From 3a1ad22a6383c85ee80ca4fe2bb8241ed76c56d8 Mon Sep 17 00:00:00 2001
From: Mayank Jobanputra <mayankjobanputra@gmail.com>
Date: Mon, 7 Nov 2022 10:33:45 +0100
Subject: [PATCH 001/206] New tutorial: 19_multimodal_retrieval (#52)

* Added first draft of tutorial_19

* Added markdown and changed index.toml

* cleared cell outputs, added the same instruction to contributing guidelines

* documentation changes

* updated action
---
 .github/workflows/markdowns.yml               |   4 +-
 Contributing.md                               |   2 +-
 README.md                                     |   1 +
 index.toml                                    |  10 +-
 ...arch_pipeline_with_MultiModal_Retriever.md | 212 +++++++++
 ...h_pipeline_with_MultiModal_Retriever.ipynb | 418 ++++++++++++++++++
 6 files changed, 643 insertions(+), 4 deletions(-)
 create mode 100644 markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
 create mode 100644 tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb

diff --git a/.github/workflows/markdowns.yml b/.github/workflows/markdowns.yml
index 75412e30..0cfec054 100644
--- a/.github/workflows/markdowns.yml
+++ b/.github/workflows/markdowns.yml
@@ -22,7 +22,7 @@ jobs:
 
       - name: Get changed notebooks
         id: changed-files
-        uses: tj-actions/changed-files@v32
+        uses: tj-actions/changed-files@v34
         with:
           files: |
             tutorials/*.ipynb
@@ -50,4 +50,4 @@ jobs:
             echo "#"
             echo "##################################################################################################"
             exit 1
-          fi
\ No newline at end of file
+          fi
diff --git a/Contributing.md b/Contributing.md
index 5670f701..743ab2f8 100644
--- a/Contributing.md
+++ b/Contributing.md
@@ -23,7 +23,7 @@ Here's what you need to do to add or edit tutorials 👇:
    - Add your new tutorial to [index.toml](/index.toml). Here, `weight` is the order in which your tutorial appears. For example, a tutorial with `weight = 15` comes after a tutorial with `weight = 10` and before `20`.
 3. Edit an existing tutorial or create a new one in the `/tutorials` folder by editing or creating `.ipynb` files.
 4. Pre-commit hooks will ensure the `markdowns` folder reflects your changes but you can update the docs at any time:
-    - Run `python /scripts/generate_markdowns.py index.toml --notebooks /tutorials/your-tutorial.ipynb --output /markdowns`. This generates or updates the relevant markdown file in `/markdowns`.
+    - Run `python scripts/generate_markdowns.py --index index.toml --notebooks tutorials/your-tutorial.ipynb --output markdowns/`. This generates or updates the relevant markdown file in `/markdowns`.
 5. Create a pull request.
 6. Wait for the [CI](#ci-continuous-integration) checks to pass.
    These checks pass if the relevant markdown files are created.
diff --git a/README.md b/README.md
index 6a3eabdd..d0d4de7c 100644
--- a/README.md
+++ b/README.md
@@ -35,3 +35,4 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 16. [Document Classifier at Index Time](./tutorials/16_Document_Classifier_at_Index_Time.ipynb)
 17. [Audio](./tutorials/17_Audio.ipynb)
 18. [Generative Pseudo Labeling](./tutorials/18_GPL.ipynb)
+19. [Text-to-Image search](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb)
diff --git a/index.toml b/index.toml
index 06b51b37..e827ca9f 100644
--- a/index.toml
+++ b/index.toml
@@ -145,4 +145,12 @@ description = "Use a Retriever and a query generator to perform unsupervised dom
 level = "advanced"
 weight = 140
 notebook = "18_GPL.ipynb"
-aliases = ["gpl"]
\ No newline at end of file
+aliases = ["gpl"]
+
+[[tutorial]]
+title = "Text-To-Image Search Pipeline with Multimodal Retriever"
+description = "Use a MultiModalRetriever to build a cross-modal search pipeline."
+level = "intermediate"
+weight = 95
+notebook = "19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
+aliases = ["multimodal"]
\ No newline at end of file
diff --git a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
new file mode 100644
index 00000000..462392e6
--- /dev/null
+++ b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
@@ -0,0 +1,212 @@
+---
+layout: tutorial
+colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
+toc: True
+title: "Text-To-Image Search Pipeline with Multimodal Retriever"
+last_updated: 2022-11-07
+level: "intermediate"
+weight: 95
+description: Use a MultiModalRetriever to build a cross-modal search pipeline.
+category: "QA"
+aliases: ['/tutorials/multimodal']
+---
+    
+
+**Level**: Intermediate
+
+**Time to complete**: 20 minutes
+
+**Prerequisites**: This tutorial assumes basic knowledge of Haystack Retrievers and Pipelines. If you want to learn about them, have a look at our tutorials on [Build Your First QA System](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb) and [Fine-Tuning a Model on Your Own Data](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb).
+
+Prepare the Colab environment (see links below).
+
+**Nodes Used**: InMemoryDocumentStore, MultiModalRetriever
+
+**Goal**: After completing this tutorial, you will have built a search system that retrieves images as answers to a text query.
+
+**Description**: In this tutorial, you'll download a set of images that you'll then turn into embeddings using a transformers model, OpenAI CLIP. You'll then use the same model to embed the text query. Finally, you'll perform a nearest neighbor search to retrieve the images relevant to the text query.
+
+Let's build a text-to-image search pipeline using a small animal dataset!
+
+## Preparing the Colab Environment
+
+- [Enable GPU Runtime in GPU](https://docs.haystack.deepset.ai/v5.2-unstable/docs/enable-gpu-runtime-in-colab)
+- [Check if GPU is Enabled](https://docs.haystack.deepset.ai/v5.2-unstable/docs/check-if-gpu-is-enabled)
+- [Set logging level to INFO](https://docs.haystack.deepset.ai/v5.2-unstable/docs/set-the-logging-level)
+
+# Installing Haystack
+
+
+```bash
+%%bash
+
+pip install --upgrade pip
+pip install farm-haystack[colab]
+```
+
+# Initializing the DocumentStore
+
+A DocumentStore stores references to the images that Haystack will compare with your query. But before it can do that, you need to initialize it. In this tutorial, you'll use the InMemoryDocumentStore.
+
+If you want to learn more, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store).
+
+
+```python
+from haystack.document_stores import InMemoryDocumentStore
+
+# Here Here we initialize the DocumentStore to store 512 dim image embeddings 
+# obtained using OpenAI CLIP model
+document_store = InMemoryDocumentStore(embedding_dim=512)
+```
+
+# Downloading Data
+
+Download 18 sample images of different animals and store it. You can find them in data/tutorial19/spirit-animals/ as a set of .jpg files.
+
+
+```python
+from haystack.utils import fetch_archive_from_http
+
+doc_dir = "data/tutorial19"
+
+fetch_archive_from_http(
+    url="https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/spirit-animals.zip",
+    output_dir=doc_dir
+)
+```
+
+Add the images you just downloaded into Haystack Document objects and write them into the DocumentStore.
+
+
+```python
+import os
+
+from haystack import Document
+
+images = [
+    Document(content=f"./{doc_dir}/spirit-animals/{filename}", content_type="image")
+    for filename in os.listdir(f"./{doc_dir}/spirit-animals/")
+]
+
+document_store.write_documents(images)
+```
+
+You have successfully stored your images in the DocumentStore.
+
+
+
+# Initializing the Retriever
+
+Retrievers sift through all the images and return only those that are relevant to the query. To run a search on images, you'll use the MultiModalRetriever with the [OpenAI CLIP model](https://github.com/openai/CLIP/blob/main/model-card.md). 
+
+For more details on supported modalities, see [MultiModalRetriever](https://docs.haystack.deepset.ai/docs/retriever#multimodal-retrieval).
+
+Before adding the Retriever to your pipeline, let's configure its parameters
+
+
+```python
+from haystack.nodes.retriever.multimodal import MultiModalRetriever
+
+retriever_text_to_image = MultiModalRetriever(
+    document_store=document_store,
+    query_embedding_model = "sentence-transformers/clip-ViT-B-32",
+    query_type="text",
+    document_embedding_models = {"image": "sentence-transformers/clip-ViT-B-32"}
+)
+
+# Now let's turn our images into embeddings and store them in the DocumentStore.
+document_store.update_embeddings(retriever=retriever_text_to_image)
+```
+
+ Your retriever is now ready for search!
+
+# Creating the MultiModal Search Pipeline
+
+We are populating a pipeline with a MultiModalRetriever node. This search pipeline queries the image database with text and returns the most relevant images.
+
+
+```python
+from haystack import Pipeline
+
+pipeline = Pipeline()
+pipeline.add_node(
+    component=retriever_text_to_image, 
+    name="retriever_text_to_image", 
+    inputs=["Query"]
+)
+```
+
+Now, you have a pipeline that uses the MultiModalRetriever and takes a text query as input. Let's try it out.
+
+# Searching Through the Images
+
+Use the pipeline `run()` method to query the images in the DocumentStore. The query argument is where you type your text query. Additionally, you can set the number of images you want the MultiModalRetriever to return using the `top-k` parameter. To learn more about setting arguments, see [Pipeline Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments).
+
+
+```python
+results = pipeline.run(
+    query="Animal that lives in the water",
+    params={"retriever_text_to_image": {"top_k": 3}}
+)
+
+# Sort the results based on the scores
+results = sorted(results["documents"], key=lambda d: d.score, reverse=True)
+
+for doc in results:
+    print(doc.score, doc.content)
+```
+
+Here are some more query strings you could try out:
+
+1.   King of the Jungle
+2.   Fastest animal
+3.   Bird that can see clearly even in the dark
+
+
+
+You can also easily vizualize these images together with their score using this code:
+
+
+
+
+
+```python
+from io import BytesIO
+from PIL import Image, ImageDraw, ImageOps
+from IPython.display import display, Image as IPImage
+
+def display_img_array(ima, score):
+    im = Image.open(ima)
+    img_with_border = ImageOps.expand(im ,border=20, fill='white')
+
+    # Add Text to an image
+    img = ImageDraw.Draw(img_with_border)
+    img.text((20, 0), f"Score: {score},    Path: {ima}", fill=(0, 0, 0))
+
+    bio = BytesIO()
+    img_with_border.save(bio, format='png')
+    display(IPImage(bio.getvalue(), format='png'))
+
+images_array = [doc.content for doc in results]
+scores = [doc.score for doc in results]
+for ima, score in zip(images_array, scores):
+    display_img_array(ima, score)
+```
+
+Congratulations! You've created a search system that returns images of animals in answer to a text query.
+
+## About us
+
+This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
+
+We bring NLP to the industry via open source!  
+Our focus: Industry specific language models & large scale QA systems.  
+  
+Some of our other work: 
+- [German BERT](https://deepset.ai/german-bert)
+- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
+
+Get in touch:
+[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
+
+By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb b/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
new file mode 100644
index 00000000..d0ebcf79
--- /dev/null
+++ b/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
@@ -0,0 +1,418 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CQyfa3akfIEZ"
+      },
+      "source": [
+        "**Level**: Intermediate\n",
+        "\n",
+        "**Time to complete**: 20 minutes\n",
+        "\n",
+        "**Prerequisites**: This tutorial assumes basic knowledge of Haystack Retrievers and Pipelines. If you want to learn about them, have a look at our tutorials on [Build Your First QA System](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb) and [Fine-Tuning a Model on Your Own Data](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb).\n",
+        "\n",
+        "Prepare the Colab environment (see links below).\n",
+        "\n",
+        "**Nodes Used**: InMemoryDocumentStore, MultiModalRetriever\n",
+        "\n",
+        "**Goal**: After completing this tutorial, you will have built a search system that retrieves images as answers to a text query.\n",
+        "\n",
+        "**Description**: In this tutorial, you'll download a set of images that you'll then turn into embeddings using a transformers model, OpenAI CLIP. You'll then use the same model to embed the text query. Finally, you'll perform a nearest neighbor search to retrieve the images relevant to the text query.\n",
+        "\n",
+        "Let's build a text-to-image search pipeline using a small animal dataset!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fzn2uA1Be1Km"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in GPU](https://docs.haystack.deepset.ai/v5.2-unstable/docs/enable-gpu-runtime-in-colab)\n",
+        "- [Check if GPU is Enabled](https://docs.haystack.deepset.ai/v5.2-unstable/docs/check-if-gpu-is-enabled)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v5.2-unstable/docs/set-the-logging-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tJU29jj0fX5m"
+      },
+      "source": [
+        "# Installing Haystack"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hl92D-ZlycPh"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install --upgrade pip\n",
+        "pip install farm-haystack[colab]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KkVAG7FdXsEU"
+      },
+      "source": [
+        "# Initializing the DocumentStore\n",
+        "\n",
+        "A DocumentStore stores references to the images that Haystack will compare with your query. But before it can do that, you need to initialize it. In this tutorial, you'll use the InMemoryDocumentStore.\n",
+        "\n",
+        "If you want to learn more, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dK86aFlSYQXv"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.document_stores import InMemoryDocumentStore\n",
+        "\n",
+        "# Here Here we initialize the DocumentStore to store 512 dim image embeddings \n",
+        "# obtained using OpenAI CLIP model\n",
+        "document_store = InMemoryDocumentStore(embedding_dim=512)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oGNwBu0yYcDq"
+      },
+      "source": [
+        "# Downloading Data\n",
+        "\n",
+        "Download 18 sample images of different animals and store it. You can find them in data/tutorial19/spirit-animals/ as a set of .jpg files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7yk_Prp3yYUa"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.utils import fetch_archive_from_http\n",
+        "\n",
+        "doc_dir = \"data/tutorial19\"\n",
+        "\n",
+        "fetch_archive_from_http(\n",
+        "    url=\"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/spirit-animals.zip\",\n",
+        "    output_dir=doc_dir\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oOJC6m8cqzCl"
+      },
+      "source": [
+        "Add the images you just downloaded into Haystack Document objects and write them into the DocumentStore."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3pdDsSVp40vr"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "from haystack import Document\n",
+        "\n",
+        "images = [\n",
+        "    Document(content=f\"./{doc_dir}/spirit-animals/{filename}\", content_type=\"image\")\n",
+        "    for filename in os.listdir(f\"./{doc_dir}/spirit-animals/\")\n",
+        "]\n",
+        "\n",
+        "document_store.write_documents(images)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_U-RlPJWHMjO"
+      },
+      "source": [
+        "You have successfully stored your images in the DocumentStore.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nfmeRIE9wz9o"
+      },
+      "source": [
+        "# Initializing the Retriever\n",
+        "\n",
+        "Retrievers sift through all the images and return only those that are relevant to the query. To run a search on images, you'll use the MultiModalRetriever with the [OpenAI CLIP model](https://github.com/openai/CLIP/blob/main/model-card.md). \n",
+        "\n",
+        "For more details on supported modalities, see [MultiModalRetriever](https://docs.haystack.deepset.ai/docs/retriever#multimodal-retrieval).\n",
+        "\n",
+        "Before adding the Retriever to your pipeline, let's configure its parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xuL1mtq6qx0d"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.nodes.retriever.multimodal import MultiModalRetriever\n",
+        "\n",
+        "retriever_text_to_image = MultiModalRetriever(\n",
+        "    document_store=document_store,\n",
+        "    query_embedding_model = \"sentence-transformers/clip-ViT-B-32\",\n",
+        "    query_type=\"text\",\n",
+        "    document_embedding_models = {\"image\": \"sentence-transformers/clip-ViT-B-32\"}\n",
+        ")\n",
+        "\n",
+        "# Now let's turn our images into embeddings and store them in the DocumentStore.\n",
+        "document_store.update_embeddings(retriever=retriever_text_to_image)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mcopKii2MBCd"
+      },
+      "source": [
+        " Your retriever is now ready for search!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vMNYvDjd9sqY"
+      },
+      "source": [
+        "# Creating the MultiModal Search Pipeline\n",
+        "\n",
+        "We are populating a pipeline with a MultiModalRetriever node. This search pipeline queries the image database with text and returns the most relevant images."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-a6ltABP40vs"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack import Pipeline\n",
+        "\n",
+        "pipeline = Pipeline()\n",
+        "pipeline.add_node(\n",
+        "    component=retriever_text_to_image, \n",
+        "    name=\"retriever_text_to_image\", \n",
+        "    inputs=[\"Query\"]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wbEkh5oSMJbq"
+      },
+      "source": [
+        "Now, you have a pipeline that uses the MultiModalRetriever and takes a text query as input. Let's try it out."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TSjizWzAF6T9"
+      },
+      "source": [
+        "# Searching Through the Images\n",
+        "\n",
+        "Use the pipeline `run()` method to query the images in the DocumentStore. The query argument is where you type your text query. Additionally, you can set the number of images you want the MultiModalRetriever to return using the `top-k` parameter. To learn more about setting arguments, see [Pipeline Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qRH5UbIdF7CW"
+      },
+      "outputs": [],
+      "source": [
+        "results = pipeline.run(\n",
+        "    query=\"Animal that lives in the water\",\n",
+        "    params={\"retriever_text_to_image\": {\"top_k\": 3}}\n",
+        ")\n",
+        "\n",
+        "# Sort the results based on the scores\n",
+        "results = sorted(results[\"documents\"], key=lambda d: d.score, reverse=True)\n",
+        "\n",
+        "for doc in results:\n",
+        "    print(doc.score, doc.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FoEt2cHHTdIZ"
+      },
+      "source": [
+        "Here are some more query strings you could try out:\n",
+        "\n",
+        "1.   King of the Jungle\n",
+        "2.   Fastest animal\n",
+        "3.   Bird that can see clearly even in the dark\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KBukVUVVU0if"
+      },
+      "source": [
+        "You can also easily vizualize these images together with their score using this code:\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "qSjZHuv68Hut",
+        "outputId": "0cb0d794-3dc2-4034-e0c9-277ca318f23f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from io import BytesIO\n",
+        "from PIL import Image, ImageDraw, ImageOps\n",
+        "from IPython.display import display, Image as IPImage\n",
+        "\n",
+        "def display_img_array(ima, score):\n",
+        "    im = Image.open(ima)\n",
+        "    img_with_border = ImageOps.expand(im ,border=20, fill='white')\n",
+        "\n",
+        "    # Add Text to an image\n",
+        "    img = ImageDraw.Draw(img_with_border)\n",
+        "    img.text((20, 0), f\"Score: {score},    Path: {ima}\", fill=(0, 0, 0))\n",
+        "\n",
+        "    bio = BytesIO()\n",
+        "    img_with_border.save(bio, format='png')\n",
+        "    display(IPImage(bio.getvalue(), format='png'))\n",
+        "\n",
+        "images_array = [doc.content for doc in results]\n",
+        "scores = [doc.score for doc in results]\n",
+        "for ima, score in zip(images_array, scores):\n",
+        "    display_img_array(ima, score)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rcteNc-Rn5R7"
+      },
+      "source": [
+        "Congratulations! You've created a search system that returns images of animals in answer to a text query."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8RyMcCI2_yHf"
+      },
+      "source": [
+        "## About us\n",
+        "\n",
+        "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
+        "\n",
+        "We bring NLP to the industry via open source!  \n",
+        "Our focus: Industry specific language models & large scale QA systems.  \n",
+        "  \n",
+        "Some of our other work: \n",
+        "- [German BERT](https://deepset.ai/german-bert)\n",
+        "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
+        "\n",
+        "Get in touch:\n",
+        "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
+        "\n",
+        "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3.6.9 64-bit",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.6.9"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From e80be74c8e1ec8813c23f71c1367518a1bd3a95c Mon Sep 17 00:00:00 2001
From: Grzegorz Sajko <42315895+gsajko@users.noreply.github.com>
Date: Mon, 7 Nov 2022 19:58:59 +0100
Subject: [PATCH 002/206] Fixed link  in tutorial 01 (#63)

fixed link
---
 markdowns/01_Basic_QA_Pipeline.md    | 4 ++--
 tutorials/01_Basic_QA_Pipeline.ipynb | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/markdowns/01_Basic_QA_Pipeline.md b/markdowns/01_Basic_QA_Pipeline.md
index d0598a94..2432b0a2 100644
--- a/markdowns/01_Basic_QA_Pipeline.md
+++ b/markdowns/01_Basic_QA_Pipeline.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb
 toc: True
 title: "Build Your First QA System"
-last_updated: 2022-10-12
+last_updated: 2022-11-07
 level: "beginner"
 weight: 10
 description: Get Started by creating a Retriever Reader pipeline.
@@ -247,7 +247,7 @@ from haystack.nodes import TransformersReader
 With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
 Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
 To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
+You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelines).
 
 
 ```python
diff --git a/tutorials/01_Basic_QA_Pipeline.ipynb b/tutorials/01_Basic_QA_Pipeline.ipynb
index afbbabd4..35257055 100644
--- a/tutorials/01_Basic_QA_Pipeline.ipynb
+++ b/tutorials/01_Basic_QA_Pipeline.ipynb
@@ -403,7 +403,7 @@
     "With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.\n",
     "Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
     "To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.\n",
-    "You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd)."
+    "You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelines)."
    ]
   },
   {

From 5297d8bda2ff5627648f03bf0c40cf99699ca85d Mon Sep 17 00:00:00 2001
From: Agnieszka Marzec <97166305+agnieszka-m@users.noreply.github.com>
Date: Wed, 16 Nov 2022 14:09:56 +0100
Subject: [PATCH 003/206] Add tutorial template (#65)

* Add tutorial template

* Add Branden's comments

* Remove prereqs
---
 Contributing.md          |   2 +-
 tutorials/template.ipynb | 113 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 114 insertions(+), 1 deletion(-)
 create mode 100644 tutorials/template.ipynb

diff --git a/Contributing.md b/Contributing.md
index 743ab2f8..a9429fa4 100644
--- a/Contributing.md
+++ b/Contributing.md
@@ -21,7 +21,7 @@ Here's what you need to do to add or edit tutorials 👇:
 2. If you're creating a new tutorial:
    - Follow the [naming convention](#naming-convention-for-file-names) for file names.
    - Add your new tutorial to [index.toml](/index.toml). Here, `weight` is the order in which your tutorial appears. For example, a tutorial with `weight = 15` comes after a tutorial with `weight = 10` and before `20`.
-3. Edit an existing tutorial or create a new one in the `/tutorials` folder by editing or creating `.ipynb` files.
+3. Edit an existing tutorial or copy the [tutorial template](/tutorials/template.ipynb) to create a new tutorial. 
 4. Pre-commit hooks will ensure the `markdowns` folder reflects your changes but you can update the docs at any time:
     - Run `python scripts/generate_markdowns.py --index index.toml --notebooks tutorials/your-tutorial.ipynb --output markdowns/`. This generates or updates the relevant markdown file in `/markdowns`.
 5. Create a pull request.
diff --git a/tutorials/template.ipynb b/tutorials/template.ipynb
new file mode 100644
index 00000000..aeb34afd
--- /dev/null
+++ b/tutorials/template.ipynb
@@ -0,0 +1,113 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: *Give your tutorial a meaningful title starting with an imperative, for example: \"Build Your First QA System\"*\n",
+    "\n",
+    "- **Level**: *Who is this tutorial for? Choose one of the options: Beginner, Intermediate, Advanced.*\n",
+    "- **Time to complete**: *Enter the time it takes to complete this tutorial, in minutes.*\n",
+    "- **Nodes Used**: *List the nodes used in this tutorial, for example: `FARMReader`.*\n",
+    "- **Goal**: After completing this tutorial, you will have achieved/learned about.. *here state the goal of the tutorial. What will the users have done when they complete it?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "*Here provide a short description of the tutorial. What does it teach? What's its expected outcome?*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 1\n",
+    "\n",
+    "*Divide your tutorial into tasks. Each task is a separate section. The title of the sections should start with a verb in the -ing form, for example: Installing Haystack, Preparing the Collab Environment, and so on.*\n",
+    "*Divide your instructions into steps:*\n",
+    "*1.*\n",
+    "*2.*\n",
+    "*3.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add some code to run, if applicable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "*Are there any pages, tutorials, or guides that could be useful to read after completing this tutorial? Are there any other tasks the user should complete now? If yes, list them here. If no, delete this section.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## About us\n",
+    "\n",
+    "*Leave this section as is. It's a footer that we add to all our tutorials.*\n",
+    "\n",
+    "\n",
+    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
+    "\n",
+    "We bring NLP to the industry via open source!  \n",
+    "Our focus: Industry specific language models & large scale QA systems.  \n",
+    "  \n",
+    "Some of our other work: \n",
+    "- [German BERT](https://deepset.ai/german-bert)\n",
+    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
+    "\n",
+    "Get in touch:\n",
+    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
+    "\n",
+    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.13 64-bit (microsoft store)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9075e6086e4e65b56cd3eb170a15e0fca54180da9a114ef73f891ab1378b8e41"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 7237d6e58ba9d3e09462547aece98a9003347654 Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Tue, 22 Nov 2022 19:06:50 +0100
Subject: [PATCH 004/206] Tutorial 08: fix typos in bash cell (#68)

* fix typos in bash cell

* ask users to enable GPU
---
 markdowns/08_Preprocessing.md    | 20 +++++++++++++++-----
 tutorials/08_Preprocessing.ipynb | 25 +++++++++++++++++++++----
 2 files changed, 36 insertions(+), 9 deletions(-)

diff --git a/markdowns/08_Preprocessing.md b/markdowns/08_Preprocessing.md
index 307768e0..cf5b8808 100644
--- a/markdowns/08_Preprocessing.md
+++ b/markdowns/08_Preprocessing.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb
 toc: True
 title: "Preprocessing Your Documents"
-last_updated: 2022-10-26
+last_updated: 2022-11-22
 level: "beginner"
 weight: 25
 description: Start converting, cleaning, and splitting Documents using Haystack’s preprocessing capabilities.
@@ -30,6 +30,16 @@ docs = [
 
 This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
 
+### Prepare environment
+
+#### Colab: Enable the GPU runtime
+Make sure you enable the GPU runtime to experience decent speed in this tutorial.
+**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
+
+<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+
+You can double check whether the GPU runtime is enabled with the following command:
+
 
 ```bash
 %%bash
@@ -38,12 +48,12 @@ pip install --upgrade pip
 pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr]
 
 # For Colab/linux based machines:
-!wget https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz
-!tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin
+wget https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz
+tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin
 
 # For macOS machines:
-# !wget https://dl.xpdfreader.com/xpdf-tools-mac-4.03.tar.gz
-# !tar -xvf xpdf-tools-mac-4.03.tar.gz && sudo cp xpdf-tools-mac-4.03/bin64/pdftotext /usr/local/bin
+# wget https://dl.xpdfreader.com/xpdf-tools-mac-4.03.tar.gz
+# tar -xvf xpdf-tools-mac-4.03.tar.gz && sudo cp xpdf-tools-mac-4.03/bin64/pdftotext /usr/local/bin
 ```
 
 ## Logging
diff --git a/tutorials/08_Preprocessing.ipynb b/tutorials/08_Preprocessing.ipynb
index 7de1117a..671428b5 100644
--- a/tutorials/08_Preprocessing.ipynb
+++ b/tutorials/08_Preprocessing.ipynb
@@ -45,6 +45,23 @@
     "This tutorial will show you all the tools that Haystack provides to help you cast your data into this format."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Prepare environment\n",
+    "\n",
+    "#### Colab: Enable the GPU runtime\n",
+    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
+    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+    "\n",
+    "You can double check whether the GPU runtime is enabled with the following command:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 26,
@@ -65,12 +82,12 @@
     "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr]\n",
     "\n",
     "# For Colab/linux based machines:\n",
-    "!wget https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz\n",
-    "!tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin\n",
+    "wget https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz\n",
+    "tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin\n",
     "\n",
     "# For macOS machines:\n",
-    "# !wget https://dl.xpdfreader.com/xpdf-tools-mac-4.03.tar.gz\n",
-    "# !tar -xvf xpdf-tools-mac-4.03.tar.gz && sudo cp xpdf-tools-mac-4.03/bin64/pdftotext /usr/local/bin"
+    "# wget https://dl.xpdfreader.com/xpdf-tools-mac-4.03.tar.gz\n",
+    "# tar -xvf xpdf-tools-mac-4.03.tar.gz && sudo cp xpdf-tools-mac-4.03/bin64/pdftotext /usr/local/bin"
    ]
   },
   {

From 58a5d7883e469e143e1d8cc6b4792434b221913c Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Thu, 24 Nov 2022 11:56:33 +0000
Subject: [PATCH 005/206] Add colab buttons to readme (#71)

Adding colab buttons to the readme.
We can make it a list again, but it looked messy with the buttons so I'm trying this
---
 README.md | 41 +++++++++++++++++++++--------------------
 1 file changed, 21 insertions(+), 20 deletions(-)

diff --git a/README.md b/README.md
index d0d4de7c..83be5eaa 100644
--- a/README.md
+++ b/README.md
@@ -16,23 +16,24 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 [![Publish tutorials on Haystack Home](https://github.com/deepset-ai/haystack-tutorials/actions/workflows/publish_tutorials.yml/badge.svg)](https://github.com/deepset-ai/haystack-tutorials/actions/workflows/publish_tutorials.yml)
 
 ## Tutorials
-
-1. [Basic QA Pipeline](./tutorials/01_Basic_QA_Pipeline.ipynb)
-2. [Fine Tune a Model on Your Data](./tutorials/02_Finetune_a_model_on_your_data.ipynb)
-3. [Basic QA Pipeline Without Elasticsearch](./tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb)
-4. [FAQ Style QA](./tutorials/04_FAQ_style_QA.ipynb)
-5. [Evaluation](./tutorials/05_Evaluation.ipynb)
-6. [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)
-7. [RAG Generator](./tutorials/07_RAG_Generator.ipynb)
-8. [Preprocessing](./tutorials/08_Preprocessing.ipynb)
-9. [DPR Training](./tutorials/09_DPR_training.ipynb)
-10. [Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)
-11. [Pipelines](./tutorials/11_Pipelines.ipynb)
-12. [Long-Form Question Answering](./tutorials/12_LFQA.ipynb)
-13. [Question Generation](./tutorials/13_Question_generation.ipynb)
-14. [Query Classifier](./tutorials/14_Query_Classifier.ipynb)
-15. [Table QA](./tutorials/15_TableQA.ipynb)
-16. [Document Classifier at Index Time](./tutorials/16_Document_Classifier_at_Index_Time.ipynb)
-17. [Audio](./tutorials/17_Audio.ipynb)
-18. [Generative Pseudo Labeling](./tutorials/18_GPL.ipynb)
-19. [Text-to-Image search](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb)
+| Name | Colab | Source Code |
+|---   |---    |---          |
+|Basic QA Pipeline|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)|[Source Code](./tutorials/01_Basic_QA_Pipeline.ipynb)|
+|Fine Tune a Model on Your Data|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb)|[Source Code](./tutorials/02_Finetune_a_model_on_your_data.ipynb)|
+|Basic QA Pipeline Without Elasticsearch|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb)|[Source Code](./tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb)|
+|FAQ Style QA|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)|[Source Code](./tutorials/04_FAQ_style_QA.ipynb)|
+|Evaluation|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)|[Source Code](./tutorials/05_Evaluation.ipynb)|
+|Better Retrieval via Embedding Retrieval|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)|[Source Code](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)|
+|RAG Generator|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)|[Source Code](./tutorials/07_RAG_Generator.ipynb)|
+|Preprocessing|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)|[Source Code](./tutorials/08_Preprocessing.ipynb)|
+|DPR Training|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)|[Source Code](./tutorials/09_DPR_training.ipynb)|
+|Knowledge Graph|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)|[Source Code](./tutorials/10_Knowledge_Graph.ipynb)|
+|Pipelines|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)|[Source Code](./tutorials/11_Pipelines.ipynb)|
+|Long-Form Question Answering|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)|[Source Code](./tutorials/12_LFQA.ipynb)|
+|Question Generation|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)|[Source Code](./tutorials/13_Question_generation.ipynb)|
+|Query Classifier|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)|[Source Code](./tutorials/14_Query_Classifier.ipynb)|
+|Table QA|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)|[Source Code](./tutorials/15_TableQA.ipynb)|
+|Document Classifier at Index Time|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb)|[Source Code](./tutorials/16_Document_Classifier_at_Index_Time.ipynb)|
+|Audio|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb)|[Source Code](./tutorials/17_Audio.ipynb)|
+|Generative Pseudo Labeling|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)|[Source Code](./tutorials/18_GPL.ipynb)|
+|Text-to-Image search|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb)|[Source Code](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb)|

From 89c6b1d72501086845b9464b84bf9968a28ca671 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Thu, 24 Nov 2022 15:04:08 +0000
Subject: [PATCH 006/206] Optional slug to unbind the notebook from the
 generated url name (#72)

* Optional slug to unbind the notebook from the generated url name

* Updated Contributing guidelines

* reverting changes to 10th tutorial
---
 Contributing.md               | 6 ++++++
 index.toml                    | 1 +
 scripts/generate_markdowns.py | 4 ++--
 3 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/Contributing.md b/Contributing.md
index a9429fa4..b08e7e07 100644
--- a/Contributing.md
+++ b/Contributing.md
@@ -30,6 +30,12 @@ Here's what you need to do to add or edit tutorials 👇:
 7. Update the [README](./README.md), if necessary.
 8. Wait for a review and merge 🎉. Thank you for contributing 💙.
 
+## Slugs
+
+The default behaviour for markdown files is that it gets the same name as the corresponfing `.ipynb` notebook of that tutorial. The name of the markdown file is also the location at which the tutorial will appear on the website. 
+For example, "01_Basic_QA_Pipeline" will be on https://haystack.deepset.ai/tutorials/01_basic_qa_pipeline
+
+In `index.toml` you have the option of adding an optional `slug` entry for a tutorial which will generate the correspoinding markdown under the name you give for the `slug`. This is useful for scenarios where you are updating a tutorial to the point where it makes sense that the name of the `.ipynb` file changes, but you would still like people to access the tutorial on the same URL.
 
 # Continuous Integration (CI)
 
diff --git a/index.toml b/index.toml
index e827ca9f..945c795a 100644
--- a/index.toml
+++ b/index.toml
@@ -10,6 +10,7 @@ level = "beginner"
 weight = 10
 notebook = "01_Basic_QA_Pipeline.ipynb"
 aliases = ["first-qa-system"]
+slug = "01_Basic_QA_Pipeline"
 
 [[tutorial]]
 title = "Fine-Tuning a Model on Your Own Data"
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index 14646128..74955d7a 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -36,8 +36,8 @@ def generate_markdown_from_notebook(config, tutorial, output_path, tutorials_pat
     md_exporter = MarkdownExporter(exclude_output=True)
     body, _ = md_exporter.from_filename(f"{tutorials_path}")
     print(f"Processing {tutorials_path}")
-
-    with open(f"{output_path}/{tutorial['notebook'][:-6]}.md", "w", encoding="utf-8") as f:
+    filename = tutorial.get('slug', tutorial['notebook'][:-6])
+    with open(f"{output_path}/{filename}.md", "w", encoding="utf-8") as f:
         try:
             f.write(frontmatter + "\n\n")
         except IndexError as e:

From ad9267afe44ac44685e7673589f69af5876d3c22 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Thu, 24 Nov 2022 15:48:05 +0000
Subject: [PATCH 007/206] First commit for adding the download path to the
 frontmatter (#70)

* First commit for adding the download path to the frontmatter

In this PR, I also make it so that the first line of the .ipynb is excluded while generating markdowns. This way we can display it wherever we want on HSH

* Download links for all tutorials
---
 markdowns/01_Basic_QA_Pipeline.md                        | 4 ++--
 markdowns/02_Finetune_a_model_on_your_data.md            | 4 ++--
 markdowns/03_Basic_QA_Pipeline_without_Elasticsearch.md  | 4 ++--
 markdowns/04_FAQ_style_QA.md                             | 4 ++--
 markdowns/05_Evaluation.md                               | 4 ++--
 markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md | 4 ++--
 markdowns/07_RAG_Generator.md                            | 4 ++--
 markdowns/08_Preprocessing.md                            | 4 ++--
 markdowns/09_DPR_training.md                             | 4 ++--
 markdowns/10_Knowledge_Graph.md                          | 4 ++--
 markdowns/11_Pipelines.md                                | 4 ++--
 markdowns/12_LFQA.md                                     | 4 ++--
 markdowns/13_Question_generation.md                      | 4 ++--
 markdowns/14_Query_Classifier.md                         | 4 ++--
 markdowns/15_TableQA.md                                  | 4 ++--
 markdowns/16_Document_Classifier_at_Index_Time.md        | 4 ++--
 markdowns/17_Audio.md                                    | 4 ++--
 markdowns/18_GPL.md                                      | 4 ++--
 scripts/generate_markdowns.py                            | 3 +++
 19 files changed, 39 insertions(+), 36 deletions(-)

diff --git a/markdowns/01_Basic_QA_Pipeline.md b/markdowns/01_Basic_QA_Pipeline.md
index 2432b0a2..03e5bd75 100644
--- a/markdowns/01_Basic_QA_Pipeline.md
+++ b/markdowns/01_Basic_QA_Pipeline.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb
 toc: True
 title: "Build Your First QA System"
-last_updated: 2022-11-07
+last_updated: 2022-11-24
 level: "beginner"
 weight: 10
 description: Get Started by creating a Retriever Reader pipeline.
 category: "QA"
 aliases: ['/tutorials/first-qa-system']
+download: "/downloads/01_Basic_QA_Pipeline.ipynb"
 ---
     
 
-# Build Your First QA System
 
 <img style="float: right;" src="https://upload.wikimedia.org/wikipedia/en/d/d8/Game_of_Thrones_title_card.jpg">
 
diff --git a/markdowns/02_Finetune_a_model_on_your_data.md b/markdowns/02_Finetune_a_model_on_your_data.md
index a9e06ec4..a4572607 100644
--- a/markdowns/02_Finetune_a_model_on_your_data.md
+++ b/markdowns/02_Finetune_a_model_on_your_data.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb
 toc: True
 title: "Fine-Tuning a Model on Your Own Data"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "intermediate"
 weight: 50
 description: Improve the performance of your Reader by performing fine-tuning.
 category: "QA"
 aliases: ['/tutorials/fine-tuning-a-model']
+download: "/downloads/02_Finetune_a_model_on_your_data.ipynb"
 ---
     
 
-# Fine-tuning a Model on Your Own Data
 
 For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
 However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
diff --git a/markdowns/03_Basic_QA_Pipeline_without_Elasticsearch.md b/markdowns/03_Basic_QA_Pipeline_without_Elasticsearch.md
index cb4f2922..fb7978b5 100644
--- a/markdowns/03_Basic_QA_Pipeline_without_Elasticsearch.md
+++ b/markdowns/03_Basic_QA_Pipeline_without_Elasticsearch.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb
 toc: True
 title: "Build a QA System Without Elasticsearch"
-last_updated: 2022-10-26
+last_updated: 2022-11-24
 level: "beginner"
 weight: 15
 description: Create a Retriever Reader pipeline that requires no external database dependencies.
 category: "QA"
 aliases: ['/tutorials/without-elasticsearch']
+download: "/downloads/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb"
 ---
     
 
-# Build a QA System Without Elasticsearch
 
 Haystack provides alternatives to Elasticsearch for developing quick prototypes.
 
diff --git a/markdowns/04_FAQ_style_QA.md b/markdowns/04_FAQ_style_QA.md
index 5e3c8b1b..dcb44ca4 100644
--- a/markdowns/04_FAQ_style_QA.md
+++ b/markdowns/04_FAQ_style_QA.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb
 toc: True
 title: "Utilizing Existing FAQs for Question Answering"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "beginner"
 weight: 20
 description: Create a smarter way to answer new questions using your existing FAQ documents.
 category: "QA"
 aliases: ['/tutorials/existing-faqs']
+download: "/downloads/04_FAQ_style_QA.ipynb"
 ---
     
 
-# Utilizing existing FAQs for Question Answering
 
 While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
 
diff --git a/markdowns/05_Evaluation.md b/markdowns/05_Evaluation.md
index b67cddc0..e533b465 100644
--- a/markdowns/05_Evaluation.md
+++ b/markdowns/05_Evaluation.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb
 toc: True
 title: "Evaluation of a QA System"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "advanced"
 weight: 100
 description: Learn how to evaluate the performance of individual nodes as well as entire pipelines.
 category: "QA"
 aliases: ['/tutorials/evaluation']
+download: "/downloads/05_Evaluation.ipynb"
 ---
     
 
-# Evaluation of a Pipeline and its Components
 
 To be able to make a statement about the quality of results a question-answering pipeline or any other pipeline in haystack produces, it is important to evaluate it. Furthermore, evaluation allows determining which components of the pipeline can be improved.
 The results of the evaluation can be saved as CSV files, which contain all the information to calculate additional metrics later on or inspect individual predictions.
diff --git a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
index 90c68805..6a8a732e 100644
--- a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
+++ b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
 toc: True
 title: "Better Retrieval with Embedding Retrieval"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "intermediate"
 weight: 55
 description: Use Transformer based dense Retrievers to improve your system’s performance.
 category: "QA"
 aliases: ['/tutorials/embedding-retrieval']
+download: "/downloads/06_Better_Retrieval_via_Embedding_Retrieval.ipynb"
 ---
     
 
-# Better Retrieval via "Embedding Retrieval"
 
 ### Importance of Retrievers
 
diff --git a/markdowns/07_RAG_Generator.md b/markdowns/07_RAG_Generator.md
index 949f624c..d6f55ed5 100644
--- a/markdowns/07_RAG_Generator.md
+++ b/markdowns/07_RAG_Generator.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb
 toc: True
 title: "Generative QA with Retrieval-Augmented Generation"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "intermediate"
 weight: 60
 description: Try out a generative model in place of the extractive Reader.
 category: "QA"
 aliases: ['/tutorials/retrieval-augmented-generation']
+download: "/downloads/07_RAG_Generator.ipynb"
 ---
     
 
-# Generative QA with "Retrieval-Augmented Generation"
 
 
 While extractive QA highlights the span of text that answers a query,
diff --git a/markdowns/08_Preprocessing.md b/markdowns/08_Preprocessing.md
index cf5b8808..59470f53 100644
--- a/markdowns/08_Preprocessing.md
+++ b/markdowns/08_Preprocessing.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb
 toc: True
 title: "Preprocessing Your Documents"
-last_updated: 2022-11-22
+last_updated: 2022-11-24
 level: "beginner"
 weight: 25
 description: Start converting, cleaning, and splitting Documents using Haystack’s preprocessing capabilities.
 category: "QA"
 aliases: ['/tutorials/preprocessing']
+download: "/downloads/08_Preprocessing.ipynb"
 ---
     
 
-# Preprocessing
 
 Haystack includes a suite of tools to extract text from different file types, normalize white space
 and split text into smaller pieces to optimize retrieval.
diff --git a/markdowns/09_DPR_training.md b/markdowns/09_DPR_training.md
index 61cca5d5..0e440d5c 100644
--- a/markdowns/09_DPR_training.md
+++ b/markdowns/09_DPR_training.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb
 toc: True
 title: "Training Your Own Dense Passage Retrieval Model"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "advanced"
 weight: 110
 description: Learn about training a Dense Passage Retrieval model and the data needed to do so.
 category: "QA"
 aliases: ['/tutorials/train-dpr']
+download: "/downloads/09_DPR_training.ipynb"
 ---
     
 
-# Training Your Own "Dense Passage Retrieval" Model
 
 Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
 This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
diff --git a/markdowns/10_Knowledge_Graph.md b/markdowns/10_Knowledge_Graph.md
index 2909d154..3916ce66 100644
--- a/markdowns/10_Knowledge_Graph.md
+++ b/markdowns/10_Knowledge_Graph.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb
 toc: True
 title: "Question Answering on a Knowledge Graph"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "advanced"
 weight: 120
 description: Experiment with a question answering system that draws upon knowledge graph.h
 category: "QA"
 aliases: ['/tutorials/knowledge-graph']
+download: "/downloads/10_Knowledge_Graph.ipynb"
 ---
     
 
-# Question Answering on a Knowledge Graph
 
 Haystack allows storing and querying knowledge graphs with the help of pre-trained models that translate text queries to SPARQL queries.
 This tutorial demonstrates how to load an existing knowledge graph into haystack, load a pre-trained retriever, and execute text queries on the knowledge graph.
diff --git a/markdowns/11_Pipelines.md b/markdowns/11_Pipelines.md
index 809a22eb..e30462bc 100644
--- a/markdowns/11_Pipelines.md
+++ b/markdowns/11_Pipelines.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb
 toc: True
 title: "How to Use Pipelines"
-last_updated: 2022-10-28
+last_updated: 2022-11-24
 level: "intermediate"
 weight: 65
 description: Learn about the many ways which you can route queries through the nodes in a pipeline.
 category: "QA"
 aliases: ['/tutorials/pipelines']
+download: "/downloads/11_Pipelines.ipynb"
 ---
     
 
-# Pipelines Tutorial
 
 In this tutorial, you will learn how the `Pipeline` class acts as a connector between all the different
 building blocks that are found in FARM. Whether you are using a Reader, Generator, Summarizer
diff --git a/markdowns/12_LFQA.md b/markdowns/12_LFQA.md
index 6e522f25..f04ff30e 100644
--- a/markdowns/12_LFQA.md
+++ b/markdowns/12_LFQA.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb
 toc: True
 title: "Generative QA with LFQA"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "intermediate"
 weight: 70
 description: Try out a generative model in place of the extractive Reader.
 category: "QA"
 aliases: ['/tutorials/lfqa']
+download: "/downloads/12_LFQA.ipynb"
 ---
     
 
-# Long-Form Question Answering
 
 Follow this tutorial to learn how to build and use a pipeline for Long-Form Question Answering (LFQA). LFQA is a variety of the generative question answering task. LFQA systems query large document stores for relevant information and then use this information to generate accurate, multi-sentence answers. In a regular question answering system, the retrieved documents related to the query (context passages) act as source tokens for extracted answers. In an LFQS system, context passages provide the context the system uses to generate original, abstractive, long-form answers.
 
diff --git a/markdowns/13_Question_generation.md b/markdowns/13_Question_generation.md
index cfac1533..eb8fa491 100644
--- a/markdowns/13_Question_generation.md
+++ b/markdowns/13_Question_generation.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb
 toc: True
 title: "Question Generation"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "intermediate"
 weight: 75
 description: Generate a set of questions that can be answered by a given Document.
 category: "QA"
 aliases: ['/tutorials/question-generation']
+download: "/downloads/13_Question_generation.ipynb"
 ---
     
 
-# Question Generation
 
 This is a bare bones tutorial showing what is possible with the QuestionGenerator Nodes and Pipelines which automatically
 generate questions which the question generation model thinks can be answered by a given document.
diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
index cccf5387..244c8044 100644
--- a/markdowns/14_Query_Classifier.md
+++ b/markdowns/14_Query_Classifier.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb
 toc: True
 title: "Query Classifier"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "intermediate"
 weight: 80
 description: Classify incoming queries so that they can be routed to the nodes that are best at handling them.
 category: "QA"
 aliases: ['/tutorials/query-classifier']
+download: "/downloads/14_Query_Classifier.ipynb"
 ---
     
 
-# Query Classifier Tutorial
 
 One of the great benefits of using state-of-the-art NLP models like those available in Haystack is that it allows users to state their queries as *plain natural language questions*: rather than trying to come up with just the right set of keywords to find the answer to their question, users can simply ask their question in much the same way that they would ask it of a (very knowledgeable!) person.
 
diff --git a/markdowns/15_TableQA.md b/markdowns/15_TableQA.md
index 55d271e9..131dcfbc 100644
--- a/markdowns/15_TableQA.md
+++ b/markdowns/15_TableQA.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb
 toc: True
 title: "Open-Domain QA on Tables"
-last_updated: 2022-10-31
+last_updated: 2022-11-24
 level: "advanced"
 weight: 130
 description: Perform question answering on tabular data.
 category: "QA"
 aliases: ['/tutorials/table-qa']
+download: "/downloads/15_TableQA.ipynb"
 ---
     
 
-# Open-Domain QA on Tables
 
 This tutorial shows you how to perform question-answering on tables using the `EmbeddingRetriever` or `BM25Retriever` as retriever node and the `TableReader` as reader node.
 
diff --git a/markdowns/16_Document_Classifier_at_Index_Time.md b/markdowns/16_Document_Classifier_at_Index_Time.md
index 6acdbe3f..6f8d5d24 100644
--- a/markdowns/16_Document_Classifier_at_Index_Time.md
+++ b/markdowns/16_Document_Classifier_at_Index_Time.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb
 toc: True
 title: "Document Classification at Index Time"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "intermediate"
 weight: 85
 description: Generate and attach classification labels to your Documents when indexing.
 category: "QA"
 aliases: ['/tutorials/doc-class-index']
+download: "/downloads/16_Document_Classifier_at_Index_Time.ipynb"
 ---
     
 
-# Extending your Metadata using DocumentClassifiers at Index Time
 
 With DocumentClassifier it's possible to automatically enrich your documents with categories, sentiments, topics or whatever metadata you like. This metadata could be used for efficient filtering or further processing. Say you have some categories your users typically filter on. If the documents are tagged manually with these categories, you could automate this process by training a model. Or you can leverage the full power and flexibility of zero shot classification. All you need to do is pass your categories to the classifier, no labels required. This tutorial shows how to integrate it in your indexing pipeline.
 
diff --git a/markdowns/17_Audio.md b/markdowns/17_Audio.md
index 7aab4096..55d93e87 100644
--- a/markdowns/17_Audio.md
+++ b/markdowns/17_Audio.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb
 toc: True
 title: "Make Your QA Pipelines Talk!"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "intermediate"
 weight: 90
 description: Convert text Answers into speech.
 category: "QA"
 aliases: ['/tutorials/audio']
+download: "/downloads/17_Audio.ipynb"
 ---
     
 
-# Make Your QA Pipelines Talk!
 
 <img style="float: right;" src="https://upload.wikimedia.org/wikipedia/en/d/d8/Game_of_Thrones_title_card.jpg">
 
diff --git a/markdowns/18_GPL.md b/markdowns/18_GPL.md
index 7092a13e..86cbb265 100644
--- a/markdowns/18_GPL.md
+++ b/markdowns/18_GPL.md
@@ -3,16 +3,16 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb
 toc: True
 title: "Generative Pseudo Labeling for Domain Adaptation"
-last_updated: 2022-10-12
+last_updated: 2022-11-24
 level: "advanced"
 weight: 140
 description: Use a Retriever and a query generator to perform unsupervised domain adaptation.
 category: "QA"
 aliases: ['/tutorials/gpl']
+download: "/downloads/18_GPL.ipynb"
 ---
     
 
-# Generative Pseudo Labeling for Domain Adaptation of Dense Retrievals
 
 *Note: Adapted to Haystack from Nils Reimers' original [notebook](https://colab.research.google.com/gist/jamescalam/d2c888775c87f9882bb7c379a96adbc8/gpl-domain-adaptation.ipynb#scrollTo=183ff7ab)
 
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index 74955d7a..1a0eb8b8 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -2,6 +2,7 @@
 from datetime import date
 import tomli
 from nbconvert import MarkdownExporter
+from nbconvert.filters.strings import get_lines
 
 
 def read_index(path):
@@ -26,6 +27,7 @@ def generate_frontmatter(config, tutorial):
 description: {tutorial["description"]}
 category: "QA"
 aliases: {aliases}
+download: "/downloads/{tutorial["notebook"]}"
 ---
     """
     return frontmatter
@@ -35,6 +37,7 @@ def generate_markdown_from_notebook(config, tutorial, output_path, tutorials_pat
     frontmatter = generate_frontmatter(config, tutorial)
     md_exporter = MarkdownExporter(exclude_output=True)
     body, _ = md_exporter.from_filename(f"{tutorials_path}")
+    body = get_lines(body, start=1)
     print(f"Processing {tutorials_path}")
     filename = tutorial.get('slug', tutorial['notebook'][:-6])
     with open(f"{output_path}/{filename}.md", "w", encoding="utf-8") as f:

From 23e662df188f4e92b0e57d865d2a577aba001821 Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Mon, 28 Nov 2022 10:02:05 +0100
Subject: [PATCH 008/206] use the name of the file as link (#78)

---
 README.md | 42 +++++++++++++++++++++---------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/README.md b/README.md
index 83be5eaa..ae94c088 100644
--- a/README.md
+++ b/README.md
@@ -16,24 +16,24 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 [![Publish tutorials on Haystack Home](https://github.com/deepset-ai/haystack-tutorials/actions/workflows/publish_tutorials.yml/badge.svg)](https://github.com/deepset-ai/haystack-tutorials/actions/workflows/publish_tutorials.yml)
 
 ## Tutorials
-| Name | Colab | Source Code |
-|---   |---    |---          |
-|Basic QA Pipeline|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)|[Source Code](./tutorials/01_Basic_QA_Pipeline.ipynb)|
-|Fine Tune a Model on Your Data|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb)|[Source Code](./tutorials/02_Finetune_a_model_on_your_data.ipynb)|
-|Basic QA Pipeline Without Elasticsearch|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb)|[Source Code](./tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb)|
-|FAQ Style QA|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)|[Source Code](./tutorials/04_FAQ_style_QA.ipynb)|
-|Evaluation|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)|[Source Code](./tutorials/05_Evaluation.ipynb)|
-|Better Retrieval via Embedding Retrieval|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)|[Source Code](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)|
-|RAG Generator|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)|[Source Code](./tutorials/07_RAG_Generator.ipynb)|
-|Preprocessing|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)|[Source Code](./tutorials/08_Preprocessing.ipynb)|
-|DPR Training|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)|[Source Code](./tutorials/09_DPR_training.ipynb)|
-|Knowledge Graph|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)|[Source Code](./tutorials/10_Knowledge_Graph.ipynb)|
-|Pipelines|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)|[Source Code](./tutorials/11_Pipelines.ipynb)|
-|Long-Form Question Answering|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)|[Source Code](./tutorials/12_LFQA.ipynb)|
-|Question Generation|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)|[Source Code](./tutorials/13_Question_generation.ipynb)|
-|Query Classifier|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)|[Source Code](./tutorials/14_Query_Classifier.ipynb)|
-|Table QA|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)|[Source Code](./tutorials/15_TableQA.ipynb)|
-|Document Classifier at Index Time|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb)|[Source Code](./tutorials/16_Document_Classifier_at_Index_Time.ipynb)|
-|Audio|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb)|[Source Code](./tutorials/17_Audio.ipynb)|
-|Generative Pseudo Labeling|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)|[Source Code](./tutorials/18_GPL.ipynb)|
-|Text-to-Image search|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb)|[Source Code](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb)|
+| Name                                     | Colab                                                                                                                                                                                                                                     | Source Code                                                                                                                                      |
+| ---------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
+| Basic QA Pipeline                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)                                       | [01_Basic_QA_Pipeline.ipynb](./tutorials/01_Basic_QA_Pipeline.ipynb)                                                                             |
+| Fine Tune a Model on Your Data           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb)                           | [02_Finetune_a_model_on_your_data.ipynb](./tutorials/02_Finetune_a_model_on_your_data.ipynb)                                                     |
+| Basic QA Pipeline Without Elasticsearch  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb)                 | [03_Basic_QA_Pipeline_without_Elasticsearch.ipynb](./tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb)                                 |
+| FAQ Style QA                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            | [04_FAQ_style_QA.ipynb](./tutorials/04_FAQ_style_QA.ipynb)                                                                                       |
+| Evaluation                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              | [05_Evaluation.ipynb](./tutorials/05_Evaluation.ipynb)                                                                                           |
+| Better Retrieval via Embedding Retrieval | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                | [06_Better_Retrieval_via_Embedding_Retrieval.ipynb](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                               |
+| RAG Generator                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           | [07_RAG_Generator.ipynb](./tutorials/07_RAG_Generator.ipynb)                                                                                     |
+| Preprocessing                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           | [08_Preprocessing.ipynb](./tutorials/08_Preprocessing.ipynb)                                                                                     |
+| DPR Training                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            | [09_DPR_training.ipynb](./tutorials/09_DPR_training.ipynb)                                                                                       |
+| Knowledge Graph                          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         | [10_Knowledge_Graph.ipynb](./tutorials/10_Knowledge_Graph.ipynb)                                                                                 |
+| Pipelines                                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               | [11_Pipelines.ipynb](./tutorials/11_Pipelines.ipynb)                                                                                             |
+| Long-Form Question Answering             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    | [12_LFQA.ipynb](./tutorials/12_LFQA.ipynb)                                                                                                       |
+| Question Generation                      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     | [13_Question_generation.ipynb](./tutorials/13_Question_generation.ipynb)                                                                         |
+| Query Classifier                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)                                        | [14_Query_Classifier.ipynb](./tutorials/14_Query_Classifier.ipynb)                                                                               |
+| Table QA                                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)                                                 | [15_TableQA.ipynb](./tutorials/15_TableQA.ipynb)                                                                                                 |
+| Document Classifier at Index Time        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb)                       | [16_Document_Classifier_at_Index_Time.ipynb](./tutorials/16_Document_Classifier_at_Index_Time.ipynb)                                             |
+| Audio                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb)                                                   | [17_Audio.ipynb](./tutorials/17_Audio.ipynb)                                                                                                     |
+| Generative Pseudo Labeling               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)                                                     | [18_GPL.ipynb](./tutorials/18_GPL.ipynb)                                                                                                         |
+| Text-to-Image search                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) | [19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) |

From 3aac361877dcb3fb80961f27b8fecd9bc2ba1bfa Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Tue, 6 Dec 2022 12:29:41 +0100
Subject: [PATCH 009/206] remove attempt to fix PIL error (#77)

---
 markdowns/08_Preprocessing.md    | 12 +-----------
 tutorials/08_Preprocessing.ipynb | 17 -----------------
 2 files changed, 1 insertion(+), 28 deletions(-)

diff --git a/markdowns/08_Preprocessing.md b/markdowns/08_Preprocessing.md
index 59470f53..8e08793c 100644
--- a/markdowns/08_Preprocessing.md
+++ b/markdowns/08_Preprocessing.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb
 toc: True
 title: "Preprocessing Your Documents"
-last_updated: 2022-11-24
+last_updated: 2022-11-25
 level: "beginner"
 weight: 25
 description: Start converting, cleaning, and splitting Documents using Haystack’s preprocessing capabilities.
@@ -30,16 +30,6 @@ docs = [
 
 This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
 
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
-
-You can double check whether the GPU runtime is enabled with the following command:
-
 
 ```bash
 %%bash
diff --git a/tutorials/08_Preprocessing.ipynb b/tutorials/08_Preprocessing.ipynb
index 671428b5..3764599b 100644
--- a/tutorials/08_Preprocessing.ipynb
+++ b/tutorials/08_Preprocessing.ipynb
@@ -45,23 +45,6 @@
     "This tutorial will show you all the tools that Haystack provides to help you cast your data into this format."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### Prepare environment\n",
-    "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
-    "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 26,

From 3ce119d5457d282312fee418c1d989314228b77a Mon Sep 17 00:00:00 2001
From: Benjamin BERNARD <benjamin.bernard@openpathview.fr>
Date: Tue, 6 Dec 2022 15:46:46 +0100
Subject: [PATCH 010/206] fix: Tutorial 2, finetune a model, distillation code
 (#69)

* fix: Tutorial 2, finetune a model, distillation code

Fixes #67

* re-generate markdown and merge master

Co-authored-by: Tuana Celik <tuana.celik@deepset.ai>
---
 markdowns/02_Finetune_a_model_on_your_data.md    | 11 ++++++-----
 tutorials/02_Finetune_a_model_on_your_data.ipynb |  9 +++++----
 2 files changed, 11 insertions(+), 9 deletions(-)

diff --git a/markdowns/02_Finetune_a_model_on_your_data.md b/markdowns/02_Finetune_a_model_on_your_data.md
index a4572607..dc0d1bc5 100644
--- a/markdowns/02_Finetune_a_model_on_your_data.md
+++ b/markdowns/02_Finetune_a_model_on_your_data.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb
 toc: True
 title: "Fine-Tuning a Model on Your Own Data"
-last_updated: 2022-11-24
+last_updated: 2022-12-06
 level: "intermediate"
 weight: 50
 description: Improve the performance of your Reader by performing fine-tuning.
@@ -121,18 +121,19 @@ To get the most out of model distillation, we recommend increasing the size of y
 # Downloading script
 !wget https://raw.githubusercontent.com/deepset-ai/haystack/main/haystack/utils/augment_squad.py
 
-doc_dir = "data/tutorial2"
+glove_dir = "data/tutorial2/gloves"
+squad_dir = "data/tutorial2/squad_small"
 
 # Downloading smaller glove vector file (only for demonstration purposes)
 glove_url = "https://nlp.stanford.edu/data/glove.6B.zip"
-fetch_archive_from_http(url=glove_url, output_dir=doc_dir)
+fetch_archive_from_http(url=glove_url, output_dir=glove_dir)
 
 # Downloading very small dataset to make tutorial faster (please use a bigger dataset for real use cases)
 s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/squad_small.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
+fetch_archive_from_http(url=s3_url, output_dir=squad_dir)
 
 # Just replace the path with your dataset and adjust the output (also please remove glove path to use bigger glove vector file)
-!python augment_squad.py --squad_path squad_small.json --output_path augmented_dataset.json --multiplication_factor 2 --glove_path glove.6B.300d.txt
+!python augment_squad.py --squad_path data/tutorial2/squad_small/squad_small.json --output_path augmented_dataset.json --multiplication_factor 2 --glove_path data/tutorial2/gloves/glove.6B.300d.txt
 ```
 
 In this case, we use a multiplication factor of 2 to keep this example lightweight. Usually you would use a factor like 20 depending on the size of your training data. Augmenting this small dataset with a multiplication factor of 2, should take about 5 to 10 minutes to run on one V100 GPU.
diff --git a/tutorials/02_Finetune_a_model_on_your_data.ipynb b/tutorials/02_Finetune_a_model_on_your_data.ipynb
index d2d23af8..343d55b2 100644
--- a/tutorials/02_Finetune_a_model_on_your_data.ipynb
+++ b/tutorials/02_Finetune_a_model_on_your_data.ipynb
@@ -219,18 +219,19 @@
     "# Downloading script\n",
     "!wget https://raw.githubusercontent.com/deepset-ai/haystack/main/haystack/utils/augment_squad.py\n",
     "\n",
-    "doc_dir = \"data/tutorial2\"\n",
+    "glove_dir = \"data/tutorial2/gloves\"\n",
+    "squad_dir = \"data/tutorial2/squad_small\"\n",
     "\n",
     "# Downloading smaller glove vector file (only for demonstration purposes)\n",
     "glove_url = \"https://nlp.stanford.edu/data/glove.6B.zip\"\n",
-    "fetch_archive_from_http(url=glove_url, output_dir=doc_dir)\n",
+    "fetch_archive_from_http(url=glove_url, output_dir=glove_dir)\n",
     "\n",
     "# Downloading very small dataset to make tutorial faster (please use a bigger dataset for real use cases)\n",
     "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/squad_small.json.zip\"\n",
-    "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
+    "fetch_archive_from_http(url=s3_url, output_dir=squad_dir)\n",
     "\n",
     "# Just replace the path with your dataset and adjust the output (also please remove glove path to use bigger glove vector file)\n",
-    "!python augment_squad.py --squad_path squad_small.json --output_path augmented_dataset.json --multiplication_factor 2 --glove_path glove.6B.300d.txt"
+    "!python augment_squad.py --squad_path data/tutorial2/squad_small/squad_small.json --output_path augmented_dataset.json --multiplication_factor 2 --glove_path data/tutorial2/gloves/glove.6B.300d.txt"
    ]
   },
   {

From e3d9f889c8a2e88ff4c8e989a69c4937c0a5f279 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 6 Dec 2022 19:58:30 +0300
Subject: [PATCH 011/206] Customize `colab` attribute to be able to skip Colab
 button (#81)

Issue: #73
---
 Contributing.md               | 4 ++--
 scripts/generate_markdowns.py | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/Contributing.md b/Contributing.md
index b08e7e07..613aabfe 100644
--- a/Contributing.md
+++ b/Contributing.md
@@ -10,7 +10,7 @@ To make a request for a new tutorial or to suggest edits and fixes, submit an is
 
 ## Contributing Edits or New Tutorials
 
-All of the Haystack tutorials live in the `tutorials` folder in this repo. Each tutorial is an interactive `.ipynb` file that you can run on Google Colab, too. For each `.ipynb` file, we also generate a Markdown file to accompany it.
+All of the Haystack tutorials live in the `tutorials` folder in this repo. Each tutorial is an interactive `.ipynb` file and we generate a Markdown file to accompany it.
 
 Here's what you need to do to add or edit tutorials 👇:
 
@@ -20,7 +20,7 @@ Here's what you need to do to add or edit tutorials 👇:
    tasks right before all git commit operations.
 2. If you're creating a new tutorial:
    - Follow the [naming convention](#naming-convention-for-file-names) for file names.
-   - Add your new tutorial to [index.toml](/index.toml). Here, `weight` is the order in which your tutorial appears. For example, a tutorial with `weight = 15` comes after a tutorial with `weight = 10` and before `20`.
+   - Add your new tutorial to [index.toml](/index.toml). Here, `weight` is the order in which your tutorial appears. For example, a tutorial with `weight = 15` comes after a tutorial with `weight = 10` and before `20`. Each tutorial comes with a Google Colab link and `Open in Colab` button on the top of the tutorial by default. If your new tutorial cannot be run on Google Colab, set `colab = false` not to display `Open in Colab` button on top the tutorial.
 3. Edit an existing tutorial or copy the [tutorial template](/tutorials/template.ipynb) to create a new tutorial. 
 4. Pre-commit hooks will ensure the `markdowns` folder reflects your changes but you can update the docs at any time:
     - Run `python scripts/generate_markdowns.py --index index.toml --notebooks tutorials/your-tutorial.ipynb --output markdowns/`. This generates or updates the relevant markdown file in `/markdowns`.
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index 1a0eb8b8..2f75b97b 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -18,7 +18,7 @@ def generate_frontmatter(config, tutorial):
 
     frontmatter = f"""---
 layout: {config["layout"]}
-colab: {config["colab"]}{tutorial["notebook"]}
+colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')}
 toc: {config["toc"]}
 title: "{tutorial["title"]}"
 last_updated: {date.today()}
@@ -39,7 +39,7 @@ def generate_markdown_from_notebook(config, tutorial, output_path, tutorials_pat
     body, _ = md_exporter.from_filename(f"{tutorials_path}")
     body = get_lines(body, start=1)
     print(f"Processing {tutorials_path}")
-    filename = tutorial.get('slug', tutorial['notebook'][:-6])
+    filename = tutorial.get("slug", tutorial["notebook"][:-6])
     with open(f"{output_path}/{filename}.md", "w", encoding="utf-8") as f:
         try:
             f.write(frontmatter + "\n\n")

From 5d5180e5289eb4d6c40ba1be568c5eedf00f4563 Mon Sep 17 00:00:00 2001
From: Vladimir Blagojevic <dovlex@gmail.com>
Date: Wed, 7 Dec 2022 13:59:36 +0100
Subject: [PATCH 012/206] Use tqdm auto instead of plain tqdm (#84)

---
 markdowns/13_Question_generation.md    | 4 ++--
 tutorials/13_Question_generation.ipynb | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/markdowns/13_Question_generation.md b/markdowns/13_Question_generation.md
index eb8fa491..3135b73f 100644
--- a/markdowns/13_Question_generation.md
+++ b/markdowns/13_Question_generation.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb
 toc: True
 title: "Question Generation"
-last_updated: 2022-11-24
+last_updated: 2022-12-07
 level: "intermediate"
 weight: 75
 description: Generate a set of questions that can be answered by a given Document.
@@ -55,7 +55,7 @@ logging.getLogger("haystack").setLevel(logging.INFO)
 # Imports needed to run this notebook
 
 from pprint import pprint
-from tqdm import tqdm
+from tqdm.auto import tqdm
 from haystack.nodes import QuestionGenerator, BM25Retriever, FARMReader
 from haystack.document_stores import ElasticsearchDocumentStore
 from haystack.pipelines import (
diff --git a/tutorials/13_Question_generation.ipynb b/tutorials/13_Question_generation.ipynb
index 75b76381..b2e37110 100644
--- a/tutorials/13_Question_generation.ipynb
+++ b/tutorials/13_Question_generation.ipynb
@@ -100,7 +100,7 @@
     "# Imports needed to run this notebook\n",
     "\n",
     "from pprint import pprint\n",
-    "from tqdm import tqdm\n",
+    "from tqdm.auto import tqdm\n",
     "from haystack.nodes import QuestionGenerator, BM25Retriever, FARMReader\n",
     "from haystack.document_stores import ElasticsearchDocumentStore\n",
     "from haystack.pipelines import (\n",

From 0fd49628eb2c9828d00ee145fa296ae6b3de2293 Mon Sep 17 00:00:00 2001
From: Julian Risch <julian.risch@deepset.ai>
Date: Fri, 9 Dec 2022 10:40:15 +0100
Subject: [PATCH 013/206] bug: remove duplicate ES daemon execution (#86)

* remove duplicate daemon execution

* generate markdown files

* fix broken link

* remove duplicate daemon exec from 15,16,17

* generate md files
---
 .github/workflows/markdowns.yml                      | 2 +-
 markdowns/04_FAQ_style_QA.md                         | 4 ++--
 markdowns/11_Pipelines.md                            | 4 ++--
 markdowns/15_TableQA.md                              | 4 ++--
 markdowns/16_Document_Classifier_at_Index_Time.md    | 4 ++--
 markdowns/17_Audio.md                                | 4 ++--
 tutorials/04_FAQ_style_QA.ipynb                      | 3 +--
 tutorials/11_Pipelines.ipynb                         | 3 +--
 tutorials/15_TableQA.ipynb                           | 3 +--
 tutorials/16_Document_Classifier_at_Index_Time.ipynb | 3 +--
 tutorials/17_Audio.ipynb                             | 3 +--
 11 files changed, 16 insertions(+), 21 deletions(-)

diff --git a/.github/workflows/markdowns.yml b/.github/workflows/markdowns.yml
index 0cfec054..5fa77165 100644
--- a/.github/workflows/markdowns.yml
+++ b/.github/workflows/markdowns.yml
@@ -44,7 +44,7 @@ jobs:
             echo "#"
             echo "#    python scripts/generate_markdowns.py --index index.toml --output markdowns --notebooks ..."
             echo "#"
-            echo "# or see https://github.com/deepset-ai/haystack-tutorials/blob/main/CONTRIBUTING.md for help."
+            echo "# or see https://github.com/deepset-ai/haystack-tutorials/blob/main/Contributing.md for help."
             echo "#"
             echo "# If you have further problems, please open an issue: https://github.com/deepset-ai/haystack-tutorials/issues"
             echo "#"
diff --git a/markdowns/04_FAQ_style_QA.md b/markdowns/04_FAQ_style_QA.md
index dcb44ca4..012ef3d5 100644
--- a/markdowns/04_FAQ_style_QA.md
+++ b/markdowns/04_FAQ_style_QA.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb
 toc: True
 title: "Utilizing Existing FAQs for Question Answering"
-last_updated: 2022-11-24
+last_updated: 2022-12-08
 level: "beginner"
 weight: 20
 description: Create a smarter way to answer new questions using your existing FAQ documents.
@@ -92,7 +92,7 @@ If Docker is not readily available in your environment (e.g. in Colab notebooks)
 wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
 tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
 chown -R daemon:daemon elasticsearch-7.9.2
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d
+
 ```
 
 
diff --git a/markdowns/11_Pipelines.md b/markdowns/11_Pipelines.md
index e30462bc..a8e6ca9e 100644
--- a/markdowns/11_Pipelines.md
+++ b/markdowns/11_Pipelines.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb
 toc: True
 title: "How to Use Pipelines"
-last_updated: 2022-11-24
+last_updated: 2022-12-08
 level: "intermediate"
 weight: 65
 description: Learn about the many ways which you can route queries through the nodes in a pipeline.
@@ -85,7 +85,7 @@ If Docker is not readily available in your environment (e.g. in Colab notebooks)
 wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
 tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
 chown -R daemon:daemon elasticsearch-7.9.2
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d
+
 ```
 
 
diff --git a/markdowns/15_TableQA.md b/markdowns/15_TableQA.md
index 131dcfbc..ad702986 100644
--- a/markdowns/15_TableQA.md
+++ b/markdowns/15_TableQA.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb
 toc: True
 title: "Open-Domain QA on Tables"
-last_updated: 2022-11-24
+last_updated: 2022-12-08
 level: "advanced"
 weight: 130
 description: Perform question answering on tabular data.
@@ -99,7 +99,7 @@ If Docker is not readily available in your environment (e.g. in Colab notebooks)
 wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
 tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
 chown -R daemon:daemon elasticsearch-7.9.2
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d
+
 ```
 
 
diff --git a/markdowns/16_Document_Classifier_at_Index_Time.md b/markdowns/16_Document_Classifier_at_Index_Time.md
index 6f8d5d24..8df6fcba 100644
--- a/markdowns/16_Document_Classifier_at_Index_Time.md
+++ b/markdowns/16_Document_Classifier_at_Index_Time.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb
 toc: True
 title: "Document Classification at Index Time"
-last_updated: 2022-11-24
+last_updated: 2022-12-08
 level: "intermediate"
 weight: 85
 description: Generate and attach classification labels to your Documents when indexing.
@@ -151,7 +151,7 @@ If Docker is not readily available in your environment (e.g. in Colab notebooks)
 wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
 tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
 chown -R daemon:daemon elasticsearch-7.9.2
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d
+
 ```
 
 
diff --git a/markdowns/17_Audio.md b/markdowns/17_Audio.md
index 55d93e87..ff57c7b1 100644
--- a/markdowns/17_Audio.md
+++ b/markdowns/17_Audio.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb
 toc: True
 title: "Make Your QA Pipelines Talk!"
-last_updated: 2022-11-24
+last_updated: 2022-12-08
 level: "intermediate"
 weight: 90
 description: Convert text Answers into speech.
@@ -84,7 +84,7 @@ If Docker is not readily available in your environment (e.g. in Colab notebooks)
 wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
 tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
 chown -R daemon:daemon elasticsearch-7.9.2
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d
+
 ```
 
 
diff --git a/tutorials/04_FAQ_style_QA.ipynb b/tutorials/04_FAQ_style_QA.ipynb
index c3bcc845..a92af09a 100644
--- a/tutorials/04_FAQ_style_QA.ipynb
+++ b/tutorials/04_FAQ_style_QA.ipynb
@@ -157,8 +157,7 @@
     "\n",
     "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
     "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d"
+    "chown -R daemon:daemon elasticsearch-7.9.2\n"
    ]
   },
   {
diff --git a/tutorials/11_Pipelines.ipynb b/tutorials/11_Pipelines.ipynb
index 204f8158..b9406566 100644
--- a/tutorials/11_Pipelines.ipynb
+++ b/tutorials/11_Pipelines.ipynb
@@ -166,8 +166,7 @@
     "\n",
     "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
     "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d"
+    "chown -R daemon:daemon elasticsearch-7.9.2\n"
    ]
   },
   {
diff --git a/tutorials/15_TableQA.ipynb b/tutorials/15_TableQA.ipynb
index 54acd803..e623677d 100644
--- a/tutorials/15_TableQA.ipynb
+++ b/tutorials/15_TableQA.ipynb
@@ -179,8 +179,7 @@
     "\n",
     "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
     "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d"
+    "chown -R daemon:daemon elasticsearch-7.9.2\n"
    ]
   },
   {
diff --git a/tutorials/16_Document_Classifier_at_Index_Time.ipynb b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
index 5d943828..0a1c8d01 100644
--- a/tutorials/16_Document_Classifier_at_Index_Time.ipynb
+++ b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
@@ -268,8 +268,7 @@
     "\n",
     "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
     "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d"
+    "chown -R daemon:daemon elasticsearch-7.9.2\n"
    ]
   },
   {
diff --git a/tutorials/17_Audio.ipynb b/tutorials/17_Audio.ipynb
index 7bc5fd10..d15bfe94 100644
--- a/tutorials/17_Audio.ipynb
+++ b/tutorials/17_Audio.ipynb
@@ -153,8 +153,7 @@
     "\n",
     "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
     "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d"
+    "chown -R daemon:daemon elasticsearch-7.9.2\n"
    ]
   },
   {

From c7684ba5c897f0301e6356c63eb0d16754bc267c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 9 Dec 2022 20:35:07 +0300
Subject: [PATCH 014/206] Update titles in tutorial 19 (#74)

* Update title convention

* Create one main title and make other titles h2
* Format .ipynb with thw new Black config
* Format .md according to new .ipynb

Issue: #64

* Remove the title and add download button

PR: #74
---
 ...arch_pipeline_with_MultiModal_Retriever.md |  43 +-
 ...h_pipeline_with_MultiModal_Retriever.ipynb | 807 +++++++++---------
 2 files changed, 422 insertions(+), 428 deletions(-)

diff --git a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
index 462392e6..37ec4d37 100644
--- a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
+++ b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
@@ -3,15 +3,17 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
 toc: True
 title: "Text-To-Image Search Pipeline with Multimodal Retriever"
-last_updated: 2022-11-07
+last_updated: 2022-11-25
 level: "intermediate"
 weight: 95
 description: Use a MultiModalRetriever to build a cross-modal search pipeline.
 category: "QA"
 aliases: ['/tutorials/multimodal']
+download: "/downloads/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
 ---
     
 
+
 **Level**: Intermediate
 
 **Time to complete**: 20 minutes
@@ -34,7 +36,7 @@ Let's build a text-to-image search pipeline using a small animal dataset!
 - [Check if GPU is Enabled](https://docs.haystack.deepset.ai/v5.2-unstable/docs/check-if-gpu-is-enabled)
 - [Set logging level to INFO](https://docs.haystack.deepset.ai/v5.2-unstable/docs/set-the-logging-level)
 
-# Installing Haystack
+## Installing Haystack
 
 
 ```bash
@@ -44,7 +46,7 @@ pip install --upgrade pip
 pip install farm-haystack[colab]
 ```
 
-# Initializing the DocumentStore
+## Initializing the DocumentStore
 
 A DocumentStore stores references to the images that Haystack will compare with your query. But before it can do that, you need to initialize it. In this tutorial, you'll use the InMemoryDocumentStore.
 
@@ -54,12 +56,12 @@ If you want to learn more, see [DocumentStore](https://docs.haystack.deepset.ai/
 ```python
 from haystack.document_stores import InMemoryDocumentStore
 
-# Here Here we initialize the DocumentStore to store 512 dim image embeddings 
+# Here Here we initialize the DocumentStore to store 512 dim image embeddings
 # obtained using OpenAI CLIP model
 document_store = InMemoryDocumentStore(embedding_dim=512)
 ```
 
-# Downloading Data
+## Downloading Data
 
 Download 18 sample images of different animals and store it. You can find them in data/tutorial19/spirit-animals/ as a set of .jpg files.
 
@@ -71,7 +73,7 @@ doc_dir = "data/tutorial19"
 
 fetch_archive_from_http(
     url="https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/spirit-animals.zip",
-    output_dir=doc_dir
+    output_dir=doc_dir,
 )
 ```
 
@@ -95,7 +97,7 @@ You have successfully stored your images in the DocumentStore.
 
 
 
-# Initializing the Retriever
+## Initializing the Retriever
 
 Retrievers sift through all the images and return only those that are relevant to the query. To run a search on images, you'll use the MultiModalRetriever with the [OpenAI CLIP model](https://github.com/openai/CLIP/blob/main/model-card.md). 
 
@@ -109,9 +111,9 @@ from haystack.nodes.retriever.multimodal import MultiModalRetriever
 
 retriever_text_to_image = MultiModalRetriever(
     document_store=document_store,
-    query_embedding_model = "sentence-transformers/clip-ViT-B-32",
+    query_embedding_model="sentence-transformers/clip-ViT-B-32",
     query_type="text",
-    document_embedding_models = {"image": "sentence-transformers/clip-ViT-B-32"}
+    document_embedding_models={"image": "sentence-transformers/clip-ViT-B-32"},
 )
 
 # Now let's turn our images into embeddings and store them in the DocumentStore.
@@ -120,7 +122,7 @@ document_store.update_embeddings(retriever=retriever_text_to_image)
 
  Your retriever is now ready for search!
 
-# Creating the MultiModal Search Pipeline
+## Creating the MultiModal Search Pipeline
 
 We are populating a pipeline with a MultiModalRetriever node. This search pipeline queries the image database with text and returns the most relevant images.
 
@@ -129,25 +131,18 @@ We are populating a pipeline with a MultiModalRetriever node. This search pipeli
 from haystack import Pipeline
 
 pipeline = Pipeline()
-pipeline.add_node(
-    component=retriever_text_to_image, 
-    name="retriever_text_to_image", 
-    inputs=["Query"]
-)
+pipeline.add_node(component=retriever_text_to_image, name="retriever_text_to_image", inputs=["Query"])
 ```
 
 Now, you have a pipeline that uses the MultiModalRetriever and takes a text query as input. Let's try it out.
 
-# Searching Through the Images
+## Searching Through the Images
 
 Use the pipeline `run()` method to query the images in the DocumentStore. The query argument is where you type your text query. Additionally, you can set the number of images you want the MultiModalRetriever to return using the `top-k` parameter. To learn more about setting arguments, see [Pipeline Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments).
 
 
 ```python
-results = pipeline.run(
-    query="Animal that lives in the water",
-    params={"retriever_text_to_image": {"top_k": 3}}
-)
+results = pipeline.run(query="Animal that lives in the water", params={"retriever_text_to_image": {"top_k": 3}})
 
 # Sort the results based on the scores
 results = sorted(results["documents"], key=lambda d: d.score, reverse=True)
@@ -175,17 +170,19 @@ from io import BytesIO
 from PIL import Image, ImageDraw, ImageOps
 from IPython.display import display, Image as IPImage
 
+
 def display_img_array(ima, score):
     im = Image.open(ima)
-    img_with_border = ImageOps.expand(im ,border=20, fill='white')
+    img_with_border = ImageOps.expand(im, border=20, fill="white")
 
     # Add Text to an image
     img = ImageDraw.Draw(img_with_border)
     img.text((20, 0), f"Score: {score},    Path: {ima}", fill=(0, 0, 0))
 
     bio = BytesIO()
-    img_with_border.save(bio, format='png')
-    display(IPImage(bio.getvalue(), format='png'))
+    img_with_border.save(bio, format="png")
+    display(IPImage(bio.getvalue(), format="png"))
+
 
 images_array = [doc.content for doc in results]
 scores = [doc.score for doc in results]
diff --git a/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb b/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
index d0ebcf79..b0fe17c6 100644
--- a/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
+++ b/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
@@ -1,418 +1,415 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CQyfa3akfIEZ"
-      },
-      "source": [
-        "**Level**: Intermediate\n",
-        "\n",
-        "**Time to complete**: 20 minutes\n",
-        "\n",
-        "**Prerequisites**: This tutorial assumes basic knowledge of Haystack Retrievers and Pipelines. If you want to learn about them, have a look at our tutorials on [Build Your First QA System](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb) and [Fine-Tuning a Model on Your Own Data](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb).\n",
-        "\n",
-        "Prepare the Colab environment (see links below).\n",
-        "\n",
-        "**Nodes Used**: InMemoryDocumentStore, MultiModalRetriever\n",
-        "\n",
-        "**Goal**: After completing this tutorial, you will have built a search system that retrieves images as answers to a text query.\n",
-        "\n",
-        "**Description**: In this tutorial, you'll download a set of images that you'll then turn into embeddings using a transformers model, OpenAI CLIP. You'll then use the same model to embed the text query. Finally, you'll perform a nearest neighbor search to retrieve the images relevant to the text query.\n",
-        "\n",
-        "Let's build a text-to-image search pipeline using a small animal dataset!"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fzn2uA1Be1Km"
-      },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in GPU](https://docs.haystack.deepset.ai/v5.2-unstable/docs/enable-gpu-runtime-in-colab)\n",
-        "- [Check if GPU is Enabled](https://docs.haystack.deepset.ai/v5.2-unstable/docs/check-if-gpu-is-enabled)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v5.2-unstable/docs/set-the-logging-level)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tJU29jj0fX5m"
-      },
-      "source": [
-        "# Installing Haystack"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Hl92D-ZlycPh"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install --upgrade pip\n",
-        "pip install farm-haystack[colab]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KkVAG7FdXsEU"
-      },
-      "source": [
-        "# Initializing the DocumentStore\n",
-        "\n",
-        "A DocumentStore stores references to the images that Haystack will compare with your query. But before it can do that, you need to initialize it. In this tutorial, you'll use the InMemoryDocumentStore.\n",
-        "\n",
-        "If you want to learn more, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "dK86aFlSYQXv"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.document_stores import InMemoryDocumentStore\n",
-        "\n",
-        "# Here Here we initialize the DocumentStore to store 512 dim image embeddings \n",
-        "# obtained using OpenAI CLIP model\n",
-        "document_store = InMemoryDocumentStore(embedding_dim=512)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "oGNwBu0yYcDq"
-      },
-      "source": [
-        "# Downloading Data\n",
-        "\n",
-        "Download 18 sample images of different animals and store it. You can find them in data/tutorial19/spirit-animals/ as a set of .jpg files."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "7yk_Prp3yYUa"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.utils import fetch_archive_from_http\n",
-        "\n",
-        "doc_dir = \"data/tutorial19\"\n",
-        "\n",
-        "fetch_archive_from_http(\n",
-        "    url=\"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/spirit-animals.zip\",\n",
-        "    output_dir=doc_dir\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "oOJC6m8cqzCl"
-      },
-      "source": [
-        "Add the images you just downloaded into Haystack Document objects and write them into the DocumentStore."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3pdDsSVp40vr"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "from haystack import Document\n",
-        "\n",
-        "images = [\n",
-        "    Document(content=f\"./{doc_dir}/spirit-animals/{filename}\", content_type=\"image\")\n",
-        "    for filename in os.listdir(f\"./{doc_dir}/spirit-animals/\")\n",
-        "]\n",
-        "\n",
-        "document_store.write_documents(images)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_U-RlPJWHMjO"
-      },
-      "source": [
-        "You have successfully stored your images in the DocumentStore.\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nfmeRIE9wz9o"
-      },
-      "source": [
-        "# Initializing the Retriever\n",
-        "\n",
-        "Retrievers sift through all the images and return only those that are relevant to the query. To run a search on images, you'll use the MultiModalRetriever with the [OpenAI CLIP model](https://github.com/openai/CLIP/blob/main/model-card.md). \n",
-        "\n",
-        "For more details on supported modalities, see [MultiModalRetriever](https://docs.haystack.deepset.ai/docs/retriever#multimodal-retrieval).\n",
-        "\n",
-        "Before adding the Retriever to your pipeline, let's configure its parameters"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "xuL1mtq6qx0d"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.nodes.retriever.multimodal import MultiModalRetriever\n",
-        "\n",
-        "retriever_text_to_image = MultiModalRetriever(\n",
-        "    document_store=document_store,\n",
-        "    query_embedding_model = \"sentence-transformers/clip-ViT-B-32\",\n",
-        "    query_type=\"text\",\n",
-        "    document_embedding_models = {\"image\": \"sentence-transformers/clip-ViT-B-32\"}\n",
-        ")\n",
-        "\n",
-        "# Now let's turn our images into embeddings and store them in the DocumentStore.\n",
-        "document_store.update_embeddings(retriever=retriever_text_to_image)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mcopKii2MBCd"
-      },
-      "source": [
-        " Your retriever is now ready for search!"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vMNYvDjd9sqY"
-      },
-      "source": [
-        "# Creating the MultiModal Search Pipeline\n",
-        "\n",
-        "We are populating a pipeline with a MultiModalRetriever node. This search pipeline queries the image database with text and returns the most relevant images."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "-a6ltABP40vs"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack import Pipeline\n",
-        "\n",
-        "pipeline = Pipeline()\n",
-        "pipeline.add_node(\n",
-        "    component=retriever_text_to_image, \n",
-        "    name=\"retriever_text_to_image\", \n",
-        "    inputs=[\"Query\"]\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wbEkh5oSMJbq"
-      },
-      "source": [
-        "Now, you have a pipeline that uses the MultiModalRetriever and takes a text query as input. Let's try it out."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TSjizWzAF6T9"
-      },
-      "source": [
-        "# Searching Through the Images\n",
-        "\n",
-        "Use the pipeline `run()` method to query the images in the DocumentStore. The query argument is where you type your text query. Additionally, you can set the number of images you want the MultiModalRetriever to return using the `top-k` parameter. To learn more about setting arguments, see [Pipeline Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qRH5UbIdF7CW"
-      },
-      "outputs": [],
-      "source": [
-        "results = pipeline.run(\n",
-        "    query=\"Animal that lives in the water\",\n",
-        "    params={\"retriever_text_to_image\": {\"top_k\": 3}}\n",
-        ")\n",
-        "\n",
-        "# Sort the results based on the scores\n",
-        "results = sorted(results[\"documents\"], key=lambda d: d.score, reverse=True)\n",
-        "\n",
-        "for doc in results:\n",
-        "    print(doc.score, doc.content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FoEt2cHHTdIZ"
-      },
-      "source": [
-        "Here are some more query strings you could try out:\n",
-        "\n",
-        "1.   King of the Jungle\n",
-        "2.   Fastest animal\n",
-        "3.   Bird that can see clearly even in the dark\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KBukVUVVU0if"
-      },
-      "source": [
-        "You can also easily vizualize these images together with their score using this code:\n",
-        "\n",
-        "\n"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CQyfa3akfIEZ"
+   },
+   "source": [
+    "# Text-To-Image Search Pipeline with Multimodal Retriever\n",
+    "\n",
+    "**Level**: Intermediate\n",
+    "\n",
+    "**Time to complete**: 20 minutes\n",
+    "\n",
+    "**Prerequisites**: This tutorial assumes basic knowledge of Haystack Retrievers and Pipelines. If you want to learn about them, have a look at our tutorials on [Build Your First QA System](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb) and [Fine-Tuning a Model on Your Own Data](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb).\n",
+    "\n",
+    "Prepare the Colab environment (see links below).\n",
+    "\n",
+    "**Nodes Used**: InMemoryDocumentStore, MultiModalRetriever\n",
+    "\n",
+    "**Goal**: After completing this tutorial, you will have built a search system that retrieves images as answers to a text query.\n",
+    "\n",
+    "**Description**: In this tutorial, you'll download a set of images that you'll then turn into embeddings using a transformers model, OpenAI CLIP. You'll then use the same model to embed the text query. Finally, you'll perform a nearest neighbor search to retrieve the images relevant to the text query.\n",
+    "\n",
+    "Let's build a text-to-image search pipeline using a small animal dataset!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fzn2uA1Be1Km"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in GPU](https://docs.haystack.deepset.ai/v5.2-unstable/docs/enable-gpu-runtime-in-colab)\n",
+    "- [Check if GPU is Enabled](https://docs.haystack.deepset.ai/v5.2-unstable/docs/check-if-gpu-is-enabled)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v5.2-unstable/docs/set-the-logging-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tJU29jj0fX5m"
+   },
+   "source": [
+    "## Installing Haystack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Hl92D-ZlycPh"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KkVAG7FdXsEU"
+   },
+   "source": [
+    "## Initializing the DocumentStore\n",
+    "\n",
+    "A DocumentStore stores references to the images that Haystack will compare with your query. But before it can do that, you need to initialize it. In this tutorial, you'll use the InMemoryDocumentStore.\n",
+    "\n",
+    "If you want to learn more, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "dK86aFlSYQXv"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.document_stores import InMemoryDocumentStore\n",
+    "\n",
+    "# Here Here we initialize the DocumentStore to store 512 dim image embeddings\n",
+    "# obtained using OpenAI CLIP model\n",
+    "document_store = InMemoryDocumentStore(embedding_dim=512)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oGNwBu0yYcDq"
+   },
+   "source": [
+    "## Downloading Data\n",
+    "\n",
+    "Download 18 sample images of different animals and store it. You can find them in data/tutorial19/spirit-animals/ as a set of .jpg files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7yk_Prp3yYUa"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.utils import fetch_archive_from_http\n",
+    "\n",
+    "doc_dir = \"data/tutorial19\"\n",
+    "\n",
+    "fetch_archive_from_http(\n",
+    "    url=\"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/spirit-animals.zip\",\n",
+    "    output_dir=doc_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oOJC6m8cqzCl"
+   },
+   "source": [
+    "Add the images you just downloaded into Haystack Document objects and write them into the DocumentStore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "3pdDsSVp40vr"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from haystack import Document\n",
+    "\n",
+    "images = [\n",
+    "    Document(content=f\"./{doc_dir}/spirit-animals/{filename}\", content_type=\"image\")\n",
+    "    for filename in os.listdir(f\"./{doc_dir}/spirit-animals/\")\n",
+    "]\n",
+    "\n",
+    "document_store.write_documents(images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_U-RlPJWHMjO"
+   },
+   "source": [
+    "You have successfully stored your images in the DocumentStore.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nfmeRIE9wz9o"
+   },
+   "source": [
+    "## Initializing the Retriever\n",
+    "\n",
+    "Retrievers sift through all the images and return only those that are relevant to the query. To run a search on images, you'll use the MultiModalRetriever with the [OpenAI CLIP model](https://github.com/openai/CLIP/blob/main/model-card.md). \n",
+    "\n",
+    "For more details on supported modalities, see [MultiModalRetriever](https://docs.haystack.deepset.ai/docs/retriever#multimodal-retrieval).\n",
+    "\n",
+    "Before adding the Retriever to your pipeline, let's configure its parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xuL1mtq6qx0d"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes.retriever.multimodal import MultiModalRetriever\n",
+    "\n",
+    "retriever_text_to_image = MultiModalRetriever(\n",
+    "    document_store=document_store,\n",
+    "    query_embedding_model=\"sentence-transformers/clip-ViT-B-32\",\n",
+    "    query_type=\"text\",\n",
+    "    document_embedding_models={\"image\": \"sentence-transformers/clip-ViT-B-32\"},\n",
+    ")\n",
+    "\n",
+    "# Now let's turn our images into embeddings and store them in the DocumentStore.\n",
+    "document_store.update_embeddings(retriever=retriever_text_to_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mcopKii2MBCd"
+   },
+   "source": [
+    " Your retriever is now ready for search!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vMNYvDjd9sqY"
+   },
+   "source": [
+    "## Creating the MultiModal Search Pipeline\n",
+    "\n",
+    "We are populating a pipeline with a MultiModalRetriever node. This search pipeline queries the image database with text and returns the most relevant images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-a6ltABP40vs"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "\n",
+    "pipeline = Pipeline()\n",
+    "pipeline.add_node(component=retriever_text_to_image, name=\"retriever_text_to_image\", inputs=[\"Query\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wbEkh5oSMJbq"
+   },
+   "source": [
+    "Now, you have a pipeline that uses the MultiModalRetriever and takes a text query as input. Let's try it out."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TSjizWzAF6T9"
+   },
+   "source": [
+    "## Searching Through the Images\n",
+    "\n",
+    "Use the pipeline `run()` method to query the images in the DocumentStore. The query argument is where you type your text query. Additionally, you can set the number of images you want the MultiModalRetriever to return using the `top-k` parameter. To learn more about setting arguments, see [Pipeline Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "qRH5UbIdF7CW"
+   },
+   "outputs": [],
+   "source": [
+    "results = pipeline.run(query=\"Animal that lives in the water\", params={\"retriever_text_to_image\": {\"top_k\": 3}})\n",
+    "\n",
+    "# Sort the results based on the scores\n",
+    "results = sorted(results[\"documents\"], key=lambda d: d.score, reverse=True)\n",
+    "\n",
+    "for doc in results:\n",
+    "    print(doc.score, doc.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FoEt2cHHTdIZ"
+   },
+   "source": [
+    "Here are some more query strings you could try out:\n",
+    "\n",
+    "1.   King of the Jungle\n",
+    "2.   Fastest animal\n",
+    "3.   Bird that can see clearly even in the dark\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KBukVUVVU0if"
+   },
+   "source": [
+    "You can also easily vizualize these images together with their score using this code:\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "qSjZHuv68Hut",
+    "outputId": "0cb0d794-3dc2-4034-e0c9-277ca318f23f"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "qSjZHuv68Hut",
-        "outputId": "0cb0d794-3dc2-4034-e0c9-277ca318f23f"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
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viEhzypbS2pyYA5EAUDLeGqf7HwUArsWDi9Dp0YmI1IZL291VtQtUMuG9YF9dUfQ3x673IknvV/uw+LAcjz4JBjxJuhPugsEBoQevP7AAIIdr58Nk7j6bzbZnijZ3CrjigTNj1zZmJoeZWWpzbnJu3T1Ys9Vpl//bPFXEZKYSPYsIRE4EBxOIjOCbeJOZ3A3MXmzMICInMGBwd8+rVwCYg4RqJlHmgX1eVzKv+PJqdXGRVk2GKYswB2G4pYNLtfBU3ujeN0K7uXtvB9T3957w3jFiTzxujbzt0XzMzlZj+CQY8BgmxvxmeNvejO8N2/bffDz3jdHZgLuOEhEiapqmK9Opppk55QbYSMzCxV2ZmVngMM85o9Ws65xbmIMMrJsnRYqSu9QmIr51n+6+yZ2cvNiXFU5uZERECgWEmdm9UxozucNZG3c3gqBmns1jlNP6pI4ndXVSx8i0XK5TNjOoZcua3fp27k7xXlXETMW+a2Yp5bZtVTP4vOw8ukc++Bn1EWHfBvxoR+dt25gfEB8kA37MHTrhw0W3XN8G98XWC6nPkAoTatu234DuolEInFlEJEoUkcASiFWTu7Ztcm036lwywMmUmQUkcIYXORfYKLs7ng/apJ3IgcgBNhhIiNzI3UHkcDM4k4B9wziNSUxV1c2tzWbmIQYSFg6LuhauY7VcN6vlerVqLq5Wqu3JSdzuBzYoVcUYu+ucc9Nw0yAlukgysPs+ckL/iWBMBf3G9fSf3Vfw3BOTCnrCJ4oPfWNU1utb4r4AzGyf+xLRyclJ30+4805iYi7/T+UE77VJOOesqXHPwmBxd4e5UYYzuRd5lZ231V77XRfTcrnIFbEDxmCQmRQ5F56zMdyZxYlEwIGFhIgtEcPM3CznJWskDs6REKrIIrN5HZd1dVWtqijrJlanm6xofWdvIoTgzCZCRKgqrusqZ1atmu8rVc05Fx48cd/HDHcvWe+OeqT37LUT4gM26eD1Y8YHyYCPHbYPZTA+dHzoDNjsbbHegg1b3Spju/NIRRHdceXyJxHNwozMVT3nbLkcwLVsLkKFUQHKhQiaqyrTdQpCAO4bUZKIuuyE3evc3Sw4OdzIS7JDFy9Jl2BugJV0SARmBGGGODsVFq8GIgc5EZrUMgemGALP61ogs9nMzK/0BwC2RU52vbdgFpEQQtmFhEixijFG7MULe3v7oQl3x0ESeuy49PdVEwreLQO+R77Pe7HQ0RGfvCsfEh/6RodpuxxKqqLt/TfY8HXSW6fWZuZ16+dPToKY6Voote3l6upHt1aIT09PKVRVPT87fRbDPKcSWdmMrNW20dS0DVSFmZm///13z549y1iKSMp53ejJycnF6x+CEJHDVq46q/j0ZF7F4K4SGDAF3JGd1MiZ3GjWKjMHRkUugLhq1uxaVVWIwYG15eCRJCaidt1WPNfcMpNpCmx1MMuX5BTcyATE7AQicPZsavos/TZlW7eWFcQBwtmSWmveVlUIJr5y1xCrs8X8ySzOVqdXr18tLSVYBCrjijhI5GQNkEG+cfB2AAwQH5tf/BPLR/5Q67Hnb7WzEMZieo9h35v97VGMD4XNf5AS8IQJHyJiJcxw95RS9ibnBCCEYJpSSjmbqlf1gqkqcdPXzdq6iFYpb5yHmefzeda2/Nm2bUpJRNq2vWrblBpYrmO0ee2WZ3UVArtHM3MCqDhnESAs5K5lh6Bw9hJRGkRSlMAiEmMtLOv1us2Wc155K8T1LAqTcBQRGPsmCbHDi7q5KM3diGxLXnPOWZMzhQgWNkWzWi9V2VmocmXxQDOqqtMQWuKW3JmFWMyRc97bJ/MU5HLCx4GJAU+Y8IY4dv9erJ6pba6WF5quclqy5armugogSynnbEEu2sbMnClkS+6uqimlnJOZqYKJqqparVY5ZxFu27ZtWzP7/vsfU7t+9eqVpfbpk7P85LRZx5OT+ZPzs+VyXRrKIYhzICYhgEwBmFMwhQFMYBLiout2IrRtbtvl5dUy50xBKPPJyZwwm1VRoU2jDDNVEQKEGO7GTlSCTpM2Oa/X7dUqpazupG5NblJer1YXaqmScHZyfjI7F5MGkSH12YuqakNorRi8mdzczJj6SYgZoE9Nlp3wsWJiwBMmvCHGGPCY+ovF4alNq8vL18vLH9L6tWk7q+X58+fz+dyNzIzpat2oO2KoIeburmaqcIObuyvQtr5erwC4W9M0OefvvvvuP/2n//S7v//t1fLiZDb/o1/9wxLcQzjWdU4puTsxU7DKWI05EhHFyoJyJiViI3IKEGKSrC0z52RNaq6urq6W66ZpVH29bF68ePb0ydmLZ+euosizujhSE2DuIDBImZ3FQUwkbduuVmtzAPz68uLv//7r777/5vLq+3ldf/HFF/VP49nsFKauGdaKUKzDbDYz9+zXBuPSeYACTF40ycx+L5PWhAmPARMDnjDhFjyUpYrZQOrWpuZqtby8ePX9enURhJbL5ctnz+fzE3Al3AYDcYzR2nXT94tWTcWbSVXbthWRq6u0Xq+bpvmzP/uzf/Nv/s2f/6//0cx+8uXnAObzeXz5Ika9WrZF700GchAbSMlBRCyAECkpXIiMQEGY2R2qZgbLXsXF0vOv//av/+Iv/uKH73786U++/NnPfvLP/4v//IvPX5Cn+SwSsVupWoqrhpCbEJFVVVXNZ3Xyy8ur777/8de//pu/+/rXr159Z96+fPH05dMnbEaaiYyhwtS4xRirua1Tq2s1z8Qigd0VAK7PMDM7w2liwDfjQ/fJ+BQwMeAJE94Qxzp6BAFL8S4GYGZ5vV6bJgFmsSKSuiINCWCHEVHbLFHIaHFa9lzQti0RmYXLy9dt237zzbf/7t/9uz/90/9Qkgn9+tffAP8fIor/4k/Onj6L9Szn7HAQEQU3diMnJiLNCncJbkSaPTA5mKmazUgbbZGrWF2tlr/+26//u//uX/+P/+P/o7nKX3z57B//6pezIKezfxbE57MIV+Zy1sjARGBiCg4nNOyz2Sy1/u233/31X/7Vn/+n//Xy8iJW/tnnzz978ezJ+amQ5tQEjsInTFqs2iEEomSm5lmEmWUTQ7t0om+57+RBOeHDx8SAJ0x4Q4wx4FHJg8wdDmVBVYW6rkPg7KyqTdPEuCZINTsBTJOt8pV523uRqWpOKeeccyIiILdpnbP9/d9//Rd/8RfuOD2pm6bJGX/3699+8cVv/sk/+WdVPZcwS3nthY87AUFdxNkd6mAndWJHdkQnImEOMcaMXDE1Tfr13/z5v/7X//3/8N//D9995wRc/cUP3/7u3/6Lf/5P//gf/2pWU26TWjOrQkk1TE5gIWcWuFMIQdVLy1NuAJyeLl48O/vqqxcvXz598exJDIEIdQwhiOfkRtnNdHNepTusheL5TDvcl5wn+W7Ch46JAU+Y8I5gluGumgHEGGd1PavmydE07XK5JBLhKERVVRGpmXnrAIhLNkJXcoeZq5uqF8GYmPni4uL162WMuLpqykEPqTjWs/nJKUl1cVUyNBAAJyLz6JsTz25km7gd5MbugDMRkVGMcTZbfJd++Ju//tv/97/9X777zqsKJxVeX+LiAlevL2AuLCJCzmZGBIa7MDuh/A+2WCxyvnT3uq4+//xzMgPZ2Wm1OKmfnJ0tZnNmrkKczWYhhKZNzuQZSXM2OIGlMGDd3+eQ8yQBT/gIcC8GfGtM4E5E+FBOZU2YcHcMIs32L7yXVqFExtjYcU0rCXWsrF0t5qfyEqlZmdmsqheLk9ls5m7kyoy2zbCM4t0EuKtaSqnNObubqZuTCF1eXs7n85//4qd/89dfE2CGquJ//s//y3/1r/6bl599AZKqju7etuVBCoGIRCQQUcr56dMzV0vNenZSC0vbphhCrOqrqysief7k6a9+9atf/OIXf/mXv2taXCUQ8PI5mLlpV8+evVhdXcxnAeZOpgYy9hAZzAQQmLmu49OnT0ViIDmdzzW3szkvZrGeSV3X8/kJIa7bRJzPnpy/ktll82q5XJt5CNFB2Y3IVVMx97K7+jaCNUrKxSPAPSrUp10TdZrwvjBJwBMmvEV0xN3dc6vFZ1g4xlAzKAa22Wwxm1dVFWMUicScUrtu03q93ixOMiJy12zJNallADnnpOZGIvLZF5//03/6T58/f/7tb3//9OnTP/zDP/zP/vif/PznP6+qat2mYlWN9SxGV9XVum2apmh3pcbvv/2xrkIdgxulpPAEN22TCKmm5TL/gz/42f/l//p//pM/+ZO/+qu/+rP/358S4R//0S//0R/9sgqxWS4toIpzwImdiBhgL/GzqJxpcp8RSaBgKbvpanVVBcQKs1kV64o4EESk4hDUuEmpTd5mMytJm7gI7UQlJ4QTnHybpAlkE+Oc8IFjYsATJtwL/bhXGBGnfJM/FSIsHGOsLNfCTIiCRV1VdR1DCGbIqqoGU4ZbVnclciMAljWl3GjO6pRyTtkIMQT+7LPPZrPFP/j5L7775psXL17+9A9+9tnLL+r5zJ3UnTl8+/2PIYTIwsyzOtZ1bVnXzbJVX6/bZ0/Oz0/PYl1FcuHackophVDnnJumOT8//d//7/6bX/3qH/7VX/3Vf/Vf/ElVh598+cUv/vBnT58sLDfMSQgptcxgEWcQREAlfqW5EnkIYXHCOT9JbStCsLaqZT6fSYwpOzNirCjMkmO1TM1ac3I1ZiJw8RUDEaEotomInQp7Bh0tuU5G4wmPDG9FBT1hwieLgf2lH8pYOEYSE4lBEzXuKqAgZIpiiDXTnJKauRszzBQEMwOZu6kmTW3W3LZZnUAsgYJUIYpIXMxPvvz8RQz1bDFXz6oKDuTU5vTFV18yqGmaH7/7/re/+02zWrfNqmmay3YtoK++/PwnX33+9Oz0qy8/++LlU2YWspQaImKxlFcpr07m8Y//8S//8Cc/M8tBaD6LQh6iZE2Xl5cxCkp4Z7/OJ8HuqmpmZM4sdVXN5/O2XefWJQaSaE5NalniTKLHKmdPrbfZ3QWF93pJSwXiEmTL4NsUi+QlVfKRQ/OgIz1hwr0xScATJtwXg51oP8NaXwUNF3cmk0BBqMrWGtSZYgyRoxAlSzm3OWd1c/cgcHcvMTgsm2W1pJrbtiGJIc5CCDFERyAS4UCeQ4xFm01BCNzmnNZptWyWy+XXf/e3//7f//v/+B/+/Xe//5bIZ3XdOofAL58/ffb07MuXz/7rf/Uv/9v/+l8+e3p2MqtBOQoHETVtcwohnJ6dzqSGq2piMhGKgWPWddOapi6MtrtbViIGTIS8uFsriWyMvmvyEATM2Sy7VBIpziA14KmFOxGYKWx8rJy2KSUMAMEAAhnb28qiM2HCu8TEgCdMeEMMUpnu0/FBAUtIpExOFGKsNWXyxASR2Hlp9fTYBkMRKQF1KCy7q5kROcgIzgyJIchsNmNTb1av5yeLWb0AS5tbNTZiCK9WzZ/+2b//N/+v/+mv//KvVpeviyZ2vVolZcC+++YbAGdzWS0vn5zO/sU//2eBbDavQJ61iTGenT11svV6PZ/NZ7OqrqJrk3NjtsouVYir9VXx7DIDA8bGaoCQUCBWMVcnohBCVVXAggM5OwH1TKrFOVczpdAi5ww3dgSiABKFu2uP0xYezICBeNInT/gI8FZU0JNX4YRPBB2LvWEtdAWKW3RgDhyqOPfaLJOwQy3DHTk3xVdZnWBm2mbADAYydS1hsOC6yXgAAGAmEQGY4IvFLFYVgKZp1m0yJ+cI4NWrV3/+53/+H/70TwvTCoFztpytZIfQtIL7cqn/4T/8h1/+4qd/+Is/mEWuaoYE1SQlJ4K7eQbM3cwywUxTu16v1pepWaulwl/JSpiRbR5iTUQiIs5efMHquiZysGc3jkHqeVWfO8uybZtGc+qyJoKIeipjB9kex53CYE344DFJwBMmPBjG/LA2SXApGDIAkUgwDSEbA0ok7prV2nbdtI1ZzqY559oDYCAHu3veRGQEHApszK5EZJZT8rZJdTTDCmA1ZmY3XFy+fvX68m//9u9+//vfgwjuLJKzAqhrWbeqbExBxCzpb37z6j/+x//4r/6r//LZ+UlYIcg8xmieX79+XdfV6WKBNjRNs17lKF5Fns1mbslyatqViJiZmBfNMZEULhokEAUB3MXUAYjQum3cLYjM5/NQz9ZrLNfr1TJlfaLq7jAnLtE2yLDRAbi7wRmu7HAnuByvOp4EgwmPC/eTgEdi0Zjb9vDA1hUFDoDsuAVzdKShI8/mm+ejyr9L9L99VKMw5YR5UPTZ5+BQb7/AoHxXDGQEGKDXymTGhk0yETWSoCF5Tp5igMwjJOaU1rmBq+W1eSLPri2ZRUKbL4kITJZyzrnJKees2dUtBEMAg9gNyPAMtBeN1pjPZrWrr1dJIW1jP3z36s/+v3/21//pL6EcRDSvBagip0YRArIZYMoCYuS/+PNf/8//z39zNg//8A+/wAkISG2ezRan85PXry8DtyCwOMhzzpbbnBXgOs6YxBQuTkRCMEspKSpRNaYkQtXcQCBhktqoIgSuzqg6X1v1Y169yrFlNSVIYbrJkbnw3uzCFbza9Dlhq5U2ghw3wISb1RX3xLHVHq8pfNvr/U5KhWu3BvvA6Q8dPkdegq8dKv/w/GKSgB8jpgAm7x39w0VvQK+3A9fzwALK+RkiiQxhhXMXFRreRe3YnnwFYoxmljTnnFLaRIgCeV3VzCEQb/yNaRMSy3IqNHSrrKarq6vvf//t119/nZsGPQ6xITGqIBAJzItN9dWrV3/7t3/9zTe/+slXz0rgjtKSnLO7iwS4Ay4McgOziIgIIZRExYPpambXp4iAooU2I6lCsqhUNU2+WK9fXzVNdvBQov3gJv8H1+A3w/Uw0VvZx3xSuB8DHpPAvERuJYBKwLzNmB1Px468/2HjTlLvhHeF/gmiuz3A2KTNcwBcwkKCSxRmc3MzhgdhEQpwJXI3MyMHTAsHLmGOnd3MqGRsaFOTmpKQoERnjBLAAsChZgYY3BhEbsV7y9UAqOrV64uvv/76h9//vjRQTRnE5Nstvm1aCCMiOFYNfvOb3/z93//9H/9nv8g5m6GqZm7UtpkgzFwYMJETqPBcZpaS3ZAZgLu6MwFEMM/YZIHYxt+KQkTGVUDduqxXul6n1SplcKyqLnwY9vQQEyY8Ajy8xP92JeBCwiYv/2MxkICnDnxfGGgs78gPuGQeAjnT1n7pgLsapLAqJieYqapnZYHDYU4wd3VXVzW33G6EX1UtT4UQIKzZyN0pQA2SUSJEWyYi6U0Yd12tVj/88ANgFIKrbjIyuDsQBZkIqr4NeFmoS4m/UXYPqmX9QlVDiADKoSCHA+qu7g5XYgYMTmYGI2a/Vg2bO8HcmLn4aYHFEoOFPBKYuBJ2h8BD5wc+6OeHmv9vm50fW/8Huq57y+ED94Mba/87/K63roLesZO97Zd9LBgEVxpdqB/mAu7w2OSbse1OJ5MdKxOzo+jpyn8kEoViYCEnB2DkABkzqwKuVg4aec6aVLVtiy45CzEHYQaRw1wtMYUAE6Zyk9XIPEogApmDzC0Xp+trUZKofJkWMd2AYCAAOw7Gs9ns7Owkxghns6TZnXOQWagqptaIyLXTZW1U31ZsY0ZgUnLRcpyXHF48P9gAJiKiIAgWWZWblJfrrJmEawc7+NrL7BAbfvw4lqF+iKdIdijSh86Afaz9D+NjdBe8FQY85gX6UOziMU/Q++AD3RF/xOjITcd9+yztOhnAri3men6SAdiYQEHV1l4qpAYrzr5CBE1mmnObc6uWct4kXVDdnOdhZmGAyMxUk5AIgxkiElgANlImlzqSwzx71pRSu1ZiPz8/g8NSAsDEnf+MldPFADGoOC8bqojnL569ePHC3dfrNQvatiUKdUUAiQiRw5jchNiYlMGMnG3z1e7m2UzMTCizuGPjiVYM2wYi4dnstL3K69Xq9euryzUZVSA2ZyLbP1f9AS2KD6ipR2Fw1u56Ubzndn0MeIsS8AEx7iNlnA+FD3FH/BFjwAyOkoDJcW0uLWAIE4MAs5w0Jc2t5+TuOTepXbdtm7VRzTnnnNuUkubgrlufLOJy/Nc9VkIkDECzsxAxw9gtBslZi+K6Xa+ulg2Dnj19ev7s2esffgAQQki5YSAIZzUQ4CACMzyDgKdPF7/61a9OTk6urq5ybhaLheY1U3Wy0LbJVbCNzpnc3ToSzG7OVDISAkWUv17vREX8ZWYGgiEQB2JWX2elnM2ZiEmNJND+ueoPSBR++17Q7x+7o/OBS8CPAPdkwLeK5GWGFasT4eg905FOWB+4SqRvaxw7UTrh3WD/6NE+J9i4MO08x1vP0BLAGeROAuEiIJqTQ9VSm9p1Sq3m1jXltk0p5dTknAxmllVVVVPyDQNmF69KkEYRFmbAYaq5pXK82AxuUaRtW1PSnJumaVdrd10sFl988cXrV69gm+NRBouxztpAAIU54CCHAM+ePfvlL38pIpeXlznHqqpUjQmqmpBUActm5jCHumWzDM3F/7mkEi4RvTbhujIXizcVzspCxEBo22ygUNVV1JBVLaiLqUtAV0XX5w8oVk6r6UHwsQr6t+MtHPu8FwO+qzSwPVbxvrzWj114Y+fA3vYC3vrNDvEp24D7Ze6iIbgPdTj4bDFzHixM4gDMiDfPsruRQ0TMHGZETqE8beSoKlG1nNucV5rXltuck+WmbVvNbZl15JvjOkSkOZtROeoDoG1bZq5jZaruzuxw0dS22rg7c8i5Dczr9Xp1tcxtk3ObmkZVFyfzk9PTq9evUk7MzKC2bYGNNVgImiDAi+eLP/7jfxxCWK/XTBIjr9ZX89lJNQuqOptxaq5CCExuKWlJleCuqllT2Z1QkOCRIjFAECZXh5mxbcTlIkEboapmTjmplrzBMEhVq16flerMwA+46EbPd94Pb7xL6NpzZ6fLt73eD9e/48ezMyIftsAzhvEp9/D0/4M8B/z+JuiEd4E31gTsK40ftj0j968lNgaVaA9UvK6IBC6EjfLYkmmGtTAlN3himMOoRDmm0mAi467y4oUVA7MIs5jnNikANxJx5i6sMjPcNLs6w4gBsiIHN8srIqpjvCJikFl2mFExzAJWDMEQwWKxYOamWdV1pWpqSbXKW6SUQnTVBHfLGZ4JlnOrllUVMObA5i4dU3GUvISdNLsxnIOErQR6drfdo9KDrv6A9M8TPkC8/0AiHyQDHsOnqxv5MHEbY9u5cyshvrv/zgMzZncnKkHhiBiAq4KcHVJCZGCTN8/y2jVpbkyTWUtucIUbkQu5g5xou8tmdyrxo4tSl5mJXVtPKZG5uysHqJtkGDEHCsGzGpwc7HC1lNrV6ury8lJAMYa6rl2tTRnAxj7LQAaA2Sw8OTk5Pz83sx9//DHGsyhVSjGlJoRQEjGZKWCmXrIiumYm95xyzpYTMztpaTyhGL+3Gq9rqU6dom+6xNXVXH3zO9wVe6P2Bp7nHzQep+FpnK4+rnYejyP5xVvgL/dMxvC48nH6g0rAj20ZfHy4OwO+lRDvc98b6n+okTUrefdKbEQm2kh7vnG/IiIIg2WjfDVtTLNp69qatmatuwJWQrQSkTgpMWBmbubl+K9IcV+Cu5M5NLcpmYHRasrCAYBIjDFWVeVOqlk1baJnNW3TNKpetJ39DsmO4sQdAi0Wi7Ozs7quVfXy8vLsLM4qV61Ug2pyN2IlVjMzTSklTa1bS27k5pqLDla2Blxsjxx1EsZGSVu0yuLOsHJsGAqIk2nh2Q8yKh8UDk7Fx6aC/mhxtE33kdmAP254/wzlhLeAWzUWA0f6sYE4yHEf0H9ntB6Ce5f8h93QiX0CIkJxUBa4wxyevXFT10YtuTZu2U3ddes8yN1Hu8IUTdPEGEUC4Dlnhqkmd3c1zTk7eVblAFAIGZZVxME5e2pby22Jh0Xsq8tV0zRt23ZOGExs3h3/ZTdKbU5JS7yt9XqZF6KaUuaQQklCnFLDuUkp5bZRTXCFKcMZzgIWZgEL2EswLAKkROEop4uym5h10eOZwWVfwepQELl9cgz4SO6L98WARwngBx+L/v23/4NkwDcQxIeqf2K67wC3MtR+gTdQQb/tQRw6fxUNzPbNws6CwCiGbHclqCO7Z7fWPFvhvmpd/MVSjxuZQtVzziGE4lTsUHaoqmXd3FFNFIgyOcOcHMmviCSrN01q2zblpk3r1LQXF69Wq1U5+otyGrgI6wQ4ctLLy0tt1oHs5YvTlNLr182s9npG5tnMtrK1K5q2bTW15EYwMiU2Ia5D7FzVHOpGqgq0hIqINuedzVyVRQE1KEngyByZzE3VnYhBeyz4k1qGj9aCNt6w98/A7oVj+/steBG/lXzA4+Xv87bbMbZcj13GnQdmnxP3T0dMeNs4ivViRAX97lECOxf9a0lDsGm7lYiPxsigEm/SvERzxCYDX2k2G7tnM1h2ESmzzswqZglilpPnlBtNKWcDWoYwhaL0VpYSXDKltF6vLy4uvvvuu29//+PVcp1SAlDkc3MT2gaLdABISS0p2bfzGVWSFwu4PQO1J80ipdMqzubzFREBuW0by1kIwuau7Ciu4JvKSmxr3YR0jhRcGMZGZlAmQwlw7Zl4FqOEyNSauxVnc9rbQd/mFfxhY+zrHpsK+uPdBh27gTicPek++Kgk4Lc3TT7eKfg+cWuv3pENj3HfdyUBU7FvwkGAuwfirQ24eENvtnRCbgC5Mdxh5WAugI77wrnob4vzc13XRawkohhjCMFd24battU2paS0Ce9hAJg5zgVMDmZH2zaFAf/ud9+GWBe3KybWkpUXINpG4tvygdXafvOb31m6/OyzkxhyrFw1M3N60qhqZ8Ut72LeRPAQKRp4c3cymJtuhyAEhzvR5vOvLcHwEsZrE1qLzMsh4pEe/lgZ8AePT85o8PC4FwNmez8DsL9TLlAfmGxLchiY+/DIWrG6jdkwyrLfnpronM1uVZkOMLa/OraeUXzogUfGfqCdAtecbGS8OjI9PMQysmMtvsq3tej2sSDbqHGZWNkBN3eC52CzOoRAgYyQ3Vqzddusgl1aztquTRNIIQpLGa1HzSnlpi0TjWFCSSjN5jHnzMyzWlzzjxc/NE3DTOzeWptyA5gQiwiZkFary+bk9DyESiRFtAtJTyoso1eV0lyY4TAzB0FEQ5C0phBCFCZ2S21Oqgnf/fbKV1fnkb88f1IvcE7yVPjc17xqm9SUBAwtewgcYyQWJyEhgztZrGY1x7KB0GwU3WHmLZkwEcG4eGpxzNkqqU5np6ury1YtePQE4y4n4wa+ienxuALv9LcF7o4NtTgaW0PLhhD1PnM7P981hxvLbvduW/HucFO8h33PkjG+cx98kBLwhAmPCl4CvQHokdEtMbOOgJmZqpZ/HWpWAl7parXKOVvKRFJEXmYu+ucSlCNKMCoJhYgdIhJCMDPXbGYpJZAR0WxRxRCYuIpycjp/8eKZKWb1vEjPLJRS06S1WhujVFWVlgiRZ7PZvIpVZFib2nVOa8vrly+fP3lydn5+9uTJk+IgbWZi1LbWNI1DYxQAItecw8xyzs7EvEk+6CXHUjlHVc5Gb6TojXd3CCHGGIJqZvNrQRn7TG6k49/KcE745PHOlC4TAz4CY6Myev9tNmbCI8T28I3x5tK2PEVpo2fu5RswLzKcaoIWYd0ABrkQC3HOmRxCzMxFcWtmpta2rZkJsYsURhVDVddVjMIMZlRVOD89qePs7OT8i88+TylxEGJfr5fL5VXKTVWFuo7LyxQCn84XT56cPXt6erqYxwC4pmb52cunX7x8cX62eHJ+enZ2EpjW6wTA3dWSu4qQFV9s9xgj4B0DFiERkcBmxbOZro8Cm8PUyYlJAtVVVVWpbbPDPcO2qviODXcC8XsYyzug7y046ck/GrxLh5KJAU+Y8MbYYQxEJCCw8SZvgns5UVS4L1S4iLYECBwq7hDAFvVMQzkCxORQdWUwc9OuRcTdzYr/c9Y2FRmanCUwUKJkWAyhCjXDYQkOIZvXMovV6XyWzs8uLi4UnjUxZULKaiFIjCGyRwmzGS8WfHZaffby/NmT89PFLIg/OTs9mc9YEJiqIGaZyFkggaoqACHGICJqKWs7m1eMsI2n4e5akgtnN4KTSzEDY2sDhhozhDiEEEIQccrq29xw7sMkLo+NAR8kyhMP/viwr4V+cEwMeMInhwdcTu4OMOAoPs8gYmYqGlRjmLuZZfPsqsxEMAIIBgILggGgqg6qZGnDa90zM7PQtTXU3FWLZ5aqVjEC2MSpVHezEnajqltTEG88sYgRhKsoRLOmaZbLFJAWNbtHIgLlJ0+quq5ns9mskhBat2UMJ/OZzObVyaKuKzHNailrDiHM5lXOAMw9mimxO7RYxXLOMRJzCGFzwKl4QRucmeEl5EcgOJmTu7qRbzIYXtvvxwMLP3IV9GPbH0x4ELwD7ouJAR+Ft62Cnlbyh4X9cSciZiIyomLyVLiSKzyXLEhmVsI6kqtbVlXzbFnhxRnYfJOb3gReSahCrCRs8wp7YIEURpzJUUy/W59kFzhZApycGOyuDAFxHaEpkbeEdWAndvdsZmZNYCxmdV17HZSwzulSMwd+ArRuaqauSSkUrXKQCm45tTklU4PFuq5DDDAlj6FLTmxwyyBxhrsTigh7zV+ZiABymGWomWv/JPTg4N8jXBSPs1UTHgqTCnrChA8F3FkpBUTsDDBIyAEjmJUjv67kCZY9J9cMy+bq7nB1VTN1dzInc5iqmUMdGiuJlUggM4cpgyQwKKRm7WoGZebIRBKqqqpiBc9upJbdyY0AZg5CHDgH1sBJKJdsCsVUTLZ0A5QFCxGrJARJhDaIEpK5MZyChCAEN80iJEIxkiqrKbFLoKoORdBlBjFMyzEndldAum4q+nkmh0N4G51EzUxLmEwjCDbScOcFXZ59hKrdgafYxI8/SrwDTjwx4AkT3hg8uNiG4DAADDM3ci266JJtAeSbaMkggwJO7FHIzEseBYK7ZsvJ1UVEiBle/KUdxiCHn5yc5LbE1jBmBplDTRMHNitBMIgMBKEQOIQqwCvTmjyjsQxYYBaRk5NYVQjSkLv4ibAGVubctpeepbgo11VdBQI8Z0tJAVR1IK5zJiIqfBfbTJqdFzNtmJM5gShsrOMoHeDM5CDeFgc2onzHxKboNxMeFR6pDfjY86xj5ffzdO5vLe+2Jq8PLdzlCH+/ZL9+3bikDh8fa//+Vn1T85H5fbu9f/dnQUkHe+t7b50lx+7T+/3Tv9O1Z/DeG+bDoJKbv+uaLg/av9e2grG8qrY3fN3p0oPvHRQe+2nn1dt6SzM8u5HNKuGidHVVzdB1Tuus6+AKU3KQm3lWzbAM3QTGIidhUAg55/aqXa1Wi8VCAgLVwiRMQqAo81pyblepWa4uyVFVVYwBRIq8uliFEOpYiYgzpdQsl5eeNYTAjHnlnlFiZVRCVcWpuazCiWXz4HWYBcqpudSaU+MJqEJNmNdVAIkp3DW3S2YWZg4SOJoZTHPbCAcjVhDEAKYSExtigJToYKoGYVd3Z0BV3Vv3uooxxkjUuvv24P1O9mXfBqS7dbzeJfbnxmNr4cPi4Dn7j1joP2haekvveiuhKI+djmOJ6ItlaJ+VjhJ6XHOC7ZK+nZL2F/nNZqdbNxZ3pNq3otsTdLzq5vfe+vabC9zQtv1XuHvH8AYbjrFxfIN50n/v9aZkpPig8Fidb4yxdm63jZuzsJvgUCXSMoxg7kowc+v+dPcSfpLMqOTjc03tuoRicHdXWE4EF6YqshBcc0qpaa/a9Uo1tUTm+ery9dXV5Xw+Pz2bV1VsV+ur1aqeCeDOBHK4s2W2lC3DFIA4ZpHIyIyDsLDO59ViEYPEGDkGCBshwzWnNVMwZvdKNaVEJSVwu74iohBCCBsHZgDucJhqAmAGItnkGiajUBERsRNRMWOzbw4jbbNWbDq2xNnCx0vTP0p83NuOd4NHoYLel4ALxnaaNxPZu7DDm99yfyJ+F/m74OaNxcYHdqR/xnZqD7UwDvbD/ivu/ro7cspbhZ6hpuHGeTIoc8OGZgyl//uaku2GYLg7YSYmk6JnLmn3PBGMYOyGwgstQ9U8l5S4cCWiksjQ3LKpmRF5FGKBeavJ07pJ61WblqpaJEjzFCua11IHgaes6zYtg8yoBLlwZyJ4hmdBgmkJiRFJpQpmABmDZjHMgoTAQSQSBTijJIIACTHAcKipNTmlnEtWYGvbdYwym82qqtrujUpIERd2kcgUiQxgpmtrLoPYQTA4XLPRVnewzY9k8G4r3B+4ico/TrzBOpqwj7fCgI9lYPsE96Cq82DJ/ff2GPDOj4OiAJh3yg/efneMaapv1c0OsP/gzY/vM+878q1jsc/nDl7fujG6mx1hh/L2lRNjxcb2BMwP40Y7Nhyb+Bi9m+SbVLgMg6ttHIkzu4HULLuXk0MOc1dzqFkWArqAyTlZTq7J3cmTqeWc29SYJ+bC8M1hT87mzCcxBvPUNq1bms+im5lqTonJmZhhkUwJ7GqWPWuxFzMUDmb2tkUIbrCqSOPKFpm8ClEkRmEiMlfXjS5qPq/btm2aZt00Wdu5zWezWZAKYFVzs+L5RUEIslUQGJH0lf/sCgQyJ9KSxAkoQUO3OY97c2Mi8Y8EHbvd39p+fHiX2vW3ooI+VtXZtwX2h/mwIfBu4tFescEjpcxx2TDuaDu8Jsp7P+1XuC+l7ZP7e24I7tLmsfv9fa5vTdQDiXxs37Dfnj55vVXVf0C+3y3TFbtVg/KWKMU2Ssb127d7DHd3t1z+M89WEvyaww3F+dnVsbmpXryg4aruTuTMbJZLIt2cG9XEpEHIGe4URcyIyAHT3GhuAQ9BdG2AOZIREEUYIixkbmqpVVWQu5u7x8BVqLgEt3RQNpiRu4AYNKtrligiME8pwbcnktlDZHNJyVRz2zYhBBEhgAgOKm7N7g4CMxuctz5WgBCsiLtbPbyX3tuOjn3w6e0+Uuzvhx5kXzsBb8kJ69jyhYTts96+TfEuYlbfBrxX+IAETNjhbW9MpjsX0EE91AlG+9rL2xxMusLUcwobYF9DcBcV/RtgX4vbb+RdBP2BBrhgzGY8eKQTncc0JftPbZ+9VmneZ6APDl//1/IK5vKfAnAvMZ+Tqbpl1+zuIHdzbI3BVAyi5G5eAlWKSFiEWuucUkqJOLll02TaACCCubmqka+bZW5TCCFGCYFTSiknodrMYM4gEwrEzOJsruwM1w3vJzUJ9awKJ6eLEuQZQFVqCqEOtYgIS3EhtKzqRmAiWl0tRaQKIYqUHAmumtskJXw1kRvcsibhSCySVBlklpm5y6VKvrnsuG/RmhPBbVJBfzCYxuVBcC8GHDcReYYYk0jGCG5JVtqhLyH1+VBfh3n4vbzDJ2gnf/KBbEhuQ45y86y62cmo4yvdzX7jscdIxtgJbdTjG9CuX2gfRQLDLle7of1juEGCPMjh+hqLPlu6maEO2nn3TfT1uF/LT9cfO+jn/jzpsiHdc8O+/12bV9tO9h5mlkCBCbp2+Cb1giZ2dVfzTP3JCXMYyrlYkLq5uwjVsQa4YQawbi7bdr1eL1WVhYjIc8o5N+1KUyZygB2kpubqauRw82yZ2KsMkoqZmIK5WQhMYDdVUkp1FRaz+bMnZzHGnC2rzqu6ms0X8/lisRBiFhERA5XIl3AnIrNcPjxElo2eGTlnhYvEovDPOSk5EYkEM8qk4uJdPrHtgDrgWlzB9Xq3bZMK+pGiv9wwvhmdcCzuxYCrqjp4P+d8+GXh8Ov6hwj7Fx0lLXzoVh7sW76wLdNPZHaAAWveWefdah9r/w3fW57d7uh9f5rus+HrZu9ZMWmbBkdEmPlmJ6zyuu7VB0v2yx+FAQ8u7ezGcbAOxzZkY00da8/gfvcW21MEDPp5ME/yiObgWOyPV7no1KfdLBXhIEjZHV7EXDODm7uzX1tBiDb5CcqDqimljfI2cAA0pbRer5u2Wa/XTbt294AQucTKypeXl4tZfXJyEqvQtm3TrGAeY0Qu0SnNMzQUbTYTgciDsHMMhKytJsQY67qezRYhBOYkqlVVz6t6NpstZvM2J2aOMRo2mYlzkZKrysyytg6JMbK4G5mpWQaYSMxM1QwIoQTOpDJ7B2PdzfbdDqQ9O9GECR857sWAT08W2KWt5T7R7CCFFQnY02TeJMNRok2kPe4zsIFXal+svH50h8BpV8x6QL3D9roLns9tmysOPWEUgk7WQY/d1rPYZZrb1Fzq8QMqdOwK94NXbx5kLinnStLyEEJHs/q9FHC9cdlE3+2F9DvwXsJ+JYO9bb83ZHOi5rpMtyfAdlfUHx3inQC/+ygtzDlvsvLlgfV0Z4N1YE8z51LDYGiKOpQ3Ypt07WnbzbjknA8GOxx0VJ+bYneiHuTBjnbjPAWA2SEOdjany5xXnpfil0INrIU16ql1ctXiFA3ywEQOYW+RQ1UODa/a5hU52tVls7y6XK5yzp4yMwtHd85tm9r2ZFbPq1oczeUqpSQuROQZy/bbwMIhOMS1Yq+CR1a1TIJIlBlJCF7pLKzZf2yxmM1rEtThPIQqZRNZeJh5rBJDg8Oy0drzleQUmVaWQwh1CEzmqfWWRGQm1Sq17AbPgsjMzgC1ajRPn1F2jibeyFwF6qTqmrJRnCnFlNC2bdZQ3LSFFAMmvDtQw2Uyki53nw4UGK6VJQcrHCCPbAjGND00UtnoW/zwhvV6q7b7fWLHHfPDiOWOdn07+jfHanCUGDHeb9J4vu0DlM3HTU53PwUzqHNYHqNxBQ6X55H7ezqY7RJ/eB+FezHgxWJRLgZE/OAdIgohHiRkHZEdbJaJqo7EHxSCb5gxOwuvx7A7Cu7u1ltgg6o6hoqt0pWIuLoWs7qqyr8dV9iIIGXAtnm2B3uFwjz2p2kZ4FKssLqOB2OXsW3K2/Xj3XtvWkV7DHhQeDBksM1GoZTsWtWNRceDb2DA/Vf02ecYA8YufexPjLSVKQcM2DcmWO63jYjMrtd2N5r7Yz2A3+YZ1/UScTesw+iJ5CgzA9v/3B0GkLm5mZGblbBV2zlW3mBZyx4l5yzEJCHwJjHwpt9SjjGaWdu2ZRqXLYiqBhbmTc57YgcZw4mcuGjL1VHaGQAyvf40YoQQHMbMDjNTB5FzJWIxegiq2QzVLApIVbOZqkYW5tnunFTQ9bYMgLu6b1YTQtmQMgCF6xalD8ydRzjYqOnHjiuPEc322HzgMWXbyHsfm/x+wzLEoa8+uHHpU/Ihz74DP+rvAEb7eTtePjJAY3UO7w/dXnGX2m54he+ei3sbuBcDPjs7G4zN4N/BiMZY4ZBCr9CRPhcpPzF7x30LC+z4QVd/n+t0rLF7Y7koEnD3ur6wuD8vuzJdq643AXGHzvZ5WJ8H9x68bnMfRcV9AwMeSMBdbvYhl8rXHOXgFw3B1G/w/hhhMFltZ+Z1HK4biwEDBin1thr7y6nr2E135Z2db7+L+g9237XObbnoOrkj9P329NrMnQTcF4LHTAyDlTZoRp8Tbzp8c5gVJVXfpiXmZF6yILmZq242Ama2meF5y4U3+t3N1ohom9eoSSmRe2QBb8z8qqpt0rbNOdV1bXnDukq4SiKoZQ68jfho5A5L5sQOZpibmxMTcyAQnDSDmUuTiChGEZEYGEAdq7U2uU1OjtJvauoeEcs8U1XPmtmFEjnD3MwERRHhvF0vrg64qhGBlKEKdoeTBIBNN7sidy+y3q1kblhgpPjBenwvzNzN73oDHC0fjQuuh+8eyQZu4Hk31NZnmeXi2L46WP6Gxg+43c2Fb/p1pJnjtd3pu97qnLkXA57NZjjEbgf8uPuAEGL3Z39yFIK4zz9CoI6wDmirH0K3kxr0+EEGPGhk/2JHTd0zLjofZpy0jQ/VcZcNA/bDqtqc8zUT3duQljKFAYcQijXuMIOJO7uHDmMTbkwCtp4Xeh/brPLXrHFfG9G7Ronou9//g4nRbYAOMmDszvh+O6tNuCXrtkfUE7Z2WS8AiFyroPsbo5tDjd5KgLr2JOucB52RaRN7Ut0damSqZlzMwJs2Z6jBs7kBTjAqyXKZ3ZNpyjm1bWs5kWuMUg7g5qztBjnn7GaaE7Lbxn2JWECgQMwg93JmCOZtzsSkgRBDIHMwRIRZCEZEbpTdVLXN2d1DCACLELmdLE502V5ernPbkCol5axCtF6vhZmZA7EJYJ5Sm3NmDpbJmUiKE3gkB2CAbRm2kyqZl+NIBHFDzjmnjVZiM33GVIh2vTR2xutgacB7mqGd4WXu/Pj6T4/VYyME91gG8EjQpzCDf/fZ3mAV9And9c1xFfSAqPqNKtx9FXT/pTd8yP4Ph+8fif31/iDVHsQDOGENOJkf8u/dDueOJrYD7x7j6RF9dOX7QxIjd9W7X/87hgGDuWbztjNLuou+RN5vgGJ9c/0DmF1LZn2+1VeJ99/b7wHuOWEV56b9fiM/rCnaZyF0owp6bAEwrh+nnu59+y+IUOIvboeXu2vavGunGe7kDncpP+lWEPVrT+brp/qdUxCwmW99Bkxbk/yAX7o7cyjstlNO9L960Of7n99dDEwYXZOKa5J5hmrWVvPaU6OWTdU9w31jY9pKu7Bsbma6ySTIYMCdDKZZU06Wsmsyy4FFRLRpm5TTcr1aLtu2NTNmrji0Vyva2AVAgJYdHpEQmZuTCguRm7c5qwkFImJjoo05wxxAQMg5p3KCKbdwIyYhJ2SYWk6aWk2J1CSbmTFIczbyQLzZZzmXHlFXtgRhokQeyti6B7g6zCyXOW8KBMApu7emTbI2ZzODlyzCd9I69i/GbK6D+Xy9rHAd2/XgMhnAj+So43xh7IcjFZt+u821D9tVlXfN2ERLvSarm6b32+nXT20MwGXWF5Lg5aW3SZwDUWf0sw4dZMAN/TYGGk0pfRT26fPbw70YsEjpJmAzs8tt6v27wYaB6dBUtv11WLLQ8SKodL+6d7ImBo/vE+7BBqDb85aLLYPZ8t3di3Kgwsy8J1ITEdHhHTp4Y6PFrl46m1NPT9sxYN/bb+5M/e1ucWDU7L+wX0P3b58B93nkdf9seqlwwfJfv56dLgU2nuPu14y2/FnulGLd/SLC9IejXLtvEuZ0T3UQxu6c2RTYSkXd6wgg92sJqdM09BnwoHPcvQuu1Jk2+qR5nw13vx5kwP0HN+XB5XyRtm3baJORzCy1llqowpW4eOJvFemqgBaBjs1A5mYgs5xSbnLTqCWGg5jYWaDmljW169SuTVUklj3Z5brpWqhERcGTcw4SCSCQlDCWpplUFFlBDuFIITKkdDlTTJo5h7RBIxJNIwtdXb7K6zW5RRFhgWcYyDQQzDxbMjMhFhGJdQjSNsmYSZQpdrs0s0zuhXYbASCnMvckZ21zbhvknA0kwu5wgEco3mCeX6+FEVts8Znob5U2xXvJWgaVH8So7fltikRvgLH29Df02NUwdURpUHJ/UdxQ+agz2pbKFf+M/f39wfK4JrPXjXyz792v/6jyg0feKid+mEhYA6kCu9+2z2kGFL9jXTd/akcVNylHDxQYykDlz4P2TiJyPZxUYPSL5PCn9ScKbQVcd+/brQcz/uCH+yHQnoR33Y278mX3095LN73XaQs67rtl9v3Zdn1BjMHaKR5F1wWoxN/ffWpPAh7s2/vt7SswSgsPykLbYgdstPv76/5GBL2VPCjZH+L+WA+4L25hwO6aWnfNIqDWsuVkObFngjEBBCfapE1iJTW4mWcDoGaeXXNu17ltNLfMgAi5mppa8tQiJ4bVUagKQSoiAnheRzNL6gCqEKKEnHNuk4aglpnhzuV0UAk/yYJAJd7zxjpMiOBotnZ3VU0pNU0TOIlQgBqT5haaBcTmZspFagfgbpZh7uzsQqZQBkAwBgXiUCJRoswwcyio60l2sDs5gkOcWBgxulAIoJxzHOHABxkw0agEPDa+hqGvYqltjEP4SHvGCPeIU+0NTbwly9kANnKs7uYNAe2iu9mJBH1Sg+2GdUCFcGih3fDG/kV/P3Rz+f4X3cAOjmWod2x2h4Epc8BWHhb3YsB9W1qfhPWHqr9gikTC23Mj3efs2yC3j3Q6T/R+Ql+87vdJJ2kVgWkr8nYlhxpa5u1LO41HT5rcJ8q+qyrv7pfP6T7ct4ucJDq8MKUieBel0JgKeuOb3TlRb2XfsDWGX7dkr220dwqLd0EE7Y1O/wPNDk84oc0GlojcNyev+i7cgx1rLzvf8NP682Fb5noEB5yvN9A7BWx7sLsvztI2Utj+9qszARxcSH2603V4v5HdRd+HAL3pnTYqbQgXRYCRKVzdWiCzm5C7EKkDcCjDjAyeYeZQ16yqsJxzyqkxy1ECM6fsOSdVZc2wHAmxihJrZjZDzvlkvmhSizYT0byuq1hf+crMNCXVBCEVAiHnZJ6JPMYI4apE7XByIyEA4hA3didV1ZSMvKoCi8VZnTwna9tkSErJg1MMIbVNmRUhlpnpxb4eQgUiZohsJpsTuRtcQQY4wE7iYIe4M4VIiCFIqKVmgCRTYcCHGVK3vgajKSNuuN1kG/IP2lGHdBWORZo7luCObQjG0m5i1Pp8GGPtHLs/YL095YTR9mRHn3B1dG+gMeq6a8AXx5wZ99/e3TlY0g/RpRvKH8uAx/pnDGMM+FZR/g1w30hYvgcc2jL0qXCnOexK7vc4bSTLrp6d9x5Qlu4V25XJCuMc7g/GOrSLztN9ju9ajKin7+1T8K7montBj2p0/TCYlDg0b7pXdI5g/cr3Hxz0W183Sz0/KfUDtmqME4g+gesvp/4o747j9XnrwSLpt3ysTIeBwHrNUHv0tr9J399ib/vhWn/Q95Anoj4Vxt5K25uH3KfXvrUriwR3VSOzrKlp27VbYigLQd0tZWthjbsSGzPVMeScW4dbhjuTswAsMfBJfQay1LQpNURE8JzaqL5eLlX15Ox0Pp/lnNsmh8BmsJWqprqem9nri1epzZWEy8vL+bwOdUWEpm3Ns4iEKNl4fnJSzxZ1mFv2n/z8Z69/eK3qWZByNsfV1YrNA9N6eXX29DS0FZjYLKeGMqoQAgSqddzEvs5tU+ZYVc2qagZiJzcztcQe3QOLEMnmkPR2jauDDMbkEHgglrqqKLAa2LT4dg1mRbeaDs7DTgM0mEj9c//YinQA9NrzfOe4/H4s+q6eG+bn/iq+QSIfoXh0sMz+n6V8zjsRAw+2eb+pnSWrc1csTqDdivDtpj+E0GfA3RLrO0/039U54Q6WKg8td7cw4IFTZJ9r7BMr9DYE6K1cIgKuTX79rhurn1mwS2e6C9oFdlWb2KNOb4z7ZUMiQ1GweTHMOtG1HImNUrFQLgBwLWzMAULPVFxW0van/sXh19qI7ecGHOy1G86xFX8Nws7U8e1gePlv+22dt6Rv/+s+vnv7PpnAnqjtPeXPloWYu5dIWzst30pmYx/bTZ3+dFTfUbl3/47ZWsYY8JhE4q44RBDRm6b9R26wIfWn9fWClAOCdX+59msAUAIgbiX46y1Up2m4gRAPSOGA9Gx83ckAKzpn80Se3DI8uSX2RCgKWBcyZwQhmMIUnsjVNKuZW3bXWQwESynl1GhOpWFkbppYQMzCcNWUUpsaN1qn3LRr4lDXsa7nANZtapvGNKXEaJhD2WyRMbmxgEGBq3k9P40Iz1++mM0WAL71b8V9TSFdXazX64rdVZZXVuWaAqkjt0mMzb14i2naSDwCIhAc2jbLnE9OzpikdJJqZlWwiWznQwl/wcIcwAEUHMFBDgETY5NFmeDVbqAe7M72/XlYnBD7jK2bEr05eY0S73N/7zVYDn1C36+5f7FL97e4kS4dWhejDHh3WXUz/DgJzLYBavpOoOht0LfLZIeD+h4DHmik99s/+GlgMusuxgSe4mQ6aAawYzo8tLqvb2776oDY7Xv5y3tPMfbGfZ9SdQOxLwvt9sab4L7pCGlXHupujhQ+IDbRrmV0t56xlx792WUrQD07KMZVRluJZyjqOW+6q5sf+4t2dz0f0Fv2v3F/7A8O7RgDPrgj679i8K+OLOCx8WIMx7RjwN11n8H3CV+/nkHDuhEXOfze/Y7qWnSw5qIKO/T510q2roXo9eeAEI85YZWfBqTZzMDqUE9N1tZyclNYcm3dWrfGLQGJSMspYYJHAjksU6KSGVfJ1D07qE3NerVqmjWZE7trMs+W28AkEkQka5uapmkaM7tcrk9Ozp6/ePHkybM2W5uzCMcYUTEzmzucq9np/GRxcnJS1/XZ6ZOz09N5VbOhWa7Xmq6aKwa9/OrLwHjN8bvlslmuPJobE2fVhgMTsZmxixOBKJK0qd0QdOEYopllNUvlbB6YmTcRsBMFCRQIbgBtQ3MQB0gABTirizu5EZyJEOQ6Su3+bDxIZPaHCbtrgXb1Uv2BHhCcAe/p6tk/hneQcA3ePoZ9BtxnAP0XDS66ysfp4eg62l+ktN2M9pdtv8e6ddGtjoMMmHo2xD5p8q1Gar95Ywy40Ntb0ZXvJPVBh5f3DBjw4L27j1zThP1/aRfYJdTYI7lvhgfLhtQNXv/jvWfYx27+3f7i6akssHMxMuFY3iQkWNdZ12R0xISxy0R7De45Dbp7N2vGdlgldNz+e7u3bG52uWDhRhtP7P6QqypKhnff8STmELo39ncDB7lUqX/w9v7oHOix3Ur6C2lwXbBfz6BDBk+NaTJ8V+F/3c7tncEHDlRMPXo9VGf1+6rDPv0azNu+NLDTY2SkZiXvr7emjea1pZVgw4aJEiGDjMjAMM3mGa7w5K7kSV0tp1XTtM06rRvzHEIgI3JjuEJjFUIIIM85t2mdUlLHixcvOAgRtZolyIsXL1589jKE8P13v8vq6kYhzuaLk7Mnp+dP6mouInFWswRrm2VqLpeXl6urOspPX/yjWkQyvf72u5VdWVaIw8nVjUREQokGZW1xxlBVBpFAJFRVRUQ55zZlaGapQmCWoNvJbJ4ZRoXybzSgARSZQqtkhmxQ9QzrxtMNROTY6WR3743jRoW2mW+bJ69H0HsbMuwtvYOzrVsUtjm03NNCbedbcaEgujaKERGKZz51yrzRDf1mDhe1QW+O8ZZjuDt1zH734npyEgyHGdiYCanju4d4+c4iGszt/hrBeNCe/gYCe2Ske+n+gt1vZ/+l+4wfuzRkQJ97nzYUAAb0bb/9+z91bzzIgPv/jpHNo3AvBsy8mYKD7uijf7O/hesPSeem2/u1/N99WrePjkPQ9r2jXoj9L+oGtX9sr0/Bbc9LtlxEO3wcAruf3/3U2VrQkzW9tysf1FCSOe0vmCHd6abp7gS9/q6RmbRPUAbreX+OYnedD/4dFL55R79XmLQX8bHfgWM24EG+ZxrR1gy+aFAJdm2KO46jbAbNWV0bmKq1rq3mNSHDG7IEtEbuyDB319SsS0wu1wRTM3PdBtZQY0HFdYxiZmYsRBCOMYQQm7ZNqVGzknu3aVZiVT0/OT8/P3/yJGf7/vvvX7169cOrH2ez2enZs/PnL8+evKhnJ8RBnUIIsa7ryBpCvV7GWVxoPV/UdXUyq+Pp6flicZKWr9iNiIQIruRGbkzBNeficS2ZQmFXVsJZM4IQxxBUVaBUzhmzGDOX/BPMRmAGi7BECDsTEM3JnKyXoqKE2hAO/ZnZY8CHw7zfTNlvIEr7T+2/Bb1J67u7tIPT/mC1B+dY97rBkhnc2a9/7JjWmCYphGEI2+1HbdhVr4UAaHfj4tiaFLcax+7PIf0c9FsXJpb28qMcbGc3Un0G7NtTTNhb2n1N1W7lJRPocPt+cGcAgGhHo9mhr+Hrt3yfoN2fB9+TAaOj7WWuAugLaf3muftYrDbi60d2xmhkwh3rjdYb+J3+4puOPVBvpl5LYP3l5r75wLH2BN05ZncrA+4z0X1HpP09geL6p74EPCaRO+28/Qbitf3C60r6T7kfZuSDCse2ir3yI4x/r9pN/299BQZLorMh7XXvtTPIoPe6zRPtstXdx9H9Wv7sW9HMrMnrnNuU2pSalBpoNsvkZkhk2ayFtSAlN4e6O5MJuVNZCEROGpgJs1ipEKMOgYmoXa8T4O5VVdV1bfASn1ICxzAnktW6efbixc9+9vP54vTi4uLrv//t7373u6urq2dPF6dnZ599+cWzl19JmL+6XP/+229evbqoqurZ0/MXz85nEa1mhVL0OAtNslkV6vnp06fPsb5aXX3rmsipRI8kKJFZ0tSoUqxDjbOKiAhsZjlnIjWFuoOgqq7qUYUjb3MJMwPUy2UCBopRQErijOwkTsQbysC0Y2Lo86qD85D21lG5GKTp7Mpoj7AOHulqLjxj4zfQ0+50Tw14Sb+Gm2NT7/P4sXXh7vvvBTAWhmCMDdj49+4/0m/bYCl1TRos/L5z4n57aJCp5ca4mGP9MBQhdtdjN17bN1yPe7+d++t6++xh9tedetgfAoyTtTfDQzFgoMduNwolH9ynsQZvNTHDO6Mq6DfNSjGcKKPl4A70BKbCem3btp5aCO6gMSem3rf3/u23B74JNAF3hCCFBXeD7r5DgIY7NdqZDX1Gfv2NOxqIw10x6Jbr+z2/8X7Jsf4f3O83ab/9RGPdttPIa+pDRD0G3P+6wU65t4APC/qFQHd/dgt4zAbcEbJS0rbJEtbrZWpW69WyXa20XaslLse1VMvxWlh2JCrpDdwXcCUvqlMGOYMUDhA7gwNTCMFyTim17TrndHK6iDE2qU25zaazWT1bnEio/uDnz+r5SdL89f/253/766+Xy/WTJ09+8Ytf/PRnn3GoWOoffnj122/++n/767//y7/+u2+/vcoZP//Z2R/96uc//+nLOmiIHigvvGoa9QVOTs4+/+xLSctv0mXbrFIiCWbu5Oxk2uS8bltnDUnq0xIVVUBk7m5qMDgzcs5t2xhLRVE4MDMYzNwxYCICkYMNCCEo6pqEAju4MGB3R2+8BuOLg8xyd7bsT7Y+AyYi2zOQdQW6677OCXuMcMgRd+3HYxv6/ckMDFfWYOL5rsz9ZthnwAP014sfOk0wKLnXzptU3+gtvd2nhujud7ro/hsHRGP/Wbr2KdkRVLrBGlvX/fb3a+57ud/KgO/Jhu9pA96wkO7Pwa/lp44f9wvc0OzD0vChAndv50Gk0fNhtLEMlZZ3N3fSC95U+WALcscPYd6wDdqamwt7LhudLiDUdf3X554H/47szvjwJnR0Q2OHacT+8bAtobyeqJ164OAWoSCEW85xDnYJW1PFDvf1PQmpV6YspK6fN/8nwl3jqRdQU7W/IK/boyrUCyiW8+Y7V6tVbtfr9TK1K+QM15J6yNTgRl5MnGbIZmaW29xuciNm1ZJOIWXVpCmRw4XIPaW0Xi+bprGsMUYO5O3GDV5EZrNZiHVK6cfXv3t9ebVer09OTr788idnT5/MZrPl8vWq0R9+vPrN7374u6+//fq37XffY9UgBDx5ctW22cFGmrVNul6t4kIVzvP5SXz+XJc//vDdb5qlmSYWdld3AtQ855zFWNFq2zIzO8oRUjNj8eKlZWZt2xoHls2RZTATO9N1KDffbgGFQwjBqKLAICHenOBhkj7J7sZ3ENmKBsPZmzYH0RFQluv69ydknwFv5vOeyLvPzPr18IjE0D81MLjf/7OrvNwfWHABON10LGrs/j4LGVsvN2sWB6uSiLaRCodfXfKF73PQm9vZ52cDmtNtFAYTo2tPuR/C9bD274/vA0bTF+5z3z5JH/Dg++B+EvAOfwJ6RG3z5+7NfmvvwpOOzq85grHS1SaE5PDtZoeF7/mR5+mtuknFug/Nw+CwRKBDm/1N+eKuVWbM9l/c1D939YLezLlw8LVDL8fyBDAMxeqO/nooxXZUJuPHkA7e3yu+KZbzzrnP6ws+7GXH+yWJAMwDADhtQt8abaZOxUgJnhEEImD39nK5fP06vv4eqanSVdCV40p92eZXOV94uoJnt0Zzck1mVrYyVdKkraWl5nXWdduu2zanpOtGXzx7AYRls2Qm44bD+unz0xOOMHz/+vXljz9Ui/n8dBYXQYT/7uvf/vY3v6149tPPf3Z28mQRT9L36Xe/+3W7plXbvL64+vHHV+myPV2hnsEWOHuKP/rlT/7zX/7k5Wfn5s3l5Svz9nL1+sXpL5roS5u52uLzz395+l99/eu//Nu//I/PzueBcl6taqHnNacZtctLNvCy4UZyOqlPz6mag0hCXdULNbaSK8tDyB5TnpMEr5rKRIJUAhFzmDGRBJlnxCrUsa4VwcmcM7E5e8g7ARD2DQL9Qe//sJ1gm/2lqh5ckxT7Nkvqym99LErl14458MPkkcaOyY2su7H5fDP2nxLZib3fFRur/4Z8t+X/B/d5hCHty3+b9oz0zxj2JVHc2DkDRnvNyMfOj3IJPTdEjEOH7c217QgS3U/mJcy1Y+dgcceDdwBgjJHfBfc9htTHns55RyzDONM9dhvxZhN67L0DMf3W8m8Powtp5HPlWhLtE6wbNARHfACNh+g7CkTUlzjRm+j5yP4cCyjUd6bbkVztcEcU55T98h2dL5y3+4mBGEECJrgi57xer9frdc45a2tmJeIzM4fAgLStm2rJLOiaXDfm+eVy6VB1dVfPqimt16uU/GTxRERyUs8a6upscZLrcHq6WH23aprm4uKCQzg/Pw8hfPfdd5eXS3M5Ozv77NkXXzz/Mrd28eri22++/fpvvyapVXXdtKt1csfZOU6enM7PFydni1/+6hdffPUTCbZeqoR5lJqIXr/+cbW6koBZHZ8/PX1yfqpffmnt1avvf8diAri7GsyMmes6rnJWM7RtbNsoFUIotCkENgQiJnjRz6cQZCcmOXVCMEq0TZLyuDk7Y8OAcfs54D4Gklx3UZLE7MPlwCTcZ6XXotUIedynFQMKPsDNqumx+g9ioFi6WXK9jQG/CQ4ysL36x54e2Pi7Og+X7uUa2Cn2BprRnjq2JwPQzq/dT6O2bRiA60i+WxEDiKMvvg33jAV9y/XxnHXn+qEY9hh2F8yBmXFj+d6Tt9k27oijJxYdLjb+2uMWjB+braVX29biUAjEYWJHI17oY7g1otzAIHLzhuZAdw3omF/fZgcx4EjJ18vL5epyvboyTWZKruYOeMnOJqAQgnHOxsxQReE8RGSe3dVdc5vaZtU0a8sZShULZSM1BgmDEchzbtqrq9XVaplSms9Pzs6fOtN6vV6tVl99+Qci4Wz+pG3bb7/5/odvvv/u9z98+/v2at2IgAXGqOd4+vL0Zz//g+eff/bFV182ubm8XJ6czM+ffqGaXr364fXr1/n7vwSMBS+fn1fhD+azp3WU2bxaVWEmTuYBJboOhRCqOFtbCyDnvE4tQiPMsKyaq7oSEVBwI7dNtmaNSkSdVoQ6IYKcmJ0B3gThcBJiInJi7g9NoYHu+5qP4Xh2HHNLQA+X051B76w5dCsnGL6Pxsjd4fk2GolyvP4ByitMN6/2Xho0ovGANiP98MZkebB9GatmjJ4cjDl/Q3sOlvG9nC69+g/fHyR36Sto++Rit+ai5PBtwXLe51r87R9lPPzWu+FhJODd6dhXjwxujhH0ne3GrXzrgQTgfu9fz+Ybyz/MjnKU0R7pW2aHqMAN9Y9h8Fndn8fu3PvYXzz7osax7bzh3DAKSd28mgD4DTbmQsiu23r9y+Z/oKKjLNxDFQLAkVu9urq4uLhYXl40zSogA+aubtktmWbN2XIWZjIyAgMOAhkFEpAI5Wxtu75aXqyWF23bwolImtUyQhbVPESWgNRo06Rls2yTZEOsZqenp1VVtapnZ2fPn7+cz85U7erq6vvf/fDNb7+9/GF1dYGrNVZr1DXmFU7Ocfasevnli69+8vzpy+dNc/Xq8kIdry9mTZN+/PGViLx48eLbH/4KMAmUmkum3K5fVsHmVWXnp2w5rdWzdvRGVeeLmTqc4K7qRqau6mhiVQtXEgJAOW2SEvRFzI0ZhbwkIHPLTg5XdlECrNiAixllh73dRSO1L9TamGTGO+W7QvsXN0u0ZYb5XitHp7MdubBHmBOXP33j+4mygm7foNxU/7EPbr66MLNjP2t349tdjyna+iNLvQsa+4CRDVBHxrwXzMTde8ao0pptr27SbPYnwGYzSIDDzY3crsXge+ABGPD+AhizKd6Fge0++3Z1vt3EHQhDY4T+oRjt+ANHlr8H0915bU+ldq/pRDtpfYFNC8PmnOKuAdh9JKnVKMJo5Kydl97aFWXbemA0SbHN4VvMwIWTR4Ya2iatri4vX/94efG6Xa80raEtrDVPZsksmSazDNd1s3JttGlyak0TmZf8BOLqpqbJcrKULbVmgDISVRZOqtlMopk68yzEtIITE3EIsapnMVbG+UTC6cn5N998u143P377+tV3P66udL3G1RVWK3zxFeoaJ6c4fXby5OXz8+dPFidiWLnAyV+/vlyvflQX5iqg/vb7PF++ijEAcnmRf/P12nT9859+9uVXn//Om7S6Wl+ppRQDA6SOtm3n56fRyYvMWgJZk4NMLbOrCBEFJrgTmAzOheS5uxu7yyb/YmHqup0Tm7Sg7t6l9+qPVTcWB8ax7ynp16V4bJ7Qpvhg5Mc2r2MSbbddH0jeb6AaHSt/0JzHdE3w72I4e1iBoafC3VQ7xvhvfu/+Oh3TEHRZ72g3pPEYX7jDZ/U2hXTt1DbwMuj7WO3YvDenJ0BMZPQQ/PchVdCjDemz15F6DnDuG1SID4X9vdh+M/rg0RCYx0308b467nsHC+D23rqRQPQX9qaF91BBj6nRsDPch2s4dk73+2GHgI46ne2UvPblsbKkiiy2jbviCAzNaNer5eXr5dVls7xKeeWmmpeAubUOdc8EE7gzW8qm2c3cMtQAM1UBmSmR10FoMa8EyyWvL9dtaq+uki6ztlpVwd3qmUhgoWAujgD2EKpQzdr1arW8Su0Pr1+/Xl5c/f6bV+srRIFUmC2wWOAP//BUIse6XpzN52eLuqKkl+urFcf54qRat1Wb05ef/4Ovvvz5b77+/f/8b/+XP5h9c35+/mz2pK6Cuzarq7Z9QkSnp6dLb5ciADEzQWAiElQ1xighOhMFFqEQgzGrJtZkmiQIC238ubeiM8i4JAej4uukRRUtTM4ghhGXruYDR29Gzy52A7fPGMYesWuH+Z0J0G24dwyE4wzm0Lzd0eHdofxN6Kt8O94DuDvvCf031/OQ9HPXjOoY19iNSahj9HYMYee0xYGBu6H+PjoJu6+c7WmYAQxmXicBb0qWX4vP7pbvHggS8gZ4SCesDv1QhT1byy1PHbTPvxsMNmVjC49HmnW8TeW4ekbvX1c4qH/sxWPtGb5u4Od5c/n9Hwb92c/fvFPtkQtJx5xN9ibb5ubRhI9ADt/GhaFi/UGbsF6ul5eXy6vLdrU0TQwHKUhBRuRQBUy9hIhyYQjIjFiCupGxu25C1bgSURViJBMzb7O2ebVufnjdXvx4QUSO/PTp+eKkurh4tZandV0vFot6dqrql5fLVz/8AOaUtAQbms+xmM3b2J6f4MWLFyenc8CNctu27etWVuAqIsbvfrz4+T/4oy+//CzOVlnbH16/MsLLz744sd+EsEnbxcwpNa9evaojPz2bW5rFGG1NOWfT5EkDS0opxBiCOLPDhZzImUAMWEqpMQNLxUTurO5sToBRpm1eQod6CaRFXjTS2wiNXjY8B4f+Zhq33T72tYsjOs0eC8F2ngPojP/dne39MZPHWNuOXHijILqOXd/xCe9xvJ2GHWsqOp5e7b12czFyyuBGnfi+qfEG57WDXX28BH+A/rh7FynvoAmyr4LezgrvV3JP1lvwUDbgnS8s2Oumu/baHVj1Ua07Ar7J3vsw732IAboJm4BFAHbVO28Wp6TbXHeKl6M52C66LXMI17vXvkwzpoIem9lxRFd1LcFgZyHdrJrrYm53F7QhZJsZsP0fLi5Xy8uLq6uL1epKdU2s4i5AMRapOtyNMnkyUzIVbPL0FH2nWYaVGMM5p9S0V26J3cyMHAIJzMvWVleaM9Rg+fVyyd99b9Xz9cnZ06fPn8e6Xq6ufvzh9fJqPTtZNMsVMz97drqoF4vZyasfXnu2L7/6/OJ1o5rWKa/ai8ZXoaLZ2Xy2mP/8D76SoKv1awI5csqrJ8+e/suXf3K61Fevfri8uHj16tVsXhMv2rZtmgZni2LfSym1qU3tktRUZ1VNxC4iRlA31QRm8kpCcFdNrRnYEAOLwLdxAbfQkh7YXcmyWSbLzGIbYl6U0L7p9R2VzDDUa4eDFNz3kgF06EI2DlQgZjLYMm5MUXdYv7vq6xHN2Rt6QRdh6zrWB2+7pa8bJ7phnh++f2wyuUP8l3b+3sVYnuadOnc0VSMSs+++ottb31r7oD3coz/dHn2bHx1782HTpYSOiW0n5O4seQjciwHbSDYD2UmWMNw17WNfablxr/fc/7O7uD2E0t1wvRvaVr5vn9i5HtlYj9tCDl93dwacpnMm2qv/8HjLzv0RQ9Yd0KdvO204cmHLnip4vxt9x9Vw7JjTiOQxsvRkxNlqTIU+SigdEIaT6kYWckfOsKyaUtusc7s2bQkKzW4ZtnIozAJbqCoEs+zIYI+Ro7VheZlS9lhFS3m1astb6li5IrdNFaPG2UVz1bbWrLFcbiy45nh1aRwQYl3NF7E+aZrVDz++UjURWV5chhBO5id1PSdzTfbs2RN3Wi6vLq/o9cWPF1evnHy2QDULp4vF+fOn8Ky5uVpeOs9fvHgeYh0iv3j+vH79VVL8+uvfNqvl02en5+fn9ew0G0BiBlVlZoi0WVNqK5Gmaep1U9hbNsTaY4whwHICg1jIFKzm2T0ESM6Zg0iJSLjZz7jAQU5SImSBGUZwCsROh52nSHVMY3TQljFKHvY1Pfv3+zreMVvy4HXdlerYfD5aQu0Lat1LDzYY4+txjAyM2VwHmu19iXOoaRs9x38ch79hg0I3/LWHsY27De5vq+n41IBM+bij9YPjwbIhPSAOukO/bXvwAO9kDAYbvHf9jbstOXj9hu0Z7GPGCcRxDPJ4HGebLweONmWKws8Ag6VsWaHmml21ZPyFZRaDqXoyS6StW4uczVJgiQwt+mwzNzB5XYW0zE3TZF1bu07NmiFpnXK2nNG2MEVV4/T8FOzeXDUJCLWEWYi1mjEHIiFikTCbzZnZNakCTnDL2dq2/fGV//jq4vLS6xOcnvPZyfnZ4nRW1UnzyVl1enrOYX7+5EzCiXmczaz9Mf7u24uvf/c9O6rZfL22NuMs1CmpmjOFGGsW9jRr4CGym3tWy1qUBWxqKWVzUCQBCSEKmcMc5mZG2KgC2K3IvoCVuFfsRjDszpPxffVYxLSRUX8g1dNYvu23P297dtY7hFB+qNdSz9epU+kXb+GHecEIHmq8xjA6LjTcYaB8+1hM77dAn98KA77PRBxjunc/pHTUu/Zk0OvZ3F2/yw3RB419/cG+bNHfyB9vuxpbqMcx2tEp5GCHdYoWhxpUtWmatl3n3LplWHZPsNY8k61V1XIyTZ4b19bymjQJNLPntk3tWpuGCCIibohxtUbOOSW1bFG4rmfnT8Jy+d3JKZ6+qF5+/kWsw48XP7RrcvYvf/LTJ8+eS6zTct0kb5ISOEae1zMighlIHcg5Xby+ury8/OYbJ8aTZ/L02cmzF4uTRU3O2rQSw+lsPlucO9ezWQwhqBNhnXz2N7/59td/hyfnePKMl60t1/qCZ20yMzCHGGMkYUuRqK5CKwDMc2IRJnLN2qwzGCIcLFSIXBEbu5M7TGVz9rfLsGu9/7B1XAIR4OO5fnCDs89hjJ5iOHK+8Vj2g3eC7uRITy83UnRkXYwFyjjqpTfQ24fysn6oesZwgzPsUY5Hb4ML3C8U5ej8PG5Hs6vr6F+/dQn45g1mnwfj6Pn/Jo3p1fmWDchvBwdZb3dnnwffXMkA/EZe2QfqH7nfKRB71kFYyuvVKrftNuxGYhjICWSezNWR3BM8qyZtG8+NNkvS1KyXzdXrnNpIAExTbnX5+tUPuVnDs6UcQ6hkBog6LU4X589enjw5v1yvVmrzJ09ffP7yV3/0x0R+dXV1dbVcLpeqWkcpGZNKhgN3b5r15XL546uLV69ghJcvTr/6yedn5zUogTLAUeKsnldcsUNzsrahULHz6mJ12dDf/eb1dxc4eyZUnWavlq2pkTqrk4OdICKzugpkVRAK4DJ4rkTsSZO1aoCEULtQQNAAZ2bpktluTBEGeDmzBHKClWvZBjlxBnxPVfjG43uDieEYjPHr0f3bkSabMfRlAPSSxo+XP9wgO1IVfP2WoYp77MNG2nOkkfkGDn9cPWMb7uv6fHi/z3o6weC4ff59IlG+HS/oN8OhPdeQMT84G+65Gm42RDtutHsy8UjLH6YxPZ3PtTro1vL7eNs7yjdGnwcDMIwmwzh49+jAICMM+waVOBGJb5w7zaBtbppmtVrltrGsJba9UKnZW7XA5sTMwcTYU6usSiklbVbN8jKt1kV/3azWzeoqk61WyxiwmM2MWk26ym1ucLn0qmrCug0L41g9+/yrz796+Q/+8OdVfHpx+Wq5XK/WbZAqnITIxAIzZXg2a1ar5XK1Xi+dECo8//zk6dMni/NIbDknFp/F6mR2okary1VoEauFRK5Y2mQXP7769d/rr397mRyLs5fV/CmFRcqSlYkrUDBzTepgZg5MQSgBMEdOcHFSVW2zqiPEWrjyOoubMEcmEWEJzgwiAaEQNYe5MbmhqKMBgGkb0Pztz81j35DzYZvuaOSpIxn2GAbLpOet/TD1j9czrGjji3NkSNpjhYeH0qTeeuxz/wLXdP52/jJq449vrqJ/Kwz4oVTQHfz6AN8DZOm6Af36ey/dTnEaO95wlybtT8p9GzD7bUeA7oWx9o/ZgI8NdbOnZ+67Yt1R/H1AjIbiu42gFEU0FCmlpmnatvWUzUqOegBGpAxnMTIQwCzOUTyL1wqr+TyvYoqc65hT06yW1jZZxIiqqjo7XTw7P2ub1eWry2aZHRYi4my+OD///Cc/OX3xjCuKs8iz+uLHy9evL69WyxDC2ZMnwu45aW5bzSnl1Wq1vLxKKYnws2f1s2c4ff5MhMyXKRszC9NquVxermKM5nz29NnLz85m1WweFuuri+Wri7/8m9ff/ojPXsrJky8lnorMjaIigCNzKAmOWiDAYJazJ1Ui8sAxBiJSVXIPzIE4ECqSIBKZA0tgIZZMW39yL3OvdLsBTjCGg6yE1veNLvpYgnv4/ujwHi2Jjm0ER6p/uIl90BD2tjH43u6vsQ3Hw9ngxxjwkdXcXn/nUl7+HGajupkNv4FK/1Y8Igl4H/3ueEs8+JAZ+DAPxrvYo3c+eDvJsd+75HpH9HrywEreV0GPL+wHVj0NcON7rSSdJIMqUsrtuvGsZrq1ZSpcHeauRGZuMDdXmBIQQhBUHBkx+qLK69l6dbUmqgKfnZxcabq6uoyVUBBqRERmi7g4mX/50384O3329MVnT168rE8WDZpXl6+/+f4HaupV28K5ms2FzVWbnHLOZpbbtmmanHOM8eTkZDabMbPXGU6as6s5grs0q3a9bhfzs6bNVTytvpydVKe1zGpuBeH331858OKzPzg9f6GuSuIUgODEDlYnVU3JCDk3ywys3ZnhVRWFWYiFREKoZqAQqlhHqYRlm/1XiJSoc9/xTbiyMd3Mm+zMjtVIHbuKwuFsYO9IBX1gL3ukSvmN6UZ/33xrsYO/vNl771z/YdxtPlCPosq2AB0qed/23AWPQgI+eMK60wbvewM+Nob0UCrfLfe964q9VeXyvjDcuIzYg0fPGY8RuIcKETr6Q98GD3cv6Xs3M3CTcsHdjDyZq5KqJk2ZXNkg5CVk1PrqqiISEYRQ3IBnVS3z+WdPzr799vea26piqInE85Oni5NnX371ixBPFPK6aX58/WrtKXsiYaYQQogxqsFyKuJ40zRErqruHmNcLBZnZ2chhJxzgyshMW+XqzUZnS7Oz07Pnz2drVc5pyZKPJmdzOqTGGfzuZ6fPlH7u2pWPX/+YjY/yc1rd4GzgTrNRzmDmy23bUtmLZEI1SESkYgwc13Xs8Vp02oIMYTAG9N0F1Nye8j6brg51veh4XogU8UIxpbRmIR96/Geo3B3n4lxvMljGw/EcWeO69pHqj/2lOjD+dCM3t83/o49eENvP9Dp1x3cjwGPTHTrB7rvbS7GN3AHVPM3WD7GnXRGah9dSIZD5ifui8XbqBQA9MiFHTZH6X2EggzfnAnYngHHJhHwJhzTQchYEPN+eoHeq3MeGto3f/Ve0G+Q3Tl/8OZd/f7pXR5OO3jTfBir/3BpHWjyt4+zHbYxj+3kEjsjEIMAUlgGkrI3QVZOl9kuzZaqa9UMc3dH8xpkbK6a1Vojz3ByowoX61VKyYG2ni2dRGS2WCwTyWdPgkOInwSOEioJRPSaWJvLVrPBLTAZexa3wLSqYraYbL30tPJ2xdpU5JHpd69fN0168eLFs6fPVLVdtyGERa4vl83FZWpapjiT+mQxe37y/Il4/uJ0UVXhIlxK/q6iEzD/4S9//n/4F7//xbM/ODt59eWJXebf4lKfffnLmUTgyenTF9/+/vffX6QvzquT2vVyacnPWtQzzKumVg31mcznMju1OMtMHmagUIwogZWpYU8RDUmgwB7YWRCIK/LgWZJhlYIwKWgOj8VFmo9lGDltp/G1wxcRjZkYjj2fOubzd7RNl70fO6lr507u4b6Fcqs3KLuX6+l6pGR5dA4IcyLy0ofbXt005GB51963XHvSbOjqvk8PbdhNj7ju/DmsH3q4noOFiXQksk9/Y8fU78WbQtUewCat6j53e/MN3/1iQY8YpTsVX+daRjewkeMlmDFj+A3ONUfVf3BVDK7vUv8b7OwGj9wsEN9a/6DxY1P5oSTmnv/aARvwQ7zgThV1HzMWTH+w/guIyKwk+yHPyA2Wy+VyuVytVikleC4JyNBtiwAO5M4oSmnVpNlcyY0FRUJ1oIiJAFy1qhYMInImYoe7p9wSUZsady/vVnhWNfXya25TztlSVtUS4ZUZV1dXRDSf11UVCr1zzckd3hakhCCViMxms8ViwYFOT2dhC2huk6Hln/70K82XddDTU7a0YErMnFJS1TAPp6eni/mpe7tet012dggjE5qUrUmR28CNUCBnNQDMIeachTOzFFtwSQjMFJzZmZ0IJCBihxFKD5QxKOMwFthhdH6SXM+wDc+AAzp2jnMksMYYRhO/j8BHQjNiq8Abbn9vY6n9p95kkR7rw+G+FXs6G1wh3Uf2GxdvxSG12R+WTud3EHchU/vmwn0MLLu9YscSpn3R+b6k7a2fA75LC+/DIO+Dd6DKvnnS7JXekKKdnTLeTJM0unW4+1ffY1x2FvDRzjLjbziu9IgI0B+Tfs8oOZDJomY0q+bq6urq6mq1WuXcEvImjkQh9Vw4h7O7QgFTTTm1lhPIZrGKLCKiqiKxJlK31CpMSSSIkLlZtqwKMKhtWxJmZiOYQ003hmi3LoIjkUeRQE5Gq8uLxbwWESZfr5c557L3f3X1vWbPmYzKEVxyd886P1mcLBYhMJHUQdyCpmyOF8/OV5fPXS8Ct1VgJsk5L5fLl88/E5EQ6+z44dXFglc1y9lZmDGTMEmUUDko5axtS05czQNxYGFmM0umYsZgLfkVOg2Qc9EcOBzb1Kobol+EkVHTw4iq+ZpBdulaQeP5cW1k4z46z4+mz4fJaZE+iPbWdU+o9VFS2WlrjmwMcPwHHLfkiQQ7jK1wwQO1bbZJezXcwH0BlFjNNxP7u7CCfda7/f/jvNm3QsVdsxvcBfdiwPFG77hOe9E1dMyJ7FgJcszGc6wK+mbs257HcBNDPVRy1EY1yDWGzb9vakMatvxYm/H9N0Zlsj6ct+QtNsLh9+rh6b3L+K97KZECRgbN3LZ5vV6n3JqpmTE5YEQOciY3N8Bdk7m7ZnKFm6YWZswws7qu3X3VpqwaYySwabtq28AihQGrQo0ZgSXnliFEFYjM1IrmnMmh8ATLrpncnIwZzHR+dgIUD+1Vm0rIZTKzb37vJyeYL+qKK5aYc766vHBkCVpH8joyiXKIHKoocL5aL+vI65xN2xhIQhCGu3IQCqGen5ycni2by1ZXMQRFDGdPivWXgnAQlggOzhSZq1rqeS1SKwUHJ2dyWHaHu4AUELiqsTopBZB7seiblxEgHz+UNobsu/R9O1vHbHVj9Y9FODr23PB4hvpuCRRZdnPZV26Nr5HjhJm7tGeUru4JTre9cUghBuvdd2MZ9e3K2z4ov47Qn21g8N0dM5gPaAq72g7XtCm3a3Q71sSwJc7dpun+PPh+gThG7nemjP7XEr1ZdrtD7x2pZ5SvvSFj3lc4jJQbH7C+Eqkzu95Yfsf4ffMyuKH53WYNfeXMXj39Yvt4YyevO+rNHkqTUWw8fTPbpg1+EwPep31K5kZCGztWUSOHEMyI4Ax3gru5q7uZWSSoGcEMDlNXNctMbAq3aIrlcrletxJrEUkpmWUtAqGamQkohBAl5JylWPuYVNWKAprI2mWb2qZZtu3KcgvNJdV9jKEUSymZKnFQzW3bkmA2r86ePDOXkjFpvV6D8kWFKrLmyBBxkVlgF2Zmz4EzIwtpXMyqSPN5fXp62ratz+n5y5es/+jifNG++l1afbtqlmgphDALcRarWFdVVZVozk1ShkcmqYQRFRHECoaXbiMiKX3Mm3yPIIfByZzIDWYucOR0eHxH5/9IGsoxTjvq/T52uvDI6TkWmGgw34BdNjBiExkWPp4Bj8XDGGfA3St72/8b6CQPRdiN/DDy0vHAJrcJYNS7Q4f2LgTckGSCRq5HcHM9vnfnPrifDXjsBzo8aY7eUY5Wf9z9sYG/RQPaTb6OHY6ZosbuU///bt9U0nZfdkcVx03H0voTdLdjxhSwh+oYee/IUzvOTb3rsbccz4DH3Ezh7lQkqa1ES3cLyFPKbhvC5ORFwaxmZoAFISUHFG6u2SybK8zIvQqcXM1hgKqm1MC84lhVgcwtaW41Na0pTCSZMVKjmnPWlN0pEBdfYiKSLCLiTO6aTYu9OaZkKZkmy62mFm7uINg6NSmltm3dLcYAZocS0cvP6/Pzp/Vs0aw1aYlIxSjpQd2YHG4MK0ZoOHK6stxYu0btVUWBiYjm87kb2pxmVX1y/iR6g7Oa0xNtr75PQkJUR6lCqCUKF7NhbtocU24TRaMgIhWkQojMYWMPFiEKxtGZjTm7kztJ0UKbwdyK6/RxmpgxZ6ujGe2NEs/dwWNOkYeUQGWJDGS7Gxr2BsLWGH24Ydn1TOrXf46a4G3YsAHhGviCHMrLe7Pt9vbNx0Dgvhl0N9J6q8DWVbK5874iYd303u47ex88ktTkBs3N28XNph/vldn2+HEEgntjNmYF2Sm/q1rp1sDoArixusFs29raDlT4gLbwwUGyB69/7KWdwL1j7R6PIVz6wn07zA4AAdEAAruRmWmbcpsQUkly5NqataapJPf1DfcwVS1JGiyrkDPXMcac83rdkHmMNTObw7M2usoptU02MyJR4sJ/6romcmamIEWDnbXNqguylNucW7PsMLgXJ6+2bZsmtW3LFEiYAaniItayOK2qOqmvUwtQjIEFmtr1ilI+WXjFJUqGCJzdaHVxcXX54/LyR0KoQ9WkXKWZiFSz2pyalNtW22yLqn765AtBsjbANcAC2f+fvX9rkmNH0gTBT1UBM3OPCPJcMiuru6V6d2Vl/7/I/ImReZnZ3Wnpna6u3erJyqzMcycZ4e5mAFR1H2A3vwUZjGAeZiZVQpxOczMYDAbg07syuyKjZDNjZlfLOVMpIs4SKDYIEc6Te3K1SVeNENjNyMjUyRxO7jZahq+938vHfeVU9SHM3DUAvnr+E3Xij9sOsbJq1f+uAMnPTzgzWD7ZFOX8tAfw1YKd1XWP3PciTlcV8XzC+uSVILu0eGI2PurPNBq1O/Pt1kB+xMQ8Oj7r/XC2Aly575UmTkyE87P/WgB8zdZix5zRi2+/T5WAP07wnlubZeCPqI+7DMKH3ZFWWo73c38f0mI9c1Jrn07BF6WLiLvEMpzRtbCBpxJP9T5Ht6MJhq8C8LRf+LKiHABDAGenAqsSbS4DKJsX92JWTLNbIdfqI1Vl2aLFR5dnCMcQgqsNh7Tb7VSdQ2TmnHLOue/3OeeclEhCgBFUNacCFpAzc3ATEcBUteTc21BSTimVlNwUpuRObiJiNpQCRwluFELXbjebzQ7cFx2GPiVrQlO9wPp+d+gfbrdNZGmarmzUjdxdiw2H+37/MPT7bdvBpWgqpdTIYxIJFBPTkNVzT0akPX39n7zkYhmayTVCnBwobbuhGIHqDk0gEAuTmEINXCvaupM5TEEqPqW+cjBM3cncgGteu9c1bZd1izk90dnqWvNPdhK80s6kBJoQZTy4TnV57Ca5fGLV7WvVma7SlTjF6xqCOXzIVxh8VYO1BumxBQeAcuTvvepOmE+j1fnXXWi89gdEYKYZgGdB5QSAH4/TvbD1PVFFv0ZfTAL6MzfST5KI49pAvMdm/Gz61O08NQ77oxmPl2JZFpvOZCNZWqaTU57QDXtieMbV+qxPHdErL2bkiBeF/7RhPaF5AuBGzGgEvfOQDrkkIlfNOSdGgivggJmZWYFR2ffkSCnvdzsvZbu9velaEfn5p5/u7+/NSURKUTg5oZRy/65mjoz3D/fb7fa7775vmm673fYp39zcuOvt7c39w/1h/3B7u3379u1XGykl5ZSsFDclB6y4u2rqU8kFHKsZueUQwVJKaZq45ZYwHA798MsQAzPb19+8+v0f/j3G+Jtvb1Q9Ff35pzduNAxDHhIzd113c3PzSm6727uU0qtv2iCdZSsGEi7JdochIN8/DLfb9u7mphOKKKQpHx7S0B/6oQvStF27ueF2g9CAGSJkXLdvM2NSN4MVYs25dxGmjtgBMjixBxc7c0oaGaNr+QZeiIu8rgK9CmAzoJ4oXZ5E16IArgHtxfu6+zVA1ZKxSJA0Y+q13M7XbDZz+yduFtdU3CFchhXOAQDzqR/MtX2mTOopIiIjIqozgWyG3qPBd8PqKS8onE84m8XEfNwBsyuwekXXfdNe7v+H0GeSivKFJKEXaeU6PZUh5k+sRH/q874QQ/9y/MELtXM1N688bV4xiEcexcjUXeEGV0IBzKFwn/DX3Q2FzEopYAqhDU0TzTznlLL6WDeIVAuTOFBK4RDZ4c7uyAqnCG44dA/7A7ghcvCwf9jtdjsOwiHe37+t/sHuxGM2BzY3A4OZghELKLK0selit40laTLVVDPhpJSJwm3XDsMQQkjF3u12obkr1v/xzz+8e/dA+V1KidlUVVUlRoCrUj0IilsuQ5+GBtaJMLsLKXGBKJEQGHDpFE5BjGNx4uISHMHIDVrYm4q+osVIYAUmrkowwAEFjFDYAwAnZyY/3eMeZRCfGPZwjeG7aoq68gMRVrEo9NEYfI2xeMQpcg3AOEPE8zvU60aniMnX4Rpgf4DosUjweAQ4j4tY0DHiuZ0C8DXysGjmp7C6RSg/534qAONsANfPeyS1P6r6PrEL4DGB4eM3smcB8FNzU39qm+4Xepw+N+B8cjuP2uwvHX8ay0FS4xrhblbTa7gCyswENquuu1X/pXBmiubkphK6GIXID4f9YfeQc5VgOGnJOYdIBEpa2mYrXFKx2DpxdMR3D4fDYE3THIYC937I796+ffPm5/3+0HWdHt6KSBtiEGKHubqau6saSGLThNhKbDg0CA0ohmD3+13OGhBZhFVjbO9ef6U63L26ldCqC4e2L/6nH37+7s8/3so706FrMQy5ZOs2oWkaicHMwOSuh77v+73EQl0IPIqzqlpIQowhNuIheCB1kGQTKx5VRZ3ZqqVc4eRqxrDCRlABJZHGSQ3GZAZnMtRyhKv97XHvpPF9XX3xLzNBr2pujgWslRb0ZXRkjzzvWjFwjhBnrT8V4C/fd22L/fDzz/uJCXrPLceX+8NHLZyA6zkAE8n8I1ZsynFO77V6/MgUff4UJ5oGotPc0RN9PIw+zwv6qWzfU/36v9CvSi/FMF11rnmh9q8RPVGz4gAxyKGWVbO7ggrciJzgzAwPXmVYiDNCd0c5EYlbgVtK6WF/2D3sRKTa3HMpKWeEICxElIsVpZQ9xC7ETWiH73/608PDT//pP/2nIZuZuubD/uH+IQ/DL3d3NzcBBNIAdlZTM0LNkJW9aZrYdjG2ICkgG7TPfZGStWb3sBBC0zSv7m5vb2+zNje3twgxdjdxc/vDn3/57se337952JefQqRXt3G3O9zctjd3t03TbDYbjEKG5pJSGjbBiQikkQHXUiiRxxgRuxg3aLZpKOpmTg4WJ3KHFSI3Bo8bmMKoep6NGgU3RnFTZnd2gTsfCRgfAsB+RYK8DkhX2rly/vvrnU94SMeHn0nvZTjqDVcOnpeBXybPRFx/xg+68fTzGoNnHcCF+x4nxV7w2Ayr/LsnDqqXujOm8sUY2nAkfx95exABEOG1v9uspj5znp3/e8Sgn/MNZ3SU6WwFw78WAH+RgF+UvrAnL0tPlUeq3OoO1QrARlSNvkpusGJmcCcIMblRiBtQBKSkQ+oPh/0hDeYIVjcXd3fPZlwKMbPI4T7tdvt397ubm9d3r2+6zas+//mHn1WaX5qmIVfTYdM2m+1WSyJuus1GGCLibsRM1bsFCITQNCxNdi7FzNycgKwyuLs5W87cbu5ubra3N8QBpaiLIGzu7ozDn7778Y8//PJwfzBLbQRTbjtqW9lsuruvv66mNsBARqTE6m5qLqSbVojI3VQ5FYRGYozCmxAMpZgpMTsLEXHNKUZO5D5WsSCGEBRQ8uJeyM251PgoZyc+2k+OZJYncvpPXUfPZBA/3sn/iU5As8g4g+B7OrbYbj8Ihq9JzBcFUCK6FjArstz36KaLinjd+PXEKSSAz2A6nz+poJcuj7biVW8rYFZrd1nZ1I9U1isBev2lXjWj7vzfmgt2efzly83lB/gAep4K+i95sw+gTy1gP7XIylPX5VMzAT3V+/0Z3vLPoierEK/SlRzgV3eyJ78wAtTdSiqaHFpzcnhR8+xWYFoFPTixk0Lc3FyK0ZB0SKaO2G00DaqFAlEISGlISeHqFmOX8u7773/e3mrx4BQPfb5/wFeDgcxU+/0hfNO8un11f/9WFURiDivGIGFyisRuZje3W4CLoR9SLqZONa1/kZ4pkJmrx9hSiMIhm8Zu0262sd1sb796tx/+7U8/3u+HZORAVqTs/SG9fdiFn8PN69d3r75pb17VoodtE7RrmHvX7CgNg1mKuZqnUihbISKO0ogEIzPAmce9ad5V3auzlJqVYApWd3UoWXZS5+yIRGC4HZcEPhH4zomvzYerMHP5+JMB/pK6EgCuJH55Kl2V4K86i10+36bEWxX1MFlPr9ue39Ofkw7Ildza9dX7Kq1QbUCirTXAj/R8aofdjxzIL/Zq7ttkIcYkLvsE1bS6ZPleU86t26+f1QnuogralyxJF4zNH0HPTMTxxDizKwvp+Y/xhf6S9JQScwCuOzt8xJ0vN39tXj3ZLY+rjKZFc1bNTuqlVOCvzLCPMiYb4IasWrKVDFVXdQMJWOGplDbEpm2HlB72O6QeoCZ+DfDb+31Shmy6zXZ/yPsDYtO13TYNu3cPaNtD1zX7fW9mr7dsZkJoQmyaLgjV8ojgBoC7gsSJmDgXHYZBNjakAxRt21XHE2fadJtu225ubhzy05u3//2//+G//vN/3w8m3DTNxm0gYg41b1e/3/fZ1N1T6lVL20a5vcGQzHpnT4eH2HYirREVMxQ1cQa1oaVAQkYo5AW1JBTIYOrO1bRLAJFZTdupZOoVid1BRjCQSZhtbMvnYy/rWtjSkyXLp9liL3kRrwSxD6br/XwqQF7p/+VEGddqXlxVuZ9ovKeDHq4D+Yl1fHT+GtHXT+KyrhFxbeHIY4rOEpjggjmZTjow93l9U7NRRb8WmrHyNj+5ZM59PSsDfnUAfipdk5n/VgH4c3uuT+0n/qnpifPnidVgpn2/gmwxMyZTy00Qd4EHd7NivqQgFlOUompQJzU3dfJSMzMTUdM0IYScUykFwpqHfT/0PSSUfkhNu62xx0YcQpOGw/6A/T6lu9InrwmjABA4Nu12u22axoqmlA6Hg8QoHNs2SkRRO+T9fkivttT3AxRdt4kxAhCRu7s7c3end7vddz/++//8v/y//+v/583rr3i7fRU2TYEGCU3sQqh1ElxE3L2UoqpN0wRtsta6cLbbP2zdm01kClrTlbgAXIDAzCwMQnFMRYHUrJZnGF8RmZmRGQu8WrPdaWRqnHCawGF+qddW0bXiRlfl3ydmwroepr72qr12zvvpSrXMD6IPCePxK8WZrqHe46l8Tz6JiK+gxwkAz99rItWpw4va+GoY1TGyzmzZOhHHmlc7jTK6kCRrdAifutSsnmgRdi+Gabl7fe9r9P2VATg8Ual5bb5de4jwVC/WJ0o81/rzUprsp76c68EB11RM1+57hTP9wAebZqvSYvtZt/zUhCRXUww+eYTiukLquBKuN/JUDbQCDKghaeLgEqFqLE0qfeANkRjYJYUIVy3a6y5bHjTd9/uHYbiHJ+ZC8DbS23cPuewNX6kbUVA3FP7p3bBpv+n3/7pp+ba5/frmtR1SUxBzuW3Dz9/dlx4A+iGzUHGuJQVFhCQcEiU1IgZvpGtUqzBc6j/ietsF/Pj2dzedIt9s7OYmb7+mV7+7PXAwbBK//v9+/4f/6X/6X/71fww3N2Dlu7vbxH/oOsqh/LTXb9vXt9uv2+bboLedb0UPh8Mhp544tK9+A75TLyE33DRDGVzs5mbLDRcdTC16dAtFyT0E6poQQZSLou0dRE7uTCUyGpYoTq4ZNa9mUCcTMFEgSHhqfPlTF+oT54NfzTF7LdHHE7fTKw9wLQ74xLnpvURPSY5PE2xWWFnD2Il8uXaeOgcg91X9XToac7qCF+t7rb/Qup0VCR9vSvPli9VjVDWfMA3uPm4Xo1A91hs+l4Av9nPaduab+qRR/3gY/kzigK/RX7vE9pnRh82Tv1tfsDrbaprlagSlI/7D3KsT9Oia4ZprmV4/5oVijDG2NcsVkcTQvr3f/fjjz9+8+r/nnPseh8Oh3qJpZLtVdyeitm03myEEDiHc3Nzc3NzUjXjy8JSqGau4W6N1bZQeR13ut7/9hiNnTa+++vruq1dNt+XQBWqGTP/Hf/1v/8///Z+//34ggMZkm86MGGNspGskRhERImIZ7+ju8BoW7DCvMquqGhjspRT3gbjruuZiyiRafE8JvoTEj1Y9c2aUlXHxenDqZ0cvZTK71s61oXiqDfjDaVR+HFln179eUOTWHvmlYgzX7/JBv66avyqA+Xo0lh4sjlEVgyfWYTH9ghyTqRgrWXbm6f16YpMjvv+F5sBnDsBf6Av95cgB9xGAc85uRgRmMprSFU86tZq5oqRUSqpi6MREUwWrEGIpOgw5xHazudEffv7Tv7+57VJKKWeU4sNw2O12Xde9fr1PqddcYozbLWKMIrLZtN98+5WUPQCwkAQSJuaScypZJ5oSgrgTSHh7d9ttW2d/9fp13HYeWpFGeHvYl//yv/+3/9f/+m+HAwgIBLil4cAbxEa2282mjV3XxRgl0ErMYiPASYuTAMQhBDDDhYjMTEuiGAKxu4OrBxXVPEIy2kR5lQ6U3WBmMHMyJ6/cjJlDPlMAfirgPXVPvnb+030XnuakRjwxWPPnnP2WqKLbfPL6YU8so/V89xXH9h5u4LIEfA69I9R9gNfrFa+9uUsn56zEXz8y4q7l4KvlYnHhXs+kzwKAr70yfaJbMz9R5/hS5RG/0OP0ETm0f0WaNVGYEk8CVr9UAHZTtZxzTsNQSsrDUMZgIANczfu+hzOchyE7YttsYtzkjDdv3oQQNhtst20p5aeffmLmu7ub/X6/3z/AbbuhNoYgFANHCa20VQiut65SbxV8VXNKqRqrJFDTBiJysttXr2IbOMSscKc8cNLy048Pf/7DD/0BAkRBZASg9DsHRLht42bTdl0TGwkhVKttZSmIBC5OTGDh2LQbdTJ14SgiaqY55TxI5LqTuKtD3NXJQdVmRgYW8DSwZGYGBcXZ7AZ3AQldKyZ0lT697+Y1oH0pCfhlzr+W0lKu1U0fAZim4V9MqefJLk4UG1NPapXVy+5yT3VqWy6cofcDzj+PCBpVWB8IGjSOw1l09V/UJ/izAOAv9IU+BxrVs2BmFpEyumONaSfNi3khL1CrUvIwHIomzUPRAjcYzJXMCxTgELp+2O9L7xaZ4u1N8+OPP3777bdffSXffPONqv7ww3dt2242m/v7h/1+T4ztttvedDEKM5eShb2oqWoxN4KIqNcCB5ZNkyZVDSHE0HWbTdM0my1uX90V04d+UA8S+ccf7v/wh5//8Mdffv6hj0CMEEHHiIQIcweRN4HbNrZNjDGIEDNKSarugHAkLmqZpQmB1MnMSjEOJiLBech+OBxuuDGulY6hnkmrbR6j8hlcxV9nMjNWqbrtKgobWajI/NfEpP06dFUuu/JDvArAAGYH4yWVpsmpV9FsIsCRYXUtLh/Jl5Pi9woAP8phrfykZjn7/ZqAo1vR9OkfBMMn3MOZK/UH0ZF+++n0BYA/IT01XOevS1L8myRfadJq0okqAbtZ1TuTK8xKKSmlWj7IVN114tnJTc0cIkGEqXn3sNvvdOj17vbr3//03d1datv27u7u/v7+3Tv75hs1MxHkkroYNtvtTbfhqri10qc8m3vdPTZhCogaQ2lBFiJvtu2rV7dd13379TZuu/tf3j7sE8C2H/783Y//2//6z3/6bnd/QEsIhEhoBVGwbSRKjkIiFKI0TWgCE8HMkqZSijsFCY7IaAiJJRyGAiIzS6l0GwshZtWSUymJmYmCq5kTkEABgFgEObPAaIJhMjIDkVVVvbmrsaLxE83n50BX1+MLdfNqMy8kGV/zEl+ummTIijp6/MAOB40LoV7gk0p3dkdaO0I6HJNkfKVDlw9f8VWGXXdDv6h5XvUclxJ0nG7HdIbBmOxQj9ORr8N7zn0PfRYAfO2Bnwpg1+Lhrp5/5fgXZvzvlqwg51yGpLmoZiYIQ3NxK6oKLQZzVctF01CvADmBmBjwmo6xlOJaRCKRHPZpv38HDl13C3xXrcuAiUgpAJCHgxCgxh2aJogQyEUoBN735k4k7IRixZKBrKKvegFRiLHtus12u7252Ww2sWkcrC6g5t27/rs/ff/7f/vpf/yPXam6YIcntLdomNh8G6lpEEJgISGEyCEwyEpJ5CFpYXdmdoRciJVMAeKu63LWVCzn3LYxCqXipllVIFCtOTesWtcEAHh2Tq2ua1zlYHdU7sYzNJsprDDHX+etX6Hr8tdHqlg/9L5P3H+u3veD25lw9LzlimWzkHh61RqyjxyMn9SdCz9Ub+THHuDcB8snEZaOv4zf+VwytslafALnj73ImcNYwfDHpzj6LAD4Oj3NGeGpxSH+3uhDo5DmYfw784d2hSpy1pRKztndWBzkDq1/5gVm5qXaYpnm7LhEZCRERg6UUop50whApZTdwyG0XdduYpSc836fc861KFtg5JwBmBWiVhhu6q5NbJoogwgzE3ut6aeWzYpZYWaHsVCMoZ2oaZrdIXddCHEbY3z75s0//8v/+eP3qkADOCAgwKNIINUM8RIji5AQs9Qice7uaoXMzZSJQQJndxSAjdq2u72961Mp9/ellKZxIhIyM3UrRuTwbO4MeCFQoM2U35lrrq5q5GMyd2U3wBzuVmAFrsDnBcB/LXRdYPj4+mej8EPuR/A+O7aPWuiLqH291WtA5Re/0PVk3LgIwKiuVUvc8Dpiyucu4/TXs5bfDySzYuCZLujPLMbwVH35Y5zj+YDqFaPB2kdg3Rm54j14bUCveiFePgystBnz5fTpcepqjtYnFxt4okr8bIAcxxzxlVucxOk+ct/zpj6kk0fZ7J4+/Nf6z4ycfNgfcs4VVs1MNQEwU9WKlKY55zyYmauKsJD0/ZDyQPDqzWtmIcRSSikaY5vSLw/7t7/7Xfftb7+Fe9/3P/zww93dXQjouk6Ed7t7uB52D5tObm+/Ms1Wsps2MY4OWGbEFpic2Uy6rkspEdFms2maRkvRbO1dtyvImd+9PfyX//LP//Lfv3vzFjlBAAVuaBMDta1vG2fXTQdxbWMD97Ztv/7667vtJoRws+mapimD9X3fSGjbSBxiaMGsmqPEISvAbbupWnImMiuBoVqKavXGNjMzrUVh64AIZE5iUP3Z2G1MSIkCGHnBkTvuX6U55m9Jc7ZE/NPpQTz6dj7ixS3NLhgA4Fqq6ev7tp+dsJIlVtHA45eqQmeeo5UwhvVN4HqSbGO2fR83/Sx6FgA/1V3sOsdx7fs14BxZjzUP8iJ5Sb7Q3znVsFcrNazIqjbVYXBlmPqoBAZADtXizjCtNYIJXuOVRISY+z4NQ6mJNPIh930fQsg5V5dmVS0FKQ1t2zYxEgVQcTUticiJYWYibEaAOUxLMi+lZNUcozCDWYhIRLp2u9ncxNjqu/Luze73//bnP//p53dvkQa4IwIGMDnBhRDEhSEMJiMSt6oQhojUvF1EXjTFGCWElFLRgUWDCJDdaYzideJJ9GdQKclJmAOxoMrrBHVTVTATDOzsCjBRzYTg7kquhGxQsqxWoAO8G/e2z2Mhfxad+AvSp48De6JEfsUJ69p70bH906tGHfO5Z9aEryvZ74gVmCHlorP3i9DzAPjK8afaVq9JwNckZqr8y5TrZMVAvafDz6S1+IuJh/orUtM+VSH12QVmfmIigxfLOauqmxGDYAIvrlPAjJJ5zSMBYAz/tTEhBtFo5gyBIZxSn5K2bbe96d7cP+wPu9u7u1KKk4uImZljGIaua5omNE2AFxZAS2gC4OzGLAZisJkZUc7OMAeslBhrPd7Yxe52cxs5Dvvhuz+/++677/71f/z+z3/K/QFwMEBMZCEIMwpcAwcRBAETyMhVy5BzViKKIbCglJJzZg4hcC5D3+9ipBBbMOWiWCuZzOHm0OHQSwyI1WhH1VcaYFWFM0OAQhAa8yqMygV3dVK35JZMh1yC+Ct8Nuj7hX51oiemOpuAwJcDJ/8Q4CCfzMNTceL5BOZZRF7SVmOl7Hxx+gvngr5M1yTgazbd2WFg8XZz4CMA49nOCy9LL5ey8fOil1Khf2qas3CUUtydzcEgdtWimkvJpoUmebcSAEzoO2eSMIO7l5Lcsd12d3c38sMPw3D4zW9/W+Xg2ASQN4KcIczM2HQNEBmjxEwguIo0cLCwtC0olBJUY2UFuqbtum0T29vtq67Z7O53P//05r/9Hz99//33P/445IKmgRvcQKDNtiWHluJucDAhMAJD1SjTMAwppRpenHM2fVCFuscYQwgG9H0vwdou5mKAMnOVTWpoMrn1KQVvI0mUUMVbJ3WHWa3aEJgFMCImYmIigpM5G0FB6q6uSYuQGc8KwS/0AvTXzkI/dT+sKTznpz6y+q5heBThjl3PRp37VFUJa5nwo3r/IfRJbMAfF4h97rr2GdJkPxiV3p95b7/Qk8gMOWtJelQBFFYTX6iqaYaaFa0xwaUUZqZqy5yiIh0omkzZXInB4ptNu9m2+11fDaIxxhhjKaXr6mx3MyXqmsBEHIOM3ppVSesqQm0jIkGV1YSItPjN9mazuWniZru5VcUPf/rpX/7l//fHf/N37/ohoWkQBIXghMC82TZWcjIjh6qGAAAicCNA3Emz5aHkJpsZITM3w2HHglev7pqmefP2DXptt60buxEYIlGYZsp5ICIKQoUZIHHyAChggIAMVWddw6vYQQYiuBE7wwnqns3iut4qVjamL/SFnkIniuij8LYFbi/NrAl411FGn3YG/kWdsJ4aJvTIsy/ech/QhZcSdNfoiyVU9AsG/42Q2aiAHQEY6lbgecz4aKVmUjQvRzn5qtcGAJijpofUIQ3MIHBKPYvf3W1zHlRVRNq2jTGq6u1t6PsCQIiJICIhQKRm8ightDCFZ4IEjiFwNWIx093r27a5ibEVDnnI797t//zv3/3x3x4O+60WYrg7VOEGEYQIpiziFohqZCdG5ZsV50YCR3dPKfU9hxAIImIPDw8i8urVnQRS1ZyzWibqnEAQEakADHJiuJpq5iyFmcEMMJiCzHZ0kE0bok2f5rV2IRVihyssm9k6IeXale8LfQQ9Vb/0uQ30dfve5eNyEsYMfJwOYN36cYsvrzL9JGFI14D5fWB57hD72IkXMohetTFfDTT+wJ78ZeipGoUv9LLkhqnCAYgcDlV1KyPSAABoCj0iorZtAcAKqZI7E9TU3Zom9jm1bVTHod8xha++flUzR8YYt9tt13Vm9tVXX71583MIYdM2MYYYuW1CCJT64kTdprViUBGRGKMENyNAmHm73RKxmR/2+91D/+OPb77//ofDAZZYvDEMVmAMkTGoMaUhCklwYYTVik+pdJuGKZh63/cga5omSJOzDsNwOOz3+4dSigQGbBgG4g0zM5QVDHZ3K9lUQWOaTMuZQYFAwlRzUrpURYJRjUYGE5zdHGYEVrC7F3dVzbQkAf1CX+gjyM4Q9/zI0+hTm8k+iRf0xXqKeMwL+siZeQHU93Vg3eAji/cqsH12PN8X+jWpJuczs1oxpSKHmxGRgBYtMxERMajpOnd3ZVaDF3LyokbUdd1+GGKQVPT+Yd+14fb2drfbmaJpGnfv2raUEtqm7/cxxpubmxApirdtw+wlMTE2m03aD+QeAzdRYhSCVRlaiM1oOPTv3u5/+vHd99///PDOGOjTwCAHgVwYImCGqSZVIWFGjJCwTPphSCV31SV7GAAyIiKIajGzUsp+vzdPIiKClBJLZmZyI3YYmZmVrKp1WMwMqs7KYhgXsru7wXlWFDjM3OAAubibEbmTmyu7w5xWrqrk71ufn3r5PlmE/Pi4209Df+024I+gC4j7ePDkGmIfR4RPgRfPk4CXJGXAoqEFjgpbLRraxxNlHOUDq05V1+purjKzLOBKKPloxawCk67cWOhEjB5zoprOl6/91I8zowKAmwMQIVxiLxSLB93JA16kaxvOtWXNdtT+B9ziSvtXNhp+YjGMWrEOC8fzfuf9J92A/In1p8f+2+pz6RhNwan18QnQUobDG/OeI1I2dZjEVASmjsbI1UychS24DblXVnf/+ec3/bC/u7sjsyGVzeY2G222r9w1vXsD64W5C3630bQ3K/0NO+vh25tYSg6/uSnpwOA2dHc3GxFSzZtNC/L9fr+5TdvQbtpbU8pZb7avGwm7/b0EevPTj/cP+/uH/sf7h4eSaQMGupiHATbADa6wA2KMm6ZxSzpkD0BECOg26FpwwFd3v3Ez4Y7Q/vDnH7Y37T/9x1fusKRiwskolW0TMjHDtjH+3L8RjvCOacOxo5ocJA+Hh/t228bgwmAXMQTlSIHQO7IjI7QagnN0akBC3JiLKxNFIQj1TJn84VB+E5pXIIIHV4xFgaoJj8rq3YW/kPfeE/dbfyLgPdkEdvX4NY3je/IonND1eu10cpePsw7Ik5/4ieN5NWzpyvMeu2dhjnN5Yj+fA6LPlICXz/plim4+OmH+/kgY0gmQP5Km5NH+0Ml/nzpFaMxlenTVeU9WCVYuQ+/JyZ/Uv+xzMkLzMoeBaWZ/Lp0DgGt5UxwO5JxLSTW4aE1VeKvZr2AOn0oSWWZmDhItiogzGjQxxsrAAdWsW2sNeIyRu05zLiWJSAgSIhHrgBJjrULEzATUorwkIkEgEpmZwAxpmqYNUS3XAoj1s/pi17+maVSTJjhABGEeTbUC2BiFUZNrEqAFwzC0baxNNU0jIsMwhBCGwwBwIBwOA0GIUUsRE5GZ5TwwJFCY7bXL4LACxisD+ewuHoh4SutVeVoQzSWDqxt5bSSGurkvr+bzmkFf6LOlTzxPPgXX96lSUV4Uy65mMLkC2NfjiZeKGevvdZtYn/Z4APUaOGlJYHaCvj6feQbwV/p35UarW3zohR/e/lpGf7H2rxnV/7o3xOP3CxDVwCH0fT/X+Bt/nTn9aa6YWU0XCbCZVacqZrAIHERRRNzNASIKMTZNY6allLZtHfFgJWdzpxBjCIHcdKxkQDVQRwILOIQQAjul6jjNEiFUY39jkcO73TDWQByhq8KqhJhSYgahBhpxCCzB4QIqY0YyF3jQrGblgCFGefv2vh9o0wln3N/f397elVJibIko5zxwaVpyp1IKwKUUWIFJI00IoSqNichVc85ODCipuhrCGBWtqiIFaJl5wl1iGh2ja7QlAHctpeQ8xNAJh2X9X5xsV/ioL7SmL74jnz99EgB+ilL06LRTLfS1clSrLBwAaKqeQbSYkOcGH+nAieS6OpOmE9YK1aMJfa4wPz/++O0+nK5dqPBrXbrczhPve10l9Z5bfCL592MtWjxZhkZkdTjX+rUj/gKAqg3DkHM205oAC6hhwGBiG42fREQ22h3EWChIKxtKBDUnMHMxq8KsuYlIbJrDfpdzbttWCVVr61AgikiIzExmBUZmDIrMzIzAJMTGLMQhhEZad2okgMzd+/4wBSuDSIQggijWq5qBCBLRRgksIiQMRrBSipaUMPQmLEJcim+DEtHDw8P9w/Db33zNgpRSjLEk7bpuu9kSWymlGmPNWEm0qKkHKqOl3F1Vg4jDVJXNzIvZmCtbpDpbFbPgbu4OUrPqHUYTg2zuhBpQoMVKspIlNqv8CF9opJdyCLo+sp8XU/O55UX47CTgWVd8YgNeQ+CRgvSKTfE0IJrGfdGvIcD6zDOZby1PP4kFvHQyTbFGpz1fP9faIrI+fmw5/iR07dZf6AOI3W0eOlUdcp81uVcbkDPciJgZyK5V1etzcUBmJmcHWEREhlKYqcYXWYXnUoioaZp+OKgbMxdkGuvmZncVbmLk2BCxA0YMZoTAwmAmEQocmUPTNJtmYwYB5TJMySyNHDAncy2WEvoDDjbkDCI0DbVNFAaRCxADGQftS07oyZk0suTM+SZziJZzzsVraTmRtm13DwemQEGgnkpWU3eODWfPpZgb+5gORGtoVggha+9qrg0F0Oi8ViiMeTcqTiNnZjd3kkJcfyFfhXJVTXYpKYYOV/K6f6Ev9LdEzwXgE3vtjMGXjbiPwsNs63kvYK0bv/b9g287Xnjtv1Ok76m708xn1J3jovF4lsI/dWyFL2mxT/v/902zppIvurLV8N06XKo+WlW9EBTm7s4wJtKspRTLRVWtFC3FVInI3E1LoKBuWcsmNKFtvB9MCwVBgcGbphlrJwiblRC47YKrCbsIEYeNtkKIMYbAIhSEQmARYebYbNypjW3btl5cVYGaI1oiSxFndjMfBux2uH+HBAfQBIgIi9WCS8IIIYLJnAb30WAMuNObN/j6mz4EEYmqWrJZY1rcnfq+NzMhNR+I1Z1BcVBV9UCxlFTSIARXJTMXs6JOCNNCqDbh4AIyouDuOWdzMDsxqaqQVwOwu8+mDiZ3VVfzOWOA10qK9Q3O/PhzY0v+TuivRYfwYkVlHo2y+eDTr9KnGM3nAvDFI9Vz5dJjP/bIi0vGiRvt5ZMv4O7F/jxiSz5v8/HjJzfyVeTTOQYfqdAWWflyPx/vwNXzLx++ev5TJ9z1oMzLd/4sgX9tLZxh+ML2raqq2azWGVS17KqAkWktG29TqRQzTKbZoCmrataSc26aQERV+9pEAWBmXdPe4IaIooSdH0IUpqaUQuzEGlm2XVvVzsL1zRmRVBtwbFh1mVEOhTmDmLlm7YgRIgKoFaSMVGuwy+j9BC8hIsTInGNDEoNIhiFECyRE8vCA/a6/e9WZ++7hQOTb7dYIXdft9/39/X3TchuZpYAE7GpmisKUh9RLz47qU6W51KKKbVvIq57LyFxVWQTVS6sUc8QYhE1ViY3cVy4WRiSBQTDzcq77mhzVHfPk+xwn26elv7c8AX8Pz/simbDo7MjyfR3X+wgErNFrZXl9bKCvSZZrj6dnvqlTyRjAkbi/APBFCfjoQiz2xpcSiNd3fOTuz6CnNkjHWowX7s/H1ns+wWBMgtXpAJppNWGC1MzclEnrOVztwcTKbMzkCCG4NAfVWl8v62j4TCkRgSgAgHkIgamzoiJimmNgaQRUao0jIUYUZoaru6tmIg+BiTyIMJPOYreqFTUrNSUIM4twjGiarou6E42EweEONzalQkUEgcCSQYhNrHZVy2gaD2SsSg91BNgMfd93mybG2HXbyO3Dw/7hYd9pkJsuUE0QRgpzI9ecEcqQEnETRYQPfdaUncnMQA7YVKyiEFemh8zMQSFodaU2L+wNjTkpAQioBi86uy3vi686An6hL3RCn3qqfHYScI1/rVRVsidOyMxHmttr8aa45O70CJxM9xq/45Lstf615pg9b/K9kuhJq+v+E2EdhoHZkr1WU1/pv/DRCXM31vlLPkgaplX9VFrK7l6VXK/FyV1xKLtG/MhMHLXuq4nxcs4Lj4clHxkOpluvaNFC8yQBB+GiYIEZhmF42N8TUdc1WoY8FHIIk7nDfSxUUJKbmlmtPF1dps2sZpcsabi/vw8icEuHnpk3m42pqioz536IUVLqQwjm2sTGi+76w7fffnvYP7RtOwwHVdtuOzcb+v7u9hbwsInuSCnVfFju2rZtSmm73TaZ3j1k2L5qldUR0YoQkaWUANzcyDff3nz1atP3bxlKRJtNk4eUUiGUTRdVcb/PjjexgQfknMHCzK9/+3V/GJg5MKVySLnfbBpmDGUIoWnitglBGARzhxmihJ5ccy4pC6htWzPuD7u4jY5sZiIRHKYopiwNo/qTjwg8vrmSD0SREPvdPm5EQksCjKpnmqavO5zc/7YEoQ8ivmIX/9TZms7bf3zon1p85Vqc8bXn/fB+PofWe8jopXC8ub3I7HsZL+iTXXttkjzWFV9TXS6C70kLj9CHy7hr7L0ogz6is73Y/tmtT6EXj7pEneDuezFvfcIxi3P5/F9rZ/q8fCiPiCap90wFPTFqNaSn1vodB5mqo5NqGqzoXK0bI6/NNLpoQYSEmKK5USAuOTGzqZqq1KJqamkYQM5CElgodk3LDFUyzTnnEAIRCTE5gyyEEGMEXFUtFwc3IcYYzYqINE1wbzOTHMwUJZsbhDDUugZaFDDFzdZKKYd+l9PQBDStBJYgITalkdi2LTmIzeDM3ERpYpdzfvPmHSGEEG5v7kruh2QO9H2/P5TtXSegQByFQwhCtfqgaUkMIqKch2EYQqOEUQMPVoGgrkFeZqa7A+bOGBNmqRkTK0jcFWSwC+kQ7PTN/Z0h8Bf6m6MXswFPkuipw/DaR5quuE7M4LiWoR3g6wzQRf3z4zYDOs4QctFgfC7LXsPRddzUNTi/iusfBv9nV50KuC+1/XyII9uarmQafb8R/YSeqop/Cb5ixGBz46ncaEVfVXWoQR0KMoKRw2Fk7mrQAoBBEKk1A0gYBp4i4pg5SgCBHMTCxFlLyYWIYxPEoSmzMJkHYgRumsDMNfIYgJkxiIMAYArCEUAIwcxn2zMRReHIFGM0g5nDWVVLcS8wR0CQQFZUK/ZxdJOhz3BygXDourYiZdc0m83mpvP9/mFID169wJqYk755+9YNIrFpGrXRK61o2e3fbTa/NTaCzSk0zAzQUkpgISIvmvphu1FpiHWZqMxMU51Bn+y77j4XfK1GayIjryNvgI2KrsWHgwk2YfDfox/W51a+83OjaxkDP2c27QUA+CQaB5PEeaLyfe/OvlYsr9s/p9rUuQr6uoR91M/5qlp+eXX3y/fz4zirdWs09+fRDj9OqwF8/LRRTzA/pq3LZn3ALHukvvXJA46q+xeauVedxZ7azrP6M1uC2eHui57MHdV2666LjOsO12roJyIRCSpgcoxlgKm6Qbu7mpqBmNxcDe6BBa4wKymzhBBjlCDERKxc3bhsfI/mOWnTNOQAmDlUqTqKWDaEICAicdWUkpC7GzNDrZSSs6kqEQuxuxkgwUMgdapKWzdJg/f73LVsuWhJVmTTNrERQnDj3/zm2zdv+O274qjJuSIR5VweHvYi0sUm5SFnNVdQNsUwDOScpSmxBFaqUiwKO6SJAh9SVsuARRZjczJmlpoyi6uXoh6XRQJNXluAMRwwPk6guHYFmb7+PaLvF5rpxbymPwNofq4T1rGP1UhVL7eWOB/3/p2h1x1V0T5Jou85/6TNRyTmk8sxweq5htzdT/iG88tnFfTjvkbvA9QPPX9+Lve5k8An4IjpUlz1Ob3Xdv75k0/RvpiqEFaDrrsCTqaAw93c3CwwO7OJAGSOCp+qGljAnoq6KsEqOpdcKIiXnPpBU9YIV2NQE6IL1TDinHNKDYMqGAfZpOEAQETcKXIUjiVb01itH5zUcs7kWp0qcs6Hw6E/qBULLG27CWFngOvgINNsQCnY71JKyIN+9Zo1w2Gbbne7HbpN24YUwuE//IdNMQ8hgFyaNoRAIu5eTB/u9/eCwN4Pg+pBxJk5p4GJQmqakBiCwIDX3CDELoAw13rJqkpuFXtHTnzUjbmP0u3I2RAzkUzvwsgV5JUTGt8UVawmnKKuAU/LDf6FvtDnRs+1Aa91tpd+PfpyJTc4dELElQRMeJ8EthaX3wsbdBysPEk5p9zDHBWxZh0e8fAaBcfrPbzWmfOWH+/80e3eZ/m+5gTxiIr4SSrol6JPDthXrIQ0T9hRj4qc8zAMWq3AiwTs7Oo+5mxnEIjMyN1LKQCaNgIoOSdVgDhwFedyP5Q05H6AO8w1Z1dtJWRBCKGUUkrJQ4oSULlV95QKM4fguc+RBRiFchEZE1mk3BcPQYi87/v9fn/Y2dAzwDHGGJuIlKiojbZuBw57JYZlbJqQ+lQKhoi0LzFaFBKRnP/AYkHQbZiI3Cjn7AqA+743L4GwPzyo910jIVIpRJQCDT0fBMRognDFRy/qI9RSzpnQG9BsakXCYGYOBTETgUZtP7i4Rxp3jzqzFRSm4hjmL+i/96vS55bR6VPT9ef9Qqf0AmFI5xE41ev43HXI7DER9WTfJ7pa1pCIVyC6cADXAe+0M/WqUgxXuIdZ1sQZ4F1Tkn84nJz383HYu2hU9ivo8nH0JAx+qq33qe38ZYgwbfuOUkpOOqmgFe4Vw9wdalVfWsU1Xk31IKKluHutQlhTOwEoKedhgHnXtI0EVxvDkGScrlX2BcAcmEP9LiLu/rB7KKWIxBBC0V5EXJFSslyIPSV3191ud3jY7Q9+ONDhoDkTU2g4eYOcASASGMgFwmhCGA7iGhkGo35vPSwQh2iGtyEiRmx6qPXQIoE026a7gTkzH9J+tztIsCZQzp6SuYO9JxKuOUyaKOIsY5RRjG2M0cxSSmDmUio/YOwUQDwWZCilsDM4MCuxuPtqy3Ycz+uXned/1fRU7+K/Vboa5XFVDnzMN+jXpRfLBX3iw7zG5vlXvxIGcx2QrhpljySViexKnBMf66bna2eAn7yu6OQ0HEM7r+JvzuH/xBf6ERibjYhrmH+EPsQu/nz6cNn3qfP211RZH23fsxn4iLsyQy1/e7pQp6JGo3+yCMgEEJPJMUorQpdShNmDEI0WZncPIWw2XWQZ+gOZC7MyQggAqlxbKbBUr6sYI5nv9/uUUtu2TdOAEpHAvJRC5ixQLTkPh8Oh7/u+98MBu1057IOqCBOxLxKwQwEyju3mcDhElhgjAXlIWS2SNQ1v7jAMOByw3yPnwUtu2mDFD/vh1e3dZrPpS+n7vL1hEcllyFldnSwIJJAEFmEC0DUxayKiGEPTNFVLT0SlFIYD7AImFhrdoYuqQ0lVxCadg7oT8ZjEY35NL+WF8IW+0OdJzwLgQY1HIw8AjLXRzOYSbIBX8KvChEzOU2uPpxMEWusGuXLHKzivpKOpePGDqm2VUuiIxjZ9dV/M1xy1P/eFznGI6MgKO3nlkDvUxihPAARnZp8xGzVL0XzV8jdJ8EdCJwBiXQZkiZs6xSmfeAjBRWQ+4iqmBybAfEGdUYyrPLVNKZGnnjsR3F084hLN7Z6K5nTczsSjyFSlas2v0Fju9Vj2AbAy9fnx8QqI9V0QrccToCNepv4SwFWNqfC5vgIRKYyIi3sxcCBV60tWNk65cctacknMUKakaiGoJYTgxVWhxUyFDHDXQUlDGzfo2HIahuxWXEvRZGQG26cDuZmXsOGmjYchSRM0WXvbqeY3734KQiE25IftBqaHMqTIg+X9/S/59vb2cDhsNpvAUid25FiG/LAbkLY28MPPD2/f5SFJGmy/L0OBaxM8OUavpkhoGuN43zCICggKECE63DGg/PIDfvvb2HTN/f3uhx1KH7/6+pbFybLboOpNbF/fbNJwSPfl1evXBaZERjHBWRMpkbkjDIccY4wcs5PmxNHaDrHpWATERGRU55xDYAIXMZAxFC5mYyCSUeZCCBFkDnZ3dwZVL2gDGFy9Sgioc+epvOivheYvpWq+1sp1yfhpdK1e7zUJ2574XP7ofwE62U/KlQeogtxa1Kmko/pk2jnnfo5AccSJY1089QwD6HhfcjgIk8/T8a79PHoWAPMSWlD7M2vpfHay8KNkQ8u1a3g4ljyOjq+HZnXaNRunTHcZe1VvMmuyZ3AdO8xHTU3OIhfckmsL0+XrFykETDn9eU6FO15+6R0Rja2tX/mx1fnoNS9geapO8JPGpyEVd6e1g/Q03xgyriU6GsnVu1jYn4+YXSeKhPXbP5kJM49ydPmJ7vFC+xOn8mF9W5Zo3bQnnmPaxGmyqXutiYvVUjyRhueDJ++lzpjakKqaFis5pwQyqcZOV7jGKLVscM5Zc2nbdtPcDn2fUm9mMGVQIEYITYyDmRXNQ6odc7WcMztMLKeiOedcW/aZ0WQBF5TRt3tmTmEOMzCDJ8aFVxEENy2mQsTIhsMwyL1LAHtJyftD2EQwlCaDtLNT5MIshCKsWUopIuKuJlIDowkgc1Xnmm7zeHpUMnPAa8pPYwOUIBcH/At9oY+mWfl1KSnw8t9RdviAPeVIpHkh9d0zbcB0oiiugqmZT9vpWs45Yh3OLcTX26eVQhtYFR88+axnrnfJtT/2fOZa9MQRYByduWbHpjseQebqcsEsd+BE3X1t8Ga4WkP+JDGf6NIXuKpDQVMD55NgtmrPbOCxggHkcBprw57yth9uTLqoFae1OmJ1hCoztO7JmQ/5Nehdf/9AL/c10ZIyZZH4p77N/JlV52TyWhnPT2ZR/W5HvziAUkrVRTNz4dGl2d2bpiF2cnMtOWe3QuQiEkM7HHoze3WzffXq1cP925IG12xFXUgkxMAlhJJyKanvnTmUlM1KzpmMmLMWS6kcDjoMpZRSUyhLoKYJ8KJGUCZSYaozJdTZxWCARm5xmc9NI+6UtVRY7nu4p7ZBE5AGpH1OLTYtugalaL/r401Udc2lEGIRVdWUC8jIRATmPBUopFySu0R3M6qbAtGciG0cTjNVLVRC4IsynK9jxb7Q3xZ9yEbzcaarur/NdzmRLU6++CSCfEB3Pgk9H4BPVcQAlt1/wQhyp1qsFDgHucsqBTesIadaTBdcxCjLHF9IU0qjWfYat+BaHGa+qdkYR3sCY2thlI58p7124ERlXvXYgNOkIhtvUd04L9EMJGvIJ0AXn7P6vE+TCeaBepw785FNWffniSnf3rd61hxSpROWa5wGjzdydoRWxz9QD7RSeDjRwnzQWglwrIxZaJyrZKtj42kEOGazsYiEEKyIGwNCTOQ6VjCsLZillFpuyFzgMQQCUj/kYQhoSinBSN2EmEECV3MvagE5q6qWIbkTs5p6KdbvD6n3ks0L3D2EKCxt2w7vCoOrtbpaSETQRDFTTIwhMWCj5TsXzWXPTCBnruFYiGFSEdRxM5hCEw6pSMcEUVI3hk4IWoq7amzhDnO4GSF7DhWJsSjEpqUxjqFpzXLi57qc9ez8Igj/6vTibNCi3T3bKD6mtbNGTjAY17caWnVmPue9U25Btaf19AK9mBNWpbUN9VyaNPMzABvPBE5wdDwfZ/vj+l44BgOqmO3k5lVQrgrjEVN9Qj6vpy3OUDPrMGPzSU/Gu1tNp7cSI6Y9et3NsUvOtpoZR6rv4zNpkhvPRuZcX3I8XCsZHaP64ei6Rcqsot6xKXomvqAVeGxVPALA68dc9/bk8esbWc94Gy+/wBJhddq1u1ykSdUPH8vOjhhsbhMujCrkiqClimbuZqZYRsQnyXjuAxEZebW2EBETmZXxeYX7w941uxaQNyIxRCJX1aHfwzWGYEXfvfnll59+6ve7TfwGplqzbxGTKRNV686ITkXdHQYzNXMYUtKcq+8YCCYEbiNz+OX+HbMTUZA6PTgIhyDuRL6orEGucDiGUhXUHiNIxndghpzRCGJAI2AAiqJwx/4hhQBtJMA1BJiTM8y1mJfRAcTdzYmjOVlNbmU1csu9nkBkdduhs73AfXRgO5lOX+hviU5W8UcQXXIXdR8VeJN560h9soZhxxErf9TyleOrW1/VZn8cPQuAJzGGFrl20jeOJ8xYZTB1VScCM62FUfcj1eKJOLvG5hMJeDpy4WXMFseVwnbk+tfkRgDo1Ed6xEJaufmYHUnGc88xIQHx0pl5o54R8RiEjrpLtCgG1h25ODvng3UA10ryqUuOY2/t9UDVpxiB5HgYp7/H8G91/qKKXz3IKbqvUfyyynriPPxY9Jyd105uMbd80v61lbwMC6ZXtXqEWaXBzDX3cjnuxiQ4w2jy8VrxakQUutZszNzEmc0slxKESil1urOMRuKc0+FwaEIrIsQYDru+7x/evYWZmQqxe/ECFyMgEJyJmZTh7hBEBFXVUsvswh2BEQUaUKyymgowkzkc0DFXNYwBAUuUCoIMVKaBWZlt5CYJTpBpRqUBZQBt0IVxqYo4G9Rwf48QsO00cN820WxD5lQV72peVMkMDhIxZ2bU0GAwxj+4Uc3eORqMiYiERsvLhVCFLwrovyU6Mi19APo+sg9dXPhzatlp0xh3s5Nt7RFJ1x+F1RdHXzwTgPW0aiewGpT1dlwKVE3ValY6jLvZqTS2lp5nwDvZx8+0VcvnCq3PmeujBjExB5MbzWWZb0bf6vMSY1hv+jNwqjoZYUEUw7EwN3dmPrgoM1enrTec88f0yQH75KmnX6vowCfHZ0F5wjlgNKIfiewTNi8D/iFq6bWeH8dv/HwkcfyasAoyOYK963Jw7eTJEF3kwOYTRlF7VqoCVWAlkI63o4rBIqJTH2wUeuGzoFb7z0RGIGJmM6uMAk+MS3GbGwwxIgoThMndUsl933u0rmkMyO556IXImaxo2wSvTRMJHCJuhikymEFe8ziWbLnk7FHAFEDE4v3gQyo5Zzh1TVTNZjUMyRyT0sPqFyOR2mciIlKWeZCNmAPDSskDmlAHioW4aeImMpnmkobeHNACzebFLJcsXH3rzcyKEhV14kDMHLm6dy1UX5gbORmNVqfZi9N9labHr73RL/Q3Qcfs+PUqrh+Mcn6saF63ud5VJuF43m+PpxmttYqPEZ19+Wh6FgCXfCQJzeMlMolc07ZrCi3rTXZEkUf26zX6noie5yevab7kpOUTDF4D8CyRn5M7qoeOmVXT3YnoSaNX8+J4PL9vPsuUN0moTGPsxVHXtaxABadPXfXlXlNYo6oeFql67RDn6/DJ9XNNw3LCiKwaWV7o3NqlMbkiKDt8BeS1h8Rz+0eD5j5yKo+3uV5L1SQxq1LPubETOvl1ZoZosZQvb6D+uh45W680Ih+TPtUHHbkoMzOYgJwwA3mfhk3bdE0UJiJntxhj07WeSinFtbi7EHVda0VNc+TWxNy5EYaTmxZzcgizmbkImYOQ3EspmiAiQZogArKi6TDk1CMrupuuFFfNmJgVgjPIilqt8kQEV2YGw81DiFUgNqt5uLhYMUPXoWkQQggS2qbbdhGWeODG9swIMorRqppT0sJNFxnwqnR2ImfGDLtS/yEijJwPqRnTkXxbrx2ZnuNF8d63/IX+eulxTuuRPfmpd1kj7rn8g0Vx/X5MfdnJ+CwAzjnXTWfeEAG4uxnN3qEiPG30kyHOfTbuTlh1JPmdIGgVNGsjo91rdcKsVeapWGg9YY4WrWEYizZycshyB/mo08Mk6Byn2ph7O7vSjH0AjT2pzarqdDkzo/5hgi9fBT1P1eswzQSqPSkFqirHiD3Ps+pWVk8WWemfpxGYNKa+7udJI+5omropL6NR26lazdmaUH1SmUBMa8l75FQA8zX/tEiuBDkRo2dhug7XHM215niWy6e5hOOFUU+kRbV+KnMTHS3WhcVaWDda5w32qeV6t1JKzrnG2jJz1ZvWuWqqcA0hpDRoKQyCjNZjELl7zpnIk9nhcGDm7ubGoV9//fXDu7e9le1242bFStu22+32zfffq2aYd10XhUtKRrltW2Yc9oOmwbuuicHdYQ6iIR2YQhOCOfKQdvd9TghCObt6LqqmTrA2EuCNgcnaNpRSqyYgBDhT36e2lWl+wt1zHkSk65q23ez3D+7eNo1Dh6Hfds0//sOr222ze/fGqajbmzdvHthutl0bZLOBCLab9uZmEwMfDofhQLGRpv2aaUz7HCSE0JjZbre72bzGWEdZWUqzFZYIIIamqmpUVUkJQiQiksxC5FqZse4ezFMQ/6hbGrnC+lbVVnHzRzrGXwex1XSxUDzah8k75a+Ds7geHPGR/Z8X6wnDfXL82l15nAzrPaQev1Zt7/j43G5d6SswpkmKOrn1up9HivQrPfxwehYA101npqNeumMM8KhK0arTqyXGXHUpSebuTdNM19GJanT+WwGw18U5b8E4EuPqrWlGF6xAaG521B4fhykCpFox2+e+rfFlLVLP8DNePEo/I/qO/MFyGs1YBWDOQU0rlTsRXZvoDsM4dsxzTaNFgV+b9RmtVI+q3p6olNdjcnF8cKpSHomZKzcz/2SrErmYF+q0POo3rfzKaIatUg7BaylZPxlAelQLtF6tj0hF72VjfTqlcj81yLXv+8WNyFaOzxgF3dFYQWRTvpGi2g8DXEfAEHGGqzPzZrNJuT8cDsJwV80WY1TVzWYTgzCzF2VBK+1m03axYXIrDTMLMWAEM7ObbjPG+wIxxs0mErIZPdybw4pCbfScjwJluBUCB3EVKACviUjgqu5gcZEAU6ibF3V9yLkUJULVRUeWGtTb9/2+37MjM7SgFQj3tGm3dy0RhRiYA4Fi5KaJbRtTSrv+UNyIgzTxRkJsmxjbme2EM8bUGuRmTHWFGhOZwgg1gc2FN7VaEo4LJuFr2svPhz48tO8LfSK6Nkk+hxfzLABOKc1qt5pdb8x7NXKvI5NbTyaa2YvRRLoqdDpnXKorc5Sn1yA3y5rThbPYXYUzxuhS7tMOvORTWilU5x2f3MFnjjn1S70LZr5hMSfAHEzACplocs0RQQgQOVYd40hWni5ZMmTNOuFHYoGIaAYGs9maNsq+gDPP+lgiGo2Ra7FyFJoxoaPDl6FYvAdHYtCxk1elKcX36LVWNfPHCDrKpifK8yphV3hbDfVyg+XW01s8YTlnVL6IwQtXNO3i0653eeGZ25w1whw5577v+77H6KhrizGYAHObJC1ihrnzWO+v2miLljCuABGGZpim21d3b9/k3bt3bRcZPmgyQs7p1au7TdflPGQrLCIMZgZZt+1cGyupysclhCEdbm+3/X7Y7/elWMmquaQBQ2/DAJsMu+bj0NTZEyofrDqoFhtjrTSDRluAAlCDK1Q9D8oBIsh9YUaIMCt93+9yrwWvb/nutrMyeFESLqbqgRnV2YWCMENEmINCAYz+WEWHnGIqBnYaAjxE8qNK4GxmgKHOal740vWEH5c2jgwNs2trnT/rSfI5IPG5CF7nYZ3280qZGM1fv8N/J3RVlf2X7cZFel4mLJnUiewgIx5rnDORCFd3j7pHj3g8QUK9fApP8BBrBqvZwocjxvdooKzGiXhNHTupGYmcmSbl5AkM0KSvpjlwefnhmOY+zJ+0ojF+dJEafbXnG5GcYM+q2SOGY27WJpPjtMtcnhJEXg2OANWsfDSNf+UkTmQDFrixmT3i1TU/75rDOB63tf726JKJoVn+O7Mgs9q5UgXcUqp30mgmr8bWeQNdf2LiklZ72dEMmBUGq0E7fqJVj9cnnz84ACdStap/rtEytpobdiyM+7RturvC2auT8KizVkAmdkfNXm1uD/vm3oyZhZDLOKPMrE+H4dC7OwtM7X73ronxpusYlE3Z0TVRQjQvTGRecs79fshZSxkf+fUd1JALkqEY1KAKNZhmJmIOQSgTUOCzw3zldtzNRt9JYgTGpgUJFzUAgYnhQzpoRgzY3G6++uaVadI0xMDCGIb7ymZsiEITo7DD9kPfNMHB0sTQRCYhosMwBFXyYGCQkNRXLDTZgAEDMS95BE7R92hKrMRjh+MMus6R79eiY8Zx0W2ebDt0bGd5pJG/JP2tCuvXRnPm9X0FTI9o4D4FPQuAb25u5qWykmFA1aQj5D6HPTAz3NZnLtJttU3WTB0VlTGJ0RUycTxMo5w2Kb2n72CBTd7IE+gu2zThSBuMabmu1vnCotYvJ083ma4xfdZf3d2IZPSKOn6FPjkYz+pNHC+8dYfPw4embvCMamt4W6mYp9sBAEIQMycVMztfzrWFmg7Qa0D0tCeMhtOp56usy0fRXzTpzGnlATCpPMCrztRXwJMt+ZhDIpFToX+du+RkLCqrMPftEQzGtJWcB0aP40asNcsjiU5eACEEzSAiMDGzgsjJFkUHqs1DfVJTE7mOkrG755w1axPFXXPOtUFpYtM0gcm8iIg0kjTlPpeUNm0TYhzK7nA/3N5sQ5DAyDkzo0Ws43t//zalUnKu6BsF3V3jFE0lZR+SDllzQVLNxVRtd0BGitGZXASiYIYEuIPGOCBU7JOAKIiC7aYNIQy5d/fYRQlUipOrOQ59/+6BXROTNu226Vo97IlZzbIpEUkMZlZUd/teiUPbNF0bYgSH0MQQgk+WqXmSjJPsCoJOs2i5ZP1CfcrX4X5sOvos0Xemayj7hT4HOtG3/eUn0LMAeLvtsJph8yoys9kOStN6I0INi1zHAVcBrjolza5DVVCsSjNMKQyZUct+m5HZmbcOeXWxrG4mRDR7Ks2G2zUkjxjsvJbYJhEZFRjmR5jJfTlS5Xl3EDHxKNvNj7A28c54j2mzPt9fJgC+PM5T5XKeB3mSAmwdPemYu1SxfNF1r5iG+ZNnUzcmBD6xrM9bx8nG4tNPM6dSL/SV6zuwHnk62ZtoSv510iwuib9YZAiaO3MNg6eh9vOflrujBq+aE1UAdncRseOzJzW0mRnczY1AMyPFzLPn8zgCbmAiIzMbhgFA27bV5TjGyMzddkOgnLN6kbiNUVIWMLrtRqTmejd3yqbkWjTpkN1snGkFxhBGIN6XUoqXUkoxm23UBitQB3uGcAwgBweJMZZSnEyEjKyuqBDQtqAecIWTMLsrA+xgwXCAGg69xnbvqoERhl5hXdcSEbn1abjfkaJrJIiIEdw9lSxDEncJLjFQkKbdhKaR0IAZXp3G4WRE4j7ztTLDM2boHWH4VB6Z58aogl4xrL86+s50lP9htbgeUbx9oRchvsLqXBvvK+Xp/6L0LAAOgdcAM2+zzDIjQc2pMO3FNkf+YcKnE5gEjGgp20M0isI0OdOuGWp3r2gN1Iyzl/MwV+PlCX9DNCbMWsPSfOHszEyLp/GYl2DlhUR1MyeqmX282jsxLTxmXoPQDFeh1q3j5annZi8TGfEkQtYCP6NnsqJaIycPF7Na2WgMsgTh5OnWuoGKuXX7nhiLEdknjdkiY69pjv9eaQimZscBnDTtZzI9zaqDlfr+iBM5OX9SSs8C0PpZrtG82Y2cxDnYLydUT3g9KT49J4hWVZi6aXUdWrn3SzGom5uJSNM0QkEClTQA2O/3bkVEUkrMILiq1iphsRExCiGA0DRNF+Pt7a1ZyUOqA6maSymplMgsIu7UNeVQ3B0pJdNyGCxnDAlDgRO0hvnpqEkCIRBUBF7z3jAzk3CMwoykmUsRQYxRU7ZSrPpkwEtJ7oxQfcCgDgAhkASXKCQcAjVNI0IlD30aAOCmizHcbDowEXNxs5yLujNREImmqmB1A0hZVYKj6k4QsChOhJkJ4rMEPEUOj8Ul3idDfobou9Y/X3TvqG4Ylxv5tVTQn1l/XoquzR86VljOe9FfjQr6RLyrNCPlDMBEUF2QafQjWklI8+daF78+gpWMaLYOFjrSHvvifETVS2uSnLQqw6eWpy3dmFcV1udpNsuvM9ZiJf7OAFbjkWgKJcJpKQhf1z9eVHAYx4RpdNECpvCeDxrzxZoISlMWIa1Fbc3GdAtjdfhRoD1SX1fRk6gajke7+Lp781PM4VWzDqM+2er4wnW5j/FLs5p+Fnvt2MSyfhYHpj5OC4DGJNrni/4aAM8T8GQqPqKgPgfgKgevacbgCsBOTsSVmasAfNjnvu9Nc9u2UZiCAFbLUw7DUGdfGgYib5tgZgHOzLFp4O5MZhpCuN1smrYdejUvAMA0e7exEJPESJvNRljNOCcbeuUxmgBCUBp1/gZ0WyllmupOCjWzWuVJiEMIIbAXgIzImblpuYqgItBJso8kd3e2u/ecsd9rjHjdhVdff3W33eweftne3t5s2mEY9rt7w6IJ4CmdJ7hWC3U19H0vqqJGEh0kUzjfqIUmqlbhaeYtX6e44fEtH4fVXbABfyZ0jQ84P/63am39Qk+l56WirIkXbDGOigBzlGeNMc0L0KqNkqsdq3bnVFNEJCJT2KjBdBQcfRSXuP7xiH9mEMhoOHSYZyISCpN6uvaRquOlFiMiEeaalNGsAOSQmihvZgJqx32MpqXRjdnMLMRQbzrLx+5jguv6ZZai1hpmIgfM3UE25kNABMggDMxm0PqI1UemiuBhEovDxK/UnriORXikvRmv9fFNuOm8Q1XHtLWOfS2OOqoyUokgkfgkJ4aruw8aMen91jC85ugrv1F/r0Mx3mgVF34i5s6vvrnGWR8fF4xR3mkMr5rRH0fQPY01AEdw98xwd/bCVgKbAAC5iVDLHovCAB/Ysqlm1UwWGBC4UWZYsWzeM2WnAhTyquAVYoZaKqkfHmJDUbZt26aUfvj+x/v7e9dyc7PZbjpzPdz/kgxdGxEjA8HscDh8/frbzbb78fvv+v3+9atbJipDYvPgogZxjhI2YVtQNH0nEoUIohBTZvJAFlIyHeywA4AgCAICbUXQlZyhCqGiBe5gA4nV6eEFRhIbkaYFzMj+4R87Ar9797C/R9OgbYkIEuz+rf8//i/d7V27u3+7P6CVsgkPtzf8sLfvfvxz18SmaUruA5PEm7ZtARhcSLZtp04ppcjx1fbmEKCWAWqlYw42qFLqbphgRoN5cC0JBpcYuiBt8lvyFkpMWagPLCQNEaLAQU5SX44iOzKA4G2dDZPJf+Sr5DwDzjznn0LXYPJDBO7Fv+SKEQSAXzM5XW3zaUrTk7q508Hlc2x20ZNdfq5zyfjamTwb+y60cimMbPrl4nG9csG1baNc204uHz69q58dXD+4u199YZen2wfRcyVgYLY4LuNSyuk5OM0StUhymNz0UaWfyXd33rgn5eoyUWr758L32AhXj+hFdTxjxiwSjvw2bFKAz4UGT98WjfwEM7OqTR2m2a3bUUuu+exjRcduddOTVWu/jGAMnhW28yjZCLHHDkQ4kgCqgDAL95jgaHZdcfcYw+r85Vo1gBYrGo1adKsKQKwyaI5xukdi7vKK5+Fdy5HuCHI0DWZF9zXV1kdTZTIe1yDNGE2TjhyrUGkSCIC1XoSManQPWV1nzByIlYhFHKMDPRzQqpqe+ZLqKjiKgCR8d3fH5CkfauNmALhpAg0DTzmkKlvTSOi6Togf3vWllE232bSd5tLvDsMw/Obr36hht9sf9ofdgxlykJa5g9dZBFO4AQXMbqYIUAX5yARPb9Sq5rykgZSR3MjUYYQfPW82ABA7hMiqNgzujtsbiAhAoQ2NF2YMQ3q43xMkhMAcUkr9fh+Y2rYlRwg18Dc2TWPgKtf2fa+bCA+zZXcOVjQzjDvG4gwxZu6ackRPZqbLE2Ce/3Rcc/plp9nnR0+vx3lG06o8OjJ+weVUBPOOMR/xeev/DOild5eF6NNb7l8kF/RRQmBMKQOBZbuvJMKzVLTW1latXV2cKxXujJpHFQkxJZqYV93sLWUuzCwyKZonDXn1x54so1PXiJjm9uHr9EzH2ub5Ek2zMZKIaKqlc7oqFqXa6ggwnunHjiTzQ7lDbY1qawBeTOxExJNrVZoSS80oWS8VHv2kVvHHVZXnq/c167znKoqjiAgoJtP7qvPLODDN7b88uD5C6w13Gb35Z1owGCv5+BiAp0fg8WLmtali4WlmhgYkcAACM1czd7OSUimliITaopnBXCRu2hbw7WaT8xBVNt0Nw4vmqrgehqEm/agmYTPLOeec2+1N1f3EGN19t9vtdnt2pBQAdotuIaeUCmKwGMyUqwGxKkuEQQIiqmWT6lPz5CcINY7BzFRdi5LAGQaY4V7BjBg5BmbiZCnnOmR8/9APwwFkxMiK3WFQeErCzEKqalq827YxRiJOKbOElNLDw55DIBJV3e0OQRhMIVT+NVRzyajcwrH6ecLjGaqZA/Nlr5r6vmT04Vh2RvqMbMF/HXSOKY+N4GhpWgSn+e3M6qiXBalre8u6euVfcPv5JPRMCdimNYPZEAiAZZH8ViXi19kiiaaUHQAcFoLMccNj1iGiMOlnT/itEEYopckwPNmVVukGZ9vqZKOdTZhzayEsya1oUqETgVYFwsdNvAKU8MwTMZPIGAmtWkVJnrF5Xa1o2iBWbU7q3BMTeFVi+5nXxuRENj6LzJrXE0Zh7u541exPvgiy62YXE/EpcZ3ki3WWls9lWEZbwEmbOPnvy66QMwyebzSC8VrdzahePOPYOFaKGgcYVIVDL+Y1DljdDTCgZjRmMJNxsVI5ownGSs1e2bADY2qp6h4YQwsyOHtxZtlut8K4v387DFmIa1LGgQeCRxFnSSnt93tyMHPVY+/uH969eatqTdP8+ENp2zZIs918lfLOHnJOnoeSk7lTgBuDCTGiaZoQQk97nwpuAiCGAO4QIXAolmvQWbWemIFG245MkXLEwZuGROIwDIcD2g6bLYKQSBCOw9CLiBATo+u629tXm81NtVZUvuHdw45D8+rVq257AyDnHJqwVtxVngPsXFUswiJEAiInduYgHEXqn5xOJkyWlVUA/OonAr4g8BPoqWB5Lh+f6fk+ticfqiQ++9nX318safgkhQHHQvCnoOd6QVf8WxItjQt+AZVqh6ilTmYRrW7cNEWdMrc1y3GtAVMbZ+YYZyl2uelaFJv/O/5NOYerP01FkBUQjl2aL2RZ2mdgTiTJq/7TCiNZgMnSyQKWSZCSWdc68hmjRK449kNmEOakQFWduTzglNlxPcKPT6o5Tnc8eTV11th/ziGNj78Co2U8QfOKuKinWp2JBdzOf1pm8Ith8Hqpz4qER4gnJDo+NrE+Yx5sVVUvClV3A5mbAkYOonGOuprVappqo/rZspmZwo2qOKuqAnFSgqSUh2EwKzFQxWozzRKihGx+OBy05E0bN5tNYMQY+77ftBHk6ZDMrNtu8pCHYWjiq5zImHJh01By6g/FTM0FTjG6jwAcY4wismnInSxbKVAgMMAwRxBiJwtSimquk5bhZo6ihDHU3lNyAExNaDYKD57aNnRdbBqWwGAx7U01irZtE5vIzCml1A+3t7cAl6KHoZeom82mA0RESQJxCEE4TqnxqhcbjCBVzSDMzBAGcZBGYhAOfBYVUDnWOo8E9Niq+Hulc3700jnj58XfZ3x9zJuM8EkB6cNp7sUn6g+tMPjT3eVZABybYwF0gpP19+lINX2Nx7ESqk7UvESLWW6BluNLsJpJMxFBQkXfJUUiU6Ap5/66S/Otl5vOnVnrUghY6rr7ibTnU+zyCIRzr2zcMNZo56sS9FUtMIm8XkVn+KK1XiuwfYmYHJHjZCLYShHtWEThdTaRNSLOIzADpOmpjPs49D4+E9faj5eds9e2lZUScqRFObacxZircxkcgE3ef1pG6EV2d4YXqGNhhtynRCqqXtRdycHkXrOoOLs7OytAzuZFU06p5LTvyUoectIwAhATUX84HPZGr27bGKjGL7Us5KkfYox3//i7rmnfvHn7pz/++83dVw8PDz/9/Ga3O+x33h+gCpC3bc0ByVI9nCNYDGRt28IsQ80UlS2GmLnDJAQxMkPKUIdEc4cCQdziqF/pD0gJaRhEhIVJYjE/DEmVhmREGAZTBVNqmgbgoc+qWtIgIk3XElGQhphVPaVkZqHtqjjLzEyBQ2RmhY41gHnMeUJBggSCUNOG0FAQ4oBLGHA8oxYN9hfBt9K5SHqyW84WtEmbMF9YN5ZxJkwWnPcut0877I+zEdP3FdtxZbu5xq29d3dazze61pvn0fNSUfLpK6w0IRaAOip1WoBWCZXWXFjV4q5q/hCtom9nQ+YMmXPx2rXBmIjqpukrJDMvMK6ZuFZtjl5aduJUSJMr8jFYMo95o6ZZu1h4Z4bD9Cjn85HkzaMsRQTiseCGj0kBa+yHwRlwCkcAPN9izhg1PsJkrVQsavw1n0G04iFWZDqNQH19Uz/NHKNNAKsFuLypE6KzUo/14JVEXi9GF5eAz/HB85Fjjq1OCIMz2Md48dFCURzuamZMzgyHkau5uqu7k7uauRkzm9Ey7j6W3KvZKYXJ3dWdIcWzF82mruburgaga5qmCW2IJR+iBBHRpLUuYUlom/ibb77d3b/d7XZw3m63IYSmaTY32xDC0OdfftnvHqAKLVCFOUKw0IYQYjQGEAKLsNfAXeYQyFR1zA3iBVRKaWL0KFFKn2CKGAAhK+4GN2ERMxtS3t2j7xHC/uZGQvT93kC4ucPNjbRBQihmVm3VpuVhf4iNNCGmlDhI13VN2zjPRaKIVNfFLYjImciFq2WIiIi8isBBmIKEJkhkiWtb0gnJOLUJGDnfExjwC1Hffxd0shlihb5+nAp38W9YrWV3n9KNTX5tl2B4PgqcjvLJuns+XQXOTyyBHynwXlqKOKFnAfDFsnd1JmCUvGzu/myL9ePEHTRGCesYsFhdSQlAta26rwoC1qlTcweuJxwmeXTOZFezUqiquU5yKs9X+GgSXs3XqUCpAWQgWgKEaFSe13RXNbkBrYsCqcKt8hA2dVWICKgldCr6WmVBiI1qEZiR4G7VnaQWYjsf5xMuZ15Iunr8dUCznSaHmv4mhqZ6Y1n1fyZCDSizxZ3t8Um30m0cvYImnkLwy07fEy36dPBoyOYf3Z2XqCQHaOSOquf3sjc5yIlcGAY3GMhqWjN3rUM2GoOJ2F3hNOkk2Gv+Ca5YS0Q1w1bF7DY2gDEC8VQgF9RtNyBLh0MTZRgOeUj7tsl3r+7v739+84sX9Pt90zS1pFLOuWhyR9MiCNTQ90hDdX4sIQRiIUJsSITd/aFXEWEyEWEYM5k6mzrA4pElRsQEiWjbJoSQSx9jy8zwWocYNd20GQjipsMAEF5/hW+++eb17c27d/vD4XC73Ww224e3bx4e+hjp7nZ7c7MJIbRt23RtDR2p/+1rku1cChUJambENNZRYXLAqjlmFIiDSAQ3J1EdJ/aD6UitGXW6WP6242sffzYHT49/QRScN9LZKWTC2mUhLyroSelKqxJt69H+y7A41+6xXuNYff80MuqnxeAXCEOav8+C2TIQVENva7Y55HSEee7I2U4gdubdmGHZ14SpttLaR2lOjMDMbbs4Z7nDxjwbDsAUFeCnW5CqVz57tLy6zm0GohACr/N9EJhZqu8HTkVztyVWeKrOmzFGA9c0ki4yJnqksQjTUhQZziA3Q85ZREKQ+nw6aYaXx51imaYR4emmcwYSmQOUZ3F/Ni2bnmQKq5ISiMi5QjjXYkeTxH0ByNev+8RonbPREc0nO45Xy3gci3PZ+GpGb6nLtiia3sVZO0uXplcMIuKx5gNP5mAC4CS5GIHdoWruGgKzwEt2ZJCTGbGJuTM7M0iGfW+uRBARArKZqRYt4sWdYcFrhU0rIiLUFk27+/2Qdps23Nxs2iaYFXPtum44HKB2e3s7AlCUUsof/vCH/cO7nDO7HIZBRNzw5u3b/u1POUMacAATGg43N42IHA69u6n1bQhNKyIwG9RSSrTZbKKEknJRF2hgMUFQCCMw32wjUTZwnY0xNMJBVXPua3BZ1yEEECFnRdEQ0HZghxe9ublhbn7729+m/pDzwMwp4WHnN1szs6ZpalFREdpsbkg4pZ63N0TOMmbiNLNAoYmxLonKJRd1Lwq2QMipdGwSyceSaIyVZ8MZXYjJeVlg+OQq1ie2f6zXWbVzpZm1cUqEJwnEZeXcut5g1/EcH6LYX/9cU6IS6GJGP/cxOu7o4PtMzhfaWalOZxl/Wv5XG1lLtHN/sBq3E0S3KwO6CJY4uvw59AJhSLOydNp0WTWPZziBF7arBvn5lKsZwJRD0Ylodn5eNzg3u8IYzEzcLBNXD4/ZiAtg8tGFO5Wi5mNe/amT5u60qlUMI9Ao0as5kYrwsRA8KqjXXFd9uao+tUNTAiVz9xijO5idjudHhYeaZbA+vKoTjU9oc4JMH7+cTF0andfIHKtskeMgVAdXnK2u1QvC+KQ2cifMU/TlB3u31Dcz8zfTwZOMWvPxjzXCPJvq6KxNDU4887SqmnPWlL1Uo6+6m6PmOdHKk43T1WlkJ2clg1WFiRsVBoQczK5F3VPfu2YyL6UM/V4LS+AqrlatTAybzabdtPHh4V1Ow6bt2s1ms9mwS/WvTkM+HIoIWkbXMSESRTdoqRHnuc4JtWQmxG4wBw4Pzj40TePuMrKJWhJiixBAzDFKLFaMmZkclVssOff7rIqiiBF3t/T67vb2rn111969au9edarD/cObf/3Xf91uX4UQGP7q9e3d735zs21/+vkHEdofHpws5yG2TexaIuIg7h59U43napksQIXNeFKaGJhABgkQR3AIcySWWk3tnF07EX8/5az5K6BJxnB3rymB1+Rj8oNL151aiBezF+iIIX4vzfecwzcAmjTcT3+kS+2fa9SutUx0mgt6HpOlPIwffbEndpJHgeGcb/j4p30WAK9ptj4CcK/NzqUJCFNdT5+yJs1/NaOyTYWS5lIN7pOJB6i4uRb1pmDB5b7MRzbI8VUJ3JGze+XBlnI6Fetm4cxG7TLRlG+fa+oPWbV5zkOtiVlmw0FtFROQriIxaGa+RgHbyY5LJU5Avtz6hIObp+D0vON8n/P30ezzNR6fgXZ+mNFtrBaIZWaho/dycqMTmts/wvSRYzhdvec76aejk1vPL5zBU8bjmW+GG0opqe/7YV9KIjd4QVXhuGrdUOYI7CndqFthuLuaFWeDTiNLxHAF3KolheBMsJyzFQvV3TkQM1czcAiBYU3TwC2EQNRt2jaG9rDb7/d7o1xvKMwiURC0IBfLpZRsOYMAFrCiqM7Q1PcIQQUqxMZGQFGoIRhMEwMMJZirplJqKUa4933ePYz53TZ3eHVzy7DU9zsutzfxm6++/vY3rw79/c8///jufgiBmyi3t9tN197e3hbtY2Az7YfDkPq269rtpuuGdrPpuq56XpqlUhpHcDBrYBEfI3xryg4BBUIACUsUiZAAjMECa2eSySZXwy0YsL83U6/Nkt/kqmJTBIeP4sE4ves+cIK/89KuO9t8xOf9edofPlAknZIATjKHjSlfmib6sRXsvVati+T+WFfWstDFneqc/6DjiJin0jHv8jKT73mpKOfP6os0PXCNrz3P0LXKeMXrakXTvn8i/o5l4cdLjMgIOr51CRTCUiwBk4Q692q6ETAlhjQzMAiyTJrlNJnUm04UwMY8jsypsuIsLAqLzhMi5C5mMAtmNiUIq3LhWg27rBNM7V/T+SwjcGkOT8O1XOhjJsjRIxpjtuejpuYW3QXkPKX2uDip5gW/1n2t+a2531MSiOWl+CVIXujKmvw41d/lzq8S4J3cTQ0p9X1/6Pu+5OQ+FV0mqxVBKkfGTsaL88KsnnIou9tYnZCDCMjcimrZtDFRNgGokFUvhKIKgwQWEiaiMqSiiUFt22LSzcQYabsVkRgbZs67gSmYUd+X/lA0WcmuipJADLZJ26FTOFyBZ1gEC4QEpCKIAe5QM6cCcpCrIqmbeilwzTkBiihoWjQBcH1993q3f/Pm54d+9yYNv/xf/2//tNlE13J7u91sNjHwMBx+//s/Pdy/3Wzi69dftW37w0/f39/fq5eCopYlcgg3QHFXtYKSzJk41NAtdiISkDgJcUPcgIRojACGT0lS6jivq2P52sfqBdJC/XURzbhrR+4XfmYeGs9e0SNYtVrLHw8qo0RzpfLEeX8+uFnMDl9rS9NasTfFF0z3efRG89b0HFom5LN5wGcBcEqL4ZYZPMX44xixgKvPvGbEKlzNr28FzPPpoyQnIiIsMqZkqifPBo91y/W+U9UmwcSLuZMZl3Wk0Kj8JwAioaYBqCLQ3Emf3HnqPQHMBQGnR1g64z6X9iNg5i591oTjdK6MxvKTyXQ+n5aVtgzvInm7r9zHFlF41iisbziKrMxjTo+zeyzH5gUwtzb/fArr83B9mjyUzyC26pRPMEcpllJKKZWSzIpDiZ3gUCNyqpoDGBkLsWFU0QAgrlkjmEi9ZhVXK26uplpcM0lggsEiE4UwStUTB1ZrLEUOAFJK/XDYtN0w9K6qxUMIXdcR8eFwaG9eAbzfDX1/uL93lJHXMQM5mOEGKJwBBtMYSZ+zBnNml4AmjGwxMYgNIjFayk4Z5rACD2Bgu8HtLTdNMC/DcHh4Z7n0McIMf/zD24eHt9st7w/2+tuv7u7u7m63h8P+p59+cNNvv3391VevROR+1xwOXB3+AXMoKt57NiuwDApmxSdPCwMLMVNkDsyRpAVHpuATK2wj41QVYKNCiTCO4keKMH8TpOqllMmQxzPgXRySiW+ctKZTJoZr5z+JJo3abHeqFWDIX+j9rEHEVohbvXnWzH01yc0nf1IMfkH0xTMBeL/v64BXlVqtsucOLdMQHAuOk1PqaC6dj0/qvZEWyfjIKaAi3FzZG5gQaBWVtPh/rY0ZswCzVsJgRM71T+P9ZGLBZ76ygquPq9/ODf6jUcGmt+tj/BIWLFt6Ncne5JPMOEvqM/pW1q92wC5Bnfs55+s1ZZaeGciWVbc+upKtj6dsTZHtdCXL+JrXpjMT9dL80au41M7lw+enLdr3R047Gpz5H7qgOXCHKmoayJpFEgBMiZzI2VFgswWhFscjJa3SmANTmmhGYXhxNYPmopY15VISPKc0uA6IFIVZPIQoQmLc933OQxQOm44Fb39JP/305ne//baafoc+d113c3PT9/3PP7991b0mkmHQ/c53DyCgi2BmYSNCzd+NKYsLgFplpCR1QRMBAgW0ATrqco2Fm8A9KwhkaCOaAGOEgG++etW2cbe73x/6f/9jv9nin/5ze3vXvH1zn3q0jQnhX/7lze3dm3/8h9u7VzfffPPNV69vfveP/7Bpm+9/+LNajo3EpoldG0IALOchdg0ouitgIK+eBxO/zsxBJLLUNB1RuD2yyTlXprjqriqT6/5JQoyeU3ThL09rNK0bBh5F37Wn5ImLxvNhsorNNFfFxpH/xzPbXws6mO2Gk8cuVjvLiWbxvRxa/VUfO+UCVViYdJYvMzee6QVdmXpU1QiA6oW7CK/jaeNYLjqTCSom7uxIR3qi451l6ElKptFt2ecZNiHBSi0zzbP5LkcNusPhzCt+0OE2Qv4MycvWVo+vrPw+P9Wx1O4+xpjitIjC8neevpFqIakV+nqtAE8EcNHCzLNT2CzXrubZ0uA5c3dRjF6RTS6KR1eez7C62E40XWNPjt7vkTL9kVv/ZUXjdQWnEYDHogg8Z+c2d1tx21Mdp2kEx/hgokAMIbI5Li6V2lrJ7locqtkrJrOFwNtt17QbO6iIwERVD4eDQ909BHL3pmnMTJMOwxBjvN89/Pl7vOO3mw3cJOcxu4sWGPm229aKI0J5kqlNiIUErqbGPM4NIkgAE4pVfTqbI4RaLBibgBiRc5WeKcYYAwvh1Su0Lb75+vXrrzbDcH/owYzbLcI9UkJKifnuq1e3v/vtb25vN/vd/f3921KyCEtgCRyiMJNqCVB3JXYi1NSeEwMtRMIUqIYeSRCOxAHgypKO28IEEV6Z9xP0vcb3/a3T7M+8OG2875L3WIKeh5e0ZqpQJeCXjAiq4oETzRrQ8fhKxEKF5+WKesJLdeGsSy/X9LMAuG3bNeZNmSWohrOu8GZcSjYpQ2Z1wnQ5T+/sgvriRAwFaqZJWvn6zmeuNS3LJJiKENC6QTOjVdmyEU7qNjc5KdQuzkC+ustRF2vKzHUijnlApv6AfDRpE63bGluaCisttzCzWSVeSqmVKmbl9EWBeDl+hrhnN5y/TNzxaZTAWKcBV2hG3/m/o9D8uesG5ylhNet4LQbFVOfh2rrkcHVMinSMP46tEEn1swJq/dtSSimJzImcRaJINiolD/0QApomBJbectd1HqXvD7/88otDA9Nvf/tbV+u6hoj29/vafkrp/h67hK9eo+tUGE0DAgRRi8e4IXJiZQCkpk7OJtXbrgapuwiYx0CqZtPkYoZAHFloyF40l4KGSURyLjkjD6kJAkDC6P3HzE0TI+OgY6LW//gfu5RS27buejgcdvsHR/nl5x/n+tBmxSyOgzNaH8ZpvKbqc+9juSNhCkRCxDYNsI8xLQv9NUytT07VHDZn5cEY/PMsRuRoIV+tCXf92rMTP5HViQBhmrzMJmPQIsKdSl8vIoKf0eI1dHz8490RngfAW5kfvh6pDCukKo8mqWp2yTEeTaFEqGgEIoJqTbxrIKr1cqsM5UJVQTgaOyd5l5nVx3QZwCx+gSR6dc2vzqs+O1TPMswkxjGIJU59q2rekSEwZ+FZdVNXvhqKUoiY7left24KXiZdRo1wmTXks85tHrFxkkwT3aaH5UAcAB+NuMxwoppG28xsNKsZSJiXJBKT7t1XHC7RcfiBT7pxdzTH88QdcK6ikh3DttVKorPS/ljwWNYXr5Xd5kIOg7PZotU+5wZ80pcEDic/XKR5O3hypq1FdV0DV5yhBuYACSU2xrEYD4pDwWAyNO+IzL0467jbVdGtmCm8AMbiDchaisLeyGE3DKnowTwFSWRlyAdNuTDFwBHGsJs2bjZdJ5zv72/a3/zw43d9v//q9d03v/vaNKe8Z/fD4RBcCNRKOOz7H9/9uH83/MNrvPkRu7fYvyO3Ub1DyAQg/LTdoG24qGkGfCy6ACxqXlU1hxgiYPsUAtpQgEHIqUFnSD1y6/e74ozXXzft7aYvpfeDRmxe4etvkOXPf/gessF//pYa2easd1//R2FzpED97Q1HeUgPb3zY/eb2zi2k7GlQye2r7evbdsshhMa5aYkFTNS24eZGug1TjB6CUVNMJIsk9t5ZPLgpmY7FueLoV2YOZR7VLDXSpK429/dsXotARFePnEywE6LJC9vMvMYaEI/7ldDatFHrmGVFzWlfJ+qYitzBU/53H61YdaladZgHQCReYQNT5e9F2bkQH1usvL731fa/lkaIwGQgCPNFhKiVOcaFWplxq4zUufPsqZxdbySyyJwi8zYxqi9ONw46uhxYFIG+khyOnvekw9Pjy7QRTKLUWN9zTsM02y596v9FWt90Ul5Wb/DL5xuNXkQnE+k5KP/MVJSjzHqiGXhc57ng0+pI/Wf5DgAoZRQrsRomrKwX6y+YB3HFCi3veGKIrjFns3J4ZrXXQa71kGqeGY41R3/V8e/ptFYpryR4my0rp++esFK5jFec+KNNw3JZ0bSwJsf71Kx+P9+flhaWb7awFZ89MSiEEEKIMcbYBGlERG2sFsDMZoXmLFpVG6FjAKsDcMNUugqAEJsIuWguZG5a2AWBRUS4EXJmNoWj/OmXP97f3zdBbm5u/sN/+Ect6Q9/+P0P3/9pu92UUlSt3/fDkK0oV5F3ZJ682p5ZEAUi1duRWMA+ohEzRKKZpazMaKOEIA41gyp8LDRCzIFhFlQF1IIblIQiCBwJkQlEwlT+8z/99u4VB8773duSNIat0MZYH97tUzoE0a++aoI05ASitm3rlhejUJC2aTfbGFqptnPmwBKdhZkJUmsvoCpRmQhidZ6Zudl6x12rm6cNdbU2cHWXnOloYx2n9JEY/eHi0Wp3c4CYl6nuqIn8zMzUyZ1pigbExO4DUICP5MUjTDhdOOdJKx7v3mr3Wx+cFHx2gmV+ZV2v+nZ0/rK9rTYfoqux2HRkg3o/new85wwTlmd0HIVTzj1knwKQzxWo12989H0S6N9jQD7X0T6HnheGNG3c88SeoGs55xgzLnecxxI1S2bjeqkqrXKn8QwVNVXFeTsnrMDUGmG1GteAvXghTapyWh3xUYpfdOkpj21Wen7A+cnla3XQrGMBanX0UbczJ4wE1r2dMRgAVH3tmuFTwP6c6ubs8gt9qH+EMbx9PW7rTQxARd969XsfeX3GX0KnOC/m45XIzCGEKM2Upo14dF+Y1KRkPqpErZRiVmvtGo1qD3NXAoRYhMg5Gc9lhc0LGYXYMouQE401l/b7B9fS3LSvX9/90z/9kzByHn76/ofd/a4X0aS73aEUCMENTGhauMMUriBGG6jtuG09BAtRYhTYkAHzqi5uQsy7PRigV95tOtWhqIaAXOAOoRAkFFcRbRo0DR6UiQyKlAphKKUcUtGC3HPz+u6rrzffvPpd7nN/yO/e7N79/PAm3R8OZbNBG+7oa4YHJt9sghngIqCGQ7tp4gYSisEhWxGhIB6EqxxMBBaRCBbmUItFuqM4xHy96y9L1ZasPdVP472z5mSLnDRZvmKpn7BwR9l0DQbLpBrtbqUUMzOCg0kDcJTDR0cTmI3qrhPzNRNgtaQHyE7USh/Uw5XUcUVXf2RRcrd1KVKfnFhXPM0FC9RqY37PO7iIvnP82FqEOEHH9QZ4rsc+F6vWktJKKDpq8BwUjno6KzOfIj2c4Mhz9rGXSUU5Q9QjoqD7Eais6VhXQbPkNXsR01RzaWrKp3WxvIkV1i4S4VkHjgbaTgb9WHCnFVDVv4joPqo71kLw9Wc+Gw5fSawzak3gYKsOzYoEohGAV2vs6J4X73/CTFTw1tWzHPVgvmrSMPukSCKC+ao+Ms2KoLmrx9vF+SOvJ/dyOz4f/henZcIsbtHu1c/AMAVNiDt5Tc1dk2dUy4C7mZoVkNlYtUAZXkNpDWDyIFzcokhSJTf3mlaaqpsMjTZ7s1LxtxAsNsKMlFJ/ONzebm83t69ff/2H3/8bM2vS/R6uaJoq81nbiJkVeBnd5DwIYsNNsLbhpgnw0vdao0JFpOvo/m0qBUTetq25q6am5Yf7vNJSOgwxIIRw2Alj8IK0L+WwL6XsBhTFv/7379JhcP3dpm1Ksnc/H77797c/fJ/3ChDwFYZdSQe7aTnEhshLMXd1Eg6GoEapeHaY+C1ImIJTMHANAxt5SSKQEElN6FwhiNzdDUcZjdjJJuaOdApNqkeuebHqmJ6WCHCCM6maT2Hqo3nrA2TMtSqWiHz0qD+6dNT8j39UJ447wcimba26Ghiq3tp83iWPJTmnuSngUgdtbUeb+ACv73R2oZw0N7RK4zw3MAo5dCRkO8HczW3RIh+fj8r6TGmLaFw5j2QjO7rvrA6YfJePfpvPrvv3mpM40SyuGICxG2Prk5Q0d3o+f9zHrggG7CfvEVjpEc9Jr2hQnqP/fB4AT5mE6zvhqajf/DDuq8k5Q8LswQtgxrnFmoD5FRGDiWolIoIRRhFjtiMfMTsLBJ+AUjWeERHhmHOxddrqYz53jX/zKSFU8Atz0QVcZTnfT2uxu7JpZ+71VH+qPqtmc6qsI378XH6d59Aag5l5ElXfL0KsXtgFGd1stfXRsgyJgpOjJoVf3sWFR8eR6eqTUd3+pv/5dFMfTV9CJDSOKqbqR6pw81Kjdc2zWXHTKZbGyAxmBIVbFKl2M5iaGcMDC0WJUbgWblDOqiUPlWkbhkOMcRj6f//jH9LQv7rZPjw89PuhFIvM7kxuAJhaIQapI3mFXkNRMCEXjRndXdu2oe0CPIho7qHqqto1UaQabhwwEQnSdG0Y+kyEGi1lpZiNPs+bptXGveSS4QbN5hma8fYnMN4c7rPA81DSIe122D8gA69eYRvhCYeHw802gKiU3t2NABJhITUUcZYQ2ImJGBKchcDmgLPV7Z/GRFcEAXisxwDD4hgxv0WuM6oYKjzTUoft8sZaLQjVmoBx8teFNjpt4FhKu7ZBLzSFS/hqtU84N2pTvBbqmNaew2YjTo1+JqLqaXE+7yddW52uM+wdq+kwDdhycESuaaxmCJ7lnDVEnmdlnRq1MZWVjIuWTs73mh2w5i7y2VJzXYVt6z2BQPNoPz7OvkbfE/MfVrtZrS4/S3Rr9F3aWrV27b51Eo7nrMb12n7ubmv15LoDjz7XY/RMFfQocc49XusQzk4++nIknSyciJ8MdxV2K7TOmpbqYznfHRMK+pWBMLuASWPrE0+H+U1M3Zzn4onOhKa4t/UseSrNuH7Mr/i6JzSFtPPiJDX+Wo3C13B/Pn6Sc+7CND1f40fzz6e7jONQVbKPPNbE016A7eO+1U3qsgzzUvGX1YsNBKupIeatr06UWgLIx+QQjDnfuNo4NdTMRvStuSddefwlw8w0uxaYgnxMPRklSGwkuBV3L1pSSkPfVz9hLbmJQZj3+/0f/s/fE5Hm0veDJieBK5kCBs1uQM4WBRRgATFU5zE0ASLoWokNN0G8aUIcAOSMlJJI07ZjpqSUUhCT4O7ShPHdMSBEteYGu8XAbWxKsGSUVYU5kKkjCnQI9z8PJZfcQzNUYQVNxLbBJgYrurt/aBoL0Ye83263EJYY2LmYwiDMoQ3CDYcmSGPSuETm4CzMUuG2Tm2n8X1X8W7R7hpq1RI3cobOG+QoJ72/6OCspsKkpVxvrOa2PvNyCzOnP60JHdfAvJeBGMLEo9Pi7HQ5CvLjeuGVoLdCUR4tPMvd9QiLp8mKs2PHNO+E8zNeGptFnKWqTsEMdRdHYCX+ui4dIJu52Ec6tdaH192yik842l7Gfvox6zCfdl2XOeuiLws/Jw0+QnM/P/D8k/tO4P0rAXAIMwfhmHBu/vVETXp5sMgBTGEM88kTWLITfFqVS/EsFkzYMPOzAJbwoTX5Kvv0aa9WKo71FBzlbJ7m2XRwNpwwH6Vu/rDRukCzOL6+4zwI697SqN6v+oBx2czOCKt2lqc+NxAQjtpcLbzl+MRkLKzVetc6lgFqK+tsKZd5zfUsXevGrw7dNcbiicA8hfGOswaAoeZEw5jASl2Lq6oVNQPDRvaiSp1k5OaAWzErrtlMDTYCs/vhcCBzuAoIwtRIECJnd9dMUFPVkrQ/pFoft6oiYxR2HA7DYbfPuZCRKcxd1UuCFphmgeVcmgYhQOT/T9yf9cqyHFnC2DLzISJy2MOZ7sx7SfZAVqm7qj+0PkEvAhotCdCrXvST9aGlAhrqGthVHC/Je+5whj1lZkS4m5kezCN2nkOyUcX7AR04ONg7d2ZkhIe7m9myZcuiKUyE2XLHXSZmY3iXodCncGSRinmuROgHEIMDyjxapAaGEkGNTEMgRtCoZjCVYJKDlmikFpgDkQtmsIE0kIWIYDRTsEhQxm6PLiOw5hRS5CqTmoqKEkLg1A2py2Av3oohdDGnlLrYdRo64y6klFIKIfFy0GNvbEXr0/A4JZYUBfleT80UNt3jP2KqloN5bev5WNZ4HiSsVse/7k+RKOUstDozky2RbGcere8L/C4xjFpbnSXx7DdATuk+j08XWwjD4jLi/ZX0z9pk/rT1ffdtILQOpLogav+j81NzQZYWwotx/FOf8c5XurSCpqXRjuv7Pj6R8ys6izd8bzf7IyBZc1w4rIDEH84BH+uz8PRPHvputP3ox/zJT5xfz/8+QcL3MsBL/SuZPTId8DjX37nEP2Kbz4Sx8G5OF86wBLnM/epJnXu1blXfuyR7B7x63Prfs77Njp5DEGeHOwTnfpb/76zvVW7TjPze/zwb/O70oPdeeS9kpzZAWAfBL+BPOBwAcN77M4T3z3/2/vXiH79x9VqwPEUDAHapij+P9f3eowEgf6KDGP8Jhuu/1AD7u88Cdo82qFaQohbVUmsppZRaq2gJBGMjBqkRG6mBjCFiAq1aK3RWM4N4Xdc8nhjEjEDgyEzBCGYkZZa2sElVS5F5rq70MoexppxzTwYRlRkwSx4dSjUTcrtP5uBzIOQIZm/EYilz7kCoIANibC1JpBqkGrH1fR/CSOxWzahitppjE1uJHCiy1lqrQWsOFVEkG5NlZUmUKAQWqZBpngp72B8i+j6llC6vJzXpI64uh6vrDqkYCceeY8rd0A/blPsidZbZjXsMOcUuxk5DZyHFmGNIMeZAzC0B3wywtWqH5srb2cTDGjLS2Uv/w/pXb7f3h/7lumuvtuEPY7J3j0fmMrXdyuXCG4j9GAkt73ln+jVZTdM/AI2W979LL143nPcvg9552x87/mgM9+638ns/q9nCr2+spjUf/977rcXqLljIILI/bX3huADz+TgvcfA/y9ycB/TLD/46zraqP46zNsT5HHL7ZzovZ4f+qf38bMP9U8H3v/T4ft2QFAS01P1ZUPiHNHE/3r2vR5/yEU8GEdkS1prz+bV16DO05H8zyTCYvdM/67xz0bkPoOo54PcNwBKTNXdpNWwuS7S2KHaHbrX9tuiKELXsrMj7keXa6/ds2QNoWtkiLQhrJ2EvridZ+gqvn/UPzvWxgSPzYzJ4ndnrzyFwjIvQZovgvdUjiFCtCXpgydMwcwB5Rx9bnsjjYsb6XAB3+QPDPJX1zlg2BAIw8lt4vP0z16HpBtDypJYAu51paWzwmEh7D6H6IzMKjz4fgdbEz+Kik7YwJQBWVGCRAAiq6DzP0zSVUrQKyAIo5ShCVYXIliyjqtY6jyZiMplWM1UptVaREqS19/C6bTLMVbRKSomBCrq/vXn9+m3XdUO3+e67h6FHraZVEM0MWrTMYCAyucRWYkgAgwKH2AWV+fKiyzmP46RqgShwjSGGSHWebo/j9fWTi4vL25vvxhPevkW/m3KOQLy7rSfGk2v0fW8yMSOQcYCUiRkpIjRx1imEmj33mgpTnxNSBFGqRed57nLa7HOtM5Pt93m7mQzYDNZlyx1RikVUtHZpgxCZUuBetMAINgTezGNRHDtKadOHEIJbLDWKj9CKLdGoN39U15EzqFopyhQQIBXMj61Z1m1E33WvsQA/qhoCe5NNVxFoLmlbgxQ5GCCyOKn8WN+5mu22TPwjdP6NpNL2eSLCWdbMINTgVhCaAqgBoKUd2eJZLo7G43R1FSn/Nb6HuKJpsy0B6DuEmdVrXn0FW/6nswh+OXhZUMTEFPjcTeHFZNtyga522k5xtlY9zUGB6N0vPfuZQoghRACKVgUwzfXxgt/9wVqDO5ihViWiGEkEy3bVvhHLo/T3pPjo/TwO7NKsYv0K10hv9780I1VtAIUt+wYWtu8Cmj2ewTfYxMFff29jT39csfefdfzvwIL+A6v2z/QMzt26ZcPlx+j2vXNisYXrQ3v3Sv44P81/WJ/ie59b7cpiU6k1TliyvFjM2BrzrTb4fBBCeOdXavXE7ZVzRWifwbWKG8vzfeHdczohi5czvOM9+BU9Gp4VDnhnxN67S5gtStqPfqKpd6F79zj38s5PZ02WoE3693bDdSR18WPW8wU+YzAurEQihLO+jQCamF1zDh59jjML/TiGaIuNzFwiB7JwSXxsmcj3SbHmhldT00pIfmsi4nYUpGwwNpizVaU571pVitY5EBmJsTeAU4ayFlNRKYFTJKjWeTqVeVJVZoYKgFrKNE1mVopMpcwTdgNMUIqEUEmJEKAiBqhx5D4xGyoVQD2EDexug6nMIYRhyCDWOnGM3sZ+mkqZyzRiGjGOQBxD2AIsgjphGtCnYrAux4aum3iBk1eIqtaUMGSME+YK0TEb0LPUqiKsgBQmzllTwGawrjMQugGpU44C1hQohoFjYE4cuhR74g42JcokGYnCOkHdkq2TgDyTqwQlUyMls6oF1d9PImJKxsYazUx0FdUBFv4zLwbvvETCTy/yByHK8tlzYsQ5lvOerA1gHFgM4ki0QVWYOcbzSNwXcLu9c+IPES11zY+hgC0WQkzNEIjNw4qV+W1k2u6OFpImzJfWYhXPFt27nsejOKX/Sc8U/Ra3HkT0npDF2UCdiWy9e9i7PyyG6p2BsD/wV9otK1RgSqti2rK7rm6YD9jqW6xBzjv79hr2+KCtF+MPKHDz78+ZrD4x1mYB66n8T9VrH86AECISeQy+XZmfGuYeFrf1j5qgP/P4ngbYA9DHC1qswb/oNI/W9/Hn9W/n8fH5ly0fPd/r/9DKrnNxfZetGZg/fintf8/yLqMPM2IOIu62vjcI75uws+96Zw9oe4e9//p6eetq8ZtyXZq4IAABAABJREFUW2JnueHVSV8H7uwka0jxR+69nR9LCaKB0DJu7tT/0dGw1cye2dr3fEOgRe1mxtFzM64mc26GHy/svDk2B1q2gPZi80+JcfYQ17OI2PmaXLMe+ugeEVFYp4lbeo/IG9HKGqgo1eZ5nssodYZUYiFTBVzteWFBF5GiUkMAWl8sIatFC1RMKqkERCarpUzjaZ7nxBRyBjQye4RNFOpUDwcrpT0LLSIsUo0RiEQrSik5pJQSwQhiVYgUhBicujwzkCM2fTLgcECIbNVHQKVSs60VUi3n3KV4H+d5cjq0MqHrOkYlCEGZkVzEg/nhofQ9UQxprIcHTDMoIgSUGSZWnIHPc8rUZdpsNWdWaIxIKcQIY1KmkCKHSBwDReaYmSGBwVbZrS+3pHLbHSko1MOtyOxorokKEFRVpPiEKeLzh9lxlzPPXluCECYlhOAW0aeAb7UizVR5ork1bXxXEYnOKhvPMSpfR/5zqQ2sMrNaq4jEGHuNIT4asEWuxV9x99FXB3SBimJo03iZjX7aNs1BIPabguvPnk9+Xm0eUxVbD7dhIZAjav4WkQaeLSEHvxugYslWN7u4Lr3FrVld4cdrIDQCIN4ZpcYMPT9EFsW9/Pity83yYlBp9Zn8H6HVqa/PxTeQ9ZGd9QJ95/GtD3TVJF5dqGUQ2vWvH/d/rlZGQAqwwLXyI6aipqopsftJsCZO7B8Ue+dK1m28y/izj+/Lgj73ub7HefAePeiM+/B+NNw+8i7ygBau/fFI948+v9WZ+qOP9tyRXH9dBKn+5PHuk7bVlcOy8PyIMb43aOcdfP/wO9xSvmvLV6B1vcjHTeG9EVinS7U20uStFfmdUP69EQBg7z4VUAuCdalAXBezGKlaEP/4OTRnaEJ9hiV9TsvKxHLVtMjQLBtAO+2ZyfcF1ph3tvQAX6F4V7Z6BCqAqusMYYESMVMAQwUiqLXWeZ7HqZRJrZIpyFRqNTWIdwX2rZgZbGoENhUVqFgtUmaRmUXLPErleR7n01jrjMTE1qUeQK1zrTVQKiLj2FoHEkOF56lqtVpbcrDOkFyTEZExxJqMM7YZjoMMXQghqBRmjQFaS2svZBwoxEiBzVshBU4pcgw3ptACM+JgpBYiYgiARUZKMUUGdD4iJ8RIUMwJoiCBqcY+QkMdBUBg6zrrO+o6MaBWlGJmFmMUrt4ab9snAwMKNSLEQEQwrQGZWhmVmFn735SMiITZrY3BhExgsgRqoiBVmBEMymriPRdhrQukK3erKoLBvEL3bJXVpvCIECi4DVuk2n03F2kgp8+XeW5wyxowicCr0LCgX1KsiphRCHHdNN9bxZ4R8pMIWn8X1VZQTAuOKgYjJl4QHZ/nCrPmMwWQK529E3AYqj7qrzFTCk3UA+zIky9DZSY0w6bLcK1pKRAwFTuPLrAYY13KSteA53FfXqol1ywSM+pZSCqCWptzEFLAu64GAKKgtqSUHMFuwqmYZwAICk+f1QozsL6zA+NsZ3uve1772R/uumsBpggEBYo8bvUqIEK0phjq8dja2bZWMgu1YomAz3m6mJd55T69WduOtv+zDHBYuwu8+/qfUligP/HL+SSms7/ZCtysezEAWtoAt8f5GH/r2VnPv8uWx/IYurld/APfit69qvf+9Icay3/4kfM7XKf4H7pmi0V5PLNvoHh0a2hxz/8IR3F9w+qQrr682eOv6xsWi+Vwetsj6I/dyDuOyzL4f3isX8QtniZYqGqeeAM9SgHZUvJBRPoYo0AV1RshLBvfas6nac2CvzOgqo34bUYiJtKwoxgTQLU+5um5tbtbXA0lAyjADPOsVq20BPDscLehmhUTUa1LZt1CJLMAilpHghopyKBiWlUmKUXnWkYTLbWWWmeQYmatQgNgcZ5nEyhprS2LOZ7ABIkmRaZpmkczQ/BpJtWMySYOYHa7GK538fZ2Sgnb7TBN0+FwzJmYcTwagFIgvQApcIqxENk4YjpN6Nt+IAKohRCOx+MwMBMFEhCYGkAx9MiBiS1w+1ApkIrkBUWTqCBEBAZHCzweRhxPKILr2YwSEc3zOJVpt7/23VStRGhkMFNgoSZbIjAzrVCFCWkEKYygAlVmVVMyhYqRmlE1JTCRQ5QBTCowmMC8TXObFMamagQVq0aL4ZSzNETw+LGFeivHy6BiRDAmdwTLXNc5A6DWOk3TPM9VFh4QN0u86SMBVZvgwWqZABDDGXm8bGFVIApp8SNUURfRSl6/bFl64uatmqhUogAKgZjZ3kHXCeAzZlMgQguv3bSLLYaBAIioO+6ebXEV/RCCiBACEcUY1sbnIo1ssexatO48RKTy6ASv2xQe9zGVBfRn5lJWk1xFBID3q63lsezwfE8rpWARp1NVb/cbQlgVAFc6yxmW7kDFoxqSmTnV1BPA5yidmXm+z2eImdUQYqSp1uV72Y16KepN0vz1EEJKKYSm1VjLo8OhDaHxof7zJQ2+fwT8J+Hc/+En/9Qf7L2/vRsD2bLh8vr6H/WSzi3TcqmP4MZyQiOj5vothpAWIMudP3vMKIAIHNYreecK15Xkrtd7lvvMwj3msRYj9DiZiGBLbaLzEWi5gD+Mic8RmPPQ3yfoUuLxeAGt+U8DZgmLlMFy/sfBPLffKo+vnD8LapsnsJRvNvQJ6vvju+CeAsYcfQGUWea5KfelZWWmFGN8/NTxMC4IW1jqVQDAnFLVNrhAC2CwGUKtmCaZprnWyszJK16YPRZZtMvCPJXTacohz/O8tCNUkFcZzSrrK8rMMI4xAlLK4q0ZlNxaKLQSUa11niZV4WAhsJmJlNMJpcg8TgDKLLUiBISAUiAFlaVWOZ1MCphBASmAyMgKGJGQE7o+xBj32+10mnLKXQynhyIzKBmWpJcqVMDEKeWcOMXxNGMcZwCRqessRoQQtkN/PDwwEIlB4l25AomoDV0KkcVqZOdeIQaUgkDMXZZtnUtNEcQgg5qWOZ4OWgXzLKokosdxPk3TOI4pApygI2KMIYTAiWFQbjXYAlbjCq0mwUIgZURlqJIahEyVpLrro01oKXACM4E3A0RQdHFVuUVX/gBVKxpZrvqOb2YppWEYvLTqfKkuv/L5PM85ne8bbslE7Pb+4POEDCGEvs90xSl1ImCDu0phne3vKcKJowVaVU7TTGAwqWqtWqSlrrvYtUjL9wA2EzVDSm3Ouzlv10xggyoBLFIBUy0icUWhS6mrQXI3N6W01muYiVRVUtdMcZUQMzJj5yiZgVrSYNlMFN4eRlTXNNO6n6hq6jrfcJbGYgghINB4Et+LnHu1nsrbPCym19afS5El4qyq6vY4hND3/RlezaotESAia7st3yWIfGyXGNzML8knwzAMXdellADUqrVWZokxxtzMX5nVEbF5nmutFxcXZiZVHe2ilZ+r5PvV+a0REdDhzz2+HwsawFmMtAZbfwqo/aOAJ/BuovQs5jJ1g9GcmhV1JJzF/O9wIO3x1bPDEaeVyuSTSVVF4uLTrUT5R6VlPCYwAIAIaYmY3ytgqLXlLN/zA2q18wvzReVaRWiBL5/fF9A89xAWshmBCLW+qyxzBrWvTonf8ur+v9dyuH0FNQ/xHDhSRa1yfublhz8WExtgrgiGWpsb7ucXEY4WovmSaUvVP4DWEMYIPtFrVRjlnAE2o8ZzefzXsodNBenRlWmug98vc1yj5xAQYxDJy42zGZUZFNs9AqSwtrwFpZRaZ4N4cKNWVSseU2xGAJEFkC7UP2EEghEHhyt9YEzMXOXjsS58HqfjcVS1GHk8qRmGAcwItc3GWgyKEBDXEXZAmYwJKXufmTrPoz+RUkop2O7o8nJ/Oh1rqAqoYJ5LIDFrQ9FleKwTY+66yb8LxilRSikEU4Vh0YtgJEs+a8gkAGCEjBSGabSUwqYfAh9jElcNFQVZr/ZQFbVymW3WOs+1Frx+/d12e0FEMTKgjOhdwYALp+SpKmlVVRNVrtGYoGzmsp4MUxMo1VqJjMgCIwQmJmJe0U6TZneJICJVZRzn1QCU5fAR2G63Fxc6DMPSx5NDoFobsVHkkVehihA8XGvPIoTYdUMIaZwNzUd36iWNUwmHQ4wxgJgRY4yB3bsiAgVIRW3Bn85Sm5paKSl1MSdmNhJVEalmJqFNGGaOvMgLAU2C5L3DVcG1/WvXamSKKpjnUmsloq7rPAWgipAWBmigyslCZY4eWJai81xEVDWmFHwd2VlN/7LrekqbnLBIrdFkM66hAXu0ckJFzIlBIYQYQwjRI34PExtvy8O25VaJINK7vTSvhq8udEWuae9bAREAVkWtak0CoYkiLD2BaJrKGvLWKrVWnw9EgTm6O8Icmw6JUd/7esQ0cSlSigCcUpdSt3oVqtZE8VQDxZTcQLAHO+v+9mcf39cAvxsLPs6nP/H2c4D5n3tyj1b17HgXkqU/ZidWpRIA8KgICAsyI8vzpvMTruIAPiOxYP1rxY4Z6bKhrNfgYRCdHetlTNNMZ9VsHhvQ4on7olWlWtkvyUzehaba0Zptnd3t4i48qnG9Z5LdiKxhcfudz4ub2w2K6Ol0Ws95buD7Pi8/Lyc/qwTwS/YSCRWISGaiJqgAWy2wKS3tbAKBOQKVGUxhGPr3Ig83wH3f/cEDbTeyAvtoHX7cQQERckaMUSSKNCLMPJvLvTZPmSmEFEJqG+P63OEuc1lN/Xrpnph8HG3mlhUGEWM6TqXMpRS1WquRQ64KIpomCcwpdcCJCMPQARz4FEOEAdAYEQJIWyc7dwo5wDN/amqKw/2xVqRU59MsgouLi6dPn3z73Xw4VhHMM5hKoNM4yjyHWpG7pOqdFdQM04zDQYfumHNzzkqBAcEzOApljYEdgheFGkKkEPLpOKfIKSW1EKg1RTYFU/KSObNQixVtQcDbtwcz6rqu75KGrGzknDc1MzcaZmYul01qCOvyeUeOSkQAYwaMmLOZeW3N8Yhx1nme1SoAMylSa50hyeOeUoqDxvM8i8g8z/NUmaJp60zsna9KKbCOCLUoMySyMaSiloY9AogxxhgCh5DDBx9002SlFDOxFk5pmaWUEqlhuRpjSslXdFwi0VKk1lpUTKEwgDmGnENgUAhuZlRlnKe2YQbK0VVKiImmqQiBmUOkcNY1QailHn2bijGmhBBwOjUvJMYYAqXkyxPTBAeWPKFOFBxa6zp4NqrWCmMmxxpQlyY3S5jRdp6cY60N7w0cmQFjFT+n84RtxcZFLAQOgbvOSy65Vna0zCFc50A9krAImyGWEqtL4giY1MyYIoxh7FhX43wZm1IIMXAkIheZCS7MBOq7jS9Vn0vzPDPNtVapBuPAiNGTZRkAM06nlRMgALqu67qu7+n+fl430DXUNjOZkVPuMphRawgc9F3Y4884vpcBrjqt6RYiUgMxRQqtrlQfs7Ntx1ylAA141DqnVVqYqPULFkEpdhCNkVMCRTIJRTCXKiKTHFfoMsTH/rjGFBiiOJ0gIn0fcoYZYp/NULWtNBFnJ8a6VIKGEHLOzqiMMchsYfEiRYw5xIgYwQFqVCuV0p5ZjDFGIm4tyWjhH3r+YzrM1jDz2HVdTCESyBCAeUKtGiP3PVKmxHmeMQMhgAKUMOujc6OU3MktpXimxAe8lHJxkeYZ41iGgc0wz+j7LIBHj05AOI1UKxMFmWcHqPs+dx3MMM16PB49gPAFnHOOMbpzN0+nFeHB4ta4KxMCYozTdFDVrutSCrWaCsEYSqZrpjkQhy5hKlDVlLkfAnE0s5QSMWAopUqpbNzFLAYtmGUKwYUbGmJRK0REYNBg8siMEAUxEJzeomYGhm8dqiqhQ4uZAgc2grJyqmO9V9wb3ZndmxxNalBWy6HOpGpq3FwaBrExUZ/rWKapzOVY61hlEp1rHcEnsTEkbLqOTA+H0/hQzHB1sXtx3dep3t4eQgUbepq32939QyAig5eNIwWkDAaGPjAZqZJiCGnbDap1PJ6OxUSRsxjAEbG3vAn9ZlNejTmFoZfDAWQqEk7jTAEXqZinXRGC0TQbHVFzyLt4qqMN2OzYoPMEVWy3iBsVKTDjAkzoMobBarnFHrBTn7EVTCNKAQvsFDabh2kCDHQ62k0MUrclUlQztbcPp3mK0z1d7DXH1OXMudM5WYg0EZuiQEfUWbEhbCkGKyAEAygqcU1swR6MmC3DOhE2MCyAKDDGSb/99s3t7dsU6frJ5eVuOwwbUJ9SqrXe39/7Lnl/f//69euf//znL168+PTTTz/99NMnT56IyDRNvsCPx+Nms+n7TkRrRdchRoxjc1LdkMcY+77PmVMChOpUayld1+02m3Ec7+7u5nmOMQ7D0FOvwFRLSqnrUEbfJWIdeZwkhG676boOToAngVZwRbZIbIqgdJ6BIhOdZlkh9BgjEfmv7peLTssKTefJrJwphC6lDoAq5rk594iYFoJxUSmlRIsIORjyAKVuupvN5qFLMWOeERLNM0Tax6vAbf1UMFctVYnMqKYUETh2Oee1isGLRFrSt+uSGUpBjM3irgg5ADY2BAMaPZ4AR30ilUJVlFhySsPQdzmNo81zEQke55hZSnlBNZoUI1pIQzFGADG6Lx5KyQ8PcjgczOx0Os3znHN2INp9Ne/v7r4LEQ3D0CXqM+JlfnjQ+/v7WmvXdZvNpuuZGW9ez+M4p5RzRikoRZ0u8OHT/Z9tQ79nBMwtUdeabJiqGmlkNsBT9OtxHtC0dKAHPfrOGxz0aOkcXir2qfUGjs3fNM8KADB97KLlTp8ZvFJCBHd3IiIpJT+Jt25eMxabfnDvAQ21aFMkhODZBM9VhhDMkhk5ucAWQrI7WQD8ktD6Bgbm5i1uNps1KTVNk0gLcN3XJiKzqPoYbY/zjGVBujfgHu7QO1M/2bvsX+c7lNKkyUvReZ6Z+xjJzI1We9FzG/v93vepWhv7KUbebDY3NzeeN80555wBOJqnZ9A0L2AQgGHYLqhDS4q0WFzMbTmAECmlEGPkQF9/e0tEXZeYh5iQUhapIgJd8aLqu6Q/i/AnlLDWSH7NWwOAIgaAIMSqEKm+okREEf39IQT3uyOH85JuWwb07PHBJfo8GUJEBozjqGviuiUvxGMvaimDIEXMJEbEGHKO3h2hVjAjZaQUVIXJRIuTBjggBqQUsqcbTMyZmdzaGqoz2gJyjoCBhJk92jNDCCElYtQi8zRiLqgFtGnOSmDJMRUuXtSoVbpt6joKwThg6LRti1X7oR+G4TScRA7Ocev7mDMeHso8IQb0AwNaK9Qk57zdFjMEIiedhUghBDErauNYcHNbpG72u4uUc+pEJLSc0brGiYiWWnGH73R9HB5NPS7GOomJGgfuRKTve+AyBtpsNl3XxcTbXZcSVPN+/7TWpwDmWQ6HQ611GAZmPp1O7l+eTie3vimlJ0+eAJchBBGeJkxTnecGZfvT9O3Cc0+lVCLKOTPzOI7jOK5TJTbve5UQJ7dbq76HmYn4Tu0pEhcWhTMbfJmc4VW2+sH+v69Ef+J+wqvrHRaehw+qKtWKWpvwiBlKkWlS35ZDcB6WERGMmIKTJmtpdOih3/iSrgW1wALmWWptcj2+Ca+X53ftm1gInBKfZ7LWN4Tg5WRSqyP/rUo7peg22E8blkwbEWLENDUb6WHAfr+/uGh+/DwntNRAKkU8xZBzJmKvS1wyYiJSiUg1elweArbbkPPFV199466Vhx8ewAC4uGz7MxF1XeeJM4fT+p6JLvz2ReRwqGb2+vWbUsrd3cY39mmapml6eHj4Nz/8yf/ARv6Pj+9lgJvgiFGTGDOCBQCyFGbhXcrSGQkCOIM4Hn3ARR5BVUktDD2BTCGKKtXMQgju9fj4npN9zrlCteJ0mk6nU9d1l5fDOKoHZMyUUso5+QZUJjDHnMPSFBYhICWMo/f4bFkA15IiaoCDnB2+HTNHa4irAhwC9T3FGE4PAAVi0aV5ghqkaqmaUoopEaFUXbAOtiZxpURkSrAAgwYIY5owjcWXkzvOZqZa5qmqakqpFkxjKUVywmmuPsPce+hyD+N5enj96q2TU7rMBKg4/qZSLcaYYtflvuucjBNiyLIMgXuXKxfRnaFpKrVqSsnXmGpks1KmaRp9FfVDBhAslCI5R4CdPZEDaUhzEVFPuhNQ3HD6XAiRXWHDoRHPqLlgsxm1vhsA2oZu87xErAGuvxNjUtVx9hQD6LHIpIkxmYmaiBTVuu4vHt+rj7+T/lQ9bWy1LgDAO41AnO3FjDqbmYWIruscTy11rBUp4eIi59yXUnJO4ziZISXkwBwQIw9Dr7XUUo3AjJiYiETKXKGKEH1rMwWmabq7u7u7uydCjNEyQqjTiFpQJkgFCCmhzFBB7kPuSq1QrSLadf2wCYYxB95sN2by8HCK0fvrUtd1u/3JFJvNhigwRZG3UjVlSrELYT4eRQUx0HYTACM2KROzxQgOgdmgVYFSKo6nGGPdbEQEJiIS2/A2brk/u2W9PB5EFlOAkYGIWcEiJFWqyFhn5tj3fdelGGjY5JRjYl7JaD4hU8J2G/r+4j/9p/+0Rr0XFxcAxnEE8NVXX7kHn3OOsRUjrMditAIRuSXwFHLOMUaI4HSa3UOd57nruu12k5IzrdoZROCpAXd5QwhmNk2cUlKFCK82OGdizkvm5VFAxofFl62vOF/Ia267kS3OslR+tQ5ceUhgZl3XOYNpnavuQHsGl5aCi5zDso0DwPFY/Mb9zWsO1Y2TX4lfgzsfywJsH3dzGwLmWderXamUHs3LGVl6NQrHY3U5WCwZvYeHh1r7FFOtmOeyQAIUYzCznPNCxFP3rR1GnufZccFp4vOA4cmTJ/M8H49HF8ZxPDKltNnmcRynaWLmruuW2KNh48NAImma9Hg8juPoo/H27dtvv/3WR9vj6Ri/Xz+F7/PhMkNEgSmlFBY5Nw44HR/JUOtKwyJ/2P7wmGskqY/0dLdrIYTYpZzJaVD+LM1MxaRy18dzu+sbtKqOk07T5O5Vznm32zHzNNnxeFyhVHqk1zaD7bPEzEppzmbf9z4hrDELRERKCTnnWuvqCK8usD9CX2+1VhEaRwKQUjQm5kjLedzhqrUKrJoCcH8QADOX2pLNa3K3NmEgOFTuI7ZObixiHTHGaZpub2/NzHec9WKYWVVvbm6+++67w+FwfX390UcfPX/+fBiGUopPypQSMy/Ba1pYkbY6+CGElKJDSbXW47Fl2vxP67NrhRO2iJKqc8H4+upJiA6OTarYbAIDTMH1Epy0EWM+u3FqRYTmU6IZYBH1cpWV8ASAyDy+6bpuGLrovKLABgZDJJTiVA6PD8o8To2tU+qSXFcn/qA1vNMmx6FiUqQWZlCMRqqsBDWJMUbVlMOSc5JWx0JmTHYaD6Qu0YWuw24/5NRNI5ilyiQFKVGKpCIcYkpBWaWaATGx85hEqxpqBQfniLoBOJVSjkffxIOwMXNKph3Gk3kx5W6XVMrDLWqch6Gb56mWukZd81hqmxuoFZeX/TzPDw8PMcbNZuO7yXiaUqbtrgMmIs4p5G7L4eHwoFKOyaMWlFotRRCnQBJS7ENGAAW2wL4i7u/u9vun76l9n4dTBrE2VwRKRgSpSsycmDmERMRiUMPqcbo59CknokVYFhdNRIC2Fz9/flkKHh4ORNT3KQS4z53zFx48hUC1mmrDsWgJCzw8Wpd8rXWzSZ6sATAMeRiyGaYp55z6vvEPPBulGtZ7xMI4cns5DF0pcjqd3GPLOYSAnBvB0+2iWQupa63uInRdt8R2m7beqS35Uh4ZDABOp1MIEy8tEGKMMa5sRFq6jDtrqSHD3rNywcNabOMUeiKfHrKAd5RzvwboLTpaqrZaucFSL7TqBwKmKrWqd8P2C1vfFkJwWHAdZz9VjIlISynTVERM6gkLGhdCGIbB65QA3N3dHY/Hrut2u51I9qFwn2PdadfrdLcppbSSaTwIvrgIOXfTNLuBx6Il0ih19Egxc/LXmg/2QMK/fZ08f97xvQzwNKqIsreaClA1D1nmeaal8ba/03dVBDrbNx3GIWaUWddUh7PZY+hSRys7saXxGo+/Zo1e0+3Kw/MsDrGKhH/87z//27/921rrX/7lX/77f//vY4zfffedquach2HIOTNTqTLKrKoEeJua7XbbdRRCErFSpru7B8cr3An1uR5CYD45nDWOoyPMXQcA/vDQQqbGvqu1Pn/xYr39UsrpdDocDtM0YQm2fFtcxsfm+RED993Q/zoMg7tg/us4jqfTaYVHfFK+evXqzZs3OefLy0v3iH3OdV0H4O7u4eHh+OWXv729vR/H+f7+0Pe9mXmVxfX19WbjNAoXSgwe9uXYoHUClBvttpSSczZVmIev0SEKFXBECDHnTlVijCll363dR6xF5rmqwhesb52lWClFijBzDElVy1wEFGMkcEjkWIVn2sWkxaYL1LzMMd85c1iqTqqgVnCEGbw60E2OuzvjOPqNn/PvDEwGZlbzOJjWQDulRAFaVFBVmCnGGM1SF7oqpdZqQIxx02dVJVgpNTFiQj94OgoxqmbMswQGJeRIHIyAHNBlnucmdBwChUAm4p5q7BAzjEjMVuZLY28RiVRi2263fcdlvhtHMGO327Gd7t6O46i7Xcodn46nUjHPYx5pnCCKUorXrmgVKTUwpc7Jse42lTLb0KdAOJ0mojL0PSGbjlLBJBwXVgcBWgihSznkxDm1yuvApDSN5eLCQ15iZiVvXuByKqJaozrHjc2CmZFJqRMhWGCiyCRMgVRNdLPZnk7TPM8hUGBwMBFSrVIp5xyj5xfC6pTf3Y3rsp1niTGoWgh0cdGNo0c5cjqdPBTzpO+KwzkFyzHn0+nkS/t0qiLS953zh5eo1Pe6upjw0DKvIJHsEZg7lMwL9t4QPgYe9bn8YAZAqzu7BojMSIliTGbggFp1dcSxQPoe9XrkunA47HQ61Zrc8mGJ7wG48VhQt6Bq6y34x0MIvlmtBky1Cad7fGwLC9W3l77v3YMH4CSVczdLVadpcni573uP7N2t931snuerqyvfxGSpF+y6bhiGw8Po3+Xhx+Fw8Lsjol//+te3t7dPnz598eKFewB937cCuIVLu16DO+jrZugHEbmt7brsyKLrS/vAloI1wbTZbGKM8zyfjvPTp0+dIuAWwY36v9xyPh7f0wCLmYXAMbAppkncMHS594W3jkIbi7YVGphCCAYmJhBK1VLq6iKZmaEQ5wFQgQoM5EwoIgbiGUUZpYhvprV60KD394fXr18Pw/bJk2e73e7t29s3b97s9/unT59eXl76kPkT9WAohFBmmcZBRI7Ho888x6y8gMwvnllub289UvS8cokidSylxBgPh8PDw8M8zyml3W633++7rrt7OKyDUGs9ncbjOLnx9pmRc+77PsYIgqnWKqWUeaFK0UJ92mw24zgudKfki0FVv/nmGzPzQP/+/iGEeHV1fXl5+fr1a+aQc7fb7YZhANB1/eXl1cXFpS+AcZzGcVon6Js3b0WUOQDEvK4R9hwwM6MpEzVznLO34YlEFGNYtDVUjQnsSM4jymQYT1CrIkKITMGfaSkK4loxT6rVUmJlrlXL7ElYJkIkxOgd2MHEVXkhYCsRiBpmlSLFEJnYUx6imGfUKja3Og+F13xIqR67+1ZSyBRQJhNqhYrEi1ilAcxLV4e2LPVMcCDGnBPbqFWLmcXEKQwixUS7jhlg0sAgQOQ0aVHVuYwhoiPEBIJSoH5I/ZBUZiIQIwQitjoX3xo4egJYPR/sFNjk4hJVtMwBtB26wHk6nh7uSg7IkXm3y3k8HgHSi+2ulJMq5rFMrXcZpmnqujR04euvTynhaU993wMKaAgxhN3t7W1KKUQ3dZMlcurcPFUzMCElx8bBBGakGKhlshFSjF3PMdhjaulxFMOyJZDBTGECC1Bxfn5kMgIFcx0o06aAkXI4nbSUSYRTZCIvZJVa4A5oSilGB0jF8aQloJEltUTOB3aPXyRME9dFMWslhbh/z0tx4OFwKKW4n+qzOoTgyLYsssarlSJqik6l1OPx6BC027N5Vl/s/nVm5t/sJKY/evi2LvJYzm9mx7sHXbqV+F27vVmxOg+dAczzfDqdprHYolyRc06pRQL+a+eJKOIYssosVfpdzjkQoVZaLTeAw+HgMaVH8H7XnvlaYcUVmfOR8U1ktZfW6rjalRORm2o/w5ova/J04+i24PmzD5c6IiIihzY9MlmZoQsrwuZ53m43Kyq5Ph3Ppp+DHKvsz/397APo7ou1FGecZ3Vvg5k98dH3Oee832EcW8GLj8l6GX/28f1Y0IVqFbNxngSAG4ZaqwxYa65tqYlW1eN8Wsk+XdflDKmRI1eB0/9SanCr6jHGeH2x94DSl4pHsV2PaUKtUsrkdnQNZUrFixcv/vN//s+n08mfhIPDDw8P/uw9evMnLSKbfnAo9dtvvz2dTre3t3d3d9M0ffLJJ16Zc3Fx4ebNL9tdsDWbsgDX5e3bt2/fvn316tU0Tbvd7sWLF257jvNICy7js8G9J6durtD3uvjdrPpa8onlKYrdbnc8Ht3WbrdbP6HzMH16hRD2+/1ut/voo4/6vn/69KlPyhijO7PuMfzVX/3VWm9+PnR++HellPb7vfuqdS6+rnSpjkdL0pgtiRxbCoJVtc51XU5mUqsAgcidpEkWNlwpUyMxyYKAidaqLmVVq5KBoEwSENeKCHW2nZctwXUKsHqsgJWinh1bn+9U6ooazYBqHaexZZu07VnEbnpMVUWLNffexYaqWlXVwG56y0qp89vMiVWk1qJaGa7axEJ2dbE9Ho9zRYxgoNbZbGYmKPqMnCPURJAjpUzMIBZQY2YBJlIBxAiw015czwhSveQLImDyK0EKnHPuu5RiSRnjeMxpt9ni5gbT6fTs6cV2G0vLrqHvAcJ0EjLPs5xqxfE4DsMxpZBSSjlEw1a6aZqqlBANQKnjZrO5vLrWudzf39cqfR9icgNmKcUFnzACYsx934eYV7hvXQLg9h9UDGKQVpIE8T4eHMjUyJRJAsEYzMTB6vwoTmStsziYY99HWfQWziLgBpZ6qsh9YgBuD9wkl1IeHh58Qxd5VFqws3JH39P9bfv9/vr6ek1j+TrFkuixJsuQPKo+Ho+3t7fjOOac9/u9A2/njC1dlLCIsn+cFnXVVWjC1925F25mb9++xRnzcf2UXw8R+Q4ZQvBlDmt8KDeWawbN48tSmkxVSsmR6mmaag2q6oFpW9S1+k5ybk3Px8rDYh9Jt1t+s+4KeMLVk6yOrvvlmRkz73a7lJJ/nW+5DjF6IJdif39/X0oZhiGltD6XGOOLFy92u51jhM6HOhwOtRb/Ot8S/bnTAko72HkOOooW/9WtwAoDeETn7/faJL/xGLr7+3vfPx8eHgBst9uUEn6w+bNt6PcywCI2jrNjqm5LABDx8TjC+GxqNkT+mzdf55w3m81ut9tsNlUlpcSFzWx2RFFZVR9OR8+XlPHkE3EcR+ewXF1dXVxcjOM4zSenOE7TpNq8kmfPPzazruuJWFVFdLvdXl8/6fthxSX8IRJxjBxjIuIQ4jiOZicz5Nzl3H399TfH4xHA6TSO47RiuTF6loIAxJhydt6/MYeU8n5/wczb7dYZmLXKSR6N1iMqAuqGTajVH/N0GmWpSX3zzeuUUt/3fd8zM+x0lHE8zZcXiRBEqlRTgSMtKaV/93/4K7/OcRxh3Hcbpng6Tn3fl1nUaTDVVDD02ydPnsxTIaK+2wz91i3uw8PDOI6b/SbnPE/l4eFBRPb7h/1+n7zb8yrisXivIQTHatzU0iMLtAUKy4xvcso+3afJCzwm3/7cmz5Nc0sLGTyn4jtaCtGXdylS66NnvdntznL/RggEYqKYWhbQ41rfNVRVYQ6OaeNFl2k8zeNxyYgAUDaIiad7q8xmZlrNRKX6Z6rMgeM6DqrqaCMzx0g5Z5Fqq4SWGWD90I2ngygigxjmJbZmgTH03HVpnkcTEKkTVwENBGU4f14AeJk4OyPXqswuH68VAEpB5HkRvVUi42DOsx3HMaVh0weYTLMxY7PppT6kjJzDZoilTuMIZut7vHjBNzf68IAYb3e7jeebp3m8vLr89ttvS7WcwQHTCA71+smWKgzT6XDquxhjnEnVLOfoel7gwDG6b21gEamqyUxhkQhkxK07giqsqYOqt142KANlOpgGCgoEUAYCTEl1HEff4ltxjrcUjPHwcPLck+8qOWefpb6g7u/vPU3oqUGHqdzjPB6Pr1698iITIvIFu3rDq+twc3Pz+9//vtb6xRdf7Pf70+nEzJvNxs0DVplokRWyEpG7u7vXr18fj8e+731p1Fp94dhCy/BRur29XfcHWgBqP3hBos52Ld0Mu2Umy8PpeDweff3aksd14stut/M4wQMfHyJ3GZ2LdH197Tb7eDwS0eXl5WazMbN59uZdxdlMbim9fGO73Q59akmiMru9Px5Pfu9ucc3MicFu/3LOfV/dbAMcOOWh9+V/PIx3d3ci0vf9ZrOpRW/e3t3e3hKR75+bJztmvrm5ef36tYg8ffp0v9+vTDRns+92u5W65aVov/nNr3PO2+324uJiv99vt1sfh91u5xvO+sjWLUhqE+c5nU4ePa7RsC7QvTsTIYQ3b956URMWWp/Pn//4V5/82Tb0+7GgBYGT1FFENpuNmnpg7t4KlpjV8f1SSiXHqHWeKzBOpZZS7u/vN/vd9fW1A63TXFLuRXF/f39bbz0f6cGZA7CrH+cQgc8qVc05rjGlO2LuuQDYbrc+j908vHnz5v7+PqX0UMXf03Xd1dXVkydP3EE7HA6/+c1vXr16dXNz8/Llyxjj8+fPr66uLi8vv/76axG5vr4upTgd7smTJ+5S1FoPh4Mna91mX11d3d3duS/sntrqgMsZG3D1NN0h9SnrO4JnIJ4+ffrs2bM12lt/KKXs9/t5nj1w9yJFItrv9wDW1IhXST08PGw2G1/n0zQdj0cHyvzZicjDw8Pt7a0T0O7v79d0iju2quolcbTQI/1O3WPwrSflfHNz4+/sug6k7pz1fX9/f39/f++B9f39/e3tba21HzZd151Op7vb2+12+8UXX2y32+PxyCD3XhMH10/wwpJpcgPfdH9CCCGwCFKKtej9w93d3d2ahRIRZmx223XCmFRipJTGkzn/GYCZiBaDxMjmtb/GpUxlLiIzNeyOSmv5TF3XsRlMPFAgQheT5TyOFaZd3236vEQPSB36vpumMh1VBMOA7ZBCoDobMWIAB9Nap2la5CopZ9cSqTC46xkCdTkRWwCAWgp2Gz4cFIbdpiPTWk4MI+DuDruNRaZh0+92h1oxzeP15fbrbx82ERf77Wab7u7qw4OEgBhz33XzdBMCTkcQjl3maSyXl/u7m9fPnlxeXdRXr25qxZPr1HX522++enb5NIWAoev6FGME1XGeTqdDzCGlnkJwkSemGHOimMoiYDdLIRCzxNBq5SPYTEhEqarz5cw2fVfVSrVS5rk8VJ1OE+YZc0VVVcHxeARpIOq6rh/yNBYi8tWXc/DitBBirdUJa77tuO1xK/j27VvP7HZd5zUku91umqZhGFT15ubm7du3RPT06dMnT574G37+85//9re//au/+qsf/ehHXdcdDgePhxyZc7LF8Xh88+YNgGEY9vv9j3/8Yw+yx3H87rvv+r53Y7CaKyf6+sL0JfP8+XMAb968qbX6fuVYnSy1jv6lbk3XuNmPUorbqtPp1Pf91dWVR5ZXV0/XQJw5zHO5vz+8efPm5ubus88+I6Ivv/zdNE2ff/758+fP53me55PPcF/RDnptNpv1Yo7Ho5OhAIzjOAyDD7Vva7e3t9999900Tc+ePSulbLfb6+trtxR+har69u1bx958h/n4449973V7vLrLIYTLy8uUjtM0/eIXv/jlL3/Z9717D5vN5pNPPvnoo4+22+3Dw4OqXlxcXFxcxBi3281XX331N3/zNymln/70p8+fP885X11dHQ4HxyS22+3V1VUIwc3t4XDwENZjISzxt8cSPhSelwwhbLfb16/f3N7e/uxnP+v7/sc//rEP2n/9r//1//X//L/82Tb0+1GoYwTgNtLMOKAfMhF57R0AqkazzYVqnUsp83TKKXR972D98Xh8eLgXqcfjQ63zdrvd7ne7/abWSmy5i/PdPRYZI7eU7um4cXK/1ZZqXSJLybW8yzieTqfTPE8ida1t9eTufr8PIeSczGypR6Kuy/4KACIMQ//FF59//vkPbm9vf/GLX7x9+3aaRiL0fffpp5+4Gz7Pc63lcHh4eLi/urryK+y6rCq3t7dv3kzM/IPNj0CWu5SSl3JaTKHrunE6+Xdx4JRzT035bEjZ0SG3l6VOw6a7ur4I0XPGZs0j8y0llDKXSmrCASkHEREttdbcRQeCOFz1oS91Oh6PtdZ0l1aH2vGW7W7wwiQAoN3F5c5J1Dc3Nw+Hu4vdHgCRqdZpmk6nw81N22WczuAp8xCCJ5tHVzWJlLtoEKni4JJLBXkC3j0zR7ljyqfT6eXLl+5l+xYGwH9V1YBG8HZ7/+T5C2Y1s8Ph4Ji8c3BubhqGv2ak2oRkmDjSjRSiMYl6OqNpxDCZd60JICKEGA1iYl6uFhkkTNBaTj4JU0pMFgDTqqrjdLTq6E6NkZmyb1v3t3eq2G2x2+6YWUopDCJ0oeUgu8xM6tJIalUNOaPrWprQzGJEjDxPjh84KC7mmXjBcdJxRA7gjjkQqXSJLveucASQ7nab/cXhcMB4vJP99WZDKnY8PvTDvutS10nfYej6fkgi1Uzu7k8iGKdTrUIkZlZljinuL7ppmmJkMyGyh4eHcTqa2cBdSkEk2jTNBff395e522+3QqQcqkrgvkvZyhK91QolpkiYoSGi01ATIrEFQghMgZHCUWcoc+shJDBmDjExhRSqVDZZsrXZjCnudh0A5+EuBamqyp7pWLkIaxbMt1H3a7fb7aeffrr6kb7iXr9+/c033/gjyDn77D0cDnd3d7XW3/72t9vtNsb4ySefrGfz6KLruufPn798+dKhVPenPaJiZvfRY4y+769onNvgUsrt7a3bJ49HXeujpfOaFlV2CzSOY4zx8vIyxvjw8ODJtaurq9evX3vw8/Dw8Hd/93dv3741s//1f/0/f/HFF1dXV7/61a+madrv91dXV37Xb9688RV0eXk5TdOvfvWrw+HwwQfP3H/dbrfuavuNOPbgyTgHYLuue/r0qfsiK57s4aDfvvPC3DR4pszjhF//+tellKurK0fyfV969erVMAxffPHFykFzIlhK6eLi4osvvnj9+vUvf/nLv//7v7+/v7+6uvJ44OOPP/b3r6QZXkjsX3/99fF4fPr06Q9+8INhGJyd4+7Umzdv+r73ufHVV1/5Q/QIZE3A3d7ergRYnwDuQAAUY/z4448vLy+fPn3qw/LRRx99Lxv6fT6822eRbJBxHLuuSbf0fX88HktplLlay+k0emg4lrrdblUVZiHGb7/99ubmZrPbDsPw+vXrlNJHn3zsIZ2ZdcPQ7RuhfMGIGpqvVldNEzNLKTj0EZf83Ha79cjPo8kQwt3d3ZdffvnmzZvnz5//4Ac/2O12IQStrWzfz7bmdTyD4tEtAOdeATgejy9evHjy5ImZeSrl9evXHii7y9b3vZO2XFPi9vbWLYEvPLdVm83G3W0sbGfHpgBcXV25dfdL2m63HsIeDgf32dd6IXeEV/fzeDz6FARcB3FyByXnPI6jr8b9fq9nTBMz6/vek9w+zxzl87Sx21Ra6lx98dcFNncv2ANiTxY4cHRzd5u76O/38xyPx/v7eyIMw7DZbPwiLy4u/OOiFmMcFyGiEIIDOyYNEoCo58vX+3L76o6q32aMgRf2puduVxBJ5DF17VOlTvM4HbDkrKEVJp4PAhmxo8hKbDEyOIBMZTqdTqzKRF3XBQapSuVaZ1IttVaZiajPHbFZraVMpZQYsem3wzDUeZZqzOi6mCCBQWROXkkhqtZSxCq6Do4VLcl77roOj61siojXEEAF44jphDAgMkhlnqta7Xqk6Nxy3e02Ty7T6aE8POjV9fjken//cDeODkgoDPOMw+HQ9ZebzUa1nsZTrS5lgOOxhgif3j4/zQSEnPPl9lK0PDyMt7d3fZ9Cirvddh/Cw3gkaqpOAKppMmMg5+y9+8wMZiqlUiIrHANLUJagAlaYGISVxWZCYMpqCqmqFDkEzojdPMk4FUdfzWwqc5xiXE++iqiY+XLzmMwjOUewAbgtdPxms9lcXAzzbF474OP84sWLV69enU6ni4uLFy9e+F785s2br7/++rvvvgOwSmutpbcOvcYY3bPnpRxxHEe/JIdAr64utlvUujudMI6je5BuM/w8fpGbzabv+7qIPKxmzO/LqSpuPHLO9/f34zimlF6+fPntt9+O4/jhhx9eX1+/ePHiH/7hH37/+9/f3d3c39/e3Lz5zW9+M47j5eXlJ5988sknn3z44Yc5x5SCr9A3b155yHRz83q3211fX3dd57mtldXl+6Tv3p77O51OwzD0fe+mK8bofoavTY9ofdVvt1sfh4uLCw9wPY349u3bUsrXX3/tCd2u63x/8MdRa/322+98a/3888/9VKUUZ7o4wuy4Aj3yv6oHBj//+c+/++67h4cHInKK9eocHA4Hz0nP8/z73/++lNJ13YsXL7wycxzHb775Zq2t8h1vmiZP/dYq/rAuLy8vLy/dK3qxlLr8ecf3FOJQM/XWnq6Rm3IYNuHFB/t53o8j/FHd35PorDZxSH0O03S6n0+570/H+1LHzXC9u9iM0+E0Pty8fSXS5lyM1KXsOyytapeqosVBkhCodQ2r9XQ63d/f3z8cYyu0jzH2KwGn7/P9vWtEnI7Hh/v72xCo7/vT8fjq1auHhwdPRbgD5Sc/jYdxOl5eXj5/8XR/sXUX9f7h1iBq1V3smLjrU+6ir8SUgwfuT59dX17tmTkM3ZoEEinRCZzQVTre/fSVxJ8pzPMEoOu6y8uLq6vL5lhEL8GSUhpTw1HZrutKmed5VpW+9zEJIvL69WtVKmUex9PhcLi/vxuGoevy9fW1O6duvVyyNQQex9FMQ2DATqfj4XAgoidPnmgV34VLKYDVWuZ5Oh6Pw/Cs1sJMKcVhuPYgYJ6nlKLH077xqcp2uyHyupfgdtTxohBomkqp4v779dUVFt6ZiGz2Q9tG57IyX5olgKaUd7udhyzz3NI2qkr8WNEYF3Eu5hhCWhO4IiJlZohI9dfcFgNCCiVVfaQOiWqd5zKfpmnKjJRizpHJvJ3TSn2IFCNTjAzSqsqgYRg8qy2lnI5TKcaMLnVWDyKFXT0zsCOK09ELE0OMUaQpwjMzwGCDucOEWlvjKRhSwKk1JxCjMk/jeNQ6IyziupHRDznlUgrG0+Hq6nLYELHVMjkwIILb24fLq81m04NUdBrHY8qxlDJNIMbxOE/T7HWuZhh67rd5v99P03Q8jscT5lq2W91fXu4u9nnsLcTjeBKibtjkwFVlmut+e4kYOJCFQJHBgRiufOLNQUUEVLQYTBGqhp5TZjI2rRBCICYOERRmLJWvxqrFzZWTBOOSeHasa8WH25pyz0yVmQ+Hw3a7yRmnkytFgJncqIQQLi52H3xw+dFHH93d3QHYbDbPnz9LKW02m9///ven02m73br1cnPlLvIabQ/D4KeSpUS+LpoV8zxPU8k5xYjdDsPQr0xgAG5LdGnDtxKInG65GnVmHobNmtMJIQzDxveQaZq6rr+/v3cr+NlnP/j88y9evnx5PB6/+uqrv/3bv/3973//7Nmzjz76aLPZfP7556fT6erq6unTp2/evLm5uXn16tU333xze3sbQnj69Olnn332wQcfeFbY3R0noHkhvgPRPrwffvhh3w9eNEzEOedh2KiqF015inCeSwhTzjnG1HX9D3/4I38c9/f3u93eYxWApmme5xJj3G63XedGfa61ek7NSaY/+clP1hjP2azu93uiQUS+/PI3zHx9ff3ZZ58xs2cWROTy8nIYBk94rwl+Edlud+5P/P73X03T/Pz58+Px+Pvf//6nP/1p3w9ea7Tb7Z89e+6lp/5Zf0zH49GNhQcwf/bx/foBR07Mal3KvPiD7I1f+gExYdgM+4vh6nr77HQ5TRMkhhBubm6qyna73V9sp2n68KOPcs6Xl/u7w0MIod8MK49cZmdXeRsjBVFMnKi/urpwuVFHYh0iM5PU9T533X9Z+RRqNXfx408+fPHBM4dbHw53VebIoYkwBHI+fIwx53h7+1a17vf7zaYHEMKw3Q4ffPD87u4u56xaiWLXeVHBhSsDuI6B47pXVxctdV0bk9DMRParGz6O48QgEyLqUhiGJbSdtVavh+6228Hzow8PdHV14R7G4XBQVTdyu93GxTTcTXZ3DECt9eJi5+q4V1cXH330ga9zn7Wqug4XAJEyz+M4HkMIKYVhGIiolGkcx3E8phgNAqIQqR9yiBQT90PebHszK3USLYZsEDfqITbkhsiYabsdhqHzFesyWB52dx2JoJTiXWmJqMsNvPVx2PSDX3CdZhHx4unj8fh0t0VDaONut3PXwR0OQytfRiuddPGdzrk5DqGLlBAopVTLDACt+6kRG5SoCZmB2GBkgEid53EaRyKKMfj2DhNpYEDtQoiJiTKZug4nAOaYktbZy5vLNM1mYLAqAsFUxGiJk9SqzTOGAc4SmMcii15prXUcCxFJkVKgipjQJTCTRBtHFwowMjFTNWjF6YSLCwSieZ6ZsdmgCk4nPBzu9hd936HUSdW22yAi4wmRedP3IVAXwzSfap1O42E81gBYbU0PpUIVM+YUwqs3b6ZSUs6gomrjLDg8FJNu2BqTV7BxjCBSp4epmgjF1g4BHNF6Y6haNZMAVYANYkYmajVooKVmGAvd4TQeT6d5HAuc2WDUHCBt1D+PC93zc1DXi/1yzsPQ5xydRl5rtygU8jyzNysbhv50atx4p3HxUvmac37+/Plms/nxj398d3fnBJy7uzuP0hybWaEmD6o8Rl+rzN14OJT96lUzJJeXw3YbQ7j+6quvzMxjOwBrOaUXODiRwgNQXzteRujMABG5uLjwRKbTPuysxOjTTz/94IMPDoeH3/3ud7/4xc9rLaXMIfB2u7m6ury7uxOp7pR/+uknqvLy5VdfffX7vh/evn375s2bf/2v//W/+Tf/Zr/f39zcfPPNNz//+c+/+OKLjz/+eBxHj1j8QXhY6abOx80jUSdUetp4ZRd7whiAh/trZfB2u3VPZcUCj8ejb2s+FCJye3vrb3aD+ubNG9+pfNNwEMLXvpltt9vPPvtsv997yL6WKauqD1cI4fnz58+fPy/llx50vXr16re//e3d3d1ut7u8vHz58qXDe04vePLkiQ+7G5RXr1797Gc/e/v27dOnTz/++OPr6+t//9Pnf7YN/V4G2JWKzXLK1HWd10Sl7Crerv+J3GG760V6VQQBEa6fXNZahyFP84uplt2uV+DyycU8z1U1xkjBd58x1jPysDftihxjdFUmAMPQ5RzNNhcXF7XW7X6nS7sel332j0xTcWjFp8vDw4NDGSnEi4sLVfWxxtJv4MsvvwRweXnpjSFjjNtt5/Ixp1MLPbvOCZBQxel02m4HZhwOk5ltNj0RxrHs0n7N4pznovoXHzg2oKruvPvMk2rrNcQYYwz7/c6tLC0Vh25rHarysNLxeZ+Fzk1z6HutYG4nl9ak06E593LcWXEnvWWFt1vnSszzHEPw9zuPI0ZPATTQzzkRCwMu55zH+RQTg7TKzBpDCCl5G2B1vsk0TaVMt7fewFWa6WMs/YfUe5aOI3LOOac+JzMzaJViUADONVsLTnLOxKZ26RQB9yp4oT3nkHNw9qnAWxt6Q/KleVVkUmvhZmAzYZBCg2qtS7PPEEKXhy5wTjGEptrWwJLgHza3x42TaVaK1CK1CqkBHKgpd26yay8YkwXynqxeHZT6vifjWk+lFKfx11rGk4bY2vD5ahqGPqV0Ohw3nRDBZcT7HCBiFbd3uL4AoPM8hhA2GxwPmCYcj/bkadhsurv7qdbWTXIccXNz43j4br/Z7vppOsXEh8NbD6NdujUEKwVlxm05HUIFrR2OQIpxnIvoqdTNxeXuYhv73sDGFBKnbiinYipKCDFySiG4GJNpFUtN/yQEssQxRsRwbO07jck4wHsQipaHh4fTJFU1p77rOkMspdRadputRz/ug64pEln0U52CNAw9EUTs4qIvBePYNq5pauKF59U1nsByO+ocw+vr6/1+f3Fx4TZ4Na5lOfyDawmAH7xIIfr5/f229K8FcDqdHh4e3H4TkXuZvhusm97KX/HFO47TyqNca6hqrS9fvnz16tU4jk+fPv3ggw98wW632xcvnv/0pz/94Q9/+F/+y3+5vb198uTJBx984DyYu7s7Vf3ss88cVL++vv4P/+E/fPrpZ2vhlssd39/f39zcuCn1m/XMl0PQ3377raeNHL3/+OOPr66uNpvNy5cvsdQ9e0Wop/w8+nKW8jRN//RP/1Rr9YDVtzKnSnna2NNbbmiY+dmzZ9fX174ZOoXNQ/yrq6sPPvjAvXCRmlJ69uyZX4wPzuFwcGzA0WbPnbtJ/su//Mubm5u7u7vNZvO73/3uZz/7mWfKnz596q7Vfr9//vz506dPN5tNCMGzBq9fv/5v/+2//e3f/u0wDB988MGHH374//i//vWfbUO/nxRlnatgnEZVZSZtUsY5ZQLBADVwkwkFM0wQAvYbLiUbY7sJA0JVMNBnDEMGoN6noeaUQo9HdRs9a8ft2oREFAKFENUsIYbQ85m4a61ExDlz32G3TUA6nrankwM4OYSw23V1xrNn167/7Ol/1/x78uTfHg5ezh9dsE0VDw/zfp+BVOujcLSrNjIPOYMI221nS7vcrkvbHYAkmlZBbFeL3W4wzfl02jpCFULrNR1Cci09b72Zsl9VuLur293gcafZO7WVIsFdBJ/BoqVKuLxqCRJforaU8bmjCoSuTyuuxYu61uFwcHn9i8sdsZnZph9WLLeJKKvWWne7TIScOwcPAPjbButX8oKD26q69mSkJkEnjkjHGKu6rCtHbuSLNZ3Tri0Ez655wEGBHSK2pUNlTNx1vVcEhkVnTReRneZ5zKWIQB61TXQ5vLskqbjwM7VKuia4ASDnnFMIbBFGprVWqRVAI89jhJpIMWvyZ6UUV4Mzg1WpYiow9bayQhFw2RxS5bUDRKMCmKCUcjqBaHY0xRtwYWl643pPIQSi0PdeJNr6anS9VdHyGmowM2fpnU7h4UG8H5Rp8wLhahUFJri7G83EDfDFxcU4xpj4cDiM47yoV8aUKCeM4ziOMPKCfoSArk+570JiBU7T6MFNCIFiUg4hpZyzI70yzxwCxUyhC2whMgS0tKEkWrpAt06TTd2MmSNzURWtjZ2Qc059CEENTvnpu84njNu2NYRaIQQn/jjuAjTlqUUTzdxUM3MpxQv/pLU4C/RuGa7DnjlnL0Zwrq8sGpa+rIZhuLy89Bnolju25jzRV6LbMHcLnNrjkZ/zqjxv7Y61+wHkzX2XPDcRhRD7PsWIlK58Pjj5se/729vbf/zHf/zqq6+Ox+Pnn3++3+9FxFC3u+EnP/03+4vt27dv+7738sKU0nY3xBj3F9vNtu+Hjz77wSd9n159d7c66+7B/OhHPyqleJDtN+scqPv7+4eHh3/6p3+6vb19+fJlrfXTTz89nU4fffTRs2fPfDtyJvY4ju7xm9kvf/nL77777vr62mNZZ6g4nu9x0ddff31/fz8Mw9XVlWO8XpSx8nju7u76vv/444+dNvTb3/725cuXjgt++eWX9/d32+32X/2rf/XZZ5/5bQLwmNVz0j68Ttxh5u12dzgc+r53m+2bxsXFRd/3TlJzG+zpfE9NelHykydPnFz9i1/8wll7f/bx/UhYu+xapqp1s+lCgAEpNY1Tawet60oKw8ARanY6Tl3XpY7KXGOMomDyeBq1ggnbXcCpKTAs26L30dTdLs4zm5lbXJl9c0kcXOwGBPdwm3DSXJAT+h7T1NJCWCK5poVZ4HUg/npKyDm6iZ1nrx5DjLEUpISUeFUfU8U8awg8z3DNRWdjetPN0/jYLoKWRpgAqrjZ5pRi1yEE1EqlpHWDYOauIye1jiMuL7umjbx8Ly9d6L1BU4xwsYHdri/Fuq6FqqUoM3tB7zzrdjuorosZq6DYPMtmMwzDwIy+h2qI8SoE1LmNzzICALjW7O3VVNH3/WbTu67kPFcKqzAkdZ23teBSaNh2tYoKfBKvtyDmSvpVawOpdrstEeaxLInntvf1fb/dxoejtAppx+tq6xxXa120xbCgu2ImAW0FQjSEwCGbVZVJxHd1IRXV2URgYmyBzZwr5Hwksy7nGEhlJqkyl1pmlUKLQxOqSKnelcyPUopK3W93JlanuRRzUxeCpUTrEyQCc/MkHCEgCgap1cYRIShBa9FhGDhASjUrvqZ8qzKznGPkJCIyVyV4+xezRcQ7hRgSBxIByGfR2A8cQug6DSHmgECl77GcLW+3W0AN0nXd6TSbuXK7ppS63DGz6rEqARwjttvh8vpq2PSieppPFllM7x8ehHm7T13XUQhVJcYkUuvS2KQ9HbhIlnkbZlPVUtUUGimuPTa85a2ZWZWaUkqcU8ockohIbYx0pyD57uwJObdzvnd7CdA8z9fX19fX147B7Ha7zYZd0tyR280G44hhgAjevp09cegRkpMfVzvKS7OBED588+aNg6Jm5mRgL0t1L9N3GJ+oDtX6fPbo2c+53++dmuoBmc/zbnEpHD/vuuiL130n5s7XXUrNaUkpbTZPLy4unj596vHcMAxO9bi9vd1fPPnd737n/vfTp09bcSAQQvBqHK9/9TTqq1dvt9sLW3KceRFP9qDQsS73pFNKT58+3e12f/VXf/Xtt9/+5je/mabp+vra64+dT+Ngu1ss9ydKKX/xF3/x8uVLZvbwwAthVNVxfkf4PRxnZoedr6+v3Tv5+uuvf/vb33Zd95Of/MTMvvjii7/+67+epunLL7+8u7vzjUWk+lP77W9/61kJfyjH4/Ef/uEf3rx58/nnn3/88ce11q+//vru7u758xeeRfZA3+uUnj17BmAluG23291ud3FxsdlsHDn48MMPLy4u/uN//I83Nze/+tWv5H+iFCUTiLHbJtVkZqaUEhgg9pYga4Nr8o7saYt5hszgSH3w2puw38TWPUmNzDJRn8i7ZekAa5KT2rQXiAA2w7BBYAJQBYAL4tRSatd1jGYyU1IY1wqYi2cZTEspZZpTSpQSEXnDOJ/Njie7axmTN5VFWEYod+wotMtfhwBiMJA7Uq+3ICJmzyUaMFfLjzJtq5cPAALEjCGzKrSiFgDoAmaaRdRMYFxLgsUQ0HcggjGwdFY2wBt6K4WQ/XXPuiEmcCBmQEEBgdkMrtgwJK4YPb3e6EjS2o+7opAve+/UHaPUWi8vFikZcwUiMqUYYCSBg+8FVeEFr8M2MtyoVG/9TcC2x6brnPGtatq6HYCJmdEAR4tmK4VVAESWGIk5mZksfQ6Y7eKalogdIkmNVBWYSylMkTn4JDSjIfab7UBzLSKq0QhidppPUlCgFlhIRIRqDaqkYNNgmMtkJqSWzHKICExQajKatTqeHDMAlVJEOs1KTKyKyRulpRhijpGYRGRUmpEEZgiagsSaqUpVtSo4jSIKZnQDEDDVaRrLcYYACLEinUrZ2enf/Yd/94tf/CKhxBhubiVsy3QcLy+2X788xFC7FA8jmHB52feoF/18/xbXF4fNhgAMXY0MCBJFsjzPE4WaBpS5GGF32UkfjyKnwxi+/mb//DmG4e2rV/3Fhd7dh9xQ06EPu11kHscR04NwjCFmjX2NPW2fdH0OVnvT+/v7qU5cjafJU3d9n6fCasqgWC2X1JUcwRRImC2QNBJgipxMycQ24dY0kB2UNkadoIvcGXUHK6KJaLvf5tRtVVs2se/iPM/TfACQu23qkkg5jveH06Hrhu2++zS/ePXqzdff/O43X/4qhPDxpx8dxp2X+XlaJBpNNfVbroKpFM41QMZ6N4nLUGyIEsBEiAEElBmHWeZx2g2XcR+JCCpP9k883FLC2tCFiTZ935aV2Xg81lpzjNl5GCHkYeBPP/U3MPPFdtt1nauaPDyYZ9PFYBVYGhap1pwjCKWaqroIthEurtJm9+zZiycrIXGe56fPr6cHqYHGcVTFZrPpQqez1loPh5v5onRdV+psZmVbdrvd5e6ymN3d33sCy9nLIQQKPNfy+u0bN4du0sxsrqXrui+++OKLL75YkIO213dduL09rAnph4eHVjwd6OLqYp7n129fH4/HaZovLy+9jCeGvA/pJxdXXnY4z/WDDz+9ff1KVfu+v7643nSbTbcJIVxsL7rU/ernv9rv93/xb//icnfpgiRvvnujEl+8ePZwM/3Tz345juNHH33w8ScfdF26efPtr3/1s//+33/25W+e/9/+7//5888/O53yPI9///d/95Of/OTjjz/a7XbD0G82w8uXL/+3/+3//fTp04uLix/96IcXFxe73TbGIFIPhwey6o+SrAbCduj/T//x/7jWOv95x/czwItVALC0Fjn/K6O1wWpxoXNJ0CIAgncRttbSx6X9z6SOoA18s/MTEhETwtKPa7Ftj9Cio6JutmEg4hj9e8mshUTSxGu8UtAxK3o0kPrO9xKR69mWgpVEx9z86Fpr16dH+0sA2onSmUqoLTdlhmmqDUokrM2EiBAo+GdNackZk7sFPtoOehMjRlKNszyCDC16XnqRkuE8HRVcc5Gavoct/EZud/74sLzWa0XeCASwOz8efJiB4tr4pYVcIgQgBb+YsFjJZmIdqHdHxEfPP+gAhn/VCpAC6Lpu7V+0PnQApUiI5k1vQmBG9Cvsuq4RhJcR9lkRujxXiEDMZimzzEQELy8lFmqJeVM1FTUlVhgMMCjMCH7f1kC5MqtVxgoJMJswR0au0ummrxEEi0y3b9/Oc+GAGCEGrTAIKNRaXdXZKdNB3U2M6xMBEGLLs4QQTie5ubkRkVLArK4lHFIYx7Hr0OU8z3Uc0eVVas39s9b7L+XQ98KEFdI8nUABXUbXhy4Pb063PnPevHn961//en+x9Vl5eXl5fLhPOfQpVTEKfHX15HR6eXerihpSq0N3mIGZc4hd1yl5BsEYEmMkCrOTEEHg1hIHrri9aGsvKRVt6qIw8qGHEMCtTRBf7LdFA3sX+qUTABFdX1+UovM8mskwDP0QRTQEWkQK43iavG51HJtoj4d0/nNKidlXtCdNQoqtDtvfsO07EUxTHU8j1FJKHKCq43h0wiktgI9aFY25Tx6ar0lcB0VWWj4vIoiN53zZ1QovbfClKmIiWMhNDWfSpW0rBxElIhLXkAERMwzHU8tDh8gpeau+QUTsItze3noo6XeUc1SNIsXMTuNxFRIZhi5GBIPZ3jH5ZQUjhPDy5cu/+Zu/+dnPfnZ1dfWXf/mXn3/++dXVFYCHh/thGIZhSCkuCdcHz7s7GFDK7JQxR1l2F3vH1T3h9fBw70pHT58+Hcfx/u7Q973zpwB9eLi7vNwvoULZ77dPnvxbx9sBfPvtt0SUUvrss09CCPf39zHy//f/8/8TKTFxzvmDD55fXu5vbm5evvz93//93/72d78Zx+PNzc3f/d3ffffdN3d3dyGEH/7wh59//vnnn3/+7Nkzz3OvVWcffPDBD37wA5cMe3h4cET9+HDn0ynn/Nlnn3lf6sD/89oRNrF9g2cubWkOiCVp4W9btHxtLuaT283MYjWBlvVpwegq/4uluwiWxpNwi7tcgC2+4bKSyQyPrRARGoGaGgKcEofQeXmoP1e0thi15USDt6dtl+RYmbsLC7YcgNaK2I3KHw7LH75mj7aqJS+pdZZt99sMRgge7KkAcGE8rKkjZ/Ayt271zGBb7G7D6BtED4AfB42IwN4EFMzMgT0kjiKCxjZtbCNmLMuOzaLpoxmHGYzNqqoFeMDq/7x1DxsotIf1KDDvvxDaDGmuUqPCPibsVxfKn3VOnstsry/j9v4gM5ZHtcwIO/8rIUaYW2YlUiLvZWhGWNqekPe4LJAKFQrVzGgRggYUJmzGZIm98bd3DVCRFU32thBupKtpFbJpmmCWczTGpLXMMFVgDk3EGByaV+eMcTOb53EukxMdQN4XgYrh5uaGmasiVOt7iEhO/ekwdl0Y+mGa7kpBYN/TKWU3ZGuWIaQkBJxOLUAJtVBAzkgxMXOtlgIiYx7xzTffMJ5vNoNHP7e3t8EixfjwcNjMdbje525AOFRFtOrVF/1mE/occjocjykl8y5RzSwFGCOw08zNWzzC1AWeYzAihVTTYBrM1uVtjtjAzGumAzNC7vqiwSh579cUKHJq8zRQzpEo5S52HQg8DJu19tudm2EYRIyZQ2r2j4g8DclMIjpNsr7uNtgdmu0G04S51ConrWJICYnYQlQOQtza2ocADsKBzMLS0LzNRPf1x7FRCsJSGOn640KdndF3PRRxV2xBfdamuU0U1kN/WRiC3LqNtSZ9MYazLFgwQ9/nacoxsjM3U45EtNl6K9IHVXRd2m6H7RYx4u6IYYgpXa56WyFwjPGjjz788Y9/JFJrrff3d7/4xc/d7r54/pQDuj4FTmRW6vRwuPPuli5aME6NExdjdM3UGJvwnyeGPZXuSPibt69g7KVQjsz/4OOPHOT3mo5nz555fWPXcdd17tk4OOzZ92fPXjgdb7/fPnnypNT5H//xZ1999eX9wy0zdV13/3D7j//4j7e3b588efLZZz/6wQ/+3YcffuiCWT7anut98uQJETmx6+bm5nA4ODD+wQcfOKDtPLVh2Krq8XD6QxPwzz++lwE+3xNXq2PmXVva35y14f6vofHFl/4Wbdf1afTe4ejKcnJav84Mpdi6XNeTAyBiUzIFMZhAkXy/W7Fl51uZcUqelSR4uOPfGGht071+F0C8tIeM9P4t80I/xtJy8fxGRO2d+1wsjespNvdW2s0SISRfVMHYh7G1PdAmsen32xwQVaWzsJ2sGRsi6lJeEW+ixoMDsBQir6i+rdfW3vzukxXxjoNgQiBCIKKsqopz20nMtjTuQwOBVRfNc+DMTwI8znlM6jc7ePZvvQB37AgI3DCJGOO5i+bjxmhtg30wRQygEMDU0p+29Dz3xxQZJ1W1ujywlomEKll1hXnzWlUVMiEYM0g1MJgYMJlFyjTPs9tRkyJSREuV2aQy0TB0UCMNGlSrTZOooIruWyM/IlMY2CEgVW9zXidjfwaioBpI0gallJyjKqaCYRPHsQ59AHv/RGHmkEQUpUx93+cMA0IgM6+aFRGoYH/RJIuZQUsXvK4b+sylaAjoEshknucYwzzPfT9cXl9BTbSCeJzq8TTnzW6zlfv7sZSqkBjZC6+raQNdmT0F6A9eiYfNVlXFELijGEDBmDz9oEQCMxNIAYUQWEmhbn0VVDkCZIEQmAXgEBAjhRBCIrARiKiW4svWZ5EqAoMJMUEqAKSEi8thuxtgiBEIjRihqinFnJ0bwfPUys3DIolDRMwEAgfkDoMEE0ophkhmGLodM7vqGi+tEUIgEYyjzXPTT17gJfJkHDVP0JEtlAKdp3Xf59a+0GMYXXLwZ6ktIuJ3GFKrJQth6cBtrdFvM+SjqknX54F7j3R9tqcUzIo/pVrnw+GeaMcB1bzmGF2X3FKGdmz+l//lr//6r//9PM+vX7/+7rvvfFp6EUp72mQ5x/1+GyPXWne7bUppmibvEe58qPv7+2ma3r596yy2nHt/fb/fXV5e7vf7+/t7kHpiOKVUpXT9xTZuROv9/f3N7dtSZ99yp3mMMRp0motBQwjXT66ev2hKXrvdbrsd5nnc74cf/6vPf/oX/+brr7/+7W9/8+vf/PJ4PLoKiog8ffbF4bjdT1tiCyEEBBdz3Gz7r7/++ue/+EdXBt3tdhyw3Q05RM8ZO7YxjkdVzGV633T9S47v14zhPIhhr60H/H96jFRVVUzVNKc1cnJ/kLyD5grPrgC1/6uPhtDOWF1WSzOZjgD5z/4eadWYZ+dZU6dLUOWhtvfRA2DGbpBo4Un5GdTEw2mRFgemyM6jcYPr3xJC8KZp6+Wt40OLAX4P4vbo2RFsFRCRu65AMxtoaU6YEeCK/I4QsK9PEVGVkPsVcG5YARmRuReMFiZiGXPvBgk11GK1CgCKxKE9p/Xy2jZoHAJ7KLletmPCIoblGXltCdZUsao381GNHlITtY7Obu+ZGvJshlIfW7n54NNyAbU6EkhE0DMO/HIv5rlkA+yRqvo4Mx0I8Q96nwYtc60zmQDQMmsV1eqhMDcdGSI1GAhq8EaYFTA10VJNq2kFrFGtpZLnCVjJNERKKZJlQowcIFrmWYsKsfUqKnWCKXIOS9jd7i4szVhUDYTWV5oAkpRC17FqZc4tfWO22M5cp+oikfu9HI+YZtlsydU7iNynJKdy14IPPnhOdDoeZ46IjHlGCrbZbK509+bNXa1IHft26SkVV0J+/fr1q7dvtpdPjOjm4XRxcXH1FGMZjyMejsfjeEpk4zxNVYbNZpqmqiCELm9S7h1Up8SqYKMQUswdp8gxcAi2eM8iYhDVYkxsbvzMIIAbZqEgAaJaOMYYiGNY1hLMQF0634583gbv0ZEgAthjmsxRqxQQmUshU5XSVL27nMIj/LZiQlADMVLm3gKZQ9ZsZn2Oq3F16qhPPQNiCiFu0xjWImBmdgT4PJx1rzrmfiV2AY8bYEqNdKm6YnuOUVGttcIc7WcCwyI3aXQiYhgxmwjMYgjdJuccRbpl8br8SB02XdeHfvCyCA0RorN5pglMzDFwinFdSoFDTiFGxLj54MXVw2efLKhyi3SXTa/f77deou21VV6hFBcV6xACQWEyT9M0js1DFnz91curq6vIPHTehEZyiv1+mxLVOhKlvo/zHKbpMM9H59wxc4xDCAEwkQkIObNqTYm6Lvd9BNWY+KOPX3zxw0/+4i9++vbt69evX3/521+/efPm22+//eUvf6lav/zyyxijJ7ZXmrqz5DabjZetOn/t8vJSRA7jycUSeOk4UEo5Hkd8j+N7KmE95ko9VFoiWlqjGTN4a0JVdfKeiNcINcx5eU+LVM6DRZEGzgCPbp2ZuYFaDFtwb1FVvceBZ5pXK65neVaDibZvMKCZ/2W79wsx8NKNTs10nf0eWYLXddLCayLYAuQuAFoL2b1E573YDksxkhe0mZIHdjEuJSrL29wbMGt1io8QfQv91aqaCZn3rl26URm8NCVyQ55pDdwNqihFyyI+jpwZ2btAukfhrgUZIp+Nm0GxAgmrk0FECGeOhSrJUmhjpmZEwkRorYmAECIFIAAGO+tG/t5RKxYlo0YsUHUko2VtQWqmzI0McI48O2StAjCKoFaZ59l7BhyO94fD3TwdSp1rnaGe0GUwO8IQObplFxQgKBlM2dRARWSeJ2hNgXMXh01PRDmKmdV5rBKcgc/EgTmmyAShqmyBMxE0C8AhaoyByNu/W1x63SzlYZozpClgWNdHVpPSukq4VCRxa6g+oUgpOfX7fa3VqqJq4QQQEEwhBAqJY1YQOIapzMa42GO7o9PR5lruHw777a5M8+EwBuIck4OBMcZqdr3f3zw8iNp+2AE4HA7FaHdx0T/cj/UoWow0dxFMOo2OcpVSTqcxpj6mros5ME86GYGYPXhtLI8YRE1MQAEwtRqNq1IgsKm4CwQFKmQ2jMbEpIFSgJDnBSgBwVQjGM0LfNxGmg+nCjnLdgFsKNVCIA5gVanLvrUsq/fQFyKEDhAlFmJjUEzkXYdTaIAqeUvMs6mbElzv4jxv0nV5RYaXlQIRU0IIj9vgOoedM7Est7ZV+m4zTZEDrdiVG++UUykJgLMESmEzpITMqJVLZdV1iSQz3WxCCCDqRfalQLUBXWOxZVDa3igCEe179qhDDTFhf9EPmywikbzjC7ygz8MhibTf9k67yZFsEeiIbPv95cXFxXa79TYGzGxG8zy/efOm1IkQuq4hKESUc9ru8vF4eDhIznl/se3m5LuWPwWDxMQXlzszcxmDYdiaWYyx6xJgLoU8jhJjfPbs2Ucff/Djf/VDl0n48ssvReTuvrx48eLZs6cu3iJSPfXDjI8++uDTTz92ZVwnbI/juOl6x0i6rlPd1qpv374Vuf+TG9k/4/heBhiLG+tDv+7Eqwu5/mMOvjGcGWZTJc8ZO9P4LBHoOAzOMVIHYVrAhyayykxL5RLDWdkLsescK17sBUydfNRSgBRju0JecHNjVbJmHGwJrR5h5NWVXhO3TKiLLVnfyL4RLOPw7og9NtBV1aVvo2sCYfnQoxmmhbgELGuVmCg1w49gaAiwR4tunslsCUwXDACoLqBgDtQTAKlWSaU+XuISsjeEfEUmRGFo7shqfmkNuRwEliUg9VEw0ubH0IJt2NmdtYT66qitbpPnBJ0QZoD3CWZ+7H29IiI+UKUqQDASURFTRUszl+KiSMfxcDw9HI8P4+lBysnjVwNASmg9qcgsUjATATOCuPKje1oxmgZogCIm7lP2UtEUUGs181bBs5owBQ5gQs4RIWrVmYqZUI4x5ir3KQUvawG1zl1eVBMCMUdmnqa5ngDfmglEAWx97/VaCMEbDqYQgpK69OkwlFJQ67zdBlHx7uZEnHIYNiqluWtVIIKcc2A+HXQcR5Bc7Hcx8FwqEZlaKSXm/vWrt6K4uTtOxSro6uoqdP081zTEPPTxdCy1TtO0Jxo2HQIfjmNKKaQcQjKjUiQEjZGFmIiIIzGDgxEJCKbVyMBm1UDBWLz5Agoxk4o/F5NqmGCsZCALIamRoIBSzkYhMVCqmUkIIZ5NJABVqydoODhVCUQIjKpKFAje0BPMnrPgWs24TVGPO9sRz7pwEq11xsRON2lrQZeNotRHp5wJqvBsS4gWIvEC/rt9rZWO4wwE33OwVPoFbmWKIo/s1PM9B60bZvtICIhLSWEMvl7gf1LzidQiIqdwMod5BjECgQNYoaYOHqQUfOdYd+nF6ssK0XliKGcGWGslNgOJVjNjYmYORF5paIYQY6srEYTYjVMNgS4v95eXewDA2gjucxFbEqtDrVWqbTabvjMXYfYWNR5Gr2XfADab4eKiU8U4RgClnsgblLKmxDF1jg1UmUMILlPhbTO8NozDdhUuVVWXQQwBNzeHvu9jDCJ7M/PZstn0XcQ8Q9U2m9z3MMPFxe7Jkyt8j+N7GeDVlC5hYtPNWMIXPjPAAHCamkiFnXWhr7X2Q164uH7CFu+yEzrOgmmzYAZXjFrn5UpEct0l93qtUYQUwDjWGJvYg6os05pbErCd3y1X84ptEZBaMWRim+Z3POUVMFd7hMTbBcPHQR9fOZvT57CSj4bHdiEvwZyd88twnk9y+jdHYs7iicu2LI28qyqQQnCCj4O6q8XzFRgjmDsnboQQQuDD4Xh+/Qtpk+ZpGZQWzxIRE7fUvN+EiwYuzxRE7A/F7beIh62rD7E+l5axW49zn4kIZ4jdmq4O7mypkikD0r4UrhHGbkkBM1UzBbg19PWOCaWaCkONzIuLyEl+/qHHgwPUWyStLpxpDSBKiRFTIMDmMmqVEqtIKdNU6kRkOeehy5uhO94/xBwCgpQqtRBZiMgd9CQxmCdifDuQKvM0hZCobaZUABMogUlTas2Vh20uMvuDHieolr7PjUbH6HoYUMWu90OpDykTsRFrCDFnaIgXFzu7v79/M08FHKbrq+3FxUUMw++/+vVnnz3vu2Ga72qtpcpxnEOaT9N8fxqrmoIM8frJiyuz3/zut6IKomqYDtO3r19Z5JjzVCpTHLa7bthwSGZUFeNcohpvkzvJHBOIqsvNWGFOLdCFGtRM1AjGZgltQhUyMmVTAkHIaCKKUjVw6HQhFSMHEQpn00isEFFkVoZD0UZrzgiBG16yrgtjaJGWBFnm3rpOp7Ewc+AUcoqBUmIAtSIy2eLWM+HM2T5LeSwoGTW8PZzPcF+bm01ed5LFpyRzO9o2qzb3vdkPEbzlAFEEvEMDm+E0orTu8dEMi3BeVFkyO35HDANEW6B8fiUebNhicdd8HxGFQF3HtaIU8T02RpRi4zjuNq0Pqet7p9Q5ueVxFQdYQK1gDsyBY/Mq4NuVGXNy9GKemwHOmacphUAXF5hHur6+PNsqLYSu1rrZ9EtxNqWEWtH3sesA7Nt4ut8T0HUppeA6uH4Sp0oQUU79yh2JMfuN12qnU3n2bDuONs9TWLT8fOnlzkFzSgkgiCAmvrza43sc3zcH7Nq7bmE8KWdmx3HMOSdqmcu0IJkhoBSPmB1/a0y/hZ3/iEW7m9OnAX9A9SLCVCszE4XVV40RIYT5LB1uLaZ9XAaOLYTQrfwjg6gRE3NA5thw7Dp1XSe1mb0YA3saUh4NKgDvJuu/9j2LttSsT1pdgsI1mkRDnlVVRZoBDiHQwr8ww1zEbT0WyjQWpaQ1UVRldS/UzJV3QQZVEmneQ5cXVHxxmdvoLWPYd+i74NW3AMocVs9juRiRarAYAjGRPPZ8pBCCcavTUIGKGhppk4hTwrmnb0a11fE6Ya3RTDzOcJGB80TGgnYgRnKwHUDXxVptmmrqIhphrRHLvJq5lAqAEBa5SZ9OVExP0zjPs0qZ51G1hkC1KLTGyMSBtZIahcA5G4TrSVUNYIoUALVaZqlzTi1nWWSSYswcE6eUzGb3r2PksOkj8dDl7aaHKBNNx9PDw12Z6rDJiZOIcKBpHlXV66zmWRxSC4FKwfFYY6ybbR/iNM+mVkNosgwAhoFUrRaoYnexvb68evXqVaugS7jc5PEw73a9mB6Ox2GDy8v97dtZBIHhnXBO08NxciWZTiv6fhMDHu4PP/zhD0OKVW26u+u6oYhudhffvvru6ZPnovj21eurJ09TSiGmqZ7yZgt+q6rDdlNKeTgeL64ua5Xj8aGqbbZ75uxDmHIvkZhCSJkp2gJgmNlciu/OFAKRAaZazXzqe5WZqVUYMwWlEgwmEeCUhpAoBYuBcw6VER+trwEamjNlDDJ4fRGHxa0PZMsqaKTOAPQ5iPOzPQWKx54WKXaPAbHBB9/MjsU9V4QAGMTxZNXmHXtUjJZzcSaEY86+G6wTXmptCm7UzmDCBnYqe5eQQhRx0E5MJKa06SPRSkVs0i4pwCdCjlBFJTNTaOBAZlBreYA1jKlLoYrvhLrY+Xa1xAYTFZgFDiEwFCYCrQRACRRypLjpu0Rq8IIDM3PY7w/YtEixbWVDM/y8xBWtwGGehYEuBSKDYMjEjDK2PQQgM9/haZ5bX2ffK1Qxz17eAjPIqpa4XEdgGFw3qX2wlFJmZorzPIs9ljgCj1vQ6aQL79V04ZMSYRWNWGdFznjPf/+XHt8zB9wMyRIp+su02WzcQ/U0RpXmVSWXkpe44iFmEOFxmhaoh90wpxRXRuLy/2PguDKa7MxQYeESY0knu43EEnR6QmX17Ghh7uqisYwlrwxrKHT7QiciMYk81u8yx3X6GlyZkv1RtQdCsPA+Hk6trVO7F4fT16FrV2UwXSWX6Ryh9XsB+WWZf5fnuKjVSoWwEJL/cCX8qcNVo5cxpEeIwmjJOjMzr/US0uqPKxG5704EY0vRq43R9jkCGBRYzdz/pQAwVFEVoprbAmi7yXo9Z7Fv+2uMrjm6DiOYmuCKJzhMqSH6i99DBJccUo21MDGoipnAhMwIwg7hN74gARRDUlILgVQMQStHglQu5URMgULg5FETADElE1qKiZk5p7g29NVFczvFSkSRECJP7iDSWiPknpym1JXSsufUSPKl65KYlXmW2YObrtbqatOllKridxcTcwAzlwqKBjFOiB1iDq4TOY31cDr0u82wxaxOLOLU9QZ+/vzJw/FU1X7wgy/A9LN//Pn94eFqd1kNfbcxoiL68HD83VcvLy8vD4fDND845FAFp9Mp9/1ut+v7/nA4+ZMJIcTcBTUQi5gojGG1ghFACJFb1awHXuzSaeSqMY4/kcBa1oSsgiYCEQWjwgjQSBahQlZUGe8UT6ybvzopwolK5/NcsC6NZZGsIS+986tPe1GYNrKCwqMfqCKAYa3AjZurTQ6oLqQqXtNwvFTLefDnq3gJkc2Mm8dhvu5UxGIMC0pkqgJ49drj3dqZY01kMbYMoGsrOC/SnV0RqBKgZmRL+d/KmiAikC7miriJMfhiXIUBwAQNnptb6ugYIZC2UgVijljyXO8ddEbRWLlqC5Dewq2cg7oi3iOvOxCh79tW4PvAEpCEszItWtPV5/vGI3b1TvBGdv472ko8Gw03ZAD5zuDoqBnMpaKSOzSNH/d4qiH94X3/c4/vmwP2YwVa/eh79ttyK7LMrcf3r7exos1uFL1cz+XKQ6CxtPo8vDuUYWmQuUz95WbSo5fHTGaJmsSVAlCBoZ5DQ3EZuHNM2BPMZ25ROzkvNnLNx9TaOvzUupTrLN6vT2l6RMJb7nOlpz3am7ORWbBek2oezhKF5hO0vy5T2C/p7Ap9TUZGU9I4n/fLO/8U7anrGpR9/nTMLKa22FhgSECDj3ipiiAighG18XS2MxbaFBZRZS8UMfMmSChFvHAlUU/Lmmc+31MekWosuxgBx+kRHsTib5HXQAAiVqvqMm6PA6qqWiFqoqYCqQZhM4OwgVyyJMCMmROZQcVtsDJzoFijamVSr8SGVRExKaqqtfjJSykxcOgHLMrAZsaM7W6oc5nnGaohcEpeRB5C6zHn7iC5cEEIMxFijF4u2XXdNMo4jqVgN6RhGKZpOp2qEcZxnqYJbEwUYxQptVaPEqosStQonnecZ9zc3Dx7sd3tdg/Hh3FErRLZxjq+en17fy/PX5w+/uSzfrv59tXNaZyVeJ6nYbs1M4CPx/nly5eEMI7jxZNLMxWjw/Heezo5IzSlNBeptdRaY6cUginElCj7gjKwKYzNq9vJXUhRIgVV5QAowJWViBiVyFpJvxFZATOBQYkgZqJWVBNpEElnyR1bprzZ2c/nHIx17b9nKESq65yb+h7daAyItDq+vk254exzQourcOY3NglJWvJEj2agzeolbbxuNWdvRiCgtfNa1uBjxVFosu2Pi8KWgg7AmdjnNI5l1RBc8NOthTbdghaBtBJ2eNdONjOmsEJQLm66DpWv+Pf23BXMXw9Do3Sdv0jLB9QefR03fLxcsAhUvUub7yctQlvvaDEWbRjXfeP8m2yBlM+P1aNyuRUATn8hosDvbPXLSVqM5yOPJQVmZoXS0r/Uv2MFRN9h4/+Lju8pxEHnd7vOjFVXGUss4vfoGI6IAerT12dY7prkIT9W2gBnC2b1dwAQNe1GbZWdLTuyfgvWLXtxPyV4+ZAbtmbsAeSlX5B/C8zNo5+KPUJuGwUAeES1litgNTxefBZjRKNXVJ/TAfTuYnuH9Y1lFq7jtihjWGMSLaZuzco4GZhaStXWwTcDedHOapMW5U6DhT/imC7fvv7M7UqaR2i2Bg/nrk+DqnIC2Iw89F6G0FpdtZnrD/gnzCxaNHWqZCP6u+PSLX0IPHP8uFEudhcLfuB5/aXAzOe9sTKxMZMtNU4+yNoEL3kex3E8zmWc57mUSaQQCf7/xP1pkyTJkSUIPmYWUVUz8yOOTCCB6erpXupZ2pn//0uWlra/7VSjUADyiAgPdztUVUSY9wOLiKp7JqqrEU3UWlkJTz/M1ERF+Hj8+LEVNgUpK4gMBqKqOGKFAHKURCvQT8Y0TkemwgRDQmFwylYF0UREJQKzl63NaFlSydlUB+EYIxnmeS5pDSEcx0lVmUNgJohZJiUCDzLkkMewENkYIjMSkoC4sWEhTIEkSwg0TaaA1+8d/iSCSz+wQG0113QuOYR4ONLlbF+/as55PIzM58sFl8tlCGGZby+XYgFzyr98eTolvX949yFlAhvoeHe6nG8x6jCwNm2mME7zPA/TBKYqwMIEIEbJrjixXAsIFFhihI10ZGKRyMxG/pGNVEsBASZkyVQUuXVEiw/GJkJxq83IRgIEUIYlQ1ILqrmUBA5JK2Tl3XetLuHZSSEiWA1h215yLwJs/Mp6ymqe0AZCmxmMoxyaTVerxaOiVjyw8M2ptu1SayIHe6veQ8n9Fm2NIdYdJxOsiQO2v7IdLsX7loTXiV1v4zSAun2rYYhVFa0ex1OTvQNe9U/6cvl70q5wZi0awL6k7Sn/3vntYpRhGOzvJMRv0rCKVtbUlpg9QuUQXCG/GsxSvJWx2jvnoHU73GUefIRMz2266+GqnE9EUvNBA3GLX5opbvdGqtYkCqitj5qph9a+xTxN7NSif/j6NgccAG1DkHbtMVBEEvZjZTCCopZRUdeFW6jl8VfFOqiix417sGkp942LtiLW9o2/prep9OTJmn1kZoTKaCAi6VKLRMTwg+veo+JVvm+YYSqqPs2mft5xrDfQu4HJUxBNziYjCtoGcTNz8eRKZH/z2AlQoCXK9X2NehLuIaffvzfXO3kbvmnNVAtT8PCa1JyKBJfi3nxnaVUxx+V+e7NYOxi7OIaIKCefIARVq+MLPCQPsXYbGdkOTmeqRR5nWatlxzNQU2ErmncQ06vjuUeZPM7o6JOvdgtj6w5xv8+AGQVhEYqRzFhL7fIyw3ytRrNemsSKaoEVWPHgoN+EWQ06fG1VTYs5EDWdjmTFNGlGtkJKJIEJQzgwcxIBlEExDFpyyXa53JhMpoNHxp6El1KccFIL7WDfjWZG4BiG6uTq9Ju1dnaKh/+ac15LZuZxdMSoxDisKQMWBpGEgyDnBMI04XjEMIYYDvf3eH46f/6M2+02HiRGLCuWJQlZTiYjqdqnz0+X68yhTqA73p1+9/AYYpxvf1MXTA7hcDjc3d39+Pz1drsxsuvIzusyLYvrEhCR+5I1L7mswiHbgZZFwkAkzCIQBaxosYJCxkxqxobaF+iJe9mnWEQEKoQCLkAxKoZsmosmLgMkFSMWQuv3g7FLm7Un2hLZtsvJ0A4Y7QGiaWDzxm99ZVtU6ulQZSWgGIgFcDZWTeY2zVrzjlin/e9NliNwaElwdR6oavbYYU7VdVsr4BC1DKOia83QYpedmmlN6/wLJiMCrOQspXjBqLSOgy0N8Bvq2Yg5Aq7eccAEwc6WmpnUEhfXtKGujHZ+096kvznd/hnb7faKuz9sLjkzc5BKhaHWRZLzZjO7Se+hTwcn+uqhKTTYLkUGtcXyrlSlfuh33Sv1H9uFAntgsn5VXPaOmF2NlR0j/JbrW4cxeF8s0COFuky/BpxL0ZIq/bjHL0QEkHdB1L23e+QxROwg6A67v3kYaD5MWrOdv50p+Vw/Zq4hEnl1eccM7AWhtvpmcACdelOptvuJ4q/TuXwembJEx1TN0DllImK5tED7FaDdQ06qH7vejHPiAdiOtsZU+wr227ruezYAbKpm3taiqgxpWEsLpxv23K3Or2NTq2tQvy9Mxtz7wpgJHJk55KCq7IamGFw9ufosK1RPL2BGHvHA92opydRYZBjH/ghsLT0wb5UVJ4MI7VDoDoSECgl6gU2pg+AbHLd9sFIgQwwhZA9CSMlgVshKQycVVipC5wYXLe8iMSsuTSAsIhFqxZz/zUYMZqEYKBARDYPZSXPJOXuD9bIs0zAC28cxEaJNjcu5wapslj19DyGMwwQyaRMkzYwkxMglayl5XVfVChUui96WWQKt62ImwzAYIQ605IUC7h/C/cMhhMHKdLjjMJ6Ncbnd7ux4uINdQSJhHI4Snz9/vt4QhuXx/Xeqer7ertfrMB0e371blkVVz+ezKydfLy+n0+njd78Xxrou1/PT9fayzOnl5VKfIKnIEIIoSy55LdnWGZc5xmLFdLQgYA7G7LA/AQICVVfkoU+GAeZIFKjKh4KKUXFak5fwVXPSjMLMAxsJSCDCABnAPnitSgXs9zlBK+S4N+u+E+pZcyfVN1Gq+wSlQMmbgIWInPJT/9wBTX+FwKreZGi92ORwWju2HjrX9l/ZG6uefzVpGiam7sz8IGxZR7cnm450NzUNNuN5aZlMS2XeYLbwLoZNuoBdCM5UmQQE561XoixTr7b6zZakPjeMiGKMPhzCz/I+P3aPC3M8HC1GMSdCewXJDWkD3rUU6bkvkfduUCniYXopSgSz7eO4rc7ZWuhWyTc9rO/0oP7xiaDZg783EDTandRfc9tjZiR7DMMFZfGN17f3AW9f0y6ewi4w8b3IzMa+P7YQpv8hNUsKvMoa/ed9d3b7ZdterOabCEzWNhl5MupnOefaG+CNUtwUKt7URGvyXbBvbABg5nNj4SSa9s6lQzqBfc6EmlkQcl1vEd5GKe1STL//3etQB0los0fb4hgQggdu0RkHqGegTU71Mba7Luq2agVEZoWIbR/w/9b1W3ARwm53CGAQ9UGTjJJJhVRhIFXKxsZlbQGHSCAqAIQlRgkBKQWf8juOPMRaolaRHarxNoDb2yavmJJsMW+jX4EZXfsX8M7O6s6r2XDTAzKCECtzUWNoVXzoJkdVqMLRanVeLTODbU3FielGtceTlNW2sRzDMCRb19s83245lxjH4/F4PE4E1VxijGSWs4+CYBERjsxBtZSsIDIjkTAMjtuzw6dExINM02R288b1YYir5bQsKWFdc5lKSsWsSKCUIGJxFAnD6XQYBkmpQHUYwjQNp9PqQcz9/VHkGkIY4hQPx/P6EML8/e9////6v/7PZVk+f/78pz/9ya18zvl4PMYYVTWE8Msvv6SU/o//9P/84x9/OL98/a//9f9zu11QxRA1hKA5rfO1EIdozMISYoie0OScAS5iIViMbGSmBjbz1hwAgE/GUoJz2R0iJjJj4+pZTc09cSmmsFyKcGUjUgumqW3V374ajWM7WT3OA96eEDOjLgvPIDaCiYiwn4WWj5pbaCOqpKTtTLWN3amz2IJv2Vv+vUHrDrVxlHYNypabgSWAOliIispKOxpuOutXzR9Ld5/dtfR1c4lzFqhutU/aNUzWw7UTOMJmt3szyx7I3a9kNa0Gst1FXu6rf+6etbFG2ACPP2DmnZPVIKhat8/Wq2b+4Jvw0e5N/evirHWiV+Wt1n+Fna/Zp8JWv1GVXoh0F1Rtqnz/xo7771/fxoLeMWXQqgstwKk+D86NdP0j4r463iHqu2c6SN+jrQJBzGiS5mgDEuqeLdmdfa8+1ttY1ltnuOxAEXfApRQjKoCgCdRxEwBB876lOP3eZR/efl4fLt125+YqAFibwk0Q4doKw4EAlJZD+7Nk7hEWOdN4/yKqveK7nZM+nGCbATUEImghqebHe/ioWSsHNt8+rt3Xv+FurdJP69eqShyBqo1lO+PVLVdbt1rad2XXGD1FkJxFBNOhf9hazwZQDCkhWI9YPc6tyE83E7W7w+lPAel1xLQLXHxHmSq0VLo7dmOvtrwTLCKNHb+5fP+d5DPYa6ZBRgATU1jXG1MhdqIawxgoAIlEAIHhtd71NnuscDwe7+7ujtOQljVzEhFlVtUwjgCHEH3qBlDMrBRto3JCv9UqwTsMJZsPoYoxxjiaLsvtFiNC4Bijq0s1nAbTNLCIBEopnc+3KIH5ME3T3d3q1b5pmlSLqadx8eHx/XRYp/FoSmktWjAvy5cvX3744Qci+vjx47qul/kG4PPnz8MwfPn69b/8l/+Hal6WZVmWu9N0Op0eHh5U9XK7Xq6zZDue7obxFIdhGg88Hvz57ugagUlUjTQhoAZGr1E8JfCrp+zgTG3ssRapKcyyoTYPtqPuhYlfdYaYAYa/1zCy5Wq1kJkrEjtOqBlwMVVi8xPczk+lC1GNJ312mfVSJZpd6nyUzi6uY83aHgZgRqXUCq7tmkr8FxoFaS/BWzdwB43bX20/2ufEIttJcSixU2q6N2XCFrBWXiT1d1HdPG7V9YxEFJuDp35mf1VfQjtlXmjYlLyIicDyOpWkKkVAuQGNnT/lx/nuLrbsbrOTAKJ7s2Y9OnjZOAFCrczdQMpeFK9L3d6l5gNooH21SE23vyLbr9PIf+z6tgzYYFq0KEFEWBimMJCxsJP4C8yKMoyFCIVMhAjICWsupViQECOSawV3LRj3Wy7y5J/w1ePBFIHWw++0YReXnoYjNme2LU1gCLFJq58T163fXt9PlSOaSjA1J9O6g2+bwMahj60177RlQAimOAzDGAYAOUEEgQEgF4h4WyF5EKBKvUXB97dqaUEgrMAj11q0JuSsOeUoQwfUfCWYEIaQcmYK9QCrK1oj1/CCwMERvTo7gcS5ekXLlj0zb43zbYX75AK13aEt2LUvszDC6OSIAs0cbAgYhtof3JGoUlQXiLAUDcIxEgPrDTkrgDywcIXZ/UF4KOkqVWYWUGIgIggVUoqsBMcu+zNmGDMsq5lCC3na6lVtLZZzrVGBKaWcbNGyZi3eMkwUYTC1kqElSbCiBZrNFFbIipaSkYXUUFByyYuWlawwWQhM4YHMihXTrELhiJElp+X9w30QXvI8l1kD4nCwgQLrl5flw4cPd4+PzHy73VLSlfOSliWvU5jGu3HAKee8rnpbcX1++d8OH46nAaQvz5d5vpVScimpQAsO0/00PZYSLufnlWQaph//Op+kTNMQHr4/X54GOd0/Pvzpv/054ZoMCKK4u810nTO4PIzr4x+Inu5++unnOIY//fjnH3/+QiFM3//uPK9/ndcffvjd+/fvxx9+/6//8n//9NNP8d3xhvTjX//l//tfw+1yPZ/P03QcximGw/Hw8Hx+OR3fh5hT1ttlnW/6/n2Y7h5X8aGfHMVrKzOZRlJlEAdmGLSomAlYmETyi/AQMBFPhAGIRkFJKAjIBJlwg1kwEY2S+RKWEKYw1lNJDNpBOW4zu9HwLoTfMGJvYlEmhRSD1hIwzBBFfAQnVbO3la72YgMS1irqB6GGXXvkXekvgMf1VlB0wzDNYKpMKnHzZf2G1IdSm8IJP664oC49G1iiZ37qRe6dQ5XQCfnMEgnkRBwnjZoFfo3GmwIECZEZRigGMGQgEa7UlgyAmIN4FkoAgYeaq3inFjOlslmqamCFJAoVp4Og99G6M8uNu1MK1AgkLCQB2dJ4IIIA5vMpQjTP1K0lUdjSuYqgE8jhgFJUAomw2968YWn1hoem7ZVTdbp+P85BiaHi2PWRA8TFzHIhNm4Bh5Vi1dz/Q9c3OWCn+6KGSEBbdMcAiyKlZFaIrSfHXrDvuKIvRB3stutFUQf8Xmdxfa/s+kF7xCeA8q4+sQ9MPJTZ54S7rNd6pMNMIZAxcq7R3BsExsxExJXJ9m/UErj+ml18Rx1vdmq0e4KcaZqGhn3ViHh3DAwgl0OEpwwhdArSfjkcKtg+EUPaPTh5oeIkvQ+KNAh3ghtaHN0okZvp6quqO23I/WqoGlprf6dTeVzsH6GNr0CMtQTQQLYet/ZieBXN9GbE2lhV4+42BMbxIkKpOtP1RjsWFyOHCEqgjKzbCU/teqNMRFQ9uLZP5T+qBTNTM20zm4wIt9uNkIVUSEWCFU0pp3S9O5K0nJ531+VymcaB65y4ur7ZahNLhw3HKfq0VNe/VXV2cwZ0moZpGrrwrN3RcltTSuuamXGd8fz8TERm5qJ6OedxnL9+fVE9ffr0aU3X4+lUsoUw3G7X8w13aw5huH84lLJ+ef7p69evj4/31+t8PB4/fvd+Kfp8vnIcstmaCoDT6fT+/btxCiXd4iBfPn3+8uXzy8vLX/78r37zMUYmUtV5nh/u7uc1YV4MKRsMnoKfh/eP5lK1HnGCGFJKYQnkPAbPZ8k4tHXsp6Adj/o1rIln1YSMqU+zJjaA9Y0vtbqh/r7m+N+/OtD1mxe9/a8ewkqr9TAaRm1mKdVKsVUWGLoD2CGf5A3BW1G2frFDkiX0d3dUqZ+p9iKbDyZClFHhm41VAR/pXTSlQqRM8P5da2MQmQRkjkV5S6enfx2V4cohJ1XVwsSZiMxYZB/5oI0L7xhu9ZGO7YFq0GtmakRKIsINVkfVc2KtExXriva0GHDpfqAxP6hP2gjkMXx1CX1HBWZmzx8c7n7DnKLOHHDEcdjhajVWUCIqpXS75WwA7OznP3Z9mxJW7Xtjq+28dXQdc5U968BIreETFUNfiMa/gfYBgv6xO6M9OTSwF3Xr0lsEgHhHZWpIfHWuu1NiO9wfO8fpeJE2ZcrqVNotAbAm0OY3YObqV6/DxvbiO3Td3N2GwEBw5ZcO5qA1jDcUtwJBjlD5B/cP5GC1b503p75Xm/z2rUKj/bm0TbljNRtg6o2BPTzx/3yVMvjaMUk/PG/Qobb++1i7VxZeSW9u03rJ1bapO2C/cs6qbIW0NZLFds8AvPdze4gAwL4Q6mlwfaYb5uantXgM1EjjHabbfwqCj/2t68jMIEl5V6az7CulpqUU05QtRwFFIfSEIHtjC6yklJZ5Xuc5p4W0EGwaIzgwsuZcsnmt1MyWTXYmTIeh2OF6PYvEaZp82LgPswshLMuyriuRxBg12/l8XRYI43SCmd1uNxExrcPvzPD5M1Qvz8+X44mPp5NzmJcZy4xlKTHGjx++T2X5609/+/Tp+fHxq2lcUrotKY7D4+OjsTxfLoD+5S9/LiX95S//uqaZtMQYPUTIJX3+/Pl4nB7vT6WUZZkBm+eJOazr6gq9IbBIDCEQrJIKtS6S71yiCkWqaTEx7wZlISpiYqidoB6akZqRPyN1hjPV/y+GElmiO3NCH8TQoVf8oz647+fy3/3Nt9/gVrGhtmUJoJycVec3yr1ugh01o56a+rLdo1fLg1/dDxGMev5QFZ3egoU1suUO1ZYCLZwTAK/HFW99dpscIlC7gBgkPr10b8NRe0TdbCbygShEfcmbWQHQdKlactxLSzXFamEoVVVgInLxhj1/ioEq9Eu98ReYcz2ibSjA5jg8l2sSTwZUtBxA55jXNM/8/6r+ie8dYCuBWWvmrLk9G7NYEw5rj8zVov5urPbfvb5RC5qYhZkV7lYBEEmFVsx6lxEzV0KDELTxArrHot2Q11K6v6OsqRrzaklLy8xaB7S2abe1affXdwg0h0T0xvP10hR6OQKtwNl+v2eovncMQM6atrEh3KlYfvX2+W7xW4AmPvEKu8yy/6H/WwREVHbnrAVi1IGBvcuPIsrI2d+xniWX9PPOLuzVYUzMBUAat8QPdl+znYMCAN/dVLn7/jrcP057zVdfrOsaQohx0wf0AQC7xan/66BZzzOc98tQl+IcYiVn6U5sxGoPSIcBrd20qbXaG4FQ2RYEHoYhhMCp66YZ4NpyQuqmHehhIqA+EU8VVeDazJSgMcaSi2affptdoTzGMaVUl9eK59nOsRqGOI7jMEYtqSTv/A3TeCRWszIvSw0820RxVHCFfLrqukqMcjqdXr58fnl5MaOH+3fDMIQgt1sxxcPp4N6ulLIu6zyvvlxpxeWCZcHxXqbpmLMCrIQQkBJyLsN0OB5PZjif8fXry+9/+KdPf/7zv/zLv3z8/rvj3QOFeJlvInI+n//bf/tnMxsCf/fdh+8+fpym6eHhMSdelkVETqfTy8vXy+WS0ioi1+tVUcnlLJEjhxBCCGQgUxCcLqWlSsyqetIR3foRk2r2QJ4AswIUsmBmZgVGZqWyAKtEEbhizlvv79+7au7977veROr/vr/BVg9p3CivEVJDrQpzJ6wYtPuYtbx6r/3J+s2rWCEibum154umW7H2TW6Qmwi2NRqX91eYOlOnebH2/qV4N4oxw6wTsNnZR57ktpyhl4ob/Wq3htpGporIEBne5bwPx8nnx2j9RiPPAiDy921L2kyM+Sj0Dbx0qT44qO//5GwOyjo45BNXmXmaXFLU8/jN5HnhsrVHebyuakiZe+ju26xONRaXHLbep4ANh/sHr29ywCHEmgwBZp6h10adxiSql5ONAkOr20Nt0gJUN/C2FADq0noitcq/BYztIJWyZVoAtTKwaZFfezUA65q3jbJbK91aiTbWu+rGlmRGx6KIXOK3PpndR9uULPsjkTaj28ulQOdPsZmlqv9Ob44foebBbxzbfn0cx3ZWMAAXwTETIhibFmemZoDVwMbWUoMeduyvf2Pj0K/OczsMtM9iX/1JXZxaI/CRi6UUkUhExA4ncicxhrE/CJQCsrJPoOuPqrXFPtI0AEogVQfHWAAyEvZSRC1h0NLCtTcZMHuNq8qO9kY6Ih5UsxUrRdXIHNWBekNVCEFzKSVbMT+u1hr2PUQahoGHKHwaBj6MgxDWBTUvHIYwxHxdlmUxTcN4yDmfz2dmfnh4GKd4uVy+Pl8v1xe/z8fHxw8f31lezufz5VLSlMbjdDgcLpfz9QLGjQl3dycJlNOSEkLAMEAeUAqMcDgcxvFwPp+XeQ0BxyNywpen55eXFxjHwLebfv368oc/8uF0MsPT0xOH4Xc/PKp+IKKUVyKapuHu7m4Ywvl8znkdhuH9u8e//e1vt9vtOA0hhHEcS8nn83kYhjDEIDGb5pyN01CSarjdLr4yKkJqOecEJFmMRMIoARyEpT5sswJSqMG7eanAxwRRFmWHt7xx3xu4fWioqqIVPl4dp//x67e29K4A+FtevCHlNb3T2uBGZo1jBYAQo8dzfnBIm19cZutmEK8i3T1frGOjWr1p15rdiZCg56C7GD0n7L+p2iUN2FEhVUPrnGSugDS9MUyAMHfUELVwS80Nv5Lj8Mu9r1uqIrUGV0pVWvDEJoRNkqHOHd+yo45p9TcCGsPLzLySY2beVt3zWmYCQktyjFpTeG4ZMzNjl6+2upiyMJGgkbRRxQw9RVGupFpGNeNkxqrqqc437rpvcsC1/Ld1YbpCZEMgGSEEMxIOfpNLqjTjnnH23+wP2BeEBOzT0rdeum1jhbAlnWZbQLdvCe+7iKhmus4H2fx2Vf7cLH5DaQoFAb1tWiCCGolQCCBIUfHafrfsbU9Q9+h7dp8Ph3eNDgBWQfhNKkuVumr5/vPWw7w5QmshHvnL9ml9AJhNC+ecQaqKUjwerx9Qd8l1xRJQvXi/3uAp1kjOOW9lVGxYOnd+PwCR0eogDe1N28ysmkMYqm4a1bY0f2U/daH2RG7B7P4eFHDRaGsrSQZP7qzbRq4lpNYxWVd7Xdd1XXNNT9Gefl1z2wWwBBImIjGYWTHdpMLXeY4BQ2CJY8lW0ur6+HfT6Dal5FxKKWl1Stg4nrSYweYlXa/XdV2j0DAMRTXl7Jxn7/f1weYAlmXxvSHCIQTAlmU+HA6Hw+F6PV+uZ9+4ZrhcoBmPjzjdHWKMeU2lLC4V9PgwXq8LRzCJT5tecjpMEMb5gl9++eWvf3k4HMPhcJqXl/PFnp6ff/jhhzCM//wvf/r05fPH77+7fzgpYVmW8TA8PDwQ0c8///j506e7u+Mf//jH0/Hh6evnzz//ImS/+/7j+P33Ly/PaVlVVYhFxE08M1ggIktOZhaoT53LHr05tMESURs7rKvOoZU1DZtzU8tQB26s6jtZUa3Ti4vUXW1kHS37zes3o0bs0NG3v+aFQTIY/+pk1N+0XQm2FD/ORkQ+erUdgfrLzJUdohmqmkqSLi4P1G3JVltjhVpq60BziXGq99VgIM8bcik+HMxnm/aIk2nst6oK3eTlPXklIoJ1nUuKY23M7cX1akJRy1ZO/qCdwPW2FLsF4T6bnHxCaA2CRQZq/Q7eWYvqgOu7wO3tDpaj3etb0y3ePiAzsY/T6UvtmCX5pwDAHJrBJ2o3v79nIkEjzve5kB163HWW13swQxUgaOOcv+X6VhZ0T4ZaHxHtHxJAZsEBCjPkpPvcrgtXvXaxbp4BwIGp7iWtFfP6iCjUp6LNEb5FcJpjkPamG27QPbFVnnr1ytzqkT1dtibdsg93HC+qmsNN3Ko7YK/z7w8nWpDenW6/n91/bgMQqTIXVFVLIfdzHos1F46iyLk4HsAuyuilFHFuRTErrvxCry3TGyNVdLuHejhb8LFf5DcOuPvg/np1bJS3c2Jb55xzC0caQOfx7JqqK2qr4X/C4dV57s9xLWAvWFSLuYVZDgQWwKc5l5JVt1utGbBVtKmUQk382hpQQ1AJRL1YWa+KtaiWnI1hRCJ1cBY5yOYv4iocVjKgWtIUgwRebrfz5TbfLiGEaSqSs5mllC7X6ziO796/n+fZdaTv7k7M7LJTRKSqLy8vyCUOYRhwucDKy/FwOh7pcDQUDINMkytpV7OeM3z83PFI5/OVmcfjIYTh8fH9+eU2L/P1Wn7++dP3v3t3ur9by+Vy0c9PX959eH+YRhF5efn65z//aTwch2H44YffjeNIbE9PT89fv67rOk0f3r9///x89mLw8/PT3ekwDAPUYowcpIe2EjAMwxAisTGsaMlq/olUlTkQCwfxwjlpMS4EJjZnlZoZrHgxCiZAgTM3jRgCSgZhCrBoVPK6ZGGpZ1Y8R/11R3s/gloUv3V5EPYG7DEz6taRtGLgrVS4wc5t+xChAZMGoDQ0zgw+suwVa1dTKaWkjsE5VON/r551xSgUO6RHDSODwRRb7Qz7QezQmoloUdXA4z6zJHZtMKhSCKGnQP2UxcjdAvOOEmu7T9o+NSltBxk7BMJfMwRxa9Z/xOz4Rf0knSzVjUV1h+3f+3hHWwewf2QfpbozmxuP2vOEfvCJuoZ2ownsFX5agzKREQlasgFUVlfVRqxK5lUQCY2iBABgGP27Sxy/cX3jNCR0qY3ucgCklN3vaq0zsAdBewFCVW0cfXTvghaDtPRoG+4OAM2JuL6VpzJ+sN0B+5j0/id95/l0kXYzu1pnc+oA/BhwFfF4HeI1KU3/gClVbTlm9gHaWvEl6WAUKmCyZV3MJEIiYiYp5Y5d7AHSPuVDpE7G9tvLOQ/DIBKYsWtCN2Hv62Iiz8udvwZhUoNq6G2FvqV5pwTUY1sAtyW/vs/+uUEEn1+0Dx28mN1TebNWtxYACIFFvCdKu5o8cxWdb8fV44Pu9Terhzpsw0tW2iMSADkTEcRzLMAYLkHogBGzzyOkwiCJSTXG6EPUSxv4WA2ki2lVv9vDICUrauoEnz1zZxzHnG5pTYQSAwcR4iHnfD4/D8MwDSMzD8NApmpCWi6Xix7Gg40KMrNczChzTtHIWJZlmdPzh/jhdx+/U9UvX76kvELN66NeXMg5L8sycAwhjONwPq85YxzHaTowf/35bz7wtQJ9IVAczIDzeQHheBy+PC/Z8n98fDgejziEeckxzsuC63zLen84HJblbp6fb+vy159+/pALMZdS/vmf/5mD/NM//dPvf/97sF2v18+fP1+vVyJKqfzyyy/PX57u7u6mafj0y/OPP/4YJZRSHh8f7w8nDuLBNYOErJSkt2xxzCnlvFJb4GEYVGQII2q8rtDMwmRCYo3MDJB6lRdqRFa8cZsElMlcFzIrc+/zhmeZ2lr7u4F6ba/y69iqX1z9WDtBzTT/PYl9D+S6He9RfU/RUDsF+taKXUaqx7J+77SrXmmVnSkAQiQzInBTMN6G7/QkEACREFloJ9YfQTNoSPkmImgDXbqV9mbIGLemiZxRijVtf9rjjsDWcbuv9wEOcG6jdKiRe2w3KMK9tf/nln7tXOw+x/2NpW5TXK2HOcA4cincuyIdAjSzENj3Q3McspUdrefQ9QmqwnsSTAkgZgPIyZIeWMeh5zxos102S1UtG4SZD/HfLN3/m9c3OeDbLYmIA8L7MdQxBsByFQ+uzBTHJUIglzUphXPuaH4NLloMVV+QoV3ApXdVmzUeDfUhSxTjwMxDJGskdfe+XpYfhkgEEe4cY38w85xRKwFUgz7fcL7QqIveP1oIsRTtdf7oc5nRWmiag7EGDLSxYltbvccrvkWaQFjoEEJKVbHFU0kzEImIHI9DSna7rWbmK+knMCXEGGN0lGobh1laTFrV41v3Wx0e7Ntat1hhWVdmDl61ZrKdSioa8tNdIzOPIxNJB6ZKaxH2FucGVcGXqB/j0hSevUEo5xxDEKYYGgvCmyWw2am9jrynCVoluC0IEcF2sWd9Eb8BgggT0TRN54sUV9jgYJpDCEZKamQKJlLSUqcdl3U1r9E5uh6j6ytYAdNQ6rQJNbNlXq/X63EMJeUVNE7RzOZ5ZqH74yGlVIo9X853x8PhdPdyOZNxThpjuM2zqT48PByPd3Nay5pEhHkixnKbL5eLag4hlJyv16WEdZqOh8Ph/j7fLnq73abpICIiaV3zuq6lJE+ar7f5cMDlBgiuy3I6yTiOX758CWHwKD7G8MunnPX2wx/K8Xg/z9P9/czT8eN3310ul59++ikOMowhpfT508+fP//y7t07F/o4HA5PT08//vijqpLlsMjd/Smt6/V8wThO0zTPs4jEcRARU0UQT8IItN4uOeecM9REZBzHEJlN12WWMPreZjKu2aQSGxlUM1sAF5B4U5grPpNnt2ze/09qwjDNpsoh1ODeNlKR9Wy18RKYuRMke1TnJsWBnB6mo6elAEHUtKjbseqqtenR7rNeMyGiDop0g8ZMqrauuY9pGYbhcBjX1S0Jg6BqKatqabBqIA4ktQeDaEsHhEREVNDB2B4yi6+piEuY7Y+HE538JI7TqwRXAogRjKQlUUQgrkdbFSGg2zEPjt2wd2AD2AL0nDWEzSG9QWh7XusP1PVF13V1+Ifb77jrIDRt+doeYr3lGhVt7lmBc7kLbzrz1UguSxGOu8dax2D4/QPBzAjS5KmdT8MAlQwY1Js5E8wQIkIAEzQwENJOSfcfvr7JAfcMpjVjud5VdT8N+N0kWrzY3v2TD5IkwrrmDQCqpHkDEKOI0K4WW+OtfnLQ2rDcoWp75S3DIyKifca5y3q5C5T7i5htWlD9JpvjqdShlp5uQHG/aky3ixwbfPoqojQj163cr2E/tB7nvWYMorkl6q5o98tmRqgBihL1JkJmrrbAfXAphalD8dvrM+F0OrUVrj9tSMYG5nCTYpc62WLLANAOQ18K5s4je1s5oyq3EoioN6IZtgCrz3GqRLxXVYZghmIqZoWE3Bl73GNQ1WxWTNVIdwX++sggpjUFijF6r5Jjfl4qzmkJQzEXtEaxkskyrBBUiEIIbJpMtSgzDcMgIvn2klLKZTVMbkHm5fbly3o8TiDNtzyv6XKb1zUfDlHiQEQfPnw3DAMRXW/LutxyzqTl/v6eIT6VWVVN8zLP1yvinV6v1xBCjEJHDMMwDMP9PS3XOcbAjJR0nudcLCWMo8hQtLYSlGVZUkpxKOPIqkrMw+CSfjyO48P7B4r46+cvl9uVg3z3/Ye7u7uc8+fPn1NazeyXn38OMR6Px8fH+1LK5XJ5fn4+HYZlWcjATNEVUoqCyNnvMgwcuJjlnAEYkxqsFDIlpiFICBxYSKBN2NnTKY8e25HxXVrBFWIFxKAOQrkeFptX+7s0ciFvTAMArc24u0PUe/aoDQLquZR/obvGv2pPGvQKYNd4UmeTtNqhm44tkdMmiUqNouH3N8+Lb8UYo+9qzz5DCD0ITiuXQmYCUBAnKFH/af0EDfduR4mFGWS59/O450HtFPQSHhH5wbd2P2qZKRBx53Ywsys5exVfDciVOeVq0HCESaQmKiKi1NNy2l1o9biecvTCrQtYdAvWEbJK1yqFmxi3v9G6VAQRzUr767g8tYh0o+4W0pX4OshcYwKlPmkYVXWrvqCWulqtYlahcC1UsnnTDYsjl8TsjB8mIlOYeveU/bpH9H/o+kYHvJUz0ern1Eyxlt7UtVVGsdlu885yIahHala1GppeAYYg/bP5dqdNSrqLFEoXl06pFRH3oRyRW4QQwhuO8T5Sww6g8I3Yj52fGdf2AkAkPWc1ZwNtE8G2V+sFVNo1xRLBaINu6r4k8px+c8A7Bwn0u6ouEPvi574QhWKG3thOmzpERbk7jLV5ayOj2jhvO3y4vfLrwjDXpjpt2pO79SER5LxF/Wb1vG/r3DuqgRhIQoBup8WZk2b94ND28QEjtp3Wbss56nOvv0ZkMO+K9grvzpFL0c629YFdaqWUnMu6LvN8u93SOiueiUjImH0AIJiMSYuWKBJCyGlNORtZCGEYRBcupSy327Ish8OBiczs5XI+n58BrGm+O55yXlPRAZZNj8Pp8f37u7s7zflyuazDkJZ1TfM8ryG4Ko1HJDGG8TBdh2G43VYgxzBQ5KLJp/m68/PNT2x5qfWa6SDzvPrTWVIBisQwjjGVFWTEWFdcLi+K7x8fHw/H4WldKcgQ4jQNH96/zzmZ6e12e3l5CcFlkugwTvnhYVmWl5eXvPI6T9M0jeM4xsFy3VeWi2MzJIRSSkk5r1rRHU+bAnskrcV8h3gqU2s7hSBmrFqawqvBvPGXgPLGTFVJSCIU9WYlVWUuoECthN8nJOy1f2nnKtBicWsg6j5wb/vf91Lt2KlgVQ+OGexzNNuV8quUqIFhNgyxWz/f7TkhJxxOFc32K+dglogkBKmMwz5BCJRzYhpehc4VsaO1njuQbJ/UuuHdsmcHD1SE2kyjnMtiZiGOwrIHgbeDppRyYQH56JHerEyUX/db9nXem0GrrMzU09MQiFyBpA5nABMxSU8PgDrTrVRc1HHBjcfTFY59Baytdu8W6SbReWf+7m5d+gMiIqYRKF6C4C1cwLrmUly9J0vgGOM4xhiDUclJOxLghrWU3iz5j1zf5IBjjLs25w3id4/IAm8m8+9194a6RgoCqZBgiF070Ie6q4csuRRQCCFwzZyoEa3Rioub960E5jrIYf9GddG9Xovmad74yzdXC2/ZP8h26mjzrOiNrYIGvb56kb4yHjVTr9EKd7DLrUAr5oU3bnV3P/Vz9UI7dl3IAEDeFacASWOPEBDEMXY2w5K3EN4p/KROjNg+Xf0zALsAZf95GzCl/f77TaaU9gaul4tibKPNdi/PPc8w5N1qEDBOwR38kvaJLBV1iILALIGoq3YQYCAlKJvl7EE3LCefVF/jgI4YLOtCmqysJaey1iuta9YZgJARmbCTI41QhGyMIdT42xn1a3XtgXTWy+WSc56G6JzSH3/8RWAseP/+/YfvPj6+fzeEaGaiHMJIFDjQuw8fxxjO5/Onn396fn42M6gt601VA0sI4fHxXnzOp9smVWbRorfb7XYtRDge1xDC8XgErtcrVIvIWHea1H0SQhgPg5l6eneZ8fPnL9+/PP3u+H6S6XA8ppRutxubFs1WdF1usFLyenz3wBJv81VVYxzu7+/V7OvTZR6Xd4/2/vEwHIa8pGVZVEEkqppSCiYwzTmteSmljMMphCCBBWY5FYMGZQkSqQtrtFS4wAjq+7SAClUSBECVW+Te2u0tezZM5mMv6yPuPKBa9auIlCNbaPULbSFeiwaNGCDWxrXs+ySKmJEqaSO+csuXQL/hbFw9qkO7JUPrFI0N+UsJXmgEEA5iZuITQwUUiBGIyDxPUHDL6wssG6RUcHgPs6EW0bCvovrxAcHMtCnSdEtCRJ6Udr/YDshmB1zukUhUJaXEG/QGrZrMPoQazUTU5IFoI6t2BLHnrzWLYABgAbOoVrO2zyK1sam9RmtEFLyAJczs+jfA9mSpinhsdlhVG+sbwzCUYl720jazDtVDWauksdWw3HJeqwO2YvDc1zlZb8cC+drjG65v7AOuVT206qZ/Hp9+hebGGlnnFRhrxg4iqZLwtqeZIRIBLyfXAot61r9lrkxEpY6O2QOh2yFpGHKFqV+5quZHm2Dkq7C3+1eRV/FwaZ3g/dq9fq9Gb3sOLcRGY5b1W+34s0d23axLmwHVfq3u3T2Cvb9kN02IyRMwHyGqLXTuTwEA5lR89biqbtHm9tq77O9TWmDx5vuOKNAmqEJWe7hfXdRo4c2vk1nR/nY7Bgbb1iHmtTfP84ttnh6AUTAYTH1UxhuWpus8lGIpazHVYnldXZJMd5dDit6SycwUgg0DgBgYnCvCldeU1iWtWpJpGgKnIQ6BCYU5MDTnNeccaRERH4i7rmtJq++zdS0pQQBVfXx8BB7N7Hx+vj3drrelKLSkGKONw3xbLpcbc2Ams+xDEQplr+yqLuM4isS0Zo5yd/egBeuanpaiisvlcjiMDinP87KuqumWM2TAOIYYJeecc/r69au3upweMgTLgi9fPo0TmDFNx6LFzArser3mdSk5h8Ah8Pl8XtZVFfcPjyPxMAwfP36fbpcQYChqGRhavFhRrrSspbCirHlZcyqlDHGqSkxap0kPGMBMnv5qB6LFzEiLoaBAWQVmXKpMmxGpD22whqEY1W+4xak++DfTEPMynh/h3dS/3pvnrSZ7F9Wh0cDSaLHELPtf2r6gqgljlQRafYn7/lJAhT2R62Cs1c4fqM0AG6KDAixg9VODFjHWT2wV1wG5ar2bXDdfW1FPAaFQqzxedjDr3ckbNJizFa0SrY6kqGLNWRof5c3Fu25DM2eB+WDvOt96O7k7V9rxKqnTcZBz6ZyhN5e2rq1utx0UROOUmfrUSe/8Gfzj9BSgl+q69/Wow19hmkjVB7LVCma7oy1rMicUZ2UgRJIgrvQvgbwG5KIOzfH36RT4X1kDXmblOo8FjTv6KsfvG1ZVYSRMxB0oIJAz+5FLi7+MiBACQhCY+JCYmiNqnVNEREP0lHiroFSfKq+I9R25bV5w+5HvjH3CuvdtbWU7/mNtaN1WY8CrKuyrOaMd/+yew/cnmnv2TLFta/JizL5wgteOvMG624/Qyt7UAkBykBEKkMd/1bJAetK8HRLXsWi7Z6/d2g+A1REr9UfM+9XrtZxX4UKrwWzosbXInKqhqMtSKSEW2y9TgClXTMlZzcQYB+QiKemScs45RnZMka3407f29MFgBltgVeZiSiSU7Lp7prWgQF6KMyIjgrENdhhzzlp8gp+WtKxrTGvIC+dEppTXedaigYUQg7AwcwDyPM8uxUxEl8vlej1rLsQ4naavX+fLDefrnHMOIeScb7flx59+MaP7+/uUFs0lDmK5qFopWRDjMD4Ok1VXVcxKzhqjMMOgfeysqo6jM1fz+ZxjEK9KpIS0AsAgGIZwOI7ruj59vf38y3w8SpDh48d39/fzbZ7P5+fPn4m48OPvUXA4Hk/HyUo+PyusENH/9sMf/vVvf336Mt8/jNM0pZzXdZ0Opz/+8SMzCwVmJrM4DDGOzOwwg5KCYVW0hGMUJsp59TmMIYRxPHQqBtcqbxGLZmooZqSqLrZU09lXxkZfk5q9OdyDP0ffTDUzRexisqLV+7ZUeF9S2ReJtjjV0yxqwr8eYO9AnQ3N6ndCgKGAzKdj9cy6vqCgZN/2Vopq2c6si3W4NsSe19FusqJNxNUJ9b7NvbsCoGq5rGYWgvnYYgJCqDBAKbVJYbMVRDAhWAwxhOD7p9G2/Sao9+Q4JfhN9E8OyNXZEfvvvy2H+sp5faXrN7T+n22+S7/2eJXv7e7hqt0j+FzwbovepBlEIDC1CVTdMTPXG2BGjC1vbPokvtQsnHOWIEShZYPCdX48aVVi9MxtL7705kP/D1zf2IZU4W8nNlWZGvgqkGOD7Xk3JTaF7p6lS1SmDhOag/8ggAWhTdlufq7iJ5vEmrfwGtdh2q/9K6pUCPnG7SWZnvP1D7LfA57hObLAzB06pVZe6pku2tFd1+LeiNtTd1Ofc0Jze2g1Y4B25d5aERMB8zZQGs11+X329qTeWCWVFvoaNaoadWZVzqajvHWvN9YAld3r9zijGBz57+/bD/k+/zZrFKqdrNiGGeyu+ui9O8K3Q/2DKjRjmsGhv9MugtnIk0Gg4IhIRCLkmBw3/nPN1Fu939u0IjMrzKzEWFJw4Yv+uIkoDgMKyIwJAiN4ITEv6aaqVmJKKaexrKNnwC9fv5CplpRzyok09vHm7KCWv/jtdrueryD8p//0H5l/Yb6O4xiGSYSyqhGdz5fT6W4aD8RCBC0IEu/uH6+XlyAyjYfHh7vDOKW8PD09vby8QJcOlqS0vLy8aMG6rqdTHdJwvd5yLj4dJCWcTs63Qi4r0XQ4HM6X27yAqHC4fTgeTneHNc23my7LLU5hvl7n+Xp/PMXAJa3X63Vdbmx4vLt/d/9gRsN4CCGY2u12O5/Pj6cwjqNQ8JqaT3+zUtELCIqVJc3rukrkcRw1l+v1ut7mGOP9u8fjkUQEWoD4BsozM6NS8UAzs0L+o3r4Sn3e9OrkElVVqH6aVJWEVbUYFd8DfTO3c9e9L2q4aX0wH7dp3NzUSbs96w5DrYTAjVRsVDmP5C+GCgSz1ZleBCAObrvJzBvrXHuZxgqTK+A1uGIwq5W1ethLgfttVbPcnBNKj4CdHJST9g4cbmaLCOakqjoh2/eShhAAGWJts3HbiJpQvnVpzNW67pyZI8DKO+uxzULeiQd0j+gXN7nmfUtLzyltK8a3HyGKIMZqE/o0wA5bbjacqiX9dQZPhHl+Fa/sIwkmkECblWcrYgjexd5mOHZblFtPVM/cGoNsePuu/+7rmxzwNAWu3cpkxgbti0KtOMeti9RckLINuUPTPSD0BiHzgrYZ+QYKjc/v264x33C7rczsRPNSCkGIQgfDd1kaMW8zdK116VmbB9nrx+2v3JKaqhqKVf72NlZz/xSxBc7ksvje275fn4bwtLyzHehhCC4oqqqlbMfc+hZur+9+zh084GvQ3GFrJWq/3L6o+TD21IBslXZve6EcZndOABRY15xScuSnqjXxKwZps0FNAa2t6v4e9v+JFhFXq+f3xGhK3q6rX0ChL6affWmlJqulIIwjjWPsJQN5XTRaVi1GZlQMRU3NqeA0DENJawhBRHKzu35HRG00aJ/IzXx3d9cy4DUJZ0LJpAV3d3clrcutzOuqJZchOJ31eDy+vLwsy+KeeFmW6xVWh0zUs2BmOZuXmcdxBFBMD2HikYSYrOS8fvz4EUWHIT48PDzc3S/LcrvdHDp2FdUQggZNKcF4GKLA+1gOqlqy49Vrzvjw4d28XC+XeZ71dErH4/39/el8vpxfAMbhcJkOw5rABUaYpiGMh3m+Lssyz3NJdZoCEf/pT3/64x//eLx/+PL0vCzL4e4+5/zLpy/pdn18fDyMx5w1a4bROqdlWabjIcYYxpByulwu59t5HGOMEdlu58vz8/M4jqfTSUSYKOXMVbi9EGI/4aj7pE7JqBkwdXpzv9Rh2RYjbo7cWle3qhb102HqCTcFMIa45dV7HGsfi6MaCi2lxCj+a9SsRympaAYCM1jQ+E3VBzcHbKrFjMwIXtMuVQVChNFMExGyrQQBgeEsDds7v17aU62zg/zo1l4mQQhhGFxbSoCkqjkhSWIe3UtxO7POdvaWMFUNQYZhGMfg1c0Gs4NYG7q2t6VU05sNIXM0QqkFvoZtdDdtekSbwfR/gkCLl8b9Dzns/HopMC0N/lcACUkkuhf0ZmU18yif9hUo2xhnZh5tWDfy2EX2u+cLZhiKuWQgXKy7AAoqUvUkvDm12xklYlVLKbUiAuecU8r/yxxwHOpSoiX+cLCeHHXp5tgkQFqV21RFJEYOUqeHqEKYhAmGdVXVIhxiREo9PnrV8tkwkwwYs7nemSZC6ZAmiIkYxM5KgiqKOkuY2OlzjJReTUMSYT/YoW5rABWa8PhxHJtHaXGc778wDP64cseuBQREHOodE7KitFQyeQnV9aoIuQFKIdSZvtrK5n4xSwgSwqtg01qMueeWu7pNKei6HClV1xujaMkiwjGaVdVyMRO3L1l9jwtRAAbmGOg2q/cd1SCmxbOtLQoie/iayM+lVmn1bpxcSzbXrRL6VBPftxvOXnF7nzXapHJ2J6fouSbSICoCJoAVPER2vNG3SlYntZlGywNF4cqitACeGAGWQQwWwDU7i2pWQ8BAMJYwhiEo51VzSZpsYs6BhomFwm2db+l8XouIhHQIJiTh+eV6WXgtDz8/fyHC8d1/kfHy6b/+v//856cfvluPx8ApvZtGfLw7n5/43WGaplJyjOM85zmtxiOLGOinr7cv16yqzysvdBBTGUYfAitEeVlKzuEopmQh3FKW8Y6CfXm5Xq+IYTyvzyGG4RhTyucbjPU2c8oA4907GcdDWlefRJtu0/DueyoXvb6szGskEh4PU0ry9PQE4ZlwGCMi3dJ1xPjw7gjO16/j5WKllPv7E4tdb5eSVxEhWw1TXmie1zQvgw1HOY16+Nvl/0bA/ftpGAYezLAW1VwsrKEEL3+ImrGNAtd2TiQ+PG4lglkinUAMCCyCJqaRJIDUdMm6EBvyAIbJSHJXgKVACxTMAgpgFDYTkxCIgbQb62nNiLuZ9l5/alwhn2IZwhAiYsMqc/bOn5CLsbEQaeczOJjUuAhwEgMhZ5Rs3k/OjGHwBI5SQs7G6dG9SC7I2VAQnb7KRqRkhAKtTGMmotVmg2ltGWHAG/0lrTOTTaNIYObMVkiNiZb1aIaczLvGVcEkIkOUCMUyY+3kkgqt1Ro2qI4bo3bucy+XGkC1uZ8MaakSDo5OpYycdZy4HnDvE7HMjCBBARO44ggAlkCty1QEFFAgRCwSqFjOWUI2oGgUAQuiWLM/3pPG1eNQ9lUK8ZBN17SUUkQiS5TATC2H9ljEsst8Og0UMIBz0lLUjJjiEAcCHIEvTVIiBIjwbQVAFCLMSgPfovzj3hffPA+4Boa+CTs4oG3aT6OOV8h+vtXGoRCqvrFVZ1a0eMeqwalJXM/GPlDFDvTuEFbf/e2nPTQmImgLA/3qUPAuenp1+etwG1SMVhTxlHFZKiCDXd+qVfIU2v3UsINakalXghtboUrT4Vclk1LVSLZuRb+c3LFnILePrL08QzutEgA5M7c5xB0jHcfYAgjyepXv/mV1HbHQsGWv8WEnsfL20b+KW1sENsTtiewvAhprtOf6qFbwNSL06799/aZcHTARgX2eAtoLU4vZoQSIWU6/HgMMgoGZydh0W/7+9IkVChZwjMPxiEHYjms6lzwMSYZRhhTn9XJbl1KSFWOGyDBNmpFUn5+fsa749OnT+8f73//+94PUVx7HMQb59POnl5cr84/zPB8Op8fHwT9vKWUcx2EYlmV5/vS8LIvjEEJCBhdQizES0bpkUz1fr7Mly0YUYXS7zSkhukszJqo4pBYsy3K5AMDj4+N33314enq6XK8h0DCEUkpBIaKcc0pp5DGl9PT09Pmz/eEP0Ts9vDmYmb0QOE2TY0Lruh5Ph4eHh7Sut9vFjK7XqxUQ0TAMJdv1ejufLw+/f9eCKlXV2+02DKMhXC6XMChYIg9kZlZyBpFJmFqz7373tNFnO01JEGCcc6YQPCmErQWxEBmBA8xrxpYbgiahERretCrsN4B/IcJA7AikG48d9Lpt0Q6fwtFIFYcoiWrnqL+jJxI9p0SrInVD0dpfQ38pZvZXCCH0G6YgDUc0Zo5cMWThg49kIAZRCQz4/NrWDgti5sGRSGbnTtXboNqHX+GGapd493VjS6pVt2SNjppWdNzb24j7CjNvFp40NA/QIhVmoLULtkTC681+k8RgoY1D0w65Pwth3ss/MgKk8lWoSoXUZcyJPe5BQ5vJBFACYyf68MbszHMh4tak7qBdEMEY/NO9Gk3xjdc3zgPedMiYufeeDwP1DNJtdBshaTGGGKsHqh8D/msOPaAzElURWlvbGx/MdYrF5jX7n/TvMHPlHvOWpmMP1W4o1lZO75f/Tv/+mw/e8s7abj8M2D+SN5Cs7hSdHLjYI9XdfWIXuLSzWl8/xurUS9nGOaB6QfJp1tjFE3GTkO3M8CpqsZP/3NoDeSeejh1BNAR/x3r/Ils/Xo3cs4tR1+4vD+D9ZXkXW1DzwfWwtafeUaN+t+3Xf/sSEXemALY5lLt36evpl1Oge/RjzKYOBlK/Aa+6+TNa1huKWs7IiTVF00hEQiOPGkgihlGGPMzrMMyX6zKvT2dTCkMQkRirgmbO+Omnn06HYQhhXc5PT0/AMZJeLufb7ZYz5nlmDmZ0PB6Z+e7uzh1wCCGllPLiDtgdgKqSksdGzDyNEOavT5fbLS8XDEMJgssF6wqRBTZ5jQV16FZalpQSVDHPs6qGyESYZ3t5eYkxjI/heLzz+zmd7sFi9jUES6mcX65MIYRAkLRkEo5xRCxmJaXUHPA7LYmZ53kOYQhDzEm/fn1+enoyo2maTlmcl1dKuV6vOefDoQxxIh4avT0HDRQohEAcipXWuGhmBU2RA6S9v3dDTKjTstTMSl6zmXIAk8QWv1ZysBKRgrELjvdbp/Jm2xFwJoF3RO1+wfovVGrQDuNx6HZZN+1Vs+DzdEWoFOsHvMNmRG8tOO36kr3WFrzK3qHEJtFagbc2iF6G7ZC5v/CpYD7fNxhCkDLIG+pWW89tJVUDsfkItVoB2E0PaidLvWVfC9alKwEEgEOo2UtO1ukg5FVbkJo1yV8iktpAnrMTvJlZm3YvsytsMJTeWIO/ZxrclrvybwiBSEo2LV5OpFiBURDARFZlvbn9YfUF/eVboC8uC0psLF5xqAnx/xTX69e3KmF1d9XTLABonIUOqPr+DiF6OaGlazUEC0Fy8p1XhWlgUgqENyWm/kbucqy162HLb2q82/2TbUS+rf2gxV/+dZVc2XvxfSy8/yvA1Q2BXRsxtXJsjfU692GrK1sr1KOTw0t55e/3EFY92LtgGc17oc4lDGWbRlmNDDYWEgNbuzM1Ir7Xwl2YsyHS0RNcVcRAapRS8f4iEQlBunJIvz1qbQxdsBO7JoT+9N8ksf2/nN7cDagZGOrusK3i6z/49UX1zGyvgvZJ979l1dR1B7xFWvDHVztCTItatpJLyaWUnC6Wi+WEnFhLgAYUIRUqRAoyGDFzjHHCkYgsrsua1XJRhRYiDAE2wVBSSvM8X84v94fh/hiXdfnTv/zp8pKJME1HEUkpXS6XGOPd3Z1PODCzYRg+fvy43q/n89k1sIplJzB64BXDeDoej8fpep2XBSFARibSlHC9YllW1LDYSjaipRQbR5jh8+cr89/u7o/DwF++aErLMD7bePRc2cxCCIdxOp1Oh4MCmKbJ5Qw9GgghSAytbzADqFXtODzcv9PyxBwCi2lalvT1SUPAYYpPT8+Hw8G7MPceLg5CImYl50ycqUSKFAInzWwMLSalzoVFtduGQu6VrTSKQz0sngYamM311jEMMHBRsyIEiLnmZWUV1L23oxDuBKV531nQSy39FPRgbh/n+fdLKW/K1R0C9JpLC6C7NCztsLQtmdnZnPo63W1bEyKumOJrBsbuYm9I2bQ0h1ejxGvjTXWIZj2OyUYghvlkv3Yma4W74UfEsKLeDehDX7rlrPeccw45Zk+f2mKaUa59wHU9c87OHeMmLwh4Ms3MpEpWBiJ2YlBPVGh33g1b9Zda5h0C+9ysnNUFrr2Lt3OpzOUEWhJd8TRmVXjdd5qqu27zJKzKXeiG2v7Pur7JAbdEc9PnBGBm61rjuLbVJEYWQU41fMjZUU3zPKzBMpsH912YUqbWbLrDEbk5mJogduMdI0pTF2s5cYuz2s52iyMibepDD3u5hYfU/eJrB0/t5q2f2Nbi9ttOo52W5hfrRS0z9lZmtJDZuDfSvUJlsaaaFvs/ItJ0aJOq9hkd1JjS/WTucDMYwEKkUpUIOsxOUOdzwSvnRCyerbZ2eFe9rrCSn2qvWhFRCOR4dSkg1zUnInRFzNcBLFVxgPrf+ivv+29eLQkyqpauBVLt3/1g2Otr/02YlZKh2TSbZmoOWFWLJYIZKYsXpEspRS1fl0uMTGw5z0kzxMgQON5/eH+53bJCbV3W5KN5i+Ld/cPd6SB4Nw50PB6DDKvezs/55QWn0wbSrOt6vV5TSqfTqUV44i7Z+TXp8sUZWCGwM7NCWJno4eHhcpmXKw4HvHv3Dva1lEKE52dlBur5yt5u8fCAcRyWZV3W25hkGOLxtJSC5+frNV3DEFLKy7KKBDMjo2k83N/f/+53v5tOx+fn55xdxkjX9TbFSZuWwvl8vlwup8Px/v5+HA/n82VRCjKcjo/Hw21d8zLncF83f4xRQMMwxHEUcYlEYQ3eHZbLagtSzhqjclZlUiYw2BuOg1mBiVqG+fgRQZ1S1YNXExaiaAIS1JnrIr7/xIQZvJsL66e57kGtXQ+OEu1TVdXaCt99xi7fMNWKFRlcVC2TNzD53Hg2T0qdplqPvIHYYMYCCQ13bXNUWdoLqIqQZ5+VvFKKVYfZwSqxngnoZh92FxtcG5MJxK44DZgiBD8zXCVQAEMBKse8atRvEYZLeQiBhMFCZExmqhAZXLnCGsHWr5w1JTVwKRnQOEiMAkALYLXG7NxsX7c4CPx9mUQ4RHKqJjg4Mzfn6gjceNK+27g0M9MxP/KmRKqoNN5So6klNNbsxs7LgBnSvKIq1GnrUDWKXdHz1Ut90/Vt4wjrTWwZEgAzi5FUq6YVGmArDPUwMDsQWnovTc7FD4UIb20ChJxzU9Z+5QVfv/urHMsYLkbYwZaezmqbCuyvKSI9lexv4f/Zszq8hqzdAbuERb8lZlqWtF+BfTqI5qSp6qS/RYHe5NmV5Nycn3/tgYjXznmbOsWOJ3eUlaoQvHQX3lLutkq0f0xt/xHmub5+jNxxaUMt/XLrfSLavp+S+tPxXLkxlo2Z+1vZryLF+qzqCtRW3DcZbH8cv7485QbQXrmfoP76NZj3klg/VN0Te+5bSoEW7/7bR3beWUHEYsxCKCABF9xuysWguq5rtVIGVZVAMYaBuCjmJZkVNUfGTUTev3/88HgSKl5bCQECoGC9zWy4e3w3TdOnT5/OX59fnr761CYzc2WPw+HwcLr7009/FuLj8SgSXb50WZbAtxDCNI2n03I8Tnd3Rxjl/KkU3K5ok06wLABwOuFwiHd3d/f3lst6u92I9cMHnmc9n0ErYszM0JK+fP5au+OQUyrrmsfRiGQYJuaQUlrXrJKJarf3+Ty/vLw8ydPj4+PpeJ+SHcbD8XC6nOfLJc8zQljfxSnGYQyDQ7YhBFJNqiyIweIQhjEihGKUUrJ1HeSuFGJmFBIwmZgxcS4lkRFBQBKoGBVux97tM6u6S9OapfmGbA8X1fl5II42bgtwYnPpZ4e7p6ygNDmu13LH2n0UBOaDjavELJlLRpSKwfp26kEwdpiZkzZc079sorwswv5NAGpv4oBN6tnMWuhg3bXk0g4p92BU0KY6oQ4UCG4gjdAZLr5A1vBYDlui2S5tJ6seUhEAHiUYittMyjmX8kpyrhQrJaW8EIFo9E/taaTDUGSVBCVkLgImIn5wXKijAGCYkrOaAWNRVQYwDNJtIxH6AFwY1BXVmni+r9NvmhN65YDhWcSbbMBZuXVAJNjj/m93uvvrW/uAO3TTnYcZxeBZFpfCqurpkSe+qnDxnSr61D6MCDsH2N32/kc9POmxT2+/2QWnm2Pr/qyXaTvOvM9ombeCqDXg2r9wLlJ/x/ay9e18DLDIK/EN7JLpLcPb5db7l2p0qtIBUlQTQJ2i2QNw7DhZpXWCU63Rhu5g/KVKm9rUV2C/WzqboyNm+0Xu/rU9r7qt+1LvX6elEeSmrVff/z3bhhoX49db+d9+BXoF8dXuqe1ve9EhqxWFVrUQas1XFbdw1kJdnzpnx3cH08gGaGFVVhgVKsKk0zQxSi6riIzTMI5xTmvOOaVFS2IZmAykIHV24cv569/+ZsdpuD9OVNa8zlpWNDqlKnJWZh7H8XA4iMiXL186+das5LwCOk1TCKGkvCxLiDXvKaXM8+xbxaklOWeQxRhENCf1vknfqOtaj8O6rsMQAE0pE+NwCMOgEursvmmi4/EoIYQQACzL8pe//OWXX35xzecQgnHVKj+fz+M4eqxwPB5Lsfl6+/TL1+thFYmDTKq4XJYvX6AFH95LTqpJSzCz4hNu/NWmaeIm26yarZKPuJQUiFSFIEpKnAkC88EYwkhGg3J2X8JwicuS8xqksBc/65g5T1M2B6z6RhCqEaysusw3qDIziNj5zG67uufeT4/vlzDDWIv2MtPeLu098WsGqBs03mNLaP2yjT3TJs8T0U7y1vwT+n+2s8yKHb2EqTYWGMBtOMWrQ0cg66UhoEr/vfa+hjbZGJUAKwwy9vaN+ntaC8N9hVWVyPsvYB7sV8u5O8B1cpGf0zqqh1v5TK1ApYOjAGBUsql5Y14dxa51TrB5zf5VlR0VMS4ZJh149/PuP1ICN35o/Sa/frRVOqQWjH+Lv9d+6x+7vpEFXdHPniz68qW8bbi+rMwVvC1aWxs7GFtpz+RgC7xi3JgO9XSgF2OAnJu4pX/65p7RhihQ20dUWUgo5ZXsYiu1viJUo4UUvaFtSxYNZuZ2s8MgXkZEm2W039r+dYtO0CPrfc2G9rLgzF3HAzsn7Vt3GLiTjbUN9UNlK9RXzpm7R1/Xjfrby8lmzumgqvy3e2QiQasK+aauTkQxxu5937hGbj3ZaLHC30tb//3XPkL6zV/Ybf9XO94aJNgdbdFcukDE7sUdgq7mkZjJhImYXIbpMBzNiuaCnJGhEAKYdJiiFmJbRYb7+/vT6XC5vKy3a7Q11frsuq5zSqsZYsTtdrlevgbguw/vDzFQWYWpFKwrTievIJR5Xscxxxh98B+AUpJLpPm4pHmef/jhh69fnuZ5XuaklpdlWZY18SoSzcCMlJfz+TmlkksmwuEAZhqGUURinC+X4jDSPM/LWkQoRgGVZcnEeP8ec0IpOB7u3r97T0QhhGK6LJ+u5/zXl6x6ff8e797dOQUsjuPzy9dHenc4HEIIj4/vT6f7L5+efv750/U6Xy9fL8+377//fck2Dh47huW6DpJjzB4CxRjjIEMMIgwty3KblwQRCZPEUSrF132tOOzsfkYtE4Q4qmW2wLV4qSKkpUpBE1s9lQW82Vmibr237n8OYet+8aSKdw0XaJFrbszHotkqCZHcO7oSmWdsXPVtikvTdzO4WfNXrRzUj4z/2qZx0YrE4+iMpK2FwR0/C7SCrlCtw7Id2KFaDaxjBFFVxIMX0Q3kI20dDNhpjPG+oPmb3rfZCve+rRzLgO4/1GYf3Pv2r4nIu5tgFdrtmUxHHcyUmXnX+FRnmxUnjnmjR0WwSrbECYhtJdXdEDMzBV80gISlY6U5b6RuMwOp01e59TX1f/vzSrlqSKDOBXe+9K9b0v8nXN8oxDGabSVMT15rB4VsdAa//AExM0v030+pDb8kUqViSMmWJamqcAAgEuvOY6BuevU42u3sPjlGg0zbFF50rfCc6/SMcRwdo7Nd0rbvVOm5b3sA/bZBRClRy/n9R7bPmNHYFj3ybXPft14dNJ/nIbbDyLsXREo9LgaRH8LqlnqkTI1gVV8QHl0S0DVXa8nEewOoZYcesXJoNHqgNA1nj3uImFvLk3tzn4AEoKHudfX2WI2hfjO8HofesZyipQMK9ZM2FjJ2lrGl1P9GEvzbkWZjqJoZ3IGN40iEy3U1s9vlbG12lg/2KNnIlImCyBiZYa69HuJSSspqChiTQIIIk+S5xMMAHYquYwwx8DgMh2koy9fT6ZDVjOXlcl1n3J3wT//7YRqmH//yZQW+2Of/3yf84Xv8H//lP5+/PlspeUVa1hjjcpv1dHcYp69fnvzeHt89DMPw9PR0vV7HcZimyQx3d3fjOKaU1lSGYcg5L7NOk8Yh3s6IUYZhAPLpRLfbMk4DM8c4uJSKyO1yWV9e8OEDqdrtZiI43WEceTqM7969+/Off5JDjDG6CbtcLqo6DMMf//g9/+3nnz/h61cwn9+9e2BmqJ5Op/Pl5XQ6PTw8fP36krPGOIrI9bKkhGVJKaVxHA8H3G6O6dE8ryGE+7tjCJxLenl5IaL0yy/j4Tgd78N4BEdKhdbMQXg8jOMY4wgPXq0QioQ+DWnnKtiIqKiqZqPQYzM/brdbilGGgb3WSX5+hbXyfaizCL2SYq1DIUbpBEaX22s5KHm25t7L54gDcGWGPvUPqOdFFS4WRlvjg7d+bAF93dBN7mrXKMF+vkKASGgpYPMT2HwkEfmYjV1GUX9kSiDLCuYgzY5l70YpdQHbn5DDQL951Rhmd3m04/8mBhOCkSqVndiWiOScY5SH+4dx2oYU5bSZU2GEyGasKuLm0zoCryVnK7quThflEFAKLUtuupjUMhx4YORRMEF6nTGnLYkKga1aezUrPgSCmXNeuE4lgPN/PTJzOndb77dr828nCf+j1zeyoGG/KqA6TaNP7d1fw1AF1d3wOqnSdTk60d95SkEkBKDVQt7Y5DfUVrQG4q6E2f/X/2ccw37T7zPRbvTbUaG9g3mTs3aSpLY5mth6i19NW3KwfU9Y32O5uUVYNexq8bJrxraIkluSrT3EQStCNIDBRGhzdKjdFG8y8o7N5oL9M+kBrMcP3Tr4W/Ru7B2G5qHSdtv91fYg2x7K80v4V5FjxcPEF1ibN3epPvxqi9c1edMh2klYHv1YxYi4hfIxxqFdJcWMbKreFMkQJiIv0Jp6z1/OOeeS1jWti66L5hmliGXoKlDoalrmga5nWdbb5XwOJa2XZV7TeU7Pz19CwPsPuD8dReK7dyMTiSnpuq5gCh8+fPz69BOcdh5HEVnXlWh0/3e+PF9vZxc2GschxriuqwlBzaEIYqsYdVnGMXpaoFpyWdd1BTiEPtNGSzHfn97v1/qMs2pRxTwr0VpKOd0dAFhJl2vyY3t3d3d/f2+Aamb+kjOYsSy3UpKIhEgOU18ut6enJ5E4xilINFtE6kEIkQ/HOC/pfHn+w8d3wxijDGY2z/OabmZFYshaJIeUFuJAkYUDkQlxDJGZSymKzMQsQYS4NwJ7ZOz+pmaSRuzNr1b5iYARrIiLEhYYlHzqUp8PvEWuLk2FKgbXf2RNcdCU98q1pvCQfhjC7vj0/Wl9z9quR0NV+1jr/bmjvZqbwDtzegLdt/7e8uXtbILZNd/ZCfiqWopXMUkIRAZz6dYWRuTeBFELfB6LSKiTCZhbJtCgaiKvDavBYFWzouXc1LP5LsTtTQcARKYq7AVT3TIZZ7T1z+7mkRsO6h+WGiLYl72tZ8W3QBpocAkgE5h5758y8zKvDcH0N6/F+FICsQuOdhp2XQFCHUbnSVrOIiLj0AN9/0Ktqefif57r9esbWdBbtQOAqnMWOuhX/70Fca/yfXIUn5iYqcZlRLV5zh+P0m+9Du1zVrTCrTZW4s4vb3/Ym8xcHdL/qhpuZqvTBch5ELZz0lqH6FL3OsIo5HTrXsaoP+0i4y6q3FqAqH9qf82UUqUv7XwwtTB+B+nUt3gdcNTDrKqeN/Zn0Zcixtg/Xf/4Wscz7FfGWsxUHXbPFL1HQt6uYUWnW01987X+Cysy/foB7G4PO9tkZrEaCH+u2y39qgTQLtm2qxcQ/WsR9hpXy6QrkS9GijH6ZCERKbnqivfHqaqlrNDirFovkJRipWjKSdesaYGubKuVlTSRqdzKC0pJKedcrl+WZcmGZS3Lenv3PpzuH+/uHv76lx+J7TBN94cjl59iCPM8Pz+fXUnNr5TK5XIBcH9/8iVdlmVdFyI6Ho8558vlcpwGIe9XEWYehkHbsEKAQ0QMcRhiSskUzDLGSUQAXpZlnm/LoqXCleqz1VTBtRhWnr++kHgHczSzZVlamyY/PDwQUQjhdrupqlW8nkOoPP7r9fr8fD4e7g7jMYQQYwghTMMYAp9Ox++//1jK39YV87yaKoOKUilLLosISeQphhA4RAmRZQhxGMI4xTDSMALQAtKMEFj8jEsUMXAdi17PuDBRYR91VP2jU52FYJWuZLlkKyA1b+4lfrUxqca+3Kd+mW0CqMAr4MrMnBihlkMY8MqU+YttHe1tz29nc29M9jaz/wJVEAtEyHlD0fYHMOtOcx5Ag6MyfGpQMSNmD+6JGjVSq/dFzqXJQCoRSVBAfMC6AdYrnC07wu6IdQjalABubcl+80wUiE1EmokwIm/99XS5RsTc1IReffC2frvvk++3GKtuUrOuWS1r9iIO97DGai9/aYxdNiuqZpZNDcaG4udChLjOUyJmBIkASusCV2/ohJVXiQT1PFix1TE729Tw22O4/p3XNzngrdTRvlDlnfWse66Dto5EsrR20vpPVYYFNoazKVICweWLtzy4B0f7bLv/Z2ntBMCr4l9KfX3rK/ilrXbyZrv7srd6vsO/xOwAeMVvRZgo9oxZFcuykarcTPjtoT3Lv4et9l3IDE/FsXv8RChle8T9kLub3B9RtHK4CHzfY+c7VZUlvD75PikSpZFHdg5YuJWHevhpu9ayvk799Xf3bPUQ73qyW824AkdO2aAxNnfYOzqqKEBrnOhpsVrHQ9pDajLAPbDbjKs3UQj1992oWFayaQYroWhJmldoIWqzICEi0UQpqsIKGxXVlL1fjEVNS06zqgaBjwIYhkGCFbM4ne7u7uc1z8u1rNA13x+Of/jDH1B0uS4//u36/XcxxhiHQETX69X9SYwiIu7zrtfL+XxOKTmL+3K5uAMOIbhw+jgOY4zzvAAqwofD4Xi8U7V5ntclHyaEyDBeFs1Z17XmNPf3cVnS+QIA797B2xzneSVZ379///333x0Oh+v1+vT0tOT16enzMIRcVhYMY2ibgQ0grj1Ft9viZQ4Cu4K064SUkkLgDx/ezcvl8+eXTz9fY8Tp7nK6G4eBJcgwhHEYJAQZBp97OESJQxyGUeKIOPihAwURCSH6g2MK4FjJIESM+qTMrPKd4bpedVywF4xAoArFoW/CzcLsmMn9i/3FtU+PzLi1zMKTP+dYcGsa5Na50MRQ659jK4LuErgdZ2U/pKifVqLOHdmqeB1/6jdvBu6pJHnK7sIRu/IZNodHhBBCEEfgitdca5zdtBkE/b1ICwzOCCke8AHqw3LM2uR3Jqp6IBzCqFO9ufP55hB3t6udnu1nmUDepdzPM7UPVVuKSJh5nLabV3O/63LlVcBnH9Mw8/E4qCInuOh1ztlT4Rijk3wlUEPdd3EYdbiiPrJ1db8AZqr0bQjgky3EzEDkxwHAJqj1D13fyIIGWv1v72CkjQX0q6Mx3jkqHqf2nwI5G+okZ0fMKhuLvJtIti3uxpbbQGn33H4uWVDysne0/d2lzZVs3pTbFBFzreMWK9TYcxcRdwDK9zp73xvXCSGkWj/UBnxs+Ws/hG9RWalF8t3pamHyb4bGMTqEbl2jyppUjRlynSNdWh7/Crd/ZVm4Nuf6d7RHqqoVXjYYiIltl9eabaEPWtfyLr3eHHOcYl9zZ4T1ZQkhDMwudWugUlDaGEo4SFA/MNro1X10TExi9paguMfeXwVPu/9sxm7bDH5vZMq0nw7LzEyIRoUMFCFkhTUITE3ZghQqYEpWQBDfdffDh9syg0MuFsZhnO5CHJf09cPjw/n58uVzCfzzDx+/HyQA+XgAgPv7++PxeJuv1+vVsY2Xl5fD4TCMYRxHL/cOQzgcDtM0ff3yhayp/RWEEEKQOERmNsN8mamO+RtSSimv5/Pz6XSKcSQ2p0N3fhx5gQMwwxAn50O9nL8Q43CcHh8fxmlIeU1PT/N8+/Tpl3meb8sM4HA4DEMkH89HwAbkkpnN8/z0dFkWAJcsoeg6HeI0TYdjHK94fvERb2WabLofpylyNBE2MrOCkkvJOWeSzLIqYRzviYyJOUQOo8tj+ab2LkX/B2AY15wETt3SnLNyUgoKkghymQUWDkAxwNvH6qbSJuna4j/ZAzNojjMEUkUpZMbAVvPyI9Ci/3a+docOaLXVJh4AwKyJihiA+o5oeUXfnKq2txtVD8exwqZe6aVcIQq1s6fu7dfHA9bEOtCyVdTSjOyn9W0ppv+VP2QRUxi5ghVXMJbYo04DmVlOixdT3TZ0+suYI2A9gGiBdIUcPEX2Rdpu9c3JrlhAJbsQfJBGFCHAhmHwYYsF5p4+QEJkIQhDBCGHnKE6VLsURQQSGsW6C4a0xLfpOviPa4/bG1MMXxZGXYEOGHyD98U3S1G6gFkfCbxtgn73tBPr8IHG/ZdLsVIA0nVdhWMIoWM1aMu/c37b5n4FJNVcEERI2pvr97JcPUHnXlLtvbxoxtpx4+Zu2XYsLTRXFAJy7gql1P4qN8AktmzYukQqdm6sv91b79vZ/LnTyrZF8IBA1ZPjPdjQ138TwNu7RrxOl4moE/HfmBu/9tEJOSqlrx7Hlj78nauXes0IFAxsKAQ1VZB4i6yjC8SBwdofkT/ldvi5tXLuHjFA2ynt99HC6ha7vP4s2C0I2lY0Y23fERHiyAjeC2EazMgYVmk14rthGAOFiYoLpxhDUKCWzQoRrWnJ6kraDGAahmmayPj569N8zbfjLREH4vfv3j99+XJ3nMcYSXEYxuPxGKPksuZ1vV1e6gqTBh7ITHO+u7uzoj7MXi07GaCgvH//HqA0LzlrKSmEME3T9XorBWbKjBBCiKRqngTP8xqjnE5lTUgJy7KGEKfxeL58uV2uP/744/l8VtXr9eq8IR+KRQawI8+hutt1gdEwDKWUlPRyuczX+fNnMONwMKY8z/l6O8eBxjE+PBxAq2oZBhpqMT6CssEdzIZbefmQFeMEVMQiEgUybgVgJg5E5Pa2PWK0h69mqpatFCUyYtkrtHR4pO+NnjK/0qcDsLFYfNvEEFDTDOqGCCBuRdp+hPsZ2dvAHi53IK1fbkCY90YMZuS/2Tv7RcRLcpVtBDJCcYpwViNiCm4cGgFKmcNmUnbZZ8vhYUDrUgS8vFnaTSnRzo8yCQlL9xHW5LdAagaQBGKqULBSfQuzyrkBjJlYtoMp3hJSK2v1ENaFsebz/K4UMGTA6+I+8WUcOMYBgJqi6t2pWSESz1RTkyQCVRXP2t/McIp1/6nnrf63VOWjAXCQIcZdsOIPtFnjPscJOwdnhtOIf/j6VinK9sWWI3ZwoDtO6g/pdUDU3MA2AzJnqn1dRu6oOkiC1hjXnUeDW7m3lw1D7AMHeyQLoJRN3YN2JWTUo1j9aEdf33xGM/MouAnLdXkHoBVEnf3IVXCOmqSt37Z1xS6/dtjARoAEoDn3+YBoXUz7kMXaLO72dW3Ham67Xr92+W41yg6O3oVKG+WhZfZvQz9sG473a+tL6kGLmaWyO72CwEzFx7G5GC/W3F6Z4VFzzx6sWUbVNp7TXu11IoQdfvhbu7Hvq+3n2gQp+6feu2RmDhwZZlbxA1WAjDgwB0gWitASmSOFnGy9rTCJgfI6L9dlvt2MsK45gw6nezNb1xsRHL8tBWt9uMWID+N0Og7X6/Xl5cXMpmkkopylaAJwubyEIHd3d3EQ34rzfD2d7iVEokk157LmnFVLKnq73e7u7mOMt9v5alfm4B+aBZ1IGGM0zarqoM4wDCJJ5iwBpdjtdiOiEPh6vV4u1zCws6uICMIDh2EMIbKaOVDsnuB8uaqqj628XHC9rIFxu+F4RIgYx2goqlm1DKM8yn1Kt6LZBWVzzimBRUlIGCTCAmkFAtRSUaIgYDEU0qRKEYPPhnN7vkXVrSVkO9UeMYmgAbAKmBYrIIUTffeY0A7cotaxtn+l2tVjVkPenjxVEuLugOwPWjdlHu+6vMObYLd/7bTqX9mEmpNZBZaCs3xLKRICNYCd2GDU217beeSd0J4hkifuTaffj4eD/L4IpZpfFDMTOpICYTvFXufpUGtLS+r3Y4z9kTiHy3T3dcNmbSuNu1JefUmDaXHzxb9aBGgBfHSSklVh3No9UaoegvqxNTNt9WY4Xta9jEN9u6Yvq7dBgAYJBKriQS4EvMv0rP3t3ha1tuO64P7up/F/UR+wtLkTezTDjFLa1Dt7xkaEZG8cMPlDMjMmIaKc1en7wkFExnHzvgC4SmdsnikE7m7PM1RV8o7YfpNUVR6pseHZo14izLNT4LxmkAF0/7fHH2yTudgliK1VoO+e7ssdU3JHvj94+wwV7dHu3eHOt2137htrT17Y2RFrNe++REyb99rcZH3/9ld7V0SvfyOEdt5aAIjdwWh33mvnW1oA0DXp5sK9EBt8NbyvUXtxy62u8AaR7fZ3fYtKrGv4MO/yBTP4zBza1dtAtreLZqYgf7I+Xa6UYqpkTXHF1NyWNUWk6BNdfDhrCEEGMjBIkEaxJEXTFeAgbIWsaAghxki0BuLj8Xibl8vl4k2bt3n+8gWi+O5xGYcBhBDCf/7P//nPf/7zjz9e1hWHw/z16xwjWDBNoZR8PD4+PDywuH2xw+GQc+YQh2FgjrnIPM/OM/jXf/3XH374w7qu8zwvSCJRVS8XjANSXvxUOu8shCKy3t/fxxjXVYiWaZpYcL1eP3/++vs/HC+X67IgFR2GNfgYVNUUkjPXegnfz4U1tmPO+XZDyTiM9cF5oqwG1ZLyEgKPYyzlrKYEXdd1ntUsjJPEIJ5mslntg6iXllKYmKCmGQBrENFA1CGPdpSqw3YRJ68ncd15RK1PL6uWlKyAjUSqgK3ZflfXv1vX1atC/TsexCzzPovo7sdSqn1Qff+/MRc9h+7jh/f5wO6DEHb+qVuDvtTtfmBGgDgTl+oAdSbd/pwq3QE+PLQdouyhBL0q12BdvdvAVEsv9AKgCOYNiG5GZhf4Uv1Hq1RIBrPQLmMhoIUse/XpluX2QKRO3VDVks1rzK9E+AxWWdONQE7wioXVMRcgCAenoZrfG8cdKxWt3KZIWZuZNB/swQwC265+W1eDdvhBNzW1fGDjUVAA3bHtzOV3//HrW6Uo3bFVhNBMkdVUkUuxNXk1LzTNbnjDtbx1byzD4L8QAlPFORRQ/37tuvdXYBBhXbOICEs252o1Xv6rtK+fJXiGqW3WUI/j1LK16ikxMbMEAqmhcuhZwM2Pqqop7cYVeEEUqh3u6CFIjS3i0DDSTkUuMAUztDj7LgMqgUIIIpyXzByYYVWfnUzJDOrQB5SZQiCQN8ysYTqB0ef1qiEXaMEw+HKRGYrVmFfVLDifQKRKzBgDjgGC1G2UeFecaSmFOIIroak6dQKJaDEWUpSUM7GNYxSIIp8k1PijVMzH4JpoBgGEcraiydRAQiSGmHMtoscIYbBAa2ekM+m8PayF5C782uc3kBFTw5Y2u0EEQImAzKRBEAOJEJeaL7EqXPdXyEjNQIIoEsmeHX11aDplJVjgAEpzuuUVsGiwdb2l1QA+Btai0awsy3X9zMP0/u6U1pIPp09/+fT+AUJ4uazT8RTHw98+fwn3/PCHD59ul5//GyzgeA9HS9clTwMYOafbcZiGGNa85pKsxDmv67o6P7ZoiXEIh0Aiz+eXeU0ZZlbEuBQdjkCEHJlH8IrLz9chyLuHx7+cP6fb9fH04e549/nzPC8vHz58ePfhd7fb7Zafjo/TcMpfv+a14HAXzejr19vR6PTd/el0tyxLzio0ksW05JCe76aj3uax3L/8fD4eSYaP8+2XIJTzd8PxDnRb8/W6rAeOhcqHj+OXL1/MsCxpWfHdd4/38X6+zg/v7g0EjdCD5ZghA8VhnMD3IUwQ1uydt6Oa3NY00AK2IF58h5CChZkLTcYgBC6JIyIHNaw3xAlaYAWtWwaAFmOC9sKTZ/z+U+bgcy0BL/TBFGqIQz22a8pd7aH1j9UZkb45HZxT32EQrkXl6nhCwLLquiYRmaY4DC7miltu/fFEbjpCkBglJbjyqAf9WqWeKTSDYwWk3npUltWOxzEMG0MiGXJRVSXbdQ3s3MQwkJmoAnB/U0tmVmbmGFiI6lh7ojp0tzR5KTbAkJOmlORupHbwcoHmiuTFIETEsFzysmZ2DZYYzdW5CQCKoqiaMgnNaxJhNqDRzpkhEYLkLbn1kQkKcSlF2NrT8p82QhYjJdUqzABVEBACgnApSBnseREhJ6iCm7JvEAi7iAdyRkrZkwTarDozA4oooCpmZ2YmzLLD8/+B61vHEXKdIO0KLNYzObTNKhx6I/ayWAcK/LLGKeh/BYCpqmEsa6HdxnG8wox8NuqGtBg5UjRO/prU+mUr3dobukvpsHO9QtxrQjkAK70frn8KtOR1N367xsL9NvbXvjxQQz/djmiljpn5yNIKKL26Mepxt/+5CErZFGKZPb0O5gM166SYGkGbGXPTSt1KUFBVK9LxYaJKhCMLKWmPBP20+1KX/KqZu8ezLpnZm5IJ3s7bZ/zVjwzuf1JRwRACFTIzFnjJ1PtGAKNGVmUXVNH6vFmEUa0jtWfRSNj/RuzJgHb0b48xAEbcHsz2yAgAU43KmTmIEA8lF7NcSm7DRmtHjg9LWK/n+ofMalY0q3HORYju7o7Lcr28YFaQ4XA4lFKWZTmdTn/4w3eafykJ7LIqhukQxhhEZJ7nebkyMwdmZlMH2SriqVa5PwA8rXeQM4RgmkvB3T1EAoCSLWd4WxWAeS7X63U6HkTkMuevz18Od7//j//7f/iXv87jOAIIYZO089NaXJcrpWVZSknH412M8e5uGobj5aws+vE7TNMDMYeAT59M8dO8vDy8i+OEUvR2yyyY4tE53uMk1+vt6ekrM47Ho3cAc2sqU+9vBolk5swU3jBbrD3IN88LpEItHgPQOnHLZseZyHWGmRkuetUPrCNVZuajAmpc3vSZARi87Yc7AECVbXvofgJtYLaqMnE/hr0QRkTXa2bmYRiGgV0otIHb3bbUo7d93h2BoeOIVcq3wDkB/XfqC+6yGhEW4Q6b2a4bxS2f35v7lRjJaZ7aphXVc91ezofB+B1qqevmQ64AQqtYdcROqxYTMUcRIS1eGem9tI204XAFhRD2Vba2CJ0lVyEJAETq0ZInSLbxeAhASpZSNnOBQs45w3zOEu07g9FM2XioSC2aqfT0qWkwAnjFg4kRtfrPzsCt/uVbrm9jQZe6Ucw8L2FGMJg3FjGLcKxwhLrvARprur6CbsDODqXZzH53kE7dMHPywua5VdXUiWA8bCVDws4Rhpaibv5tBwj3Y9Ab7Oy1T/UgwyVEPN33Pn2tYzupa0q3l92Ihe19m4OsOhPmpMyuJdl+xxfhbWWIGh+SdipazDHDxxBtOF7biFsbQ6tdmZmVtIoIi1S6YH0PILT+evPJJ+SbtbmuJnzTTEVofEJQZ7Nv2xrNlrlYnXmLrVVav0NPTEx4havjNQr9ukrd9r2HJq/NcyMv8mvaFoA6YdRnRtUXNDNTYSJqxgi2x66pSXFJbTOMltIwDGKknNeyrHnRXIcD0q6inHPWXLJSLqZq9/f3Oetym0uCwoZheHh4eHr5awjh8fGRQZ9/+ZldIqbo4XA4jMM4xVzKvCyqOh7Gw+Hg0gpm5o84l9XdgEtfppTc7g8DiGhdIRy0YMllWdKywIKj2SDgcrlkTWplTbj8YhJ/vrs7DsMkEkRUZAbAHETkeByFo6qmtJZS0XsRGcd4mE7DeDi/vADlD398R3R6eUkxYlnw9AUh3IaRmUnzOh3o/nC4P92fz89mZRxH1XK5rNfr1WMRMuLNfxRVKSX7oaIW63QrsHfA9BsX7/pc/MD6v7lOgK+2YrOVvYRUN7FsJ5pqT63vmMrJ8EpT3947T+l+1G0ae5yd81bE9XtOKU3T5N5Xq9x9NSw9St5Asq3TlDq1uG71HdpMqID5r/z31rXRDYh/zB5DrOu6q+J5saIlpqV4orJ5IENXCFE1Z6q6cPhtTUCgPkKmjRV3Xqp3HTIzNW6sWa89W4tg3Nx3bYNX8K9SfUF2IhUxiEkc3LZObas7gJxF28IRK1rg8WuMwTGkCgQqtZhG7XUTr7W63lYUaNGYGslr/0WtB+xbrm8dR0jY9hARumEGnDQBM+SMnCznzFJNuTaVqLaDyUcY+YNhNh9WGKJXFxnoIuPVszb2ganHfpUMzO01a4mlFNCurYhaVYYadLF9kJ0AjXvkfabuf2JN7dJalbc5vLR3wN70TbSNtdpXaW1XFK//sJlBtezfrh9IL4eg0aFFmtwrWKxqiflmlFq7qq2ZPg/I2odhmGoiUmYTBNlFo15kehMGEbmzdMPk0WYlqngU7MtVD7/7MXt7/2iByy6soRaiEhvItOb5WlElMpAV/z4RBYa0DsV+NvfF3n2kZMBeML20eWdtVIs7/E76rJuFqMZ5NfcNtWm4p8lEJCyk0hRIaiYUh8F8qm3Oy7JmNSMWGQEMYzgdjsfjvFyxLMu6rsfjES/4/PmzPj4ej8d0f7fONxHxKT00jTHGOAzE5g6PmTlKi+00l9Wq4HAtio/jCKRlUS2uHwct7EXivOScwbVajyFiTVjLOo4YBqji+bn8tz/987t3DwYjpchiZgIaJITDUUQAs5KiiA0x5wwt7OgvSSmpaHr/4XFZcb6uw4jjEUQwRV7lRqvpKiFM03Q8TuM4ns/POefD4eBk1DXNEgPMzAqgIGMS73XWXQjeE8MaWbZHtjuwRObYiHmPN7QOQRtcM9mgRqVozsX39tTa5HqA3s+m0zABBAnMYJZSiGxrZmXq43j3sTLcgjuhQZtlwI6qCWAYhhAEQErbsBk3HpU606Yv+J/7iNzNSuwqsqjlNgKF/XHrH2fv3nrfRPPHhKoWErtufKkTUZmIck7apPRiHFwgzwxNaRcAwSq61t7CAKpFZoY3dKXVg4+3SQhVm2+Nsw13wcxb2N2/b8YSejq+NSqaoWSgtVN07MMMBBGWHnw4AO84E2/KvtUpMHuTzTat1QCn8NZPCvcm2oOVlNC5KHtNlf9lfcB++XzjXmEFID6IsW+ItrIiEUZaaiueX9R/p5hHH73XMFdeee8DqJhnK7q0vIyJwNQG3b+9PYMLcfQcsUeO0qSe9htlj/v3nBItEvSNKyIxBp9ss/db7Te9ma+2/bzKYutEYe3pqUeAVqN+aUYHjbVkAHIxZo6RfTP5ihBBACJPpn33katraTY11aKlwGohgEPgwLHiaVRV2M0Aq+L1BBi4VPSCiRDrYC82szZMxgMOjxsgAnLaMJFnEtil+21tqUKCzq1QMiUvsgsT9yC3R5Lez1AVFYg99QeAV8Hm3ge3JNi/XwVl4bR5TbW+ZdYz4A3vdphavSfBTF17NjZQtBCMmef5qqxUFfmDjKMF1mHQc0kp+VikXJKBxzjGacxZ16UQ2TBQXux8vj0/P9/f33/8+PHPf/7z58+fv//4XYwxLfO6rlaMhdZ1HVII3rEIsLCZ1XElUKIQjHPORAgh5FyISDi+vFwul3PO+e7u7nicS4FmNQNzEE7WKLIe75KAGccjjicYoRRd1xwCOuvH9zYRjeO4LIuqDkM4HMZ1JUDn+ZoXZSmGZLDDide8EK/jFA6HPN9wPeN8SEcjEcxz/vLly930cHd3NKScE7ONU/TtXUoiGOWYc+ZgJCYicZDMDfszNvr3WLXaV9HMuhIxFOsKbRG2bTgl+uRBAMyuUrf5kpZP99yAh1gVr8y5FG1vO9mzh3ctX+QQSA1qmQVErpFXz74qHKgCQYJbfK/vkkhrQG23sTcmaOfUDOu6SdgaHBpxwVGnoHELtjc3vBmf9mpEmEYqGlKSN3L69FtrvvejIoihwgmeKO//hIAgUAOPY4/L/ftu/RRbBtz5faCef7/p5oBaxSZt01upqKewdLfaUt56hz2163ihzwzapW31cxV1bURpjHoHn30+dMvRdhDpbgxPm9CsXAoP4z/ugb+xDcmxdVLduM3ApvnpSVIIIJIQpIu5dG+9K6Job8vrqEsTauj0xTrmqJGNG1TosebO4r+JvNz4dlTTaaIOXOy9Rc0Fd9TzHt/0MMKNlBMOPShWtU54xnaMHZb0adHS78SXK6XEDG8wcMJwDei5FZX91z1pqx9qg6rQw0+vkhJcY6+7qyGGnHM2NTMmCFMdyMqv1kY9K92gLVJtZgA1MIerldE2pouogmxEFqIPMylEJIEC1/VSRdkex/5BoK08M1Xu2K8DSHuVaP6mWagRa+NivPHBfc9YZ1NXo0ZqWhVlmVyIyKwq1xXTXAcTadaUiE1cs54CkEzdHTMhqhXNZGZZm9OKA3EYD9NhOqyrms5kRmrMmGc8Pz+/f//+/XePT09Pl/P5crk4s3RdzQpOdwFA8jgRpRt0UwNUREIIoODpbIzx69dnrymanV9eAEunE4nIuiiDQgjjMB4OX0tyLBQJOB4xHgZDKjARSAxEtq6r1WG0Mee8rtn39uFwcFpsffeIUtJtTsFCVeniEmIpdpWgIWIcZbmVyxkhzMfT/f2dgM+//LIch0/v3j3EQZ6evuScBVUZNOdMBgqlnqYmr9+zRndUu+O8FQjaXrK6b0hBHtYVtQxjM75cVghYvBIpMUaX5kgp97/tsXUF2bgWqrY4kOBjVfe4aMel+tbaxfT0eqaCuBC3GZalfjMEiREEpDZXhohUeY95WhsEt2VytjNHvbzlaJHVkkRvhvRk1AAzctb/dmSaowqhDZ1regn+EYZh2Gcp/fe7hQwBvVyKFiuo1mG5fjGh06JbrXczAgSvQ23DYQ3qSBtVOYdNJP+WXnW0djSOXCmoZr01GigFKIBBCTmjFHOprxoKvKqvmxnULOekWsxCW/JqkTwRagg89SwstM+VSx2v1zpI/3E3+u1SlDVjswLbZpLUgR7WZudRxaJpt5lqPdXTWSJuLnTLOEOItuHJaGFahXGa7djEvntA50e1u173/WidtahxTd1etZD8WkISLQvkNn2hAVaVlEEbatSlGdHuvPMdPHzzr5sUotlrf7FzUa8SZQ+4yKzmtf2G0aLaN+9bnbe9Go9YqXBCVAOB7rma0PjueACVXelsbcgWMPZQuv5WXcnCQqqOmHFqwXJlnLXkuPVWoK+JP/2/R1+IDKOmadoKBWaoaHVDHTxo6I781Zq2B9Ev1FI04JkQG9USoWfFWVU9AzYrKa9sGoRFGKoxRjb1ces+6rwUSyn5SRaRw+FAJApzsc1S0hhijC7grKp4eVn+8q9/4/j+4eGBgOv5EoUCc4yAYF1XIUig7oDBEJGSV9SeOTeUZn34mJKn6ZczYtMcePqyRMHdHd+fpsOUVppbIOhcfY7DMVvKec05E2O5lXHUaZo8JF3XNeciIiktRERsahnVRmspZeTjuiwAiNWwFp1ZuJQc43Q6jarXy8Uu5/n77x+GCZfb+cuXL8fjFCKHEMx8hAKYkbK6qZUagdaj4d2c3m4AEJuxSxvTW0Qa1YkWsmqAVJWKgn0BPbBEc41wyEdfdwZaI/av63Z8OgAWwtaYvttRNQrvhwuAiMRIwj6G1RFpDjvdpYbxsDTKPnN/x/6htvBirxLf39fsbYO+CETYjHOuAkd4VZFBSonb1TJFHzpStcP2Rg9ACGOzOa8gxr4CKSHVIquamVIVAlKVGCs3UrW2LVlbyTfxsyfKDlb7L6RcASovRuxL9X4bzRSwB0gEmKLUGW4bCc5y5dN5kTsEjoNUZEJRM2xBKzlXJYmGnWyRXocZ+sr7zXTUjRo+upG1/tHrG/uAt3TeDJXBb6YtzNSqS1x/fx/loVVz6143aBu/3JrYEIf6d3tI5tcuecfJcgO6eUStOoh1xfvZIyI319qEGvpe7IADmg+m3YHxMca7xH3/gujv4v5pT/7qpFxV9YGv+1z/zUXseuf1YIxjXRCzmq3W0lFTb+/5vzVWSB9NuEm3A9YCXkMhEmquuHfwMAPe+9SYU/v4tz9EXxuzYobiibYVZl5yr6j53BXX/kYLlcTfqtbId0AI8Opd2rLwmz1T1LU/VS2T9udFMtTQ9K0PdqZVHerZL1Ut/KppuGbJML/tlEsapA08drEzLZoKFA4loIwaSlmZmWuLBVFKJaWyLPPtejkeHqZhnMYYApnNX5/wr399pnj9D//hPxyPxy+fvhbG4/1hGo5C/Ne/nfPpRmwhRme3FhcGsUBERRMXmKmX9j33LdmYg5epRDAM0zCkL5+XcUAImSAxjloKUyBCSrjdMJ2Wx3cf4/RwvZ6fLy/rivkKsjyEEsNgxE1jEJpLiCwSzExzIiIGGUhEbksupRjDn7DB1hWAnk53pdinT7eff16/+27+4eEYBzx9Op/P5xDZzO7u7gxlXVfVCut5F3WM0YeKWBegYEJvM+nF1F0ejJa/kilZqQqqVswKmZHicBhBUOr5AIpBCSG486BmBupP53l2mKGd+roDfa75PofzfTiOtZbcY3f3yD5wUFV6LuUuPMbQd7I2g+mg1P5YtX9Td7S0c2N4rTVNbG6IROB9Jn0STF8iJxNIAxi629hD8T0CAJBScvuG3zKzqpZSWpc+8khkqDoQqgpENA2QTmbZQDu/7d3hFN7yrdttbbCBsUX/LQMkQA0lucQZiVAIW+KUKifcej3bVjMdiJFSMSshTN4v0qDHwoJAgWvDjg61/bV+BLNX1DN6DW478tfF/5sv2GQq/rHrWzPglmhW+p+zQ2tgWzl1JCKllHVd4xCYWVj2msYAlmX7nBXwcZr4tG2p/hSJtohyzxJkRnNsHayowOOypJ59tqCYW42W2xhtR42YGSlV4Q5pOWOHoXy/vqEM+EduncGkbQ6SDzxuaJK7be7JtxMObceMF2y8RxFEf0NUZWwXntzc9q7Krm2LdADKO+7ePK+OL4G2/isAupNZtqZzssMDN9doVSze9b+4iVbWeZwwp+YTM41jJPJPsLWZ9eFuqqyGuDuQv/bEjZphLdJ0CQ0QkXqvHuAiTSuL8JYHq8GylZKWJd1uF+/ajDGutuRUtJS0LEUMQyCB5pLzOkg4HA7jIM/PT8uySGDHonPOVApzKHk2sxhCFCFNKLmEwSmkzLwsy7IsQ4wxxufn82EcTfOyzsfjUXhclp89WPnLX3LO//zD73//T//0x8vL18v5MgQcp8PjI52fbVnnDx91nOLtdsuax3H8D//0x6enp69fv95ut+PxME1TSul2u43jdH9/b0bffffdu3d/Phzw9evX6/UqAi24nMv5fH24O863y8vLy+9+d//16SUX3G729PQUxhACT9NgtsYIAGta1IqIDENYV12W8uXL0+lu+v777w+H8XK5pJTiEMzs/OXsqbkETmsZBvnLX8rlij/+/v7yUqYp3j/clgXPz+eP30/+OJ+enu/vT9NhuN2WUlIc6rwagqgipcQhc5xAyEltsJyzMAURFnEXYgXToZI1upOuTSkG1cxUGGTMXkwQGYboeo09Iucgr+RdG25nPjLcja6qepRItc2h6i71KL/tUinFPOWNgVKWDo85u8ej7tzKal6mrXUESLfvbvn9ddDOuEfP/TDq60ERDTp2X7WlAdTouFZ5RrknAD3J2zU30jCEHrX7xHEPl7kNS3ZPH+M2/cQDF+YhyODE+JxzQenKRQ5n+yOLQfqnHoZhaFSbfczbK31EWxDQ0vIKHGflUvYpDXfzW3KdOOc22Jus2DDPs5lJ4Bhdaa6kZMQWgjCzi54OQxgiAWLbR6ur6suYVdvCbmkxbQEciBBjR8URv8EHf1sGHDzxhTWFyJZW+nrW8phaUdWiucx5GIYwiTfDlYxlUVUXdtmss7NtzbAuvkDtGDTnxI3v/iY/2xgKhr0YZNd6xOuS0kYQbmc753oarcla+dUyy/rpujvvGXOnR/kL+//4OC1qo3766ziVv8XOHRCz4zjAXr3j9oqMxsqzUkhahsBtMFllanSQ5LeeF3F/3BtapYApVFqpux/WNqbNz602+vf2alQDcBA7Obzk0upSXMmH1Zz5ASOXR3+T+tuv2ZK7JooWeBkRpVRX3h9BAVhBhHVdmTmwMJPB9qhGL9tja9o2qxNAVSgATbIbJee1g1Go0LSiFPK5eBzIFDDPwnPOMYSutOVunqig5HnORgwtwzDFMA3DwHzzD7UseH5+HuOgqjEKk+Wch2GIcckF/3/i/q5JchzJEgWPqgIkzdwjIrO6Z3qu3Pv/f9WK7D7c2e7qrsrMCA83MxJQ1X1QAIR5Zs1OZ1yRpkR5eZqb0UgQ+n306L4fL6+Xn3/+uWi53W6xILXWx+N+HPvlcsk5r+tqZgQqJXQcasX9fofz64seO0rB43FctyXu6ziOr1/x5SfkjPu9cq3rykam2tqrzcysRhH09fVKREd5PB6Pv//979fr1ltKNhE2O2LYMACuXAsfuz4eWLdUSwXpRYEdZtgfmrJsWzqO+ve/f79c+OX1knPS6nfd07IGnvE4qvmDMnJeWRxc3cmZCAIWYg+uYe8o6K4Q29OJHA47QEZuTkatxAD1YAyOLdWxBhNJXGsM7adFU1iGNocbZjCfxv/RyHbqsgjR6ZeHxJhhZJiBqIMOgCEPQzialMwspSXC5ZHYjJ/aoXOjwzAucsmRmibq9bv4YNVTiHw6YtI9/W7g23BEqCkuMju1pWMs0Yj2zo+IgKhhOSnRuK/mInsbSjhgj0OduDtG2wma7opjWdKIjkb9GwC3KRBkUTczdWUH3E7VTwCTt96JRmzp6PW+6EeKoV6Aj50QEcuYPBv2m5qNaRstUtbzcxmXfeYUn1Xinzh+dB4wwiB1WHZU2ohosHSigWlcJLjsLagOzHCUeuwlFFBkEkZ6qtUraTSq0ygYx54wp7DZI5X9v7rJngru2/G00wAGYGc2q6NajGZpaHx1OLbWmtapD8U816TL+TkzKxxboqA+t95Ez0A7W/guvpyBvjt6J2EjdmGON5u7G+cI0IfgUXcqgQFuOo+hac50UKRl+y9BTTGgbaE7iAnduw9wcs/IxeZuo7ypJYW0lNoflkQ8H/CWJbdrMIP7mVhuqe/nUN2nApJFnwpAwb0OBwVfBreZrBxPR5iZJEIHgosw4MRchzFm1tnRdliBJ6bEoUTczIoWO6cxMmBaDepuldQFIAiRDl3EKXlph5nCjCUJB8o3LcuSUg5wqhnWFZJRCr5+vW/LY1tSigqz2XW9+Gu93fU48Hg8fvrpp7Skx+Px17/+tdaSkgSX5MiRllIIdr/vbryuKAX7wz5/Xj99uv5W3t5v+O239+uWiMiBx2P/9An/1//1F7D/8suvYXGPCncMlFCtBhwitK4vl8vl+zv99tvb9+83tAHmVEoRE/PD3LUSPFHJZT9MCxHy4sumYDOQkZeC79/v25Z//umVcD/KboZyKDXmdvYGradarerBJnDJa4IZkUGVUMHC0qoMs/AO79ndARdyij0SkQA7YKXEsJrRPdGMTeyOyKZiKhtFCbzZRUUPGb3UI6XEvAzrFeFako9ZMe9Q8w95p/niu2fvU0g3BOqsKw2l+qx/osujOfqn7qLhNw9oyNjktiwnFqQvWvvJw4MnOGPASK01iDbDGbXVyZOgCPGZxV0oYTgrPUlA7lKOit8dRKTdpYi67LisnMS8DZc8hzQg3C+Is4PD6eyduw1MECd2B3Mys4aqpOjKC8/gnLeBYNYDn2brHILX8GgDeTN2DiYDPD8UPGdG//Txo+MIRzLWcSZzmNmdzIxblUKI3L2zvcNK0QYCZMpLXEN4TBbzxUCRO3pai/EvZ9SKDmtynIOEfSRWhwuGPi8Bv8tzjrWbQupTMofzxkwBqaj1jBHndRhS1LMZ1h26HOFWs6CtHdkHMsIUMWntQ89xP6eb6biM8CJbUothJg5IbyweETP1QdzTv9PTnzP5HxZk/GRmIFCdg0wqwmvqjkVUlRzNR0K4m9JJYx2mCiKPzrHmTXWZGcpipIA+qC3z+TE153QkoACiRMGoBcBAic7UfXfqxUyiBIBGANsu2Ky6u+oBJ8uJJDFHMtNco9FIIrFm3rg7jlISlAksTABzyjnDHPUej4eZUmDdhEVkWzMhLwvvBfu+Px6P48DlBS8vQXQHVycvvCYRIiYzyzmvpu83/Md/7Mfx7z//089fvnx5+/otQLzruubcyNJHQH8cx7pcL5flOI7jQM6LLhvR2+0GOH76fLtecmzaf/rL9uXLl9vjPWdZryuAervlnEjryHBahfLuujDWy7Le0zuAy3IBUEo57oe778cdJKW47mKP8va1muJ6AZGlpRgei/uqqBXv33dCrlW37fLp0yeQHcfj9v5Yt/zysphFy+ZKJKBEYJGc0zr4k4BTiJ4l8Wm4ZBv7S0QOI2/04FGSZOqtAWel1U9RfdIqQ42qarTjU0t1jqPlbFKCTO4tdfxKCH4w7o0Arsvj03eF4HeT3yV0EkZqGiks+qDfAjMed5OeS9eJie/0/ttJaEQakxD5dNkN2TtDvpkZiKTR2QcV0ldKif8Ukd4nPR7AGQK2sJXPqGmUseKvteho42ReRpk8ahLW83cRfbi7IBMBBGJyYjpD+ZhCIVP9LvTsxANBMTaYiYLB1jsAheBwg9ZWGnt6xlCi9Idl3aFgfaqKdu36v4z//pfHDxngGvwG1oJcEg6uLyKomiRmjipdG0qFho+1WmvExDF4OaWkNUoXAEAdxv6hT3fcuZxnG13bscutp0TQP0jjbbPv3EW6eYh0trsQ2u6nqOYO9xZ9/CL18UFoGC6OqSY9GXX2J7g3nKFZg9oDpxCGADCTSI4/CU8VoFYwBsiC+R1PdVkHUNTrNHiRgjaZYkLwkyvQnxiPz1Kj3qTmukYo7BBw76mHQhEDMBkc1peoU5IxcDKNxBq+vq7e8A7Dj3H3PiZsJmwBzKH19AwmyOhTHBCXT4BblPnibRT0ReGRHECOXFRcz5l1CJRAKxSMhtD+/OGuRHm8gA4RaIQbU5zUhb+5bEwpZ69KzElE3ZOruRa1quX4dH0xJHN++4/f3t7u+66qWNf19ZWO49gfZhXHAaGatpRTrrW6Bsc4joK3N18vt5eXl//xP/7H/X5/f3//9u23lNK6rjmvy7K4oxYTyZfL5XK5fPt61IrOUg4Ax4F9Ly/XZVmyWSml/PWvf/36djPD9VNm5mXR6/Va7t8GHqd1nNcaPS3LsoTtj6Jc7KVSIElN5XbT7/f7b7/Vonj5BNCRl6quYtgusIJaYDV/+/r1cl1zzkkWS1ZrrQX7o3DKSX0hTmkjzrysOV1SWnjbmBOk9QE3tRhUnNMmH/qHyINl3NkjRxKJnDBKQoQznRlwnnlGy9njwI19VtxdT4IFWtdVegIjgrw26uAU5KZ6I2kc6fpg5et2qMXTzzF0WFbsezUzVe567/w5mLnCX3fnaa5DpMd0qLWo6fb7auFs2/M9v9qKpQBaRpBVybr/0Q3JGVp0j7/Z4PN1yLD6auNSNYKuUSLsF8AD7vM7UcKHo+vfFtqHW8KEJC7cWW/7v4YZ6oXtaBbSBFUcBaWoqUUozEzHcXAbZNeUnjuOo5ZyZsWaXBuZYhT45rAEvYRhT2xlPM2I+TPHD0bALebnmJLdVC7MWvI4JU4JxI1LwYHESMw5LzW7qroRwMGxyZxOl7AHFiOMG6TkIQaqqFp7Gifonk6DzY2s8YyLf3/xISqzg/bhX5zJ2rj7OO2J4YoNHWKQc57AcmeuqRYQR6/F+S3RE2VmXTxbJtl0/t7WTifCDQyMRkwRechQLYdHaRMjUg8XuCo6MMGH4wZAOw11iFACoXdojHVupDZTlzZAcPZotmwh8ljDWF4Qec4SLEvBUdBdEYBMz0zDqZvc/egKcTjL8ZmU5HS5vLn1TFjSid0A4Elc4+moW2cKjDx/OLlnZznTU/nptM0t79J92hFgDaXDzMuyJJiguLmaeo3iU7NPQg74/rg9Hqqq9/v+cn1VJza53d7f3+8pIWfknFslxPfDvW/phndTRUr8ZUssIKKU0u12++nzl549agzV65qXZUsp394fRPn19fWXX76FPrrd7sKXdV1fXvZyoBSoarAZ1Xr8+uvtt29ICS/fb+u6EtgNIAVBuJGrq+pIP6aUmFOtdhwV4JS41poXpCSum6nf3+v9hmXDly+fiG3JWh17wbKBEsgzYanV3r7djuP48uXT6+vr9Xo9jqOUPTOZQatLIpEkkpmFiFNKIhnCbqQ+R3JPgc4I8rjjd8hBMEYLJTOLRSAdIMqOb6qtTk+9YNnUSzi+IjBjb71SLEJ5aXCQWttecpeg3xnSMaqAsZ3mCx7iPOjzukIfAUBMY3saXo7WKXuqqXnTnrbn97nl0wZTb0R8+kY8Z/hmvPRY2HEGasU1ok5N4xNsM47Af42TP1/ncGT75QHMDdvY+pWno+HdRq0MxMQeXHvc0mVDM3uHguIDENgRCiFm1XQ/QJrvQSYi3orrOI7awdXSsiWEE6jdcn7jwTnQ9FKctdeM/2tR0AFOgYQsnFlQhlci8lan7HmG84NAThSzozu0FdHrAOAcaE+L+zmxbFBiRdpArQ5rh8mLHwH0B89rvIheNj6O0zjNW2g4pNyaaiKE9VEe+OgVTvc2a3AAwmng5WrVqEwwUymqagTJWTiGI2nwaZ/lGaI2LcdgDu4EkBwszd7J04FhSFrf/YAOYrJtRKQldpW1qmqb49VwWC36nG4nHM8O/mqOf2zfAUtxR5CrppRm1eDuoEBBxH/GY+wPi9v2/r1Uzy4RAO7TPiLacG/B+lgoMyJKLJBJFKM/ZN/3McQGHp1m3rsyooOcYy/BjMgjYAcFv6lpz0a2+NygqlYrtKYAA8sS5QBXNbNyHK7mWpnZFcXrfrub2fV65XwrpQSIjJlz1iiuR4ka6mtOeREW2S7Luq7V6v1+/+WXvwGIXp1QXpEej32+ruvLy0uU05bFjwNJyrqunz7t375i33G/P+SaliWldRE9Xl6w7/j11/L5s+Z1ud/v0hrcXRKLCJW2b4dWChc554WIHvcDMfFM3S25MsO2dfv86SfF3/Ni2FEVS8YirMdqxpdl+f59fzyqyO319fXl+mlZ9scjsWTh5G3GbUuhmcLMRMABK3AQSycheUpdNB3CDFRipxM/BMDcVViC9ss6TrVoC6ZxRmYnLnrsN+ZhgD90graSbQTkQ+lbM+0uQikByB+0wbB/8w4fBi/ak+Y/eivxhO6aw2gQwVq23ACwnHm4sJ5zkxL32QazBuieN6XEIzKer23WlgEoE4GZpESB0YnLDopyYKipJ0rqIcXdz4adSvbk20eroTbF7u4UpDdTX2SKXmE6vys+5tZxWICPOTI9eRawsmDmDaZr6kX9EJY228bcjVga6B70NFnHe1khJmsMF6U77gCGEsY/JDT43zh+dB4wEXEw8vcrDjRgPOuUWujWF2esYavzCYPkJKCw5tfQZEhg5uZ12ODw6ENRch9UEs9noJ1HHQI4fSWcOh1EIIz+4zP+o070Ea7Nifky7vd11gzGhu5P6yzHT2piQLfUvLpzbIm4WiZyPyex7KUSefAVEBkLkp9da9YICiTwUGbegdCRa6Kx/rP1DXsf91LaF7WZnwYI4HG2ZoONidHhY9Qaaw1EbjAPpFEoyhwXphotd7HRzxWOqbp979rJ9Q1FT21xGty8A1pCZuThw6EF/s05A/aj9V4DkEREjWd/pK9NYd4gytH5ptNk6P5dETxxh2m0i+vh07gpU1OGE5Opqatb6yyRKAMnIZOUksFKV3AtecscHkCtlTldLhei2/2+Xy7hsIZ+d2luWX2/+X//55Rzfuy7QyP96+77vofRVW2cl0QkkoMBcVmuEbnmnNf1UEUpZb1e1jUx133H7YYsVYQk87ZtxEcp9vaGbbPLy3Ich0gL5pobRK5qpezMF5HMzKqasyzLEu1PRcFkj/f7435RFUCYsohUVWJzR6lIAk5QQGskxnAcuN329/f3nFNK+bIJJwk0XexYAcKnbFi5JBRRVOOQEetji4ZkjeCYRiw4mbEwvV0Sm5ejqtdFov6aOsnw7Fnidxnj4ziTPdPmQQ/JUKt39PtCPf8/lMkQww5iOC87zpZ6l+Pzp85IZj58mhsYUj0ySUbIu7MAAKedSURBVO4e8FWgecMRYuo0vAGTIZ9V4ljP8bO9M37pha0AZMVXDUc/rcOJ4eGv/NFlty9TIwE3vg5v7VJh0wGwQERCvcXiye8QKvFLJOfDDZ3TnDH3dlmIaA27kDPljJi9GPiyJYdX0SKZQAVGrD8exLjyplG9aXtVzTnnnIYHFniMS/rzUfCPGWBOEW2cY6EAJsCRBMQtR0HcQDbjAbW39YMjq8kwhqrDi3Fxq2lJWk2tqJp7mwFg5mYSeDlZU5Ic4yeDOwK9Oz72J/WU9QjO0GO4uOyRBhQhlgBv63ZZ2mMlN6twZYYQWV3RR32552HtjgO1GpwCpqoK1WgbPKq6PobIJVM61N9LzBNdBuVNypCEUk6PuMV8Lchr+RDv21HhBruseWz6GAYcGIecc4wiiZu1Aj0AQEhTEllghlJUq6qSM2ubcAwiNoMatXlKBhEK2AI8yj8mEux3h7ujtTXnKFlFnbv73U3yiWEmBFA0CB1ee1jpCAKEHJygtaAUq0WXpRlmEUgCE1xRw48zNa3uDhcRaZObnQjwBOKASrNaUhMlPszvpWhVYgfYWZx4WTa3o6G/3ciIiZOw14LIZ5ol5i1nJiMvR6lMCnJlcyYjJhFkOd5U8no7jm/7bpSQpJpdrmtaOC387V//QwSvW/71t7//y1/wr/+K9co/ff4CK4/79+si6yrQQ6u/rPj73x732+Of/tsngP6///Pf1nX9l3/5F+jBzLXW8jhUi2wbqtpRHt9vW8oL0duvvy7s+/tx3PDygsyeuRT4JYEdpKg7sJEwuxYUyw446m84tGzrq+RHJjLz47bLi1wvL8X08XjAjnW9MKVy96Po49j1KEwXzvl2u+1W346bMtYrqu7lJkv6mW+Lf/v3/e8PueLn/5GvX8rj8T+t4vML1gXHjq9ff7t8umJJt+Oo1a+vn7brhVL2nGnNtpBB92qr2AWyLleSTJRdUq0MSeLiBHclAghMJpxIX4kXI65JlYryA0SZCYTsIIc6DoW6ITsvnGgF4Ib9caJJRrs/EzgjZ3jH5evepu8BlCTHZ48d29Y6aIl8WYK20EciefaA44gSaQzda/KramaqefIn2pvdh6OP7nPE654bO0qXr1ClBCDwkoMngMxgQNWjnZwpKFmGmQcgTASJhmx6SviBe527U02FjFHPtw3vx+DgDqCJACYJtGazYOAPuygDI2mOo7TbDFgu8wiRn2yEO+pIJ8QgnL4gRy3MTMLupEGaxpAENY1asGSXHHUEGeyGUXGsRtWgpusWbeLmqDqNeiMiNQFAHIxdbLbUqtHCY1aPQ6c6WlTi/4sM8L4fLT+jCDMaJiLSO9G2GzmF8FD/Ubl61AhHgqVhH9yrluM4SlF3gjdo47rmFtilhpeJhpfZTfYGMmyTl+xs+ga6ka5lsE6COXJdMbC+Ny2AiMW8A24Ns5ygJ7RjEXrIFq9HGmeMJR5lrAh6Wq5PlVp+9NluoddvAn7YiNYm339I5vAN0SJ7CaOOHlVrH3FBBBYZTsnsifcVGzH0E/rU7eNjizVXVeCs6QIo5YlhZ4yjGGsy73IA67q1NfRxCxzd7uM6/ZkLkBuj1gjuzzWPedqDrn0owHingEYsbGax7hKRNbObqWpwi6DXyOOdEYqXUkwPgiZmFnK3/VHcgvMt2h7kcAcQDAAdaQgRWlcuChG7vdunF72smzDEa60HGZaM79/gjuPA169v67qs60pEv/zyy5fXCxGJyLquZpJzjrA4PA5VfTyO2+2mChGkhCUldzweKoLoLVgWWZblOO4507YJkR4PuGLf9/1R/vLfExMxu3MUI1VNvQ2bS8LZFfv+vt8f5VBV3T6/3G6342g1CLRr/vov//IvObewslYcx3G5rOuaU6rruuZVH49C7Pu+k4i75yULZxHJec3ruq6r5OxGe9+KZkZkvcGvE9Q9Q6BDwvkcojZXN1v7hPfEsnB0r7XLtk6VGmph36MDFdz5/dHqIzSEq8sXiHCUeDHE5Gw5XRbpib4nkdm2laZgrks69XTduKOWXTuO0UAVoKEG/aW+zwekOVJKUVgdSzfgOCclbR8lHj/3vRIRQcaVx5VskubbHEnLkVakud2AEPOV58BmDoKZOeLyce/4ncqK19eV+uPw0kfbmZlpSqkPP6VzYX+nuNrrsQdERgmSxpp489NP/dM3yNDPZ9J03OB8wXMav5l2im6LH+pD+tFxhABigKyZA+4T2J0x7j9oYN3/sacw7GKQQqa0iLA6gMU0bAMxs4x5PgyemFF7vDWAFecBYADrxwPor9f+VIQ6/xQRTJ9YNby37c9gRTwVeAgYU+NbvpQII++KKRdthpRa47n15jO0FAqN83db6F1IZtxd00Qx5WP42qNwFQ6HOdRMTUMCoiVsSkM1Qx72g6ZE+thSIbrjkohaD0a0v0cbWKxomODjsKE15twRTnl7kh/poy9MTw9JhAbiFB1yNcRepEMHpvaMKKJ363uCp9Cly4gGCKtLUbswacOkzAw5sbvDlYj7QEJX08hh1nJk4bQlSVyP/TiOS0runtJyvV7F/Xjcmfzz58/MXMqx7zsRcs7bBtqPnO1+x3HU1+tlkavV2/32YMNlzXkruS0g1hUvLy/7vv/yyzfUsm7Ltm0555y3SDjXaubK4Frr9+/vb29vtUZ7DF1etn0v+4HLhss1Hfe6bOuyLPfHI2XJWXK2I+vx0P1h+8NKQc5JOAWSWFW9TX96uNO6NEVjZqDG21dKvd9RK65XCAfJZWuezrl1PR2HX690vb64flvXvMkKuVXV43ikZUk5S15FhMAppXW9LMtKnBSNRzawjQQmMknEzQT9zvq6u5tDwR+VmKqCUjBDEUEiWiKIt8CRzgquWW/op1Y+bNvEJ7gfAIrCCIMZ90cDFlBDgZ32gGj+SBPnkceetbm3ptuT5WNIn49BEd1lHZu8pwCf2jrCyRhiG7YGaBmzQG5MympUweFOpjLaFp7V0Viik42En8ffhvwOTdU1A6arar/Mxsys5cnH86TedjW7/gDe3285p2VZcj7bHTGp8XHEmnGb5cAf9snQA/2uAbSmPuqE5L+rNbRLqtVHZTOSf/0CBv7rByrAP1wDTu7OFBrT3MmbIWkRMBC209zU3dX/2FlY10wj8d/4UDjnGLcnRFtK1T1aPogaFeKwZz3PMTluYbDH6g8TNY54QqNvlSjqKxQ2ICjpJY3X20R0eoLGOYCAiOWchnvYbVVYUx7IhYjFQ+aj+vjsl53i9AEKjlaGaa0F0glXzXDsBXNk6TRH+WjbDkOAP/h06Bs05zT96TRUfQBw++AQp07SNr6lvSEQ2vFogCFsrX0Lk8qIo2q4FzHViYbWCJ9m3KaZBocfTVmyWk9FM1RtuAj9Lsi7cMzfywgYCJHFAK7W5dK1HvW5IE0loWPd0SAYKfJwAEQk6rJL3nQ5FC5EeVvJyHR/PB4CMPO25lJqYpDhuD/uOW3LwkhkVKuXoy6ZlgR3N0LOed2ymRHhdrs7LNqBgjnZzAIGEXaxlL1WpzYoWl5eLmZVHXnB5bKWUolQ9Ui5OW0ilBcBGF7d9X6DX+omWZIAbgYSWpbl7f17UBMssopQzikanW/Hse+oFcxYV+SUHo8KYN/v65pFZFmw7zgOmNV1/Xw82pemxNa3+rquNup2JMRCFCU6hrR4pdbKzaYY5MlvG24rEZmrUG9Up6en7O7mhB450UDqdJzEmMDm7svSSH1Zguuhlf36MOamvuMS6LSmTtRffK5WTnom9MkZDOA0BmdT/sg8xZPNWWZRHUmgMXWg64Sop7YMVEScs7sfESqem6qJaF0bRrrBuXtkWGsfjutDylRVI6k283yFdyLclqu7Dk2fCJ/Yjr5W7T+bxnabzaSd+KlnQJ09VKnW6k7E3mf3TvlqohkxzmdyfETwwa1xboD+dad/wzHaboJwltJGUASuomMvGplELOZwAp5V2n/6+LE2JAUQhoSCecOm4QRBmta4DgNa9Q8MsMhTZqaZjcHrJExr7rxg8U5zb7vNXNFgOKc1nc0w0PrzxvJN3zs7SqoaPXxoA+1dIomq2pDYeYrJcMa+Q0THw7AwqMwUzYV0xpdu1sqoHVdpsxM9e7jcZ3za1H8VYhPzoAJKcHIyu1t1hwEp3p8zuWfgDwB+Ph1Dfcx+AHUK2Qmv0d4zzjBW0ltLwBmSoj++8c7f+fW073s8bSIh4i6c02mnS41Hg2nHR/WlKYvWJ9bWR5VUPaglgWCnazeFAEK7mdpxaCKkTivSZ/DFxu2pFIM7MaW0RI2f3ALJvLofwUPJbrVaLUrCbiQpATiOGnmahAQ3JlxW1MN+++XbyyV/erkypb2U79/9ckGkkdctMad6FPN6fclUTTgBCBBW3PJxHMuy5JRVfVmWdZVyUeEYdsQslBLU4a4ieOy3qlhXApWA+wnnZRE3NpRaY2Ami2QnU6/Rxx+9T1pqBQlRzgJXM2XwknBdoQlbJsDJwY66H7C6ZnnZAIMVlL3UY1/XTNJ2OEvfBu38AhaRTCRBkKTVKTUJJZirMmuK/qveawdgbs6OUZI06jJ92xBRK2pMCU/vhBtaowSLDzvNDOgjg9SKmdkZzxEIjfDQwcxu5OZVLXxijuGkZ4Q3bG1s10YZ1K3jIGNqXZRDp4tIStLHWwVSCX2SGzF34txnnPYwHkQIeBQzBjs6AHOulbuRay+OVuP+xJHzGVJPK+OqwcPcOxE6BdNI6fWSdtcnGAxcT9ZpRFmYFObwAyJfDtBQsNfrNSyIu5sq957+J1emA9+AkZ9r/6j3Us5Ru/kZ1E6VjTloOU84UxqENo7Ope4FdtjpfxUKul+xAKM5DwD2fWfm6G3g1pILd/d/cKVBoK8GAyNgO0CxYYCbpRwDxmMQNRCJInSccFv0flXtl5GuxFQPiP+cDHBsC+/GoLU1RxXZneAJTQuMLFPcbXjBcRMddm8GMoIwBxTqaaYQM3cU3+iyH9eTWlfr2TDQmp7xnMmJt6WUqaO10dqlWsQZ4KmebyGHE5HrqY8wxYXDstJzLqvTKcfSnYY5PtKsKQhPWYEnD3r49d2IBkTzbOtsO7shGqBK1vb9fNfnOUcEE4IRD7PWuL8z8i6llKJWq2rpSgEgY29cqa29QA+wCCciisCLiIR7jqvLVqt9EDMhZn2JJBGh8s7M8JbMSCnlJCQsIjGUNGdij6YIFUFeRFXvd1gpWY4sYlq+P/Dly+ZeUpacspl9//49EPLoq6SqtTaCzGVZRDIcRB6dSKpvAFJKzmpUtxfcb8j5sV3SftRScVTfKmyrS2LJICYWAowFzEySUloMqo3dPjqshOC11kR82ZYkdByHu1yvIqLlQMpSjhr9Ao5AwvP1JVWtVXG/4/397b//Hy/x7JKzOpvVo+6pbmvanEk4Cyem1GoQQcozFXpnH9G7LfXp9TPg6cewqOE5te0ZjhTgTmbeCS76R5p3eOoNZgYJM2sNythBXts5eZy9I2PNMBoZWpvWVC2KilhOUOOYIDsLQu8tbjNsRiVSp+kOw0b27BeZneUxa+NQQy+MxM0Z7jeN0fPbvS4zRzunoWqfeloNYuZSiplEP1IDrHHLhHkbTUijnZCIQNZbJfsqTyYwfG4RmjtoqWPLRzzt7tdrtO2yqlvPjDJzZ49pNNo0cZkNNTtOix4y9/WMJW3V4g/FMm9dFafCifdM0JOBJx/oa/zI8YPTkIYGJ2aYiZK5+35oeGY9vnFAiJ6iz/kI+Ez3xdoEwwb0ADBqkNKKrOKNB4MinUgto8vMYznm1A1zmnB65G6xDXoXTLThngMbBumut47S5oQeO9zH42zshtSoZd1dzVitNIIwtxiGFXcR0Alm6jXOoWTQ40PvsI6RpGo3Hr3/dvofcVORFDvFqX2QTi9nKAW08skJmmA+exhUrcv++OAUZwCBFu6OwmmJCafNc4eadhqgIY3N33/iZm9xAATS7fH5oFtpqpeE0ZsoYvX6Cni//pYw8NaGETggK6Xse4nmEwR7rDs5IovlzYAHyZYBcLWqh1tlDnYHHj2ODuScyeDm7hoqJe5aUjJby36oEkveri9LkmVZezt7jKMhEU6MbYFDlpSs7rVg3w9ekztqQZKFOIs4QLVWs4BWOrfvOjvdl2Vh5vv9vu9Fq5kjoE9mzgJJLEKfv9D/5xfPC/7lf3z59Ze/E8EN1VAKmC1FYic5JSazIDaKTIuTVa8ivCW5rJu7P95vqpqXRLRY1VKdybOAFyw5wZC4CsG1WlVJtCS5bHXfQYCpAsYsJPmSkuHxqCUy20VrRPbcpuNBAaYUhj+lxCROzzsSY4f8w2PevW5u2lI/IxIcVie6nL03+M6ubUs1sbi7iHVJlPkbiIb4tJcCQ9QoH7oBdneiFDLY45OniJA7rgfA3C9XikdsEMHlcNPDFZ7DtUiSlxKq9rRDkYWKJhQAtbYBMC4a5SwmsABCjjQsiPmTJfsjPdCXCMDZy8SAfQhM3YZnc8JE+vK2xsgB9m6C3HkSBzvbrBDadIBWFJPhNtGUeMBzoIKehFM9AzDtsxSHxptSeqciNRt7IxzfyIw+VejRhwb9AAj6h7mgx8adrz54A1JKjdP+DAH/OF9e+2yp3gzTijT+kdcwvtQi5AXCjspkSwA8PZKQh2VJfPbjUmegbEhRB+oEmBoeEMIw13NYwlFruxg+EUBkJIm9Z9cjkIqUgHqNbPCAewASzNKqRJRmj7hFH1OJaNxIdEsPA4zWCSAIx7b2qkh4stH1NXZVm8558l2M3dmleuz4J364COkmA89DlhoYrTvCYwN0H6ht0VkSAJo7crtMjlD4o+s6HsSIPLwRIo4wOny+2F30TB13rmdz1NxHo6gQPbQQjByCFjRFRx+xuXtMZ5hVfU5Zi6kdBAc1Nhb3KF27me2lwhDIXosGDyJ3iBCTC1MSZMFRdN02XY5HdSuqPQT4+va25pwXAmlKvG5BX1XWtKzruuS1jzq3WOFSyu12q0VTWiL1F6VqyZCFXj5dq74XxefPr/f7m7CXWoRhQK0ocrDknMW2/Hiv1a3VOjIZGYM50fV6/fTyWmv9237cb7cg4NiP++OQfTd3EJAXIZLtUlNC1Vr1AAkLlgXmgcrmGAi/5AymvSpqiV1XSgFndzKQxuRQMHc27cBnOf0uvI1N03/h38W/586JWphREAShOVJOMZcstc1pNvoR2n+CLEpm4ZZF2W9G6AxIIDDQBue/sd9G5GdmRFxq2ydj84fTOboQVaWU6h0980FOh886DTs5nRMiPo7ClJhpql49YSZCiNBaKFErpE0uad60uZt7dIXM9i++PdB/T8KOU9zGm+fw2hAWjIb1DYcA3abOkJ3aNYNqGdU3Zj6O3o9AkQGJaKGZ5v7djelMow+yl+0wZeCsl2y76885c/SmzsrHp7LaWIfO2wN3HMfRUy+NzXtYYvzZ44cMcCNw6K3W4+ojUbYs8MbiZiIsjLFrIrIMPgEzW9cVXeFNNqaBd8b+m3/BoAHhliSkqbt8mG0RXpYWFneLDgzHqkXUyIJ0kVIkRmPWasGrH88rrCmA6ACJsImZU+KUmJmrFm6s17Jt27ZtMQSPkNzRn1qkTO04ECCLlIBBMtVTSaMrsXfgnUYXZ7LlxBDGx0fHV3N49Sw4dV+BiUj49Gx63kncm/C3xySRJA/D3IShP/B4Oq6qTDIThcZer7WOdM3gSQfa1B3pE1eoMfCF4xLxK524OYtaVP/02Q7RCoTWYKunOkAv/4zxbSKSkptVV1Mt5IWptdJ7PUTEjkNVM3NKAtdSih7F+Mg5Y/SzOTFzWpZjf2Thdb3UclfVAObGfjz2RmBeayXh68unbbvut/v7+/uXv2xrytu2HI8ihCUjp0WEHnBV3G4wO5ZE//zPy+O+mx3LehXhfb9XxeWyJmZVjVnx5vp43H7++edt275+/VprFSHmTKCUeV3lftfbbf+Pv/31n/7y397e7j/95b0e+Ovf/uP19fXvv/zyT3/ZzAtFOzvLcRzHoeXQahD32+NerayXvF5XYtrL4ziOutbH4/Ht27f7TT+/UkqJHeTqits7Pn/GZcnvZV8EbigPvP329fOXl9fLtmZe0p0YS2LmZOCc88un17xs+gup2V7q9eWSl626vb+/bxdeL69uVMqZghIRkpzzmpc1yXJi1psUNOTqxFY5bDARyIEU7n8CBAdQzYrWlZY4Qxerpo69JR5bdrSdBjj22je/DEvmDm5USk0k0UO3vLQsctREaz1JkogopZapHr6me8NnWUdImVkpUYRKOQdHb+6T4iylJlOBDLUOG3ajYoU78VOtfhxWKw9ocVgfIiql7LtFcywziUjQ/sQVmlkpJWDPwQATImNm4SvM6S4AS8bjgUByrOuSl6aghKHGpbhTOD2dVwetp8gdpbRrCx0VVm1Z8rDTfTCz1W4Ix5CE2/semRJpqUFXq2qaaIFzG5kWKCIN6DiZO9AMZ0oNPha03maN2wdd6RzHgd7x2KqfDjNnSuZGhCQphiWrotYpsPjPHz9kgEdMQ4iR2KdaHLYBDgKbAn72C1B8ACkRm9lAFvYSRrcrFLazJXjRFyg8MprivBCnASFqvQbUOKjHqRuHGRBA11L9xOL1yDKeNXVkfMhOmI2U1FvSMmCBwgwWElmYEWjPIEVREgBW4/aDVapfG5HpuUp9ATEMahjIue4blbZuMkf/A+rR7uUp3GztWG1PMz/leGd3bw6FcVq88wjgT1duZ56lkxo/ZauIzjT+iEji/3stn0TSuFSihoL2lkZuAuktWRoZlI/NZgOL6J0evOuXuKP5EJg7jE+HXakvenNKuHl4FLOoETHQ8+E9++fqRkzcGyidTr7oRKLsTMIg2Wsxp5R4WVJeRGvNQlmCuw1LxpKRErK0auu6ppSYOBGZKqyCfXfgsm2vrxcAb29v7hoejGq53e7rui7Lamap8LKm202/f8flZ61W0iKvr/j2De/3W0p8ueR//9vj8ydcLikx57Quy1Z2/fb1ezmOopasclpSSmEKmNnVvn//bqXmnA/W9/fH0vhyLXitA3S9LCll7A8QQQ21HkAagVfOqZpJy/s5M2/bVtEoS4O8e7iGLKLeVHnOZ+Xm3E6YdxT17fqcdu5bO6VE3qstzT3lRMmKA+jZ0bOSMqIx7lyzE8fcmaQZMttLiT5SemEHj6OE1h7RedjjWUyG1BNhWQK3QVEetqlOY2aqJ61Nj8MMcICmd7m7M6Xu+LY7ij/X2lZPpKGFG+tnA7Kxu8MIZF1dSFRY42Jae07XNt4QYadecoAZyxJpGCsHpwTueiKaQuc02HF4JK4whW1deON+fFhloFED9YB3ROTobZNu2rC+0bk+6PSf1Z2PaU4ppZkEbWhUmubGYuokHm/zqexFbarmiHl+iAz6R1PQAMK3YiI+wdyhKTAYtAO4K9PSoCVG2gMeu3MOqmLgdpAgAtTLrjTQtvH+WmDqvV/tbDkfumCQoU+YIAIakdiY74Gm4omo0Q6PJxTyfrmucZFEZ2ED0xfNRSuaarFdZbR/YWInW9WD17Px5ozgMV0Jug+Ongjy55QO0ChsWqBOThDuZx4YBD9biuOc0zhunzXFSLBQX/m5ae98M9FpHbuT/kQxz9xgF+M6AVCDd7WsRhvb0gs2AzMy7wqc1evzzGZhDW0wP0cGaaiz+BoGFAY0bxFkwbTdNdnZWhDaHF2/xrW5OtyJiVngZmbURsIF6jlC8xRxBoBlWbZ1XVJWPhJHo1EMfO+01UA1LdVKVVXNi+QUnJgwgwMi8vr6+ng8brdj21hEVEsQzV+v15wlcnQiRIz9wPe7Xi+PlLb1An7HscM+0cunz7/++nfJsm1Xd5WcL5drEn3s5fv7cRxIqRCRM5VSjDQlVtV93xPxp5fPAvn1l/f3h14uFEBuBtZFtnXJKd3fb1rUHVqgtQiTiNQCIixZKiCSwaLVAd4uL9XciQ6tpJqmvnYQJ/jtOERkWZ56Pc0s3LZpQz79Mn6Pp2ZuOTGsedutUsWg1q/hOHvTTqkfMti92MipDprYc/BfyO/IbY58CdHJWh+n7uEjjZRVd4i7DT4hFIRz2uZZG659XFioNTmL2dRML9PIbEc6bcgLANVwCFLYRVV0vmjvLkjYIg5ImQip8rDfAGY1Oxq3MGqohJSQM9UqpVitNpyA8amxgL3CFmOdzlmxRJSalmsFWu1Z5pxi5HATipFpI5KeC2sElgE4fVQLeE3gh9xHuHLyQHBr0ILqqbpnygF3uH/Uh8M/6MpqVkf4keP/ARQ0OvA1FmhYXDOn3roefyrlrObOkVAc3qEraOl7lzS4mXyQZaNxgVLDqjVLQKPpNnb5fEnTyg7xCEeBEU/2hPg2f2cGPaEXOwct/LiFfvdn5Npv1iMFfa4RPf0bDBvUWwxn+Q/ncSzLvFA9h+yTKplP2z5iz+2zfUlbcszdO6hv/JW6RMy2LagxTzBFxBNjWcZyxbFtW7/m0z/HZMjdaTbAYfiZYyyaE4VZ8zbxqV/2uAZrSNGnlQkZ8+d6j6qWomZGYwAU92gV5OwMYfNgH3dt1pd7y6mZETncCBaOfk/AtCKlVZgZq5GHF5kTYFYhbCBQAxNFER3kLL4kmLtBE5+JuFqd6DiKA5B0x5ZNEdh8BlJK8ZhSwvV6ZeZgHr5cLo3/jeEwVSXC5QIAEPa+OZlBEFVfLiCwOoSEOTlRzuunT5++v709HtgLStnp8FJ3MIlsalpKIVm2S359+Xzc6u226wFlT5m6NfJlWXLORMq9Uy5nkZRq3c1r5DByXpmWam6gZVlI7bGrcWutdkDRqrUiQqrB4C28rGkJLd81tRPIR7W/bfsn6zvhDT164nF6825uLWPR3bgeLcB6R8049ey5mcWKnoBea4Xz7sD2SEs4j/1zxqdT913siqF/Sp31OE16KfTJqSLi49EmRGiD0cb4uFra1wUD7hh6zwIRknTGAlMA0y6/P7sW5kYipEcpHijukX4PiOUwTvQUezy18OKpht0ocpmXuMeuZ85ulIk3/nRigriJu8LpOnlU04eDbnH91hibaa4Ep0TueSg96qF8KQqykFG0HOR58bMjRX8ElAtwTzz3S54++Z88fhAFPS4XXXvb2IJmYBKaMjy138DkD54bznte1AecgdMwhICzhMlsWFzVc4aPO4i41sZ9OpO5hJrr13luHSIIjxLskw12h+lJNxPuJzMGUPo0wNSifG8oIRrD49xderSKKYXVv53mDQeIO6W+0adVbZc3MmYDngd0GMWcb8FsZUnViTT68UeOaxLpDw7QqSwmB/AEj2gdbQw0fMlxnWbI6UQmT7cGkZP0bt4AeUGXwFhnMmPjc0iGBcQApw6ijkDuNbzAOsXysFma0exq1T1KBpPHzc7ODCMw3E0DLwthjnGT3nJ0Bldy62Pemdh59LBCO+4azCmw0gCCRSTnLEuWh8R/w1RASJAlqXotkRnEUWAxKdlRFceOlMKFhyvAKKV8+/a91uPz588//fTZ3aMJuD8gW9f18Tj2o5jh0ydcvyxfvnwuB4rCHXnNzPx+fwD47a2UWr58+ZwkRmdaXi+fPm2GhzuKFRQ7aslZROR47KXocf9+PEqWZduu5FxKPWphIWF3reU4CFqORzlwvcIIiTmnlDPVdS0FrhW+ghJApg5hkeQEsdL0L1nMyyp6JFqYWjJz33emnNdLmAjrSs87emDsz3lX9C5I6g41cQx6aTORfHzQO4aAJ2KZWS0MmbVzjGDswMiohtPsSeatSO5I0lJ9UTRrVSc6me8oyHq9nbnUUx+OS6KerBq3PA47UT+Y3wznSA6HoiAiEU4JoDwMXrdbUQBOkUSNiFqafuYuidn6oKTgikqJmKPdkVRlVtS1+xBdp6GU53nJvbnRG2PxKf4jHyCCDhk5XRaeWlqIzoE90QpBRCL5mT0UKaXAFQUYpQH66Gm39FDea60pn6gxtbGAp6rEcxkipTQeytBlob3xZ48fo6JEy21Ol+JDTrwh1xpTCROIUwtl3LkN/QExdHqKTTjBjsGEHjmH8U2NLljrSYQ2z0IZF9PdlhO1hOkxROm5e6A0mZyIqk8o40jwspyezjBdw4HoXxoLw80R7y/Flc8Gb8gSemw3nIBhg2NP1FpjutZYH5kGbXqQ/vT8wdi+6OW3yXHzsRfHOvR/p8MxFipkYEQhfrLDtw7jJ9vvJ5YBk07pS0RDqnFuYoxLHbdMGCxX8ey9J/yp+aoTS22Uq2ol7p0Jfe+5qqKPNKf2aIz9FH4E/MvMG1OESIuAIxlewwA7NSYO4czU4rAOmmNmjgiJiMANRLqubRhL6BRmsMANORm5iWBZ4AR1oEYIi+NAFqybCiMnrCtJ4sfjcRzH9br90z//5fX1+ng8iGhZloAuppRSWpgRjKTXqyzrmpallmIGBZIsy7Yxs3vZH2UvIHBasiv2R6m1fvrpqjj2PUhLiaixQB/Yzez9eyl72Ra8Xl5EkhktXJYlEalWqBZTNQsWTHTsXsl5WZYERL8ABwqGRIiFKeVMkHSoubTw0ayG3yx5zXkNDpwImD5Y2W5hP+aiZwPMxDHUjmKwJsNjGLkR/OlTw30cSTseIy8JjuiGj00ZBAA6tErKTw7okMrg4mBGdF1OhgGqEo9pstmTTLaDW5qp20vqGOz41CAUIjxRZXHHt477E0aSU6iG6HWz1+ugaLU2OJigFsV7mFEpHAUdAMyZAk9DoHQiQ0c2joj6NDPUilL3CK5aDoia6rv3MQYhGWF6RdpkPCJilqEtxx19CM+IIufc+M9PFQQsC2qVkQqNLCAR1apx4809ahqsyWg8jlJsEDCMGvOkusHMeTvXfP73I8f/AzXg0TE5Ggciic9M6GSE6BslFDHatqOxrHF8iBHjd46H32rAANA3RhMbeGvf5vPx/oFR6SYntDMGQTTQPdV+uJ8BUwzpjciS+ZSWIWPc++0C4EB9a581p+csbhzD0cNk83AGdug7w39XCvW+PqCWym7x9ri7No8hJqGWOiS585acPt0g2xtO3/zX/rXkTm5wbqHt7MT4VDgYxALjNnlCp1Mn1JzWcFaqfVN5Q1PHFuqEpj5vmO4SUXhOpZQkmNYw+oADst4uqW24KWACURtmEn0FzESN1twazLRG5xqTg6Po5R5zG1TNIDmIyhS9lqyOorZI7tfPOYnlvDMZgUlBSoyUQSxqVqqnJETqHZ8vQjnTtl1ylm/f32uty7J8+fIl8s+Ndp9sWZaU0nEc7+/vtQb35MXDVSIQYX/g27f319fXT6+vX356+f72W6l3EVnXS04r4f3r12/Lsmzb5n4DEDMlo4Fw2zYzHPkoO+536PGeQrTX7pgyuXvVgxmfPnXRgN7vdxZPSZZlqXpQWqJLUFKKeQ+gtK4iDiWKJml3j6GNQiNJKNInMBJLNHcRuPW6nulTHqqvmV5iBwvO+Z5wOEO5UcU0LTxVT/DsQE8awN29W9DY7WkWZzOr9cyEMZPwiVEKrMM8nXBEV8P2fAgburI6S7BxtW0UwaQZ4u0YM2BAwe0FNJ7aThkN4t4ucbJknJiy5htNWbSm8KYVwIQSt8a0dYJU3Liv6lktNoNjDHUYdJuxnicDz3CuiGYqyvj+yDzTKKipei0R/0ct/Alw432RqdNp9VAVYxQN95kW41mLiGoFBDjbt4KJjAiqT4nAuOzB2PgB/vIjx4+ioImmqUfSEEydK5h9ZEWm9I6fWVAeO7jf53w/NDBcLdYOh5T8fn9w59Dujy082TTqMnH+YZBil7iHBxB731XLcAnncK1VX1yZeOAdQpvPRp05PePUO6T23GQExNe19wzDGXu7fyNN5SgblexxnmVpj2mKVjHhCeOonWfDwz2MNa91jEzo2dtuhDoywkTk9wUYIhrcsLNX7u7R2TXXyHHKsD8vUbsk5kGLhs7Y1UoyJ1J9wk2E6oncshkN9jhrvP+p8bsSois/J2GGGddqvQmkN237x/xIGGAfWXcgcsuj3aU5NWbuMaotcO8Mj5i6wUT5mbbJFMxuZp3cMnh9s6YkIkxVhJP7mi0JKG0OMz9S2pgqaE85EhvuQYtKvq5rrfX19fV6ve77nZmJUq1VRLbL4kZvb19/++1mhm2jy3XDNeectZII3W5+v2Nd//5y/T//+eefk+Dtu7vTsiz//E//7dPLZ5FU699EiFKU30bOw6/XK8BktKT9/dt+e4Opr+J+QU7VDCKAHzH8al3z41FCah6PR8qU8zXnbF7zsqSURHJKuZqpOrElzmvKFWBJzOwIWtkKrKrhBCzCKQxzypYTh339vQG2zug7NpuDqTezaXWDO7sKKZv3LGvf/2fkGjoqUDltq7uO/TmMkyp3EbApSxn9Mwt6oyOedF10qsxcV3ls3dqJe4dI9gtrRbSJ5C7ISmXs3lkhhK8pkjE5H2YQ0tarKejAIiKilql6Br7QmX2cHf0hwsPJbs460DhyhjkcFjG5TPE9rM9jTilwCk2TxNhWZpaEoNnpl3Fy8sebS9FazAyB004pxuCeWcyW2/NmnifKXoqJc6FMWobcGovDfpQR3ngb5s0xYYKolaWHgBON6Pmpja2Zqj97/FgELN+bYk+JU6KAybtzjh1aIODO72ruYlcY4EyR5UHbSt00BuC5B4swo8aUwT3zZAZTSmmLMVXuKAWlo+3BWgP8FBstUWpVSXMio6gQ2SgxO1HOKeVwedALq4xAUarX5p6ziHCUE8zQgPJOBKeOOjYAwc+FwZ5c6ehR4FDTFun3eGRT0Jli9gOIHWPoVQBJvPaCUJiW2ILEuD9atMbM3XHzWuvWr0odYAG7AdUcdmZNq7aweMmiCoq0XY+8tVFdpiGKzSMmOHDfj5wzC7k5OjrfzFjSh7xMA0u4m7m69qXo4TDvA5cW3yyJHB6gYgK7Q9VcFabkJsfCbsyc2ZIxM8jVcbDvRJk5kwgnOOj+cPPdjzfWynqoFoZJLzeZkqRFE+rDj1IksQhccOxHzokA8+qukUWo9XFZF5DDiwiBHNWKF5B+d5GXL/a47cedJDlQYZXc2dOSL5eL1+N228k1pfT12HEpBsi6kG1aE7uL0+N2L8WWjLzARZFRCN/rnrDkvK+f1od9/3//3/8vM7ter9t2VavregGn2/3+y9dv3+9ICdvLlrds62V7+ctvX7++P162y/v7u//r3+rLX+75RstrvW7yuN/e6u3nvOXPP+fb5fvf//VwVTcWXK7bti2JpNzvSTljMZbDklhOVA+122Fi263sLP7p0+VyvVxzudFvt3tZM14/41Fwu/tiD0uvqn5TuujNaUF2U1ez6lgkERLTyuopLZf1pare73txv6xXI4VJpJxS648IgmB2YoCMhECAkIu77FyFJHlltwRnp+qucHNTEstkTZsYmVBH5ktC4MZb6o5BrMSMqW0BnuCo1UUo5xOFpEruY3qER1U4CUUxdVndQaYw86O4RR+RxZhOYs4jldX6hThxn2dQitVaR3Vz2LNh1JkhLKWgKqixCMCtjfQhIqAF5dwSqzCloWrC4Y6gO2eiVml2M3O0pG4prVECHeccgv94PHLOy9Ic6Fpb+35eGux52J9oq+0x9HAF2G0hQMeXeodnmpFr5hy2Y6Q2ReAEg8QsBHdXVzXNkpd1s95bjDYkGBEyrLl975g12XDLZGpCTCxYJQIAc/dt29xb90PUkl1RKlTDC2FhloQxy+FRWyag6BOb9P+uvfyj48dAWLQAQbcqbjx10VCvZ1B4ndGEhhPXTu6Yu25GHoNaSxZ5m630B9/7IflOHZoRQ7PnnEBPWVv/OvcGsHIzD5QKuptm9gFkxCMJEzvcJq8wKhHMFIUfmvFWrb6Njng846pxwcNt7+Em8LtpJ94JKEbgGlO0xiKItKxAbINwHkVk389cAjNyTrEUx1HHZXjLPJMqB8jiuWmPABlFoJGPip/Sudla62yXcLR2Z+oyPxZEprc9/Sl6nFqG4NmRDNtPdAYHpZSAPhI7pwyQM7Hztm2cMhGpU1SFo4g4RRUEB8y9E+qYVbIRl7eE6lhb9qhhNskGAPPYpRjDkfTM/sOchd3bJhjhEfXdEvmVUsCNbRu11uPQWqtrPG5EVN6TaTG0QwJUEpnhuJHYVPu+v79HghrbhkhNr+sFnYutFM8ZOfPj8fjrX99ePmFZWwng/ngnJ4dGXBK7bt93IkeysHn7vu+7xvrUetsPg6P4Q61yRUr3y8bbRZZlU7tb1+9jcHUEhaoq3LhcHGBHxLXbyilSDiFQSeLhepKx08auM7NEvfVQEtGUs5qOJrPE5sSNPwAURGcUFVMXag2Hw7bFAqzrMqJAs+bFmlmSBaPQNumTkSwdQWFLbkkF2K2P8XBXNdMmgHH0IAxE4Ehp9q+YVbn3roo4T+wfmdLa1rtFho6Kndx2a2AU5Emghs5pm7zle9nMag2oJkWjUWyznJsqrpXN7Dgwklg9GAjFexakG+AGZ2JySiF4rAwAnClxAig6SHu1+Gm1U8oimQiqVkohosCWjih5eCfUwXST4W+ZiryIu49p5R0EjuN4si7DrKhiTKcdtW11G5MHmdtQjB+2vz9qgDuZctQIz16XMFTMIpEI9d6fys+z6lQRzLfSyTlnYKH6VCah/pPM3M2lle657eMOKTjzJ+MYSjjeMDKu3GfX92wqTTgpJuLesU5jDyHkxLmJ+5S96aaiLQ07qDd9j/sNKv/umjQri49C3szVAHHgtFhEJOcuCWq9PpSDWgmEI3VDnbKOO46f+UzRewNXYxAvz19EDRMx2pDaOJr4KZIGvnHUZSMBP5brwz9/Lpw038JTK2g3F9P7Nzs8qrQUyfbIANNK7k7sUWONm6XwAjuYgiBjnAYRCZM2AxzOcAvZ3atpIaKUJcq3tZQlEU8Z60jDEIU7Ze7kOoy7mhbqlW93j91uWqmTCbg79x6z2BfHgXVBMBsGW/VxVDOQPHGZjWexbdu+72GAiWK+UwXghPt9f3t7K8W3Da+v1229MqV1uWj146j3++PxwJcv8vJyvd/vpRQwUt62y4XY396+adn3h5PklDSloopSLdWac15TYoFqMCKtlF3N9h0AjCoRmHC/1/d8T+nCUbwlZ07MpYuSpcQ554O6oIhQTqTuzAo3d4h4tEUIb9vmgAaGItq9euFQtTAltcrEwixoOz7+OorArXDSDZa5OcSH2uj5s5EpjQAxvMswP2HEuTXVnDAc1Q+O6Wn8iKJ/pkV17i5walNYGC0rBocyf0jJtv2/yBmlUfdIPuiuyWsfXzrCu6ZKgvUrCFJwUutHDa4Z+7EYXbpH6pHMePh58Qv3meshk4H7C4QHc2ibltdk5mCSR+98CR9LRNBbK/sDsjEesemgPjujVhVx5vTBvSBvsb4IVJloGXHn/DY8G+Cxzt6P8WJ33qZmSB9oWbgjQEtqxmjVd6Zm16pWtWU+1Ycn9eeOH+sDHr2q1smGDG7k1gO1YUEd0UIKnCo4UGpm0SUCM4CoYRVji/QBkwC6cR0FGJhJNwPja5pk4jloGxHt8Muipb0lSKetPDyAMFqdbbHRoKOVoYh6K+rgq+qm5ey5IvqD0gD1UVbuNHynuM4onPs5prBd8Pxv9l3MwNQasUCwnsknQFgshgdQewLh7MdIuMAwo8Ec3N1Hf3YYvH5V5zWPZew/T1dxdM2HInuOT9p5Buv6ADvEd1WxHpFE2Dy8l7Pbk5nMwufwde1DGsKrQMtcd+4RsMMIIlk4JclGQRfuDFN3twqvIE8iWouqmlVEZzApAAnEF7TtJDg7uI+3aJo9OL21urtVg6ibAUiBVTF6lkx292b7+yb1jtB214BlXhZQQl4iN+gGiHBKvCzLvu+DinwQkyVZ7o/H/fFwYFnXdbtGS647HXu53W63G/Ydl8v65cun77eHO/YHSinyKsxcyl6LOSTi5mRmj71WHEdNqSyymHtKSRfSUpl9uWLZcb/jqLhcAtOL2+1IGctKqp5XTinlVNfVgy7RCcy8LElEQmgkJWKAhSTVWpkYZCBLOS3rNgcoA/QOWAzmLaVIK19Ko5QLjQ4iqmbGGnm3qp7cqQZFbhtcQ2iIE4M05zWebhJgEWbu1VzqbjQiHChHOFwDt9i2WS9L8ag1qnaqh567De6/KMqae0xNG171CSB6IoSiobjo5F5uYGAi/J7piXqhavi46BBrMxOmYYBnD+DUzc/osxH7doPavmtZwJxqtenFMR/3VA5E3CS9gTHP6xn6b9xLuERNd5kB3B30preJKEpm434j7jSzZ3Sb9QwBCeaVwVD78YZg1ou/axBxTLMJRiD0wao28+XuRmO4xUDDdNPw56PgH+OCnsKm8VzR64iYgvowGNEnN/ZceLvM4OgTdbXaJDB2vPtHLHj/utGs+ZRUiaowMJLYzbZF3GBTS3u7zjTo1NsjnEOQblbdxwyNJVLkI2x1M8NoTvcWyg0bfNST9AMYbjg/ewYA/oCIDlP2cqR8RzQZH696AibbuNBnkz9HnO4+G8hh7AeYCM9WNlasn+f0ZoBIolJootkAj9b4fnLqkvMkLeOLavHuWPhgN/u939L5f4i8u//UnKehT8MbMI3p0yOs9/CyYncFnyS7cuZarephVYnNyaM/kogcYe2MKJADNHqT3DUYVqx176LqkSgBJiAigiEKbmTOIGYOAgLXqm4AcgYz28hwMhdWM+QMzlgWXhYBmSlIOKVUipaicS+B0DYF4MdxlFLcKeeYTiiS03rZdrVa/djr/Q41LGt6eb2Ar8fx9Tjw/U2vl8dlrUnWDKKcHrYAKEWMWv0PeLjTRTUvm7t/Px7E9vPPn0Xe7Bd//xu2DZIW96MW3G6HOTEjg0nytsHpsbXGJ3e3vF6DkebQmhNH9TVYJxHZFG4zCBXuaieFIxHIRm1RVamluVprEM15pEmi43GHf05EYHJXIhqTnluGeeotzDlU96ypvKt1GcLY5RdDqwxn/fxyZ0wGIN6QkoTLOGuGeMNIvIUli3xSeFoR1MqzYo82IQDRvTnSb7WaxVCWKfZwd7bF+8XMETx6n30P+4gpgVD1yDlH5rlW3/c27TinllrTXvsMA2zW26K6qYtlQZa+mKE3WrYWk/UdTgCAtDTQsinMuh/G1Hm7EMxW4Rh1fuJhXAhonJezWxP5ieAyoylu4WmGce/fpXlxuj4MDNfZ4cvMpGdvN6hR6IH+6www+q2On+5Pvzeg2vmnNntn6HFutOBsVrsRGqFYdUh0XQJoAHqHcIDTYK4RcKBnIKcawFMQnHOOX2zq2gGQZfFWQmiuGfrz/qM79ahnR18/DV4dNy1KkOiZmaQUA0Q3PJJug08T06XxVCXdoJ70de4eqZfBURr3OIZV9A82S2o9cxB2PBTBsiSdZoSNm+ru9pMNRjf/45Xxzv6pcK5tXKe7i/A4Vb+hdrVoKulpZlkdGX4DcxuJSNToZEfN4cNBcu73aLkJ01sKiqkbjqPue9n34lrMLKbVupp5JShTxFXV3T1mtqCNiSQyU4/otn0Xe0oJHp2y5mHFzR0KMq9KLInYJDRN0009WcdWaynFagnksAiYc6lea1V1Is6CkpBzkqQBGTavZpWcYf7167dSIOJmJpxTykHpsO+lFCWidV2XZWFJkvK6XY6D1yUx84hdUuIvP30qh90fb6p4f39f0mVbmckJum6b1BQhZq0lEChyHKr65SWB3byC8XLJKX9S+/br31rOI6cEqlpRiy9roHs4LeuFZVlWZi7aJuma6eM4nISEU24ZmJRSSHlEUUcbROxJnChYIwMcHfvG2r4gIiIhOEfKhExEWJiFE4EE4ARRF1EIj/7xqDQ3AVdt8Pju1/LwuUc9lYgC0ggPwE7D1g49Fg1UQ9Ktc+NwMzmIz45tn/O5eyfL59L62puwDHdZtTBDNc9CNFRT91lPmxpdCXMBvqlQHZJOw3cfNzuivRMDG/NgWs8xer43daAThvaOpZtck9Zm0mrVCZ2ZLhSOxRU9t12dZnjJDb0VZgGN5ytws42VgYRTAjHUUfZOpdJzksMZGjYIPV0PQuDL9r1hnmMWBdHH0lu/pNEvY2aMzmoZensUKVRlVqR/+vhRJqxxAbPFjUi0V4IHhugMgIJBW+RsTQlPONRi6HNEyIMmR/1tPOxTmNduJ4iIajkN0mxjcpbOqcbzX7U/p7EVah9of5oQpjZV1B1nQEnmTt5gSNWq9HqGY2w+mx2jIa7UuwA/PLth3phP+Hu0OQ3VMK12OOYf3P928lh5btMGHWgkzAI5kYHTQo26iPuQmbbP4mb78z1tZw9tx4xC6s/otL3TR3rKYaLYBACLBJ0BrhpBXic3JgrKofPR9/QXuu/clIjhOFTVS6lFnYhK0VJKOZSsuDuZOiLfW5nMyaoGstw6sF0ZlDjDzay6BfuVRyZGEtf9IJh4VVO36h7zniMvYiJCaoj5Ze4MOkrpOR4vpVhVAnIWJ0ssRq6qRwHcmGldPWUWoSRE7FasVocXd3+/YVkgnGMYqoiktBzHcdTHEUnklJd1yzmnlJZluchlyev1epUEqyj1Xsrx01+u/Crf3/V2v+27vr29mXJOTiQ/f74w77Vuy3bUWis7gKpKRLsepDAUB6ofYL+80OtnqsX2XSXJmjOshLyXQ1U1cQ52pQgTjMLHivboMwtpZsykanBfkjDzUauZSV6op1LIDGRkxu7k6Ijos5uu1WhDXUbmkZwRxQiTlBwxTDlqEC0L6j2hNWd93ak257/hxVJKKYE7Z/sctA3xmZET3YyNwqQGR2zIAdFH2zDAQaDIb9HsAXhrfwpu87PRtl+A966H03BKD8TNziiTGcce4nxqhhHlD9NLI3vUmd38mc9nROoz7reX81KPZ6w/WSainE8Uyyi0oSd4fWIspu5AD9gmdV9/KIrxvOJUA6YaPah0fnBcZ7My0UXW619WSgmwETWCsHNl5vOEJn7WzGPb6ADcdBTef60BlnNXeXSbuKqpYDFXCzwFaEIlOMi5TRF8KqKErhfJT8ErzR4idS7yM0b0Pqq6m66WShpHnG3fT8o3TJhh7QnS+fmZobOK0yQ8T+TMYaGcQ8pRihu59L/GGcxsWVJY8Lav0fziMNLDQSGKfgDUWkZyuHmyQHD3tyL0yL0YudGH+x07YwJDBmKwO4MOOLSeBYzAE63L1mrF3rwHIrJRK5ry2LGky0KBTWBK3kPeCAHR2rXPOvEUOZ8+e5xNotfeAxMX5fSWTieiYKBkGSh0Cm80oMJaUavGOgeWtRZTuEg2M4/lUoun6q4OhTcQSCnFvHJ4GC0xTu4GVzI3aChvcyOiRFyswk1dYwYD3GFK7m6VaWMODhZzNYYTUfSToIUsbqqZJaW020EkzHBFPQLe4mtgPcLnMHPVUmIX1Vqxbeg1OY5JD9VUq0XQCGFZVsmZ80Ipb3l7ub5ermtKsIRSylFuy/LiduS88n5TRaghFhPOBrcxWofgjuKwA5dLtlhE8up2v7+7O4t/+vz6229vMd1WRCqKKkpxDZpSiWR+Vw5TMYI7L6ARmcccUmvcJhxNwMg5cwVgDnU1YyOr1PpV7MRluYt3WTZ31qgBQ1oGAj2t0ly/8ALNHWp09jUELXNvyeUo4g6mdCCLDJ75UwpUEQE0nVgHIop+BOri3wzS8Gs/4CqacgNMwURMID7jVyKKrmJ0ZhtmYhY+HffmHxMA6ixAU0p5BLsDYTrQZx8CxIFdMkNQM5p5Kc0yBdFCx3w1dPSyNDKY6EvugJVm86wxsbdynkwKZF7G+BAF0w5B7bzmMCOxGgIBBmimqfrIz8+pC3RfHAgB0lZtJGPJ7ggWAepDk1KfnTJSx8+GnHptlIm9EyIFyD/syEB9/6j1xQ8a4Kq9phhXbyAmSWJWWIiFVVW1Wi19u0ukLbuP07ZLzrG5T3pkzCLU3RL30fQtZhLg+CEJRPC+1YCxF4kI+87Dw52HzJufmZwGa2o7tdckmjXqO7UVHs/NZAAbUlqI4G0rnxxy47n2PXf6oaoeqpAaiFFinLD2Gcl05nKfvNfxFWam6o3utFUsbN/V3VsBqY0WJlXfd3s8nCcmr7HI0exLUwXlg/nEpDLGUQpq1eFU5pxz5qoNZNjXv3Uzz0+kfcFUlOrCQ2jMLNQ51VrRwdSCmyzyrn0/UGyuMJ9V3UCJREt9f3+/v99gZlaJYWZaD8DWdSUrpqXWw7UkQRKptbhWsOQs5b6zQA3HsQvjsi7k9v7+Bjctx5o5JS67miszPx6PdVkYnjhBrBRNKcHq9+/fvaHQBUA1c9XMwpK3nMzsOGopOA7AsSx4eb1YPSqUhXNOIiKkpaIYLiuEoOopLa+vr0R0f+y1tET39vL68vIp57yua16W/Ti2a/7111+/f/+2XZATri/iKH/993/9y08v23Yh8vfbm5lVPUpJBUUrf/r06fr66fvt3Z22S9Ja3t6wbXsVSSKyZPdj3y360VLi61X2Q1Wt1kMSV7X3O14T7vsDDE6y77tYoiTkrqpCxCklFjOrtRoxiL1UArvq/f12fX25Xq9mpqW6CYAKdhZiF8mxP2MHRuEgRDoZg5FSikl7bXsRQq23cQveKhQASSJ3CTh97J/ofJ1guicokjkUF8KNG9ESNURunuFCqmcpOiWYQYRHfDlIKlRPCoiUeKIEgSqswMxBMcf6DIX5BHuaGQ1odnQNRRCiqnnKcXsHdrh7TpcuvzRyzu4opeac4kO1tgrrtqWqVVVjRHEoVfTZycM5wGREH/djWZbL1ngUtHeT1mrNp5P2NDRUVu0OUKwztS6SuBILYktz7pyGScg9Ji2Iu7tVIlqXREQBjXYP7EKzl9UKgDA9sRT7vgNY13Xbskh2Q61tBjNNqdnZKUmJAukdlHxmlLMQkaoKefyj6NNg/BdHwMvSMh5oGdRQ8c2AAZEAaewtzMxOqgKnIJoBnvQ+c2SYT89izFfHlGgdHxlly+ENjalSeHK1MJMoqWqQwri7cUtNA6cH6j0F9MFIuKN2zDMwIPRQb2MWieAM8tNwWp9i5h13hlOe5yrU6TtjaoGnNiKb5jdM7osPR2HktaavmBfB+gfbt4/ibluHPnwUOH18nElsG08kzvt4BBVfHfw+Zqaa1i3PDg3RKLw9bRvq5ZbGRU/R6czubjA4yEMyY1wCzLjW2hQ4UWKJIR/CwtkDqQccqko+FFA1qyJkZlaLqhLUzMmrWrV6SMPgeGLyxMxsWswrACFyaTB5tVpLSRITHdq2IQfIiSHUmhjcW+XYtKpq2XetRbyz73ZmSvNq2rrSI/bKiUTI3BLD3Y5yaKdZcEfO2Lbt9fry+vp6uVzdg/jE39/vad1eX1+vLy8imTip+nEcf//1f/72229/+9u/ryuWV6QM97ptL+EOruslfN4waaq1FF+2Lef86dMnIjItZpaSvt3sp0/pcn2xqm+1VteUsCxIfGVRev8Oh8EEyLlpT3dyImeKMTQR5qJWIl5SalmBopyXvKyqGi5WLAt7g9T1wb1MfEq3uxIt0hNhwmABsRO5uZM5yCEGBPYN1b0l5k6KNeq5RQxHHAAg1hpwT30yeXg+tKtP0xGGFIx3ztAqTNp86B8HqkKt9omXT2MGTtlvs6hlQMZ63gzuDjL3HCmrIYzdCdDJX6f5Tz0wHWHMcB3OWHkAs8doeZraMboP3bDix1FKaaDRAKnVSt3PCM2IcT3RcBGVKTOhqZ41rZVF5twBkEkioPnx3gaKNv+bqMGf3BGWq+v82EtEns/lmvTbgHYSB4EJ1Ro7QebbH4+jzSDX8xETwJTANgKeAKLNCfY/d/yQAU55KvV5Q6QBlDP3B8xDHbs7at9wMeLXFRCfqvH9l3ZagYwdM1vEEd2G8h1SFHSsgzsm6hZhJLrLw85ufQA11uiTa9mnRrAAJPRSxyS37l4LdQMGYMqKBQI+Ej5wh3O7wp4G60c8zkGqHiXwcanD7M2i5QFaeT5i/SclNdXXh/ltweXpEXsvmY+vGJ8aKSzt47gxedND5uODcT2ztor3mGb0xBdGfQWYKoAfs9DTQ488D7m3/DFRW02tYUOtlCIiLpwSAuFI3hNEssCra2vUrfWo9UjkZrXqYVYJ7lUNpf1pTcLi3gb3EqzWwzvvR04cy1NrLWVPspCZW7VmDwIr1IScTD0ILaxYVWjd9920LA3Zx8wRNhkMqoceJQywCFISIa5tCrXvOxRICamzAomknBciLqWUUu6P4zhKMV2Xy+X6mvMKgHMCczkqC90ft6rlp5/5si778Xjs3z/7pdaa0hLncVURAZl5VcXjsRNRXi9Xs/v9LrVuF/v+ze9LWdcDXZdVBxSSVFZfait9FUUCcgZ6Yyg1Rk92r6VWHGVZ1sQioOMoVX0B87KpGsjJIaCYmtDta1SmJMY5Nu2mjRAPndGYhi4gc2fyYKzQblaUKMfOI8QwYEfgpu00n+ggfz9Hc57hdACIZu06i9hQu+NCWoasD5XxwRFBrd7UZJ89+N3iEkZbSxgYOBki1zrkIopX6u5w2GmHZvN/4jB6aZaH5Da7dkK2HHBib+lKBlOzN0wolTFZL/TcckTY/DxmG0CSQPC5mQy+MOtA4pCmMH4ssBMhfmob94gd4l6sxx0kQsKorTHs7ByJ24z7opbJQL9r8Gndzxze0DkjzxeJwChaMXPKjQNkUIT6hzpm4/cOv/AcO9GvFj9y/Fgbkj3FXkMjW+NwAXfCMFPUqtAUbQB99VsTS8dkSQjAuKV09o9SfF2QPcQMrJEYQS+3CEMVx9HAAu4eaisMXlSh/EykOHvDTbhTVFUDPeFGam5k453xU2s8wtEO62iAJlNmUQIwgnsAS8odf0Rz9X4MehlJobNu0SDZg93Ce87Gx78hmXO7V3h4s/WddEEvEkQ3IsbYpXZVzyni0wGIs3mbK2Kx9Zk5pYA/kAgBYZvZnR6PR2Po7iJBdMZzmLY19er7uZeaeHTmdz0L3mYMTwQ2NxgTiynTdJ4R6KtHWH6oFfNKCCfPZMyeKCV60ojIXasWgecUhOxgBEmERh3bTLtODacEDLiak7filqoCzmxVTbWWolrIvNZKqp6FYsha76ZjTmaPWgFF6mBvhxJcJIEUcAGWheBs1kbHuPu+77XY4/F4HHsxFc6SEzMXUzhnSJJMUn/69NO3b7+9vKwAcuL9wHHg/fbmugFY15UgLJFQ4ZQchMexq9u2LJwW5l3SktNa61dV//Xr+5KYJW1XLbvuOzLfQGXdmMhN7TigDnEsuUOQwtIADiYScidzMoe3nzCnGBPuIIepWqmgxMIi4k4sxIzUbDC1mgnCU28l5rPQbDE1LapCDjJv9NDxnxSS0wcUNo7fWVOFdE0R5BzI+geo8xCuIeCxB8868UQ1gx4ZR1JQhDoeG+gGeIj8KbkNeNjiTmpORicOIh7yOZzvHox2SA0CRUUxNBMd/t0rpq1Fc6jZJokEAEHcOwvUCANoirBPzWANCRxpxdFLwn9kmpjg8pRT7AoczU3i8WgarCxnOMgnZuk4f60UZKLUUwRx1iaV/RZO3dLAWU0dWbA8qZdSJeZ0cZpNdYRwgS8hIji3EL/DvmKP0clW8OePH6sBV8xRFFEjuCjlxK0FWUytWqt6jU3D4YOYWSSZVUuDRWTpc9wAIC8D2PYHc42A89vdHRBTCkpVnwiKR+xLjdqmj8cBpLMVDp9unHxEjej5cyJyTg6LukT/VkUwFMLATOYRcMaXVpzhJroYMGNZTjRyzwidca1OGdtZTYxX/tHj6JL5sfuezqOt2DhVq51P5d55EZgpCvM8tQ+5dz6mCbTS77HVg8dDjDLY8DMmzxfo00U65V4TD+op69r7GomG+xRDfEmtpR7b4TiOo9ZS9ShlL3U307CP7srMwhmoVmFmsJqSELlq8aom1kSZnJnVitYYA9zml4q07WENPV2InFjMguHYiMLm1lIKtA4VPz8ahbtZz2SAGZCGD1ItzBQgrLw4AWvK1UCk23Zdlo1IarF93/d9Nwd3KIlIZkrmpm5ey77v8LeUeLss9/sNJNsF+47b7SEIdcwRWDBTSrJIeux13/dSyrquOeeybOGZvVxfa5NYSpSWbVsXq7XWHSDyRRJx2Y/IpXNCzllSaoGXekwEFJHEAndVZbagSRRiN8u8gBhmWupBO5FssuWcj2pC3bowCZ02hszE3Fwphgr4KPo+H9QpgYCgUxsK+eMbu51zp+A4BEBt9tYgST5HAU6+41Mb4XCUAWCMd+uup0gb89dCK6d5/1MDhQ1IV1jdp8wwwA40HeBNwfaLb1If2m5cFJ0WV7qxbJGotySwfPQ2AAC5Ue+Pm2KzVhGfdAgNjWqtPZeiblVrI6UfGQV0xzpO22ZEdufbGWZjiqjPq0p4upcPDzolnm/2D8uws3V07gXmAQftg4+YQYHf0/bgQm90/U8EcVIiCWB8nPZ8gvyjNvjHpiHVU6UiansWKNDWTG3kWkPLRI2DSBqoz1ufkqqWtt1Dzan3EIpAZHbOEhnLt21rd9+k2zBypxjFYB2YR11zD1ShdSLfFtT2VDB6PmrWmyMTO1w/ZrizhZh75K8omJV6HdqhCEfdp0EO1Hul4szLEv2UNC3dE0HH8DHjT4PsYvy1x/0nx3IcfdN7j6F9vgX0rKl1kFd8vE1/em4iRGsz4ygZ8nPHUf+Wp68eL/Qbid/bDuk+r4/UX1pSvBI+BxExCRFKqe40nngPqgHO5m4Ojs3SJq2ru9dSat1dS1hisxoeQsQx3PBxNVqPkrBZNa3kCpCqNkwMmapa1Y5m8BiRG9Dc2FaqSuTkMK/Nv5NWEQ8DLH3kIpwbDQiBwMQg4pjk2iIVh7sCSYTIjZhybolHhkvro6B4NqrqxEkaZ27Oa5Rv3++32+1m5u+32432lPl6vTz2N2b69Pkq7/f39xbElKOaiJL1Nk5pXo1IWPRcaikVaiktItlEa62PUhfD5XJ5ff387dfv0UdroOpWewZVRFLmlJLGfjMW4ZRSluQe80s8JTFiAqxqXggON7dSKyBcsKxCzGzzdppd7Z5qahIAdzh6gQsRODrAUauPDiVg/G9UtYbsDGVCU852vNhkvwE1eOSowp9elnG22bk8uY5DDbbXCR2oNaSj/SIS9S/qNrhXH6e6DABCy+dp9XlVxnnSH82DJzonBfVIvq1HG0Tf3znEeH4PMDwAIvpoJkK55ejX4vCVk2oNNYhW5LbQQtbXKuJa7yqLWg4sADHtCn1ymPiPCC4cnp692/4ReBtx9pSEmBfKO4M0MzGnmedE1WtVkEUUMdSmN0Dc+c6uzSJ78VSn+BPHj0XAZSjWcY0dwN31e7d21DIE3hwaai/IiPDifmbN60iqUDVV74ypICKtbXPESEuLnn1378imyURxR1S1b5mQBU9MkHP8F7/MlZ52wr7v1Y2dAqxDlAac0onY2fwjTeiHeAhdwDq2uRWz/xEZ7BQINsvXV7WM8Hq6lzOgn6u5zNydhkH7xf0yqFd2zvHD7m4Wgzl5FH3HZXhPTA0IIjObnTYjuNpVmRmlNBDcnFcAUDQaamkE5Wg13biX9nWmbNGu1rnJqllQLsMc0TBZimp1jYpvsCE89Z5aPSJwzzkT9CiHq65ZUmLzasWCrCP4KRvQImq9xB5Elu7uWmtlOGVTVTDcHa0JuOhRoo3YqsJUicWdSFgo6IW0VAAiRAtppVpViJh5WZL74W7MrdJm1lhNvOMATuQQkJY1pbSuq4jUN317e4sun1qO7fKSswDuqOt6JSLVdxFBw8EWNz2KqS6rJlkuWFchMrPH4xGBYMpZtq0cx63eSimq6tK6/o5yP0oxQ4yIiQQGEdxVZJOcg2I7ZEREsogGlycFb5BG8c6StSY/AGqmat1v7jteuQMGSZ6Sxk11dIBVvNDclp5DYWammALRBBZgbzRqMrf/xREKV6draBLkoyYySqrtExhWZOK96Z8N77xdGDNU66jL4CxKtWRP7ZVjIKzT9J9tD4+U1dkWgWdLgx65zse4YGv9Ba2INX5Bu8r4rHtzPuZwf2TLMUwXQEGJNb+eEgZF/EgCh3SP34ePPp1/um20XgjvrP5q5feaEyNR8Qehrwx71PW8hJn4sCbUO6DQtI0D3jseZQp+xI1qrSPCHCfpaysA/QAR1o+moENl++i+il24rqtININ6RLfcIsTTZ+yJCCISoq25uM0WEhED/mFnj+AsyL5D+8/fO2ztkKX43oFvAtAjKmHm+kf0TzTlWD6kXgHgdKKJeo/NEH30rRyXFKxA/bLPt5USJ4zrwZDwgX/GM2nXXPWZXfg5wv7905l1SjeurazYjzNRFiSrYwX6GT6sPKYt++RVxKfmutTsN5RS/PloS1rG13VcuoYeaKMUiFoKqOUAUusMCfPadKSdh7dFqyA7L89PzHYS5JxrUVdzmEhOjKCmYjfrZBpxL2Pc7Bl5NcHrRfHe7RBmOJruKEB2rsYSbcHCknNm4HYUil0KglMv3UlKUuqhGsXyYOliZs45x5Zoe8CjXqOXGDDMbPDH4/F+u3369OnTp09/+4//KKUc5XF/NP7py2Vlkrq3+r2Z1XK411qPUpa//MtL2Pv7/b7vu1VflmVdLz/99NOvv/zt7e2tVhVhEtn38vb2boZakDMulxUA+aOU6L1p3XROqOV8vkSUQCTCRFW1qIGdWFwN7NxDoeauVaXEH3YI/e44N25Pkg29SJ2X5kw2hUh2cxUt9UQ0egvRbYz7uW/Hzhli0hvTh2SNnXVaXzPjk9KS1DWidCIfpjp6ed0mddGm3HuvzgItoe2TE9nRIZRGqnk2PiGmNjF59fjkbGfAhLuUPoeqi3HoPg/WjmGAP9SJ3Z2ZUmoE7IEh7yNhGoIJp/d8LlH/JearnHkIIu+ZifP3uKHxu0+5wPOauxo+72A6/Kmf24iolqFaR3TH7mwaDoGxQIRz5pxBCI+60WpqRSe9Oh/ZuJjhYfzp48fmAYcoH0dUktC66yTl3HwMJqEUnI3m7pkZsN6GGw8bGiXGjuyeeGHAHgDwASMKfOyyJIv5UAJICh4sJ4IFvga5x9btJvOgDWP3M8pUaUwu5CQpDxeVu5vmfbPEx1kLERGTc/MgOXKLwJgHQUyUsrGYWcrh4p0VFATBRUh+/woWZKaMZGcGusmjCQFUzAht2F8sggk7E4wMqHomz+Nni6RBzhKU9AoE6tFB8c8cE1Mdemjq7kadguqYwq55l4uwOxnYJYPE3SviC1atH0HUPvUzoCPFxjlFhIm7gdQub0ZngbeMReH0AiixgbXdihPAqnBarVCt9VG1mBbdYYfXf7Njp1IyPGu9qHkhHAsRxDfOwokMDy13P24gPZyKlcSUFoYXq/dE/rJt+3shr/Vxd92vxExOR0mqOSerO9xtP/a372I1M5fvt4WJXL2+V3hmFng5vru7Js6vix53SuANdICSfr3fty8/OTZXPWqDMTFzXnK131yTOoOEhLOwgd3t7e3XvP33auXxOPayA6huCv98XUp5WNVtBRE9iif3YnX7tIlI2Y/b+1sptgi45lrL+n5d15UZjsNxUDJH/f64XQpk0ctLfuzvt4flXJe0pGXVh+21EKCewHTgUQk58266ObJDjYQvSbI7HfeDVn55/VRM7/c7QRJLlA8ENVMqZU9JX1++HMd++/tt+4tRft1eNhEpR616zyllrlVtXTKvyAuTMKIlN/aPZXBGWlW2SqkATswGIqRQbS0QtWBOAZK7MzmxAxwBLhyuCCo0odQg+F0o6nH67lmW1tT+jGnojh9uw2CAhdgc5YArUsaaIQwEiyobYIBX5IACELEZjjocdBKhlCAsPA0eIIE5asdMAB9MAtybrg2Rybw8R6gQQUyoM3S+PAGI3CPBTu4W80bg7M7hLpq1Wq87jr257OtCt/sOgEBu5GCWGNEKB9gjETJidxeGY5CFUWMWotAnQHcprDdNEQUXtBNRdJ1Ro/8J2Cch3J0pE3Av8dTIrds1BxzOKAZTs6DGYwi7s1vp2KOUpPFdAz1xHdgjEJYV7lbVvClGkcySuWGQfx+H/2eOH6SibMlJ9IRtKy61EBO9LbhFdbta1G3mvPmwHAM9NHZ8d1FPlT2K70Rg4tlZBlr/cT/h+RUj4uo/nejJn8JkYNjbZRPB7Yx+ugFth59R8lPmZ44FB67vw108X9X8ymns/cO9TVd4nuo5IP7wFeN37+lo4dOXnI/hGo//bALwD/aW9iEQcz4ZgPdxfePFYX3/8C5GAGR9yN9Y1XlNxgUzKpG7KbEC1mj5g+1LYaUEKoiE2cRdzJO6kim5kZIb3I3cFNQ4fhBoLWZOBGRmVxMGuwmzJ4bqaHc2KAGNC8JaMNoWxC0oiykCTZDD2EEB3SIPhDxnHml/YbLkOYuqHvu96rRQREEtuS7MzGBR1arNLSNOy5KZUlSttJqq6qH7XjJlMCXZktwBEEQ4W2aSJCK08FrU63tsmZTS/X4HkLNElVAVe9nN7N/+7d+Es5mty0qo7qimphBrXXNHLXGlManCg7fLLYa7xcZkxv2+y7K2RwyDgiVzTqoaDZvWO9yIKFqkVc8kDc3NeKqsyiRnzW3KdbpHtNlcZ3I3d2l1YPdoeyQEU6q1gQ2xgXuAjEB4PBURfSK1oKZ6ZW7TGI2OsUOq2AgXwpCbmWkhzsqiCm7zJXjUR+dIccRYEaCbnWEldcLIbvJngZrPcMpd3F1/cXRVnACxM4MAODEYkalqQn1emHwAnY0rHHQizR0xtn6pgIBOrtwPqg/n287EYddW53vaE/FOak/GfKKPY8HMrDfdeK3eYO9n+eHpsgEO820Gd80pDW3jBu3he/ANcNeW8UOEic/siJ/Cih85fpQLmoiJlkiXeUd35PyEHhzbxcyoDyQfD2CcShhmJ/g7tNx4w4ggxw1TBzGelqR3gmNUOOZ0TZSBT7kFnNy8jaybZkjE0R3tUSUB81M+hCaO1vmDZyTN45s/PqUh4cPAjLuc30bo5eSprjzsJp6tYOwknkjScYqiuTvZQJOdkgPQIDzpBrjdVPWnk4zrnK34fLV9oMZMmvP0FpoCWyJwOl2BD5puvv4zNscBONhB1cm62DJZ0lq1qJuJCLASaoEXyxrDiEzFKGbbkR+QFD6ianFVIiEWV2X2JMStIlDbLLxgfrLqHiO5Qze5wQ89uKmSGJaVOW6EmZyr15heRPDIgVxScveAVQOQtlX0ft/btOxGyIosadu2xhAEJzMmN0biLHnhlAEqh5VDzViVjqPutz1tKzNJWpNc1I2QwDlzTikJMWdfF9vvx34/NNWXy/K47xLMx2kxM7NHVS+lfvv22HISiZHbHHuPiAyecnLX/VGC3XEEDdVNtQDMlMwbZvDt9p63Neds7mZqTpkspfTY92U1IjZrvd2cuJTiqLnWlFJ4tmO/lVKYmTgzuBHZPgkqOkVtkF/BzMjBLlFWNInWva6DiWNcYXMWDTFuGd2mT3br3PzdKcXscBOdou3u5pWiKGjuJO6u9VDVlLiSiyRW0GmDz5yrt9oJIp9Za3UXIOaNdw2D0X55OspoaaQBaW5dRnE9Wk7N0BpqHLDGPDisb+gZIulj/nzAtmZZDJ9gTl4S5ikTvU0UrbEXp9KOLzijVff53nEcdVSvetAc2dCAv5k3BxdRiuljQ03VTIMmzNz9KP1xOAHsJ619d/oZzLGFiIhSv724NfTYz1xjHDL6BXMb1EYAzMdd+B+p9v/c8aPTkKhxCOT+pIn5nP4REIPBjk18mq7+FI0szDY6L3QjmBRh9Tq+qH0Kz8+1/ymO4hMHxHRIq9WhlxzatVtnqeir2JymVuP838stfAi1h/UawukOTOpiXF9cz0gfAZjHjzUHFu7upRR6qqZQs4ZT+ROAdHU4m7H4axRFjomVt5V1mQAcpT5ZU+9XjifCrPGeUUOatcD8zr4y55/+wcHxr/kTaO3Fg0nNPdIV4QBFetZg1cnco+GH4JQY5qTemZSQXVdlPWw71FyrqiY3V4NWZ1oSm9WjmunB0ERsRuUw4tqAdVrV1coh5EFn2CrOncpUq5t51GaDRIIT55zcnToMUFwUgFpYBQISi7OJSOMTZoQ2fjxABGKIND+PGWteXDhgUMzrsiWWJCKcF1OY0m5aisMIyrX4416u66s7ExaWtWqtlpJLSinJQuQJVFN148cdzM44LnlFUAuktDEDbBrqu94e1WuN3GOcQZisVhF2l33fq4IzmKEwSgQEKM/A6MRGVlXVLBMxp2qllIrWplj2WpIs6hZz4IXZTN0syEWpcz5IysyNyNmswlLQLyJyjMygFKEceQuJYHAi8kblHZB1sJBbd80/hIne1TSIenR6etgtEO9BeYtEexCJnJl5TSlpzOTCSY5uCmeOKSBmokqsQi1uIerTcLuMqPe4XLUCRixU2V2idhkiBXikr0PgQjR6X/6pheKG7seg2fHogAIAsuBHevoIgOC/fj6GpqLngbMtXgdwTkdt1h1AKTaCkxGJhbBi6LFJHw4WhFlXUCcQjC0BuKRg8RztZ9ElHncVj6a6N2lzV9OWT+1RBxJzzjml1oIvUy2/34IDWHIOYxT2y8xS4tRz1N671DAskfx5I/xjNeBpqEXPNjRvIp5HrW1kbKyCbAv5aZaa5dCA3TbvUgRAar9Y8qlN1sPrbjavEz2fHuv/f5v5IWLzRnOKruGb9JVSprk+7WBHp8w7T+VtVutTBDzW4UN+6cMFTLLh06eGQx31GHN3Uwe0Y0noPI/5MK6zJ356fE8bmlTP2N8b5tEBxKjt+Wif60/KOyXIiAqme3lqsZi/bljlj3/rxygVj8a7P7TW9NwJbe7uFXCFA8Rm2qfEuMPgcFRzU6hvahVW2atZcata7wqXjFofxZShqzCR6V6O+11yXdcswqa17AfDKAgiiBrdVgIlCSPgMSwNbgRJnDRJTuSW1pRzbtE2ag2lQCTUqcvDYDMz87qsh9b73YgggLeECjEnZqZ8Pcqtgta8rtdrEG8CUNcaD74aXADWYgW1qpg5KEu+ZqosDCwOysvGIFglXkDZ+aGK+6NcPl/MYMYBTMzZ3JGSHru+H/v3d2hFSthyzZmIKIFFMlyN9mpIfdcsy0JEAf2NlHs4VQ0eyELipKRwqjVGRbXAl8WsOpBTijA37o7UDU5VUl4kZ2aWlBrFLzmH8+1DzJrcRxe+u8UQ125mIgJzQqo9bzgCI+p0KIEUPoMzasSQRIEGjIY9V4um3lPXUcRVIsk5EvNhXBnk5BSX4WFWlaq79jiMO5cSO3u0mxuxuSEvKXL4RB4RVwgciwBE54CjJmJjznwXmAnk/OQfN5+dOucXnZ/42FBrjTvazWxZ5LzfyQYPwoIzrgXcvVYjjrDqTLMTAaTjYuZjONz9npq812JmpmZmlZm5zXoaM1nb+5mDuA5rbtmaptXZqK8dIVoAkxBSANnmRuHz+nl4G0HffRyHu7svRMQS7aMt5qYJSv2njx+NgKerbwbJHccBTLGXT92lTB8+2yow3ksggw+MOoOSO1kbvtv2Vau9PcdeAFof9e+NnH40MOOvw/65n0leMxtbISSFKNLU2vcq+ucCKT3dTiuJPSVR56ua//OjDfbhBrZcZjWNcZgALMANM6/j6byf1nesdrxCM/Nzhxabu3V+7PHt80fahVFfnJPkxwFUrfMdDSEX41GgCgMfJ/+9Je7nb1l9M4tOb+o9UdM7w/xjuP/ubm4aTQvE5malaFVtcyqqaynlqFXJN8LhvgcrvFvVutd67HQ8ylFrXSSpMGmxY0ctftFFyICy30spWZgle/XjeFAADMndrTiMjZIkQWAPSSTKLuQc3UHcGMiJHB4YOpF4c2yanHOSfLlciunXr7f+OlKSnNecMzPTcuHqqdZlvaS8qeoRfUHufSRDzK1DrWqGx1GJSPLlNW8gU4/IsSZZWVAPuFHO+fXlomVXtWNX+C6cIy9iYKZEIiwr6AiZrgXfC4BChJ8/rWwqIikv6kcEPMKUOgtHlx6KUkNetyhgExGnRaoZvNYaZWCMQd2uxCFLAAwWzmettZpWzmldLizCWahtAHVVYoYpgiQLhk6NIw43c2eKAmroE5BPbTmxbUchhulJj3YfiEdBOoQy8mXuvq6Ngx2T9nP3LIMxMSjniDhl5WDiA6DVDRZjLpk5LTHujFwgxEIUjPQp9YFxHMLVxCFIjZxkFj2igHE99fW6AR18095zBpQq1uYNfDgmIzpq5DZijGdhbAHi/EGPwk3TpXP/yIhGbOiKUT8GWu25vQ4hGs2rbBaTzeJGmCDRVu8n6juANiDCZZNONhBzHcS7fRvX3O8O7ujEX6dXEfWFPo7MIv9EbeiF+B4nb1fXOt0mgoc/cfyQAY5GQEzB++8vZZgxEbEpSYthG9y60WrA9z4AC+EYfYigvON6fBjJ0XPcivOzum9Zpz88el8dCYIVtV18lgXdrkg3TP5HxDtxfAhA8Szqvw8Bx19n8w/AfaSGrMeFE11z4KZ/Zyz5dxQZz4oG0zvPO5iy1yZT6mnWRCNCnT2V+fUP56eJ7H4YXe+sZPNHzrvmBgFzNbgTE4Nmh6ndb4g3mbqaFzOtMeySmECq3nLEZlqr1aLV3F1AycWJMyCw4hX1buV+K/p+vzPA66Za9/d3cbusW8pU7n64H48bALmsB1S1PO7v65q3JZnpfn9UPYLdJQkCxySJopeUiBpxboTGEcoZnMUhpdxZkoU7Y6DUvPXWUMtIKa/rum3bsiwppYOIclqWvG0XAPf9dr/fzYwpqaqQiET+oOz7ISL3fV+W5eXl5frpJaV01P39/e3+eK9u4lRNDztyzuvPuRzH+7e32+1Rq6WUl2WlRdxE3Yko5/Wyudvj8SiPO/YdAFLCflQQrZzyujl5LQWOlDIJmztcmVJQjLStyNzGdaQl52xmpaqZbdfLcVQiWtd8HFVVXQ2ujCDrcRFWN9dSys4itBrBoNWc1Au4mNRUEnOmbGbQLEbSWpuAWJwg5pg2EmSAFvsk7wAZh2FDNyRM8Oh2bfwYXSaEnMXd5TRFrWzbZGYhMw8vWsgJDBFPEuN0MKS3NR2dYTRayxOi5SkM8IhDRvw6Ownjs0RP1gU4G2GHXA9Yb4hnOaJ3X9IcTH7sIKLho9R6DpVR0wEUH0a0W3dnZocGo5eIBEHC+Qioedh8Uqjg1BXW8gUAAWymOTNRJqpR6WHmyGrYCT6H6akPU7cXkUwdcLOeSf4QqT8lKcfT70/W3cEsvWLMqg03bhZJEicINZLgP3/8KAgLU1dcHNTHC5rxzCYIwMxa+aO7mdQ2eDsPPc/d87Pg3xta5vrNeRmjP/gPokxMlBofDiJIUJJPfiIcnBi9Genp6PnzcUfxvR+M4oSSiA3+0XBPaWTqvguNN55f0TF7RBRh5Igm+8Rve3p/N8AzDnl+3ex88/zXcdqPf9UWeLZERF9SaY7Osw12Z1izLWjbkjr6Gr97KO6uvaLvnbu7Y6P0wzq3y1E1U4WaqZsagcjF2T04M8gnn0YoMrbGpIlMtML3w+5cb+/373XfRQReymN//+3XhenTzz+jLqWW4zj2xy3nvAh5xX5/P47Hlj5l2YrZUR7H/XG5XPK2Ugx+cV/SokSqyoyUuZYT0GxwdXSi0mYRzFDMvapRedwP4ohXIJLzsuVlS3mVtBz1AFNeFl7l2Pf7cb/tNyLKXFUdshDIoEHCpap7Uckk6/Lp85fLdbvvt2plL4+j7hlSyl7KLkIvL5fjwff799tbdd9LuZxFHJA7lu3FKTllw+N2vx0VKUEy70UhnFZac8qutVYwIi/aqSdaZx4RiaTqXos5+8oWzR6B9tvyUooyc05JY5hzI4JRdktsJAaFeXUtWg/XQ92JVA3qgJPJXlJe8kqmDnHK4IyGHSJTc3F/rlma+ci/TehOiwnNGHqMW38gTUxz8z+AOnXGyGM3Iy0CI7bexTf10fb2GyXVYKCPpJqFUzxiuKhlzvlj6oohDM8fHkT4fWbR+1AHTLYZYPd6HAewjMLf2KzxEKbTNr0RfAP0u16JlJf5SwO+4y7BK9n5k087p0Yd4P6kEy+XZAbrLcVAsASmsNZm2d2IfazG8H5Ugy6Ww+fjPnWxZUjHlKcAr3mFRW9VLP68XE/BvQiYaaBS3KMb2KPtpm8JaTWOHwh/8aNUlHomKzChfyd2XxqAHTOjxL3NvMeXwqPnJ7ZCjJ9s5++NccJEEFVzO2k3DAOv60A88lFyoF6zdHQU5egSGZLDaJEcEZkbvI1CK0UFBH423Ia8LuOc4yTep6ngOVh0987Ndp5obN84Rqja7B9Oq8MkTE6IgV/REBdBXuSFfUS9mLICjTG5R+TjUrtcPQ1O4T5ibNziuKoRuY5LnZ/7h7s4g+8+zWb+1NgAcUnDWyKi+1HmiDn+NMatzKY3zlBrbQgUOLGzO4zUC5lb9BCYJoIJqVOtdeV6vab64AV0vD/c9396Wf72+PVC9bJJKcUeJRtexEirlNv3397f39/3+yMleX19fSt7rUfZ79u27bc7u3/7+uvlcvnl21eYfn59IQPUH8djXVcA9SjbZSEi8+OyXFyolGoGN0IiB4Pl7Xbfi9eqYjCn/ahO7A41ZJbtcs15UbX7caR1rcC6Zkr0/f3b4/FQr5LZzNbrers9ihfhTERF6/3xYOZ0v4HpZ/55u14kye3r49B6uVzevv+mxlUPEsBUtXLCp88vbruZ3d4fRL9drtfBY2MOltVRQXJ9vajdS4GDv75ZupdSv375/MrMlCS690H02I91WZZlVdVSKi0L3JiWWBlrjNCyZKQYer0/+PVTeexW6nq91LLv+36YM33Kib3upgAvqvq4vVup10+fJeVy1GpY1wuT6PHY9c7qRALKlhMoG1cmIcoR6qKQu5sgGHBpJGrblJeYgVbdZYoKzn0+qL5GCrc2Yr6mhagN8+Y2IYfgQmbn8IAoTk+42UjmhCXzZBS207w5zSKyJHFEyKFuxMKt9GI9muxGqMvGiQHy7joIszDPhCHeuoET93ajWsxt9Bm7t2EPNOlzQkNRnQQ7DT9IxMwpnbFmN2A81e8+OC4gTqGwaLpaD4pKDmf9TGuLtIFgRGAOJtfAeYwRh8IMpU7c7V4e3PPDDmcSaQWItkTkWusB8pxzmyhs1gbojaQ3EZUSWCMEvJzbjB6K6X/dqfq4W/7c8WPjCFMLqqwDvseT7scZ4hDRXitPMOZ4Fi2T0324uQ8YJ3XZaRjQU75jpCgGbxQGE83T0U9GH37ymUKhEZoDWBqdbw/KY7kFxM91EuB3+QcHgr8mfII0DOEcaP6jIzZ6DLvt54u6r7Zv8jPjQfQHJxuGc35l/obps+SR8kYDGuB3BphgaNDk+ZEBOKn7vE/Pbpp7LEIHgjienc0p/hhjFvtVAXAiBJf4uPDxnmgCdCcj4mjZjIYDRo9eLEgIQAp2xs11R3ljuifstT4e5e37r//++vrCLKyupVzz+vM/fbFjR93fv9/3fQ9+5vv33w5pOctvv/7y9Ze/LUtyd/75y3G7f696yYsQf/31G8gu6yWKlno00IBqgDBHBx6p1Wp6FDeQExuoauhjuEBE8nqVvHLK6hZoKXhx9+N43O7399ubmS6Sck4xwsiN3u+32/t+exyPAsDkKPnie9Hb43j5dP305Sf1+v3t6+vnL+T1Yfpwr25FDwZFaa+aG+p+VJaSUmsjrNWYZFk2NTbfwY+jOKiywAnVvVRjiQHsbOBazZ1AwpTUncBJtpzz/XF03BmTAVEUSEmY3Wx/3KyqqiYWAMlJxLXc399KObS652XbtitJ/v726/v3b3nZtu2FRPZabNm2bVNT10MPcbAqeSIWFuZE7EhQBbGTN5PgLJNDGZs5iUDATHgOlwPcS5htBdxg1aLttxkhkVDuUQbUcxufapCiO5mGGabgfnanUg4zmQlouyMeDWkfET6/zzW212eFhKGfvGvXln3t+pP6ZLbTIYkY2wzz+gx9lZOE2QzO+7PGRO2+yE6t2xTgaXtnBdRCSMJTzr8N9ZmsYLw75xatxrcP9VP+f+29Sa8kSZImJouqmpm7vy22zMiltu6qmp6uruluTIOYwZDggTcCvMx/5YUHEuCtQQ44JNBDNrvZVZVZmVWZkRFv8cVMVUWEB1GzZy+yBpipJJEXFwQeXkSYm6stKp+sn5TqQYX50ryp2kqZaq2liPdBpdg3npnmNXlhHamKCHmI08Pyoo17xMu4mX38YisKXsHRt0yK7xeAVzGBR6hTbZQasFKdLmamfwCEVop9Pgxm2H4PEmwOwy5OHD4lefbPL0HeFjgFY0TyEoB5Vy1fivhkpgUZEM/P1d8W8gxEC5vb05bcP3RbFkeWlr+u7YD3bILlF4deNzdnkxkXHxGQAezpp56swZbs+Or86zuruoR2ve/ZrVRcvaNm5ixUjgv+v3PgaDWnQWfqjOVbiMj7DlXXnXIrtTLfB4OZYhPa6PQlqd8Om3PA7xlSHKMrEGh8Xq3zhMCZwtw/rgSiVsFKCpPoqejeKKNMmk/377758rPPLq6uttstIso0cpqGix2xHg97q8ogw2boum487qfjwYuJ8jSO4zGE0Kdwp3L/9i4lGmJA6O7f3ccYEz8ggmhhtE0/RA4IJipgGogVwIdkTaWIqBoIcBVDp2gN0UBiSrHrIXRKDGZAQaHFEkqR4/F4Op2Yqeu61A/jOFII0yT7h8PD/enhWI4nyBPcld+L4u7iqt/cF9AQiLnjkEwrqJlTiNUpG0QmwuDDD0Qk54yIKaUYI8cgqhRDiF1nLMYcTwLTaYLdxm1QyiJkaGLMCEBjrlWwt2AQoPVcBYTAKARkPrhKW+dKQCrjVKc80jHTCADer0REHUk+QRHJUwWArt+V/kghAcXAqdtsyDT1Q+AUQcgKsBoaQBVTsAqmBD64XhSqWkA0JbVqymYKPlh4eSe9HW/eEOv923TuU6UEqlZr8YA/trZZWugsYMYNWxmdS7TZDPjpoFlTKBmX/v4F82DG4GXnGjhpHeA6ILc6lciTQNrKmF6KklRXsS7naXdq7qVC873ttvg2ZuieLiJQACI2e4zMW6u9MrOZOLMlrRsJmRngXMsJtpSPPSpOs6VgTREMFAGWjNj8FL7lbjroIgIxIvq4RqpSRYvzohAFZjYI4O0FrEiI6OXliLQYJL42xaZRAIEXdofHJAI96bB5z9aBPzQM4z9Rvus4wiXhvzw2ALC5BG6tf8Fpzdqbjet/93nINKPOXLwHpT6S+LcTzl4vIs5tOY/5S5s5ymB5lVtIXAgep5vBYjoQ4pzJhsW5VfPCf8KGLXPE4/ENWMPwexi84Kv7svDUClnwD566sE8dZQCYSwwer52W93JOFAFAnVfezq8r6sdv32er5RF1FxsGkbu0WDnr8DLP0Mtt3NMTx3ot/o8FqqqCgKonjNv+XEWdWxoJFVVbqMDMZ3ivWDgWqw7MGngpAJAGMDNQr8MA8wnMYGYgaqqmBTSLnLQeVYrKPk/3ebpLEYgM0HLO+4fDrz97uL7m589vNn2nUiJpYlSQOk21TJeb4XIzoGSZxlpqHkdi2HZ9Cmxmx3f3OoKZTg8HNT3cHTeb4UhsVogRVYbUMaOIahUCTIHNUMSkonEyEFVUrVXEFClSF0gMDYNiqKKixhyRImBIMVYpOddSBIwCd4E7pAAYDeB0yu9uj8fDpMJichzh9h0ofNMNF4bpbn/iQIRCRDVLYEzdZhjkKDLmqSLFwCklVS1FRMRBBZnYEoAWsSlnEWMOfb8Jccr5cdeYv2lAZiiKHgpRNVV0W1cEay0Elsep9Re1DlfRXKdpur+/n7qTB733ROM4xhh3ZYet3KGxtNQyITJxuthdQQrj/h60pu0lCuf9hNsrBMMYCQzQFKpapur0q6QECKgK6ojTguWPc22XDWhPdesjCM2ppfdcBl+2Z7U97+Yuph+O4DVe7VO6OsM6GmQEhBGXKioKc4U/4qo4aM24RKvVriNxUlvWeTF5fb+IPm7YR/N33rDLZl/+y7lg/WtnPeajOlpJNodZE7q2eVyJb1bfvJ7/YjdeZ4XmVvvjVz+9n367iHkJ0QFiywrPlTEAAF4tn7rgX7c8MqmsCj4+kplFAgJ7y74BcECPVyO2nHSzHkDNgAxm6kp/IZwEuLnmy6NfCFjW919VzGyIfzyMfvcc8BN0+ba3t37TU1ya3B9/LsEfe5xbp/ONwBmH/AkheDwYyZz2UNuk6MYsQbJ879qmNZMFROcDmh814xGAZ1nMDK3WStBc0ccayFVDm32rgm75Xlu5+N8+Zm3tru+Yy9pKNUMRqc6p36aJ+zn5vROu32b/61LE9D4At6ksTLMds5ByLIbOAsAAwATLMesNrKoLVxHMffSqihhrrdBYAh6vyI9f7gk108afh3fR+IUbERItw5GaR+72qZlhVYPFQZdFnZBHtKyCZtMRNJsW1fF+/9V42k/H27BJGzKOKaYNd7vffnH/+Zfy4x+++bN/9uPLm8s+YccYEts3D3dv94FgO3RsEJlR9FgyZbi63A79gCqj2e5FV3K+f3M/yfH+XrpQtWitxaAeH6qIbLdDKUXEEDhw55ufWVPYTFO2amI6VatVEwCHzigABzXOxYgQI0OISgwCdbTxVEypS7vNZkDiPNnpWKvg8VSnyt3m5vLihYje3T7Il7+fJnz7dhyGYhBUJ4PSJdYqmyEOXbfZcJ3KdKwiCj0yAzPW2kZaKfjooiAA5XR6eDiUCsOw4RBSirUWUacOZgDy4a4KJGJICaVWJanO2YVjkZxzjKXW6uCqqmg2TZNTYJ5OpylGnGtExnG8vLyMRwMA5JmcqyKZGgdQO+xvVcowbKVM437fpb7v+6qbkDYRGYiBk0AxmQxgSFGtooHPXmgl/yAADTKXog19n5JPlzccAETnTQqICEjGAQ2wCx20umVy8FBTEfHQJUDrC/Kz0mPk+IkDi7C0IAMzEKAZO7bpkyye+yRt7yzLXrTlrB+axptdUjCzOtegOLDpTBvgfdst4bValDx2H6w1t3OJKAB5kH3dd+NROve4fKACQmjFqouSnzvEVu2FTyya1fcuIbcnDujqpq01+JNbZGBdiogJoEX4W3i5lhCCn3MZW9du43LTwNYjjRxxavVWTSMKTqO9bruSWczsu8DodwJgm129FZy09bmsHXlEEAJ4DK4+HunJdpg5gXWhopxHEz4JL88av2o7ctlOYXavEZYlAQAAMdNChOyRMEAAbaq8OZ2NLdwMyRAAyWdEq8Hcnfg0K7P8rk8ZJFZY9f69WuzTNfQux6+8z5kFd9nFTd7r51ks2sVi/QM9QsvvBMrEKTY1RG16tu9k9Dp7XVmsXeDZuPHLBGcGQTBCJGyxCvVpuwiVEed+y/miSFWnyaeytLi3Yy3AY9egapU2uYU9QWUzTwiiLSFx8Ckxqt732TxgQyI2VIOqIGAVrIAVsJptrDoVyWNWQ5tyKYZGXVH4+hvot/Dn3fblhx9vOgIdj/vw6uV0f/eQxwlUuxjjdpfjeHq4f7jXPpx6jrvdZkjdzeXVV1///qsv8tuHOk7w4asYOZrUKY/v7qTUt/V6U0oBoC4N2CFzQKOATJwMqhqKQhGbJqgiBlPoElBSJFULwYPSIRebpjyOYz4pYuw2Q5c2VSRPk1lHHC8uN5tdeHbzwYcffEKhOxxOH/3Tr2/v3iLRdvf85cvnBnI83qvkd/dfqQBUQiC0AWwk1ECDyMHvv4hUFZJqZj67dhrrw2FfCyCSIRuhM9qTH2+KYGhghFWhCxEYTWkaa84ZjQBomqZN1ya1QAe1z6o6nUYJwUSdB0JFDVBEQG3TDwjVubrFFERDUOugTymm8PDwUMcJ1FTh4eFwcXHxyaefnvYPcTBLMXU9WCU0AfMOPp84gmbeZ4toTkkGc9R40SqIyI9MRo9m8fqAGavIN06cPZ5Fn9i8XZuPMJ9rTd3TuD4e1RiQPIIBIvDqg8uCVJuL4h7h7HjBwpJhK5YF13nqcNR2WWMn9BOb4dzMsw5yt2XEEG1FN+TmBaKHnds3+hTzxwtsi8EWeVzoG59ULHsVC/o84xZKm6Ncfj+We7S4p/itwMN/TKy9w6VLTORVawAAtULOrmyf0AY/ftfi1Tw9YTPbgMxEBZeibni8IljGx73nzf/nyv8HRBxrh2/9yxp9/WcRb+hc/zG3JgAAzJbxgnNE5UnRhFkLc0CLgD2Jb5uZ16T7F89c2ggAwd1jatZuyzXO61/Q3VvTiSjG2BpP25ywdpcJddmN0Bxcr7fS5XzYxjwYIqg8Np4vlrWZeWP+klVd7uTihi6mohG6y4uroXjw+Mvijz5+y/qK4OlHetCA0AVOKXr9uZcC5uzeP5KhrqommIAIAs/TKcAnREpgAjAwaSTpAIEbJSmgqiEqrtQcVsHlERmAza3aIvOYPBPRxs5Iq44sAGv5+eb0YtOmzjYHaGYIPupGVQQ0m1bTYpJBs0CpVorkw1RHkXrMpZBxxwmMgBOk4Qq7AaMxAJcppVgrSCkpxKHrVOvpgNHHpqtFhG0/HB72YBoMhwBlBK3QhTikDkDUEtiJAKbT6IXr2AKqhMBEVMNQquYiolAVxgKWIct003Vq6NMPFUiBplpLKdNDLlJFNHUh8BB4MKtI8OzmMvXb1F2kuL28evnixYcxDdNYXn34w9vb22k6XV1fvHjxPHWU82ka91/89jd53Jc8ajGFGHgg0xD6Wve+A6CxxqrZKGCBk4I/QaumC0eua9uiYpMhWWQyCAAooqIGptPxcH+/R6W+7wGw4wozl3+Z6jgep2nq+54MuhD7vvcX9WG6H4+1TCNvXF2yFKtWMk69aiDqQuxiyDl//fsvT6cJEYcUy+k4ppMS09RDN3EQZCUGYo93q4EgIhMIEpqufLLHYI9f45LEe8+MtifiCoSWXo/VkQ2kW4RMn/YfgxCggAGQQm0dwEZmpnXZaza32aCzVXiLi4iTnralLQ0C815qvy8ZojkhjQ6Ziyy+MgDMPVGuKFr8z+be2YaXT+ntvCBZVVvTz5w2RmuEJC3eTi2isNI8/qcVFau2CRCLG6qGZuAG0OLOwTKmEN4T17QwGzxI1Lg05lvheu+x+zlGXuZnID5Johssx8NMQOoUqoBeEswAwEK2jEA1e7wtPpiBVvNq/zj5rh7wovThieOL6zjt8khEHi/52y7amtl49oBtjTeIrt3nyIj/XL16Hnp96pE//sO3m4ENvLrYzMzHgvnuSoFETGSmIZ25xUFbtPYP+sFrWXbvcgDOA1mttWTQtz/eQq4Ki2E7pyWeHLnswGUbvnceN2hWh7VPdaF5AN444TEZ/4PYQkmw2gmoSghki90OZGAGAaldC8B8QiYigYUy97HNCRFTSovWW9Yz26T+PT6SsvUB1/rIAb4WdsIWQgAkQEUENFR3oVS0mIhKqTWLlCr54XBfTsdxPFiIUQENuOu2u2tKn19ew4sPnw8Xl2OWWvJuIErp888//+JzePYMSinboQsUK7PWshlgs9lcXFxs+uHN736vtUipz675y7eCBF03xNipFcZhN5yurjallGYDqUnNUsEMibn0YSw5S9W5070UqAKXV6aq1RQAuRX7TIfDoR4NEZGJMBEGpmiBUqTrZ89322fb3U1I2767TGlHlFJMH3+0/ej1D9SqkQ1Dd3WxiQlV8g9/8IPfffHZ7778bH/7TmotNGrNtfhWMiJSbSNiihrWisAcur7vgZTIHWNbHJpaVVAZYeloyDmboYIc98dv3lQ0uL7Wvu89rwwAqno4HO7vs2TQ7QgA22233W6ZudR8eKhffw2Mb15vtwhO/Wm1aqlaazUBFbi5eXGvx3/8x/97/3D44Q9/HCLd3t6Wi4sKYLGLYYjcMyROHZBaGzY8v2YMKIiIoABqWv1iG5PaQvzi5vq8xwBanB3MDNQQwe1G/+URHBAQgJGWUHY7wG9XawTwn2JtXCCaCRiRsra2TAWs8xhB6boOGuWflty6EBFIrfxBdY+z5wBLcs0D10+pE9umfvQgHt1N/73MHrYZWkvlIqIzCmsppdk0c0JK6rIAMJsJuZaQ/ryMFfzbcs7FpofHEILzSy191AhOeWJeLOLI0cinPCSOKIBLRZhnJt1pZVzRUH9bZvSdn1ND34bB0qoFfJGozmUH4O4wzlGOWbF/hxKs7wjA4/6Nh2U4JmZ2Sk8DMtMlzuAPwDuDSaGU1u4GC6MkM7QBIK2yX81ElZkjISLBXMpUPSQtYM3S8/gn48yUTXEQH+KoxozMiARs4EztS3BIrc0e0BKIiMm1oRegEweYBNDQLMwVEtrmSagyI6KKTjNAgkjxy5mBjRFQRESFsEVjiYg5IIAUzdNUi88tZkQkDOvqYi8T9bJDQ9+WhYjm8fPUzDQkQOgqASoQVxGtxUCDU+tHFBGRIuZAPncKgRILUlW1WhXRB7AHVI0MKaDjl852bgjxdDqqhhDiNE2iJcaYOirlMLsOiOilWkgEW8YJAqkUEUQgRs82ISL6zqoCAuAzIZDHYRStU8mINQT/0oJYU6Kc62nMpSgCMyXmSIRdeJBqVQSRiaJYKKVUyZJPqQPmPOYHk0NiFVXJZSOH3735MhKnjghp2Owe7vbv7t8djvDnf/npf/vf/ds/+8VffXW7f3e//6rCXX4In4ZfvK6Xl5e23R1DvLq8vKjTq+7n/9P/8N//6e76Gl+OlV9/9OPj3ZvD8TiQPGSQCl+/fXj9yccbSd+8+e2LqwR56lC3u0gxZcWx6ARwzGU85PpgUk00ieDxYONxMgBDEKPjNA5kIcXTdMpFpOLhcJqmqR+2l5fX0O2O2o/HLvSbcHVhF6/k4rleP4Nue0B+AAqpS7E/TW9EBKDf9MOwu5xSN6kWnezy2Yvh4+HVT7958+Xvfveb6esv9/fv9mWUA53KqWrlEJV0lBEKJEysAQsyM2+oKkx1yhQmHL+Zao/9wDEGTpGV6JSLHseLzcWnH38AVk+/+fXuRY6RQevdtD8e4PpmA0IppqvdxfTwxZsHu9ptxkmGlz94/uFPDhn//f/y7//d/wHcwXH3aXerNzfP+267HS4Ph9O7zz/rUrjsn111l73F7W73TQq/+/Jhz5/T1bMp3x9v4osPX/d91tt3VOz6g00gO+ajBjXuETeCRBoDxAAACKe+qmqWCjNcjSXrpCESPO1Qd/3GFpdAaAt1zVE0xKUGCABAERRJ5tSkhzJcYQDwNJUQghuaiBgCikLOOZ9OMNu7TZEyM3VgTumqtbZO5eaWBHxqvzZjFwMCgqIisi0LBsjO0wCgSDb7aqYWfMySA8wKI09TbgUfBG6LE4DP94VATF1zZGc7o+KTcuUW3VKLcdVl5N49GCAQi6JNKrk88SWc+QuQDVaTexCtZbLwkcnkSZFQM+Jbp3JIucw20NxB9D4/SfvZoqdg8fF5rQQRRaFMM6GHjw6qILLwPSy5wu/VAx7HEduMzJHZx5IwIsbYAaJ3wZrB4sjW+jgsdnUHnWsU3/On2zG0BOqbt0SEGKjRR7qsgvtEAIpOSYcEIUB01rnGozZnQ4lgnpI0m2yNnc4Eci4e8W04bdVdtzxmatxs1UzcElRV4nYtc7lSc3bJWp+r51tFJOeac86Th6Cd94CXq47EiAgMq9mTbQo0AwCyNmN67ou32tqqCAjdf1VQ9yMRICDOtpzfPSkxxhBCDEG18fnnk3FoeR2tXm0BXgZzOo00j3w28BFdNE0TMy8BCERehpeJtN3ZupXVBAR9J81TZpAMjXyXh8ikSERdDN7rDGwAkKfKzClGAjGfJGdi5pOIBMW0VXk007jvU0xoCrWEolTKlMfxeDz22Me0YyTEngJzGKocTyf55b/40b/5r/+bv/kv/vWr1z+4fLf/fz7/Yn+YXrz6+KNfvBaRFKIqnPaHgCDTpAKf/PBP7g93X3z9zc3Pf/Ts4jkzF6klp1zvTyd4e/+Qi1xfXY/5cDpi3zEyFqlZlVO8vtoJ8t39Ud7dHu5rLSZiucJUalUgBgoAGImtqoFY3/cIMZdsQPvDFPpt6jfXz57HfkvcUeopbW5unoduG2PHMQUOhtGARMRfwBBC121iGEJIqN7rVS2yzxju+36323399ZcP+7sv7j5L/cWz3e7y8pKZfSpc3/ebzQbmWEit9Xg87ff70+l0mLKZ1FLKOIKJIcQwYIzPXzx7/up1QCXDw/5BtZ6Oe+RjfcilwsevP3r5/Pndu7dTMQ4gCn/y059xt3nYH7/8+va3X3759dewuwYFo0i73eby4loEb29v37x58/z5zbDdUAzIEGK6ur6+uv7cEMZpQmQFOZ1OsT+kXSKCKlnzUS0SJHNyCy1k4HRXSxgR5nibIy0C5HHCVSxt8RB1hXYNUVY1yQsYz3lZdU6VJRRlM0mRzXmiJXiG2FJdSyR5Df9LP+6iVXx5io9Jx+Vg36F+Wv/Q8tlqj5Q4T3GCFoBcfs6XaetANxGprnlqH3+aAaLNEe91enUBV1jdclw8lsW+WaRWWda/FGYjIrVOFGgFsEvr4azHm5dK2EaWzeodySm03q+6mteH1NyY+Un5H328FTM7x6NDvxBULCYarsKcf7R8txywFTMqUtx+mctlac5xBq/AwxWzRHOH5ujo/E6261lO3J4H+jtVnf5JfTgumrdzNSPU3Ltqz4kQKIAq+ZjfgBACiIAamIC48cttGT6BWGSp422seCXnFf+LLuHTcTo5uDqrfghhaXHzPL/bsB5NNRCoLYeNyESkarXWWrSW4vcEZ+dexcwshECMITjSAbFDlqnHeprBQdDMLoLsnnSL5SqYSJljBDAvBtVNILXo9aBOn2hWSqm1WpXUyOXNRNoyZnUQY+y6WKswc9/3zChS2lBWDIgEQKZeK4eyqlvRdtM8kiYArVMI1Vl6kYi6EBfvWAG8WhIAxjBKNa9CVGll8KJKOiEic0RiZlYEkSRSA2lMZDqmCNOI04igJeeYDxFoS8wchsQppk23gcubD3/28z//q7/+169f/5i21x9uXkzW3d4fX37w6ibeMSARj8fT7e2tlWIlczd88pMv/t3f/s9f/v7XRvznP/vJ9ctPIHVf/Paz7c3x9lR//Vv7we+/efXRL14EfPuuSxFzzWzaA3LXd9tLDIm7EePlZA8P94eH/f6wr9MIChATVIOH/eny2UUpUk+nNNo41fu7I1GYslxD6nfXlzevNtsLSj2FnmJ3/eyVACMlohBCp0ilSC4VKcYQ+n4z9NuU+hASAsQwTOORQkyx3252l9fPnr/44OUHnzzc3376yZ9tNptnz55dX18TUSntKasqURtYqarTNJ2OU85Zap6m8e7u7vbtm+P+Aa0iKGnZbTevP3oVA11c3Eyn4zgd796+u7+/7T9KeZwunr9Kw/b3//Crr76BZ8/o5euPd9fP33xz+/mXX3z59bu3tyAGKcJ2e/H27W/McHh3fzicfvub3371VZ7q9PrjF9vLV4dcBZg3fb+7yKDHmq8vbyRQ0fEw7i0lrkMqp67f9v2mtj7gqdlozhKCAfF9dYetDJgXMFuQwN/btVIyazwEMPsGy//avM8fddeqr28N6p7xIQKAEDCs0XFeQMsHudJYzgMA1R7HhtLTuaL2VHxVVZ4koRY1+5jj09XiZ+NgqYRdlgQr1HkqrQbWjLg5Ie+jHawAvkXvVm0vAA75ijg7mrYUgeJ7GeV1v+vqHz0WjYg+1W1eNqO3Qbsr/JQLaKYZeByZg2YzyaC1FlNthThr5/uRn+TbmPXHyXfjggZPXakqGqFpBAtMseQJkZg5ho65i4GZA6L3e9mSBwUzU9RVntPfPg9u0qNN1xq9fRwQAIgWwkBeCYRkBmAECmDiZb3kMztVVUkVDBx958ycmbktGbpacs7ZqXRrzdM0lVIIlIhCaC83WCsNUxAA8KZJANCKiDhXjTUaSGameYCD5PXWCrNtq0xoVkxB54YBXxQIM7MpeQgfEQDVANygQURt09HbfgvQcj8z6otPBSolMzMG9mLn5TZKoFqmkkc3mWutOY9SSnPb5zc7xlhKyTmbgUhFBA4+8QaIaLPZNPh3LkhddqxHl40ADZHM6mxK55ypAbySERAzgRIEZvJJc8ENnbbz42bj1XjO9arzPCiYnJKTEJE4wqwsOFgMCBC7DvPI09j1KXRd9/mbTZGKGAS3WVGn0A0vfv7P/+YvfvnXu8sP7g+aNId+e3H1KnS567eKlWPk0HWpXHdXoJVUr18d3z6MX3zz9u/+t7/9X//Dr05K/9W/+pe715fHL97tnoP+/ot//Byu/v5XP/r5zz948WEnmMf93TjtLi+3u8vQbYwjUHq24etXMFzdf/XV11P97Hdvvn7zBsxgu4XNBbzqLhT7h/07Mfjg8qpjlTvJxV5++MMXrz+6efXRxfXLYXMZui50G45D6jbFwIABWb3yu4iUGlIM3Hdp18UtcSSMhEiBVBihIilCjmkT07bfPp/yiULy4Q3DMCCiqoUQqO+Pt7dMMYTAIQCAipQitVbUSUve7/d3t2/393c1n/J0kjJJnWJ/QSiby3BxDTmPnL4Km+v7r7+pjKcSKGKlfl8B93rM+Pb28KvPfv93f//l7T0cM3iWCTkep/Grf/hNHmEaQQRM4O1D/tUXXz7/8Fmpx5R5MoOhr1kr0ebZTXd5wzEChdN0rA93PGzj7jKQoolpVlNq5jMzs5EhRp7ZzaAR2eJ7qLwoKDehF0fT/1dXQPVejRDOKZnlPMuplj1I9OiHN+8bnsT8zNo5Zl+zdUUvKmIRB3WvMPK8nq3M3wVK18tb/muNYevrZY6LvoIV42wIYb3CxbBwtUErngA/fwiPo5YewwNmS+rUVrcIANAH4TQ/FExbm4quDKD19y7rec8ssMbpAQDOuzLndMUAtFX82COELxfbPmWzhwOPNsd6/eub892h1+U7AfDp+KCqbjggUwjRJChFDAxGpgrGbCFQjAGwERwhInuVymJQLH1E8K3Qirue/jKQYoOeKsIGyJ7XbCWFiqYTcyIKDFAN1UQNavEeuGoz82JjDRRBLDnnaTpJLiIiWnLOUgoAEEMIIaA7cy0H4yFrkaJeJUSkqlWyiDj0hkC6zLpHLKURRTFF5IoU2JMWgVSg1ipqBAbY8sHgRU+qKB47Nn9pSs4O4d7GIOKkB9oKj5nNZrfbqpkSgZmA1GJP0lp5tILoMWQHbG2sMW04mb/f43Q8jYfIYcpVVZ89e/bBhy9jjKXkZed7eE2qmekc9JoD5G2MYaPaNbMqhaxVLTfiDENQQyVCZVM2RARRMxUTIfNOeDFVMHUGG8LQbbbeHi0KIlqkilZRCZgQjRgTJIKeUMmUmS+ffWL4zswodeN+PJ1ON1fPfvSnP/3Rn/7FVLRoRI0P9xOGmDb9/X5PlwNyJ4AYYn+5M1XJOXbbf/E3/yb0m1evP/n7//C/31X5+gQvNhd09UF36F99cpz09qR8e9TnYRe3dbQAE4XNs92zl9wNgJFSl/ptTOn61fTJw+Hi5p8O09/+9ne/e9hDfwHPXjz75Mc/N6sV42az+Yu/+Mthc/HZZ18+3B8++eEPnj17fn19HWKHHELq+27LMVWlAGgQxLBWlapqQBQQImGK1IfQEUYFJiAA6oZABARSZZzysZoCY+oH4SBEoybULqVEkZATUIibyByJGv1utVKpCJtVBY7dJu4wpuFSSi55LNPx9u2bseI0TmC42QwYex6kk55P0It+dXcP7w6Vt3FLx6oFe+wu4uZS4cu7A5wmOChcVei64Xr34e39b989QJ7g6gr6HqrA/eF0qlYFK1rlGLa7bNPJOFPcbTepH4pRPk7H6RhP+zAeshGlTa2ElCKAkJihWgQU5AERlrDTsjWWMZELvDXokkddhE9lUVXwBHQf1fL6yDUSrw1iV4g2g7rZovQfvfD1zyqPHvACCfPG/wOjRdFWRByrPzQXQuvMn9I+6AOMAYmYAKvVKlVFlobldnIDMKDGQglmKmpGngJHAKC+f7x6A7BGG7sAL8yngwUC/T7MRbj+/24QvAd17yHu+jmu/ovMVAXMRBVLmQAA56nRq5O5p2A+1WYpkm/TqABgLiBffyP8Afnjwfg7AfDD/VvP/7kRZJpMg2DZhp1YsUoCJmyCZloAYCptbmgp2b06ZobGQdq4F7ARNQDznH1ctSeJNu4kBmPwPhQzEGhu7qRkggrM4INYqyzlm0vqXqSWcfIAbK01l9Erj/xUBFZrBoNaUTwQourJUYWy2I+ISAyllGmaYoxgzJTAgu8DAEDEOvkQCDauBgwQAdFLjxHBsDrpCrZWRQQOiAIA5kRP2Dx1BiEI6LxRijaXZ1RzSgFCRQEwQiJiMs/PmXkcAAHBmIhIxBBRpPic0zlupUTse1K0iBYpNcOEiPcPB1WNiV/qc2YspeQ8OkkCAACQLVkUVABQrTh3b4s4+bEAgLMGI86D27CigZnWAqZoWlXI77NHKeaOM0UQnONgzNBFNjOJLNWqGlUnFUYO1korPB5AMcYOAH7y07989+5dPo1a5Z5vJ7kLw83Nyx9U63LNQ9hi3BwPt1CsGzanrJyxIqgKI202HZqMUgJ0Fy8vf/HX6eMf/Phnv/jl57/+Bx1ijRcf/+yXw6vTP/vlXx/2dwh689GrdPUibJ/x/v7iZU3DMGwvUr/rt9t+c0kcVe36BcSYXr7+02Kb++P/+Pbt25/89E/++Z//7KOPXw3b7idaUkqvP/y077bPP/wJYRp2wzBsmfk0TqUIhqTUqRJxZAoKQatYraqKyCEwhRhCYg4IjMgApEgALGodMcbEHMkIhMCSmQAFCiF0feiG2HXE7BxOcdgAkXNRoQhBYayoigiqOsloaEbEqcewDd1l0VCmqRZEFQgbY4kDbOPVj3/2i1LKP/z9/3X79s3Ny577y4e7++2zDyrFtL2BFPensjdQgFIBQup3l932mwsZH+7BCAS64zSdBCdMYYgYyHJOl1JonKi7nVQOp74ipY5j4rhBxFIm4wSGpSJSBSBlVQGiCqDEdVbTT6pj145jgxAiACiiZoCA5mFkpLXTtdBO2TwkbY6duhjMpDFEsJBMLRg/a79HiF0B+dM6mAV15PHr19C+RIkdvZavMOrWJ1w+uOjVBbCXj68Pc3NhCYCvbRFqXSflPXS0VejedbufbLmapwCywK47v+8PuogUERHncnZ71Fi2vkUtBQiwKkhWAFBz8meoxVOiQOSpFZrv2GMYwAzV1BTBlFipsRGAmYEKKiKAPrKU2eLEAwBAhD9WvhMA7+9u3U+NMWLsABU0EpU8UVVRAWZWKbVMFBgRM2z92Ttrtkc1oTFerQvbCFttF65y+D5r07kiEFAAAVQWo8nMRE+lnpg5cpjvMrlq9+dKBmYmYuM45pxrOXnANuesNQNACF4UrSYqAGYCuoxLk9N4nBOrEELQAnmaxtMBhwFjVBAD1nljIKJWBW/hxmAQRFvqVysBAAIxmgECeAQJyKI5A7UJtFGDpghmpsAkwRBUoJTqIUHmefwRtNamlFLo0jxcQWE23El8WEKr5jCRUkqzOcwIUMnaBq7i/BhExAFRsdZ6e/vWtdtsoVuMXUophEQY3HAVlUbQYFal1lJKbcasSOGW3EIncFD3ZHOVOYw2R8B9zrwRUSJUIEU1p31QHU8nIERkdxwQIQZQQK2t1cJAWi0XRyKKly+H7bP9fp/Hadg9v3p2HPptf3Hz5m7PqcPjGKoUMZU6yR4RT2MFjFIrEaUOEQhD6vt+f3pI2+sPN9uLm+tPf/Tj/cMtmCDap3ETY0iRteRapqELzDieToCIyMghpG7YXvTDhZiNOXfBum7g/vqvTjZcvDwejy9fvXj1+gWx3dxc7C42uZRpEjG+ef7BdnuRrfTdBgCUTlyFOAJQrUphQERquTRFZLfq+q7vYmqlGExgBEiGIGqTqCARB05DIsbS15qzAqcudANwLEo4p9/MIyoEACiCoqxeeRHEas0mxwnGyXzCEBj2F69iP1HcMVrXxTJliBcppZvnV11MLz784TQeA8Htm7f/9I//8O6bt2/v93envJ/EInywBSPYXQ5T1v3RFIaLq0ulEyJTCCQ4XH3QbV/sLpIR4Gm8jjdXL3nod7urGyR493CipM+ef5C2F9V4PNVdR6VIqcaBF2xzFVHzXOXbQjRP8GYNbPPPx8NUlZlxxiRw4rD35akfNvffaK1aq5kRABK5x0nMbXzDDIRLseq6nXItS//P8nM5fg2ltnjDsALjFWItbX5r9AUAAFkcUH/8ROD1KGa2xNyZw7eoaZ+Ex5181My+xaD3h4+HR3sCW5+0j5Gw2vqkWsgBlnopxCdmisGTbIItLA7gds/q5OquOAJ42bULLbffzGqtXlVj85Sq9mjJYLa61obO9wfAD7deWmk6gClbMCqIrFalerA+gYlaFyQgMPaDQyqYgPmscq+fEzABY6/m9Vy+AyessikiUmtVrSEEQPBJCyJS6uS3SWW02bSMMabQMbMi1iJaay2PjGh5nHLOVRyASx6P0zSBSYwcY+hSQBPVqlqlFhH/XQ+H+xhjSilGJkwAIHWsZdrXqes6lb7WJ+TPKSWEaKgqalpwVUVFRF6k5sV76vaBFr/SahWg9XojYpmL2pz5z0upaxGKrdLK7wwR9ZtusCHnbLO52mxVf4s1RmYzrWbOKa+qYKJSiIgRFnXjp91ut16U9tVXX5UyMXPqgoiYYtd1u93lZoOBUdUB3bUJWEPgKnNrmdSKCBrJaccQDCoaAWCnis4Iz62lQRFATYgYAzEBmEN7NYAsGREBSA2rQlXxXUDeyerk0lK9pI2RD2MlThh7FLzZXoUQQAFDUIU+dXcP90Xl5tkLUdnf3/XDcNzf5Zyrd6+KEiCaRGJVsEC1WEjDD//kZyLlmzdf3d2963c3WvJwcdF3MY/HGMPQ9afTydNUyCyGRAFCQsDEFmPJVavFjz79yYcf/ykzC0jOx2GTDAoxU9LQaYrbENI0qTIppRhjzx0YhRCq2uk4EpGZMzCLc5a7RkgphEAUCGiZBatgtNltPMMi4glGUiJBDoiJU6RIRlYNkFJKGCNUW3rDyQoIIiggCERFAuwobSKGOFMcHh7uuOuHsAnEHFD40NXdxcUFdChIl68uteYy5cub18Pl8//z7/4Ovn4jNFy/+mhzVX7xi1+mbri9vd/tdsdyV3Fzsbu+ihJi6vvN6XT4+Ic/vXr+6cX1Tq3Cw/3mKu62Vxe7m+3lZTm8+9VvPs9ZMGw57u7uH2w8hO6auwQGCJGICZOiEhFCkJoRAqFBcxYfVZnpwlH1KEQJnhLE0nyrYU6lrDFsbg14WpllNo6jTwCMMfI8LZSZF3ycnYgnFUY0C89jIiFEWMVgl+OXGhFYlU2ZWda6gPH6KqDAcp61R0vQNInNHfnLt9tcHQ1L++iqmqnt+/lPqYWIDIKBLosBgFpWx6/Eq8HfWz8iSmnXtYZ6M3MvDp5iMLbCK78zs4GiBCBzztiB0x+fAgDxEwbD5TwrAHbv/P1ks85lTE/u6h8l3wmAv/79l5eXl3R9FY1LMdUqFIAwahrHURX67QbRpulEXnNfyA3JnPN+vw8hXVxchBAQOYTgw6dsNlprrQotboyItebT6ZhzNpDNZnMaDyJljoRIlQIAU74PIXQxEUapMkklCgxIFHLOTpKnqrW0qZZeapTH0WeVm1aAFAMy8jgdpRZEA61SChFshz5PyGR5PNRMjDszm057k3o4HkvX1ZyIaJnrGWOciHa7HcZogiGkKqIKXdf5DD0zKUVFzBdjZmTJE0AitdbqRVnIXJsriYCMyCYgeSq5askAwMwxJUScpnI6PdwiKhgR9X0/DINAM0iHfktmXdz4q8OMIi27PE2nQJxnTUEMMcZIbBxijI2KGSQXPY1tx07TlHOutQ7D1hRzLrXWQCwiRVyVIBlUMxEJCICKPlbFpJZJVSIxSEtDhBBct5mqSI0xIgIBAaiaQJ1mXVNLrVXNu26yqAHM8XCQmkUkMfVdB6B1yvvjcTaEYRJRIgQ4jUcRGR9KjHHYDlUmA4iRxtM+EY/7AyKmEI/3d6BmJof720AAVlCVUIiByYB4d/0cuS8UH6Z8KsIYJoMsNcZNlzoiAgoR0YAQmQk7DKb3YhUZ4hBj6IdhIKKi035/K5rVBEI39Iko1QJGFnpW4mKEoSNkZGaFbsMqUKYJjFLsmWSa2iwjihZ7ChHMqqgFjugmjGVihMCzgUWMgQJjNgLUKo1pBa1mDGqYEmgjpEcDAqhVaq1FRFWLoiiJctN5iLG/BFStJedcThNDF4coFIlxn/M+HyOHLgyTlO2zD/76X728v70zkX/5r/7L3fby+vp6HKdpmk6n6fbd39cqsdumuDGlfjNsNr2APntxBSwhhbT9YBzz9uLm5auPhuGi7r9J21f742nK9f4+Ewyp31UBqJjiEGKvQrmKGVaTaTxRv9TfNq3qsbcQgmhxn2EGBQ/zPNJPAnhjbmveQ0Tkx5LpxRdycFqKHP2v0+Q5yMdkcxN9AkhuE9daF+KaxZ1ojQmxSykR0VzC0pBpqUFZPtUKNZT8hNDosL0LOUzT5Imzruv88vu+B0QKHJkjc631dDpN00REXdeN4+iLxNZp2dbjxTQLWsMMn1qqAtQpLzbEbGgEX1sIAcy8xiqGQNa0iiIItIl5CAiiHAIhOaWgxx5UBLDR2a/nrWELKJv7VG7xhBBiZDCIMRJRKXmd78/T5PozpZRSIgr+iMFMSrsumKn1jYg5+PUSETRkePpA//PlO05DyqVMZTyhCoYYY4yhY2YLAbxFTKVK9itBsvvbt06OU2s97I8hhGk6EYWu60JI7nV5RVWzmEC15U5UVUudzIwZcx6nPNZaCTypKSIFAICmEEJNKYTEzAEC+XhQ4OmUcy6+tSS34qD96cGBBE0QLYaQIodApUzTeJqmE6OZSckTAIyn/WF/JKKcR0SseRKRw+HgRVh5iuMpIj5amr5PLi8vh2FIKfX9BgBM0aQ2S7rlSh9NS6YOEZ3ou9YqVv3NzrnWVm1PhKGqTWOZpinrEQDcKfftXaQiohMfenU3AOScmVlySXHDjL7VmaiUMk0nRDRRZjYQB+CAVGPMzBBijBxjpFa+3vZ8CMHamKNQq4K52QGnWmqRUkrVBnt+NzggmnHAGMAkl2kErUSAceMmtncnk6f0q4ywDyFE9pbrLLW4JjKEIlXFFEEUslRErrW66qml1FqFUGpV1VLKmNu4Pa8AEDAy8A3mmhNIzZuzaqlSErD5NSogspmyAZtpFdCqKkgSDJVmL4SxAoqSEwaQQFWLXisPuvAWYQgxRmZLTEgcU0+BY+xi7NWqSC7qXakcOHHo3fECAIUCSAZoRoBIhiqm7sVSoGjMjNJoUFNKRE78a43OMBi1oJkKoE+VEpPF2WIzL7pw2x4B0RRUQCrYatScs3uqP1IMIcTUG3iCiFRFkE2tKhYjxQ7RlGKFoKZZQASTWTFDAREEteevPwpIw+X1pt9uNpvj8agKIvLJj5+pQN9vY9yqQggpdiFLDhENKyIeTpPyMaRLCDdKQ3/B1F0N23KapsM05SocUuABIQEwWjAlM1IBt3BxcgVSZ61NZjYXRogDMAAA0Ow17pfWBn/invFyFTyTGsOj/yp/GIDbpKmnzjHMoeMlztd2vcjhcFhCwQvmOQDHGP2Ea6/X5jbiOVnWAHjYXC4ER75O37y11pzz4sa5Uw4AKXWLu+wg7d81juOik7ElB4mIfDgjzLQBsGqOsqWXd8W87UOH3Blb1iOryTFu3MAM5AEEVzXb/suaqxhWthQi1pZhFE9xIqJZQkyuHpc73IwBVVEfK+nl2UTURqy2w/Rx2LwbLiK6ALCZLQr2u8h3AmCn8M1lylKYQ9/3RIRkIsVMkNhAS5lEnMMBctZxHP0uj1Pm2l7rnAdm9rydWevHAoAKmvNYa0VrzbsphdRFKXUajzmPAAD4OL/B8BBClJICJ+bIHAkDIyPyOOY6VRUzQ5+/BgCncRzHU845Beq6GELgGIio5nGapsPxgcEQLOexSkbEOok7fwBQcyml7A/3IpJSmnBcRS2ajHkcx9NudzEMw3ZbGlNYC1mQmVUvvtb2AoXg3fo+87uqViBk5mkqqiCGREwYPIc9TWWqD+76SNcZYs6z3ZciEYkWE1UwB+Ba624bPNpTSyEC34doUOrkeyafxlIm3yHMjDHVGqs4/Yj5plpvdQAopRCFNidSVErxZchinQJwBYMaqgmZ1pLHvWhlBAvZNUJKKcaOPUQvWuoUPPRlIFpEmq5BplqkqiCiAIqqYmGOFqOq1lJKKdU0T1OzgimABwz1MVqlpZJPnakZSRAU0VQKqQAh+gB53+Emnmc1rSpVfBiFYQiEgRCNOJioiIICIlUzUqgqxrpkTAAgxtj3fYxR6mSK6sFFDGY25no6jSWLoXFAwhBCCtwVQkM181xdiwyJafVh4tQUWQjRvQJE5BSJCzEQgw93IVYmXiUxvcblMW5GAGRMZjSXDxEIAoJW8OpA8z6z6n9UW82BK1CDVhdpZlUsl+rk3hCCEhcDVMuipdQqHFhRTauhARcd+gShywA25eOUQ0gxJEwXZtgNV323E3UiASh53F5uxKqZTHJAJoUuS7DMKW1St+GkmCahvZ4mRQBjRAYL3raoYkXVy4fJCoEa88ILYQoi4gRBHpxExMUHOE6je3vNN1iFnYmIn3rGIqLVFgAGgAWAF4scn0anpeZ18Fn1sQRp7QEvXjj34C64G8q4mkPj51w8P//INtcFPHBuYJ3Xls0EQB2odA5WO8D44pdSWc9VLQtbjAmvjvU33F1M/94WIl7Fk9tFShvj6Ijo98QsfvuSG8YzKgIYLc9FVaVVHT5G2mEG4LFW9y78zrvdIkKIyesHfUv6NYqIT06cra7GcLkc02DeKaGdOh8aObHjVK2l1j9MDvqfLvgdP3+Ws5zlLGc5y1n+CPmPkFWf5SxnOctZznKW/z/lDMBnOctZznKWs3wPcgbgs5zlLGc5y1m+BzkD8FnOcpaznOUs34OcAfgsZznLWc5ylu9BzgB8lrOc5SxnOcv3IGcAPstZznKWs5zle5AzAJ/lLGc5y1nO8j3IGYDPcpaznOUsZ/ke5AzAZznLWc5ylrN8D3IG4LOc5SxnOctZvgc5A/BZznKWs5zlLN+DnAH4LGc5y1nOcpbvQc4AfJaznOUsZznL9yBnAD7LWc5ylrOc5XuQMwCf5SxnOctZzvI9yBmAz3KWs5zlLGf5HuQMwGc5y1nOcpazfA9yBuCznOUsZznLWb4H+X8BD4FD/iqoJfwAAAAASUVORK5CYII=",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "from io import BytesIO\n",
-        "from PIL import Image, ImageDraw, ImageOps\n",
-        "from IPython.display import display, Image as IPImage\n",
-        "\n",
-        "def display_img_array(ima, score):\n",
-        "    im = Image.open(ima)\n",
-        "    img_with_border = ImageOps.expand(im ,border=20, fill='white')\n",
-        "\n",
-        "    # Add Text to an image\n",
-        "    img = ImageDraw.Draw(img_with_border)\n",
-        "    img.text((20, 0), f\"Score: {score},    Path: {ima}\", fill=(0, 0, 0))\n",
-        "\n",
-        "    bio = BytesIO()\n",
-        "    img_with_border.save(bio, format='png')\n",
-        "    display(IPImage(bio.getvalue(), format='png'))\n",
-        "\n",
-        "images_array = [doc.content for doc in results]\n",
-        "scores = [doc.score for doc in results]\n",
-        "for ima, score in zip(images_array, scores):\n",
-        "    display_img_array(ima, score)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rcteNc-Rn5R7"
-      },
-      "source": [
-        "Congratulations! You've created a search system that returns images of animals in answer to a text query."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8RyMcCI2_yHf"
-      },
-      "source": [
-        "## About us\n",
-        "\n",
-        "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-        "\n",
-        "We bring NLP to the industry via open source!  \n",
-        "Our focus: Industry specific language models & large scale QA systems.  \n",
-        "  \n",
-        "Some of our other work: \n",
-        "- [German BERT](https://deepset.ai/german-bert)\n",
-        "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-        "\n",
-        "Get in touch:\n",
-        "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-        "\n",
-        "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [],
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3.6.9 64-bit",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python",
-      "version": "3.6.9"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-      }
-    }
+   ],
+   "source": [
+    "from io import BytesIO\n",
+    "from PIL import Image, ImageDraw, ImageOps\n",
+    "from IPython.display import display, Image as IPImage\n",
+    "\n",
+    "\n",
+    "def display_img_array(ima, score):\n",
+    "    im = Image.open(ima)\n",
+    "    img_with_border = ImageOps.expand(im, border=20, fill=\"white\")\n",
+    "\n",
+    "    # Add Text to an image\n",
+    "    img = ImageDraw.Draw(img_with_border)\n",
+    "    img.text((20, 0), f\"Score: {score},    Path: {ima}\", fill=(0, 0, 0))\n",
+    "\n",
+    "    bio = BytesIO()\n",
+    "    img_with_border.save(bio, format=\"png\")\n",
+    "    display(IPImage(bio.getvalue(), format=\"png\"))\n",
+    "\n",
+    "\n",
+    "images_array = [doc.content for doc in results]\n",
+    "scores = [doc.score for doc in results]\n",
+    "for ima, score in zip(images_array, scores):\n",
+    "    display_img_array(ima, score)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rcteNc-Rn5R7"
+   },
+   "source": [
+    "Congratulations! You've created a search system that returns images of animals in answer to a text query."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8RyMcCI2_yHf"
+   },
+   "source": [
+    "## About us\n",
+    "\n",
+    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
+    "\n",
+    "We bring NLP to the industry via open source!  \n",
+    "Our focus: Industry specific language models & large scale QA systems.  \n",
+    "  \n",
+    "Some of our other work: \n",
+    "- [German BERT](https://deepset.ai/german-bert)\n",
+    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
+    "\n",
+    "Get in touch:\n",
+    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
+    "\n",
+    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3.6.9 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.6"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }

From 210cdb9346ffa517f97bb06585a20c204eddfdf1 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Mon, 12 Dec 2022 14:31:52 +0100
Subject: [PATCH 015/206] Fixing broken link in Pipelines Tutorial (#88)

---
 markdowns/11_Pipelines.md    | 6 +++---
 tutorials/11_Pipelines.ipynb | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/markdowns/11_Pipelines.md b/markdowns/11_Pipelines.md
index a8e6ca9e..37a0ae92 100644
--- a/markdowns/11_Pipelines.md
+++ b/markdowns/11_Pipelines.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb
 toc: True
 title: "How to Use Pipelines"
-last_updated: 2022-12-08
+last_updated: 2022-12-09
 level: "intermediate"
 weight: 65
 description: Learn about the many ways which you can route queries through the nodes in a pipeline.
@@ -211,7 +211,7 @@ Haystack features prebuilt pipelines to do:
 - generative QA (GenerativeQAPipeline)
 - FAQ style QA (FAQPipeline)
 - translated search (TranslationWrapperPipeline)
-To find out more about these pipelines, have a look at our [documentation](https://haystack.deepset.ai/docs/latest/pipelinesmd)
+To find out more about these pipelines, have a look at our [documentation](https://docs.haystack.deepset.ai/docs/pipelines)
 
 
 With any Pipeline, whether prebuilt or custom constructed,
@@ -466,6 +466,6 @@ Some of our other work:
 - [FARM](https://github.com/deepset-ai/FARM)
 
 Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
+[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
 
 By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/tutorials/11_Pipelines.ipynb b/tutorials/11_Pipelines.ipynb
index b9406566..3085177d 100644
--- a/tutorials/11_Pipelines.ipynb
+++ b/tutorials/11_Pipelines.ipynb
@@ -405,7 +405,7 @@
     "- generative QA (GenerativeQAPipeline)\n",
     "- FAQ style QA (FAQPipeline)\n",
     "- translated search (TranslationWrapperPipeline)\n",
-    "To find out more about these pipelines, have a look at our [documentation](https://haystack.deepset.ai/docs/latest/pipelinesmd)\n"
+    "To find out more about these pipelines, have a look at our [documentation](https://docs.haystack.deepset.ai/docs/pipelines)\n"
    ]
   },
   {
@@ -830,7 +830,7 @@
     "- [FARM](https://github.com/deepset-ai/FARM)\n",
     "\n",
     "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
+    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
     "\n",
     "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
    ]
@@ -852,7 +852,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "3.10.6"
+   "version": "3.8.9"
   },
   "vscode": {
    "interpreter": {

From 9b71be9240d7b02c7215b69d0dae32dc02614135 Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Fri, 16 Dec 2022 09:19:35 +0100
Subject: [PATCH 016/206] make tutorial 14 testable (#19)

* make tutorial 14 testable

* comment out draw

* minor

* enable on nightly
---
 .github/workflows/nightly.yml       |   1 +
 markdowns/14_Query_Classifier.md    | 114 +++++++++++++-------
 tutorials/14_Query_Classifier.ipynb | 161 ++++++++++++++++++++--------
 3 files changed, 196 insertions(+), 80 deletions(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index b7cf1b35..fdd9584a 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -33,6 +33,7 @@ jobs:
           - 10_Knowledge_Graph
           - 11_Pipelines
           - 12_LFQA
+          - 14_Query_Classifier
           - 15_TableQA
           - 16_Document_Classifier_at_Index_Time
           - 17_Audio
diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
index 244c8044..46844bec 100644
--- a/markdowns/14_Query_Classifier.md
+++ b/markdowns/14_Query_Classifier.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb
 toc: True
 title: "Query Classifier"
-last_updated: 2022-11-24
+last_updated: 2022-12-15
 level: "intermediate"
 weight: 80
 description: Classify incoming queries so that they can be routed to the nodes that are best at handling them.
@@ -44,20 +44,27 @@ Make sure you enable the GPU runtime to experience decent speed in this tutorial
 
 <img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
 
+You can double check whether the GPU runtime is enabled with the following command:
+
+
+```bash
+%%bash
+
+nvidia-smi
+```
+
 Next we make sure the latest version of Haystack is installed:
 
 
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
+```bash
+%%bash
 
-# Install the latest main of Haystack (Colab)
-!pip install --upgrade pip
-!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
+pip install --upgrade pip
+pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
 
 # Install these to allow pipeline visualization
-!apt install libgraphviz-dev
-!pip install pygraphviz
+apt install libgraphviz-dev
+pip install pygraphviz
 ```
 
 ### Logging
@@ -156,38 +163,43 @@ And as we see, the question "Who was the father of Arya Stark" is sent to branch
 
 Now let's see how we can use query classifiers in a question-answering (QA) pipeline. We start by initiating Elasticsearch:
 
+#### Start an Elasticsearch server
+You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
+
 
 ```python
-# In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
+# Recommended: Start Elasticsearch using Docker via the Haystack utility function
+from haystack.utils import launch_es
 
-import os
-from subprocess import Popen, PIPE, STDOUT
+launch_es()
+```
 
-es_server = Popen(
-    ["elasticsearch-7.9.2/bin/elasticsearch"], stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1)  # as daemon
-)
-# wait until ES has started
-! sleep 30
+#### Start an Elasticsearch server in Colab
+
+If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
+
+
+```bash
+%%bash
+
+wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
+tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
+chown -R daemon:daemon elasticsearch-7.9.2
+sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d
+```
+
+
+```bash
+%%bash --bg
+
+sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
 ```
 
 Next we fetch some data&mdash;for our example we'll use pages from the Game of Thrones wiki&mdash;and index it in our `DocumentStore`:
 
 
 ```python
-from haystack.utils import (
-    print_answers,
-    print_documents,
-    fetch_archive_from_http,
-    convert_files_to_docs,
-    clean_wiki_text,
-    launch_es,
-)
-from haystack.pipelines import Pipeline
-from haystack.document_stores import ElasticsearchDocumentStore
-from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader, TransformersQueryClassifier
+from haystack.utils import fetch_archive_from_http, convert_files_to_docs, clean_wiki_text
 
 # Download and prepare data - 517 Wikipedia articles for Game of Thrones
 doc_dir = "data/tutorial14"
@@ -196,9 +208,23 @@ fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
 
 # convert files to dicts containing documents that can be indexed to our datastore
 got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
+```
+
+
+```python
+import os
+import time
+
+from haystack.document_stores import ElasticsearchDocumentStore
+
+
+# Wait 30 seconds only to be sure Elasticsearch is ready before continuing
+time.sleep(30)
+
+# Get the host where Elasticsearch is running, default to localhost
+host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
+
 
-# Initialize DocumentStore and index documents
-# launch_es() # Uncomment this line for local Elasticsearch
 document_store = ElasticsearchDocumentStore()
 document_store.delete_documents()
 document_store.write_documents(got_docs)
@@ -212,6 +238,9 @@ We start by initializing our retrievers and reader:
 
 
 ```python
+from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader
+
+
 # Initialize sparse retriever for keyword queries
 bm25_retriever = BM25Retriever(document_store=document_store)
 
@@ -228,6 +257,9 @@ Now we define our pipeline. As promised, the question/statement branch `output_1
 
 
 ```python
+from haystack.pipelines import Pipeline
+
+
 # Here we build the pipeline
 sklearn_keyword_classifier = Pipeline()
 sklearn_keyword_classifier.add_node(component=SklearnQueryClassifier(), name="QueryClassifier", inputs=["Query"])
@@ -237,14 +269,17 @@ sklearn_keyword_classifier.add_node(
 sklearn_keyword_classifier.add_node(component=bm25_retriever, name="BM25Retriever", inputs=["QueryClassifier.output_2"])
 sklearn_keyword_classifier.add_node(component=reader, name="QAReader", inputs=["BM25Retriever", "EmbeddingRetriever"])
 
-# Visualization of the pipeline
-sklearn_keyword_classifier.draw("sklearn_keyword_classifier.png")
+# To generate a visualization of the pipeline, uncomment the following:
+# sklearn_keyword_classifier.draw("sklearn_keyword_classifier.png")
 ```
 
 Below, we can see how this choice affects the branching structure: the keyword query "arya stark father" and the question query "Who is the father of Arya Stark?" generate noticeably different results, a distinction that is likely due to the use of different retrievers for keyword vs. question/statement queries.
 
 
 ```python
+from haystack.utils import print_answers
+
+
 # Useful for framing headers
 equal_line = "=" * 30
 
@@ -320,14 +355,17 @@ transformer_question_classifier.add_node(
 )
 transformer_question_classifier.add_node(component=reader, name="QAReader", inputs=["QueryClassifier.output_1"])
 
-# Visualization of the pipeline
-transformer_question_classifier.draw("transformer_question_classifier.png")
+# To generate a visualization of the pipeline, uncomment the following:
+# transformer_question_classifier.draw("transformer_question_classifier.png")
 ```
 
 And here are the results of this pipeline: with a question query like "Who is the father of Arya Stark?", we obtain answers from a reader, and with a statement query like "Arya Stark was the daughter of a Lord", we just obtain documents from a retriever.
 
 
 ```python
+from haystack.utils import print_documents
+
+
 # Useful for framing headers
 equal_line = "=" * 30
 
@@ -407,7 +445,7 @@ pd.DataFrame.from_dict(sent_results)
 You can also perform zero-shot classification by providing a suitable base transformer model and **choosing** the classes the model should predict.
 For example, we may be interested in whether the user query is related to music or cinema.
 
-*In this case, the `labels` parameter is a list containing the candidate classes.*
+In this case, the `labels` parameter is a list containing the candidate classes.
 
 
 ```python
diff --git a/tutorials/14_Query_Classifier.ipynb b/tutorials/14_Query_Classifier.ipynb
index 49cc0b0d..5d941275 100644
--- a/tutorials/14_Query_Classifier.ipynb
+++ b/tutorials/14_Query_Classifier.ipynb
@@ -49,7 +49,24 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.  \n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">"
+    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+    "\n",
+    "You can double check whether the GPU runtime is enabled with the following command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "nvidia-smi"
    ]
   },
   {
@@ -72,20 +89,21 @@
     "id": "CjA5n5lMN-gd",
     "pycharm": {
      "name": "#%%\n"
+    },
+    "vscode": {
+     "languageId": "shellscript"
     }
    },
    "outputs": [],
    "source": [
-    "# Install the latest release of Haystack in your own environment\n",
-    "#! pip install farm-haystack\n",
+    "%%bash\n",
     "\n",
-    "# Install the latest main of Haystack (Colab)\n",
-    "!pip install --upgrade pip\n",
-    "!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]\n",
+    "pip install --upgrade pip\n",
+    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]\n",
     "\n",
     "# Install these to allow pipeline visualization\n",
-    "!apt install libgraphviz-dev\n",
-    "!pip install pygraphviz"
+    "apt install libgraphviz-dev\n",
+    "pip install pygraphviz"
    ]
   },
   {
@@ -284,27 +302,66 @@
     "Now let's see how we can use query classifiers in a question-answering (QA) pipeline. We start by initiating Elasticsearch:"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Start an Elasticsearch server\n",
+    "You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Recommended: Start Elasticsearch using Docker via the Haystack utility function\n",
+    "from haystack.utils import launch_es\n",
+    "\n",
+    "launch_es()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Start an Elasticsearch server in Colab\n",
+    "\n",
+    "If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "fCLtLItU5aWl"
+    "vscode": {
+     "languageId": "shellscript"
+    }
    },
    "outputs": [],
    "source": [
-    "# In Colab / No Docker environments: Start Elasticsearch from source\n",
-    "! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
-    "! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "! chown -R daemon:daemon elasticsearch-7.9.2\n",
+    "%%bash\n",
     "\n",
-    "import os\n",
-    "from subprocess import Popen, PIPE, STDOUT\n",
+    "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
+    "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
+    "chown -R daemon:daemon elasticsearch-7.9.2\n",
+    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%bash --bg\n",
     "\n",
-    "es_server = Popen(\n",
-    "    [\"elasticsearch-7.9.2/bin/elasticsearch\"], stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1)  # as daemon\n",
-    ")\n",
-    "# wait until ES has started\n",
-    "! sleep 30"
+    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch"
    ]
   },
   {
@@ -331,17 +388,7 @@
    },
    "outputs": [],
    "source": [
-    "from haystack.utils import (\n",
-    "    print_answers,\n",
-    "    print_documents,\n",
-    "    fetch_archive_from_http,\n",
-    "    convert_files_to_docs,\n",
-    "    clean_wiki_text,\n",
-    "    launch_es,\n",
-    ")\n",
-    "from haystack.pipelines import Pipeline\n",
-    "from haystack.document_stores import ElasticsearchDocumentStore\n",
-    "from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader, TransformersQueryClassifier\n",
+    "from haystack.utils import fetch_archive_from_http, convert_files_to_docs, clean_wiki_text\n",
     "\n",
     "# Download and prepare data - 517 Wikipedia articles for Game of Thrones\n",
     "doc_dir = \"data/tutorial14\"\n",
@@ -349,10 +396,28 @@
     "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
     "\n",
     "# convert files to dicts containing documents that can be indexed to our datastore\n",
-    "got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n",
+    "got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "\n",
+    "from haystack.document_stores import ElasticsearchDocumentStore\n",
+    "\n",
+    "\n",
+    "# Wait 30 seconds only to be sure Elasticsearch is ready before continuing\n",
+    "time.sleep(30)\n",
+    "\n",
+    "# Get the host where Elasticsearch is running, default to localhost\n",
+    "host = os.environ.get(\"ELASTICSEARCH_HOST\", \"localhost\")\n",
+    "\n",
     "\n",
-    "# Initialize DocumentStore and index documents\n",
-    "# launch_es() # Uncomment this line for local Elasticsearch\n",
     "document_store = ElasticsearchDocumentStore()\n",
     "document_store.delete_documents()\n",
     "document_store.write_documents(got_docs)"
@@ -380,6 +445,9 @@
    },
    "outputs": [],
    "source": [
+    "from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader\n",
+    "\n",
+    "\n",
     "# Initialize sparse retriever for keyword queries\n",
     "bm25_retriever = BM25Retriever(document_store=document_store)\n",
     "\n",
@@ -413,6 +481,9 @@
    },
    "outputs": [],
    "source": [
+    "from haystack.pipelines import Pipeline\n",
+    "\n",
+    "\n",
     "# Here we build the pipeline\n",
     "sklearn_keyword_classifier = Pipeline()\n",
     "sklearn_keyword_classifier.add_node(component=SklearnQueryClassifier(), name=\"QueryClassifier\", inputs=[\"Query\"])\n",
@@ -422,8 +493,8 @@
     "sklearn_keyword_classifier.add_node(component=bm25_retriever, name=\"BM25Retriever\", inputs=[\"QueryClassifier.output_2\"])\n",
     "sklearn_keyword_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"BM25Retriever\", \"EmbeddingRetriever\"])\n",
     "\n",
-    "# Visualization of the pipeline\n",
-    "sklearn_keyword_classifier.draw(\"sklearn_keyword_classifier.png\")"
+    "# To generate a visualization of the pipeline, uncomment the following:\n",
+    "# sklearn_keyword_classifier.draw(\"sklearn_keyword_classifier.png\")"
    ]
   },
   {
@@ -444,6 +515,9 @@
    },
    "outputs": [],
    "source": [
+    "from haystack.utils import print_answers\n",
+    "\n",
+    "\n",
     "# Useful for framing headers\n",
     "equal_line = \"=\" * 30\n",
     "\n",
@@ -546,8 +620,8 @@
     ")\n",
     "transformer_question_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"QueryClassifier.output_1\"])\n",
     "\n",
-    "# Visualization of the pipeline\n",
-    "transformer_question_classifier.draw(\"transformer_question_classifier.png\")"
+    "# To generate a visualization of the pipeline, uncomment the following:\n",
+    "# transformer_question_classifier.draw(\"transformer_question_classifier.png\")"
    ]
   },
   {
@@ -567,6 +641,9 @@
    },
    "outputs": [],
    "source": [
+    "from haystack.utils import print_documents\n",
+    "\n",
+    "\n",
     "# Useful for framing headers\n",
     "equal_line = \"=\" * 30\n",
     "\n",
@@ -672,7 +749,7 @@
     "You can also perform zero-shot classification by providing a suitable base transformer model and **choosing** the classes the model should predict.\n",
     "For example, we may be interested in whether the user query is related to music or cinema.\n",
     "\n",
-    "*In this case, the `labels` parameter is a list containing the candidate classes.*"
+    "In this case, the `labels` parameter is a list containing the candidate classes."
    ]
   },
   {
@@ -757,7 +834,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3.7.11 ('haystack-dev')",
+   "display_name": "Python 3.10.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -771,11 +848,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.11"
+   "version": "3.10.6"
   },
   "vscode": {
    "interpreter": {
-    "hash": "a1c4180befe5334d9af26d84758dc08f43161c3b98a4eb4d4a43d7491d015a65"
+    "hash": "bda33b16be7e844498c7c2d368d72665b4f1d165582b9547ed22a0249a29ca2e"
    }
   }
  },

From 266cb09656eb30e3ef99d0cab20321cfca656824 Mon Sep 17 00:00:00 2001
From: Sebastian <sjrl@users.noreply.github.com>
Date: Fri, 16 Dec 2022 15:16:11 +0100
Subject: [PATCH 017/206] Remove torch-scatter reference since it is no longer
 needed (#89)

---
 markdowns/15_TableQA.md    | 17 +---------------
 tutorials/15_TableQA.ipynb | 41 ++++----------------------------------
 2 files changed, 5 insertions(+), 53 deletions(-)

diff --git a/markdowns/15_TableQA.md b/markdowns/15_TableQA.md
index ad702986..7778e232 100644
--- a/markdowns/15_TableQA.md
+++ b/markdowns/15_TableQA.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb
 toc: True
 title: "Open-Domain QA on Tables"
-last_updated: 2022-12-08
+last_updated: 2022-12-15
 level: "advanced"
 weight: 130
 description: Perform question answering on tabular data.
@@ -47,21 +47,6 @@ apt install libgraphviz-dev
 pip install pygraphviz
 ```
 
-
-```python
-# The TaPAs-based TableReader requires the torch-scatter library
-import torch
-
-torch_version = torch.__version__
-```
-
-
-```bash
-%%bash -s "$torch_version"
-
-pip install torch-scatter -f https://data.pyg.org/whl/torch-$1.html
-```
-
 ## Logging
 
 We configure how logging messages should be displayed and which log level should be used before importing Haystack.
diff --git a/tutorials/15_TableQA.ipynb b/tutorials/15_TableQA.ipynb
index e623677d..0c564ea1 100644
--- a/tutorials/15_TableQA.ipynb
+++ b/tutorials/15_TableQA.ipynb
@@ -71,41 +71,9 @@
     "pip install pygraphviz"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# The TaPAs-based TableReader requires the torch-scatter library\n",
-    "import torch\n",
-    "\n",
-    "torch_version = torch.__version__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash -s \"$torch_version\"\n",
-    "\n",
-    "pip install torch-scatter -f https://data.pyg.org/whl/torch-$1.html"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -123,7 +91,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
@@ -811,7 +778,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3.10.4 64-bit",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -825,7 +792,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.8"
   },
   "vscode": {
    "interpreter": {
@@ -834,5 +801,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
-}
+ "nbformat_minor": 1
+}
\ No newline at end of file

From a70b54465607eb4b51fa910cc8fc800fc06888d6 Mon Sep 17 00:00:00 2001
From: Julian Risch <julian.risch@deepset.ai>
Date: Thu, 22 Dec 2022 16:02:14 +0100
Subject: [PATCH 018/206] bug: remove duplicate ES daemon start in tutorial 14
 (#92)

* bug: remove duplicate es start in tutorial 14

* generate md file for tutorial 14
---
 markdowns/14_Query_Classifier.md    | 4 ++--
 tutorials/14_Query_Classifier.ipynb | 3 +--
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
index 46844bec..1b66a55f 100644
--- a/markdowns/14_Query_Classifier.md
+++ b/markdowns/14_Query_Classifier.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb
 toc: True
 title: "Query Classifier"
-last_updated: 2022-12-15
+last_updated: 2022-12-22
 level: "intermediate"
 weight: 80
 description: Classify incoming queries so that they can be routed to the nodes that are best at handling them.
@@ -185,7 +185,7 @@ If Docker is not readily available in your environment (e.g. in Colab notebooks)
 wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
 tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
 chown -R daemon:daemon elasticsearch-7.9.2
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d
+
 ```
 
 
diff --git a/tutorials/14_Query_Classifier.ipynb b/tutorials/14_Query_Classifier.ipynb
index 5d941275..ecbb8950 100644
--- a/tutorials/14_Query_Classifier.ipynb
+++ b/tutorials/14_Query_Classifier.ipynb
@@ -345,8 +345,7 @@
     "\n",
     "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
     "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch -d"
+    "chown -R daemon:daemon elasticsearch-7.9.2\n"
    ]
   },
   {

From 94aa68660fd774a4b6da9f9f6c4dbe1cb7fbeaf5 Mon Sep 17 00:00:00 2001
From: Julian Risch <julian.risch@deepset.ai>
Date: Thu, 22 Dec 2022 17:39:54 +0100
Subject: [PATCH 019/206] fix xpdf installation in tutorial 16 (#91)

* fix xpdf installation in tutorial 16

* generate md file for tutorial 16
---
 markdowns/16_Document_Classifier_at_Index_Time.md    | 6 +++---
 tutorials/16_Document_Classifier_at_Index_Time.ipynb | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/markdowns/16_Document_Classifier_at_Index_Time.md b/markdowns/16_Document_Classifier_at_Index_Time.md
index 8df6fcba..9812573b 100644
--- a/markdowns/16_Document_Classifier_at_Index_Time.md
+++ b/markdowns/16_Document_Classifier_at_Index_Time.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb
 toc: True
 title: "Document Classification at Index Time"
-last_updated: 2022-12-08
+last_updated: 2022-12-22
 level: "intermediate"
 weight: 85
 description: Generate and attach classification labels to your Documents when indexing.
@@ -30,8 +30,8 @@ This tutorial will show you how to integrate a classification model into your pr
 pip install --upgrade pip
 pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr]
 
-!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz
-!tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin
+wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz
+tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin
 
 apt install libgraphviz-dev
 pip install pygraphviz
diff --git a/tutorials/16_Document_Classifier_at_Index_Time.ipynb b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
index 0a1c8d01..7faae66d 100644
--- a/tutorials/16_Document_Classifier_at_Index_Time.ipynb
+++ b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
@@ -48,8 +48,8 @@
     "pip install --upgrade pip\n",
     "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr]\n",
     "\n",
-    "!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz\n",
-    "!tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin\n",
+    "wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz\n",
+    "tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin\n",
     "\n",
     "apt install libgraphviz-dev\n",
     "pip install pygraphviz"

From f2e6b15bf58c361e3eea56f3f99d3950a790fe94 Mon Sep 17 00:00:00 2001
From: Julian Risch <julian.risch@deepset.ai>
Date: Thu, 22 Dec 2022 17:42:09 +0100
Subject: [PATCH 020/206] bug: import TransformersQueryClassifier earlier (#93)

* bug: import TransformersQueryClassifier earlier

* generate md file for tutorial 14
---
 markdowns/14_Query_Classifier.md    | 4 ++--
 tutorials/14_Query_Classifier.ipynb | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
index 1b66a55f..b44edf65 100644
--- a/markdowns/14_Query_Classifier.md
+++ b/markdowns/14_Query_Classifier.md
@@ -299,6 +299,8 @@ The above example uses an `SklearnQueryClassifier`, but of course we can do prec
 
 
 ```python
+from haystack.nodes import TransformersQueryClassifier
+
 # Here we build the pipeline
 transformer_keyword_classifier = Pipeline()
 transformer_keyword_classifier.add_node(
@@ -398,8 +400,6 @@ The first label we provide corresponds to output_1, the second label to output_2
 
 
 ```python
-from haystack.nodes import TransformersQueryClassifier
-
 # Remember to compile a list with the exact model labels
 # The first label you provide corresponds to output_1, the second label to output_2, and so on.
 labels = ["LABEL_0", "LABEL_1", "LABEL_2"]
diff --git a/tutorials/14_Query_Classifier.ipynb b/tutorials/14_Query_Classifier.ipynb
index ecbb8950..0e108fb5 100644
--- a/tutorials/14_Query_Classifier.ipynb
+++ b/tutorials/14_Query_Classifier.ipynb
@@ -549,6 +549,8 @@
    },
    "outputs": [],
    "source": [
+    "from haystack.nodes import TransformersQueryClassifier\n",
+    "\n",
     "# Here we build the pipeline\n",
     "transformer_keyword_classifier = Pipeline()\n",
     "transformer_keyword_classifier.add_node(\n",
@@ -689,8 +691,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from haystack.nodes import TransformersQueryClassifier\n",
-    "\n",
     "# Remember to compile a list with the exact model labels\n",
     "# The first label you provide corresponds to output_1, the second label to output_2, and so on.\n",
     "labels = [\"LABEL_0\", \"LABEL_1\", \"LABEL_2\"]\n",

From bdf24843bb08c636aaabc8f9e0dd2299e5d03bda Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Tue, 10 Jan 2023 10:48:47 +0100
Subject: [PATCH 021/206] Update issue templates (#90)

---
 .github/ISSUE_TEMPLATE/content-improvement.md | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/content-improvement.md

diff --git a/.github/ISSUE_TEMPLATE/content-improvement.md b/.github/ISSUE_TEMPLATE/content-improvement.md
new file mode 100644
index 00000000..b3482fd5
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/content-improvement.md
@@ -0,0 +1,10 @@
+---
+name: Content Improvement
+about: Tell us if there's any explanation on tutorials that should be improved
+title: ''
+labels: enhancement
+assignees: ''
+
+---
+
+

From e1d78c46329f3ac43b72961365bb9ed43ee840d3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 11 Jan 2023 12:33:57 +0300
Subject: [PATCH 022/206] New Tutorial: REST API (#61)

* add first draft of REST API tutorial 20
* Change the structure of the tutorial 20
* Remove the demo setup process
* Pay attention to the language
* Add more description to all steps
* Update README and toml file for the new version
* Change the language of the tutorial
* Add more explanation
* Make the language cleaner
* Make fix the language of texts
* Run `rest_api` with docker instead of gunicorn server
* Additionally language changes
* Remove `bash` cells and turn them into code md cells
* Update tutorial 20 according to the new docker-compose.yml file
* remove the submodule
* Remove Colab url and generate files according to the new convention
* Remove `txt` highlighting
* No highlight language needs to be given
* Add download button to readme
* Update language to comply with Haystack voice
* Generate the markdown file
* Fix some text and the example output
* Add version info
* Fix comment spacing

Co-authored-by: agnieszka-m <amarzec13@gmail.com>
---
 README.md                                     |   1 +
 index.toml                                    |  11 +-
 markdowns/20_Using_Haystack_with_REST_API.md  | 313 ++++++++++++++
 .../20_Using_Haystack_with_REST_API.ipynb     | 403 ++++++++++++++++++
 4 files changed, 727 insertions(+), 1 deletion(-)
 create mode 100644 markdowns/20_Using_Haystack_with_REST_API.md
 create mode 100644 tutorials/20_Using_Haystack_with_REST_API.ipynb

diff --git a/README.md b/README.md
index ae94c088..92929e13 100644
--- a/README.md
+++ b/README.md
@@ -37,3 +37,4 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 | Audio                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb)                                                   | [17_Audio.ipynb](./tutorials/17_Audio.ipynb)                                                                                                     |
 | Generative Pseudo Labeling               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)                                                     | [18_GPL.ipynb](./tutorials/18_GPL.ipynb)                                                                                                         |
 | Text-to-Image search                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) | [19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) |
+| Using Haystack with REST API             | <a href="https://haystack.deepset.ai/downloads/20_Using_Haystack_with_REST_API.ipynb" target="_blank"><img alt="Download" src="https://img.shields.io/badge/DOWNLOAD-blue?&style=for-the-badge"/> <a>                                                                                                                                                                                                    | [20_Using_Haystack_with_REST_API.ipynb](./tutorials/20_Using_Haystack_with_REST_API.ipynb)                                                       |
\ No newline at end of file
diff --git a/index.toml b/index.toml
index 945c795a..3c82c478 100644
--- a/index.toml
+++ b/index.toml
@@ -154,4 +154,13 @@ description = "Use a MultiModalRetriever to build a cross-modal search pipeline.
 level = "intermediate"
 weight = 95
 notebook = "19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
-aliases = ["multimodal"]
\ No newline at end of file
+aliases = ["multimodal"]
+
+[[tutorial]]
+title = "Using Haystack with REST API"
+description = "Create a production-ready pipeline and interact with Haystack REST API."
+level = "advanced"
+weight = 115
+notebook = "20_Using_Haystack_with_REST_API.ipynb"
+aliases = ["using-haystack-with-rest-api"]
+colab = false
diff --git a/markdowns/20_Using_Haystack_with_REST_API.md b/markdowns/20_Using_Haystack_with_REST_API.md
new file mode 100644
index 00000000..5c0b790f
--- /dev/null
+++ b/markdowns/20_Using_Haystack_with_REST_API.md
@@ -0,0 +1,313 @@
+---
+layout: tutorial
+colab: False
+toc: True
+title: "Using Haystack with REST API"
+last_updated: 2023-01-10
+level: "advanced"
+weight: 115
+description: Create a production-ready pipeline and interact with Haystack REST API.
+category: "QA"
+aliases: ['/tutorials/using-haystack-with-rest-api']
+download: "/downloads/20_Using_Haystack_with_REST_API.ipynb"
+---
+    
+
+
+- **Level**: Advanced
+- **Time to complete**: 30 minutes
+- **Prerequisites**: Basic understanding of Docker and basic knowledge of Haystack pipelines. 
+- **Nodes Used**: `ElasticsearchDocumentStore`, `EmbeddingRetriever`
+- **Goal**: After you complete this tutorial, you will have learned how to interact with Haystack through REST API.
+
+## Overview
+
+Learn how you can interact with Haystack through REST API. This tutorial introduces you to all the concepts needed to build an end-to-end document search application.  
+
+With Haystack, you can apply the latest NLP technology to your own data and create production-ready applications. Building an end-to-end NLP application requires the combination of multiple concepts:
+* **DocumentStore** is the component in Haystack responsible for loading and storing text data in the form of [Documents](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document). In this tutorial, the DocumentStore uses Elasticsearch behind the scene.
+* **Haystack pipelines** convert files into Documents, index them to the DocumentStore, and run NLP tasks such as question answering and document search.
+* **REST API**, as a concept, makes it possible for applications to interact with each other by handling their queries and returning responses. There is `rest_api` application within Haystack that exposes Haystack's functionalities through a RESTful API.
+* **Docker** simplifies the environment setup needed to run Elasticsearch and Haystack API.
+
+
+
+## Preparing the Environment
+
+1. Install [Docker Compose](https://docs.docker.com/compose/) and launch Docker.
+If you installed Docker Desktop, just start the application. Run `docker info` to see if Docker is up and running:
+
+   ```bash
+   docker info
+   ```
+
+2. Download the *docker-compose.yml* file. Haystack provides a *docker-compose.yml* file that defines services for Haystack API and Elasticsearch. 
+    1. Create a new folder called *doc-search* in a directory where you want to keep all tutorial related files.
+    2. Save the latest [*docker-compose.yml*](https://github.com/deepset-ai/haystack/blob/main/docker-compose.yml) file from GitHub into the folder. To save the *docker-compose.yml* file into the directory directly, run:
+
+         ```bash
+         curl --output docker-compose.yml https://raw.githubusercontent.com/deepset-ai/haystack/main/docker-compose.yml
+         ```
+
+    Here's what the */doc-search* folder should look like:
+    ```
+    /doc-search
+    └── docker-compose.yml
+    ```
+
+Now that your environment's ready, you can start creating your indexing and query pipelines.
+
+## Creating the Pipeline YAML File
+
+You can define components and pipelines using YAML code that Haystack translates into Python objects. In a pipeline YAML file, the `components` section lists all pipeline nodes and the `pipelines` section defines how these nodes are connected to each other. Let's start with defining two different pipelines, one to index your documents and another one to query them. We'll use one YAML file to define both pipelines.
+
+1. Create a document search pipeline. This will be your query pipeline:
+   1. In the newly created *doc-search* folder, create a file named *document-search.haystack-pipeline.yml*. The *docker-compose.yml* file and the new pipeline YAML file should be on the same level in the directory:
+
+      ```
+      /doc-search
+      ├── docker-compose.yml
+      └── document-search.haystack-pipeline.yml
+      ```
+
+   2. Provide the path to *document-search.haystack-pipeline.yml* as the `volume` source value in the *docker-compose.yml* file. The path must be relative to *docker-compose.yml*. As both files are in the same directory, the source value will be `./`. 
+
+      ```yaml
+      haystack-api:
+        ...
+        volumes:
+          - ./:/opt/pipelines
+      ```
+
+   3. Update the `PIPELINE_YAML_PATH` variable in *docker-compose.yml* with the name of the pipeline YAML file. The `PIPELINE_YAML_PATH` variable tells `rest_api` which YAML file to run. 
+
+      ```yaml
+      environment:
+        ...
+        - PIPELINE_YAML_PATH=/opt/pipelines/document-search.haystack-pipeline.yml
+        ...
+      ```
+   4. Define the pipeline nodes in the `components` section of the file. A document search pipeline requires a DocumentStore and a Retriever. Our pipeline will use `ElasticsearchDocumentStore` and `EmbeddingRetriever`:
+
+      ```yaml
+      components:
+        - name: DocumentStore # How you want to call this node here
+          type: ElasticsearchDocumentStore # This is the Haystack node class
+          params: # The node parameters
+            embedding_dim: 384 # This parameter is required for the embedding_model
+        - name: Retriever
+          type: EmbeddingRetriever
+          params:
+            document_store: DocumentStore
+            top_k: 10
+            embedding_model: sentence-transformers/all-MiniLM-L6-v2
+      ```
+
+   5. Create a query pipeline in the `pipelines` section. Here, `name` refers to the name of the pipeline, and `nodes` defines the order of the nodes in the pipeline: 
+
+      ```yaml
+      pipelines:
+        - name: query 
+          nodes:
+            - name: Retriever
+              inputs: [Query]
+      ```
+
+2. In the same YAML file, create an indexing pipeline. This pipeline will index your documents to Elasticsearch through `rest_api`. 
+   1. Define `FileTypeClassifier`, `TextConverter`, and `PreProcessor` nodes for the pipeline:
+
+      ```yaml
+      components:
+        ...
+        - name: FileTypeClassifier
+          type: FileTypeClassifier
+        - name: TextFileConverter
+          type: TextConverter
+        - name: Preprocessor
+          type: PreProcessor
+          params: # These parameters define how you want to split your documents
+            split_by: word
+            split_length: 250
+            split_overlap: 30 
+            split_respect_sentence_boundary: True 
+      ```
+
+   2. In the `pipelines` section of the YAML file, create a new pipeline called `indexing`. In this pipeline, indicate how the nodes you just defined are connected to each other, Retriever, and DocumentStore. This indexing pipeline supports *.TXT* files and preprocesses them before loading to Elasticsearch.
+
+      ```yaml
+      pipelines:
+        ...
+        - name: indexing
+          nodes:
+            - name: FileTypeClassifier
+              inputs: [File]
+            - name: TextFileConverter
+              inputs: [FileTypeClassifier.output_1]
+            - name: Preprocessor
+              inputs: [TextFileConverter]
+            - name: Retriever
+              inputs: [Preprocessor]
+            - name: DocumentStore
+              inputs: [Retriever]
+      ```
+
+3. After creating query and indexing pipelines, add `version: 1.12.1` to the top of the file. This is the Haystack version that comes with the Docker image in the *docker-compose.yml*. Now, the pipeline YAML is ready.
+
+```yaml
+version: 1.12.1
+
+components:
+  - name: DocumentStore
+    type: ElasticsearchDocumentStore
+    params:
+      embedding_dim: 384
+  - name: Retriever
+    type: EmbeddingRetriever
+    params:
+      document_store: DocumentStore
+      top_k: 10 
+      embedding_model: sentence-transformers/all-MiniLM-L6-v2
+  - name: FileTypeClassifier
+    type: FileTypeClassifier
+  - name: TextFileConverter
+    type: TextConverter
+  - name: Preprocessor
+    type: PreProcessor
+    params:
+      split_by: word
+      split_length: 250
+      split_overlap: 30 
+      split_respect_sentence_boundary: True
+
+pipelines:
+  - name: query 
+    nodes:
+      - name: Retriever
+        inputs: [Query]
+  - name: indexing
+    nodes:
+      - name: FileTypeClassifier
+        inputs: [File]
+      - name: TextFileConverter
+        inputs: [FileTypeClassifier.output_1]
+      - name: Preprocessor
+        inputs: [TextFileConverter]
+      - name: Retriever
+        inputs: [Preprocessor]
+      - name: DocumentStore
+        inputs: [Retriever]
+```
+
+Feel free to play with the pipeline setup later on. Add or remove some nodes, change the parameters, or add new ones. For more options for nodes and parameters, check out [Haystack API Reference](https://docs.haystack.deepset.ai/reference/answer-generator-api).
+
+## Launching Haystack API and Elasticsearch
+
+Pipelines are ready. Now it's time to start Elasticsearch and Haystack API.
+1. Run `docker-compose up` to start the `elasticsearch` and `haystack-api` containers. This command installs all the necessary packages, sets up the environment, and launches both Elasticsearch and Haystack API. Launching may take 2-3 minutes. 
+
+   ```bash
+   docker-compose up
+   ```
+
+2. Test if everything is OK with the Haystack API by sending a cURL request to the `/initialized` endpoint. If everything works fine, you will get `true` as a response.
+
+   ```bash
+   curl --request GET http://127.0.0.1:8000/initialized
+   ```
+
+
+Both containers are initialized. Time to fill your DocumentStore with files.  
+
+## Indexing Files to Elasticsearch
+
+Right now, your Elasticsearch instance is empty. Haystack API provides a `/file-upload` endpoint to upload files to Elasticsearch. This endpoint uses the indexing pipeline you defined in the pipeline YAML. After indexing files to Elasticsearch, you can perform document search.
+
+1. Download the [example files](https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/article_txt_countries_and_capitals.zip) to the *doc-search* folder. The .zip file contains text files about countries and capitals crawled from [Wikipedia](https://en.wikipedia.org/wiki/Category:Lists_of_countries_by_continent).
+
+   ```
+   /doc-search
+   ├── docker-compose.yml
+   ├── document-search.haystack-pipeline.yml
+   └── /article_txt_countries_and_capitals
+       ├── 0_Minsk.txt
+       └── ...
+   ```
+
+2. Index files to Elasticsearch. You can send cURL requests to the `/file-upload` endpoint to upload files to the Elasticsearch instance. If the file is successfully uploaded, you will get `null` as a response.
+
+   ```bash
+   curl --request POST \
+        --url http://127.0.0.1:8000/file-upload \
+        --header 'accept: application/json' \
+        --header 'content-type: multipart/form-data' \
+        --form files=@article_txt_countries_and_capitals/0_Minsk.txt \
+        --form meta=null
+   ```
+
+   This method is not the best one if you have multiple files to upload. That's because you need to replace file names in the request by hand. Instead, you can run a command that takes all *.TXT* files in the *article_txt_countries_and_capitals* folder and sends a POST request to index each file:   
+
+   ```bash
+   find ./article_txt_countries_and_capitals -name '*.txt' -exec \
+        curl --request POST \
+             --url http://127.0.0.1:8000/file-upload \
+             --header 'accept: application/json' \
+             --header 'content-type: multipart/form-data' \
+             --form files="@{}" \
+             --form meta=null \;
+   ```
+
+## Asking a Question
+
+That's it, the application is ready! Send another POST request to retrieve documents about _"climate in Scandinavia"_: 
+
+```bash
+curl --request POST \
+     --url http://127.0.0.1:8000/query \
+     --header 'accept: application/json' \
+     --header 'content-type: application/json' \
+     --data '{
+     "query": "climate in Scandinavia"
+     }'
+```
+
+As a response, you will get a `QueryResponse` object consisting of `query`, `answers`, and `documents`. Documents related to your query will be under the `documents` attribute of the object.
+
+```python
+{
+  "query": "climate in Scandinavia",
+  "answers": [],
+  "documents": [
+    {
+      "id": "24904f783ea4b90a47c33434a3e9df7a",
+      "content": "Because of Sweden's high latitude, the length of daylight varies greatly. North of the Arctic Circle, the sun never sets for part of each summer, and it never rises for part of each winter. In the capital, Stockholm, daylight lasts for more than 18 hours in late June but only around 6 hours in late December. Sweden receives between 1,100 and 1,900 hours of sunshine annually...",
+      "content_type": "text",
+      "meta": {
+        "_split_id": 33,
+        "name": "43_Sweden.txt"
+      },
+      "score": 0.5017639926813274
+    },
+    ...
+  ]
+}
+```  
+
+Congratulations! You have created a proper search system that runs using Haystack REST API.
+
+## About us
+
+This [Haystack](https://github.com/deepset-ai/haystack/) tutorial was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
+
+We bring NLP to the industry via open source!  
+Our focus: Industry specific language models & large scale QA systems.  
+  
+Some of our other work: 
+- [German BERT](https://deepset.ai/german-bert)
+- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
+- [FARM](https://github.com/deepset-ai/FARM)
+
+Get in touch:
+[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
+
+By the way: [we're hiring!](https://www.deepset.ai/jobs)
+
diff --git a/tutorials/20_Using_Haystack_with_REST_API.ipynb b/tutorials/20_Using_Haystack_with_REST_API.ipynb
new file mode 100644
index 00000000..9bbb46da
--- /dev/null
+++ b/tutorials/20_Using_Haystack_with_REST_API.ipynb
@@ -0,0 +1,403 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: Using Haystack with REST API\n",
+    "\n",
+    "- **Level**: Advanced\n",
+    "- **Time to complete**: 30 minutes\n",
+    "- **Prerequisites**: Basic understanding of Docker and basic knowledge of Haystack pipelines. \n",
+    "- **Nodes Used**: `ElasticsearchDocumentStore`, `EmbeddingRetriever`\n",
+    "- **Goal**: After you complete this tutorial, you will have learned how to interact with Haystack through REST API."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "Learn how you can interact with Haystack through REST API. This tutorial introduces you to all the concepts needed to build an end-to-end document search application.  \n",
+    "\n",
+    "With Haystack, you can apply the latest NLP technology to your own data and create production-ready applications. Building an end-to-end NLP application requires the combination of multiple concepts:\n",
+    "* **DocumentStore** is the component in Haystack responsible for loading and storing text data in the form of [Documents](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document). In this tutorial, the DocumentStore uses Elasticsearch behind the scene.\n",
+    "* **Haystack pipelines** convert files into Documents, index them to the DocumentStore, and run NLP tasks such as question answering and document search.\n",
+    "* **REST API**, as a concept, makes it possible for applications to interact with each other by handling their queries and returning responses. There is `rest_api` application within Haystack that exposes Haystack's functionalities through a RESTful API.\n",
+    "* **Docker** simplifies the environment setup needed to run Elasticsearch and Haystack API.\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparing the Environment\n",
+    "\n",
+    "1. Install [Docker Compose](https://docs.docker.com/compose/) and launch Docker.\n",
+    "If you installed Docker Desktop, just start the application. Run `docker info` to see if Docker is up and running:\n",
+    "\n",
+    "   ```bash\n",
+    "   docker info\n",
+    "   ```\n",
+    "\n",
+    "2. Download the *docker-compose.yml* file. Haystack provides a *docker-compose.yml* file that defines services for Haystack API and Elasticsearch. \n",
+    "    1. Create a new folder called *doc-search* in a directory where you want to keep all tutorial related files.\n",
+    "    2. Save the latest [*docker-compose.yml*](https://github.com/deepset-ai/haystack/blob/main/docker-compose.yml) file from GitHub into the folder. To save the *docker-compose.yml* file into the directory directly, run:\n",
+    "\n",
+    "         ```bash\n",
+    "         curl --output docker-compose.yml https://raw.githubusercontent.com/deepset-ai/haystack/main/docker-compose.yml\n",
+    "         ```\n",
+    "\n",
+    "    Here's what the */doc-search* folder should look like:\n",
+    "    ```\n",
+    "    /doc-search\n",
+    "    └── docker-compose.yml\n",
+    "    ```"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that your environment's ready, you can start creating your indexing and query pipelines."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the Pipeline YAML File\n",
+    "\n",
+    "You can define components and pipelines using YAML code that Haystack translates into Python objects. In a pipeline YAML file, the `components` section lists all pipeline nodes and the `pipelines` section defines how these nodes are connected to each other. Let's start with defining two different pipelines, one to index your documents and another one to query them. We'll use one YAML file to define both pipelines.\n",
+    "\n",
+    "1. Create a document search pipeline. This will be your query pipeline:\n",
+    "   1. In the newly created *doc-search* folder, create a file named *document-search.haystack-pipeline.yml*. The *docker-compose.yml* file and the new pipeline YAML file should be on the same level in the directory:\n",
+    "\n",
+    "      ```\n",
+    "      /doc-search\n",
+    "      ├── docker-compose.yml\n",
+    "      └── document-search.haystack-pipeline.yml\n",
+    "      ```\n",
+    "\n",
+    "   2. Provide the path to *document-search.haystack-pipeline.yml* as the `volume` source value in the *docker-compose.yml* file. The path must be relative to *docker-compose.yml*. As both files are in the same directory, the source value will be `./`. \n",
+    "\n",
+    "      ```yaml\n",
+    "      haystack-api:\n",
+    "        ...\n",
+    "        volumes:\n",
+    "          - ./:/opt/pipelines\n",
+    "      ```\n",
+    "\n",
+    "   3. Update the `PIPELINE_YAML_PATH` variable in *docker-compose.yml* with the name of the pipeline YAML file. The `PIPELINE_YAML_PATH` variable tells `rest_api` which YAML file to run. \n",
+    "\n",
+    "      ```yaml\n",
+    "      environment:\n",
+    "        ...\n",
+    "        - PIPELINE_YAML_PATH=/opt/pipelines/document-search.haystack-pipeline.yml\n",
+    "        ...\n",
+    "      ```\n",
+    "   4. Define the pipeline nodes in the `components` section of the file. A document search pipeline requires a DocumentStore and a Retriever. Our pipeline will use `ElasticsearchDocumentStore` and `EmbeddingRetriever`:\n",
+    "\n",
+    "      ```yaml\n",
+    "      components:\n",
+    "        - name: DocumentStore # How you want to call this node here\n",
+    "          type: ElasticsearchDocumentStore # This is the Haystack node class\n",
+    "          params: # The node parameters\n",
+    "            embedding_dim: 384 # This parameter is required for the embedding_model\n",
+    "        - name: Retriever\n",
+    "          type: EmbeddingRetriever\n",
+    "          params:\n",
+    "            document_store: DocumentStore\n",
+    "            top_k: 10\n",
+    "            embedding_model: sentence-transformers/all-MiniLM-L6-v2\n",
+    "      ```\n",
+    "\n",
+    "   5. Create a query pipeline in the `pipelines` section. Here, `name` refers to the name of the pipeline, and `nodes` defines the order of the nodes in the pipeline: \n",
+    "\n",
+    "      ```yaml\n",
+    "      pipelines:\n",
+    "        - name: query \n",
+    "          nodes:\n",
+    "            - name: Retriever\n",
+    "              inputs: [Query]\n",
+    "      ```\n",
+    "\n",
+    "2. In the same YAML file, create an indexing pipeline. This pipeline will index your documents to Elasticsearch through `rest_api`. \n",
+    "   1. Define `FileTypeClassifier`, `TextConverter`, and `PreProcessor` nodes for the pipeline:\n",
+    "\n",
+    "      ```yaml\n",
+    "      components:\n",
+    "        ...\n",
+    "        - name: FileTypeClassifier\n",
+    "          type: FileTypeClassifier\n",
+    "        - name: TextFileConverter\n",
+    "          type: TextConverter\n",
+    "        - name: Preprocessor\n",
+    "          type: PreProcessor\n",
+    "          params: # These parameters define how you want to split your documents\n",
+    "            split_by: word\n",
+    "            split_length: 250\n",
+    "            split_overlap: 30 \n",
+    "            split_respect_sentence_boundary: True \n",
+    "      ```\n",
+    "\n",
+    "   2. In the `pipelines` section of the YAML file, create a new pipeline called `indexing`. In this pipeline, indicate how the nodes you just defined are connected to each other, Retriever, and DocumentStore. This indexing pipeline supports *.TXT* files and preprocesses them before loading to Elasticsearch.\n",
+    "\n",
+    "      ```yaml\n",
+    "      pipelines:\n",
+    "        ...\n",
+    "        - name: indexing\n",
+    "          nodes:\n",
+    "            - name: FileTypeClassifier\n",
+    "              inputs: [File]\n",
+    "            - name: TextFileConverter\n",
+    "              inputs: [FileTypeClassifier.output_1]\n",
+    "            - name: Preprocessor\n",
+    "              inputs: [TextFileConverter]\n",
+    "            - name: Retriever\n",
+    "              inputs: [Preprocessor]\n",
+    "            - name: DocumentStore\n",
+    "              inputs: [Retriever]\n",
+    "      ```"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. After creating query and indexing pipelines, add `version: 1.12.1` to the top of the file. This is the Haystack version that comes with the Docker image in the *docker-compose.yml*. Now, the pipeline YAML is ready.\n",
+    "\n",
+    "```yaml\n",
+    "version: 1.12.1\n",
+    "\n",
+    "components:\n",
+    "  - name: DocumentStore\n",
+    "    type: ElasticsearchDocumentStore\n",
+    "    params:\n",
+    "      embedding_dim: 384\n",
+    "  - name: Retriever\n",
+    "    type: EmbeddingRetriever\n",
+    "    params:\n",
+    "      document_store: DocumentStore\n",
+    "      top_k: 10 \n",
+    "      embedding_model: sentence-transformers/all-MiniLM-L6-v2\n",
+    "  - name: FileTypeClassifier\n",
+    "    type: FileTypeClassifier\n",
+    "  - name: TextFileConverter\n",
+    "    type: TextConverter\n",
+    "  - name: Preprocessor\n",
+    "    type: PreProcessor\n",
+    "    params:\n",
+    "      split_by: word\n",
+    "      split_length: 250\n",
+    "      split_overlap: 30 \n",
+    "      split_respect_sentence_boundary: True\n",
+    "\n",
+    "pipelines:\n",
+    "  - name: query \n",
+    "    nodes:\n",
+    "      - name: Retriever\n",
+    "        inputs: [Query]\n",
+    "  - name: indexing\n",
+    "    nodes:\n",
+    "      - name: FileTypeClassifier\n",
+    "        inputs: [File]\n",
+    "      - name: TextFileConverter\n",
+    "        inputs: [FileTypeClassifier.output_1]\n",
+    "      - name: Preprocessor\n",
+    "        inputs: [TextFileConverter]\n",
+    "      - name: Retriever\n",
+    "        inputs: [Preprocessor]\n",
+    "      - name: DocumentStore\n",
+    "        inputs: [Retriever]\n",
+    "```\n",
+    "\n",
+    "Feel free to play with the pipeline setup later on. Add or remove some nodes, change the parameters, or add new ones. For more options for nodes and parameters, check out [Haystack API Reference](https://docs.haystack.deepset.ai/reference/answer-generator-api)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Launching Haystack API and Elasticsearch\n",
+    "\n",
+    "Pipelines are ready. Now it's time to start Elasticsearch and Haystack API.\n",
+    "1. Run `docker-compose up` to start the `elasticsearch` and `haystack-api` containers. This command installs all the necessary packages, sets up the environment, and launches both Elasticsearch and Haystack API. Launching may take 2-3 minutes. \n",
+    "\n",
+    "   ```bash\n",
+    "   docker-compose up\n",
+    "   ```\n",
+    "\n",
+    "2. Test if everything is OK with the Haystack API by sending a cURL request to the `/initialized` endpoint. If everything works fine, you will get `true` as a response.\n",
+    "\n",
+    "   ```bash\n",
+    "   curl --request GET http://127.0.0.1:8000/initialized\n",
+    "   ```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Both containers are initialized. Time to fill your DocumentStore with files.  "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing Files to Elasticsearch\n",
+    "\n",
+    "Right now, your Elasticsearch instance is empty. Haystack API provides a `/file-upload` endpoint to upload files to Elasticsearch. This endpoint uses the indexing pipeline you defined in the pipeline YAML. After indexing files to Elasticsearch, you can perform document search.\n",
+    "\n",
+    "1. Download the [example files](https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/article_txt_countries_and_capitals.zip) to the *doc-search* folder. The .zip file contains text files about countries and capitals crawled from [Wikipedia](https://en.wikipedia.org/wiki/Category:Lists_of_countries_by_continent).\n",
+    "\n",
+    "   ```\n",
+    "   /doc-search\n",
+    "   ├── docker-compose.yml\n",
+    "   ├── document-search.haystack-pipeline.yml\n",
+    "   └── /article_txt_countries_and_capitals\n",
+    "       ├── 0_Minsk.txt\n",
+    "       └── ...\n",
+    "   ```\n",
+    "\n",
+    "2. Index files to Elasticsearch. You can send cURL requests to the `/file-upload` endpoint to upload files to the Elasticsearch instance. If the file is successfully uploaded, you will get `null` as a response.\n",
+    "\n",
+    "   ```bash\n",
+    "   curl --request POST \\\n",
+    "        --url http://127.0.0.1:8000/file-upload \\\n",
+    "        --header 'accept: application/json' \\\n",
+    "        --header 'content-type: multipart/form-data' \\\n",
+    "        --form files=@article_txt_countries_and_capitals/0_Minsk.txt \\\n",
+    "        --form meta=null\n",
+    "   ```\n",
+    "\n",
+    "   This method is not the best one if you have multiple files to upload. That's because you need to replace file names in the request by hand. Instead, you can run a command that takes all *.TXT* files in the *article_txt_countries_and_capitals* folder and sends a POST request to index each file:   \n",
+    "\n",
+    "   ```bash\n",
+    "   find ./article_txt_countries_and_capitals -name '*.txt' -exec \\\n",
+    "        curl --request POST \\\n",
+    "             --url http://127.0.0.1:8000/file-upload \\\n",
+    "             --header 'accept: application/json' \\\n",
+    "             --header 'content-type: multipart/form-data' \\\n",
+    "             --form files=\"@{}\" \\\n",
+    "             --form meta=null \\;\n",
+    "   ```"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asking a Question\n",
+    "\n",
+    "That's it, the application is ready! Send another POST request to retrieve documents about _\"climate in Scandinavia\"_: \n",
+    "\n",
+    "```bash\n",
+    "curl --request POST \\\n",
+    "     --url http://127.0.0.1:8000/query \\\n",
+    "     --header 'accept: application/json' \\\n",
+    "     --header 'content-type: application/json' \\\n",
+    "     --data '{\n",
+    "     \"query\": \"climate in Scandinavia\"\n",
+    "     }'\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a response, you will get a `QueryResponse` object consisting of `query`, `answers`, and `documents`. Documents related to your query will be under the `documents` attribute of the object.\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    "  \"query\": \"climate in Scandinavia\",\n",
+    "  \"answers\": [],\n",
+    "  \"documents\": [\n",
+    "    {\n",
+    "      \"id\": \"24904f783ea4b90a47c33434a3e9df7a\",\n",
+    "      \"content\": \"Because of Sweden's high latitude, the length of daylight varies greatly. North of the Arctic Circle, the sun never sets for part of each summer, and it never rises for part of each winter. In the capital, Stockholm, daylight lasts for more than 18 hours in late June but only around 6 hours in late December. Sweden receives between 1,100 and 1,900 hours of sunshine annually...\",\n",
+    "      \"content_type\": \"text\",\n",
+    "      \"meta\": {\n",
+    "        \"_split_id\": 33,\n",
+    "        \"name\": \"43_Sweden.txt\"\n",
+    "      },\n",
+    "      \"score\": 0.5017639926813274\n",
+    "    },\n",
+    "    ...\n",
+    "  ]\n",
+    "}\n",
+    "```  "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Congratulations! You have created a proper search system that runs using Haystack REST API."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## About us\n",
+    "\n",
+    "This [Haystack](https://github.com/deepset-ai/haystack/) tutorial was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
+    "\n",
+    "We bring NLP to the industry via open source!  \n",
+    "Our focus: Industry specific language models & large scale QA systems.  \n",
+    "  \n",
+    "Some of our other work: \n",
+    "- [German BERT](https://deepset.ai/german-bert)\n",
+    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
+    "- [FARM](https://github.com/deepset-ai/FARM)\n",
+    "\n",
+    "Get in touch:\n",
+    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
+    "\n",
+    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.6 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From e243704b0fbfaa67118296b79630b025b1b1b680 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Wed, 11 Jan 2023 15:52:58 +0100
Subject: [PATCH 023/206] Attempting fix for the failing workflow (#98)

@masci and @bilgeyucel really unsure here..
---
 .github/workflows/markdowns.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/markdowns.yml b/.github/workflows/markdowns.yml
index 5fa77165..e14db335 100644
--- a/.github/workflows/markdowns.yml
+++ b/.github/workflows/markdowns.yml
@@ -22,7 +22,7 @@ jobs:
 
       - name: Get changed notebooks
         id: changed-files
-        uses: tj-actions/changed-files@v34
+        uses: tj-actions/changed-files@v35
         with:
           files: |
             tutorials/*.ipynb

From f009727695b36e3e07e87744282aef7e63f44f93 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 11 Jan 2023 18:08:08 +0300
Subject: [PATCH 024/206] change the title of the last section (#97)

* change the title of the last section

* This is no question

* Attempting fix for the failing workflow

@masci and @bilgeyucel really unsure here..

Co-authored-by: Tuana Celik <tuana.celik@deepset.ai>
---
 markdowns/20_Using_Haystack_with_REST_API.md    | 4 ++--
 tutorials/20_Using_Haystack_with_REST_API.ipynb | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/markdowns/20_Using_Haystack_with_REST_API.md b/markdowns/20_Using_Haystack_with_REST_API.md
index 5c0b790f..df1ceee5 100644
--- a/markdowns/20_Using_Haystack_with_REST_API.md
+++ b/markdowns/20_Using_Haystack_with_REST_API.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: False
 toc: True
 title: "Using Haystack with REST API"
-last_updated: 2023-01-10
+last_updated: 2023-01-11
 level: "advanced"
 weight: 115
 description: Create a production-ready pipeline and interact with Haystack REST API.
@@ -256,7 +256,7 @@ Right now, your Elasticsearch instance is empty. Haystack API provides a `/file-
              --form meta=null \;
    ```
 
-## Asking a Question
+## Querying Your Pipeline
 
 That's it, the application is ready! Send another POST request to retrieve documents about _"climate in Scandinavia"_: 
 
diff --git a/tutorials/20_Using_Haystack_with_REST_API.ipynb b/tutorials/20_Using_Haystack_with_REST_API.ipynb
index 9bbb46da..987d796e 100644
--- a/tutorials/20_Using_Haystack_with_REST_API.ipynb
+++ b/tutorials/20_Using_Haystack_with_REST_API.ipynb
@@ -300,7 +300,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Asking a Question\n",
+    "## Querying Your Pipeline\n",
     "\n",
     "That's it, the application is ready! Send another POST request to retrieve documents about _\"climate in Scandinavia\"_: \n",
     "\n",

From cc771da6bbb70d853c94203155a5233b4a0dbe1a Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Wed, 11 Jan 2023 16:10:48 +0100
Subject: [PATCH 025/206] pass the host to document store init (#99)

---
 markdowns/14_Query_Classifier.md    | 4 ++--
 tutorials/14_Query_Classifier.ipynb | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
index b44edf65..2b98d534 100644
--- a/markdowns/14_Query_Classifier.md
+++ b/markdowns/14_Query_Classifier.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb
 toc: True
 title: "Query Classifier"
-last_updated: 2022-12-22
+last_updated: 2023-01-11
 level: "intermediate"
 weight: 80
 description: Classify incoming queries so that they can be routed to the nodes that are best at handling them.
@@ -225,7 +225,7 @@ time.sleep(30)
 host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
 
 
-document_store = ElasticsearchDocumentStore()
+document_store = ElasticsearchDocumentStore(host=host)
 document_store.delete_documents()
 document_store.write_documents(got_docs)
 ```
diff --git a/tutorials/14_Query_Classifier.ipynb b/tutorials/14_Query_Classifier.ipynb
index 0e108fb5..cb95f0c3 100644
--- a/tutorials/14_Query_Classifier.ipynb
+++ b/tutorials/14_Query_Classifier.ipynb
@@ -417,7 +417,7 @@
     "host = os.environ.get(\"ELASTICSEARCH_HOST\", \"localhost\")\n",
     "\n",
     "\n",
-    "document_store = ElasticsearchDocumentStore()\n",
+    "document_store = ElasticsearchDocumentStore(host=host)\n",
     "document_store.delete_documents()\n",
     "document_store.write_documents(got_docs)"
    ]
@@ -847,7 +847,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.6 (main, Aug 11 2022, 13:36:31) [Clang 13.1.6 (clang-1316.0.21.2.5)]"
   },
   "vscode": {
    "interpreter": {

From 8c6a2c086632d71faf0deae0ea471f031e16f86f Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Wed, 11 Jan 2023 16:11:58 +0100
Subject: [PATCH 026/206] adding only tutorial 1 and 3 updates (#95)

* adding only tutorial 1 and 3 updates

* resolving conflict 1

* resolving conflict 2

* update to readme

* Fixing links for enabling GPU in Colab

* doc path for tutorial

* fix 2

* Update the lg to match Haystack voice

* Generate markdowns

Co-authored-by: agnieszka-m <amarzec13@gmail.com>
---
 README.md                                     |    4 +-
 index.toml                                    |   13 +-
 markdowns/01_Basic_QA_Pipeline.md             |  269 +--
 ...Basic_QA_Pipeline_without_Elasticsearch.md |  258 ---
 markdowns/03_Scalable_QA_System.md            |  297 +++
 tutorials/01_Basic_QA_Pipeline.ipynb          |  429 ++--
 ...ic_QA_Pipeline_without_Elasticsearch.ipynb |  446 ----
 tutorials/03_Scalable_QA_System.ipynb         | 1805 +++++++++++++++++
 8 files changed, 2324 insertions(+), 1197 deletions(-)
 delete mode 100644 markdowns/03_Basic_QA_Pipeline_without_Elasticsearch.md
 create mode 100644 markdowns/03_Scalable_QA_System.md
 delete mode 100644 tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb
 create mode 100644 tutorials/03_Scalable_QA_System.ipynb

diff --git a/README.md b/README.md
index 92929e13..2ae22b08 100644
--- a/README.md
+++ b/README.md
@@ -18,9 +18,9 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 ## Tutorials
 | Name                                     | Colab                                                                                                                                                                                                                                     | Source Code                                                                                                                                      |
 | ---------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
-| Basic QA Pipeline                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)                                       | [01_Basic_QA_Pipeline.ipynb](./tutorials/01_Basic_QA_Pipeline.ipynb)                                                                             |
+| Build Your First Question Answering System | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)                                       | [01_Basic_QA_Pipeline.ipynb](./tutorials/01_Basic_QA_Pipeline.ipynb)                                                                             |
 | Fine Tune a Model on Your Data           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb)                           | [02_Finetune_a_model_on_your_data.ipynb](./tutorials/02_Finetune_a_model_on_your_data.ipynb)                                                     |
-| Basic QA Pipeline Without Elasticsearch  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb)                 | [03_Basic_QA_Pipeline_without_Elasticsearch.ipynb](./tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb)                                 |
+| Build a Scalable Question Answering System  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb)                 | [03_Basic_QA_Pipeline_without_Elasticsearch.ipynb](./tutorials/03_Scalable_QA_System.ipynb)                                 |
 | FAQ Style QA                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            | [04_FAQ_style_QA.ipynb](./tutorials/04_FAQ_style_QA.ipynb)                                                                                       |
 | Evaluation                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              | [05_Evaluation.ipynb](./tutorials/05_Evaluation.ipynb)                                                                                           |
 | Better Retrieval via Embedding Retrieval | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                | [06_Better_Retrieval_via_Embedding_Retrieval.ipynb](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                               |
diff --git a/index.toml b/index.toml
index 3c82c478..ba9ed8ee 100644
--- a/index.toml
+++ b/index.toml
@@ -4,13 +4,12 @@ toc = true
 colab = "https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/"
 
 [[tutorial]]
-title = "Build Your First QA System"
+title = "Build Your First Question Answering System"
 description = "Get Started by creating a Retriever Reader pipeline."
 level = "beginner"
 weight = 10
 notebook = "01_Basic_QA_Pipeline.ipynb"
-aliases = ["first-qa-system"]
-slug = "01_Basic_QA_Pipeline"
+aliases = ["first-qa-system", "without-elasticsearch", "03_basic_qa_pipeline_without_elasticsearch"]
 
 [[tutorial]]
 title = "Fine-Tuning a Model on Your Own Data"
@@ -21,12 +20,12 @@ notebook = "02_Finetune_a_model_on_your_data.ipynb"
 aliases = ["fine-tuning-a-model"]
 
 [[tutorial]]
-title = "Build a QA System Without Elasticsearch"
-description = "Create a Retriever Reader pipeline that requires no external database dependencies."
+title = "Build a Scalable Question Answering System"
+description = "Create a scalable Retriever Reader pipeline that uses an ElasticsearchDocumentStore."
 level = "beginner"
 weight = 15
-notebook = "03_Basic_QA_Pipeline_without_Elasticsearch.ipynb"
-aliases = ["without-elasticsearch"]
+notebook = "03_Scalable_QA_System.ipynb"
+aliases = []
 
 [[tutorial]]
 title = "Utilizing Existing FAQs for Question Answering"
diff --git a/markdowns/01_Basic_QA_Pipeline.md b/markdowns/01_Basic_QA_Pipeline.md
index 03e5bd75..c3407da6 100644
--- a/markdowns/01_Basic_QA_Pipeline.md
+++ b/markdowns/01_Basic_QA_Pipeline.md
@@ -2,62 +2,55 @@
 layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb
 toc: True
-title: "Build Your First QA System"
-last_updated: 2022-11-24
+title: "Build Your First Question Answering System"
+last_updated: 2023-01-02
 level: "beginner"
 weight: 10
 description: Get Started by creating a Retriever Reader pipeline.
 category: "QA"
-aliases: ['/tutorials/first-qa-system']
+aliases: ['/tutorials/first-qa-system', '/tutorials/without-elasticsearch', '/tutorials/03_basic_qa_pipeline_without_elasticsearch']
 download: "/downloads/01_Basic_QA_Pipeline.ipynb"
 ---
     
 
 
-<img style="float: right;" src="https://upload.wikimedia.org/wikipedia/en/d/d8/Game_of_Thrones_title_card.jpg">
+> We've modified this first tutorial to make it simpler to start with. If you're looking for a Question Answering tutorial that uses a DocumentStore such as Elasticsearch, go to our new [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_Scalable_QA_System) tutorial.
 
-Question Answering can be used in a variety of use cases. A very common one:  Using it to navigate through complex knowledge bases or long documents ("search setting").
+- **Level**: Beginner
+- **Time to complete**: 15 minutes
+- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `FARMReader`
+- **Goal**: After completing this tutorial, you will have learned about the Reader and Retriever, and built a question answering pipeline that can answer questions about the Game of Thrones series.
 
-A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports. 
-In this tutorial we will work on a slightly different domain: "Game of Thrones". 
 
-Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the 
-marvellous seven kingdoms.
+## Overview
 
+Learn how to build a question answering system using Haystack's DocumentStore, Retriever, and Reader. Your system will use Game of Thrones files and will be able to answer questions like "Who is the father of Arya Stark?". But you can use it to run on any other set of documents, such as your company's internal wikis or a collection of financial reports. 
 
-### Prepare environment
+To help you get started quicker, we simplified certain steps in this tutorial. For example, Document preparation and pipeline initialization are handled by ready-made classes that replace lines of initialization code. But don't worry! This doesn't affect how well the question answering system performs.
 
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+## Preparing the Colab Environment
 
-You can double check whether the GPU runtime is enabled with the following command:
+- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)
+- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)
 
 
-```bash
-%%bash
-
-nvidia-smi
-```
+## Installing Haystack
 
-To start, install the latest release of Haystack with `pip`:
+To start, let's install the latest release of Haystack with `pip`:
 
 
 ```bash
 %%bash
 
 pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
+pip install farm-haystack[colab]
 ```
 
-## Logging
 
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
+```python
+Set the logging level to INFO:
+```
 
 
 ```python
@@ -67,127 +60,60 @@ logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logg
 logging.getLogger("haystack").setLevel(logging.INFO)
 ```
 
-## Document Store
-
-Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`,  `SQLDocumentStore`, and `InMemoryDocumentStore`.
+## Initializing the DocumentStore
 
-**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).
+We'll start creating our question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, we're using the `InMemoryDocumentStore`, which is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store).
 
-**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.
-
-**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.
-
-### Start an Elasticsearch server locally
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
+Let's initialize the the DocumentStore:
 
 
 ```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-### Start an Elasticsearch server in Colab
-
-If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
+from haystack.document_stores import InMemoryDocumentStore
 
-
-```bash
-%%bash
-
-wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-chown -R daemon:daemon elasticsearch-7.9.2
+document_store = InMemoryDocumentStore(use_bm25=True)
 ```
 
+The DocumentStore is now ready. Now it's time to fill it with some Documents.
 
-```bash
-%%bash --bg
+## Preparing Documents
 
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
-```
+1. Download 517 articles from the Game of Thrones Wikipedia. You can find them in *data/build_your_first_question_answering_system* as a set of *.txt* files.
 
-### Create the Document Store
 
-The `ElasticsearchDocumentStore` class will try to open a connection in the constructor, here we wait 30 seconds only to be sure Elasticsearch is ready before continuing:
+```python
+from haystack.utils import fetch_archive_from_http
 
+doc_dir = "data/build_your_first_question_answering_system"
 
-```python
-import time
-time.sleep(30)
+fetch_archive_from_http(
+    url="https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt1.zip",
+    output_dir=doc_dir
+)
 ```
 
-Finally, we create the Document Store instance:
+2. Use `TextIndexingPipeline` to convert the files you just downloaded into Haystack [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore:
 
 
 ```python
 import os
-from haystack.document_stores import ElasticsearchDocumentStore
-
-# Get the host where Elasticsearch is running, default to localhost
-host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
-document_store = ElasticsearchDocumentStore(host=host, username="", password="", index="document")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
+from haystack.pipelines.standard_pipelines import TextIndexingPipeline
 
-In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
+files_to_index = [doc_dir + "/" + f for f in os.listdir(doc_dir)]
+indexing_pipeline = TextIndexingPipeline(document_store)
+indexing_pipeline.run_batch(file_paths=files_to_index)
 
 
-```python
-from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http
-
-
-# Let's first fetch some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/tutorial1"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt1.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is:
-# {
-#    'content': "<DOCUMENT_TEXT_HERE>",
-#    'meta': {'name': "<DOCUMENT_NAME_HERE>", ...}
-# }
-# (Optionally: you can also add more key-value-pairs here, that will be indexed as fields in Elasticsearch and
-# can be accessed later for filtering or shown in the responses of the Pipeline)
-
-# Let's have a look at the first 3 entries:
-print(docs[:3])
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(docs)
 ```
 
-## Initialize Retriever, Reader & Pipeline
-
-### Retriever
+The code in this tutorial uses the Game of Thrones data, but you can also supply your own *.txt* files and index them in the same way.
 
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.
-They use some simple but fast algorithm.
+As an alternative, you can cast you text data into [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore using `DocumentStore.write_documents()`.
 
-**Here:** We use Elasticsearch's default BM25 algorithm
+## Initializing the Retriever
 
-**Alternatives:**
+Our search system will use a Retriever, so we need to initialize it. A Retriever sifts through all the Documents and returns only the ones relevant to the question. This tutorial uses the BM25 algorithm. For more Retriever options, see [Retriever](https://docs.haystack.deepset.ai/docs/retriever).
 
-- Customize the `BM25Retriever`with custom queries (e.g. boosting) and filters
-- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)
-- Use `DensePassageRetriever` to use different embedding models for passage and query (see Tutorial 6)
+Let's initialize a BM25Retriever and make it use the InMemoryDocumentStore we initialized earlier in this tutorial:
 
 
 ```python
@@ -196,58 +122,28 @@ from haystack.nodes import BM25Retriever
 retriever = BM25Retriever(document_store=document_store)
 ```
 
+The Retriever is ready but we still need to initialize the Reader. 
 
-```python
-# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes with SQLite document store.
-
-# from haystack.nodes import TfidfRetriever
-# retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
+## Initializing the Reader
 
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
+A Reader scans the texts it received from the Retriever and extracts the top answer candidates. Readers are based on powerful deep learning models but are much slower than Retrievers at processing the same amount of text. In this tutorial, we're using a FARMReader with a base-sized RoBERTa question answering model called [`deepset/roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2). It's a strong all-round model that's good as a starting point. To find the best model for your use case, see [Models](https://haystack.deepset.ai/pipeline_nodes/reader#models).
 
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
+Let's initialize the Reader:
 
 
 ```python
 from haystack.nodes import FARMReader
 
-# Load a  local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-
 reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
 ```
 
-#### TransformersReader
-
-Alternative:
+We've initalized all the components for our pipeline. We're now ready to create the pipeline.
 
+## Creating the Retriever-Reader Pipeline
 
-```python
-from haystack.nodes import TransformersReader
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
+In this tutorial, we're using a ready-made pipeline called `ExtractiveQAPipeline`. It connects the Reader and the Retriever. The combination of the two speeds up processing because the Reader only processes the Documents that the Retriever has passed on. To learn more about pipelines, see [Pipelines](https://docs.haystack.deepset.ai/docs/pipelines).
 
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelines).
+To create the pipeline, run:
 
 
 ```python
@@ -256,59 +152,55 @@ from haystack.pipelines import ExtractiveQAPipeline
 pipe = ExtractiveQAPipeline(reader, retriever)
 ```
 
-## Voilà! Ask a question!
+The pipeline's ready, you can now go ahead and ask a question!
+
+## Asking a Question
+
+1. Use the pipeline `run()` method to ask a question. The query argument is where you type your question. Additionally, you can set the number of documents you want the Reader and Retriever to return using the `top-k` parameter. To learn more about setting arguments, see [Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments). To understand the importance of the `top-k` parameter, see [Choosing the Right top-k Values](https://docs.haystack.deepset.ai/docs/optimization#choosing-the-right-top-k-values).
 
 
 ```python
-# You can configure how many candidates the Reader and Retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
 prediction = pipe.run(
-    query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
+    query="Who is the father of Arya Stark?",
+    params={
+        "Retriever": {"top_k": 10},
+        "Reader": {"top_k": 5}
+    }
 )
 ```
 
+Here are some questions you could try out:
+- Who is the father of Arya Stark?
+- Who created the Dothraki vocabulary?
+- Who is the sister of Sansa?
 
-```python
-# prediction = pipe.run(query="Who created the Dothraki vocabulary?", params={"Reader": {"top_k": 5}})
-# prediction = pipe.run(query="Who is the sister of Sansa?", params={"Reader": {"top_k": 5}})
-```
-
-Now you can either print the object directly:
+2. Print out the answers the pipeline returned:
 
 
 ```python
 from pprint import pprint
 
 pprint(prediction)
-
-# Sample output:
-# {
-#     'answers': [ <Answer: answer='Eddard', type='extractive', score=0.9919578731060028, offsets_in_document=[{'start': 608, 'end': 615}], offsets_in_context=[{'start': 72, 'end': 79}], document_id='cc75f739897ecbf8c14657b13dda890e', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,
-#                  <Answer: answer='Ned', type='extractive', score=0.9767240881919861, offsets_in_document=[{'start': 3687, 'end': 3801}], offsets_in_context=[{'start': 18, 'end': 132}], document_id='9acf17ec9083c4022f69eb4a37187080', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,
-#                  ...
-#                ]
-#     'documents': [ <Document: content_type='text', score=0.8034909798951382, meta={'name': '332_Sansa_Stark.txt'}, embedding=None, id=d1f36ec7170e4c46cde65787fe125dfe', content='\n===\'\'A Game of Thrones\'\'===\nSansa Stark begins the novel by being betrothed to Crown ...'>,
-#                    <Document: content_type='text', score=0.8002150354529785, meta={'name': '191_Gendry.txt'}, embedding=None, id='dd4e070a22896afa81748d6510006d2', 'content='\n===Season 2===\nGendry travels North with Yoren and other Night's Watch recruits, including Arya ...'>,
-#                    ...
-#                  ],
-#     'no_ans_gap':  11.688868522644043,
-#     'node_id': 'Reader',
-#     'params': {'Reader': {'top_k': 5}, 'Retriever': {'top_k': 5}},
-#     'query': 'Who is the father of Arya Stark?',
-#     'root_node': 'Query'
-# }
 ```
 
-Or use a util to simplify the output:
+3. Simplify the printed answers:
 
 
 ```python
 from haystack.utils import print_answers
 
-# Change `minimum` to `medium` or `all` to raise the level of detail
-print_answers(prediction, details="minimum")
+print_answers(
+    prediction,
+    details="minimum" ## Choose from `minimum`, `medium`, and `all`
+)
 ```
 
+And there you have it! Congratulations on building your first machine learning based question answering system!
+
+# Next Steps
+
+Check out [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_Scalable_QA_System) to learn how to make a more advanced question answering system that uses an Elasticsearch backed DocumentStore and makes more use of the flexibility that pipelines offer.
+
 ## About us
 
 This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
@@ -319,10 +211,9 @@ Our focus: Industry specific language models & large scale QA systems.
 Some of our other work: 
 - [German BERT](https://deepset.ai/german-bert)
 - [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
 
 Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
+[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
 
 By the way: [we're hiring!](https://www.deepset.ai/jobs)
 
diff --git a/markdowns/03_Basic_QA_Pipeline_without_Elasticsearch.md b/markdowns/03_Basic_QA_Pipeline_without_Elasticsearch.md
deleted file mode 100644
index fb7978b5..00000000
--- a/markdowns/03_Basic_QA_Pipeline_without_Elasticsearch.md
+++ /dev/null
@@ -1,258 +0,0 @@
----
-layout: tutorial
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb
-toc: True
-title: "Build a QA System Without Elasticsearch"
-last_updated: 2022-11-24
-level: "beginner"
-weight: 15
-description: Create a Retriever Reader pipeline that requires no external database dependencies.
-category: "QA"
-aliases: ['/tutorials/without-elasticsearch']
-download: "/downloads/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb"
----
-    
-
-
-Haystack provides alternatives to Elasticsearch for developing quick prototypes.
-
-You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
-
-If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1. 
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
-
-You can double check whether the GPU runtime is enabled with the following command:
-
-
-```bash
-%%bash
-
-nvidia-smi
-```
-
-To start, install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-## Document Store
-
-
-
-```python
-# In-Memory Document Store
-from haystack.document_stores import InMemoryDocumentStore
-
-document_store = InMemoryDocumentStore()
-```
-
-
-```python
-# Alternatively, uncomment the following to use the SQLite Document Store:
-
-# from haystack.document_stores import SQLDocumentStore
-# document_store = SQLDocumentStore(url="sqlite:///qa.db")
-```
-
-## Preprocessing of documents
-
-Haystack provides a customizable pipeline for:
- - converting files into texts
- - cleaning texts
- - splitting texts
- - writing them to a Document Store
-
-In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.
-
-
-```python
-from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http
-
-
-# Let's first get some documents that we want to query
-# Here: 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/tutorial3"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt3.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
-# It must take a str as input, and return a str.
-docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# We now have a list of dictionaries that we can write to our document store.
-# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
-# The default format here is: {"name": "<some-document-name>", "content": "<the-actual-text>"}
-
-# Let's have a look at the first 3 entries:
-print(docs[:3])
-
-# Now, let's write the docs to our DB.
-document_store.write_documents(docs)
-```
-
-## Initialize Retriever, Reader & Pipeline
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered. 
-
-With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1.
-
-
-```python
-# An in-memory TfidfRetriever based on Pandas dataframes
-from haystack.nodes import TfidfRetriever
-
-retriever = TfidfRetriever(document_store=document_store)
-```
-
-### Reader
-
-A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
-on powerful, but slower deep learning models.
-
-Haystack currently supports Readers based on the frameworks FARM and Transformers.
-With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
-
-**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)
-
-**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
-
-**Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
-
-**Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"
-
-#### FARMReader
-
-
-```python
-from haystack.nodes import FARMReader
-
-
-# Load a  local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-#### TransformersReader
-
-Alternatively, we can use a Transformers reader:
-
-
-```python
-# from haystack.nodes import FARMReader, TransformersReader
-# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelines).
-
-
-```python
-from haystack.pipelines import ExtractiveQAPipeline
-
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k for retriever, the better (but also the slower) your answers.
-prediction = pipe.run(
-    query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
-)
-```
-
-
-```python
-# You can try asking more questions:
-
-# prediction = pipe.run(query="Who created the Dothraki vocabulary?", params={"Reader": {"top_k": 5}})
-# prediction = pipe.run(query="Who is the sister of Sansa?", params={"Reader": {"top_k": 5}})
-```
-
-
-```python
-# Now you can either print the object directly...
-from pprint import pprint
-
-pprint(prediction)
-
-# Sample output:
-# {
-#     'answers': [ <Answer: answer='Eddard', type='extractive', score=0.9919578731060028, offsets_in_document=[{'start': 608, 'end': 615}], offsets_in_context=[{'start': 72, 'end': 79}], document_id='cc75f739897ecbf8c14657b13dda890e', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,
-#                  <Answer: answer='Ned', type='extractive', score=0.9767240881919861, offsets_in_document=[{'start': 3687, 'end': 3801}], offsets_in_context=[{'start': 18, 'end': 132}], document_id='9acf17ec9083c4022f69eb4a37187080', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,
-#                  ...
-#                ]
-#     'documents': [ <Document: content_type='text', score=0.8034909798951382, meta={'name': '332_Sansa_Stark.txt'}, embedding=None, id=d1f36ec7170e4c46cde65787fe125dfe', content='\n===\'\'A Game of Thrones\'\'===\nSansa Stark begins the novel by being betrothed to Crown ...'>,
-#                    <Document: content_type='text', score=0.8002150354529785, meta={'name': '191_Gendry.txt'}, embedding=None, id='dd4e070a22896afa81748d6510006d2', 'content='\n===Season 2===\nGendry travels North with Yoren and other Night's Watch recruits, including Arya ...'>,
-#                    ...
-#                  ],
-#     'no_ans_gap':  11.688868522644043,
-#     'node_id': 'Reader',
-#     'params': {'Reader': {'top_k': 5}, 'Retriever': {'top_k': 5}},
-#     'query': 'Who is the father of Arya Stark?',
-#     'root_node': 'Query'
-# }
-```
-
-
-```python
-# ...or use a util to simplify the output
-from haystack.utils import print_answers
-
-
-# Change `minimum` to `medium` or `all` to control the level of detail
-print_answers(prediction, details="minimum")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/03_Scalable_QA_System.md b/markdowns/03_Scalable_QA_System.md
new file mode 100644
index 00000000..1e307c6c
--- /dev/null
+++ b/markdowns/03_Scalable_QA_System.md
@@ -0,0 +1,297 @@
+---
+layout: tutorial
+colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb
+toc: True
+title: "Build a Scalable Question Answering System"
+last_updated: 2023-01-02
+level: "beginner"
+weight: 15
+description: Create a scalable Retriever Reader pipeline that uses an ElasticsearchDocumentStore.
+category: "QA"
+aliases: []
+download: "/downloads/03_Scalable_QA_System.ipynb"
+---
+    
+
+
+- **Level**: Beginner
+- **Time to complete**: 20 minutes
+- **Nodes Used**: `ElasticsearchDocumentStore`, `BM25Retriever`, `FARMReader`
+- **Goal**: After completing this tutorial, you'll have built a scalable search system that runs on text files and can answer questions about Game of Thrones. You'll then be able to expand this system for your needs.
+
+
+## Overview
+
+Learn how to set up a question answering system that can search through complex knowledge bases and highlight answers to questions such as "Who is the father of Arya Stark?". In this tutorial, we'll work on a set of Wikipedia pages about Game of Thrones, but you can adapt it to search through internal wikis or a collection of financial reports, for example.
+
+This tutorial introduces you to all the concepts needed to build such a question answering system. It also uses Haystack components, such as indexing pipelines, querying pipelines, and DocumentStores backed by external database services.
+
+Let's learn how to build a question answering system and discover more about the marvelous seven kingdoms!
+
+
+## Preparing the Colab Environment
+
+- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)
+
+
+## Installing Haystack
+
+To start, let's install the latest release of Haystack with `pip`:
+
+
+```bash
+%%bash
+
+pip install --upgrade pip
+pip install farm-haystack[colab]
+```
+
+Set the logging level to INFO:
+
+
+```python
+import logging
+
+logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
+logging.getLogger("haystack").setLevel(logging.INFO)
+```
+
+## Initializing the ElasticsearchDocumentStore
+
+A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. Here, we're using the [`ElasticsearchDocumentStore`](https://docs.haystack.deepset.ai/reference/document-store-api#module-elasticsearch) which connects to a running Elasticsearch service. It's a fast and scalable text-focused storage option. This service runs independently from Haystack and persists even after the Haystack program has finished running. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store).
+
+1. Download, extract, and set the permissions for the Elasticsearch installation image:
+
+
+```bash
+%%bash
+
+wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
+tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
+chown -R daemon:daemon elasticsearch-7.9.2
+```
+
+2. Start the server:
+
+
+```bash
+%%bash --bg
+
+sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
+```
+
+If you are working in an environment where Docker is available, you can also start Elasticsearch using Docker. You can do this manually, or using our [`launch_es()`](https://docs.haystack.deepset.ai/reference/utils-api#module-doc_store) utility function.
+
+3. Wait 30 seconds for the server to fully start up:
+
+
+```python
+import time
+time.sleep(30)
+```
+
+4. Initialize the ElasticsearchDocumentStore:
+
+
+
+```python
+from haystack.utils import launch_es
+launch_es()
+```
+
+
+```python
+import os
+from haystack.document_stores import ElasticsearchDocumentStore
+
+# Get the host where Elasticsearch is running, default to localhost
+host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
+
+document_store = ElasticsearchDocumentStore(
+    host=host,
+    username="",
+    password="",
+    index="document"
+)
+```
+
+ElasticsearchDocumentStore is up and running and ready to store the Documents.
+
+## Indexing Documents with a Pipeline
+
+The next step is adding the files to the DocumentStore. The indexing pipeline turns your files into Document objects and writes them to the DocumentStore. Our indexing pipeline will have two nodes: `TextConverter`, which turns `.txt` files into Haystack `Document` objects, and `PreProcessor`, which cleans and splits the text within a `Document`.
+
+Once we combine these nodes into a pipeline, the pipeline will ingest `.txt` file paths, preprocess them, and write them into the DocumentStore.
+
+
+1. Download 517 articles from the Game of Thrones Wikipedia. You can find them in *data/build_a_scalable_question_answering_system* as a set of *.txt* files.
+
+
+```python
+from datasets import load_dataset
+
+from haystack.utils import fetch_archive_from_http
+
+doc_dir = "data/build_a_scalable_question_answering_system"
+
+fetch_archive_from_http(
+    url="https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt3.zip", 
+    output_dir=doc_dir
+)
+```
+
+2. Initialize the pipeline, TextConverter, and PreProcessor:
+
+
+```python
+from haystack import Pipeline
+from haystack.nodes import TextConverter, PreProcessor
+
+indexing_pipeline = Pipeline()
+text_converter = TextConverter()
+preprocessor = PreProcessor(
+    clean_whitespace=True,
+    clean_header_footer=True,
+    clean_empty_lines=True,
+    split_by="word",
+    split_length=200,
+    split_overlap=20,
+    split_respect_sentence_boundary=True,
+)
+
+```
+
+To learn more about the parameters of the `PreProcessor`, see [Usage](https://docs.haystack.deepset.ai/docs/preprocessor#usage). To understand why document splitting is important for your question answering system's performance, see [Document Length](https://docs.haystack.deepset.ai/docs/optimization#document-length).
+
+2. Add the nodes into an indexing pipeline. You should provide the `name` or `name`s of preceding nodes as the `input` argument. Note that in an indexing pipeline, the input to the first node is `File`.
+
+
+```python
+import os
+
+indexing_pipeline.add_node(component=text_converter, name="TextConverter", inputs=["File"])
+indexing_pipeline.add_node(component=preprocessor, name="PreProcessor", inputs=["TextConverter"])
+indexing_pipeline.add_node(component=document_store, name="DocumentStore", inputs=["PreProcessor"])
+
+```
+
+3. Run the indexing pipeline to write the text data into the DocumentStore:
+
+
+```python
+files_to_index = [doc_dir + "/" + f for f in os.listdir(doc_dir)]
+indexing_pipeline.run_batch(file_paths=files_to_index)
+```
+
+The code in this tutorial uses Game of Thrones data, but you can also supply your own `.txt` files and index them in the same way.
+
+As an alternative, you can cast you text data into [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore using [`DocumentStore.write_documents()`](https://docs.haystack.deepset.ai/reference/document-store-api#basedocumentstorewrite_documents).
+
+Now that the Documents are in the DocumentStore, let's initialize the nodes we want to use in our query pipeline.
+
+## Initializing the Retriever
+
+Our query pipeline is going to use a Retriever, so we need to initialize it. A Retriever sifts through all the Documents and returns only those that are relevant to the question. This tutorial uses the BM25Retriever. This is the recommended Retriever for a question answering system like the one we're creating. For more Retriever options, see [Retriever](https://docs.haystack.deepset.ai/docs/retriever).
+
+
+```python
+from haystack.nodes import BM25Retriever
+
+retriever = BM25Retriever(document_store=document_store)
+```
+
+The BM25Retriever is initialized and ready for the pipeline.
+
+## Initializing the Reader
+
+Our query pipeline also needs a Reader, so we'll initialize it next. A Reader scans the texts it received from the Retriever and extracts the top answer candidates. Readers are based on powerful deep learning models but are much slower than Retrievers at processing the same amount of text. This tutorials uses a FARMReader with a base-sized RoBERTa question answering model called [`deepset/roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2). It's a good all-round model to start with. To find a model that's best for your use case, see [Models](https://docs.haystack.deepset.ai/docs/reader#models).
+
+
+```python
+from haystack.nodes import FARMReader
+
+reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
+```
+
+## Creating the Retriever-Reader Pipeline
+
+You can combine the Reader and Retriever in a querying pipeline using the `Pipeline` class. The combination of the two speeds up processing because the Reader only processes the Documents that it received from the Retriever. 
+
+Initialize the `Pipeline` object and add the Retriever and Reader as nodes. You should provide the `name` or `name`s of preceding nodes as the input argument. Note that in a querying pipeline, the input to the first node is `Query`.
+
+
+```python
+from haystack import Pipeline
+
+querying_pipeline = Pipeline()
+querying_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
+querying_pipeline.add_node(component=reader, name="Reader", inputs=["Retriever"])
+
+```
+
+That's it! Your pipeline's ready to answer your questions!
+
+## Asking a Question
+
+1. Use the pipeline's `run()` method to ask a question. The query argument is where you type your question. Additionally, you can set the number of documents you want the Reader and Retriever to return using the `top-k` parameter. To learn more about setting arguments, see [Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments). To understand the importance of the `top-k` parameter, see [Choosing the Right top-k Values](https://docs.haystack.deepset.ai/docs/optimization#choosing-the-right-top-k-values).
+
+
+
+```python
+prediction = querying_pipeline.run(
+    query="Who is the father of Arya Stark?",
+    params={
+        "Retriever": {"top_k": 10},
+        "Reader": {"top_k": 5}
+    }
+)
+```
+
+Here are some questions you could try out:
+- Who is the father of Arya Stark?
+- Who created the Dothraki vocabulary?
+- Who is the sister of Sansa?
+
+2. Print out the answers the pipeline returns:
+
+
+```python
+from pprint import pprint
+
+pprint(prediction)
+```
+
+3. Simplify the printed answers:
+
+
+```python
+from haystack.utils import print_answers
+
+print_answers(
+    prediction,
+    details="minimum" ## Choose from `minimum`, `medium` and `all`
+)
+```
+
+And there you have it! Congratulations on building a scalable machine learning based question answering system!
+
+# Next Steps
+
+To learn how to improve the performance of the Reader, see [Fine-Tune a Reader](https://haystack.deepset.ai/tutorials/03_fine_tune_a_reader).
+
+## About us
+
+This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
+
+We bring NLP to the industry via open source!  
+Our focus: Industry specific language models & large scale QA systems.  
+  
+Some of our other work: 
+- [German BERT](https://deepset.ai/german-bert)
+- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
+
+Get in touch:
+[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
+
+By the way: [we're hiring!](https://www.deepset.ai/jobs)
+
diff --git a/tutorials/01_Basic_QA_Pipeline.ipynb b/tutorials/01_Basic_QA_Pipeline.ipynb
index 35257055..1f3261c4 100644
--- a/tutorials/01_Basic_QA_Pipeline.ipynb
+++ b/tutorials/01_Basic_QA_Pipeline.ipynb
@@ -1,107 +1,80 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Build Your First QA System\n",
+    "# Tutorial: Build Your First Question Answering System\n",
     "\n",
-    "<img style=\"float: right;\" src=\"https://upload.wikimedia.org/wikipedia/en/d/d8/Game_of_Thrones_title_card.jpg\">\n",
+    "> We've modified this first tutorial to make it simpler to start with. If you're looking for a Question Answering tutorial that uses a DocumentStore such as Elasticsearch, go to our new [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_Scalable_QA_System) tutorial.\n",
     "\n",
-    "Question Answering can be used in a variety of use cases. A very common one:  Using it to navigate through complex knowledge bases or long documents (\"search setting\").\n",
-    "\n",
-    "A \"knowledge base\" could for example be your website, an internal wiki or a collection of financial reports. \n",
-    "In this tutorial we will work on a slightly different domain: \"Game of Thrones\". \n",
-    "\n",
-    "Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the \n",
-    "marvellous seven kingdoms.\n"
+    "- **Level**: Beginner\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `FARMReader`\n",
+    "- **Goal**: After completing this tutorial, you will have learned about the Reader and Retriever, and built a question answering pipeline that can answer questions about the Game of Thrones series.\n"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
    },
    "source": [
-    "### Prepare environment\n",
-    "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "## Overview\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+    "Learn how to build a question answering system using Haystack's DocumentStore, Retriever, and Reader. Your system will use Game of Thrones files and will be able to answer questions like \"Who is the father of Arya Stark?\". But you can use it to run on any other set of documents, such as your company's internal wikis or a collection of financial reports. \n",
     "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
+    "To help you get started quicker, we simplified certain steps in this tutorial. For example, Document preparation and pipeline initialization are handled by ready-made classes that replace lines of initialization code. But don't worry! This doesn't affect how well the question answering system performs."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "%%bash\n",
     "\n",
-    "nvidia-smi"
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To start, install the latest release of Haystack with `pip`:"
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]"
+    "pip install farm-haystack[colab]"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "## Logging\n",
-    "\n",
-    "We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
-    "Example log message:\n",
-    "INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
-    "Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
+    "Set the logging level to INFO:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import logging\n",
@@ -111,21 +84,15 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Document Store\n",
+    "## Initializing the DocumentStore\n",
     "\n",
-    "Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`,  `SQLDocumentStore`, and `InMemoryDocumentStore`.\n",
+    "We'll start creating our question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, we're using the `InMemoryDocumentStore`, which is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store).\n",
     "\n",
-    "**Here:** We recommended Elasticsearch as it comes preloaded with features like [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html), [BM25 retrieval](https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25), and [vector storage for text embeddings](https://www.elastic.co/guide/en/elasticsearch/reference/7.6/dense-vector.html).\n",
-    "\n",
-    "**Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the [Tutorial 3](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb) for using SQL/InMemory document stores.\n",
-    "\n",
-    "**Hint**: This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can configure Haystack to work with your existing document stores.\n",
-    "\n",
-    "### Start an Elasticsearch server locally\n",
-    "You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source."
+    "Let's initialize the the DocumentStore:"
    ]
   },
   {
@@ -134,60 +101,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Recommended: Start Elasticsearch using Docker via the Haystack utility function\n",
-    "from haystack.utils import launch_es\n",
+    "from haystack.document_stores import InMemoryDocumentStore\n",
     "\n",
-    "launch_es()"
+    "document_store = InMemoryDocumentStore(use_bm25=True)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Start an Elasticsearch server in Colab\n",
-    "\n",
-    "If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
-    "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash --bg\n",
-    "\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch"
+    "The DocumentStore is now ready. Now it's time to fill it with some Documents."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create the Document Store\n",
+    "## Preparing Documents\n",
     "\n",
-    "The `ElasticsearchDocumentStore` class will try to open a connection in the constructor, here we wait 30 seconds only to be sure Elasticsearch is ready before continuing:"
+    "1. Download 517 articles from the Game of Thrones Wikipedia. You can find them in *data/build_your_first_question_answering_system* as a set of *.txt* files."
    ]
   },
   {
@@ -196,114 +130,59 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import time\n",
-    "time.sleep(30)"
+    "from haystack.utils import fetch_archive_from_http\n",
+    "\n",
+    "doc_dir = \"data/build_your_first_question_answering_system\"\n",
+    "\n",
+    "fetch_archive_from_http(\n",
+    "    url=\"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt1.zip\",\n",
+    "    output_dir=doc_dir\n",
+    ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we create the Document Store instance:"
+    "2. Use `TextIndexingPipeline` to convert the files you just downloaded into Haystack [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
-    "from haystack.document_stores import ElasticsearchDocumentStore\n",
+    "from haystack.pipelines.standard_pipelines import TextIndexingPipeline\n",
     "\n",
-    "# Get the host where Elasticsearch is running, default to localhost\n",
-    "host = os.environ.get(\"ELASTICSEARCH_HOST\", \"localhost\")\n",
-    "document_store = ElasticsearchDocumentStore(host=host, username=\"\", password=\"\", index=\"document\")"
+    "files_to_index = [doc_dir + \"/\" + f for f in os.listdir(doc_dir)]\n",
+    "indexing_pipeline = TextIndexingPipeline(document_store)\n",
+    "indexing_pipeline.run_batch(file_paths=files_to_index)\n",
+    "\n"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
-    "## Preprocessing of documents\n",
+    "The code in this tutorial uses the Game of Thrones data, but you can also supply your own *.txt* files and index them in the same way.\n",
     "\n",
-    "Haystack provides a customizable pipeline for:\n",
-    " - converting files into texts\n",
-    " - cleaning texts\n",
-    " - splitting texts\n",
-    " - writing them to a Document Store\n",
-    "\n",
-    "In this tutorial, we download Wikipedia articles about Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http\n",
-    "\n",
-    "\n",
-    "# Let's first fetch some documents that we want to query\n",
-    "# Here: 517 Wikipedia articles for Game of Thrones\n",
-    "doc_dir = \"data/tutorial1\"\n",
-    "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt1.zip\"\n",
-    "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
-    "\n",
-    "# Convert files to dicts\n",
-    "# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)\n",
-    "# It must take a str as input, and return a str.\n",
-    "docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n",
-    "\n",
-    "# We now have a list of dictionaries that we can write to our document store.\n",
-    "# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.\n",
-    "# The default format here is:\n",
-    "# {\n",
-    "#    'content': \"<DOCUMENT_TEXT_HERE>\",\n",
-    "#    'meta': {'name': \"<DOCUMENT_NAME_HERE>\", ...}\n",
-    "# }\n",
-    "# (Optionally: you can also add more key-value-pairs here, that will be indexed as fields in Elasticsearch and\n",
-    "# can be accessed later for filtering or shown in the responses of the Pipeline)\n",
-    "\n",
-    "# Let's have a look at the first 3 entries:\n",
-    "print(docs[:3])\n",
-    "\n",
-    "# Now, let's write the dicts containing documents to our DB.\n",
-    "document_store.write_documents(docs)"
+    "As an alternative, you can cast you text data into [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore using `DocumentStore.write_documents()`."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Initialize Retriever, Reader & Pipeline\n",
+    "## Initializing the Retriever\n",
     "\n",
-    "### Retriever\n",
+    "Our search system will use a Retriever, so we need to initialize it. A Retriever sifts through all the Documents and returns only the ones relevant to the question. This tutorial uses the BM25 algorithm. For more Retriever options, see [Retriever](https://docs.haystack.deepset.ai/docs/retriever).\n",
     "\n",
-    "Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.\n",
-    "They use some simple but fast algorithm.\n",
-    "\n",
-    "**Here:** We use Elasticsearch's default BM25 algorithm\n",
-    "\n",
-    "**Alternatives:**\n",
-    "\n",
-    "- Customize the `BM25Retriever`with custom queries (e.g. boosting) and filters\n",
-    "- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging\n",
-    "- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)\n",
-    "- Use `DensePassageRetriever` to use different embedding models for passage and query (see Tutorial 6)"
+    "Let's initialize a BM25Retriever and make it use the InMemoryDocumentStore we initialized earlier in this tutorial:"
    ]
   },
   {
@@ -318,102 +197,60 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes with SQLite document store.\n",
-    "\n",
-    "# from haystack.nodes import TfidfRetriever\n",
-    "# retriever = TfidfRetriever(document_store=document_store)"
+    "The Retriever is ready but we still need to initialize the Reader. "
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Reader\n",
-    "\n",
-    "A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based\n",
-    "on powerful, but slower deep learning models.\n",
-    "\n",
-    "Haystack currently supports Readers based on the frameworks FARM and Transformers.\n",
-    "With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).\n",
+    "## Initializing the Reader\n",
     "\n",
-    "**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)\n",
+    "A Reader scans the texts it received from the Retriever and extracts the top answer candidates. Readers are based on powerful deep learning models but are much slower than Retrievers at processing the same amount of text. In this tutorial, we're using a FARMReader with a base-sized RoBERTa question answering model called [`deepset/roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2). It's a strong all-round model that's good as a starting point. To find the best model for your use case, see [Models](https://haystack.deepset.ai/pipeline_nodes/reader#models).\n",
     "\n",
-    "**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)\n",
-    "\n",
-    "**Alternatives (Models):** e.g. \"distilbert-base-uncased-distilled-squad\" (fast) or \"deepset/bert-large-uncased-whole-word-masking-squad2\" (good accuracy)\n",
-    "\n",
-    "**Hint:** You can adjust the model to return \"no answer possible\" with the no_ans_boost. Higher values mean the model prefers \"no answer possible\"\n",
-    "\n",
-    "#### FARMReader"
+    "Let's initialize the Reader:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from haystack.nodes import FARMReader\n",
     "\n",
-    "# Load a  local model or any of the QA models on\n",
-    "# Hugging Face's model hub (https://huggingface.co/models)\n",
-    "\n",
     "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### TransformersReader\n",
-    "\n",
-    "Alternative:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack.nodes import TransformersReader\n",
-    "# reader = TransformersReader(model_name_or_path=\"distilbert-base-uncased-distilled-squad\", tokenizer=\"distilbert-base-uncased\", use_gpu=-1)"
+    "We've initalized all the components for our pipeline. We're now ready to create the pipeline."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Pipeline\n",
+    "## Creating the Retriever-Reader Pipeline\n",
     "\n",
-    "With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.\n",
-    "Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
-    "To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.\n",
-    "You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelines)."
+    "In this tutorial, we're using a ready-made pipeline called `ExtractiveQAPipeline`. It connects the Reader and the Retriever. The combination of the two speeds up processing because the Reader only processes the Documents that the Retriever has passed on. To learn more about pipelines, see [Pipelines](https://docs.haystack.deepset.ai/docs/pipelines).\n",
+    "\n",
+    "To create the pipeline, run:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from haystack.pipelines import ExtractiveQAPipeline\n",
@@ -422,44 +259,54 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Voilà! Ask a question!"
+    "The pipeline's ready, you can now go ahead and ask a question!"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asking a Question\n",
+    "\n",
+    "1. Use the pipeline `run()` method to ask a question. The query argument is where you type your question. Additionally, you can set the number of documents you want the Reader and Retriever to return using the `top-k` parameter. To learn more about setting arguments, see [Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments). To understand the importance of the `top-k` parameter, see [Choosing the Right top-k Values](https://docs.haystack.deepset.ai/docs/optimization#choosing-the-right-top-k-values)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# You can configure how many candidates the Reader and Retriever shall return\n",
-    "# The higher top_k_retriever, the better (but also the slower) your answers.\n",
     "prediction = pipe.run(\n",
-    "    query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
+    "    query=\"Who is the father of Arya Stark?\",\n",
+    "    params={\n",
+    "        \"Retriever\": {\"top_k\": 10},\n",
+    "        \"Reader\": {\"top_k\": 5}\n",
+    "    }\n",
     ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# prediction = pipe.run(query=\"Who created the Dothraki vocabulary?\", params={\"Reader\": {\"top_k\": 5}})\n",
-    "# prediction = pipe.run(query=\"Who is the sister of Sansa?\", params={\"Reader\": {\"top_k\": 5}})"
+    "Here are some questions you could try out:\n",
+    "- Who is the father of Arya Stark?\n",
+    "- Who created the Dothraki vocabulary?\n",
+    "- Who is the sister of Sansa?"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now you can either print the object directly:"
+    "2. Print out the answers the pipeline returned:"
    ]
   },
   {
@@ -470,58 +317,51 @@
    "source": [
     "from pprint import pprint\n",
     "\n",
-    "pprint(prediction)\n",
-    "\n",
-    "# Sample output:\n",
-    "# {\n",
-    "#     'answers': [ <Answer: answer='Eddard', type='extractive', score=0.9919578731060028, offsets_in_document=[{'start': 608, 'end': 615}], offsets_in_context=[{'start': 72, 'end': 79}], document_id='cc75f739897ecbf8c14657b13dda890e', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,\n",
-    "#                  <Answer: answer='Ned', type='extractive', score=0.9767240881919861, offsets_in_document=[{'start': 3687, 'end': 3801}], offsets_in_context=[{'start': 18, 'end': 132}], document_id='9acf17ec9083c4022f69eb4a37187080', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,\n",
-    "#                  ...\n",
-    "#                ]\n",
-    "#     'documents': [ <Document: content_type='text', score=0.8034909798951382, meta={'name': '332_Sansa_Stark.txt'}, embedding=None, id=d1f36ec7170e4c46cde65787fe125dfe', content='\\n===\\'\\'A Game of Thrones\\'\\'===\\nSansa Stark begins the novel by being betrothed to Crown ...'>,\n",
-    "#                    <Document: content_type='text', score=0.8002150354529785, meta={'name': '191_Gendry.txt'}, embedding=None, id='dd4e070a22896afa81748d6510006d2', 'content='\\n===Season 2===\\nGendry travels North with Yoren and other Night's Watch recruits, including Arya ...'>,\n",
-    "#                    ...\n",
-    "#                  ],\n",
-    "#     'no_ans_gap':  11.688868522644043,\n",
-    "#     'node_id': 'Reader',\n",
-    "#     'params': {'Reader': {'top_k': 5}, 'Retriever': {'top_k': 5}},\n",
-    "#     'query': 'Who is the father of Arya Stark?',\n",
-    "#     'root_node': 'Query'\n",
-    "# }"
+    "pprint(prediction)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or use a util to simplify the output:"
+    "3. Simplify the printed answers:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from haystack.utils import print_answers\n",
     "\n",
-    "# Change `minimum` to `medium` or `all` to raise the level of detail\n",
-    "print_answers(prediction, details=\"minimum\")"
+    "print_answers(\n",
+    "    prediction,\n",
+    "    details=\"minimum\" ## Choose from `minimum`, `medium`, and `all`\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And there you have it! Congratulations on building your first machine learning based question answering system!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    },
+   "source": [
+    "# Next Steps\n",
+    "\n",
+    "Check out [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_Scalable_QA_System) to learn how to make a more advanced question answering system that uses an Elasticsearch backed DocumentStore and makes more use of the flexibility that pipelines offer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## About us\n",
     "\n",
@@ -533,10 +373,9 @@
     "Some of our other work: \n",
     "- [German BERT](https://deepset.ai/german-bert)\n",
     "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
     "\n",
     "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
+    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
     "\n",
     "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
    ]
@@ -544,7 +383,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.9 64-bit",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -558,11 +397,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]"
   },
   "vscode": {
    "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+    "hash": "3fd1520721e0740ccf655b87fc1a15949508d880e32fa0665a9483d3c4c5735c"
    }
   }
  },
diff --git a/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb b/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb
deleted file mode 100644
index ccb312af..00000000
--- a/tutorials/03_Basic_QA_Pipeline_without_Elasticsearch.ipynb
+++ /dev/null
@@ -1,446 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Build a QA System Without Elasticsearch\n",
-    "\n",
-    "Haystack provides alternatives to Elasticsearch for developing quick prototypes.\n",
-    "\n",
-    "You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.\n",
-    "\n",
-    "If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### Prepare environment\n",
-    "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
-    "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "nvidia-smi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To start, install the latest release of Haystack with `pip`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Logging\n",
-    "\n",
-    "We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
-    "Example log message:\n",
-    "INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
-    "Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "\n",
-    "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
-    "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Document Store\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# In-Memory Document Store\n",
-    "from haystack.document_stores import InMemoryDocumentStore\n",
-    "\n",
-    "document_store = InMemoryDocumentStore()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Alternatively, uncomment the following to use the SQLite Document Store:\n",
-    "\n",
-    "# from haystack.document_stores import SQLDocumentStore\n",
-    "# document_store = SQLDocumentStore(url=\"sqlite:///qa.db\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Preprocessing of documents\n",
-    "\n",
-    "Haystack provides a customizable pipeline for:\n",
-    " - converting files into texts\n",
-    " - cleaning texts\n",
-    " - splitting texts\n",
-    " - writing them to a Document Store\n",
-    "\n",
-    "In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http\n",
-    "\n",
-    "\n",
-    "# Let's first get some documents that we want to query\n",
-    "# Here: 517 Wikipedia articles for Game of Thrones\n",
-    "doc_dir = \"data/tutorial3\"\n",
-    "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt3.zip\"\n",
-    "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
-    "\n",
-    "# convert files to dicts containing documents that can be indexed to our datastore\n",
-    "# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)\n",
-    "# It must take a str as input, and return a str.\n",
-    "docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n",
-    "\n",
-    "# We now have a list of dictionaries that we can write to our document store.\n",
-    "# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.\n",
-    "# The default format here is: {\"name\": \"<some-document-name>\", \"content\": \"<the-actual-text>\"}\n",
-    "\n",
-    "# Let's have a look at the first 3 entries:\n",
-    "print(docs[:3])\n",
-    "\n",
-    "# Now, let's write the docs to our DB.\n",
-    "document_store.write_documents(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize Retriever, Reader & Pipeline\n",
-    "\n",
-    "### Retriever\n",
-    "\n",
-    "Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered. \n",
-    "\n",
-    "With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# An in-memory TfidfRetriever based on Pandas dataframes\n",
-    "from haystack.nodes import TfidfRetriever\n",
-    "\n",
-    "retriever = TfidfRetriever(document_store=document_store)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Reader\n",
-    "\n",
-    "A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based\n",
-    "on powerful, but slower deep learning models.\n",
-    "\n",
-    "Haystack currently supports Readers based on the frameworks FARM and Transformers.\n",
-    "With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).\n",
-    "\n",
-    "**Here:** a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)\n",
-    "\n",
-    "**Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)\n",
-    "\n",
-    "**Alternatives (Models):** e.g. \"distilbert-base-uncased-distilled-squad\" (fast) or \"deepset/bert-large-uncased-whole-word-masking-squad2\" (good accuracy)\n",
-    "\n",
-    "**Hint:** You can adjust the model to return \"no answer possible\" with the no_ans_boost. Higher values mean the model prefers \"no answer possible\"\n",
-    "\n",
-    "#### FARMReader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.nodes import FARMReader\n",
-    "\n",
-    "\n",
-    "# Load a  local model or any of the QA models on\n",
-    "# Hugging Face's model hub (https://huggingface.co/models)\n",
-    "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### TransformersReader\n",
-    "\n",
-    "Alternatively, we can use a Transformers reader:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from haystack.nodes import FARMReader, TransformersReader\n",
-    "# reader = TransformersReader(model_name_or_path=\"distilbert-base-uncased-distilled-squad\", tokenizer=\"distilbert-base-uncased\", use_gpu=-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pipeline\n",
-    "\n",
-    "With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.\n",
-    "Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
-    "To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.\n",
-    "You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelines)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.pipelines import ExtractiveQAPipeline\n",
-    "\n",
-    "pipe = ExtractiveQAPipeline(reader, retriever)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Voilà! Ask a question!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# You can configure how many candidates the reader and retriever shall return\n",
-    "# The higher top_k for retriever, the better (but also the slower) your answers.\n",
-    "prediction = pipe.run(\n",
-    "    query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# You can try asking more questions:\n",
-    "\n",
-    "# prediction = pipe.run(query=\"Who created the Dothraki vocabulary?\", params={\"Reader\": {\"top_k\": 5}})\n",
-    "# prediction = pipe.run(query=\"Who is the sister of Sansa?\", params={\"Reader\": {\"top_k\": 5}})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Now you can either print the object directly...\n",
-    "from pprint import pprint\n",
-    "\n",
-    "pprint(prediction)\n",
-    "\n",
-    "# Sample output:\n",
-    "# {\n",
-    "#     'answers': [ <Answer: answer='Eddard', type='extractive', score=0.9919578731060028, offsets_in_document=[{'start': 608, 'end': 615}], offsets_in_context=[{'start': 72, 'end': 79}], document_id='cc75f739897ecbf8c14657b13dda890e', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,\n",
-    "#                  <Answer: answer='Ned', type='extractive', score=0.9767240881919861, offsets_in_document=[{'start': 3687, 'end': 3801}], offsets_in_context=[{'start': 18, 'end': 132}], document_id='9acf17ec9083c4022f69eb4a37187080', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,\n",
-    "#                  ...\n",
-    "#                ]\n",
-    "#     'documents': [ <Document: content_type='text', score=0.8034909798951382, meta={'name': '332_Sansa_Stark.txt'}, embedding=None, id=d1f36ec7170e4c46cde65787fe125dfe', content='\\n===\\'\\'A Game of Thrones\\'\\'===\\nSansa Stark begins the novel by being betrothed to Crown ...'>,\n",
-    "#                    <Document: content_type='text', score=0.8002150354529785, meta={'name': '191_Gendry.txt'}, embedding=None, id='dd4e070a22896afa81748d6510006d2', 'content='\\n===Season 2===\\nGendry travels North with Yoren and other Night's Watch recruits, including Arya ...'>,\n",
-    "#                    ...\n",
-    "#                  ],\n",
-    "#     'no_ans_gap':  11.688868522644043,\n",
-    "#     'node_id': 'Reader',\n",
-    "#     'params': {'Reader': {'top_k': 5}, 'Retriever': {'top_k': 5}},\n",
-    "#     'query': 'Who is the father of Arya Stark?',\n",
-    "#     'root_node': 'Query'\n",
-    "# }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# ...or use a util to simplify the output\n",
-    "from haystack.utils import print_answers\n",
-    "\n",
-    "\n",
-    "# Change `minimum` to `medium` or `all` to control the level of detail\n",
-    "print_answers(prediction, details=\"minimum\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.8.9 64-bit",
-   "language": "python",
-   "name": "python3"
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-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.9"
-  },
-  "vscode": {
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-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/tutorials/03_Scalable_QA_System.ipynb b/tutorials/03_Scalable_QA_System.ipynb
new file mode 100644
index 00000000..a10f5b8b
--- /dev/null
+++ b/tutorials/03_Scalable_QA_System.ipynb
@@ -0,0 +1,1805 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: Build a Scalable Question Answering System\n",
+    "\n",
+    "- **Level**: Beginner\n",
+    "- **Time to complete**: 20 minutes\n",
+    "- **Nodes Used**: `ElasticsearchDocumentStore`, `BM25Retriever`, `FARMReader`\n",
+    "- **Goal**: After completing this tutorial, you'll have built a scalable search system that runs on text files and can answer questions about Game of Thrones. You'll then be able to expand this system for your needs.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "Learn how to set up a question answering system that can search through complex knowledge bases and highlight answers to questions such as \"Who is the father of Arya Stark?\". In this tutorial, we'll work on a set of Wikipedia pages about Game of Thrones, but you can adapt it to search through internal wikis or a collection of financial reports, for example.\n",
+    "\n",
+    "This tutorial introduces you to all the concepts needed to build such a question answering system. It also uses Haystack components, such as indexing pipelines, querying pipelines, and DocumentStores backed by external database services.\n",
+    "\n",
+    "Let's learn how to build a question answering system and discover more about the marvelous seven kingdoms!"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the logging level to INFO:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
+    "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the ElasticsearchDocumentStore\n",
+    "\n",
+    "A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. Here, we're using the [`ElasticsearchDocumentStore`](https://docs.haystack.deepset.ai/reference/document-store-api#module-elasticsearch) which connects to a running Elasticsearch service. It's a fast and scalable text-focused storage option. This service runs independently from Haystack and persists even after the Haystack program has finished running. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store)."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Download, extract, and set the permissions for the Elasticsearch installation image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
+    "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
+    "chown -R daemon:daemon elasticsearch-7.9.2"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Start the server:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash --bg\n",
+    "\n",
+    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are working in an environment where Docker is available, you can also start Elasticsearch using Docker. You can do this manually, or using our [`launch_es()`](https://docs.haystack.deepset.ai/reference/utils-api#module-doc_store) utility function."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. Wait 30 seconds for the server to fully start up:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "time.sleep(30)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. Initialize the ElasticsearchDocumentStore:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.utils import launch_es\n",
+    "launch_es()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from haystack.document_stores import ElasticsearchDocumentStore\n",
+    "\n",
+    "# Get the host where Elasticsearch is running, default to localhost\n",
+    "host = os.environ.get(\"ELASTICSEARCH_HOST\", \"localhost\")\n",
+    "\n",
+    "document_store = ElasticsearchDocumentStore(\n",
+    "    host=host,\n",
+    "    username=\"\",\n",
+    "    password=\"\",\n",
+    "    index=\"document\"\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ElasticsearchDocumentStore is up and running and ready to store the Documents."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing Documents with a Pipeline\n",
+    "\n",
+    "The next step is adding the files to the DocumentStore. The indexing pipeline turns your files into Document objects and writes them to the DocumentStore. Our indexing pipeline will have two nodes: `TextConverter`, which turns `.txt` files into Haystack `Document` objects, and `PreProcessor`, which cleans and splits the text within a `Document`.\n",
+    "\n",
+    "Once we combine these nodes into a pipeline, the pipeline will ingest `.txt` file paths, preprocess them, and write them into the DocumentStore.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Download 517 articles from the Game of Thrones Wikipedia. You can find them in *data/build_a_scalable_question_answering_system* as a set of *.txt* files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "from haystack.utils import fetch_archive_from_http\n",
+    "\n",
+    "doc_dir = \"data/build_a_scalable_question_answering_system\"\n",
+    "\n",
+    "fetch_archive_from_http(\n",
+    "    url=\"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt3.zip\", \n",
+    "    output_dir=doc_dir\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Initialize the pipeline, TextConverter, and PreProcessor:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "from haystack.nodes import TextConverter, PreProcessor\n",
+    "\n",
+    "indexing_pipeline = Pipeline()\n",
+    "text_converter = TextConverter()\n",
+    "preprocessor = PreProcessor(\n",
+    "    clean_whitespace=True,\n",
+    "    clean_header_footer=True,\n",
+    "    clean_empty_lines=True,\n",
+    "    split_by=\"word\",\n",
+    "    split_length=200,\n",
+    "    split_overlap=20,\n",
+    "    split_respect_sentence_boundary=True,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To learn more about the parameters of the `PreProcessor`, see [Usage](https://docs.haystack.deepset.ai/docs/preprocessor#usage). To understand why document splitting is important for your question answering system's performance, see [Document Length](https://docs.haystack.deepset.ai/docs/optimization#document-length)."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Add the nodes into an indexing pipeline. You should provide the `name` or `name`s of preceding nodes as the `input` argument. Note that in an indexing pipeline, the input to the first node is `File`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "indexing_pipeline.add_node(component=text_converter, name=\"TextConverter\", inputs=[\"File\"])\n",
+    "indexing_pipeline.add_node(component=preprocessor, name=\"PreProcessor\", inputs=[\"TextConverter\"])\n",
+    "indexing_pipeline.add_node(component=document_store, name=\"DocumentStore\", inputs=[\"PreProcessor\"])\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. Run the indexing pipeline to write the text data into the DocumentStore:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Converting files: 100%|██████████| 183/183 [00:00<00:00, 708.99it/s]\n",
+      "Preprocessing:   0%|          | 0/183 [00:00<?, ?docs/s]WARNING:haystack.nodes.preprocessor.preprocessor:We found one or more sentences whose word count is higher than the split length.\n",
+      "Preprocessing: 100%|██████████| 183/183 [00:00<00:00, 419.58docs/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'documents': [<Document: {'content': '\\n\\n\\'\\'\\'\\'\\'A Game of Thrones\\'\\'\\'\\'\\' is the first novel in \\'\\'A Song of Ice and Fire\\'\\', a series of fantasy novels by the American author George R. R. Martin.  It was first published on August 1, 1996.  The novel won the 1997 Locus Award and was nominated for both the 1997 Nebula Award and the 1997 World Fantasy Award. The novella \\'\\'Blood of the Dragon\\'\\', comprising the Daenerys Targaryen chapters from the novel, won the 1997 Hugo Award for Best Novella. In January 2011, the novel became a \\'\\'New York Times\\'\\' Bestseller and reached #1 on the list in July 2011.\\n\\nIn the novel, recounting events from various points of view,  Martin introduces the plot-lines of the noble houses of Westeros, the Wall, and the Targaryens. The novel has inspired several spin-off works, including several games. It is also the namesake and basis for the first season of \\'\\'Game of Thrones\\'\\', an HBO television series that premiered in April 2011. A March 2013 paperback TV tie-in re-edition was also titled \\'\\'Game of Thrones\\'\\', excluding the indefinite article \"A\".\\n\\n==Plot==\\n\\n\\'\\'A Game of Thrones\\'\\' follows three principal storylines simultaneously.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '6ad052f1c1a94fdddf96cc27be2fba13'}>,\n",
+       "  <Document: {'content': 'A March 2013 paperback TV tie-in re-edition was also titled \\'\\'Game of Thrones\\'\\', excluding the indefinite article \"A\".\\n\\n==Plot==\\n\\n\\'\\'A Game of Thrones\\'\\' follows three principal storylines simultaneously.\\n\\n===In the Seven Kingdoms===\\nAt the beginning of the story, Eddard \"Ned\" Stark, the Warden of the North in the Seven Kingdoms, executes a deserter from the Night\\'s Watch, the military order that guards the Wall on the Seven Kingdoms\\' northern border. On the way home to his castle Winterfell, he discovers six orphaned direwolf pups, which are adopted by his six children. Upon the death of Lord Jon Arryn, the principal advisor to King Robert Baratheon, Robert recruits Ned to replace Arryn as \"Hand of the King\", and betroth Ned\\'s daughter Sansa to Robert\\'s son Joffrey. Ned accepts the position when he learns that Arryn\\'s widow Lysa believes Queen Cersei Lannister and her family poisoned Arryn. Shortly thereafter, Ned\\'s son Bran discovers Cersei having sex with her twin brother Jaime Lannister, who throws Bran from the tower to conceal their affair, paralyzing his legs.\\n\\nNed and his daughters Sansa and Arya depart for the capital city, King\\'s Landing. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '5a135d20e69aa364c65e8704c2a54dfc'}>,\n",
+       "  <Document: {'content': 'Shortly thereafter, Ned\\'s son Bran discovers Cersei having sex with her twin brother Jaime Lannister, who throws Bran from the tower to conceal their affair, paralyzing his legs.\\n\\nNed and his daughters Sansa and Arya depart for the capital city, King\\'s Landing. During the journey south, an altercation between Arya and Joffrey leads to Arya\\'s direwolf being driven away and Sansa\\'s direwolf being executed to appease the Lannisters. Upon arriving in King\\'s Landing to take his post as Hand, Ned finds that Robert is an ineffective king whose only interests are hunting, drinking and womanizing, leaving his Small Council to govern the realm.\\n\\nAt Winterfell, an assassin attempts to kill Bran, and Ned\\'s wife Catelyn travels to King\\'s Landing to bring word to Ned. Catelyn\\'s childhood friend, Petyr \"Littlefinger\" Baelish, implicates Tyrion Lannister, the dwarf brother of Cersei and Jaime, in the assassination attempt. En route back to Winterfell, Catelyn encounters Tyrion by chance, arrests him, and takes him to stand trial for the attempt on Bran\\'s life. In retaliation for Tyrion\\'s abduction, his father Lord Tywin Lannister sends soldiers to raid the Riverlands, Catelyn\\'s home region. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '94c21b1014caac9012ccdb53de3479f3'}>,\n",
+       "  <Document: {'content': \"En route back to Winterfell, Catelyn encounters Tyrion by chance, arrests him, and takes him to stand trial for the attempt on Bran's life. In retaliation for Tyrion's abduction, his father Lord Tywin Lannister sends soldiers to raid the Riverlands, Catelyn's home region. Tyrion regains his freedom by recruiting a mercenary named Bronn to defend him in trial by combat.\\n\\nNed investigates Jon Arryn's death and eventually discovers that Robert's legal heirs, including Joffrey, are in fact Cersei's children by Jaime, and that Jon Arryn was killed to conceal his discovery of their incest. Ned offers Cersei a chance to flee before he informs Robert, but she uses this chance to arrange Robert's death in a hunting accident and install Joffrey on the throne.  Ned enlists Littlefinger's help to challenge Joffrey's claim; but Littlefinger betrays him, resulting in Ned's arrest. Arya escapes the castle, but Sansa is taken hostage by the Lannisters.\\n\\nNed's eldest son Robb marches his army south in response to his father's arrest, and in order to relieve the threat on the Riverlands. To secure a strategically necessary bridge crossing, Catelyn negotiates a marital alliance between Robb and the notoriously unreliable House Frey. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '31938ace0bef496e0b2a764fbe5e9316'}>,\n",
+       "  <Document: {'content': 'To secure a strategically necessary bridge crossing, Catelyn negotiates a marital alliance between Robb and the notoriously unreliable House Frey. Robb defeats a Lannister army in the Riverlands, capturing Jaime. Tywin sends Tyrion back to King\\'s Landing to act as Hand of the King to Joffrey. When Ned is executed, Robb\\'s followers declare the North\\'s independence from the Seven Kingdoms, proclaiming Robb \"King in the North\".\\n\\n===On the Wall ===\\nThe prologue of the novel introduces the Wall: an ancient barrier of stone, ice, and magic, hundreds of feet high and hundreds of miles long, shielding the Seven Kingdoms from the northern wilderness. The Wall is manned by the Night\\'s Watch: an order of warriors sworn to serve there for life, defending the realm from the fabled Others, an ancient and hostile inhuman race, as well as from the human \"wildlings\" who live north of the Wall.\\n\\nJon Snow, Ned’s bastard son, is inspired by his uncle, Benjen Stark, to join the Night\\'s Watch, but becomes disillusioned when he discovers that its primary function is as a penal colony. Jon unites his fellow recruits against their harsh instructor and protects the cowardly but good-natured and intelligent Samwell Tarly. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '6d4dbfa626d0497853a1c10298824920'}>,\n",
+       "  <Document: {'content': 'Jon Snow, Ned’s bastard son, is inspired by his uncle, Benjen Stark, to join the Night\\'s Watch, but becomes disillusioned when he discovers that its primary function is as a penal colony. Jon unites his fellow recruits against their harsh instructor and protects the cowardly but good-natured and intelligent Samwell Tarly. Jon is appointed steward to the leader of the Watch, Lord Commander Jeor Mormont, making him a potential successor to Mormont. Benjen fails to return from an expedition north of the Wall. Six months later, the dead bodies of two men from his party are recovered; these re-animate as undead wights before being dispatched by Jon.\\n\\nWhen word of his father\\'s execution reaches Jon, he attempts to join Robb against the Lannisters, but is persuaded to remain loyal to the Watch. Mormont then declares his intention to march north to find Benjen, dead or alive, and to investigate rumors of a \"King-Beyond-the-Wall\" uniting the wildlings.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'a1bc114deb532c8199d2d03120b07fb4'}>,\n",
+       "  <Document: {'content': 'Mormont then declares his intention to march north to find Benjen, dead or alive, and to investigate rumors of a \"King-Beyond-the-Wall\" uniting the wildlings.\\n\\n===Across the narrow sea===\\nAcross the sea to the east of Westeros, the exiled prince Viserys Targaryen, son of the late \"mad king\" and rightful heir to House Targaryen, the royal house of Westeros before it was overthrown by Robert Baratheon, betroths his 13-year-old sister Daenerys to Khal Drogo, a warlord of the nomadic Dothraki, in exchange for the use of Drogo\\'s army to reclaim the throne of Westeros. Illyrio, a wealthy merchant who has been supporting the penniless Targaryens, gives Daenerys three petrified dragon eggs as a wedding gift. Jorah Mormont, a knight exiled from Westeros, joins Viserys as an adviser. Initially terrified of her new husband and his people, Daenerys eventually embraces her role as Drogo\\'s queen. Drogo, however, shows little interest in conquering Westeros before the birth of his child with Daenerys, and an impatient Viserys tries to browbeat his sister into coercing Drogo. When Viserys publicly threatens Daenerys, Drogo executes him by pouring molten gold on his head.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'a447290ba61e322277ffbdbddc315bd5'}>,\n",
+       "  <Document: {'content': \"Drogo, however, shows little interest in conquering Westeros before the birth of his child with Daenerys, and an impatient Viserys tries to browbeat his sister into coercing Drogo. When Viserys publicly threatens Daenerys, Drogo executes him by pouring molten gold on his head.\\n\\nAn assassin seeking King Robert's favor attempts to poison Daenerys and her unborn child, finally convincing Drogo to conquer Westeros. While sacking villages to fund the invasion of Westeros, Drogo is badly wounded, and Daenerys commands a captive folk healer to save him. The healer, angered by the Dothraki raids against her people, sacrifices Daenerys' unborn child to power the spell to save Drogo's life, which restores Drogo's physical health but leaves him in a persistent vegetative state.\\n\\nWith Drogo completely incapacitated and unable to lead, much of the Dothraki army departs to follow a new Khal. Daenerys smothers Drogo with a pillow, and the healer responsible for his condition is tied to Drogo's funeral pyre on her orders. She places her three dragon eggs on the pyre and enters it herself; she soon emerges, unburned, with three newly hatched dragons. Awe-struck, Jorah and the remaining Dothraki swear allegiance to her.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '80cf938890f2375f800efaed2ece2810'}>,\n",
+       "  <Document: {'content': 'She places her three dragon eggs on the pyre and enters it herself; she soon emerges, unburned, with three newly hatched dragons. Awe-struck, Jorah and the remaining Dothraki swear allegiance to her.\\n\\n== Themes ==\\nThroughout the novel, characters are often faced with decisions that match one redeemable trait against another. \\'\\'The Guardian\\'\\' outlines characters who are frequently \"forced to choose between their love for those close to them and the greater interests of honour, duty and the realm.\"In Westeros, Ned ultimately decides to venture south with Robert, leaving much of his family in Winterfell. At the Wall, Jon wrestles with the predicament of joining his half-brother Robb in rebellion or staying with his sworn brothers in the Night\\'s Watch. Daenerys has issue with the Dothraki treatment of those they conquered in Essos. These conflicts characters encounter oftentimes reflect inconsistent decision making. Catelyn initially is overwhelmed by grief and does not leave Bran\\'s bedside while he is comatose, ignoring her political responsibilities, choosing family over duty. But soon after, Catelyn leaves Bran and her family for Kings Landing to inform Ned of potential Lannister treason, effectively displaying a more duty fulfilling role. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '5ff68b1b8bb556e89df86c90b07ed6bb'}>,\n",
+       "  <Document: {'content': 'But soon after, Catelyn leaves Bran and her family for Kings Landing to inform Ned of potential Lannister treason, effectively displaying a more duty fulfilling role. Family, duty, and honor play major roles in conflicts that arise in the story arc, and qualities traditionally categorized as noble oppose each other in resolution. Character decision conflicts and consequence analysis are particular to how Martin wants to portray fantasy.\\n\\nMartin characteristically deviates from the traditional fantasy model and clear-cut lines of good versus evil. Martin reflects: \"I think the battle between good and evil is fought largely within the individual human heart, by the decisions that we make. It\\'s not like evil dresses up in black clothing and you know, they\\'re really ugly\". This viewpoint characterizes the book and is evident in the actions of several different families which frequently have conflicts with each other. The Starks\\' and Lannisters\\' conflict is a central component of the novel, and the reader receives points of view from both sides. Likewise, Daenerys\\' storyline develops around the Targaryen\\'s upheaval in Westeros, in which the Starks played a significant role. Martin argues: Having multiple viewpoints is crucial to the grayness of the characters. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'd37bf82e67b6f11a3694bccebafd5207'}>,\n",
+       "  <Document: {'content': 'Likewise, Daenerys\\' storyline develops around the Targaryen\\'s upheaval in Westeros, in which the Starks played a significant role. Martin argues: Having multiple viewpoints is crucial to the grayness of the characters. You have to be able to see the struggle from both sides, because real human beings in a war have all these processes of self-justification, telling ourselves why what we\\'re doing is the right thing.\\n\\n==Viewpoint characters==\\nEach chapter concentrates on the third person limited point of view of a single character; the book presents the perspective of eight main characters. Additionally, a minor character provides the prologue. Chapter headings indicate the perspective.\\n* Prologue: Will, a man of the Night\\'s Watch.\\n* Lord Eddard \"Ned\" Stark, Warden of the North and Lord of Winterfell, Hand of the King.\\n* Lady Catelyn Stark, of House Tully, wife of Eddard Stark.\\n* Sansa Stark, elder daughter of Eddard and Catelyn Stark.\\n* Arya Stark, younger daughter of Eddard and Catelyn Stark.\\n* Bran Stark, middle son of Eddard and Catelyn Stark.\\n* Jon Snow, illegitimate son of Eddard Stark.\\n* Tyrion Lannister, a dwarf, brother of the twins Queen Cersei and Jaime, son of Lord Tywin Lannister.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'da61d85f2e6b4653ccf41793d12c74f1'}>,\n",
+       "  <Document: {'content': \"* Jon Snow, illegitimate son of Eddard Stark.\\n* Tyrion Lannister, a dwarf, brother of the twins Queen Cersei and Jaime, son of Lord Tywin Lannister.\\n* Princess Daenerys Targaryen, Stormborn, the Princess of Dragonstone and heiress to the Targaryen throne after her older brother Viserys Targaryen.\\n\\nIn the later books, certain viewpoint characters are added while others are removed.\\n\\n==Writing==\\nMartin acknowledges several authors who lent their time and expertise during the writing of the novel: Sage Walker, Martin Wright, Melinda Snodgrass, Carl Keim, Bruce Baugh, Tim O'Brien, Roger Zelazny, Jane Lindskold, and Laura Mixon.\\n\\n==Editions==\\n\\nAccording to author George R.R. Martin, the Bantam 1996 edition was the true first. The HarperCollins/ Voyager 1996 edition was the British first edition. Its official publication date was earlier than that of the US Bantam edition, but Bantam went to print several months earlier to hand out copies at the American Boksellers Association (ABA). The novel has been translated into many languages and published in multiple editions in hardcover, paperback, e-book, and audio book form. In different languages, the number of books may not be the same. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'fa60b05c6c85a6a2c168bacbc90c6202'}>,\n",
+       "  <Document: {'content': 'The novel has been translated into many languages and published in multiple editions in hardcover, paperback, e-book, and audio book form. In different languages, the number of books may not be the same. In June 2000, Meisha Merlin published a limited edition of the book, fully illustrated by Jeffrey Jones.\\n\\n==Adaptations==\\n\\n\\'\\'A Game of Thrones\\'\\' and the subsequent novels in the \\'\\'A Song of Ice and Fire\\'\\' series have been adapted in a HBO television series, a comics series, several card, board and video games, and other media.\\n\\n==Reception==\\n\\'\\'A Game of Thrones\\'\\' has received critical acclaim. Lauren K. Nathan of the Associated Press wrote that the book \"grips the reader from Page One\" and is set in a \"magnificent\" fantasy world that is \"mystical, but still believable.\"Steve Perry told readers of \\'\\'The Oregonian\\'\\' that the plot is \"complex and fascinating\" and the book is \"rich and colorful\" with \"all the elements of a great fantasy novel\". Writing in \\'\\'The Washington Post\\'\\', John H. Riskind commented that \"many fans of sword-and-sorcery will enjoy the epic scope of this book\" but felt that the book \"suffers from one-dimensional characters and less than memorable imagery.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '3e6af0a9016329a642445f1d1a24ae5c'}>,\n",
+       "  <Document: {'content': 'Writing in \\'\\'The Washington Post\\'\\', John H. Riskind commented that \"many fans of sword-and-sorcery will enjoy the epic scope of this book\" but felt that the book \"suffers from one-dimensional characters and less than memorable imagery.\"Phyllis Eisenstein of the \\'\\'Chicago Sun-Times\\'\\' wrote that, although the book uses many generic fantasy tropes, Martin\\'s approach is \"so refreshingly human and intimate that it transcends them.\"She described it as \"an absorbing combination of the mythic, the sweepingly historical, and the intensely personal.\"John Prior, writing in the \\'\\'San Diego Union-Tribune\\'\\', called Martin\\'s writing \"strong and imaginative, with plenty of Byzantine intrigue and dynastic struggle\", and compared it to Robert Jordan\\'s \\'\\'Wheel of Time\\'\\' books, \"though much darker, with no comedy or romance to relieve the nastiness.\"On November 5, 2019, the \\'\\'BBC News\\'\\' listed \\'\\'A Game of Thrones\\'\\' on its list of the 100 most influential novels.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '75c3d8eda5fd7a1c24c5f9ea4e4029c'}>,\n",
+       "  <Document: {'content': \"On November 5, 2019, the ''BBC News'' listed ''A Game of Thrones'' on its list of the 100 most influential novels.\\n\\n==Awards and nominations==\\n* Locus Award – Best Novel (Fantasy) (Won) – (1997)\\n* World Fantasy Award – Best Novel (Nominated) – (1997)\\n* Hugo Award – Best Novella for ''Blood of the Dragon'' (Won) – (1997)\\n* Nebula Award – Best Novel (Nominated) – (1997)\\n* Ignotus Award – Best Novel (Foreign) (Won) – (2003)\\n\\n==References==\\n\\n==External links==\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '2c3df17510e19c515b32315c751d6d7d'}>,\n",
+       "  <Document: {'content': \"\\nThe soundtrack album of the fifth season of HBO series ''Game of Thrones'', titled '''''Game of Thrones: Season 5''''', was released digitally on June 9, 2015, and on CD on July 17, 2015. The album was composed by Ramin Djawadi.\\n\\n==Reception==\\nThe soundtrack received positive reviews from critics.\\n\\n==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* Czech Film Orchestra and Choir – primary artist\\n* Bradley Hanan Carter – featured artist\\n* D.B. Weiss – liner notes\\n\\n==References==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '4ee95a362a4f7e7272ad5031e523748f'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Blood of My Blood\\'\\'\\'\" is the sixth episode of the sixth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 56th overall. The episode was written by Bryan Cogman, and directed by Jack Bender.\\n\\nBran Stark and Meera Reed are rescued from the White Walkers by Benjen Stark. Samwell Tarly returns to his family\\'s home in Horn Hill, accompanied by Gilly and little Sam; Jaime Lannister attempts to rescue the Queen, Margaery Tyrell; Arya Stark defies the Faceless Men; and Daenerys Targaryen rides on Drogon and emboldens her newly acquired khalasar.\\n\\n\"Blood of My Blood\" was positively received by critics who praised the return of several notable characters, including Benjen Stark, Walder Frey and Edmure Tully. Further praise was given to other plot points, such as Samwell\\'s return to Horn Hill, and Arya\\'s decision to return to being a Stark rather than a disciple of the Many-Faced God. The episode title is a reference to a famous Dothraki saying used between a Khal and his bloodriders. Filming of Bran\\'s visions was put together precisely and also very carefully chosen. In the United States, the episode achieved a viewership of 6.71 million in its initial broadcast.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'b86de13b285afdab87d78db42e7cc63f'}>,\n",
+       "  <Document: {'content': \"Filming of Bran's visions was put together precisely and also very carefully chosen. In the United States, the episode achieved a viewership of 6.71 million in its initial broadcast.\\n\\n==Plot==\\n\\n===Beyond the Wall===\\nBran and Meera continue to flee the wights. Bran, still in his visions, witnesses several events: Jaime Lannister killing King Aerys Targaryen, a dragon flying over the Red Keep, his fall from Winterfell which left him paralyzed, the Night King transforming Craster's last son into a White Walker, the massacre at Hardhome, Ned Stark's beheading, the murder of Catelyn and Robb Stark at the Red Wedding, and wildfire exploding beneath King's Landing. As the wights close in, a black-clad rider appears and pulls Meera and Bran onto his horse, allowing them to escape.\\n\\nBran awakens to find the rider is his uncle, Benjen Stark, who had gone missing beyond the Wall. Benjen explains that he was stabbed by a White Walker during a ranging and was left to die and become another wight, but was saved by the Children of the Forest by being impaled with dragonglass. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'f402fc193d2f589b95e28dfd798144fd'}>,\n",
+       "  <Document: {'content': \"Benjen explains that he was stabbed by a White Walker during a ranging and was left to die and become another wight, but was saved by the Children of the Forest by being impaled with dragonglass. He tells Bran that he needs to become the Three-Eyed Raven and control his skinchanging before the Night King comes south.\\n\\n===At Horn Hill===\\nSamwell, Gilly, and Little Sam arrive at Horn Hill, the seat of House Tarly. Sam warns Gilly not to mention that she is a Wildling, due to his father Randyll's hatred of Wildlings. They are warmly greeted by Sam's mother Melessa and sister Talla.\\n\\nAt dinner, Randyll insults Sam's bookishness, weight, and lack of fighting prowess. Gilly defends Sam, mentioning that Sam killed a Thenn and a White Walker. When Sam's brother Dickon insists that White Walkers don't exist, Gilly declares that she saw the act herself, but in doing so reveals her Wildling heritage. Disgusted, Randyll further insults Sam and Gilly, prompting Melessa and Talla to leave the room with Gilly in anger. Randyll tells Sam that Gilly and Little Sam can remain at Horn Hill, but that he must never set foot in Horn Hill again. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '6d1806499a5317d563e14f6a21bb8ea8'}>,\n",
+       "  <Document: {'content': \"Randyll tells Sam that Gilly and Little Sam can remain at Horn Hill, but that he must never set foot in Horn Hill again. Sam bids farewell to Gilly, but then changes his mind and decides to bring her and Little Sam with him to the Citadel. As they leave, Sam takes House Tarly's ancestral Valyrian steel sword, Heartsbane, as well.\\n\\n===In Braavos===\\nArya returns to watch the play featuring Lady Crane. She sneaks backstage during the last act and poisons Lady Crane's rum. As she attempts to leave, Lady Crane stops her, and they discuss acting and Lady Crane's early life. Back in the dressing room, Arya stops Lady Crane from drinking her rum, and warns her that her rival Bianca wants her dead. The scene is witnessed by the Waif, who returns to tell Jaqen of Arya's failure. Jaqen gives the Waif permission to kill Arya, on the condition that Arya doesn't suffer. Meanwhile, Arya retrieves her sword, Needle, from the rocks where she had hidden it and goes into hiding.\\n\\n===In King's Landing===\\nThe High Sparrow, along with Tommen, prepares for Margaery's walk of atonement. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'bef29c74ce910360f883d2f9c9901c2f'}>,\n",
+       "  <Document: {'content': \"Meanwhile, Arya retrieves her sword, Needle, from the rocks where she had hidden it and goes into hiding.\\n\\n===In King's Landing===\\nThe High Sparrow, along with Tommen, prepares for Margaery's walk of atonement. The High Sparrow allows Tommen to visit his wife, where he discovers that Margaery has become a devout follower of the Faith of the Seven and repented for her sins.\\n\\nJaime and Mace Tyrell lead a company of Tyrell soldiers to the Great Sept of Baelor, where the High Sparrow is presenting Margaery to the people of King's Landing. Jaime orders the High Sparrow to release Margaery and Loras, threatening force against the Faith Militant. The High Sparrow declares that Margaery will not have to perform a walk of atonement, and instead presents Tommen, who announces that he has agreed to unite the Faith and the Crown. When Mace asks Olenna what this means, she bitterly replies it means the High Sparrow has won.\\n\\nIn the throne room, Tommen relieves Jaime of his duties on the Kingsguard as punishment for taking up arms against the Faith, to Jaime's dismay. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '87f2839ee49182eac93874f0c903af27'}>,\n",
+       "  <Document: {'content': \"In the throne room, Tommen relieves Jaime of his duties on the Kingsguard as punishment for taking up arms against the Faith, to Jaime's dismay. While speaking with Cersei, Jaime reveals that he has been given orders to oust the Blackfish from Riverrun, but that he would rather massacre the Faith Militant to release Tommen from the High Sparrow's influence. Cersei warns him that, if he does so, he will be killed, thus defeating the purpose of liberating Tommen. She counsels him to instead lead the Lannister army on Riverrun as a show of force to their enemies. Cersei expresses no concern about her upcoming trial, as it will be a trial by combat and she has chosen The Mountain as her champion. Cersei and Jaime then passionately kiss.\\n\\n===At the Twins===\\nLord Walder Frey receives word that Riverrun has been retaken by the Blackfish, and chastises his sons Lothar Frey and Black Walder Rivers, who had been ordered to hold the castle. Walder demands that the Tully stronghold be taken back, refusing to be humiliated by not being able to hold a single castle. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '2ceaf82547449ad8e91b756cc7ca081b'}>,\n",
+       "  <Document: {'content': 'Walder demands that the Tully stronghold be taken back, refusing to be humiliated by not being able to hold a single castle. He orders his men to bring in Edmure Tully, held as a prisoner of the Freys since the Red Wedding, and declares that they will use him to retake Riverrun.\\n\\n===In the Dothraki Sea===\\nWhile riding, Daenerys is discussing with Daario how many ships she needs for her army to cross the Narrow Sea when she notices an unusual gust of wind and rides out alone to investigate. Daario grows impatient, but as he prepares to follow her, Drogon flies over the khalasar, with Daenerys riding on his back. Daenerys lands nearby and rallies the Dothraki by stating that she chooses them all to be her bloodriders, rather than the traditional three chosen by khals. She then asks if they will cross the Narrow Sea with her and help her retake the Seven Kingdoms, which they unanimously agree to do.\\n\\n==Production==\\n\\n===Writing===\\nSeries veteran Bryan Cogman wrote the episode, his first of two episodes of the season.\\n\"Blood of My Blood\" was written by Bryan Cogman. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '356a021f86ca70b3feca20446b119017'}>,\n",
+       "  <Document: {'content': '==Production==\\n\\n===Writing===\\nSeries veteran Bryan Cogman wrote the episode, his first of two episodes of the season.\\n\"Blood of My Blood\" was written by Bryan Cogman. Cogman has been a writer for the series since its beginning, previously writing seven other episodes, as well as the subsequent episode. The title of the episode, \"Blood of My Blood\", is a reference to the famous Dothraki saying between a Khal and his bloodriders.\\n\\nIn an interview with \\'\\'Entertainment Weekly\\'\\', Cogman described some of the thought process he had when writing the reunion between Samwell and his family, saying, \"As much as Sam has gone through, I love exploring those family dynamics. His mother and sister and brother are all fundamentally decent people but his father is just a cold hearted bastard when it comes to his distant son. There\\'s a painful part of the scene where his father just unloads on him and tells him every hateful thing he ever thought about him and Sam can\\'t defend himself. We found that fascinating – Sam has killed a man, he\\'s killed a White Walker, he\\'s emerged as such a great hero, but he still can\\'t stand up to his dad.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '6c09038292a2a072f19a9193b19e972'}>,\n",
+       "  <Document: {'content': 'We found that fascinating – Sam has killed a man, he\\'s killed a White Walker, he\\'s emerged as such a great hero, but he still can\\'t stand up to his dad.\"Cogman noted, about the Arya storyline in the same interview, that he comes from a theater background, and that \"being able to comment on the show and the reactions to the show through the players were so much fun. The show is often accused of being gratuitous in all kinds of way – the violence and the bigness of the characters. It\\'s a huge operatic story. We\\'re able to lovingly spoof ourselves but also play with ideas about how audiences view the show, good and bad, and how a perspective of a story changes. Plus there’s the dramatic deliciousness of Arya watching her own life play out on stage.\"Weiss also spoke about Arya, saying \"We were excited to do the play within the play, and it\\'s a distorted fun house mirror representation of things we\\'ve already seen.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'b4e2ef245af6e0ef778fbbf8c9196558'}>,\n",
+       "  <Document: {'content': 'Weiss also spoke about Arya, saying \"We were excited to do the play within the play, and it\\'s a distorted fun house mirror representation of things we\\'ve already seen.\"Benioff continued, \"Part of Arya\\'s amusement is just that she knows that they\\'re getting so many details wrong, but she always regretted that she didn\\'t have a chance to watch Joffrey die, and now she gets to. It\\'s obviously a comic version of it, but that gives her great pleasure.\"Weiss also noted, \"Arya is slowly getting seduced by these performances, and Lady Crane, the actress that she\\'s charged with killing, this is somebody who like her has taken as her life\\'s work the job of becoming other people.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'c80ee8a91bab228230dffead911aa666'}>,\n",
+       "  <Document: {'content': 'Weiss also noted, \"Arya is slowly getting seduced by these performances, and Lady Crane, the actress that she\\'s charged with killing, this is somebody who like her has taken as her life\\'s work the job of becoming other people.\"In regards to the final scene of the episode with Daenerys Targaryen riding Drogon and emboldening her newly acquired khalasar, Benioff stated in the \"Inside the Episode\" featurette that the scene is a reflection of the speech that Khal Drogo gave before his death, with Benioff saying \"One of our favorite moments from season one was watching Khal Drogo deliver a speech to his gathered khalasar, that speech clearly lingered in Daenerys\\'s mind, and she\\'s echoing almost the exact same language when she\\'s talking to the Dothraki now. She\\'s basically telling them the promise that one of the great Khals had made years before and saying now is the time to live up to that promise and fulfill it.\"===Casting===\\nActor David Bradley (\\'\\'pictured\\'\\'), as well as Joseph Mawle and Tobias Menzies returned to the series after a nearly three-year absence.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '3049a32bc7d240cba229a5732506eb45'}>,\n",
+       "  <Document: {'content': '===Casting===\\nActor David Bradley (\\'\\'pictured\\'\\'), as well as Joseph Mawle and Tobias Menzies returned to the series after a nearly three-year absence.\\nThe episode saw the return of several characters from previous seasons and the introduction of new characters that had either been mentioned or had some connection to established characters. Joseph Mawle, who was previously cast to play Benjen Stark and was featured in three episodes in the show\\'s first season before disappearing (as he does in the \\'\\'A Song of Ice and Fire\\'\\' series that the show is based on). In an interview with \\'\\'Entertainment Weekly\\'\\', Bryan Cogman spoke of the re-introduction of Benjen and Mawle to the show saying \"It was great to have Joe Mawle back with us – it must have been a trip for him to step back into the character after so long – but he\\'s also decidedly not the Benjen of season 1. So that was fun to explore.\"Mawle had been interviewed in 2013, where he expressed his desire to return to the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'f0437239170ccbf1337fa74579c973df'}>,\n",
+       "  <Document: {'content': 'So that was fun to explore.\"Mawle had been interviewed in 2013, where he expressed his desire to return to the series.\\n\\nAnother return involved the story of the Riverlands with actor David Bradley returning to the show as Walder Frey, who last appeared in the aftermath of the Red Wedding, as well as Tobias Menzies as Edmure Tully, who also had not appeared since the Red Wedding episode \"The Rains of Castamere\", in the third season. Tim Plester, who plays one of Walder\\'s sons also returned but his other son, Lothar, was re-cast, with Daniel Tuite taking over the role.\\n\\nSeveral new actors were cast as Samwell Tarly\\'s family in Horn Hill. Samwell\\'s father, Randyll Tarly, had been referenced several times and had been described as \"cruel\" in his treatment of his oldest son. In an interview with \\'\\'The Hollywood Reporter\\'\\', John Bradley (Samwell Tarly) spoke about the introduction of his family to the series, stating, \"When you see Sam with his father and mother and brother especially, he starts to make sense. The character is contextualized. Why is his psyche the way it is? Why does he behave the way he does? ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'ffefd025cac423cb89b6637e69106dd3'}>,\n",
+       "  <Document: {'content': 'The character is contextualized. Why is his psyche the way it is? Why does he behave the way he does? Ever since you first saw him, he comes from this very sincere and heartfelt maternal love, and then there\\'s this monster. You can see why he\\'s so damaged. His emotional life has been pulled in so many different directions. He\\'s so incredibly confused.\"Actor James Faulkner was cast in the role of Randyll, with Samantha Spiro portraying Samwell\\'s mother and Freddie Stroma and Rebecca Benson portraying Samwell\\'s brother Dickon and sister Talla.\\n\\nFor Bran\\'s brief vision sequence at the beginning of the episode, actor David Rintoul was cast as King Aerys II Targaryen in a scene that had only been described to that point. It depicts the murder of Aerys, who is repeatedly yelling \"burn them all\", at the hands of Jaime Lannister.\\n\\n===Filming===\\nThe Cathedral in Girona, Spain stood in for the Great Sept, with some extending CGI altering the building.\\n\"Blood of My Blood\" was directed by Jack Bender, who also directed the previous episode \"The Door\", his directorial debut for the series. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '7594d8407c94d8aa8a30a429af61acb1'}>,\n",
+       "  <Document: {'content': '\"Blood of My Blood\" was directed by Jack Bender, who also directed the previous episode \"The Door\", his directorial debut for the series. Bender had been approached to direct for the series but declined due to the extensive time commitment involved in shooting, which he noted in an interview as having to commit to \"four-and-a-half to six months because of the enormity of the episodes\".\\n\\nFor the primary King\\'s Landing scene at the Great Sept, with Margaery being presented to the city by the High Sparrow, the grand staircase of the Girona Cathedral in Girona, Spain was used. The cathedral was constructed in the 11th century, and continued its expansion throughout the 12th and 13th centuries, as well as the 18th century. According to prior reports, filming took place over the course of approximately two weeks, with many different challenges involved in shooting at the location, including \"extras requiring medical attention due to exhaustion and dehydration,\" as well as the need for extensive security to close off certain areas, during filming, from the public. Parts of Braavos were also filmed in Girona. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '349ec6ab5d7c19e7de1f3d8b631562cb'}>,\n",
+       "  <Document: {'content': 'According to prior reports, filming took place over the course of approximately two weeks, with many different challenges involved in shooting at the location, including \"extras requiring medical attention due to exhaustion and dehydration,\" as well as the need for extensive security to close off certain areas, during filming, from the public. Parts of Braavos were also filmed in Girona. In nearby Canet de Mar, Catalonia, Spain, the Castell de Santa Florentina, an 11th-century medieval castle, was utilized for the castle of House Tarly.\\n\\nThe director of the episode, Bender, conducted an interview with \\'\\'The Wall Street Journal\\'\\' following the airing of the episode, and spoke about filming the play with Arya in Braavos, stating \"I staged the whole play, we did it, and the producers came in to watch the rehearsal, including all the fart jokes, all that stuff, some of which was written. So, after we watched the rehearsal, and the guys laughed a lot, I said, \\'My only concern is, am I mocking your brilliant show too much?\\'And they said, \\'No, do it more!\\'They\\'re completely unpretentious, David and Dan, and they loved it.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '7d94c51e0c58f9dcc51332b48b8c8a81'}>,\n",
+       "  <Document: {'content': 'So, after we watched the rehearsal, and the guys laughed a lot, I said, \\'My only concern is, am I mocking your brilliant show too much?\\'And they said, \\'No, do it more!\\'They\\'re completely unpretentious, David and Dan, and they loved it.\"He also noted that several other extra scenes were filmed, but were cut from the final version of the episode, noting that they will likely be released as deleted scenes on the DVD for the season.\\n\\nIn the behind the scenes video published by HBO following the airing of the fifth and sixth episodes, Benioff described Bran\\'s visions at the beginning of the episode as being something that was put together very precisely and purposefully, noting \"Even though some of those images flash by in just a fraction of a second each of them was very carefully chosen. And the Mad King was probably the most dramatic of those because we\\'ve been hearing about the Mad King from the very beginning of the show, but he\\'s never appeared on screen before. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'f82a15076e9cbe4feb7d8f517da31b93'}>,\n",
+       "  <Document: {'content': 'And the Mad King was probably the most dramatic of those because we\\'ve been hearing about the Mad King from the very beginning of the show, but he\\'s never appeared on screen before. And shooting it, you know, spending a lot of time on what ended up being maybe like a second and a half, couple seconds, of screen time.\"The new scenes of the Mad King, Aerys II Targaryen, are interspersed with previously shown footage of the White Walkers, as well as wildfire explosions, and other landmark moments from throughout the series, such as Ned Stark\\'s beheading, the Red Wedding, and Bran falling from the Broken Tower in the series premiere. In the \\'\\'A Song of Ice and Fire\\'\\' series by George R. R. Martin, the Mad King is described as having long, unkempt hair and beard, with nine inch long fingernails. The show\\'s version portrayed his look in a more tidy fashion, with David Rintoul playing the role of the Mad King.\\n\\nEllie Kendrick, who portrays Meera Reed in the series, in an interview with \\'\\'The Hollywood Reporter\\'\\' spoke about the re-introduction of Benjen Stark, or Coldhands Benjen, and working with Joseph Mawle, saying \"It was so fantastic. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'df31215869b9895304451c9f53b5dc25'}>,\n",
+       "  <Document: {'content': 'Ellie Kendrick, who portrays Meera Reed in the series, in an interview with \\'\\'The Hollywood Reporter\\'\\' spoke about the re-introduction of Benjen Stark, or Coldhands Benjen, and working with Joseph Mawle, saying \"It was so fantastic. And it\\'s interesting. If you\\'ve read the books, then you know about the Coldhands character, who was cut out of the Bran, Meera and Jojen storyline on the show. It was always interesting, having read the books, watching this amalgamation of story lines happen. Benjen is sort of like the Coldhands character in that he\\'s a slightly suspicious guy who is half-dead and half-alive with blue hands. I thought that was very cool, the way it happened. It was a nice marriage between the book and TV revelations rolled into one. I was very excited having him on set. I loved working with Joseph Mawle, and I loved having another member of our rapidly dwindling gang.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'ba355e741833c625b3f2ec048e87aaeb'}>,\n",
+       "  <Document: {'content': 'I was very excited having him on set. I loved working with Joseph Mawle, and I loved having another member of our rapidly dwindling gang.\"==Reception==\\n\\n===Ratings===\\n\"Blood of My Blood\" was viewed by 6.71 million American households on its initial viewing on HBO, which was a modest decrease from the previous week\\'s rating of 7.89 million viewers for the episode \"The Door\", likely due to the Memorial Day holiday in the United States. The episode also acquired a 3.25 rating in the 18–49 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was viewed by 2.684 million viewers on Sky Atlantic, making it the highest-rated broadcast that week on its channel. It also received 0.126 million timeshift viewers.\\n\\n===Critical reception===\\n\"Blood of My Blood\" was positively received by critics who praised the return of several notable characters from past seasons, including Benjen Stark and Walder Frey, as well as Samwell Tarly\\'s return home to Horn Hill and Arya\\'s decision to return to being a Stark, abandoning the teachings of the Faceless Men. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '149cd13e61259f937e90b3aaba28e002'}>,\n",
+       "  <Document: {'content': '===Critical reception===\\n\"Blood of My Blood\" was positively received by critics who praised the return of several notable characters from past seasons, including Benjen Stark and Walder Frey, as well as Samwell Tarly\\'s return home to Horn Hill and Arya\\'s decision to return to being a Stark, abandoning the teachings of the Faceless Men. It has received a 88% rating on the review aggregator website Rotten Tomatoes from 44 reviews with an average score of 7.6/10, a decrease over the previous episode. The site\\'s consensus reads \"Crucial power dynamics are reassessed and significant characters return in the skillfully plotted and gratifying \"Blood of My Blood\".\"Matt Fowler of IGN wrote in his review of the episode, \"Some sluggish stories took better turns this week – albeit sometimes by just ending things for now – while a book character many thought would never appear on the series made a notable splash, answering a big mystery that the novels hadn’t even revealed yet in the process. There were some great moments in \"Blood of My Blood\", but mostly the big returns made it possible to salivate for the payoffs (and possible characters) to come.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '1a8ec0584b9dc59c76360554c2f514d9'}>,\n",
+       "  <Document: {'content': 'There were some great moments in \"Blood of My Blood\", but mostly the big returns made it possible to salivate for the payoffs (and possible characters) to come.\"He also continued by noting, \"this installment probably speaks, more than any other episode so far this season, to the accelerated rate of payoffs we\\'re now getting that the show isn\\'t directly following the books.\"Fowler gave the episode an 8.6 out of 10. Laura Prudom of \\'\\'Variety\\'\\' also praised the pacing of the episode as well as the season\\'s payoffs, writing \"There\\'s a clarity of purpose in Season 6 that certainly gives the impression that we\\'re barreling towards a conclusion instead of just meandering through Dorne or killing time in Qarth.\"Emily VanDerWerff of \\'\\'Vox\\'\\' likewise praised the structure of the season, noting the recent absence of Ramsay Bolton as a positive, but continuing, \"Though it wasn\\'t as good as the last two episodes, \"Blood of My Blood\" continued the general upswing in quality \\'\\'Game of Thrones\\'\\' season six has undergone.\"VanDerWerff also wrote in her review, \"\"Blood of My Blood\" is another exciting hour of television, even if it\\'s a bit more piece move-y than, say, \"The Door\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '1f3af391340906369877fef03fcb52c1'}>,\n",
+       "  <Document: {'content': 'VanDerWerff also wrote in her review, \"\"Blood of My Blood\" is another exciting hour of television, even if it\\'s a bit more piece move-y than, say, \"The Door\". The revelations are milder, the characters mostly talk about what they\\'re going to do next, and the stories go from Point A to Point B, instead of Point X, Y, or Z. But there\\'s still plenty going on that\\'s worth checking out.\"Comparing the episode to \"Book of the Stranger\", Jeremy Egner of \\'\\'The New York Times\\'\\' said, \"The problem with emerging naked from a flaming temple is that it\\'s hard to top. It\\'s hard to top, that is, unless you happen to have a dragon with a flair for dramatic timing.\"Aaron Riccio of \\'\\'Slant Magazine\\'\\' criticized the episode, writing in his review \"The largest problem with tonight\\'s episode is that it either changes course so abruptly or restates certain theses so redundantly that it feels like a bit of a tease, especially to those not invested in Samwell Tarly\\'s storyline.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': '1da4c40cf5379069697fa967a2ed5ba3'}>,\n",
+       "  <Document: {'content': 'Aaron Riccio of \\'\\'Slant Magazine\\'\\' criticized the episode, writing in his review \"The largest problem with tonight\\'s episode is that it either changes course so abruptly or restates certain theses so redundantly that it feels like a bit of a tease, especially to those not invested in Samwell Tarly\\'s storyline.\"===Accolades===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron\\n\\n2017\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Blood of My Blood\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': '6ea833652206dd2b5f2492dc0c16a66f'}>,\n",
+       "  <Document: {'content': \"\\n\\nThe fifth season of the fantasy drama television series ''Game of Thrones'' premiered on HBO on April 12, and concluded on June 14, 2015. It was broadcast on Sunday at 9:00 pm in the United States, consisting of 10 episodes, each running approximately 50–60 minutes. The season primarily adapts material from ''A Feast for Crows'' and ''A Dance with Dragons'', the fourth and fifth novels in George R. R. Martin's ''A Song of Ice and Fire'' series, though it also uses elements from the third novel, ''A Storm of Swords'', as well as the upcoming sixth novel ''The Winds of Winter''. It also contains original content not found in Martin's novels. The series is adapted for television by David Benioff and D. B. Weiss.\\n\\nLike previous seasons in ''Game of Thrones'', the fifth season continues storylines primarily set within the fictional land of Westeros, whereas one storyline is set in another continent, Essos. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'c2626c5cc086558e8ec18b730be32f67'}>,\n",
+       "  <Document: {'content': \"Like previous seasons in ''Game of Thrones'', the fifth season continues storylines primarily set within the fictional land of Westeros, whereas one storyline is set in another continent, Essos. The season continues the aftermath of the deaths of King Joffrey Baratheon and Tywin Lannister, as Tywin's son Tyrion, now on the run after killing his father, travel to Essos, where he crosses paths with Daenerys Targaryen, who is struggling to balance her political ambitions with her dragons. Erstwhile, the Lannister family continues to struggle coping with Tywin and Joffrey's deaths while encountering new enemies; Jon Snow and the Night's Watch now serve under Stannis Baratheon; the remaining Stark children face new hardships in their respective journeys.\\n\\nHBO ordered the fifth season on April 8, 2014, together with the sixth season, which began filming in July 2014. The season was filmed primarily in Ireland, Northern Ireland, Croatia and Spain.\\n\\n''Game of Thrones'' features a large ensemble cast, including Peter Dinklage, Nikolaj Coster-Waldau, Lena Headey, Emilia Clarke, and Kit Harington. The season introduced a number of new cast members, including Jonathan Pryce and Alexander Siddig.\\n\\nCritics praised the show's production values and cast, giving specific accolades for Dinklage's portrayal of Tyrion Lannister. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '5173233b73ccd2eda66dda4b4430705'}>,\n",
+       "  <Document: {'content': \"The season introduced a number of new cast members, including Jonathan Pryce and Alexander Siddig.\\n\\nCritics praised the show's production values and cast, giving specific accolades for Dinklage's portrayal of Tyrion Lannister. Viewership yet again rose compared to the previous season. This season set a Guinness World Record for winning the highest number of Emmy Awards for a series in a single season and year, winning 12 out of 24 nominations, including Outstanding Drama Series.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '397228d322efd561c1e2e556c81797'}>,\n",
+       "  <Document: {'content': 'This season set a Guinness World Record for winning the highest number of Emmy Awards for a series in a single season and year, winning 12 out of 24 nominations, including Outstanding Drama Series.\\n\\n==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n* Peter Dinklage as Tyrion Lannister\\n* Nikolaj Coster-Waldau as Jaime Lannister\\n* Lena Headey as Cersei Lannister\\n* Emilia Clarke as Daenerys Targaryen\\n* Kit Harington as Jon Snow\\n* Aidan Gillen as Petyr \"Littlefinger\" Baelish\\n* Charles Dance as Tywin Lannister\\n* Natalie Dormer as Margaery Tyrell\\n* Stephen Dillane as Stannis Baratheon\\n* Liam Cunningham as Davos Seaworth\\n* Carice van Houten as Melisandre\\n* John Bradley as Samwell Tarly\\n* Sophie Turner as Sansa Stark\\n* Kristofer Hivju as Tormund Giantsbane\\n\\n* Hannah Murray as Gilly\\n* Conleth Hill as Varys\\n* Gwendoline Christie as Brienne of Tarth\\n* Michiel Huisman as Daario Naharis\\n* Nathalie Emmanuel as Missandei\\n* Dean-Charles Chapman as Tommen Baratheon\\n* Indira Varma as Ellaria Sand\\n* Maisie Williams as Arya Stark\\n* Jerome Flynn as Bronn\\n* Tom Wlaschiha as Jaqen H\\'ghar\\n* Alfie Allen as Theon Greyjoy / \"Reek\"\\n* Michael McElhatton as Roose Bolton\\n* Iwan Rheon as Ramsay Bolton\\n* Iain Glen as Jorah Mormont\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 5. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '3efbd816e07bb84d7b114611c529693f'}>,\n",
+       "  <Document: {'content': '==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n* Peter Dinklage as Tyrion Lannister\\n* Nikolaj Coster-Waldau as Jaime Lannister\\n* Lena Headey as Cersei Lannister\\n* Emilia Clarke as Daenerys Targaryen\\n* Kit Harington as Jon Snow\\n* Aidan Gillen as Petyr \"Littlefinger\" Baelish\\n* Charles Dance as Tywin Lannister\\n* Natalie Dormer as Margaery Tyrell\\n* Stephen Dillane as Stannis Baratheon\\n* Liam Cunningham as Davos Seaworth\\n* Carice van Houten as Melisandre\\n* John Bradley as Samwell Tarly\\n* Sophie Turner as Sansa Stark\\n* Kristofer Hivju as Tormund Giantsbane\\n\\n* Hannah Murray as Gilly\\n* Conleth Hill as Varys\\n* Gwendoline Christie as Brienne of Tarth\\n* Michiel Huisman as Daario Naharis\\n* Nathalie Emmanuel as Missandei\\n* Dean-Charles Chapman as Tommen Baratheon\\n* Indira Varma as Ellaria Sand\\n* Maisie Williams as Arya Stark\\n* Jerome Flynn as Bronn\\n* Tom Wlaschiha as Jaqen H\\'ghar\\n* Alfie Allen as Theon Greyjoy / \"Reek\"\\n* Michael McElhatton as Roose Bolton\\n* Iwan Rheon as Ramsay Bolton\\n* Iain Glen as Jorah Mormont\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 5. They are listed by the region in which they first appear:\\n\\n====At and beyond the Wall====\\n* Peter Vaughan as Maester Aemon\\n* Owen Teale as Alliser Thorne\\n* Brian Fortune as Othell Yarwyck\\n* Michael Condron as Bowen Marsh\\n* Dominic Carter as Janos Slynt\\n* Ben Crompton as Eddison Tollett\\n* J. J. Murphy as Denys Mallister\\n* Will O\\'Connell as Todder\\n* Brenock O\\'Connor as Olly\\n* Ciarán Hinds as Mance Rayder\\n* Birgitte Hjort Sørensen as Karsi\\n* Zahary Baharov as Loboda\\n* Ross O\\'Hennessy as the Lord of Bones\\n* Murray McArthur as Dim Dalba\\n* Ian Whyte as Wun Wun\\n* Ali Lyons as Johnna\\n* Richard Brake as the Night King\\n\\n====In the North====\\n* Elizabeth Webster as Walda Bolton\\n* Tara Fitzgerald as Selyse Florent\\n* Kerry Ingram as Shireen Baratheon\\n* Charlotte Hope as Myranda\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Rupert Vansittart as Yohn Royce\\n* Daniel Portman as Podrick Payne\\n\\n====In Dorne====\\n* Alexander Siddig as Doran Martell\\n* Toby Sebastian as Trystane Martell\\n* Keisha Castle-Hughes as Obara Sand\\n* Rosabell Laurenti Sellers as Tyene Sand\\n* Jessica Henwick as Nymeria Sand\\n* Nell Tiger Free as Myrcella Baratheon\\n* DeObia Oparei as Areo Hotah\\n\\n====In King\\'s Landing====\\n* Julian Glover as Grand Maester Pycelle\\n* Anton Lesser as Qyburn\\n* Ian Gelder as Kevan Lannister\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Finn Jones as Loras Tyrell\\n* Diana Rigg as Olenna Tyrell\\n* Eugene Simon as Lancel Lannister\\n* Ian Beattie as Meryn Trant\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Paul Bentley as the High Septon\\n* Jonathan Pryce as the High Sparrow\\n* Hannah Waddingham as Septa Unella\\n* Will Tudor as Olyvar\\n* Josephine Gillan as Marei\\n\\n====In Braavos====\\n* Mark Gatiss as Tycho Nestoris\\n* Gary Oliver as Ternesio Terys\\n* Oengus MacNamara as the thin man\\n* Faye Marsay as the Waif\\n* Sarine Sofair as Lhara\\n* Hattie Gotobed as Ghita\\n\\n====In Slaver\\'s Bay====\\n* Ian McElhinney as Barristan Selmy\\n* Jacob Anderson as Grey Worm\\n* Reece Noi as Mossador\\n* Joel Fry as Hizdahr zo Loraq\\n* Enzo Cilenti as Yezzan zo Qaggaz\\n* Adewale Akinnuoye-Agbaje as Malko\\n* Meena Rayann as Vala\\n\\n==Production==\\n\\n===Crew===\\nThe production team during a location scouting at Klis Fortress, Croatia prior to season 5.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8116723ea144970ce89242830c72b80a'}>,\n",
+       "  <Document: {'content': \"They are listed by the region in which they first appear:\\n\\n====At and beyond the Wall====\\n* Peter Vaughan as Maester Aemon\\n* Owen Teale as Alliser Thorne\\n* Brian Fortune as Othell Yarwyck\\n* Michael Condron as Bowen Marsh\\n* Dominic Carter as Janos Slynt\\n* Ben Crompton as Eddison Tollett\\n* J. J. Murphy as Denys Mallister\\n* Will O'Connell as Todder\\n* Brenock O'Connor as Olly\\n* Ciarán Hinds as Mance Rayder\\n* Birgitte Hjort Sørensen as Karsi\\n* Zahary Baharov as Loboda\\n* Ross O'Hennessy as the Lord of Bones\\n* Murray McArthur as Dim Dalba\\n* Ian Whyte as Wun Wun\\n* Ali Lyons as Johnna\\n* Richard Brake as the Night King\\n\\n====In the North====\\n* Elizabeth Webster as Walda Bolton\\n* Tara Fitzgerald as Selyse Florent\\n* Kerry Ingram as Shireen Baratheon\\n* Charlotte Hope as Myranda\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Rupert Vansittart as Yohn Royce\\n* Daniel Portman as Podrick Payne\\n\\n====In Dorne====\\n* Alexander Siddig as Doran Martell\\n* Toby Sebastian as Trystane Martell\\n* Keisha Castle-Hughes as Obara Sand\\n* Rosabell Laurenti Sellers as Tyene Sand\\n* Jessica Henwick as Nymeria Sand\\n* Nell Tiger Free as Myrcella Baratheon\\n* DeObia Oparei as Areo Hotah\\n\\n====In King's Landing====\\n* Julian Glover as Grand Maester Pycelle\\n* Anton Lesser as Qyburn\\n* Ian Gelder as Kevan Lannister\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Finn Jones as Loras Tyrell\\n* Diana Rigg as Olenna Tyrell\\n* Eugene Simon as Lancel Lannister\\n* Ian Beattie as Meryn Trant\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Paul Bentley as the High Septon\\n* Jonathan Pryce as the High Sparrow\\n* Hannah Waddingham as Septa Unella\\n* Will Tudor as Olyvar\\n* Josephine Gillan as Marei\\n\\n====In Braavos====\\n* Mark Gatiss as Tycho Nestoris\\n* Gary Oliver as Ternesio Terys\\n* Oengus MacNamara as the thin man\\n* Faye Marsay as the Waif\\n* Sarine Sofair as Lhara\\n* Hattie Gotobed as Ghita\\n\\n====In Slaver's Bay====\\n* Ian McElhinney as Barristan Selmy\\n* Jacob Anderson as Grey Worm\\n* Reece Noi as Mossador\\n* Joel Fry as Hizdahr zo Loraq\\n* Enzo Cilenti as Yezzan zo Qaggaz\\n* Adewale Akinnuoye-Agbaje as Malko\\n* Meena Rayann as Vala\\n\\n==Production==\\n\\n===Crew===\\nThe production team during a location scouting at Klis Fortress, Croatia prior to season 5.\\nThe writing staff for the fifth season includes executive producers and showrunners David Benioff and D. B. Weiss, producer Bryan Cogman, and Dave Hill, who was promoted to staff writer this season after previously working as an assistant to Benioff and Weiss. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'a37ecb4dae71ca4540112309b8b72f0e'}>,\n",
+       "  <Document: {'content': \"The writing staff for the fifth season includes executive producers and showrunners David Benioff and D. B. Weiss, producer Bryan Cogman, and Dave Hill, who was promoted to staff writer this season after previously working as an assistant to Benioff and Weiss. Author George R. R. Martin, who had written one episode for each of the first four seasons, did not write an episode for the fifth season as he was working to finish writing the sixth novel of the series, ''The Winds of Winter''. The directing staff for the fifth season is Michael Slovis (episodes 1 and 2), Mark Mylod (episodes 3 and 4), Jeremy Podeswa (episodes 5 and 6), Miguel Sapochnik (episodes 7 and 8), and David Nutter (episodes 9 and 10). Nutter is the only returning director with the rest being first-time ''Game of Thrones'' directors.\\n\\n===Writing===\\nThis season features more original material than previous seasons. The deviations from Martin's novels are in part attributable to the way the television series has covered most of the novel series' published material and in part to the directors' opinions of the actors' abilities. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '4c03fc99666517ccf8f9e9abb957c198'}>,\n",
+       "  <Document: {'content': 'The deviations from Martin\\'s novels are in part attributable to the way the television series has covered most of the novel series\\' published material and in part to the directors\\' opinions of the actors\\' abilities. For example, Sansa Stark arrives at Winterfell to marry Ramsay Bolton in \"High Sparrow,\" a plotline that had been given to a minor character in the novels. In an interview, show writer David Benioff explains that Sophie Turner\\'s development as an actress was one of the reasons that they decided to give her character more dramatic scenes, saying, \"Even if child actors come in and do a great audition, it’s so hard to know if they’re going to quite literally grow into the parts. With Sansa and Arya in particular, their storylines have become quite dark. It was such a gamble and the fact that they’ve both become such great wonderful actresses is a bit of a miracle.\"Bryan Cogman added that it made more sense to give the Winterfell storyline to a proven actress who was already popular with viewers than to bring in a new character.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '1d37a4128a872e3bcf5990cd3cc0b976'}>,\n",
+       "  <Document: {'content': 'Bryan Cogman added that it made more sense to give the Winterfell storyline to a proven actress who was already popular with viewers than to bring in a new character.\\n\\nOther changes include the portrayal of Tommen as old enough to interact with Margaery and the Faith Militant as an adult, the speeding up or streamlining of several subplots, and the introduction of fewer new characters. According to Benioff and Weiss, \"We felt we\\'d capsize the show if we put in every single character from the books.\"Critics particularly liked the decision to have Tyrion actually meet Daenerys at the end of \"The Gift,\" which has yet to happen as of \\'\\'A Dance with Dragons\\'\\'. David Benioff cited the television adaptation\\'s faster pace as part of the rationale behind this decision.\\n\\n===Filming===\\nThe Alcázar of Seville stands in for the Water Gardens of Dorne.\\nFilming for the fifth season began in July 2014 in Belfast and ended in December 2014. Locations in Northern Ireland included Titanic Studios, Belfast and the cliff edge of Binevenagh Mountain. The Winterfell sets were in the village of Moneyglass.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'd9e9e9bcccfe9680ddd3eb3c0d3214d8'}>,\n",
+       "  <Document: {'content': 'Locations in Northern Ireland included Titanic Studios, Belfast and the cliff edge of Binevenagh Mountain. The Winterfell sets were in the village of Moneyglass.\\n\\nSome of the scenes that take place in the principality of Dorne were filmed in Spain, beginning in October 2014. Locations explored for the production included the Alcázar of Seville and the University of Osuna. On October 14, some scenes were filmed on the Roman bridge of Córdoba. Benioff and Weiss said that season 5 would include flashbacks, which they had previously avoided, and that Córdoba would represent the city of Volantis.\\n\\nCersei\\'s \"walk of atonement\" from \\'\\'A Dance with Dragons\\'\\' was filmed in Dubrovnik, Croatia, in the Stradun street between the Dubrovnik Cathedral and the Sponza Palace in early October 2014. The production reportedly employed a body double for Lena Headey for part of the scene in which Cersei appears naked.\\nMeereen scenes were once again shot in Diocletian\\'s Palace in Split, and on Klis Fortress north of Split.\\n\\nA town on the coast of the Bay of Kaštela in Croatia, the 16th century Kaštel Gomilica stood in for some parts of Braavos.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '2a5c668cf8f966adc4de33bb07f7cd4c'}>,\n",
+       "  <Document: {'content': \"A town on the coast of the Bay of Kaštela in Croatia, the 16th century Kaštel Gomilica stood in for some parts of Braavos.\\n\\nA very small portion of one episode was filmed in Calgary, Alberta, Canada: the scenes featuring Jon Snow's wolf Ghost (played by animal actor Quigly) who also appears in season 6.\\n\\n===Casting===\\nJonathan Pryce plays the High Sparrow.\\nThe fifth season adds previously recurring actors Indira Varma (Ellaria Sand), Michiel Huisman (Daario Naharis), Nathalie Emmanuel (Missandei), Dean-Charles Chapman (Tommen Baratheon), Tom Wlaschiha (Jaqen H'ghar) and Michael McElhatton (Roose Bolton) to the series' main cast.\\n\\nIn the fifth season, the region of Dorne is introduced as a location. Alexander Siddig joins the cast as Doran Martell, the ruling Prince of Dorne, and elder brother of Oberyn Martell, while his son Trystane Martell is portrayed by Toby Sebastian. The role of Cersei's daughter Myrcella Baratheon, who is Doran's ward and betrothed to Trystane is played by Nell Tiger Free. The role was portrayed by Aimee Richardson in the first two seasons. DeObia Oparei portrays Areo Hotah, the captain of the Dornish palace guard. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'bcdc1b81e723d7a8ec7dff371cec96fc'}>,\n",
+       "  <Document: {'content': 'The role was portrayed by Aimee Richardson in the first two seasons. DeObia Oparei portrays Areo Hotah, the captain of the Dornish palace guard. The three eldest bastard daughters of Oberyn Martell (collectively known as the \"Sand Snakes\") are portrayed by Keisha Castle-Hughes (Obara Sand), Jessica Henwick (Nymeria Sand), and Rosabell Laurenti Sellers (Tyene Sand).\\n\\nIn King\\'s Landing, Jonathan Pryce joins the cast as the High Sparrow, the leader of the militant faithful in King\\'s Landing, while Hannah Waddingham portrays Septa Unella, one of the \"Most Devout\", the ruling council of the Faith of the Seven. Nell Williams  was cast as a young Cersei Lannister seen in flashbacks, while Jodhi May was cast as Maggy the Frog, a fortune-teller.\\n\\nAcross the Narrow Sea, Enzo Cilenti joins as Yezzan, a Yunkish nobleman and slave trader, while Adewale Akinnuoye-Agbaje was cast as Malko, a slaver, who does not appear in the novels. At Castle Black and beyond the Wall, new cast members include Michael Condron as Bowen Marsh, First Steward of the Night\\'s Watch. J. J. Murphy, who was cast as Night\\'s Watch officer Denys Mallister, died in August 2014 shortly after filming his first scenes; his role was not recast. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '6d5dbb71ef48dd9838d4828f15f23bf6'}>,\n",
+       "  <Document: {'content': 'J. J. Murphy, who was cast as Night\\'s Watch officer Denys Mallister, died in August 2014 shortly after filming his first scenes; his role was not recast. One role that was recast for season 5 was Ross O\\'Hennessy as the Lord of Bones. O\\'Hennessy replaces Edward Dogliani who was last seen in the season 3 premiere \"Valar Dohaeris\". Charles Dance, whose character died in the previous season, returned for one episode to portray Tywin Lannister\\'s corpse.  Isaac Hempstead-Wright (Bran Stark), Kristian Nairn (Hodor) and Ellie Kendrick (Meera Reed) are absent this season because their part in the story has reached the end of \\'\\'A Dance with Dragons\\'\\'.\\n\\n===Music===\\n\\nThe soundtrack for the season was released digitally on June 9, 2015, and on CD on July 17, 2015.\\n\\n==Reception==\\n\\n===Critical response===\\nOn Metacritic, the season (based on the first four episodes) has a score of 91 out of 100 based on 29 reviews, indicating \"universal acclaim\". On Rotten Tomatoes, the fifth season has a 93% approval rating from 50 critics with an average rating of 8.61 out of 10. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '787d472cb3c90791ae12e65f7cc245c3'}>,\n",
+       "  <Document: {'content': 'On Rotten Tomatoes, the fifth season has a 93% approval rating from 50 critics with an average rating of 8.61 out of 10. The site\\'s critical consensus reads, \"Bloody action and extreme power plays return full throttle, as \\'\\'Game of Thrones\\'\\' enjoys a new-found liberation from the world of the source material, resulting in more unexpected thrills.\"One notable exception is the sixth episode, \"Unbowed, Unbent, Unbroken\", which was heavily criticized due to the showrunners\\' decision to have a well-liked character suffer a sexual assault. It received a rating of 54% on Rotten Tomatoes, lower than any previous episode of the show. The eighth episode, \"Hardhome\", received significant praise and was immediately regarded as one of the best episodes in the series. According to \\'\\'Business Insider\\'\\', \"Despite the attacks, \\'\\'Game of Thrones\\'\\' remains a steady hit.\"Season 5 featured more significant changes from the novels. Some of the changes were more well-received than others (the sexual assault scene was heavily criticized and caused great controversy). The various plotlines for the season also differed significantly in reception. The Dornish subplot received an overwhelmingly negative reception. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'c2f7ec7b363203870aa5d82ab00b5ea4'}>,\n",
+       "  <Document: {'content': 'Some of the changes were more well-received than others (the sexual assault scene was heavily criticized and caused great controversy). The various plotlines for the season also differed significantly in reception. The Dornish subplot received an overwhelmingly negative reception. In particular, the hyped Sand Snakes were maligned by critics and fans alike, as many found them cheesy and unlikeable. In contrast, Tyrion\\'s storyline was highly praised. Many fans felt that his subplot progressed overly slowly in the novels, and his meeting with Daenerys was particularly lauded.\\n\\n===Ratings===\\n\\nThe gross viewing figure per episode for the show, which include streaming, DVR recordings and repeat showings, averaged at 20 million this season.\\n\\nLive +7 ratings were not available, so Live +3 ratings have been used instead.\\n\\n\\'\\'Business Insider\\'\\' noted a significant difference in the ratings between this season and previous seasons. The typical pattern involves \"Solid premiere viewings followed by a slow but steady gain of momentum through to the finale. Historically, the finale episodes of each season have beaten the premiere for total number of viewers\". However, this season showed a significant decline from 8 million viewers at the premiere to 5.4 million at the seventh episode, \"The Gift\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'ea75daf400fa7ece48a757c12602ef01'}>,\n",
+       "  <Document: {'content': 'However, this season showed a significant decline from 8 million viewers at the premiere to 5.4 million at the seventh episode, \"The Gift\". \\'\\'Business Insider\\'\\' cites two likely reasons for the lower ratings: backlash over the rape scene in \"Unbowed, Unbent, Unbroken\", one of many deviations from the novels throughout the season that \"have upset fans,\" and increased online streaming through HBO Now. It also acknowledged a possible effect of the first four episodes leaking online before the season premiere and the decision to air on Memorial Day weekend in the United States (a weekend that had been skipped in previous seasons). \\'\\'Business Insider\\'\\' reached out to HBO for comment, HBO responded that it is seeing an increase in overall viewer numbers this season taking into account streaming services, which are not included in Nielsen ratings. The dip in ratings did not extend to the eighth and ninth episodes, \"Hardhome,\" and \"The Dance of Dragons\", which were both seen by over 7 million viewers. The season finale, \"Mother\\'s Mercy\" was seen by 8.11 million people, setting a new record and making it the most watched episode of the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'd25ac3839fb3c8335bb167138d352853'}>,\n",
+       "  <Document: {'content': 'The season finale, \"Mother\\'s Mercy\" was seen by 8.11 million people, setting a new record and making it the most watched episode of the series.\\n\\n===Accolades===\\n\\nFor the 5th Critics\\' Choice Television Awards, the series was nominated for Best Drama Series. For the 31st TCA Awards, the series was nominated for Program of the Year and Outstanding Achievement in Drama. For the 67th Primetime Emmy Awards, the series received 24 nominations, the most of any series. It won 12 awards, including Outstanding Drama Series, Peter Dinklage for Outstanding Supporting Actor in a Drama Series, David Benioff and D. B. Weiss for Outstanding Writing for a Drama Series for \"Mother\\'s Mercy\", and David Nutter for Outstanding Directing for a Drama Series for \"Mother\\'s Mercy\". Nominations included Lena Headey and Emilia Clarke both for Outstanding Supporting Actress in a Drama Series, Diana Rigg for Outstanding Guest Actress in a Drama Series, and Jeremy Podeswa for Outstanding Directing for a Drama Series for \"Unbowed, Unbent, Unbroken\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'dba7f59b098f34d9642d65ca2088b371'}>,\n",
+       "  <Document: {'content': 'Nominations included Lena Headey and Emilia Clarke both for Outstanding Supporting Actress in a Drama Series, Diana Rigg for Outstanding Guest Actress in a Drama Series, and Jeremy Podeswa for Outstanding Directing for a Drama Series for \"Unbowed, Unbent, Unbroken\".\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n5th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n31st TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nSophie Turner\\n\\n67th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter for \"Mother\\'s Mercy\"\\n\\nJeremy Podeswa for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\n67th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\nAnette Haellmigk for \"Sons of the Harpy\"\\n\\nRob McLachlan for \"The Dance of Dragons\"\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham for \"The Dance of Dragons\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount for Mother\\'s Mercy\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews for \"Mother\\'s Mercy\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower for \"Hardhome\"\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \"Hardhome\"\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit for \"Hardhome\"\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau for \"The Dance of Dragons\"\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nE! ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '7391e0412687b3fa706579ba1a90485c'}>,\n",
+       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n5th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n31st TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nSophie Turner\\n\\n67th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter for \"Mother\\'s Mercy\"\\n\\nJeremy Podeswa for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\n67th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\nAnette Haellmigk for \"Sons of the Harpy\"\\n\\nRob McLachlan for \"The Dance of Dragons\"\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham for \"The Dance of Dragons\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount for Mother\\'s Mercy\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews for \"Mother\\'s Mercy\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower for \"Hardhome\"\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \"Hardhome\"\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit for \"Hardhome\"\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau for \"The Dance of Dragons\"\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nE! Online Best. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'a65be222eee4271207bf1323ee7636d7'}>,\n",
+       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n5th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n31st TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nSophie Turner\\n\\n67th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter for \"Mother\\'s Mercy\"\\n\\nJeremy Podeswa for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\n67th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\nAnette Haellmigk for \"Sons of the Harpy\"\\n\\nRob McLachlan for \"The Dance of Dragons\"\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham for \"The Dance of Dragons\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount for Mother\\'s Mercy\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews for \"Mother\\'s Mercy\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower for \"Hardhome\"\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \"Hardhome\"\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit for \"Hardhome\"\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau for \"The Dance of Dragons\"\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nE! Online Best. Ever. TV. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '859d3ca71dd4fd11a3186d84f79ce774'}>,\n",
+       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n5th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n31st TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nSophie Turner\\n\\n67th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter for \"Mother\\'s Mercy\"\\n\\nJeremy Podeswa for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\n67th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\nAnette Haellmigk for \"Sons of the Harpy\"\\n\\nRob McLachlan for \"The Dance of Dragons\"\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham for \"The Dance of Dragons\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount for Mother\\'s Mercy\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews for \"Mother\\'s Mercy\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower for \"Hardhome\"\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \"Hardhome\"\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit for \"Hardhome\"\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau for \"The Dance of Dragons\"\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nE! Online Best. Ever. TV. Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Post Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Color Grading\\n\\nJoe Finley for \"Hardhome\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Hardhome\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani for \"The Dance of Dragons\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"Hardhome\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley for \"Hardhome\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n20th Satellite Awards\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\nIGN People\\'s Choice Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nGuinness World Records\\n\\nLargest TV drama simulcast\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost viewers sharing a single torrent file simultaneously\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n42nd People\\'s Choice Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\nShorty Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGIF of the Year\\n\\n\\'\\'Game of Thrones\\'\\' Come At Me Bro\\n\\nEmpire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nArt Directors Guild Awards 2015\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley for  \"High Sparrow\", \"Unbowed, Unbent, Unbroken\" and \"Hardhome\"\\n\\nScreenwriters Choice Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nLocation Managers Guild Awards\\n\\nOutstanding Locations in Period Television\\n\\nRobert Boake and Tate Araez\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss for \"Hardhome\"\\n\\nAmerican Cinema Editors Awards 2016\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nTim Porter for \"Hardhome\"\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton for \\'\\'Game of Thrones\\'\\'\\n\\n68th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nDavid Nutter for \"Mother’s Mercy\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\n73rd Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n13th Irish Film & Television Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nMTV Millennial Awards\\n\\nKiller Series of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n21st National Television Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProducers Guild of America Awards 2015\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n42nd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O\\'Connor\\n\\n22nd Screen Actors Guild Awards\\n\\nOutstanding Action Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aiden Gillen, Ian Glen, Kit Harington, Lena Headey, Michiel Huisman, Brenock O\\'Conner, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams and Tom Wlaschiha\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nGlamour Awards 2016\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Hardhome\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"Hardhome\"\\n\\nVisual Effects Society Awards 2015\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon for \"The Dance of Dragons\"\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer\\' for \"Mother\\'s Mercy\" - Wounded Drogon\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen for \"Dance of Dragons\" - Drogon Arena Rescue\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur for \"City of Volantis\"\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson for \"Drogon Arena\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés for \"Hardhome\"\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela for \"Hardhome\"\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"Drogon Arena\"\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding for \"Drogon Lair\"\\n\\nWriters Guild of America Awards 2015\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan for \"The Dance of Dragons\"\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast to 170 countries by HBO and its broadcast partners. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '7f8a95fcae771c61b52a5010f3302dec'}>,\n",
+       "  <Document: {'content': 'Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Post Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Color Grading\\n\\nJoe Finley for \"Hardhome\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Hardhome\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani for \"The Dance of Dragons\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"Hardhome\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley for \"Hardhome\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n20th Satellite Awards\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\nIGN People\\'s Choice Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nGuinness World Records\\n\\nLargest TV drama simulcast\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost viewers sharing a single torrent file simultaneously\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n42nd People\\'s Choice Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\nShorty Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGIF of the Year\\n\\n\\'\\'Game of Thrones\\'\\' Come At Me Bro\\n\\nEmpire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nArt Directors Guild Awards 2015\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley for  \"High Sparrow\", \"Unbowed, Unbent, Unbroken\" and \"Hardhome\"\\n\\nScreenwriters Choice Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nLocation Managers Guild Awards\\n\\nOutstanding Locations in Period Television\\n\\nRobert Boake and Tate Araez\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss for \"Hardhome\"\\n\\nAmerican Cinema Editors Awards 2016\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nTim Porter for \"Hardhome\"\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton for \\'\\'Game of Thrones\\'\\'\\n\\n68th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nDavid Nutter for \"Mother’s Mercy\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\n73rd Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n13th Irish Film & Television Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nMTV Millennial Awards\\n\\nKiller Series of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n21st National Television Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProducers Guild of America Awards 2015\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n42nd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O\\'Connor\\n\\n22nd Screen Actors Guild Awards\\n\\nOutstanding Action Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aiden Gillen, Ian Glen, Kit Harington, Lena Headey, Michiel Huisman, Brenock O\\'Conner, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams and Tom Wlaschiha\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nGlamour Awards 2016\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Hardhome\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"Hardhome\"\\n\\nVisual Effects Society Awards 2015\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon for \"The Dance of Dragons\"\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer\\' for \"Mother\\'s Mercy\" - Wounded Drogon\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen for \"Dance of Dragons\" - Drogon Arena Rescue\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur for \"City of Volantis\"\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson for \"Drogon Arena\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés for \"Hardhome\"\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela for \"Hardhome\"\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"Drogon Arena\"\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding for \"Drogon Lair\"\\n\\nWriters Guild of America Awards 2015\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan for \"The Dance of Dragons\"\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast to 170 countries by HBO and its broadcast partners. In some countries, it aired the day after its first release. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '4a53b2aadbe92075acdc7ca63001422b'}>,\n",
+       "  <Document: {'content': 'Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Post Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Color Grading\\n\\nJoe Finley for \"Hardhome\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Hardhome\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani for \"The Dance of Dragons\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"Hardhome\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley for \"Hardhome\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n20th Satellite Awards\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\nIGN People\\'s Choice Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nGuinness World Records\\n\\nLargest TV drama simulcast\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost viewers sharing a single torrent file simultaneously\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n42nd People\\'s Choice Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\nShorty Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGIF of the Year\\n\\n\\'\\'Game of Thrones\\'\\' Come At Me Bro\\n\\nEmpire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nArt Directors Guild Awards 2015\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley for  \"High Sparrow\", \"Unbowed, Unbent, Unbroken\" and \"Hardhome\"\\n\\nScreenwriters Choice Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nLocation Managers Guild Awards\\n\\nOutstanding Locations in Period Television\\n\\nRobert Boake and Tate Araez\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss for \"Hardhome\"\\n\\nAmerican Cinema Editors Awards 2016\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nTim Porter for \"Hardhome\"\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton for \\'\\'Game of Thrones\\'\\'\\n\\n68th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nDavid Nutter for \"Mother’s Mercy\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\n73rd Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n13th Irish Film & Television Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nMTV Millennial Awards\\n\\nKiller Series of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n21st National Television Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProducers Guild of America Awards 2015\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n42nd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O\\'Connor\\n\\n22nd Screen Actors Guild Awards\\n\\nOutstanding Action Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aiden Gillen, Ian Glen, Kit Harington, Lena Headey, Michiel Huisman, Brenock O\\'Conner, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams and Tom Wlaschiha\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nGlamour Awards 2016\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Hardhome\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"Hardhome\"\\n\\nVisual Effects Society Awards 2015\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon for \"The Dance of Dragons\"\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer\\' for \"Mother\\'s Mercy\" - Wounded Drogon\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen for \"Dance of Dragons\" - Drogon Arena Rescue\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur for \"City of Volantis\"\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson for \"Drogon Arena\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés for \"Hardhome\"\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela for \"Hardhome\"\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"Drogon Arena\"\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding for \"Drogon Lair\"\\n\\nWriters Guild of America Awards 2015\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan for \"The Dance of Dragons\"\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast to 170 countries by HBO and its broadcast partners. In some countries, it aired the day after its first release. Sky Atlantic, the network serving the United Kingdom and Ireland, aired the premiere the day after HBO, but joined the simulcast for the rest of the season.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': 'd55e5cef91a9778a472621b5344ef06d'}>,\n",
+       "  <Document: {'content': \"Sky Atlantic, the network serving the United Kingdom and Ireland, aired the premiere the day after HBO, but joined the simulcast for the rest of the season.\\n\\n===Marketing===\\nA half-hour documentary, ''Game of Thrones: A Day in the Life'', aired on HBO on February 8, 2015. It covered one day of production of season 5 on three sets in Belfast, Dubrovnik and Osuna from the viewpoint of key crew members. The first official trailer for season 5 was released on January 30, 2015, and the season's second trailer was released on March 9, 2015. The world premiere of the first episode of the fifth season was held at the Tower of London on March 18, 2015.\\n\\n===Home media===\\nThe season was released on Blu-ray and DVD on March 15, 2016, in region 1 and March 14, 2016, in region 2.\\n\\n===Illegal distribution===\\nOn April 11, prior to the airing of the season's first episode, screener copies of the first four episodes were leaked to several file sharing sites. According to TorrentFreak, 18 million different IP addresses downloaded the leaked episodes, totaling 32 million downloads during the first week. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': '74951450a7bdf08c9250fa9ba28fed44'}>,\n",
+       "  <Document: {'content': \"According to TorrentFreak, 18 million different IP addresses downloaded the leaked episodes, totaling 32 million downloads during the first week. The fifth season of ''Game of Thrones'' was the most-pirated TV series in 2015.\\n\\n==References==\\n\\n==External links==\\n*  – official US site\\n*  – official UK site\\n*  ''Game of Thrones'' – The Viewers Guide on HBO.com\\n*  Making ''Game of Thrones'' on HBO.com\\n*\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': '3ad0560ccfad8ba389bb6d683f9107ad'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Baelor\\'\\'\\'\" is the ninth and penultimate episode of the first season of the HBO medieval fantasy television series \\'\\'Game of Thrones\\'\\'. First aired on June 12, 2011, it was written by the show\\'s creators and executive producers David Benioff and D. B. Weiss, and directed by Alan Taylor, his directorial debut for the series.\\n\\nThe plot depicts Eddard Stark (Sean Bean), imprisoned and accused of high treason, struggling with the decision whether to falsely confess to save his daughters, and he is ultimately beheaded by King Joffrey Baratheon (Jack Gleeson). Stark\\'s wife Catelyn (Michelle Fairley) negotiates with Lord Walder Frey (David Bradley) for the use of a strategic river crossing as their son Robb (Richard Madden) fights his first battle in the war against the Lannisters. Meanwhile, Jon Snow (Kit Harington) discovers a secret about Maester Aemon (Peter Vaughan), and Daenerys Targaryen (Emilia Clarke) stands up to Dothraki soldier Qotho (Dar Salim) and challenges Dothraki traditions to care for Khal Drogo (Jason Momoa). The title refers to the great Sept (church) in King\\'s Landing where Ned meets his fate.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'ef4990d40c60ff4a9853a71ced4698dd'}>,\n",
+       "  <Document: {'content': 'Meanwhile, Jon Snow (Kit Harington) discovers a secret about Maester Aemon (Peter Vaughan), and Daenerys Targaryen (Emilia Clarke) stands up to Dothraki soldier Qotho (Dar Salim) and challenges Dothraki traditions to care for Khal Drogo (Jason Momoa). The title refers to the great Sept (church) in King\\'s Landing where Ned meets his fate.\\n\\nThe episode received great acclaim among critics, who cited the final scene with Eddard Stark\\'s beheading as a highlight for the series, calling it a \"daring, tragic finish.\"In the United States, the episode achieved a viewership of 2.66 million in its initial broadcast. The episode was nominated for an Emmy Award for Outstanding Writing for a Drama Series and Peter Dinklage won the award for Outstanding Supporting Actor in a Drama Series for his performance.\\n\\n==Plot==\\n===In the Riverlands===\\nThe Stark army reaches the Twins, a bridge stronghold controlled by Walder Frey, who agrees to allow the army to cross the river and to commit his troops in return for Robb and Arya Stark marrying two of his children.\\n\\nTyrion Lannister suspects his father Tywin, who decides Tyrion and his barbarians will fight in the vanguard, wants him killed. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'e4a82c0744b78cedc52768fa911d5d29'}>,\n",
+       "  <Document: {'content': \"Tyrion Lannister suspects his father Tywin, who decides Tyrion and his barbarians will fight in the vanguard, wants him killed. As Tyrion, Bronn, and the prostitute Shae swap stories, Tyrion reveals he was married to a woman his father revealed was a prostitute, and made Tyrion watch as his guardsmen raped her.\\n\\nAs a Stark force approaches, Tyrion is trampled in the rush and regains consciousness to find the battle over. Tywin discovers the Stark host was only 2,000 men, not the 20,000 he was led to expect.\\n\\nRobb, having divided his forces, defeats Jaime Lannister's army with his remaining 18,000 men and captures Jaime.\\n\\n===At the Wall===\\nJeor Mormont gives Jon Snow his ancestral Valyrian steel sword Longclaw. Jon is upset he cannot join Robb against the Lannisters.\\n\\nMaester Aemon reveals to Jon that he is Aemon Targaryen, the Mad King Aerys's uncle and Daenerys Targaryen's great-uncle, and advises Jon that the choice he must make between the Night's Watch and his family will haunt him the rest of his life.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '49955c729238d425a1dc43f963adff96'}>,\n",
+       "  <Document: {'content': 'Maester Aemon reveals to Jon that he is Aemon Targaryen, the Mad King Aerys\\'s uncle and Daenerys Targaryen\\'s great-uncle, and advises Jon that the choice he must make between the Night\\'s Watch and his family will haunt him the rest of his life.\\n\\n===In Lhazar===\\nKhal Drogo is weakened by his infected wound, and Jorah warns that if Drogo dies, his bloodriders will fight to be his successor and kill Daenerys and her unborn child. Daenerys refuses to abandon her husband, and encourages Mirri Maz Duur to use blood magic. She prepares a spell, warning that no one may enter the tent, and Jorah kills Qotho when he tries to intervene. Daenerys goes into premature labor, but the Dothraki midwives refuse to help. Desperate, Jorah carries Daenerys into Drogo\\'s tent to seek Mirri\\'s help.\\n\\n===In King\\'s Landing===\\nAfter Varys tells him that Sansa Stark\\'s life is also at stake, Eddard \"Ned\" Stark agrees to make a false confession and swear loyalty to King Joffrey Baratheon.\\n\\nArya Stark finds a crowd gathering to watch her father be judged, and climbs onto the statue of Baelor the Blessed. Ned notices Arya and alerts Night\\'s Watch recruiter Yoren. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'ec5c1df06f24c1f79006b017887c5b60'}>,\n",
+       "  <Document: {'content': 'Arya Stark finds a crowd gathering to watch her father be judged, and climbs onto the statue of Baelor the Blessed. Ned notices Arya and alerts Night\\'s Watch recruiter Yoren. Before Sansa, Cersei Lannister, Joffrey and the Small Council, Ned confesses to treason and swears fealty to Joffrey. Instead of sparing Ned as promised, Joffrey orders him to be executed. Seeing that Arya has been rescued by Yoren, Ned accepts his fate and is beheaded.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by the showrunners David Benioff and D. B. Weiss, based on the original book by George R. R. Martin.\\n\\nThe title of the episode refers to the Great Sept of Baelor, the main religious building in King\\'s Landing, where the episode\\'s pivotal scene takes place. In the world created by George R. R. Martin, Baelor I Targaryen was a king during a previous century, revered as a patron and supporter of the Faith of the Seven. \"Baelor\" includes the content of the book\\'s chapters Eddard XV, Catelyn IX, Jon VIII, Tyrion VIII, Catelyn X, Daenerys VIII and Arya V (59–61 and 63–66).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '189b6dcd64bb6151a205559ad128b6e2'}>,\n",
+       "  <Document: {'content': '\"Baelor\" includes the content of the book\\'s chapters Eddard XV, Catelyn IX, Jon VIII, Tyrion VIII, Catelyn X, Daenerys VIII and Arya V (59–61 and 63–66).\\n\\nThe scene with the drinking game between Tyrion, Bronn and Shae was written specifically for the episode, but the story of Tyrion\\'s ill-fated marriage to Tysha was taken from a previous chapter of the books. Shae\\'s background was changed from Westerosi to foreign to accommodate Kekilli\\'s accent. Other notable divergence from the books include the modification of the whole strategy of Robb Stark when dividing his forces, and a change to the Targaryen genealogy as explained by Maester Aemon: in the TV adaptation the Mad King is described as the son, rather than the grandson, of Aegon V (thus eliminating Jaehaerys II from the succession of kings).\\n\\n===Casting===\\nSibel Kekilli plays the role of the prostitute Shae.\\n\"Baelor\" marks the first appearance of the German actress Sibel Kekilli, in the role of the prostitute Shae. Executive producer George R. R. Martin commented that she was extraordinary in her audition, in which she read the scene where Shae meets Tyrion in a tent the night before the battle of the Green Fork. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '55dfeee8abf3a9f7d01cdfa9d630f52b'}>,\n",
+       "  <Document: {'content': 'Executive producer George R. R. Martin commented that she was extraordinary in her audition, in which she read the scene where Shae meets Tyrion in a tent the night before the battle of the Green Fork. According to Martin, \"a lot of beautiful young women read for Shae. ... But there\\'s another dimension to Shae as well. She\\'s not as practiced and hardened at this as a more seasoned pro. There\\'s still a girl next door quality to her, a sense of vulnerability, playfulness, and, yes, innocence. ... All of our Shaes were hot as hell. But only a handful of them captured that other quality, maybe three out of twenty, and Sibel was the standout. ... Watching those auditions, any red-blooded male would want to take every one of our Shae candidates to bed. But Sibel made you fall in love with her as well.\"Also introduced in this episode was the English actor David Bradley, playing the role of Walder Frey.\\n\\nThe scenes at the Great Sept of Baelor were filmed at Fort Manoel, in Malta.\\n\\n===Filming locations===\\nThe interiors of the episode were filmed at the Paint Hall studios, close to Belfast. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '131a352ee8805186bf1e8fb8d5989e11'}>,\n",
+       "  <Document: {'content': 'The scenes at the Great Sept of Baelor were filmed at Fort Manoel, in Malta.\\n\\n===Filming locations===\\nThe interiors of the episode were filmed at the Paint Hall studios, close to Belfast. The area of the Castle Ward estate, also in Northern Ireland, was used to film on location the Stark and Lannister camps, the Crossing, and the battlefields of the Green Fork and the Whispering Woods.\\n\\nThe climactic scene before the Great Sept of Baelor was shot at Fort Manoel, in the Maltese town of Gżira. The filming took place in the last week of October 2010.\\n\\n===Execution===\\n\\nYears later, showrunner D. B. Weiss commented that the explicit style of the execution scene was selected in part to make it clear to the viewers that Eddard Stark, despite being the arguable protagonist of season one, actually was dead: \"It’s that rule: \\'If you don\\'t see the body then they’re not really dead.\\'Like when we cut Ned’s head off, we didn’t want a gory Monty Python geyser of blood, but we needed to see the blade enter his neck and cut out on the frame where the blade was mid-neck. ... we needed Ned\\'s death to be totally unambiguous.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '5bac2ec9e3eabd9f251bb0ecfd600694'}>,\n",
+       "  <Document: {'content': 'Like when we cut Ned’s head off, we didn’t want a gory Monty Python geyser of blood, but we needed to see the blade enter his neck and cut out on the frame where the blade was mid-neck. ... we needed Ned\\'s death to be totally unambiguous.\"==Reception==\\n\\n===Ratings===\\n\"Baelor\" gathered 2.7 million viewers in its premiere telecast in the United States, equaling the season high reached with the previous week\\'s episode. The total for the night, including the repeat, was slightly lower, with 3.4\\xa0million viewers. In the United Kingdom, the episode was viewed by 1.043 million viewers, making it the highest-rated broadcast that week.\\n\\n===Critical response===\\nThe episode received great acclaim among the critics. Review aggregator Rotten Tomatoes surveyed 19 reviews of the episode and judged 100% of them to be positive with an average score of 9.5 out of 10. The website\\'s critical consensus reads, \"Veering slightly from the source material, \"Baelor\" sports excellent performances and a bold, unprecedented climax.\"From the reviewers of \\'\\'The A.V. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'b6a29640af1da0dee7d5333394e40547'}>,\n",
+       "  <Document: {'content': 'The website\\'s critical consensus reads, \"Veering slightly from the source material, \"Baelor\" sports excellent performances and a bold, unprecedented climax.\"From the reviewers of \\'\\'The A.V. Club\\'\\', where it was rated  with an A, Emily VanDerWerff called it \"unquestionably the finest episode of \\'\\'Game Of Thrones\\'\\' yet,\" and David Sims found it \"terrific\" and with a conclusion that would be \"sure to blow the minds (and break the hearts)\" of the watchers. Matt Fowler of IGN TV gave the episode a perfect \"10\" saying that it was a \"clean and epic entry with a daring, tragic finish\" that had \"an admirable undercurrent of audience contempt.\"The focus of most reviews was in the climactic final scene, whose directing and acting is universally acclaimed by critics. Writing for Cultural Learnings, Myles McNutt stated: \"the final shot, with Arya looking to the sky as everything goes to silence and all she sees is the birds flying was just wonderfully haunting. Alan Taylor\\'s direction sold both the chaos and the resignation of that moment.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '1261bd910a505cf8a0d0b7705f425be8'}>,\n",
+       "  <Document: {'content': 'Writing for Cultural Learnings, Myles McNutt stated: \"the final shot, with Arya looking to the sky as everything goes to silence and all she sees is the birds flying was just wonderfully haunting. Alan Taylor\\'s direction sold both the chaos and the resignation of that moment.\"HitFix\\'s Alan Sepinwall felt that \"that final scene was so gorgeously shot, and the weariness of Bean\\'s performance and the horror of Maisie Williams\\' so perfectly conveyed the emotions of it, even as things seemed so chaotic.\"The emotional charge of the scene hit home for many reviewers: Scott Meslow of \\'\\'The Atlantic\\'\\' called it \"an absolutely nightmarish scene\" and labelled Eddard\\'s death \"horrific in its indignity.\"Jace Lacob from Televisionary and Maureen Ryan from AOL TV admitted having shed tears at the episode\\'s dramatic conclusion. The latter found the scene \"masterful\" and felt that the visual medium and Alan Taylor\\'s excellent work had made it more powerful than the book\\'s original version.\\n\\nBesides the final scene, other aspects were discussed: Garcia noted the acting of Richard Madden and how the Freys had been introduced. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '7d9ec4ac665b197b041c1f8c57e8c45e'}>,\n",
+       "  <Document: {'content': 'Besides the final scene, other aspects were discussed: Garcia noted the acting of Richard Madden and how the Freys had been introduced. Ryan praised the wide range of emotions used by Emilia Clarke while playing Daenerys, and how Peter Dinklage played Tyrion\\'s frustration and confusion during the episode. Both she and McNutt were glad that Tyrion\\'s exposition scene in the tent with Bronn and Shae did not use sex to keep viewers, as was done in past episodes.\\n\\nThere was debate about the merits of the producer\\'s decision to avoid depicting the two battles between the Starks and Lannisters. Ryan criticized it and confessed being \"a little disappointed that many of the major characters are caught up in a war and we\\'re not seeing it.\"Sims regretted not seeing the fight, and although he falsely claimed to understand the budget constrictions, he felt that \"all this off-screen fighting is just getting my blood rushing for some on-screen fighting.\"Sepinwall concludes: \"Ideally, we\\'d get a few epic, \\'\\'Braveheart\\'\\'-level battle scenes at some point, but I also respect the demands of time and budget here. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'acb6b08a4bafcdc92117a9b59b6c2aa3'}>,\n",
+       "  <Document: {'content': 'Sepinwall concludes: \"Ideally, we\\'d get a few epic, \\'\\'Braveheart\\'\\'-level battle scenes at some point, but I also respect the demands of time and budget here. Those kinds of sequences cost a fortune, and they eat up a lot of screen time, and I think ultimately I\\'d have rather had the time, say, that we spent in Tyrion\\'s tent the night before the battle, with the mortifying story of his ex-wife, and then whatever it cost to make the execution sequence look as good as it did, than for the episode to have given us one or two long fight scenes.\"In 2013, \\'\\'TV Guide\\'\\' ranked the final scene as the second greatest twist of all time.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'c4736dc6d7490fbc64e64470bae7fec0'}>,\n",
+       "  <Document: {'content': 'Those kinds of sequences cost a fortune, and they eat up a lot of screen time, and I think ultimately I\\'d have rather had the time, say, that we spent in Tyrion\\'s tent the night before the battle, with the mortifying story of his ex-wife, and then whatever it cost to make the execution sequence look as good as it did, than for the episode to have given us one or two long fight scenes.\"In 2013, \\'\\'TV Guide\\'\\' ranked the final scene as the second greatest twist of all time.\\n\\n==== Awards and nominations ====\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2011\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\nBest TV Twist\\n\\nIGN People\\'s Choice Awards\\n\\nBest TV Episode\\n\\nBest TV Twist\\n\\n2012\\n\\nAmerican Cinema Editors\\n\\nBest Edited One-Hour Series for Non-Commercial Television\\n\\nFrances Parker\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Mark Taylor\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Baelor\" at HBO\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '7cbefa35692834f37b14f01a173fd3b5'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Fire and Blood\\'\\'\\'\" is the tenth and final episode of the first season of the HBO medieval fantasy television series \\'\\'Game of Thrones\\'\\'. First aired on June 19, 2011, it was written by the show\\'s creators and executive producers David Benioff and D. B. Weiss, and directed by Alan Taylor.\\n\\nThe title of the episode is the motto of House Targaryen, and alludes to the aftermath of the previous episode\\'s climactic events. The episode\\'s action revolves around the Starks\\' reactions to Eddard Stark\\'s execution: Sansa is taken hostage, Arya flees in disguise, Robb and Catelyn lead an army against the Lannisters, and Jon Snow struggles with his divided loyalty. Across the narrow sea, Daenerys must deal with the blood magic that has robbed her of her husband, her son, and her army.\\n\\nThe episode was well received by critics, who singled out the closing scene as a particularly strong way to end the first season. In the United States, the episode achieved a viewership of 3.04\\xa0million in its initial broadcast. This episode was nominated for the Primetime Emmy Award for Outstanding Special Visual Effects for a Series.\\n\\n==Plot==\\n===At Winterfell===\\nMaester Luwin informs Bran and Rickon of Ned\\'s execution.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '9745dd6b3af224033b1d9e2624d3de87'}>,\n",
+       "  <Document: {'content': 'This episode was nominated for the Primetime Emmy Award for Outstanding Special Visual Effects for a Series.\\n\\n==Plot==\\n===At Winterfell===\\nMaester Luwin informs Bran and Rickon of Ned\\'s execution.\\n\\n===In the Riverlands===\\nAt the Stark army camp, Robb vows revenge on the Lannisters after Ned\\'s death, but Catelyn says they must first rescue Arya and Sansa. The Starks followers now support Northern independence, proclaiming Robb the \"King in the North\", rather than support Stannis or Renly Baratheon, who have both claimed the Iron Throne. Jaime tells Catelyn he pushed Bran out of the tower window, but does not explain why.\\n\\nAt the Lannister army camp, Tywin, unable to sue for peace with the Starks after Ned\\'s execution, orders Tyrion to go to King\\'s Landing in his stead as \"Hand of the King\" to keep Joffrey under control. Against his father\\'s orders, Tyrion brings Shae with him.\\n\\n===In King\\'s Landing===\\nJoffrey forces Sansa to look at Ned and his household staff\\'s severed heads on spikes. When Sansa says she wishes to see Joffrey\\'s head mounted there after Joffrey says Robb\\'s head will be, Joffrey has Ser Meryn slap her. The Hound advises Sansa to obey Joffrey for her own safety.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '132911597e419d813963709f1f97f19d'}>,\n",
+       "  <Document: {'content': 'When Sansa says she wishes to see Joffrey\\'s head mounted there after Joffrey says Robb\\'s head will be, Joffrey has Ser Meryn slap her. The Hound advises Sansa to obey Joffrey for her own safety.\\n\\nArya, rescued by Night\\'s Watch recruiter Yoren, escapes with him under the alias of \"Arry\", a boy, to the Wall with his new recruits, including Lommy, Hot Pie, and Gendry, the late Robert\\'s unknowing bastard son.\\n\\n===At the Wall===\\nJon attempts to desert the Night\\'s Watch to join Robb and avenge Ned, but Sam, Pyp, and Grenn convince him to return. The next morning, Jeor, despite knowing Jon attempted to desert, orders him to join him in an expedition beyond the Wall, intended to counter the threats of the wildlings and the White Walkers, and to find Benjen Stark.\\n\\n===In Lhazar===\\nDaenerys learns that her unborn son died due to Mirri\\'s spell. Furthermore, although Drogo\\'s life was saved, he has fallen into a catatonic state, causing most of his followers to abandon him. Mirri reveals that she caused this to avenge the destruction of her village and her people. Daenerys mercy kills Drogo.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '6ef2546140c8f92f2097d36addf8e4e0'}>,\n",
+       "  <Document: {'content': \"Mirri reveals that she caused this to avenge the destruction of her village and her people. Daenerys mercy kills Drogo.\\n\\nDaenerys has Mirri tied to Drogo's funeral pyre, with her dragons eggs placed on top. Daenerys declares herself queen of a new khalasar and steps into the lit pyre. By daybreak, Jorah and her remaining loyalists find her unharmed in the ashes, carrying three dragon hatchlings, the first born in decades. Amazed, they bow to Daenerys.\\n\\n==Production==\\n===Writing===\\nThe episode was written by showrunners David Benioff and D. B. Weiss. Like the rest of the first season, it adapts the plot of ''A Game of Thrones,'' the first novel in the ''A Song of Ice and Fire'' series by George R. R. Martin. The episode covers the novel's chapters 66 to 73, that is, Arya V, Bran VII, Sansa VI, Daenerys IX, Tyrion IX, Jon IX, Catelyn XI, and Daenerys X. It also covers part of the second novel, ''A Clash of Kings'': Arya I (chapter 2) and part of Catelyn VII (chapter 55). \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '5c3628f383b2adf27f47a1d483a65d7b'}>,\n",
+       "  <Document: {'content': 'It also covers part of the second novel, \\'\\'A Clash of Kings\\'\\': Arya I (chapter 2) and part of Catelyn VII (chapter 55). Scenes added for the adaptation include Catelyn and Robb receiving news of Eddard\\'s death, the revelation of Cersei and Lancel Lannister\\'s relationship, as well as interactions between Grand Maester Pycelle, the prostitute Ros, Varys, and Littlefinger.\\n\\n===Filming===\\nThe dragons featured in the episode\\'s finale were implemented by BlueBolt, the lead VFX agency for the first season. VFX supervisor Angela Barson confirmed that the CGI dragons were among the most stressful effects, prompting sleepless nights. Commenting on the episode\\'s climactic scene where the hatchling dragons are revealed, actress Emilia Clarke told VH1, \"You see the relationship that Dany has with her eggs, and you see that grow and grow and grow and kind of the intuitive connection she has with them, you see that develop really beautifully\". Clarke also hinted that she expected to \"get to play with some more dragons!\"in the second season, based on her conversations with book author and executive producer George R.R. Martin. \"VFX Data Wrangler\" Naill McEvoy later confirmed that dragon presence would be increasing in season two.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'a54b9dd9233f5b82efdda34d6aae98be'}>,\n",
+       "  <Document: {'content': 'in the second season, based on her conversations with book author and executive producer George R.R. Martin. \"VFX Data Wrangler\" Naill McEvoy later confirmed that dragon presence would be increasing in season two.\\n\\nIn the scene where Joffrey forces Sansa to view the heads of Ned and his entourage on spikes, one of the prosthetic heads briefly seen in profile is that of former U.S. president George W. Bush. In their commentary on the DVD release of season 1, David Benioff and D.B. Weiss explained that this was not meant as a political statement, but rather because the production used the prosthetic heads that happened to be at hand. Following media reports in June 2012, HBO apologized for this shot, which their statement described as \"unacceptable, disrespectful and in very bad taste\". The statement also said that the shot would be edited for any future home video production and TV broadcasts. HBO removed the episode from digital download services until the scene was edited. In the edited scene, the head bears no resemblance to Bush.\\n\\n==Reception==\\n===Airings and ratings===\\n\"Fire and Blood\" was first aired on HBO in the U.S. and Canada on June 19, 2011. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'cef83c00803182da9354b0e231b356a2'}>,\n",
+       "  <Document: {'content': '==Reception==\\n===Airings and ratings===\\n\"Fire and Blood\" was first aired on HBO in the U.S. and Canada on June 19, 2011. The episode was the most watched episode of the season and was viewed by an estimated 3.041\\xa0million viewers and received a 1.4\\xa0share among adults between the ages of 18 and 49. This means that it was seen by 1.4% of all 18- to 49-year-olds at the time of the broadcast. With repeats, the episode brought in 3.9\\xa0million total viewers. In the United Kingdom, the episode was viewed by 1.314\\xa0million viewers, making it the highest-rated broadcast that week.\\n\\n===Critical response===\\n\"Fire and Blood\" received positive reviews, and much critical acclaim for the closing scene.\\n\\nReview aggregator Rotten Tomatoes surveyed 32 reviews of the episode and judged 100% of them to be positive with an average score of 9.2 out of 10. The website\\'s critical consensus reads, \"\"Fire and Blood\" creatively provides closure after a shocking penultimate episode while strategically setting up arcs for season two.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'ae7230698942c89dd28faf7ce8d14c32'}>,\n",
+       "  <Document: {'content': 'The website\\'s critical consensus reads, \"\"Fire and Blood\" creatively provides closure after a shocking penultimate episode while strategically setting up arcs for season two.\"Matt Fowler of IGN wrote that \" \\'Fire and Blood\\' wasn\\'t exactly a powerful roar of an episode, but that book fans would definitely appreciate the small parts of the second book, \\'\\'A Clash of Kings\\'\\', that got included to help set up season two next year\". He rated the episode 8.5 out of 10.\\n\\nEmily VanDerWerff of \\'\\'The A.V. Club\\'\\' gave it an \"A-\", commenting:\\n\\nDavid Sims, also writing for \\'\\'The A.V. Club\\'\\', called it a fitting end to the season, \"leaving absolutely everyone salivating for season two\". Writing for the \\'\\'Star-Ledger\\'\\', Jenifer Braun praised the episode for its set dressing (\"I have to say, it\\'s a pleasure just to look at all the shiny stuff the HBO set dressers came up with for Tywin Lannister\\'s tent\") and the authenticity of the baby dragons (\"And wow, HBO, seamless special effects here. Baby Dragon looks every bit as real as the series\\' dogs\"). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '437fdbcb05b8a9f9107c30a0e631f431'}>,\n",
+       "  <Document: {'content': 'Writing for the \\'\\'Star-Ledger\\'\\', Jenifer Braun praised the episode for its set dressing (\"I have to say, it\\'s a pleasure just to look at all the shiny stuff the HBO set dressers came up with for Tywin Lannister\\'s tent\") and the authenticity of the baby dragons (\"And wow, HBO, seamless special effects here. Baby Dragon looks every bit as real as the series\\' dogs\"). HitFix said it \"Wrapped up its terrific first season...it was damned entertaining along the way--with the finale as possibly the most entertaining so far--and we know that at least one more season is coming. And if the creative team can keep up this level of quality, it\\'s hard to imagine HBO shutting things down anytime soon, even with a budget that only figures to get higher. Dragons aren\\'t cheap, but they\\'re also amazing\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '9e04c09757d7eaed4367f7e069ffa1c8'}>,\n",
+       "  <Document: {'content': 'And if the creative team can keep up this level of quality, it\\'s hard to imagine HBO shutting things down anytime soon, even with a budget that only figures to get higher. Dragons aren\\'t cheap, but they\\'re also amazing\".\\n\\n==== Awards and nominations ====\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2011\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Special Visual Effects\\n\\nRafael Morant, Adam McInnes, Graham Hills, Lucy Ainsworth-Taylor, Stuart Brisdon, Damien Macé, Henry Badgett, and Angela Barson\\n\\n===George W. Bush controversy and removal===\\nThe use of an effigy of George W. Bush\\'s head on a spike caused HBO to issue an apology and to edit the scene.\\nThe episode contains a scene in which Joffrey forces Sansa to look at her father and family household staff beheaded on spikes. After a scene of the disfigured heads is shot, one decapitated head resembled former president of the United States of America George W. Bush. HBO quickly began removing and editing the scene as it sparked political outrage on social media. The beheaded prop was edited to resemble an ordinary head prop.\\n\\nA small amount of politicians began calling for a boycott of the series, describing the scene as \\'despicable\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '854ccc4941ece25d20e4bf10a3cb3e97'}>,\n",
+       "  <Document: {'content': 'The beheaded prop was edited to resemble an ordinary head prop.\\n\\nA small amount of politicians began calling for a boycott of the series, describing the scene as \\'despicable\\'. Creators David Benioff and D.B. Weiss both apologized for the filming of the prop, stating:\\n\\n== References ==\\n\\n== External links ==\\n\\n*  \"Fire and Blood\" at HBO\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '4971acff41e8779287becea809c606ee'}>,\n",
+       "  <Document: {'content': '\\n\\nThe sixth season of the fantasy drama television series \\'\\'Game of Thrones\\'\\' premiered on HBO on April 24, 2016, and concluded on June 26, 2016. It consists of ten episodes, each of approximately 50–60 minutes long, largely of original content not found in George R. R. Martin\\'s \\'\\'A Song of Ice and Fire\\'\\' series. Some story elements were derived from the novels and from information Martin revealed to the show-runners. The series was adapted for television by David Benioff and D. B. Weiss.  HBO ordered the season on April 8, 2014, together with the fifth season, which began filming in July 2015 primarily in Northern Ireland, Spain, Croatia, Iceland and Canada. The entire season cost over $100 million to produce.\\n\\nThe season follows the continuing struggle between the Starks and other noble families of Westeros for the Iron Throne. The Starks defeat the Bolton forces in the \"Battle of the Bastards\", culminating with Sansa Stark feeding her tormenting husband Ramsay Bolton — who had earlier murdered his father Roose and stepfamily — to his starving hounds, and Jon Snow is proclaimed the King in the North. Tyrion attempts to rule Meereen while Daenerys is held captive by a Dothraki tribe.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'e05ffbe9d51f347cb542b6974e79234c'}>,\n",
+       "  <Document: {'content': 'The Starks defeat the Bolton forces in the \"Battle of the Bastards\", culminating with Sansa Stark feeding her tormenting husband Ramsay Bolton — who had earlier murdered his father Roose and stepfamily — to his starving hounds, and Jon Snow is proclaimed the King in the North. Tyrion attempts to rule Meereen while Daenerys is held captive by a Dothraki tribe.  At King\\'s Landing, the Tyrell army attempts to liberate Margaery and Loras, but Margaery capitulates to the High Sparrow, who becomes more powerful by influencing King Tommen. At her trial, Cersei burns the Great Sept — killing her rivals — and is later crowned Queen of the Seven Kingdoms, while Tommen commits suicide. Ellaria Sand and three of Oberyn Martell\\'s daughters kill Doran and Trystane Martell and seize control of Dorne.  In Essos, Daenerys Targaryen is captured by Khal Moro who takes her before the khals; she burns them alive and takes command of the Dothraki. Olenna and the Dornish ally with Daenerys.\\n\\n\\'\\'Game of Thrones\\'\\' features a large ensemble cast, including Peter Dinklage, Nikolaj Coster-Waldau, Lena Headey, Emilia Clarke and Kit Harington. The season introduced new cast members, including Max von Sydow, Pilou Asbæk and Essie Davis.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '789201589c816766066e3d6cbb44a248'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' features a large ensemble cast, including Peter Dinklage, Nikolaj Coster-Waldau, Lena Headey, Emilia Clarke and Kit Harington. The season introduced new cast members, including Max von Sydow, Pilou Asbæk and Essie Davis.\\n\\nCritics praised its production values, writing, plot development, and cast. ''Game of Thrones'' received most nominations for the 68th Primetime Emmy Awards, with 23 nominations, and won twelve, including that for Outstanding Drama Series for the second year in a row. U.S. viewership rose compared to the previous season, and by approximately 13% over its course, from 7.9 million to 8.9 million by the finale.\\n\\n==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 6. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'ef6b5b817406a726f5083ee59c6925be'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' received most nominations for the 68th Primetime Emmy Awards, with 23 nominations, and won twelve, including that for Outstanding Drama Series for the second year in a row. U.S. viewership rose compared to the previous season, and by approximately 13% over its course, from 7.9 million to 8.9 million by the finale.\\n\\n==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 6. They are listed by the region in which they first appear:\\n\\n====In the North, including the Wall====\\n* Daniel Portman as Podrick Payne\\n* Natalia Tena as Osha\\n* Art Parkinson as Rickon Stark\\n* Owen Teale as Alliser Thorne\\n* Ben Crompton as Eddison Tollett\\n* Brenock O\\'Connor as Olly\\n* Charlotte Hope as Myranda\\n* Elizabeth Webster as Walda Bolton\\n* Paul Rattray as Harald Karstark\\n* Dean Jagger as Smalljon Umber\\n* Tim McInnerny as Robett Glover\\n* Bella Ramsey as Lyanna Mormont\\n* Sean Blowers as Wyman Manderly\\n* Tom Varey as Cley Cerwyn\\n* Richard Rycroft as Maester Wolkan\\n* Michael Condron as Bowen Marsh\\n* Brian Fortune as Othell Yarwyck\\n* Ian Whyte as Wun Wun\\n* Murray McArthur as Dim Dalba\\n\\n====Beyond the Wall====\\n* Max von Sydow as the Three-eyed Raven\\n* Ellie Kendrick as Meera Reed\\n* Kristian Nairn as Hodor\\n* Joseph Mawle as Benjen Stark\\n* Kae Alexander as Leaf\\n* Vladimir \"Furdo\" Furdik as the Night King\\n\\n====In the Riverlands====\\n* David Bradley as Walder Frey\\n* Clive Russell as Brynden Tully\\n* Tobias Menzies as Edmure Tully\\n* Richard Dormer as Beric Dondarrion\\n* Paul Kaye as Thoros of Myr\\n* Tim Plester as Black Walder Rivers\\n* Daniel Tuite as Lothar Frey\\n* Jóhannes Haukur Jóhannesson as Lem\\n* Ricky Champ as Gatins\\n* Ian Davies as Morgan\\n* Ian McShane as Brother Ray\\n\\n====On the Iron Islands====\\n* Gemma Whelan as Yara Greyjoy\\n* Patrick Malahide as Balon Greyjoy\\n* Pilou Asbæk as Euron Greyjoy\\n* Michael Feast as Aeron Greyjoy\\n\\n====In Dorne====\\n* Alexander Siddig as Doran Martell\\n* Toby Sebastian as Trystane Martell\\n* Jessica Henwick as Nymeria Sand\\n* Keisha Castle-Hughes as Obara Sand\\n* Rosabell Laurenti Sellers as Tyene Sand\\n* DeObia Oparei as Areo Hotah\\n\\n====In King\\'s Landing====\\n* Diana Rigg as Olenna Tyrell\\n* Julian Glover as Grand Maester Pycelle\\n* Finn Jones as Loras Tyrell\\n* Anton Lesser as Qyburn\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Eugene Simon as Lancel Lannister\\n* Ian Gelder as Kevan Lannister\\n* Hannah Waddingham as Septa Unella\\n* Nell Tiger Free as Myrcella Baratheon\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Josephine Gillan as Marei\\n* Nathanael Saleh as Arthur\\n* Annette Hannah as Frances\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Rupert Vansittart as Yohn Royce\\n\\n====In Braavos====\\n* Faye Marsay as the Waif\\n* Richard E. Grant as Izembaro\\n* Essie Davis as Lady Crane\\n* Leigh Gill as Bobono\\n* Eline Powell as Bianca\\n* Rob Callender as Clarenzo\\n* Kevin Eldon as Camello\\n\\n====In Meereen====\\n* Jacob Anderson as Grey Worm\\n* George Georgiou as Razdal mo Eraz\\n* Eddie Jackson as Belicho Paenymion\\n* Enzo Cilenti as Yezzan zo Qaggaz\\n* Ania Bukstein as Kinvara\\n* Gerald Lepkowski as Zanrush\\n* Meena Rayann as Vala\\n\\n====In Vaes Dothrak====\\n* Joe Naufahu as Khal Moro\\n* Andrei Claude as Khal Rhalko\\n* Tamer Hassan as Khal Forzho\\n* Staz Nair as Qhono\\n* Chuku Modu as Aggo\\n* Deon Lee-Williams as Iggo\\n* Souad Faress as the High Priestess\\n* Hannah John-Kamen as Ornella\\n\\n====In the Reach====\\n* James Faulkner as Randyll Tarly\\n* Samantha Spiro as Melessa Tarly\\n* Freddie Stroma as Dickon Tarly\\n* Rebecca Benson as Talla Tarly\\n\\n====In flashbacks====\\n* Robert Aramayo & Sebastian Croft as Eddard Stark\\n* Matteo Elezi as Benjen Stark\\n* Aisling Franciosi & Cordelia Hill as Lyanna Stark\\n* Wayne Foskett as Rickard Stark\\n* Fergus Leathem as Rodrik Cassel\\n* Annette Tierney as Old Nan\\n* Sam Coleman as Hodor\\n* Leo Woodruff as Howland Reed\\n* Luke Roberts as Arthur Dayne\\n* Eddie Eyre as Gerold Hightower\\n* David Rintoul as Aerys II Targaryen\\n\\n==Production==\\n\\n===Crew===\\nThe writing staff for the sixth season includes executive producers and showrunners David Benioff and D. B. Weiss, producer Bryan Cogman and Dave Hill. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'ecdf0bb81b76c5fc9bed10781d5264be'}>,\n",
+       "  <Document: {'content': 'They are listed by the region in which they first appear:\\n\\n====In the North, including the Wall====\\n* Daniel Portman as Podrick Payne\\n* Natalia Tena as Osha\\n* Art Parkinson as Rickon Stark\\n* Owen Teale as Alliser Thorne\\n* Ben Crompton as Eddison Tollett\\n* Brenock O\\'Connor as Olly\\n* Charlotte Hope as Myranda\\n* Elizabeth Webster as Walda Bolton\\n* Paul Rattray as Harald Karstark\\n* Dean Jagger as Smalljon Umber\\n* Tim McInnerny as Robett Glover\\n* Bella Ramsey as Lyanna Mormont\\n* Sean Blowers as Wyman Manderly\\n* Tom Varey as Cley Cerwyn\\n* Richard Rycroft as Maester Wolkan\\n* Michael Condron as Bowen Marsh\\n* Brian Fortune as Othell Yarwyck\\n* Ian Whyte as Wun Wun\\n* Murray McArthur as Dim Dalba\\n\\n====Beyond the Wall====\\n* Max von Sydow as the Three-eyed Raven\\n* Ellie Kendrick as Meera Reed\\n* Kristian Nairn as Hodor\\n* Joseph Mawle as Benjen Stark\\n* Kae Alexander as Leaf\\n* Vladimir \"Furdo\" Furdik as the Night King\\n\\n====In the Riverlands====\\n* David Bradley as Walder Frey\\n* Clive Russell as Brynden Tully\\n* Tobias Menzies as Edmure Tully\\n* Richard Dormer as Beric Dondarrion\\n* Paul Kaye as Thoros of Myr\\n* Tim Plester as Black Walder Rivers\\n* Daniel Tuite as Lothar Frey\\n* Jóhannes Haukur Jóhannesson as Lem\\n* Ricky Champ as Gatins\\n* Ian Davies as Morgan\\n* Ian McShane as Brother Ray\\n\\n====On the Iron Islands====\\n* Gemma Whelan as Yara Greyjoy\\n* Patrick Malahide as Balon Greyjoy\\n* Pilou Asbæk as Euron Greyjoy\\n* Michael Feast as Aeron Greyjoy\\n\\n====In Dorne====\\n* Alexander Siddig as Doran Martell\\n* Toby Sebastian as Trystane Martell\\n* Jessica Henwick as Nymeria Sand\\n* Keisha Castle-Hughes as Obara Sand\\n* Rosabell Laurenti Sellers as Tyene Sand\\n* DeObia Oparei as Areo Hotah\\n\\n====In King\\'s Landing====\\n* Diana Rigg as Olenna Tyrell\\n* Julian Glover as Grand Maester Pycelle\\n* Finn Jones as Loras Tyrell\\n* Anton Lesser as Qyburn\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Eugene Simon as Lancel Lannister\\n* Ian Gelder as Kevan Lannister\\n* Hannah Waddingham as Septa Unella\\n* Nell Tiger Free as Myrcella Baratheon\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Josephine Gillan as Marei\\n* Nathanael Saleh as Arthur\\n* Annette Hannah as Frances\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Rupert Vansittart as Yohn Royce\\n\\n====In Braavos====\\n* Faye Marsay as the Waif\\n* Richard E. Grant as Izembaro\\n* Essie Davis as Lady Crane\\n* Leigh Gill as Bobono\\n* Eline Powell as Bianca\\n* Rob Callender as Clarenzo\\n* Kevin Eldon as Camello\\n\\n====In Meereen====\\n* Jacob Anderson as Grey Worm\\n* George Georgiou as Razdal mo Eraz\\n* Eddie Jackson as Belicho Paenymion\\n* Enzo Cilenti as Yezzan zo Qaggaz\\n* Ania Bukstein as Kinvara\\n* Gerald Lepkowski as Zanrush\\n* Meena Rayann as Vala\\n\\n====In Vaes Dothrak====\\n* Joe Naufahu as Khal Moro\\n* Andrei Claude as Khal Rhalko\\n* Tamer Hassan as Khal Forzho\\n* Staz Nair as Qhono\\n* Chuku Modu as Aggo\\n* Deon Lee-Williams as Iggo\\n* Souad Faress as the High Priestess\\n* Hannah John-Kamen as Ornella\\n\\n====In the Reach====\\n* James Faulkner as Randyll Tarly\\n* Samantha Spiro as Melessa Tarly\\n* Freddie Stroma as Dickon Tarly\\n* Rebecca Benson as Talla Tarly\\n\\n====In flashbacks====\\n* Robert Aramayo & Sebastian Croft as Eddard Stark\\n* Matteo Elezi as Benjen Stark\\n* Aisling Franciosi & Cordelia Hill as Lyanna Stark\\n* Wayne Foskett as Rickard Stark\\n* Fergus Leathem as Rodrik Cassel\\n* Annette Tierney as Old Nan\\n* Sam Coleman as Hodor\\n* Leo Woodruff as Howland Reed\\n* Luke Roberts as Arthur Dayne\\n* Eddie Eyre as Gerold Hightower\\n* David Rintoul as Aerys II Targaryen\\n\\n==Production==\\n\\n===Crew===\\nThe writing staff for the sixth season includes executive producers and showrunners David Benioff and D. B. Weiss, producer Bryan Cogman and Dave Hill. Author George R. R. Martin, who had written one episode for each of the first four seasons, did not write an episode for the sixth season, as he was working to finish writing the sixth \\'\\'A Song of Ice and Fire\\'\\' novel, \\'\\'The Winds of Winter\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'b1fa85846d5ab04543d5dbb44d1aa9f0'}>,\n",
+       "  <Document: {'content': 'Author George R. R. Martin, who had written one episode for each of the first four seasons, did not write an episode for the sixth season, as he was working to finish writing the sixth \\'\\'A Song of Ice and Fire\\'\\' novel, \\'\\'The Winds of Winter\\'\\'. The directing staff for the sixth season was Jeremy Podeswa (episodes 1 and 2), Daniel Sackheim (episodes 3 and 4), Jack Bender (episodes 5 and 6), Mark Mylod (episodes 7 and 8), and Miguel Sapochnik (episodes 9 and 10). Sackheim and Bender were first-time \\'\\'Game of Thrones\\'\\' directors, with the rest each having directed two episodes in the previous season.\\n\\n===Writing===\\nWith the end of the fifth season, the content of the show has reached the plot of the latest novel in Martin\\'s \\'\\'A Song of Ice and Fire\\'\\' series, \\'\\'A Dance with Dragons\\'\\'. Season 6 director Jeremy Podeswa said in August 2015: \"Right now in season six, what we\\'re shooting currently isn\\'t based on anything in the book. It\\'s fully based on discussions the writers have had with George Martin, because the series has now surpassed the books in terms of what\\'s available.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '5e55fa91468a1bfcceb8dad27f3e0108'}>,\n",
+       "  <Document: {'content': 'It\\'s fully based on discussions the writers have had with George Martin, because the series has now surpassed the books in terms of what\\'s available.\"Actress Natalie Dormer, who plays Margaery Tyrell, later added that the show\\'s writers \"know where it\\'s got to go and what George Martin\\'s intentions for the characters are. But they are just filling in the gaps.\"The season premiere starts off right where the fifth season ended. A plot from \\'\\'The Winds of Winter\\'\\' regarding a traveling theater troupe located in Braavos that stages a play called \"The Bloody Hand\", about the events that have taken place in King\\'s Landing since the beginning of the series, is included in the sixth season.\\n\\n===Filming===\\nCastle of Zafra in Guadalajara, Spain is among the filming locations.\\nFilming for season six began in July 2015, and ended on December 17, 2015. The budget for the sixth season increased compared to the previous seasons as each episode did cost over $10 million per episode, totaling over $100 million for the full season and setting a new high for the series. The season filmed in five different countries, Northern Ireland, Spain, Croatia, Iceland and Canada.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'ec1f7824869a2f33cd589de75289112f'}>,\n",
+       "  <Document: {'content': 'The budget for the sixth season increased compared to the previous seasons as each episode did cost over $10 million per episode, totaling over $100 million for the full season and setting a new high for the series. The season filmed in five different countries, Northern Ireland, Spain, Croatia, Iceland and Canada.\\n\\nLike the previous seasons, a large amount of production took place in Northern Ireland, mainly in Belfast and on the Causeway Coast, including film locations in the Binevenagh, Magilligan area which was used to film scenes for the Dothraki Grasslands, and Larrybane Quarry and Ballintoy Harbour, both used for scenes in the Iron Islands. The small village of Corbet was also used, for the siege of Riverrun. As in previous seasons, some of Castle Black was set at the abandoned Magheramorne quarry.\\n\\nFrom September 3 to October 23, 2015 the show also filmed in Spain, specifically in Girona, Navarre, Peniscola, and Almería. Some filming locations in Spain included the Castle of Zafra in Guadalajara, the Bardenas Reales Natural Park in Navarre, the Alcazaba in Almería, and the Castle of Santa Florentina in Canet de Mar.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'f75f7275559d42a3a3a7b4cd96eaea4e'}>,\n",
+       "  <Document: {'content': \"Some filming locations in Spain included the Castle of Zafra in Guadalajara, the Bardenas Reales Natural Park in Navarre, the Alcazaba in Almería, and the Castle of Santa Florentina in Canet de Mar.\\n\\nIn August 2015, HBO announced that for the first time since season 1, the show would not be filming any scenes in Croatia. The Croatian city of Dubrovnik has stood in for King's Landing since the beginning of season 2; nearby cities such as Klis, Split and Šibenik have been used to depict various other locations. Contradicting the statement by HBO, cast of the show were seen in costume in Dubrovnik in October 2015.\\n\\nOnly a very small portion of the season was filmed in Canada (north of Calgary, Alberta): the scenes featuring Jon Snow's wolf Ghost (played by animal actor Quigly). However, some of the special effects were created at Montreal's Rodeo FX studios which has won Emmy Awards previously for its work on the series.\\n\\n===Casting===\\nPilou Asbæk plays Euron Greyjoy.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '96a6421d0c5818c4e13431d2137d8e5d'}>,\n",
+       "  <Document: {'content': 'However, some of the special effects were created at Montreal\\'s Rodeo FX studios which has won Emmy Awards previously for its work on the series.\\n\\n===Casting===\\nPilou Asbæk plays Euron Greyjoy.\\nThe sixth season saw the return of Isaac Hempstead Wright as Bran Stark, Kristian Nairn as Hodor, Ellie Kendrick as Meera Reed, Gemma Whelan as Yara Greyjoy, and Rory McCann as Sandor \"The Hound\" Clegane,  who did not appear in the fifth season. Clive Russell, Tobias Menzies, Patrick Malahide, Richard Dormer and Paul Kaye also returned to the show as Brynden Tully, Edmure Tully, Balon Greyjoy, Beric Dondarrion and Thoros of Myr after not appearing since the third season. Jonathan Pryce as the High Sparrow was added to the series main cast after appearing in a recurring role in the previous season.\\n\\nAcross the Narrow Sea, Melanie Liburd plays a Red priestess from Asshai who is in R\\'hllor\\'s service. At the Reach, House Tarly is introduced, with Freddie Stroma joining the cast as Samwell Tarly\\'s brother Dickon Tarly, a character who was mentioned in the novels but has yet to appear in them. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '75f0ca86e21a8b0bf403c9ba51a6c148'}>,\n",
+       "  <Document: {'content': 'At the Reach, House Tarly is introduced, with Freddie Stroma joining the cast as Samwell Tarly\\'s brother Dickon Tarly, a character who was mentioned in the novels but has yet to appear in them. Other members of House Tarly that were introduced were Randyll Tarly, played by James Faulkner; Melessa Tarly, played by Samantha Spiro and Talla Tarly, who was portrayed by Rebecca Benson.\\n\\nVeteran actor Max von Sydow was cast to play the Three-Eyed-Raven, who is training Bran. The character was previously played by Struan Rodger in the fourth-season finale \"The Children\". David Bradley confirmed in August 2015, that he would be returning to the show as Walder Frey after last appearing in the third-season finale \"Mhysa\", but he did not confirm when he would be returning. After the second official trailer was released, it was confirmed that Bradley would appear in the sixth season. Danish actor Pilou Asbæk joins the show as Theon Greyjoy\\'s uncle, pirate captain Euron Greyjoy. Members of the Icelandic band Of Monsters and Men made cameo appearances. Ricky Champ played Gatins, an outlaw who is a part of a band using religion to justify extorting the people of the countryside. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '92ea8991c9f4e2782ff0e3e418719ed6'}>,\n",
+       "  <Document: {'content': 'Ricky Champ played Gatins, an outlaw who is a part of a band using religion to justify extorting the people of the countryside. A young Ned Stark was portrayed by Sebastian Croft in a flashback scene.\\n\\nThe sixth season also included a traveling theater troupe located in Braavos that stages a play called \"The Bloody Hand\", about the events that have taken place in King\\'s Landing since the beginning of the series. Essie Davis and Kevin Eldon joined the cast in this theater troupe, portraying actors playing Cersei Lannister and Ned Stark, respectively, while Richard E. Grant was cast as the troupe\\'s manager. Members of Icelandic indie band Of Monsters and Men appear as the musicians of the Braavos theatre group.\\n\\n===Music===\\n\\nThe soundtrack for the season was digitally released on June 24, 2016, and was released on CD on July 29, 2016. The album reached  on the \\'\\'Billboard\\'\\' 200,  on Soundtrack chart, and  on the Canadian Albums chart on its digital release, with the track from the season finale \"Light of the Seven\" reaching  on \\'\\'Billboard\\'\\'s Spotify Viral 50 chart.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'fadd1f462bf41db27526acb2702b238a'}>,\n",
+       "  <Document: {'content': 'The album reached  on the \\'\\'Billboard\\'\\' 200,  on Soundtrack chart, and  on the Canadian Albums chart on its digital release, with the track from the season finale \"Light of the Seven\" reaching  on \\'\\'Billboard\\'\\'s Spotify Viral 50 chart.\\n\\n==Reception==\\n\\n===Critical response===\\nThe season received highly positive reviews, with special praise directed at the episodes \"The Door\", \"Battle of the Bastards\" and \"The Winds of Winter\". On Metacritic, the season (based on the first episode) has a score of 73 out of 100 based on 9 reviews, indicating \"generally favorable reviews\". On Rotten Tomatoes, the sixth season has a 94% approval rating from 34 critics with an average rating of 8.28 out of 10. The site\\'s critical consensus reads, \"Bloody and captivating as always, \\'\\'Game of Thrones\\'\\' plunges back into the midst of a world touched by grief, dread, and precarious sexuality.\"===Ratings===\\n\\nThe season finale had 8.89 million viewers on its initial airing on HBO, up ten percent from the previous season\\'s finale, which was the most-watched episode prior to this episode. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '847eb1b9bb597a52dc7dcc0aeb05b20b'}>,\n",
+       "  <Document: {'content': \"===Ratings===\\n\\nThe season finale had 8.89 million viewers on its initial airing on HBO, up ten percent from the previous season's finale, which was the most-watched episode prior to this episode. The average gross viewing figure per episode for the show, which include streaming, DVR recordings and repeat showings, reached over 25 million this season, and it was described as the last consensus show on television. The figure went up by 25% compared to previous year, and viewing figures of the show this season on its on demand services HBO Now and HBO Go went up by over 90%, which were new records for HBO.  Almost 40% of viewers of the show watched this season on HBO digital platforms.  The show also broke records on pay television channels in 2016 in the United Kingdom with an average audience of more than five million across all platforms, and in Australia with a cumulative average audience of 1.2 million viewers.\\n\\nLive +7 ratings were not available, so Live +3 ratings have been used instead.\\n\\n===Accolades===\\n\\nFor the 32nd TCA Awards, the series was nominated for Program of the Year and Outstanding Achievement in Drama. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '9b8075e44053267903360baad2841316'}>,\n",
+       "  <Document: {'content': '===Accolades===\\n\\nFor the 32nd TCA Awards, the series was nominated for Program of the Year and Outstanding Achievement in Drama. For the 68th Primetime Emmy Awards, the series received 23 nominations, the most of any series. It won 12 awards, including Outstanding Drama Series, David Benioff and D. B. Weiss for Outstanding Writing for a Drama Series for \"Battle of the Bastards\", and Miguel Sapochnik for Outstanding Directing for a Drama Series for \"Battle of the Bastards\". For the 7th Critics\\' Choice Television Awards, the series won for Best Drama Series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'cbdd17245301ced34323d02f16867a68'}>,\n",
+       "  <Document: {'content': 'It won 12 awards, including Outstanding Drama Series, David Benioff and D. B. Weiss for Outstanding Writing for a Drama Series for \"Battle of the Bastards\", and Miguel Sapochnik for Outstanding Directing for a Drama Series for \"Battle of the Bastards\". For the 7th Critics\\' Choice Television Awards, the series won for Best Drama Series.\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n32nd TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nTV Choice Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nDragon Awards\\n\\nBest Science Fiction or Fantasy TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold, Robert Sterne, Carla Stronge\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nSophie Turner\\n\\n68th Primetime Emmy Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nKit Harington as Jon Snow\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\nMaisie Williams as Arya Stark\\n\\nOutstanding Directing for a Drama Series\\n\\nJack Bender for \"The Door\"\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Battle of the Bastards\"\\n\\n68th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow as Three-Eyed Raven\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton for \\'\\'Home\\'\\'\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary for \\'\\'The Winds of Winter\\'\\'\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora for \\'\\'The Door\\'\\'\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth for \"Battle of the Bastards\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \\'\\'Blood of My Blood\\'\\', \\'\\'The Broken Man\\'\\', and \\'\\'No One\\'\\'\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower for \\'\\'The Door\\'\\'\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \\'\\'Battle of the Bastards\\'\\'\\n\\nKatie Weiland for \\'\\'Oathbreaker\\'\\'\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit for \\'\\'The Door\\'\\'\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney,  Sam Conway,  Matthew Rouleau,  Michelle Blok, Glenn Melenhorst for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Main Titles 360 Experience\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch  for \"Battle of the Bastards\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau  for \"Battle of the Bastards\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n3D Award for Visual Effects Design\\n\\nIloura for \"Battle of the Bastards\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"The Winds of Winter\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nSean Savage, David Morgan & John Ferguson for \"Battle of the Bastards\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Battle of the Bastards\"\\n\\nAnette Haellmigk for \"Book of the Stranger\"\\n\\n7th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN People\\'s Choice Award\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMTV Fandom Awards\\n\\nFan Freak Out of the Year\\n\\n\\'\\'Game of Thrones\\'\\' – Resurrection of Jon Snow\\n\\nInternational Film Music Critics Association\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\nFilm Music Composition Of The Year\\n\\nRamin Djawadi for \"Light of the Seven\"\\n\\nWorld Soundtrack Awards\\n\\nTelevision Composer of the Year\\n\\nRamin Djawadi\\n\\n2017\\n\\n43rd People\\'s Choice Awards\\n\\nFavorite Premium Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n44th Annie Awards\\n\\nOutstanding Achievement, Character Animation in a Live Action Production\\n\\nNicholas Tripodi, Dean Elliott, James Hollingworth, Matt Weaver for \"Battle of the Bastards\"\\n\\n74th Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\n21st Satellite Awards\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWriters Guild of America Awards 2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n23rd Screen Actors Guild Awards\\n\\nOutstanding Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, Leo Woodruff\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunnungham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carcie Van Houten, Gemma Whelan, Maisie Williams,\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nAmerican Cinema Editors Awards 2017\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nArt Directors Guild Awards 2016\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley for  \"Blood of My Blood\", \"The Broken Man\" and \"No One\"\\n\\nProducers Guild of America Awards 2016\\n\\nThe Norman Felton Award for Outstanding Producer of Episodic Television, Drama\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\nVisual Effects Society Awards 2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway for \"Battle of the Bastards\"\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback for \"Battle of the Bastards\" – Drogon\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben for \"Home\" – Emaciated Dragon\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta for \"Battle of the Bastards\" – Meereen City\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik for \"The Winds of Winter\" – Citadel\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies for \"Battle of the Bastards\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen for \"Battle of the Bastards\"\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond for \"Battle of the Bastards\" – Meereen City\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar for \"Battle of the Bastards\" – Meereen City\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson for \"Battle of the Bastards\" – Retaking Winterfell\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren for \"The Door\" – Land of Always Winter\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss for \"Battle of the Bastards\"\\n\\n69th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker, Kay Bilk\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nBest Special Makeup Effects – Television\\n\\nBarrie Gower, Sarah Gower\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Fantasy Television Series\\n\\nMichele Clapton, April Ferry\\n\\nDorian Awards\\n\\nTV Drama of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n22nd National Television Awards\\n\\nBest Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Battle of the Bastards\"\\n\\nSociety of Camera Operators Awards\\n\\nCamera Operator of the Year – Television\\n\\nSean Savage\\n\\nZulu Awards\\n\\nBest Actor\\n\\nNikolaj Coster-Waldau\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Home\"\\n\\nLocation Managers Guild Awards\\n\\nLMGI Award for Outstanding Locations in Period Television\\n\\nMatt Jones and Naomi Liston\\n\\n43rd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\n22nd Empire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIrish Film & Television Academy\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest VFX\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nUnscripted (Branded)\\n\\n\"Battle of the Bastards\" Featurette\\n\\nHugo Award\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Miguel Sapochnik (director) for \"Battle of the Bastards\"\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Jack Bender (director) for \"The Door\"\\n\\n2017 MTV Movie & TV Awards\\n\\nShow Of The Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actor In A Show\\n\\nEmilia Clarke\\n\\nTearjerker\\n\\nHodor\\'s (Kristian Nairn) Death\\n\\n2017 British Academy Television Awards\\n\\nMust-See Moment\\n\\n\"Battle of the Bastards\"\\n\\nGlamour Awards\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast around the world by HBO and its broadcast partners. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '8cde72af54363c7dbdbcbadef960ae0f'}>,\n",
+       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n32nd TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nTV Choice Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nDragon Awards\\n\\nBest Science Fiction or Fantasy TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold, Robert Sterne, Carla Stronge\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nSophie Turner\\n\\n68th Primetime Emmy Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nKit Harington as Jon Snow\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\nMaisie Williams as Arya Stark\\n\\nOutstanding Directing for a Drama Series\\n\\nJack Bender for \"The Door\"\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Battle of the Bastards\"\\n\\n68th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow as Three-Eyed Raven\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton for \\'\\'Home\\'\\'\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary for \\'\\'The Winds of Winter\\'\\'\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora for \\'\\'The Door\\'\\'\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth for \"Battle of the Bastards\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \\'\\'Blood of My Blood\\'\\', \\'\\'The Broken Man\\'\\', and \\'\\'No One\\'\\'\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower for \\'\\'The Door\\'\\'\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \\'\\'Battle of the Bastards\\'\\'\\n\\nKatie Weiland for \\'\\'Oathbreaker\\'\\'\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit for \\'\\'The Door\\'\\'\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney,  Sam Conway,  Matthew Rouleau,  Michelle Blok, Glenn Melenhorst for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Main Titles 360 Experience\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch  for \"Battle of the Bastards\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau  for \"Battle of the Bastards\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n3D Award for Visual Effects Design\\n\\nIloura for \"Battle of the Bastards\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"The Winds of Winter\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nSean Savage, David Morgan & John Ferguson for \"Battle of the Bastards\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Battle of the Bastards\"\\n\\nAnette Haellmigk for \"Book of the Stranger\"\\n\\n7th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN People\\'s Choice Award\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMTV Fandom Awards\\n\\nFan Freak Out of the Year\\n\\n\\'\\'Game of Thrones\\'\\' – Resurrection of Jon Snow\\n\\nInternational Film Music Critics Association\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\nFilm Music Composition Of The Year\\n\\nRamin Djawadi for \"Light of the Seven\"\\n\\nWorld Soundtrack Awards\\n\\nTelevision Composer of the Year\\n\\nRamin Djawadi\\n\\n2017\\n\\n43rd People\\'s Choice Awards\\n\\nFavorite Premium Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n44th Annie Awards\\n\\nOutstanding Achievement, Character Animation in a Live Action Production\\n\\nNicholas Tripodi, Dean Elliott, James Hollingworth, Matt Weaver for \"Battle of the Bastards\"\\n\\n74th Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\n21st Satellite Awards\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWriters Guild of America Awards 2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n23rd Screen Actors Guild Awards\\n\\nOutstanding Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, Leo Woodruff\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunnungham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carcie Van Houten, Gemma Whelan, Maisie Williams,\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nAmerican Cinema Editors Awards 2017\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nArt Directors Guild Awards 2016\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley for  \"Blood of My Blood\", \"The Broken Man\" and \"No One\"\\n\\nProducers Guild of America Awards 2016\\n\\nThe Norman Felton Award for Outstanding Producer of Episodic Television, Drama\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\nVisual Effects Society Awards 2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway for \"Battle of the Bastards\"\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback for \"Battle of the Bastards\" – Drogon\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben for \"Home\" – Emaciated Dragon\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta for \"Battle of the Bastards\" – Meereen City\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik for \"The Winds of Winter\" – Citadel\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies for \"Battle of the Bastards\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen for \"Battle of the Bastards\"\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond for \"Battle of the Bastards\" – Meereen City\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar for \"Battle of the Bastards\" – Meereen City\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson for \"Battle of the Bastards\" – Retaking Winterfell\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren for \"The Door\" – Land of Always Winter\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss for \"Battle of the Bastards\"\\n\\n69th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker, Kay Bilk\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nBest Special Makeup Effects – Television\\n\\nBarrie Gower, Sarah Gower\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Fantasy Television Series\\n\\nMichele Clapton, April Ferry\\n\\nDorian Awards\\n\\nTV Drama of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n22nd National Television Awards\\n\\nBest Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Battle of the Bastards\"\\n\\nSociety of Camera Operators Awards\\n\\nCamera Operator of the Year – Television\\n\\nSean Savage\\n\\nZulu Awards\\n\\nBest Actor\\n\\nNikolaj Coster-Waldau\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Home\"\\n\\nLocation Managers Guild Awards\\n\\nLMGI Award for Outstanding Locations in Period Television\\n\\nMatt Jones and Naomi Liston\\n\\n43rd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\n22nd Empire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIrish Film & Television Academy\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest VFX\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nUnscripted (Branded)\\n\\n\"Battle of the Bastards\" Featurette\\n\\nHugo Award\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Miguel Sapochnik (director) for \"Battle of the Bastards\"\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Jack Bender (director) for \"The Door\"\\n\\n2017 MTV Movie & TV Awards\\n\\nShow Of The Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actor In A Show\\n\\nEmilia Clarke\\n\\nTearjerker\\n\\nHodor\\'s (Kristian Nairn) Death\\n\\n2017 British Academy Television Awards\\n\\nMust-See Moment\\n\\n\"Battle of the Bastards\"\\n\\nGlamour Awards\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast around the world by HBO and its broadcast partners. While in some countries, it aired the day after its first release.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '1369e11a77cb0c1daca95a47877eff03'}>,\n",
+       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n32nd TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nTV Choice Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nDragon Awards\\n\\nBest Science Fiction or Fantasy TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold, Robert Sterne, Carla Stronge\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nSophie Turner\\n\\n68th Primetime Emmy Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nKit Harington as Jon Snow\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\nMaisie Williams as Arya Stark\\n\\nOutstanding Directing for a Drama Series\\n\\nJack Bender for \"The Door\"\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Battle of the Bastards\"\\n\\n68th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow as Three-Eyed Raven\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton for \\'\\'Home\\'\\'\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary for \\'\\'The Winds of Winter\\'\\'\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora for \\'\\'The Door\\'\\'\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth for \"Battle of the Bastards\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \\'\\'Blood of My Blood\\'\\', \\'\\'The Broken Man\\'\\', and \\'\\'No One\\'\\'\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower for \\'\\'The Door\\'\\'\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \\'\\'Battle of the Bastards\\'\\'\\n\\nKatie Weiland for \\'\\'Oathbreaker\\'\\'\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit for \\'\\'The Door\\'\\'\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney,  Sam Conway,  Matthew Rouleau,  Michelle Blok, Glenn Melenhorst for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Main Titles 360 Experience\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch  for \"Battle of the Bastards\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau  for \"Battle of the Bastards\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n3D Award for Visual Effects Design\\n\\nIloura for \"Battle of the Bastards\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"The Winds of Winter\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nSean Savage, David Morgan & John Ferguson for \"Battle of the Bastards\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Battle of the Bastards\"\\n\\nAnette Haellmigk for \"Book of the Stranger\"\\n\\n7th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN People\\'s Choice Award\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMTV Fandom Awards\\n\\nFan Freak Out of the Year\\n\\n\\'\\'Game of Thrones\\'\\' – Resurrection of Jon Snow\\n\\nInternational Film Music Critics Association\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\nFilm Music Composition Of The Year\\n\\nRamin Djawadi for \"Light of the Seven\"\\n\\nWorld Soundtrack Awards\\n\\nTelevision Composer of the Year\\n\\nRamin Djawadi\\n\\n2017\\n\\n43rd People\\'s Choice Awards\\n\\nFavorite Premium Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n44th Annie Awards\\n\\nOutstanding Achievement, Character Animation in a Live Action Production\\n\\nNicholas Tripodi, Dean Elliott, James Hollingworth, Matt Weaver for \"Battle of the Bastards\"\\n\\n74th Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\n21st Satellite Awards\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWriters Guild of America Awards 2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n23rd Screen Actors Guild Awards\\n\\nOutstanding Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, Leo Woodruff\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunnungham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carcie Van Houten, Gemma Whelan, Maisie Williams,\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nAmerican Cinema Editors Awards 2017\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nArt Directors Guild Awards 2016\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley for  \"Blood of My Blood\", \"The Broken Man\" and \"No One\"\\n\\nProducers Guild of America Awards 2016\\n\\nThe Norman Felton Award for Outstanding Producer of Episodic Television, Drama\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\nVisual Effects Society Awards 2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway for \"Battle of the Bastards\"\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback for \"Battle of the Bastards\" – Drogon\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben for \"Home\" – Emaciated Dragon\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta for \"Battle of the Bastards\" – Meereen City\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik for \"The Winds of Winter\" – Citadel\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies for \"Battle of the Bastards\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen for \"Battle of the Bastards\"\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond for \"Battle of the Bastards\" – Meereen City\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar for \"Battle of the Bastards\" – Meereen City\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson for \"Battle of the Bastards\" – Retaking Winterfell\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren for \"The Door\" – Land of Always Winter\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss for \"Battle of the Bastards\"\\n\\n69th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker, Kay Bilk\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nBest Special Makeup Effects – Television\\n\\nBarrie Gower, Sarah Gower\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Fantasy Television Series\\n\\nMichele Clapton, April Ferry\\n\\nDorian Awards\\n\\nTV Drama of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n22nd National Television Awards\\n\\nBest Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Battle of the Bastards\"\\n\\nSociety of Camera Operators Awards\\n\\nCamera Operator of the Year – Television\\n\\nSean Savage\\n\\nZulu Awards\\n\\nBest Actor\\n\\nNikolaj Coster-Waldau\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Home\"\\n\\nLocation Managers Guild Awards\\n\\nLMGI Award for Outstanding Locations in Period Television\\n\\nMatt Jones and Naomi Liston\\n\\n43rd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\n22nd Empire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIrish Film & Television Academy\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest VFX\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nUnscripted (Branded)\\n\\n\"Battle of the Bastards\" Featurette\\n\\nHugo Award\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Miguel Sapochnik (director) for \"Battle of the Bastards\"\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Jack Bender (director) for \"The Door\"\\n\\n2017 MTV Movie & TV Awards\\n\\nShow Of The Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actor In A Show\\n\\nEmilia Clarke\\n\\nTearjerker\\n\\nHodor\\'s (Kristian Nairn) Death\\n\\n2017 British Academy Television Awards\\n\\nMust-See Moment\\n\\n\"Battle of the Bastards\"\\n\\nGlamour Awards\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast around the world by HBO and its broadcast partners. While in some countries, it aired the day after its first release.\\n\\n===Marketing===\\nOn November 23, 2015, a teaser poster displaying Jon Snow was released on the official \\'\\'Game of Thrones\\'\\' Twitter account. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '74d1dd3a5a526756cb9339cb8c20247d'}>,\n",
+       "  <Document: {'content': '===Marketing===\\nOn November 23, 2015, a teaser poster displaying Jon Snow was released on the official \\'\\'Game of Thrones\\'\\' Twitter account. A 41-second teaser trailer was released on December 3, featuring Jon Snow from the fifth season episode \"Hardhome\", as well as many of the previous seasons\\' highlights, and a voice-over from Max von Sydow as the Three-Eyed Raven and Isaac Hempstead Wright as Bran Stark. The first footage from the season was revealed in a new promotional video released by HBO highlighting its new and returning original shows for the coming year on December 6, 2015, showcasing scenes involving Daenerys Targaryen, Ramsay Bolton, Cersei Lannister, and Tommen Baratheon. On December 28, 2015, \\'\\'Entertainment Weekly\\'\\' released its \"Exclusive First Look\" issue, featuring an image of Bran Stark, who was noticeably older from his last appearance in season four, and with shorter hair.\\n\\nOn January 22, 2016, three teaser trailers were released, with each teaser depicting the banners of the houses Targaryen, Lannister and Stark, and included voice-overs by Iwan Rheon as Ramsay Bolton, Jonathan Pryce as The High Sparrow and an unknown character speaking in Dothraki. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'a4f6ae0c6f538be668e7c5a38c26050f'}>,\n",
+       "  <Document: {'content': 'On January 22, 2016, three teaser trailers were released, with each teaser depicting the banners of the houses Targaryen, Lannister and Stark, and included voice-overs by Iwan Rheon as Ramsay Bolton, Jonathan Pryce as The High Sparrow and an unknown character speaking in Dothraki. On February 11, 2016, HBO released 28 exclusive photos from the sixth season, picturing several of the main characters during the season and confirming the fates of Theon, Sansa and Myrcella, while Jon Snow was notably absent. HBO released a teaser trailer on February 14, 2016, that shows the faces of a number of living as well as deceased characters such as Ned Stark, Robb Stark, Catelyn Stark, Joffrey Baratheon, Tywin Lannister, Stannis Baratheon, Ygritte and, controversially, Jon Snow in the House of Black and White. On February 24, 2016, HBO released 16 character posters of both deceased and alive characters, and two official posters featuring various characters.\\n\\nA behind-the-scenes video of the sixth season was released on February 29, 2016, focusing on camera operators working on the show. The first official trailer for season 6 was released on March 8, 2016. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'e065b32375821ff343ced7fb6bd680cf'}>,\n",
+       "  <Document: {'content': 'A behind-the-scenes video of the sixth season was released on February 29, 2016, focusing on camera operators working on the show. The first official trailer for season 6 was released on March 8, 2016. Another behind-the-scenes video was released on March 22, 2016, focusing on the creative process of prosthetics, specifically the White Walkers. The video also contained new footage of White Walkers from the sixth season. On March 24, \\'\\'Entertainment Weekly\\'\\' revealed a series of new issues titled \"Dame of Thrones\", featuring six of the female lead characters from the series and focus on the sixth season. On March 26, 2016, new photos from season six, in addition to a new promo named \"March Madness\" with new footage, was released by HBO. After the screening of the season premiere \"The Red Woman\", HBO released a second official trailer. As well, in July 2016, HBO released a \"blooper reel\" online, a video of outtakes from season 6.\\n\\n===\\'\\'After the Thrones\\'\\'===\\n\\n\\'\\'After the Thrones\\'\\', a live aftershow in which hosts Andy Greenwald and Chris Ryan discuss episodes of the series, airs on the stand-alone streaming service HBO Now on the Monday following each episode of the show\\'s sixth season.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '4954dfb4c6112141cbe16b6056ad615c'}>,\n",
+       "  <Document: {'content': \"===''After the Thrones''===\\n\\n''After the Thrones'', a live aftershow in which hosts Andy Greenwald and Chris Ryan discuss episodes of the series, airs on the stand-alone streaming service HBO Now on the Monday following each episode of the show's sixth season.\\n\\n===Home media===\\nThe season was released on Blu-ray and DVD on November 15, 2016. The set includes extra background, behind-the-scenes material and deleted scenes.\\n\\n===Copyright infringement===\\nThe sixth season of ''Game of Thrones'' was the most-pirated TV series in 2016.\\n\\n==References==\\n\\n==External links==\\n*  – official US site\\n*  – official UK site\\n*  ''Game of Thrones'' – The Viewers Guide on HBO.com\\n*  Making ''Game of Thrones'' on HBO.com\\n*\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '8ba14dcb1ecae81f513f6b84ef6be329'}>,\n",
+       "  <Document: {'content': '\\n\\nThe \\'\\'\\'Night King\\'\\'\\' is a fictional character appearing in the HBO high fantasy television series \\'\\'Game of Thrones\\'\\', based on George R. R. Martin\\'s novel series \\'\\'A Song of Ice and Fire\\'\\'. He is depicted as the leader and the first of the White Walkers, having existed since the age of the First Men, and is the most dangerous and powerful of his race. The Night King is an original creation of the television adaptation, thus far having no counterpart in the novels upon which the show is based.\\n\\nThe Night King was portrayed by British-American actor Richard Brake in seasons 4 and 5 and then by Slovakian actor and stuntman Vladimir Furdik in seasons 6 to 8.\\n\\n==Description==\\nIn \\'\\'Game of Thrones\\'\\', the Night King is physically distinguished from the other White Walkers by his \"crown\" of pointy, frosty horns.\\n\\n==The Night\\'s King in the novels==\\nThe \"Night King\", as presented in the show, has not appeared in \\'\\'A Song of Ice and Fire\\'\\'. In the novels, the title \"Night\\'s King\" is given to the long-ago legendary 13th Lord Commander of the Night\\'s Watch, who supposedly married a female White Walker and led the Night\\'s Watch to commit atrocities. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'e1b5cdbb7677a5cb341a2fbd288a9b80'}>,\n",
+       "  <Document: {'content': 'In the novels, the title \"Night\\'s King\" is given to the long-ago legendary 13th Lord Commander of the Night\\'s Watch, who supposedly married a female White Walker and led the Night\\'s Watch to commit atrocities. Regarding whether the character in the television series is the same as the one in the novels, Martin said, \"As for the Night\\'s King (the form I prefer), in the books he is a legendary figure, akin to Lann the Clever and Brandon the Builder, and no more likely to have survived to the present day than they have.\"==Development==\\n\\'\\'\\'Top:\\'\\'\\' Vladimír Furdík as the Night King with only practical effects and prosthetics.\\'\\'\\'Bottom:\\'\\'\\' Added VFX in post-production, with altered eyes and an icy layer.\\nIn order to create the Night King, according to Barrie Gower, it is primarily practical prosthetic, but also incorporates some visual effects (VFX) to create a more icy look and feel, saying about the eyes, \"The effects department alters the eyes in post-production. They give them that blue-glowy hue to them, which we can\\'t really achieve with contact lenses.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'ba218d9615a9c20a4f372d9fb9a34e90'}>,\n",
+       "  <Document: {'content': 'In order to create the Night King, according to Barrie Gower, it is primarily practical prosthetic, but also incorporates some visual effects (VFX) to create a more icy look and feel, saying about the eyes, \"The effects department alters the eyes in post-production. They give them that blue-glowy hue to them, which we can\\'t really achieve with contact lenses.\"In regards to the overall prosthetic, \"they\\'ve added this sort of icy layer over the top of him to create this—it\\'s something incredibly difficult to achieve practically, prosthetics are cast in a translucent rubber, which can only give you so much of that icy quality, so visual effects help augment it a little bit further to give it more dimension.\"In the fourth and fifth season, the Night King was portrayed by Richard Brake, with a head mold of Brake being created in order to accurately mold the prosthetic to his face. In the sixth season he was portrayed by Vladimir Furdik. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '4dae655e6fc817b7e99b8f4bf7d6c58f'}>,\n",
+       "  <Document: {'content': 'In the fourth and fifth season, the Night King was portrayed by Richard Brake, with a head mold of Brake being created in order to accurately mold the prosthetic to his face. In the sixth season he was portrayed by Vladimir Furdik. The White Walker army was first shot in front of a green screen in Magheramorne quarry, and according to a piece in \\'\\'The Hollywood Reporter\\'\\' \"A scan was taken from a drone and used as the basis for a CG model of the location, which was augmented with VFX and joined with volcanic hills that were photographed in Iceland.\"Crowd replication was used to create the 1,000-man army, with special effects supervisor Joe Bauer saying \"It\\'s scans of those actors in the makeup and costumes, with variations we used to make a digital army that extends up onto the hills.\"Bauer also noted that VFX were used to create the weather conditions in the scene, noting \"It\\'s wind, mist, fog and heavy atmosphere that you\\'d get in a marine layer. The dramatic value is to say something\\'s wrong; it\\'s a mystery what they are facing. The temperature drops, and our characters can see their breath. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '548595e95ad3dd7eb0d3d72f18fe24e1'}>,\n",
+       "  <Document: {'content': 'The dramatic value is to say something\\'s wrong; it\\'s a mystery what they are facing. The temperature drops, and our characters can see their breath. The weather obscures their vision.\"Spain-based El Ranchito, one of several special effects companies used by \\'\\'Game of Thrones\\'\\', was responsible for the White Walker army shots.\\n\\n==Storylines==\\n\\n===Season 4===\\nThe Night King\\'s first appearance is through a vision Bran Stark has with a Weirwood Heart tree. He experiences a flood of images from the past, present, and future, many of which he was not physically present for. He does not comprehend what all of these images are but, in retrospect, one of them is an image of the Night King picking up the last of Craster\\'s sons off an ice altar.\\n\\nAfter Rast leaves Craster\\'s final son in the Haunted Forest, a White Walker retrieves the child. The Walker travels to the White Walkers\\' fortress in the Lands of Always Winter and lays the child on an altar made of ice. The Night King approaches and places his finger on the boy, transforming him into a White Walker.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '672dfe2066974c9d39f2fe6c11379c83'}>,\n",
+       "  <Document: {'content': \"The Walker travels to the White Walkers' fortress in the Lands of Always Winter and lays the child on an altar made of ice. The Night King approaches and places his finger on the boy, transforming him into a White Walker.\\n\\n===Season 5===\\nThe Night King appears again when Jon Snow and Tormund Giantsbane are coordinating the evacuation of Hardhome. While loading the ships with wildlings, Hardhome is attacked by an army of wights. While assisting in the defense of Hardhome, Jon and Tormund see the Night King observing the battle along with his generals. The Night King also observes Jon destroying a White Walker with his Valyrian steel sword Longclaw. Jon and his allies are forced to flee Hardhome after the walls fall to the army of the dead; as they sail to safety, they witness the Night King reviving the dead as wights.\\n\\n===Season 6===\\nThe Night King appears in a vision being observed by Bran and the Three-eyed Raven, where they witness him as a human being forcibly transformed into the first White Walker by the Children of the Forest by impaling him with a dragonglass dagger. Bran subsequently confronts Leaf about creating the White Walkers. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '9a2de49f0677a93aae89b1ae732b243f'}>,\n",
+       "  <Document: {'content': \"===Season 6===\\nThe Night King appears in a vision being observed by Bran and the Three-eyed Raven, where they witness him as a human being forcibly transformed into the first White Walker by the Children of the Forest by impaling him with a dragonglass dagger. Bran subsequently confronts Leaf about creating the White Walkers. She explains that they were at war with the First Men and had no choice.\\n\\nLater on, Bran decides to observe a vision without the Three-Eyed Raven. He witnesses a massive army of wights led by the Night King, who touches him whilst in the vision. Bran awakens to find a mark where he was touched, and the Three-Eyed Raven warns that he must leave, as the Night King is now able to bypass the magic protecting the Three-Eyed Raven's lair. The Night King and his army quickly arrive and the Night King kills the Three-Eyed Raven, but Bran and Meera Reed are able to escape.\\n\\n===Season 7===\\nThe Night King is shown leading his army south. Through ravens' eyes, Bran locates the Night King's army beyond the Wall. When the Night King looks up, the ravens disperse, and Bran is pulled out of the warging. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '6c8239e7e402c69f509d3eb84d6ed5e4'}>,\n",
+       "  <Document: {'content': \"Through ravens' eyes, Bran locates the Night King's army beyond the Wall. When the Night King looks up, the ravens disperse, and Bran is pulled out of the warging. He requests that ravens be sent throughout the Seven Kingdoms to warn of the threat.\\n\\nThe Night King is present when the wights and White Walkers battle Jon Snow at the Wight Hunt, who traveled beyond the Wall with the Hound, Jorah, Beric, Thoros, Gendry, Tormund and several other Wildlings to attempt to capture a wight to use as proof for the gathered high lords of Westeros. Remembering their encounter from Hardhome, the Night King keeps his gaze on Jon for some time. Beric Dondarrion suggests trying to kill the Night King; as they know, killing a White Walker kills any and all wights it raised, so killing the Night King might destroy every such monster under his command, putting an end to the Great War before it truly begins. Jon dismisses the plan, however, arguing that trying to fight their way through the wights to reach the Night King would be suicide. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'fbe0d7b656deaed268fd6d44f08bc4a9'}>,\n",
+       "  <Document: {'content': \"Jon dismisses the plan, however, arguing that trying to fight their way through the wights to reach the Night King would be suicide. When Daenerys Targaryen arrives and her dragons burn many wights, the men try to evacuate on Drogon but the wights continue their attack; using an ice javelin, the Night King kills Viserion. The Night King keeps his gaze on an enraged Jon, who appears on the verge of calling him down to fight directly, but his lieutenant hands him another spear. The Night King throws it at Drogon, but the dragon takes flight and dodges in time. Jon stays on the ground to cover the others' departure, but is pulled into the water, and the others are forced to flee on Drogon. Jon emerges from the water and is saved by Benjen Stark, who gives Jon his horse to ride to Eastwatch while sacrificing himself to hold off the wights. After their battle is over, the Night King reanimates Viserion.\\n\\nThe undead army arrives at Eastwatch; when the Night King appears, riding Viserion, Tormund orders the defenders to evacuate. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '8bb1498629489392fa41a1e82c72b532'}>,\n",
+       "  <Document: {'content': \"After their battle is over, the Night King reanimates Viserion.\\n\\nThe undead army arrives at Eastwatch; when the Night King appears, riding Viserion, Tormund orders the defenders to evacuate. Breathing blue fire, Viserion destroys Eastwatch and a section of the Wall, allowing the White Walkers and their wight army to finally invade the Seven Kingdoms  while the Night King flies overhead towards the North.\\n\\n===Season 8===\\nLed by the Night King, the dead quickly advance south and attack Last Hearth, massacring the population and adding them to their army, before marching on Winterfell. Bran, knowing that the Night King plans to destroy him, plans to hide in Winterfell's Godswood as bait, with Jon and Daenerys lying in wait with the dragons to ambush him. However, Jon and Daenerys leave their position to attack the wights when they begin overwhelming Winterfell's defenders, and the Night King engages them with Viserion, resulting in Jon and the Night King being dismounted. Daenerys attempts to burn the Night King, not realizing that he is immune to fire, and flees when the Night King attempts to kill her dragon Drogon. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '74dfb5a396729b155f3d67f739a5e9bd'}>,\n",
+       "  <Document: {'content': 'Daenerys attempts to burn the Night King, not realizing that he is immune to fire, and flees when the Night King attempts to kill her dragon Drogon. Jon attempts to fight the Night King, but the Night King reanimates those killed in battle to protect him. The Night King and the White Walkers head to the Godswood for Bran, and are confronted by Theon Greyjoy, whom the Night King kills. However, before he can kill Bran, he is ambushed by Arya Stark, who destroys him by plunging a dagger made out of Valyrian steel into the Night King\\'s chest. With the Night King dead, the White Walkers shatter and the Army of the Dead collapses.\\n\\n==Merchandising==\\nDark Horse produced a Night King bust in March 2016, which was followed by an 8-inch Night King figure by Dark Horse Deluxe in late 2016. Funko also released a \"Night King\" POP! figure that same year.\\n\\n== References ==\\n* 50px Content in this article was copied from  Night King at Game of Thrones Wiki, which is licensed under the  Creative Commons Attribution-Share Alike 3.0 (Unported) (CC-BY-SA 3.0) license.', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'c3aa18c2dec1317a864aceb134ae33b0'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Mhysa\\'\\'\\'\" is the third season finale of the American medieval epic fantasy television series \\'\\'Game of Thrones\\'\\', and its 30th episode overall. Written by executive producers David Benioff and D. B. Weiss, and directed by David Nutter, it originally aired on  on HBO in the United States.\\n\\nThe episode revolves on the aftermath of the events instigated by \"The Red Wedding\", in which Tywin Lannister is revealed to be the mastermind behind the massacre — with Walder Frey and Roose Bolton having conspired with the Lannisters against the Starks. As a result, House Frey receives the Seat of Riverrun and Roose Bolton is appointed the new \"Warden of the North\". Elsewhere, House Greyjoy begins a new military campaign. In the North, Maester Aemon sends out ravens to alert the whole of Westeros about the arrival of the White Walkers. And across the narrow sea, the freed slaves of Yunkai hail Daenerys as their \"mhysa\", the Ghiscari language\\'s word for \"mother\".\\n\\nAccording to Nielsen Media Research, \"Mhysa\" was seen by 5.4 million household viewers in the United States, a twenty-eight percent increase compared to the second season finale, \"Valar Morghulis\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '93865fe1862df748dc3e381c4941dc52'}>,\n",
+       "  <Document: {'content': 'According to Nielsen Media Research, \"Mhysa\" was seen by 5.4 million household viewers in the United States, a twenty-eight percent increase compared to the second season finale, \"Valar Morghulis\". After its broadcast, the episode received generally positive reviews from television critics, with some of them addressing its anti-climactic closure of the series\\' third season, and its establishment of potential storylines for the fourth season, such as through the final scene\\'s \"glimmer of hope\". The episode received a nomination for a Primetime Emmy Award for Outstanding Cinematography for a Single-Camera Series at the 65th Primetime Creative Arts Emmy Awards.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nTyrion learns of the deaths of Robb and Catelyn during the Red Wedding. Tyrion tells Tywin that northerners will never forget the role the Lannisters played in the Red Wedding. Varys gives Shae a sack of diamonds to sail for Essos, but she refuses. Jaime, Brienne, and Qyburn arrive in King\\'s Landing.\\n\\n===At the Twins===\\nSandor and Arya find a group of Frey men. Arya kills one, who claimed to have desecrated Robb\\'s body and Sandor kills others. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '9e7166ef4d952cc33fa2a51e70669cb3'}>,\n",
+       "  <Document: {'content': \"===At the Twins===\\nSandor and Arya find a group of Frey men. Arya kills one, who claimed to have desecrated Robb's body and Sandor kills others. Walder and Bolton discuss the escape of Brynden and their new oppositions as Lord of Riverrun and Warden of the North.\\n\\n===In the North===\\nBran and his group arrive at the Nightfort and meet Sam and Gilly. Sam realizes that Bran is Jon's brother. Bran asks Sam to take them north of the Wall. He takes them through the passage and gives them his dragonglass weapons. Sam and Gilly arrive at Castle Black and meet Aemon, who, after hearing of White Walkers, sends all ravens with messages warning the return of White Walkers. Ygritte finds Jon, but before he escapes, Ygritte shoots three arrows into him. Jon arrives at Castle Black, where his wounds are treated. At the Dreadfort, Theon's torturer, revealed to be Bolton's bastard Ramsay Snow, nicknames Theon Reek.\\n\\n===On the Iron Islands===\\nBalon reads letter from Ramsay, demanding to have the Ironborn soldiers withdraw from the North. Balon allows Theon's torture to continue, but Yara takes 50 best Ironborn men to attack the Dreadfort.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'e2f34a69ff1601a1ef9856c0587c4dd7'}>,\n",
+       "  <Document: {'content': '===On the Iron Islands===\\nBalon reads letter from Ramsay, demanding to have the Ironborn soldiers withdraw from the North. Balon allows Theon\\'s torture to continue, but Yara takes 50 best Ironborn men to attack the Dreadfort.\\n\\n===At Dragonstone===\\nStannis informs Davos of Robb\\'s death and Melisandre plans to sacrifice Gendry to the Lord of Light. Davos gives Gendry a boat to escape Dragonstone. Stannis orders Davos’ execution, but after he shows Stannis a letter from the Wall, Melisandre tells Stannis that only he can save the North from the White Walkers and that he will need Davos\\' help, preventing his execution.\\n\\n===Outside Yunkai===\\nDaenerys frees slaves from Yunkai, who begin to shout mhysa, which Missandei tells Daenerys is Ghiscari for mother.\\n\\n==Production==\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\\n===Writing===\\n\"Mhysa\" was written by executive producers David Benioff and D. B. Weiss, based on George R. R. Martin\\'s original work from his novel \\'\\'A Storm of Swords\\'\\'. Chapters adapted from \\'\\'A Storm of Swords\\'\\' to the episode were chapters 43, 49, 53 to 55, 57, 63, and 64 (Daenerys IV, Jon VI, Arya XI, Tyrion VI, Davos V, Bran IV, Jaime VII, Davos VI).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '6d1cf2d41eed8182323309040fb9a485'}>,\n",
+       "  <Document: {'content': 'Chapters adapted from \\'\\'A Storm of Swords\\'\\' to the episode were chapters 43, 49, 53 to 55, 57, 63, and 64 (Daenerys IV, Jon VI, Arya XI, Tyrion VI, Davos V, Bran IV, Jaime VII, Davos VI).\\n\\n===Casting===\\nAfter being absent for the entire second season, Peter Vaughan returns as Maester Aemon and Josef Altin returns as Pypar. This episode also marks the return, after a long absence, of Patrick Malahide as Balon Greyjoy and Gemma Whelan as Yara Greyjoy.\\n\\n==Reception==\\n\\n===Ratings===\\nIn its original American broadcast in HBO, \"Mhysa\" was seen by an estimated 5.4 million household viewers, according to Nielsen Media Research. It marked a year-to-year increase in viewership of 28 percent compared to the second season finale, \"Valar Morghulis\", which was seen by 4.2 million. The second broadcast of the \"Mhysa\" during the night was viewed by 900,000 viewers, bringing its total viewership for to 6.30 million. According to analysts, the success of the episode significantly helped \\'\\'Game of Thrones\\'\\' to surpass \\'\\'True Blood\\'\\' as the second most-watched series on HBO, after \\'\\'The Sopranos\\'\\'. In the United Kingdom, the episode was viewed by 1.154 million viewers, making it the highest-rated broadcast that week. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '6d39a052364b087c5e4cf62c180246bb'}>,\n",
+       "  <Document: {'content': 'According to analysts, the success of the episode significantly helped \\'\\'Game of Thrones\\'\\' to surpass \\'\\'True Blood\\'\\' as the second most-watched series on HBO, after \\'\\'The Sopranos\\'\\'. In the United Kingdom, the episode was viewed by 1.154 million viewers, making it the highest-rated broadcast that week. It also received 0.110 million timeshift viewers.\\n\\n===Critical reception===\\n\"Mhysa\" received generally positive reviews from television critics, with some of them addressing the finale\\'s anti-climactic closure of the third season and for establishing new storylines for the fourth. Review aggregator Rotten Tomatoes surveyed 20 reviews of the episode and deemed 100% of them to be positive with an average score of 8.5 out of 10. The website\\'s critical consensus reads, \"\\'Mhysa\\' wraps up several of season 3\\'s lingering storylines while subtly setting the table for season 4.\"James Poniewozik of \\'\\'Time\\'\\' wrote in his review, \"The end of season 3, then, spent some time among the winners and losers in post-Red-Wedding Westeros, giving the audience a chance to soak in the shock, seethe at the winners\\' glee, and get a reminder of the larger forces – White Walkers, dragons – well beyond the war between the Lannisters and the Starks. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'b47c44b02fb421d246d64d4a1cc83089'}>,\n",
+       "  <Document: {'content': 'James Poniewozik of \\'\\'Time\\'\\' wrote in his review, \"The end of season 3, then, spent some time among the winners and losers in post-Red-Wedding Westeros, giving the audience a chance to soak in the shock, seethe at the winners\\' glee, and get a reminder of the larger forces – White Walkers, dragons – well beyond the war between the Lannisters and the Starks. If the Red Wedding seemed to kill hope, \\'Mhysa\\' made clear that it didn\\'t end anything. And it weaved together the many, many threads of \\'\\'GoTs\\'\\' tapestry by returning to a recurring theme: that \\'\\'Game of Thrones\\'\\' is ultimately about family.\"Matt Fowler of IGN described that the finale had \"nicely set up a lot of cool stuff for Season 4, but it was also lacking some of the power and majesty of previous finales. Especially the ending with Dany, who herself had a better ending back in \\'And Now His Watch is Ended\\' when her dragons torched Astapor and she left with a full army.\"He also praised the scene where Arya killed a Frey soldier. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'd3ebe29866be25b4f27bff5b912ea77'}>,\n",
+       "  <Document: {'content': 'Especially the ending with Dany, who herself had a better ending back in \\'And Now His Watch is Ended\\' when her dragons torched Astapor and she left with a full army.\"He also praised the scene where Arya killed a Frey soldier. Writing for \\'\\'Today\\'\\', Drusilla Moorhouse remarked that \"After last week\\'s shocking massacre, most fans braced for more tragic deaths in season three\\'s finale. Instead, the blow was softened with poignant reunions and surprising saves, setting the stage for an explosive fourth season.\"In her review for Zap2it, Terri Schwartz wrote that \"Nothing can ever quite redeem the deaths of Robb and Catelyn Stark, but at least larger forces are taking shape that are propelling this series into Season 4. Daenerys is as powerful as she\\'s ever been, Jon Snow returns to the Wall while Bran heads north of it and the Greyjoys ready an assault to finally save Theon from his captor. Then there\\'s the fact that Stannis decides to sail north to the Wall to aid the Night\\'s Watch in their fight against the White Walkers, which seems like it\\'s going to end up being the greater, global conflict in the future of \\'Game of Thrones\\'.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'e81eb41ca3fcb4ae4c04929df94c5127'}>,\n",
+       "  <Document: {'content': 'Then there\\'s the fact that Stannis decides to sail north to the Wall to aid the Night\\'s Watch in their fight against the White Walkers, which seems like it\\'s going to end up being the greater, global conflict in the future of \\'Game of Thrones\\'.\"Writing for \\'\\'The A.V. Club\\'\\', David Sims gave \"Mhysa\" an \"A-\" rating, while Emily VanDerWerff gave it a \"B+\". Sims, writing for audiences who have not read the novels, described the episode as lacking \"a lot of serious plot movement or major twists and may have fans gnashing their teeth a little bit as they wait nine months for season four. The previous season finales have also had that quality, but they each ended on a barnstorming note. \\'Mhysa\\', not quite so much.\"VanDerWerff, writing for audiences who have read the novels, wrote that \"On a plot level, not a lot happens in \\'Mhysa\\'\", but praised the episode in addressing the series\\' \"value of one human life is in the face of a kingdom.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '1c9580cd48a950550ba738ce741cd4b3'}>,\n",
+       "  <Document: {'content': 'VanDerWerff, writing for audiences who have read the novels, wrote that \"On a plot level, not a lot happens in \\'Mhysa\\'\", but praised the episode in addressing the series\\' \"value of one human life is in the face of a kingdom.\"He also mildly criticized the series\\' template of when \"something terrible happens in Westeros, Dany offers a glimmer of hope over in her story line\", referring to it as its \"Achilles\\' heel\". Kevin Fitzpatrick of ScreenCrush wrote in his review, \"All in all, the air (or blood) of the season had mostly been let out by \\'The Rains of Castamere\\', as even a returning Jaime’s most interesting moments arrived much earlier in the season, but \\'Mhysa\\' gave us a good course-heading for season 4, with some much-needed catharsis along the way.\"The episode\\'s final scene, in which Daenerys, \"the blondest possible savior figure\", appears with \"uncharacterized brown people\" as \"being lifted up as their messiah and praising her for saving them from bondage\", was criticized by at least four commentators as having colonialist or even racist undertones. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'f8bf54b4d5d7bc96341bbedb8f48c04f'}>,\n",
+       "  <Document: {'content': 'The episode\\'s final scene, in which Daenerys, \"the blondest possible savior figure\", appears with \"uncharacterized brown people\" as \"being lifted up as their messiah and praising her for saving them from bondage\", was criticized by at least four commentators as having colonialist or even racist undertones. They asked why the series chose to portray the Yunkish as nearly uniformly dark-skinned, rather than as ethnically diverse as in the source novels, to which George R. R. Martin replied that this was because the scene was shot in Morocco with local extras. Commentators also criticized, more broadly, that the series\\'s inclusion of people of color was limited to only a small number of characters.\\n\\n== References ==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '8a9390fc1041619d81dfcaa56ec6524c'}>,\n",
+       "  <Document: {'content': \"\\n\\nThe fictional world in which the ''A Song of Ice and Fire'' novels by George R. R. Martin take place is divided into several continents, known collectively as The Known World. Some unofficial fan groups call it Planetos.\\n\\nMost of the story takes place on the continent of Westeros and in a large political entity known as the Seven Kingdoms. Those kingdoms are spread across nine regions: the North, the Iron Islands, the Riverlands, the Vale, the Westerlands, the Stormlands, the Reach, the Crownlands, and Dorne. A massive wall of ice and old magic separates the Seven Kingdoms from the largely unmapped area to the north. The vast continent of Essos is located east of Westeros, across the Narrow Sea. The closest foreign nations to Westeros are the Free Cities, which is a collection of independent city-states along the western edge of Essos. The lands along the southern coastline of Essos are called the Lands of the Summer Sea and include Slaver's Bay and the ruins of Valyria. The latter is the former home of the ancestors of House Targaryen. To the south of Essos are the continents of Sothoryos and Ulthos, which in the narrative are largely unexplored.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '3099b7d06df8807e298e579c084a5338'}>,\n",
+       "  <Document: {'content': 'The latter is the former home of the ancestors of House Targaryen. To the south of Essos are the continents of Sothoryos and Ulthos, which in the narrative are largely unexplored.\\n\\nThe planet experiences erratic seasons of unpredictable duration that can last for many years. At the beginning of \\'\\'A Song of Ice and Fire\\'\\', Westeros has enjoyed a decade-long summer, and many feared that an even longer and harsher winter would follow.\\n\\nGeorge R. R. Martin set the \\'\\'Ice and Fire\\'\\' story in an alternative world of Earth, a \"secondary world\", such as that which J. R. R. Tolkien pioneered with Middle-earth. Martin has also suggested that world may be larger than the real world planet Earth. The \\'\\'Ice and Fire\\'\\' narrative is set in a post-magic world where people no longer believe in supernatural things such as the Others. Although the characters understand the natural aspects of their world, they do not know or understand its magical elements. Religion, though, has a significant role in the life of people, and the characters practice many different religions.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'a5d3affa8a2e9403e92aac8664bfd89b'}>,\n",
+       "  <Document: {'content': 'Although the characters understand the natural aspects of their world, they do not know or understand its magical elements. Religion, though, has a significant role in the life of people, and the characters practice many different religions.\\n\\n==Maps==\\n\\nMap\\n\\n\\'\\'Game\\'\\'\\n\\n\\'\\'Clash\\'\\'\\n\\n\\'\\'Storm\\'\\'\\n\\n\\'\\'Feast\\'\\'\\n\\n\\'\\'Dance\\'\\'\\n\\n(\\'\\'Lands\\'\\')\\n\\nThe North of Westeros\\n\\nUS\\n\\nThe South of Westeros\\n\\nUS\\n\\nKing\\'s Landing city map\\n\\nBeyond the Wall\\n\\nSlaver\\'s Bay, Valyria, and Sothoryos\\n\\nThe Iron Islands\\n\\nThe Free Cities\\n\\n\\'\\'A Game of Thrones\\'\\', the first installment of the \\'\\'A Song of Ice and Fire\\'\\' series, has two maps of Westeros. Each new book has added one or two maps so that, as of \\'\\'A Dance with Dragons\\'\\', seven maps of the fictional world are available in the books. Martin said in 2003 that complete world maps were not made available so that readers may better identify with people of the real Middle Ages who were uneducated about distant places. He also did not \"subscribe to the theory put forth in \\'\\'The Tough Guide To Fantasyland\\'\\' ... that eventually the characters must visit every place shown on The Map.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'fd3dfe1e99f0b62df361e46d88d79be8'}>,\n",
+       "  <Document: {'content': 'He also did not \"subscribe to the theory put forth in \\'\\'The Tough Guide To Fantasyland\\'\\' ... that eventually the characters must visit every place shown on The Map.\"He conceded, however, that readers may be able to piece together a world map by the end of the series. He was intentionally vague about the size of the \\'\\'Ice and Fire\\'\\' world, omitting a scale on the maps to discourage prediction of travel lengths based on measured distances. A new map artist was used in \\'\\'A Dance with Dragons\\'\\' so that the maps are available in two versions by James Sinclair and Jeffrey L. Ward, depending on the book. The old maps were redone to match the style of the new ones.\\n\\nA set of foldout maps was published on October 30, 2012 as \\'\\'The Lands of Ice and Fire\\'\\' (). The illustrator and cartographer Jonathan Roberts drew the maps, based on drafts by Martin. The twelve maps in the set are entitled \"The Known World\", \"The West\", \"Central Essos\", \"The East\", \"Westeros\", \"Beyond The Wall\", \"The Free Cities\", \"Slaver\\'s Bay\", \"The Dothraki Sea\", \"King\\'s Landing\", \"Braavos\", and \"Journeys\". The latter tracks the paths taken by the novels\\' characters.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '5ac45046c1eb62939c277c51fb22b622'}>,\n",
+       "  <Document: {'content': 'The twelve maps in the set are entitled \"The Known World\", \"The West\", \"Central Essos\", \"The East\", \"Westeros\", \"Beyond The Wall\", \"The Free Cities\", \"Slaver\\'s Bay\", \"The Dothraki Sea\", \"King\\'s Landing\", \"Braavos\", and \"Journeys\". The latter tracks the paths taken by the novels\\' characters.\\n\\n==Westeros ==\\n\\nThe story takes place primarily on a continent called \\'\\'\\'Westeros\\'\\'\\', which is roughly the size of South America. The continent is home to the Seven Kingdoms and lands beyond the great Wall, which makes up a large chunk of Westeros, being roughly the size of Canada, with most of it largely unmapped and unexplored.\\n\\nAt the novel\\'s beginning, the majority of Westeros is united under the rule of one king, with each of nine regions controlled by a different major house. Martin here drew inspiration from medieval European history, in particular the Hundred Years\\' War, the Crusades, the Albigensian Crusade, and the Wars of the Roses.\\n\\nThe first inhabitants of the continent were the Children of the Forest, a nature-worshipping anthropoid species who carved the faces of their gods in weirwood trees. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '7e8ae98911f2f73f7012006975356f4a'}>,\n",
+       "  <Document: {'content': 'The first inhabitants of the continent were the Children of the Forest, a nature-worshipping anthropoid species who carved the faces of their gods in weirwood trees. Some time later, the First Men\\'s attempts at cultivating the land led to a war with the Children of the Forest that eventually was settled by an agreement known as \"The Pact\". This was the beginning of the Age of Heroes. During that time, the First Men adopted the gods of the Children of the Forest. Those gods later became known in Westeros as the Old Gods.\\n\\nEight thousand years before the events of the novels, an enigmatic species called the Others emerged from the furthermost north during the decades-long winter known as \"The Long Night\". The Children of the Forest and the First Men jointly repelled the Others and then built a massive wall barring passage from the far north.\\n\\nSometime later, the Andals invaded Westeros and established the Faith of the Seven, writing, and steel. Only the North remained unconquered. The Children of the Forest disappeared from Andal lands. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '940c3cc431cbb154149fc67a5d1dd200'}>,\n",
+       "  <Document: {'content': 'Sometime later, the Andals invaded Westeros and established the Faith of the Seven, writing, and steel. Only the North remained unconquered. The Children of the Forest disappeared from Andal lands. Over time, \\'\\'\\'seven kingdoms\\'\\'\\' were forged across Westeros: The North, The Iron Islands (inclusive of the riverlands and parts of the crownlands), The Vale, The Westerlands, The Stormlands (inclusive of the remaining parts of the crownlands), The Reach, and Dorne. The Seven Kingdoms were constantly at war with one another, and no kingdom remained dominant for long.\\n\\nThree hundred years before the novels begin, Aegon the Conqueror and his two Targaryen sister-wives came from Dragonstone and landed at present-day King\\'s Landing. The three assembled a temporary bastion, known as \"Aegon\\'s Fort\", which grew into the capital city, later known as King\\'s Landing. Their powerful dragons overwhelmed six of the Seven Kingdoms through conquest or treaty, with Dorne remaining independent for another two hundred years until it was absorbed through a marriage-alliance. The Targaryens built the Iron Throne, which consists of the swords of defeated rulers, fused together by dragonfire. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '7c2d706d37a59f49e75e2a8e35aead95'}>,\n",
+       "  <Document: {'content': \"Their powerful dragons overwhelmed six of the Seven Kingdoms through conquest or treaty, with Dorne remaining independent for another two hundred years until it was absorbed through a marriage-alliance. The Targaryens built the Iron Throne, which consists of the swords of defeated rulers, fused together by dragonfire. They also established King's Landing as their capital city and consolidated parts of surrounding regions of the riverlands and stormlands into the crownlands region. They remained the ruling power on the continent until deposed by a rebellion led by Robert Baratheon.\\n\\n===The North ===\\nThe North consists of the northern half of the Seven Kingdoms and is ruled by House Stark from their castle at Winterfell. The North is sparsely populated, but it is nearly as big as the other six kingdoms combined. Martin compared the North to Scotland. The climate is cold overall, with hard winters and mild snows common regardless of season. The region's northern border is the New Gift, which is a stretch of land 50 leagues wide and in possession of the Night's Watch. An isthmus of swampland named The Neck separates the North from the South. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '156a3750949be37c7c81a342fd0caf52'}>,\n",
+       "  <Document: {'content': \"The region's northern border is the New Gift, which is a stretch of land 50 leagues wide and in possession of the Night's Watch. An isthmus of swampland named The Neck separates the North from the South. It is home to small, marsh-dwelling crannogmen and is ruled by House Reed of Greywater Watch, bannermen of House Stark. The Neck's narrowness, difficult terrain, and almost impenetrable Moat Cailin help protect the North from invasion. The city of White Harbor is a thriving port. Illegitimate children born of a noble parent in the North are given the surname Snow.\\n\\n====Winterfell====\\n\\nWinterfell is the ancestral castle of House Stark and was built over a natural hot spring. Scalding water runs through the castle walls and warms its halls and rooms. There are several open pools where heated water collects within the godswood. The hot spring also prevents the ground from freezing. The castle has deep catacombs where bodies of the Starks are entombed behind statues in their likeness with a direwolf at their feet and their swords left in their hands. The tombs have been used since the old kings of the North, known as the Kings of Winter, were in power. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '6a97eacf1e096f420efa42518b868dcc'}>,\n",
+       "  <Document: {'content': 'The tombs have been used since the old kings of the North, known as the Kings of Winter, were in power. They ruled before Aegon the Conqueror and the arrival of the Andals before that.\\n\\nTo depict Winterfell, both the pilot and season 1 of the television adaptation used the 16th century clock tower and ancient courtyard of the Clearsky Adventure Centre located at Castle Ward in County Down, Northern Ireland, Doune Castle in Stirling, Scotland, which previously featured as Castle Anthrax in the film \\'\\'Monty Python and the Holy Grail\\'\\', was also used for exterior scenes. Saintfield Estates stood in as Winterfell\\'s godswood, which is an enclosed wooded area where characters can worship the old gods beside trees with faces carved in their bark. A car park stood in for Winterfell\\'s courtyard, and a wine cellar was used to depict the Stark family crypt. Tollymore Forest featured prominently in the prologue of the pilot episode and in the pivotal scene where the Starks first find the direwolves. Cairncastle, meanwhile, served as the location where Ned Stark beheads the deserter Will.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '25496ce841ae3d3821b8998032cb3d18'}>,\n",
+       "  <Document: {'content': 'Tollymore Forest featured prominently in the prologue of the pilot episode and in the pivotal scene where the Starks first find the direwolves. Cairncastle, meanwhile, served as the location where Ned Stark beheads the deserter Will.\"The interior of Winterfell, such as the Tower of the First Keep, the Great Hall, and Catelyn\\'s bedchamber, were filmed at The Paint Hall studio. Set designer Gemma Jackson said, \"Winterfell was based on a Scottish castle.\"====The Wall====\\nThe Wall in the \\'\\'Ice and Fire\\'\\' series was inspired by Hadrian\\'s Wall in the North of England.\\nThe Wall is a huge structure of stone, ice, and magic on the northern border of the Seven Kingdoms. It is home to the Night\\'s Watch, a brotherhood sworn to protect the realms of men from the threats beyond the Wall.\\n\\nThe Wall was inspired by Martin\\'s visit to Hadrian\\'s Wall, in the North of England close to the border with Scotland. Looking out over the hills, Martin wondered what a Roman centurion from the Mediterranean would feel, not knowing what threats might come from the north. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'fc52e412a86dd53e8edd75a265cc5d67'}>,\n",
+       "  <Document: {'content': 'Looking out over the hills, Martin wondered what a Roman centurion from the Mediterranean would feel, not knowing what threats might come from the north. This experience was so profound that a decade later, in 1991, he wanted to \"write a story about the people guarding the end of the world\", and ultimately \"the things that come out of the fictional north are a good deal more terrifying than Scotsmen or Picts\".\\n\\nMartin adjusted the size, length, and magical nature of the Wall for genre demands; Jon Snow\\'s chapters describe it as approximately  long and  high in general, rising up to a perceived  in spots due to huge foundation blocks. The top is wide enough for a dozen mounted knights to ride abreast (approximately 30\\xa0ft or 10\\xa0m), while the base is so thick that the Wall\\'s gates are more like tunnels through the ice.\\n\\nThe novels\\' legends claim that the First Men, or more specifically Brandon the Builder with the possible help of children of the forest and giants, constructed the Wall some 8,000 years before the events of the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'bf0656124f6eb60462cb3711d2c08141'}>,\n",
+       "  <Document: {'content': 'The novels\\' legends claim that the First Men, or more specifically Brandon the Builder with the possible help of children of the forest and giants, constructed the Wall some 8,000 years before the events of the series.\\n\\nThe Wall has since been maintained by the Night\\'s Watch to guard the realms of men against the threats from beyond, originally the Others, and later against wildling raids.\\n\\nA strip of land known as \"the Gift\", now stretching 50 leagues (about ) south of the wall, was given to them in perpetuity thousands of years ago for cultivation. In \\'\\'A Game of Thrones\\'\\', out of the nineteen castles built along the wall, only three are still manned: Castle Black with 600 men, and the Shadow Tower and Eastwatch-by-the-Sea with 200 men each. Parts of Castle Black have fallen into ruin.\\n\\nThe TV series\\' Castle Black and the Wall were filmed in the abandoned Magheramorne Quarry near Belfast, Northern Ireland, whereas the scenes shot atop the wall were filmed inside Paint Hall Studios. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'c52e692f8751898eaba439a9017cf6a2'}>,\n",
+       "  <Document: {'content': 'The TV series\\' Castle Black and the Wall were filmed in the abandoned Magheramorne Quarry near Belfast, Northern Ireland, whereas the scenes shot atop the wall were filmed inside Paint Hall Studios. The composite set (with both exteriors and interiors) consisted of a large section of Castle Black including the courtyard, the ravenry, the mess hall, and the barracks, and used the stone wall of the quarry as the basis for the ice wall that protects Westeros from the dangers that dwell beyond. They also made a functional elevator to lift the rangers to the top of the Wall. A castle with real rooms and a working elevator were built near a cliff  high. \"Working construction lifts were discovered at a nearby work site and rise 18 feet; CGI fills in the rest to make the wall appear 700 feet high.\"The area around the elevator was painted white to make it look like ice. Martin was surprised by the height and thought: \"Oh I may have made the wall too big!\"Martin observed: \"It\\'s a pretty spectacular, yet miserable location. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'cd9a96a6a62d9f7feac5b331d9efae67'}>,\n",
+       "  <Document: {'content': 'Martin was surprised by the height and thought: \"Oh I may have made the wall too big!\"Martin observed: \"It\\'s a pretty spectacular, yet miserable location. It is wet and rainy, and the mud is thick....it really gets the actors in the mood of being at the end of the world in all of this cold and damp and chill.\"====Beyond the Wall====\\nSeason 2 of the TV adaptation featured scenes set north of the Wall that were filmed on the Vatnajökull glacier in Iceland.\\n\\'\\'A Clash of Kings\\'\\' takes the story to the lands Beyond the Wall, although the first five books do not explore \"what lies really north ... but we will in the last two books\". The TV adaptation used Iceland as filming location for the lands Beyond the Wall. Martin, who has never been to Iceland, said Beyond the Wall was \"considerably larger than Iceland and the area closest to my Wall is densely forested, so in that sense it\\'s more like Canada Hudson Bay or the Canadian forests just north of Michigan. And then as you get further and further north, it changes. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '6dacf4321149864e6eaba4fb7c23726a'}>,\n",
+       "  <Document: {'content': 'Martin, who has never been to Iceland, said Beyond the Wall was \"considerably larger than Iceland and the area closest to my Wall is densely forested, so in that sense it\\'s more like Canada Hudson Bay or the Canadian forests just north of Michigan. And then as you get further and further north, it changes. You get into tundra and ice fields and it becomes more of an arctic environment. You have plains on one side and a very high range of mountains on the other. Of course, once again this is fantasy, so my mountains are more like the Himalayas.\"In an HBO featurette, Martin stated the lands beyond the wall make up a big part of Westeros, being roughly the size of Canada. The Valley of Thenn is one such location beyond the Wall, and north of that is the Lands of Always Winter, where the Others come from.\\n\\nDuring the first season, the HBO team used places that they could decorate with artificial snow for the north of the Wall, but a bigger landscape was chosen for Season 2. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '569f7a02b01af13e0e4d01202eb8ae13'}>,\n",
+       "  <Document: {'content': 'During the first season, the HBO team used places that they could decorate with artificial snow for the north of the Wall, but a bigger landscape was chosen for Season 2. \"Primary filming for these scenes, which encompass both the Frostfangs and the Fist of the First Men, occurred at the Svínafellsjökull calving glacier in Skaftafell, Iceland, followed by shooting near Smyrlabjörg and Vík í Mýrdal on Höfðabrekkuheiði. Benioff said, \"We always knew we wanted something shatteringly beautiful and barren and brutal for this part of Jon\\'s journey, because he\\'s in the true North now. It\\'s all real. It\\'s all in camera. We\\'re not doing anything in postproduction to add mountains or snow or anything.\"===The Iron Islands===\\nThe Iron Islands are a group of seven islands to the west of Westeros – Pyke, Great Wyk, Old Wyk, Harlaw, Saltcliffe, Blacktyde, and Orkmont – in Ironman\\'s Bay off the west coast of the continent. Ruled by House Greyjoy of Pyke, the isles are described as bare and barren, and the local weather as \"windy and cold, and damp\". The members of this seafaring nation are known in the rest of Westeros as Ironmen, and to themselves as Ironborn. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'd13f0b626e4b6aaa78183107e7c6d0bb'}>,\n",
+       "  <Document: {'content': 'The members of this seafaring nation are known in the rest of Westeros as Ironmen, and to themselves as Ironborn. Illegitimate children born in the Iron Islands are given the surname Pyke.\\n\\nFor fierce raids, the Ironmen are titled the \"terror of the seas\". They worship the Drowned God, who \"had made them to reave and rape, to carve out kingdoms and write their names in fire and blood and song\". The appendix of \\'\\'A Game of Thrones\\'\\' summarizes that the Ironmen once ruled over the Riverlands and much of the western coast of Westeros. When Aegon the Conqueror extinguished Harren the Black\\'s line, he chose House Greyjoy as the new rulers of the Ironmen.\\n\\n====Pyke====\\nFor the TV adaptation, the harbour of Ballintoy in Northern Ireland was redressed as the port of Pyke.\\nPyke is the seat of House Greyjoy. The television adaptation filmed the scenes of Pyke\\'s port at Lordsport Harbour in Ballintoy Harbour, in Northern Ireland\\'s County Antrim. The sea has worn away much of the rock on which Pyke originally stood, so the castle now consists mostly of a main keep on the main island and smaller towers perched on rocks.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'eed75de86f4fb709d4d9d4fc097c920d'}>,\n",
+       "  <Document: {'content': 'The sea has worn away much of the rock on which Pyke originally stood, so the castle now consists mostly of a main keep on the main island and smaller towers perched on rocks.\\n\\n====Old Wyk====\\nOld Wyk is the smallest and holiest island in the Iron Islands. It is where Kingsmoots are held, and where the Grey King slew Nagga, a sea dragon, and made a court of his bones.\\n\\n===The Riverlands===\\nThe Riverlands are the populous and fertile areas surrounding the forks of the river Trident on Westeros. While they form one of the nine regions of Westeros, the Riverlands\\' central location and geographic features made the region an inter-kingdom battle zone that changed hands rather than becoming its own \\'eighth\\' kingdom of the Seven Kingdoms. Centrally located between the Westerlands, the Crownlands, the Vale, and the North and lacking the natural defenses of other regions, they have seen frequent warfare. The first ruler to unite the Riverlands was Benedict Justman, but the Justman dynasty died out three centuries later. The Durrandons conquered the Riverlands, but lost rule of it to Harwyn \"Hardhand\" Hoare, King of the Iron Islands. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '50c201ad37fa165432cac4bb59945aa0'}>,\n",
+       "  <Document: {'content': 'The first ruler to unite the Riverlands was Benedict Justman, but the Justman dynasty died out three centuries later. The Durrandons conquered the Riverlands, but lost rule of it to Harwyn \"Hardhand\" Hoare, King of the Iron Islands. At the time of Aegon\\'s conquest, the Riverlands were ruled by Harwyn\\'s grandson, Harren the Black, king of the Iron Islands, and the Tullys were local nobles who rebelled against him by joining Aegon the Conqueror. As with Westerosi customs to give bastards a surname showing their origins, illegitimate children born in the Riverlands are given the surname Rivers.\\n\\n====Harrenhal====\\nHarrenhal is an enormous ruined castle and is the site of many important events in the novels. Harrenhal was built by Harren the Black, after his conquest of the Riverlands, intending to make it the largest fortification ever built in Westeros. The castle has been described as so large that an entire army was needed to garrison it. The Great Hall had 35 hearths and seated thousands. Shortly after the castle was completed, Aegon the Conqueror\\'s dragon slew Harren, his sons, and his entire army by setting the castle alight.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '38252a3e8803beb451e1ff6744190182'}>,\n",
+       "  <Document: {'content': 'Shortly after the castle was completed, Aegon the Conqueror\\'s dragon slew Harren, his sons, and his entire army by setting the castle alight.\\n\\nSince then, the ruins of the castle have been occupied by a variety of houses, all of which eventually became extinct. As a result, the people of Westeros believe the castle is cursed. The logistical and economic difficulties inherent in keeping such an enormous castle maintained and garrisoned has made it something of a white elephant. At the start of the War of the Five Kings, the castle is in ruin, with only a fraction of it habitable, and held by Lady Shella Whent, the last of her House, who is stripped of Harrenhal when the Lannisters seize her castle. The castle changes hands repeatedly over the course of the novels, many of those holding it meeting unpleasant ends.\\n\\n====Riverrun====\\nRiverrun is the ancestral stronghold of House Tully. The castle is located along one of the \"forks\" of the Trident and controls access to the interior of Westeros. The castle is bordered on two sides by the Tumblestone River and the Red Fork. The third side fronts on a massive manmade ditch. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '63f7bd7aae70514770d935c5d12fbb42'}>,\n",
+       "  <Document: {'content': \"The castle is bordered on two sides by the Tumblestone River and the Red Fork. The third side fronts on a massive manmade ditch. It was built by Ser Axel Tully on land he received from the Andal King Armistead Vance.\\n\\nThe castle is the location of Robb Stark's great victory over House Lannister and the site of his crowning. By the end of the ''A Feast for Crows'', Brynden Tully surrenders the castle to Jaime Lannister to spare further bloodshed. Riverrun then passed into the hands of Emmon Frey, an ally of House Lannister.\\n\\n====The Twins====\\nThe Twins is the seat of House Frey, which has grown wealthy by charging a toll of all those who cross for the past six centuries. Because the Freys are both wealthy and numerous, theirs is one of the most powerful houses sworn to House Tully. The castle's strategic position gives House Frey enormous importance in times of war.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '6a7cd3744b304ac9fe5dbfb641cd24ef'}>,\n",
+       "  <Document: {'content': 'Because the Freys are both wealthy and numerous, theirs is one of the most powerful houses sworn to House Tully. The castle\\'s strategic position gives House Frey enormous importance in times of war.\\n\\nWhen Robb Stark goes to The Twins to repair his alliance with House Frey, the Freys massacre him, his mother, and his army (and in the TV adaptation, his wife): an event known as \"The Red Wedding\", which violates native customs of guest right and incurs enmity throughout the Seven Kingdoms, especially in the Riverlands and North.\\n\\n===The Vale of Arryn===\\nFor the TV adaptation, images of the Greek rock formations of Meteora were used for the composite views of the Vale.\\nThe Vale is the area surrounded almost completely by the Mountains of the Moon in the east of Westeros. The Vale is under the rulership of House Arryn, one of the oldest lines of Andal nobility and formerly Kings of Mountain and Vale. Their seat, the Eyrie, is a castle high in the mountains, small but considered unassailable. The only way to reach the Vale is by a mountain road teeming with animals called \\'shadowcats\\', rock slides, and dangerous mountain clans. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': 'f1c06e65a87d676e6a3e886754dbbe3'}>,\n",
+       "  <Document: {'content': \"The only way to reach the Vale is by a mountain road teeming with animals called 'shadowcats', rock slides, and dangerous mountain clans. The mountain road ends at the Vale's sole entrance, the Bloody Gate: a pair of twin watchtowers, connected by a covered bridge, on the rocky mountain slopes over a very narrow path. The protection of the surrounding mountains gives the Vale itself a temperate climate, fertile meadows, and woods. The snowmelt from the mountains and a constant waterfall that never freezes, named Alyssa's Tears, provide plentiful water. The Vale has rich black soil, wide slow-moving rivers, and hundreds of small lakes. Illegitimate children born in the Vale are given the surname Stone.\\n\\n====The Eyrie====\\ncastle of Neuschwanstein, on which the Eyrie is based\\nBased on the German castle of Neuschwanstein, the Eyrie is the seat of House Arryn. It is situated on the Giant's Lance and reachable only by a narrow mule trail, guarded by the Gates of the Moon and three small castles, titled Stone, Snow, and Sky. Travelers must enter the Gates of the Moon and its upper bailey before reaching the narrow path up the mountain. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': 'caf447419074e577ef8e711f659525b2'}>,\n",
+       "  <Document: {'content': \"Travelers must enter the Gates of the Moon and its upper bailey before reaching the narrow path up the mountain. The steps up the Giant's Lance starts directly behind the Gates of the Moon. The Eyrie clings to the mountain and is six hundred feet above Sky. The last part of the climb to the Eyrie is something of a cross between a chimney and a stone ladder, which leads to the Eyrie's cellar entrance. Due to the Mountains of the Moon's harsh winters, travel to and from the Eyrie is possible through the mountains only in summer.\\n\\nThe Eyrie is the smallest of the great castles in the story, consisting of seven slim towers bunched tightly together. It has no stables, kennels, or smithies, but the towers can house 500 men, and the granary can sustain a small household for a year or more. The Eyrie does not keep livestock on hand; all dairy produce, meats, fruits, vegetables, etc., must be brought from the Vale below. Its cellars hold six great winches with long iron chains to draw supplies and occasionally guests from below, with oxen are used to raise and lower them. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': '7bc6ae2d3db3c17b320c382be33ecd7c'}>,\n",
+       "  <Document: {'content': 'Its cellars hold six great winches with long iron chains to draw supplies and occasionally guests from below, with oxen are used to raise and lower them. Winter snows can make supplying the fortress impossible. The Eyrie\\'s dungeons, known as \"sky cells\", are left open to the sky on one side and have sloping floors that put prisoners in danger of slipping or rolling off the edge. Executions in the Eyrie are carried out via the Moon Door, which opens from the high hall onto a 600-foot drop.\\n\\nThe Eyrie is made of pale stone and primarily decorated with the blue and white colors of House Arryn. Elegant details provide warmth and comfort through plentiful fireplaces, carpets, and luxurious fabrics. Many of the chambers have been described to be warm and comfortable, with magnificent views of the Vale, the Mountains of the Moon, or the waterfall. The Maiden\\'s Tower is the easternmost of the seven slender towers, so all the Vale can be seen from its windows and balconies. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': '1cbb6ff63ad37c7f17a502fd72e6d75a'}>,\n",
+       "  <Document: {'content': \"The Maiden's Tower is the easternmost of the seven slender towers, so all the Vale can be seen from its windows and balconies. The apartments of the Lady of the Eyrie open over a small garden planted with blue flowers and ringed by white towers, containing grass and scattered statuary, with the central statue of a weeping woman believed to be Alyssa Arryn, around low, flowering shrubs. The lord's chambers have doors of solid oak, and plush velvet curtains covering windows of small rhomboid panes of glass. The High Hall has a blue silk carpet leading to the carved weirwood thrones of the Lord and Lady Arryn. The floors and walls are of milk-white marble veined with blue. Daylight enters down through high narrow arched windows along the eastern wall, and there are some fifty high iron sconces where torches may be lit.\\n\\nThe Eyrie was held by Lord Jon Arryn, who fostered Ned Stark and Robert Baratheon prior to Robert's Rebellion. After the war, Lord Arryn served as King Robert I Baratheon's Hand of the King (prime minister). After Lord Arryn was assassinated, his wife, Lady Lysa Arryn, took her sickly child, Robin, and fled to the Eyrie. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': '857c852fbc99c426461a22b580d8599d'}>,\n",
+       "  <Document: {'content': 'After Lord Arryn was assassinated, his wife, Lady Lysa Arryn, took her sickly child, Robin, and fled to the Eyrie. Lysa refused to align herself with any of the claimants during the War of the Five Kings, but eventually pretends to a possible alliance with House Lannister after Lord Petyr Baelish agrees to marry her. Later Baelish kills Lysa after she attempts to murder her niece, Sansa Stark. As of \\'\\'Feast for Crows\\'\\', Baelish rules in the Eyrie as the Lord Protector and Regent for the sickly, epileptic Lord Robert \"Robin\" Arryn, and plans for Sansa to marry Harold Harding, who will become heir to the Eyrie and the Vale in the event of young Robin Arryn\\'s death.\\n\\nFor the CGI compositions of the Vale of Arryn in the TV series, as seen in the establishing shot of the Eyrie and from the sky cells, the visual effects team used images and textures from the Greek rock formations of Meteora. Initially they had been considering the Zhangjiajie Mountains in China, but because the landscape base plates were shot in Northern Ireland, using Meteora resulted a better option. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': '836e529a330e67d0e499b4fabeaee2a3'}>,\n",
+       "  <Document: {'content': 'Initially they had been considering the Zhangjiajie Mountains in China, but because the landscape base plates were shot in Northern Ireland, using Meteora resulted a better option. Set designer Gemma Jackson said, \"A lot of the mosaics in the Eyrie were based on a beautiful chapel I visited in Rome.\"The interior of the High Hall of the Arryns was filmed at The Paint Hall, occupying one of the four soundstages there. Martin acknowledged that the set differed significantly from its presentation in the books: \"In the books, the room is long and rectangular. But The Paint Hall soundstage had essentially a square space, which they chose to put a round hall in, with a staircase curving up to a throne that was high above.\"===The Westerlands===\\nThe Westerlands are the Westerosi lands to the west of the Riverlands and north of the Reach. They are ruled by House Lannister of Casterly Rock, formerly Kings of the Rock. People of this region are often called \"Westermen.\"Lannisport, lying hard by Casterly Rock, is the chief town of the region and one of the great ports and cities of Westeros. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 28}, 'embedding': None, 'id': '85fb05953b90664ad418db8c47363110'}>,\n",
+       "  <Document: {'content': 'Lannisport, lying hard by Casterly Rock, is the chief town of the region and one of the great ports and cities of Westeros. The Westerlands are rich in precious metals, mostly gold, which is the source of their wealth. As with Westerosi customs to give bastards a surname showing their origins, illegitimate children born in the Westerlands are given the surname Hill.\\n\\n====Casterly Rock====\\nCasterly Rock was inspired by the Rock of Gibraltar (pictured).\\nA stronghold carved from a mountain overlooking the harbor city of Lannisport and the sea beyond, Casterly Rock is the ancestral seat of House Lannister. According to popular legend, the hero known as Lann the Clever tricked the Casterlys into giving up the Rock, and took it for himself. The Rock is renowned as the wealthiest region due to its abundance of gold mining resources, and it is one of the strongest castles of the Seven Kingdoms. It has never been taken in battle, despite attacks by the Iron Islanders and the plans of Robb Stark in the War of the Five Kings. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 29}, 'embedding': None, 'id': 'e65129a611873d2f8f71505068375167'}>,\n",
+       "  <Document: {'content': \"It has never been taken in battle, despite attacks by the Iron Islanders and the plans of Robb Stark in the War of the Five Kings. It was held by Lord Tywin Lannister before the War of the Five Kings, but after his death, Queen Regent Cersei Lannister made one of her cousins castellan of the castle. As of ''A Dance with Dragons'', the narrative has not actually taken place in Casterly Rock, yet descriptions of it have been offered by the Lannisters in the POV chapters.\\n\\nWest of Casterly Rock is the coastal city of Lannisport. A busy port under the governance of the Lannisters of Casterly Rock, Lannisport thrives as a protected and wealthy city. The city is also home to many lesser Lannisters and other people with similar surnames, such as Lannys.\\n\\nGeorge R. R. Martin stated on his blog that he drew inspiration for Casterly Rock from the Rock of Gibraltar.\\n\\n===The Reach===\\nThe Castle of Almodóvar del Río in Córdoba, Spain, which represented Highgarden in ''Game of Thrones''\\nThe Reach is the most lush and fertile region of Westeros, ruled by House Tyrell from Highgarden. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 30}, 'embedding': None, 'id': '750837ad0de8f0c643e20b99b9a67de1'}>,\n",
+       "  <Document: {'content': \"===The Reach===\\nThe Castle of Almodóvar del Río in Córdoba, Spain, which represented Highgarden in ''Game of Thrones''\\nThe Reach is the most lush and fertile region of Westeros, ruled by House Tyrell from Highgarden. The Tyrells were stewards to House Gardener, the Kings of the Reach before Aegon's conquest. After the last Gardener King was killed on the Field of Fire, the Tyrells surrendered Highgarden to Aegon and were rewarded with both the castle and the position of overlords of the Reach. The wealth and power of the Reach comes from their bountiful harvests of the most sought-after wines and foods. During times of war, the lengthy distance of the Reach and its abundance of foods protects their inhabitants from initial famine and sickness. In a significant political maneuver during the civil war in Westeros and the War of the Five Kings, House Tyrell provides the starving populace of King's Landing with hundreds of carts of food, ensuring the positive image of House Tyrell foremost, and the alliance for the Iron Throne with House Baratheon as secondary. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 31}, 'embedding': None, 'id': '195705a56943dad4c9709def7c22e3e8'}>,\n",
+       "  <Document: {'content': \"In a significant political maneuver during the civil war in Westeros and the War of the Five Kings, House Tyrell provides the starving populace of King's Landing with hundreds of carts of food, ensuring the positive image of House Tyrell foremost, and the alliance for the Iron Throne with House Baratheon as secondary. However, the Tyrells were responsible for the starvation in the first place, as part of their plan to help Renly usurp the Iron Throne. The most prominent city in the Reach is Oldtown. It is the oldest city in Westeros, home to the Maester's Citadel, and the previous seat of the Faith of the Seven. Illegitimate children born in the Reach are given the surname Flowers.\\n\\n====Oldtown====\\nThe Hightower of Oldtown bears similarities to the Lighthouse of Alexandria (3D reconstruction pictured)\\nOldtown is one of the largest cities in Westeros and is by far the oldest, built by the First Men before the Andal Invasion. It survived the invasion by welcoming the Andals rather than resisting them. The city is located in the southwestern part of Westeros, at the mouth of the River Honeywine, where it opens onto Whispering Sound and the Sunset Sea beyond.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 32}, 'embedding': None, 'id': '40cd5a64f1820aa97c5f66f6d19208d6'}>,\n",
+       "  <Document: {'content': \"The city is located in the southwestern part of Westeros, at the mouth of the River Honeywine, where it opens onto Whispering Sound and the Sunset Sea beyond.\\n\\nOldtown is primarily known as the location of the Citadel, home of the order of Maesters who serve as councillors, doctors, scientists, and postmasters for the Seven Kingdoms. The city's Starry Sept was the seat of the Faith of the Seven until the construction of the Great Sept of Baelor in King's Landing. Aegon the Conqueror's reign is dated from his entrance into the city of Oldtown and his acknowledgment as king by the High Septon.\\n\\nOldtown is the second most important port in the Seven Kingdoms after King's Landing: trading ships from the Summer Islands, the Free Cities, the eastern cities, and the rest of Westeros constantly crowd into its harbors. The city itself is described as stunningly beautiful. Many rivers and canals crisscross its cobbled streets, and breathtaking stone mansions are common. The city lacks the squalor of King's Landing, which usurped its position as the preeminent city of Westeros.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 33}, 'embedding': None, 'id': 'b3d569ee3160a9b11bf515192426448f'}>,\n",
+       "  <Document: {'content': \"Many rivers and canals crisscross its cobbled streets, and breathtaking stone mansions are common. The city lacks the squalor of King's Landing, which usurped its position as the preeminent city of Westeros.\\n\\nThe largest structure in the city, and the tallest structure in Westeros, is the Hightower, a massive stepped lighthouse which extends some  into the sky and is topped by a huge beacon which can be seen for many miles out to sea. Oldtown is ruled from the Hightower by House Hightower. Originally kings in their own right, they later swore fealty to the Gardeners of Highgarden, and became vassals of the Tyrells after the Conquest. The Hightowers are known for their loyalty and stalwartness. The current ruler of the city is Lord Leyton Hightower.\\n\\nOldtown remained aloof from the War of the Five Kings, but late in the war the Ironborn under King Euron Greyjoy launched a massive raid along the coast, conquering the Shield Islands and parts of the Arbor before trying to blockade the mouth of the Honeywine. An attempt to attack the city harbor was repulsed by the city's defenders. Oldtown remains under threat from the Ironborn.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 34}, 'embedding': None, 'id': '820b4e33089e35fc93a11d9c910689cc'}>,\n",
+       "  <Document: {'content': \"An attempt to attack the city harbor was repulsed by the city's defenders. Oldtown remains under threat from the Ironborn.\\n\\n===The Stormlands===\\nThe Stormlands are the Westerosi areas between King's Landing and the Sea of Dorne. In the east they are bordered by Shipbreaker Bay and the Dornish Sea to the south. Before Aegon's conquest they were ruled by the Storm Kings, and afterwards by House Baratheon, bastard relatives to the Targaryens. The Dornish Marches are located within this region, and were common battlegrounds between the Stormlands and Dorne until Dorne joined the Seven Kingdoms. Illegitimate children born in the Stormlands are given the surname Storm.\\n\\n====Storm's End====\\nStorm's End is the seat of House Baratheon and, before them, the ancestral seat of the Storm Kings extending back many thousands of years. According to legend, the first Storm King in the age of the First Men was Durran, who married Elenei, the daughter of the sea god and the goddess of the wind. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 35}, 'embedding': None, 'id': '906bfca1f0f3a5afdabf4937cf9d8de7'}>,\n",
+       "  <Document: {'content': \"According to legend, the first Storm King in the age of the First Men was Durran, who married Elenei, the daughter of the sea god and the goddess of the wind. In a rage her parents sent vast storms to shatter his keep and kill his wedding guests and family; whereupon Durran declared war against the gods and raised several castles over Shipbreaker Bay, each larger and more formidable than the last. Finally, the seventh castle stayed in place and resisted the storms. Some believe the Children of the Forest took a hand in its construction; others suggest that Brandon Stark, the builder of the Wall, advised Durran on its construction. The truth of the matter is unknown.\\n\\nStorm's End has never fallen to either siege or storm. Its outer defenses consist of a huge curtain wall,  tall and  thick on its thinnest side, nearly  thick on its seaward side. The wall consists of a double course of stones with an inner core of sand and rubble. The wall is smooth and curving, the stones so well placed that the wind cannot enter. On the seaward side, there is a  drop below the wall into the sea.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 36}, 'embedding': None, 'id': '94807bc04de1a3975af5e8d28a56deff'}>,\n",
+       "  <Document: {'content': \"The wall is smooth and curving, the stones so well placed that the wind cannot enter. On the seaward side, there is a  drop below the wall into the sea.\\n\\nThe castle itself consists of one huge drum tower crowned with formidable battlements, and so large that it can comfortably contain stables, barracks, armory and lord's chambers in the same structure. Although never taken in battle, Storm's End has endured several sieges and battles in recent history. The last Storm King, Argilac the Arrogant, abandoned his impressive defenses to meet the Targaryen commander, Orys Baratheon, in open battle during Aegon Targaryen's War of Conquest, and lost. This led to Orys Baratheon marrying Argilac's daughter and becoming Lord of Storm's End.\\n\\nDuring the War of the Usurper, Storm's End was besieged for a year by the host of Lord Mace Tyrell, who commanded the landward forces, while Paxter Redwyne's fleet of the Arbor kept the castle cut off by sea. Stannis Baratheon, commanding the defense, refused to yield and his men were reduced to eating rats. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 37}, 'embedding': None, 'id': 'f9ac7b97e524ac8d8da78666d4b0f881'}>,\n",
+       "  <Document: {'content': \"During the War of the Usurper, Storm's End was besieged for a year by the host of Lord Mace Tyrell, who commanded the landward forces, while Paxter Redwyne's fleet of the Arbor kept the castle cut off by sea. Stannis Baratheon, commanding the defense, refused to yield and his men were reduced to eating rats. A smuggler named Davos ran the blockade to resupply the castle and Stannis rewarded him by knighting him and giving him lands, thus founding House Seaworth, but he also cut off the fingertips of his left hand as punishment for all his previous smuggling. After the war, Stannis was furious when his brother Robert, now king, gave the castle to their younger brother Renly and placed Stannis in command of Dragonstone. This led to many years of bitterness on Stannis' part.\\n\\nDuring the War of the Five Kings, Storm's End supported Renly when he attempted to usurp the crown, and was besieged by Stannis. When the castellan, Cortnay Penrose, refused to yield even after Renly's death, he was killed by Stannis' ally, the priestess Melisandre, and the castle surrendered. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 38}, 'embedding': None, 'id': 'f85e2dbd6be8e73c3bbc5ade3da44147'}>,\n",
+       "  <Document: {'content': \"When the castellan, Cortnay Penrose, refused to yield even after Renly's death, he was killed by Stannis' ally, the priestess Melisandre, and the castle surrendered. Later, the castle was besieged by a strong army under Mace Tyrell, but he abandoned the siege after a few weeks to return to King's Landing after the arrest of his daughter Margaery by the High Septon. As of ''A Dance with Dragons'', the castle remains in the hands of Stannis Baratheon.\\n\\nAt the end of ''A Dance with Dragons'' an army lands in the Stormlands led by Jon Connington and a young man claiming to be Aegon Targaryen, the son of Rhaegar Targaryen and Elia Martell and heir to the Iron Throne. To attract support, Aegon plans to conquer Storm's End and raise the banner of House Targaryen above the battlements.\\n\\nIn the TV adaptation, scenes in the Stormlands were filmed in Larrybane, Northern Ireland. The scene where Stannis' red priestess Melisandre gave birth to a shadow creature was filmed in the Cushendun Caves, also in Northern Ireland.\\n\\n===The Crownlands===\\nThe Crownlands are the lands in Westeros surrounding King's Landing, ruled directly by the crown of the Iron Throne. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 39}, 'embedding': None, 'id': '62307ac791b1b40e0e52dd7a98520cc4'}>,\n",
+       "  <Document: {'content': \"===The Crownlands===\\nThe Crownlands are the lands in Westeros surrounding King's Landing, ruled directly by the crown of the Iron Throne. The Targaryen kings consolidated this as one of the nine regions of Westeros, after their conquest of the Seven Kingdoms, from sparsely populated pieces of the Riverlands and Stormlands. The Crownlands form the entire coastline of Blackwater Bay, and include the original Targaryen homeland on the island of Dragonstone, at the Narrow Sea entrance to Blackwater Bay. Besides King's Landing, which is the largest city in Westeros, the Crownlands include many towns and castles. The illegitimate children born in the Crownlands are given the surname Waters.\\n\\n====Dragonstone====\\n\\nDragonstone was once the westernmost outpost of the ancient Freehold of Valyria. A century before the Doom, the Targaryen family moved to Dragonstone. When the Doom came upon Valyria, House Targaryen survived along with the last of the Valyrian dragons. Another century later, Aegon Targaryen and his sisters Rhaenys and Visenya launched a massive campaign of conquest from the island and eventually conquered all of Westeros except for Dorne, and North of the Wall. Aegon's progeny reigned as kings of the Seven Kingdoms for centuries.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 40}, 'embedding': None, 'id': '47b9d96203246378d1ee6d3b88561e18'}>,\n",
+       "  <Document: {'content': \"Another century later, Aegon Targaryen and his sisters Rhaenys and Visenya launched a massive campaign of conquest from the island and eventually conquered all of Westeros except for Dorne, and North of the Wall. Aegon's progeny reigned as kings of the Seven Kingdoms for centuries.\\n\\nDragonstone is a massive, forbidding fortress, taking up a large portion of the island of the same name. The castle is unique in that the builders and sorcerers of Valyria carved its towers and keeps into the shapes of dragons and made ferocious gargoyles to cover its walls using both magic and masonry. The castle's lower levels are warmed by residual volcanic activity deep below the keep. There is a small port and town outside of the castle.\\n\\nDuring the War of the Usurper, before the sack of King's Landing, the Targaryen Queen Rhaella, who was pregnant, and her son Viserys were sent to Dragonstone along with part of the Targaryen fleet and a garrison of loyal soldiers. But after King's Landing fell, Robert Baratheon dispatched his brother Stannis to take the island stronghold. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 41}, 'embedding': None, 'id': '302417250b252cf9a076639c19070528'}>,\n",
+       "  <Document: {'content': \"During the War of the Usurper, before the sack of King's Landing, the Targaryen Queen Rhaella, who was pregnant, and her son Viserys were sent to Dragonstone along with part of the Targaryen fleet and a garrison of loyal soldiers. But after King's Landing fell, Robert Baratheon dispatched his brother Stannis to take the island stronghold. After a storm destroyed the royalist fleet, the Targaryen garrison tried to betray Viserys and his newborn sister, Daenerys, to Stannis (the queen had died in childbirth). But Targaryen loyalists led by Ser Willem Darry took the children away. Stannis conquered Dragonstone easily, and King Robert granted him ownership of the castle. Stannis took this a slight because his younger brother Renly then inherited Storm's End, the ancient seat of House Baratheon. Ser Axell Florent, one of the uncles of Stannis' wife Selyse Florent, acted as castellan.\\n\\nUpon Robert's death, Stannis declared himself King of Westeros and condemned the queen's children as bastards born of incest, as he had discovered with Jon Arryn. Dragonstone became his main seat. He returned there after the disastrous Battle of the Blackwater. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 42}, 'embedding': None, 'id': '806be06bae089790a25f6164519257ce'}>,\n",
+       "  <Document: {'content': 'Upon Robert\\'s death, Stannis declared himself King of Westeros and condemned the queen\\'s children as bastards born of incest, as he had discovered with Jon Arryn. Dragonstone became his main seat. He returned there after the disastrous Battle of the Blackwater. His councilor, the red priestess Melisandre of Asshai, tried to convince him to let her raise the \"stone dragon\" of the castle through blood magic, but Lord Davos Seaworth convinced Stannis to go north to the Wall to help the Night\\'s Watch instead. After Stannis abandoned Dragonstone, leaving the Bastard of Nightsong Rolland Storm as castellan, Queen Regent Cersei Lannister dispatched a fleet to barricade it. However, Ser Loras Tyrell, impatient to free the fleet to protect his home castle of Highgarden, attacked Dragonstone directly. He took the castle but lost a thousand men and was himself reportedly gravely wounded. As of \\'\\'A Dance with Dragons\\'\\', Dragonstone is now controlled by troops loyal to House Tyrell, and theoretically, once again under control of the Iron Throne.\\n\\nOne scene set at Dragonstone, in which Stannis burns wooden sculptures of the Seven gods, was filmed at the beach of Downhill Strand. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 43}, 'embedding': None, 'id': 'e07789bcc07701794577ab3250029c0'}>,\n",
+       "  <Document: {'content': \"One scene set at Dragonstone, in which Stannis burns wooden sculptures of the Seven gods, was filmed at the beach of Downhill Strand. In Season 7 of the show, filming for Dragonstone took place at several locations in the Basque region of Spain: the islet of Gaztelugatxe in Bermeo, Itzurun Beach in Zumaia, and Muriola Beach in Barrika.\\n\\n====King's Landing====\\n\\nKing's Landing is the royal capital of Westeros and the Seven Kingdoms. King's Landing has an estimated population of 1 million people, making it the most populous city in Westeros. It is situated on the Blackwater river on the spot where Aegon the Conqueror landed in Westeros to begin his conquest. The main city is surrounded by a wall, which is manned by the City Watch of King's Landing, which is nicknamed the gold cloaks, after the cloaks they wear. Within the walls, the city's natural landscape is dominated by three hills, named after Aegon and his two sister-wives Rhaenys and Visenya. Poorer smallfolk (commoners) build shanty settlements outside the city. King's Landing is described as extremely populous but unsightly and dirty. The stench of the city's waste can be smelled far beyond its walls.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 44}, 'embedding': None, 'id': '93d766a13e41412d6b7509b125d18170'}>,\n",
+       "  <Document: {'content': 'King\\'s Landing is described as extremely populous but unsightly and dirty. The stench of the city\\'s waste can be smelled far beyond its walls.\\n\\nThe royal castle, called the Red Keep, sits on Aegon\\'s Hill. It is the seat of the royal court. The Keep holds the Iron Throne. Aegon commissioned the throne\\'s construction from the swords of his defeated enemies. According to legend, he kept the blades sharp because he believed that no ruler should ever sit comfortably. Centuries later, kings still cut themselves on the throne. It is a common belief that one who cuts himself on the throne has been \"rejected\" by the throne and is therefore not fit to rule.\\n\\nThe city also holds the Great Sept of Baelor, where the Most Devout convene with the High Septon. It is the holiest sept of the Seven. The slums of King\\'s Landing are called Flea Bottom, where residents are so poor they regularly subsist on \"bowls of brown\", a mystery stew that can include the meat of puppies and murder victims.\\n\\nMartin compared King\\'s Landing to medieval Paris or London. It was inspired by the view of Staten Island from his childhood home in Bayonne, New Jersey.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 45}, 'embedding': None, 'id': 'cb15aeef4a87f16bf8f22ec04665d79a'}>,\n",
+       "  <Document: {'content': 'Martin compared King\\'s Landing to medieval Paris or London. It was inspired by the view of Staten Island from his childhood home in Bayonne, New Jersey.\\n\\nThe first season of the TV adaptation used Malta\\'s former capital Mdina to represent King\\'s Landing. \"Like King\\'s Landing, Mdina is a walled medieval city built upon a hill, but unlike King\\'s Landing, Mdina is an inland city so the production was limited to interior shots such as side streets and the town gate, which can be seen when Ned Stark arrives. Nearby Fort Manoel doubled as the great Sept of Baelor,\" which can be seen when Ned Stark is executed. Various other locations around Malta represent the Red Keep, \"including the real-life residence of the president of Malta, San Anton Palace. The gates of Fort Ricasoli doubled as the Red Keep\\'s gates; Fort St. Angelo was used for the scenes of Arya Stark chasing cats; and St. Dominic monastery stood in for the scene where Ned Stark confronts Cersei Lannister in the godswood.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 46}, 'embedding': None, 'id': '217639e74ebd43cd506b71389651342c'}>,\n",
+       "  <Document: {'content': 'The gates of Fort Ricasoli doubled as the Red Keep\\'s gates; Fort St. Angelo was used for the scenes of Arya Stark chasing cats; and St. Dominic monastery stood in for the scene where Ned Stark confronts Cersei Lannister in the godswood.\"\"In season two, filming for King\\'s Landing and the Red Keep shifted from Malta to the historic parts of Dubrovnik and the Minčeta, Bokar, and Lovrijenac fortresses in Croatia, which allowed for more exterior shots of an authentic walled medieval city.\"Parts of Season three were filmed there, too, as well as in nearby Trsteno. \"Known as \\'\\'the Pearl of the Adriatic\\'\\', the city proved to share many characteristics with the fictional capital: it had a well-preserved medieval look, with high walls and the sea at its side. According to David Benioff, executive producer of the show, \"King\\'s Landing might be the single most important location in the entire show, and it has to look right\", and \"The minute we started walking around the city walls we knew that was it. You read the descriptions in the book and you come to Dubrovnik and that\\'s what the actual city is. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 47}, 'embedding': None, 'id': '6657448ebdf308e050a5edd0aa4c27e9'}>,\n",
+       "  <Document: {'content': 'According to David Benioff, executive producer of the show, \"King\\'s Landing might be the single most important location in the entire show, and it has to look right\", and \"The minute we started walking around the city walls we knew that was it. You read the descriptions in the book and you come to Dubrovnik and that\\'s what the actual city is. It has the sparkling sea, sun and beautiful architecture.\"Co-Executive Producer D.B. Weiss added \"To find a full-on, immaculately preserved medieval walled city that actually looks uncannily like King\\'s Landing where the bulk of our show is set, that was in and of itself such an amazing find\". The Tourney of the Hand in season 1 was filmed in Shane\\'s Castle, Northern Ireland.\\n\\nThe Red Keep interior are filmed at Belfast\\'s studio The Paint Hall. Set designer Gemma Jackson said, \"When I was thinking about King\\'s Landing, the whole red aspect of it, that immediately made me think of Rajasthan. The floor at King\\'s Landing was from the Pantheon in Rome.\"Martin said that \"Our throne room is a spectacular throne room – we actually redressed a throne room built for another film. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 48}, 'embedding': None, 'id': '937f3d4f5a707285881a04404ff1e943'}>,\n",
+       "  <Document: {'content': 'Martin said that \"Our throne room is a spectacular throne room – we actually redressed a throne room built for another film. And again, it occupied a quarter of the Paint Hall, so it\\'s very big, but in my mind in the books, it\\'s Westminster Abbey, it\\'s St. Paul\\'s Cathedral.\\n\\n===Dorne===\\nCastle of Zafra in Guadalajara, Spain, which portrayed the Tower of Joy in Dorne in \\'\\'Game of Thrones\\'\\'\\nDorne is the southernmost and least populated land of Westeros. The capital, Sunspear, is the seat of the ruling House Martell. As of the first five books, Doran Nymeros Martell is the Prince of Dorne and Lord of Sunspear. Doran\\'s sister, Princess Elia, was married in a political alliance to Prince Rhaegar Targaryen, the Prince of Dragonstone and heir to the Iron Throne. They had two children, a daughter, Rhaenys, and a son, Aegon. During the Sack of King\\'s Landing at the end of Robert\\'s Rebellion, Princess Elia was raped and murdered by Gregor Clegane, a House Lannister bannerman (vassal). Her children were also killed in front of her. Prince Doran and his wife, Princess Mellaria, have three children, Arianne, Quentyn and Trystane. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 49}, 'embedding': None, 'id': '35025602a80d9980a33e53d2599455ce'}>,\n",
+       "  <Document: {'content': \"Her children were also killed in front of her. Prince Doran and his wife, Princess Mellaria, have three children, Arianne, Quentyn and Trystane. During the War of the Five Kings, Tyrion Lannister, as Hand of the King, turns the historical enmity of House Martell and Dorne into an alliance by sending King Joffrey's middle sibling and sister, Myrcella Baratheon, as the betrothed future bride to Trystane, the youngest child of Prince Doran, who is about her own age. The eldest child of Prince Doran, Arianne, is heir to House Martell, Sunspear and the rule of Dorne. The wealth of Dorne comes from their famous Sand Steeds, purebred horses of endurance, speed, and grace, and from spices, wines, fishing, fabrics, and textiles.\\n\\nDorne is bordered by the Sea of Dorne to the north, the islands known as the Stepstones to the east, and stretches from the high mountains of the Dornish marches, the Red Mountains, separating Dorne from the remainder of the Seven Kingdoms by land. The two major passes though the Red Mountains that connect Dorne with the rest of the continent are the Stone Way Pass and the Prince's Pass. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 50}, 'embedding': None, 'id': '828d474089cfda7e94cd605664cfb64f'}>,\n",
+       "  <Document: {'content': \"The two major passes though the Red Mountains that connect Dorne with the rest of the continent are the Stone Way Pass and the Prince's Pass. The Prince's Pass leads to the Reach, while the Stone Way exits the mountains near Summerhall. The southern coast of the continent is bordered by the Summer Sea. Described as tropical in climate by George R. R. Martin, Dorne has the highest temperatures kingdom in Westeros, is arid, with a rocky, mountainous, terrain that includes the only desert on the continent. Its rivers provide some fertile lands and during a long summer there is enough rain and other supplies of water to keep Dorne habitable. Inland water is almost as valuable as gold, and wells are jealously guarded. Notable locations of Dorne are Starfall, the seat of House Dayne, and Yronwood, the seat of House Yronwood, the most powerful of the Martell bannermen. Planky Town is a trade port town at the mouth of the River Greenblood.\\n\\nDornishmen have a reputation for hot-bloodedness. They differ both culturally and ethnically from other Westerosi due to the historical mass immigration of Rhoynish people. They have adopted many Rhoynish customs as well, including equal primogeniture. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 51}, 'embedding': None, 'id': '57301cd897971eaa0fd2d392d640936b'}>,\n",
+       "  <Document: {'content': 'They differ both culturally and ethnically from other Westerosi due to the historical mass immigration of Rhoynish people. They have adopted many Rhoynish customs as well, including equal primogeniture. Dorne was the only kingdom in Westeros to successfully resist Aegon\\'s conquest, even killing one of his dragons during the war. It was conquered by Daeron I over a century after the Targaryen invasion, but rose against him leading to his death. Finally under Daeron\\'s cousin Daeron II they joined through marriage. This accomplishment has allowed Dorne to retain a measure of independence. Lords of the ruling House Martell still style themselves \"Prince\" and \"Princess\" in the Rhoynish fashion. Unlike most of the rest of Westeros, illegitimate children born in Dorne are treated nearly the same as legal offspring and given the surname Sand, as with Westerosi customs to give bastards a surname showing their origins.\\n\\nAccording to \\'\\'A Storm of Swords\\'\\', \"There were three sorts of Dornishmen .... There were the salty Dornishmen who lived along the coasts, the sandy Dornishmen of the deserts and long river valleys, and the stony Dornishmen who made their fastnesses in the passes and heights of the Red Mountains. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 52}, 'embedding': None, 'id': '696888dffd6297f9202827d15602f1df'}>,\n",
+       "  <Document: {'content': 'There were the salty Dornishmen who lived along the coasts, the sandy Dornishmen of the deserts and long river valleys, and the stony Dornishmen who made their fastnesses in the passes and heights of the Red Mountains. The salty Dornishmen had the most Rhoynish blood, the stony Dornishmen the least. All three sorts seemed well represented in Doran’s retinue. The salty Dornishmen were lithe and dark, with smooth olive skin and long black hair streaming in the wind. The sandy Dornishmen were even darker, their faces burned brown by the hot Dornish sun. They wound long bright scarfs around their helms to ward off sunstroke. The stony Dornishmen were biggest and fairest, sons of the Andals and the First Men, brownhaired or blond, with faces that freckled or burned in the sun instead of browning.\"In the show, Dornish scenes were filmed in the Alcázar of Seville, Seville, Spain.\\n\\n==Summer Sea==\\n====Basilisk Isles====\\nEast of Naath, the Basilisk Isles have been a festering sore of the Summer Sea, and a safe haven for pirates, slavers, sellswords, and outlaws. Ruins have been found on the Isle of Tears, the Isle of Toads, and Ax Island. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 53}, 'embedding': None, 'id': '8a7419519e38b5884a350718c6b54e13'}>,\n",
+       "  <Document: {'content': \"==Summer Sea==\\n====Basilisk Isles====\\nEast of Naath, the Basilisk Isles have been a festering sore of the Summer Sea, and a safe haven for pirates, slavers, sellswords, and outlaws. Ruins have been found on the Isle of Tears, the Isle of Toads, and Ax Island. The Isle of Tears is the largest island, with steep valleys and black bogs. It was conquered by the Ghiscari and it was called Gorgai for two centuries, until the dragonlords of Valyria captured it and renamed it Gorgossos. It was used as a prison by the Freehold, a place where they sent their most despicable criminals.\\n\\n====Naath====\\nNaath, also known as the Isle of Butterflies, is an island off the north-west coast of Sothoryos that lies west of the Basilisk Isles. The Naathi people have dark skin and golden eyes. They practice extreme pacifism, making music instead of war and refusing to eat meat, only fruit. This makes them especially vulnerable to slavers from Essos. Daenerys' interpreter Missandei is from Naath.\\n\\n====Summer Islands ====\\nAs indicated on a map in ''A Storm of Swords'', the Summer Islands are situated to the south of Westeros, with a local fauna of talking birds, apes, and monkeys. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 54}, 'embedding': None, 'id': '6ac7f9a61205c42eeb859fd23c1a05ec'}>,\n",
+       "  <Document: {'content': '====Summer Islands ====\\nAs indicated on a map in \\'\\'A Storm of Swords\\'\\', the Summer Islands are situated to the south of Westeros, with a local fauna of talking birds, apes, and monkeys. The novels describe the island natives as dark-skinned people who speak their own language. They wear colored feathery clothes and live on fruit and fish. From their port city named Tall Trees Town, the Summer Isles export rare goods to Westeros such as wine, spices, feathers, but also a special kind of wood from which bows are made that have a longer range than most others. People of the Seven Kingdoms call the Summer Islanders\\' great vessels \\'\\'swan ships\\'\\', \"for their billowing white sails and for their figureheads, most of which depicted birds\". Samwell Tarly, who spends two chapters in \\'\\'A Feast for Crows\\'\\' aboard a swan ship, describes the Summer Islander women as wanton, and their gods as strange; they \"revered the elderly and celebrated their dead\" through sexual intercourse. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 55}, 'embedding': None, 'id': 'e2b89ff3d8cfac49f3e1ab1234aba89d'}>,\n",
+       "  <Document: {'content': 'Samwell Tarly, who spends two chapters in \\'\\'A Feast for Crows\\'\\' aboard a swan ship, describes the Summer Islander women as wanton, and their gods as strange; they \"revered the elderly and celebrated their dead\" through sexual intercourse. As a prostitute explains to Tyrion in \\'\\'A Clash of Kings\\'\\', the Summer Islanders regard their sexuality as the gods\\' gift to worship them through mating, and hence many of their highborn youths and maidens serve in pleasure houses for a few years to honor the gods.\\n\\n==Essos==\\nPart of the narrative in \\'\\'A Song of Ice and Fire\\'\\' lies across the Narrow Sea from Westeros, an area comprising the large eastern continent named Essos. Being roughly the size of Eurasia, Essos has geography and climate that vary greatly. The western coastline is characterized by green rolling hills, the massive Forest of Qohor, and extensive island chains such as Braavos and Lys. The middle of the continent is covered by the flat grasslands of the Dothraki Sea and the arid lands known as the Red Waste to the east. Beyond the Red Waste lies the city of Qarth. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 56}, 'embedding': None, 'id': '2239e950ed1e0465b3b2aa569738020a'}>,\n",
+       "  <Document: {'content': \"The middle of the continent is covered by the flat grasslands of the Dothraki Sea and the arid lands known as the Red Waste to the east. Beyond the Red Waste lies the city of Qarth. The south is dominated by dry rolling hills and has a Mediterranean climate, with a coastline along the Summer Sea and Slaver's Bay. The north coast of the mainland is separated from the polar cap by the Shivering Sea. To the south, across the Summer Sea, lies the uncharted jungle continent of Sothoryos.\\n\\nMuch of the fictional history of Essos relates to Valyria, a city located on a peninsula in southern Essos and the origin of House Targaryen before the destruction of the Valyrian Empire in an unspecified cataclysm. After the destruction of Valyria, the cities of Astapor, Yunkai, and Meereen regained independence and ruled their respective areas as city-states. The area is known in the books as Slaver's Bay.\\n\\n===Free Cities and vicinity ===\\nAcross the Narrow sea on the western side of Essos lie the nine Free Cities, independent city-states that are mostly on islands or along the coast. They are Lys, Myr, Pentos, Braavos, Lorath, Norvos, Qohor, Volantis and Tyrosh. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 57}, 'embedding': None, 'id': 'ea7a8debaa388e127d1cd3ada29c3ed'}>,\n",
+       "  <Document: {'content': '===Free Cities and vicinity ===\\nAcross the Narrow sea on the western side of Essos lie the nine Free Cities, independent city-states that are mostly on islands or along the coast. They are Lys, Myr, Pentos, Braavos, Lorath, Norvos, Qohor, Volantis and Tyrosh. Although most Free Cities are named early in the first novel, the books only provide a map of this region in \\'\\'A Dance with Dragons\\'\\'. Mountains to the east separate the coast from the plains of the Dothraki Sea, though gaps in the mountain range provide the Dothraki people some access to the Free Cities. The Free Cities were colonies built by the ancient Valyrian Freehold, and later declared independence after the Doom of Valyria. An exception to this is Braavos, which was founded by refugees fleeing Valyrian expansion, escaped slaves and other rabble. The languages of the Free Cities are derivatives of High Valyrian.\\n\\nThe Free Cities span an area characterized by the river Rhoyne, which the local character Yandry describes as \"the greatest river in the world\". Its banks are the homeland of the Rhoynar, who worship the river as \"Mother Rhoyne\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 58}, 'embedding': None, 'id': '4a60313c1e5ef7d022daa3880e50c3ac'}>,\n",
+       "  <Document: {'content': 'The Free Cities span an area characterized by the river Rhoyne, which the local character Yandry describes as \"the greatest river in the world\". Its banks are the homeland of the Rhoynar, who worship the river as \"Mother Rhoyne\". As mapped in \\'\\'A Dance with Dragons\\'\\', the Rhoyne originates from the conjunction of two of its tributaries, the Upper Rhoyne and the Little Rhoyne, southeast of the ruins of Ghoyan Drohe. The headwaters of the Upper Rhoyne lie in Andalos, the homeland of the Andals between Braavos and Pentos. The Rhoyne\\'s course runs southeast to turn due south after Dagger Lake, where river pirates hide on and around the many lake islands. The Rhoyne gains in width considerably as it gets fed by more tributaries, until it opens into the Summer Sea in a delta near the Free City of Volantis.\\n\\n====Braavos====\\nUnique among the Free Cities, Braavos was not a Valyrian colony, but a secret refuge from Valyrian expansion. It is a city spread over hundreds of tiny islands in a lagoon on the northwestern end of Essos where the Narrow Sea and Shivering Sea meet. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 59}, 'embedding': None, 'id': '326a13ac37c0cdff51889ec92511446f'}>,\n",
+       "  <Document: {'content': \"It is a city spread over hundreds of tiny islands in a lagoon on the northwestern end of Essos where the Narrow Sea and Shivering Sea meet. Braavos is home to the 'Iron Bank', one of the wealthiest banks in the known world . Braavos is also known for its swordsmen known as 'bravos', and its mysterious assassins, the Faceless Men. It is also famed for the Titan of Braavos, both a fortress and a statue. The ruler of Braavos is known as the Sealord and it is from the sea that the city's power and wealth flows. The hulls of Braavosi ships are painted purple and their merchant ships sail to many distant lands and bring their trade and wealth back home. Braavos has many moneylenders and the Iron Bank of Braavos lends money to foreign nations, especially The Crown, which has borrowed millions.\\n\\nBraavosi dress in flashy colors while the very rich and powerful dress in black and in blues that are almost black. Officials of Braavos, called keyholders and justiciars, wear drab coats of brown or grey. The city is also renowned worldwide for its courtesans. Every courtesan has her own barge and servants to work them. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 60}, 'embedding': None, 'id': '550ed103d12f11b119707751e1ba2bff'}>,\n",
+       "  <Document: {'content': \"The city is also renowned worldwide for its courtesans. Every courtesan has her own barge and servants to work them. The beauty of famed courtesans has inspired many a song. They are showered with gifts from goldsmiths and craftsmen beg for their custom. Nobility and rich merchants pay the courtesans large amounts of money to appear alongside them at events, and bravos are known to kill each other in their names. The character Syrio Forel, former first sword of the Sealord of Braavos, introduces Arya Stark to a unique form of Braavosi sword fighting, called Water Dancing. The style is a refined form of fencing in which the practitioner stands sideways and wields a slender blade. Pugnacious bravos fill the city, frequently dueling to display their skill.\\n\\nBraavos was inspired by Venice, Italy. It was filmed in Croatian towns of Šibenik, and Kaštel Gomilica in the TV series.\\n\\n====Pentos====\\nThe TV series filmed Illyrio's mansion in Pentos at Verdala Palace in Malta.\\nPentos is a major trading port on a bay of the western coast. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 61}, 'embedding': None, 'id': 'eb8b974aed88027b218c60f895433009'}>,\n",
+       "  <Document: {'content': '====Pentos====\\nThe TV series filmed Illyrio\\'s mansion in Pentos at Verdala Palace in Malta.\\nPentos is a major trading port on a bay of the western coast. Dominated by an architecture of square brick towers, it is headed by a Prince who is chosen by the de facto rulers of the city, known as Magisters. Khalasars occasionally make their way this far from the Dothraki Sea, but the Pentoshi are spared much of the raiding and invasions by paying tribute to their khals. Men from Pentos wear dyed and forked beards. As in many Free Cities, slavery is outlawed, but the wealthy and powerful members of the city have the ability to flout these laws by keeping servants collared in bronze.\\n\\nDaenerys\\'s scenes in the pilot episode were filmed in Morocco. The television adaptation re-used the Jerusalem sets of \\'\\'Kingdom of Heaven\\'\\' near Ouarzazate, Morocco. \"One small portion of the Jerusalem set, redressed and repainted, became the courtyard of Illyrio\\'s manse where Dany first meets Khal Drogo.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 62}, 'embedding': None, 'id': '2372d61234bcb758211fe08cec212b6d'}>,\n",
+       "  <Document: {'content': '\"One small portion of the Jerusalem set, redressed and repainted, became the courtyard of Illyrio\\'s manse where Dany first meets Khal Drogo.\"\"When the pilot was delivered, HBO asked for extensive reshoots, including the scrapping of all the footage shot in a landlocked part of Morocco which was supposed to take place in Pentos, a fictional port city and filming it again in Malta.\"The exterior scenes at Illyrio\\'s mansion in Pentos were shot at Verdala Palace, the 16th century summer palace of the president of Malta. \"One of Malta\\'s most spectacular natural attractions, the Azure Window on the island of Gozo, stood in for the location of Daenerys Targaryen\\'s wedding to Khal Drogo.\"When Pentos reappeared in Season 5, it was filmed in Croatia.\\n\\n====Volantis====\\nVolantis is a port on the southern coast of Essos, and is the oldest and proudest of the Free Cities. A fortification known as the Black Wall protects the oldest parts of the city. The city is ruled by three triarchs, who are elected every year by free landholders of Volantis, and defended by slave soldiers called the \"Tiger cloaks\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 63}, 'embedding': None, 'id': '48ff3c494a478684830645acff61096d'}>,\n",
+       "  <Document: {'content': 'The city is ruled by three triarchs, who are elected every year by free landholders of Volantis, and defended by slave soldiers called the \"Tiger cloaks\". Volantis is incredibly important to the slave market, and in the city there are five slaves to every free man. All Volantene slaves have facial tattoos denoting their profession: for instance, sex slaves have tears tattooed on their faces, and the tiger cloaks have tiger stripes. The worship of R\\'hllor is the most influential religion of Volantis, especially among slaves.\\n\\nThe TV adaptation used locations in Córdoba, Spain.\\n\\n====Other Free Cities ====\\n* \\'\\'\\'Lorath\\'\\'\\' is a port city on a group of northern islands. The character Jaqen H\\'ghar poses as a Lorathi in \\'\\'A Clash of Kings\\'\\', wearing long hair dyed red on one side and white on the other.\\n* \\'\\'\\'Lys\\'\\'\\' sits astride a series of southern islands. The Lysene have a tendency to be tall and fair of skin and eyes, unlike most inhabitants of the Free Cities. Lys is well known for its pleasure houses, training slaves in the arts of love and selling them as concubines and bed-slaves. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 64}, 'embedding': None, 'id': '9ec31a8c6f97dfbcf33d7bfd5015ec39'}>,\n",
+       "  <Document: {'content': \"Lys is well known for its pleasure houses, training slaves in the arts of love and selling them as concubines and bed-slaves. Lys also frequently fights over control of the Stepstones and the Disputed Lands. There appears to be a love goddess whose worship is peculiar to Lys. Dany's handmaiden Doreah and the pirate Salladhor Saan are Lysene.\\n* '''Myr''' is a coastal city renowned for their master lenscrafters, intricate lace, and fine carpets. The dark eyed and dark skinned Myrmen are similar to Norvosi and Pentoshi in that they are ruled by magisters that are known to pay tribute to passing Dothraki khalasars. Myr is a hub of trade in both slaves and their signature green nectar wines. Myr frequently fights over control of the Disputed Lands.\\n* '''Norvos''' sits on the main continent in two parts, one atop a high hill and the other beside a low river. The city has three large bells, each with its own name and distinctive voice, that are rung frequently. The surrounding area is a land of rolling hills, terraced farms, and white-stucco villages. The climate is fairly mild. Norvosi can be recognized by their dyed and upswept mustaches. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 65}, 'embedding': None, 'id': 'c763cfd05425776aca1f270bd7574d3f'}>,\n",
+       "  <Document: {'content': \"The surrounding area is a land of rolling hills, terraced farms, and white-stucco villages. The climate is fairly mild. Norvosi can be recognized by their dyed and upswept mustaches. The city is run by a council of magisters that are known to pay tribute to passing Dothraki khalasars. It is also home to a group of bearded priests that train elite guardsmen. These guardsmen swear oaths of duty and consider themselves wedded to their distinctive long axes.\\n* '''Qohor''' is situated on the main continent, in the vast Forest of Qohor. It is known for its fine tapestries and its smiths, who have the rare ability to reforge Valyrian steel, even directly infusing the metal with a variety of different colors. The Black Goat is a prominent god in the city. Qohor's city guard has been composed solely of Unsullied eunuch slave soldiers ever since the Battle of the Three Thousand, when 3,000 Unsullied soldiers successfully defended the city against over 25,000 Dothraki horsemen. Guardsmen tie braids of human hair to their spears to commemorate the Dothraki cutting their braids in salute to Qohor's defenders.\\n* '''Tyrosh''', a coastal city-state ruled by an Archon, is infamous for its avarice. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 66}, 'embedding': None, 'id': '386fda3da94f686a50dad8b62a834bd3'}>,\n",
+       "  <Document: {'content': \"Guardsmen tie braids of human hair to their spears to commemorate the Dothraki cutting their braids in salute to Qohor's defenders.\\n* '''Tyrosh''', a coastal city-state ruled by an Archon, is infamous for its avarice. Traders deal extensively in slaves and Tyroshi pear brandy. The city features an abundance of pleasure houses, but they are not as highly regarded as those in Lys. Tyroshi master armorsmiths make intricate armor in fantastic shapes. Tyrosh is a popular center for the hiring of sellswords. The city is often drawn into the ongoing conflicts over the Disputed Lands and the Stepstones. The Tyroshi often wear forked beards and pointed mustaches dyed in bright colors. The character Daario Naharis is from Tyrosh.\\n\\n===Central Essos===\\nThis section covers the Essos locations east of the Free Cities that Daenerys Targeryen passes through on her travels in ''A Game of Thrones'' and ''A Clash of Kings'' before moving on to Slaver's Bay.\\n\\n==== Valyria ====\\nValyria is a peninsula in South-Central Essos, west of Slaver's Bay. Before the Doom of Valyria, it was the seat of the Valyrian Freehold, a massive empire thousands of years old. The Valyrians are characterized by their silver hair and violet eyes. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 67}, 'embedding': None, 'id': 'e80cc224b4a21801d32a278719fc84c3'}>,\n",
+       "  <Document: {'content': 'Before the Doom of Valyria, it was the seat of the Valyrian Freehold, a massive empire thousands of years old. The Valyrians are characterized by their silver hair and violet eyes. Valyria was called The Freehold because every man who owned land was allowed to vote for their leaders. The Valyrians also used slaves to mine the Fourteen Flames, a series of volcanoes rich with ore. They subjugated the Ghiscari and the Rhoynar and established all of the Free Cities, save Braavos. They did this through their knowledge of dragonlore. Many Valyrians rode dragons. However, hundreds of years ago, an event known as the Doom of Valyria, apparently involving a violent eruption of the Fourteen Flames, destroyed the Freehold and made Valyria an archipelago. The Targaryens are of the blood of old Valyria, who escaped before The Doom.\\n\\n====Dothraki Sea ====\\nThe Dothraki Sea is a vast, flat grassland on Essos. It is inhabited by the Dothraki people, a copper-skinned race of warlike nomads with their own language and unique culture. The Dothraki live in hordes called khalasars, each led by a chief called a khal. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 68}, 'embedding': None, 'id': '91432015dec34f013c9953a198fef70d'}>,\n",
+       "  <Document: {'content': 'It is inhabited by the Dothraki people, a copper-skinned race of warlike nomads with their own language and unique culture. The Dothraki live in hordes called khalasars, each led by a chief called a khal. Khalasars are broken into groups, called khas, which are each led by one of the khal\\'s captains, called kos. Each khal and his khalasar owe fealty to a ruling council of royal priestesses, called the dosh khaleen, whose members are each a former khal\\'s consort, called a khaleesi during the reign of her husband, one who became part of the dosh khaleen following his death.\\n\\nDothraki are expert riders and their horses are of prime importance in their culture, used for food, transportation, raw materials, warfare, and establishing social standing. They regularly raid other peoples.\\n\\nGeorge R. R. Martin said \"The Dothraki were actually fashioned as an amalgam of a number of steppe and plains cultures ... Mongols and Huns, certainly, but also Alans, Sioux, Cheyenne, and various other Amerindian tribes ... seasoned with a dash of pure fantasy. So any resemblance to Arabs or Turks is coincidental. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 69}, 'embedding': None, 'id': '98f883df13eef8fc57f73fb82e2fcaea'}>,\n",
+       "  <Document: {'content': 'George R. R. Martin said \"The Dothraki were actually fashioned as an amalgam of a number of steppe and plains cultures ... Mongols and Huns, certainly, but also Alans, Sioux, Cheyenne, and various other Amerindian tribes ... seasoned with a dash of pure fantasy. So any resemblance to Arabs or Turks is coincidental. Well, except to the extent that the Turkic peoples|historic Turks were also originally horsemen of the steppes, not unlike the Alans, Huns, and the rest.\"However, he also noted that \"In general, though, while I do draw inspiration from history, I try to avoid direct one-for-one transplants, so it would not be correct to say that the Dothraki are Mongols.\"The Dothraki have only one permanent city, called Vaes Dothrak, which serves as their capital. The dosh khaleen hold the city as their seat. It is filled with statues stolen from other cities the Dothraki conquered or raided. There is a law that no Dothraki may shed blood within the boundaries of Vaes Dothrak and that those who do are cursed. Two gigantic bronze stallions, whose hooves meet midair, form an arch above the entryway to the city. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 70}, 'embedding': None, 'id': 'f33f75c341a4f29fe09c75ee6f8dc892'}>,\n",
+       "  <Document: {'content': 'There is a law that no Dothraki may shed blood within the boundaries of Vaes Dothrak and that those who do are cursed. Two gigantic bronze stallions, whose hooves meet midair, form an arch above the entryway to the city. For the first season of the TV adaptation, Sandy Brae in the Mourne Mountains of northern Ireland was chosen to stand in for Vaes Dothrak; the bronze stallions making up the Horse Gate as the main entrance of Vaes Dothrak were later C.G.I.ed on two pedestals erected on location.\\n\\n====Lhazar====\\nLhazar is an area of the semi-arid lands south of the Dothraki Sea. A region of pastures and hills, it is inhabited by the Lhazareen, a peaceful people with bronze skin, flat faces, and almond eyes. They are predominantly shepherds, called the Lamb Men by the Dothraki, who frequently prey on them. They worship a god called the Great Shepherd and believe that all of humanity is part of a single flock. The scenes at the village of the Lamb Men that is sacked by the Dothraki were filmed in Malta, at the farming town of Manikata.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 71}, 'embedding': None, 'id': '1d3845d7384dd23f3298afc784518da2'}>,\n",
+       "  <Document: {'content': \"The scenes at the village of the Lamb Men that is sacked by the Dothraki were filmed in Malta, at the farming town of Manikata.\\n\\n===Slaver's Bay ===\\nSlaver's Bay is a marginal sea of the Summer Sea, lying to the south of the Dothraki Sea, to the west of Lhazar and thousands of leagues to the east of the Free Cities. The climate is very hot. After a first mention in ''A Game of Thrones'' in relation to slavery, Daenerys Targaryen conquers the three great Slaver's Bay port city-states Astapor, Yunkai and Meereen in ''A Storm of Swords''. She stays in Meereen throughout most of ''A Dance with Dragons''.\\n\\nThe cities were built from the rubble of Old Ghis, an ancient rival of Valyria that was crushed by Valyria thousands of years before the series' events. The economies of the cities are largely based on slave labor and the slave trade. Treatment of slaves is often harsh, while citizens live in relative luxury. Professional soldiers of all three cities wear outlandish costumes and hairstyles that limit their usefulness in battle. The cities' militaries are highly dependent on additional slave and mercenary armies for the actual fighting.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 72}, 'embedding': None, 'id': '71c1337ba2650b567443d8e107461ca3'}>,\n",
+       "  <Document: {'content': 'Professional soldiers of all three cities wear outlandish costumes and hairstyles that limit their usefulness in battle. The cities\\' militaries are highly dependent on additional slave and mercenary armies for the actual fighting.\\n\\nPresent inhabitants of the bay are a mixed race that no longer speak the old Ghiscari tongue but variations of High Valyrian with a characteristic growl. The ancient folk of Ghis, who name themselves the harpy\\'s sons in Astapor, are said to have bristly red-black hair. The Good Masters of Astapor all appear alike to Daenerys as \"thick fleshy men with amber skin, broad noses, dark eyes. Their wiry hair was black or a dark red, or that queer mixture of red and black that was peculiar to Ghiscari\". Only the freeborn men of Astapor are permitted to wear garments called tokars, whose fringes display their status. Many Astapori women veil their face for the dust. The Astapori are drenched in sweet perfumes.\\n\\n====Astapor====\\nAstapor lies on the banks of the Worm River, a wide, slow and crooked stream with wooded islands. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 73}, 'embedding': None, 'id': '67ceb92305bd51d67a4d467c27ff4021'}>,\n",
+       "  <Document: {'content': 'The Astapori are drenched in sweet perfumes.\\n\\n====Astapor====\\nAstapor lies on the banks of the Worm River, a wide, slow and crooked stream with wooded islands. Entering Astapor at the beginning of \\'\\'A Storm of Swords\\'\\', Daenerys experiences it as an ancient and dilapidated city that has long passed its glory days. The city is dominated by its red brick architecture, and Arstan Whitebeard explains to Daenerys that the saying \"Brick and blood built Astapor, ... and brick and blood her people\" refers to the slaves who make the bricks. Astapor\\'s stepped pyramids, its fighting pits, streets, the surrounding walls and the Plaza of Pride are all made of red bricks. The so-called Plaza of Punishment at Astapor\\'s main gates is even larger than the Plaza of Pride.\\n\\nThe Plaza of Pride, which has a red-brick fountain and a huge bronze harpy statue in its center, serves as an open air slave market and a marshaling area for the Unsullied, elite eunuch spearmen with a renown for discipline and effectiveness. Astapor is the only city to sell Unsullied, but also sells bed slaves, fieldhands, scribes, craftsmen and tutors. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 74}, 'embedding': None, 'id': '1189d958a6e71d2bd4c34ba996d2b410'}>,\n",
+       "  <Document: {'content': \"The Plaza of Pride, which has a red-brick fountain and a huge bronze harpy statue in its center, serves as an open air slave market and a marshaling area for the Unsullied, elite eunuch spearmen with a renown for discipline and effectiveness. Astapor is the only city to sell Unsullied, but also sells bed slaves, fieldhands, scribes, craftsmen and tutors. The Unsullied require a huge investment in both time and money by the Astapori who raise and train them, but they earn the most profitable of returns for the Good Masters of Astapor. The Unsullied wear spiked bronze hats, and they obey at all costs, even if it demands their death. They are given new slave names each day to be reminded of their worthlessness. In times of attack, unsold Unsullied are deployed to the massive, crumbling red-brick walls that the Astapori no longer man.\\n\\nDaenerys decides to buy all of Astapor's trained and untrained Unsullied, over 8600 in number, and tells them to kill all adult Astapori slavers and soldiers when she leaves the city. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 75}, 'embedding': None, 'id': '23d708300322e675f1f2f2478471d248'}>,\n",
+       "  <Document: {'content': \"Daenerys decides to buy all of Astapor's trained and untrained Unsullied, over 8600 in number, and tells them to kill all adult Astapori slavers and soldiers when she leaves the city. She gives the power over Astapor to a council of former slaves led by a healer, a scholar and a priest, and tens of thousands of former slaves join her on her travels to Yunkai. A former butcher named Cleon fends off a scheme to have the Good Masters re-established, and was crowned as the King of Astapor in reward.\\n\\nThe TV show used the coastal town of Essaouira, Morocco to film scenes in Astapor.\\n\\n====Yunkai====\\nThe smallest of the three cities, Yunkai, like Meereen, does not trade in Unsullied but is known for its fighting pits and its pleasure houses, both of which turn out slaves at a brisk pace. The city is similar to Astapor in architecture except for its smaller size and its use of yellow brick in its buildings instead of red. The slavers of Yunkai are known as the Wise Masters. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 76}, 'embedding': None, 'id': '16a92112fa0ec03346df1525ab5161f7'}>,\n",
+       "  <Document: {'content': \"The city is similar to Astapor in architecture except for its smaller size and its use of yellow brick in its buildings instead of red. The slavers of Yunkai are known as the Wise Masters. Because of the city's lack of Unsullied, it relies on a mixed professional and slave army of approximately 4,000 with at least 1,000 mercenaries. Typical for Ghiscari, Yunkai soldiers wear impractical armor and oiled hair teased into enormous shapes, limiting their effectiveness.\\n\\nYunkish scenes were filmed in Aït Benhaddou, Morocco in the TV show.\\n\\n====Meereen====\\nThe largest of the three slaver cities, Meereen has a population equaling that of Astapor and Yunkai combined. The city has architecture similar to that of its neighbors, but it is made of bricks of many colors. Its landscape is dominated by a massive pyramid, named the Great Pyramid, and the Temple of Graces, which is capped by a golden dome. Meereen is unique among the Ghiscari cities in that it is filled with many temples and pyramids. The slavers of Meereen are known as the Great Masters. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 77}, 'embedding': None, 'id': 'dc4dc2e4a874026ebad708231aab1678'}>,\n",
+       "  <Document: {'content': \"Meereen is unique among the Ghiscari cities in that it is filled with many temples and pyramids. The slavers of Meereen are known as the Great Masters. They field a force of lancers equipped in traditionally extravagant Ghiscari fashion with scales of copper and lances as long as fourteen feet. It is built on the banks of the river Skahadhazan.\\n\\nFor the HBO television series, many of the scenes in Meereen were filmed in Split and the Fortress of Klis, Croatia. In Season 5, Daznak's Pit in the city was shot in the Plaza de Toros in Osuna, Spain.\\n\\n=== Eastern Essos ===\\n====Red Waste ====\\nThe Red Waste is a great desert-like area in the eastern part of Essos. Not much is known about it, since it was only briefly seen in ''A Clash of Kings'' when Daenerys Targaryen and her ''khalasar'' crossed it. The only known settlement in the region, Vaes Tolorro, is in ruins.\\n\\n====Qarth====\\nFirst mentioned in ''A Game of Thrones'', the city of Qarth has not yet appeared on any maps in the books. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 78}, 'embedding': None, 'id': 'cff7fbfa6e205cda09503fff1c5a8ad6'}>,\n",
+       "  <Document: {'content': \"====Qarth====\\nFirst mentioned in ''A Game of Thrones'', the city of Qarth has not yet appeared on any maps in the books. However, the HBO ''Viewer's Guide'' world map and the opening titles of the TV series' second season show Qarth located at a strait between the Summer Sea and the Jade Sea in the south-east of Essos. Upon Daenerys' first visit to Qarth in ''A Clash of Kings'', the warlock Pyat Pree describes his city as the center of the world and as a gateway of commerce and culture between the east and west, and the north and south. The reader learns through Daenerys's eyes that the city is surrounded by three graded walls of thirty to fifty feet in height, respectively engraved with portraits of animals, war, and lovemaking. The city's buildings are of many colors, including rose, violet, and umber. Slender towers rise throughout the city, fountains adorn every square, and thousands of colored birds, blooming trees and flowers fill the city. The TV adaptation filmed Qarth on the island of Lokrum near Dubrovnik and constructed a set at the Dubac quarry in Croatia to double for the gates of Qarth.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 79}, 'embedding': None, 'id': 'fb1fb5cf36925de01d23016d5b30d72f'}>,\n",
+       "  <Document: {'content': 'The TV adaptation filmed Qarth on the island of Lokrum near Dubrovnik and constructed a set at the Dubac quarry in Croatia to double for the gates of Qarth.\\n\\nThe Qartheen are described as \"tall pale folk in linen and samite and tiger fur\", with the women wearing gowns that leave one breast bare, while the men sport beaded silk skirts. Daenerys perceives them as \"nothing if not polite\". Slaves serve their needs. The Pureborn, descendants of the city\\'s ancient kings and queens, govern Qarth and also command the city\\'s defenses. Three principal merchant groups battle amongst themselves and against the Pureborn for dominance of the city: the Thirteen, the Ancient Guild of Spicers, and the Tourmaline Brotherhood. Qarth\\'s warlocks, whose lips are turned blue from a potion called \"the shade of the evening\", are said to brood over these factions; they are still feared although their power and prestige have waned over the years. Qarth is also home to the Sorrowful Men, a guild of assassins named so for whispering \"I am so sorry,\" before killing their victims. Daenerys leaves Qarth again at the end of \\'\\'A Clash of Kings\\'\\'.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 80}, 'embedding': None, 'id': '651da7952515faf0c48a723e9714e35e'}>,\n",
+       "  <Document: {'content': 'Qarth is also home to the Sorrowful Men, a guild of assassins named so for whispering \"I am so sorry,\" before killing their victims. Daenerys leaves Qarth again at the end of \\'\\'A Clash of Kings\\'\\'.\\n\\n=== Unvisited lands  ===\\n\\n====Asshai and the Shadow Lands ====\\nAsshai and the Shadow Lands are mysterious locations in the \\'\\'Ice and Fire\\'\\' world. They are first mentioned in \\'\\'A Game of Thrones\\'\\' and were first mapped in \\'\\'The Lands of Ice and Fire\\'\\', lying on the far east of the known world. Martin is unsure if the books will ever take the readers to Asshai, but said that readers may learn more through the POV character Melisandre (who originates from Asshai) or through the memories and mentions of other characters. Jorah Mormont describes Asshai as a port city far to the south of the Dothraki sea, at the end of the known world. Asshai exports such goods as black amethysts, amber and dragonglass. At another time, Jorah Mormont tells Daenerys of great kingdoms to the east of the Red Waste, and lists Asshai by the Shadow as one of the cities full of wonders there. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 81}, 'embedding': None, 'id': '3382bb3d242e13298dc059696ded2b17'}>,\n",
+       "  <Document: {'content': 'At another time, Jorah Mormont tells Daenerys of great kingdoms to the east of the Red Waste, and lists Asshai by the Shadow as one of the cities full of wonders there. According to Martin, all ship travels between Westeros and Asshai go via the Summer Sea and the Jade Sea through the straits at Qarth, and that the common folk still believe the world to be flat. However, according to Martin, \"Asshai is not nearly important to trade as Yi Ti, and the rich port cities of Yi Ti (and Leng) and more easily reached via Qarth.\"Quaithe of the Shadow prophesies Daenerys in Qarth that \"To go north, you must journey south. To reach the west, you must go east ... and to touch the light you must pass beneath the shadow.\"When Daenerys interprets this to mean she must go to Asshai, Quaithe says she would find the truth there.\\n\\nThere are many tales about the Shadow Lands, though how much truth they hold is unclear. The Dothraki believe that ghost grass covers the Shadow Lands, with stalks that glow in the dark and grow taller than a man on horseback. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 82}, 'embedding': None, 'id': 'bc65dec1382cb91ee09decfcc7499d51'}>,\n",
+       "  <Document: {'content': 'The Dothraki believe that ghost grass covers the Shadow Lands, with stalks that glow in the dark and grow taller than a man on horseback. Daenerys heard that \"spellsingers, warlocks, and aeromancers practiced their arts openly in Asshai, while shadowbinders and bloodmages worked terrible sorceries in the black of night\". There are also Westerosi maesters in Asshai. The mages of Asshai teach others their healing powers, but also their spells requiring blood sacrifice. Ancient books of Asshai record the Azor Ahai prophecy followed by members of the R\\'hllor faith. Daenerys heard that dragons themselves originated from the Shadow Lands beyond Asshai and the islands of the Jade Sea, and they possibly still live there. Bran dreams of flying Dragons in Asshai. The petrified dragon eggs Illyrio gives to Daenerys are said to come from the Shadow Lands. The \"dour and frightening\" Shadow Men cover their bodies in tattoos and wear lacquered wooden masks, and the appearance of the Asshai\\'i is described as dark and solemn. The Dothraki believe the Asshai\\'i to be the spawn of shadows. The Asshai\\'i have a language of their own.\\n\\n====Ibben====\\nIbben is a collection of islands north of Essos in the Bay of Whales. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 83}, 'embedding': None, 'id': '8c90de32defc3f936e3df2bb795b8229'}>,\n",
+       "  <Document: {'content': 'The Asshai\\'i have a language of their own.\\n\\n====Ibben====\\nIbben is a collection of islands north of Essos in the Bay of Whales. The largest of these islands is Ib, which contains the cities Port of Ibben and Ib Nor. Until the Doom of Valyria, Ibben was ruled by a God-King. Now power is held by the Shadow Council, which is made up of nobles, priests, and wealthy guildsmen. Ibben is first mentioned in \\'\\'A Game of Thrones\\'\\', where Tyrion talks of rumors that mammoths \"roam the cold wastes beyond the Port of Ibben\". In 2002, Martin said the narrative would \"probably not\" take readers to Ibben, which he described as a \"cold, mountainous, Iceland-sized island\" (i.e. 40,000 square miles) in the Shivering Sea, with the Port of Ibben as the major city; some Ibbenese also live on smaller islands nearby or in colonies on Essos. Ibben is unmapped in the books as of \\'\\'A Dance with Dragons\\'\\', but similar to Martin\\'s descriptions, the HBO \\'\\'Viewer\\'s Guide\\'\\' world map gives the island\\'s location as to the north-east of Essos. Martin said that due to a large whale population in the Shivering sea, many of the Ibbenses were whalers. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 84}, 'embedding': None, 'id': 'ba997e482a3ddb30b71ed72ece872c45'}>,\n",
+       "  <Document: {'content': 'Ibben is unmapped in the books as of \\'\\'A Dance with Dragons\\'\\', but similar to Martin\\'s descriptions, the HBO \\'\\'Viewer\\'s Guide\\'\\' world map gives the island\\'s location as to the north-east of Essos. Martin said that due to a large whale population in the Shivering sea, many of the Ibbenses were whalers. The Ibbenses are known to chew whale blubber in order to maintain their metabolism in the cold climate. Several characters see Ibbenese whalers and cogs at the ports of King\\'s Landing, Braavos, Maidenpool, Eastwatch-by-the-Sea, and the Iron Islands. The novels describe the people of Ibben as squat and hairy; Arya even meets an Ibbenese woman with a mustache. Tyrion and Varys meet foul-smelling Ibbenese,  who \"were as fond of axes as they were of each other\". Arya sees \"a dark brutal axeman from Ib\" in her dreams. The Ibbenese are said to speak with low, raspy voices and to have their own language.\\n\\n====Yi Ti====\\nThe novels repeatedly describe Yi Ti as a city full of wonders, lying in the far east. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 85}, 'embedding': None, 'id': '747446a6b16a268de6bddd9a74fc9290'}>,\n",
+       "  <Document: {'content': 'The Ibbenese are said to speak with low, raspy voices and to have their own language.\\n\\n====Yi Ti====\\nThe novels repeatedly describe Yi Ti as a city full of wonders, lying in the far east. As of \\'\\'A Dance with Dragons\\'\\', Yi Ti has not appeared on any maps in the books, but Martin specified that \"Yi Ti is to the south east of Qarth, generally, across the Jade Sea.\"The city is first mentioned in \\'\\'A Game of Thrones\\'\\', talking of rumors that \"basilisks infested the jungles of Yi Ti\". Sailor stories presented in \\'\\'A Feast for Crows\\'\\' mention that a grey plague has hit Yi Ti. The god of the people of Yi Ti is called the Lion of Night. Daenerys sees people of Yi Ti as bright-eyed men in monkey-tail hats in the markets of Vaes Dothrak. Martin is unsure \"to what extent those peoples like of Yi Ti will ever enter this present story, however... their lands are very far away.\"====Plains of Jogos Nhai====\\nNorth of Yi Ti, the Plain of Jogos Nhai are windswept, with rolling hills. They are dominated by a race of mounted warriors called the Jogos Nhai. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 86}, 'embedding': None, 'id': '8839dd9ea1ca89dbef082b8bb6c9d675'}>,\n",
+       "  <Document: {'content': \"====Plains of Jogos Nhai====\\nNorth of Yi Ti, the Plain of Jogos Nhai are windswept, with rolling hills. They are dominated by a race of mounted warriors called the Jogos Nhai. The Jogos Nhai live in yurts and tents, and are a nomadic people. They are short, squat, and have large heads and small faces. Men and women both have pointed skulls, a result of their custom of binding the heads of newborns. They also ride zorses, a striped mount that can withstand much more than average horse. The Jogos Nhai do not fight between themselves, and live in small clans bound by blood. They live in a state of perpetual war with outsiders. Each tribe is commanded by a ''jhat'', or war chief, and a moonsinger, who is a priestess, healer, and judge. Moonsingers are generally female, and ''jhats'' are mostly male. (Paraphrased from ''The World of Ice and Fire)''\\n\\n==Sothoryos ==\\nTo the south of Essos is the continent of Sothoryos (mistakenly spelled Sothoros in early novels). Sothoryos is the third continent of the known world, and is vast, plague-ridden, covered in jungles, and largely unexplored. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 87}, 'embedding': None, 'id': '2a96da3d0d5c66ba77e5d63a805ba64b'}>,\n",
+       "  <Document: {'content': '(Paraphrased from \\'\\'The World of Ice and Fire)\\'\\'\\n\\n==Sothoryos ==\\nTo the south of Essos is the continent of Sothoryos (mistakenly spelled Sothoros in early novels). Sothoryos is the third continent of the known world, and is vast, plague-ridden, covered in jungles, and largely unexplored. It is reported to be as large as Essos and described as a \"land without end\" by Jaenara Belaerys, a Valyrian dragonlord from before the Doom of Valyria.\\n\\nThe continent is first named on a map in \\'\\'A Storm of Swords\\'\\' (2000), showing the cities of Yeen and Zamettar on it. The narrative itself first refers to the continent in \\'\\'A Feast for Crows\\'\\' (2005). Martin had described Sothoryos in 2002 as \"the southern continent, roughly equivalent to Africa, jungly, plague-ridden, and largely unexplored\". The novels provide little other information. The swampy nature of Sothoryos is briefly referenced by Victarion in \\'\\'A Dance with Dragons\\'\\', and teak from Sothoryos is said to be used to build ships. A corsair\\'s road runs along the continent\\'s northern coast. \\'\\'A Dance with Dragons\\'\\' refers to the diseases on Sothoryos in regards to the wealthy but sick Yunkai slave trader Yezzan zo Qaggaz. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 88}, 'embedding': None, 'id': 'f1e25fd970686b4eaaf80361e97e3b20'}>,\n",
+       "  <Document: {'content': '\\'\\'A Dance with Dragons\\'\\' refers to the diseases on Sothoryos in regards to the wealthy but sick Yunkai slave trader Yezzan zo Qaggaz. Victarion describes some people as \"squat and hairy as the apes of Sothoros\", and some people fighting in Daznak\\'s Pit for Daenerys\\'s entertainment in \\'\\'A Dance with Dragons\\'\\' are described as \"brindle-skinned half-men from the jungles of Sothoros\". Martin said that, unlike other peoples in the novels, the brindled men of Sothoryos were pure fantasy constructs.\\n\\n==Ulthos ==\\nThe map collection \\'\\'The Lands of Ice and Fire\\'\\' also shows the north tip of a landmass named \"Ulthos\" to the south of Essos and east of Sothoryos. Asked whether this was another continent, Martin replied, \"Well, it\\'s a large landmass. I am a little unclear on the formal definition of \\'continent\\' as opposed to \\'big island.\\'Also on the size of Ulthos, which after all sits at the edge of the known world. Terra incognita and all that.\"==References==\\n===Secondary sources===\\n\\n===Primary sources===\\n\\n===Bibliography===\\n*\\n*\\n*\\n*\\n*\\n*\\n*\\n*\\n\\n==External links==\\n*  Wiki of Ice and Fire, Geography', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 89}, 'embedding': None, 'id': 'a29e5dbffa6c4e655177a2e39a370d6a'}>,\n",
+       "  <Document: {'content': '\\nThe soundtrack album of the sixth season of HBO series \\'\\'Game of Thrones\\'\\', titled \\'\\'\\'\\'\\'Game of Thrones: Season 6\\'\\'\\'\\'\\', was released digitally on June 24, 2016, and later released on CD on July 29, 2016. The album reached No. 1 on the \\'\\'Billboard\\'\\'s Soundtracks chart, and the track from the season finale \"Light of the Seven\" reached No. 1 on \\'\\'Billboard\\'\\'s Spotify Viral 50 chart. The \"Light of the Seven\" is the first time piano is used in the music for \\'\\'Game of Thrones\\'\\'. The album was composed by Ramin Djawadi. The soundtrack has received favorable reviews and peaked at #1 on the U.S. Soundtrack Albums chart and #27 on the U.S. Billboard 200. It won an International Film Music Critics Association for \"Best Original Score for a Television Series\".\\n\\n==Reception==\\nThe soundtrack received positive reviews from critics.\\n\\n==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* D.B. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'a7f0a201dd811abaf7c491ab5b28e623'}>,\n",
+       "  <Document: {'content': \"==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* D.B. Weiss – liner notes\\n\\n==Charts==\\n\\nChart (2016)\\n\\nPeak position\\n\\nUK Soundtrack Albums (OCC)\\n\\n2\\n\\nUS Soundtrack Albums (''Billboard'')\\n\\n1\\n\\nUS Digital Albums (''Billboard'')\\n\\n5\\n\\nUS Top Album Sales (''Billboard'')\\n\\n12\\n\\n==Awards and nominations==\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nInternational Film Music Critics Association\\n\\nBest Original Score for a Television Series\\n\\nWorld Soundtrack Awards\\n\\nTelevision Composer of the Year\\n\\n==Notes==\\n\\n==References==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '6d8be95995f0206a44d17bca174ff57c'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''Renly Baratheon''' is a fictional character in the ''A Song of Ice and Fire'' series of fantasy novels by American author George R. R. Martin, and its television adaptation ''Game of Thrones''.\\n\\nIntroduced in 1996's ''A Game of Thrones'', Renly is the youngest of the three sons of Lord Steffon Baratheon and Cassana Estermont, and the younger brother of Robert and Stannis Baratheon. He is Lord of Storm's End and Lord Paramount of the Stormlands. He served as master of laws in his eldest brother's small council, before crowning himself king in the wake of Robert's death with the support of the Reach and the Stormlands, an act that helps kick off the War of Five Kings. This brings him into conflict with his older brother Stannis, whose claim is greater.\\n\\nRenly's homosexual relationship with Loras Tyrell is alluded to in the books and is made clearly evident in the television show. Both men are among Martin's most prominent LGBTQ characters, although Renly and Loras' adapted relationship and the show's portrayal of the latter has received mixed criticism.\\n\\nRenly Baratheon is portrayed by Gethin Anthony in the HBO television adaptation.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '8734113490279f03dfe70ea47b2278c8'}>,\n",
+       "  <Document: {'content': \"Both men are among Martin's most prominent LGBTQ characters, although Renly and Loras' adapted relationship and the show's portrayal of the latter has received mixed criticism.\\n\\nRenly Baratheon is portrayed by Gethin Anthony in the HBO television adaptation.\\n\\n== Character description ==\\nRenly Baratheon is the younger brother of King Robert  and of King Stannis. He is a handsome and charismatic man that wins friends easily, which makes him popular with the smallfolk. He is thought to look extremely similar to his older brother Robert, albeit smaller and slimmer, inheriting the Baratheon height and long black hair. Although he is well-liked and charismatic, many powerful lords at court secretly consider him to be vain and frivolous. He is described as disdainful of reading, although he enjoys hunting and jousting. Renly is also a closeted homosexual engaged in a relationship with Loras Tyrell.\\n\\nRenly is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, such as Ned and Catelyn Stark. He is also often mentioned and remembered by Brienne of Tarth, who had fallen in love with him at a young age. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'c67d8a963e1205bd37f2a52478cbccdd'}>,\n",
+       "  <Document: {'content': \"He is also often mentioned and remembered by Brienne of Tarth, who had fallen in love with him at a young age. Renly is mostly a background character in the novels.\\n\\n== Storylines ==\\n\\nleft\\nRenly Baratheon is the youngest of the Baratheon brothers and Lord of Storm's End. He is described as handsome and charismatic, winning friends easily. Renly serves on Robert's council as Master of Laws. After Robert dies, Renly declares himself King of the Seven Kingdoms in ''A Clash of Kings'', wins the support of the Baratheon bannermen as he is their Lord Paramount, and seals an alliance with House Tyrell by marrying Margaery Tyrell. Before he can march on the capital though, he hears Stannis is besieging Storm's End. Renly marches there, intending to kill his brother in battle, and turning down an offer to become Stannis' heir. Before the battle he is assassinated by a shadow conjured by Melisandre, though it is unclear if Stannis is aware of this or not.\\n\\n==TV adaptation==\\nBanner of arms of King Renly Baratheon in ''Game of Thrones''\\nRenly Baratheon is played by the British actor Gethin Anthony in the television adaption of the series of books.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '3e5d106df8df16ca298ee34e12161fa3'}>,\n",
+       "  <Document: {'content': '==TV adaptation==\\nBanner of arms of King Renly Baratheon in \\'\\'Game of Thrones\\'\\'\\nRenly Baratheon is played by the British actor Gethin Anthony in the television adaption of the series of books.\\n\\nGethin Anthony on his character: \"The reason why I like playing him is I think he\\'s a fantastic man who believes in Westeros being a cultured and enlightened place. His major advantage -- and why he gets on well in the world -- is that he gets on well with people.\"====Season 1====\\nRenly Baratheon, Lord of Storm\\'s End, is the youngest brother of King Robert and Master of Laws in the Small Council. He is popular with the people because he is handsome, jovial, and throws extravagant balls and masquerades. He is not fond of fighting or bloodshed, and would rather make friends than kill enemies. He is secretly the lover of Ser Loras Tyrell, the Knight of the Flowers, who convinces him that those qualities make him better ruling material than either of his older brothers. While Robert lies dying, Renly attempts to convince Ned of this, and that the two of them should kidnap Joffrey and rule the realm themselves. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '4ed57eb30a37b00966edc6077a2dc68c'}>,\n",
+       "  <Document: {'content': \"While Robert lies dying, Renly attempts to convince Ned of this, and that the two of them should kidnap Joffrey and rule the realm themselves. However, Ned refuses, so Renly, Loras, and their followers flee south. Once Joffrey becomes King and has Ned executed, Renly challenges his alleged nephew's claim to the throne.\\n\\n====Season 2====\\nRenly declares himself King of the Seven Kingdoms during Season 2 and wins the support of the Baratheon bannermen and the support of other houses, despite Stannis' better claim. He also seals an alliance with the powerful House Tyrell and its bannermen (including Randyll Tarly, father of Samwell Tarly) by marrying Margaery Tyrell. He leads his massive army slowly through southern Westeros, biding his time. Catelyn Stark tries to convince Renly and Stannis to put aside their differences and unite against the Lannisters but it fails as both brothers refuse to give their claim for the throne. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8457d8c15418d17a65ac518fd9990072'}>,\n",
+       "  <Document: {'content': 'Catelyn Stark tries to convince Renly and Stannis to put aside their differences and unite against the Lannisters but it fails as both brothers refuse to give their claim for the throne. On the night before a battle between his and Stannis\\'s forces, Renly agrees to Catelyn to allow Robb Stark to keep the title \"King in the North\" and rule the North and Riverlands, but on the condition that he swear fealty to Renly as King on the Iron Throne. Before Catelyn can offer a real negotiation, Renly is assassinated by Melisandre, who gives birth to a shadow demon and sends it to kill Renly in order to remove him from Stannis\\' path. Stannis is initially unaware of the exact nature of Melisandre\\'s crime, and is later visibly shocked and saddened of the role he played in his brother\\'s death, which he visibly regrets.\\n\\n====Season 5====\\nRenly is later avenged in the Season 5 finale when Brienne of Tarth executes his brother Stannis after the battle outside Winterfell against the Boltons, spitefully telling Stannis that Renly was the rightful King.\\n\\n=== Family tree of House Baratheon ===\\n\\n== References ==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '9881897fca71bbdb3afa6a55b3d2cb81'}>,\n",
+       "  <Document: {'content': '\\n\\nA \\'\\'\\'White Walker\\'\\'\\' is a humanoid creature from the HBO television series \\'\\'Game of Thrones\\'\\', and the George R. R. Martin novel series \\'\\'A Song of Ice and Fire\\'\\' on which it is based. Primarily referred to as \\'\\'\\'the Others\\'\\'\\' in the novels, White Walkers are a supernatural threat to humankind who dwell north of The Wall in Westeros. \\'\\'The Verge\\'\\' named them among \"the most visually iconic creatures on the show\". White Walkers are also featured in the show\\'s merchandising.\\n\\n==Description==\\nMartin introduces the Others in the prologue of \\'\\'A Game of Thrones\\'\\' (1996), describing them as \"Tall ... and gaunt and hard as old bones, with flesh pale as milk\" with eyes \"deeper and bluer than any human eyes, a blue that burned like ice\". Accompanied by intense cold, they wear armor that \"seemed to change color as it moved\", and wield thin crystal swords capable of shattering steel. The Others move silently, and they speak their own language; Martin writes that their voices are \"like the cracking of ice on a winter lake\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '3bd02732b5daab9adcf1c4c2bd3d9a48'}>,\n",
+       "  <Document: {'content': 'The Others move silently, and they speak their own language; Martin writes that their voices are \"like the cracking of ice on a winter lake\". In \\'\\'A Storm of Swords\\'\\' (2000), they are shown to be vulnerable to weapons made of dragonglass (obsidian), as Samwell Tarly kills one this way:\\n\\nIn \\'\\'A Dance with Dragons\\'\\' (2011), Sam uncovers ancient record fragments which suggest that the Others are also vulnerable to something called \"dragonsteel\", which he and Jon Snow surmise is another term for Valyrian steel.\\n\\nCreatures killed by the Others soon reanimate as wights: undead with pallid skin, black hands, and glowing blue eyes similar to the Others\\'. Dragonglass has no effect on them. Wights may be physically injured, but even dismembered parts remain animated, so they must be destroyed by fire. The humans who live in the north beyond the Wall—called \"wildlings\" by the inhabitants of Westeros—burn their dead so they will not become wights.\\n\\n==Novels==\\nIn 2012, Chris Lackner wrote in \\'\\'Dose\\'\\', \"Fans of the novels are eagerly awaiting Martin\\'s final two installments of the seven-part series. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '4f6edb34639955903421e2113915bd8'}>,\n",
+       "  <Document: {'content': '==Novels==\\nIn 2012, Chris Lackner wrote in \\'\\'Dose\\'\\', \"Fans of the novels are eagerly awaiting Martin\\'s final two installments of the seven-part series. In particular, they are eager to learn more about the White Walkers—or The Others—a mysterious, undead race seemingly bent on humanity\\'s destruction.\"===Backstory===\\nIn the novels and the 2014 companion book \\'\\'The World of Ice & Fire\\'\\', Martin establishes that, millennia before the events of \\'\\'A Song of Ice and Fire\\'\\' series, a pact existed between the Children of the Forest (elf-like creatures) and the First Men (mankind). This was weakened by the emergence of the Others, an enigmatic and malevolent nonhuman species who inflicted a night that lasted a generation and a winter that lasted decades. After the Others were pushed back, the Children and the First Men raised The Wall, a vast barrier of stone, ice and magic from one coast of northern Westeros to the other, to bar the passage of the Others south.\\n\\n===\\'\\'A Game of Thrones\\'\\'===\\nAs \\'\\'A Game of Thrones\\'\\' (1996) begins, the general belief across Westeros is that the Others are a legend to scare children, or else \"gone eight thousand years\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '50770e83e0a0fe099c7931d338490858'}>,\n",
+       "  <Document: {'content': '===\\'\\'A Game of Thrones\\'\\'===\\nAs \\'\\'A Game of Thrones\\'\\' (1996) begins, the general belief across Westeros is that the Others are a legend to scare children, or else \"gone eight thousand years\". But the Others have re-emerged, and are gaining power—and creating wights—beyond the Wall. In the Prologue, a ranging party from the Night\\'s Watch come face to face with a group of the Others, who kill Ser Waymar Royce. Reanimated as a wight, he then kills the ranger Will. Out ranging beyond the Wall, Jon Snow and the Night\\'s Watch find the corpses of two of their fallen brothers. Brought home to Castle Black, the dead men rise and kill several of their living fellows before they are destroyed.\\n\\n===\\'\\'A Storm of Swords\\'\\'===\\nSam Tarly kills an Other with a dragonglass dagger in \\'\\'A Storm of Swords\\'\\' (2000). His former comrade Small Paul is killed and reanimated as a wight. The undead Paul is unfazed by the dragonglass, but Sam is able to put him down with fire. Bran Stark recalls the story of the Night\\'s King, a Stark and the 13th Lord Commander of the Night\\'s Watch who had been seduced by a female White Walker. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '1b629dce89a5636ecc0366d7b060d40a'}>,\n",
+       "  <Document: {'content': 'Bran Stark recalls the story of the Night\\'s King, a Stark and the 13th Lord Commander of the Night\\'s Watch who had been seduced by a female White Walker. The Night\\'s King and his queen enslaved the brothers of the Watch until the Starks and the wildlings joined forces to defeat him.\\n\\n===\\'\\'The Winds of Winter\\'\\'===\\nMartin said in 2012 that readers will see more of the Others in his forthcoming novel \\'\\'The Winds of Winter\\'\\'. He noted in a 2012 \\'\\'Dose\\'\\' interview, \"We\\'ll learn more about their history, certainly, but I don\\'t know about culture ... I don\\'t know if they have a culture\".\\n\\n==TV adaptation==\\nThe White Walkers portrayed on HBO\\'s \\'\\'Game of Thrones\\'\\' differ slightly in appearance from their literary counterparts, but Aaron Souppouris of \\'\\'The Verge\\'\\' named them among \"the most visually iconic creatures on the show\". In the TV series, the primary White Walker has been portrayed by Ross Mullan. Their apparent leader is the Night King, portrayed by Richard Brake and Vladimir Furdik, who first appeared in the episode \"Oathkeeper\", in which he places his hand on Craster\\'s baby son, transforming him into a White Walker. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8f3023cc341da8053820472f2aa29c49'}>,\n",
+       "  <Document: {'content': 'Their apparent leader is the Night King, portrayed by Richard Brake and Vladimir Furdik, who first appeared in the episode \"Oathkeeper\", in which he places his hand on Craster\\'s baby son, transforming him into a White Walker. In \"Hardhome\", the effectiveness of Valyrian steel against the White Walkers is proven as Jon shatters one to pieces with a single stroke of his ancient sword Longclaw. Unlike in the novels, the TV series has established that wights can be destroyed by dragonglass.\\n\\nIn the season 6 episode \"The Door\" (May 2016), Bran Stark experiences a vision of the creation of the Night King by Leaf, one of the Children of the Forest, by stabbing a human prisoner in the chest with a dragonglass dagger. Leaf explains to an incredulous Bran that the Children were at war with the First Men at the time of the Night King\\'s creation. In season 7, when Jon kills a White Walker, the wights under its control are also destroyed. The Night King kills Daenerys Targaryen\\'s dragon Viserion in the seventh season episode \"Beyond the Wall\", and then reanimates him as a wight. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'be6b0beed55ab4b83b35c8e59316e6c3'}>,\n",
+       "  <Document: {'content': 'The Night King kills Daenerys Targaryen\\'s dragon Viserion in the seventh season episode \"Beyond the Wall\", and then reanimates him as a wight. In the season finale episode \"The Dragon and the Wolf\", the Night King uses the dragon to breach the Wall.\\n\\nIn the eighth season premiere, \"Winterfell\", a group of survivors from the Wall\\'s destruction discover that Last Hearth, the home of House Umber, has been attacked by the Army of the Dead in their conquest of the North, with all of its inhabitants killed. The Night King has also left a message in the form of the deceased Ned Umber impaled on a wall and surrounded by a spiral of severed limbs. In the second episode \"A Knight of the Seven Kingdoms\", the inhabitants of Winterfell make plans for defending the castle against the Army of the Dead. Bran states that the Night King is seeking an \"endless night\", as he plans to cleanse the world of all life and memory and intends to kill Bran as a major step towards this goal. The White Walkers are later seen viewing Winterfell from a distance as the living prepare to fight them. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'efc2864183898d7c5f977b6e839619c4'}>,\n",
+       "  <Document: {'content': 'Bran states that the Night King is seeking an \"endless night\", as he plans to cleanse the world of all life and memory and intends to kill Bran as a major step towards this goal. The White Walkers are later seen viewing Winterfell from a distance as the living prepare to fight them. In the third episode \"The Long Night\", the Army of the Dead marches on Winterfell and nearly wipes out the combined armies of the living. The Night King eventually reaches the Godswood, kills Theon Greyjoy, and prepares to strike down Bran. However, the Night King is ambushed and killed by Arya Stark with the Valyrian steel dagger that Bran had previously given her (\"The Spoils of War\"), which causes both him and the other White Walkers to shatter and results in the complete obliteration of the Army of the Dead.\\n\\n===Merchandising===\\nIn 2012, Funko released a White Walker figure as part of their POP! Television line, which are 4.5 inch vinyl figures in the Japanese super deformed style. The company later produced a Mystery Mini Blind Box figurine of a stylized White Walker. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '97e07ff2570008971773944f828f8afc'}>,\n",
+       "  <Document: {'content': 'Television line, which are 4.5 inch vinyl figures in the Japanese super deformed style. The company later produced a Mystery Mini Blind Box figurine of a stylized White Walker. Dark Horse released a 9-inch White Walker bust statue in 2013, and later a 9-inch full figure statue. In 2014, Funko released a 6.5 inch articulated White Walker action figure (with spear accessory) as part of their HBO-licensed Legacy Collection line, which features \"some of the series’ most popular characters\".\\n\\n==See also==\\n* Themes in \\'\\'A Song of Ice and Fire\\'\\'\\n\\n==References==\\n\\n==Further reading==\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '2297c34014b4a3799961a56ec978a990'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'No One\\'\\'\\'\" is the eighth episode of the sixth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 58th episode overall. It was written by series co-creators David Benioff and D. B. Weiss, and directed by Mark Mylod.\\n\\nArya Stark fights for her life and chooses her destiny; Jaime Lannister returns Edmure Tully to Riverrun; Cersei Lannister is thwarted; Daenerys Targaryen returns to Meereen; and Sandor Clegane tracks down the Brotherhood Without Banners.\\n\\n\"No One\" received mostly positive reviews from critics, who listed the conclusion of Arya\\'s story with the Faceless Men, the reintroduction of the Brotherhood Without Banners, and Jaime\\'s scheme to retake Riverrun as high points of the episode, but also received some criticism as being anticlimactic. Filming of the episode\\'s foot chase scene between Arya and the Waif required a month of practice in Belfast to get the choreography right. In the United States, the episode achieved a viewership of 7.60 million in its initial broadcast. The episode was Peter Dinklage and Maisie Williams\\' selection for the 68th Primetime Emmy Awards to support their nominations.\\n\\nThe episode marked the final appearance of Tom Wlaschiha (Jaqen H\\'ghar), Faye Marsay (the Waif), and Clive Russell (Brynden Tully).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '380ec6032289b8430c9845c888c8cf84'}>,\n",
+       "  <Document: {'content': \"The episode marked the final appearance of Tom Wlaschiha (Jaqen H'ghar), Faye Marsay (the Waif), and Clive Russell (Brynden Tully).\\n\\n==Plot==\\n\\n===In the Riverlands===\\nSandor tracks down some of the Brotherhood Without Banners and brutally kills them. He then comes across Beric Dondarrion and Thoros of Myr, who are preparing to hang the rest of the men responsible for the attack on the village. Sandor bargains with Beric, who allows him to personally hang two of the men. Beric and Thoros try to recruit Sandor into the Brotherhood, explaining that they intend to head north to fight the White Walkers and need his strength.\\n\\nBrienne and Podrick arrive at Riverrun, where they reunite with Jaime and Bronn. Brienne explains that she has come to recruit the Blackfish and the Tully army for Sansa, but Jaime points out that he is currently besieged. Brienne proposes that if she can convince the Blackfish to surrender, then Jaime will allow him and the Tully army safe passage North. Jaime agrees to the plan, but only gives Brienne until nightfall. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '52a67ba3106ed4a0b25a337f102b087f'}>,\n",
+       "  <Document: {'content': \"Brienne proposes that if she can convince the Blackfish to surrender, then Jaime will allow him and the Tully army safe passage North. Jaime agrees to the plan, but only gives Brienne until nightfall. She attempts to return Oathkeeper to Jaime, stating that she has completed her mission, but Jaime tells her that it is hers and hers alone. Brienne notes that should she be inside Riverrun for a siege she would be bound to fight alongside the Blackfish, and against Jaime.   She attempts to negotiate with the Blackfish, but, despite being sympathetic to Sansa's plight, he refuses to abandon Riverrun.\\n\\nMeanwhile, Jaime talks with Edmure, trying to win his cooperation by tempting him with seeing his newborn son, and offering to send him and his family to Casterly Rock, away from the Freys. Edmure insults Jaime, and asks him how he can live with himself. Jaime responds that he wants to take Riverrun to be back with Cersei and is willing to do anything to achieve it, even if it means killing every Tully he finds. Edmure, cowed by this threat, is sent by Jaime to parley with the Blackfish. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'f59bf0ccaab17da57d4072061b660654'}>,\n",
+       "  <Document: {'content': \"Jaime responds that he wants to take Riverrun to be back with Cersei and is willing to do anything to achieve it, even if it means killing every Tully he finds. Edmure, cowed by this threat, is sent by Jaime to parley with the Blackfish. Despite the Blackfish's protests, the Tully soldiers allow Edmure entry into the castle. Edmure then orders his men to surrender Riverrun, and also orders the Blackfish's capture. The Blackfish helps Brienne and Podrick escape, but remains to fight to the end.\\n\\nJaime is informed about the death of the Blackfish, and sees Brienne and Pod escaping on the river. Jaime and Brienne mournfully wave each other farewell.\\n\\n===In Meereen===\\nWith the Red Priests on their side and spreading pro-Daenerys propaganda, Tyrion and Varys see Meereen returning to life. After warning Tyrion not to trust the Red Priests, Varys leaves for Westeros on a secret mission to find more allies. Tyrion then has a drink with Grey Worm and Missandei, but they are interrupted by the arrival of a fleet sent by the Masters to attack the city. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '636e4bee88dcea044a3b47c4471500f'}>,\n",
+       "  <Document: {'content': \"Tyrion then has a drink with Grey Worm and Missandei, but they are interrupted by the arrival of a fleet sent by the Masters to attack the city. As the fleet bombards Meereen, Tyrion and Grey Worm are discussing their strategy when Daenerys returns to the city with Drogon.\\n\\n===In King's Landing===\\nThe Faith Militant, led by Lancel Lannister and with Tommen's blessing, arrive to take Cersei to see the High Sparrow (whom they now refer to as the High Septon). Cersei refuses to go with them and when one of the Faith Militant attacks, he is brutally killed by Gregor Clegane. Seeing the rest of the Faith Militant soldiers intimidated, Cersei remarks that the High Sparrow is free to come to the Red Keep to see her. She then attends a royal announcement by Tommen in the throne room, but Kevan Lannister, her uncle and Hand of the King, refuses to let her stand by his side. Tommen announces the date that Cersei and Loras' trials will be held, and that, in consultation with the High Sparrow, he has decided to end the practice of trial by combat as a means of resolving conflicts. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '589ef3301e3907f069debc91b7af25c3'}>,\n",
+       "  <Document: {'content': 'Tommen announces the date that Cersei and Loras\\' trials will be held, and that, in consultation with the High Sparrow, he has decided to end the practice of trial by combat as a means of resolving conflicts. The prospect of a trial by seven septons instead clearly worries Cersei. Qyburn then reports to Cersei about a \"rumor\" that she ordered him to investigate, and remarks that it is \"more, much more\".\\n\\n===In Braavos===\\nLady Crane returns to her chambers to find a wounded Arya hiding inside, and helps stitch her wounds. She tells Arya that, thanks to her warning, she mutilated her would-be killer Bianca\\'s face before kicking her out of the acting company. She then offers to have Arya join them, but she refuses, saying that she intends to travel west of Westeros to see the edge of the world. As Arya recovers, the Waif arrives and kills Lady Crane, intending to kill Arya as well. Arya flees through the streets of Braavos, but during the chase, Arya\\'s wounds reopen and she limps back to her hideout with the Waif in pursuit. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'c01a12e266292b0cd276840e8a75cfb3'}>,\n",
+       "  <Document: {'content': 'Arya flees through the streets of Braavos, but during the chase, Arya\\'s wounds reopen and she limps back to her hideout with the Waif in pursuit. As the Waif closes in, Arya extinguishes the candle lighting the room; having trained while blinded for several weeks, Arya has the upper hand.\\n\\nAt the House of Black and White, Jaqen follows a bloodtrail to the Hall of Faces, where he finds the Waif\\'s face before being held at sword-point by Arya. Jaqen congratulates Arya for finally becoming No One. However, she rejects the title, asserting her identity as Arya Stark before turning and leaving, announcing that she is \"going home.\"Jaqen proudly watches on as she leaves.\\n\\n==Production==\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"No One\" was written by the series\\' creators David Benioff and D. B. Weiss.\\n\\nIn the \"Inside the Episode\" featurette published by HBO following the airing of the episode, David Benioff spoke about the revelation in King\\'s Landing, and Tommen\\'s decision to abolish \"trial by combat,\" stating \"This moment is a grievous blow for Cersei because she\\'s been counting on trial by combat really since the end of season five. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'b402865903c28f6e18285cf8e968e430'}>,\n",
+       "  <Document: {'content': 'In the \"Inside the Episode\" featurette published by HBO following the airing of the episode, David Benioff spoke about the revelation in King\\'s Landing, and Tommen\\'s decision to abolish \"trial by combat,\" stating \"This moment is a grievous blow for Cersei because she\\'s been counting on trial by combat really since the end of season five. This was always her ace in the hole, the Mountain was never going to lose a trial by combat there\\'s no one out there who could beat him. And then Tommen takes it away from her and knowing really what he\\'s doing. He might be a weak King, but he\\'s not an idiot, he understands that this move is going to mean that she\\'s probably not going to fare that well in this trial. So it\\'s a pretty devastating moment for Cersei, and you see that in her reaction, and partly because it just bodes so ill for this coming trial, and partly because it\\'s her own son who\\'s really betrayed her.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '4daf0b5c3e9ea68c780c43651a352676'}>,\n",
+       "  <Document: {'content': 'So it\\'s a pretty devastating moment for Cersei, and you see that in her reaction, and partly because it just bodes so ill for this coming trial, and partly because it\\'s her own son who\\'s really betrayed her.\"Speaking about the siege of Riverrun, Weiss noted about Brienne and Jaime\\'s reunion that \"Jaime didn\\'t expect to see Brienne necessarily ever again, he certainly didn\\'t expect to see her marching into his military camp. And his relationship is obviously very complicated, and fraught, and has undercurrents that he\\'s uncomfortable with feeling.\"Benioff continued about Brienne\\'s escape, \"It\\'s these two who are not quite sure how they feel about the other, and he lets her go. He\\'d be well within his rights to send his men after her, to capture her, and it wouldn\\'t be that hard to capture her since she\\'s being slowly rowed away by Podrick, but he doesn\\'t. She might technically be an enemy now that she\\'s serving Sansa Stark, who is still obviously a suspect in Joffrey\\'s murder, but she\\'s not Jaime\\'s enemy.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '1094e23be35baadb144fbd49bbbe14cb'}>,\n",
+       "  <Document: {'content': 'She might technically be an enemy now that she\\'s serving Sansa Stark, who is still obviously a suspect in Joffrey\\'s murder, but she\\'s not Jaime\\'s enemy.\"Clive Russell, who portrayed Brynden \"Blackfish\" Tully in the series, spoke about his character\\'s encounter with Brienne of Tarth, in an interview with IGN, noting \"Brienne represented for him his young self, a person of integrity, a soldier of integrity, trying to do the right thing. That added to the conflict he felt of what was the right thing to do: whether to send men to Sansa or not.\"He continued by saying about their relationship to each other, \"She spoke very clearly to power. She was talking to an older man who\\'s, initially, very irritated by her being there, but eventually takes her very, very seriously. There\\'s a touching moment where he acknowledges that to her face. It\\'s very touching and sad, and I think that\\'s what it\\'s about. He\\'s really recognizing the next generation of what I once was.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'e4aa912de93daf59b03a39f9a31a4059'}>,\n",
+       "  <Document: {'content': 'It\\'s very touching and sad, and I think that\\'s what it\\'s about. He\\'s really recognizing the next generation of what I once was.\"In the conclusion of the \"Inside the Episode\" segment Weiss spoke about Arya\\'s story, saying \"Arya is in danger, she\\'s got an open wound in her stomach, and the one person who has protected her to this point unfortunately gets murdered. The fact that the Many-Faced God gets the people who have been promised to him really makes you think that there\\'s almost an inevitability about what happens to people who run afoul of the Faceless Men, as Arya has done.\"Weiss continued about the final part of the sequence, \"Arya\\'s telling Jaqen by putting the face on the wall that \\'this account is settled, and we\\'re good here, and now I\\'m going to walk away,\\' and I think she knows what the answer\\'s going to be. The implication, obviously, is that Jaqen was, on some level, rooting for the outcome that he got. He may be \"no one,\" but there\\'s still enough of a person left in him to respect, and admire who this girl is and what she\\'s become. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'cc260cdccd05e8e6bf5941fd9cdc7278'}>,\n",
+       "  <Document: {'content': 'He may be \"no one,\" but there\\'s still enough of a person left in him to respect, and admire who this girl is and what she\\'s become. Arya finally tells us something that we\\'ve kind of known all along, that she\\'s not no one, she\\'s Arya Stark of Winterfell.\"===Casting===\\nRichard Dormer (\\'\\'pictured\\'\\') and Paul Kaye returned to the series as Beric Dondarrion and Thoros of Myr, respectively.\\n\"No One\" saw the reintroduction of Richard Dormer and Paul Kaye, who portrayed Beric Dondarrion and Thoros of Myr, respectively, in the third season, last appearing in the episode \"The Bear and the Maiden Fair\", before returning for the sixth season. Dormer was previously asked about returning to \\'\\'Game of Thrones\\'\\' in an interview in February 2015, saying at the time that he was doubtful about coming back to the series, stating \"they haven\\'t told me about it, so I don\\'t think so.\"He also noted about his portrayal of the character, \"Beric was a noble character, a leader of men. Kind of like Robin Hood. So I thought, that\\'s how I\\'ll play the guy.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '4bb670e316e0f838e71ddc1f235ecd6a'}>,\n",
+       "  <Document: {'content': 'He also noted about his portrayal of the character, \"Beric was a noble character, a leader of men. Kind of like Robin Hood. So I thought, that\\'s how I\\'ll play the guy.\"Paul Kaye\\'s participation in the series was confirmed in mid-April 2016, with his agency posting about the casting shortly before the premiere of the sixth season.\\n\\nThe episode also featured the deaths of several recurring characters in the series. Faye Marsay, who portrayed the Waif, spoke about her participation in the episode, and leaving \\'\\'Game of Thrones\\'\\' after several seasons, saying \"As much as I\\'m sad that I\\'m now off of the show, I think that storyline needed to be concluded that way. I\\'m rooting for Arya and Maisie as well. Even though I won\\'t be able to hang out with everyone again, I think it was done right and done properly. I think Arya deserved to rip off her face and stick it on a wall.\"Recurring guest actors Clive Russell as Blackfish, and Essie Davis as Lady Crane portrayed their characters for the last time in the series, as they were killed off on screen.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '61cd164e98ee05fbe643298d4412dfb9'}>,\n",
+       "  <Document: {'content': 'Recurring guest actors Clive Russell as Blackfish, and Essie Davis as Lady Crane portrayed their characters for the last time in the series, as they were killed off on screen.\\n\\nIn the first scene featuring Sandor \"the Hound\" Clegane, comedian Steve Love was cast as one of the Brotherhood members killed by the Hound. Love previously had become known for his impressions of various \\'\\'Game of Thrones\\'\\' characters, uploading the video to YouTube, which ultimately resulted in his appearing on \\'\\'Jimmy Kimmel Live!\\'\\'. Following the appearance, Love stated that he had received an email from Benioff offering him a part in the show, saying \"I broached the subject by sending them an email back saying \\'Listen I don\\'t know what your plans are for me but it would really mean a lot to me if I could have one of those trademark \\'\\'Game of Thrones\\'\\' gruesome death scenes, and they wrote back saying \\'Actually Steve, that was exactly what we had in mind for you.\\'\"===Filming===\\nFilming of the exterior chase scenes throughout Braavos took place in Girona, Spain.\\n\"No One\" was directed by Mark Mylod. Mylod previously directed the fifth season episodes \"High Sparrow\" and \"Sons of the Harpy\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '608d16084551f64d5ba6847032a4a887'}>,\n",
+       "  <Document: {'content': '\"No One\" was directed by Mark Mylod. Mylod previously directed the fifth season episodes \"High Sparrow\" and \"Sons of the Harpy\". Mylod also directed the previous episode, \"The Broken Man\", for the sixth season. Mylod stated in an interview that the most physically challenging scene to direct was the chase scene, while the scene he was most scared to shoot was the conversation between Jaime and Edmure, stating that he felt the writing was \"so freaking good that I was scared about fucking it up.\"Mylod additionally spoke about filming the final scenes of the episode, the foot chase between Arya and the Waif, in the behind the scenes featurette published by HBO after the airing of the episode. Mylod stated that he did extensive research in order to create the foot chase, saying \"I devoured every foot chase in the history of cinema, but trying to take from that something that would fit into the blueprint of the \\'\\'Game of Thrones\\'\\' visual style, which is not necessarily the same kind of kinetic, handheld feel that you find in many contemporary thrillers. It\\'s a much more classic frame.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '59f5c1b3b5113fc7b5e0175c9877d160'}>,\n",
+       "  <Document: {'content': 'Mylod stated that he did extensive research in order to create the foot chase, saying \"I devoured every foot chase in the history of cinema, but trying to take from that something that would fit into the blueprint of the \\'\\'Game of Thrones\\'\\' visual style, which is not necessarily the same kind of kinetic, handheld feel that you find in many contemporary thrillers. It\\'s a much more classic frame.\"He continued, \"Everything hinged from finding this very lovely, steep stairwell and thinking \\'Wouldn\\'t it be great if she leapt off that, and then tumbled down the stairs?\\'I had this image in my head that I wanted lots of oranges rolling down, not quite sure where that comes from, but it looked pretty.\"Mylod also stated in a separate interview that it was a conscious decision not to show the final fight between Arya and the Waif, given that it took place in the dark, noting \"when the character dies in the dark, unless we switch on night-vision goggles, we\\'re not going to be able to see that, and we\\'re not going to stick around to hear a scream in the dark anyway. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'e6bc7cd0ce3ae23dc19506bc16c18bae'}>,\n",
+       "  <Document: {'content': 'Mylod also stated in a separate interview that it was a conscious decision not to show the final fight between Arya and the Waif, given that it took place in the dark, noting \"when the character dies in the dark, unless we switch on night-vision goggles, we\\'re not going to be able to see that, and we\\'re not going to stick around to hear a scream in the dark anyway. It was actually a conscious choice in the writing and I think, for me, a very elegant storytelling beat as to get that reveal a few seconds later.\"In an interview, Maisie Williams described the thought process behind Arya\\'s scenes, as well as the process of filming saying \"We wanted people to think this could be the end, or the start of the end. Like maybe her wound is going to fester – like The Hound.\"She continued, \"There was this constant spectrum of conversation with Mark Mylod during the chase of about how petrified she needs to look, but also how safe she is. Arya\\'s been very lucky with the people she\\'s encountered so far. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'ecffd34599624110ee85f97fb59dc4d2'}>,\n",
+       "  <Document: {'content': 'She continued, \"There was this constant spectrum of conversation with Mark Mylod during the chase of about how petrified she needs to look, but also how safe she is. Arya\\'s been very lucky with the people she\\'s encountered so far. The whole time she was with The Hound she took a back seat because he was really good. So I wanted her to look like she was struggling.\"Williams also stated that she gave input on how the scene should take place, stating \"I didn\\'t want the chase stunt to be unnecessary or superhuman. I got on set and they were going to have Arya rolling around, and diving, and I was like, \"That looks amazing, but no.\"I\\'d be like, \"Why would she run over there? She\\'d just duck under here and just get out.\"It doesn\\'t look quite as cinematic, maybe, but they\\'ll have to find something else if they want cinematic. And I felt awful because the job of the stunt guys is to make everything look as crazy and cool as possible. But I know Arya now. In the beginning it was a lot of guesswork and now I\\'ve figured her out.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'a6457e9683c1a742ff405885962df1c6'}>,\n",
+       "  <Document: {'content': 'But I know Arya now. In the beginning it was a lot of guesswork and now I\\'ve figured her out.\"Faye Marsay, who portrayed the Waif, spoke about her scenes and filming with Williams, saying, \"I think we did a month in Belfast before we started shooting. We went into the stunt tent and lived there. It was choreographed like a dance. Maisie is so well coordinated and good at what she does. I\\'m a little bit less coordinated and easily pissed off. I would drop the stick down and go, \"I can\\'t do it!\"And she would encourage me. She\\'s so supportive. We did a month, and then the day before the cameras turned on, we would go through the moves a couple of times so we wouldn\\'t hurt each other. There were a few times we clipped each other. I remember getting her in the stomach really bad one time, and she nearly cut my ear off at one point. It was fun. It was a lot of fun, but we were exhausted as well.\"The castle face, and drawbridge were physically built, with the rest added in post-production with special effects.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '1a7a26a3486d12b0a7c4a982b8183ba3'}>,\n",
+       "  <Document: {'content': 'It was a lot of fun, but we were exhausted as well.\"The castle face, and drawbridge were physically built, with the rest added in post-production with special effects.\\nFilming of the Riverrun sequence began in October 2015, and led to some complaints by locals living in the area following the construction of a portion of the Tully castle. The shooting of the scenes took place in Corbet, County Down, Northern Ireland. Due to some concerns over the size of the structure, filming was unable to take place until local inspectors were able to determine if the production unit followed what had been agreed upon in the \"planning application.\"Construction of a portion of the castle began in September, with tents and wooden additions being added shortly after. In addition, the production unit also created an actual working drawbridge for the scenes, rather than relying on CGI to create the set piece. Visual effects were added to create the rest of the castle.\\n\\nClive Russell, who portrayed Blackfish, spoke about working with Gwendoline Christie as Brienne of Tarth, noting \"There\\'s something very curious, by the way, I\\'m probably about 6\\'6\" tall. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '32208598d0fe12d1822599c1fe9f7ccd'}>,\n",
+       "  <Document: {'content': 'Clive Russell, who portrayed Blackfish, spoke about working with Gwendoline Christie as Brienne of Tarth, noting \"There\\'s something very curious, by the way, I\\'m probably about 6\\'6\" tall. I think she\\'s about 6\\'3\", but all the time I worked with her, I felt like she was taller than me. Now I don\\'t really know what that has to do with it. It might be something to do with being confronted by a physically charismatic very tall woman, or maybe it\\'s that she\\'s caught the impressiveness of the character of that woman, that soldier. She\\'s also an extraordinarily hilarious woman. We had great fun together. It was great fun to play.\"Gwendoline Christie also spoke about filming with Nikolaj Coster-Waldau, who portrays Jaime Lannister, again, saying \"I was excited, It was really fun to be with Nikolaj again, and I thought the way of which their reunion happens is not really expected. I just loved that it was so formal, because within the confines of such formality, and having to negotiate with each other, there are so many other stories begging to be told in those moments. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '715bbec3b4bcf9c97647cdc4263608f0'}>,\n",
+       "  <Document: {'content': 'I just loved that it was so formal, because within the confines of such formality, and having to negotiate with each other, there are so many other stories begging to be told in those moments. There\\'s a slow process of creeping familiarity among two people who haven\\'t been together for a long time; it was allowed to build.\"Coster-Waldau also spoke about their first reunion scene, stating \"It\\'s a great scene. These characters are so much about holding their cards so close to their chest and they don\\'t want to reveal how they\\'re feeling. But we know there\\'s history between them, that this is more than two knights meeting. But they would never acknowledge that.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '3921e81a3ee52427c4094786fb7283b4'}>,\n",
+       "  <Document: {'content': 'But we know there\\'s history between them, that this is more than two knights meeting. But they would never acknowledge that.\"Mylod, spoke about the decision to show Blackfish\\'s death off screen, saying \"there was a lot of tonal discussion about this, and I think I can safely speak for the writers that the choice was a conscious one in the writing not to see his actual death, because the emotional focus was really about the final moments between him and Brienne -- two warriors, two samurais, facing each other -- and Brienne\\'s acknowledgment that this fellow samurai has chosen his moment, and this is his moment of dignity and choice of his death. So it was really about that moment of choice and the dignity and acceptance between these two great warriors, rather than the actual detail of his actual death. That was actually a tonal choice as to how we saw that character.\"==Reception==\\n===Ratings===\\n\"No One\" was viewed by 7.60 million American households on its initial viewing on HBO, which was slightly less than the previous week\\'s rating of 7.80 million viewers for the episode \"The Broken Man\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': '999699120efa16322198b13a7e741804'}>,\n",
+       "  <Document: {'content': '==Reception==\\n===Ratings===\\n\"No One\" was viewed by 7.60 million American households on its initial viewing on HBO, which was slightly less than the previous week\\'s rating of 7.80 million viewers for the episode \"The Broken Man\". The episode also acquired a 3.9 rating in the 18–49 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was viewed by 2.436 million viewers on Sky Atlantic, making it the highest-rated broadcast that week on its channel. It also received 0.113 million timeshift viewers.\\n\\n===Critical reception===\\n\"No One\" was received mostly positively by critics, who listed the conclusion of Arya\\'s story with the Faceless Men, the reintroduction of the Brotherhood Without Banners, and Jaime\\'s scheme to retake Riverrun as high points of the episode, but some critics described sequences in the episode as being anticlimactic. It has received an 85% rating on the review aggregator website Rotten Tomatoes from 47 reviews with an average score of 7.2/10. The site\\'s consensus reads \"\"No One\" sees some fan favorites back in action and moves the final few pieces into place for a momentous – and long-awaited – battle.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': '191a39f5b9a2deac301e5139e2c1ec87'}>,\n",
+       "  <Document: {'content': 'The site\\'s consensus reads \"\"No One\" sees some fan favorites back in action and moves the final few pieces into place for a momentous – and long-awaited – battle.\"Matt Fowler of IGN wrote in his review for the episode, \"\"No One\" may have given us a bloodless siege, but there were plenty of bloody moments, namely from the brothers Clegane, who were both awesomely brutal this week. Though not against one another, as it looks like, for now, the Cleganebowl\\'s been canceled. I loved the end of Arya\\'s arc along with Jaime\\'s scheme to take Riverrun - though I\\'m curious to see what comes of the Tully angle. Brienne failed to get an army for Sansa and Jaime\\'s just going to head back home now? It seems like there should be more to this that him just winning one for the Lannister name. Though regardless of what the payoff might be, it was still good to see how so much of this story tied back to the first three seasons and Jaime\\'s time as Catelyn\\'s prisoner.\"He gave the episode an 8.2 out of 10. Myles McNutt of \\'\\'The A.V. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': 'e154a433c1e64043941ff7d8ddc8d604'}>,\n",
+       "  <Document: {'content': 'Though regardless of what the payoff might be, it was still good to see how so much of this story tied back to the first three seasons and Jaime\\'s time as Catelyn\\'s prisoner.\"He gave the episode an 8.2 out of 10. Myles McNutt of \\'\\'The A.V. Club\\'\\' criticized the episode\\'s momentum within the season, writing \"The lack of momentum driving these stories is surprising to me given that the season has largely been doing a fine job with internal momentum.\"McNutt continued, \"The show may have run into an unfortunate convergence of so many anti-climactic storylines in a single episode, but their very existence is a necessary byproduct of a narrative that is preparing to shed its skin and move forward with a new lease on life.\"Despite this, he gave the episode a grade of B-. Erik Kain of \\'\\'Forbes\\'\\' wrote about the episode, \"Not the greatest episode of the season, but I still didn\\'t want it to end. Sometimes I realize that even an episode I have lots of complaints about manages to be some of the best television out there. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': 'f94b8ecb1c0d57c5ebe8a63e3e1b0c28'}>,\n",
+       "  <Document: {'content': 'Sometimes I realize that even an episode I have lots of complaints about manages to be some of the best television out there. \\'\\'Game of Thrones\\'\\' isn\\'t always on the money, but even an off night can be surprisingly good.\"James Hibberd of \\'\\'Entertainment Weekly\\'\\' summarized the episode in his review by saying \"Cheers, chills, and the sound of online fan theories imploding. \"No One\" delivered Jaime Lannister\\'s finest scene in years, a thrilling chase, and set one Stark on an exciting new path.\"Jeremy Egner of \\'\\'The New York Times\\'\\' criticized the episode in his review, writing, \"It\\'s just not all that satisfying. After endless weeks of stick-beating and the events of last week, it all seemed a little too tidy,\" and additionally called Arya\\'s story \"anticlimactic.\"However, Egner also praised the episode\\'s continuation with the King\\'s Landing story, as well as the follow up with Sandor Clegane and the Brotherhood Without Banners. \\'\\'TV Guide\\'\\'s Damian Holbrook criticized as implausible the fact that Arya, after being stabbed multiple times in the previous episode by the Waif, was able to outrun and kill her faster, stronger nemesis in this episode. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': 'def2b6f1ddefea690ce1e7ea000e6b74'}>,\n",
+       "  <Document: {'content': '\\'\\'TV Guide\\'\\'s Damian Holbrook criticized as implausible the fact that Arya, after being stabbed multiple times in the previous episode by the Waif, was able to outrun and kill her faster, stronger nemesis in this episode. Hanh Nguyen of Indiewire also felt the Riverrun sequence ended in an anticlimactic fashion as well.\\n\\n===Accolades===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nMaisie Williams as Arya Stark\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron\\n\\n2017\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley\\n\\n==References==\\n\\n==External links==\\n\\n*  \"No One\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': 'b0f60389984113044ee451bd2a9b1fc6'}>,\n",
+       "  <Document: {'content': '\\n\\'\\'\\'\\'\\'For the Throne: Music Inspired by the HBO Series Game of Thrones\\'\\'\\'\\'\\' is a soundtrack inspired by the television series \\'\\'Game of Thrones\\'\\', released by Columbia Records on April 26, 2019. The album features the SZA, The Weeknd and Travis Scott collaborative single, \"Power Is Power\", which was released on April 18, 2019, as well as the two promotional singles, \"Kingdom of One\" by Maren Morris and \"Nightshade\" by The Lumineers, which were released simultaneously on April 12, 2019.\\n\\n==Reception==\\n\\nAllMusic\\'s Stephen Thomas Erlewine rated the album 2.5 out of 5 stars.\\n\\n==Track listing==\\nCredits adapted from Tidal.\\n\\n\\'\\'\\'Notes\\'\\'\\'\\n*  signifies a co-producer\\n*  signifies a miscellaneous producer\\n* \"Nightshade\" features background vocals by Lauren Jacobsen\\n* \"Turn on Me\" features background vocals by Arone Dyer and Kyle Resnick\\n\\n==Charts==\\n\\nChart (2019)\\n\\nPeakposition\\n\\nAustralian Digital Albums (ARIA)\\n\\n23\\n\\n==See also==\\n* Music of \\'\\'Game of Thrones\\'\\'\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '455fa4e148d221665b8e91e4eff8c3fc'}>,\n",
+       "  <Document: {'content': '\\n\\nThe soundtrack album of the seventh season of HBO series \\'\\'Game of Thrones\\'\\', titled \\'\\'\\'\\'\\'Game of Thrones: Season 7\\'\\'\\'\\'\\', was released digitally on August 25, 2017 on CD on September 29, 2017.\\n\\n==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* D.B. Weiss – liner notes\\n\\n==Charts==\\n\\nChart (2017)\\n\\nPeak position\\n\\nNew Zealand Heatseekers Albums (RMNZ)\\n\\n4\\n\\nSpanish Albums (PROMUSICAE)\\n\\n85\\n\\n==Awards and nominations==\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2018\\n\\n60th Annual Grammy Awards\\n\\nBest Score Soundtrack for Visual Media\\n\\nRamin Djawadi\\n\\n70th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Music Composition for a Series\\n\\nEpisode: \"The Dragon and the Wolf\"\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'a357d25e0d539ec9d52454586340c73b'}>,\n",
+       "  <Document: {'content': \"\\n\\nThe title sequence of the HBO fantasy television series ''Game of Thrones'' that introduces every episode serves as a guide to the physical landscape of the world of the series. It changes depending on the locations visited in the particular episode it introduces.  The title sequence was created by Elastic for HBO, and is accompanied by a theme tune composed by Ramin Djawadi.\\n\\nThe sequence depicts a three-dimensional map of the series' fictional world, projected onto a concave earth, and lit by a small sun contained within an armilla (or spherical astrolabe) that metaphorically depicts major events in the history of the fictional world at the sphere's center. As the camera swoops across the map and focuses on the locations in which the episode's events take place, complicated clockwork mechanisms cause buildings and other structures to emerge from the map and unfold. Meanwhile, the names of the principal cast (with the symbols of the characters' families next to the names) and creative staff are displayed. The sequence concludes after about one-and-a-half minutes with the title card and brief opening credits indicating the episode's writers and directors.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '1eda7e619e4122ce8ece54663206274e'}>,\n",
+       "  <Document: {'content': \"The sequence concludes after about one-and-a-half minutes with the title card and brief opening credits indicating the episode's writers and directors.\\n\\nCreative director Angus Wall, art director Robert Feng, animator Kirk Shintani and designer Hameed Shaukat received the 2011 Primetime Emmy Award for Outstanding Main Title Design for their work on the sequence. The title sequence has been ranked one of the best TV title sequences of all time.\\n\\n==Description==\\nThe title sequence consists of a three-dimensional map of the world, with the continents of Westeros and Essos located on the inner surface of a sphere, which is rendered in the style of a fantasy role-playing map used as a game board by participants for their battle plans. The maps used are those of Westeros and Essos that precede the novels in the book series. At the center of the sphere is a heliocentric armillary sphere. The title sequence serves as a guide to the physical landscape of the world of ''Game of Thrones'', and details of the title sequence change each week depending on the locations visited, and new locations may be added in each season.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '3d6dee7567c7e62be320cd7b7fd303ae'}>,\n",
+       "  <Document: {'content': \"The title sequence serves as a guide to the physical landscape of the world of ''Game of Thrones'', and details of the title sequence change each week depending on the locations visited, and new locations may be added in each season.\\n\\nThe sequence of every episode opens with a close-up of the sun and the sphere surrounding it. Relief depictions of the fictional's world's history are visible on the sphere, such as the Doom of Valyria, Aegon's Conquest and the rise of House Baratheon, which appear at varying points in the sequence.  The camera then pans to different parts of the map, on which different locations in the fictional world are shown.  Many of the cities and buildings on these locations appear out of the ground using clockwork mechanisms. Other elements, such as the weirwood tree at Winterfell and the Horse Gate at Vaes Dothrak, are also added at the various locations. The locations shown vary depending on the locations visited in that particular episode, and three or four variations of the title sequence are shown in each season.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '2f2487ca34b95d6660bb731a1d147e03'}>,\n",
+       "  <Document: {'content': \"The locations shown vary depending on the locations visited in that particular episode, and three or four variations of the title sequence are shown in each season.  However, due to the limitation on time for the title sequence, no more than six locations may be shown in any episode. Also, because of their importance in the show, every episode features King's Landing, Winterfell, The Wall, and wherever Daenerys may be regardless of whether or not any of these locations is featured in that episode.\\n\\nThe sigils of the reigning families are added to each location; for example, the Baratheon stag sigil on King's Landing and the Stark dire wolf at Winterfell. The names of the cast are also shown together with the corresponding sigil of the character they portray. The sigil may change depending on the storyline, for example the flayed man sigil is displayed over Winterfell when it is taken by the Boltons, but it reverts to the dire wolf sigil after it is recaptured by the Starks. Other events in the show are also reflected in the title sequence. For example, after Winterfell is burned, smoke is depicted billowing out from it. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '12a6c15da9508d4ddbab9bfb286efdfc'}>,\n",
+       "  <Document: {'content': \"Other events in the show are also reflected in the title sequence. For example, after Winterfell is burned, smoke is depicted billowing out from it. The ''Game of Thrones'' logo appears over the armilla at the end.\\n\\nHBO released an interactive 360-degree video of the title sequence in season 6.\\n\\nWhile the locations featured change from episode to episode, the general design and route of the sequence remain roughly similar. However, in the final season, the title sequence received a major revamp to denote the change in season and shift in storyline. In this new version, the first location shown is the breached wall instead of King's Landing, and moves down to the cities south of the wall following the path of the army of the dead. The interiors of two major locations are shown for the first time: the crypts of Winterfell and the Red Keep at King's Landing, as well as the latter's throne room. The appearance of the armilla and the events depicted on it also change to reflect event of the season 7 finale, such as the fall of the Wall.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '1c1f3c313403065a4abc0624b500736e'}>,\n",
+       "  <Document: {'content': \"The appearance of the armilla and the events depicted on it also change to reflect event of the season 7 finale, such as the fall of the Wall.\\n\\n==Conception and production==\\nThe title sequence was created by three teams: its design, which forms the bulk of the project, was done by Elastic, the computer graphics by a52, and the title sequence editing by Rock Paper Scissors. Elastic had previously created the title sequences for ''Rome'', ''Big Love'', and ''Carnivale'' for HBO, and they were approached by Carolyn Strauss of HBO, with whom they had worked on these shows, to create the title sequence for ''Game of Thrones''.  Angus Wall, the head of title design firm Elastic, met with Strauss, the showrunners David Benioff and Dan Weiss, and producer Greg Spence to discuss the project around a year before the show debut.\\n\\nWhen the pilot was first made, it was felt that the geography of the imaginary world of Westeros and Essos might confuse the viewers, and that maps could be useful as navigational guides for the viewers. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'e29772576e56fb6290b2d36c33690f81'}>,\n",
+       "  <Document: {'content': 'When the pilot was first made, it was felt that the geography of the imaginary world of Westeros and Essos might confuse the viewers, and that maps could be useful as navigational guides for the viewers. An early suggestion was to use animated maps as a transition between scenes to orient viewers, but that idea was dropped as it would interrupt the narrative flow. The idea of the map was therefore moved into the title sequence.  For the original pilot, the showrunners Benioff and Weiss initially wrote the title sequence as a crow\\'s flight from King\\'s Landing to Winterfell;  however, the production team at Elastic thought the idea was too flat and devised the idea of using 3-D models within a sphere that represents the world of the show. The sphere was used to obviate the question of what might lie beyond the horizon of the map, and the whole sphere is lit by the sun in the middle. According to Angus Wall, the title sequence had \"a concrete function in the world of the show, in that it serves as a legend the way the map at the beginning of a fantasy book orients you.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '8d3373db4fc3bafe33bb250df2ff851b'}>,\n",
+       "  <Document: {'content': 'According to Angus Wall, the title sequence had \"a concrete function in the world of the show, in that it serves as a legend the way the map at the beginning of a fantasy book orients you.\"The title sequence informs the viewers of the locations of the show relevant to each episode, and changes to reflect the storylines of the show and changes within this world.\\n\\nOn the use of an armillary and models with moving parts, producer Greg Spence explained that Angus Wall at Elastic came up with \"a vision of a mad monk, in a tower somewhere,\" who was somehow keeping track of all this action \"and creating as he went. He would then fashion little automatons out of the materials that would be available in his world. They would be stone, or tin, or wood, and everything would feel very hand-crafted.\"The idea is, therefore, that everything in the title sequence could be created with hammer, saw, and chisel, and operated with gears and cogs. The turning gears and cogs were meant to be reminiscent of Leonardo da Vinci\\'s inventions.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'd2593a65f16235a960027fe30012ba5a'}>,\n",
+       "  <Document: {'content': 'The idea is, therefore, that everything in the title sequence could be created with hammer, saw, and chisel, and operated with gears and cogs. The turning gears and cogs were meant to be reminiscent of Leonardo da Vinci\\'s inventions.\\n\\nThe design team were given a list of the locations where the action may take place in each episode after the shooting had completed, and they had around 3 months to create title sequences. The concepts, including details such as the machinery used, were first sketched by hand, and the models were then created with computer graphics.\\n\\n==\\'\\'Game of Thrones\\'\\' theme==\\n\\nThe theme music that accompanies the title sequence was composed by Ramin Djawadi. The production team showed the title sequence they were working on to Djawadi, who was then inspired to create the music for the \"\\'\\'Game of Thrones\\'\\' Theme\" and finished the theme music three days later. Djawadi said the showrunners Benioff and Weiss wanted the theme music to be about a journey that reflects the variety of locations and characters in the show.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'ff7402efad573e27661e1de7319ed287'}>,\n",
+       "  <Document: {'content': \"Djawadi said the showrunners Benioff and Weiss wanted the theme music to be about a journey that reflects the variety of locations and characters in the show.\\n\\n==List of elements==\\nThe following table lists the locations shown in each episode's title sequence, in the order of their appearance in the sequence as determined by the first episode in which they are shown. The locations King's Landing, Winterfell, and The Wall are featured in every title sequence, as well as the most recent location in Essos (in the first 6 seasons), even if they are not present in that particular episode. It is also noteworthy that the appearance of Winterfell has changed over the seasons: in Seasons 3 and 4 Winterfell was covered in smoke, referencing its burning at the end of Season 2, and in Season 5 the smoke was removed but the Bolton sigil replaced the Stark sigil, reflecting how the Boltons moved to Winterfell during that season. In episode 6.10, the Stark sigil returned, the result of Jon Snow and Sansa Stark's reclaiming Winterfell from the Boltons at the end of the previous episode.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '153133224a368436e12a23cf7360d78a'}>,\n",
+       "  <Document: {'content': 'In episode 6.10, the Stark sigil returned, the result of Jon Snow and Sansa Stark\\'s reclaiming Winterfell from the Boltons at the end of the previous episode.\\n\\n===Summary===\\n\\nS. 1\\n\\nS. 2\\n\\nS. 3\\n\\nS. 4\\n\\nS. 5\\n\\nS. 6\\n\\nS. 7\\n\\nS. 8\\n\\nTotal Count\\n\\n\\'\\'\\'Locations\\'\\'\\'\\n\\nKing\\'s Landing\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nWinterfell\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nThe Wall\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nMeereen\\n\\n10\\n\\n10\\n\\n10\\n\\n30\\n\\nBraavos\\n\\n5\\n\\n9\\n\\n7\\n\\n21\\n\\nDragonstone\\n\\n3\\n\\n4\\n\\n5\\n\\n7\\n\\n19\\n\\nPyke\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nVaes Dothrak\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nHarrenhal\\n\\n7\\n\\n8\\n\\n15\\n\\nRiverrun\\n\\n6\\n\\n3\\n\\n9\\n\\nDorne\\n\\n7\\n\\n2\\n\\n9\\n\\nThe Eyrie\\n\\n4\\n\\n2\\n\\n1\\n\\n7\\n\\nThe Dreadfort\\n\\n7\\n\\n7\\n\\nQarth\\n\\n7\\n\\n7\\n\\nOldtown\\n\\n7\\n\\n7\\n\\nYunkai\\n\\n6\\n\\n6\\n\\nLast Hearth\\n\\n6\\n\\n6\\n\\nThe Twins\\n\\n1\\n\\n2\\n\\n1\\n\\n1\\n\\n5\\n\\nMoat Cailin\\n\\n3\\n\\n1\\n\\n4\\n\\nAstapor\\n\\n4\\n\\n4\\n\\nEastwatch\\n\\n3\\n\\n3\\n\\nPentos\\n\\n1\\n\\n1\\n\\n2\\n\\n==Homage==\\n\\'\\'The Simpsons\\'\\' episode \"Exit Through the Kwik-E-Mart\" features a homage to the \\'\\'Game of Thrones\\'\\' title sequence, with famous buildings in the town of Springfield rising through the ground as characters watch on, dressed in \\'\\'Game of Thrones\\'\\'-style costumes. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '4a9ee1b73895ad98e7f84395d99d0908'}>,\n",
+       "  <Document: {'content': '===Summary===\\n\\nS. 1\\n\\nS. 2\\n\\nS. 3\\n\\nS. 4\\n\\nS. 5\\n\\nS. 6\\n\\nS. 7\\n\\nS. 8\\n\\nTotal Count\\n\\n\\'\\'\\'Locations\\'\\'\\'\\n\\nKing\\'s Landing\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nWinterfell\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nThe Wall\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nMeereen\\n\\n10\\n\\n10\\n\\n10\\n\\n30\\n\\nBraavos\\n\\n5\\n\\n9\\n\\n7\\n\\n21\\n\\nDragonstone\\n\\n3\\n\\n4\\n\\n5\\n\\n7\\n\\n19\\n\\nPyke\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nVaes Dothrak\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nHarrenhal\\n\\n7\\n\\n8\\n\\n15\\n\\nRiverrun\\n\\n6\\n\\n3\\n\\n9\\n\\nDorne\\n\\n7\\n\\n2\\n\\n9\\n\\nThe Eyrie\\n\\n4\\n\\n2\\n\\n1\\n\\n7\\n\\nThe Dreadfort\\n\\n7\\n\\n7\\n\\nQarth\\n\\n7\\n\\n7\\n\\nOldtown\\n\\n7\\n\\n7\\n\\nYunkai\\n\\n6\\n\\n6\\n\\nLast Hearth\\n\\n6\\n\\n6\\n\\nThe Twins\\n\\n1\\n\\n2\\n\\n1\\n\\n1\\n\\n5\\n\\nMoat Cailin\\n\\n3\\n\\n1\\n\\n4\\n\\nAstapor\\n\\n4\\n\\n4\\n\\nEastwatch\\n\\n3\\n\\n3\\n\\nPentos\\n\\n1\\n\\n1\\n\\n2\\n\\n==Homage==\\n\\'\\'The Simpsons\\'\\' episode \"Exit Through the Kwik-E-Mart\" features a homage to the \\'\\'Game of Thrones\\'\\' title sequence, with famous buildings in the town of Springfield rising through the ground as characters watch on, dressed in \\'\\'Game of Thrones\\'\\'-style costumes. The Wall is replaced by the monolithic \"Couch\" at the end of the sequence.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'bff802af02ff186a131ecee9f8af32c5'}>,\n",
+       "  <Document: {'content': '===Summary===\\n\\nS. 1\\n\\nS. 2\\n\\nS. 3\\n\\nS. 4\\n\\nS. 5\\n\\nS. 6\\n\\nS. 7\\n\\nS. 8\\n\\nTotal Count\\n\\n\\'\\'\\'Locations\\'\\'\\'\\n\\nKing\\'s Landing\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nWinterfell\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nThe Wall\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nMeereen\\n\\n10\\n\\n10\\n\\n10\\n\\n30\\n\\nBraavos\\n\\n5\\n\\n9\\n\\n7\\n\\n21\\n\\nDragonstone\\n\\n3\\n\\n4\\n\\n5\\n\\n7\\n\\n19\\n\\nPyke\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nVaes Dothrak\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nHarrenhal\\n\\n7\\n\\n8\\n\\n15\\n\\nRiverrun\\n\\n6\\n\\n3\\n\\n9\\n\\nDorne\\n\\n7\\n\\n2\\n\\n9\\n\\nThe Eyrie\\n\\n4\\n\\n2\\n\\n1\\n\\n7\\n\\nThe Dreadfort\\n\\n7\\n\\n7\\n\\nQarth\\n\\n7\\n\\n7\\n\\nOldtown\\n\\n7\\n\\n7\\n\\nYunkai\\n\\n6\\n\\n6\\n\\nLast Hearth\\n\\n6\\n\\n6\\n\\nThe Twins\\n\\n1\\n\\n2\\n\\n1\\n\\n1\\n\\n5\\n\\nMoat Cailin\\n\\n3\\n\\n1\\n\\n4\\n\\nAstapor\\n\\n4\\n\\n4\\n\\nEastwatch\\n\\n3\\n\\n3\\n\\nPentos\\n\\n1\\n\\n1\\n\\n2\\n\\n==Homage==\\n\\'\\'The Simpsons\\'\\' episode \"Exit Through the Kwik-E-Mart\" features a homage to the \\'\\'Game of Thrones\\'\\' title sequence, with famous buildings in the town of Springfield rising through the ground as characters watch on, dressed in \\'\\'Game of Thrones\\'\\'-style costumes. The Wall is replaced by the monolithic \"Couch\" at the end of the sequence.  A version of the title sequence has also been recreated with Oreo cookies.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'c89565387052fff9c59472b422ad9ea5'}>,\n",
+       "  <Document: {'content': 'The Wall is replaced by the monolithic \"Couch\" at the end of the sequence.  A version of the title sequence has also been recreated with Oreo cookies.\\n\\n==Awards==\\nThe design team behind the title sequence, Angus Wall, art director Rob Feng, designer Hameed Shaukat, and C.G. supervisor Kirk Shintani, won a Creative Arts Emmy Award on September 10, 2011.\\n\\n=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2011\\n\\n63rd Primetime Creative Arts Emmy Awards\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Hameed Shaukat, Kirk Shintani and Robert Feng\\n\\n2016\\n\\n68th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Main Titles 360 Experience\\n\\n==References==\\n\\n== External links ==\\n* Perkins, Will; Albinson, Ian (June 29, 2011).  \"Game of Thrones (2011)\". Art of the Title. May 11, 2011.\\n* Appelo, Tim (April 19, 2011).  \"Secrets Behind \\'Game of Thrones\\' Opening Credits (Video)\". \\'\\'The Hollywood Reporter\\'\\'.', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '87ff72fefc766c030ce7be5f0b02b184'}>,\n",
+       "  <Document: {'content': '\\n\"\\'\\'\\'Power Is Power\\'\\'\\'\" is a song by American singer SZA, Canadian singer The Weeknd and American rapper Travis Scott. It was released on April 18, 2019, as a single from the \\'\\'Game of Thrones\\'\\' companion soundtrack, \\'\\'For the Throne\\'\\'. The song is the second of the two collaborative singles released by The Weeknd and Scott in 2019, being preceded by \"Wake Up\".\\n\\n==Background and release==\\nFollowing the release of the collaboration \"Wake Up\" by Travis Scott featuring The Weeknd, as the fourth single from Scott\\'s \\'\\'Astroworld\\'\\' on March 26, 2019. In early April of that year, rumours regarding the possibility of a \\'\\'Game of Thrones\\'\\' inspired track by SZA, The Weeknd, and Travis Scott began to circulate around the web. The artists\\' involvement on the \\'\\'For the Throne\\'\\' soundtrack was then later confirmed on April 8, via an announcement of the soundtrack by Columbia Records. Following the release of the promotional singles \"Kingdom of One\" by Maren Morris and \"Nightshade\" by The Lumineers on April 12, SZA then later shared the cover art and release date of the song, on her Instagram profile on April 16.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '6a962445cf0cb6f26890dff6704d2022'}>,\n",
+       "  <Document: {'content': 'Following the release of the promotional singles \"Kingdom of One\" by Maren Morris and \"Nightshade\" by The Lumineers on April 12, SZA then later shared the cover art and release date of the song, on her Instagram profile on April 16.\\n\\n==Lyrics==\\nThe title of the song is a line spoken by the character Cersei Lannister in the HBO TV series \\'\\'Game of Thrones\\'\\', the song\\'s lyric however revolve around the character Jon Snow. Writers at the time interpreted the lyrics as possibly foreshadowing the aforementioned character winning out the conclusion of the show.\\n\\n==Music video==\\nTeasement for the music video first began on April 20, 2019, the video features a medieval theme, reminiscent of its source show \\'\\'Game of Thrones\\'\\'. It was directed by Anthony Mandler and released on May 5, 2019.\\n\\n==Charts==\\n\\nChart (2019)\\n\\nPeakposition\\n\\nAustralia (ARIA)\\n\\n30\\n\\nChina Airplay/FL (\\'\\'Billboard\\'\\')\\n\\n22\\n\\nFrance Digital Tracks (SNEP)\\n\\n140\\n\\nIreland (IRMA)\\n\\n31\\n\\nNew Zealand Hot Singles (RMNZ)\\n\\n3\\n\\nNorway (VG-lista)\\n\\n33\\n\\nSweden (Sverigetopplistan)\\n\\n41\\n\\n==Release history==\\n\\nRegion\\n\\nDate\\n\\nFormat\\n\\nLabel(s)\\n\\nUnited States\\n\\nApril  18, 2019\\n\\nApril 23, 2019\\n\\nContemporary hit radio\\n\\nRhythmic contemporary\\n\\nItaly\\n\\nMay 3, 2019\\n\\nContemporary hit radio\\n\\nSony\\n\\n==References==\\n\\n==External links==\\n*\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'd5fa8511ac704de276cc348c3184cbd'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Bells\\'\\'\\'\" is the fifth and penultimate episode of the eighth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 72nd and penultimate episode of the series. It was written by David Benioff and D. B. Weiss, and directed by Miguel Sapochnik. It aired on May 12, 2019.\\n\\n\"The Bells\" features the final battle for control of the Iron Throne, with Daenerys Targaryen\\'s forces commencing their assault on Cersei Lannister\\'s forces at King\\'s Landing.\\n\\nThe episode received criticism from critics and audiences alike. Critics praised the episode as visually impressive and commended the acting and direction, but criticized the pacing and logic of the story, as well as its handling of the character arcs of Tyrion, Jaime, Cersei, Grey Worm, Varys, and particularly Daenerys. It received three Primetime Emmy Award nominations and was also picked by Lena Headey to support her nomination for Outstanding Supporting Actress in a Drama Series. It eventually was awarded the prizes for Outstanding Fantasy/Sci-Fi Costumes and Outstanding Special Visual Effects.\\n\\n==Plot==\\n===At Dragonstone===\\nVarys writes a letter about Jon Snow\\'s true heritage when a girl informs him that Daenerys Targaryen continues to refuse food. He tells her to \"try again at supper\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '8fc5971192395101d381df9216d208dd'}>,\n",
+       "  <Document: {'content': '==Plot==\\n===At Dragonstone===\\nVarys writes a letter about Jon Snow\\'s true heritage when a girl informs him that Daenerys Targaryen continues to refuse food. He tells her to \"try again at supper\". Varys implores Jon to take the Iron Throne, but Jon refuses to betray Daenerys. Tyrion Lannister informs Daenerys of Varys\\' plot; she executes Varys. When Daenerys says she does not have the people\\'s love in Westeros, Jon assures Daenerys he loves her. However, when he stops her kiss, Daenerys resigns herself to fear. Tyrion implores Daenerys to spare the common folk in King\\'s Landing if the city bells ring, and she nods toward Grey Worm. She informs Tyrion that Jaime Lannister has been captured on his way to King\\'s Landing and warns Tyrion not to fail her again.\\n\\n===At King\\'s Landing===\\nTyrion arrives at the Unsullied camp and frees Jaime so Jaime can take Cersei Lannister away from King\\'s Landing to Pentos. Jaime makes for the Red Keep.\\n\\nThe next day, Arya Stark and Sandor Clegane enter the Red Keep. The battle begins with Daenerys and Drogon burning Euron Greyjoy\\'s Iron Fleet, the Golden Company, and the city\\'s defenses. Grey Worm kills Harry Strickland. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'e6bb5a6d08b709df9476f750debef0cb'}>,\n",
+       "  <Document: {'content': \"The battle begins with Daenerys and Drogon burning Euron Greyjoy's Iron Fleet, the Golden Company, and the city's defenses. Grey Worm kills Harry Strickland. When Daenerys' army storms King's Landing, the remaining Lannister forces drop their weapons and the city surrenders. Daenerys refuses to accept the surrender and, atop Drogon, lays waste to the city, while her army, led by Grey Worm, massacre the unarmed Lannister soldiers. Jon unsuccessfully tries to stop his men from attacking, kills one who attempts rape, and eventually, he and Davos Seaworth have the rest fall back as the city burns.\\n\\nJaime fights Euron, who badly wounds him, but Jamie is able to mortally wound Euron, leaving him to die. Sandor convinces Arya to give up her desire for revenge against Cersei and he and Arya part ways as friends. As Cersei and Qyburn attempt to escape, Sandor arrives and kills the Queensguard in order to fight his brother, Gregor Clegane. Gregor kills Qyburn and Cersei flees. Gregor resists all attacks but when Sandor tackles him through a wall, the brothers fall to their deaths in the fire below. Jaime reunites with Cersei, but find their escape passage is blocked by rubble from the attack. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'f7f222e145f4dc8c377986442056e833'}>,\n",
+       "  <Document: {'content': \"Gregor resists all attacks but when Sandor tackles him through a wall, the brothers fall to their deaths in the fire below. Jaime reunites with Cersei, but find their escape passage is blocked by rubble from the attack. They resign themselves to their fate and embrace as the Keep's cellar ceiling collapses on them, killing them and their unborn child.\\n\\nArya experiences the chaos in King's Landing and tries to help the smallfolk she encounters but is knocked unconscious when a bell tower falls near her. When she wakes up, she discovers the burned corpses of the smallfolk on the street. She encounters a white horse coated in blood and rides it away.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by David Benioff and D. B. Weiss.\\n\\n=== Filming ===\\nThe episode was directed by Miguel Sapochnik. This was his final episode of the series.\\n\\nFor the filming of the episode, the city of Dubrovnik (the stand-in for King's Landing) was recreated on the backlot of their Belfast set. Filming Varys's death scene ultimately ended up taking seven months as rain kept postponing the shoot.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '58d862f5ee9c824dbc9c17f0a0f1462e'}>,\n",
+       "  <Document: {'content': 'For the filming of the episode, the city of Dubrovnik (the stand-in for King\\'s Landing) was recreated on the backlot of their Belfast set. Filming Varys\\'s death scene ultimately ended up taking seven months as rain kept postponing the shoot.\\n\\nIn a promotional photo of the scene in which Jaime and Cersei embrace at the Red Keep, his right hand is visible instead of the metallic prosthetic the character received in season 4. In the actual episode, only the metallic hand is seen. The error in the image garnered attention in light of the previous episode\\'s coffee cup mistake.\\n\\n===Casting===\\nLaura Elphinstone was cast as one of the inhabitants of King\\'s Landing who helped Arya Stark during the dragon attack on the city. The American football quarterback Aaron Rodgers appears in a cameo in this episode.\\n\\n== Reception ==\\n=== Ratings ===\\nThe episode was viewed by 12.48 million viewers on its initial live broadcast on HBO, surpassing \"The Dragon and the Wolf\" as the most-watched episode of the series. An additional 5.9 million viewers watched on streaming platforms, for a total of 18.4 million viewers.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'f6841f0213ff2f336d536cbd5b0767e6'}>,\n",
+       "  <Document: {'content': '== Reception ==\\n=== Ratings ===\\nThe episode was viewed by 12.48 million viewers on its initial live broadcast on HBO, surpassing \"The Dragon and the Wolf\" as the most-watched episode of the series. An additional 5.9 million viewers watched on streaming platforms, for a total of 18.4 million viewers.\\n\\n===Critical response===\\n====General====\\n\\nOn the review aggregator Rotten Tomatoes, the episode has an approval rating of 49% based on 107 reviews, and an average rating of 6.32/10, with the critical consensus stating, \"Death, destruction, and the deterioration of Daenerys\\' sanity make \\'The Bells\\' an episode for the ages; but too much plot in too little time muddles the story and may leave some viewers feeling its conclusions are unearned.\"It is the second lowest-rated episode on Rotten Tomatoes, behind \"The Iron Throne.\"Lenika Cruz of \\'\\'The Atlantic\\'\\' wrote that although she found the special effects stunning and the acting spectacular, this was \"the worst \\'\\'Game of Thrones\\'\\' episode ever\" because the plot was either too obvious or illogical, with the massacre seeming \"an unearned negation of the identity Daenerys had spent years building for herself\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '5edb5e76fb60bbfcfb25e02ed9fcf6d0'}>,\n",
+       "  <Document: {'content': 'Lenika Cruz of \\'\\'The Atlantic\\'\\' wrote that although she found the special effects stunning and the acting spectacular, this was \"the worst \\'\\'Game of Thrones\\'\\' episode ever\" because the plot was either too obvious or illogical, with the massacre seeming \"an unearned negation of the identity Daenerys had spent years building for herself\". Emily VanDerWerff of \\'\\'Vox\\'\\' found the episode grim and absent of sense, but credited director Miguel Sapochnik for \"the gorgeous visuals that extended beyond the battle scenes.\"VanDerWerff praised the acting of the cast such as Maisie Williams, Lena Headey, Peter Dinklage, and Emilia Clarke; the latter two made their characters\\' actions believable even when they were illogical. Alex McLevy of \\'\\'The A.V. Club\\'\\' wrote that the episode successfully demonstrated that \"the chaos of war makes villains and victims of us all\", with the \"progression from exhilarating hope to tragic denouement being skillfully executed by director Miguel Sapochnik\" with better large-scale choreography than in the \"The Long Night\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'c1103c4daa45c0e2577059f7e98adf96'}>,\n",
+       "  <Document: {'content': 'Club\\'\\' wrote that the episode successfully demonstrated that \"the chaos of war makes villains and victims of us all\", with the \"progression from exhilarating hope to tragic denouement being skillfully executed by director Miguel Sapochnik\" with better large-scale choreography than in the \"The Long Night\".\\n\\nHugh Montgomery of BBC Culture wrote that characters such as Tyrion, Jaime, Daenerys, and Cersei were \"definitively sacrificed to the show\\'s scrambled plotting\", and that \\'\\'Game of Thrones\\'\\' was previously \"a show that intelligently delineated a senseless world\", but that it was now nonsense. VanDerWerff stated that Cersei and Jaime\\'s deaths were not given much thought by the writers and were instead simply something to check off a list. Kelly Lawler of \\'\\'USA Today\\'\\', saying that the episode lacked substance and was an \"absolute disaster\", argued that Grey Worm, Daenerys and Varys took actions which were wildly out of character.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '37005f7bda29653b14f2cfa4238f648e'}>,\n",
+       "  <Document: {'content': 'Kelly Lawler of \\'\\'USA Today\\'\\', saying that the episode lacked substance and was an \"absolute disaster\", argued that Grey Worm, Daenerys and Varys took actions which were wildly out of character.\\n\\nAlan Sepinwall of \\'\\'Rolling Stone\\'\\' wrote that the episode\\'s \"technical genius\" and \"the visual clarity only made it easier to see how muddled the show has been, from both a narrative and character standpoint, in this home stretch\", with the scorpions turning from accurate to useless, Euron surviving Drogon to have a \"pointless fight with Jaime\", the setting of Cleganebowl being invulnerable while the castle fell, Jaime easily entering the gated Red Keep, and the appearance of \"seemingly hundreds of Dothraki\" despite most of them apparently dying in \\'The Long Night\\'. Lawler also said that the writers \"threw out their own rule book (suddenly the scorpions don\\'t work and Drogon can burn everything?)to pursue gross spectacle\".\\n\\nSeveral critics criticized the pacing of events; Lawler wrote that the pacing started off rushed, while Spencer Kornhaber of \\'\\'The Atlantic\\'\\' said that the show was \"sprinting through plot check marks\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'c6511ae05914710a0cbf5e999990a906'}>,\n",
+       "  <Document: {'content': 'Several critics criticized the pacing of events; Lawler wrote that the pacing started off rushed, while Spencer Kornhaber of \\'\\'The Atlantic\\'\\' said that the show was \"sprinting through plot check marks\". Will Bedingfield of \\'\\'Wired\\'\\' also felt that the show rushed storylines, seeing the death of Varys as being without the climactic buildup previous seasons had. Sarah Hughes of \\'\\'The Guardian\\'\\' attributed season eight\\'s pacing problems to season seven having had pacing issues that ensured that the rest of the series would feel \"breathless and rushed.\"====Daenerys\\'s arc====\\nCritics generally considered Daenerys Targaryen\\'s (Emilia Clarke) villainous turn in this episode unearned.\\nDaenerys\\'s turn from hero to villain was especially criticized, with many reviewers and fans feeling that, because she was built up as a morally good protagonist for seven seasons, it made no narrative sense, or that it was not executed properly within the provided timescale. Mike Hogan of \\'\\'Vanity Fair\\'\\' said that although the show had been clear that Daenerys has a temper, \"we have seen her balance that violence with mercy, kindness, and above all shrewdness.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'a5c0fc006c867debc2174ad32c6c23b7'}>,\n",
+       "  <Document: {'content': 'Mike Hogan of \\'\\'Vanity Fair\\'\\' said that although the show had been clear that Daenerys has a temper, \"we have seen her balance that violence with mercy, kindness, and above all shrewdness.\"Zack Beauchamp of \\'\\'Vox\\'\\' argued that Daenerys\\'s previous cruelties were somewhat logical because she killed people who \"committed crimes deserving of punishment\" and Samwell Tarly\\'s father and brother because they \"refused to submit to her rule,\" but that there was no reason to go after harmless civilians. He said although the show had been \"building toward Daenerys becoming the Mad Queen\" and this route could have worked, \"its execution was sloppy and rushed\" and \"it felt as if Daenerys had become a monster simply because the show needed her to become a monster, not because it was paying off a thoughtfully developed character turn.\"\\'\\'Slate\\'\\'\\'s Sam Adams said \"the show spent far more time making Dany a hero\" than building her up as the Mad Queen.\\n\\n\\'\\'The Atlantic\\'\\' staff opined, \"\\'\\'Game of Thrones\\'\\' could have easily demonstrated the nasty reality of Daenerys\\'s fight for Westeros without putting the choice to massacre innocents directly on her shoulders. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'fd29e403f9eaf0ff8b33873cabcb29ca'}>,\n",
+       "  <Document: {'content': '\\'\\'The Atlantic\\'\\' staff opined, \"\\'\\'Game of Thrones\\'\\' could have easily demonstrated the nasty reality of Daenerys\\'s fight for Westeros without putting the choice to massacre innocents directly on her shoulders. Instead, \\'The Bells\\' ended up painting one of the most pivotal plot points in the final season as an emotional lashing-out from a tired, lonely, paranoid young woman.\"Kathryn VanArendonk of \\'\\'Vulture\\'\\' also criticized the episode for \"ultimately hinging on a trope as painfully stale as \\'and then the scary powerful woman goes crazy\\'.\"Eliana Dockterman of \\'\\'Time\\'\\' criticized the analyses of Daenerys\\'s advisors as also playing \"into the terrible trope of the crazed, power-hungry woman\" and Tyrion\\'s advice as having kept Daenerys from taking the Iron Throne much earlier and without as much bloodshed. She said she understood the idea of power corrupting \"a heroic figure like Daenerys\" and Daenerys falling prey to a messiah complex, but that the show needed to evolve her to that point like it evolved other characters to their points. Alan Sepinwall of \\'\\'Rolling Stone\\'\\' stated that Daenerys\\'s descent into madness is the kind of development that requires \"at least another half of a regular-length GoT season to feel earned.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '8b14195a9ca1b7e48e1589c267d61e2a'}>,\n",
+       "  <Document: {'content': 'Alan Sepinwall of \\'\\'Rolling Stone\\'\\' stated that Daenerys\\'s descent into madness is the kind of development that requires \"at least another half of a regular-length GoT season to feel earned.\"Myles McNutt of \\'\\'The A.V. Club\\'\\' said that Daenerys\\'s final arc comes down to two aspects\\xa0– story development and character development. He said Daenerys burning down King\\'s Landing is a logical and effective way of ending the story on a thematic level, adding that how \"it reshapes the rest of the episode is a striking reframing of the violence that has defined the show.\"Character-wise, he felt that the choice was \"undeniably trickier\" because one might be led to believe that Daenerys\\'s choice was purely reflexive when it was instead due to \"a collection of life experiences that left her believing that ruling with fear was the only path ahead of her.\"He said \"the writers failed to create the necessary structure for the villainous turn\" and they could have better highlighted Daenerys\\'s destiny as a villain by referencing things such as her prophecy in Qarth. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '35aac4ce23167aec964eac239acff1a1'}>,\n",
+       "  <Document: {'content': 'He said \"the writers failed to create the necessary structure for the villainous turn\" and they could have better highlighted Daenerys\\'s destiny as a villain by referencing things such as her prophecy in Qarth. \\'\\'Variety\\'\\'\\'s Daniel D\\'Addario also argued that it made sense that Daenerys would burn the city because her \"tactics have always been more deeply rooted in dominance than in empathy.\"Arguing that the change in Daenerys\\'s arc has likely been planned by George R. R. Martin from the beginning, Vox\\'s Andrew Prokop stated, \"If \\'\\'Game of Thrones\\'\\' ended with a triumphant Daenerys Targaryen heroically taking the Iron Throne, it wouldn\\'t be \\'\\'Game of Thrones\\'\\'. This is the show of Ned Stark\\'s death. This is the show of the Red Wedding. This is the ending it was headed toward all along.\"Screen Rant\\'s Alexandra August felt that \"ultimately it comes down to your interpretation of Dany\\'s arc and how much that interpretation was diluted by pacing, execution and cultural context, if at all.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '839828d5cb0048cfa33f534445fc57b0'}>,\n",
+       "  <Document: {'content': 'Screen Rant\\'s Alexandra August felt that \"ultimately it comes down to your interpretation of Dany\\'s arc and how much that interpretation was diluted by pacing, execution and cultural context, if at all.\"She pondered different ways the show could have pulled off the arc better, such as Daenerys only choosing to kill Cersei, but concluded that there would eventually be someone else to push her over the edge regardless. Also stating that \"there are many ways to interpret\" Daenerys\\'s decision to burn the city, Megan Garber of \\'\\'The Atlantic\\'\\' said that maybe it was the Targaryen madness having settled in, or her ruthlessness taking over, or a \"crazy edit\" by the writers, or a decision \"that some innocents must die in the present so that many more can live peacefully in the future,\" or maybe that Daenerys \"having recently lost her second dragon and the apparent loyalty of those left in her orbit, she simply made a blunt calculation about power and what will be required to attain it.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '28eb61fd0a5d61efe71d295d8f669146'}>,\n",
+       "  <Document: {'content': 'Also stating that \"there are many ways to interpret\" Daenerys\\'s decision to burn the city, Megan Garber of \\'\\'The Atlantic\\'\\' said that maybe it was the Targaryen madness having settled in, or her ruthlessness taking over, or a \"crazy edit\" by the writers, or a decision \"that some innocents must die in the present so that many more can live peacefully in the future,\" or maybe that Daenerys \"having recently lost her second dragon and the apparent loyalty of those left in her orbit, she simply made a blunt calculation about power and what will be required to attain it.\"Garber said that \"ambiguity is a powerful tool in storytelling,\" but that \"it is also a difficult one to wield well,\" and that \"Dany is a savior, and Dany is a monster, and it is impossible to know where one ends and the other begins.\"To Garber, this uncertainty made Daenerys\\'s actions all the more horrific because logic will not always win and \"justice won\\'t always save the day.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'eb7e9b331c2a1e7f96dea6ddd03e8da4'}>,\n",
+       "  <Document: {'content': 'To Garber, this uncertainty made Daenerys\\'s actions all the more horrific because logic will not always win and \"justice won\\'t always save the day.\"Showrunners David Benioff and D. B. Weiss attributed Daenerys\\'s decision to burn the city to having lost almost all of her friends and advisors, no longer trusting Jon Snow, and wanting to reclaim the home that her family built. Benioff said that Jon also being unable to return her affections because they are related played a factor. Weiss stated, \"I think that when she says, \\'Let it be fear,\\' she\\'s resigning herself to the fact that she may have to get things done in a way that isn\\'t pleasant.\"Benioff said that the ruthless side to Daenerys had always been there and that \"if Cersei hadn\\'t betrayed her, if Cersei hadn\\'t executed Missandei, if Jon hadn\\'t told her the truth ... if all of these things had happened in any different way, then I don\\'t think we\\'d be seeing this side of Daenerys Targaryen.\"Emilia Clarke said, \"Every single thing that\\'s led her to this point, and there she is, alone.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '6f62fbd8037e398d407605328036eccf'}>,\n",
+       "  <Document: {'content': 'Benioff said that the ruthless side to Daenerys had always been there and that \"if Cersei hadn\\'t betrayed her, if Cersei hadn\\'t executed Missandei, if Jon hadn\\'t told her the truth ... if all of these things had happened in any different way, then I don\\'t think we\\'d be seeing this side of Daenerys Targaryen.\"Emilia Clarke said, \"Every single thing that\\'s led her to this point, and there she is, alone.\"VanArendonk expressed disappointment that Weiss and Benioff explained Daenerys\\'s actions as due to her emotions, instead of some \"twisted, empathetic logic\". Emma Baty of \\'\\'Cosmopolitan\\'\\' also criticized the showrunners\\' response, stating, \"Totally, totally. A woman is definitely prone to ending thousands of innocent lives just because her dad did the same, even though in the entire series, she\\'s been insistent on not becoming that person. That checks out!\"===Audience response===\\n\\'\\'People\\'\\' magazine\\'s Aurelie Corinthios said, \"Despite the major plot developments, viewers were left wanting more from a series that has spent the last eight seasons meticulously building up complex characters.\"She added that \"others defended the episode, arguing that while the action was definitely \\'rushed,\\' the main elements still made sense. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'ef357233fb73fa0d3396b9911577e2db'}>,\n",
+       "  <Document: {'content': 'She added that \"others defended the episode, arguing that while the action was definitely \\'rushed,\\' the main elements still made sense. Plus, didn\\'t we always know we wouldn\\'t get a happy ending?\"Lauren Hill of \\'\\'Chicago Tribune\\'\\' relayed that the episode \"had fans very divided. Some are upset by the character arcs of certain fan favorites while others saw this coming for quite some time.\"Reviewers made note of the negative fan reactions, especially to Daenerys\\'s arc. Estelle Tang of \\'\\'Elle\\'\\' stated that many \"commented how unbelievable it was for Dany to turn into a violent vengeance-seeker this far into the show\\'s run.\"\\'\\'Esquire\\'\\'\\'s Gabrielle Bruney said viewers felt that the writers threw out her character development. \\'\\'The Hollywood Reporter\\'\\' staff stated that \"many \\'\\'Game of Thrones\\'\\' viewers see Daenerys\\' acts as nothing short of a character assassination, laying blame for the treatment of the Dragon Queen, Cersei and the series\\' other powerful women at the feet of creators David Benioff and Dan Weiss.\"Mehera Bonner of \\'\\'Cosmopolitan\\'\\' wrote that \"fans are absolutely devastated\" by the episode and that they bemoaned \"how the show has been completely ruined by sloppy writing and terrible decisions.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '7e51d840ebfb5756ccd355d34dc7c852'}>,\n",
+       "  <Document: {'content': 'Mehera Bonner of \\'\\'Cosmopolitan\\'\\' wrote that \"fans are absolutely devastated\" by the episode and that they bemoaned \"how the show has been completely ruined by sloppy writing and terrible decisions.\"She said, \"While you could argue that we should have seen a lot of this coming, many fans were... I don\\'t know... just hoping for better? Better than Daenerys becoming a Mad Queen just because it\\'s in her family history and better than Jaime reverting to his season 1 personality just because it’s the easy way out.\"A petition to HBO for a remake of the eighth season of \\'\\'Game of Thrones\\'\\' \"that makes sense\" was started on Change.org after the fourth episode \"The Last of the Starks\" aired, but went viral after the fifth episode \"The Bells\" aired. The petition described showrunners David Benioff and D. B. Weiss as \"woefully incompetent writers\". As of May 18, 2019 (i.e., the evening before the series finale), it has amassed over 1 million signatures. The petition was labelled as \"ridiculous\" by actor Isaac Hempstead Wright (who plays Bran Stark), and \"rude\" by actor Jacob Anderson (who plays Grey Worm). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '92a9c686c0e07bcc055923ac17784b42'}>,\n",
+       "  <Document: {'content': 'The petition was labelled as \"ridiculous\" by actor Isaac Hempstead Wright (who plays Bran Stark), and \"rude\" by actor Jacob Anderson (who plays Grey Worm). Sophie Turner (who plays Sansa) said that \"there\\'s always been crazy twists and turns\" on \\'\\'Game of Thrones\\'\\' and \"so Daenerys becoming something of the Mad Queen — it shouldn\\'t be such a negative thing for fans. It\\'s a shock for sure, but I think it\\'s just because it hasn\\'t gone their way.\"She argued, \"All of these petitions and things like that — I think it\\'s disrespectful to the crew, and the writers, and the filmmakers who have worked tirelessly over 10 years, and for 11 months shooting the last season.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '57a5a788ed4a715ec616717c1be4408b'}>,\n",
+       "  <Document: {'content': 'She argued, \"All of these petitions and things like that — I think it\\'s disrespectful to the crew, and the writers, and the filmmakers who have worked tirelessly over 10 years, and for 11 months shooting the last season.\"=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2019\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Emma O\\'Loughlin, and Kate O\\'Farrell\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron\\n\\nOutstanding Special Visual Effects\\n\\nJoe Bauer, Steve Kullback, Adam Chazen, Sam Conway, Mohsen Mousavi, Martin Hill, Ted Rae, Patrick Tiberius Gehlen, and Thomas Schelesny\\n\\n==References==\\n\\n== External links ==\\n* \"The Bells\" at HBO.com\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '22108e4ec739168f148d959c9f8361ab'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Iron Throne\\'\\'\\'\" is the series finale of the American fantasy drama television series \\'\\'Game of Thrones\\'\\'. It is the sixth episode of the eighth season and the 73rd overall episode of the series. Written and directed by the series creators David Benioff and D. B. Weiss, it aired on HBO in the United States and Canada on May 19, 2019. This is the only episode of the season that does not feature the \"Game Revealed\" and \"Inside the Episode\" behind-the-scenes specials, which were recorded yet unreleased for unknown reasons.\\n\\n\"The Iron Throne\" features the characters dealing with the aftermath of Daenerys Targaryen\\'s (Emilia Clarke) devastation of King\\'s Landing and determining who will finally rule Westeros. Some commentators took issue with the episode\\'s story arcs, pacing and tone, while others deemed it a satisfying conclusion to the series. Benioff and Weiss received directing and writing nominations for the episode at the 71st Primetime Emmy Awards, whilst Kit Harington and Peter Dinklage selected the episodes to support their nominations for Outstanding Lead Actor in a Drama Series and Outstanding Supporting Actor in a Drama Series, respectively.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '10b24f0a1bc598a504c5093ee7d64aef'}>,\n",
+       "  <Document: {'content': 'Benioff and Weiss received directing and writing nominations for the episode at the 71st Primetime Emmy Awards, whilst Kit Harington and Peter Dinklage selected the episodes to support their nominations for Outstanding Lead Actor in a Drama Series and Outstanding Supporting Actor in a Drama Series, respectively.\\n\\n== Plot ==\\nJon and Davos survey the destruction that Daenerys Targaryen wrought on King\\'s Landing after she took the city from Cersei Lannister, Queen of the Seven Kingdoms. Tyrion finds the crushed corpses of Cersei and Jaime in the ruins of the Red Keep and mourns them. Grey Worm executes Lannister soldiers, telling Jon that Daenerys ordered it. Daenerys declares to the Unsullied and Dothraki that they \"liberated\" the people of King\\'s Landing, and she will \"liberate\" the entire world. Tyrion publicly resigns as Daenerys’ Hand in protest and is arrested for treason.\\n\\nArya tells Jon that because of his Targaryen heritage, Daenerys will kill him as a threat to her rule. Jon visits Tyrion in captivity, who tells Jon that despite Jon\\'s love for Daenerys, it is Jon\\'s duty to kill her for being the people\\'s greatest threat. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '42749e49c5071efaeed4f3f82685533'}>,\n",
+       "  <Document: {'content': 'Jon visits Tyrion in captivity, who tells Jon that despite Jon\\'s love for Daenerys, it is Jon\\'s duty to kill her for being the people\\'s greatest threat. Tyrion also warns that Arya and her sister Sansa will not bend the knee to Daenerys, putting all of House Stark in danger.\\n\\nIn the throne room, Jon confronts Daenerys, who blames the deaths of civilians on Cersei for using them as human shields. She refuses to forgive Tyrion or the Lannister prisoners, arguing that their executions\\xa0– and a continued \"liberation\" campaign\\xa0– are necessary to establish her vision of a good world. Unable to dissuade her, a conflicted Jon reaffirms his fealty to her and fatally stabs her as they kiss. As he grieves, Drogon arrives. After discovering that Daenerys has been murdered, Drogon melts the Iron Throne and carries Daenerys\\'s body away to the east.\\n\\nSome time later, the lords and ladies of the Seven Kingdoms convene to discuss the fates of Tyrion and Jon. Tyrion, still held prisoner by Grey Worm, suggests that future monarchs be chosen by a council, instead of inheriting the crown. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '1db90b7dc33a694f6d5d575bf6cf7379'}>,\n",
+       "  <Document: {'content': \"Tyrion, still held prisoner by Grey Worm, suggests that future monarchs be chosen by a council, instead of inheriting the crown. This suggestion is agreed upon by the lords and ladies present, and Tyrion nominates Bran to be the new king of Westeros, dubbing him Bran the Broken. Everyone agrees with the nomination except for Sansa, who declares the North's independence from the Seven Kingdoms. Bran accepts, appoints Tyrion as his Hand, and sentences Jon to rejoin the Night's Watch to appease Grey Worm and the Unsullied, who then depart with the Dothraki for Naath, the homeland of his deceased lover Missandei.\\n\\nDays later, Tyrion convenes a new Small Council consisting of Bronn (now Lord of Highgarden), Brienne, Davos, and Sam. As they begin planning to rebuild King's Landing, Bran, King of the Six Kingdoms, briefly meets with the council and tasks himself with finding Drogon.\\n\\nArya sets sail to explore the uncharted seas west of Westeros. Sansa is crowned Queen in the North by the Northern lords. Jon returns to Castle Black and reunites with Tormund, Ghost and the rest of the Wildlings, and leads them to return to the lands beyond the Wall.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '20d5c03d30ebc3e6665098d10a09f9c5'}>,\n",
+       "  <Document: {'content': 'Jon returns to Castle Black and reunites with Tormund, Ghost and the rest of the Wildlings, and leads them to return to the lands beyond the Wall.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by David Benioff and D. B. Weiss. Isaac Hempstead-Wright stated that Benioff and Weiss told him Bran becoming king came from George R. R. Martin\\'s outline for future novels.\\n\\n=== Filming ===\\nThe episode was directed by David Benioff and D. B. Weiss.\\n\\nDuring filming of the Dragonpit scene in Seville, Spain, actors Vladimir Furdik (The Night King), Faye Marsay (The Waif), Tom Wlaschiha (Jaqen H\\'ghar), and Kit Harington (Jon Snow) were brought by HBO to the city as decoys to hide plot points. In the same scene, two plastic water bottles were spotted by the audience behind John Bradley and Liam Cunningham\\'s feet.\\n\\n== Reception ==\\n\\n=== Ratings ===\\nThe episode was viewed by 13.61 million viewers on its initial live broadcast on HBO, making it the most watched episode of the series, surpassing the preceding episode \"The Bells\", as well as the most-watched HBO telecast ever, surpassing the 13.4 million viewers of \\'\\'The Sopranos\\'\\' episode \"For All Debts Public and Private\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '3c1def87a747b6338556d5e7793fceb0'}>,\n",
+       "  <Document: {'content': '== Reception ==\\n\\n=== Ratings ===\\nThe episode was viewed by 13.61 million viewers on its initial live broadcast on HBO, making it the most watched episode of the series, surpassing the preceding episode \"The Bells\", as well as the most-watched HBO telecast ever, surpassing the 13.4 million viewers of \\'\\'The Sopranos\\'\\' episode \"For All Debts Public and Private\". An additional 5.7 million viewers watched on streaming platforms, for a total of 19.3 million viewers.\\n\\n=== Critical response ===\\nOn review aggregator Rotten Tomatoes, the episode has an approval rating of 48% based on 133 reviews, with an average rating of 6.32 out of 10. The website\\'s critical consensus reads: \"If nothing else, \\'The Iron Throne\\' is a bittersweet\\xa0– if unfortunately bland\\xa0– series finale that ensures \\'\\'Game of Thrones\\'\\' fans will linger on the fate of their favorite characters for some time. Will they ever be satisfied by the show\\'s conclusions? Ask us again in 10 years.\"As of 9 August 2019, it is the lowest rated episode of the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'bdade211303f88d1f4439abf2be3c8bd'}>,\n",
+       "  <Document: {'content': 'Ask us again in 10 years.\"As of 9 August 2019, it is the lowest rated episode of the series.\\n\\nJames Poniewozik of \\'\\'The New York Times\\'\\' felt the story insufficiently explained why Daenerys burned King\\'s Landing and how she arrived at her final character point, stating that \"a woman, abused and traded like chattel, becomes so caught up in her zeal to do good that she sees anything but blind adoration as evil\" was a solid idea, but that viewers were not taken \"inside her perspective to make that change seem real and inevitable.\"Poniewozik said \"small character moments\" like Tyrion straightening chairs, Arya sailing west and Jon rejoining the Wildlings \"made emotional sense\". Hank Stuever of \\'\\'The Washington Post\\'\\', who watched with lowered expectations, said the series \"sailed (and trotted) off to a noble and perhaps anticlimactic end...It was everything nobody wanted, but it was still quite a thing: adequately just, narratively symmetrical and sufficiently poignant. It went long on swelling imagery and somewhat short on dialogue.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'fad3883d3d680c534f377ea0fea8b42f'}>,\n",
+       "  <Document: {'content': 'It was everything nobody wanted, but it was still quite a thing: adequately just, narratively symmetrical and sufficiently poignant. It went long on swelling imagery and somewhat short on dialogue.\"Laura Prudom of \\'\\'IGN\\'\\' wrote that the finale was \"not a disaster\", but also \"not quite the dream of spring we might\\'ve hoped for\", \"struggling to resolve many of the show\\'s lingering plot threads in a satisfying and coherent conclusion, and once again falling victim to the season\\'s needlessly truncated episode order.\"Critics took issue with the episode\\'s pacing and final revelations. Spencer Kornhaber of \\'\\'The Atlantic\\'\\' wrote that the finale was \"tonally odd, logically strained, and emotionally thin\" and a \"drama turned into a sitcom\". Lenika Cruz, also writing for \\'\\'The Atlantic\\'\\', wrote that the episode had \"pacing issues, rushed character development, tonal dissonance, the lack of attention to detail, unexplained reversals, and weak dialogue\". Kelly Lawler of \\'\\'USA Today\\'\\' wrote, \"Tragedy and injustice were as baked into the series\\' identity as dragons and battles,\" but the finale was \"unrecognizable. It was hacky; it was cliched. Every character left standing received a saccharine coda...It didn\\'t gracefully swerve into another lane, it careened off a cliff.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '20f52580ff32c019c3db8182305c990f'}>,\n",
+       "  <Document: {'content': 'Every character left standing received a saccharine coda...It didn\\'t gracefully swerve into another lane, it careened off a cliff.\"Inkoo Kang of \\'\\'Slate\\'\\' wrote that \"We know governance is complicated, and the show’s depiction of those complications is one of the reasons why it initially felt so refreshingly relatable\\xa0– but the finale’s argument that an abdication of responsibility is the best we can hope for in a leader\\xa0– feels lazy and false.\"Conversely, \\'\\'TV Guide\\'\\'s consensus concluded that the Game of Thrones finale \"finished strong,\" ranking it #33 on the list of all 73 episodes. Richard Roeper, writing for the \\'\\'Chicago Sun-Times\\'\\', wrote that \"over all, the finale was a solid and largely satisfying wrap-up to one of the most exciting and enthralling TV series ever,\" and was \"melancholy, bittersweet, twist-filled and at times surprisingly humorous.\"Hugh Montgomery of BBC also rated the episode 4/5, writing that the finale largely satisfies \"on the terms the creators have previously set out\" in the \"ruinous\" penultimate episode. Bran becoming king was \"true to the show’s sense of realpolitik\" as a \"contingently happy ending\", whereas the show \"provides an efficient, if disappointingly uncontroversial, ending\" for Jon, Arya and Sansa. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '67fcffdcc643eff3656bd6796aa9951d'}>,\n",
+       "  <Document: {'content': 'Bran becoming king was \"true to the show’s sense of realpolitik\" as a \"contingently happy ending\", whereas the show \"provides an efficient, if disappointingly uncontroversial, ending\" for Jon, Arya and Sansa. Karl Quinn of \\'\\'The Age\\'\\' wrote that the show \"may have resolved itself\" too quickly in dramatic terms, but Bran\\'s ascension \"made perfect sense\" thematically according to the show\\'s \"anti-war and anti-despot themes...After all the bloodshed, butchery and burning, \\'\\'Game of Thrones\\'\\' ended not with a bang but with a ballot.\"Lucy Mangan of \\'\\'The Guardian\\'\\' gave a 4/5 rating, stating that \"the finale just about delivered. It was true to the series\\' overall subject\\xa0– war, and the pity of war\\xa0– and, after doing a lot of wrong to several protagonists last week, did right by those left standing.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'e15606f2c630a4b68cbdda57297e4cad'}>,\n",
+       "  <Document: {'content': 'It was true to the series\\' overall subject\\xa0– war, and the pity of war\\xa0– and, after doing a lot of wrong to several protagonists last week, did right by those left standing.\"=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2019\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss\\n\\nOutstanding Lead Actor in a Drama Series\\n\\nKit Harington\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Cinematography for a Single-Camera Series (One Hour)\\n\\nJonathan Freeman\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nKatie Weiland\\n\\n== References ==\\n\\n== External links ==\\n* \"The Iron Throne\" at HBO.com\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'c9695141a2397fa7b8b56a51f5c766b3'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'A Knight of the Seven Kingdoms\\'\\'\\'\" is the second episode of the eighth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 69th overall. It was written by Bryan Cogman, and directed by David Nutter. It aired on April 21, 2019.\\n\\nThe episode takes place entirely in Winterfell and is dedicated to the buildup before the battle between the living and the dead. It has been likened to a bottle episode, though it does not meet the definition of the term.\\n\"A Knight of the Seven Kingdoms\" received positive reception from critics, with many highlighting the episode\\'s balance of the show\\'s longtime characters and citing it as one of the best episodes of the series. Gwendoline Christie (Brienne of Tarth) submitted the episode to support her Primetime Emmy Award nomination for Outstanding Supporting Actress in a Drama Series. Nikolaj Coster-Waldau (Jaime Lannister) later selected the episode to support his nomination for Outstanding Supporting Actor in a Drama Series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'a588c0c8589a8a6e5820928e353674f6'}>,\n",
+       "  <Document: {'content': 'Nikolaj Coster-Waldau (Jaime Lannister) later selected the episode to support his nomination for Outstanding Supporting Actor in a Drama Series.\\n\\nThe title is a reference to the title bestowed on Brienne of Tarth after Jaime Lannister knights her and to the collection of stories of the same name by George R. R. Martin, the author of the \\'\\'A Song of Ice and Fire\\'\\' novels on which \\'\\'Game of Thrones\\'\\' is based.\\n\\n==Plot==\\nDaenerys and Sansa both contemplate the fate of Jaime Lannister, who reveals Cersei was lying about sending her army. They let Jaime fight for them after Brienne vouches for him. Jaime speaks with Bran and apologizes for trying to kill him (\"Winter Is Coming\"), but Bran holds no anger. Meanwhile, Daenerys is angry at Tyrion for believing Cersei, but is mollified by Jorah.\\n\\nSansa and Daenerys attempt to clear the air between them based on their mutual love of Jon, but Daenerys doesn\\'t have an answer when Sansa asks about the fate of the North once Daenerys takes the Iron Throne. Theon returns wishing to fight for the Starks. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '33c2920f811875dda23b96d7fd66e7b2'}>,\n",
+       "  <Document: {'content': 'Sansa and Daenerys attempt to clear the air between them based on their mutual love of Jon, but Daenerys doesn\\'t have an answer when Sansa asks about the fate of the North once Daenerys takes the Iron Throne. Theon returns wishing to fight for the Starks. Tormund, Beric, and Edd arrive and tell Jon that the Army of the Dead will arrive before the following morning.\\n\\nAt the war council, Bran persuades them to let him act as bait to lure the Night King, who wishes to kill him. Theon volunteers to defend Bran with the Ironborn, and Jon and Daenerys plan to ambush the Night King when he reveals himself to destroy him.\\n\\nArya talks to the Hound, and asks him why he came North since he only fought for himself, and he retorts that he had fought for her (\"The Children\"). Arya then visits Gendry. He reveals he is a bastard of Robert Baratheon. Wanting to lose her virginity before the battle, Arya seduces and sleeps with Gendry.\\n\\nTyrion, Jaime, Brienne, Podrick, Davos, and Tormund gather in the meeting hall to drink before the battle. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'd2d015a10c58eb883daa86e5faff4af6'}>,\n",
+       "  <Document: {'content': 'Wanting to lose her virginity before the battle, Arya seduces and sleeps with Gendry.\\n\\nTyrion, Jaime, Brienne, Podrick, Davos, and Tormund gather in the meeting hall to drink before the battle. The conversation turns to why women cannot be knighted; in response to Tormund\\'s remark that he would knight Brienne without hesitation, Jaime knights an emotional Brienne.\\n\\nJorah fails at attempting to stop Lyanna Mormont from fighting in the battle. Sam approaches Jorah and gives him House Tarly\\'s Valyrian sword Heartsbane, out of gratitude for the influence Jorah\\'s father Jeor had on him.\\n\\nDaenerys visits Jon in the crypts and he reveals his true parentage to Daenerys but Daenerys is skeptical about the reliability of the information. They are interrupted by horn blasts signalling the approach of the Army of the Dead.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by \"series veteran\" Bryan Cogman. This was his eleventh and final script of the series overall. For him \"the biggest challenge was not writing a Wikipedia page.\"The episode adapts material from the unpublished novels \\'\\'The Winds of Winter\\'\\' and \\'\\'A Dream of Spring\\'\\', along with original material not published in George R. R. Martin\\'s novels.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '40f23a534ff560155ec04ed0c9530903'}>,\n",
+       "  <Document: {'content': 'The episode adapts material from the unpublished novels \\'\\'The Winds of Winter\\'\\' and \\'\\'A Dream of Spring\\'\\', along with original material not published in George R. R. Martin\\'s novels.\\n\\n===Filming===\\nThe episode was directed by David Nutter, his second of three episodes this season. The outdoor Winterfell scenes were filmed at sets in Moneyglass and Magheramorne in Northern Ireland, with indoor scenes filmed at Paint Hall studios in Belfast. Executive producers David Benioff and D. B. Weiss let Maisie Williams decide how much of her body to show on camera during her sex scene; she didn\\'t believe that Arya\\'s nakedness was central to the narrative, and she \"kept herself pretty private.\"===Music===\\nThe episode features a song called \"Jenny\\'s Song\", sung by Podrick while many of the characters were drinking by the fire before the upcoming battle. The first line of the song appeared in the book \\'\\'A Storm of Swords\\'\\'; the writers wrote the rest of the lyrics while series composer Ramin Djawadi set it to music. Another version of the song, titled \"Jenny of Oldstones\", was sung by the band Florence and the Machine and played over the ending credits.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8538c2ca08b346848ad5a4ac3f44c2d6'}>,\n",
+       "  <Document: {'content': 'Another version of the song, titled \"Jenny of Oldstones\", was sung by the band Florence and the Machine and played over the ending credits.\\n\\n==Reception==\\n\\n===Ratings===\\nThe episode was viewed by 10.29 million viewers on its initial live broadcast on HBO, and an additional 5.61 million viewers on streaming platforms, for a total 15.9 million viewers.\\n\\n===Critical response===\\nThe episode received largely positive reviews from critics and acclaim from many longtime commentators of the series. It has an 87% rating on the review aggregator site Rotten Tomatoes based on 94 reviews, with an average rating of 8.12/10. The site\\'s critics\\' consensus states: \"What \\'A Knight of the Seven Kingdoms\\' lacks in forward narrative momentum it makes up for in cryptic callbacks, intimate moments, and the promise of imminent battle – though some fans may find their patience tested. The things we do for love.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '47344b86693fcc0094f12a50724ea10a'}>,\n",
+       "  <Document: {'content': 'The site\\'s critics\\' consensus states: \"What \\'A Knight of the Seven Kingdoms\\' lacks in forward narrative momentum it makes up for in cryptic callbacks, intimate moments, and the promise of imminent battle – though some fans may find their patience tested. The things we do for love.\"Jeremy Egner of \\'\\'The New York Times\\'\\' believed that the episode did a good job with building up the story before the eventual climax at Winterfell and wrote, \"In this last week before the big White Walker clash and the presumable carnage and loss of beloved characters it will entail, it was a reminder that the things we do for love can be heroic, too.\"David Sims of \\'\\'The Atlantic\\'\\' suggested that \"audiences didn\\'t need most of last week\\'s dire episode,\" adding that smooshing the episodes together would result in \"a good hour of fan service,\" before the potential death of some of the characters in the next episode. Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' believed that the episode did a good job of distinguishing between the two sides of the upcoming battle by showing \"the warm bodies and warmer conversations between our tragically, beautifully human characters.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '209ab0cd7de3e156499ceb5e934a6f55'}>,\n",
+       "  <Document: {'content': 'Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' believed that the episode did a good job of distinguishing between the two sides of the upcoming battle by showing \"the warm bodies and warmer conversations between our tragically, beautifully human characters.\"Rosenberg went on to analyze that the apocalyptic situation facing the characters served as a meta commentary for the impending end of the series, writing, \"It’s a sly note to those of us who have spent a decade or longer with George R.R. Martin’s characters, an admonition that even when this hugely absorbing series ends in a few weeks, its best moments will live on in those of us who have loved it and challenged it. And in the hands of writer and co-executive producer Bryan Cogman — who has always, to my mind, better understood what made “Game of Thrones” wonderful than even the men who created and ran the darn thing\"  Author Stephen King shared praise about the episode on Twitter, writing \"As a long-time storyteller, I\\'m in awe of how perfectly the minds behind this show brought all the major characters together at Winterfell. They made it look easy. Constant Readers, it is not.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'fd5df7f89b236cee2af23d8b72e0f678'}>,\n",
+       "  <Document: {'content': 'And in the hands of writer and co-executive producer Bryan Cogman — who has always, to my mind, better understood what made “Game of Thrones” wonderful than even the men who created and ran the darn thing\"  Author Stephen King shared praise about the episode on Twitter, writing \"As a long-time storyteller, I\\'m in awe of how perfectly the minds behind this show brought all the major characters together at Winterfell. They made it look easy. Constant Readers, it is not.\"Among the negative reviews, Liz Shannon Miller of \\'\\'IndieWire\\'\\' criticized the pace of the episode and unnecessary confrontations between the characters and added, \"The issues surrounding \"A Knight of the Seven Kingdoms\" would be lesser if a) Episode 1 hadn\\'t also been so devoted to moving pieces into place and b) Season 8 wasn\\'t going to be only six episodes.\"=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2019\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting  Actor in a Drama Series\\n\\nNikolaj Coster-Waldau\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nGwendoline Christie\\n\\n==References==\\n\\n==External links==\\n*  \"A Knight of the Seven Kingdoms\" at HBO.com\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '7c4fc9e6b42ea5b9c5e5f1467dab6dd1'}>,\n",
+       "  <Document: {'content': \"\\n\\nThe fire maps show the locations of actively burning fires around the world on a monthly basis, based on observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite. The colors are based on a count of the number (not size) of fires observed within a 1,000-square-kilometer area. White pixels show the high end of the count—as many as 100 fires in a 1,000-square-kilometer area per day. Yellow pixels show as many as 10 fires, orange shows as many as 5 fires, and red areas as few as 1 fire per day.\\n\\n'''Fire''' is the rapid oxidation of a material in the exothermic chemical process of combustion, releasing heat, light, and various reaction products.  Slower oxidative processes like rusting or digestion are not included by this definition.\\n\\nFire is hot because the conversion of the weak double bond in molecular oxygen, O2, to the stronger bonds in the combustion products carbon dioxide and water releases energy (418 kJ per 32 g of O2); the bond energies of the fuel play only a minor role here.  At a certain point in the combustion reaction, called the ignition point, flames are produced.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'f11e1a02e6f564970b14a3c7b6d1ad5f'}>,\n",
+       "  <Document: {'content': \"Fire is hot because the conversion of the weak double bond in molecular oxygen, O2, to the stronger bonds in the combustion products carbon dioxide and water releases energy (418 kJ per 32 g of O2); the bond energies of the fuel play only a minor role here.  At a certain point in the combustion reaction, called the ignition point, flames are produced.  The ''flame'' is the visible portion of the fire. Flames consist primarily of carbon dioxide, water vapor, oxygen and nitrogen.  If hot enough, the gases may become ionized to produce plasma. Depending on the substances alight, and any impurities outside, the color of the flame and the fire's intensity will be different.\\n\\nFire in its most common form can result in conflagration, which has the potential to cause physical damage through burning.  Fire is an important process that affects ecological systems around the globe. The positive effects of fire include stimulating growth and maintaining various ecological systems.\\n\\nThe negative effects of fire include hazard to life and property, atmospheric pollution, and water contamination. If fire removes protective vegetation, heavy rainfall may lead to an increase in soil erosion by water. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '83ebe99b0f112e76eabd9f52c8f85f4c'}>,\n",
+       "  <Document: {'content': 'The negative effects of fire include hazard to life and property, atmospheric pollution, and water contamination. If fire removes protective vegetation, heavy rainfall may lead to an increase in soil erosion by water. Also, when vegetation is burned, the nitrogen it contains is released into the atmosphere, unlike elements such as potassium and phosphorus which remain in the ash and are quickly recycled into the soil. This loss of nitrogen caused by a fire produces a long-term reduction in the fertility of the soil, which only slowly recovers as nitrogen is \"fixed\" from the atmosphere by lightning and by leguminous plants such as clover.\\n\\nFire has been used by humans in rituals, in agriculture for clearing land, for cooking, generating heat and light, for signaling, propulsion purposes, smelting, forging, incineration of waste, cremation, and as a weapon or mode of destruction.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'f4f40f1cc3c90c7fdb09ed5429efa4ca'}>,\n",
+       "  <Document: {'content': 'Fire has been used by humans in rituals, in agriculture for clearing land, for cooking, generating heat and light, for signaling, propulsion purposes, smelting, forging, incineration of waste, cremation, and as a weapon or mode of destruction.\\n\\n==Physical properties==\\n\\n===Chemistry===\\n\\nThe fire tetrahedron\\nFires start when a flammable or a combustible material, in combination with a sufficient quantity of an oxidizer such as oxygen gas or another oxygen-rich compound (though non-oxygen oxidizers exist), is exposed to a source of heat or ambient temperature above the flash point for the fuel/oxidizer mix, and is able to sustain a rate of rapid oxidation that produces a chain reaction. This is commonly called the fire tetrahedron. Fire cannot exist without all of these elements in place and in the right proportions. For example, a flammable liquid will start burning only if the fuel and oxygen are in the right proportions. Some fuel-oxygen mixes may require a catalyst, a substance that is not consumed, when added, in any chemical reaction during combustion, but which enables the reactants to combust more readily.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '7fc52dfa8407c0beeaae65e4449c723'}>,\n",
+       "  <Document: {'content': 'Some fuel-oxygen mixes may require a catalyst, a substance that is not consumed, when added, in any chemical reaction during combustion, but which enables the reactants to combust more readily.\\n\\nOnce ignited, a chain reaction must take place whereby fires can sustain their own heat by the further release of heat energy in the process of combustion and may propagate, provided there is a continuous supply of an oxidizer and fuel.\\n\\nIf the oxidizer is oxygen from the surrounding air, the presence of a force of gravity, or of some similar force caused by acceleration, is necessary to produce convection, which removes combustion products and brings a supply of oxygen to the fire. Without gravity, a fire rapidly surrounds itself with its own combustion products and non-oxidizing gases from the air, which exclude oxygen and extinguish the fire. Because of this, the risk of fire in a spacecraft is small when it is coasting in inertial flight. This does not apply if oxygen is supplied to the fire by some process other than thermal convection.\\n\\nFire can be extinguished by removing any one of the elements of the fire tetrahedron. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '5f67057e3cd47adf446c8458eeec9344'}>,\n",
+       "  <Document: {'content': 'This does not apply if oxygen is supplied to the fire by some process other than thermal convection.\\n\\nFire can be extinguished by removing any one of the elements of the fire tetrahedron. Consider a natural gas flame, such as from a stove-top burner. The fire can be extinguished by any of the following:\\n* turning off the gas supply, which removes the fuel source;\\n* covering the flame completely, which smothers the flame as the combustion both uses the available oxidizer (the oxygen in the air) and displaces it from the area around the flame with CO2;\\n* application of water, which removes heat from the fire faster than the fire can produce it (similarly, blowing hard on a flame will displace the heat of the currently burning gas from its fuel source, to the same end), or\\n* application of a retardant chemical such as Halon to the flame, which retards the chemical reaction itself until the rate of combustion is too slow to maintain the chain reaction.\\n\\nIn contrast, fire is intensified by increasing the overall rate of combustion. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '84808fa3ce6e77a5f9377a13b5e60d97'}>,\n",
+       "  <Document: {'content': \"The fire can be extinguished by any of the following:\\n* turning off the gas supply, which removes the fuel source;\\n* covering the flame completely, which smothers the flame as the combustion both uses the available oxidizer (the oxygen in the air) and displaces it from the area around the flame with CO2;\\n* application of water, which removes heat from the fire faster than the fire can produce it (similarly, blowing hard on a flame will displace the heat of the currently burning gas from its fuel source, to the same end), or\\n* application of a retardant chemical such as Halon to the flame, which retards the chemical reaction itself until the rate of combustion is too slow to maintain the chain reaction.\\n\\nIn contrast, fire is intensified by increasing the overall rate of combustion. Methods to do this include balancing the input of fuel and oxidizer to stoichiometric proportions, increasing fuel and oxidizer input in this balanced mix, increasing the ambient temperature so the fire's own heat is better able to sustain combustion, or providing a catalyst, a non-reactant medium in which the fuel and oxidizer can more readily react.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'a89a72d29430684d324284409cd90815'}>,\n",
+       "  <Document: {'content': 'Methods to do this include balancing the input of fuel and oxidizer to stoichiometric proportions, increasing fuel and oxidizer input in this balanced mix, increasing the ambient temperature so the fire\\'s own heat is better able to sustain combustion, or providing a catalyst, a non-reactant medium in which the fuel and oxidizer can more readily react.\\n\\n===Flame===\\n\\nA candle\\'s flame\\nNorthwest Crown Fire Experiment, Canada\\nPhoto of a fire taken with a 1/4000th of a second exposure\\nISS\\nA flame is a mixture of reacting gases and solids emitting visible, infrared, and sometimes ultraviolet light, the frequency spectrum of which depends on the chemical composition of the burning material and intermediate reaction products. In many cases, such as the burning of organic matter, for example wood, or the incomplete combustion of gas, incandescent solid particles called soot produce the familiar red-orange glow of \"fire\". This light has a continuous spectrum. Complete combustion of gas has a dim blue color due to the emission of single-wavelength radiation from various electron transitions in the excited molecules formed in the flame. Usually oxygen is involved, but hydrogen burning in chlorine also produces a flame, producing hydrogen chloride (HCl). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '41a0b2341e11444f8f495c43b1a6f289'}>,\n",
+       "  <Document: {'content': 'Complete combustion of gas has a dim blue color due to the emission of single-wavelength radiation from various electron transitions in the excited molecules formed in the flame. Usually oxygen is involved, but hydrogen burning in chlorine also produces a flame, producing hydrogen chloride (HCl). Other possible combinations producing flames, amongst many, are fluorine and hydrogen, and hydrazine and nitrogen tetroxide. Hydrogen and hydrazine/UDMH flames are similarly pale blue, while burning boron and its compounds, evaluated in mid-20th century as a high energy fuel for jet and rocket engines, emits intense green flame, leading to its informal nickname of \"Green Dragon\".\\n\\nThe glow of a flame is complex. Black-body radiation is emitted from soot, gas, and fuel particles, though the soot particles are too small to behave like perfect blackbodies. There is also photon emission by de-excited atoms and molecules in the gases. Much of the radiation is emitted in the visible and infrared bands. The color depends on temperature for the black-body radiation, and on chemical makeup for the emission spectra. The dominant color in a flame changes with temperature. The photo of the forest fire in Canada is an excellent example of this variation. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'ea906689fb967fe4df221dc7c392b475'}>,\n",
+       "  <Document: {'content': 'The dominant color in a flame changes with temperature. The photo of the forest fire in Canada is an excellent example of this variation. Near the ground, where most burning is occurring, the fire is white, the hottest color possible for organic material in general, or yellow. Above the yellow region, the color changes to orange, which is cooler, then red, which is cooler still. Above the red region, combustion no longer occurs, and the uncombusted carbon particles are visible as black smoke.\\n\\nThe common distribution of a flame under normal gravity conditions depends on convection, as soot tends to rise to the top of a general flame, as in a candle in normal gravity conditions, making it yellow. In micro gravity or zero gravity, such as an environment in outer space, convection no longer occurs, and the flame becomes spherical, with a tendency to become more blue and more efficient (although it may go out if not moved steadily, as the CO2 from combustion does not disperse as readily in micro gravity, and tends to smother the flame). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'c776a92731920cdf861a7b90c7155a5d'}>,\n",
+       "  <Document: {'content': 'In micro gravity or zero gravity, such as an environment in outer space, convection no longer occurs, and the flame becomes spherical, with a tendency to become more blue and more efficient (although it may go out if not moved steadily, as the CO2 from combustion does not disperse as readily in micro gravity, and tends to smother the flame). There are several possible explanations for this difference, of which the most likely is that the temperature is sufficiently evenly distributed that soot is not formed and complete combustion occurs. Experiments by NASA reveal that diffusion flames in micro gravity allow more soot to be completely oxidized after they are produced than diffusion flames on Earth, because of a series of mechanisms that behave differently in micro gravity when compared to normal gravity conditions. These discoveries have potential applications in applied science and industry, especially concerning fuel efficiency.\\n\\nIn combustion engines, various steps are taken to eliminate a flame. The method depends mainly on whether the fuel is oil, wood, or a high-energy fuel such as jet fuel.\\n\\n===Flame temperatures===\\n\\n====Temperatures of flames by appearance====\\nIt is true that objects at specific temperatures do radiate visible light. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'bf751b26cdf23b75a3c613400fd62b02'}>,\n",
+       "  <Document: {'content': 'The method depends mainly on whether the fuel is oil, wood, or a high-energy fuel such as jet fuel.\\n\\n===Flame temperatures===\\n\\n====Temperatures of flames by appearance====\\nIt is true that objects at specific temperatures do radiate visible light. Objects whose surface is at a temperature above approximately  will glow, emitting light at a color that indicates the temperature of that surface. See the section on red heat for more about this effect. It is a misconception that one can judge the temperature of a fire by the color of its flames or the sparks in the flames. For many reasons, chemically and optically, these colors may not match the red/orange/yellow/white heat temperatures on the chart. Barium nitrate burns a bright green, for instance, and this is not present on the heat chart.\\n\\n====Typical temperatures of flames====\\n\\nThe \"adiabatic flame temperature\" of a given fuel and oxidizer pair indicates the temperature at which the gases achieve stable combustion.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '34af782daab33c3eb48fce3dd8f58dae'}>,\n",
+       "  <Document: {'content': '====Typical temperatures of flames====\\n\\nThe \"adiabatic flame temperature\" of a given fuel and oxidizer pair indicates the temperature at which the gases achieve stable combustion.\\n* Oxy–dicyanoacetylene\\n* Oxy–acetylene\\n* Oxyhydrogen\\n* Air–acetylene\\n* Blowtorch (air–MAPP gas)\\n* Bunsen burner (air–natural gas)\\n* Candle (air–paraffin)\\n* Smoldering cigarette:\\n** Temperature without drawing: side of the lit portion; ; middle of the lit portion:\\n** Temperature during drawing: middle of the lit portion:\\n** Always hotter in the middle.\\n\\n==Fire ecology==\\n\\nEvery natural ecosystem has its own fire regime, and the organisms in those ecosystems are adapted to or dependent upon that fire regime. Fire creates a mosaic of different habitat patches, each at a different stage of succession. Different species of plants, animals, and microbes specialize in exploiting a particular stage, and by creating these different types of patches, fire allows a greater number of species to exist within a landscape.\\n\\n==Fossil record==\\n\\n==Human control==\\n\\nBushman starting a fire in Namibia\\nProcess of ignition of a match\\nThe ability to control fire was a dramatic change in the habits of early humans. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'daee9108d9a3a17ffe3c739f67794cc8'}>,\n",
+       "  <Document: {'content': '==Fossil record==\\n\\n==Human control==\\n\\nBushman starting a fire in Namibia\\nProcess of ignition of a match\\nThe ability to control fire was a dramatic change in the habits of early humans. Making fire to generate heat and light made it possible for people to cook food, simultaneously increasing the variety and availability of nutrients and reducing disease by killing organisms in the food. The heat produced would also help people stay warm in cold weather, enabling them to live in cooler climates. Fire also kept nocturnal predators at bay. Evidence of cooked food is found from , although there is a theory that fire could have been used in a controlled fashion about 1 million years ago.  Evidence becomes widespread around 50 to 100 thousand years ago, suggesting regular use from this time; resistance to air pollution started to evolve in human populations at a similar point in time.  The use of fire became progressively more sophisticated, with it being used to create charcoal and to control wildlife from tens of thousands of years ago.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '704e8bdbee03dd3e72bd0a27d9edba2a'}>,\n",
+       "  <Document: {'content': 'The use of fire became progressively more sophisticated, with it being used to create charcoal and to control wildlife from tens of thousands of years ago.\\n\\nFire has also been used for centuries as a method of torture and execution, as evidenced by death by burning as well as torture devices such as the iron boot, which could be filled with water, oil, or even lead and then heated over an open fire to the agony of the wearer.\\n\\nBy the Neolithic Revolution, during the introduction of grain-based agriculture, people all over the world used fire as a tool in landscape management. These fires were typically controlled burns or \"cool fires\", as opposed to uncontrolled \"hot fires\", which damage the soil. Hot fires destroy plants and animals, and endanger communities. This is especially a problem in the forests of today where traditional burning is prevented in order to encourage the growth of timber crops. Cool fires are generally conducted in the spring and autumn. They clear undergrowth, burning up biomass that could trigger a hot fire should it get too dense. They provide a greater variety of environments, which encourages game and plant diversity. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'd267ee84428adb053223e63db390c027'}>,\n",
+       "  <Document: {'content': 'They clear undergrowth, burning up biomass that could trigger a hot fire should it get too dense. They provide a greater variety of environments, which encourages game and plant diversity. For humans, they make dense, impassable forests traversable. Another human use for fire in regards to landscape management is its use to clear land for agriculture. Slash-and-burn agriculture is still common across much of tropical Africa, Asia and South America. \"For small farmers, it is a convenient way to clear overgrown areas and release nutrients from standing vegetation back into the soil\", said Miguel Pinedo-Vasquez, an ecologist at the  Earth Institute’s Center for Environmental Research and Conservation. However this useful strategy is also problematic. Growing population, fragmentation of forests and warming climate are making the earth\\'s surface more prone to ever-larger escaped fires. These harm ecosystems and human infrastructure, cause health problems, and send up spirals of carbon and soot that may encourage even more warming of the atmosphere – and thus feed back into more fires. Globally today, as much as 5 million square kilometres – an area more than half the size of the United States – burns in a given year.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'c1345cdf370d5aa364f232d81b4ca27a'}>,\n",
+       "  <Document: {'content': 'Globally today, as much as 5 million square kilometres – an area more than half the size of the United States – burns in a given year.\\n\\nThere are numerous modern applications of fire. In its broadest sense, fire is used by nearly every human being on earth in a controlled setting every day. Users of internal combustion vehicles employ fire every time they drive. Thermal power stations provide electricity for a large percentage of humanity.\\nHamburg after four fire-bombing raids in July 1943, which killed an estimated 50,000 people\\nThe use of fire in warfare has a long history. Fire was the basis of all early thermal weapons. Homer detailed the use of fire by Greek soldiers who hid in a wooden horse to burn Troy during the Trojan war. Later the Byzantine fleet used Greek fire to attack ships and men. In the First World War, the first modern flamethrowers were used by infantry, and were successfully mounted on armoured vehicles in the Second World War. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'cd7b7e40a1288daf5aff21869790f7e5'}>,\n",
+       "  <Document: {'content': \"In the First World War, the first modern flamethrowers were used by infantry, and were successfully mounted on armoured vehicles in the Second World War. In the latter war, incendiary bombs were used by Axis and Allies alike, notably on Tokyo, Rotterdam, London, Hamburg and, notoriously, at Dresden; in the latter two cases firestorms were deliberately caused in which a ring of fire surrounding each city was drawn inward by an updraft caused by a central cluster of fires. The United States Army Air Force also extensively used incendiaries against Japanese targets in the latter months of the war, devastating entire cities constructed primarily of wood and paper houses. The use of napalm was employed in July 1944, towards the end of the Second World War; although its use did not gain public attention until the Vietnam War. Molotov cocktails were also used.\\n\\n===Use as fuel===\\ncoal-fired power station in the People's Republic of China\\nDisability-adjusted life year for fires per 100,000\\xa0inhabitants in 2004\\n\\nSetting fuel aflame releases usable energy. Wood was a prehistoric fuel, and is still viable today. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '28dc57283fd2463638d0f6814d5190a8'}>,\n",
+       "  <Document: {'content': \"===Use as fuel===\\ncoal-fired power station in the People's Republic of China\\nDisability-adjusted life year for fires per 100,000\\xa0inhabitants in 2004\\n\\nSetting fuel aflame releases usable energy. Wood was a prehistoric fuel, and is still viable today. The use of fossil fuels, such as petroleum, natural gas, and coal, in power plants supplies the vast majority of the world's electricity today; the International Energy Agency states that nearly 80% of the world's power came from these sources in 2002. The fire in a power station is used to heat water, creating steam that drives turbines. The turbines then spin an electric generator to produce electricity. Fire is also used to provide mechanical work directly, in both external and internal combustion engines.\\n\\nThe unburnable solid remains of a combustible material left after a fire is called ''clinker'' if its melting point is below the flame temperature, so that it fuses and then solidifies as it cools, and ''ash'' if its melting point is above the flame temperature.\\n\\n==Protection and prevention==\\n\\nThis visualization shows fires detected in the United States from July 2002 through July 2011. Look for fires that reliably burn each year in western states and across the Southeast.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '114b639ac00e7e99e48ea485e21e29a1'}>,\n",
+       "  <Document: {'content': \"==Protection and prevention==\\n\\nThis visualization shows fires detected in the United States from July 2002 through July 2011. Look for fires that reliably burn each year in western states and across the Southeast.\\n\\nWildfire prevention programs around the world may employ techniques such as ''wildland fire use'' and ''prescribed or controlled burns''. ''Wildland fire use'' refers to any fire of natural causes that is monitored but allowed to burn. ''Controlled burns'' are fires ignited by government agencies under less dangerous weather conditions.\\n\\nFire fighting services are provided in most developed areas to extinguish or contain uncontrolled fires. Trained firefighters use fire apparatus, water supply resources such as water mains and fire hydrants or they might use A and B class foam depending on what is feeding the fire.\\n\\nFire prevention is intended to reduce sources of ignition.  Fire prevention also includes education to teach people how to avoid causing fires. Buildings, especially schools and tall buildings, often conduct fire drills to inform and prepare citizens on how to react to a building fire. Purposely starting destructive fires constitutes arson and is a crime in most jurisdictions.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'bf7f8f032e7e7024b7a923f78859fa8e'}>,\n",
+       "  <Document: {'content': 'Buildings, especially schools and tall buildings, often conduct fire drills to inform and prepare citizens on how to react to a building fire. Purposely starting destructive fires constitutes arson and is a crime in most jurisdictions.\\n\\nModel building codes require passive fire protection and active fire protection systems to minimize damage resulting from a fire. The most common form of active fire protection is fire sprinklers. To maximize passive fire protection of buildings, building materials and furnishings in most developed countries are tested for fire-resistance, combustibility and flammability. Upholstery, carpeting and plastics used in vehicles and vessels are also tested.\\n\\nWhere fire prevention and fire protection have failed to prevent damage, fire insurance can mitigate the financial impact.\\n\\n==Restoration==\\nDifferent restoration methods and measures are used depending on the type of fire damage that occurred. Restoration after fire damage can be performed by property management teams, building maintenance personnel, or by the homeowners themselves; however, contacting a certified professional fire damage restoration specialist is often regarded as the safest way to restore fire damaged property due to their training and extensive experience. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '7bd4af8c709e620cfb057c38b93e2bdf'}>,\n",
+       "  <Document: {'content': 'Restoration after fire damage can be performed by property management teams, building maintenance personnel, or by the homeowners themselves; however, contacting a certified professional fire damage restoration specialist is often regarded as the safest way to restore fire damaged property due to their training and extensive experience. Most are usually listed under \"Fire and Water Restoration\" and they can help speed repairs, whether for individual homeowners or for the largest of institutions.\\n\\nFire and Water Restoration companies are regulated by the appropriate state\\'s Department of Consumer Affairs – usually the state contractors license board. In California, all Fire and Water Restoration companies must register with the California Contractors State License Board. Presently, the California Contractors State License Board has no specific classification for \"water and fire damage restoration.\"Hence, the Contractor\\'s State License Board requires both an asbestos certification (ASB) as well as a demolition classification (C-21) in order to perform Fire and Water Restoration work.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': 'f5b58f828986cf4114e6b2ef0c35c409'}>,\n",
+       "  <Document: {'content': \"Hence, the Contractor's State License Board requires both an asbestos certification (ASB) as well as a demolition classification (C-21) in order to perform Fire and Water Restoration work.\\n\\n==See also==\\n\\n* Aodh (given name)\\n* Colored fire\\n* Control of fire by early humans\\n* Deflagration\\n* Fire (classical element)\\n* Fire investigation\\n* Fire lookout\\n* Fire lookout tower\\n* Fire making\\n* Fire pit\\n* Fire safety\\n* Fire triangle\\n* Fire whirl\\n* Fire worship\\n* Flame test\\n* Life Safety Code\\n* List of fires\\n* List of light sources\\n* Phlogiston theory\\n* Piano burning\\n* Prometheus, the Greek mythological figure who gave mankind fire\\n* Pyrokinesis\\n* Pyrolysis\\n* Pyromania\\n* Self-immolation\\n* ''The Chemical History of a Candle''\\n\\n==References==\\n\\n==Bibliography==\\n*\\n* Haung, Kai. 2009. Population and Building Factors That Impact Residential Fire Rates in Large U.S. Cities. Applied Research Project. Texas State University.  TXstate.edu\\n*\\n* Kosman, Admiel:  Sacred fire. In: Thu, January 13, 2011.\\n\\n==External links==\\n\\n*  How Fire Works at HowStuffWorks\\n*  What exactly is fire? \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': 'd287f03ce4a38564c5e884be219ef21d'}>,\n",
+       "  <Document: {'content': \"Texas State University.  TXstate.edu\\n*\\n* Kosman, Admiel:  Sacred fire. In: Thu, January 13, 2011.\\n\\n==External links==\\n\\n*  How Fire Works at HowStuffWorks\\n*  What exactly is fire? from The Straight Dope\\n*  On Fire, an Adobe Flash-based science tutorial from the NOVA (TV series)\\n*  20 Things You Didn't Know About... Fire from ''Discover'' magazine\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': '1858abe9d71d119d917fa91e5051ec'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Climb\\'\\'\\'\" is the sixth episode of the third season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 26th episode of the series. Directed by Alik Sakharov and written by David Benioff and D. B. Weiss, it aired on May 5, 2013.\\n\\nThe episode\\'s title comes from climbing of the wall by Jon Snow and Ygritte, and also the references from dialogue between Lord Petyr Baelish and Lord Varys.\\n\\nThe episode marks the final appearance of Esmé Bianco.\\n\\n==Plot==\\n===In King\\'s Landing===\\nMeeting with Olenna, Tywin threatens to appoint Loras to the Kingsguard, thereby renouncing his right to inheritance and marriage and leaving House Tyrell without a male heir, and Olenna consents to Loras and Cersei\\'s marriage.\\n\\nTyrion accuses Cersei of trying to have him killed during the Battle of the Blackwater, but deduces it was Joffrey who ordered his death and Cersei tells him he is not in danger now that Tywin is the Hand. Tyrion informs Sansa that she will not wed Loras, but himself.\\n\\nBaelish tells Varys he has given Ros to Joffrey to kill for his pleasure. Sansa watches tearfully as Baelish departs by ship, losing her chance at leaving the capital.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'f61131d2cd3f7e8f48321941d66d216d'}>,\n",
+       "  <Document: {'content': 'Baelish tells Varys he has given Ros to Joffrey to kill for his pleasure. Sansa watches tearfully as Baelish departs by ship, losing her chance at leaving the capital.\\n\\n===In the Riverlands===\\nArriving at the Brotherhood\\'s hideout, Melisandre is shocked to learn of Beric\\'s six resurrections, and takes Gendry into her custody. Arya unnerves Melisandre, who declares Arya will \"shut many eyes forever\" and they will meet again.\\n\\nAt Riverrun, Robb and his advisors discuss an alliance with Lame Lothar Frey and Black Walder Rivers. Lord Walder Frey\\'s demands include a formal apology from Robb, the castle Harrenhal, and for Edmure to marry one of his daughters.\\n\\n===At Harrenhal===\\nRoose Bolton agrees to send Jaime to King\\'s Landing if Jaime assures Tywin that Bolton had nothing to do with his maiming, but keeps Brienne under arrest for abetting treason.\\n\\n===In the North===\\nBran defuses tensions between Osha and Meera, and Jojen tells Bran his vision of Jon surrounded by enemies.\\n\\nTorturing Theon, the boy threatens to sever his finger if he cannot guess the boy\\'s identity and their location. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '5dad6d33498e41e70474c82df21ad9a9'}>,\n",
+       "  <Document: {'content': \"===In the North===\\nBran defuses tensions between Osha and Meera, and Jojen tells Bran his vision of Jon surrounded by enemies.\\n\\nTorturing Theon, the boy threatens to sever his finger if he cannot guess the boy's identity and their location. After his finger is flayed for several wrong guesses, Theon guesses the boy is a Karstark and he is being tortured at the Karhold for betraying Robb. The boy pretends Theon was correct before continuing to flay his finger, admitting his torture is solely for his amusement. Theon finally begs his torturer to remove his finger.\\n\\n===Beyond the Wall===\\nEn route to the Wall, Sam shows Gilly the dragonglass dagger he found and tells her about Castle Black.\\n\\nYgritte reveals she is aware Jon remains loyal to the Night's Watch, but tells him their loyalty to each other is greater. As Tormund's party climbs the wall, Ygritte inadvertently causes an avalanche that kills some wildlings and leaves her and Jon hanging by their rope. Before Orell cuts the rope, Jon secures himself to the Wall and reaches the top with Ygritte, where they embrace.\\n\\n==Production==\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '8db59acae29596ddfc361782af79b0e3'}>,\n",
+       "  <Document: {'content': 'Before Orell cuts the rope, Jon secures himself to the Wall and reaches the top with Ygritte, where they embrace.\\n\\n==Production==\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"The Climb\" is the sixth episode of the season written by showrunners David Benioff and D. B. Weiss, and 18th overall. It is based upon George R. R. Martin\\'s novel \\'\\'A Storm of Swords\\'\\', namely, chapters 30, 35, 37, and 48 (Jon IV, Catelyn IV, Jaime V, and Samwell III).\\n\\nGilly\\'s baby, appearing in the fourth episode and in \"The Climb\"\\'s first scene, was played by ten months-old Arya Hasson – named after Arya Stark – from the Waterside in Derry.\\n\\n==Reception==\\n\\n===Ratings===\\n\"The Climb\" set a new record for \\'\\'Game of Thrones\\'\\' in ratings, the fourth consecutive episode to establish a new series high. 5.5 million viewers watched the premiere airing, with 1.27 million additional viewers watching the second airing. The episode also set a new series high in viewers aged 18–49, with a rating of 2.9. In the United Kingdom, the episode was seen by 0.926 million viewers on Sky Atlantic, being the channel\\'s highest-rated broadcast that week.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '8438149f1b17e47789c50b2f998fdd5e'}>,\n",
+       "  <Document: {'content': 'In the United Kingdom, the episode was seen by 0.926 million viewers on Sky Atlantic, being the channel\\'s highest-rated broadcast that week.\\n\\n===Critical reception===\\n\"The Climb\" was met with positive reviews from critics. Review aggregator Rotten Tomatoes surveyed 21 reviews of the episode and judged 90% of them to be positive with an average score of 7.9 out of 10. The website\\'s critical consensus reads \"Tywin Lannister and Olenna Tyrell steal the show with their exquisite negotiation as \\'The Climb\\' builds towards the Wildlings\\' death-defying scaling of The Wall.\"Writing for IGN, Matt Fowler gave the episode an 8.8/10, writing \"This week\\'s Game of Thrones started off slow, but then built to a roaring crescendo.\"Two reviews were published by The A.V. Club. David Sims gave the episode a \"B\" rating for people new to the series, while Emily VanDerWerff rated the episode a \"B+\" for people who have read the novels.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8c34f280bc83c66bad0aa4d5ae05e207'}>,\n",
+       "  <Document: {'content': 'David Sims gave the episode a \"B\" rating for people new to the series, while Emily VanDerWerff rated the episode a \"B+\" for people who have read the novels.\\n\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2013\\n\\nHollywood Post Alliance Awards\\n\\nOutstanding Sound – Television\\n\\nPaula Fairfield, Brad Katona, Jed Dodge, Onnalee Blank and Mathew Waters\\n\\n2014\\n\\nVisual Effects Society\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nKirk Brillon, Steve Gordon, Geoff Sayer, Winston Lee\\n\\nOutstanding Created Environment in a Broadcast Program\\n\\nPatrick Zentis, Mayur Patel, Nitin Singh, Tim Alexander\\n\\n== References ==\\n\\n== External links ==\\n\\n*  \"The Climb\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '710be7a1e297c8bfad9146db0ca6bd8d'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Light of the Seven\\'\\'\\'\" is a piece in the HBO\\'s series \\'\\'Game of Thrones\\'\\', the television series adaptation of \\'\\'A Song of Ice and Fire\\'\\' by George R. R. Martin. It first played during the season six finale of the show and was composed by Ramin Djawadi in 2016. The \"Light of the Seven\" is the first time piano is used in the music for \\'\\'Game of Thrones\\'\\'. It was nominated by the International Film Music Critics Association for Film Music Composition of the Year.\\n\\n==Background==\\nIn an interview, Djawadi spoke about \"Light of the Seven\", which largely consisted of piano, something unusual for the series. Djawadi stated, \"The interesting thing to me was the use of the piano. When we started the season showrunners David Benioff and Dan Weiss, and Miguel Sapochnik, the director of the episode, reached out to me and said, \\'There\\'s something coming up in episode 10.\\'We talked about the \\'Light of the Seven\\', and how it needed to be a new piece of music. Any kind of character theme could tip it, and we didn\\'t want to tip the audience. Miguel brought it up: \\'What about the piano?\\'We discussed it. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'bce0334d76f8b513f70e1e07c9a85fc9'}>,\n",
+       "  <Document: {'content': 'Any kind of character theme could tip it, and we didn\\'t want to tip the audience. Miguel brought it up: \\'What about the piano?\\'We discussed it. The piano is not really in the language of the \\'\\'Game of Thrones\\'\\' score.\"Djawadi had tried other instruments, including playing the whole tune with harp, but none of them sounded right, eventually he settled on the colder sound of the piano.\\n\\nDjawadi said: \"It all felt like a perfect fit. What\\'s great about the scene, too, is there\\'s hardly any dialogue. It\\'s nine minutes long. I knew I had to start minimal and give it space. Let notes ring, then give it space, and build up the anticipation from there, without tipping in either direction.\"Djawadi stated that he refrained from using the typical Lannister theme, \"The Rains of Castamere\", in order to create more of a mystery. The piece also featured vocals by two young boys singing in unison, and Djawadi instructed the boys to sing in such a way that although it is \"not out of tune, but you get that feeling of Something’s wrong.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '977c3b65c1be94ddf8fa65b0bce87690'}>,\n",
+       "  <Document: {'content': 'The piece also featured vocals by two young boys singing in unison, and Djawadi instructed the boys to sing in such a way that although it is \"not out of tune, but you get that feeling of Something’s wrong.\"Djawadi describing how he pieced all of the separate pieces of the music together by saying \"The boys I recorded completely separate. The strings I recorded all together. Even the solo instruments, I recorded them separately — the solo violins and solo cellists were recorded separately. The piano, I played. And the organ as well.\"In another interview, Djawadi talked about the process, saying, \"That was the big guidance for me, in how I wanted to build this piece, It\\'s a different instrument, and I put it in an upper register, but the idea is that it\\'s building something that stays the same but changes over time. Of course, now that I say that, people might be like, \\'No, it\\'s not really that.\\'And it\\'s not staying true to the form. Obviously the picture is guiding me, so I have to pull back and break away from it. I couldn\\'t keep it as a passacaglia all the way through. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'de1125d197f81839dbd845901bea7414'}>,\n",
+       "  <Document: {'content': 'Obviously the picture is guiding me, so I have to pull back and break away from it. I couldn\\'t keep it as a passacaglia all the way through. But there are definitely moments where it defaults to that.\"==Composition==\\n\"Light of the Seven\" has a duration of approximately ten minutes. When Djawadi originally composed the piece, he intended at first to make it a passacaglia. The instrumentation of \"Light of the Seven\" consists of piano, organ, strings and two boy soloists. On why he decided to use two young soloists instead of a full choir, Djawadi said, \"I felt that two of them were more haunting than using a full choir, because it\\'s a smaller environment, when they\\'re running around in the catacombs.\"==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* Ramin Djawadi – composer, primary artist, producer\\n\\n* David Benioff – liner notes\\n* D.B. Weiss – liner notes\\n\\n==Live performances==\\nDjawadi performing \"Light of the Seven\" at the \\'\\'Game of Thrones Live Concert Experience\\'\\'.\\nDjawadi has performed the piece with a live orchestra at the Game of Thrones Live Concert Experience, which consisted of 24 dates in cities across the United States and Canada.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '988fc448e24d85bd87dcf5ea3572e82b'}>,\n",
+       "  <Document: {'content': 'Djawadi has performed the piece with a live orchestra at the Game of Thrones Live Concert Experience, which consisted of 24 dates in cities across the United States and Canada.\\n\\n==Reception==\\nThe piece received universal praise from critics and fans, with Lili Loofbourow of \\'\\'The Week\\'\\' calling it the \"real winner\" of the season finale. Djawadi responded to the universal praise the composition got, saying, \"I never would have thought that would happen, It\\'s so exciting because it\\'s such a special finale.\"==Aftermath==\\nA remixed version of \"Light of the Seven\" was used in a season 7 trailer. The final motif from this piece is used in the season 6 track \"Hear Me Roar\", and is also used from season 7 onwards in tracks involving Cersei Lannister and her machinations, such as \"The Long Farewell\" or \"No One Walks Away from Me\", acting as an alternate theme to \"The Rains of Castamere\" for her character.\\n\\n==Charts==\\n\\nChart (2016)\\n\\nPeakposition\\n\\n==Accolades==\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nInternational Film Music Critics Association\\n\\nFilm Music Composition of the Year\\n\\nRamin Djawadi\\n\\n==See also==\\n*\\'\\'Game of Thrones\\'\\' Theme\\n*Music of Game of Thrones\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'd6b637002ac38897140234e82a4f7144'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''Varys''' is a fictional character in the ''A Song of Ice and Fire'' series of fantasy novels by American author George R. R. Martin, and its television adaptation ''Game of Thrones''.\\n\\nIntroduced in 1996's ''A Game of Thrones'', Varys is a former slave eunuch from the fictional city of Lys and the master of whisperers in King's Landing. He subsequently appeared in Martin's books ''A Clash of Kings'' (1998), ''A Storm of Swords'' (2000), and ''A Dance with Dragons'' (2011). He proves to be a key ally to Ned Stark and Tyrion Lannister at court, but his true motives remain shrouded in mystery to those who employ his services.\\n\\nVarys is portrayed by Conleth Hill in the HBO television adaptation.\\n\\n==Character overview==\\nVarys is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, such as Ned Stark, Tyrion and Cersei Lannister.\\n\\n===Background===\\nVarys was born as a slave in the Free City of Lys and joined a travelling acting troupe. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '76e3a8a5c2d1ad872842f8301b933800'}>,\n",
+       "  <Document: {'content': \"==Character overview==\\nVarys is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, such as Ned Stark, Tyrion and Cersei Lannister.\\n\\n===Background===\\nVarys was born as a slave in the Free City of Lys and joined a travelling acting troupe. While the troupe was performing in Myr, a sorcerer bought the young Varys from the troupe's leader, drugged Varys before removing his genitals and burning them in a brazier in a blood magic ritual, and afterwards cast him into the streets. Varys turned to begging, prostitution, and ultimately theft to survive, but soon became known in Myr and fled to Pentos. There he befriended a poor sellsword, Illyrio Mopatis, with whom he teamed up to steal valuables from other thieves and return them to their owners for a fee. Varys eventually realised that there was more to gain from stealing secrets instead of valuables and trained his spies to copy information from the wealthy and powerful. Varys and Illyrio became rich, and Varys' reputation reached the ears of the King of Westeros, Aerys II Targaryen, who appointed Varys as his Master of Whisperers. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '411f6808d892a1aa0739081093e19a16'}>,\n",
+       "  <Document: {'content': \"Varys and Illyrio became rich, and Varys' reputation reached the ears of the King of Westeros, Aerys II Targaryen, who appointed Varys as his Master of Whisperers. Jaime claims that Aerys saw traitors everywhere, and Varys was quick to point out any he missed. Apparently, when Rhaegar Targaryen intended to use a Tourney at Harrenhal to call a Great Council to deal with his father's instability, and possibly dethrone him, Varys warned Aerys, and they attended the tourney. Sometime prior to the Sack of King's Landing, Varys had Rhaegar's infant son Aegon swapped with a lowborn baby and smuggled to Essos to be raised in hiding by Rhaegar's friend Jon Connington, who was exiled by the Mad King for failing to defeat Robert. Varys remains Master of Whisperers after Robert Baratheon seizes the Iron Throne but secretly remains loyal to House Targaryen.\\n\\n===Description===\\nVarys, also called the Spider, is a eunuch and courtier who serves as Master of Whisperers, the spymaster for the king of the Seven Kingdoms at the royal court in King's Landing. As Master of Whisperers he is on the privy council. He is feared by nobles and common people alike. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '1d268e3eb3193136f5b7305e76bb660a'}>,\n",
+       "  <Document: {'content': '===Description===\\nVarys, also called the Spider, is a eunuch and courtier who serves as Master of Whisperers, the spymaster for the king of the Seven Kingdoms at the royal court in King\\'s Landing. As Master of Whisperers he is on the privy council. He is feared by nobles and common people alike. He knows all of the secret passages in the royal castle, and his spies are found everywhere. He is a skilled manipulator and commands a network of informants across two continents. He often puts on the public persona of being nothing more than a bald, pudgy man well suited to the pleasantries of court life; humble, obsequious, fawning, and a little effeminate. This is simply a facade that Varys has developed, which often leads those who do not know him well to underestimate him as a cheerful and vapid flatterer. In reality he is a cunning and ruthless manipulator of court politics, on par with Master of Coin, Petyr Baelish (\"Littlefinger\"), with whom he frequently spars. Unlike Littlefinger, Varys insists that his goals are to achieve what he honestly feels is best for the realm.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'bf1b376c53d6df00400a93d9f9279f1c'}>,\n",
+       "  <Document: {'content': 'In reality he is a cunning and ruthless manipulator of court politics, on par with Master of Coin, Petyr Baelish (\"Littlefinger\"), with whom he frequently spars. Unlike Littlefinger, Varys insists that his goals are to achieve what he honestly feels is best for the realm.\\n\\n==Storylines==\\n\\n===In the books===\\n====\\'\\'A Game of Thrones\\'\\'====\\nSoon after Eddard Stark\\'s arrival in King\\'s Landing, Varys warns him that Robert\\'s wife Cersei Lannister intends to kill Robert and offers his help to Eddard in investigating the Lannisters. He later meets in secret with Illyrio to discuss stalling a war between Houses Stark and Lannister until the right moment; this is overheard by Arya Stark, but she is unable to identify the men. When Robert is ultimately killed, Varys remains spymaster for his heir Joffrey and suggests that Ser Barristan Selmy be blamed for Robert\\'s death. Selmy is removed from the Kingsguard and ultimately defects to Daenerys Targaryen, which may have been Varys\\' plan all along. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8d8cf01e8087566f2ab9489074319837'}>,\n",
+       "  <Document: {'content': \"Selmy is removed from the Kingsguard and ultimately defects to Daenerys Targaryen, which may have been Varys' plan all along. Varys visits Eddard in captivity and convinces him to plead guilty to treason and join the Night's Watch in order to save the life of his daughters Sansa and Arya, which would also avert war between the Starks and Lannisters, and Ned eventually agrees. Varys is horrified, however, when Joffrey has Ned executed anyway and tries in vain to dissuade the King whilst Littlefinger smugly looks on. In the aftermath, Varys arranges for Robert's bastard Gendry to join the Night's Watch, to avoid being killed by Cersei.\\n\\n====''A Clash of Kings''====\\nVarys is the first to learn that Tyrion Lannister has brought his mistress Shae with him to King's Landing, and tells Tyrion of a route that he can use to visit her. He forms an uneasy alliance with Tyrion to share the information gathered by his spy network.\\n\\n====''A Storm of Swords''====\\nVarys continues to facilitate Tyrion and Shae's trysts, but testifies against Tyrion when Tyrion is tried for Joffrey's murder. After Tyrion is sentenced to death, Jaime Lannister forces Varys to help Tyrion escape. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '5d6ce185e969b6b8637750c5c0712955'}>,\n",
+       "  <Document: {'content': \"====''A Storm of Swords''====\\nVarys continues to facilitate Tyrion and Shae's trysts, but testifies against Tyrion when Tyrion is tried for Joffrey's murder. After Tyrion is sentenced to death, Jaime Lannister forces Varys to help Tyrion escape. As Varys and Tyrion escape through the secret passages of the Red Keep, Tyrion decides to seek out his father Tywin's chambers. Varys protests but gives Tyrion the exact directions to Tywin's room. It is likely Varys took advantage of the situation to have Tywin killed, weakening a chief rival of House Targaryen's.\\n\\n====''A Feast for Crows''====\\nAfter helping Tyrion escape, Varys disappears. A Red Keep turnkey, Rugen, disappears at the same time, and a Tyrell coin is found in his chambers. This cements Cersei's distrust of House Tyrell, although she is unaware Rugen is merely a disguise of Varys'.\\n\\n====''A Dance with Dragons''====\\nVarys sneaks into the Red Keep to kill Grand Maester Pycelle. He also lures Lord Regent Kevan Lannister into Pycelle's chambers and mortally wounds him. As Kevan dies, Varys explains that his death was necessary to destabilise the Seven Kingdoms in preparation for Aegon Targaryen's invasion, before ordering his little birds to finish Kevan off.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '99fa8267c79a8eb3d9b0fd75df84102f'}>,\n",
+       "  <Document: {'content': 'As Kevan dies, Varys explains that his death was necessary to destabilise the Seven Kingdoms in preparation for Aegon Targaryen\\'s invasion, before ordering his little birds to finish Kevan off.\\n\\n===In the show===\\nConleth Hill plays the role of Varys in the television series\\n\\nCoat of arms of House Targaryen\\n\\n==== Season 1 & 2 ====\\nVarys\\' storyline remains, for the most part, identical between the first two seasons of the show and the books.\\n\\n====Season 3====\\nAfter Littlefinger\\'s confidante Ros is severely beaten by Joffrey\\'s guards and Littlefinger fails to intervene, Varys takes Ros into his service as a spy. Littlefinger finds out and sells her to the sadist Joffrey, taunting the eunuch that Varys could not protect her. Varys affirms his course as a means to prevent chaos befalling the realm, claiming that Littlefinger \"would see the Seven Kingdoms burn, if he could be king over the ashes.\"====Season 4====\\nVarys informs Tyrion that Cersei has discovered his relationship with Shae. Although Varys claims he will not lie for him, he implores Tyrion to send Shae away for her own safety. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '87353708e5785390cf802029dfad3b71'}>,\n",
+       "  <Document: {'content': \"Although Varys claims he will not lie for him, he implores Tyrion to send Shae away for her own safety. Varys later testifies against Tyrion at his trial for murdering Joffrey, but when Tyrion is convicted, Jaime enlists Varys' help in smuggling Tyrion out of King's Landing to Essos. As Varys prepares to return to the Red Keep, he hears the tolling bells, making him realise that Tyrion's escape has been discovered (along with his murder of Tywin), and he joins Tyrion on the voyage to Essos\\n\\n====Season 5====\\nVarys and Tyrion arrive in Pentos, where Varys reveals his allegiance to House Targaryen and convinces Tyrion to travel with him to Meereen and aid Daenerys Targaryen in retaking the Iron Throne. While in Volantis, Tyrion is kidnapped by Daenerys' former advisor Jorah Mormont. Varys journeys on to Meereen, where he arrives to discover Tyrion in control of the city in Daenerys' absence. He offers Tyrion the use of his spy network to maintain order in the city.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '30347b7d7d68b0b2fc8b53d47ec89904'}>,\n",
+       "  <Document: {'content': \"Varys journeys on to Meereen, where he arrives to discover Tyrion in control of the city in Daenerys' absence. He offers Tyrion the use of his spy network to maintain order in the city.\\n\\n====Season 6====\\nVarys discovers that the Meereenese insurgency, the Sons of the Harpy, are funded by the masters of Yunkai and Astapor and the slavers of Volantis, and brokers a meeting between Tyrion and representatives of those cities. The slavers agree to a truce with Tyrion, and Meereen begins to prosper. Varys departs for the Seven Kingdoms, telling Tyrion that he will seek out allies for Daenerys. His ultimate destination is Dorne, where Ellaria Sand has killed Doran Martell in anger at his inaction against the Lannisters and seized power. There he meets and forms alliances with Ellaria and Olenna Tyrell, who also seeks vengeance against Cersei, as Cersei's machinations have caused the death of all the other Tyrells. Varys returns to Meereen with ships from Dorne and the Reach, and sets sail for Westeros with Daenerys and her army.\\n\\n====Season 7====\\n\\nVarys, along with Daenerys and her entire army, reaches Dragonstone. While in Dragonstone, Varys helps in planning the attack against Cersei. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '3cc2a29968936f397cab201d9ca5bacf'}>,\n",
+       "  <Document: {'content': \"====Season 7====\\n\\nVarys, along with Daenerys and her entire army, reaches Dragonstone. While in Dragonstone, Varys helps in planning the attack against Cersei. Daenerys thanks him for getting the support of The Reach and Dorne, but confronts Varys for his past allegiances. Varys defends himself by saying that he doesn't serve any King/Queen but serves the people. Daenerys forgives him but warns Varys not to betray her, or else she would burn him alive. Varys then tells Daenerys of Euron Greyjoy's attack as well as defeat of Highgarden against the Lannister army. While talking with Tyrion, Varys criticizes Daenerys for burning Randyll and Dickon Tarly alive. He also reads the raven sent from Winterfell before informing Jon Snow. Varys accompanies Daenerys, Jon, and others to King's Landing to discuss a truce with Cersei.\\n\\n====Season 8====\\nDuring the White Walkers' attack on Winterfell, Varys shelters in the crypts with the other non-combatants, and survives the Night King's reanimation of those buried in the crypts. In the aftermath of the battle, Varys observes Daenerys' dismay at not being lauded by the Northerners. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '1fc0a3bf324a7e9eeb58bd6cc38c3239'}>,\n",
+       "  <Document: {'content': \"====Season 8====\\nDuring the White Walkers' attack on Winterfell, Varys shelters in the crypts with the other non-combatants, and survives the Night King's reanimation of those buried in the crypts. In the aftermath of the battle, Varys observes Daenerys' dismay at not being lauded by the Northerners. He accompanies her court back to Dragonstone to prepare to attack King's Landing, and is told by Tyrion that Jon Snow is secretly Rhaegar's son and thus the rightful heir to the Iron Throne. The fleet is ambushed by Euron Greyjoy's Iron Fleet and Missandei is captured and executed. After failing to convince Tyrion to help him install Jon as king, or (it is implied) to poison Daenerys, Varys openly approaches Jon and suggests that he become king. Jon rebuffs him, and Tyrion reveals Varys' betrayal to Daenerys. Daenerys has Varys brought before her and has Drogon burn him alive.\\n\\n==TV adaptation==\\nVarys is played by the Northern Irish actor Conleth Hill in the television adaption of the series of books. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '3b61736919a1a97f3e44aebadba35979'}>,\n",
+       "  <Document: {'content': '==TV adaptation==\\nVarys is played by the Northern Irish actor Conleth Hill in the television adaption of the series of books. Author George R. R. Martin wrote about the casting of Hill as Varys, \"Hill, like Varys, is quite a chameleon, an actor who truly disappears inside the characters he portrays, more than capable not only of bringing the slimy, simpering eunuch to life.\"===Recognition and awards===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nResult\\n\\n2011\\n\\nScream Awards\\n\\nBest Ensemble\\n\\n2012\\n\\nScreen Actors Guild Award\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\n2015\\n\\nScreen Actors Guild Award\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nEmpire Award\\n\\nEmpire Hero Award (Ensemble)\\n\\n2017\\n\\nScreen Actors Guild Award\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '5289aa061bbd9270e6d91201b4fd6469'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''Stannis Baratheon''' is a fictional character in the ''A Song of Ice and Fire'' series of epic fantasy novels by American author George R. R. Martin, and its television adaptation ''Game of Thrones''. He is the second son of Steffon Baratheon, the lord of Storm's End, and his wife Lady Cassana Estermont, and brother to King Robert and Renly. He is the Lord of Dragonstone and a claimant to the Iron Throne after his elder brother's death. Stannis's goals are frequently impeded by his lack of manpower and resources, owing to his unpopularity with other noble houses. He must therefore rely on the counsel of the foreign priestess Melisandre and his right-hand man, lowborn smuggler Davos Seaworth, who later becomes his Hand of the King. Stannis often struggles to escape the shadow of his two more overtly charismatic brothers, particularly Robert.\\n\\nThough first mentioned in 1996's ''A Game of Thrones'', Stannis formally appeared in ''A Clash of Kings'' (1998), ''A Storm of Swords'' (2000) and ''A Dance with Dragons'' (2011).  In December 2011, Martin posted a sample chapter from the yet-unfinished ''The Winds of Winter'', told from Theon Greyjoy's viewpoint, which confirmed Stannis' return in the sixth book.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '6d51432d5a55c90a7bb92fdb5215be75'}>,\n",
+       "  <Document: {'content': \"In December 2011, Martin posted a sample chapter from the yet-unfinished ''The Winds of Winter'', told from Theon Greyjoy's viewpoint, which confirmed Stannis' return in the sixth book.\\n\\nStannis is portrayed by English actor Stephen Dillane in the HBO television adaptation of the series, who has received significant critical praise for his performance. Stannis is a divisive character among fans of the books and television show alike, enjoying both great popularity for his dedication to justice and dark horse status, as well as opposition for his unrelenting attitude towards the Iron Throne. He has earned particular attention for the differences in his characterization between the novels and show, particularly during season five.\\n\\n== Character description ==\\nStannis Baratheon is the younger brother of King Robert and older brother of Renly. He is portrayed as a brooding and humorless man with a harsh but fair sense of duty and justice whose dour demeanor is often off-putting in comparison to his two more charismatic brothers. He is renowned as a skilled and prescient military commander. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '522d9ecfddc59c4ac7b7c858ea6bbea'}>,\n",
+       "  <Document: {'content': \"He is portrayed as a brooding and humorless man with a harsh but fair sense of duty and justice whose dour demeanor is often off-putting in comparison to his two more charismatic brothers. He is renowned as a skilled and prescient military commander. Although he is initially said to be extremely stubborn and inflexible, in later books he has shown some ability to use diplomacy and deception to achieve his goals.\\n\\nStannis is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, predominantly Davos Seaworth and Jon Snow, and later Asha and Theon Greyjoy.\\n\\n===Background===\\nStannis was born as the second of Steffon Baratheon and Cassana Estermont's three sons, and in his youth he was constantly overshadowed by his older brother Robert.  When Stannis was fourteen he witnessed his parents' death in a shipwreck off the coast of House Baratheon's castle of Storm's End, and subsequently lost his faith in the Seven Gods.\\n\\nDuring Robert's Rebellion, a teenage Stannis holds Storm's End in Robert's absence, but is besieged by Mace Tyrell and Paxter Redwyne for the best part of the year. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '742aad591829819999c419a8245199ad'}>,\n",
+       "  <Document: {'content': \"During Robert's Rebellion, a teenage Stannis holds Storm's End in Robert's absence, but is besieged by Mace Tyrell and Paxter Redwyne for the best part of the year. Stannis' garrison only avoids starvation thanks to the smuggler Davos Seaworth, who evades the blockade by the Redwyne fleet to bring the Baratheon soldiers a cargo of onions and fish.  When Eddard Stark arrives to lift the siege, Stannis knights Davos as reward for his aid, but also insists that Davos have four fingers removed as punishment for his years of illegal smuggling; Davos agrees, on the condition that Stannis removes the fingers himself.  Stannis is subsequently tasked with building a new royal fleet to assault Dragonstone and capture the island, but arrives to find that Aerys II Targaryen's children Viserys and the newborn Daenerys had fled. Robert names Stannis Lord of Dragonstone, giving him control over the islands of Blackwater Bay and the nearby peninsula of Massey's Hook - but Stannis feels slighted, as their younger brother Renly is named Lord of Storm's End, giving him control over the entirety of the Stormlands. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '5d9fde6af2fb69b7ad9e773552ad355f'}>,\n",
+       "  <Document: {'content': \"Robert names Stannis Lord of Dragonstone, giving him control over the islands of Blackwater Bay and the nearby peninsula of Massey's Hook - but Stannis feels slighted, as their younger brother Renly is named Lord of Storm's End, giving him control over the entirety of the Stormlands. It is suggested that this was because Robert was following the custom of granting Dragonstone to the king's heir, but Stannis perceives it as an insult.\\n\\nSeveral years later, Stannis is married to Lady Selyse Florent, but Robert soiled their wedding bed by deflowering Selyse's cousin Delena, further insulting Stannis. Stannis and Selyse have a single daughter together, Shireen, who nearly dies in infancy after contracting greyscale and is left disfigured.  Stannis, who is named Robert's Master of Ships, destroys the Iron Fleet under the command of Victarion Greyjoy at Fair Isle during the Greyjoy Rebellion and leads the conquest of Great Wyk, the largest of the Iron Islands, adding to his significant military resume.\\n\\nAt the beginning of the first novel ''A Game of Thrones'', Stannis comes to suspect that Robert's children with Cersei Lannister are not actually his, and reveals his suspicions to Robert's Hand of the King, Lord Jon Arryn. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '4d623cbd3c5c071b1a0c43b0099464a4'}>,\n",
+       "  <Document: {'content': 'At the beginning of the first novel \\'\\'A Game of Thrones\\'\\', Stannis comes to suspect that Robert\\'s children with Cersei Lannister are not actually his, and reveals his suspicions to Robert\\'s Hand of the King, Lord Jon Arryn. The two discover several of Robert\\'s bastard children in King\\'s Landing, and begin to gather proof, before Jon\\'s sudden, untimely death.\\n\\n===Appearance and personality===\\nStannis is frequently described as a large and sinewy man that towers over others, such as Davos Seaworth and Jon Snow, a Baratheon trait. He lacks the long black hair of his brothers, and is instead balding, although he keeps a close-cropped beard of the signature Baratheon black. His face is described as \\'tight like cured leather\\' with hollow, gaunt cheeks. His eyes are described by Asha Greyjoy as \\'deep, sunken pits\\', with a powerful stare that suggests an \\'iron ferocity\\'.\\n\\nStannis\\' most prominent characteristics are his flinty and austere demeanor, unrelenting stubbornness, and powerful sense of duty and justice, the latter of which he is notorious for throughout Westeros. He rarely forgives a slight; Jon Snow once comments that \"Stannis Baratheon with a grievance was like a mastiff with a bone; he gnawed it down to splinters.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '3e2e88445d30b30cae600d21579699cf'}>,\n",
+       "  <Document: {'content': 'He rarely forgives a slight; Jon Snow once comments that \"Stannis Baratheon with a grievance was like a mastiff with a bone; he gnawed it down to splinters.\"His signature tic is grinding his teeth, often in the face of unpleasant or unforeseen circumstances. He is a renowned commander, sailor, and warrior, although he is a better tactician than fighter. Stannis is known for his brusqueness and lack of tact in social situations and finds himself uncomfortable around women, including his own wife. He abhors brothels and once tried to have them banned from King\\'s Landing, which made him unpopular with the smallfolk there. He dresses plainly in dark clothing and is rarely seen without his sword and dagger. Stannis is plagued with bitterness at the lack of respect and affection he has been shown by his older brother King Robert, and even in childhood was described by the Baratheon family maester as \"the most unloved of the three\"; a \"solemn and joyless\" child \"mature beyond his years\". Stannis is an atheist, despite claims that he is being manipulated by the red priestess Melisandre.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'add8f357b6a5fc72e2daa111fc8c72ef'}>,\n",
+       "  <Document: {'content': 'Stannis is plagued with bitterness at the lack of respect and affection he has been shown by his older brother King Robert, and even in childhood was described by the Baratheon family maester as \"the most unloved of the three\"; a \"solemn and joyless\" child \"mature beyond his years\". Stannis is an atheist, despite claims that he is being manipulated by the red priestess Melisandre.\\n\\nAlthough Stannis suffers from a lack of support and resources during the War of Five Kings, he is frequently described by his adversaries, including Tywin Lannister, as the most dangerous rival claimant. Above all he is characterized by his fearless and uncompromising pursuit of justice and duty - he is described by Varys in this way: \"His claim is the true one, he is known for his prowess as a battle commander, and he is utterly without mercy. There is no creature on earth half so terrifying as a truly just man.\"== Storylines ==\\nPersonal coat of arms of Stannis Baratheon\\n\\n==== \\'\\'A Game of Thrones\\'\\' ====\\n\\nWhen Robert travels to Winterfell to name Eddard as his new Hand, Stannis flees to Dragonstone with his forces, including most of the royal navy. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '87fa3ebb96aeb4fa258c954ca8796fab'}>,\n",
+       "  <Document: {'content': \"== Storylines ==\\nPersonal coat of arms of Stannis Baratheon\\n\\n==== ''A Game of Thrones'' ====\\n\\nWhen Robert travels to Winterfell to name Eddard as his new Hand, Stannis flees to Dragonstone with his forces, including most of the royal navy. Stannis suspects Cersei's involvement in Jon's death. It is later revealed that the true culprit was Jon's wife Lysa, who was persuaded to poison her husband by her lover, Petyr Baelish. Eddard continues Stannis and Jon's investigations, and discovers that Cersei's children are the products of her incestuous affair with her twin brother Jaime Lannister. After Robert's death, Eddard tries to replace Robert's presumed heir Joffrey Baratheon with Stannis, but loses the political struggle against the Lannisters and is executed.\\n\\n==== ''A Clash of Kings'' ====\\n\\nStannis comes under the influence of the red priestess Melisandre, who believes that Stannis is the reincarnation of Azor Ahai, a messianic figure in her faith. Stannis declares himself the one true King; however, most of the Baratheon bannermen support the claim of his younger and much more charismatic brother, Renly, as does the powerful House Tyrell due to Renly's marriage to Margaery Tyrell. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'b282f0ffcf4a1ac92ff13bf79afe67df'}>,\n",
+       "  <Document: {'content': \"Stannis declares himself the one true King; however, most of the Baratheon bannermen support the claim of his younger and much more charismatic brother, Renly, as does the powerful House Tyrell due to Renly's marriage to Margaery Tyrell. Stannis tries to negotiate with Renly, offering him the chance to become his heir, which offer Renly rejects, planning to have Stannis killed the next day. Renly is subsequently assassinated by a shadow conjured by Melisandre using Stannis' life force, and many of Renly's bannermen immediately swear allegiance to Stannis, although the Tyrells side with the Lannisters due to their offer to have Margaery marry Joffrey.\\n\\nStannis assaults King's Landing by sea, but many of his men are killed when Tyrion Lannister detonates wildfire in the River Blackwater. Stannis' forces still nearly claim victory, but Lannister and Tyrell reinforcements arrive in time to drive Stannis' men away from King's Landing. However Rolland Storm, the Bastard of Nightsong, commands his forces well enough that Stannis is able to escape.\\n\\n==== ''A Storm of Swords'' ====\\n\\nStannis retreats to Dragonstone with what is left of his army. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '4dc3c67e373d9665ac02b922422add50'}>,\n",
+       "  <Document: {'content': \"However Rolland Storm, the Bastard of Nightsong, commands his forces well enough that Stannis is able to escape.\\n\\n==== ''A Storm of Swords'' ====\\n\\nStannis retreats to Dragonstone with what is left of his army. He imprisons his Hand Lord Alester Florent, one of his wife's uncles, for trying to offer terms of surrender to the Lannisters. Another of Selyses' uncles, Ser Axell Florent, castellan of Dragonstone, aims to become Stannis' Hand of the King. He suggests a plan to attack Claw Isle in retaliation for Lord Celtigar's bending the knee to Joffrey, but Davos derides an attack on this defenseless island as evil. For this honest counsel Stannis names Davos as his new Hand. Melisandre tells Stannis to burn Edric Storm, one of Robert's bastards, as a sacrifice to raise dragons from Dragonstone. Stannis is conflicted about burning his nephew but uses Edric's blood collected by three leeches to curse the rival kings: Balon Greyjoy, Joffrey Baratheon, and Robb Stark. After Joffrey's, Balon's, and Robb's deaths seem to prove the power of king's blood, Stannis considers sacrificing Edric. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '198ec749acf329d298b8000dbd824c7a'}>,\n",
+       "  <Document: {'content': \"Stannis is conflicted about burning his nephew but uses Edric's blood collected by three leeches to curse the rival kings: Balon Greyjoy, Joffrey Baratheon, and Robb Stark. After Joffrey's, Balon's, and Robb's deaths seem to prove the power of king's blood, Stannis considers sacrificing Edric. Davos smuggles Edric off Dragonstone to the Free Cities, and then persuades Stannis to sail to the North and save the Night's Watch from the wildling army. After Melisandre burns Alester Florent as a sacrifice to R'hllor to ensure favorable winds, Stannis takes most of his remaining army and sails north to crush the wildling assault, managing to rout the whole host with few casualties. He remains at the Night's Watch headquarters at Castle Black to negotiate a settlement with the wildlings, and offers to legitimise Eddard's bastard Jon Snow if Jon will fight with Stannis' army. Jon, who has been elected Lord Commander of the Night's Watch, declines, as he has made an oath binding him to serve the Night's Watch.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '88b34d3211ff38e893eee061feee413b'}>,\n",
+       "  <Document: {'content': \"Jon, who has been elected Lord Commander of the Night's Watch, declines, as he has made an oath binding him to serve the Night's Watch.\\n\\n==== ''A Feast for Crows'' and ''A Dance with Dragons'' ====\\n\\nStannis sends ravens to the Northern houses naming himself as king and asking for their support, but only House Karstark and a faction of House Umber swear allegiance. Stannis has the King-beyond-the-Wall Mance Rayder burned at the stake, and most of the wildlings bend the knee to Stannis, although Stannis is unaware that Melisandre had used glamours to switch Rayder and his lieutenant, the Lord of Bones. To rally the North, Stannis intends to attack House Bolton's castle The Dreadfort with the help of Arnolf Karstark, castellan of Karhold. In reality Arnolf is working with the Boltons, hoping to entrap Stannis and have his hostage great-nephew Harrion Karstark executed so his branch of the family can take Karhold. On Jon's advice, Stannis instead rallies the northern mountain clans and attacks Deepwood Motte, ousting the Ironborn holding the castle and taking Asha Greyjoy captive. He restores the castle to House Glover, thus winning their support and that of House Mormont. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '6ec701757df6488227eefd4d3f2187b7'}>,\n",
+       "  <Document: {'content': \"On Jon's advice, Stannis instead rallies the northern mountain clans and attacks Deepwood Motte, ousting the Ironborn holding the castle and taking Asha Greyjoy captive. He restores the castle to House Glover, thus winning their support and that of House Mormont. He marches on Winterfell to confront the Boltons, joined by the men of Arnolf Karstark and Mors Umber (one of the castellans of the Last Hearth), but his host is waylaid by heavy snows and forced to stop at a crofter's village three days away from Winterfell. A letter is later sent to Jon Snow by Ramsay Bolton claiming that Stannis has been defeated and killed, though it is unknown how much of the letter is true. George R. R. Martin confirmed to a fan in 2015 that Stannis is still alive in the books.\\n\\n==== ''The Winds of Winter'' ====\\n\\nIn December 2011, Martin posted a sample chapter from the yet-unfinished sixth book ''The Winds of Winter'', told from Theon Greyjoy's viewpoint, where Stannis is actively and efficiently preparing for the looming battle against the Bolton-Frey-Karstark alliance.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'fdc35f52b4166a28b0ba6879636940b6'}>,\n",
+       "  <Document: {'content': '==== \\'\\'The Winds of Winter\\'\\' ====\\n\\nIn December 2011, Martin posted a sample chapter from the yet-unfinished sixth book \\'\\'The Winds of Winter\\'\\', told from Theon Greyjoy\\'s viewpoint, where Stannis is actively and efficiently preparing for the looming battle against the Bolton-Frey-Karstark alliance.  He secures a loan by signing a blood contract with the Braavosi banker Tycho Nestoris, whose convoy managed to find Stannis\\' army, and plans to immediately send Tycho back to the Wall for safety.  Tycho also brought a message from Jon Snow informing Stannis of Arnolf Karstark\\'s planned treachery, after which Stannis arrested Arnolf, his son Arthor and his three grandsons and plans to execute them. He also subdued Tybald, the Dreadfort maester, who out of fear confesses that he has already revealed Stannis\\'s position to the Boltons in Winterfell.\\n\\nStannis then sends Ser Justin Massey to escort Tycho and the escaped \"Arya Stark\" back to Castle Black to reunite her with Jon Snow, in gratitude for Jon\\'s counsel of gathering the northern mountain clans. Massey is to then travel to Braavos and use a loan from the Iron Bank to hire twenty thousand sellswords for Stannis.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'c57e1237f2969da02215bcc017da0da4'}>,\n",
+       "  <Document: {'content': \"Massey is to then travel to Braavos and use a loan from the Iron Bank to hire twenty thousand sellswords for Stannis.  Stannis tells Massey that he may hear of Stannis's death in Braavos, but even if that news is true, Massey is to follow the orders as to seat his daughter Shireen on the Iron Throne.\\n\\nStannis then interrogates Theon for information regarding the Boltons' military strength. When Theon taunts Stannis for not taking Ramsay seriously, Stannis confidently reveals that he already has a battle plan to utilize the terrain against the incoming Frey army. Stannis next receives Asha Greyjoy, and informs her that her brother Theon must be executed, as showing him mercy would mean losing all of the northmen. To spare Theon the agony of death by burning, Asha pleas Stannis to personally execute Theon by beheading in the tradition of the northmen.\\n\\n=== Family tree of House Baratheon ===\\n\\n== TV adaptation ==\\nStephen Dillane plays the role of Stannis Baratheon in the television series.\\nStannis Baratheon is played by Stephen Dillane in the television adaption of the series of books.\\n\\n==== Season 2 ====\\nStannis' personal banner in the TV series.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '9cd3810dd4d90563c9160220b7f44ee1'}>,\n",
+       "  <Document: {'content': \"Stannis Baratheon is played by Stephen Dillane in the television adaption of the series of books.\\n\\n==== Season 2 ====\\nStannis' personal banner in the TV series.\\nStannis falls under the influence of Melisandre, a priestess of R'hllor who believes Stannis is the reincarnation of a legendary hero from her religion. After Robert's death, Stannis claims himself the true heir to the Iron Throne as Cersei's children are bastards born of incest. However, most of the Baratheon bannermen support the claim of the younger but far more charismatic Renly. Stannis confronts Renly and offers to make him his heir if he supports him, but Renly refuses, only using the negotiations as an opportunity to mock his brother, and intending to kill Stannis in battle the next day. Melisandre, who had seduced Stannis, gives birth to a shadow baby bearing Stannis' face that kills Renly, and many of Renly's bannermen immediately swear allegiance to Stannis. He then attacks King's Landing by sailing up Blackwater Bay. Stannis' force breaches the walls, but due to Tyrion Lannister's use of wildfire and the arrival of last-minute Lannister and Tyrell reinforcements, he is defeated. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '74ce08c3bc3be3722a97543574435753'}>,\n",
+       "  <Document: {'content': \"Stannis' force breaches the walls, but due to Tyrion Lannister's use of wildfire and the arrival of last-minute Lannister and Tyrell reinforcements, he is defeated. Nonetheless, he is convinced to continue fighting by Melisandre, and is further convinced by her magic as she shows him a vision of a battle in the flames.\\n\\n==== Season 3 ====\\nStannis acquires one of Robert's bastards, Gendry, from the Brotherhood Without Banners and plans to sacrifice him to further his quest for the Iron Throne. After Davos Seaworth questions his course of action, Stannis has three leeches drawn with Gendry's blood and throws them into a fire, calling for the death of Robb Stark, Balon Greyjoy and Joffrey Baratheon. Upon the news of Robb's death, Davos releases Gendry to prevent him from being sacrificed. Stannis subsequently sentences Davos to death but is swayed by Melisandre, who encourages him to travel North to aid the Night's Watch against the White Walkers emerging from beyond the Wall.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '67f1cb5a4e64706416522848011580c'}>,\n",
+       "  <Document: {'content': \"Stannis subsequently sentences Davos to death but is swayed by Melisandre, who encourages him to travel North to aid the Night's Watch against the White Walkers emerging from beyond the Wall.\\n\\n==== Season 4 ====\\nStannis hears of Joffrey's death and chastises Davos again for releasing Gendry and not finding him a suitable army, prompting Davos to write to the Iron Bank of Braavos in Stannis' name to help pay for an army. The Iron Bank nearly refuses Stannis' request, but Davos manages to convince them that Stannis is the only one they can turn to since Tywin Lannister is nearing old age. Stannis and his army arrive at the Wall shortly after the Battle of Castle Black, interrupting a parley between Jon Snow and Mance Rayder and crushing the wildling force. Stannis learns that Jon is Ned Stark's son, and on Jon's advice, he places Mance and his men under arrest. He is later present at the funeral of the Night's Watch brothers who died during the Battle for Castle Black.\\n\\n==== Season 5 ====\\nStannis begins plans to retake the North from Roose Bolton, hoping to recruit Mance's wildling army, if Mance will bend the knee to him. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '15fd7a5820d20d0709f88a166ccf6e55'}>,\n",
+       "  <Document: {'content': \"==== Season 5 ====\\nStannis begins plans to retake the North from Roose Bolton, hoping to recruit Mance's wildling army, if Mance will bend the knee to him. Mance refuses, and Stannis has him burnt at the stake. Stannis offers to legitimise Jon as a Stark to win the loyalty of the Northerners who refuse to recognise Stannis as their king, but Jon decides to remain loyal to his vows to the Night's Watch. Stannis marches on Winterfell, but his army is delayed by a large snowstorm. In the chaos Ramsay Bolton and his men infiltrate Stannis's camp, and destroy all supplies and horses. Melisandre persuades a reluctant Stannis to sacrifice Shireen to ensure victory. Although the snowstorm lifts, half of Stannis's army deserts him, Selyse commits suicide out of guilt, and Melisandre flees to Castle Black. Stannis decides to complete the march on foot. As his army arrives at Winterfell, it is swiftly defeated by a cavalry charge led by Ramsay Bolton. Stannis survives the battle, but is confronted by Brienne of Tarth, a former member of Renly's Kingsguard. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'da7ca350e93f3b86674f23e9c401067c'}>,\n",
+       "  <Document: {'content': 'As his army arrives at Winterfell, it is swiftly defeated by a cavalry charge led by Ramsay Bolton. Stannis survives the battle, but is confronted by Brienne of Tarth, a former member of Renly\\'s Kingsguard. Stannis confesses to killing Renly with blood magic, and Brienne executes him, telling Stannis she is killing him in the name of the \\'rightful\\' King Renly.\\n\\n=== Critical reception ===\\nThe English actor Stephen Dillane has received positive reviews for his performance as Stannis Baratheon, especially in the fifth season. On her review for \"Sons of the Harpy\", Sarah Hughes of \\'\\'The Guardian\\'\\' wrote, \"Stephen Dillane has always been wonderful at showing us the core of this rigid, complex man.\"His performance on the same episode also received positive reviews from \\'\\'Vulture\\'\\'.\\n\\nHarri Sargeant of \\'\\'Hypable\\'\\' wrote, \"Stephen Dillane has always been one of the classiest actors on the show, and his last moments as the King Who Almost Was saw as commanding a performance as ever.\"In her review for \\'\\'Mother\\'s Mercy\\'\\', Meghan O\\'Keefe of \\'\\'Decider\\'\\' wrote, \"Whether you like Stannis or not, you have to admit that Stephen Dillane delivered a monumental performance this season.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '47ada5ae629cc8d2082fd73d87ac3e8f'}>,\n",
+       "  <Document: {'content': 'In her review for \\'\\'Mother\\'s Mercy\\'\\', Meghan O\\'Keefe of \\'\\'Decider\\'\\' wrote, \"Whether you like Stannis or not, you have to admit that Stephen Dillane delivered a monumental performance this season.\"Cindy Davis of \\'\\'Pajiba\\'\\' wrote \"After Shireen’s seemingly pointless death, it’s a testament to Stephen Dillane’s phenomenal skills that we could feel anything at all for a man who watched his daughter burned alive.\"Nick Steinberg of \\'\\'Goliath\\'\\'  wrote, \"Credit has to go to Stephen Dillane for his performance, especially in these last few episodes where he’s had to convey a form of grim tragedy that’s effectively conveyed the character’s downfall.  For his part, Dillane expressed misgivings about his performance, telling \\'\\'The Times\\'\\' newspaper, \"I didn’t know what I was doing until we’d finished filming and it was too late. The damage had been done. I thought no one would believe in me and I was rather disheartened by the end. I felt I’d built the castle on non-existent foundations.”\\'\\'\\'\\'\\'\\n\\n== References ==\\n\\n==Sources==\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': 'd009bfb76568ef6498116374a60a1da4'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Winterfell\\'\\'\\'\" is the eighth season premiere episode of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 68th overall. It was written by Dave Hill and directed by David Nutter. It aired on April 14, 2019.\\n\\nThe episode centres on Daenerys Targaryen and Jon Snow arriving in Winterfell with Unsullied and Dothraki forces, after Jon Snow has pledged his allegiance to her. It also reunites Jon with his Stark siblings Bran and Arya for the first time since the first season\\'s second episode.\\n\\n\"Winterfell\" received positive reception from critics, who listed Jon Snow and Daenerys\\'s arrival at Winterfell, Jon\\'s long-awaited reunion with Arya, the destruction of Last Hearth, Sam\\'s revelation to Jon Snow about his true lineage, Jaime and Bran\\'s tense encounter, and the performances of Sophie Turner and John Bradley as highlights of the episode. The episode received a Primetime Emmy Award nomination for Outstanding Single-Camera Picture Editing for a Drama Series and was Sophie Turner\\'s pick to support her nomination for Outstanding Supporting Actress in a Drama Series.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nQyburn informs Cersei Lannister that the White Walkers have breached the Wall, which pleases Cersei. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '789ae3bc1c0c5aefc1746d80e14ff3fa'}>,\n",
+       "  <Document: {'content': \"The episode received a Primetime Emmy Award nomination for Outstanding Single-Camera Picture Editing for a Drama Series and was Sophie Turner's pick to support her nomination for Outstanding Supporting Actress in a Drama Series.\\n\\n==Plot==\\n\\n===In King's Landing===\\nQyburn informs Cersei Lannister that the White Walkers have breached the Wall, which pleases Cersei. Euron Greyjoy arrives with Yara Greyjoy as his prisoner and with the Golden Company. Euron uses his deeds to convince Cersei to start their relationship, and the pair have sex. Euron insists that he will impregnate Cersei, who appears to be visibly upset when he leaves the room.\\n\\nQyburn visits Bronn and presents him with the crossbow Tyrion used to assassinate his father Tywin Lannister. Should Jaime and Tyrion survive the war against the dead, Cersei wants Bronn to assassinate them.\\n\\n===In the Narrow Sea===\\nTheon Greyjoy secretly boards Euron's flagship in the night and frees his sister Yara. Yara decides to retake the Iron Islands while Euron is distracted by matters in King's Landing, whereas Theon decides to journey to Winterfell to fight the undead with Daenerys and the Starks.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '88431eaea9002ed2b0de8739091ac042'}>,\n",
+       "  <Document: {'content': \"Yara decides to retake the Iron Islands while Euron is distracted by matters in King's Landing, whereas Theon decides to journey to Winterfell to fight the undead with Daenerys and the Starks.\\n\\n===At Last Hearth===\\nTormund Giantsbane and Beric Dondarrion, having survived the fall of Eastwatch, lead their party into House Umber's seat of Last Hearth, already sacked by the dead. They encounter Eddison Tollett, who had evacuated the Night's Watch from Castle Black. They find young Lord Ned Umber, who had been evacuating Last Hearth's inhabitants, dead and impaled to a wall. Tormund declares that they must reach Winterfell before the Night King. Ned reanimates as a wight, and Beric burns him with his flaming sword.\\n\\n===At Winterfell===\\nDaenerys Targaryen arrives at Winterfell alongside Jon Snow, Tyrion Lannister, Jorah Mormont, Varys and Missandei, with Grey Worm leading her Unsullied and Dothraki armies. Jon reunites with Bran and Daenerys is greeted by Sansa Stark, the townspeople, and the Northern lords, though the pleasantries are cut short when Bran Stark reveals to Daenerys and Jon that the Night King has reanimated Viserion and breached the Wall. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '224868eb8735b122bec3f7b1edd5768e'}>,\n",
+       "  <Document: {'content': \"Jon reunites with Bran and Daenerys is greeted by Sansa Stark, the townspeople, and the Northern lords, though the pleasantries are cut short when Bran Stark reveals to Daenerys and Jon that the Night King has reanimated Viserion and breached the Wall. Sansa has ordered the Northern lords to retreat to Winterfell to make the stand against the undead, but at council, Lyanna Mormont and the other lords express displeasure towards Jon for abandoning his position as King in the North by swearing fealty to a Targaryen. This tension is magnified when Tyrion declares that the Lannister army will soon march north too; Sansa fears that the combined forces might overstretch their supplies, but in private implies to Tyrion that she does not believe Cersei will send her army.\\n\\nArya reunites with Jon, Gendry (who is working in Winterfell's forges crafting dragonglass weapons), and the Hound. Davos Seaworth suggests to Tyrion and Varys that the Northerners could be won over by betrothing Jon and Daenerys. Jon and Daenerys ride the dragons, deepening their bond. Later, Sansa accuses Jon of bending the knee out of love for Daenerys.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '80e86822114c852613ac35bca52d3158'}>,\n",
+       "  <Document: {'content': 'Jon and Daenerys ride the dragons, deepening their bond. Later, Sansa accuses Jon of bending the knee out of love for Daenerys.\\n\\nSamwell Tarly meets Daenerys, who thanks him for curing Jorah of his greyscale, but after discovering his identity admits that she executed his father, Randyll, and his brother, Dickon. Bran instructs a heartbroken Samwell to tell Jon about his true Targaryen heritage, the news of which leaves Jon stunned.\\n\\nJaime Lannister arrives at Winterfell and is shocked when he encounters Bran in the courtyard, waiting for him.\\n\\n== Production ==\\n=== Writing ===\\nThe episode was written by Dave Hill, his final script of the series. The episode adapts material from the unpublished novels \\'\\'The Winds of Winter\\'\\' and \\'\\'A Dream of Spring\\'\\', among them the released sample chapter \"The Forsaken\", in which Euron Greyjoy converses with a captive member of his family aboard his ship.\\n\\n=== Casting ===\\nThe episode saw the introduction of Marc Rissmann as Harry Strickland, the commander of the Golden Company. On preparing for his role in the season, Rissmann said, \"I did some research into who this person was, where he comes from, and the books are quite precise. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '43455d19d7a5d9b8d244e1cd156213f5'}>,\n",
+       "  <Document: {'content': 'On preparing for his role in the season, Rissmann said, \"I did some research into who this person was, where he comes from, and the books are quite precise. And then you see in the scripts what is there, what are the similarities, what are the differences. So I did a bit of research on that, especially since this world was so properly thought through. It\\'s a functioning world, and that\\'s why it\\'s actually so appealing.\"Jacob Anderson (Grey Worm) is listed as a series regular in the opening credits for the first time in this episode; he has previously appeared as a supporting/guest actor. Actors Rob McElhenney and Martin Starr and writer Dave Hill made cameo appearances as Ironborn soldiers killed when Theon rescues Yara.\\n\\n=== Filming ===\\nThe episode was directed by David Nutter who had previously directed two episodes each in the second, third and fifth season. The outdoor Winterfell scenes were filmed at sets in Moneyglass and Magheramorne in Northern Ireland, with indoor scenes filmed at Paint Hall studios in Belfast.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '5b111e706df0efb674492f03a48b5c8c'}>,\n",
+       "  <Document: {'content': 'The outdoor Winterfell scenes were filmed at sets in Moneyglass and Magheramorne in Northern Ireland, with indoor scenes filmed at Paint Hall studios in Belfast.\\n\\n== Reception ==\\n\\n=== Ratings ===\\n\"Winterfell\" was viewed by 11.76 million viewers on its initial live broadcast on HBO, and an additional 5.6 million viewers on streaming platforms, for a total 17.4 million viewers.\\n\\n=== Critical response ===\\nThe episode received largely positive reviews from critics, and is the best reviewed episode of the season. It has a 92% approval rating on the review aggregator website Rotten Tomatoes, based on 107 reviews with an average score of 7.72 out of 10. The site\\'s consensus reads, \"Though surprisingly bloodless, hard-fought reunions, bone-chilling surprises, and a welcome dose of humor help \"Winterfell\" set the stage for what should be an epic final season.\"Sarah Hughes of \\'\\'The Guardian\\'\\' wrote, \"This was a thrilling episode with its pedal to the floor, in which new alliances were made, old ones tested and long-awaited reunions occurred (hurrah for Jon and Arya\\'s tenderly scripted meeting)\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'c0aca0cb3791a8c7c78acb9a66de872a'}>,\n",
+       "  <Document: {'content': 'Sarah Hughes of \\'\\'The Guardian\\'\\' wrote, \"This was a thrilling episode with its pedal to the floor, in which new alliances were made, old ones tested and long-awaited reunions occurred (hurrah for Jon and Arya\\'s tenderly scripted meeting)\". Todd VanDerWiff of \\'\\'Vox\\'\\' praised the episode\\'s visual quality, stating, \"For as much as I grouse about \\'\\'Game of Thrones\\'\\', it\\'s often casually stunning in a way that no other TV show comes close to pulling off\". Ron Hogan of Den of Geek similarly highlighted the episode\\'s pacing, while also praising John Bradley who \"runs through the entire gamut of emotions in a very short time on screen\".\\n\\nAmong the negative reviews, Willa Paskin of \\'\\'Slate\\'\\' criticized the show\\'s pace, stating \"Momentum, the idea that we are hurtling toward some conclusion that will explain it all, has been so encoded into the \\'\\'Game of Thrones\\'\\' experience that in the absence of any forward motion, the show is ... kind of dull.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '9a58a549c08d02b72f2eff8c97abffec'}>,\n",
+       "  <Document: {'content': 'Among the negative reviews, Willa Paskin of \\'\\'Slate\\'\\' criticized the show\\'s pace, stating \"Momentum, the idea that we are hurtling toward some conclusion that will explain it all, has been so encoded into the \\'\\'Game of Thrones\\'\\' experience that in the absence of any forward motion, the show is ... kind of dull.\"=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2019\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nSophie Turner\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nCrispin Green\\n\\n==References==\\n\\n==External links==\\n*  \"Winterfell\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'b008e340d0eae5f90f4f6be27985740f'}>,\n",
+       "  <Document: {'content': '\\n\"\\'\\'\\'The Queen\\'s Justice\\'\\'\\'\" is the third episode of the seventh season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 63rd overall. The episode was written by series co-creators David Benioff and D. B. Weiss, and directed by Mark Mylod.\\n\\nAt Dragonstone, Jon Snow and Davos Seaworth meet with Daenerys Targaryen, from whom Jon asks for aid in defeating the White Walkers, but Daenerys iterates her desire to take the Iron Throne instead. In King\\'s Landing, Euron Greyjoy returns bearing gifts to Cersei Lannister, in the form of Ellaria Sand and Tyene Sand. At the Citadel, Samwell Tarly and Archmaester Ebrose examine a now healed Jorah Mormont. In Winterfell, Sansa Stark is reunited with her brother Bran Stark. At Casterly Rock, the Unsullied infiltrate and capture the castle, but Euron\\'s Iron Fleet arrives, and begins destroying their ships, leaving them stranded. Meanwhile, Jaime Lannister leads the Lannister army to take Highgarden.\\n\\nThe title of the episode refers to Cersei Lannister exacting vengeance, her own way of justice, on both the Sand Snakes and Olenna Tyrell. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '5af9d0cdc1b4906a8284be277cb7777b'}>,\n",
+       "  <Document: {'content': 'The title of the episode refers to Cersei Lannister exacting vengeance, her own way of justice, on both the Sand Snakes and Olenna Tyrell. \"The Queen\\'s Justice\" received positive reviews from critics, who considered the long-awaited meeting between Daenerys and Jon, the agonizing fates of Ellaria and Tyene Sand, Sansa and Bran\\'s reunion, the bait-and-switch scene concerning Casterly Rock and Highgarden, and the final fates of Ollena Tyrell and Ellaria Sand as highlights of the episode. In the United States, it achieved a viewership of 9.25 million in its initial broadcast. It was also Diana Rigg\\'s pick to support her nomination at the 70th Primetime Emmy Awards for Outstanding Guest Actress.\\n\\n\"The Queen\\'s Justice\" marked the final appearances of Diana Rigg (Olenna Tyrell), Rosabell Laurenti Sellers (Tyene Sand) and Indira Varma (Ellaria Sand).\\n\\n==Plot==\\n===On Dragonstone===\\nJon and Davos arrive at Dragonstone with their soldiers. They agree Daenerys is not responsible for her father\\'s crimes, but Daenerys denies Jon\\'s assertion he should not be held by his ancestors\\' oath to hers. Jon explains the threat of the White Walkers and wights, but Daenerys wants to claim the Iron Throne before considering other threats. Jon is forced to remain at Dragonstone.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '141d83cbbc04da67378f7f0fa211e387'}>,\n",
+       "  <Document: {'content': \"Jon explains the threat of the White Walkers and wights, but Daenerys wants to claim the Iron Throne before considering other threats. Jon is forced to remain at Dragonstone.\\n\\nVarys asks Melisandre why she hides from Jon; she admits they parted on bad terms due to her mistakes. She plans to return to Volantis, and Varys urges her to stay overseas forever, but she predicts that both of them will die in Westeros.\\n\\nSpeaking with Jon, Tyrion concedes that Jon's willingness to meet with Daenerys has convinced him that the White Walkers are real. Tyrion explains that Daenerys' followers are loyal because Daenerys dedicates herself to protecting others from threats she understands. Tyrion relays Jon's request to mine dragonglass on Dragonstone; Daenerys accepts Tyrion's recommendation to agree as a gesture of goodwill.\\n\\n===In the Narrow Sea===\\nOne of the few remaining ships of Yara's fleet rescue Theon. The Ironborn disbelieve his claim that he tried to rescue Yara from Euron.\\n\\n===In King's Landing===\\nEuron parades his captives Ellaria, Tyene, and Yara through abusive crowds in the streets of King's Landing. He presents the Sands as his gift to Cersei. Ellaria and Tyene are bound and gagged in the dungeon. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '6cf213e07395b2cfdc7fcf5253486c19'}>,\n",
+       "  <Document: {'content': '===In King\\'s Landing===\\nEuron parades his captives Ellaria, Tyene, and Yara through abusive crowds in the streets of King\\'s Landing. He presents the Sands as his gift to Cersei. Ellaria and Tyene are bound and gagged in the dungeon. Cersei kisses Tyene with the same poison Ellaria used to kill Myrcella, and tells Ellaria she will be kept alive to watch her daughter die and decompose. Tycho Nestoris of the Iron Bank comes to collect repayment of the Lannisters\\' loans. Cersei denigrates Daenerys as a possible investment, and secures a fortnight to pay the debt.\\n\\n===At Winterfell===\\nSansa competently manages Winterfell, preparing it as an emergency refuge for all Northerners. Littlefinger counsels Sansa to \"fight every battle\" mentally. Bran and Meera arrive, and Sansa is reunited with her brother. In the Godswood, Sansa is confused by Bran\\'s revelation that he is the Three-Eyed Raven, and troubled by his specific knowledge of her suffering (\"Unbowed, Unbent, Unbroken\").\\n\\n===In Oldtown===\\nArchmaester Ebrose proclaims that Jorah is healed of greyscale and releases him. Jorah plans to return to Daenerys. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '57445d745395501e3be1887d806a22c2'}>,\n",
+       "  <Document: {'content': '===In Oldtown===\\nArchmaester Ebrose proclaims that Jorah is healed of greyscale and releases him. Jorah plans to return to Daenerys. Sam admits that he administered the forbidden treatment; Ebrose praises his skill, but punishes his disobedience with an assignment to copy a large number of old documents.\\n\\n===At Casterly Rock===\\nThe Unsullied infiltrate Casterly Rock via its sewers, which Tyrion designed. They capture the castle, finding far fewer defenders than expected. Meanwhile, the Iron Fleet arrives and destroys Daenerys\\' ships, stranding Grey Worm and his army.\\n\\n===At Highgarden===\\nJaime, Randyll, and their armies take Highgarden, having abandoned Casterly Rock to trick Daenerys into committing her Unsullied forces to a strategically useless position. Jaime grants Olenna the mercy of a quick and painless death by poison, over Cersei\\'s original plans to execute her. After drinking the poison, Olenna confesses to murdering Joffrey and asks Jaime to tell Cersei. Incensed, Jaime leaves Olenna to die alone.\\n\\n==Production==\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"The Queen\\'s Justice\" was written by the series\\' creators, David Benioff and D. B. Weiss, and directed by Mark Mylod, his second of two episodes for this season. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'e96340339305bc444c448eeda5d384c'}>,\n",
+       "  <Document: {'content': '\"The Queen\\'s Justice\" was written by the series\\' creators, David Benioff and D. B. Weiss, and directed by Mark Mylod, his second of two episodes for this season. Mylod joined the series as a director in the fifth season, working on \"High Sparrow\" and \"Sons of the Harpy\".\\n\\nIndira Varma made her final appearance as Ellaria Sand.\\n\"The Queen\\'s Justice\" was the final episode for actress Indira Varma, who had played Ellaria Sand since the fourth season. \"The Queen\\'s Justice\" was also the last episode for recurring cast members Diana Rigg and Rosabell Laurenti Sellers, as Olenna Tyrell and Tyene Sand were killed. The episode featured the return of Mark Gatiss as Tycho Nestoris, who was last seen in season 5\\'s \"The Dance of Dragons\".\\n\\nKit Harington spoke about the scene he shot with Peter Dinklage in Spain, saying \"It was a beautiful location with 50 mph winds and I was wearing a cape next to a cliff—There was a danger of me being blown off! I\\'m not sure it\\'s the way I would have wanted to go.\"Indira Varma and Rosabell Laurenti Sellers\\'s last scene was technically difficult for the actresses, because they were shackled. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '8e238d23589f156ea70ebb9ec22dfeea'}>,\n",
+       "  <Document: {'content': 'I\\'m not sure it\\'s the way I would have wanted to go.\"Indira Varma and Rosabell Laurenti Sellers\\'s last scene was technically difficult for the actresses, because they were shackled. The shackles were felt-lined but tight, and Varma and Sellers ended up \"bruised and battered\" due to the physical intensity of their acting. Varma had to be cut from the shackles at the end of the day.\\n\\n==Analysis==\\nD. B. Weiss talked about the return of Bran to Winterfell, saying \"One of the things we loved about Game of Thrones from the very first book is it\\'s not a world where magic is the primary driver of the story, it\\'s a world where human psychology and behavior and desire are drivers of the story. We try very hard to make sure it stays that way because that\\'s a lot more relatable to the vast majority of the audience than magic powers—as much fun as those are. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'f03d536a95af335ca33eeb1bcba33768'}>,\n",
+       "  <Document: {'content': 'We try very hard to make sure it stays that way because that\\'s a lot more relatable to the vast majority of the audience than magic powers—as much fun as those are. So with Bran, ideally you want to use him in a way that adds to the story and enriches the story and not in a way that\\'s a magic bullet to conveniently deal with things that you haven\\'t come up with a better way to deal with. So it was a balancing act to account for who Bran is now without letting that overtake the story.\"Isaac Hempstead Wright explained how the magical conversion has altered the characters personality, saying \"It\\'s like imagining you have all of space and time in your head—Bran is existing in thousands of planes of existence at any one time. So it\\'s quite difficult for Bran to have any kind of semblance of personality anymore because he\\'s really like a giant computer\". He went on to add that \"Bran really at this stage is not the Three-Eyed Raven. He\\'s got the title but hasn\\'t had thousands of years of sitting in a cave looking through time. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '6f906c9ff2d637410b2d63ad266b1291'}>,\n",
+       "  <Document: {'content': 'He went on to add that \"Bran really at this stage is not the Three-Eyed Raven. He\\'s got the title but hasn\\'t had thousands of years of sitting in a cave looking through time. Somebody put in front of him a massive encyclopedia of all of time and he\\'s only opened page one. He can look stuff up but doesn\\'t have this all-knowing all-seeing capability just yet.\"Indira Varma expected the seventh season would be her character\\'s last, reasoning that a \"less important character\" like Ellaria would have to be phased out to make room for the story\\'s climax. She expected audiences to be \"more invested in Cersei,\" who had more screen time than Ellaria, but she hoped positive fan reception for Pedro Pascal as Oberyn Martell would carry over to sympathy for Ellaria. Varma had hoped for her character to die on screen, but found the fate Weiss and Benioff crafted for Ellaria to be \"really clever\" and beyond \"every parent\\'s worst nightmare.\"Weiss praised Diana Rigg\\'s performance in her final scene as Olenna Tyrell, for contributing to the character uniquely \"winning her own death scene.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'f8f5dbceb543ad8ae5625ed159fb280d'}>,\n",
+       "  <Document: {'content': 'Weiss praised Diana Rigg\\'s performance in her final scene as Olenna Tyrell, for contributing to the character uniquely \"winning her own death scene.\"==Reception==\\n===Ratings===\\n\"The Queen\\'s Justice\" was viewed by 9.25 million total viewers on its initial viewing on HBO, which was slightly less than the previous week\\'s rating of 9.27 million viewers for the episode \"Stormborn\". The episode also acquired a 4.3 rating in the 18–49 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was viewed by 3.272 million viewers on Sky Atlantic during its Simulcast, making it the highest-rated broadcast that week.\\n\\n===Critical reception===\\n\"The Queen\\'s Justice\" received positive reactions from critics. Review aggregator Rotten Tomatoes surveyed 40 reviews of the episode and judged 89% of them to be positive, with an average rating of 7.9 out of 10. The website\\'s consensus for the episode stated \"\\'The Queens Justice\\' saw the much-anticipated meeting between Jon Snow and Daenerys Targaryen -- but had much larger surprises in store before its shocking end.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '4b1f47097920809b3400c7a18a95c4f1'}>,\n",
+       "  <Document: {'content': 'The website\\'s consensus for the episode stated \"\\'The Queens Justice\\' saw the much-anticipated meeting between Jon Snow and Daenerys Targaryen -- but had much larger surprises in store before its shocking end.\"Matt Fowler of IGN described the episode as \"amazing\", saying \"\"The Queen\\'s Justice\" did justice to the long-awaited coming together of Jon Snow and Daenerys Targaryen. With a devilishly good script and some pointed action (along with a few twists and turns), this episode contained a ton of long scenes, but no fat. - This was prime Thrones.\"He gave the episode a 9.5 out of 10. Shane Ryan of \\'\\'Paste Magazine\\'\\' wrote \"It was, by far, the best episode of the season. I\\'ve always contended that the show does best when it pushes the drama and the narrative ahead by means of two-person scenes, and the examples in \"The Queen\\'s Justice\" were phenomenal\". Glen Weldon of NPR also praised the episode and Diana Rigg\\'s performance, writing \"It\\'s a hell of a way to go out, but a character like Olenna -- and an actress like Rigg, who always let you see the danger flashing behind those eyes\\xa0– deserves nothing less.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'a2e6fae2c93b5be21c2e0a3ec47ac064'}>,\n",
+       "  <Document: {'content': 'Glen Weldon of NPR also praised the episode and Diana Rigg\\'s performance, writing \"It\\'s a hell of a way to go out, but a character like Olenna -- and an actress like Rigg, who always let you see the danger flashing behind those eyes\\xa0– deserves nothing less.\"Bennett Madison of \\'\\'Vanity Fair\\'\\' wrote, \"\\'\\'Game of Thrones\\'\\' has spent so much time building up tension and drawing out plots that many of its ongoing story lines have literally been brewing for years\\xa0... With this episode, \\'The Queen\\'s Justice\\', we\\'re finally starting to see some real payoff.\"Andy Hartup of GamesRadar similarly gave praise to the episode for granting victories to antagonists like Cersei and Euron, while criticizing the character dynamics in scenes taking place at Winterfell and Dragonstone. Sean T. Collins of \\'\\'Rolling Stone\\'\\' wrote of the episode, \"As befits its title, this week\\'s installment asks us to consider what it means to be a ruler\\xa0... and what it means to seek justice.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'fd73abb51f62030095b8f8ac5cfe6b89'}>,\n",
+       "  <Document: {'content': 'Sean T. Collins of \\'\\'Rolling Stone\\'\\' wrote of the episode, \"As befits its title, this week\\'s installment asks us to consider what it means to be a ruler\\xa0... and what it means to seek justice.\"Kevin Yeoman of Screen Rant praised the final scene of Olenna Tyrell, writing \"As much as a momentous encounter between two major characters grabbed the spotlight, \\'\\'Game of Thrones\\'\\' proved that a character\\'s last words could be just as important as their first.\"Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' praised the performances of Rigg, Coster-Waldau and Dinklage, but was critical of Emilia Clarke and Kit Harington\\'s scenes together, writing that \"in the Dragonstone scenes, Clarke and Harington mostly served to bring out each other\\'s most wooden line readings\". She also addressed the way the episode approached themes of female empowerment, writing that \"if any show has been a cautionary tale about the difference between female empowerment and true social change, \\'\\'Game of Thrones\\'\\' has been it\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '5663620d3c943eb8543a977de024197e'}>,\n",
+       "  <Document: {'content': 'She also addressed the way the episode approached themes of female empowerment, writing that \"if any show has been a cautionary tale about the difference between female empowerment and true social change, \\'\\'Game of Thrones\\'\\' has been it\".\\n\\n===Accolades===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2017\\n\\nHollywood Post Alliance\\n\\nOutstanding Editing\\n\\nJesse Parker\\n\\n2018\\n\\nArt Directors Guild Awards\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley\\n\\n2018\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\n==References==\\n\\n==External links==\\n\\n*  \"The Queen\\'s Justice\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'a41431dcca659d6ac4a444cea676f4ea'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Last of the Starks\\'\\'\\'\" is the fourth episode of the eighth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 71st overall. It was written by David Benioff and D. B. Weiss, and directed by David Nutter. It aired on May 5, 2019.\\n\\n\"The Last of the Starks\" shows the aftermath of the battle against the Army of the Dead while setting the stage for the final confrontation, with Daenerys, Jon, and their remaining forces going towards King\\'s Landing to confront Cersei and demand her surrender.\\n\\nThe episode received mixed reviews. Critics praised its return to the political intrigue of earlier \\'\\'Game of Thrones\\'\\' episodes, but criticized the episode\\'s writing. It received a Primetime Emmy Award nomination for Outstanding Directing for a Drama Series and was picked by Emilia Clarke to support her nomination for Outstanding Lead Actress in a Drama Series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '94742c832df92c2bff9b5e2bfd74560b'}>,\n",
+       "  <Document: {'content': \"It received a Primetime Emmy Award nomination for Outstanding Directing for a Drama Series and was picked by Emilia Clarke to support her nomination for Outstanding Lead Actress in a Drama Series.\\n\\nThis episode marks the final appearance of Nathalie Emmanuel (Missandei), and Hannah Murray (Gilly), as well as the final appearances of six actors whose characters died in the previous episode, but were seen as corpses: Alfie Allen (Theon Greyjoy), Iain Glen (Ser Jorah Mormont), Bella Ramsey (Lyanna Mormont), Richard Dormer (Beric Dondarrion), Ben Crompton (Eddison Tollett), and Staz Nair (Qhono).\\n\\n==Plot==\\n===At Winterfell===\\nJon leads a mass funeral cremation for the dead. At a feast, Daenerys legitimizes Gendry as a Baratheon and makes him Lord of Storm's End, the ancestral home of House Baratheon. Gendry proposes to Arya, but she declines. Brienne leaves a drinking game in discomfort when Tyrion guesses she is a virgin. Jaime goes to Brienne's room and they have sex. Daenerys is uncomfortable at the acclaim the wildlings give Jon and, in private, begs him to never reveal his true parentage. Jon reassures Daenerys that he's renounced his claim for hers but insists he must tell Arya and Sansa the truth.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'a44c33ee8d93324bc5663eacf226ae8a'}>,\n",
+       "  <Document: {'content': \"Jon reassures Daenerys that he's renounced his claim for hers but insists he must tell Arya and Sansa the truth.\\n\\nDaenerys plans to immediately storm King's Landing, but Sansa argues the soldiers need rest. Ultimately, they agree Jon will lead the army on foot while Daenerys and her fleet will sail to Dragonstone. Afterwards, Arya and Sansa tell Jon that they distrust Daenerys but Jon defends her. After swearing Sansa and Arya to secrecy, Jon has Bran tell Arya and Sansa of his true parentage.\\n\\nBronn arrives to kill Jaime and Tyrion, but accepts their offer of Highgarden in exchange for their lives. Arya and Sandor Clegane ride for King's Landing. Sansa reveals Jon's parentage to Tyrion, who then tells Varys. Jon gives Ghost to Tormund, who plans to return north of the Wall with the wildlings; Tormund suggests that Jon join them. Jon also bids farewell to Sam and a pregnant Gilly.\\n\\nAfter learning of Cersei's capture of Missandei and Sansa suggesting Daenerys will execute Cersei for this, Jaime leaves for King's Landing during the night in hopes of saving her, despite Brienne's tearful plea that he stay with her.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '93891fd493a911edb3abdeb11e43f69'}>,\n",
+       "  <Document: {'content': \"After learning of Cersei's capture of Missandei and Sansa suggesting Daenerys will execute Cersei for this, Jaime leaves for King's Landing during the night in hopes of saving her, despite Brienne's tearful plea that he stay with her.\\n\\n===At Dragonstone===\\nEuron's Iron Fleet ambushes Daenerys's fleet and kills Rhaegal. Missandei is captured. Daenerys is convinced to talk to Cersei first instead of attacking King's Landing. Varys fears for Daenerys' mental stability and tells Tyrion he believes Jon would be a better ruler, but Tyrion stands by her.\\n\\n===At King's Landing===\\nEuron reports his success to Cersei, who claims to be pregnant with his child. She orders the gates of the Red Keep remain open, making her subjects human shields to deter an all-out assault on the city.\\n\\nDaenerys and her army arrive at the walls of King's Landing. Cersei and Daenerys demand each other's surrender, with Cersei threatening to kill Missandei. Tyrion attempts to appeal to Cersei's humanity to get her to surrender. Cersei refuses and has Gregor Clegane behead Missandei, horrifying and enraging Grey Worm and Daenerys.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by David Benioff and D. B. Weiss.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'a7a5c6b32df1f35ca19ce6f95f21c452'}>,\n",
+       "  <Document: {'content': 'Cersei refuses and has Gregor Clegane behead Missandei, horrifying and enraging Grey Worm and Daenerys.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by David Benioff and D. B. Weiss.\\n\\n=== Filming ===\\nThe episode was directed by David Nutter. This was his final episode of the series overall.\\n\\nDuring filming of the banquet scenes, a disposable coffee cup was accidentally left on the set; it was briefly visible in the original broadcast of the episode, but was digitally erased two days later.\\n\\nActress Nathalie Emmanuel was digitally inserted into the long shots of her own death scene, because filming took place at a great physical height and was exposed to photographers. To keep the plot secret, Emmanuel did not participate in those takes, while her close-ups were shot separately on a sound stage.\\n\\n== Reception ==\\n=== Ratings ===\\nThe episode was viewed by 11.8 million viewers on its initial live broadcast on HBO. An additional 5.4 million viewers watched on streaming platforms, for a total of 17.2 million viewers.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '79eea431733099ce5d08c48dc5268c5a'}>,\n",
+       "  <Document: {'content': '== Reception ==\\n=== Ratings ===\\nThe episode was viewed by 11.8 million viewers on its initial live broadcast on HBO. An additional 5.4 million viewers watched on streaming platforms, for a total of 17.2 million viewers.\\n\\n=== Critical response ===\\nThe episode received mixed to negative reviews; on the review aggregator Rotten Tomatoes it has an approval rating of 58%, based on 107 reviews and an average rating of 7.01/10, with the critics consensus stating: \"\\'The Last of the Starks\\' strains to set the board for \\'\\'Game of Thrones\\'\\' conclusion, but serves up enough political intrigue and touching character interactions to satisfy\".\\n\\nAmong the positive reviews, Spencer Kornhaber of \\'\\'The Atlantic\\'\\' wrote, \"despite all of the logical head-smack moments ... I\\'m not brimming with complaints. The episode recaptured some classic \\'\\'Thrones\\'\\' qualities, really.\"Chris Barton of \\'\\'Los Angeles Times\\'\\' praised the storytelling and the interaction between characters and believed that \"the bulk of this episode was filled with the sort of palace intrigue and character dynamics that made the show more than the sum of its dragons.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '3756b0d98b2d49dcdca42ab764d670bc'}>,\n",
+       "  <Document: {'content': 'Chris Barton of \\'\\'Los Angeles Times\\'\\' praised the storytelling and the interaction between characters and believed that \"the bulk of this episode was filled with the sort of palace intrigue and character dynamics that made the show more than the sum of its dragons.\"Sean T. Collins of \\'\\'Rolling Stone\\'\\' believed that the episode did a good job of showing the conflicts between humans and wrote, \"It hasn\\'t missed a step thematically, moving from humanity\\'s need to stop killing itself and face a common threat to its compulsion to annihilation even after seeing what it can accomplish as a united front.\"Sarah Hughes of \\'\\'The Guardian\\'\\' wrote in her review that the pace of the episode was just right and added, \"Where a lesser show would have hurried the action, moving us swiftly on to the battle with Cersei, The Last of the Starks took time to show what the true cost of the Battle of Winterfell was.\"Among the negative reviews, Shirley Li of \\'\\'The Atlantic\\'\\' wrote that she wasn\\'t impressed by \"The Last of the Starks\" and criticized the \"nonsensical storytelling\" in this episode. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '37e3d660cb1b56737f377542948b81b5'}>,\n",
+       "  <Document: {'content': 'Among the negative reviews, Shirley Li of \\'\\'The Atlantic\\'\\' wrote that she wasn\\'t impressed by \"The Last of the Starks\" and criticized the \"nonsensical storytelling\" in this episode. David Malitz of \\'\\'The Washington Post\\'\\' also questioned the logic in this episode, and, referring to the confrontation between Daenerys and Euron, wrote, \"Maybe it was just good luck on those initial kill shots, though, because the next few dozen arrows that fly toward Daenerys and Drogon all miss their mark. Every single one of them. Yep.\"Steve Johnson of \\'\\'Chicago Tribune\\'\\' believed that the show had lost its exciting storyline and, referring to the downfall of the Night King in the last episode, wrote, \"How are we supposed to get thrilled about fighting mere Cersei again when we\\'ve already bested the greatest enemy of all time?\"Alan Sepinwall of \\'\\'Rolling Stone\\'\\' labeled the episode as a \"structural oddity\", and wrote, \"It never feels solid enough. The script races through the transition, piling one personality-altering event on top of the next so that none of them gets to breathe.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'c198b1d50e1825f1fe9d5f43359f7120'}>,\n",
+       "  <Document: {'content': 'The script races through the transition, piling one personality-altering event on top of the next so that none of them gets to breathe.\"Longtime \\'\\'Game of Thrones\\'\\' reviewer, Erik Kain of \\'\\'Forbes\\'\\', stated that \"so much happened and so little of it felt right, that today I\\'m left feeling letdown more than anything, and dreading the next two episodes instead of eagerly awaiting them.\"On the \"Inside the Episode\" featurette for \"The Last of the Starks\", showrunner David Benioff explained that Daenerys lost Rhaegal and her fleet because \"Dany kind of forgot about the Iron Fleet and Euron\\'s forces\". Amy Jones of \\'\\'The Daily Telegraph\\'\\' described the explanation as \"so stupid\", while fans ridiculed Benioff by turning the statement into a meme.\\n\\n=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2019\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter\\n\\nOutstanding Lead Actress in a Drama Series\\n\\nEmilia Clarke\\n\\n== References ==\\n\\n== External links ==\\n*  \"The Last of the Starks\" at HBO.com\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '3ba5ab4e5bb7ee00e967eb818df716c0'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''''A Dance with Dragons''''' is the fifth novel, of seven planned, in the epic fantasy series ''A Song of Ice and Fire'' by American author George R. R. Martin.  In some areas, the paperback edition was published in two parts, titled ''Dreams and Dust'' and ''After the Feast''. It was the only novel in the series to be published during the eight-season run of the HBO adaptation of the series, ''Game of Thrones'', and runs to 1,040 pages with a word count of almost 415,000.\\n\\nThe US hardcover was officially published on July 12, 2011, and a few weeks later went to No. 1 on both ''Publishers Weekly'' and ''USA Today'' bestsellers lists.  The novel has been adapted for television as the fifth season of ''Game of Thrones'', although elements of the book have also appeared in the series' third, fourth and sixth seasons.\\n\\n==Plot summary==\\n\\n===The Wall and beyond===\\nStannis Baratheon, a claimant to the Iron Throne of Westeros, occupies the Wall at the realm's northern border, having helped to repel an invasion of wildlings from the northern wilderness.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'bb8cef06dc7ee7058437fe50740433a'}>,\n",
+       "  <Document: {'content': \"==Plot summary==\\n\\n===The Wall and beyond===\\nStannis Baratheon, a claimant to the Iron Throne of Westeros, occupies the Wall at the realm's northern border, having helped to repel an invasion of wildlings from the northern wilderness.  Stannis apparently executes Mance Rayder, the leader of the wildlings, for refusing to submit to him, and marches south with his army to seek support in his bid for the throne.\\n\\nJon Snow, the newly elected Lord Commander of the Night's Watch, the order that defends the Wall, brokers a truce with the wildlings, allowing them through the Wall in exchange for their assistance in defending the Wall against the hostile otherworldly creatures known as the Others. This creates unrest among the Night's Watch, who have considered the wildlings their enemies for centuries.\\n\\nStannis's advisor, the sorceress Melisandre, warns Jon that a Northern girl is in trouble, who Jon assumes is his half-sister, Arya Stark. Mance is revealed to be alive thanks to Melisandre's magical trickery, and he is sent to the Starks' ancestral castle Winterfell, now occupied by the enemy Boltons, to rescue Arya. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '3ac61694d8bc5cecd532dced3b6ec89'}>,\n",
+       "  <Document: {'content': \"Mance is revealed to be alive thanks to Melisandre's magical trickery, and he is sent to the Starks' ancestral castle Winterfell, now occupied by the enemy Boltons, to rescue Arya. However, the girl in Melisandre's visions turns out to be Alys Karstark, a young noblewoman fleeing to the Wall to escape her treacherous uncle. To protect Alys and aid the wildlings' integration into Westerosi society, Jon arranges for Alys to marry Sigorn of Thenn, a wildling leader.\\n\\nJon receives a taunting letter from Ramsay Bolton, who claims to have crushed Stannis's army at Winterfell. Ramsay demands that Jon hand over to him Stannis' wife and daughter or be killed, and also insists that Jon deliver Arya and his foster-brother Theon Greyjoy, neither of whom Jon has seen in years. Jon instead decides that he will seek out and kill Ramsay himself, asking for volunteers from the Watch to accompany him. For this perceived betrayal of the Watch's principle of neutrality, Jon is betrayed and stabbed by several of his own men.\\n\\nMeanwhile, Jon's crippled half-brother Bran Stark, traveling north of the Wall, is led to the last surviving Children of the Forest, the non-human natives of Westeros. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'd7563c9f6a8722264fd8ef5501291e26'}>,\n",
+       "  <Document: {'content': 'Meanwhile, Jon\\'s crippled half-brother Bran Stark, traveling north of the Wall, is led to the last surviving Children of the Forest, the non-human natives of Westeros. They introduce Bran and his companions to the \"last greenseer\", an ancient man intertwined with the roots of a weirwood tree. He helps Bran learn clairvoyant \"greensight\", enabling Bran to witness the past and present through the eyes of the weirwood trees that grow throughout the North.\\n\\n===Across the Narrow Sea===\\n\\n====In the Free Cities====\\nHaving killed his father Tywin, the Hand of the King, the dwarf Tyrion Lannister is smuggled out of Westeros to the city of Pentos by the spymaster Varys, where he is sheltered by the aristocrat Illyrio Mopatis. Tyrion is sent south with a party ostensibly to aid the exiled princess Daenerys Targaryen, who controls the only living dragons, in claiming the Iron Throne; on the journey, he learns that Varys and Illyrio have hidden the presumed-dead Aegon Targaryen, son of Daenerys\\'s late brother Prince Rhaegar, intending to eventually install him as king of Westeros with Daenerys\\'s aid. Tyrion persuades Aegon to invade Westeros immediately, without waiting for Daenerys. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'ce79d99c0500588db63b14222ca5f492'}>,\n",
+       "  <Document: {'content': 'Tyrion is sent south with a party ostensibly to aid the exiled princess Daenerys Targaryen, who controls the only living dragons, in claiming the Iron Throne; on the journey, he learns that Varys and Illyrio have hidden the presumed-dead Aegon Targaryen, son of Daenerys\\'s late brother Prince Rhaegar, intending to eventually install him as king of Westeros with Daenerys\\'s aid. Tyrion persuades Aegon to invade Westeros immediately, without waiting for Daenerys. Soon afterward, Tyrion is kidnapped by Daenerys\\'s disgraced advisor Jorah Mormont, who intends to deliver him to Daenerys in order to regain her favor.\\n\\nIn the Free City of Braavos, Arya is an acolyte of the guild of assassins known as the Faceless Men. Temporarily afflicted with blindness at their hands, she develops her sense of hearing, and realizes that she can \"see\" through cats the same way she could with her pet direwolf, Nymeria. After her sight is restored, she is tasked with assassinating a corrupt merchant while magically disguised with another girl\\'s face. After doing so, Arya is declared an apprentice of the Faceless Men, to be sent to another assassin to continue her training.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'def69d1880e03d47f414c28d3756969f'}>,\n",
+       "  <Document: {'content': 'After doing so, Arya is declared an apprentice of the Faceless Men, to be sent to another assassin to continue her training.\\n\\n====Slaver\\'s Bay====\\nDaenerys has conquered the city of Meereen and banned slavery, but struggles to maintain peace within the city while also trying to prevent military defeat at the hands of the neighboring city of Yunkai. A plague known as the \"pale mare\" (dysentery) ravages the city. Daenerys\\' dragons have become increasingly dangerous, and she reluctantly confines them in a dungeon for the safety of her people. Drogon, the largest, evades capture and flies off. Despite her sexual relationship with the mercenary Daario Naharis, Daenerys marries the Meereenese nobleman Hizdahr zo Loraq to secure an alliance that will hopefully appease the Sons of the Harpy, a violent Meereenese resistance group.\\n\\nQuentyn Martell, the son of the Prince of Dorne in southern Westeros, arrives in Meereen to seek to reestablish the allegiance between Daenerys\\'s family and his, but is unable to maintain her attention. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '36e68a459f060b1726eb88a61362590b'}>,\n",
+       "  <Document: {'content': 'Quentyn Martell, the son of the Prince of Dorne in southern Westeros, arrives in Meereen to seek to reestablish the allegiance between Daenerys\\'s family and his, but is unable to maintain her attention. Jorah and Tyrion are shipwrecked, kidnapped, and sold to a Yunkish slave trader; at Meereen, Tyrion escapes in the mass confusion of the plague ravaging the Yunkish army, joins the Second Sons mercenary group, and secures their support for Daenerys. Meanwhile, another Westerosi, Victarion Greyjoy, the brother of the king of the Ironborn, sails for Meereen, intending to marry Daenerys and use her dragons to overthrow his brother.\\n\\nAt Hizdahr\\'s insistence, Daenerys reopens the city\\'s \"fighting pits\" for gladiatorial combat, but the noise and carnage attract Drogon. Two hundred people are killed in the resulting panic, at which point Daenerys climbs on Drogon to calm him. He flies off with Daenerys on his back. Hizdahr is soon implicated in an attempt to poison Daenerys, and her advisor Barristan Selmy removes Hizdahr from power and prepares for battle with the armies outside Meereen. Quentyn attempts to prove his worth by riding one of the remaining dragons, but is killed, releasing both dragons in the process.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'bc532897c5904b9db75548e195cbfd90'}>,\n",
+       "  <Document: {'content': \"Quentyn attempts to prove his worth by riding one of the remaining dragons, but is killed, releasing both dragons in the process.\\n\\nDrogon flies Daenerys to the Dothraki Sea, the grassland controlled by the nomadic Dothraki people. After several days, Daenerys encounters a Dothraki horde led by Khal Jhaqo.\\n\\n===In the Seven Kingdoms===\\n\\n====The North====\\nIn the North, Roose Bolton has assumed authority after conspiring with House Frey to overthrow the Stark family, the North's longtime overlords; meanwhile, much of the region is occupied by the Ironborn. Stannis wins the support of the northern lords and hill clans by pledging support for the Starks and fighting off the Ironborn; while doing so, he captures Asha Greyjoy, Victarion's niece. He marches his forces toward Winterfell to attack the Boltons, but his army becomes snowbound and cannot advance.\\n\\nStannis's advisor Davos Seaworth is sent to win the support of the wealthy lord Wyman Manderly, who pretends to execute Davos to curry favor with the Lannister regime that controls the Iron Throne. In a secret meeting, Davos is told the Manderlys and other Northern vassals intend to feign submission to the Boltons and Lannisters while plotting revenge for Robb Stark's death. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '46c70a8b4172f52ef3959b253412eeb4'}>,\n",
+       "  <Document: {'content': 'In a secret meeting, Davos is told the Manderlys and other Northern vassals intend to feign submission to the Boltons and Lannisters while plotting revenge for Robb Stark\\'s death. Revealing to Davos that young Rickon Stark is in hiding on the remote island of Skagos, Manderly pledges to support Stannis if Davos can retrieve Rickon and unite the Starks\\' supporters around him.\\n\\nTheon Greyjoy is a prisoner of the Boltons, mutilated and driven nearly insane by torture at the hands of Roose\\'s sadistic son Ramsay, who renames him \"Reek\". To cement his rule over the North, Roose Bolton has Ramsay apparently married to Arya Stark; his bride is actually Jeyne Poole, a friend of Arya\\'s sister Sansa forced to impersonate Arya. Ramsay abuses Jeyne physically and sexually. Mance Rayder arrives at Winterfell in disguise and enlists Theon to help him free the false Arya. Mance and his spearwives are left behind while Theon and Jeyne escape, leaping from the castle wall into the snow to be captured by Stannis\\'s forces.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '66ce3b17b025800618e8253f258db5a6'}>,\n",
+       "  <Document: {'content': \"Mance and his spearwives are left behind while Theon and Jeyne escape, leaping from the castle wall into the snow to be captured by Stannis's forces.\\n\\n====The South====\\n\\nJaime Lannister, the uncle (and, secretly, father) of the young king Tommen Baratheon, negotiates the surrender of the last of the late Robb Stark's allies, nominally putting an end to the Stark–Lannister war in the Riverlands. Brienne of Tarth, whom Jaime had sent to search for Sansa Stark (and who unbeknownst to Jaime was captured by Sansa's undead, vengeful mother Catelyn), finds Jaime and tells him Sansa is in danger; he follows after her.\\n\\nAfter Tyrion convinces Aegon to attack Westeros, Aegon and his forces capture several castles in the Stormlands with little resistance. Meanwhile, Aegon's foster father Jon Connington is secretly succumbing to greyscale, a deadly and infectious disease.\\n\\nDoran Martell, the prince of Dorne, sends three of his nieces north to the capital city of King's Landing to infiltrate the Faith and the government and work in Dorne's interests.\\n\\nCersei Lannister, Tommen's mother and queen regent, has been arrested by the Faith on charges of fornication and conspiracy. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '562d27e377501054bbac611e014134dd'}>,\n",
+       "  <Document: {'content': 'Doran Martell, the prince of Dorne, sends three of his nieces north to the capital city of King\\'s Landing to infiltrate the Faith and the government and work in Dorne\\'s interests.\\n\\nCersei Lannister, Tommen\\'s mother and queen regent, has been arrested by the Faith on charges of fornication and conspiracy. In order to gain release from her imprisonment, she confesses to several of the lesser charges against her, but does not confess to having murdered her husband King Robert Baratheon, nor that her children are the product of incest. As a condition of her release, she is forced to walk naked across the city as the smallfolk pelt her with rotten vegetables and insults. Meanwhile, Cersei\\'s ally, the ex-maester Qyburn, has created \"Ser Robert Strong\", an eight-foot-tall figure encased in armor, to be an unbeatable champion in Cersei\\'s upcoming trial by combat.\\n\\nHaving taken control of the government, Cersei\\'s uncle Kevan Lannister and the Grand Maester Pycelle attempt to undo the damage caused by Cersei\\'s misrule. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '29b313e5b03cffdef524eb2cc7542e12'}>,\n",
+       "  <Document: {'content': 'Having taken control of the government, Cersei\\'s uncle Kevan Lannister and the Grand Maester Pycelle attempt to undo the damage caused by Cersei\\'s misrule. However, Varys returns to King\\'s Landing and murders both Kevan and Pycelle, revealing that he has been plotting for years for the Lannisters to destroy themselves so that Aegon Targaryen can take the throne, having been raised to be an ideal ruler. The book ends with Varys sending his \"little birds\" (child spies) to finish Kevan off.\\n\\n===Extras===\\nIn addition to the maps published in previous books, the book includes a new map of the previously-unmapped area of the Free Cities on the eastern continent. Like the previous four volumes in the \\'\\'Ice and Fire\\'\\' series, the book includes an appendix with a complete list of characters.\\n\\n==Characters==\\nThe story is narrated from the point of view of 18 different characters, including two minor one-off point-of-view (POV) characters featured in the prologue and epilogue. All but two POV characters were identified before the book\\'s release.\\n\\nIn the North:\\n* Prologue: Varamyr Sixskins, a skinchanger and one of the surviving wildlings north of the Wall.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '7468bec9ae06bbaf5977cb45f5077efa'}>,\n",
+       "  <Document: {'content': \"All but two POV characters were identified before the book's release.\\n\\nIn the North:\\n* Prologue: Varamyr Sixskins, a skinchanger and one of the surviving wildlings north of the Wall.\\n* Jon Snow, the 998th Lord Commander of the Night's Watch and bastard son of Eddard Stark.\\n* Bran Stark, rightful heir to his brother, the late Robb Stark. Seeking an old power beyond the Wall, believed dead by his own family.\\n* Davos Seaworth, former smuggler and Hand of the King to Stannis Baratheon. Has recently learned to read, sent to negotiate with northern houses.\\n* Reek, the Prince of Winterfell, the Turncloak, a Ghost in Winterfell: Theon Greyjoy, presumed-dead son of recently deceased King Balon Greyjoy of the Iron Islands, a captive of Ramsay Bolton and now tortured, starved and barely sane.\\n* The Wayward Bride, the King's Prize, the Sacrifice: Asha Greyjoy, niece of King Euron Greyjoy of the Iron Islands, fled the Iron Islands after her uncle's coronation.\\n* Lady Melisandre, a shadow-binder from Asshai and a devoted priestess to the red god R'hllor, advisor to Stannis.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'f96b47d4980a02f5475eb75a593f32af'}>,\n",
+       "  <Document: {'content': \"* The Wayward Bride, the King's Prize, the Sacrifice: Asha Greyjoy, niece of King Euron Greyjoy of the Iron Islands, fled the Iron Islands after her uncle's coronation.\\n* Lady Melisandre, a shadow-binder from Asshai and a devoted priestess to the red god R'hllor, advisor to Stannis.\\n\\nIn the eastern continent of Essos:\\n* Daenerys Targaryen, heir to the Targaryen dynasty which ruled Westeros for 300 years until their deposition 15 years before the first novel. Self-proclaimed Queen of Westeros, she now rules the city of Meereen.\\n* Tyrion Lannister, dwarf and uncle to the King Tommen of Westeros, a fugitive wanted for kinslaying and regicide. Recently fled the Seven Kingdoms.\\n* The Merchant's Man, the Windblown, the Spurned Suitor, the Dragontamer: Quentyn Martell, eldest son of Prince Doran Martell of Dorne, traveling into the East on a mission for his father.\\n* The Lost Lord, the Griffin Reborn: Jon Connington, one of the former Hands of the King to Aerys Targaryen and one of Prince Rhaegar's closest friends. Exiled, and falsely believed dead. His identity as a narrator was kept secret throughout the book's pre-release process.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '4d56eeea24e7683f857141101ddb5308'}>,\n",
+       "  <Document: {'content': '* The Lost Lord, the Griffin Reborn: Jon Connington, one of the former Hands of the King to Aerys Targaryen and one of Prince Rhaegar\\'s closest friends. Exiled, and falsely believed dead. His identity as a narrator was kept secret throughout the book\\'s pre-release process.\\n* The Queensguard, the Discarded Knight, the Kingbreaker, the Queen\\'s Hand: Ser Barristan Selmy, the former Lord Commander of Robert Baratheon\\'s Kingsguard, and the first of Daenerys\\'s Queensguard.\\n* The Iron Suitor: Victarion Greyjoy, Captain of the Iron fleet, recently gone on a quest to find Daenerys and use her for the Ironborn\\'s own ends.\\n* The Blind Girl, the Ugly Little Girl: Arya Stark, hiding in the Free City of Braavos, where she has taken on the identity of the \"Cat of the Canals\" and continues her training as an assassin by the House of Black and White (The Faceless Men).\\n\\nIn the South:\\n* The Watcher: Areo Hotah, Captain of Doran Martell\\'s guard.\\n* Ser Jaime Lannister, the Kingslayer, Lord Commander of the Kingsguard; currently occupying the lands around Riverrun.\\n* Cersei Lannister, the Queen Regent, currently imprisoned in a tower cell, awaiting trial.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '26e5b3d4b44783405c5a01bff045c450'}>,\n",
+       "  <Document: {'content': \"* Ser Jaime Lannister, the Kingslayer, Lord Commander of the Kingsguard; currently occupying the lands around Riverrun.\\n* Cersei Lannister, the Queen Regent, currently imprisoned in a tower cell, awaiting trial.\\n* Epilogue: Ser Kevan Lannister, head of House Lannister in light of his brother's death, and current regent to King Tommen. His identity as a narrator was kept secret throughout the book's pre-release process.\\n\\nChapters for several POVs, which may include Sansa Stark, Samwell Tarly, Aeron Damphair, Arianne Martell, and Brienne of Tarth were written for the book, but they will instead tentatively appear in the next book, ''The Winds of Winter''.\\n\\n==Writing==\\n\\n===Early development===\\n\\n''A Dance with Dragons'' was originally intended to be the title of the second novel in the sequence, when Martin still envisioned the series as a trilogy. Some early US editions of ''A Game of Thrones'' (1996) list ''A Dance of Dragons'' as the forthcoming second volume in the series. The 1998 anthology ''Legends'', which features the novella ''The Hedge Knight'' from the same universe, listed ''A Dance of Dragons'' as the third installment of a four-book series.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'e4b46b846dc5a9670a1a11792410204d'}>,\n",
+       "  <Document: {'content': 'The 1998 anthology \\'\\'Legends\\'\\', which features the novella \\'\\'The Hedge Knight\\'\\' from the same universe, listed \\'\\'A Dance of Dragons\\'\\' as the third installment of a four-book series.\\n\\nIn May 2005 Martin announced that the \"sheer size\" of his still-unfinished manuscript for \\'\\'A Feast for Crows\\'\\' had led him and his publishers to split the narrative into two books. Rather than divide the text in half chronologically, Martin opted to instead split the material by character and location, resulting in \"two novels taking place simultaneously\" with different casts of characters. Published in 2005, \\'\\'A Feast for Crows\\'\\' is narrated primarily by characters in the South of the Seven Kingdoms and in the new locations of the Iron Islands and Dorne. \\'\\'A Dance with Dragons\\'\\' features characters in the North and across the narrow sea, although Jaime Lannister, Cersei Lannister, Arya Stark, Areo Hotah and Victarion and Asha Greyjoy appear in both volumes.\\n\\nApproximately one-third of the published \\'\\'A Dance with Dragons\\'\\' consists of material that had been written for the pre-split \\'\\'A Feast for Crows\\'\\', although much of this has been rewritten by Martin.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '7bbd3364b026fa497256948cc4edb618'}>,\n",
+       "  <Document: {'content': 'Approximately one-third of the published \\'\\'A Dance with Dragons\\'\\' consists of material that had been written for the pre-split \\'\\'A Feast for Crows\\'\\', although much of this has been rewritten by Martin.\\n\\nIn 2009, Martin confirmed that, contrary to earlier statements, Sansa Stark would not appear in \\'\\'A Dance with Dragons\\'\\'; Sansa chapters initially slated for the novel have instead been pushed back to \\'\\'The Winds of Winter\\'\\', which is planned to be the sixth book in the series. In early 2010, Martin noted that his intent for \\'\\'A Dance with Dragons\\'\\' was for the first 800 manuscript pages to cover the alternate characters in the same time span as \\'\\'A Feast for Crows\\'\\', and that \"Everything that follows is post-\\'\\'Feast\\'\\', so that\\'s where some of the cast from the last book start popping up again.\"Stating that \"I wanted to resolve at least a few of the cliffhangers from \\'\\'Feast\\'\\',\" Martin also mentioned the possibility that some of his finished chapters might get pushed to the next novel, \\'\\'The Winds of Winter,\\'\\' depending on the length of the finished manuscript for \\'\\'A Dance with Dragons\\'\\'.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '7fcd0f70d70e02ec7566967374fc75f0'}>,\n",
+       "  <Document: {'content': 'Stating that \"I wanted to resolve at least a few of the cliffhangers from \\'\\'Feast\\'\\',\" Martin also mentioned the possibility that some of his finished chapters might get pushed to the next novel, \\'\\'The Winds of Winter,\\'\\' depending on the length of the finished manuscript for \\'\\'A Dance with Dragons\\'\\'.\\n\\n===Road to publication===\\nDespite original, optimistic predictions of possible completion in late 2006, Martin completed the novel in April 2011, nearly five years later. During this period, Martin\\'s blog featured sporadic updates on his progress, and in January 2008 he posted an update affirming his vigilant commitment to finishing the novel. In early 2008, publisher Spectra Books (a division of Random House) announced that \\'\\'A Dance with Dragons\\'\\' would be released on September 30, 2008, but Martin stated this would only be possible if he finished writing by the end of June, before his trip to Spain and Portugal; he did not meet his goal.\\n\\nOn February 19, 2009, Martin posted on his website: \"I am trying to finish the book by June. I think I can do that. If I do, \\'\\'A Dance with Dragons\\'\\' will likely be published in September or October.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '51bade9a1c320667378355bfaec8484e'}>,\n",
+       "  <Document: {'content': 'I think I can do that. If I do, \\'\\'A Dance with Dragons\\'\\' will likely be published in September or October.\"On June 22, 2009, the author expressed \"guarded optimism\" with respect to his progress on the novel, while still not confirming a publication date. When asked in a July 2009 interview with \\'\\'FREE! Magazine\\'\\' how the book was going, Martin stated, \"It is going pretty well, actually. I am hoping to finish it by September or October; that is my goal.\"On October 6, 2009, Martin said that his working manuscript for \\'\\'A Dance With Dragons\\'\\' had just exceeded 1,100 pages of completed chapters, plus \"considerably more in partials, fragments, and roughs.\"He noted that this made the upcoming novel longer than his earlier books \\'\\'A Game of Thrones\\'\\' and \\'\\'A Feast for Crows\\'\\', and nearly as long as \\'\\'A Clash of Kings\\'\\'.\\n\\nOn March 2, 2010, Martin remarked that he had reached 1,311 manuscript pages, making \\'\\'Dance\\'\\' the second-longest novel in the series at that point, behind only the 1,521-page manuscript of \\'\\'A Storm of Swords\\'\\'. On July 8, 2010, Martin spoke at a conference and confirmed the current length of the book to be 1,400 manuscript pages. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '83adaa6e0bef262766b272dd7a40a368'}>,\n",
+       "  <Document: {'content': \"On July 8, 2010, Martin spoke at a conference and confirmed the current length of the book to be 1,400 manuscript pages. He expressed his disappointment that he was unable to completely finish the novel by the conference, although he would not speculate how soon the book would be completed after his return home on July 11. At the same conference, Martin also confirmed that he has written one Sansa, one Arya, and two Arianne chapters for the planned sixth novel, ''Winds of Winter'', and had transferred two Cersei chapters from that book into ''A Dance with Dragons''. On August 7, 2010, Martin confirmed that he had completed eight POVs, excluding the prologue and epilogue.\\n\\nAt the New York Comic Con on  October 10, 2010, Spectra senior editor Anne Groell announced that Martin had only five chapters remaining to finish, with sections of the chapters already completed. She stated her desire to have the manuscript completed by December. In a December 2010 interview with Bear Swarm, Martin stated that he almost had ''A Dance With Dragons'' completed.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '3799a9a03559e11e96d4d88b6f5114d7'}>,\n",
+       "  <Document: {'content': 'She stated her desire to have the manuscript completed by December. In a December 2010 interview with Bear Swarm, Martin stated that he almost had \\'\\'A Dance With Dragons\\'\\' completed.\\n\\nOn March 3, 2011, the publisher announced that the novel, though at that point still not completed, would be officially published on July 12, 2011. Martin claimed this July 2011 publication date was different from the previous publication dates mentioned, in that this was \"real\", as opposed to earlier \"wishful thinking, boundless optimism, cockeyed dreams, and honest mistakes\". On March 12, he revealed that the unfinished manuscript had exceeded \\'\\'A Storm of Swords\\'\\' in length, making it the longest volume in the entire series. On March 27, he announced that the manuscript had exceeded 1,600 pages. On April 27, 2011, it was announced that Martin had completed \\'\\'A Dance with Dragons\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': 'caa9ea1a48ec51dc54502d06058b5243'}>,\n",
+       "  <Document: {'content': 'On March 27, he announced that the manuscript had exceeded 1,600 pages. On April 27, 2011, it was announced that Martin had completed \\'\\'A Dance with Dragons\\'\\'. After incorporating requested changes made by his book editor and her staff copy-editors, suggested final draft notes from trusted friends, and his own final \"sweat\" (a line-by-line reread done to tighten and eliminate any unnecessary \"fat\" remaining in the manuscript), the final draft had been reduced to 1,510 pages; this made \\'\\'Dragons\\'\\' the second-longest novel, by a narrow margin, to volume three, \\'\\'A Storm of Swords\\'\\'. The novel was now ready to meet its July 12, 2011 release date.\\n\\nOn June 29, 2011, Amazon Germany had mistakenly released 180 copies of the novel early. Martin requested that those who held copies not spoil the book for fans who had to wait. Several \\'\\'A Song of Ice and Fire\\'\\' websites put an embargo in place on their forums with the same intent.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': 'ad3565f907a294979bdfab9f1cd4f757'}>,\n",
+       "  <Document: {'content': 'Several \\'\\'A Song of Ice and Fire\\'\\' websites put an embargo in place on their forums with the same intent.\\n\\n==Editions==\\n\\'\\'\\'Foreign-language editions\\'\\'\\'\\n* Bulgarian: Бард: \"Танц с Дракони\" (\"Dance with Dragons\")\\n* Catalan: Alfaguara: \"Dansa amb dracs\" (\"Dance with Dragons\")\\n* Chinese (traditional): Three volumes, \"與龍共舞\" 上, 中 and 下\\n* Croatian: Two volumes, Algoritam: \"Ples zmajeva\" (\"A Dance of Dragons\")\\n* Czech: Talpress: \"Tanec s draky\" (\"A Dance with Dragons\")\\n* Danish: \"En dans med drager\" (\"A Dance with Dragons\")\\n* Dutch: Two volumes, Luitingh-Sijthoff: \"Oude vetes, Nieuwe strijd\", \"Zwaarden tegen draken\" (\"Old Feuds, New Battle\", \"Swords Against Dragons\")\\n* Estonian: Two volumes, hardcover : Varrak \"Tants Lohedega\" (\"A Dance with Dragons\") book 1 (09/23/2016) & book 2 (05/18/2017)\\n* Finnish: Two volumes: \"Lohikäärmetanssi\" (\"Dragondance\") 1 and 2\\n* French: Three volumes, Pygmalion: \"Le Bûcher d\\'un roi\", \"Les dragons de Meereen\", \"Une danse avec les dragons\" (\"The Stake/Pyre of a King\", \"The Dragons of Meereen\", \"A Dance with Dragons\")\\n* German: FanPro (2012): \"Drachenreigen\" (\"Dragons\\' Round\"). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': 'd5e7dbae4119df4c7e7139c9d949cee7'}>,\n",
+       "  <Document: {'content': '==Editions==\\n\\'\\'\\'Foreign-language editions\\'\\'\\'\\n* Bulgarian: Бард: \"Танц с Дракони\" (\"Dance with Dragons\")\\n* Catalan: Alfaguara: \"Dansa amb dracs\" (\"Dance with Dragons\")\\n* Chinese (traditional): Three volumes, \"與龍共舞\" 上, 中 and 下\\n* Croatian: Two volumes, Algoritam: \"Ples zmajeva\" (\"A Dance of Dragons\")\\n* Czech: Talpress: \"Tanec s draky\" (\"A Dance with Dragons\")\\n* Danish: \"En dans med drager\" (\"A Dance with Dragons\")\\n* Dutch: Two volumes, Luitingh-Sijthoff: \"Oude vetes, Nieuwe strijd\", \"Zwaarden tegen draken\" (\"Old Feuds, New Battle\", \"Swords Against Dragons\")\\n* Estonian: Two volumes, hardcover : Varrak \"Tants Lohedega\" (\"A Dance with Dragons\") book 1 (09/23/2016) & book 2 (05/18/2017)\\n* Finnish: Two volumes: \"Lohikäärmetanssi\" (\"Dragondance\") 1 and 2\\n* French: Three volumes, Pygmalion: \"Le Bûcher d\\'un roi\", \"Les dragons de Meereen\", \"Une danse avec les dragons\" (\"The Stake/Pyre of a King\", \"The Dragons of Meereen\", \"A Dance with Dragons\")\\n* German: FanPro (2012): \"Drachenreigen\" (\"Dragons\\' Round\"). Two volumes, Blanvalet (2012): \"Der Sohn des Greifen\", \"Ein Tanz mit Drachen\" (\"The Son of the Griffin\", \"A Dance with Dragons\")\\n* Greek: Two volumes, Anubis: \"Το Κάλεσμα της Φλόγας\", \"Το Σπαθί στο Σκοτάδι\" (\"Call of the Flame\", \"The Sword in the Darkness\")\\n* Hebrew: \"ריקוד עם דרקונים חלק א – חלומות ואבק, ריקוד עם דרקונים חלק ב – לאחר המשתה\" (\"Dance with Dragons Part I – Dreams and Dust, Dancing with Dragons Part II – After the Feast\")\\n* Hungarian: Alexandra Könyvkiadó: \"Sárkányok tánca\" (\"Dance of Dragons\")\\n* Italian: Three volumes, Arnoldo Mondadori Editore (2011, 2012): \"I guerrieri del ghiaccio\", \"I fuochi di Valyria\", \"La danza dei draghi\" (\"The Warriors of Ice\", \"The Fires of Valyria\", \"The Dance of Dragons\")\\n* Japanese: Hardcover : Three volumes, Hayakawa (2013): \"竜との舞踏\" (\"Dance with Dragons\") I, II and III, paperback : Hayakawa (2016), I, II and III\\n* Latvian: \"Deja ar Pūķiem\" (\"A Dance with Dragons\")\\n* Lithuanian: Two volumes: \"Šokis su Drakonais: Sapnai ir Dulkės\", \"Šokis su Drakonais: Po Puotos\" (\"Dance with Dragons: Dreams and the Dust\", \"Dance with Dragons: After the Feast\")\\n* Norwegian: Two volumes, Vendetta: \"Drømmer og støv\", \"Dragenes dans\" (\"Dreams and Dust\", \"Dance of the Dragons\")\\n* Polish: \"Taniec ze smokami\" (\"A Dance with Dragons\")\\n* Brazilian Portuguese: Leya: \"A Dança dos Dragões\" (\"The Dance of Dragons\")\\n* European Portuguese: Two volumes, Saída de Emergência: \"A Dança dos Dragões\", \"Os Reinos do Caos\" (\"A Dance of Dragons\", \"The Kingdom of Chaos\")\\n* Romanian: Nemira: \"Dansul dragonilor\" (\"The Dance of the Dragons\")\\n* Russian: AST: \"Танец с драконами\" (\"Dance with Dragons\")\\n* Serbian: Two volumes, Лагуна: \"Плес са змајевима Део први: Снови и прах\", \"Плес са змајевима Део други: После гозбе\" (\"A Dance with Dragons Part I: Dreams and Dust\", \"A Dance with Dragons Part II: After the Feast\")\\n* Slovenian: \"Ples z zmaji\" (\"A Dance with Dragons\")\\n* Spanish: Ediciones Gigamesh: \"Danza de dragones\" (\"Dance of Dragons\")\\n* Swedish: \"Drakarnas dans\" (\"Dance of the Dragons\")\\n* Turkish: \"Ejderhaların Dansı\" (\"Dance of Dragons\")\\n* Ukrainian: One volume, KM Publishing (2018): \"Танок драконів\" (\"A Dance of Dragons\")\\n* Vietnamese: Three volumes: \"Trò Chơi Vương Quyền 5A: Đánh thức Rồng Thiêng\", \"Trò Chơi Vương Quyền 5B: Trấn thủ thành Mereen\", \"Trò Chơi Vương Quyền 5C: Vũ điệu Rồng Thiêng\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': 'e79ec14d7dbc21c9b8384b9642b69448'}>,\n",
+       "  <Document: {'content': 'Two volumes, Blanvalet (2012): \"Der Sohn des Greifen\", \"Ein Tanz mit Drachen\" (\"The Son of the Griffin\", \"A Dance with Dragons\")\\n* Greek: Two volumes, Anubis: \"Το Κάλεσμα της Φλόγας\", \"Το Σπαθί στο Σκοτάδι\" (\"Call of the Flame\", \"The Sword in the Darkness\")\\n* Hebrew: \"ריקוד עם דרקונים חלק א – חלומות ואבק, ריקוד עם דרקונים חלק ב – לאחר המשתה\" (\"Dance with Dragons Part I – Dreams and Dust, Dancing with Dragons Part II – After the Feast\")\\n* Hungarian: Alexandra Könyvkiadó: \"Sárkányok tánca\" (\"Dance of Dragons\")\\n* Italian: Three volumes, Arnoldo Mondadori Editore (2011, 2012): \"I guerrieri del ghiaccio\", \"I fuochi di Valyria\", \"La danza dei draghi\" (\"The Warriors of Ice\", \"The Fires of Valyria\", \"The Dance of Dragons\")\\n* Japanese: Hardcover : Three volumes, Hayakawa (2013): \"竜との舞踏\" (\"Dance with Dragons\") I, II and III, paperback : Hayakawa (2016), I, II and III\\n* Latvian: \"Deja ar Pūķiem\" (\"A Dance with Dragons\")\\n* Lithuanian: Two volumes: \"Šokis su Drakonais: Sapnai ir Dulkės\", \"Šokis su Drakonais: Po Puotos\" (\"Dance with Dragons: Dreams and the Dust\", \"Dance with Dragons: After the Feast\")\\n* Norwegian: Two volumes, Vendetta: \"Drømmer og støv\", \"Dragenes dans\" (\"Dreams and Dust\", \"Dance of the Dragons\")\\n* Polish: \"Taniec ze smokami\" (\"A Dance with Dragons\")\\n* Brazilian Portuguese: Leya: \"A Dança dos Dragões\" (\"The Dance of Dragons\")\\n* European Portuguese: Two volumes, Saída de Emergência: \"A Dança dos Dragões\", \"Os Reinos do Caos\" (\"A Dance of Dragons\", \"The Kingdom of Chaos\")\\n* Romanian: Nemira: \"Dansul dragonilor\" (\"The Dance of the Dragons\")\\n* Russian: AST: \"Танец с драконами\" (\"Dance with Dragons\")\\n* Serbian: Two volumes, Лагуна: \"Плес са змајевима Део први: Снови и прах\", \"Плес са змајевима Део други: После гозбе\" (\"A Dance with Dragons Part I: Dreams and Dust\", \"A Dance with Dragons Part II: After the Feast\")\\n* Slovenian: \"Ples z zmaji\" (\"A Dance with Dragons\")\\n* Spanish: Ediciones Gigamesh: \"Danza de dragones\" (\"Dance of Dragons\")\\n* Swedish: \"Drakarnas dans\" (\"Dance of the Dragons\")\\n* Turkish: \"Ejderhaların Dansı\" (\"Dance of Dragons\")\\n* Ukrainian: One volume, KM Publishing (2018): \"Танок драконів\" (\"A Dance of Dragons\")\\n* Vietnamese: Three volumes: \"Trò Chơi Vương Quyền 5A: Đánh thức Rồng Thiêng\", \"Trò Chơi Vương Quyền 5B: Trấn thủ thành Mereen\", \"Trò Chơi Vương Quyền 5C: Vũ điệu Rồng Thiêng\". (\"Game of Thrones 5A: Awake Dragon\", \"Game of Thrones 5B: Defense on Mereen\", \"Game of Thrones 5C: Dance of Dragons\")\\n\\n==Reception==\\n\\n===Critical response===\\n\\'\\'The Atlantic\\'\\' Rachael Brown found \\'\\'A Dance with Dragons\\'\\' \"infinitely more satisfying than its predecessor, 2005\\'s bleak and plodding \\'\\'A Feast for Crows\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': '3eb84fabefc2ca973ba5310c5b158e02'}>,\n",
+       "  <Document: {'content': '(\"Game of Thrones 5A: Awake Dragon\", \"Game of Thrones 5B: Defense on Mereen\", \"Game of Thrones 5C: Dance of Dragons\")\\n\\n==Reception==\\n\\n===Critical response===\\n\\'\\'The Atlantic\\'\\' Rachael Brown found \\'\\'A Dance with Dragons\\'\\' \"infinitely more satisfying than its predecessor, 2005\\'s bleak and plodding \\'\\'A Feast for Crows\\'\\'. The aspects of Martin\\'s work that have endeared him to fans are abundant here – rich world building, narrative twists and turns, and gritty depictions of the human struggle for power. Characters who were sorely missed in \\'\\'Feast\\'\\' – Daenerys Targaryen, Tyrion Lannister, and Jon Snow – make up more than a third of the novel, and Martin is wise enough to give us at least a chapter from (almost) everyone else. Weaknesses that have plagued Martin\\'s previous books are also present: too much repetition, unexceptional prose, and characters who use the same idioms (and have sex in exactly the same manner) no matter their ethnicity, social class, or continent. But while \\'\\'A Dance with Dragons\\'\\' cries out for better editing, it remains entirely engrossing. Martin has hidden so many clues and red herrings throughout his previous volumes that it is a thrill to see certain pieces fall into place.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': '2267265af380de430821f99fcef960d3'}>,\n",
+       "  <Document: {'content': 'Martin has hidden so many clues and red herrings throughout his previous volumes that it is a thrill to see certain pieces fall into place.\"Remy Verhoeve of \\'\\'The Huffington Post\\'\\' said, \"\\'\\'A Dance with Dragons\\'\\' is just a book, of course. It is not the Second Coming or anything. And I understand that the author must feel a lot of pressure concerning this one (he should). It\\'s late and it has to repair some of the damage done by \\'\\'A Feast for Crows\\'\\', which frankly felt as if it was written by a ghost writer at times. Finally it is here, and some of the things we\\'ve been wondering about for more than a decade are actually revealed (not everything, but at least some things). It has the same structural problems as the previous book; it is sprawling and incoherent at times, but at least the characters are more interesting than in the previous installment. It does feel like I\\'m reading a bunch of separate stories within the same setting–the chapters are told through the eyes of various characters–but that doesn\\'t really bother me as I love the setting and like to see it through various points of view. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': 'f907563ae7d212560f1b927c3b903095'}>,\n",
+       "  <Document: {'content': 'It does feel like I\\'m reading a bunch of separate stories within the same setting–the chapters are told through the eyes of various characters–but that doesn\\'t really bother me as I love the setting and like to see it through various points of view. Theories that have been bandied about online for the last ten years mostly come true in this book, so in that respect the novel isn\\'t shocking but neither is it disappointing. Martin also manages to put in a few twists, but ends the book much like he did the previous one with cliffhangers instead of wrapping things up a little better so the next long wait won\\'t hurt so much.\"David Orr of \\'\\'The New York Times\\'\\' said, \"\\'\\'A Dance With Dragons\\'\\' comes in at roughly 9,574,622,012 pages, and smart money says the final two books in the series will make this one look like \\'The Old Man and the Sea\\'. Such length isn\\'t necessary, and it hurts Martin\\'s prose and his plot mechanics. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 28}, 'embedding': None, 'id': '3a4a01e6868032b989327ddec01385b1'}>,\n",
+       "  <Document: {'content': 'David Orr of \\'\\'The New York Times\\'\\' said, \"\\'\\'A Dance With Dragons\\'\\' comes in at roughly 9,574,622,012 pages, and smart money says the final two books in the series will make this one look like \\'The Old Man and the Sea\\'. Such length isn\\'t necessary, and it hurts Martin\\'s prose and his plot mechanics. Tyrion \\'waddles\\' at least 12 times here, and even if we suppose the unflattering word reflects Tyrion\\'s contempt for his own awkward gait, it seems unlikely he would indulge this contempt when he\\'s, say, fighting for his life. Similarly, when your novel\\'s terrain stretches across hundreds of miles and your world lacks jet propulsion, as an author you face some basic problems of transportation that can result in conveyance via Rube Goldberg.\"\\'\\'Time\\'\\' James Poniewozik found a possible \"weakness to the early Meereen sections by marking time (and making Dany uncharacteristically indecisive) to allow time for all the pieces to fall into place\" and said, \"All this makes for a thousand-page book that feels half as long, that moves dextrously, answers key questions and gobsmacks you with convincing feints and change-ups. As in \\'\\'AFFC\\'\\', there are sections that feel like they could have used an editor. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 29}, 'embedding': None, 'id': 'e44ce1c07cc87ef89b4e08d9fd23cd30'}>,\n",
+       "  <Document: {'content': '\\'\\'Time\\'\\' James Poniewozik found a possible \"weakness to the early Meereen sections by marking time (and making Dany uncharacteristically indecisive) to allow time for all the pieces to fall into place\" and said, \"All this makes for a thousand-page book that feels half as long, that moves dextrously, answers key questions and gobsmacks you with convincing feints and change-ups. As in \\'\\'AFFC\\'\\', there are sections that feel like they could have used an editor. In some chapters you suddenly find yourself in a strange land with a character you have little attachment to, wondering where this thread is going, as if you had stayed too long at a party after the friends you came with have left.\\n\\n\\'\\'The Washington Post\\'\\' Bill Sheehan said, \"Filled with vividly rendered set pieces, unexpected turnings, assorted cliffhangers and moments of appalling cruelty, \\'\\'A Dance With Dragons\\'\\' is epic fantasy as it should be written: passionate, compelling, convincingly detailed and thoroughly imagined. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 30}, 'embedding': None, 'id': '2adea5c9147399c3a414768eba117875'}>,\n",
+       "  <Document: {'content': '\\'\\'The Washington Post\\'\\' Bill Sheehan said, \"Filled with vividly rendered set pieces, unexpected turnings, assorted cliffhangers and moments of appalling cruelty, \\'\\'A Dance With Dragons\\'\\' is epic fantasy as it should be written: passionate, compelling, convincingly detailed and thoroughly imagined. Despite a number of overtly fantastic elements (dragons, seers, shape shifters and sorcerers), the book — and the series as a whole — feels grounded in the brutal reality of medieval times and has more in common with the Wars of the Roses than it does with \\'\\'The Lord of the Rings\\'\\'. The result is a complex summer blockbuster with brains and heart, a book with rare — and potentially enormous — appeal.\\n\\nMegan Wasson of \\'\\'The Christian Science Monitor\\'\\' said, \"\\'\\'A Dance with Dragons\\'\\' may well be one of the best books in the five-book series so far. Martin\\'s prose is concise but pithy, begging to be devoured over and over again. All the fans\\' favorite characters make an appearance, unlike in the last book. But what truly sets this book above some of the others in the series is Martin\\'s ability to keep his readers on their toes and the edges of their seats. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 31}, 'embedding': None, 'id': 'b51cf08c9cc513e4532e9e6bd28ffea2'}>,\n",
+       "  <Document: {'content': 'But what truly sets this book above some of the others in the series is Martin\\'s ability to keep his readers on their toes and the edges of their seats. No character, no matter how likeable and seemingly important, is ever safe from Martin\\'s pen (remember Ned Stark?), and on the other hand, no one can ever be truly pronounced dead. What you thought was going to happen after reading Books No. 1, 2, and even 3 and 4, now clearly will not happen, and that\\'s where Martin\\'s strength lies.\"===Awards===\\nIn April 2012, \\'\\'A Dance with Dragons\\'\\' was nominated for the 2012 Hugo Award for Best Novel.  In May 2012, it was nominated for the 2012 Locus Award for Best Fantasy Novel and won it in June 2012. In August 2012, the novel was nominated for a World Fantasy Award for Best Novel.\\n\\n==References==\\n\\n==External links==\\n*  of author George R. R. Martin\\n*\\n*\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 32}, 'embedding': None, 'id': 'a3736b25be33ef08206fb25e75a485f3'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Mountain and the Viper\\'\\'\\'\" is the eighth episode of the fourth season of HBO\\'s acclaimed fantasy television series \\'\\'Game of Thrones\\'\\', and the 38th overall. The episode was written by series co-creators David Benioff and D. B. Weiss, and directed by Alex Graves. It aired on June 1, 2014.\\n\\nThis episode marks the final appearance of Pedro Pascal (Oberyn Martell).\\n\\n==Plot==\\n\\n===At the Wall===\\nMole\\'s Town is attacked by wildlings, but Ygritte spares Gilly and her son\\n\\n===At Moat Cailin===\\nTheon enters Moat Cailin and gives the terms for their surrender to Ralf Kenning. Ralf declines, but is murdered by one of his men, who agrees. However, after the Ironborn open the Moat\\'s gates, Ramsay\\'s army slaughters them. As a reward for retaking the Moat, Roose legitimizes Ramsay as a true Bolton. Bolton\\'s army and Reek depart for their new seat at Winterfell.\\n\\n===In Meereen===\\nBarristan receives a letter with the Hand of the King\\'s seal. The letter is Jorah\\'s royal pardon signed by Robert for spying on Daenerys. Jorah admits his spying on Daenerys and she exiles him.\\n\\n===In the Vale===\\nPetyr is interrogated by the nobility of the Vale about Lysa\\'s death. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '43967ec76b2a3cf2ffc1fd0313d559eb'}>,\n",
+       "  <Document: {'content': \"Jorah admits his spying on Daenerys and she exiles him.\\n\\n===In the Vale===\\nPetyr is interrogated by the nobility of the Vale about Lysa's death. He claims that Lysa committed suicide. Lord Yohn Royce demands to speak with Sansa, who reveals her identity, corroborates Petyr's story and convinces them of his innocence. Petyr, Sansa and Robin leave the Eyrie to tour the Vale. Sansa dyes her hair black to hide her identity. Sandor and Arya arrive at Bloody Gate, only to be informed of Lysa's death, by Donnel.\\n\\n===In King's Landing===\\nTyrion's trial by combat begins. Oberyn knocks Gregor to the ground, but instead of finishing him, Oberyn screams at him to confess that Tywin gave him the order to kill Elia and her children. Gregor knocks Oberyn down, confessing to the murder of Elia and her children and crushing Oberyn's skull with his hands. Tywin sentences Tyrion to death for\\xa0regicide.\\n\\n==Production==\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'f8fee3ecd82a6e80bd72375ac20926dd'}>,\n",
+       "  <Document: {'content': 'Tywin sentences Tyrion to death for\\xa0regicide.\\n\\n==Production==\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode contains content from three of George Martin\\'s novels: \\'\\'A Storm of Swords\\'\\', chapters Daenerys V, Daenerys VI, and Tyrion X; \\'\\'A Feast for Crows\\'\\', chapters Alayne I and Alayne II; and \\'\\'A Dance with Dragons\\'\\', chapter Reek II.\\n\\n==Reception==\\n=== Ratings ===\\n\"The Mountain and the Viper\" was watched by an estimated 7.17 million people during its first airing. In the United Kingdom, the episode was viewed by 1.811 million viewers, making it the highest-rated broadcast that week. It also received 0.062 million timeshift viewers.\\n\\n=== Critical reception ===\\nThe episode received high praise from critics and audiences alike, with the fight between Prince Oberyn and the Mountain being hailed as the episode\\'s highlight. On Rotten Tomatoes it obtained a 97% score, based on 32 reviews, with an average rating of 9/10. The website\\'s critical consensus reads, \"With one of the most gruesome scenes to date, \\'The Mountain and the Viper\\' delivers a tense, twisty final scene well worth the wait.\"Writing for \\'\\'The A.V. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '639758a024fa6ba71a91be123ecb8aba'}>,\n",
+       "  <Document: {'content': 'The website\\'s critical consensus reads, \"With one of the most gruesome scenes to date, \\'The Mountain and the Viper\\' delivers a tense, twisty final scene well worth the wait.\"Writing for \\'\\'The A.V. Club\\'\\', Emily VanDerWerff gave the episode an A- and praised the staging of the final fight by director Alex Graves. Erik Adams, also writing for the \\'\\'A.V. Club\\'\\' gave the episode an A. Terri Schwartz, writing for Zap2it.com, wrote the episode was \"one of the strongest \\'\\'Game of Thrones\\'\\' episodes to date, and that\\'s just the type of episode that the death of Oberyn Martell deserves.\"===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2014\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Art Direction for a Single-Camera Fantasy Series\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi\\n\\n2015\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley\\n\\nSFX Awards\\n\\nBest TV Episode\\n\\nHugo Awards\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff, Alex Graves, and D. B. Weiss\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '82d8a7d59bb3d017cd7f597b0901f800'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Second Sons\\'\\'\\'\" is the eighth episode of the third season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 28th episode of the series. The episode was written by executive producers David Benioff and D. B. Weiss, and directed by Michelle MacLaren. It aired on .\\n\\nThe episode is centered on the wedding of Tyrion Lannister and Sansa Stark in King\\'s Landing, Gendry\\'s arrival at Dragonstone and Daenerys\\'s meeting with the mercenary company of the Second Sons before the walls of Yunkai.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nTyrion visits Sansa to ease her apprehension at the prospect of being his wife. In the Sept of Baelor, Cersei threatens Margaery with the story of House Reyne, former Lannister vassals whom Tywin exterminated when they rebelled against him. After arriving at the Sept, Sansa is walked down the aisle by Joffrey.\\n\\nAt their wedding feast, Tyrion gets drunk. Joffrey, after threatening to rape Sansa, calls for the traditional bedding ceremony to begin, but his plan is thwarted when Tyrion threatens to castrate an outraged Joffrey. Tywin defuses the situation. Tyrion leaves the feast with Sansa, and he tells Sansa he will not share her bed until she wants him to.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'cca00568aee4e0899cdab10f267cc5da'}>,\n",
+       "  <Document: {'content': \"Tyrion leaves the feast with Sansa, and he tells Sansa he will not share her bed until she wants him to.\\n\\n===At Dragonstone===\\nMelisandre returns to Dragonstone with Gendry and takes him to see Stannis, who recognizes Gendry as one of Robert's bastards. When Gendry is taken to his chamber, Stannis and Melisandre discuss what they intend to do with him.\\n\\nIn the dungeons, Davos continues to learn to read. Stannis visits him to discuss Melisandre's plan to sacrifice Gendry. Davos objects to the plan, but Stannis remains resolved. He then makes Davos swear to never raise his hand to Melisandre again, and frees him.\\n\\nLater, Melisandre visits Gendry and seduces him, tying him to a bed in the process. She lays three leeches on him to draw his royal blood. Stannis ritually burns the leeches, speaking the names of the usurpers to his throne: Robb Stark, Balon Greyjoy, and Joffrey Baratheon.\\n\\n===In the Riverlands===\\nArya tries to kill the Hound while he is sleeping, but he is revealed to be awake, and thwarts her attempt on his life. They depart their camp and head for the Twins, where the Hound intends to ransom Arya to Robb.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '7f509021deeb21d914524674d7103725'}>,\n",
+       "  <Document: {'content': \"===In the Riverlands===\\nArya tries to kill the Hound while he is sleeping, but he is revealed to be awake, and thwarts her attempt on his life. They depart their camp and head for the Twins, where the Hound intends to ransom Arya to Robb.\\n\\n===At Yunkai===\\nJorah tells Daenerys that Yunkai has employed a mercenary group called the Second Sons. Daenerys meets with Mero, his co-captain Prendahl na Ghezn and his lieutenant Daario Naharis. She attempts to bribe Mero to fight for her, and gives him two days to make a decision.\\n\\nAfter nightfall, Daario enters Daenerys' camp, disguised as an Unsullied soldier. He enters her tent and shows her and Missandei the severed heads of Mero and Prendahl, admitting that he is smitten by Daenerys.\\n\\n===Beyond the Wall===\\nSam and Gilly continue their journey to the Wall. They stop at an abandoned hut for the night. When they hear a murder of crows cawing nearby, Sam leaves the hut to investigate. Soon after, he is attacked by a White Walker. The Walker intents on taking Gilly's son, but Sam stabs it with his dragonglass dagger, causing the Walker to disintegrate.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'e2c90efbcc4092d92464264f681371f8'}>,\n",
+       "  <Document: {'content': 'The Walker intents on taking Gilly\\'s son, but Sam stabs it with his dragonglass dagger, causing the Walker to disintegrate.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"Second Sons\" was written by the show creators and executive producers David Benioff and D. B. Weiss, based on material from George R. R. Martin\\'s novel \\'\\'A Storm of Swords\\'\\'. The episode adapts parts of the book\\'s chapters 19, 29, 37, 43, 47 and 48 (Samwell I, Sansa III, Davos IV, Daenerys IV, Samwell III and Arya IX).\\n\\n===Casting===\\nThe episode introduced the mercenary captains in service of Yunkai: Mark Killeen was cast as Mero (known as the Titan\\'s Bastard), Ed Skrein the recurring role of Daario Naharis, and Ramon Tikaram the part of Prendahl na Ghezn. Tikaram is mistakenly credited as \"Ramon Tikrum\" in the closing credits.\\n\\n===Filming locations===\\nMost of the scenes of the episode were shot in the Belfast studios of The Paint Hall, including the wedding of Tyrion and Sansa that was filmed at the huge semicircular set of the Great Sept of Baelor in mid September 2012. For this scene, a few hundred extras were recruited.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'acb061ea5521482ac9a142d5036eb80f'}>,\n",
+       "  <Document: {'content': '===Filming locations===\\nMost of the scenes of the episode were shot in the Belfast studios of The Paint Hall, including the wedding of Tyrion and Sansa that was filmed at the huge semicircular set of the Great Sept of Baelor in mid September 2012. For this scene, a few hundred extras were recruited.\\n\\n==Reception==\\n===Ratings===\\nThe episode received 5.1 million viewers, an increase from the previous week, with an 18-49 demographic of 2.6. In the United Kingdom, the episode was seen by 0.907 million viewers on Sky Atlantic, being the channel\\'s highest-rated broadcast that week.\\n\\n===Critical reception===\\n\"Second Sons\" received critical acclaim. Review aggregator Rotten Tomatoes surveyed 20 reviews of the episode and judged 100% of them to be positive with an average score of 8 out of 10. The website\\'s critical consensus reads, \" \\'Second Sons\\' shines through efficient storytelling -- and a comparatively low number of storylines to keep track of this week.\"Writing for IGN, Matt Fowler rated the episode a 9.0/10, and wrote \"This week\\'s well-crafted and wonderfully acted Game of Thrones gave us a cold wedding, a hot bath and a blood-letting.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '665447811e009cc2cb8021eeed498a1'}>,\n",
+       "  <Document: {'content': 'Writing for IGN, Matt Fowler rated the episode a 9.0/10, and wrote \"This week\\'s well-crafted and wonderfully acted Game of Thrones gave us a cold wedding, a hot bath and a blood-letting.\"He especially praised the scenes between Sansa and Tyrion and between Ser Davos and Stannis. David Sims and Emily VanDerWerff, both writing for The A.V. Club, gave the episode \"B\" ratings. Sims was frustrated by the episode\\'s meandering pace, but praised the end of the episode, with Sam killing the white walker, as \"the most crucial, fascinating, electric moment of the night\". VanDerWerff praised the use of nudity in the episode, writing \"...I actually think Game Of Thrones has gotten quite a bit better at utilizing nudity and sex in the midst of everything else as a method of telling its story. It’s come a long way from the \\'sexposition\\' days of season one, when it sometimes seemed like the series would toss some breasts into the background of a scene just in case we got bored of hearing somebody talk at length.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'c07114fdc35dbf9a678f1edd7ae67bc6'}>,\n",
+       "  <Document: {'content': 'It’s come a long way from the \\'sexposition\\' days of season one, when it sometimes seemed like the series would toss some breasts into the background of a scene just in case we got bored of hearing somebody talk at length.\"===Accolades===\\nDue to his nomination, Peter Dinklage submitted this episode for consideration for the Primetime Emmy Award for Outstanding Supporting Actor in a Drama Series for the 65th Primetime Emmy Awards.\\n\\nAt the 65th Primetime Creative Arts Emmy Awards, the episode was nominated for Outstanding Hairstyling for a Single-Camera Series.\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Second Sons\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'da645181d022b5ee7f7e4cee304aab71'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''Roose Bolton''' is a fictional character in the ''A Song of Ice and Fire'' series of fantasy novels by American author George R. R. Martin, and its television adaptation ''Game of Thrones''.\\n\\nIntroduced in 1996's ''A Game of Thrones'', Roose, a Northern lord with his seat at the Dreadfort, is a retainer of Lord Eddard Stark. His family is notorious for their cruelty and custom of flaying their enemies; he frequently has himself leeched, leading him to be known as '''the Leech Lord''' across Westeros. He later appears in ''A Clash of Kings'' (1998), ''A Storm of Swords'' (2000), and ''A Dance with Dragons'' (2011).\\n\\nRoose joins Robb Stark's rebellion as one of his chief lieutenants. With the help of the Brave Companions, he takes and holds Harrenhal until rejoining Robb Stark to help retake the Neck occupied by the Greyjoys. In truth he is a chief orchestrator of the Red Wedding alongside Tywin Lannister and Lord Walder Frey, receiving the title of Warden of the North from the former after personally slaying Robb. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '51ff18519f3d4cb58207312ecb2ce6b9'}>,\n",
+       "  <Document: {'content': \"In truth he is a chief orchestrator of the Red Wedding alongside Tywin Lannister and Lord Walder Frey, receiving the title of Warden of the North from the former after personally slaying Robb. His rule as Warden in the North is punctuated by unrest, and several forces conspire to unseat him and House Frey and restore the Starks to power.\\n\\nRoose is portrayed by Irish actor Michael McElhatton in the HBO television adaptation.\\n\\n==Character description==\\nLord Roose Bolton is a significant vassal of Lord Eddard Stark. His seat is the Dreadfort and his sigil is a flayed man, an homage to the ancient Bolton tradition of flaying enemies. He is nicknamed '''the Leech Lord''' for regular leechings meant to improve his health.\\n\\nRoose Bolton is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, such as Catelyn Stark, Arya Stark and Theon Greyjoy/Reek.\\n\\n===Background===\\nRoose practiced an ancient and illicitly banned tradition called the first night, in which a lord had the right to bed a commoner's wife. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '745b2a3c05ee31c800411a2d2a6f274f'}>,\n",
+       "  <Document: {'content': \"===Background===\\nRoose practiced an ancient and illicitly banned tradition called the first night, in which a lord had the right to bed a commoner's wife. One day, prior to Robert's Rebellion, Roose was fox hunting along the Weeping Water when he came across a young woman washing clothes in the stream, whom was married to the old miller without Roose's knowledge. Desiring the woman, and angered for not being informed of the marriage, Roose had the miller hanged and violently raped the woman beneath the tree where her husband still hung.\\n\\nA year later, the woman came to the Dreadfort with a newborn boy whom she claimed was Roose's bastard, named Ramsay. Roose considered having the woman whipped and the baby killed before realizing that the baby is indeed his, owing to its pale, cold eyes. He gave the woman money and annual supplies as payment to raise Ramsay, and had the tongue of the miller's brother cut out to prevent Lord Rickard Stark from being informed of his illegal activities. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '444b51afbd73ccf9592fc5d8c3851ea1'}>,\n",
+       "  <Document: {'content': \"He gave the woman money and annual supplies as payment to raise Ramsay, and had the tongue of the miller's brother cut out to prevent Lord Rickard Stark from being informed of his illegal activities. He sends his odious and depraved servant Reek to the young Ramsay and his mother after the latter demands a servant to help raise the boy, an act he considers to be highly amusing. Roose later participates in Robert's Rebellion and (according to a semi-canonical source) the Greyjoy Rebellion.\\n\\nDespite Roose's orders, Roose's only trueborn son, Domeric, seeks out Ramsay. Domeric dies soon after, and Roose suspects that Ramsay poisoned Domeric to become his heir. Roose, left without a trueborn heir, brings Ramsay to the Dreadfort, where he later serves as the fortress's castellan and has thus far refused to legitimize or otherwise acknowledge him as his true heir. He considers his bastard son foolhardy and tactless, with 'tainted blood that would poison even the leeches'.\\n\\n===Appearance and personality===\\nRoose is most often described as an oddly average and unassuming-looking man, clean-shaven with pallid skin. He is often described as ageless, with an apathetic demeanor that betrays nothing. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '396d6c8ff3b352d157e042d31f48c12d'}>,\n",
+       "  <Document: {'content': '===Appearance and personality===\\nRoose is most often described as an oddly average and unassuming-looking man, clean-shaven with pallid skin. He is often described as ageless, with an apathetic demeanor that betrays nothing. His most prominent feature are his strangely pale and cold eyes, so light as to almost blend with the sclera.\\n\\nRoose appears to be unassuming and mild-mannered, although in truth he is remorseless and unforgiving, possessed of the same cruelty and ruthlessness his house is notorious for. Theon Greyjoy believes that he is even more cruel and menacing than his bastard son, despite Ramsay\\'s more evident depravity. While his voice is small and soft, he does not need to raise it in order to inspire silence and attention – Jaime Lannister and Robb Stark both remark that even just his silence is threatening. His personal motto is \"A peaceful land, a quiet people\", a lesson he has thus far failed to instill into his bastard son. He often dresses in a pale pink fur cloak embroidered in blood red to symbolize his family\\'s custom of flaying.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '7e06a08a4012eecac8344a51b97de4b7'}>,\n",
+       "  <Document: {'content': 'He often dresses in a pale pink fur cloak embroidered in blood red to symbolize his family\\'s custom of flaying.\\n\\n== Storylines ==\\n\\n====\\'\\'A Game of Thrones\\'\\'====\\n\\nRoose is amongst the lords who travel to Winterfell to aid Robb Stark in his campaign against the Lannisters. His intelligence and caution sees him given command of part of the Northern host when the army splits up at the Twins, and he leads the attack on Tywin Lannister\\'s army in the Battle of the Green Fork. The battle ends in a Lannister victory and Roose retreats with the survivors to the causeway of Moat Cailin.\\n\\n====\\'\\'A Clash of Kings\\'\\'====\\n\\nTo form an alliance with House Frey, Roose is offered by Lord Walder Frey one of his female family members hand in marriage, as well as offering the prospective bride\\'s weight in silver for her dowry. Roose chooses \"Fat\" Walda Frey, the fattest female member of House Frey. He also makes an alliance with the Brave Companions, Essosi sellswords employed by Tywin, to help the Northerners capture Harrenhal from the Lannister force occupying it. After capturing Harrenhal Roose takes Arya Stark as a servant, mistaking her for a commoner.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '51ae48fc8837184b0dfcf3a088c92fc7'}>,\n",
+       "  <Document: {'content': \"He also makes an alliance with the Brave Companions, Essosi sellswords employed by Tywin, to help the Northerners capture Harrenhal from the Lannister force occupying it. After capturing Harrenhal Roose takes Arya Stark as a servant, mistaking her for a commoner.\\n\\n====''A Storm of Swords''====\\n\\nHoat brings Jaime Lannister to Harrenhal, having cut off Jaime's hand in hope of blaming Roose and preventing the Boltons allying with the Lannisters. Roose has Jaime sent back to King's Landing after Jaime assures Roose that he will not blame him. Roose then travels to the Twins for Edmure Tully's wedding to Roslin Frey, but at the wedding the Freys turn on the Starks and Roose personally kills Robb Stark. It is revealed that Roose had conspired with the Freys and Tywin Lannister to betray the Starks. As reward for his service, Tywin names Roose the new Warden of the North\\n\\n====''A Feast for Crows'' and ''A Dance With Dragons''====\\n\\nRoose returns North with his forces, joined by two thousand Frey men. Meeting with Ramsay (now legitimised as a Bolton) and a captive Theon Greyjoy, the Boltons travel to Barrowton for Ramsay's wedding to Jeyne Poole, forced to assume the identity of Arya Stark. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'bd8b1cd3a9313df0a9a3c4783bbcc829'}>,\n",
+       "  <Document: {'content': \"Meeting with Ramsay (now legitimised as a Bolton) and a captive Theon Greyjoy, the Boltons travel to Barrowton for Ramsay's wedding to Jeyne Poole, forced to assume the identity of Arya Stark. After hearing that Stannis Baratheon has captured Deepwood Motte, Roose decides to move the wedding to Winterfell to bait Stannis out. The Boltons and their Northern allies (many of whom are only grudgingly pledging fealty to the Boltons, or plan to betray them) remain at Winterfell after the wedding in anticipation of Stannis' attack. Tensions are high during the wedding due to the anger of the Northmen at the Freys. Three of the Freys who had been travelling with Lord Wyman Manderly of White Harbor, who lost his younger son Ser Wendel Manderly at the Red Wedding, have disappeared, and are heavily implied to have been put in pies which Wyman gives to the Freys and Boltons, eating some himself. Lady Barbrey Dustin of Barrowton, the younger sister of Bethany Ryswell, tells Theon that Roose has no feelings and plays with people for amusement. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'ba328e225fd5be9f13c2a06c937a39f6'}>,\n",
+       "  <Document: {'content': \"Lady Barbrey Dustin of Barrowton, the younger sister of Bethany Ryswell, tells Theon that Roose has no feelings and plays with people for amusement. When one of Walder Frey's grandsons, Little Walder Frey, is found murdered (possibly by his cousin Big Walder Frey), their uncle Ser Hosteen Frey attacks Wyman, leading to a fight in which White Harbor and Frey men are killed. Roose is forced to send them both out of Winterfell to encounter Stannis.\\n\\n=== Family tree of House Bolton ===\\n\\n;Notes\\n\\n== TV adaptation ==\\n\\nRoose Bolton is played by Michael McElhatton in the HBO television adaption of the series of books. He and the rest of the cast were nominated for Screen Actors Guild Awards for Outstanding Performance by an Ensemble in a Drama Series in 2014.\\n\\n====Second season====\\nRoose declares for King in the North Robb Stark and serves as a chief member of his war council, although Robb sternly admonishes Roose when he advocates flaying Lannister prisoners to obtain information. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'c65e7f49a87247559de4a8cc61297d9d'}>,\n",
+       "  <Document: {'content': \"====Second season====\\nRoose declares for King in the North Robb Stark and serves as a chief member of his war council, although Robb sternly admonishes Roose when he advocates flaying Lannister prisoners to obtain information. After Theon Greyjoy betrays the Starks and seizes Winterfell, Roose brings the news to Robb and offers to send his bastard son Ramsay Snow with a force of Dreadfort men to oust Theon and the Ironborn from Winterfell.\\n\\n====Third season====\\nThe dagger with which Roose Bolton kills Robb Stark, and the knife with which Catelyn Stark kills Walder Frey's wife.\\nFollowing the Northern army's arrival at Harrenhal, Roose presents a letter from Ramsay claiming that the Ironborn sacked Winterfell before fleeing. Robb orders Roose and the Bolton forces to hold Harrenhal while the rest of his army rides to Riverrun. One of Roose's man-at-arms, Locke, captures the escaped Jaime Lannister and his escort Brienne of Tarth, cutting off Jaime's swordhand in the process, before bringing the two to Harrenhal. Roose agrees to let Jaime go, but keeps Brienne as a hostage, though Jaime later returns to secure her release. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'c3521d80f1b1e66b5dd21e7e09da68f6'}>,\n",
+       "  <Document: {'content': \"Roose agrees to let Jaime go, but keeps Brienne as a hostage, though Jaime later returns to secure her release. He then meets up with the Stark army at the Twins for the wedding of Edmure Tully and Roslin Frey. However, it is revealed that Roose has conspired with Lord Walder Frey to betray the Starks, and after the wedding the Freys and Bolton slaughter the Stark forces, with Roose personally killing Robb. As part of the Bolton-Frey alliance, Roose agrees to marry Walder's daughter Walda - Walder offers him the bride's weight in silver as dowry, so Roose decides to marry the fattest of Walder's daughters. In the aftermath of the massacre, Roose hints to Walder that his betrayal of Robb was motivated by resentment at having his advice ignored by Robb. Roose also reveals that Winterfell was actually sacked by his bastard Ramsay, who subsequently flayed the Ironborn garrison there and took Theon prisoner, for his own amusement. As reward for his defection, Tywin Lannister names Roose the Warden of the North.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '605f1edf10fcac7c39cc2f9597075f03'}>,\n",
+       "  <Document: {'content': 'Roose also reveals that Winterfell was actually sacked by his bastard Ramsay, who subsequently flayed the Ironborn garrison there and took Theon prisoner, for his own amusement. As reward for his defection, Tywin Lannister names Roose the Warden of the North.\\n\\n====Fourth season====\\nWith the Ironborn holding Moat Cailin - the fortification barring passage between the North and the rest of Westeros - Roose is forced to smuggle himself back into the North. Upon his return to the Dreadfort, he chastises Ramsay for having gelded Theon and sending terms of surrender to the Greyjoys without his approval, while reminding Ramsay of his bastard parentage. Insulted, Ramsay demonstrates how effectively he has broken Theon (whom he has since renamed \"Reek\") by having Reek shave him, even after revealing Roose\\'s murder of Robb, while also coaxing Reek into revealing he faked the deaths of Bran and Rickon Stark. After Ramsay points out that the other Northerners will turn on the Boltons if it is revealed that there is a living male Stark, Roose tasks Locke with hunting down Bran and Rickon and killing Jon Snow, Robb\\'s bastard half-brother. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'd5414b94206122329c63d346a536cd87'}>,\n",
+       "  <Document: {'content': 'After Ramsay points out that the other Northerners will turn on the Boltons if it is revealed that there is a living male Stark, Roose tasks Locke with hunting down Bran and Rickon and killing Jon Snow, Robb\\'s bastard half-brother. Roose also sends Ramsay and Reek to lift the siege of Moat Cailin; when Ramsay is successful, Roose presents him with a royal decree of legitimisation as a trueborn Bolton. Roose subsequently moves to rebuild and occupy Winterfell.\\n\\n====Fifth season====\\nIn the aftermath of Tywin Lannister\\'s death and Ramsay\\'s murder of a disobedient vassal and his family, Roose seeks to secure House Bolton\\'s position by arranging to have Ramsay marry Sansa Stark, supposedly the last trueborn Stark alive. In doing so, Roose seemingly secures an alliance with the forces of the Vale and its Lord Protector Petyr \"Littlefinger\" Baelish (unaware that Baelish intends on having the Bolton army decimated by the approaching Baratheon army before defeating the victor with the Vale\\'s army). After Ramsay torments Sansa by having Reek serve them at dinner, Roose announces that he and Walda are expecting a son. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '760f1800a03332ada6f6e2b7d738e335'}>,\n",
+       "  <Document: {'content': \"After Ramsay torments Sansa by having Reek serve them at dinner, Roose announces that he and Walda are expecting a son. However, later Roose privately reassures Ramsay of his position as his heir, and asks him to assist in defeating Stannis Baratheon's army. To this end, Roose permits Ramsay and his men to launch a sneak attack on Stannis' camp, destroying the army's supplies. With the supplies destroyed and most of Stannis' army subsequently deserting him, the Boltons easily defeat the Baratheons when they attempt to lay siege to Winterfell, but in the aftermath of the battle Reek and Sansa manage to escape, severely jeopardizing House Bolton's rule in the North.\\n\\n====Sixth season====\\nDespite their victory over Stannis, Roose warns Ramsay that the North will someday have to face the Lannisters, and chastises him for allowing Sansa and Theon to escape, as Sansa was crucial to unifying the North. He implies that if Sansa is not recovered, Ramsay's position as heir may be usurped by Walda's baby. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '744ba06402a9fb3808b3be6be973f032'}>,\n",
+       "  <Document: {'content': \"====Sixth season====\\nDespite their victory over Stannis, Roose warns Ramsay that the North will someday have to face the Lannisters, and chastises him for allowing Sansa and Theon to escape, as Sansa was crucial to unifying the North. He implies that if Sansa is not recovered, Ramsay's position as heir may be usurped by Walda's baby. Soon afterwards, it is announced that Walda has given birth to a boy; Ramsay immediately kills Roose by stabbing him in the stomach, before setting his dogs upon Walda and the baby, severely jeopardizing House Bolton and leaving Ramsay as the last remaining Bolton. Ramsay is ultimately killed when Jon Snow retakes Winterfell in the Battle of the Bastards, leaving House Bolton extinct.\\n\\n== References ==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'e93b46ebe3293699bd24ab071f3bef3c'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''''Rani Mahal''''' is an upcoming Indian television series, which will air on Sony Entertainment Television (India) and Sony Entertainment Television Asia. Many news sources claimed that this show is an Indian adaptation of American fantasy drama series ''Game of Thrones'' which based on best-selling novel ''A Song of Ice and Fire'', which has been denied by Sony Entertainment Television (India) and Lost Boy Productions.\\n\\n== Cast ==\\n*Yuvraj Thakur\\n*Anita Hassanandani\\n*Ankita Lokhande\\n*VJ Bani\\n*Reena Roy as Raj Mata\\n\\n==References==\\n\\n==External links==\\n*  Official website\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '5e997c167670b17cfc7479c9de62b8d5'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Winter Is Coming\\'\\'\\'\" is the series premiere episode of the HBO medieval fantasy television series \\'\\'Game of Thrones\\'\\'. The first episode of the first season, it was written by the show creators David Benioff and D. B. Weiss, in a faithful adaptation of the first chapters of George R. R. Martin\\'s book \\'\\'A Game of Thrones\\'\\'. The episode was directed by Tim Van Patten, redoing the work done by director Tom McCarthy in an unaired pilot.\\n\\nAs the first episode of the series, it introduces the setting and the main characters of the show. The episode centers on the Stark family, and how its lord, Eddard \"Ned\" Stark, gets involved in the court politics after the king chooses Eddard to replace his recently deceased chief administrator (\"Hand of the King\"). The episode received largely positive reviews, and was seen initially by 2.2 million viewers. A week before the episode first aired, HBO made the first 15 minutes available as an Internet preview.\\n\\nThe title of the episode is the motto (referred to as \"House Words\" in-universe) of House Stark, which is spoken several times in the episode and in the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '1250c7f4363788ddac725fb5b9a8dcda'}>,\n",
+       "  <Document: {'content': 'The title of the episode is the motto (referred to as \"House Words\" in-universe) of House Stark, which is spoken several times in the episode and in the series.\\n\\n==Plot==\\n===Beyond the Wall===\\nOn the continent of Westeros, rangers of the Night\\'s Watch scout wildling movements beyond the Wall, the massive ice barrier to the north, and discover demonic White Walkers and wildlings turned to undead wights. Will, the sole surviving ranger, flees South.\\n\\n===In King\\'s Landing===\\nWatching as the corpse of Jon Arryn, the Hand of the King, is tended to, Jaime Lannister assures his twin sister, Queen Cersei Lannister, that if Arryn had spoken to anyone about them, they would already have been executed.\\n\\n===In Pentos===\\nExiled prince Viserys Targaryen plots to reclaim his father\\'s throne from King Robert Baratheon, and brokers a marriage between his sister Daenerys and Dothraki warlord Khal Drogo. As wedding gifts, Daenerys is given books of the Seven Kingdoms from Ser Jorah Mormont, an exiled knight loyal to the Targaryens, and three petrified dragon eggs from Magister Illyrio Mopatis, who helped arrange the marriage.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '4028b00ef0f8cef8ffc5e645872687af'}>,\n",
+       "  <Document: {'content': 'As wedding gifts, Daenerys is given books of the Seven Kingdoms from Ser Jorah Mormont, an exiled knight loyal to the Targaryens, and three petrified dragon eggs from Magister Illyrio Mopatis, who helped arrange the marriage.\\n\\n===In the North===\\nThe Starks of Winterfell are introduced: Lord Eddard \"Ned\" Stark, his wife Lady Catelyn, their children – heir Robb, elder daughter Sansa, younger daughter Arya, ten-year-old son Bran, youngest son, Rickon, and Ned\\'s bastard son Jon Snow and ward Theon Greyjoy.\\n\\nNed takes his sons to witness Will\\'s execution for desertion. Ignoring Will\\'s warning of the White Walkers, Ned beheads him, insisting Walkers are long extinct. The Starks find a dead stag, sigil of House Baratheon, and a dead direwolf, sigil of the Starks, whose pups are taken in by the children.\\n\\nNews arrives of the death of Lord Arryn, Eddard\\'s friend and Catelyn\\'s brother-in-law. Winterfell receives the royal court, including King Robert, his wife Queen Cersei, their children – heir Prince Joffrey, Princess Myrcella, and Prince Tommen – as well as Jaime, a member of the Kingsguard, and his and Cersei\\'s younger brother Tyrion Lannister, a dwarf known as \"The Imp\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '4774695f19422618065b13e85f906e82'}>,\n",
+       "  <Document: {'content': 'Winterfell receives the royal court, including King Robert, his wife Queen Cersei, their children – heir Prince Joffrey, Princess Myrcella, and Prince Tommen – as well as Jaime, a member of the Kingsguard, and his and Cersei\\'s younger brother Tyrion Lannister, a dwarf known as \"The Imp\". Robert pays respects to Lyanna Stark, his late fiancée and Ned\\'s sister, appoints Ned the new Hand of the King, and suggests Sansa be betrothed to Joffrey. Catelyn receives a message from her sister Lysa, Arryn\\'s widow, who suspects her husband was murdered by the Lannisters. Ned reluctantly accepts the position of Hand of the King.\\n\\nBran climbs an abandoned tower and stumbles upon Cersei and Jaime having sex. To keep the incestuous relationship a secret, Jaime shoves Bran out of the window.\\n\\n==Production==\\n===Conception and development===\\nA number of Hollywood studios had contacted George R.\\xa0R. Martin about possibly adapting his book series \\'\\'A Song of Ice and Fire\\'\\' into a film. However, Martin expressed the opinion the books could not be made into a film as too much would have to be cut from the books, but thought it could be made into a television series. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '6edf0e1a6b877e8d7082ee3e5d33bdae'}>,\n",
+       "  <Document: {'content': 'However, Martin expressed the opinion the books could not be made into a film as too much would have to be cut from the books, but thought it could be made into a television series. In January 2006, David Benioff happened to speak to Martin\\'s literary agent, and the agent sent the first four books of \\'\\'A Song of Ice and Fire\\'\\' to David Benioff. Benioff read a few hundred pages of the first book in the series, \\'\\'A Game of Thrones\\'\\', called D. B. Weiss and said: \"Maybe I\\'m crazy, but I haven\\'t had this much fun reading anything in about 20 years. So take a look because I think it might make a great HBO series.\"Weiss, who then read the first book in two days, was very enthusiastic about a possible television project based on the books. They arranged a meeting with Martin, who asked them if they knew who Jon Snow\\'s real mother might be, and was satisfied with their answer.\\n\\nIn March 2006, a few weeks after meeting Martin, Benioff and Weiss pitched the show to Showtime and then Carolyn Strauss of HBO, the latter of whom accepted their proposal. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '280451609aea7df792bc798da26464a0'}>,\n",
+       "  <Document: {'content': 'In March 2006, a few weeks after meeting Martin, Benioff and Weiss pitched the show to Showtime and then Carolyn Strauss of HBO, the latter of whom accepted their proposal. HBO acquired the rights to the novels to turn them into a television series, with Benioff and Weiss as writers and executive producers of the series. The series went into development in January 2007. The series would begin with the first book from 1996, \"A Game of Thrones\", with the intention that each novel in the series would form the basis for a season\\'s worth of episodes. However, Benioff and Weiss had to resubmit a proposal after Carolyn Strauss stepped down as president of HBO in 2008. The first and second drafts of the pilot script, written by Benioff and Weiss, were submitted in August 2007 and June 2008 respectively. While HBO found both drafts to their liking, a pilot was not ordered until November 2008.\\n\\n===Writing===\\nSeries co-creators David Benioff and D. B. Weiss wrote the episode.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '53a7b9909310c3b97b45c6a00d4d4721'}>,\n",
+       "  <Document: {'content': \"While HBO found both drafts to their liking, a pilot was not ordered until November 2008.\\n\\n===Writing===\\nSeries co-creators David Benioff and D. B. Weiss wrote the episode.\\nScripted by the show creators David Benioff and D. B. Weiss, the first episode includes the plot of the book's chapters 1–9 and 12 (Prologue, Bran I, Catelyn I, Daenerys I, Eddard I, Jon I, Catelyn II, partial Arya I, Bran II, Daenerys II). Changes in the adaptation include the sequence of events in the prologue (in the books it is Gared and not Will who survives and is beheaded by Eddard afterwards, and Arya's material is set before the arrival of the royal family), new scenes showing the Lannister twins' perspective, and Daenerys's wedding night showing Drogo not waiting for her to consent to sex.\\n\\n===Filming===\\nSean Bean portrays Ned Stark in the series.\\nTom McCarthy was chosen to direct the pilot episode, shot between October 24 and November 19, 2009 on location in Northern Ireland, Scotland and Morocco. However, the pilot was deemed unsatisfactory and had to be reshot.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'e43b90f1a955a85bc30eeb0c125a2a4c'}>,\n",
+       "  <Document: {'content': 'Tom McCarthy was chosen to direct the pilot episode, shot between October 24 and November 19, 2009 on location in Northern Ireland, Scotland and Morocco. However, the pilot was deemed unsatisfactory and had to be reshot.\\n\\nThe new pilot episode was filmed in 2010 by new director Tim Van Patten, and several actors appearing in the original pilot did not return for the series. Tamzin Merchant was replaced as Daenerys Targaryen by Emilia Clarke, and Jennifer Ehle was replaced as Catelyn Stark by Michelle Fairley. Additionally, Ian McNeice was replaced as Magister Illyrio by Roger Allam, Richard Ridings as Gared by Dermot Keaney, and Jamie Campbell Bower as Ser Waymar Royce by Rob Ostlere.\\n\\nAnother difference is that the original pilot featured scenes shot in Scotland and scenes in Pentos were shot in Morocco, but in the aired series, Winterfell was filmed in a combination of locations in Northern Ireland, while scenes from Pentos were from Malta. The Doune Castle in Scotland was originally used to recreate Winterfell, and its great hall was used for some interior shots. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '5d0873156b7bac605db51b824a0ccdc5'}>,\n",
+       "  <Document: {'content': \"The Doune Castle in Scotland was originally used to recreate Winterfell, and its great hall was used for some interior shots. Some scenes survived, but as it was not practical to return to Scotland for the reshoot, an exact replica of Doune's great hall was recreated in the soundstage in Belfast for the series. Castle Ward in Northern Ireland was used in the reshoot to film King Robert's entourage entry into Winterfell castle. A car park stood in for the Winterfell castle's courtyard and a wine cellar for the Stark family crypt. The Tollymore Forest Park was used for the opening scene of the encounter with the White Walkers.\\n\\nAll the scenes shot in Morocco were reshot in Malta. The original pilot reused the sets of ''Kingdom of Heaven'' in Morocco to stand in for Pentos and the site of Drogo and Daenerys's wedding. In Malta, the Verdala Palace, the 16th century summer palace of the president of Malta, was used for the exterior scenes at Illyrio's mansion. The Azure Window was used as the backdrop for the wedding. Filming at the Azure Window, however, caused some controversy when a protected ecosystem was damaged by a subcontractor.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '59899cbfc7bd437516d083be6407253b'}>,\n",
+       "  <Document: {'content': 'The Azure Window was used as the backdrop for the wedding. Filming at the Azure Window, however, caused some controversy when a protected ecosystem was damaged by a subcontractor.\\n\\nIn the sex scene, the then-pregnant Lena Headey was substituted by a body double; the production hid her pregnancy for the rest of the season. In the scene in which the Starks encounter a stag killed by a dire wolf as they return from the execution, an actual animal was used rather than a prop. As the stag had been dead for two days, it stank so much that the actors had to take much care not to let it show on their faces. Some scenes filmed were never aired, for example a flashback to the death of Eddard Stark\\'s brother, and the death of Jon Arryn.\\n\\n===Original pilot===\\n\\nThe original pilot from 2009 was poorly received in a private viewing with friends, one of whom, Craig Mazin, said to Benioff and Weiss, \"You guys have a massive problem\", and said \"change everything\" when asked for ideas. It was so disliked that Kit Harington joked that when he annoys Benioff and Weiss, they threaten to release the episode on YouTube. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '88464f38b40fc667ff430464ee1172f1'}>,\n",
+       "  <Document: {'content': 'It was so disliked that Kit Harington joked that when he annoys Benioff and Weiss, they threaten to release the episode on YouTube. Weiss said of the viewing: \"Watching them watch the pilot was a deeply humiliating, painful experience, because these are very smart individuals, and it just clearly wasn’t working for any of them on a very basic level.\"For example, it was never established that the two major characters, Jaime and Cersei Lannister were in fact brother and sister, a major plot point.\\n\\nHBO did not make a decision for four months after the pilot was delivered. In March 2010, HBO\\'s decision to greenlight the series including the pilot was announced, with the production of the series scheduled to start June 2010. HBO however demanded extensive reshoot of the pilot, and wanted all the scenes from Morocco scrapped. A cameo appearance by George RR Martin as a Pentoshi nobleman at Daenerys\\'s wedding filmed in Morocco was therefore also cut. In all 90 percent of the pilot was re-shot in 2010, with some cast changes and a different director.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'a929b4426ffbd58e8b7885528bc9b1f7'}>,\n",
+       "  <Document: {'content': 'A cameo appearance by George RR Martin as a Pentoshi nobleman at Daenerys\\'s wedding filmed in Morocco was therefore also cut. In all 90 percent of the pilot was re-shot in 2010, with some cast changes and a different director.\\n\\n===Aired episode===\\nThe original pilot remained unaired, although some footage from the original pilot was used in the first aired episode. This includes Sansa\\'s scenes with Catelyn (Michelle Fairley\\'s footage as Catelyn was inserted over Jennifer Ehle\\'s performance), Will\\'s ride through the woods (retained though also portrayed by a different actor), most of the feast at Winterfell, and Ned and Robert\\'s scene in the crypt. That scene is one of a few to be filmed on 35 mm film, and consequently slight film grain can be seen in the HD version of the episode.\\n\\n==Reception==\\n\\n===Preview===\\nOn April 3, 2011, two weeks before the series premiere aired, the first 15 minutes of \"Winter Is Coming\" were released as a preview on HBO\\'s website. \\'\\'Wired\\'\\'\\'s Dave Banks called the preview \"much better than anticipated.\"Scott Stinson of Toronto\\'s National Post noted that \"you know you aren’t watching a network drama when there have been two beheadings in the first 15 minutes.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '21bc16cfe1a75859a189717d10f204be'}>,\n",
+       "  <Document: {'content': 'Scott Stinson of Toronto\\'s National Post noted that \"you know you aren’t watching a network drama when there have been two beheadings in the first 15 minutes.\"===Ratings===\\nThe first episode of \\'\\'Game of Thrones\\'\\' obtained 2.2 million viewers in its premiere airing, with an additional 2 million viewers in the reruns aired during the same night. The day after the premiere HBO aired the episode six additional times, adding another 1.2 million to the viewer\\'s figures. Reruns aired during the following week upped the total viewership to 6.8 million.\\n\\n===International===\\nThe show premiered on HBO Canada at the same time as its U.S. premiere. On April 18, 2011, the show premiered in United Kingdom and Ireland through Sky Atlantic, gathering 750,000 viewers, a ratings record for the network. The series was broadcast throughout Latin America beginning on May 8, 2011. New Zealand\\'s \\'\\'Dominion Post\\'\\' noted in an article on copyright laws that the popular series was downloaded via file sharing service regularly before its release to that market. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '107fc4ce2c24eb3a5190741bc70b6256'}>,\n",
+       "  <Document: {'content': 'New Zealand\\'s \\'\\'Dominion Post\\'\\' noted in an article on copyright laws that the popular series was downloaded via file sharing service regularly before its release to that market. In Australia, the July 17 premiere of the series was largely overshadowed by the release of \\'\\'A Dance with Dragons\\'\\', but according to \\'\\'The Sydney Morning Herald\\'\\' was successful \"especially with women, who aren\\'t seen as a target market for sword-fighting sagas\".\\n\\n===Critical response===\\n\\nThe critical response to the first episode of the series was positive. Review aggregator Rotten Tomatoes surveyed 10 reviews of the episode and judged 100% of them to be positive with an average score of 8.5 out of 10. The website\\'s critical consensus reads, Winter is Coming\\' is an introduction to a wonderfully bleak journey that honors its source material with stellar execution and an impressive cast.\"James Poniewozik from \\'\\'Time\\'\\' considered it an \"epic win\", and Jace Lacob from \\'\\'The Daily Beast\\'\\' deemed it \"unforgettable\". HitFix\\'s Alan Sepinwall wrote that while it was too early to say if \\'\\'Game of Thrones\\'\\' belonged to the HBO pantheon with shows like \\'\\'The Sopranos\\'\\' or \\'\\'The Wire\\'\\', it had many things in common with those shows. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '568b50b6550e93a2cb84e685fbb78e51'}>,\n",
+       "  <Document: {'content': 'HitFix\\'s Alan Sepinwall wrote that while it was too early to say if \\'\\'Game of Thrones\\'\\' belonged to the HBO pantheon with shows like \\'\\'The Sopranos\\'\\' or \\'\\'The Wire\\'\\', it had many things in common with those shows. IGN\\'s Matt Fowler wrote that the pilot \"effortlessly takes us along, faithfully, through the book, but it also manages to capture the majestically morbid spirit of Martin\\'s pages and turn them into thrilling television\".\\n\\nMuch praise was given to the production values and the acting: Scott Meslow from \\'\\'The Atlantic\\'\\' states that \"the show\\'s immense cast is almost universally strong, and the fantasy land of Westeros feels lived-in, and looks terrific\". Alan Sepinwall also qualifies the casting as \"really exceptional\", and states that the show is \"feast for the eyes\", with all the different locations having their own memorable looks. The opening sequence, with an aerial view of the world where the series takes place with the different settings emerging from it, was also acclaimed.\\n\\nOn April 19, less than two days after the initial airing, HBO announced that the series had been renewed for a second season. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '9b4fe4076d73e9f04606fe6d3a4fc107'}>,\n",
+       "  <Document: {'content': 'On April 19, less than two days after the initial airing, HBO announced that the series had been renewed for a second season. In a press teleconference, HBO executives announced their satisfaction with initial ratings, which they compared favorably to \\'\\'True Blood\\'\\'.\\n\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominees\\n\\nResult\\n\\n2011\\n\\nPortal Award\\n\\nBest Episode\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nTim Van Patten\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen\\n\\n2012\\n\\nGolden Reel Awards\\n\\nBest Sound Editing — Short Form Sound Effects and Foley in Television\\n\\nDirectors Guild of America Awards\\n\\nDramatic Series\\n\\nTim Van Patten\\n\\nVisual Effects Society\\n\\nOutstanding Supporting Visual Effects in a Broadcast Program\\n\\nLucy Ainsworth-Taylor, Angela Barson, Ed Bruce and Adam McInnes\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Winter Is Coming\" at HBO\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'f17fb53c0b0e9826cdce881ed78d5adb'}>,\n",
+       "  <Document: {'content': '\\n\"\\'\\'\\'Valar Dohaeris\\'\\'\\'\" is the third season premiere episode of the HBO fantasy television series \\'\\'Game of Thrones\\'\\'. Written by executive producers David Benioff and D. B. Weiss, and directed by Daniel Minahan, it aired on March 31, 2013.\\n\\nThe premiere continued where the second season left off, with the Lannisters consolidating their power at King\\'s Landing in the aftermath of the Battle of the Blackwater. Meanwhile, Jon Snow meets the \"King beyond the Wall,\" and Daenerys leaves Qarth for Slaver\\'s Bay.\\n\\nThe episode\\'s title translates to \"all men must serve\" in the High Valyrian language in George R. R. Martin\\'s \\'\\'A Song of Ice and Fire\\'\\' novels, which the series adapts. The title mirrors that of season 2\\'s finale, \"Valar Morghulis\" – \"all men must die.\"The episode received mostly positive reviews, set a new ratings record for the series, and was one of the most torrented episodes for an HBO series.\\n\\n==Plot==\\n===Beyond the Wall===\\nThe White Walkers’ attack leaves few Night\\'s Watch survivors. Samwell Tarly is saved by the direwolf Ghost and Lord Commander Jeor Mormont, who reprimands Sam for failing to warn of the approaching army and orders the survivors back to the Wall.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'bb33c4d6d17b41496fad7f380fc3d556'}>,\n",
+       "  <Document: {'content': 'Samwell Tarly is saved by the direwolf Ghost and Lord Commander Jeor Mormont, who reprimands Sam for failing to warn of the approaching army and orders the survivors back to the Wall.\\n\\nAt the wildling camp, Jon Snow mistakes Tormund Giantsbane for the King-beyond-the-Wall, but Mance Rayder reveals himself and questions Jon\\'s motives for deserting the Night\\'s Watch. Jon earns Mance\\'s approval, declaring that he wants \"to fight for the side that fights for the living\".\\n\\n===In King\\'s Landing===\\nNewly knighted Ser Bronn returns to the service of Tyrion Lannister, whose father Tywin, new Hand of the King, agrees to recognize Tyrion\\'s accomplishments during the Battle of Blackwater but refuses to name him heir to Casterly Rock and viciously insults him.\\n\\nPetyr Baelish offers to smuggle Sansa Stark out of King\\'s Landing. Ros tells Shae to look out for Sansa, especially when dealing with Baelish.\\n\\nKing Joffrey Baratheon and his betrothed Lady Margaery Tyrell travel through Flea Bottom, when Margaery exits her litter to visit an orphanage as Joffrey hides. At dinner, Joffrey trades insults with his mother, in contrast to the harmonious Tyrells.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'b77178260b72dca465c9447d70f7ff1b'}>,\n",
+       "  <Document: {'content': 'King Joffrey Baratheon and his betrothed Lady Margaery Tyrell travel through Flea Bottom, when Margaery exits her litter to visit an orphanage as Joffrey hides. At dinner, Joffrey trades insults with his mother, in contrast to the harmonious Tyrells.\\n\\n===In Blackwater Bay===\\nStranded Davos Seaworth is rescued by the pirate Salladhor Saan, and recounts seeing his son Matthos die. Salladhor reveals he is leaving the service of Stannis, who is in seclusion at Dragonstone and will only speak with Melisandre, who is burning people alive. Davos, intending to kill Melisandre, persuades Salladhor to bring him to Stannis.\\n\\n===At Dragonstone===\\nDiscovering Stannis is indifferent to his survival and that Melisandre believes him responsible for Stannis’ defeat, Davos tries to attack Melisandre but is thrown in the dungeons.\\n\\n===At Harrenhal===\\nRobb Stark and his forces discover Gregor Clegane has abandoned Harrenhal and slaughtered the prisoners. Lord Roose Bolton sympathizes with Lord Rickard Karstark\\'s frustration that Catelyn Stark let Jaime Lannister go, assuring Karstark that his best hunter is after Jaime. Robb finds a survivor, Qyburn.\\n\\n===Across the Narrow Sea===\\nReaching Astapor in Slaver\\'s Bay with her fast-growing dragons, Daenerys Targaryen considers buying an army of the \"Unsullied\", renowned eunuch soldiers. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '9be46d3e912d5402a15828dd05e0e6ee'}>,\n",
+       "  <Document: {'content': '===Across the Narrow Sea===\\nReaching Astapor in Slaver\\'s Bay with her fast-growing dragons, Daenerys Targaryen considers buying an army of the \"Unsullied\", renowned eunuch soldiers. A warlock masked as a young girl attempts to assassinate Daenerys with a scorpion-like creature, but is thwarted by Ser Barristan Selmy, Kingsguard to Daenerys\\' father, who swears his allegiance to her.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by showrunners David Benioff and D. B. Weiss. It was based mainly on the first chapters of \\'\\'A Storm of Swords\\'\\', the third novel in George R. R. Martin\\'s \\'\\'A Song of Ice and Fire\\'\\' series. Specifically, it adapts material from chapters Samwell I, Jon I, Davos I, Davos II, Tyrion I, Daenerys I, Davos III, and Daenerys II from \\'\\'A Storm of Swords\\'\\' and chapter Daenerys V of \\'\\'A Clash of Kings\\'\\'.\\n\\nSome of the twists that open the third book were used in the previous season\\'s finale (mainly Robb\\'s marriage and the White Walkers attacking the Night\\'s Watch). Conversely, Barristan Selmy saving Daenerys from the manticore was borrowed from her last chapter in the second book \\'\\'A Clash of Kings\\'\\'.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'f9d05a282d7b85808d6f073b6afa47b3'}>,\n",
+       "  <Document: {'content': 'Conversely, Barristan Selmy saving Daenerys from the manticore was borrowed from her last chapter in the second book \\'\\'A Clash of Kings\\'\\'.\\n\\n===Casting===\\nCiarán Hinds joins the series as Mance Rayder\\n\"Valar Dohaeris\" introduces the Irish actor Ciarán Hinds as the Wildling leader and Night\\'s Watch deserter Mance Rayder, one of the latest Season 3 roles to be cast. The producers explained that casting Mance was a great challenge because he was someone who had become \"King beyond the Wall\" not by birthright but by convincing all the tribes to unite under his leadership. They had to find an actor with the charisma required to portray this. Unusual for a production such as \\'\\'Game of Thrones\\'\\', the first scene Hinds filmed was the first one where his character appears: the meeting with Jon Snow at his tent.\\n\\nThe season premiere also marks the first appearance of guest stars Kristofer Hivju as the Wildling Tormund Giantsbane, Nathalie Emmanuel as the slave Missandei, and Anton Lesser as the wounded prisoner Qyburn. The three castings were announced at San Diego Comic-Con in July 2012.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'da99838e256a6e49dceea240cabc92a1'}>,\n",
+       "  <Document: {'content': 'The season premiere also marks the first appearance of guest stars Kristofer Hivju as the Wildling Tormund Giantsbane, Nathalie Emmanuel as the slave Missandei, and Anton Lesser as the wounded prisoner Qyburn. The three castings were announced at San Diego Comic-Con in July 2012.\\n\\nWith this episode, Rose Leslie (Ygritte) and Oona Chaplin (Talisa) are promoted to series regulars, after guest starring in the second season. After being absent for the entire second season, Ian McElhinney returns as Ser Barristan Selmy. Actor Ian Whyte, previously cast as a White Walker in the first season, was recast as the huge Ser Gregor Clegane in the second and appears in the episode as the giant seen in the wildlings camp.\\n\\n===Valyrian===\\nDavid J. Peterson, who created the Dothraki language for the first season of the show, was entrusted by the producers to design a new constructed language to depict Valyrian, the tongue of the fallen Valyrian Empire. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '43df89192697da27dff3c80fdd519cc6'}>,\n",
+       "  <Document: {'content': \"===Valyrian===\\nDavid J. Peterson, who created the Dothraki language for the first season of the show, was entrusted by the producers to design a new constructed language to depict Valyrian, the tongue of the fallen Valyrian Empire. After immersing himself in the fictional background, Peterson ended devising two languages: High Valyrian, the oldest form that was spoken at the height of the Empire and that in its purest form still exists as a language of scholarship and refinement, and the Slaver's Bay variety of Low Valyrian, a creolized version that is spoken in local dialects around the Slaver's Bay. The relationship between the two languages would be similar to the one between Classical Latin and Vulgar Latin.\\n\\nTo translate sentences in Low Valyrian such as the ones spoken by Kraznys mo Nakloz and Missandei during the episode, Peterson would first write them into High Valyrian, and then apply a series of phonological, semantic and grammatical changes to the text.\\n\\n===Filming locations===\\nThe episode used locations in four different countries: The Paint Hall in Belfast, the City Walls of Dubrovnik, Mývatn and Vinderbelgjarfjall, and the Old Fort at Essaouira.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'e9eee465a78da0d0f4be4d91c116f6bd'}>,\n",
+       "  <Document: {'content': \"===Filming locations===\\nThe episode used locations in four different countries: The Paint Hall in Belfast, the City Walls of Dubrovnik, Mývatn and Vinderbelgjarfjall, and the Old Fort at Essaouira.\\nThe production continued to use the Paint Hall studios in Belfast for most interior shoots. The scene of Daenerys at sea was filmed at the Linen Mill Film & Television Studios at Banbridge, using the same ship built for Theon's arrival in Pyke and used as Stannis' flagship in season 2. The beach of Downhill Strand returned as the island of Dragonstone.\\n\\nThe old city of Dubrovnik was again used for the exterior shots of the capital, King's Landing. Tyrion and Bronn are seen walking on the famed city walls, and the scene where Lord Baelish visits Sansa at the docks was filmed in the old port between Fort Lovrijenac and the Pila Gates.\\n\\nAlso repeating from last season, the scenes beyond the Wall were filmed in Iceland. The Wildling's camp was built in a new location, on a lava field in the Mývatn Lake region in the North, a few hours from the town of Akureyri. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '2a341235dcd97eca4f897784a9f1a933'}>,\n",
+       "  <Document: {'content': \"The Wildling's camp was built in a new location, on a lava field in the Mývatn Lake region in the North, a few hours from the town of Akureyri. Building the camp took months of work from the art department and weeks of construction by the local crew. Although the scenes at the Icelandic exteriors were filmed at minus 11 degrees Celsius, the interior of Mance's tent was filmed on one of the soundstages at the Paint Hall. The fires burning inside the tent made the set very hot, and the actors suffered the heat wearing heavy furs designed for Arctic climates.\\n\\nTo depict the slaver city of Astapor, the production used the Moroccan city of Essaouira. The Unsullied are introduced in the city ramparts of ''Skala de la Ville'', the 18th-century sea bastion that runs along the northern cliffs, and during the closing scene when Barristan reveals himself, the Genoese-built citadel by the harbour can be clearly seen.\\n\\n===Dedication===\\nThe episode is dedicated to the memory of cinematographer Martin Kenzie, who had worked in the photography department and died of cancer while the third season was being filmed on July 16, 2012.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '42cb29307c35b598758d701e43ec17d4'}>,\n",
+       "  <Document: {'content': '===Dedication===\\nThe episode is dedicated to the memory of cinematographer Martin Kenzie, who had worked in the photography department and died of cancer while the third season was being filmed on July 16, 2012.\\n\\n==Reception==\\n\\n===Ratings===\\n\"Valar Dohaeris\"\\'s first airing was seen by 4.4 million viewers; the total rose to 6.7 million viewers once the two repeats of the night are taken into account. Both figures represented a viewership record for the show. In the United Kingdom, the episode was seen by 1.173 million viewers on Sky Atlantic, being the channel\\'s highest-rated broadcast that week.\\n\\n===Critical reception===\\nThe episode received critical acclaim. Review aggregator Rotten Tomatoes surveyed 21 reviews of the episode and judged 100% of them to be positive with an average score of 7.5 out of 10. The website\\'s critical consensus reads, \"\\'Valar Dohaeris\\' overextends itself trying to reintroduce a multitude of characters and plotlines, but is strengthened by its brilliantly acted two-person scenes (and three growing baby dragons).\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'e604d36a7f5c832eec1cb69e95b3039'}>,\n",
+       "  <Document: {'content': 'The website\\'s critical consensus reads, \"\\'Valar Dohaeris\\' overextends itself trying to reintroduce a multitude of characters and plotlines, but is strengthened by its brilliantly acted two-person scenes (and three growing baby dragons).\"In an advance review for \\'\\'The Daily Beast\\'\\', Jace Lacob wrote that the season premiere lacks \"energy and intensity, but provides a necessary foundation,\" and that the season, like the novel it is based on, \"takes a little to get going.\"Matt Fowler, writing for IGN, gave the episode an 8.6/10, writing that \"while understandably not showing us everyone, Game of Thrones returned in fine form with dragons, zombies and giants.\"Neela Dabnath of \\'\\'The Independent\\'\\' felt that \"Valar Dohaeris\" was a strong start to Season 3 and \"deftly picked up the various story strands from the last season ... even if it was just a series of brief check-in.\"She also praised the show for taking \"creative liberties which always pay off in bucket loads,\" commenting on how this can \"add to Martin’s world and flesh it out in new ways.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '75d03b7916a60d2211ca4986ce6f5d41'}>,\n",
+       "  <Document: {'content': 'She also praised the show for taking \"creative liberties which always pay off in bucket loads,\" commenting on how this can \"add to Martin’s world and flesh it out in new ways.\"Forbes.com writer Erik Kain stated he felt \"Valar Dohaeris\" did \"exactly what it needed to do,\" by bringing the viewers \"back up to speed on the broader conflict and the various minor character conflicts and positioning for power. The ball hasn’t really been moved forward at all, but our feet are on sturdy ground to move forward.\"He did however express some disappointment over how some elements of the episode differed from its source material in regards to the reintroduction of the character Barristan Selmy and the apparent absence of the character Strong Belwas. Reviewing the episode for \\'\\'The Guardian\\'\\', Sarah Hughes felt that \"Valar Dohaeris\" \"didn\\'t really feel like a season opener,\" and that the opening scene was \"a little disorientating.\"However she enjoyed the scenes in King\\'s Landing and lauded Stephen Dillane\\'s brief performance. Commenting on the Daenerys\\' plot, she felt her storyline was advancing quite quickly. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'c91e130c9ff0f781dd54ca36da667b7'}>,\n",
+       "  <Document: {'content': 'However she enjoyed the scenes in King\\'s Landing and lauded Stephen Dillane\\'s brief performance. Commenting on the Daenerys\\' plot, she felt her storyline was advancing quite quickly. She also felt that the Barristan Selmy reveal was handled well as; \"there are certain conventions that work better on page than screen and the hidden identity trope is one of them. By getting the reveal out of the way early, Benioff and Weiss can concentrate on the potential rivalry between Barristan and Jorah.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'e4de1db6a90e213adbd0df291f53a21d'}>,\n",
+       "  <Document: {'content': 'By getting the reveal out of the way early, Benioff and Weiss can concentrate on the potential rivalry between Barristan and Jorah.\"===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2013\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton\\n\\nOutstanding Special Visual Effects\\n\\nDoug Campbell, Rainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen\\n\\nHollywood Post Alliance Awards\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Bauer and Jabbar Raisani, Jörn Grosshans and Sven Martin, and Doug Campbell\\n\\n2014\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Television Series\\n\\nGemma Jackson\\n\\nAmerican Society of Cinematographers\\n\\nOne-Hour Episodic Television Series\\n\\nJonathan Freeman\\n\\nVisual Effects Society\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nSteve Kullback, Joe Bauer, Jörn Großhans, Sven Martin\\n\\n== References ==\\n\\n== External links ==\\n\\n*  \"Valar Dohaeris\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '4cc439bdc13daf40e039ed827359ba89'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Gift\\'\\'\\'\" is the seventh episode of the fifth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 47th overall. The episode was written by the series\\' creators David Benioff and D. B. Weiss, and directed by Miguel Sapochnik, his directorial debut for the series.\\n\\nThis episode marks the final appearance for Peter Vaughan (Maester Aemon).\\n\\n==Plot==\\n\\n===At the Wall===\\nJon leaves for Hardhome with Tormund and some rangers, in spite of Ser Alliser\\'s complaints. Before they leave, Sam gives Jon a bag of dragonglass weapons, reminding him of their use against the White Walkers. Sam and Gilly visit Maester Aemon, who has fallen ill and dies at nightfall. In the morning, Sam eulogizes him before lighting his funeral pyre. Afterward, Gilly is attacked in the dining hall by two brothers. Sam attempts to intervene and is badly beaten before Jon\\'s wolf, Ghost, scares off the brothers. Gilly tends to Sam and the two have sex.\\n\\n===At Winterfell===\\nReek brings food to Sansa, who has been imprisoned in her bedchamber by Ramsay. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '7c11f0f5ca7ce507ad5a790554d6d961'}>,\n",
+       "  <Document: {'content': \"Gilly tends to Sam and the two have sex.\\n\\n===At Winterfell===\\nReek brings food to Sansa, who has been imprisoned in her bedchamber by Ramsay. She begs for him to help, asking him to light a candle at the top of the broken tower, the aim of which is to send a signal to Stark sympathizers to rescue her. Instead, Reek tells Ramsay, who reacts by flaying and killing the old lady from whom Sansa learned the signal. As Ramsay is talking to Sansa, however, she discreetly steals a corkscrew from the top of a barrel. Meanwhile, Brienne and Podrick wait out in the snow for the signal, to no avail.\\n\\n===In the North===\\nStannis' troops have made a camp and are trapped by the snowstorm. Davos Seaworth meets with Stannis and tells him that supplies, as well as horses, have been lost to the winter. The Stormcrows, a group of 500 sellswords, have abandoned the camp during the night. Davos suggests returning to Castle Black, but Stannis replies that doing so could delay them for years, since winters in Westeros can last for years at a time. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '2d87585b0673d1299e6b33f3c1664b9a'}>,\n",
+       "  <Document: {'content': \"Davos suggests returning to Castle Black, but Stannis replies that doing so could delay them for years, since winters in Westeros can last for years at a time. After Davos leaves, Stannis asks Melisandre if she is sure of his victory. Melisandre assures him that she saw a vision of Stannis winning, but she asks for permission to sacrifice his daughter Shireen to the Red God. Stannis is disgusted and refuses, and dismisses Melisandre.\\n\\n===In Meereen===\\nJorah is sold by the slave traders to a Meereenese man, Yezzan zo Qaggaz, who is looking for fighters for the upcoming games at Meereen. Before Yezzan leaves, Tyrion convinces Yezzan to purchase him as well by showing fighting prowess by attacking with a chain a slave herder who had been whipping Tyrion.\\n\\nDaenerys and Daario discuss her pending marriage to Hizdahr zo Loraq. Daario advises her to kill all of Meereen's nobles when the fighting pits get re-opened, but she declines.\\n\\nYezzan takes his fighters to Meereen, where they fight in front of Daenerys. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '869ff69f7f3f51d0231580cb5f1c94c6'}>,\n",
+       "  <Document: {'content': \"Daario advises her to kill all of Meereen's nobles when the fighting pits get re-opened, but she declines.\\n\\nYezzan takes his fighters to Meereen, where they fight in front of Daenerys. When Jorah realizes that Daenerys is present, he enters the pits and easily defeats the other fighters before revealing his identity to Daenerys, who orders he be taken out of her sight. Jorah tells her he brought her a gift, and Tyrion enters the arena, where he reveals his identity to a stunned Daenerys.\\n\\n===In Dorne===\\nMyrcella is brought to speak with Jaime, and asks why he came to take her back to King's Landing, when what she wants is to stay and marry Trystane. In the cells, Bronn is taunted by Tyene Sand exposing her breasts to him, before learning that her daggers, with which he'd been cut during their fight, had been coated with a slow-acting poison that would activate when his heart rate increased. He slowly succumbs to the poison, but Tyene gives him the antidote after he gives in to her request that he call her the most beautiful woman in the world.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'bb6d8ccb47b9354db01261a3a1cc851a'}>,\n",
+       "  <Document: {'content': \"He slowly succumbs to the poison, but Tyene gives him the antidote after he gives in to her request that he call her the most beautiful woman in the world.\\n\\n===In King's Landing===\\nIn the Sept of Baelor, Olenna meets with the High Sparrow to discuss the imprisonment of her grandchildren, Loras and Margaery. She demands that he release them, but he declines, stating they will be punished for their crimes. The High Sparrow tells her that the laws of the Faith must be applied to everyone equally. Olenna first tries to bribe him, but upon seeing that he cannot be bought, threatens to end the Tyrell support for the capital by stopping shipments of food. The High Sparrow remains unfazed. He counters that she and the rest of the nobility are outnumbered by the smallfolk, and tells her to consider what will come when the smallfolk stop fearing the nobility. On her way out, Olenna receives a letter from Petyr Baelish.\\n\\nIn the Red Keep, Tommen anguishes over his inability to help Margaery. Cersei offers to speak with the High Sparrow for him, and advocate for Margaery and Loras's release.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'f0039cdc3473f0bd455469a618c96a98'}>,\n",
+       "  <Document: {'content': \"In the Red Keep, Tommen anguishes over his inability to help Margaery. Cersei offers to speak with the High Sparrow for him, and advocate for Margaery and Loras's release.\\n\\nPetyr meets with Olenna in his ransacked brothel, where they share a tense conversation. Olenna reminds Petyr of the role they both played in Joffrey's murder, and warns that, should her house fall, she won't keep his involvement a secret. In the Sept, Cersei meets with Margaery and brings her food, but Margaery refuses and angrily orders her mother-in-law to leave, knowing full well that she was responsible for imprisoning both her and Loras. Cersei then meets with the High Sparrow, and after a brief discussion on Margaery and Loras, he reveals that her cousin Lancel has informed against her regarding a sin she has committed. Cersei tries to leave but is stopped by a tall septa, who proceeds to lock Cersei in a cell.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode was written by David Benioff and D. B. Weiss, the series' creators. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '5a2e0b488b27783cca9da1b0b3ab5727'}>,\n",
+       "  <Document: {'content': '==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode was written by David Benioff and D. B. Weiss, the series\\' creators. It contains content from George R. R. Martin\\'s novels \\'\\'A Feast for Crows\\'\\', chapters Samwell II, Samwell IV, and Cersei X and \\'\\'A Dance with Dragons\\'\\', chapters The King\\'s Prize, Tyrion X, and Daenerys IV.\\n\\nLike other episodes in season 5, this episode differed considerably from Martin\\'s novels in places. Aemon\\'s death takes place at Castle Black rather than at sea, which Myles McNutt of \\'\\'A.V. Club\\'\\' notes, \"establishes a shifting of the guard at Castle Black and marking Sam’s reemergence as a more significant character.\"In what Sara Stewart of the \\'\\'New York Post\\'\\' calls \"the biggest departure yet from the books,\" Tyrion actually meets Daenerys at the end of the episode, which has yet to happen as of \\'\\'A Dance with Dragons\\'\\'. The decision was largely approved by critics from \\'\\'The Atlantic\\'\\' and other publications. David Benioff cited the television adaptation\\'s faster pace as part of the rationale behind this decision.\\n\\n===Filming===\\nMiguel Sapochnik served as director for the episode, his first episode for the series.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '5a633ce359afee5eefab39e3b96024a2'}>,\n",
+       "  <Document: {'content': 'David Benioff cited the television adaptation\\'s faster pace as part of the rationale behind this decision.\\n\\n===Filming===\\nMiguel Sapochnik served as director for the episode, his first episode for the series.\\n\"The Gift\" was directed by Miguel Sapochnik. It was his first time being a director for \\'\\'Game of Thrones\\'\\'; he also directed the subsequent episode, \"Hardhome\".\\n\\n==Reception==\\n\\n===Ratings===\\n\"The Gift\" was watched by 5.5 million and a 2.5 in the key 18–49 demographic during the its first airing. This is significantly fewer viewers than the previous episode, \"Unbowed, Unbent, Unbroken\", which had an audience of 6.2 million. According to \\'\\'Business Insider\\'\\', likely causes for the lower ratings include backlash from the rape scene in \"Unbowed, Unbent, Unbroken\", in response to which many fans announced they would stop watching the show, and increased use of streaming through HBO Now. Rebecca Martin of \\'\\'Wetpaint\\'\\', however, maintains that the Memorial Day weekend air date was probably the only reason for the decrease in ratings. The season two episode \"Blackwater\", which also aired on Memorial Day weekend, also suffered a notable drop in ratings. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'ea7d673b456066e7094c8466f2162935'}>,\n",
+       "  <Document: {'content': 'Rebecca Martin of \\'\\'Wetpaint\\'\\', however, maintains that the Memorial Day weekend air date was probably the only reason for the decrease in ratings. The season two episode \"Blackwater\", which also aired on Memorial Day weekend, also suffered a notable drop in ratings. Whatever the reason, the ratings for the episode immediately after \"The Gift\", \"Hardhome\", were higher than those of either \"The Gift\" or \"Unbowed, Unbent, Unbroken\". With Live+7 DVR viewing factored in, the episode had an overall rating of 8.87 million viewers, and a 4.5 in the 18–49 demographic.\\n\\nIn the United Kingdom, the episode was viewed by 2.293 million viewers, making it the highest-rated broadcast that week. It also received 0.176 million timeshift viewers.\\n\\n===Critical reviews===\\nThe episode received very positive reviews. It scored 91% on Rotten Tomatoes from 32 reviews with a rating average of 7.9 out of 10 and the consensus reading: \"\\'The Gift\\' is a jam-packed installment that delivers long-awaited plot turns in a satisfying manner, even though it continues a disturbing theme from the previous episode.\"Erik Kain of \\'\\'Forbes\\'\\' called this a \"terrific, exciting, tense episode,\" though, like Myles McNutt of \\'\\'The A.V. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '67c19e788a02040c517c0a76dfeb5ad8'}>,\n",
+       "  <Document: {'content': 'It scored 91% on Rotten Tomatoes from 32 reviews with a rating average of 7.9 out of 10 and the consensus reading: \"\\'The Gift\\' is a jam-packed installment that delivers long-awaited plot turns in a satisfying manner, even though it continues a disturbing theme from the previous episode.\"Erik Kain of \\'\\'Forbes\\'\\' called this a \"terrific, exciting, tense episode,\" though, like Myles McNutt of \\'\\'The A.V. Club\\'\\', he questioned both the \"anemic\" Dorne storyline and the gratuitousness of Tyene exposing her breasts in the prison scene.\\n\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'ed25aa9e7b544cf6dea5a04921684e28'}>,\n",
+       "  <Document: {'content': '\\n\"\\'\\'\\'Unbowed, Unbent, Unbroken\\'\\'\\'\" is the sixth episode of the fifth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 46th overall. The episode was written by Bryan Cogman, and directed by Jeremy Podeswa.\\n\\nThe episode polarized critics and viewers for its ending, depicting a violent sexual assault. It received a rating of 54% on Rotten Tomatoes, and was the lowest-rated \\'\\'Game of Thrones\\'\\' episode until \"The Bells\" in season 8. Nonetheless, director Jeremy Podeswa received an Emmy Award nomination for Outstanding Directing for a Drama Series for this episode.\\n\\nThe name of the episode comes from the House Martell motto \\'\\'Unbowed, Unbent, Unbroken\\'\\', words about strength that are put in contrast with the fates of several main characters, especially women, as these take a turn for the worse.\\n\\n==Plot==\\n\\n===In Braavos===\\nThe Waif tells Arya that to pass the Game of Faces she must be able to convincingly lie. When a man brings her sick daughter to the temple, so that she can die in peace, Arya lies to her that she was ill like her in the past and gives her the temple\\'s poisoned water to end her suffering. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '7e9c78679cae8600462c3e8bad1b476d'}>,\n",
+       "  <Document: {'content': \"When a man brings her sick daughter to the temple, so that she can die in peace, Arya lies to her that she was ill like her in the past and gives her the temple's poisoned water to end her suffering. Jaqen takes Arya to a chamber where the Faceless Men store the faces of all the people that have died in the temple and tells her that she is not yet ready to become no one, but she is ready to become someone else.\\n\\n===In Dorne===\\nBronn and Jaime disguise as Dornish guards and infiltrate the Water Gardens to take Myrcella, but are attacked by Sand Snakes. Dornish guards, led by Areo Hotah, arrive and arrest both groups and Ellaria Sand\\n\\n===On the Valyrian peninsula===\\nTyrion tells Jorah that his father Jeor is dead. They are captured by slavers, led by Malko. After hearing that Daenerys reopened the fighting pits, Tyrion convinces the slavers to take them to Meereen, saying that Jorah is an accomplished warrior.\\n\\n===In King's Landing===\\nPetyr Baelish arrives in King's Landing and tells Cersei that Sansa will marry Ramsay at Winterfell. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '67c48a205860bf5a87eea50cb79390c0'}>,\n",
+       "  <Document: {'content': \"After hearing that Daenerys reopened the fighting pits, Tyrion convinces the slavers to take them to Meereen, saying that Jorah is an accomplished warrior.\\n\\n===In King's Landing===\\nPetyr Baelish arrives in King's Landing and tells Cersei that Sansa will marry Ramsay at Winterfell. He asks her to lead the Knights of the Vale to destroy the victor, after Stannis’ attack on the Boltons and be named Warden of the North. She agrees. Olenna arrives and tells Cersei that her actions have put the Lannister-Tyrell alliance in peril, but Cersei tells her that she had nothing to do with Loras’ arrest. At Loras' holy inquest, the High Sparrow interrogates Loras and Margaery, who both deny that Loras is homosexual. Olyvar testifies against Loras and the Faith Militant arrests Loras and Margaery.\\n\\n===In Winterfell===\\nTheon takes Sansa to the Godswood, where she is married to Ramsay. At his chambers, Ramsay orders Sansa to undress and rapes her, to Theon's horror.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series producer Bryan Cogman.\\nThis episode was written by the series producer Bryan Cogman, who has written at least one episode in every season of the show. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '295727c3dc37252d6adca6754e113e9b'}>,\n",
+       "  <Document: {'content': 'This episode was written by the series producer Bryan Cogman, who has written at least one episode in every season of the show. It contains some content from George Martin\\'s novel \\'\\'A Feast for Crows\\'\\', chapters Arya II, The Queenmaker, Cat of the Canals, and Cersei X and \\'\\'A Dance with Dragons\\'\\', chapters the Ugly Little Girl, Tyrion X, and the Prince of Winterfell, though series consultant Elio Garcia describes the portrayal of some of these events as \"vastly different\" from the original.\\n\\nLike other episodes this season, it also included content and storylines written specifically for the television adaptation.  Myles McNutt of \\'\\'A.V. Club\\'\\' points out that this changes the way the viewers interpret the showrunner\\'s decisions. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '82fd8609fe2385b35c47c882857c1023'}>,\n",
+       "  <Document: {'content': 'Like other episodes this season, it also included content and storylines written specifically for the television adaptation.  Myles McNutt of \\'\\'A.V. Club\\'\\' points out that this changes the way the viewers interpret the showrunner\\'s decisions. When describing his opinion of the decision to show Sansa raped by Ramsay on their wedding night (a storyline given to a different character, Jeyne Poole, in the books), he compares the scene to a similar one between Daenerys and Drogo in season one (which was consensual in the novels): \"While we could frame the shifted events of Dany and Khal Drogo’s wedding night in light of where we knew Dany’s story was going, here we have no idea what this does to Sansa’s storyline.\"Most critics questioned the decision to show Sansa raped on her wedding night, but, as \\'\\'Business Insider\\'\\' pointed out, \"The book version of this scene was much, much worse,\" with Theon ordered, graphically, to participate in Jeyne\\'s mistreatment. In an interview with \\'\\'Entertainment Weekly\\'\\', show writer Bryan Cogman was asked about the decision to decrease the level of violence, responding, \"Lord no. \\'\\'No-no-no-no-no.\\'\\'No. It’s still a shared form of abuse that they have to endure, Sansa and Theon. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'a42c54102e1f606d3764390aeb08255e'}>,\n",
+       "  <Document: {'content': 'In an interview with \\'\\'Entertainment Weekly\\'\\', show writer Bryan Cogman was asked about the decision to decrease the level of violence, responding, \"Lord no. \\'\\'No-no-no-no-no.\\'\\'No. It’s still a shared form of abuse that they have to endure, Sansa and Theon. But it’s not the extreme torture and humiliation that scene in the book is.\"However, in other ways, the episode veers back to book canon: \"Whereas Loras’ arrest suggested the show was replacing Margaery\\'s alleged dalliances with his homosexuality, here the show gradually builds to Margaery\\'s arrest for lying on her brother\\'s behalf.\"===Filming===\\n\"Unbowed, Unbent, Unbroken\" was directed by Jeremy Podeswa. He also directed the previous episode, \"Kill the Boy\".\\n\\n==Reception==\\n\\n===Ratings===\\n\"Unbowed, Unbent, Unbroken\" was watched by 6.24 million American viewers during its first airing. With Live+7 DVR viewing factored in, the episode had an overall rating of 8.79 million viewers, and a 4.5 in the 18-49 demographic. In the United Kingdom, the episode was viewed by 2.285 million viewers, making it the highest-rated broadcast that week. It also received 0.126 million timeshift viewers.\\n\\n===Critical reviews and controversy===\\nThe episode received polarized reviews from critics. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'a26c9b8dc110e9d6ac78bf883d1a844'}>,\n",
+       "  <Document: {'content': 'In the United Kingdom, the episode was viewed by 2.285 million viewers, making it the highest-rated broadcast that week. It also received 0.126 million timeshift viewers.\\n\\n===Critical reviews and controversy===\\nThe episode received polarized reviews from critics. On Rotten Tomatoes, the episode received a 54% approval rating from 50 critics with a rating average of 7.55 out of 10, the lowest of any episode in the series at that time. The critical consensus states: \"Unbalanced storytelling and unnecessary, excessive brutality add up to disturbing viewing, although \\'Unbowed, Unbent, Unbroken\\' still includes enough plot revelations to offer hope for future episodes.\"The majority of professional criticism concerned the decision to have Ramsay rape Sansa on their wedding night, with most critics describing the scene as gratuitous and artistically unnecessary. \"This grim scene was difficult for the show to justify,\" said Charlotte Runcie of \\'\\'The Daily Telegraph\\'\\'. Joanna Robinson of \\'\\'Vanity Fair\\'\\' added, \"this rape scene undercuts all the agency that’s been growing in Sansa since the end of last season. ... I’d never advocate that \\'\\'Game of Thrones\\'\\' (or any work of fiction) shy away from edgy plots out of fear of pushback or controversy. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '199cac14d101c3dab4eb7f4127a05b78'}>,\n",
+       "  <Document: {'content': 'Joanna Robinson of \\'\\'Vanity Fair\\'\\' added, \"this rape scene undercuts all the agency that’s been growing in Sansa since the end of last season. ... I’d never advocate that \\'\\'Game of Thrones\\'\\' (or any work of fiction) shy away from edgy plots out of fear of pushback or controversy. But edgy plots should always accomplish something above pure titillation or shock value and what, \\'\\'exactly\\'\\', was accomplished here?\"Christopher Orr wrote in \\'\\'The Atlantic\\'\\', \"I continue to be astonished that showrunners Benioff and Weiss still apparently believe that their tendency to ramp up the sex, violence, and—especially—sexual violence of George R.R. Martin’s source material is a strength rather than the defining weakness of their adaptation.\"Myles McNutt of \\'\\'The A.V. Club\\'\\' wrote, \"The issue with the show returning to rape as a trope is not simply because there have been thinkpieces speaking out against it, and is not solely driven by the rational concerns lying at the heart of those thinkpieces. It’s also that the show has lost my faith as a viewer.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '9b6363c4a07a8e17dac9e2689bcfade9'}>,\n",
+       "  <Document: {'content': 'Club\\'\\' wrote, \"The issue with the show returning to rape as a trope is not simply because there have been thinkpieces speaking out against it, and is not solely driven by the rational concerns lying at the heart of those thinkpieces. It’s also that the show has lost my faith as a viewer.\"Writers from \\'\\'Vanity Fair\\'\\', \\'\\'The Mary Sue\\'\\' and \\'\\'The Daily Beast\\'\\' all disapproved of the decision to use Sansa\\'s victimization as a motivating agent for Theon, saying that the scene undermined Sansa\\'s character development: \"Was it really important to make that scene about Theon\\'s pain?\"wrote Joanna Robinson of \\'\\'Vanity Fair\\'\\'.\\n\\nOther critics responded positively to the scene. Sean T. Collins of \\'\\'Rolling Stone\\'\\' wrote: \"By involving a multidimensional main character instead of one introduced primarily to suffer, the series has a chance to grant this story the gravity and seriousness it deserves. Sarah Hughes of \\'\\'The Guardian\\'\\' wrote: \"I have repeatedly made clear that I’m not a fan of rape as a plot device – but the story of Ramsay and Sansa’s wedding was more than that. ... The writers are walking a very fine line here. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'e13805acc400132db056176ea6bef537'}>,\n",
+       "  <Document: {'content': 'Sarah Hughes of \\'\\'The Guardian\\'\\' wrote: \"I have repeatedly made clear that I’m not a fan of rape as a plot device – but the story of Ramsay and Sansa’s wedding was more than that. ... The writers are walking a very fine line here. They handled it well tonight, telling a gothic tale of innocence sacrificed\". Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' wrote that the scene \"managed to maintain a fine balance, employing a dignity and care for the experiences of victims that \\'\\'Game of Thrones\\'\\' has not always demonstrated.\"Some critics questioned why this scene in particular should generate outrage when similar scenes have not. Sara Stewart of the \\'\\'New York Post\\'\\' pointed out that the rape and sexual abuse of both female and male characters is typical for \\'\\'Game of Thrones\\'\\': \"Why are we suddenly so outraged about the rape of Sansa Stark, when this show has served up a steady diet of sexual assault and violence against women since its first season began?\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '9663511852c9cd49ae9261c1811d74d9'}>,\n",
+       "  <Document: {'content': 'Sara Stewart of the \\'\\'New York Post\\'\\' pointed out that the rape and sexual abuse of both female and male characters is typical for \\'\\'Game of Thrones\\'\\': \"Why are we suddenly so outraged about the rape of Sansa Stark, when this show has served up a steady diet of sexual assault and violence against women since its first season began?\"Cathy Young of \\'\\'Reason\\'\\' magazine, writing in \\'\\'Time\\'\\' noted what she calls a lack of complaint in response to the sexual mistreatment of male characters in earlier seasons, specifically the literal emasculation of Theon Greyjoy and the sexual assault of Gendry.\\n\\nCriticism of the scene has not extended to the quality of the acting. Joanna Robinson of \\'\\'Vanity Fair\\'\\' wrote, \"And if we can say one positive thing about that scene it\\'s that Allen nailed his performance. Theon\\'s horror mirrored our own and the camera—focusing on his reaction—let our minds fill in the blanks.\"Sophie Turner defended the scene as an artistic challenge for herself as an actor, saying, \"When I read that scene, I kinda loved it. I love the way Ramsay had Theon watching. It was all so messed up. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '2e8b973c15b469f7aca568e0e696e80c'}>,\n",
+       "  <Document: {'content': 'Sophie Turner defended the scene as an artistic challenge for herself as an actor, saying, \"When I read that scene, I kinda loved it. I love the way Ramsay had Theon watching. It was all so messed up. It’s also so daunting for me to do it. ... I think it\\'s going to be the most challenging season for me so far, just because it\\'s so emotional for her. It’s not just crying all the time, like seasons 2 or 3, it’s super messed up.\"She had since gone on to interview for the DailyMail about it and that the scene had inspired her to work for organizations to stop violence against women and to help underprivileged girls and women in Africa in similar situations. Iwan Rheon (Ramsay Bolton) agreed, referring to Turner\\'s performances this season as \"absolutely amazing.\"Some viewers, including U.S. Senator Claire McCaskill, announced that they would stop watching the show because of this scene. According to \\'\\'Business Insider\\'\\', this scene and increased use of streaming services are likely reasons why ratings dropped from 6.2 million viewers for this episode to 5.4 million for the next episode, \"The Gift.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'bd62e1fce6209a5be2f05bea149d0467'}>,\n",
+       "  <Document: {'content': 'According to \\'\\'Business Insider\\'\\', this scene and increased use of streaming services are likely reasons why ratings dropped from 6.2 million viewers for this episode to 5.4 million for the next episode, \"The Gift.\"However, there is some question as to how much of this drop is attributable to its Memorial Day weekend air date. Rebecca Martin of \\'\\'Wetpaint\\'\\' maintains that the air date was probably the only reason for the decrease in ratings. The season two episode \"Blackwater,\" which also aired on Memorial Day, also suffered a notable drop in ratings. No episode was aired on Memorial Day weekend in either season three or season four. Whatever the reason, the ratings for the episode after \"The Gift,\" \"Hardhome,\" were higher than those of both previous episodes.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '6ac7521c4fa4251fb974766a65c9bfd2'}>,\n",
+       "  <Document: {'content': 'Whatever the reason, the ratings for the episode after \"The Gift,\" \"Hardhome,\" were higher than those of both previous episodes.\\n\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nPrimetime Emmy Award\\n\\nOutstanding Directing for a Drama Series\\n\\nJeremy Podeswa\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron\\n\\n2016\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nGregory Middleton\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Unbowed, Unbent, Unbroken\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'd3c7107628afe50d21b2fc73db35723b'}>,\n",
+       "  <Document: {'content': '\\nalt=Game of Thrones logo\\n\\'\\'Game of Thrones\\'\\' is an American fantasy drama television series created by David Benioff and D. B. Weiss. The series is based on George R. R. Martin\\'s series of fantasy novels, \\'\\'A Song of Ice and Fire\\'\\'. The series takes place on the fictional continents of Westeros and Essos, and chronicles the power struggles among noble families as they fight for control of the Iron Throne of the Seven Kingdoms. The series starts when House Stark, led by Lord Eddard \"Ned\" Stark (Sean Bean), is drawn into schemes surrounding King Robert Baratheon (Mark Addy).\\n\\nThe series premiered on April 17, 2011, on HBO. David Benioff and D. B. Weiss both serve as executive producers, along with Carolyn Strauss, Frank Doelger, Bernadette Caulfield, and George R. R. Martin. Filming for the series has taken place in a number of locations, including Croatia, Ireland, Iceland, and Spain. Episodes were broadcast on Sunday at 9:00 pm Eastern Time, and the episodes are between 50 and 82 minutes in length. The first seven seasons are available on DVD and Blu-ray.\\n\\nThe series concluded with its eighth season, which premiered on April 14, 2019, and consisted of six episodes. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'b1c5ee918ebf752f590490ee19c10b16'}>,\n",
+       "  <Document: {'content': 'The first seven seasons are available on DVD and Blu-ray.\\n\\nThe series concluded with its eighth season, which premiered on April 14, 2019, and consisted of six episodes. The show\\'s episodes have won numerous awards including four Primetime Emmy Award for Outstanding Drama Series.\\n\\n== Series overview ==\\n\\n== Episodes ==\\n\\n=== Season 1 (2011) ===\\n\\nIn the continent of Westeros, Warden of the North Eddard \"Ned\" Stark becomes \"Hand of the King\" (chief adviser) to King Robert Baratheon. When Ned\\'s son Bran stumbles upon Queen Cersei having sex with her twin brother Jaime Lannister, Jaime cripples Bran. After moving to King\\'s Landing, the capital of the Seven Kingdoms, Ned discovers that Prince Joffrey and his siblings were incestuously fathered by Jaime. When King Robert dies, Ned does not recognize Joffrey as the rightful heir and is executed. Ned\\'s bastard son Jon Snow joins the Night\\'s Watch, an ancient brotherhood sworn to watch over the massive Wall in the North, which separates the Seven Kingdoms from hostile tribes known as \"Wildlings\" and mythological humanoid creatures known as \"White Walkers\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '49fefb606d65b7dc11919b1e197a3542'}>,\n",
+       "  <Document: {'content': 'Ned\\'s bastard son Jon Snow joins the Night\\'s Watch, an ancient brotherhood sworn to watch over the massive Wall in the North, which separates the Seven Kingdoms from hostile tribes known as \"Wildlings\" and mythological humanoid creatures known as \"White Walkers\". In the continent of Essos, the exiled Viserys Targaryen, son of King Aerys who was deposed by King Robert, marries off his sister Daenerys to the Dothraki warlord Khal Drogo, in exchange for an army to return to and take over Westeros. Eventually Drogo kills Viserys and Drogo later dies himself. After Drogo dies, Daenerys manages to hatch three dragons.\\n\\n=== Season 2 (2012) ===\\n\\nNed\\'s oldest son Robb has declared war on the Lannisters and holds Jaime prisoner. However, his mother Catelyn secretly frees Jaime to ransom her daughters, Sansa and Arya, believed to be captives in King\\'s Landing. Sansa is being regularly abused by her betrothed King Joffrey, while a disguised Arya escapes Lannister forces in Harrenhal. Ned\\'s ward, Theon Greyjoy sides with his father and seizes Winterfell, the Starks\\' ancestral home, though Bran and his brother Rickon escape. Meanwhile, Stannis and Renly Baratheon, brothers of the deceased King Robert, claim the throne for themselves. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'c87e2928c3b8a596170a02209669c55b'}>,\n",
+       "  <Document: {'content': \"Ned's ward, Theon Greyjoy sides with his father and seizes Winterfell, the Starks' ancestral home, though Bran and his brother Rickon escape. Meanwhile, Stannis and Renly Baratheon, brothers of the deceased King Robert, claim the throne for themselves. Stannis has the support of Melisandre, a Red Priestess of the Lord of Light. Stannis kills Renly and attacks King's Landing, whose defense is successfully led by Tyrion Lannister, the acting Hand of the King and Queen Cersei's younger brother. On a ranging beyond the Wall, Jon Snow manages to gain the trust of Wildlings, while other members of the Watch come under attack by an army of White Walkers and their reanimated corpses. In Essos, Daenerys and her khalasar find refuge in Qarth. After foiling an attempt to steal her dragons, Daenerys leaves the city.\\n\\n=== Season 3 (2013) ===\\n\\nAfter Robb breaks his vow to marry one of Lord Walder Frey's daughters, Frey arranges the massacre of Robb, his mother, his wife, his unborn child and his bannermen, during a wedding feast. The northern House Bolton, which had liberated Winterfell from Theon at Robb's request, also betrays the Starks. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '2137caaa92082a70e9e55b2ecce87ae8'}>,\n",
+       "  <Document: {'content': '=== Season 3 (2013) ===\\n\\nAfter Robb breaks his vow to marry one of Lord Walder Frey\\'s daughters, Frey arranges the massacre of Robb, his mother, his wife, his unborn child and his bannermen, during a wedding feast. The northern House Bolton, which had liberated Winterfell from Theon at Robb\\'s request, also betrays the Starks. After Robb\\'s death, Lord Roose Bolton is appointed the new Warden of the North by Lord Tywin Lannister, the head of House Lannister and new Hand of the King. In Dreadfort, the Boltons\\' ancestral castle, Theon is tortured by Roose Bolton\\'s bastard son Ramsay Snow. Further north, Jon Snow climbs the Wall with a Wildling party and ends up on its south side, but then betrays them. In the capital, King Joffrey has decided to marry Margaery of House Tyrell, setting Sansa aside. Lord Tywin, however, arranges Sansa\\'s marriage with his son Tyrion. Jaime reaches King\\'s Landing, having had his dominant hand cut off. In Essos, Daenerys acquires \"The Unsullied\", an army of eunuch slave soldiers. She also joins forces with the \"Second Sons\", a company of mercenaries, and takes over two cities.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '804139beaa9467a2c45b62343fcc4ed2'}>,\n",
+       "  <Document: {'content': 'In Essos, Daenerys acquires \"The Unsullied\", an army of eunuch slave soldiers. She also joins forces with the \"Second Sons\", a company of mercenaries, and takes over two cities.\\n\\n=== Season 4 (2014) ===\\n\\nIn King\\'s Landing, Lady Olenna secretly poisons King Joffrey at his wedding with her granddaughter Margaery, but Tyrion is falsely accused of the murder by his family, and is found guilty. However, Jaime and Varys conspire to smuggle Tyrion to Essos. Tyrion kills his father Tywin before leaving. Petyr Baelish smuggles Sansa into the Vale, ruled by her aunt and his lover, the widowed Lysa Arryn. Baelish marries Lysa but later kills her. After attempting to reunite with her family, Arya takes a ship bound for Braavos, in Essos. Having returned to the Night\\'s Watch, Jon Snow defends Castle Black against an army of Wildlings, who are superior in strength. The Watch is rescued by the arrival of Stannis Baratheon and his forces. A crippled Bran with newfound powers travels north beyond the Wall with a few companions. Beneath a weirwood tree, he finds the Three-eyed Raven, an old man with the ability to perceive future and past. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'bd1f6776b78b687b9d3493ad9086f752'}>,\n",
+       "  <Document: {'content': \"Beneath a weirwood tree, he finds the Three-eyed Raven, an old man with the ability to perceive future and past. In Essos, Daenerys takes control of Meereen and abolishes slavery. When she discovers that her trusted advisor, Ser Jorah Mormont, had spied on her for Robert Baratheon, she exiles him.\\n\\n=== Season 5 (2015) ===\\n\\nIn King's Landing, Margaery marries the new King Tommen Baratheon, Joffrey's younger brother. The Sparrows, a group of religious fanatics, impose their views upon the city, imprisoning Margaery, her brother Loras, and Cersei for committing various sins. Jaime travels to Dorne to take back Myrcella Baratheon. However, Oberyn Martell's lover, Ellaria and his bastard daughters kill Myrcella as revenge for Oberyn's death. In Winterfell, the new seat of House Bolton, Baelish arranges Sansa's marriage with the now-legitimized son of Roose Bolton, the sadist Ramsay. Stannis's unsuccessful march on Winterfell, which leads to his death, allows Sansa the opportunity to escape with Theon. At the Wall, as the newly elected Lord Commander of the Night's Watch, Jon Snow forms an alliance with the Wildlings to save them from the White Walkers and their army of reanimated corpses. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '206495c95288ae2b36bd62912fea912f'}>,\n",
+       "  <Document: {'content': \"At the Wall, as the newly elected Lord Commander of the Night's Watch, Jon Snow forms an alliance with the Wildlings to save them from the White Walkers and their army of reanimated corpses. However, Jon is stabbed to death by some brothers who see him as a traitor. Arya arrives in Braavos, where she finds Jaqen H'ghar whom she had previously helped escape, and begins training with the Faceless Men, a guild of assassins. In Essos, Tyrion becomes an advisor to Daenerys. Ser Jorah saves the life of Daenerys against a revolt of slavers, who flees Meereen on Drogon's back.\\n\\n=== Season 6 (2016) ===\\n\\nAt the Wall, Melisandre resurrects Jon. Jon reunites with Sansa and leaves the Night's Watch. Aided by the Wildlings, loyalists and the Knights of the Vale, they defeat the Boltons and Jon is proclaimed the King in the North. Beyond the Wall, Bran trains with the Three-eyed Raven but they come under attack by the White Walkers. The Three-eyed Raven is killed and succeeded by Bran, who escapes with the help of Hodor, who dies in the process. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '6e99db0a7535f617b62798eda470ebc'}>,\n",
+       "  <Document: {'content': \"The Three-eyed Raven is killed and succeeded by Bran, who escapes with the help of Hodor, who dies in the process. Bran realizes Jon is in fact the son of his deceased aunt Lyanna Stark and Crown Prince Rhaegar Targaryen. In King's Landing, Cersei kills the High Sparrow, Margaery, Loras and many others by blowing up the Great Sept with wildfire. Tommen kills himself after witnessing the events, and Cersei is crowned Queen. In the Iron Islands, Euron Greyjoy usurps leadership by killing his brother and Theon's father, Balon. Ellaria seizes control of Dorne, and joins Olenna Tyrell in an alliance with Daenerys, who forgives Ser Jorah, takes control of the Dothraki and defeats the slavers. Daenerys sails for Westeros, joined by Theon and his sister Yara. In Braavos, Arya continues her training with the Faceless Men, but eventually leaves them.\\n\\n=== Season 7 (2017) ===\\n\\nDaenerys arrives in Westeros and takes over Dragonstone. She plans to overthrow Cersei, but Jon arrives to instead save Westeros from the White Walkers. The Night King, leader of the White Walkers, kills and reanimates Daenerys' dragon Viserion. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '1312c397699daa8c4f6070bc68b2b4bb'}>,\n",
+       "  <Document: {'content': \"She plans to overthrow Cersei, but Jon arrives to instead save Westeros from the White Walkers. The Night King, leader of the White Walkers, kills and reanimates Daenerys' dragon Viserion. Jon and Daenerys attempt to persuade Cersei to join their cause by showing the existence of a wight they had captured, but she has her own plans to increase control over the continent. At Winterfell, Sansa is reunited with her siblings, Arya and Bran. When Lord Protector of the Vale Petyr Baelish begins to turn the Stark children against one another, they manage to have him executed. In a vision, Bran sees that his aunt, Lyanna, was, in fact, married to Prince Rhaegar, and that Jon's real name is Aegon Targaryen, making him the true heir to the Iron Throne. After his sister Yara is kidnapped by his uncle Euron, Theon sets out to save her. The Night King demolishes a section of The Wall with the help of the reanimated Viserion, allowing the White Walkers and Army of the Dead to pass into the Seven Kingdoms.\\n\\n=== Season 8 (2019) ===\\n\\nJon and Daenerys learn the Army of the Dead has breached the Wall. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '377eb73b649cabbe04e43eae3728c45d'}>,\n",
+       "  <Document: {'content': \"The Night King demolishes a section of The Wall with the help of the reanimated Viserion, allowing the White Walkers and Army of the Dead to pass into the Seven Kingdoms.\\n\\n=== Season 8 (2019) ===\\n\\nJon and Daenerys learn the Army of the Dead has breached the Wall. Theon rescues Yara, then returns to Winterfell. Sam reveals to Jon that he is actually Aegon Targaryen. Jaime arrives at Winterfell, revealing Cersei won't help defeat the Army of the Dead. Jon reveals his Targaryen lineage to Daenerys, who wants it kept a secret. After a battle, Arya kills the Night King, destroying the Army of the Dead. Euron's navy kills Rhaegal and Cersei executes Missandei who had been captured, enraging Daenerys, who, after her army takes King's Landing, destroys much of the city indiscriminately. Cersei and Jaime, who returned to Cersei, are killed. Tyrion denounces Daenerys and is imprisoned for treason to await execution. Jon, unable to stop her, kills Daenerys. Bran Stark is proclaimed king, allowing the North to secede as an independent kingdom. Bran appoints Tyrion as his Hand. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'a85383bf487f01f1214a67cc90fa5b12'}>,\n",
+       "  <Document: {'content': \"Bran Stark is proclaimed king, allowing the North to secede as an independent kingdom. Bran appoints Tyrion as his Hand. Jon is sentenced to the Night's Watch, Sansa is crowned Queen in the North, and Arya sets sail to explore west of Westeros.\\n\\n==Specials==\\n\\n==Home media release==\\n\\nSeason\\n\\nEpisodes\\n\\nDVD and Blu-ray release date\\n\\nRegion 1\\n\\nRegion 2\\n\\nRegion 4\\n\\n1\\n\\n10\\n\\nMarch 6, 2012\\n\\nMarch 5, 2012\\n\\nAugust 10, 2012\\n\\n2\\n\\n10\\n\\nFebruary 19, 2013\\n\\nMarch 4, 2013\\n\\nMarch 6, 2013\\n\\n3\\n\\n10\\n\\nFebruary 18, 2014\\n\\nFebruary 17, 2014\\n\\nFebruary 19, 2014\\n\\n4\\n\\n10\\n\\nFebruary 17, 2015\\n\\nFebruary 16, 2015\\n\\nFebruary 18, 2015\\n\\n5\\n\\n10\\n\\nMarch 15, 2016\\n\\nMarch 14, 2016\\n\\nMarch 16, 2016\\n\\n6\\n\\n10\\n\\nNovember 15, 2016\\n\\nNovember 14, 2016\\n\\nNovember 16, 2016\\n\\n7\\n\\n7\\n\\nDecember 12, 2017\\n\\nDecember 11, 2017\\n\\nDecember 11, 2017\\n\\n8\\n\\n6\\n\\nDecember 3, 2019\\n\\nDecember 2, 2019\\n\\nDecember 4, 2019\\n\\n== Ratings ==\\n\\n== References ==\\n\\n== External links ==\\n*\\n*  ''Game of Thrones''\\xa0– The Viewers Guide on HBO.com\\n*  Making ''Game of Thrones'' on HBO.com\\n*\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '3270f7f2226aac27bc9cf95c4cfd87b7'}>,\n",
+       "  <Document: {'content': \"\\n\\nThe fourth season of the fantasy drama television series ''Game of Thrones'' premiered in the United States on HBO on April 6, 2014, and concluded on June 15, 2014. It was broadcast on Sunday at 9:00 pm in the United States, consisting of 10 episodes, each running approximately 50–60 minutes. The season is adapted primarily from the second half of ''A Storm of Swords'', along with elements of ''A Feast for Crows'' and ''A Dance with Dragons'', all novels from the ''A Song of Ice and Fire'' series by George R. R. Martin. The series is adapted for television by David Benioff and D. B. Weiss. HBO ordered the fourth season on April 2, 2013, which began filming in July 2013. The season was filmed primarily in Ireland, Northern Ireland and Croatia.\\n\\nThe story takes place in a fictional world, primarily upon a continent called Westeros, with one storyline occurring on another continent to the east known as Essos. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '8a5a88aa17764db954febe097dca8ee2'}>,\n",
+       "  <Document: {'content': \"The story takes place in a fictional world, primarily upon a continent called Westeros, with one storyline occurring on another continent to the east known as Essos. Like the novel and previous seasons, the fourth season mainly centres around the war of the five kings; after the death of Robb Stark at The Red Wedding, all three remaining kings in Westeros believe they have a claim to the Iron Throne. Meanwhile, at the wall, Jon Snow and the Night's Watch get ready for the battle against the free folk.\\n\\n''Game of Thrones'' features a large ensemble cast, including Peter Dinklage, Nikolaj Coster-Waldau, Lena Headey, Emilia Clarke and Kit Harington. The season introduced a number of new cast members, including Pedro Pascal, Indira Varma, Michiel Huisman and Dean-Charles Chapman.\\n\\nCritics praised the show's production values and cast, with specific accolades for Dinklage's portrayal of Tyrion Lannister. Viewership yet again rose compared to the previous season. It won 4 of the 19 Emmy Awards for which it was nominated.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'a3131856516010ddbd5192a5767693e'}>,\n",
+       "  <Document: {'content': 'Viewership yet again rose compared to the previous season. It won 4 of the 19 Emmy Awards for which it was nominated.\\n\\n==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n* Peter Dinklage as Tyrion Lannister\\n* Nikolaj Coster-Waldau as Jaime Lannister\\n* Lena Headey as Cersei Lannister\\n* Emilia Clarke as Daenerys Targaryen\\n* Kit Harington as Jon Snow\\n* Charles Dance as Tywin Lannister\\n* Natalie Dormer as Margaery Tyrell\\n* Jack Gleeson as Joffrey Baratheon\\n* Sophie Turner as Sansa Stark\\n* Maisie Williams as Arya Stark\\n* John Bradley as Samwell Tarly\\n* Rose Leslie as Ygritte\\n* Kristofer Hivju as Tormund Giantsbane\\n* Rory McCann as Sandor \"The Hound\" Clegane\\n\\n* Gwendoline Christie as Brienne of Tarth\\n* Jerome Flynn as Bronn\\n* Sibel Kekilli as Shae\\n* Iain Glen as Jorah Mormont\\n* Liam Cunningham as Davos Seaworth\\n* Stephen Dillane as Stannis Baratheon\\n* Carice van Houten as Melisandre\\n* Alfie Allen as Theon Greyjoy / \"Reek\"\\n* Isaac Hempstead Wright as Bran Stark\\n* Iwan Rheon as Ramsay Snow / Ramsay Bolton\\n* Conleth Hill as Varys\\n* Aidan Gillen as Petyr \"Littlefinger\" Baelish\\n* Hannah Murray as Gilly\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 4. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'b81940c805f56762728d0d85321b82fa'}>,\n",
+       "  <Document: {'content': '==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n* Peter Dinklage as Tyrion Lannister\\n* Nikolaj Coster-Waldau as Jaime Lannister\\n* Lena Headey as Cersei Lannister\\n* Emilia Clarke as Daenerys Targaryen\\n* Kit Harington as Jon Snow\\n* Charles Dance as Tywin Lannister\\n* Natalie Dormer as Margaery Tyrell\\n* Jack Gleeson as Joffrey Baratheon\\n* Sophie Turner as Sansa Stark\\n* Maisie Williams as Arya Stark\\n* John Bradley as Samwell Tarly\\n* Rose Leslie as Ygritte\\n* Kristofer Hivju as Tormund Giantsbane\\n* Rory McCann as Sandor \"The Hound\" Clegane\\n\\n* Gwendoline Christie as Brienne of Tarth\\n* Jerome Flynn as Bronn\\n* Sibel Kekilli as Shae\\n* Iain Glen as Jorah Mormont\\n* Liam Cunningham as Davos Seaworth\\n* Stephen Dillane as Stannis Baratheon\\n* Carice van Houten as Melisandre\\n* Alfie Allen as Theon Greyjoy / \"Reek\"\\n* Isaac Hempstead Wright as Bran Stark\\n* Iwan Rheon as Ramsay Snow / Ramsay Bolton\\n* Conleth Hill as Varys\\n* Aidan Gillen as Petyr \"Littlefinger\" Baelish\\n* Hannah Murray as Gilly\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 4. They are listed by the region in which they first appear:\\n\\n====At and beyond the Wall====\\n* Owen Teale as Alliser Thorne\\n* Peter Vaughan as Maester Aemon\\n* Brian Fortune as Othell Yarwyck\\n* Dominic Carter as Janos Slynt\\n* Ben Crompton as Eddison Tollett\\n* Noah Taylor as Locke\\n* Mark Stanley as Grenn\\n* Josef Altin as Pypar\\n* Luke Barnes as Rast\\n* Burn Gorman as Karl Tanner\\n* Brenock O\\'Connor as Olly\\n* Lu Corfield as the Mole\\'s Town madam\\n* Lois Winstone as a Mole\\'s Town prostitute\\n* Ciarán Hinds as Mance Rayder\\n* Yuri Kolokolnikov as Styr\\n* Ian Whyte as Dongo\\n* Joseph Gatt as a Thenn warg\\n* Deirdre Monaghan as Morag\\n* Jane McGrath as Sissy\\n* Thomas Brodie-Sangster as Jojen Reed\\n* Ellie Kendrick as Meera Reed\\n* Kristian Nairn as Hodor\\n* Struan Rodger as the Three-Eyed Raven\\n* Octavia Alexandru as Leaf\\n* Richard Brake as the Night King\\n* Ross Mullan as White Walkers\\n\\n====In the North====\\n* Michael McElhatton as Roose Bolton\\n* Elizabeth Webster as Walda Bolton\\n* Gemma Whelan as Yara Greyjoy\\n* Charlotte Hope as Myranda\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Kate Dickie as Lysa Arryn\\n* Rupert Vansittart as Yohn Royce\\n* Alisdair Simpson as Donnel Waynwood\\n\\n====In King\\'s Landing====\\n* Dean-Charles Chapman as Tommen Baratheon\\n* Julian Glover as Grand Maester Pycelle\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Finn Jones as Loras Tyrell\\n* Diana Rigg as Olenna Tyrell\\n* Pedro Pascal as Oberyn Martell\\n* Indira Varma as Ellaria Sand\\n* Ian Beattie as Meryn Trant\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Daniel Portman as Podrick Payne\\n* Tony Way as Dontos Hollard\\n* Paul Bentley as the High Septon\\n* Anton Lesser as Qyburn\\n* Will Tudor as Olyvar\\n* Josephine Gillan as Marei\\n* Pixie Le Knot as Kayla\\n\\n====On Dragonstone====\\n* Tara Fitzgerald as Selyse Florent\\n* Kerry Ingram as Shireen Baratheon\\n\\n====In the Riverlands====\\n* Gary Oliver as Ternesio Terys\\n* Andy Kellegher as Polliver\\n* Andy Beckwith as Rorge\\n* Gerard Jordan as Biter\\n* Ben Hawkey as Hot Pie\\n\\n====In Braavos====\\n* Mark Gatiss as Tycho Nestoris\\n* Lucian Msamati as Salladhor Saan\\n* Sarine Sofair as Lhara\\n\\n====In Slaver\\'s Bay====\\n* Michiel Huisman as Daario Naharis\\n* Ian McElhinney as Barristan Selmy\\n* Nathalie Emmanuel as Missandei\\n* Jacob Anderson as Grey Worm\\n* Joel Fry as Hizdahr zo Loraq\\n* Reece Noi as Mossador\\n\\n==Production==\\nOn April 2, 2013, HBO announced it had renewed the series for a fourth season, to consist of 10 episodes.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '31c50d7c99ec669fa9f4e39e2ea05a4c'}>,\n",
+       "  <Document: {'content': \"They are listed by the region in which they first appear:\\n\\n====At and beyond the Wall====\\n* Owen Teale as Alliser Thorne\\n* Peter Vaughan as Maester Aemon\\n* Brian Fortune as Othell Yarwyck\\n* Dominic Carter as Janos Slynt\\n* Ben Crompton as Eddison Tollett\\n* Noah Taylor as Locke\\n* Mark Stanley as Grenn\\n* Josef Altin as Pypar\\n* Luke Barnes as Rast\\n* Burn Gorman as Karl Tanner\\n* Brenock O'Connor as Olly\\n* Lu Corfield as the Mole's Town madam\\n* Lois Winstone as a Mole's Town prostitute\\n* Ciarán Hinds as Mance Rayder\\n* Yuri Kolokolnikov as Styr\\n* Ian Whyte as Dongo\\n* Joseph Gatt as a Thenn warg\\n* Deirdre Monaghan as Morag\\n* Jane McGrath as Sissy\\n* Thomas Brodie-Sangster as Jojen Reed\\n* Ellie Kendrick as Meera Reed\\n* Kristian Nairn as Hodor\\n* Struan Rodger as the Three-Eyed Raven\\n* Octavia Alexandru as Leaf\\n* Richard Brake as the Night King\\n* Ross Mullan as White Walkers\\n\\n====In the North====\\n* Michael McElhatton as Roose Bolton\\n* Elizabeth Webster as Walda Bolton\\n* Gemma Whelan as Yara Greyjoy\\n* Charlotte Hope as Myranda\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Kate Dickie as Lysa Arryn\\n* Rupert Vansittart as Yohn Royce\\n* Alisdair Simpson as Donnel Waynwood\\n\\n====In King's Landing====\\n* Dean-Charles Chapman as Tommen Baratheon\\n* Julian Glover as Grand Maester Pycelle\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Finn Jones as Loras Tyrell\\n* Diana Rigg as Olenna Tyrell\\n* Pedro Pascal as Oberyn Martell\\n* Indira Varma as Ellaria Sand\\n* Ian Beattie as Meryn Trant\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Daniel Portman as Podrick Payne\\n* Tony Way as Dontos Hollard\\n* Paul Bentley as the High Septon\\n* Anton Lesser as Qyburn\\n* Will Tudor as Olyvar\\n* Josephine Gillan as Marei\\n* Pixie Le Knot as Kayla\\n\\n====On Dragonstone====\\n* Tara Fitzgerald as Selyse Florent\\n* Kerry Ingram as Shireen Baratheon\\n\\n====In the Riverlands====\\n* Gary Oliver as Ternesio Terys\\n* Andy Kellegher as Polliver\\n* Andy Beckwith as Rorge\\n* Gerard Jordan as Biter\\n* Ben Hawkey as Hot Pie\\n\\n====In Braavos====\\n* Mark Gatiss as Tycho Nestoris\\n* Lucian Msamati as Salladhor Saan\\n* Sarine Sofair as Lhara\\n\\n====In Slaver's Bay====\\n* Michiel Huisman as Daario Naharis\\n* Ian McElhinney as Barristan Selmy\\n* Nathalie Emmanuel as Missandei\\n* Jacob Anderson as Grey Worm\\n* Joel Fry as Hizdahr zo Loraq\\n* Reece Noi as Mossador\\n\\n==Production==\\nOn April 2, 2013, HBO announced it had renewed the series for a fourth season, to consist of 10 episodes.\\n\\n===Crew===\\nDavid Benioff and D. B. Weiss serve as main writers and showrunners for the fourth season. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '31dd5205833aa60a6482f6329b02e6b'}>,\n",
+       "  <Document: {'content': \"They are listed by the region in which they first appear:\\n\\n====At and beyond the Wall====\\n* Owen Teale as Alliser Thorne\\n* Peter Vaughan as Maester Aemon\\n* Brian Fortune as Othell Yarwyck\\n* Dominic Carter as Janos Slynt\\n* Ben Crompton as Eddison Tollett\\n* Noah Taylor as Locke\\n* Mark Stanley as Grenn\\n* Josef Altin as Pypar\\n* Luke Barnes as Rast\\n* Burn Gorman as Karl Tanner\\n* Brenock O'Connor as Olly\\n* Lu Corfield as the Mole's Town madam\\n* Lois Winstone as a Mole's Town prostitute\\n* Ciarán Hinds as Mance Rayder\\n* Yuri Kolokolnikov as Styr\\n* Ian Whyte as Dongo\\n* Joseph Gatt as a Thenn warg\\n* Deirdre Monaghan as Morag\\n* Jane McGrath as Sissy\\n* Thomas Brodie-Sangster as Jojen Reed\\n* Ellie Kendrick as Meera Reed\\n* Kristian Nairn as Hodor\\n* Struan Rodger as the Three-Eyed Raven\\n* Octavia Alexandru as Leaf\\n* Richard Brake as the Night King\\n* Ross Mullan as White Walkers\\n\\n====In the North====\\n* Michael McElhatton as Roose Bolton\\n* Elizabeth Webster as Walda Bolton\\n* Gemma Whelan as Yara Greyjoy\\n* Charlotte Hope as Myranda\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Kate Dickie as Lysa Arryn\\n* Rupert Vansittart as Yohn Royce\\n* Alisdair Simpson as Donnel Waynwood\\n\\n====In King's Landing====\\n* Dean-Charles Chapman as Tommen Baratheon\\n* Julian Glover as Grand Maester Pycelle\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Finn Jones as Loras Tyrell\\n* Diana Rigg as Olenna Tyrell\\n* Pedro Pascal as Oberyn Martell\\n* Indira Varma as Ellaria Sand\\n* Ian Beattie as Meryn Trant\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Daniel Portman as Podrick Payne\\n* Tony Way as Dontos Hollard\\n* Paul Bentley as the High Septon\\n* Anton Lesser as Qyburn\\n* Will Tudor as Olyvar\\n* Josephine Gillan as Marei\\n* Pixie Le Knot as Kayla\\n\\n====On Dragonstone====\\n* Tara Fitzgerald as Selyse Florent\\n* Kerry Ingram as Shireen Baratheon\\n\\n====In the Riverlands====\\n* Gary Oliver as Ternesio Terys\\n* Andy Kellegher as Polliver\\n* Andy Beckwith as Rorge\\n* Gerard Jordan as Biter\\n* Ben Hawkey as Hot Pie\\n\\n====In Braavos====\\n* Mark Gatiss as Tycho Nestoris\\n* Lucian Msamati as Salladhor Saan\\n* Sarine Sofair as Lhara\\n\\n====In Slaver's Bay====\\n* Michiel Huisman as Daario Naharis\\n* Ian McElhinney as Barristan Selmy\\n* Nathalie Emmanuel as Missandei\\n* Jacob Anderson as Grey Worm\\n* Joel Fry as Hizdahr zo Loraq\\n* Reece Noi as Mossador\\n\\n==Production==\\nOn April 2, 2013, HBO announced it had renewed the series for a fourth season, to consist of 10 episodes.\\n\\n===Crew===\\nDavid Benioff and D. B. Weiss serve as main writers and showrunners for the fourth season. They co-wrote seven out of ten episodes. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'e50e3f8a5e9d361d8258ff33e00aa518'}>,\n",
+       "  <Document: {'content': '===Crew===\\nDavid Benioff and D. B. Weiss serve as main writers and showrunners for the fourth season. They co-wrote seven out of ten episodes. The remaining three episodes were written by Bryan Cogman (two episodes), and the author of \\'\\'A Song of Ice and Fire\\'\\', George R. R. Martin (one episode).\\n\\nBenioff and Weiss co-directed the season premiere after making their directorial debut in season 3, although only Weiss is credited as Benioff received credit for their previous directed episode; Alex Graves, who directed two episodes in season 3, returned and directed episodes 2, 3, 8 and 10; Michelle MacLaren, who also directed two episodes in season 3, returned to direct episodes 4 and 5; former series cinematographer Alik Sakharov, who directed in seasons 2 and 3, returned to direct episodes 6 and 7; and Neil Marshall directed episode 9 after previously directing \"Blackwater\", the ninth episode of season 2.\\n\\n===Casting===\\nPedro Pascal plays Oberyn Martell.\\nThe fourth season adds previously recurring actors Gwendoline Christie (Brienne of Tarth), Iwan Rheon (Ramsay Snow), Kristofer Hivju (Tormund Giantsbane) and Hannah Murray (Gilly) to the series\\' main cast.  Iain Glen\\'s credit is moved last in the rotation and given the \"With\" moniker.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '714763f36d96b95788b41cdd506fe616'}>,\n",
+       "  <Document: {'content': 'The fourth season adds previously recurring actors Gwendoline Christie (Brienne of Tarth), Iwan Rheon (Ramsay Snow), Kristofer Hivju (Tormund Giantsbane) and Hannah Murray (Gilly) to the series\\' main cast.  Iain Glen\\'s credit is moved last in the rotation and given the \"With\" moniker.\\n\\nPrince Oberyn Martell, nicknamed \"The Red Viper\", is played by Chilean-American actor Pedro Pascal. \"This was a tough one\", said showrunners David Benioff and Dan Weiss about the casting. \"The Red Viper is sexy and charming, yet believably dangerous; intensely likable, yet driven by hate. The boys love him, the girls love him, and he loves them all back. Unless your last name is Lannister. We found a fellow who can handle the job description and make it seem effortless. He wasn\\'t easy to find and he won\\'t be easy to stop\". Martin commented on the casting by saying: \"I wasn\\'t present for Pedro Pascal\\'s audition, but I understand that he really killed it with his reading. And since his casting was announced, the producer of another TV show on which he appeared recently has written me to say how terrific Pascal is, and to congratulate us on the casting. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '779106dc599370f078b26784bf62c4cf'}>,\n",
+       "  <Document: {'content': 'And since his casting was announced, the producer of another TV show on which he appeared recently has written me to say how terrific Pascal is, and to congratulate us on the casting. So I suspect that he will turn out to be a wonderful Red Viper\". Actress Indira Varma was cast as Ellaria Sand, Prince Oberyn\\'s paramour.\\n\\nRoger Ashton-Griffiths joins the cast in the role of Mace Tyrell. \"The lord oaf of Highgarden\", as his mother describes him, is otherwise known as father to Margaery and Loras. His casting was confirmed by George R. R. Martin, who introduced the actor as the solution to a riddle he\\'d set fans with the following message, \"Yes, it\\'s the fine British character actor Roger Ashton-Griffiths, who has been cast in the role of Mace Tyrell, son to the Queen of Thorns, and father of Loras and Margaery\".\\n\\nMark Gatiss plays Tycho Nestoris, a representative of the Iron Bank of Braavos, to whom the Iron Throne owes millions in borrowed gold. The role of Hizdahr zo Loraq is played by young British actor Joel Fry. Hizdahr is the young scion of an ancient Meereenese family who crosses paths with Daenerys Targaryen in Meereen. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '543612958de3cea51f08d24facf699d8'}>,\n",
+       "  <Document: {'content': 'The role of Hizdahr zo Loraq is played by young British actor Joel Fry. Hizdahr is the young scion of an ancient Meereenese family who crosses paths with Daenerys Targaryen in Meereen. Elizabeth Webster was cast as Fat Walda Frey. Walda Frey is a granddaughter of Lord Walder Frey. She is the new wife of Roose Bolton, the Lord of the Dreadfort. During the wedding feast of Edmure Tully and Roslin Frey, Lord Bolton recounts to Catelyn Stark and Ser Brynden \"Blackfish\" Tully how Lord Walder Frey proposed him to marry one of his granddaughters and offered her weight in silver as dowry. Lord Bolton then adds he chose the fattest bride available and she has made him very rich.\\n\\nPaola Dionisotti and Rupert Vansittart were cast as Lady Anya Waynwood and Bronze Yohn Royce. They are the heads of House Waynwood and House Royce of Runestone: two powerful vassal houses of House Arryn. Yuri Kolokolnikov plays Styr: One of Mance Rayder\\'s lieutenants and the Magnar – the name of the first ever Lord of Thenn which is now a title – of the Thenn people, a wildling clan. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '77692212c9127505e38185cc647c8fbb'}>,\n",
+       "  <Document: {'content': 'Yuri Kolokolnikov plays Styr: One of Mance Rayder\\'s lieutenants and the Magnar – the name of the first ever Lord of Thenn which is now a title – of the Thenn people, a wildling clan. Two mysterious characters from Bran\\'s storyline have also been cast: the Three-Eyed Crow, who is played by Struan Rodger, and a Child of the Forest, played by Octavia Alexandru.\\n\\nRoles that were recast for season 4 include Michiel Huisman as Daario Naharis. Huisman replaces Ed Skrein, who portrayed the character in season 3. Dean-Charles Chapman plays the role of Tommen Baratheon, King Joffrey\\'s younger brother. Tommen was played by Callum Wharry in seasons 1 and 2. Chapman appeared as Martyn Lannister in two episodes of season 3 (\"Walk of Punishment\" and \"Kissed by Fire\"). Hafþór Júlíus Björnsson was cast as Ser Gregor Clegane, called \"The Mountain\". Hafþór replaces Ian Whyte, who portrayed the character in season 2.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '5b164512c3c47bda4c16593dad411fe9'}>,\n",
+       "  <Document: {'content': 'Hafþór Júlíus Björnsson was cast as Ser Gregor Clegane, called \"The Mountain\". Hafþór replaces Ian Whyte, who portrayed the character in season 2.\\n\\nAfter an absence of at least a season, Owen Teale returns as Alliser Thorne, Kate Dickie as Lysa Arryn, Dominic Carter as Janos Slynt, Tony Way as Dontos Hollard, Andy Beckwith as Rorge, Gerard Jordan as Biter, Andy Kellegher as Polliver, Lino Facioli as Robin Arryn and Brian Fortune as Othell Yarwyck.\\n\\n===Filming===\\nFilming for the season began on July 8, 2013, in Northern Ireland. The series also returned to Iceland and Dubrovnik for filming.\\nNew locations in Croatia include Diocletian\\'s Palace in Split, Klis Fortress north of Split, Perun quarry east of Split, Mosor mountain, and Baška Voda further down to the south. In the commentary for episode 2, \"The Lion and the Rose,\" the showrunners revealed that parts of Joffrey\\'s death scene had been filmed in California. The Thingvellir National Park in Iceland was used as the location for the fight between Brienne and The Hound.\\n\\nFilming for the season lasted 136 days and was completed on November 21, 2013.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '127e6cce9d13a12f13d8e22bb48ed4b0'}>,\n",
+       "  <Document: {'content': 'The Thingvellir National Park in Iceland was used as the location for the fight between Brienne and The Hound.\\n\\nFilming for the season lasted 136 days and was completed on November 21, 2013.\\n\\n===Music===\\n\\nThe Icelandic post-rock band Sigur Rós appears in the second episode, as a group of musicians serenading the royal couple at their wedding reception with \"The Rains of Castamere.\"This continues the series\\'s tradition of employing noted indie bands, begun in season 2 with The National and continued in season 3 with The Hold Steady.\\n\\nThe soundtrack for the season was released digitally on June 10, 2014, and on CD on July 1, 2014.\\n\\n==Reception==\\n\\n===Critical response===\\nThe fourth season was acclaimed by critics. However, the third episode attracted criticism for the inclusion of a scene in which Jaime Lannister appears to be raping his sister and lover Cersei in the Great Sept of Baelor. In the source novel, Cersei verbally consents to the sexual encounter, but does not in the television portrayal. The final episode was also criticised for the omission of the events of the epilogue of \\'\\'A Storm of Swords\\'\\' which was expected by fans to be the final scene.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'cf8a414ecc4694daabb84201ff8f2175'}>,\n",
+       "  <Document: {'content': 'The final episode was also criticised for the omission of the events of the epilogue of \\'\\'A Storm of Swords\\'\\' which was expected by fans to be the final scene.\\n\\nThe review aggregator website Metacritic gave season 4 a score of 94 out of 100 based on 29 reviews, signifying \"universal acclaim\". On Rotten Tomatoes, the fourth season has a 97% approval rating from 44 critics with an average rating of 8.89 out of 10. The site\\'s critical consensus reads, \"\\'\\'Game of Thrones\\'\\' continues to be one of the best shows on TV, combining meticulously-plotted character arcs with the spectacular design of the Seven Kingdoms.\"===Ratings===\\n\\nSeason 4 obtained the strongest viewer numbers of all seasons aired up until that point, with a series high of 7.20 million viewers of the first airing of the seventh episode. With its fourth season, \\'\\'Game of Thrones\\'\\' has become the most-watched HBO series in history (surpassing the fourth season of \\'\\'The Sopranos\\'\\' which had a gross audience of 18.2 million viewers), averaging 18.4 million viewers across multiple platforms, including live viewing, encores, DVR views, HBO GO and On Demand views.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '314f1336f6374d195055c5c9221a70d1'}>,\n",
+       "  <Document: {'content': 'With its fourth season, \\'\\'Game of Thrones\\'\\' has become the most-watched HBO series in history (surpassing the fourth season of \\'\\'The Sopranos\\'\\' which had a gross audience of 18.2 million viewers), averaging 18.4 million viewers across multiple platforms, including live viewing, encores, DVR views, HBO GO and On Demand views.\\n\\n===Accolades===\\n\\nFor the 30th TCA Awards, the series was nominated for Outstanding Achievement in Drama and Program of the Year. For the 4th Critics\\' Choice Television Awards, the series was nominated for Best Drama Series and Diana Rigg received a nomination for Best Guest Performer in a Drama Series. For the 66th Primetime Emmy Awards, the series received 19 nominations, including Outstanding Drama Series, Peter Dinklage for Outstanding Supporting Actor in a Drama Series, Lena Headey for Outstanding Supporting Actress in a Drama Series, Diana Rigg for Outstanding Guest Actress in a Drama Series, David Benioff and D. B. Weiss for Outstanding Writing for a Drama Series for \"The Children\", and Neil Marshall for Outstanding Directing for a Drama Series for \"The Watchers on the Wall\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'ee07e11bfe4636bf2058a4f8978c6c49'}>,\n",
+       "  <Document: {'content': 'For the 66th Primetime Emmy Awards, the series received 19 nominations, including Outstanding Drama Series, Peter Dinklage for Outstanding Supporting Actor in a Drama Series, Lena Headey for Outstanding Supporting Actress in a Drama Series, Diana Rigg for Outstanding Guest Actress in a Drama Series, David Benioff and D. B. Weiss for Outstanding Writing for a Drama Series for \"The Children\", and Neil Marshall for Outstanding Directing for a Drama Series for \"The Watchers on the Wall\". For the 67th Writers Guild of America Awards, the series was nominated for Best Drama Series and George R. R. Martin was nominated for Best Episodic Drama for \"The Lion and the Rose\". For the 21st Screen Actors Guild Awards, the cast was nominated for Best Drama Ensemble, Peter Dinklage was nominated for Best Drama Actor, and the series won for Best Stunt Team. For the 72nd Golden Globe Awards, the series was nominated for Best Television Series – Drama. For the 67th Directors Guild of America Awards, Alex Graves was nominated for Outstanding Directing – Drama Series for the episode \"The Children\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'c3dea0d40d3d602c80624b57ca429f71'}>,\n",
+       "  <Document: {'content': 'For the 67th Directors Guild of America Awards, Alex Graves was nominated for Outstanding Directing – Drama Series for the episode \"The Children\".\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2014\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nTV Choice Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nYoung Hollywood Awards\\n\\nWe Love to Hate You\\n\\nJack Gleeson\\n\\nBingeworthy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nBest Supporting Actor, Drama\\n\\nCharles Dance\\n\\nBest Guest Actor, Drama\\n\\nPedro Pascal\\n\\n66th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nNeil Marshall for \"The Watchers on the Wall\"\\n\\nOutstanding Drama Series\\n\\n\"The Lion and the Rose\", \"Breaker of Chains\", \"First of His Name\", \"The Laws of Gods and Men\", \"The Watchers on the Wall\", and \"The Children\"\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"The Children\"\\n\\n66th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Art Direction for a Single-Camera Fantasy Series\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron for \"The Laws of Gods and Men\" and \"The Mountain and the Viper\"\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nAnette Haellmigk for \"The Lion and the Rose\"\\n\\nJonathan Freeman for \"Two Swords\"\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Sheena Wichary, Alexander Fordham, and Nina Ayres for \"The Lion and the Rose\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, and Nicola Mount for \"The Lion and the Rose\"\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Premiere – Facebook Live and Instagram, by Sabrina Caluori, Paul Beddoe-Stephens, Jim Marsh, Michael McMorrow, Michael McMillian\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Ann McEwan for \"Oathkeeper\"\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi for \"The Mountain and the Viper\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker and Barrie Gower for \"The Children\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Jed M. Dodge, Tim Hands, Paula Fairfield, David Klotz, Bradley C. Katona, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit for \"The Watchers on the Wall\"\\n\\nOutstanding Sound Mixing for a Drama Series (One Hour)\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, and Mathew Waters for \"The Watchers on the Wall\"\\n\\nOutstanding Special and Visual Effects\\n\\nJoe Bauer, Joern Grosshans, Steve Kullback, Adam Chazen, Eric Carney, Sabrina Gerhardt, Matthew Rouleau, Thomas H. Schelesny, and Robert Simon for \"The Children\"\\n\\nOutstanding Stunt Coordination for a Drama Series\\n\\nPaul Herbert\\n\\nWomen\\'s Image Network Awards\\n\\nActress Drama Series\\n\\nLena Headey\\n\\n19th Satellite Awards\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n4th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n30th TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2014\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nCharles Dance\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nPedro Pascal\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Lion and the Rose\"\\n\\n\"The Watchers on the Wall\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nHollywood Music in Media Awards\\n\\nBest Original Score - TV Show/Digital Streaming Series\\n\\nRamin Djawadi\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Onnalee Blank, Mathew Waters, Paula Fairfield, Brad Katona and Jed M. Dodge for \"The Children\"\\n\\nOutstanding Color Grading\\n\\nJoe Finley for \"Mockingbird\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Sven Martin, Jörn Grosshans, Thomas Schelesny, Matthew Rouleau for \"The Children\"\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN People\\'s Choice Award\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nJupiter Award\\n\\nBest International TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n1st MTV Fandom Awards\\n\\nOMG Moment of the Year\\n\\n\\'\\'Game of Thrones\\'\\' - The Purple Wedding\\n\\n2015\\n\\nGuinness World Records\\n\\nMost pirated TV program\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nPeople\\'s Choice Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley for  \"The Laws of Gods and Men\" and \"The Mountain and the Viper\"\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss for \"The Children\"\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan for \"Oathkeeper\"\\n\\n12th Irish Film & Television Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nSFX Awards\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\nBest Villain\\n\\nCharles Dance as Tywin Lannister\\n\\nBiggest Disappointment\\n\\nNo Hodor in Game Of Thrones season five\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\\'\\'The Mountain and the Viper\\'\\'\\n\\nScreenwriters Choice Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nNewNowNext Awards\\n\\nBest New Television Actor\\n\\nPedro Pascal\\n\\nAstra Awards\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nAnette Haellmigk for \"The Children\"\\n\\nFabian Wagner for \"Mockingbird\"\\n\\n20th Empire Awards\\n\\nHero Award\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBritish Academy Television Awards\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton for \\'\\'Game of Thrones\\'\\'\\n\\nDirectors Guild of America Award\\n\\nDramatic Series\\n\\nAlex Graves for \"The Children\"\\n\\nGLAAD Media Award\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGolden Reel Award\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Watchers on the Wall\"\\n\\n72nd Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHugo Awards\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff, Alex Graves, and D. B. Weiss for \"The Mountain and The Viper\"\\n\\nNational Television Awards\\n\\nMultichannel show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProducers Guild Awards\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Chris Newman, Greg Spence, Carolyn Strauss, D. B. Weiss\\n\\nSaturn Award\\n\\nBest Limited Run Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Performance by a Younger Actor on Television\\n\\nMaisie Williams\\n\\nBest Supporting Actress on Television\\n\\nEmilia Clarke\\n\\n21st Screen Actors Guild Awards\\n\\nOutstanding Action Performance by a Stunt Ensemble in a Drama Series\\n\\nLucy Allen, Cole Armitage, Gary Arthurs, Rachelle Beinart, Ferenc Berecz, Richard Bradshaw, Andy Butcher, Michael Byrch, Neil Chapelhow, Nick Chopping, Jonathan Cohen, Joel Conlan, Gary Connery, James Cox, Tom Cox, Jason Curle, Nicholas Daines, Bill Davey, Kelly J. Dent, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Neil Finnighan, Jozsef Fodor, Dean Forster, David Garrick, James Grogan, Tim Halloran, Paul Heasman, Robert Hladik, Al Holland, Gergely Horpacsi, Paul Howell, Stewart James, Gary Kane, Ian Kay, Robbie Keane, George Kirby, Cristian Knight, Laszlo Kosa, Geza Kovacs, Norbert Kovacs, Guy List, Phil Lonergan, Russell Macleod, Tina Maskell, Adrian McGaw, Nick McKinless, Erol Mehmet, Andy Merchant, Sian Milne, Daniel Naprous, Chris Newton, Ray Nicholas, Bela Orsanyi, Sam Parham, Ian Pead, Justin Pearson, Martin Pemberton, Heather Phillips, Rashid Phoenix, Andy Pilgrim, Christopher Pocock, Curtis Rivers, Marcus Shakesheff, Matt Sherren, Anthony Skrimshire, Mark Slaughter, Karen Smithson, Mark Southworth, Helen Steinway Bailey, Shane Steyn, Matthew Stirling, John Street, Gaspar Szabo, Gabor Szeman, Roy Taylor, Gyula Toth, Tony Van Silva, Reg Wayment, Linda Weal, Richard J Wheeldon, Heron White, Maxine Whittaker, Simon Whyman, Donna C. Williams, Lou Wong, Annabel E. Wood, Liang Yang, Steen Young\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nJosef Altin, Jacob Anderson, John Bradley, Dominic Carter, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Charles Dance, Peter Dinklage, Natalie Dormer, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nVisual Effects Society\\n\\nOutstanding Performance of an Animated Character in a Commercial, Broadcast Program, or Video Game\\n\\nPhilip Meyer, Thomas Kutschera, Igor Majdandzic, and Mark Spindler for \"Drogon\"\\n\\nOutstanding Visual Effects in a Visual Effects-Driven Photoreal/Live Action Broadcast Program\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nOutstanding Created Environment in a Commercial, Broadcast Program, or Video Game\\n\\nRene Borst, Christian Zilliken, Jan Burda, Steffen Metzner for \"Braavos Establisher\"\\n\\nOutstanding Compositing in a Photoreal/Live Action Broadcast Program\\n\\nKeegan Douglas, Okan Ataman, Brian Fortune, David Lopez for \"Wight Attack\"\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"The Watchers on the Wall\"\\n\\nWriters Guild of America Awards\\n\\nEpisodic Drama\\n\\nGeorge R. R. Martin for \"The Lion and the Rose\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, D. B. Weiss\\n\\n==Release==\\n\\n===Home media===\\nThe fourth season of \\'\\'Game of Thrones\\'\\' was released on DVD and Blu-ray in region 1 on .\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '25d97fdfb5d033c825e91fcced3521ad'}>,\n",
+       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2014\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nTV Choice Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nYoung Hollywood Awards\\n\\nWe Love to Hate You\\n\\nJack Gleeson\\n\\nBingeworthy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nBest Supporting Actor, Drama\\n\\nCharles Dance\\n\\nBest Guest Actor, Drama\\n\\nPedro Pascal\\n\\n66th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nNeil Marshall for \"The Watchers on the Wall\"\\n\\nOutstanding Drama Series\\n\\n\"The Lion and the Rose\", \"Breaker of Chains\", \"First of His Name\", \"The Laws of Gods and Men\", \"The Watchers on the Wall\", and \"The Children\"\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"The Children\"\\n\\n66th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Art Direction for a Single-Camera Fantasy Series\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron for \"The Laws of Gods and Men\" and \"The Mountain and the Viper\"\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nAnette Haellmigk for \"The Lion and the Rose\"\\n\\nJonathan Freeman for \"Two Swords\"\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Sheena Wichary, Alexander Fordham, and Nina Ayres for \"The Lion and the Rose\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, and Nicola Mount for \"The Lion and the Rose\"\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Premiere – Facebook Live and Instagram, by Sabrina Caluori, Paul Beddoe-Stephens, Jim Marsh, Michael McMorrow, Michael McMillian\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Ann McEwan for \"Oathkeeper\"\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi for \"The Mountain and the Viper\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker and Barrie Gower for \"The Children\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Jed M. Dodge, Tim Hands, Paula Fairfield, David Klotz, Bradley C. Katona, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit for \"The Watchers on the Wall\"\\n\\nOutstanding Sound Mixing for a Drama Series (One Hour)\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, and Mathew Waters for \"The Watchers on the Wall\"\\n\\nOutstanding Special and Visual Effects\\n\\nJoe Bauer, Joern Grosshans, Steve Kullback, Adam Chazen, Eric Carney, Sabrina Gerhardt, Matthew Rouleau, Thomas H. Schelesny, and Robert Simon for \"The Children\"\\n\\nOutstanding Stunt Coordination for a Drama Series\\n\\nPaul Herbert\\n\\nWomen\\'s Image Network Awards\\n\\nActress Drama Series\\n\\nLena Headey\\n\\n19th Satellite Awards\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n4th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n30th TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2014\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nCharles Dance\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nPedro Pascal\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Lion and the Rose\"\\n\\n\"The Watchers on the Wall\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nHollywood Music in Media Awards\\n\\nBest Original Score - TV Show/Digital Streaming Series\\n\\nRamin Djawadi\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Onnalee Blank, Mathew Waters, Paula Fairfield, Brad Katona and Jed M. Dodge for \"The Children\"\\n\\nOutstanding Color Grading\\n\\nJoe Finley for \"Mockingbird\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Sven Martin, Jörn Grosshans, Thomas Schelesny, Matthew Rouleau for \"The Children\"\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN People\\'s Choice Award\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nJupiter Award\\n\\nBest International TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n1st MTV Fandom Awards\\n\\nOMG Moment of the Year\\n\\n\\'\\'Game of Thrones\\'\\' - The Purple Wedding\\n\\n2015\\n\\nGuinness World Records\\n\\nMost pirated TV program\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nPeople\\'s Choice Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley for  \"The Laws of Gods and Men\" and \"The Mountain and the Viper\"\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss for \"The Children\"\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan for \"Oathkeeper\"\\n\\n12th Irish Film & Television Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nSFX Awards\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\nBest Villain\\n\\nCharles Dance as Tywin Lannister\\n\\nBiggest Disappointment\\n\\nNo Hodor in Game Of Thrones season five\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\\'\\'The Mountain and the Viper\\'\\'\\n\\nScreenwriters Choice Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nNewNowNext Awards\\n\\nBest New Television Actor\\n\\nPedro Pascal\\n\\nAstra Awards\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nAnette Haellmigk for \"The Children\"\\n\\nFabian Wagner for \"Mockingbird\"\\n\\n20th Empire Awards\\n\\nHero Award\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBritish Academy Television Awards\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton for \\'\\'Game of Thrones\\'\\'\\n\\nDirectors Guild of America Award\\n\\nDramatic Series\\n\\nAlex Graves for \"The Children\"\\n\\nGLAAD Media Award\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGolden Reel Award\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Watchers on the Wall\"\\n\\n72nd Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHugo Awards\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff, Alex Graves, and D. B. Weiss for \"The Mountain and The Viper\"\\n\\nNational Television Awards\\n\\nMultichannel show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProducers Guild Awards\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Chris Newman, Greg Spence, Carolyn Strauss, D. B. Weiss\\n\\nSaturn Award\\n\\nBest Limited Run Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Performance by a Younger Actor on Television\\n\\nMaisie Williams\\n\\nBest Supporting Actress on Television\\n\\nEmilia Clarke\\n\\n21st Screen Actors Guild Awards\\n\\nOutstanding Action Performance by a Stunt Ensemble in a Drama Series\\n\\nLucy Allen, Cole Armitage, Gary Arthurs, Rachelle Beinart, Ferenc Berecz, Richard Bradshaw, Andy Butcher, Michael Byrch, Neil Chapelhow, Nick Chopping, Jonathan Cohen, Joel Conlan, Gary Connery, James Cox, Tom Cox, Jason Curle, Nicholas Daines, Bill Davey, Kelly J. Dent, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Neil Finnighan, Jozsef Fodor, Dean Forster, David Garrick, James Grogan, Tim Halloran, Paul Heasman, Robert Hladik, Al Holland, Gergely Horpacsi, Paul Howell, Stewart James, Gary Kane, Ian Kay, Robbie Keane, George Kirby, Cristian Knight, Laszlo Kosa, Geza Kovacs, Norbert Kovacs, Guy List, Phil Lonergan, Russell Macleod, Tina Maskell, Adrian McGaw, Nick McKinless, Erol Mehmet, Andy Merchant, Sian Milne, Daniel Naprous, Chris Newton, Ray Nicholas, Bela Orsanyi, Sam Parham, Ian Pead, Justin Pearson, Martin Pemberton, Heather Phillips, Rashid Phoenix, Andy Pilgrim, Christopher Pocock, Curtis Rivers, Marcus Shakesheff, Matt Sherren, Anthony Skrimshire, Mark Slaughter, Karen Smithson, Mark Southworth, Helen Steinway Bailey, Shane Steyn, Matthew Stirling, John Street, Gaspar Szabo, Gabor Szeman, Roy Taylor, Gyula Toth, Tony Van Silva, Reg Wayment, Linda Weal, Richard J Wheeldon, Heron White, Maxine Whittaker, Simon Whyman, Donna C. Williams, Lou Wong, Annabel E. Wood, Liang Yang, Steen Young\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nJosef Altin, Jacob Anderson, John Bradley, Dominic Carter, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Charles Dance, Peter Dinklage, Natalie Dormer, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nVisual Effects Society\\n\\nOutstanding Performance of an Animated Character in a Commercial, Broadcast Program, or Video Game\\n\\nPhilip Meyer, Thomas Kutschera, Igor Majdandzic, and Mark Spindler for \"Drogon\"\\n\\nOutstanding Visual Effects in a Visual Effects-Driven Photoreal/Live Action Broadcast Program\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nOutstanding Created Environment in a Commercial, Broadcast Program, or Video Game\\n\\nRene Borst, Christian Zilliken, Jan Burda, Steffen Metzner for \"Braavos Establisher\"\\n\\nOutstanding Compositing in a Photoreal/Live Action Broadcast Program\\n\\nKeegan Douglas, Okan Ataman, Brian Fortune, David Lopez for \"Wight Attack\"\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"The Watchers on the Wall\"\\n\\nWriters Guild of America Awards\\n\\nEpisodic Drama\\n\\nGeorge R. R. Martin for \"The Lion and the Rose\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, D. B. Weiss\\n\\n==Release==\\n\\n===Home media===\\nThe fourth season of \\'\\'Game of Thrones\\'\\' was released on DVD and Blu-ray in region 1 on .\\n\\n===IMAX===\\nBetween January 30 and February 5, 2015, the last two episodes of season four were shown in 205 IMAX theaters in the U.S. \\'\\'Game of Thrones\\'\\' is the first TV series released in this format. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '75b350d201851f889480daefe56e7fd8'}>,\n",
+       "  <Document: {'content': \"===IMAX===\\nBetween January 30 and February 5, 2015, the last two episodes of season four were shown in 205 IMAX theaters in the U.S. ''Game of Thrones'' is the first TV series released in this format. The show earned $686,000 in its opening day at the box office and $1.5 million during its opening weekend. The one-week release grossed $1,896,092.\\n\\n===Copyright infringement===\\nThe fourth season of ''Game of Thrones'' was the most-pirated TV series in 2014.\\n\\n==References==\\n\\n==External links==\\n*  – official US site\\n*  – official UK site\\n*  ''Game of Thrones'' – The Viewers Guide on HBO.com\\n*  Making ''Game of Thrones'' on HBO.com\\n*\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'd5f237e8b356c3910826640c9c355375'}>,\n",
+       "  <Document: {'content': '\\n\"\\'\\'\\'Stormborn\\'\\'\\'\" is the second episode of the seventh season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 62nd overall. The episode was written by Bryan Cogman, and directed by Mark Mylod. The title of the episode refers to both Daenerys Targaryen, who was born during a terrible storm, and Euron Greyjoy, who declares himself to be \"the storm\".\\n\\nThe episode\\'s main plot focuses on Daenerys planning her conquest of Westeros; at the behest of Melisandre, she orders Tyrion to summon Jon Snow, who agrees to meet with her despite the objections of Sansa and several other supporters. In King\\'s Landing, Cersei Lannister warns her bannermen about Daenerys\\'s impending invasion. Meanwhile, in the Narrow Sea, the Greyjoys and Sand Snakes are attacked by Euron Greyjoy\\'s Iron Fleet, with Yara Greyjoy and Ellaria Sand being taken as hostages.\\n\\n\"Stormborn\" received praise from critics, who considered Euron Greyjoy\\'s raid on Yara\\'s Iron Fleet, the assembly of Daenerys\\' allies at Dragonstone, and Arya\\'s reunion with her direwolf Nymeria as highlights of the episode. In the United States, it achieved a viewership of 9.27 million in its initial broadcast.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '572b9f20f0bf562a07b54095994bb4a1'}>,\n",
+       "  <Document: {'content': '\"Stormborn\" received praise from critics, who considered Euron Greyjoy\\'s raid on Yara\\'s Iron Fleet, the assembly of Daenerys\\' allies at Dragonstone, and Arya\\'s reunion with her direwolf Nymeria as highlights of the episode. In the United States, it achieved a viewership of 9.27 million in its initial broadcast.\\n\\nThis episode marks the final appearance of Keisha Castle-Hughes (Obara Sand) and Jessica Henwick (Nymeria Sand).\\n\\n==Plot==\\n===On Dragonstone===\\nDaenerys confronts Varys over his previously shifting allegiances. Varys declares he is loyal to the people, and Daenerys secures his oath to tell her directly if she fails the people, threatening to execute him if he betrays her. Daenerys grants an audience to Melisandre, who urges her to meet Jon Snow. Tyrion vouches for Jon and recommends the Starks as allies. Daenerys instructs Tyrion to summon Jon to bend the knee. Yara Greyjoy and Ellaria Sand support an overwhelming offensive on King\\'s Landing. Daenerys doesn\\'t wish to destroy the territory she plans on ruling, but instead adopts Tyrion\\'s plan to have her Westerosi armies besiege King\\'s Landing, while the Unsullied will take Casterly Rock.\\n\\nBefore departing to lead his army, Grey Worm reveals to Missandei he never feared before loving her. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'b34f4964c63ef570a1bd179ffe04da87'}>,\n",
+       "  <Document: {'content': \"Daenerys doesn't wish to destroy the territory she plans on ruling, but instead adopts Tyrion's plan to have her Westerosi armies besiege King's Landing, while the Unsullied will take Casterly Rock.\\n\\nBefore departing to lead his army, Grey Worm reveals to Missandei he never feared before loving her. Missandei is moved by his words and they make love.\\n\\n===In Oldtown===\\nArchmaester Ebrose tells Sam that Jorah's greyscale is now untreatable, Jorah has about six months of sanity left, and suicide is his only alternative to exile among the Stone Men of Valyria. Sam discovers a treatment; Ebrose tells him it is forbidden because it risks transmission, but Sam performs the agonizing procedure in secret.\\n\\n===At Winterfell===\\nJon receives Daenerys' message. Sansa and Davos advocate refusal, but the latter notes that dragonfire can stop the undead. After receiving Sam's information about dragonglass on Dragonstone, Jon decides to go, despite uniform opposition from his loyalists. Jon names Sansa as regent, surprising her, and threatens Littlefinger to stop pursuing Sansa when he tries to ingratiate himself with Jon.\\n\\n===In the Riverlands===\\nArya encounters Hot Pie in an inn and learns that Jon has retaken Winterfell; she turns north. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'dafcb65590a6d1168a6bfcc535577185'}>,\n",
+       "  <Document: {'content': '===In the Riverlands===\\nArya encounters Hot Pie in an inn and learns that Jon has retaken Winterfell; she turns north. She\\'s beset by a wolfpack, the alpha being her direwolf Nymeria, having driven her off years earlier (\"The Kingsroad\") Arya invites her north but Nymeria instead leaves her.\\n\\n===In King\\'s Landing===\\nCersei appeals to lords, including House Tyrell\\'s bannermen, for support against Daenerys, portraying her as a dangerous foreign invader. Jaime offers to make Randyll Tarly Warden of the South if he becomes Jaime\\'s second-in-command. Qyburn demonstrates a scorpion, a projectile weapon able to pierce dragon skulls.\\n\\n===In the Narrow Sea===\\nEuron\\'s fleet attacks Yara\\'s navy. Euron kills Obara and Nymeria Sand; his men abduct Ellaria and Tyene Sand, while he takes Yara prisoner. Theon, triggered by the violence, jumps overboard, abandoning Yara.\\n\\n==Production==\\n===Writing===\\nBryan Cogman wrote the episode.\\n\"Stormborn\" was written by Bryan Cogman. Cogman has been a writer for the series since its beginning, previously writing nine other episodes. The title of the episode, \"Stormborn\", is a reference to Daenerys Targaryen, who was born in the midst of a great storm, earning her that nickname. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'f10871a8318faeebd7a37c704e6b088e'}>,\n",
+       "  <Document: {'content': 'The title of the episode, \"Stormborn\", is a reference to Daenerys Targaryen, who was born in the midst of a great storm, earning her that nickname. In the \"Inside the Episode\" segment published by HBO following the initial airing of the episode, co-creators and executive producers of the series David Benioff and D. B. Weiss were interviewed, saying \"We last saw Nymeria when Arya made her run away because she wanted to save Nymeria\\'s life, - She knew Cersei was going to kill her if she found her, and when she finally finds Nymeria again — or Nymeria finds her — she of course wants Nymeria to come back home with her and be her loyal companion again. But Nymeria\\'s found her own life.\"They continued noting that the line \"That\\'s not you\" was a direct reference to what she said to Ned Stark back in the first season, when he was telling her that she will be a \"lady of a castle and marrying some lord and wearing a nice frilly dress\", which Arya responded by saying \"That\\'s not me\". Weiss continued, \"Arya\\'s not domesticated. It makes total sense the wolf wouldn\\'t be either. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '916440f3061f0b47ecb4130f4b1581cd'}>,\n",
+       "  <Document: {'content': 'They continued noting that the line \"That\\'s not you\" was a direct reference to what she said to Ned Stark back in the first season, when he was telling her that she will be a \"lady of a castle and marrying some lord and wearing a nice frilly dress\", which Arya responded by saying \"That\\'s not me\". Weiss continued, \"Arya\\'s not domesticated. It makes total sense the wolf wouldn\\'t be either. Once the wolf walks away, at first she\\'s heartbroken to have come this close, but then she realizes the wolf is doing exactly what she would do if she was that wolf.\"===Casting===\\nThe episode featured the return of Ben Hawkey as Hot Pie, who was last seen in season 4\\'s \"Mockingbird\". Hawkey spoke about his return to \\'\\'Entertainment Weekly\\'\\', saying that he did not expect to ever come back. Prior to getting the script and to avoid the return of character to be leaked, the producers gave Hawkey a code name when communicating by email. He continued by saying that the scene was \"really nice\" and was a \"perfect little Hot Pie scene\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'd144f6ae2e5eb03aa504f4d1118b62f0'}>,\n",
+       "  <Document: {'content': 'Prior to getting the script and to avoid the return of character to be leaked, the producers gave Hawkey a code name when communicating by email. He continued by saying that the scene was \"really nice\" and was a \"perfect little Hot Pie scene\". \"Stormborn\" was also the last episode for Keisha Castle-Hughes and Jessica Henwick, as Obara and Nymeria Sand were killed. The episode introduced new recurring cast member Tom Hopper as Dickon Tarly, replacing Freddie Stroma, who briefly portrayed the character in Season 6.\\n\\n===Filming===\\n\"Stormborn\" was directed by Mark Mylod, his first of two episodes for this season. He joined the series as a director in the fifth season, his first episode being \"High Sparrow\", which was followed by \"Sons of the Harpy\". John Bradley spoke about what went into shooting the Jorah Mormont greyscale scene with actor Iain Glen. Before filming Iain had to sit on the prosthetics trailer for around five hours while the make up department \"applied these really detailed and intricate greyscale prosthetics, piece by piece.\"He continued \"I was basically peeling the plastic latex prosthetic off of Iain\\'s actual body. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'a83812ab8fb3d6b534776beb5b5337db'}>,\n",
+       "  <Document: {'content': 'Before filming Iain had to sit on the prosthetics trailer for around five hours while the make up department \"applied these really detailed and intricate greyscale prosthetics, piece by piece.\"He continued \"I was basically peeling the plastic latex prosthetic off of Iain\\'s actual body. He was kind of in a suit - It was the same as pulling away a prosthetic. It was a very, very big technical job for the prosthetics department. There were about five or six guys on set that day that you can\\'t see but were just out of the camera line, there with pumps and buckets of pus.\"Nathalie Emmanuel discussed her character\\'s love scene with Grey Worm, saying that the characters have shown \"interest in each other\", we have seen them \"express it for each other, but not really say it - This point is the climax and they physically act upon it\", much due to them facing \"the prospect of never seeing each other again\". Emmanuel went on to praise the relationship that has developed between these two characters, stating that it\\'s \"something sweet and pure and beautiful\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '7c77ccf972fe3aaf153f01f4a16d77ae'}>,\n",
+       "  <Document: {'content': 'Emmanuel went on to praise the relationship that has developed between these two characters, stating that it\\'s \"something sweet and pure and beautiful\".\\n\\nGemma Whelan said in an interview with \\'\\'Entertainment Weekly\\'\\' that the kissing scene by Yara Greyjoy and Ellaria Sand before the battle sequence at the end was improvised. She stated that \"It wasn’t directed that we would kiss, - It just seemed like something we should do\". The scenes with the Sand Snakes was originally planned to be a more \"drawn out storyline\", but due to Jessica Henwick\\'s schedule filming her scenes as Colleen Wing in \\'\\'Iron Fist\\'\\' and \\'\\'The Defenders\\'\\', she would have had to go back and forth to shoot both shows. Speaking about the battle sequence, Henwick said \"normally there’s a lot of CG and you watch it on screen and you see a massive epic battle, but when you’re filming it’s all quite tame by comparison. For this, the audience can’t feel the heat on their face from the pyrotechnics going off or feel the wave machine trying to knock us off our feet, or the sweat dripping off our faces.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '31206879f89ba2e72bbe55e50282d945'}>,\n",
+       "  <Document: {'content': 'For this, the audience can’t feel the heat on their face from the pyrotechnics going off or feel the wave machine trying to knock us off our feet, or the sweat dripping off our faces.\"Mylod also discussed his direction for the battle sequence, saying that \"We agreed that the violence should be brutal and feel un-choreographed, that it shouldn\\'t feel structured\". Stunt coordinator Rowley Irlam also stated that they were inspired by riots when it was being choreographed. Mylod decided to put much more of the focus to Theon, Yara and Euron Greyjoy, rather than people around them.\\n\\n==Reception==\\n\\n===Ratings===\\n\"Stormborn\" was viewed by 9.27 million total viewers on its initial viewing on HBO. The episode also acquired a 4.33 rating in the 18–49 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was viewed by 2.770 million viewers on Sky Atlantic during its Simulcast, making it the highest-rated broadcast that week.\\n\\n===Critical reception===\\n\\n\"Stormborn\" has received high praise from critics. It has a 96% rating on the review aggregator website Rotten Tomatoes from 38 reviews with an average score of 8.2 out of 10. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '25f383aa2f70f8e1704a3febe89739e0'}>,\n",
+       "  <Document: {'content': 'It has a 96% rating on the review aggregator website Rotten Tomatoes from 38 reviews with an average score of 8.2 out of 10. The site\\'s consensus reads \"While necessarily setting up the events for the season, \"Stormborn\" features compelling strategic discussion, and a cracking action sequence to end the episode with a bang.\"Ed Power of \\'\\'The Daily Telegraph\\'\\' said \"It\\'s been a cautious start to \\'Game of Thrones\\' season seven. But, following further, patience-testing re-arranging of the chess pieces, episode two went out in a literal blaze of glory.\"Matt Fowler of IGN described the episode as \"amazing\", saying \"With Daenerys now in Westeros, war is imminent and worlds were colliding in this week\\'s big episode.\"He gave the episode a 9 out of 10. Ben Philippe of \\'\\'New York Observer\\'\\' also gave a positive review, saying \"\\'Stormborn,\\' proves that the table setting always pays off in Game of Thrones by giving us a sample platter of everything that the show does best.\"Nina Shen Rastogi of \\'\\'New York Magazine\\'\\' similarly gave praise to the episode, writing \"All of this prologue is what made the final scene land so hard, and so well. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '3f79bd7e6969d2b7a8a94ff896f1e941'}>,\n",
+       "  <Document: {'content': 'Nina Shen Rastogi of \\'\\'New York Magazine\\'\\' similarly gave praise to the episode, writing \"All of this prologue is what made the final scene land so hard, and so well. The shipboard battle between the Greyjoys was very good GOT, in my mind: dramatic, unexpected, and authentic on a character level.\"Sean T. Collins of \\'\\'Rolling Stone\\'\\' praised the battle sequence at the end, writing \"It ends with a naval battle as grandiose as any we\\'ve seen in the series. But the strong second chapter of Game of Thrones\\' seventh season - \\'Stormborn\\' - navigates even more treacherous waters.\"Erik Kain of \\'\\'Forbes\\'\\' gave a mixed review, writing that he enjoyed Arya\\'s scenes but criticizing the Dragonstone scenes. Kain also criticized the plot hole of Ellaria Sand being in control of Dorne.\\n\\n===Accolades===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2017\\n\\nHollywood Post Alliance\\n\\nOutstanding Editing\\n\\nTim Porter\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Stormborn\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '886ddb817ecb9403145f57f21468cc4f'}>,\n",
+       "  <Document: {'content': '\\n\\n\\'\\'\\'Eddard\\'\\'\\' \"\\'\\'\\'Ned\\'\\'\\'\" \\'\\'\\'Stark\\'\\'\\' is a fictional character in the 1996 fantasy novel \\'\\'A Game of Thrones\\'\\' by George R. R. Martin, and \\'\\'Game of Thrones\\'\\', HBO\\'s adaptation of Martin\\'s \\'\\'A Song of Ice and Fire\\'\\' series. In the storyline, Ned is the lord of Winterfell, an ancient fortress in the North of the fictional continent of Westeros. Though the character is established as the main character in the novel and the first season of the TV adaptation, a plot twist involving Ned at the end of the novel and the end of the first season shocked both readers of the book and viewers of the TV series.\\n\\nNed is portrayed by Sean Bean in the first season of \\'\\'Game of Thrones\\'\\', as a child by Sebastian Croft in the sixth season and as a young adult by Robert Aramayo in the sixth and seventh seasons. Bean was nominated for a Saturn Award for Best Actor on Television and a Scream Award for Best Fantasy Actor for the role. He and the rest of the cast were nominated for Screen Actors Guild Awards for Outstanding Performance by an Ensemble in a Drama Series in 2011.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '184ca22544285a03c5a3be806ec9ca40'}>,\n",
+       "  <Document: {'content': \"He and the rest of the cast were nominated for Screen Actors Guild Awards for Outstanding Performance by an Ensemble in a Drama Series in 2011.\\n\\n== Character ==\\n=== Description ===\\nIn ''A Game of Thrones'' (1996), Ned Stark is introduced as the virtuous and honorable patriarch of House Stark and the father of six children. The moral compass of the story, he is content to remain far from courtly intrigues and is unwavering in his view of loyalty and honor. His family name, Stark, serves as an indication of his resistance to moral compromise, but his boundaries are increasingly tested over the course of the novel. Finding himself a key player in the escalating political intrigue of King's Landing, Ned struggles as his own sense of honor draws him into corrupt goings-on at court. As the story progresses, he begins to see the importance of moral and practical compromises to achieve a just end, and is ultimately forced to choose between the safety of his family and doing what is right.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '7dd478a5e17391528a8ecef97d4d1dc5'}>,\n",
+       "  <Document: {'content': 'As the story progresses, he begins to see the importance of moral and practical compromises to achieve a just end, and is ultimately forced to choose between the safety of his family and doing what is right.\\n\\nSean Bean said of the character, \"he’s a good man trying to do his best in the middle of this corruption, he’s a fish out of water, he’s used to being up north in Winterfell where people are pretty straight and pragmatic, and he comes down to a place where people are playing games and backstabbing … he’s a principled man who tries to hold things together. This is a journey that he makes where ultimately his loyalty causes his downfall.\"=== Development and overview ===\\n\\'\\'Publishers Weekly\\'\\' noted in 1996 that, despite the honest Ned Stark\\'s intervention in court politics, \"no amount of heroism or good intentions can keep the realm under control.\"From his very first introduction, Ned is portrayed as a noble hero and set up to be the heart of the story. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'dcda6328625198daf5d3e89bde279411'}>,\n",
+       "  <Document: {'content': \"From his very first introduction, Ned is portrayed as a noble hero and set up to be the heart of the story. With fifteen chapters devoted to his point of view, more than any single character in the novel, he is presented as a primary character in the series, and the main storyline of ''A Game of Thrones'', the drama in King's Landing, is told entirely from his perspective. In the ''London Review of Books'', John Lanchester writes that everything about Ned is designed to gain audience sympathy, from his strong sense of honor and moral compass to his compassion towards his wife and children. Readers are led to believe that Ned will be the main character of the series, but ultimately he is, from a literary perspective, a classic decoy protagonist. After struggling to keep himself and the kingdom on a moral path for the entire novel, the only option that remains to save his family is to put aside his honor; he does so, but is betrayed anyway. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'b6224ae5673e6bc2d374345adb0e5f16'}>,\n",
+       "  <Document: {'content': 'After struggling to keep himself and the kingdom on a moral path for the entire novel, the only option that remains to save his family is to put aside his honor; he does so, but is betrayed anyway. Calling Ned\\'s execution \"shocking\", \\'\\'The New York Times\\'\\' noted in 2011 that the novel was \"famous for dispatching a thoroughly admirable major character with whom readers have been identifying for most of the book\". In an interview for \\'\\'Entertainment Weekly\\'\\', author George R. R. Martin commented on this misdirection:\\n\\nDavid Benioff, executive producer and writer of the HBO adaptation, told \\'\\'Entertainment Weekly\\'\\' that when he read the novel:\\n\\nIn a review of the \\'\\'Game of Thrones\\'\\' TV episode \"Baelor\", James Poniewozik wrote in \\'\\'Time\\'\\' that \"the execution of Eddard Stark is crucial to the story and its themes and everything that follows, but it\\'s also a meta-message to the reader: don\\'t take anything for granted here\". James Hibberd of \\'\\'Entertainment Weekly\\'\\' stated that tricking the audience into thinking Ned is the hero and then killing him makes the series\\' story better. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'f689e00d50e9a75e78323d87d0a84c5'}>,\n",
+       "  <Document: {'content': 'James Hibberd of \\'\\'Entertainment Weekly\\'\\' stated that tricking the audience into thinking Ned is the hero and then killing him makes the series\\' story better. Writing that \"the big twist here isn\\'t that Ned Stark dies, but who the true protagonists of \\'\\'Game of Thrones\\'\\' are\", Hibberd pointed out that the series\\' focus proves to be the \"new generation\" of leaders, in particular the Stark children but also Daenerys and even Tyrion. He noted:\\n\\n== Storylines ==\\nCoat of arms of House Stark\\n\\n=== Background ===\\nAs established in \\'\\'A Game of Thrones\\'\\', Eddard \"Ned\" Stark is the second son of Rickard Stark, the Lord of Winterfell. Years before the events of the novel, the quiet and shy young Ned is fostered in the Vale by Jon Arryn. During this time Ned becomes close friends with Robert Baratheon, heir to the Stormlands and another ward of Arryn\\'s. Robert is eventually betrothed to Ned\\'s sister Lyanna, but before he can marry her, Crown Prince Rhaegar Targaryen absconds with Lyanna. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'aa3334298acedafa649abb4cb72bae14'}>,\n",
+       "  <Document: {'content': 'Robert is eventually betrothed to Ned\\'s sister Lyanna, but before he can marry her, Crown Prince Rhaegar Targaryen absconds with Lyanna. Ned\\'s father and older brother Brandon go to King Aerys II Targaryen and demand that Lyanna be freed; the so-called \"Mad King\" executes both Rickard and Brandon, convinced that the Starks seek to usurp him. Ned, Robert, and Arryn then rise in revolt, securing the support of House Tully with Ned marrying Catelyn Tully. Ned leaves for war the next morning.\\n\\nAt the decisive Battle of the Trident, Robert manages to kill Prince Rhaegar and scatter the Targaryen army. Robert is injured in the battle, so Ned takes command and marches on the capital. In King\\'s Landing, Ned finds that House Lannister has — through treachery — already sacked the city, and murdered Aerys and the royal family. Disgusted by the dishonorable massacre, Ned departs to lift the siege of the Baratheon stronghold Storm\\'s End, and later attempts to rescue Lyanna but finds his sister dying; her last words are \"Promise me Ned.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '6e3891080b1d24b07f9e0d57eaad2ac5'}>,\n",
+       "  <Document: {'content': 'Disgusted by the dishonorable massacre, Ned departs to lift the siege of the Baratheon stronghold Storm\\'s End, and later attempts to rescue Lyanna but finds his sister dying; her last words are \"Promise me Ned.\"Ned returns to Winterfell with his bastard son Jon Snow in tow; meanwhile Catelyn has delivered their son and Ned\\'s heir, Robb, conceived on their wedding night.\\n\\nSix years after the end of Robert\\'s Rebellion, Balon Greyjoy, the Lord of the Iron Islands, declares independence from the Iron Throne. Ned aids King Robert in putting down \"Greyjoy\\'s Rebellion\". Balon surrenders, and his sole surviving heir, Theon, is taken back to Winterfell as Stark\\'s ward and hostage. Ned rules the North for nine more years before the events of the novel.\\n\\n=== \\'\\'A Game of Thrones\\'\\' ===\\nAs \\'\\'A Game of Thrones\\'\\' begins, Catelyn informs Ned that his mentor Jon Arryn has died, and that King Robert intends to offer Ned Jon\\'s old position as Hand of the King. Content to be far from court intrigue, Ned is reluctant to accept the offer until he receives a letter from Arryn\\'s widow, who believes that her husband had been poisoned by the Lannisters. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '3560a3078881efd297fdc32781fbd7bb'}>,\n",
+       "  <Document: {'content': 'Content to be far from court intrigue, Ned is reluctant to accept the offer until he receives a letter from Arryn\\'s widow, who believes that her husband had been poisoned by the Lannisters. Ned subsequently agrees to the appointment to protect Robert. He then travels south to King\\'s Landing with his daughters Sansa and Arya. Catelyn later comes to the capital in secret, under the protection of her childhood friend Petyr \"Littlefinger\" Baelish, to tell Ned of an assassination attempt on their young son Bran. Ned\\'s longstanding mistrust of the Lannisters is further fueled by Littlefinger\\'s confirmation that the dagger used by the would-be assassin once belonged to Tyrion Lannister. Increasingly disgusted by the political intrigues at court, Ned finally resigns his position when Robert insists on having Aerys\\' only surviving child, the young Daenerys Targaryen, assassinated in exile. Meanwhile, Catelyn has taken Tyrion hostage at the Eyrie, and in retaliation Jaime Lannister attacks and seriously injures Ned in the street before he and his daughters can depart King\\'s Landing. Visiting a wounded Ned, Robert reappoints him as Hand.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '94d6cb1b308087b51e09557f03d95fde'}>,\n",
+       "  <Document: {'content': 'Meanwhile, Catelyn has taken Tyrion hostage at the Eyrie, and in retaliation Jaime Lannister attacks and seriously injures Ned in the street before he and his daughters can depart King\\'s Landing. Visiting a wounded Ned, Robert reappoints him as Hand.\\n\\nNed eventually concludes that all of Robert\\'s heirs with his wife Cersei are illegitimate, the product of her incest with her twin brother Jaime. Further, Ned suspects that Arryn had been poisoned to conceal the truth. Ned confronts Cersei with his discovery and gives her the chance to escape with her children into exile. Before Ned can tell the king, Robert is fatally wounded while hunting and names Ned Protector of the Realm, to function as regent until his \"son\" Joffrey comes of age. Ned alters Robert\\'s will, replacing Joffrey\\'s name with \"my rightful heir\" to make the succession ambiguous. With the palace in chaos, Ned rebuffs multiple offers to increase his own power, instead opting to crown Robert\\'s brother, Stannis Baratheon, as king. Cersei, however, outmaneuvers Ned, and the duplicitous Littlefinger has the city watch arrest Ned instead of Cersei. With all of his entourage slaughtered, Sansa a hostage and Arya escaped but alone, Ned is charged with treason. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '618f0222ae7467fb7c4ed165b3a0086d'}>,\n",
+       "  <Document: {'content': 'Cersei, however, outmaneuvers Ned, and the duplicitous Littlefinger has the city watch arrest Ned instead of Cersei. With all of his entourage slaughtered, Sansa a hostage and Arya escaped but alone, Ned is charged with treason. A deal is struck in which Ned will be spared and sent into exile if he declares Joffrey the rightful king. Fearing for his daughters, Ned makes a public confession of his \"treason\". The sadistic Joffrey, however, has Ned executed anyway for his own amusement.\\n\\n=== Later novels ===\\nNed\\'s arrest pits the Stark and Lannister forces against each other, and his execution causes a civil war later called the War of Five Kings. Tyrion Lannister eventually returns Ned\\'s bones to Catelyn, but it is not confirmed whether they ever arrived at Winterfell. He is mentioned in a flashback along with Robert Baratheon in \\'\\'A Clash of Kings\\'\\'.\\n\\n=== Family tree of House Stark ===\\n\\n== TV adaptation ==\\nSean Bean plays the role of Ned Stark in the television series.\\nIn January 2007 HBO secured the rights to adapt Martin\\'s series for television. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'fa230ac65a46b44f0f57617e859fc889'}>,\n",
+       "  <Document: {'content': '=== Family tree of House Stark ===\\n\\n== TV adaptation ==\\nSean Bean plays the role of Ned Stark in the television series.\\nIn January 2007 HBO secured the rights to adapt Martin\\'s series for television. When the pilot went into production in 2009, one of the first casting announcements was Sean Bean as the \"lead\" Eddard Stark. As the show premiered in 2011, the \\'\\'Los Angeles Times\\'\\' called Bean\\'s Ned \"the strong and brooding headliner of the series\".\\n\\nAs in the source novel, Ned is beheaded in the ninth episode of season 1, \"Baelor\". Though praising the character\\'s demise for its role in propelling the story, James Hibberd of \\'\\'Entertainment Weekly\\'\\' later noted that:\\n\\nHibberd echoed the show\\'s producers\\' statement that \"the move lays down a dramatic precedent for the show: Nobody is safe\". He called it a \"risky\" move that would probably lose the show viewers who had tuned in for Bean, but would hopefully attract others impressed by the boldness of it. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '9b77bd69c8835c274da60c6f36e6e41b'}>,\n",
+       "  <Document: {'content': 'He called it a \"risky\" move that would probably lose the show viewers who had tuned in for Bean, but would hopefully attract others impressed by the boldness of it. Executive producer and writer D. B. Weiss told \\'\\'Entertainment Weekly\\'\\' in 2011 that when he and Benioff pitched the series to HBO, the fact that \"main character\" Ned was slated to die \"was a selling point for them\". Noting that the network has killed off characters in other successful series, he said that this sense of jeopardy \"completely ups the ante for any moment when a character is in a dire situation if you know another character didn\\'t survive a similar situation\". HBO programming president Sue Naegle concurred, saying that Ned\\'s death made the show creatively more attractive, adding that \"The book series was filled with unexpected twists and turns. I loved this idea we\\'d bring together the group of characters, then once you started to believe all the tropes of heroes, you pull the rug out from under them. It\\'s the opposite of feeling manipulated\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '28677c484dadb8f6ca7f9e95a665d51a'}>,\n",
+       "  <Document: {'content': 'I loved this idea we\\'d bring together the group of characters, then once you started to believe all the tropes of heroes, you pull the rug out from under them. It\\'s the opposite of feeling manipulated\". Noting that the story and world of the series is bigger than any one character, Naegle said, \"Sean brings a giant following, but \\'\\'Thrones\\'\\' is not just about the promise you\\'re going to see one of your favorite actors week in and week out. The star is the story\". Bean noted that Ned\\'s death \"was as much a surprise to me as anyone\" and called it \"a very courageous move for a television company\".\\n\\nThe image of Bean as Ned Stark sitting in the Iron Throne is featured on the covers of the 2011 Season 1 DVD and Blu-ray Disc sets, released in March 2012.\\n\\nThe character makes a return in the sixth season, under a recurring capacity, via flashback visions of his youth and childhood seen by Bran and the Three-Eyed Raven using Greensight.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'ec0806f8eed370d4ba83227cb8f9f06d'}>,\n",
+       "  <Document: {'content': 'The character makes a return in the sixth season, under a recurring capacity, via flashback visions of his youth and childhood seen by Bran and the Three-Eyed Raven using Greensight.\\n\\n===Storylines===\\n====Season 1====\\nIn season 1, King Robert Baratheon asks his old friend Eddard \"Ned\" Stark, Lord of Winterfell and Warden of the North, to assume the position of Hand of the King after the sudden death of their mentor, Jon Arryn. Not interested in politics or the intrigues of the court, Ned accepts out of duty, as well as to discover how Arryn died. Robert is killed just as Ned discovers that his three children by Cersei were actually fathered by the queen\\'s twin, Jaime. Before Ned is able to neutralize Cersei and place Robert\\'s brother Stannis on the throne, he is betrayed by Littlefinger, and the queen has him imprisoned for treason. Ned makes a public confession to save his daughters from Cersei\\'s wrath, but Joffrey has Ned beheaded anyway.\\n\\n====Seasons 6 and 7====\\nIn the season 6 episode \"Home\", Bran sees a vision of Ned (Sebastian Croft) as a child with his sister and brother, Lyanna and Benjen. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '326f2850a3449cc7ae1169f2c101f541'}>,\n",
+       "  <Document: {'content': '====Seasons 6 and 7====\\nIn the season 6 episode \"Home\", Bran sees a vision of Ned (Sebastian Croft) as a child with his sister and brother, Lyanna and Benjen. In the following episode, \"Oathbreaker\", Bran witnesses the battle between a young Ned (Robert Aramayo) and the knight Ser Arthur Dayne at the Tower of Joy. In the episode \"The Door\", Bran watches Ned bid Benjen and his father, Rickard, farewell as he departs for his fosterage at the Vale. In the following episode, \"Blood of My Blood\", Bran briefly glimpses Ned\\'s hand amongst someone\\'s blood, revealed in the season finale, \"The Winds of Winter\", as belonging to Lyanna (Aisling Franciosi), dying of childbirth in the tower. She makes him swear to protect her son - Jon Snow. Bran revisits this moment in the season 7 finale, \"The Dragon and the Wolf\", where Lyanna whispers Jon\\'s true name to Ned - Aegon Targaryen.\\n\\n===Recognition and awards===\\nBean was nominated for a Saturn Award for Best Actor on Television, a Scream Award for Best Fantasy Actor, and an EWwy Award for Best Actor, Drama for the role. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'd3f0c299bebbbf1c60c26659fbb5974a'}>,\n",
+       "  <Document: {'content': '===Recognition and awards===\\nBean was nominated for a Saturn Award for Best Actor on Television, a Scream Award for Best Fantasy Actor, and an EWwy Award for Best Actor, Drama for the role. IGN named Ned its Best TV Hero of 2011, and Bean won the Portal Award for Best Actor.\\n\\n== References ==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '8b393ae6dbf5bd1bb3f9461170bb4645'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''''Game of Thrones''''' is an American  fantasy drama television series created by David Benioff and D. B. Weiss for HBO. It is an adaptation of ''A Song of Ice and Fire'', George R. R. Martin's series of fantasy novels, the first of which is ''A Game of Thrones''. The show was both produced and filmed in Belfast and elsewhere in the United Kingdom. Filming locations also included Canada, Croatia, Iceland, Malta, Morocco, and Spain. The series premiered on HBO in the United States on April 17, 2011, and concluded on May 19, 2019, with 73 episodes broadcast over eight seasons.\\n\\nSet on the fictional continents of Westeros and Essos, ''Game of Thrones'' has several plots and a large ensemble cast and follows several story arcs. One arc is about the Iron Throne of the Seven Kingdoms and follows a web of alliances and conflicts among the noble dynasties either vying to claim the throne or fighting for independence from it. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'faa34b7c837f5024230c016c4a1d0f92'}>,\n",
+       "  <Document: {'content': \"One arc is about the Iron Throne of the Seven Kingdoms and follows a web of alliances and conflicts among the noble dynasties either vying to claim the throne or fighting for independence from it. Another focuses on the last descendant of the realm's deposed ruling dynasty, who has been exiled and is plotting a return to the throne, while another story arc follows the Night's Watch, a brotherhood defending the realm against the fierce peoples and legendary creatures of the North.\\n\\n''Game of Thrones'' attracted a record viewership on HBO and has a broad, active, and international fan base. The series was acclaimed by critics for its acting, complex characters, story, scope, and production values, although its frequent use of nudity and violence (including sexual violence) was criticized; the final season received further criticism for its condensed story and creative decisions, with many considering it a disappointing conclusion. The series received 58 Primetime Emmy Awards, the most by a drama series, including Outstanding Drama Series in 2015, 2016, 2018, 2019. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '3aa78ebbfef7d960faa751bd853e64d2'}>,\n",
+       "  <Document: {'content': \"The series received 58 Primetime Emmy Awards, the most by a drama series, including Outstanding Drama Series in 2015, 2016, 2018, 2019. Its other awards and nominations include three Hugo Awards for Best Dramatic Presentation (2012–2014), a 2011 Peabody Award, and five nominations for the Golden Globe Award for Best Television Series – Drama (2012 and 2015–2018). In 2019, the show's final season established a new record for most Emmy nominations received in the same year by any regular series with 32, breaking the 25 years long record of 26 nominations established by ''NYPD Blue'' in 1994; it also established ''Game of Thrones'' as the drama series with the most overall Emmy nominations, with a total of 161.\\n\\nOf the ensemble cast, Peter Dinklage has won four Primetime Emmy Awards for Outstanding Supporting Actor in a Drama Series (2011, 2015, 2018, and 2019) and the Golden Globe Award for Best Supporting Actor – Series, Miniseries or Television Film (2012) for his performance as Tyrion Lannister. Alfie Allen, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Lena Headey, Kit Harington, Sophie Turner, Diana Rigg, Carice van Houten, Max von Sydow, and Maisie Williams have also received Primetime Emmy Award nominations for their performances.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '2cd5b13890980c040707eedd6d9a42a9'}>,\n",
+       "  <Document: {'content': 'Alfie Allen, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Lena Headey, Kit Harington, Sophie Turner, Diana Rigg, Carice van Houten, Max von Sydow, and Maisie Williams have also received Primetime Emmy Award nominations for their performances.\\n\\n==Background==\\n\\nPower and violence are central themes of \\'\\'Game of Thrones\\'\\', and the number of weapons made for the series (some of which are shown here) reflects this.\\n\\n===Setting===\\n\\n\\'\\'Game of Thrones\\'\\' is roughly based on the storylines of \\'\\'A Song of Ice and Fire\\'\\', set in the fictional Seven Kingdoms of Westeros and the continent of Essos. The series chronicles the violent dynastic struggles among the realm\\'s noble families for the Iron Throne, while other families fight for independence from it. It opens with additional threats in the icy North and Essos in the east.\\n\\nShowrunner David Benioff jokingly suggested \"\\'\\'The Sopranos\\'\\' in Middle-earth\" as \\'\\'Game of Thrones\\'\\' tagline, referring to its intrigue-filled plot and dark tone in a fantasy setting of magic and dragons. In a 2012 study, out of 40 recent television drama shows, \\'\\'Game of Thrones\\'\\' ranked second in deaths per episode, averaging 14 deaths.\\n\\n===Themes===\\n\\nThe series is generally praised for what is perceived as a sort of medieval realism. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '1be9a1a353d1ef1860fcf45b3d64ff5c'}>,\n",
+       "  <Document: {'content': 'In a 2012 study, out of 40 recent television drama shows, \\'\\'Game of Thrones\\'\\' ranked second in deaths per episode, averaging 14 deaths.\\n\\n===Themes===\\n\\nThe series is generally praised for what is perceived as a sort of medieval realism. George R.R. Martin set out to make the story feel more like historical fiction than contemporary fantasy, with less emphasis on magic and sorcery and more on battles, political intrigue, and the characters, believing that magic should be used moderately in the epic fantasy genre. Martin has stated that \"the true horrors of human history derive not from orcs and Dark Lords, but from ourselves.\"A common theme in the fantasy genre is the battle between good and evil, which Martin says does not mirror the real world. Just like people\\'s capacity for good and for evil in real life, Martin explores the questions of redemption and character change. The series allows the audience to view different characters from their perspective, unlike in many other fantasies, and thus the supposed villains can provide their side of the story. Benioff said, \"George brought a measure of harsh realism to high fantasy. He introduced gray tones into a black-and-white universe.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8011c8d05e1a913e59055eb5a523f31e'}>,\n",
+       "  <Document: {'content': 'Benioff said, \"George brought a measure of harsh realism to high fantasy. He introduced gray tones into a black-and-white universe.\"In early seasons, under the influence of the \\'\\'A Song of Ice and Fire\\'\\' books, main characters were regularly killed off, and this was credited with developing tension among viewers. In later seasons, critics pointed out that certain characters had developed \"plot armor\" to survive in unlikely circumstances, and attributed this to \\'\\'Game of Thrones\\'\\' deviating from the novels to become more of a traditional television series. The series also reflects the substantial death rates in war.\\n\\n===Inspirations and derivations===\\nAlthough the first season closely follows the events of the first novel, later seasons have made significant changes. According to David Benioff, the series is \"about adapting the series as a whole and following the map George laid out for us and hitting the major milestones, but not necessarily each of the stops along the way\".\\n\\nThe novels and their adaptations base aspects of their settings, characters, and plot on events in European history. Most of Westeros is reminiscent of high medieval Europe, from lands and cultures, to the palace intrigue, feudal system, castles, and knightly tournaments. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '82cbeed68e0c87bb01e5f35a386cea8a'}>,\n",
+       "  <Document: {'content': 'Most of Westeros is reminiscent of high medieval Europe, from lands and cultures, to the palace intrigue, feudal system, castles, and knightly tournaments. A principal inspiration for the novels is the English Wars of the Roses (1455–1485) between the houses of Lancaster and York, reflected in Martin\\'s houses of Lannister and Stark. The scheming Cersei Lannister evokes Isabella, the \"she-wolf of France\" (1295–1358). She and her family, as portrayed in Maurice Druon\\'s historical novel series, \\'\\'The Accursed Kings\\'\\', were a main inspiration of Martin\\'s.\\n\\nOther historical antecedents of series elements include Hadrian\\'s Wall (which becomes Martin\\'s Wall), the Roman Empire, and the legend of Atlantis (ancient Valyria), Byzantine Greek fire (\"wildfire\"), Icelandic sagas of the Viking Age (the Ironborn), the Mongol hordes (the Dothraki), the Hundred Years\\' War, and the Italian Renaissance. The series\\' popularity has been attributed, in part, to Martin\\'s skill at fusing these elements into a seamless, credible version of alternate history. Martin acknowledges, \"I take history and I file off the serial numbers and I turn it up to 11.\"==Cast and characters==\\n\\n\\'\\'Game of Thrones\\'\\' has an ensemble cast estimated to be the largest on television; during its third season, 257 cast names were recorded. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '5f2128f83bcf624c2f3283f26b3e96f2'}>,\n",
+       "  <Document: {'content': '==Cast and characters==\\n\\n\\'\\'Game of Thrones\\'\\' has an ensemble cast estimated to be the largest on television; during its third season, 257 cast names were recorded. In 2014, several actor contracts were renegotiated to include a seventh-season option, with raises which reportedly made them among the highest-paid performers on cable television. In 2016, several actor contracts were again renegotiated, reportedly increasing the salary of five of the main cast members to $1 million per episode for the last two seasons, which would make them the highest paid actors on television. The main cast is listed below.\\n\\nLord Eddard \"Ned\" Stark (Sean Bean) is the head of House Stark, whose members are involved in plotlines throughout most of the series. He and his wife, Catelyn Tully (Michelle Fairley), have five children: Robb (Richard Madden), the eldest; followed by Sansa (Sophie Turner), Arya (Maisie Williams), Bran (Isaac Hempstead-Wright), and Rickon (Art Parkinson). Ned\\'s illegitimate son Jon Snow (Kit Harington) and his friend Samwell Tarly (John Bradley) serve in the Night\\'s Watch under Lord Commander Jeor Mormont (James Cosmo). The Wildlings living north of the Wall include the young Gilly (Hannah Murray), and the warriors Tormund Giantsbane (Kristofer Hivju) and Ygritte (Rose Leslie).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '62cb2cde2478995175657f0eb914e68b'}>,\n",
+       "  <Document: {'content': 'The Wildlings living north of the Wall include the young Gilly (Hannah Murray), and the warriors Tormund Giantsbane (Kristofer Hivju) and Ygritte (Rose Leslie).\\n\\nOthers associated with House Stark include Ned\\'s ward Theon Greyjoy (Alfie Allen), his vassal Roose Bolton (Michael McElhatton), and Bolton\\'s bastard son Ramsay Snow (Iwan Rheon). Robb falls in love with the healer Talisa Maegyr (Oona Chaplin), and Arya befriends the blacksmith\\'s apprentice Gendry (Joe Dempsie) and the assassin Jaqen H\\'ghar (Tom Wlaschiha). The tall warrior Brienne of Tarth (Gwendoline Christie) serves Catelyn and, later, Sansa.\\n\\nIn King\\'s Landing, the capital, Ned\\'s friend King Robert Baratheon (Mark Addy), shares a loveless marriage with Cersei Lannister (Lena Headey), who has taken her twin brother, the \"Kingslayer\" Ser Jaime Lannister (Nikolaj Coster-Waldau), as her lover. She loathes her younger brother, the dwarf Tyrion Lannister (Peter Dinklage), who is attended by his mistress Shae (Sibel Kekilli) and the sellsword Bronn (Jerome Flynn). Cersei\\'s father is Lord Tywin Lannister (Charles Dance), and she also has two young sons: Joffrey (Jack Gleeson) and Tommen (Dean-Charles Chapman). Joffrey is guarded by the scar-faced warrior Sandor \"the Hound\" Clegane (Rory McCann).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '89cd9ee20955ce149f0039cc39e70818'}>,\n",
+       "  <Document: {'content': 'Cersei\\'s father is Lord Tywin Lannister (Charles Dance), and she also has two young sons: Joffrey (Jack Gleeson) and Tommen (Dean-Charles Chapman). Joffrey is guarded by the scar-faced warrior Sandor \"the Hound\" Clegane (Rory McCann).\\n\\nThe king\\'s Small Council of advisors includes the crafty Master of Coin Lord Petyr \"Littlefinger\" Baelish (Aidan Gillen) and the eunuch spymaster Lord Varys (Conleth Hill). Robert\\'s brother Stannis Baratheon (Stephen Dillane) is advised by foreign priestess Melisandre (Carice van Houten) and former smuggler Ser Davos Seaworth (Liam Cunningham). The wealthy Tyrell family is represented at court by Margaery Tyrell (Natalie Dormer). The High Sparrow (Jonathan Pryce) is the capital\\'s religious leader. In the southern principality of Dorne, Ellaria Sand (Indira Varma) seeks vengeance against the Lannisters.\\n\\nAcross the Narrow Sea, siblings Viserys (Harry Lloyd) and Daenerys Targaryen (Emilia Clarke) – the exiled children of the last king of the original ruling dynasty, who was overthrown by Robert Baratheon – are running for their lives and trying to win back the throne. Daenerys marries Khal Drogo (Jason Momoa), the leader of the nomadic Dothraki. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '60e7af960748238e8d2f8eaf37c8215a'}>,\n",
+       "  <Document: {'content': \"Across the Narrow Sea, siblings Viserys (Harry Lloyd) and Daenerys Targaryen (Emilia Clarke) – the exiled children of the last king of the original ruling dynasty, who was overthrown by Robert Baratheon – are running for their lives and trying to win back the throne. Daenerys marries Khal Drogo (Jason Momoa), the leader of the nomadic Dothraki. Her retinue includes the exiled knight Ser Jorah Mormont (Iain Glen), her aide Missandei (Nathalie Emmanuel), the sellsword Daario Naharis (Michiel Huisman), and Grey Worm (Jacob Anderson), who leads Daenerys' army of elite eunuch-warriors, the Unsullied.\\n\\n==Production==\\n===Conception and development===\\nShowrunners D. B. Weiss and David Benioff created the series, wrote most of its episodes and directed several.\\n\\nIn January 2006, David Benioff had a phone conversation with George R. R. Martin's literary agent about the books he represented and became interested in ''A Song of Ice and Fire'', as he had been a fan of fantasy fiction when young but had not read the books before.  The literary agent then sent Benioff the series' first four books. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '6a242df0509d905effa4b61456aebe3f'}>,\n",
+       "  <Document: {'content': 'In January 2006, David Benioff had a phone conversation with George R. R. Martin\\'s literary agent about the books he represented and became interested in \\'\\'A Song of Ice and Fire\\'\\', as he had been a fan of fantasy fiction when young but had not read the books before.  The literary agent then sent Benioff the series\\' first four books. Benioff read a few hundred pages of the first novel, \\'\\'A Game of Thrones\\'\\', shared his enthusiasm with D. B. Weiss, and suggested that they adapt Martin\\'s novels into a television series; Weiss finished the first novel in \"maybe 36 hours\". They pitched the series to HBO after a five-hour meeting with Martin (himself a veteran screenwriter) in a restaurant on Santa Monica Boulevard. According to Benioff, they won Martin over with their answer to his question, \"Who is Jon Snow\\'s mother?\"Before being approached by Benioff and Weiss, Martin had had other meetings with other scriptwriters, most of whom wanted to adapt the series as a feature film. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '84393501b1d1d39ae0be38edfb82b865'}>,\n",
+       "  <Document: {'content': 'Before being approached by Benioff and Weiss, Martin had had other meetings with other scriptwriters, most of whom wanted to adapt the series as a feature film. Martin, however, deemed it \"unfilmable\" and impossible to be done as a feature film, stating that the size of one of his novels is as long as \\'\\'The Lord of the Rings\\'\\', which had been adapted as three feature films. Similarly, Benioff also said that it would be impossible to turn the novels into a feature film as the scale of the novels is too big for a feature film, and dozens of characters would have to be discarded. Benioff added, \"a fantasy movie of this scope, financed by a major studio, would almost certainly need a PG-13 rating. That means no sex, no blood, no profanity. Fuck that.\"Martin himself was pleased with the suggestion that they adapt it as an HBO series, saying that he \"never imagined it anywhere else\". \"I knew it couldn\\'t be done as a network television series. It\\'s too adult. The level of sex and violence would never have gone through.\"The series began development in January 2007. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '4ec14c6db4e25fddcacbb973727570a5'}>,\n",
+       "  <Document: {'content': 'It\\'s too adult. The level of sex and violence would never have gone through.\"The series began development in January 2007. HBO acquired the television rights to the novels, with Benioff and Weiss as its executive producers, and Martin as a co-executive producer. The intention was for each novel to yield a season\\'s worth of episodes. Initially, Martin would write one episode per season while Benioff and Weiss would write the rest of the episodes. Jane Espenson and Bryan Cogman were later added to write one episode apiece the first season.\\n\\nThe first and second drafts of the pilot script by Benioff and Weiss were submitted in August 2007 and June 2008, respectively. Although HBO liked both drafts, a pilot was not ordered until November 2008; the 2007–2008 Writers Guild of America strike may have delayed the process. The pilot episode, \"Winter Is Coming\", was first shot in 2009; after a poor reception in a private viewing, HBO demanded an extensive re-shoot (about 90 percent of the episode, with cast and directorial changes).\\n\\nThe pilot reportedly cost HBO US$5–10million to produce, while the first season\\'s budget was estimated at $50–60million. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '9d21f98a10bdf5be8dbed72d7232c7c9'}>,\n",
+       "  <Document: {'content': 'The pilot episode, \"Winter Is Coming\", was first shot in 2009; after a poor reception in a private viewing, HBO demanded an extensive re-shoot (about 90 percent of the episode, with cast and directorial changes).\\n\\nThe pilot reportedly cost HBO US$5–10million to produce, while the first season\\'s budget was estimated at $50–60million. In the second season, the series received a 15-percent budget increase for the climactic battle in \"Blackwater\" (which had an $8million budget). Between 2012 and 2015, the average budget per episode increased from $6million to \"at least\" $8million. The sixth-season budget was over $10million per episode, for a season total of over $100million and a series record.\\n\\n===Casting===\\nNina Gold and Robert Sterne are the series\\' primary casting directors. Through a process of auditions and readings, the main cast was assembled. The only exceptions were Peter Dinklage and Sean Bean, whom the writers wanted from the start; they were announced as joining the pilot in 2009. Other actors signed for the pilot were Kit Harington as Jon Snow, Jack Gleeson as Joffrey Baratheon, Harry Lloyd as Viserys Targaryen, and Mark Addy as Robert Baratheon. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '9dbd4f909d641a07ad390a9ec69be57d'}>,\n",
+       "  <Document: {'content': \"Other actors signed for the pilot were Kit Harington as Jon Snow, Jack Gleeson as Joffrey Baratheon, Harry Lloyd as Viserys Targaryen, and Mark Addy as Robert Baratheon. Addy was, according to showrunners Benioff and Weiss, the easiest actor to cast for the series, due to his audition performance. Some of the characters in the pilot were recast for the first season: Catelyn Stark was initially played by Jennifer Ehle, but the role was recast with Michelle Fairley. Daenerys Targaryen was also recast, with Emilia Clarke replacing Tamzin Merchant. The rest of the first season's cast was filled in the second half of 2009.\\n\\nAlthough many of the cast returned after the first season, the producers had many new characters to cast for each of the following seasons. Due to the large number of new characters, Benioff and Weiss postponed the introduction of several key characters in the second season and merged several characters into one or assigned plot functions to different characters.  Some recurring characters were recast over the years; for example, Gregor Clegane was played by three different actors, while Dean-Charles Chapman who played Tommen Baratheon also played a minor Lannister character.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'bdd6a200c2576db4385148e13ae94513'}>,\n",
+       "  <Document: {'content': \"Some recurring characters were recast over the years; for example, Gregor Clegane was played by three different actors, while Dean-Charles Chapman who played Tommen Baratheon also played a minor Lannister character.\\n\\n===Writing===\\nGeorge R. R. Martin, author of ''A Song of Ice and Fire'', is a series co-executive producer and wrote one episode for each of the first four seasons.\\n\\n''Game of Thrones'' used seven writers in six seasons. Series creators David Benioff and D. B. Weiss, the showrunners, write most of the episodes each season.\\n\\n''A Song of Ice and Fire'' author George R. R. Martin wrote one episode in each of the first four seasons. Martin did not write an episode for the later seasons, since he wanted to focus on completing the sixth novel (''The Winds of Winter''). Jane Espenson co-wrote one first-season episode as a freelance writer.\\n\\nBryan Cogman, initially a script coordinator for the series, was promoted to producer for the fifth season. Cogman, who wrote at least one episode for the first five seasons, is the only other writer in the writers' room with Benioff and Weiss. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'bd97d194ed9fb9ea7662c57a5875075b'}>,\n",
+       "  <Document: {'content': \"Cogman, who wrote at least one episode for the first five seasons, is the only other writer in the writers' room with Benioff and Weiss. Before his promotion, Vanessa Taylor (a writer during the second and third seasons) worked closely with Benioff and Weiss. Dave Hill joined the writing staff for the fifth season after working as an assistant to Benioff and Weiss. Although Martin is not in the writers' room, he reads the script outlines and makes comments.\\n\\nBenioff and Weiss sometimes assign characters to particular writers; for example, Cogman was assigned to Arya Stark for the fourth season. The writers spend several weeks writing a character outline, including what material from the novels to use and the overarching themes. After these individual outlines are complete, they spend another two to three weeks discussing each main character's individual arc and arranging them episode by episode.\\n\\nA detailed outline is created, with each of the writers working on a portion to create a script for each episode. Cogman, who wrote two episodes for the fifth season, took a month and a half to complete both scripts. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'd8ea78984d3c4240bbf854167b8b6919'}>,\n",
+       "  <Document: {'content': \"A detailed outline is created, with each of the writers working on a portion to create a script for each episode. Cogman, who wrote two episodes for the fifth season, took a month and a half to complete both scripts. They are then read by Benioff and Weiss, who make notes, and parts of the script are rewritten. All ten episodes are written before filming begins since they are filmed out of order with two units in different countries.\\n\\nBenioff and Weiss write each of their episodes together, with one of them writing the first half of the script and the other the second half. After that they begin with passing the drafts back and forth to make notes and rewrite parts of it.\\n\\n===Adaptation schedule and episodes===\\n\\nAfter ''Game of Thrones'' began outpacing the published novels in the sixth season, the series was based on a plot outline of the future novels provided by Martin and original content. In April 2016, the showrunners' plan was to shoot 13 more episodes after the sixth season: seven episodes in the seventh season and six episodes in the eighth. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '2d99a5c25513938aaac93d75f4cd8c67'}>,\n",
+       "  <Document: {'content': \"In April 2016, the showrunners' plan was to shoot 13 more episodes after the sixth season: seven episodes in the seventh season and six episodes in the eighth. Later that month, the series was renewed for a seventh season with a seven-episode order. Eight seasons were ordered and filmed, adapting the novels at a rate of about 48 seconds per page for the first three seasons.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'f54f8ddbb48ddcb0f7c44db94062b05a'}>,\n",
+       "  <Document: {'content': \"Eight seasons were ordered and filmed, adapting the novels at a rate of about 48 seconds per page for the first three seasons.\\n\\nSeason\\n\\nOrdered\\n\\nFilming\\n\\nFirst aired\\n\\nLast aired\\n\\nNovel(s) adapted\\n\\nRefs\\n\\nSeason 1\\n\\nMarch 2, 2010\\n\\nSecond half of 2010\\n\\nApril 17, 2011\\n\\nJune 19, 2011\\n\\n''A Game of Thrones''\\n\\nSeason 2\\n\\nApril 19, 2011\\n\\nSecond half of 2011\\n\\nApril 1, 2012\\n\\nJune 3, 2012\\n\\n''A Clash of Kings'' and some early chapters from ''A Storm of Swords''\\n\\nSeason 3\\n\\nApril 10, 2012\\n\\nJuly – November 2012\\n\\nMarch 31, 2013\\n\\nJune 9, 2013\\n\\nAbout the first two-thirds of ''A Storm of Swords''\\n\\nSeason 4\\n\\nApril 2, 2013\\n\\nJuly – November 2013\\n\\nApril 6, 2014\\n\\nJune 15, 2014\\n\\nThe remaining one-third of ''A Storm of Swords'' and some elements from ''A Feast for Crows'' and ''A Dance with Dragons''\\n\\nSeason 5\\n\\nApril 8, 2014\\n\\nJuly – December 2014\\n\\nApril 12, 2015\\n\\nJune 14, 2015\\n\\n''A Feast for Crows'', ''A Dance with Dragons'' and original content, with some late chapters from ''A Storm of Swords'' and elements from ''The Winds of Winter''\\n\\nSeason 6\\n\\nApril 8, 2014\\n\\nJuly – December 2015\\n\\nApril 24, 2016\\n\\nJune 26, 2016\\n\\nOriginal content and outline from ''The Winds of Winter'', with some late elements from ''A Feast for Crows'' and ''A Dance with Dragons''\\n\\nSeason 7\\n\\nApril 21, 2016\\n\\nAugust 2016 – February 2017\\n\\nJuly 16, 2017\\n\\nAugust 27, 2017\\n\\nOriginal content and outline from ''The Winds of Winter'' and ''A Dream of Spring''\\n\\nSeason 8\\n\\nJuly 30, 2016\\n\\nOctober 2017 – July 2018\\n\\nApril 14, 2019\\n\\nMay 19, 2019\\n\\nOriginal content and outline from ''The Winds of Winter'' and ''A Dream of Spring''\\n\\nThe first two seasons adapted one novel each. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '22c1a724f4443038e260e2184bfa1879'}>,\n",
+       "  <Document: {'content': \"Season\\n\\nOrdered\\n\\nFilming\\n\\nFirst aired\\n\\nLast aired\\n\\nNovel(s) adapted\\n\\nRefs\\n\\nSeason 1\\n\\nMarch 2, 2010\\n\\nSecond half of 2010\\n\\nApril 17, 2011\\n\\nJune 19, 2011\\n\\n''A Game of Thrones''\\n\\nSeason 2\\n\\nApril 19, 2011\\n\\nSecond half of 2011\\n\\nApril 1, 2012\\n\\nJune 3, 2012\\n\\n''A Clash of Kings'' and some early chapters from ''A Storm of Swords''\\n\\nSeason 3\\n\\nApril 10, 2012\\n\\nJuly – November 2012\\n\\nMarch 31, 2013\\n\\nJune 9, 2013\\n\\nAbout the first two-thirds of ''A Storm of Swords''\\n\\nSeason 4\\n\\nApril 2, 2013\\n\\nJuly – November 2013\\n\\nApril 6, 2014\\n\\nJune 15, 2014\\n\\nThe remaining one-third of ''A Storm of Swords'' and some elements from ''A Feast for Crows'' and ''A Dance with Dragons''\\n\\nSeason 5\\n\\nApril 8, 2014\\n\\nJuly – December 2014\\n\\nApril 12, 2015\\n\\nJune 14, 2015\\n\\n''A Feast for Crows'', ''A Dance with Dragons'' and original content, with some late chapters from ''A Storm of Swords'' and elements from ''The Winds of Winter''\\n\\nSeason 6\\n\\nApril 8, 2014\\n\\nJuly – December 2015\\n\\nApril 24, 2016\\n\\nJune 26, 2016\\n\\nOriginal content and outline from ''The Winds of Winter'', with some late elements from ''A Feast for Crows'' and ''A Dance with Dragons''\\n\\nSeason 7\\n\\nApril 21, 2016\\n\\nAugust 2016 – February 2017\\n\\nJuly 16, 2017\\n\\nAugust 27, 2017\\n\\nOriginal content and outline from ''The Winds of Winter'' and ''A Dream of Spring''\\n\\nSeason 8\\n\\nJuly 30, 2016\\n\\nOctober 2017 – July 2018\\n\\nApril 14, 2019\\n\\nMay 19, 2019\\n\\nOriginal content and outline from ''The Winds of Winter'' and ''A Dream of Spring''\\n\\nThe first two seasons adapted one novel each. For the later seasons, its creators see ''Game of Thrones'' as an adaptation of ''A Song of Ice and Fire'' as a whole rather than the individual novels; this enables them to move events across novels, according to screen-adaptation requirements.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '9b9c2bfc39d03ec4c7f1b7ff6051d23a'}>,\n",
+       "  <Document: {'content': \"For the later seasons, its creators see ''Game of Thrones'' as an adaptation of ''A Song of Ice and Fire'' as a whole rather than the individual novels; this enables them to move events across novels, according to screen-adaptation requirements.\\n\\n===Filming===\\nThe Azure Window at Ras-id-Dwerja, on Gozo, was the site of the Dothraki wedding in season one.\\n\\nPrincipal photography for the first season was scheduled to begin on July 26, 2010, and the primary location was the Paint Hall Studios in Belfast, Northern Ireland. Exterior scenes in Northern Ireland were filmed at Sandy Brae in the Mourne Mountains (standing in for Vaes Dothrak), Castle Ward (Winterfell), Saintfield Estates (the Winterfell godswood), Tollymore Forest (outdoor scenes), Cairncastle (the execution site), the Magheramorne quarry (Castle Black), and Shane's Castle (the tourney grounds). Doune Castle in Stirling, Scotland, was also used in the original pilot episode for scenes at Winterfell. The producers initially considered filming the whole series in Scotland, but decided on Northern Ireland because of the availability of studio space.\\n\\nThe first season's southern scenes were filmed in Malta, a change in location from the  pilot episode's Moroccan sets. The city of Mdina was used for King's Landing. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': '1924f2a363b7fd1a7bc5838a1e94025d'}>,\n",
+       "  <Document: {'content': \"The first season's southern scenes were filmed in Malta, a change in location from the  pilot episode's Moroccan sets. The city of Mdina was used for King's Landing. Filming was also done at Fort Manoel (representing the Sept of Baelor), at the Azure Window on the island of Gozo (the Dothraki wedding site) and at San Anton Palace, Fort Ricasoli, Fort St. Angelo and St. Dominic monastery (all used for scenes in the Red Keep).\\n\\nThe walled city of Dubrovnik became King's Landing in season two.\\nFilming of the second season's southern scenes shifted from Malta to Croatia, where the city of Dubrovnik and nearby locations allowed exterior shots of a walled, coastal medieval city. The Walls of Dubrovnik and Fort Lovrijenac were used for scenes in King's Landing, though, exteriors of some local buildings, for example, the Red Keep and the Sept of Baelor, are computer-generated. The island of Lokrum, the St. Dominic monastery in the coastal town of Trogir, the Rector's Palace in Dubrovnik, and the Dubac quarry (a few kilometers east) were used for scenes set in Qarth. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': '7c25cd8bf9ac99445d60b60bdfa11de1'}>,\n",
+       "  <Document: {'content': \"The island of Lokrum, the St. Dominic monastery in the coastal town of Trogir, the Rector's Palace in Dubrovnik, and the Dubac quarry (a few kilometers east) were used for scenes set in Qarth. Scenes set north of the Wall, in the Frostfangs and at the Fist of the First Men, were filmed in November 2011 in Iceland: on the Vatnajökull glacier near Smyrlabjörg, the Svínafellsjökull glacier near Skaftafell and the Mýrdalsjökull glacier near Vik on Höfðabrekkuheiði.\\n\\nThird-season production returned to Dubrovnik, with the Walls of Dubrovnik, Fort Lovrijenac and nearby locations again used for scenes in King's Landing and the Red Keep. Trsteno Arboretum, a new location, is the garden of the Tyrells in King's Landing. The third season also returned to Morocco (including the city of Essaouira) to film Daenerys' scenes in Essos. Dimmuborgir and the Grjótagjá cave in Iceland were used as well. One scene, with a live bear, was filmed in Los Angeles. The production used three units (Dragon, Wolf and Raven) filming in parallel, six directing teams, 257 cast members and 703 crew members.\\n\\nBallintoy Harbour was Lordsport on the Iron Islands.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': '673de9797339e660df0e92430a735775'}>,\n",
+       "  <Document: {'content': \"The production used three units (Dragon, Wolf and Raven) filming in parallel, six directing teams, 257 cast members and 703 crew members.\\n\\nBallintoy Harbour was Lordsport on the Iron Islands.\\nThe fourth season returned to Dubrovnik and included new locations, including Diocletian's Palace in Split, Klis Fortress north of Split, Perun quarry east of Split, the Mosor mountain range, and Baška Voda further south. Thingvellir National Park in Iceland was used for the fight between Brienne and the Hound. Filming took 136 days and ended on November 21, 2013. The fifth season added Seville, Spain, used for scenes of Dorne, as well as Córdoba.\\n\\nThe sixth season, which began filming in July 2015, returned to Spain and filmed in Navarra, Guadalajara, Seville, Almeria, Girona and Peniscola. Filming also returned to Dubrovnik, Croatia.\\n\\nFilming of the seven episodes of season 7 began on August 31, 2016, at Titanic Studios in Belfast, with other filming in Iceland, Northern Ireland and many locations in Spain, including Seville, Cáceres, Almodovar del Rio, Santiponce, Zumaia and Bermeo. The series also filmed in Dubrovnik, which is used for location of King's Landing. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': '2f6f738f1cc790608b37951cc0a8d55e'}>,\n",
+       "  <Document: {'content': 'Filming of the seven episodes of season 7 began on August 31, 2016, at Titanic Studios in Belfast, with other filming in Iceland, Northern Ireland and many locations in Spain, including Seville, Cáceres, Almodovar del Rio, Santiponce, Zumaia and Bermeo. The series also filmed in Dubrovnik, which is used for location of King\\'s Landing. Filming continued until the end of February 2017 as necessary to ensure winter weather in some of the European locations.\\n\\n===Directing===\\nEach ten-episode season of \\'\\'Game of Thrones\\'\\' has four to six directors, who usually direct back-to-back episodes. Alan Taylor has directed seven episodes, the most episodes of the series. Alex Graves, David Nutter, Mark Mylod, and Jeremy Podeswa have directed six each. Daniel Minahan directed five episodes, and Michelle MacLaren, Alik Sakharov, and Miguel Sapochnik directed four each; MacLaren is also the only female director in the entire series\\' run. Brian Kirk directed three episodes during the first season, and Tim Van Patten directed the series\\' first two episodes. Neil Marshall directed two episodes, both with large battle scenes: \"Blackwater\" and \"The Watchers on the Wall\". Other directors have been Jack Bender, David Petrarca, Daniel Sackheim, Michael Slovis and Matt Shakman. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': '10734fb1d5590c3ed4b25eb4a37be345'}>,\n",
+       "  <Document: {'content': 'Neil Marshall directed two episodes, both with large battle scenes: \"Blackwater\" and \"The Watchers on the Wall\". Other directors have been Jack Bender, David Petrarca, Daniel Sackheim, Michael Slovis and Matt Shakman. David Benioff and D. B. Weiss have directed two episodes together but only credited one each episode, which was determined after a coin toss.\\n\\n===Technical aspects===\\nAlik Sakharov was the pilot\\'s cinematographer. The series has had a number of cinematographers, and has received seven Primetime Emmy Award for Outstanding Cinematography for a Single-Camera Series nominations.\\n\\nOral Norrey Ottey, Frances Parker, Martin Nicholson, Crispin Green, Tim Porter and Katie Weiland have edited the series for a varying number of episodes. Weiland received a Primetime Emmy Award for Outstanding Single-Camera Picture Editing for a Drama Series in 2015.\\n\\n===Costumes===\\nThe costumes of Ygritte, Jon Snow and Tormund Giantsbane reflect the harsh climate in which they are worn.\\nDresses worn at the royal court in King\\'s Landing indicate their wearers\\' wealth and status.\\nFunctional weapons and armor, like Brienne of Tarth\\'s (left), were manufactured for the series.\\nMichele Clapton was costume designer for \\'\\'Game of Thrones\\'\\' first five seasons before she was replaced by April Ferry. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': '6aa4a0cc4cd64424b8b7b3aa4d4668c'}>,\n",
+       "  <Document: {'content': \"Functional weapons and armor, like Brienne of Tarth's (left), were manufactured for the series.\\nMichele Clapton was costume designer for ''Game of Thrones'' first five seasons before she was replaced by April Ferry. Clapton returned to the series as costume designer for the seventh season.\\n\\nThe costumes used in the series drew inspiration from a number of sources, such as Japanese and Persian armor. Dothraki dress resembles that of the Bedouin (one was made out of fish skins to resemble dragon scales), and the Wildlings wear animal skins like the Inuit. Wildling bone armor is made from molds of actual bones, and is assembled with string and latex resembling catgut. Although the extras who play Wildlings and the Night's Watch often wear hats (normal in a cold climate), members of the principal cast usually do not so viewers can recognize the main characters. Björk's Alexander McQueen high-neckline dresses inspired Margaery Tyrell's funnel-neck outfit, and prostitutes' dresses are designed for easy removal. All clothing used is aged for two weeks so it appears realistic on high-definition television.\\n\\nAbout two dozen wigs are used for the actresses. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 28}, 'embedding': None, 'id': '8f6b14fc066e489340cc9c9cbdcc7d8f'}>,\n",
+       "  <Document: {'content': \"All clothing used is aged for two weeks so it appears realistic on high-definition television.\\n\\nAbout two dozen wigs are used for the actresses. Made of human hair and up to  in length, they cost up to $7,000 each and are washed and styled like real hair. Applying the wigs is time-consuming; Emilia Clarke, for example, requires about two hours to style her brunette hair with a platinum-blonde wig and braids. Other actors, such as Jack Gleeson and Sophie Turner, receive frequent hair coloring. For characters such as Daenerys (Clarke) and her Dothraki, their hair, wigs and costumes are processed to appear as if they have not been washed for weeks.\\n\\n===Makeup===\\nFor the first three seasons, Paul Engelen was ''Game of Thrones'' main makeup designer and prosthetic makeup artist with Melissa Lackersteen, Conor O'Sullivan, and Rob Trenton. At the beginning of the fourth season Engelen's team was replaced by Jane Walker and her crew, composed of Ann McEwan and Barrie and Sarah Gower.\\n\\n===Visual effects===\\nFor the series' large number of visual effects, HBO hired British-based BlueBolt and Irish-based Screen Scene for season one. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 29}, 'embedding': None, 'id': '6d4c2894f02c06419f4ce5a58ca23a7b'}>,\n",
+       "  <Document: {'content': '===Visual effects===\\nFor the series\\' large number of visual effects, HBO hired British-based BlueBolt and Irish-based Screen Scene for season one. Most of the environment builds were done as 2.5D projections, giving viewers perspective while keeping the programming from being overwhelming. In 2011, the season-one finale, \"Fire and Blood\", was nominated for a Primetime Emmy Award for Outstanding Special Visual Effects.\\n\\nBecause the effects became more complex in subsequent seasons (including CGI creatures, fire, and water), German-based Pixomondo became the lead visual-effects producer; nine of its twelve facilities contributed to the project for season two, with Stuttgart the lead.  Scenes were also produced by British-based Peanut FX, Canadian-based Spin VFX, and US-based Gradient Effects.  \"Valar Morghulis\" and \"Valar Dohaeris\" earned Pixomondo Primetime Emmy Awards for Outstanding Special Visual Effects in 2012 and 2013, respectively.\\n\\nFor season four, HBO added German-based Mackevision to the project. The season-four finale, \"The Children\", won the 2014 Emmy Award for Visual Effects. Additional producers for season four included Canadian-based Rodeo FX, German-based Scanline VFX and US-based BAKED FX.  The muscle and wing movements of the adolescent dragons in seasons four and five were based largely on those of a chicken.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 30}, 'embedding': None, 'id': '98076683556cf8a96d99c9ca1d3277ea'}>,\n",
+       "  <Document: {'content': \"The muscle and wing movements of the adolescent dragons in seasons four and five were based largely on those of a chicken.  Pixomondo retained a team of 22 to 30 people which focused solely on visualizing Daenerys Targaryen's dragons, with the average production time per season of 20 to 22 weeks.  For the fifth season, HBO added Canadian-based Image Engine and US-based Crazy Horse Effects to its list of main visual-effects producers.\\n\\n===Sound===\\nUnusual for a television series, the sound team receives a rough cut of a full season and approaches it as a ten-hour feature film. Although seasons one and two had different sound teams, one team has been in charge of sound since then. For the series' blood-and-gore sounds, the team often uses a chamois. For dragon screams, mating tortoises, dolphin, seal, lion and bird sounds have been used.\\n\\n===Title sequence===\\n\\nThe series' title sequence was created by production studio Elastic for HBO. Creative director Angus Wall and his collaborators received the 2011 Primetime Emmy Award for Main Title Design for the sequence, which depicts a three-dimensional map of the series' fictional world. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 31}, 'embedding': None, 'id': 'c9f359bc9234f18b4624747d4d3a86e'}>,\n",
+       "  <Document: {'content': \"Creative director Angus Wall and his collaborators received the 2011 Primetime Emmy Award for Main Title Design for the sequence, which depicts a three-dimensional map of the series' fictional world. The map is projected on the inside of a sphere which is centrally lit by a small sun in an armillary sphere. As the camera moves across the map, focusing on the locations of the episode's events, clockwork mechanisms intertwine and allow buildings and other structures to emerge from the map. Accompanied by the title music, the names of the principal cast and creative staff appear. The sequence concludes after about 90 seconds with the title card and brief opening credits indicating the episode's writer(s) and director. Its composition changes as the story progresses, with new locations replacing those featuring less prominently or not at all.\\n\\n===Music===\\n\\nRamin Djawadi composed the ''Game of Thrones'' score.\\n\\nThe music for the series was composed by Ramin Djawadi. The first season's soundtrack, written in about ten weeks before the premiere, was published by Varèse Sarabande in June 2011. Soundtrack albums for subsequent seasons have been released, with tracks by the National, the Hold Steady, and Sigur Rós. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 32}, 'embedding': None, 'id': '767145c9015dfd43a598c63635c66ccc'}>,\n",
+       "  <Document: {'content': \"The first season's soundtrack, written in about ten weeks before the premiere, was published by Varèse Sarabande in June 2011. Soundtrack albums for subsequent seasons have been released, with tracks by the National, the Hold Steady, and Sigur Rós. Djawadi has composed themes for each of the major houses and also for some of the main characters. The themes may evolve over time, as Daenerys Targaryen's theme started small and then became more powerful after each season. Her theme started first with a single instrument, a cello, and Djawadi later incorporated more instruments for it.\\n\\n===Language===\\nThe Westerosi characters of ''Game of Thrones'' speak British-accented English, often (but not consistently) with the accent of the English region corresponding to the character's Westerosi region. The Northerner Eddard Stark speaks in actor Sean Bean's native northern accent, and the southern lord Tywin Lannister speaks with a southern accent, while characters from Dorne speak English with a Spanish accent. Characters foreign to Westeros often have a non-British accent.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 33}, 'embedding': None, 'id': '9ec7bcd7ec3b543fa209876ae02c525a'}>,\n",
+       "  <Document: {'content': \"The Northerner Eddard Stark speaks in actor Sean Bean's native northern accent, and the southern lord Tywin Lannister speaks with a southern accent, while characters from Dorne speak English with a Spanish accent. Characters foreign to Westeros often have a non-British accent.\\n\\nAlthough the common language of Westeros is represented as English, the producers charged linguist David J. Peterson with constructing Dothraki and Valyrian languages based on the few words in the novels; Dothraki and Valyrian dialogue is often subtitled in English. It has been reported that during the series these fictional languages have been heard by more people than the Welsh, Irish, and Scots Gaelic languages combined.\\n\\n===Effect on location===\\n''Game of Thrones'' is funded by Northern Ireland Screen, a UK government agency financed by Invest NI and the European Regional Development Fund. , Northern Ireland Screen gave the series £9.25million ($14.37million); according to government estimates, this has benefited the Northern Ireland economy by £65million ($100.95million).\\n\\nTourism Ireland has a ''Game of Thrones''-themed marketing campaign similar to New Zealand's Tolkien-related advertising. Invest NI and the Northern Ireland Tourist Board also expect the series to generate tourism revenue. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 34}, 'embedding': None, 'id': 'c62b4b2ce92aab8f7d10b9a8d1701a68'}>,\n",
+       "  <Document: {'content': \"Tourism Ireland has a ''Game of Thrones''-themed marketing campaign similar to New Zealand's Tolkien-related advertising. Invest NI and the Northern Ireland Tourist Board also expect the series to generate tourism revenue. According to Arlene Foster, the series has given Northern Ireland the most non-political publicity in its history. The production of ''Game of Thrones'' and other TV series also boosted Northern Ireland's creative industries, contributing to an estimated 12.4-percent growth in arts, entertainment, and recreation jobs between 2008 and 2013 (compared with 4.3percent in the rest of the UK during the same period). In September 2018, after the filming had finished, HBO announced plans to convert its filming locations in Northern Ireland into tourist attractions to be opened in 2019.\\n\\nTourism organizations elsewhere reported increases in bookings after their locations appeared in ''Game of Thrones''. In 2012, bookings through LateRooms.com increased by 28 percent in Dubrovnik and 13 percent in Iceland. The following year, bookings doubled in Ouarzazate, Morocco (the location of Daenerys' season-three scenes). ''Game of Thrones'' has been attributed as a significant factor in the boom of tourism in Iceland that had a strong impact on its economy. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 35}, 'embedding': None, 'id': 'cb8520768babd78b75a24e0747df35d6'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been attributed as a significant factor in the boom of tourism in Iceland that had a strong impact on its economy. Tourist numbers increased by 30% in 2015, followed by another 40% in 2016, with a final figure of 2.4million visitors expected for 2016, which is around seven times the population of the country.  However, the increase in tourism to Dubrovnik, with \\'\\'Game of Thrones\\'\\' estimated to be responsible for half of its annual increase over many years, had led to concerns on \"over-tourism\" and its mayor to impose limits on tourist number in the city.\\n\\n==Availability==\\n===Broadcast===\\n\\'\\'Game of Thrones\\'\\' is broadcast by HBO in the United States and by its local subsidiaries or other pay television services in other countries, at the same time as in the US or weeks (or months) later. The series\\' broadcast in China on CCTV, begun in 2014, was heavily edited to remove scenes of sex and violence, in accordance with a Chinese practice of censoring Western television series to prevent what the \\'\\'People\\'s Daily\\'\\' called \"negative effects and hidden security dangers\". This resulted in viewer complaints about the incoherence of what remained. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 36}, 'embedding': None, 'id': '8acb6bbc32fb08237ab5911d22354493'}>,\n",
+       "  <Document: {'content': 'The series\\' broadcast in China on CCTV, begun in 2014, was heavily edited to remove scenes of sex and violence, in accordance with a Chinese practice of censoring Western television series to prevent what the \\'\\'People\\'s Daily\\'\\' called \"negative effects and hidden security dangers\". This resulted in viewer complaints about the incoherence of what remained. Broadcasters carrying \\'\\'Game of Thrones\\'\\' include Showcase in Australia; HBO Canada, Super Écran, and Showcase in Canada; HBO Latin America in Latin America; SoHo and Prime in New Zealand, and Sky Atlantic in the United Kingdom and Ireland.\\n\\n===Home media===\\nThe ten episodes of the first season of \\'\\'Game of Thrones\\'\\' were released as a DVD and Blu-ray box set on March 6, 2012. The box set includes extra background and behind-the-scenes material but no deleted scenes, since nearly all the footage shot for the first season was used. The box set sold over 350,000 copies in the first week after release, the largest first-week DVD sales ever for an HBO series, and the series set an HBO-series record for digital-download sales. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 37}, 'embedding': None, 'id': '3fe45b81f64f545dba47cefb080f0bf0'}>,\n",
+       "  <Document: {'content': \"The box set sold over 350,000 copies in the first week after release, the largest first-week DVD sales ever for an HBO series, and the series set an HBO-series record for digital-download sales. A collector's-edition box set was released in November 2012, combining the DVD and Blu-ray versions of the first season with the first episode of season two. A paperweight in the shape of a dragon egg is included in the set.\\n\\nDVD-Blu-ray box sets and digital downloads of the second season became available on February 19, 2013. First-day sales broke HBO records, with 241,000 box sets sold and 355,000 episodes downloaded. The third season was made available for purchase as a digital download on the Australian iTunes Store, parallel to the US premiere, and was released on DVD and Blu-ray in region 1 on February 18, 2014. The fourth season was released on DVD and Blu-ray on February 17, 2015, and the fifth season on March 15, 2016. The sixth season was released on Blu-ray and DVD on November 15, 2016. The seventh season was released on Blu-ray and DVD on December 12, 2017. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 38}, 'embedding': None, 'id': 'c654abf486276c5c3af19628c1fc8acb'}>,\n",
+       "  <Document: {'content': \"The sixth season was released on Blu-ray and DVD on November 15, 2016. The seventh season was released on Blu-ray and DVD on December 12, 2017. Beginning in 2016, HBO began issuing Steelbook Blu-ray sets which include both Dolby TrueHD 7.1 and Dolby Atmos audio options. In 2018, the first season was released in 4K HDR on Ultra HD Blu-ray.\\n\\n===Copyright infringement===\\n''Game of Thrones'' has been widely pirated, primarily outside the US. According to the file-sharing news website TorrentFreak, ''Game of Thrones'' has been the most pirated television series since 2012, which means it has held the record for six years in a row. Illegal downloads increased to about seven million in the first quarter of 2015, up 45 percent from 2014. An unnamed episode was downloaded about 4,280,000 times through public BitTorrent trackers in 2012, roughly equal to its number of broadcast viewers. Piracy rates were particularly high in Australia, and US Ambassador to Australia Jeff Bleich issued a statement condemning Australian piracy of the series in 2013.\\n\\nDelays in availability apart from HBO and its affiliates before 2015 and the cost of subscriptions to these services have been cited as causes of the series' illegal distribution. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 39}, 'embedding': None, 'id': 'c6f6ac95d648c64a5e5a0913306a16f9'}>,\n",
+       "  <Document: {'content': 'Delays in availability apart from HBO and its affiliates before 2015 and the cost of subscriptions to these services have been cited as causes of the series\\' illegal distribution. According to \\'\\'TorrentFreak\\'\\', a subscription to a service for \\'\\'Game of Thrones\\'\\' would cost up to $25 per month in the United States, up to £26 per episode in the UK and up to $52 per episode in Australia.\\n\\nFor \"combating piracy\", HBO said in 2013 that it intended to make its content more widely available within a week of the US premiere (including HBO Go). In 2015, the fifth season was simulcast to 170 countries and to HBO Now users. On April 11, the day before the season premiere, screener copies of the first four episodes of the fifth season leaked to a number of file-sharing websites. Within a day of the leak, the files were downloaded over 800,000 times; in one week the illegal downloads reached 32million, with the season-five premiere alone (\"The Wars to Come\") pirated 13million times. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 40}, 'embedding': None, 'id': 'c060f8159c2619b821944a9a76563793'}>,\n",
+       "  <Document: {'content': 'Within a day of the leak, the files were downloaded over 800,000 times; in one week the illegal downloads reached 32million, with the season-five premiere alone (\"The Wars to Come\") pirated 13million times. The season-five finale (\"Mother\\'s Mercy\") was the most simultaneously shared file in the history of the BitTorrent filesharing protocol, with over 250,000 simultaneous sharers and over 1.5million downloads in eight hours. For the sixth season, HBO did not send screeners to the press, to prevent the spread of unlicensed copies and spoilers.\\n\\nObservers, including series director David Petrarca and Time Warner CEO Jeff Bewkes, said that illegal downloads did not hurt the series\\' prospects; it benefited from \"buzz\" and social commentary, and the high piracy rate did not significantly translate to lost subscriptions. According to \\'\\'Polygon\\'\\', HBO\\'s relaxed attitude towards piracy and the sharing of login credentials amounted to a premium-television \"free-to-play\" model. At a 2015 Oxford Union debate, series co-creator David Benioff said that he was just glad that people watched the series; illegally downloaded episodes sometimes interested viewers enough to buy a copy, especially in countries where \\'\\'Game of Thrones\\'\\' was not televised. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 41}, 'embedding': None, 'id': 'd76af9472bb9160b57723dfe04f5df8a'}>,\n",
+       "  <Document: {'content': 'At a 2015 Oxford Union debate, series co-creator David Benioff said that he was just glad that people watched the series; illegally downloaded episodes sometimes interested viewers enough to buy a copy, especially in countries where \\'\\'Game of Thrones\\'\\' was not televised. Co-creator D. B. Weiss had mixed feelings, saying that the series was expensive to produce and \"if it doesn\\'t make the money back, then it ceases to exist\". However, he was pleased that so many people \"enjoy the show so much they can\\'t wait to get their hands on it.\"In 2015, \\'\\'Guinness World Records\\'\\' called \\'\\'Game of Thrones\\'\\' the most pirated television program.\\n\\n===IMAX===\\nBeginning on January 23, 2015, the last two episodes of season four were shown in 205 IMAX theaters across the United States; \\'\\'Game of Thrones\\'\\' is the first television series shown in this format. The show earned $686,000 at the box office on its opening day and $1.5million during its opening weekend; the week-long release grossed $1,896,092.\\n\\n==Reception and achievements==\\n\\'\\'Game of Thrones\\'\\' was highly anticipated by fans before its premiere, and has become a critical and commercial success. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 42}, 'embedding': None, 'id': 'f8a80ae14df4e1efa3470830ee217a5e'}>,\n",
+       "  <Document: {'content': '==Reception and achievements==\\n\\'\\'Game of Thrones\\'\\' was highly anticipated by fans before its premiere, and has become a critical and commercial success. According to \\'\\'The Guardian\\'\\', by 2014 it was \"the biggest drama\" and \"the most talked about show\" on television. A 2015 \\'\\'The Hollywood Reporter\\'\\' survey of 2,800 actors, producers, directors, and other industry people named \\'\\'Game of Thrones\\'\\' as their #4 favorite show.\\n\\n===Cultural influence===\\nAlthough \\'\\'Game of Thrones\\'\\' was dismissed by some critics, its success has been credited with an increase in the popularity of the fantasy genre. On the eve of the second season\\'s premiere, according to CNN, \"after this weekend, you may be hard pressed to find someone who isn\\'t a fan of some form of epic fantasy\" and cited Ian Bogost as saying that the series continues a trend of successful screen adaptations beginning with Peter Jackson\\'s 2001 \\'\\'The Lord of the Rings\\'\\' film trilogy and the \\'\\'Harry Potter\\'\\' films establishing fantasy as a mass-market genre; they are \"gateway drugs to fantasy fan culture\". The success of the show led to a number of fantasy series being commissioned on television, including a retelling of the \\'\\'Lord of the Rings\\'\\' by Amazon Studios. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 43}, 'embedding': None, 'id': 'b2b668d09942a8207cba78926e24e285'}>,\n",
+       "  <Document: {'content': \"The success of the show led to a number of fantasy series being commissioned on television, including a retelling of the ''Lord of the Rings'' by Amazon Studios. According to Neil Gaiman, whose works ''Good Omens'' and ''American Gods'' were adapted for TV, ''Game of Thrones'' did help change attitudes towards fantasy on television, but mainly it made big budgets for fantasy series more acceptable. The success of the genre had been attributed by writers to a longing for escapism in popular culture, frequent female nudity and a skill in balancing lighthearted and serious topics (dragons and politics, for example) which provided it with a prestige enjoyed by conventional, top-tier drama series.\\n\\nThe series' popularity increased sales of the ''A Song of Ice and Fire'' novels (republished in tie-in editions), which remained at the top of bestseller lists for months. According to ''The Daily Beast'', ''Game of Thrones'' was a favorite of sitcom writers and the series has been referred to in other TV series. With other fantasy series, it has been cited as a reason for an increase in the purchase (and abandonment) of huskies and other wolf-like dogs.\\n\\n''Game of Thrones'' has added to the popular vocabulary. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 44}, 'embedding': None, 'id': '4935ed59f770366985d14c5517ec8b1'}>,\n",
+       "  <Document: {'content': 'With other fantasy series, it has been cited as a reason for an increase in the purchase (and abandonment) of huskies and other wolf-like dogs.\\n\\n\\'\\'Game of Thrones\\'\\' has added to the popular vocabulary. A first season scene in which Petyr Baelish explains his motives (or background) while prostitutes have sex in the background gave rise to the word \"sexposition\" for providing exposition with sex and nudity. \"Dothraki\", the series\\' nomadic horsemen, was ranked fourth in a September 2012 Global Language Monitor list of words from television most used on the Internet. In 2012, the media used \"Game of Thrones\" as a figure of speech or comparison for situations of intense conflict and deceit, such as US healthcare politics, the Syrian Civil War and the ousting of Bo Xilai from the Chinese government.\\n\\nIn 2019, singer-songwriter Taylor Swift told \\'\\'Entertainment Weekly\\'\\' that several songs on her 2017 album \\'\\'Reputation\\'\\' were inspired by characters and plots of \\'\\'Game of Thrones\\'\\'.\\n\\n\"Khaleesi\" became more popular as a name for baby girls in the United States. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 45}, 'embedding': None, 'id': 'f409bf8752b365d213b20d962a2ba0b2'}>,\n",
+       "  <Document: {'content': 'In 2019, singer-songwriter Taylor Swift told \\'\\'Entertainment Weekly\\'\\' that several songs on her 2017 album \\'\\'Reputation\\'\\' were inspired by characters and plots of \\'\\'Game of Thrones\\'\\'.\\n\\n\"Khaleesi\" became more popular as a name for baby girls in the United States. In the novels and the TV series, \"khaleesi\" is not a name, but the title of the wife of a khal (warlord) in the Dothraki language, held by Daenerys Targaryen.\\n\\n\\'\\'Game of Thrones\\'\\' has also become a subject of scientific inquiry. In 2016, researchers published a paper analyzing emotional sentiment in online public discourse associated with the unfolding storyline during the fourth season. The analysis purported to be able to distinguish discussions about the storyline of an episode from media critiques or assessments of a specific actor\\'s performance. In 2018, Australian scientists conducted a survival analysis and examined the mortality among 330 important characters during the first seven seasons of \\'\\'Game of Thrones\\'\\'. In 2019, the Australian branch of the Red Cross conducted a study using international human rights laws to determine which of the \\'\\'Game of Thrones\\'\\' characters had committed the most war crimes during the first seven seasons of the show.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 46}, 'embedding': None, 'id': '39575949d0404d2dc1232b192c12c22c'}>,\n",
+       "  <Document: {'content': 'In 2019, the Australian branch of the Red Cross conducted a study using international human rights laws to determine which of the \\'\\'Game of Thrones\\'\\' characters had committed the most war crimes during the first seven seasons of the show.\\n\\n===Critical response===\\n====General====\\n\\nSeason\\n\\nCritical response\\n\\nRotten Tomatoes\\n\\nMetacritic\\n\\n1\\n\\n90% (40 reviews)\\n\\n80 (28 reviews)\\n\\n2\\n\\n96% (37 reviews)\\n\\n90 (26 reviews)\\n\\n3\\n\\n96% (45 reviews)\\n\\n91 (25 reviews)\\n\\n4\\n\\n97% (44 reviews)\\n\\n94 (29 reviews)\\n\\n5\\n\\n93% (50 reviews)\\n\\n91 (29 reviews)\\n\\n6\\n\\n94% (34 reviews)\\n\\n73 (9 reviews)\\n\\n7\\n\\n93% (51 reviews)\\n\\n77 (12 reviews)\\n\\n8\\n\\n56% (13 reviews)\\n\\n74 (12 reviews)\\n\\n\\'\\'Game of Thrones\\'\\', particularly the first seven seasons, received critical acclaim, although the series\\' frequent use of nudity and violence has been criticized. Its seasons have appeared on annual \"best of\" lists published by \\'\\'The Washington Post\\'\\' (2011), \\'\\'Time\\'\\' (2011 and 2012) and \\'\\'The Hollywood Reporter\\'\\' (2012).\\n\\nThe performances of the cast were praised. Peter Dinklage\\'s \"charming, morally ambiguous, and self-aware\" Tyrion, who earned him Emmy and Golden Globe awards, was particularly noted. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 47}, 'embedding': None, 'id': 'f3f52d575a7f3a38a578a3a98718a87f'}>,\n",
+       "  <Document: {'content': 'The performances of the cast were praised. Peter Dinklage\\'s \"charming, morally ambiguous, and self-aware\" Tyrion, who earned him Emmy and Golden Globe awards, was particularly noted. \"In many ways, \\'\\'Game of Thrones\\'\\' belongs to Dinklage\", wrote Mary McNamara of the \\'\\'Los Angeles Times\\'\\' before Tyrion became the series\\' central figure in season two. Several critics highlighted performances by actresses and children. Fourteen-year-old Maisie Williams, noted in the first season for her debut as Arya Stark, was singled out for her season-two work with veteran actor Charles Dance (Tywin Lannister). Stephen Dillane has received positive reviews for his performance as Stannis Baratheon, especially in the fifth season, with one critic noting \"Whether you like Stannis or not, you have to admit that Stephen Dillane delivered a monumental performance this season.\"The series has a rating of 89 percent on Rotten Tomatoes.\\n\\nFirst-season reviewers said the series had high production values, a fully realized world and compelling characters. According to \\'\\'Variety\\'\\', \"There may be no show more profitable to its network than \\'Game of Thrones\\' is to HBO. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 48}, 'embedding': None, 'id': 'f8d178557cfe98681a7ebc058f4e31b7'}>,\n",
+       "  <Document: {'content': 'According to \\'\\'Variety\\'\\', \"There may be no show more profitable to its network than \\'Game of Thrones\\' is to HBO. Fully produced by the pay cabler and already a global phenomenon after only one season, the fantasy skein was a gamble that has paid off handsomely\". The second season was also well received. \\'\\'Entertainment Weekly\\'\\' praised its \"vivid, vital, and just plain fun\" storytelling and, according to \\'\\'The Hollywood Reporter\\'\\', the series made a \"strong case for being one of TV\\'s best series\"; its seriousness made it the only drama comparable to \\'\\'Mad Men\\'\\' or \\'\\'Breaking Bad\\'\\'. \\'\\'The New York Times\\'\\' gave the series a mixed review, criticizing its number of characters, their lack of complexity and a meandering plot.\\n\\nThe third season was extremely well received by critics, with Metacritic giving it a score of 91 out of 100 (indicating \"universal acclaim\"). The fourth season was similarly praised; Metacritic gave it a score of 94 out of 100 based on 29 reviews, again indicating \"universal acclaim\". The fifth season was also well received by critics and has a score of 91 out of 100 (based on 29 reviews) on Metacritic. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 49}, 'embedding': None, 'id': '6261cb48cfbc770d966850d1539d27f5'}>,\n",
+       "  <Document: {'content': 'The fifth season was also well received by critics and has a score of 91 out of 100 (based on 29 reviews) on Metacritic. The sixth season was praised by critics, though not as highly as its predecessors. It has a score of 73 on Metacritic (based on nine reviews), indicating \"generally favorable reviews\". The seventh season scored 77 out of 100 (based on twelve reviews) and was praised for its action sequences and focused central characters, but received criticism for its breakneck pace and plot developments that \"defied logic.\"Darren Franich of \\'\\'Entertainment Weekly\\'\\' gave the series a \\'B\\' rating, stating that it was ultimately \"okay\", with both \"transcendent moments\" and \"miserable phases\", it was \"beloved enough to be criticized by everyone for something\". Franich described seasons 3 and 4 as \"relentless\", seasons 6\\'s ending having a \"killer one-two punch\", while seasons 7 and 8 were \"indifferent\".\\n\\n====Sex and violence====\\nDespite its otherwise enthusiastic reception by critics, some have criticized the show for the amount of female nudity, violence, and sexual violence it depicts, and for the manner in which it depicts these themes. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 50}, 'embedding': None, 'id': 'e6db372e90d294df3020e5a3fa59dffa'}>,\n",
+       "  <Document: {'content': '====Sex and violence====\\nDespite its otherwise enthusiastic reception by critics, some have criticized the show for the amount of female nudity, violence, and sexual violence it depicts, and for the manner in which it depicts these themes. \\'\\'The Atlantic\\'\\' called the series\\' \"tendency to ramp up the sex, violence, and—especially—sexual violence\" of the source material \"the defining weakness\" of the adaptation. George R. R. Martin responded that he feels obliged to be truthful about history and human nature, and that rape and sexual violence are common in war; and that omitting them from the narrative would have rung false and undermined one of his novels\\' themes, its historical realism. HBO said that they \"fully support the vision and artistry of Dan and David\\'s exceptional work and we feel this work speaks for itself.\"The amount of sex and nudity in the series, especially in scenes that are incidental to the plot, was the focus of much of the criticism aimed at the series in its first and second seasons. Stephen Dillane, who portrays Stannis Baratheon, likened the series\\' frequent explicit scenes to \"German porn from the 1970s\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 51}, 'embedding': None, 'id': '6abc1f0e4c577fe646d94b6b78127ac1'}>,\n",
+       "  <Document: {'content': 'The amount of sex and nudity in the series, especially in scenes that are incidental to the plot, was the focus of much of the criticism aimed at the series in its first and second seasons. Stephen Dillane, who portrays Stannis Baratheon, likened the series\\' frequent explicit scenes to \"German porn from the 1970s\". Charlie Jane Anders wrote in io9 that while the first season was replete with light-hearted \"sexposition\", the second season appeared to focus on distasteful, exploitative, and dehumanizing sex with little informational content.\\n\\nAccording to \\'\\'The Washington Post\\'\\'s Anna Holmes, the nude scenes appeared to be aimed mainly at titillating heterosexual men, right down to the Brazilian waxes sported by the women in the series\\' faux-medieval setting, which made these scenes alienating to other viewers. \\'\\'The Huffington Post\\'\\'s Maureen Ryan likewise noted that \\'\\'Game of Thrones\\'\\' mostly presented women naked, rather than men, and that the excess of \"random boobage\" undercut any aspirations the series might have to address the oppression of women in a feudal society. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 52}, 'embedding': None, 'id': '4a2db830b5b7906faf90ee22d765da25'}>,\n",
+       "  <Document: {'content': '\\'\\'The Huffington Post\\'\\'s Maureen Ryan likewise noted that \\'\\'Game of Thrones\\'\\' mostly presented women naked, rather than men, and that the excess of \"random boobage\" undercut any aspirations the series might have to address the oppression of women in a feudal society. \\'\\'Saturday Night Live\\'\\' parodied this aspect of the adaptation in a sketch that portrayed the series as retaining a 13-year-old boy as a consultant whose main concern was showing as many breasts as possible.\\n\\nIn the third season, which saw Theon Greyjoy lengthily tortured and eventually emasculated, the series was also criticized for its use of torture. \\'\\'New York\\'\\' magazine called the scene \"torture porn.\"Madeleine Davies of \\'\\'Jezebel\\'\\' agreed, saying, \"it\\'s not uncommon that \\'\\'Game of Thrones\\'\\' gets accused of being torture porn—senseless, objectifying violence combined with senseless, objectifying sexual imagery.\"According to Davies, although the series\\' violence tended to serve a narrative purpose, Theon\\'s torture in \"The Bear and the Maiden Fair\" was excessive.\\n\\nA scene in the fourth season\\'s episode \"Breaker of Chains\", in which Jaime Lannister rapes his sister and lover Cersei, triggered a broad public discussion about the series\\' depiction of sexual violence against women. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 53}, 'embedding': None, 'id': '737b24f5478a7e2e4d6a83cda8a0088a'}>,\n",
+       "  <Document: {'content': 'A scene in the fourth season\\'s episode \"Breaker of Chains\", in which Jaime Lannister rapes his sister and lover Cersei, triggered a broad public discussion about the series\\' depiction of sexual violence against women. According to Dave Itzkoff of \\'\\'The New York Times\\'\\', the scene caused outrage, in part because of comments by director Alex Graves that the scene became \"consensual by the end\". Itzkoff also wrote that critics fear that \"rape has become so pervasive in the drama that it is almost background noise: a routine and unshocking occurrence\". Sonia Saraiya of \\'\\'The A.V. Club\\'\\' wrote that the series\\' choice to portray this sexual act, and a similar one between Daenerys Targaryen and Khal Drogo in the first season—both described as consensual in the source novels—as a rape appeared to be an act of \"exploitation for shock value\".\\n\\nIn the fifth season\\'s episode \"Unbowed, Unbent, Unbroken\", Sansa Stark is raped by Ramsay Bolton. Most reviewers, including those from \\'\\'Vanity Fair\\'\\', \\'\\'Salon\\'\\', \\'\\'The Atlantic\\'\\', and \\'\\'The Daily Beast\\'\\', found the scene gratuitous and artistically unnecessary. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 54}, 'embedding': None, 'id': '202ca00aadfbc0f5d7869f75bbd84168'}>,\n",
+       "  <Document: {'content': 'Most reviewers, including those from \\'\\'Vanity Fair\\'\\', \\'\\'Salon\\'\\', \\'\\'The Atlantic\\'\\', and \\'\\'The Daily Beast\\'\\', found the scene gratuitous and artistically unnecessary. For example, Joanna Robinson, writing for \\'\\'Vanity Fair\\'\\', said that the scene \"undercuts all the agency that\\'s been growing in Sansa since the end of last season.\"In contrast, Sara Stewart of the \\'\\'New York Post\\'\\' wondered why viewers were not similarly upset about the many background and minor characters who\\'d undergone similar or worse treatment. In response to the scene, pop culture website \\'\\'The Mary Sue\\'\\' announced that it would cease coverage of the series because of the repeated use of rape as a plot device, and US Senator Claire McCaskill said that she would no longer watch it.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 55}, 'embedding': None, 'id': '9ba68eee11519345359d3338eacdbf32'}>,\n",
+       "  <Document: {'content': 'In response to the scene, pop culture website \\'\\'The Mary Sue\\'\\' announced that it would cease coverage of the series because of the repeated use of rape as a plot device, and US Senator Claire McCaskill said that she would no longer watch it.\\n\\nAs the sixth and seventh seasons saw Daenerys, Sansa, and Cersei assume ruling positions, Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' noted that the series could be seen as a \"long-arc revenge fantasy about what happens when women who have been brutalized and raped gain power\"—namely, that their past leaves them too broken to do anything but commit brutal acts in their own turn, and that their personal liberation does not effect the social change needed to protect others from suffering. \\'\\'Time\\'\\' reported before the seventh season that \"Even if Benioff and Weiss don\\'t always admit it, the show has changed. Scenes in which exposition is delivered in one brothel or another, for example, have been pared back\".\\n\\n====Lighting issues====\\nThe lighting, or lack of light, in darker scenes has been a recurring point of criticism since season 6 of the series. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 56}, 'embedding': None, 'id': '9fec7800720ec4de807168e2a6d0c400'}>,\n",
+       "  <Document: {'content': '====Lighting issues====\\nThe lighting, or lack of light, in darker scenes has been a recurring point of criticism since season 6 of the series. In 2016, \\'\\'Bustle\\'\\'s Caitlyn Callegari listed 31 examples of scenes where the lighting caused viewers problems ranging from not being able to tell a character\\'s hair color to not being able to see what was going on. Some reviewers have noted this is part of a wider trend among shows that are made by people who have experience working primarily on films, suggesting they \"haven\\'t grasped the nuances (or lack thereof)\" of television as a medium, especially the differences between watching a scene on a television screen versus watching it on the big screen in a movie theater.\\n\\nIn a 2017 interview, Robert McLachlan, a cinematographer working on the show, explained the lack of lighting as an artistic choice: \"we\\'re trying to be as naturalistic as possible\". The criticism reached a high point during \"The Long Night\", the third episode of season 8. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 57}, 'embedding': None, 'id': 'b1bbdea9b789a5344354f2974d1c9c8f'}>,\n",
+       "  <Document: {'content': 'In a 2017 interview, Robert McLachlan, a cinematographer working on the show, explained the lack of lighting as an artistic choice: \"we\\'re trying to be as naturalistic as possible\". The criticism reached a high point during \"The Long Night\", the third episode of season 8. Barely minutes into the episode, viewers took to social media sites such as Twitter to express their discontent about the fact that they were having severe difficulties watching the battle and trying to figure out what was going on.\\n\\n===Fandom===\\n\\nIn this manipulated image published by the White House in 2014, US president Barack Obama (a fan of the series) sits on the Iron Throne in the Oval Office with the king\\'s crown on his lap.\\n\\nThe popularity of the show in the country, coupled with its filming locations in Northern Ireland, led to the creation of a show-specific exhibit in the National Wax Museum Plus in Dublin.\\n\\n\\'\\'A Song of Ice and Fire\\'\\' and \\'\\'Game of Thrones\\'\\' have a broad, active international fan base. In 2012 \\'\\'Vulture.com\\'\\' ranked the series\\' fans as the most devoted in popular culture, more so than Lady Gaga\\'s, Justin Bieber\\'s, \\'\\'Harry Potter\\'\\'s or \\'\\'Star Wars\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 58}, 'embedding': None, 'id': '19f0b294819a866763463fd7f870ddc0'}>,\n",
+       "  <Document: {'content': 'In 2012 \\'\\'Vulture.com\\'\\' ranked the series\\' fans as the most devoted in popular culture, more so than Lady Gaga\\'s, Justin Bieber\\'s, \\'\\'Harry Potter\\'\\'s or \\'\\'Star Wars\\'\\'. Fans include political leaders such as former US president Barack Obama, former British prime minister David Cameron, former Australian prime minister Julia Gillard and Dutch foreign minister Frans Timmermans, who framed European politics in quotes from Martin\\'s novels in a 2013 speech.\\n\\nBBC News said in 2013 that \"the passion and the extreme devotion of fans\" had created a phenomenon unlike anything related to other popular TV series, manifesting itself in fan fiction, \\'\\'Game of Thrones\\'\\'-themed burlesque routines and parents naming their children after series characters; writers quoted attributed this success to the rich detail, moral ambiguity, sexual explicitness and epic scale of the series and novels. The previous year, \"Arya\" was the fastest-rising girl\\'s name in the US after it jumped in popularity from 711th to 413th place.\\n\\n, about 58 percent of series viewers were male and 42 percent female, and the average male viewer was 41 years old. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 59}, 'embedding': None, 'id': '59bc20096f0f99987c5c3d638a316b50'}>,\n",
+       "  <Document: {'content': ', about 58 percent of series viewers were male and 42 percent female, and the average male viewer was 41 years old. According to SBS Broadcasting Group marketing director Helen Kellie, \\'\\'Game of Thrones\\'\\' has a high fan-engagement rate; 5.5 percent of the series\\' 2.9million Facebook fans talked online about the series in 2012, compared to 1.8percent of the more than ten million fans of \\'\\'True Blood\\'\\' (HBO\\'s other fantasy series). Vulture.com cited Westeros.org and WinterIsComing.net (news and discussion forums), ToweroftheHand.com (which organizes communal readings of the novels) and Podcastoficeandfire.com as fan sites dedicated to the TV and novel series; and podcasts cover \\'\\'Game of Thrones\\'\\'.\\n\\n===Awards and accolades===\\n\\n\\'\\'Game of Thrones\\'\\' has won numerous of awards since it debuted as a series, including 58 Primetime Emmy Awards, 5 Screen Actors Guild Award, and a Peabody Award; it holds the Emmy-award records for both most wins for a scripted television series, surpassing the record of 37 wins held by \\'\\'Frasier\\'\\' since 2004, and for most nominations for a drama with 161. In 2013 the Writers Guild of America listed \\'\\'Game of Thrones\\'\\' as the 40th \"best written\" series in television history. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 60}, 'embedding': None, 'id': '5bd32b253fc174de0daa4e3c205fdaa'}>,\n",
+       "  <Document: {'content': 'In 2013 the Writers Guild of America listed \\'\\'Game of Thrones\\'\\' as the 40th \"best written\" series in television history. In 2015 \\'\\'The Hollywood Reporter\\'\\' placed it at number four on their \"best TV shows ever\" list, while in 2016 the series was placed seventh on \\'\\'Empire\\'\\' \"The 50 best TV shows ever\". The same year \\'\\'Rolling Stone\\'\\' named it the twelfth \"greatest TV Show of all time\".\\n\\nThe 2011 first season received 13 Emmy nominations (including Outstanding Drama Series), and won for Outstanding Supporting Actor in a Drama Series (given to Peter Dinklage for his portrayal of Tyrion Lannister) and Outstanding Main Title Design. Other nominations included Outstanding Directing (\"Winter Is Coming\") and Outstanding Writing (\"Baelor\"). Dinklage was also named Best Supporting Actor at the Golden Globe, Satellite and Scream Awards.\\n\\nIn 2012, the second season received six Emmy Awards from 11 nominations, including Outstanding Drama Series and Outstanding Supporting Actor in a Drama Series (Dinklage).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 61}, 'embedding': None, 'id': 'c9c7528828d83d20f09a7b0dcbaa5b91'}>,\n",
+       "  <Document: {'content': 'In 2012, the second season received six Emmy Awards from 11 nominations, including Outstanding Drama Series and Outstanding Supporting Actor in a Drama Series (Dinklage).\\n\\nThe 2013 third season received 16 Emmy nominations, including Outstanding Drama Series, Outstanding Supporting Actor in a Drama Series (Dinklage), Outstanding Supporting Actress in a Drama Series (Emilia Clarke), Outstanding Guest Actress in a Drama Series (Diana Rigg) and Outstanding Writing (\"The Rains of Castamere\"), winning two Creative Arts Emmys.\\n\\nIn 2014, the fourth season received four Emmys from 19 nominations, which included Outstanding Drama Series, Outstanding Supporting Actor in a Drama Series (Dinklage), Outstanding Supporting Actress in a Drama Series (Lena Headey), Outstanding Guest Actress in a Drama Series (Rigg), Outstanding Directing (\"The Watchers on the Wall\") and Outstanding Writing (\"The Children\").\\n\\nThe 2015 fifth season won the most Primetime Emmy Awards for a series in a year (12 awards from 24 nominations), including Outstanding Drama Series; other wins included Outstanding Supporting Actor in a Drama Series (Dinklage), Outstanding Directing (\"Mother\\'s Mercy\") and Outstanding Writing (\"Mother\\'s Mercy\"), and eight were Creative Arts Emmy Awards.\\n\\nIn 2016, the sixth season received the most nominations for the 68th Primetime Emmy Awards (23). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 62}, 'embedding': None, 'id': 'b1630ac6dd6411e653bf6bd82419c69b'}>,\n",
+       "  <Document: {'content': 'The 2015 fifth season won the most Primetime Emmy Awards for a series in a year (12 awards from 24 nominations), including Outstanding Drama Series; other wins included Outstanding Supporting Actor in a Drama Series (Dinklage), Outstanding Directing (\"Mother\\'s Mercy\") and Outstanding Writing (\"Mother\\'s Mercy\"), and eight were Creative Arts Emmy Awards.\\n\\nIn 2016, the sixth season received the most nominations for the 68th Primetime Emmy Awards (23). It won for Outstanding Drama Series, Outstanding Directing (\"Battle of the Bastards\"), Outstanding Writing (\"Battle of the Bastards\"), and nine Creative Arts Emmys. Nominations included Outstanding Supporting Actor in a Drama Series (Dinklage and Kit Harington), Outstanding Supporting Actress in a Drama Series (Clarke, Headey and Maisie Williams), Outstanding Guest Actor in a Drama Series (Max von Sydow) and Outstanding Directing (\"The Door\").\\n\\nIn 2018, the seventh season received the most nominations at the 70th Primetime Emmy Awards (22). It won for Outstanding Drama Series, Outstanding Supporting Actor in a Drama Series (Peter Dinklage), and seven Creative Arts Emmys. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 63}, 'embedding': None, 'id': 'c809980f9d8ca909e5808fbc61595ce7'}>,\n",
+       "  <Document: {'content': 'It won for Outstanding Drama Series, Outstanding Supporting Actor in a Drama Series (Peter Dinklage), and seven Creative Arts Emmys. Nominations included Outstanding Supporting Actress in a Drama Series (Lena Headey), Outstanding Supporting Actor in a Drama Series (Nikolaj Coster-Waldau), Outstanding Guest Actress in a Drama Series (Diana Rigg), Outstanding Writing for a Drama Series (David Benioff and D. B. Weiss for \"The Dragon and the Wolf\"), and Outstanding Directing for a Drama Series (Jeremy Podeswa for \"The Dragon and the Wolf\" and Alan Taylor for \"Beyond the Wall\").\\n\\nIn 2019, the final season established a new record for most Emmy nominations received in the same year by any regular series with 32, breaking the 25 years long record previously held by \\'\\'NYPD Blue\\'\\', which had scored 26 nominations for its first season in 1994. Those nominations included Outstanding Drama Series, three nominations for Outstanding Directing for a Drama Series, Outstanding Writing for a Drama Series and ten nominations for acting. Emilia Clarke and Kit Harington in the lead acting categories: Alfie Allen, Peter Dinklage, Nikolaj Coster-Waldau, Gwendoline Christie, Lena Headey, Sophie Turner, and Maisie Williams in the supporting acting categories; and Carice van Houten in the guest acting category. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 64}, 'embedding': None, 'id': 'fd73ab59dc89992efcd6e74795c30490'}>,\n",
+       "  <Document: {'content': \"Emilia Clarke and Kit Harington in the lead acting categories: Alfie Allen, Peter Dinklage, Nikolaj Coster-Waldau, Gwendoline Christie, Lena Headey, Sophie Turner, and Maisie Williams in the supporting acting categories; and Carice van Houten in the guest acting category. The series eventually won twelve awards, becoming the first series to win Outstanding Drama Series for an eighth season and tying its own record (previously achieved for season five) for most Emmys won by a series in a single season. It also tied the record for most Outstanding Drama Series wins, becoming the fifth series to reach four wins after ''Hill Street Blues'', ''L.A. Law'', ''The West Wing'', and  ''Mad Men''; unlike all the other record holders, which had won for their first four seasons, ''Game of Thrones'' achieved the record with its final four seasons. Peter Dinklage broke the record for most wins in the Outstanding Supporting Actor in a Drama Series category with his fourth win for the series; his nomination had also extended his record for most nominations in the category to eight (one for every season of the show).\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 65}, 'embedding': None, 'id': '6938ac26792161e2e0e835431b33cb70'}>,\n",
+       "  <Document: {'content': 'Peter Dinklage broke the record for most wins in the Outstanding Supporting Actor in a Drama Series category with his fourth win for the series; his nomination had also extended his record for most nominations in the category to eight (one for every season of the show).\\n\\n===Viewer numbers===\\nThe first season averaged 2.5million viewers for its first Sunday-night screenings and a gross audience (including repeats and on-demand viewings) of 9.3million viewers per episode. For its second season, the series had an average gross audience of 11.6million viewers. The third season was seen by 14.2million viewers, making \\'\\'Game of Thrones\\'\\' the second-most-viewed HBO series (after \\'\\'The Sopranos\\'\\'). For the fourth season, HBO said that its average gross audience of 18.4million viewers (later adjusted to 18.6million) had passed \\'\\'The Sopranos\\'\\' for the record.\\n\\nBy the sixth season the average per-episode gross viewing figure had increased to over 25million, with nearly 40 percent of viewers watching on HBO digital platforms. In 2016, a \\'\\'New York Times\\'\\' study of the 50 TV shows with the most Facebook likes found that \\'\\'Game of Thrones\\'\\' was \"much more popular in cities than in the countryside, probably the only show involving zombies that is\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 66}, 'embedding': None, 'id': '9a7fa95b1d6121aca12dcacfe5c9983a'}>,\n",
+       "  <Document: {'content': 'In 2016, a \\'\\'New York Times\\'\\' study of the 50 TV shows with the most Facebook likes found that \\'\\'Game of Thrones\\'\\' was \"much more popular in cities than in the countryside, probably the only show involving zombies that is\". By season seven, average viewer numbers had increased to 32.8million per episode across all platforms.\\n\\nThe series set records on pay-television channels in the United Kingdom (with a 2016 average audience of more than five million on all platforms) and Australia (with a cumulative average audience of 1.2million).\\n\\n==Other media and products==\\n\\n===Video games===\\n\\nThe series has inspired several video games based on the TV series and novels. The strategy game \\'\\'Game of Thrones Ascent\\'\\' ties into the HBO series, making characters and settings available to players as they appear on television. Behaviour Interactive is developing a free-to-play strategy game based on the series for mobile devices. \\'\\'Reigns: Game of Thrones\\'\\', a spin-off of the \\'\\'Reigns\\'\\' strategy video game series, is in development by Nerial, published by Devolver Digital, and set to release in October 2018.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 67}, 'embedding': None, 'id': 'cf99680dad336ae9d1582a6c59f879a7'}>,\n",
+       "  <Document: {'content': \"''Reigns: Game of Thrones'', a spin-off of the ''Reigns'' strategy video game series, is in development by Nerial, published by Devolver Digital, and set to release in October 2018.\\n\\n===Merchandise and exhibition===\\n''Game of Thrones'' merchandise in HBO's New York City store\\n\\nHBO has licensed a variety of merchandise based on ''Game of Thrones'', including games, replica weapons and armor, jewelry, bobblehead dolls by Funko, beer by Ommegang and apparel. High-end merchandise includes a $10,500 Ulysse Nardin wristwatch and a $30,000 resin replica of the Iron Throne. In 2013 and 2014, a traveling exhibition of costumes, props, armor and weapons from the series visited major cities in Europe and the Americas.\\n\\n===Accompanying material===\\n''Thronecast: The Official Guide to Game of Thrones'', a series of podcasts presented by Geoff Lloyd and produced by Koink, has been released on the Sky Atlantic website and the UK iTunes store during the series' run; a new podcast, with analysis and cast interviews, is released after each episode. In 2014 and 2015 HBO commissioned ''Catch the Throne'', two rap albums about the series.\\n\\nA companion book, ''Inside HBO's Game of Thrones'' () by series writer Bryan Cogman, was published on September 27, 2012. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 68}, 'embedding': None, 'id': '1cd91c08aa173ed8e645bb8fa9d1d323'}>,\n",
+       "  <Document: {'content': \"A companion book, ''Inside HBO's Game of Thrones'' () by series writer Bryan Cogman, was published on September 27, 2012. The 192-page book, illustrated with concept art and behind-the-scenes photographs, covers the creation of the series' first two seasons and its principal characters and families.\\n\\n''After the Thrones'' is a live aftershow in which hosts Andy Greenwald and Chris Ryan discuss episodes of the series. It airs on HBO Now the Monday after each sixth-season episode. The Game of Thrones Live Concert Experience, a North American 28-city orchestral tour which performed the series' soundtrack with composer Ramin Djawadi, began February 2017 and concluded in April 2017. A second tour occurred in 2018 across cities in Europe and North America.\\n\\nEach season's Blu-ray and DVD set contains several short animated sequences narrated by the cast as their characters as they detail events in the history of Westeros. For the seventh season, this is to include the animated prequel series ''Game of Thrones: Conquest & Rebellion'', illustrated in a different animation style than previous videos. The series focuses on Aegon Targaryen's conquest of the Seven Kingdoms of Westeros.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 69}, 'embedding': None, 'id': '428db9ea328c532228326c0439b815ea'}>,\n",
+       "  <Document: {'content': 'For the seventh season, this is to include the animated prequel series \\'\\'Game of Thrones: Conquest & Rebellion\\'\\', illustrated in a different animation style than previous videos. The series focuses on Aegon Targaryen\\'s conquest of the Seven Kingdoms of Westeros.\\n\\n===Successor series===\\nIn May 2017, after years of speculation about possible successor series, HBO commissioned Max Borenstein, Jane Goldman, Brian Helgeland, Carly Wray, and Bryan Cogman to develop individual \\'\\'Game of Thrones\\'\\' successor series; all of the writers were to be working individually with George R. R. Martin, who also co-wrote two of the scripts. D. B. Weiss and David Benioff said that they would not be involved with any of the projects.\\n\\nMartin said that all the concepts under discussion were prequels, although he believes the term \"successor show\" applies better to these projects, as they are not \\'\\'Game of Thrones\\'\\' spin-offs in the traditional sense. He ruled out Robert\\'s Rebellion (the overthrow of Daenerys\\' father by Robert Baratheon) as a possible idea and revealed that some may be set outside Westeros. Later, he stated: \"at least two of them are solidly based on material in \\'\\'Fire and Blood\\'\\'.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 70}, 'embedding': None, 'id': '22833aa49d7348675e5460bb4833f917'}>,\n",
+       "  <Document: {'content': 'He ruled out Robert\\'s Rebellion (the overthrow of Daenerys\\' father by Robert Baratheon) as a possible idea and revealed that some may be set outside Westeros. Later, he stated: \"at least two of them are solidly based on material in \\'\\'Fire and Blood\\'\\'.\"In September 2018, regarding the other four projects, HBO president of programming Casey Bloys said that some of them have been abandoned completely, while others remain as possibilities for the future. In May 2019, Martin stated that two other projects were still in the script stage, but are \"edging closer\". In April 2019, Cogman confirmed his prequel would not be moving forward.\\n\\n==== \\'\\'Bloodmoon\\'\\' ====\\nOn June 8, 2018, HBO commissioned a pilot to a \\'\\'Game of Thrones\\'\\' prequel series from Goldman as showrunner and Martin as co-creator. The prequel was to take place in the Age of Heroes, a period that begins roughly 10,000 years before the events of \\'\\'Game of Thrones\\'\\'. Notable events of that period include the foundation of powerful Houses, the Long Night when the White Walkers first descended upon Westeros, and the Andal Invasion when the Andals invaded from Essos and conquered most of Westeros. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 71}, 'embedding': None, 'id': '3e116ce5d8ac9d0b591a79822b76144b'}>,\n",
+       "  <Document: {'content': 'Notable events of that period include the foundation of powerful Houses, the Long Night when the White Walkers first descended upon Westeros, and the Andal Invasion when the Andals invaded from Essos and conquered most of Westeros. Martin suggested \\'\\'The Long Night\\'\\' as a title for the series. On May 13, 2019, it was reported that the show had already begun filming in Belfast, under the working title \\'\\'Bloodmoon\\'\\'. S. J. Clarkson was announced to direct and executive produce the pilot, while Naomi Watts was cast as the female lead playing \"a charismatic socialite hiding a dark secret.\"Other series regulars were to include: Josh Whitehouse, Toby Regbo, Ivanno Jeremiah, Georgie Henley, Naomi Ackie, Denise Gough, Jamie Campbell Bower, Sheila Atim, Alex Sharp, Miranda Richardson, Marquis Rodriguez, John Simm, Richard McCabe, John Heffernan, and Dixie Egerickx. In September 2019, Martin claimed the pilot was in post-production but in October 2019, it was announced that HBO had decided to not move forward with Goldman\\'s prequel series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 72}, 'embedding': None, 'id': 'ab2113356c789cb362acbbe755c4a323'}>,\n",
+       "  <Document: {'content': 'In September 2019, Martin claimed the pilot was in post-production but in October 2019, it was announced that HBO had decided to not move forward with Goldman\\'s prequel series.\\n\\n==== \\'\\'House of the Dragon\\'\\' ====\\nIn September 2019, Nellie Andreeva of \\'\\'Deadline Hollywood\\'\\' reported that a second prequel from Martin and Ryan Condal that \"tracks the beginning of the end for House Targaryen\" was close to receiving a pilot order from HBO; the project is not considered an original sixth script, as it builds upon Cogman\\'s idea from 2017. This prequel, titled \\'\\'House of the Dragon\\'\\', was picked up straight to series on October 29, 2019. The 10-episode series is to be based on material from \\'\\'Fire and Blood\\'\\', executive produced by Martin, Vince Gerardis, Condal, and Miguel Sapochnik; the latter two are to be its showrunners as well.\\n\\n==References==\\n\\n==External links==\\n*\\n*\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 73}, 'embedding': None, 'id': 'abe94170f4131433e9ef7e719c46697d'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''Jorah Mormont''' is a fictional character in the ''A Song of Ice and Fire'' series of fantasy novels by American author George R. R. Martin and its television adaptation ''Game of Thrones''.\\n\\nIntroduced in ''A Game of Thrones'' (1996), Jorah is a knight in exile, the disgraced former lord of Bear Island and the only son of Jeor Mormont, the honorable lord commander of the Night's Watch. Jorah subsequently appeared in Martin's ''A Clash of Kings'' (1998), ''A Storm of Swords'' (2000) and ''A Dance with Dragons'' (2011). After fleeing Westeros, Jorah pledges fealty to Daenerys Targaryen and over the course of both the novels and the television show becomes her closest and most loyal companion; Jorah's passionate yet unrequited love of Daenerys is central to the character's arc in both the novels and television show. He is portrayed as a skilled warrior whose knowledge of the peoples and customs of Essos proves invaluable to Daenerys' journeys.\\n\\nJorah is portrayed by the Scottish actor Iain Glen in the HBO television adaptation.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '89eee1cbee4aceaae32cba0f2b44a06f'}>,\n",
+       "  <Document: {'content': \"He is portrayed as a skilled warrior whose knowledge of the peoples and customs of Essos proves invaluable to Daenerys' journeys.\\n\\nJorah is portrayed by the Scottish actor Iain Glen in the HBO television adaptation.\\n\\n==Character==\\n===Background===\\nSer Jorah Mormont is the only child of the Night's Watch's Lord Commander Jeor Mormont, who abdicated shortly before Robert's Rebellion to join the Night's Watch and let Jorah assume the lordship of Bear Island. At some point Jorah married a lady of House Glover, who died from miscarriage after ten years of marriage. Jorah fought in Greyjoy's Rebellion, distinguishing himself by being one of the first to enter the fray during the siege of Pyke and was knighted by King Robert Baratheon. At a tourney at Lannisport celebrating the Baratheon victory, Jorah fell in love with the beautiful Lynesse Hightower (aunt of Margaery Tyrell). He named her Queen of Love and Beauty after winning the tourney and asked her father for her hand in marriage, which he accepted. However, Lynesse found herself ill-suited to the rough life on Bear Island, having grown up as a member of the wealthy House Hightower. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'd9c513ac3ea589e189f0035b8a7c3d44'}>,\n",
+       "  <Document: {'content': 'However, Lynesse found herself ill-suited to the rough life on Bear Island, having grown up as a member of the wealthy House Hightower. Jorah bankrupted himself trying to provide her with luxuries and eventually sold poachers found on his lands to a Tyroshi slaver to fund her lifestyle. His liege lord, Eddard Stark, condemned Jorah to death, but he and Lynesse fled to Lys. Jorah then made his living as a sellsword, but was still unable to provide enough to satisfy Lynesse, and eventually returned from a campaign to find that Lynesse had become a concubine of a wealthy Lysene merchant. The merchant warned Jorah that he would be enslaved to settle his debts if he remained in Lys and Jorah was forced to flee again. Afterwards he drifted among the Free Cities and the Dothraki, before becoming a spy for Varys.\\n\\nJorah Mormont is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of Daenerys Targaryen and Tyrion Lannister.  Jorah mostly serves to provide Daenerys with background information such as Westerosi history and the various cultures of Essos.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'f920976ec731533d578e5bb7a28ee9d5'}>,\n",
+       "  <Document: {'content': \"Jorah Mormont is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of Daenerys Targaryen and Tyrion Lannister.  Jorah mostly serves to provide Daenerys with background information such as Westerosi history and the various cultures of Essos.\\n\\n==Storylines==\\nCoat of arms of House Mormont\\n\\n===''A Game of Thrones''===\\n\\nIn Pentos, Jorah enters the service of House Targaryen during Daenerys Targaryen's wedding to Khal Drogo, although he is actually spying on behalf of Robert's Master of Whisperers Varys in hopes of earning a royal pardon. Although Jorah holds Daenerys' brother Viserys in contempt, he comes to admire Daenerys for her bravery and strength of character, and eventually falls in love with her. In Vaes Dothrak, Jorah is warned by Illyrio Mopatis that Robert has ordered Daenerys' assassination and saves her from being poisoned. After Drogo's death, Jorah is the first to pledge fealty to Daenerys and is astounded when she emerges from Drogo's funeral pyre with three dragon hatchlings.\\n\\n===''A Clash of Kings''===\\n\\nJorah accompanies Daenerys and the remnants of her khalasar to Qarth. There, Jorah stops sending reports to Varys, having truly fallen in love with Daenerys. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '19932e957aa990bd4baaf85227f9800e'}>,\n",
+       "  <Document: {'content': \"===''A Clash of Kings''===\\n\\nJorah accompanies Daenerys and the remnants of her khalasar to Qarth. There, Jorah stops sending reports to Varys, having truly fallen in love with Daenerys. Daenerys discovers his love after Jorah notes her physical similarity to Lynesse, but does not tell Jorah that she knows. At Qarth's docks, Jorah and Daenerys encounter the former pit fighter Strong Belwas and his squire Arstan Whitebeard. Arstan claims to know Jorah; Jorah finds him familiar, but does not recognise him. Despite Jorah's mistrust, Daenerys accepts the duo into her service.\\n\\n===''A Storm of Swords''===\\n\\nJorah confesses his love to Daenerys, who rejects his advances. Jorah recommends that they sail to Astapor to buy an army of Unsullied, and commands the army when they overthrow the masters of Yunkai. As they arrive at Meereen, Arstan is revealed to be Ser Barristan Selmy, former Kingsguard to Aerys II Targaryen and Robert, and he announces Jorah's former status as a spy. Daenerys orders Jorah and Selmy to infiltrate Meereen and release slaves. Upon their return, Jorah refuses to beg forgiveness, insisting that Daenerys owes him forgiveness as reward for his service. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'b6395ea820367ae193b0f332abff227c'}>,\n",
+       "  <Document: {'content': \"Daenerys orders Jorah and Selmy to infiltrate Meereen and release slaves. Upon their return, Jorah refuses to beg forgiveness, insisting that Daenerys owes him forgiveness as reward for his service. Daenerys agrees but notes that she cannot grant it without undermining her authority, and banishes Jorah.\\n\\n===''A Dance With Dragons''===\\n\\nJorah encounters the exiled Tyrion Lannister in a brothel in Selhorys and abducts him, hoping to return to Daenerys' graces by presenting a Lannister captive as a gift.  In Volantis they encounter the dwarf Penny, whom Tyrion takes pity on and is allowed by Jorah to accompany them.  During their voyage to Meereen, their ship is disabled by a storm and later seized by slavers, and Jorah is badly beaten and branded trying to fend them off.  In Meereen the trio are sold to the Yunkish master Yezzan zo Qaggaz.  Jorah becomes despondent after learning that Daenerys has taken the Meereenese nobleman Hizdhar zo Loraq as her husband.  When Yezzan dies from dysentery, Jorah and Tyrion escape to the camp of the sellsword company Second Sons, formerly in service to Daenerys before defecting to the slavers, and Jorah rejoins the company along with Tyrion.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '83cf21ee6c65ff4e60660099fd967ec3'}>,\n",
+       "  <Document: {'content': 'When Yezzan dies from dysentery, Jorah and Tyrion escape to the camp of the sellsword company Second Sons, formerly in service to Daenerys before defecting to the slavers, and Jorah rejoins the company along with Tyrion.  Jorah realises that the Yunkai\\'i will lose the coming battle with Meereen and tells Tyrion that they need to convince the company to defect again.\\n\\n===Family tree of House Mormont===\\n\\n;Notes\\n\\n==TV adaptation==\\nIain Glen plays the role of Jorah Mormont in the television series.\\nJorah Mormont is portrayed by Scottish actor Iain Glen in the television adaption of the series of books.\\n\\n====Season 1====\\nSer Jorah Mormont is an exiled knight in the service of Daenerys Targaryen and the son of Jeor Mormont of the Night\\'s Watch. To fund his wife\\'s extravagant lifestyle, he sold poachers on his land to slave traders, which is illegal in the Seven Kingdoms. Rather than face punishment by Lord Stark, he fled to Essos and learned the lifestyle of the Dothraki, who embrace him as one of their own and know him as \"Jorah the Andal\". Jorah serves as an adviser to the Targaryens on both political and cultural matters regarding the Seven Kingdoms and Essos. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'bd2c1f52ffb3969f2a16c7dcc1761e8e'}>,\n",
+       "  <Document: {'content': 'Jorah serves as an adviser to the Targaryens on both political and cultural matters regarding the Seven Kingdoms and Essos. Jorah is actually spying on the Targaryens for Lord Varys in exchange for a pardon from Robert, but after learning more about Daenerys, it appears that Jorah is falling for her; even preventing her poisoning, when Robert & Varys so order it.\\n\\n====Season 2====\\nAfter Daenerys is widowed, Jorah travels with her to Qarth and becomes the first knight of her Queensguard. He serves Daenerys as an adviser and protector throughout Season 2. When a mysterious, masked, female seuthsayer (privately) questions his fealty, he affirms that he will no longer help (Varys or) anyone betray (Daenerys). He explains to Daenerys that he respects her birthright claim to the Seven Kingdoms, as she \"has a kind heart\", the key to being a just Queen.\\n\\n====Season 3====\\nIn Season 3, Jorah plays an important part in the capture of Yunkai, together with Unsullied commander Grey Worm and Daario Naharis, Lieutenant and leader of the Second Sons. Led by Daario, the trio infiltrates the city and kills a number of Yunkish guards to open a gate, ensuring the capture of Yunkai. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'aa391edac5696472fadc8ac9dcd619c2'}>,\n",
+       "  <Document: {'content': 'Led by Daario, the trio infiltrates the city and kills a number of Yunkish guards to open a gate, ensuring the capture of Yunkai. Jorah later assists Daenerys and her army in conquering Meereen, and later informs her of Joffrey\\'s death, but at the same time dissuades her from invading King\\'s Landing, since she is not strong enough to take all of Westeros yet. When Daenerys appears to begin a romantic relationship with Daario, Jorah voices his disapproval.\\n\\n====Season 4====\\nIn Season 4, following the Conquering of Meereen, Daenerys discovers that Jorah\\'s original mission was to spy on her for the \"usurper\" Robert Baratheon, which almost led to her poisoning by the wine merchant in Vaes Dothrak. She exiles Jorah from Meereen on the threat of death. Jorah subsequently departs alone.\\n\\n====Season 5====\\nJorah encounters Tyrion Lannister in Volantis and kidnaps him with the intention of delivering him to Daenerys. Sailing through the ruins of Valyria, the duo is assaulted by \"stone men\" – humans turned insane by the disease greyscale – and Jorah is infected in the struggle. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '3e6565f93c04cc69ffda151262c71f51'}>,\n",
+       "  <Document: {'content': 'Sailing through the ruins of Valyria, the duo is assaulted by \"stone men\" – humans turned insane by the disease greyscale – and Jorah is infected in the struggle. As they make their way to Meereen on foot, Jorah learns from Tyrion that Jeor was killed in a mutiny beyond the Wall. Jorah and Tyrion are captured by slavers, who are convinced by Tyrion to sell them to the fighting pits in Meereen. At a demonstration of pit fighters, Jorah encounters Daenerys, who orders him freed but refuses to let him return to her service. With nowhere else left to go, Jorah returns to the fighting pits. At the reopening of the fighting pits, Jorah foils an assassination attempt on Daenerys and protects her from the insurgent Sons of the Harpy in the resulting confrontation. After Daenerys flies away on Drogon, Jorah and Daario Naharis leave Meereen to look for her.\\n\\n====Season 6====\\nJorah and Daario discover Daenerys\\' intentionally discarded ring in a grass plain, and Jorah deduces that she has been captured by a Dothraki horde and taken to Vaes Dothrak. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'ca55464fb081ccccceff6e5f7731faed'}>,\n",
+       "  <Document: {'content': \"====Season 6====\\nJorah and Daario discover Daenerys' intentionally discarded ring in a grass plain, and Jorah deduces that she has been captured by a Dothraki horde and taken to Vaes Dothrak. Upon arriving there, they are reunited with Daenerys, who rejects their offer to flee the city and instead asks them to help her with her own plan, to which they reluctantly agree. The next evening, Jorah and Daario watch as Daenerys, after trapping and burning all the Khals alive in the temple building, emerges from it completely unharmed, and they join the rest of the Dothraki in kneeling before her in awe. As Daenerys and her entourage prepare to leave Vaes Dothrak, Jorah reveals his greyscale to Daenerys and his intention to end his life before his illness overtakes him. Daenerys instead orders him to find a cure for his condition and return to her, declaring that she will need his counsel after conquering Westeros. Jorah then departs alone.\\n\\n====Season 7====\\nJorah returns to Westeros, seeking aid at the Citadel in Oldtown. By this time his greyscale has drastically progressed, consuming his entire arm and part of his torso. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '2868c73fa876dc4cfad221eb575a9619'}>,\n",
+       "  <Document: {'content': \"====Season 7====\\nJorah returns to Westeros, seeking aid at the Citadel in Oldtown. By this time his greyscale has drastically progressed, consuming his entire arm and part of his torso. Archmaester Ebrose diagnoses Jorah's greyscale as untreatably advanced, and informs Jorah that he has six months of sanity left and will be exiled from the Citadel the next day. Samwell Tarly, who formerly served under Jeor in the Night's Watch, rediscovers a cure for greyscale. Despite being forbidden to administer it, because whoever treats the patient could become infected, he treats Jorah in secret and successfully heals Jorah's greyscale. Ebrose, although unimpressed by Jorah's attempts to conceal Sam's treatment, discharges Jorah. Jorah then returns to Daenerys, who is happy to see him and accepts him into her service once again. He then joins the expedition led by Jon Snow beyond the Wall to capture a wight and prove its existence to Cersei Lannister. He and Jon talk about their respective fathers, and Jon expresses his relief that Jorah was not executed. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '890bad138e944514c4f1382a24c8a2f'}>,\n",
+       "  <Document: {'content': \"He then joins the expedition led by Jon Snow beyond the Wall to capture a wight and prove its existence to Cersei Lannister. He and Jon talk about their respective fathers, and Jon expresses his relief that Jorah was not executed. Jon attempts to return Longclaw to Jorah, on the basis that it is still the family sword of House Mormont, but Jorah refuses, claiming that he forfeited his right to claim the sword and that Jeor ''gave'' the weapon to Jon, making it his. Jorah survives the expedition and returns to Dragonstone.\\n\\n====Season 8====\\nJorah travels with Jon, Daenerys, and her forces to Winterfell to make a stand against the White Walkers. Daenerys is displeased with Tyrion when she learns that his sister Cersei Lannister is not sending her army, but she is mollified after Jorah speaks up in support of him. Samwell grants Jorah House Tarly's ancestral sword Heartsbane out of gratitude for Jeor's formative influence on him. During the Battle of Winterfell, Jorah is mortally wounded defending Daenerys from certain death at the hands of the undead, though he does survive long enough to see the army felled.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '74f98f30f781d66af5ecd8e544a49cb5'}>,\n",
+       "  <Document: {'content': 'During the Battle of Winterfell, Jorah is mortally wounded defending Daenerys from certain death at the hands of the undead, though he does survive long enough to see the army felled.  Jorah dies in the arms of a sobbing Daenerys, encircled by the wings of her dragon, Drogon.\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'a6c0538d5e0a05112372ef47c0810939'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''David Benioff''' (; né '''Friedman''' ; September 25, 1970) is an American screenwriter and television producer, writer, and director. Along with his collaborator D. B. Weiss, he is best known as co-creator, showrunner, and writer of ''Game of Thrones'' (2011–2019), the HBO adaptation of George R. R. Martin's series of books ''A Song of Ice and Fire''. He is also known for writing ''Troy'' (2004) and co-writing ''X-Men Origins: Wolverine'' (2009).\\n\\n==Early life==\\nBenioff was born David Friedman in New York City, to a Jewish family who emigrated from Austria, Romania, Germany, Poland and Russia. He is the son of Barbara (Benioff) and Stephen Friedman, who is a former head of Goldman Sachs. He is a distant cousin of Salesforce founder Marc Benioff. As an adult, he uses the last name Benioff, his mother's maiden name, to avoid confusion with other writers named David Friedman. He is the youngest of three children (Suzy, Caroline, and David) and grew up in Manhattan, first in Peter Cooper Village, then on 86th Street where he spent most of his childhood, before eventually moving near the U.N. headquarters when he was 16.\\n\\nBenioff is an alumnus of The Collegiate School and of Dartmouth College. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '6474302a93b75672c830a5fbf09ad2ce'}>,\n",
+       "  <Document: {'content': \"He is the youngest of three children (Suzy, Caroline, and David) and grew up in Manhattan, first in Peter Cooper Village, then on 86th Street where he spent most of his childhood, before eventually moving near the U.N. headquarters when he was 16.\\n\\nBenioff is an alumnus of The Collegiate School and of Dartmouth College. While at Dartmouth he was a member of Phi Delta Alpha Fraternity and the Sphinx Senior Society. After graduating in 1992, he worked in a number of jobs: for a time as a club bouncer in San Francisco, and as a high school English teacher at Poly Prep in Brooklyn, New York City for two years, where he served as the school's wrestling coach.\\n\\nBenioff became interested in pursuing an academic career and went to Trinity College Dublin in 1995, for a one-year program to study Irish literature. While in Dublin he met D. B. Weiss, who would later become his collaborator. Benioff wrote a thesis on Samuel Beckett while at Trinity College, but decided against a career in academia after writing the thesis. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '8b4b61b16e6a0504edbd61c63fa6c8b9'}>,\n",
+       "  <Document: {'content': \"Benioff wrote a thesis on Samuel Beckett while at Trinity College, but decided against a career in academia after writing the thesis. He worked as a radio DJ in Moose, Wyoming for a year—mostly as a side job, which he mainly accepted to take a year in the countryside at a writer's retreat. He then applied to join the Creative Writing Program at the University of California, Irvine after reading ''The Mysteries of Pittsburgh'' by Michael Chabon (an alumnus there), and received a Master of Fine Arts degree in creative writing there in 1999.\\n\\nIn 2001, ''People'' magazine included Benioff on its list of America's Top 50 Most Eligible Bachelors.\\n\\n==Career==\\n===Writing career===\\nBenioff spent two years writing his first published novel ''The 25th Hour'', originally titled ''Fireman Down'', and completed the book as his thesis for his master's degree at Irvine. He was asked to adapt the book into a screenplay after Tobey Maguire read a preliminary trade copy and became interested in making a film of the book. The film adaptation, titled ''25th Hour'' and starring Edward Norton, was directed by Spike Lee. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '5fa701327394f5101b66c7050c2d3376'}>,\n",
+       "  <Document: {'content': 'He was asked to adapt the book into a screenplay after Tobey Maguire read a preliminary trade copy and became interested in making a film of the book. The film adaptation, titled \\'\\'25th Hour\\'\\' and starring Edward Norton, was directed by Spike Lee. Benioff then wrote a collection of short stories titled \\'\\'When the Nines Roll Over (And Other Stories)\\'\\' (2004).\\n\\nHe drafted a screenplay of the mythological epic \\'\\'Troy\\'\\' (2004), for which Warner Bros. pictures paid him $2.5 million. He also wrote the script for the psychological thriller \\'\\'Stay\\'\\' (2005), which was directed by Marc Forster, and stars Ewan McGregor and Naomi Watts. His screenplay for \\'\\'The Kite Runner\\'\\' (2007), adapted from the novel of the same name, marked his second collaboration with director Marc Forster.\\n\\nBenioff was hired in 2004 to write the screenplay for the \\'\\'X-Men\\'\\' spin-off \\'\\'X-Men Origins: Wolverine\\'\\' (2009). He based his script on Barry Windsor-Smith\\'s \"Weapon X\" story, Chris Claremont and Frank Miller\\'s 1982 limited series on the character, as well as the 2001 limited series \\'\\'Origin\\'\\'. Hugh Jackman collaborated on the script, which he wanted to be more of a character piece compared with the previous \\'\\'X-Men\\'\\' films. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'f9a75b204b1e025e71aa48cb2c69cf60'}>,\n",
+       "  <Document: {'content': 'Hugh Jackman collaborated on the script, which he wanted to be more of a character piece compared with the previous \\'\\'X-Men\\'\\' films. Skip Woods was later hired by Fox to revise and rewrite Benioff\\'s script. Benioff had aimed for a \"darker and a bit more brutal\" story, writing it with an R rating in mind, although he acknowledged the film\\'s final tone would rest with the producers and director.\\n\\nIn 2006, Benioff became interested in adapting George R.R. Martin\\'s novel series, \\'\\'A Song of Ice and Fire\\'\\', and began working with D. B. Weiss on a proposed television series, \\'\\'Game of Thrones\\'\\'. The pilot, \"Winter Is Coming\", was put into development by HBO in 2007 and the series greenlit in 2010. Benioff and Weiss acted as the executive producers, showrunners, and writers of the show, which began airing on HBO in 2011. Benioff and Weiss had previously worked together on a script for a horror film titled \\'\\'The Headmaster\\'\\', but it was never made.\\n\\nIn October 2007, Universal Pictures hired Benioff to write an adapted screenplay of the Charles R. Cross biography of Kurt Cobain, but the screenplay was not used. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '7c0bbac4fca315d3624b3588e22ecaa7'}>,\n",
+       "  <Document: {'content': 'In October 2007, Universal Pictures hired Benioff to write an adapted screenplay of the Charles R. Cross biography of Kurt Cobain, but the screenplay was not used. In 2008, Benioff\\'s second novel, \\'\\'City of Thieves\\'\\', was published.\\n\\nOn April 10, 2014, Benioff announced he and Weiss had taken on their first feature film project to write, produce, and direct \\'\\'Dirty White Boys\\'\\', a novel by the Pulitzer prize-winning author Stephen Hunter. On July 19, 2017, Benioff announced that he and Weiss would produce another HBO series, titled \\'\\'Confederate\\'\\', after the final season of \\'\\'Game of Thrones\\'\\'. Benioff and Weiss said, \"We have discussed \\'\\'Confederate\\'\\' for years, originally as a concept for a feature film, but our experience on \\'\\'Thrones\\'\\' has convinced us that no one provides a bigger, better storytelling canvas than HBO.\"The announcement of \\'\\'Confederate\\'\\' was met with public animosity, and as of August 2019 (when Benioff and Weiss\\' deal with Netflix deal was announced) would not be moving forward.\\n\\nOn February 6, 2018, Disney announced that both Benioff and Weiss would write and produce a new series of \\'\\'Star Wars\\'\\' films after the final season of \\'\\'Game of Thrones\\'\\' is completed in 2019.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '1c29a73fcfe46cda0ce12e9e7d1627b8'}>,\n",
+       "  <Document: {'content': 'On February 6, 2018, Disney announced that both Benioff and Weiss would write and produce a new series of \\'\\'Star Wars\\'\\' films after the final season of \\'\\'Game of Thrones\\'\\' is completed in 2019.\\n\\nTowards the end of the final season of \\'\\'Game of Thrones\\'\\', a petition to HBO was started on Change.org. It described showrunners Benioff and Weiss as \"woefully incompetent writers\", and thus demanded \"competent writers\" to remake the eighth season of \\'\\'Game of Thrones\\'\\' in a manner \"that makes sense\". The petition eventually amassed over 1.5 million signatures. Richard Roeper, writing for the \\'\\'Chicago Sun Times\\'\\', described that the backlash to the eighth season was so great that in his 25+ years of reviewing movies and television, he doubts that he has \"ever seen the level of fan (and to a lesser degree, critical) vitriol leveled at\" \\'\\'Game of Thrones\\'\\'.\\n\\nIn early August 2019, Benioff and Weiss negotiated an exclusive multi-year film and television deal with Netflix worth US$200 million. Due to their commitments to Netflix, Benioff and Weiss exited their contract to produce \\'\\'Star Wars\\'\\' films for Disney and Lucasfilm.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'c886e3fb2c2a45820cd4b236faa71cab'}>,\n",
+       "  <Document: {'content': 'Due to their commitments to Netflix, Benioff and Weiss exited their contract to produce \\'\\'Star Wars\\'\\' films for Disney and Lucasfilm.\\n\\n===Directing career===\\nBenioff and Weiss together directed two episodes of \\'\\'Game of Thrones\\'\\', but used a coin-flip to decide who would get the credit on the show. Benioff was given the credit for season 3 episode 3, \"Walk of Punishment\", while Weiss was credited with season 4 episode 1, \"Two Swords\". Benioff and Weiss co-directed the series finale, which was widely panned.\\n\\n==Personal life==\\nOn September 30, 2006, Benioff married actress Amanda Peet in New York City. Together they have three children. The family lives in Manhattan and Beverly Hills.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '4b39a3e287ad1ae5b3f2f9a4e42e04fb'}>,\n",
+       "  <Document: {'content': '==Personal life==\\nOn September 30, 2006, Benioff married actress Amanda Peet in New York City. Together they have three children. The family lives in Manhattan and Beverly Hills.\\n\\n==Bibliography==\\n\\n===Author===\\n\\nTitle\\n\\nYear\\n\\nType\\n\\nNote\\n\\n\\'\\'The 25th Hour\\'\\'\\n\\n2001\\n\\nNovel\\n\\nPaperback: 224 pagesPublisher: Plume; Reissue edition (January 29, 2002)Language: English\\n\\n\\'\\'When the Nines Roll Over (and Other Stories)\\'\\'\\n\\n2004\\n\\nShort Story Collection\\n\\nHardcover: 223 pagesPublisher: Viking Books (August 19, 2004)Language: English\\n\\n\\'\\'City of Thieves\\'\\'\\n\\n2008\\n\\nNovel\\n\\nHardcover: 281 pagesPublisher: Viking Books (May 15, 2008)Language: English\\n\\n==Filmography==\\n===Film===\\n\\nYear\\n\\nTitle\\n\\nWriter\\n\\nProducer\\n\\nDirector\\n\\nNotes\\n\\n2002\\n\\n\\'\\'25th Hour\\'\\'\\n\\nSpike Lee\\n\\nNominated—Boston Society of Film Critics Award for Best Screenplay (2002)\\n\\n2004\\n\\n\\'\\'Troy\\'\\'\\n\\nWolfgang Petersen\\n\\n2005\\n\\n\\'\\'Stay\\'\\'\\n\\nMarc Forster\\n\\n\\'\\'When the Nines Roll Over\\'\\'\\n\\nHimself\\n\\nShort film based on a story from \\'\\'When the Nines Roll Over\\'\\'\\n\\n2007\\n\\n\\'\\'The Kite Runner\\'\\'\\n\\nMarc Forster\\n\\nChristopher Award for Best Feature Film (2007)Nominated—Golden Globe Award for Best Foreign Language Film (2008)Nominated—BAFTA Award for Best Adapted Screenplay (2008)Nominated—Satellite Award for Best Adapted Screenplay (2007)\\n\\n2009\\n\\n\\'\\'X-Men Origins: Wolverine\\'\\'\\n\\nGavin Hood\\n\\nCo-wrote with Skip Woods\\n\\n\\'\\'Brothers\\'\\'\\n\\nJim Sheridan\\n\\n2019\\n\\n\\'\\'Gemini Man\\'\\'\\n\\nAng Lee\\n\\nCo-wrote with Billy Ray and Darren Lemke\\n\\n===Television===\\n\\nYear\\n\\nTitle\\n\\nWriter\\n\\nProducer\\n\\nDirector\\n\\nNotes\\n\\n2011–2019\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nCo-creatorExecutive producerDirected and wrote episode: \"Walk of Punishment\"Directed and wrote episode (with D. B. Weiss):\"The Iron Throne\"Wrote: 45 episodesPrimetime Emmy Award for Outstanding Drama Series (2015-2016, 2018)Primetime Emmy Award for Outstanding Writing for a Drama Series (2015-2016)Hugo Award for Best Dramatic Presentation, Long Form (2012)Hugo Award for Best Dramatic Presentation, Short Form (2013-2014)Producers Guild of America Award for Best Episodic Drama (2015)Golden Nymph Awards for Outstanding International Producer (2012)Nominated—Primetime Emmy Award for Outstanding Drama Series (2011-2014)Nominated—Primetime Emmy Award for Outstanding Writing for a Drama Series (2011-2014)Nominated—Producers Guild of America Award for Best Episodic Drama (2011-2014, 2016, 2018)Nominated—BAFTA for Best International Programme (2013)Nominated—Writers Guild of America Award for Dramatic Series (2011-2012, 2014-2016, 2018)Nominated—Writers Guild of America Award for Episodic Drama (2015-2016)Nominated—Writers Guild of America Award for New Series (2011)Nominated—Hugo Award for Best Dramatic Presentation, Short Form (2015, 2017)Nominated—USC Scripter Award for Best Adapted Screenplay (2016-2017)Nominated—Humanitas Prize for 60 Minute Network or Syndicated Television (2017)\\n\\n2013\\n\\n\\'\\'It\\'s Always Sunny in Philadelphia\\'\\'\\n\\nWrote episode: \"Flowers for Charlie\"Bored Lifeguard #1 (cameo in \"The Gang Goes to a Water Park\")\\n\\n==See also==\\n\\n* List of awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\n==References==\\n\\n==External links==\\n\\n*\\n*\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'c02e1b9ca5d2af705d0351f933ec45bd'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'First of His Name\\'\\'\\'\" is the fifth episode of the fourth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 35th overall. The episode was written by series co-creators David Benioff and D. B. Weiss, and directed by Michelle MacLaren. It aired on May 4, 2014.\\n\\nThe title of the episode refers to a phrase used during Tommen Baratheon\\'s coronation as king. A similar style is used by Daenerys Targaryen styling herself Queen in Meereen. Both are the first of their respective names to lay claim to the Throne of the Seven Kingdoms.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nTommen is crowned as king. Cersei decides that Tommen will marry Margaery in fortnight. Tywin tells Cersei that the gold mines in the Westerlands have run dry and that the crown is deeply indebted to the Iron Bank of Braavos, but the union of the houses of Lannister and Tyrell will help rectify this problem. Cersei asks Oberyn to send a ship back to Sunspear as a gift for Myrcella.\\n\\n===In the Vale===\\nPetyr and Sansa arrive in the Vale. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'f7755f202b1dbd8979457b383f8fb825'}>,\n",
+       "  <Document: {'content': \"Cersei asks Oberyn to send a ship back to Sunspear as a gift for Myrcella.\\n\\n===In the Vale===\\nPetyr and Sansa arrive in the Vale. At the Bloody Gate, Petyr tells Ser Donnel Waynwood that Sansa's name is Alayne, hiding her true identity as she is still wanted in King's Landing for Joffrey's murder. Lysa reveals that she poisoned Jon\\xa0Arryn and accused the Lannisters in her letter to Catelyn for Petyr. Lysa tells Sansa that once Tyrion is executed, she will marry Robin.\\n\\n===In Meereen===\\nDaario tells Daenerys that his forces have taken the Meereenese navy. Jorah tells her that Astapor and Yunkai have fallen back into old habits. Daenerys tells Jorah that instead of sailing to Westeros, she intends to stay for now and rule Slaver's Bay.\\n\\n===On the Kingsroad===\\nBrienne and Podrick ride toward the Wall, believing that Sansa is there with Jon.\\n\\n===In the Riverlands===\\nArya reveals that Syrio was killed by Meryn.\\n\\n===Beyond the Wall===\\nJon's group attack Karl's group at Craster's Keep. Locke tries to kidnap Bran, but Bran enters Hodor's mind and kills Locke. Hodor frees Jojen and Meera. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '767c0b9233079393f2b8f28188509688'}>,\n",
+       "  <Document: {'content': '===Beyond the Wall===\\nJon\\'s group attack Karl\\'s group at Craster\\'s Keep. Locke tries to kidnap Bran, but Bran enters Hodor\\'s mind and kills Locke. Hodor frees Jojen and Meera. Bran wishes to reunite with Jon, but Jojen tells him that Jon will stop their journey. Jon kills Karl. Counting the dead, Edd notices Rast is missing. Rast, having fled to the woods, is killed by Ghost. Jon burns Craster\\'s Keep, at the suggestion of Craster\\'s wives.\\n\\n==Production==\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"First of His Name\" was written by executive producers David Benioff and D. B. Weiss, based upon the source material, Martin\\'s \\'\\'A Storm of Swords\\'\\'. Chapters adapted from \\'\\'A Storm of Swords\\'\\' to the episode were chapters 68 and 71 (Sansa VI and Daenerys VI). It also covers part of the fourth novel, \\'\\'A Feast of Crows\\'\\': namely chapter 14 (Brienne III).\\n\\n===Casting===\\nKate Dickie (Lysa Arryn) and Lino Facioli (Robin Arryn) make return appearances after an absence of several years (since the first season).\\n\\n==Reception==\\n===Ratings===\\n\"First of His Name\" established a new series high in ratings, with 7.16 million people watching it during its first airing. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '8a0cbafe585b20fcf4c2f5fae85a7d99'}>,\n",
+       "  <Document: {'content': '==Reception==\\n===Ratings===\\n\"First of His Name\" established a new series high in ratings, with 7.16 million people watching it during its first airing. In the United Kingdom, the episode was viewed by 1.643 million viewers, making it the highest-rated broadcast that week. It also received 0.082 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode received universal acclaim from critics. Rotten Tomatoes reported that 100% of the episode\\'s 40 reviews on the site were positive with an average score of 8.25 out of 10. Its consensus reads: \"Only slow by \\'\\'Game of Thrones\\'\\' standards, \"First of His Name\" demonstrates how carefully constructed this show has been since season one.\"Matt Fowler of \\'\\'IGN\\'\\' gave the episode a 9/10 writing that the episode \"gave us a much-needed look back at some past events on the series.\"He highlighted the revelation that Littlefinger\\'s actions \"put the story of the entire series into motion.\"Erik Adams of \\'\\'The A.V. Club\\'\\' gave the episode an A and praised the writers for their focus on the female characters.\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '309e2b21d1d7304639f4223658a304dd'}>,\n",
+       "  <Document: {'content': \"\\n\\n+ '''List of awards won by ''Game of Thrones'''''\\n\\n250px\\n\\nAwards and nominations\\n\\n'''Award'''\\n\\n'''Won'''\\n\\n'''Nominated'''\\n\\n;Art Directors Guild\\n\\n;American Cinema Editors\\n\\n;AFI Awards\\n\\n;Annie Awards\\n\\n;British Academy Television Awards\\n\\n;American Society of Cinematographers\\n\\n;Artios Awards\\n\\n;Cinema Audio Society\\n\\n;Costume Designers Guild\\n\\n;Critics' Choice Television Awards\\n\\n;Directors Guild of America Awards\\n\\n;Primetime Emmy Awards\\n\\n;GLAAD Media Awards\\n\\n;Golden Globe Awards\\n\\n;Grammy Award\\n\\n;Golden Nymph Awards\\n\\n;Golden Reel Awards\\n\\n;Hollywood Post Alliance\\n\\n;Irish Film & Television Academy\\n\\n;Kerrang! Awards\\n\\n;Location Managers Guild Awards\\n\\n;National Television Awards\\n\\n;Peabody Awards\\n\\n;People's Choice Awards\\n\\n;Producers Guild of America Awards\\n\\n;Royal Television Society\\n\\n;Satellite Awards\\n\\n;Saturn Awards\\n\\n;Scream Awards\\n\\n;Screen Actors Guild Awards\\n\\n;TCA Awards\\n\\n;Visual Effects Society\\n\\n;Writers Guild of America Awards\\n\\n;Young Artist Awards\\n\\n; '''Total number of wins and nominations'''\\n\\nReferences\\n\\n''Game of Thrones'' is an American  fantasy drama television series created for HBO by David Benioff and D. B. Weiss. It is an adaptation of ''A Song of Ice and Fire'', George R. R. Martin's series of fantasy novels. The story takes place on the fictional continents of Westeros and Essos, it has several plot lines and a large ensemble cast. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '70f34057da7241ef86034cff1c89660a'}>,\n",
+       "  <Document: {'content': \"The story takes place on the fictional continents of Westeros and Essos, it has several plot lines and a large ensemble cast. The first story arc follows a dynastic conflict among competing claimants for succession to the Iron Throne of the Seven Kingdoms, with other noble families fighting for independence from the throne. The second covers attempts to reclaim the throne by the exiled last scion of the realm's deposed ruling dynasty; the third chronicles the threat of the impending winter and the legendary creatures and fierce peoples of the North.\\n\\nThe series, mostly written by Benioff, Weiss and Martin, has been nominated for many awards, including six Golden Globe Awards (one win), ten Writers Guild of America Awards, seven Producers Guild of America Awards (one win), eight Directors Guild of America Awards (two wins), eight Art Directors Guild Awards (five wins), twenty one Saturn Awards (two wins), fourteen Satellite Awards (three wins), and one Peabody Award. The series has received 160 Primetime Emmy Award nominations, including eight consecutive Outstanding Drama Series nominations, with 59 wins to date. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '40ae02c65ebb8b11e035beadf26d919'}>,\n",
+       "  <Document: {'content': \"The series has received 160 Primetime Emmy Award nominations, including eight consecutive Outstanding Drama Series nominations, with 59 wins to date. ''Game of Thrones'' received many nominations, with awards recognizing various aspects of the series such as directing, writing, cast, visual effects, or overall quality.\\n\\nPeter Dinklage is the most awarded member of the cast, with recognitions such as the Primetime Emmy Award and Golden Globe for Best Supporting Actor in a Series. In addition, cast members Lena Headey, Emilia Clarke, Kit Harington, Maisie Williams, Nikolaj Coster-Waldau, Alfie Allen, Sophie Turner, Gwendoline Christie, Carice van Houten, Diana Rigg, and Max von Sydow received Primetime Emmy Award nominations for their performances in the series. The rest of the cast was also very praised, many receiving various awards nominations, including six Screen Actors Guild Award for Outstanding Performance by an Ensemble in a Drama Series nominations rewarding all of the main cast for seasons 1, 3, 4, 5, 6 and 7. In 2015, it set a record for winning the most number of Primetime Emmy Awards for a series in a single year, with 12 wins out of 24 nominations. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '377121bc397a9560ce24d004128b2f0a'}>,\n",
+       "  <Document: {'content': 'In 2015, it set a record for winning the most number of Primetime Emmy Awards for a series in a single year, with 12 wins out of 24 nominations. In 2016, it became the most awarded series in Emmy Awards history, with a total of 38 wins. To date, \\'\\'Game of Thrones\\'\\' has won 308 awards out of 628 nominations.\\n\\n\\'\\'Game of Thrones\\'\\' also holds six world records from the Guinness Book of World Records, \"Most pirated TV program\" and \"Largest TV drama simulcast\" among others.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '70e2207dc3015a9f55472c58dc980a44'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' also holds six world records from the Guinness Book of World Records, \"Most pirated TV program\" and \"Largest TV drama simulcast\" among others.\\n\\n==Total nominations and awards for the cast==\\nPeter Dinklage is the series\\'s most successful cast member in terms of awards, having won eight awards and received 38 additional nominations\\n\\nActor\\n\\nCharacter\\n\\nTenure\\n\\nNominations\\n\\nAwards\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\n2011–2019\\n\\n47\\n\\n8\\n\\nLena Headey\\n\\nCersei Lannister\\n\\n2011–2019\\n\\n33\\n\\n6\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\n2011–2019\\n\\n32\\n\\n5\\n\\nKit Harington\\n\\nJon Snow\\n\\n2011–2019\\n\\n30\\n\\n5\\n\\nMaisie Williams\\n\\nArya Stark\\n\\n2011–2019\\n\\n22\\n\\n5\\n\\nSophie Turner\\n\\nSansa Stark\\n\\n2011–2019\\n\\n14\\n\\n4\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\n2013–2017\\n\\n9\\n\\n3\\n\\nSean Bean\\n\\nEddard \"Ned\" Stark\\n\\n2011\\n\\n8\\n\\n3\\n\\nPedro Pascal\\n\\nOberyn Martell\\n\\n2014\\n\\n5\\n\\n3\\n\\nJack Gleeson\\n\\nJoffrey Baratheon\\n\\n2011–2014\\n\\n12\\n\\n2\\n\\nNatalie Dormer\\n\\nMargaery Tyrell\\n\\n2012–2016\\n\\n4\\n\\n2\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\n2011–2019\\n\\n17\\n\\n1\\n\\nMichelle Fairley\\n\\nCatelyn Stark\\n\\n2011–2013\\n\\n8\\n\\n1\\n\\nLiam Cunningham\\n\\nDavos Seaworth\\n\\n2012–2019\\n\\n8\\n\\n1\\n\\nCharles Dance\\n\\nTywin Lannister\\n\\n2011–2015\\n\\n6\\n\\n1\\n\\nIain Glen\\n\\nJorah Mormont\\n\\n2011–2019\\n\\n7\\n\\n1\\n\\nIsaac Hempstead Wright\\n\\nBrandon \"Bran\" Stark\\n\\n2011–2014, 2016–2019\\n\\n7\\n\\n1\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\n2012–2019\\n\\n7\\n\\n1\\n\\nConleth Hill\\n\\nVarys\\n\\n2011–2019\\n\\n6\\n\\n1\\n\\nNathalie Emmanuel\\n\\nMissandei\\n\\n2013–2019\\n\\n6\\n\\n1\\n\\nRory McCann\\n\\nSandor \"The Hound\" Clegane\\n\\n2011–2014, 2016–2019\\n\\n6\\n\\n1\\n\\nIan McElhinney\\n\\nBarristan Selmy\\n\\n2011, 2013–2015\\n\\n5\\n\\n1\\n\\nJohn Bradley\\n\\nSamwell Tarly\\n\\n2011–2019\\n\\n5\\n\\n1\\n\\nJosef Altin\\n\\nPypar\\n\\n2011, 2013–2014\\n\\n4\\n\\n1\\n\\nJulian Glover\\n\\nPycelle\\n\\n2011–2016\\n\\n4\\n\\n1\\n\\nMark Stanley\\n\\nGrenn\\n\\n2011–2014\\n\\n4\\n\\n1\\n\\n==Emmy nominations and awards for the cast==\\n\\nActor\\n\\nCharacter\\n\\nCategory\\n\\nSeasons\\n\\n1\\n\\n2\\n\\n3\\n\\n4\\n\\n5\\n\\n6\\n\\n7\\n\\n8\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\nSupporting Actor\\n\\nLena Headey\\n\\nCersei Lannister\\n\\nSupporting Actress\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\nGuest Actress\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\nSupporting Actress\\n\\nLead Actress\\n\\nKit Harington\\n\\nJon Snow\\n\\nSupporting Actor\\n\\nLead Actor\\n\\nMaisie Williams\\n\\nArya Stark\\n\\nSupporting Actress\\n\\nMax von Sydow\\n\\nThree-eyed Raven\\n\\nGuest Actor\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\nSupporting Actor\\n\\nAlfie Allen\\n\\nTheon Greyjoy\\n\\nSupporting Actor\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\nSupporting Actress\\n\\nSophie Turner\\n\\nSansa Stark\\n\\nSupporting Actress\\n\\nCarice van Houten\\n\\nMelisandre\\n\\nGuest Actress\\n\\n==Significant Guild and Peer Awards==\\n\\n===AFI Awards===\\nThe AFI Award, created in 2000 by the American Film Institute, is given annually to ten films and ten television programs to acknowledge the \"most significant achievements in the art of the moving image\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'df1f37224dbe8f0205e855aaa9b1e4f2'}>,\n",
+       "  <Document: {'content': '==Total nominations and awards for the cast==\\nPeter Dinklage is the series\\'s most successful cast member in terms of awards, having won eight awards and received 38 additional nominations\\n\\nActor\\n\\nCharacter\\n\\nTenure\\n\\nNominations\\n\\nAwards\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\n2011–2019\\n\\n47\\n\\n8\\n\\nLena Headey\\n\\nCersei Lannister\\n\\n2011–2019\\n\\n33\\n\\n6\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\n2011–2019\\n\\n32\\n\\n5\\n\\nKit Harington\\n\\nJon Snow\\n\\n2011–2019\\n\\n30\\n\\n5\\n\\nMaisie Williams\\n\\nArya Stark\\n\\n2011–2019\\n\\n22\\n\\n5\\n\\nSophie Turner\\n\\nSansa Stark\\n\\n2011–2019\\n\\n14\\n\\n4\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\n2013–2017\\n\\n9\\n\\n3\\n\\nSean Bean\\n\\nEddard \"Ned\" Stark\\n\\n2011\\n\\n8\\n\\n3\\n\\nPedro Pascal\\n\\nOberyn Martell\\n\\n2014\\n\\n5\\n\\n3\\n\\nJack Gleeson\\n\\nJoffrey Baratheon\\n\\n2011–2014\\n\\n12\\n\\n2\\n\\nNatalie Dormer\\n\\nMargaery Tyrell\\n\\n2012–2016\\n\\n4\\n\\n2\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\n2011–2019\\n\\n17\\n\\n1\\n\\nMichelle Fairley\\n\\nCatelyn Stark\\n\\n2011–2013\\n\\n8\\n\\n1\\n\\nLiam Cunningham\\n\\nDavos Seaworth\\n\\n2012–2019\\n\\n8\\n\\n1\\n\\nCharles Dance\\n\\nTywin Lannister\\n\\n2011–2015\\n\\n6\\n\\n1\\n\\nIain Glen\\n\\nJorah Mormont\\n\\n2011–2019\\n\\n7\\n\\n1\\n\\nIsaac Hempstead Wright\\n\\nBrandon \"Bran\" Stark\\n\\n2011–2014, 2016–2019\\n\\n7\\n\\n1\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\n2012–2019\\n\\n7\\n\\n1\\n\\nConleth Hill\\n\\nVarys\\n\\n2011–2019\\n\\n6\\n\\n1\\n\\nNathalie Emmanuel\\n\\nMissandei\\n\\n2013–2019\\n\\n6\\n\\n1\\n\\nRory McCann\\n\\nSandor \"The Hound\" Clegane\\n\\n2011–2014, 2016–2019\\n\\n6\\n\\n1\\n\\nIan McElhinney\\n\\nBarristan Selmy\\n\\n2011, 2013–2015\\n\\n5\\n\\n1\\n\\nJohn Bradley\\n\\nSamwell Tarly\\n\\n2011–2019\\n\\n5\\n\\n1\\n\\nJosef Altin\\n\\nPypar\\n\\n2011, 2013–2014\\n\\n4\\n\\n1\\n\\nJulian Glover\\n\\nPycelle\\n\\n2011–2016\\n\\n4\\n\\n1\\n\\nMark Stanley\\n\\nGrenn\\n\\n2011–2014\\n\\n4\\n\\n1\\n\\n==Emmy nominations and awards for the cast==\\n\\nActor\\n\\nCharacter\\n\\nCategory\\n\\nSeasons\\n\\n1\\n\\n2\\n\\n3\\n\\n4\\n\\n5\\n\\n6\\n\\n7\\n\\n8\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\nSupporting Actor\\n\\nLena Headey\\n\\nCersei Lannister\\n\\nSupporting Actress\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\nGuest Actress\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\nSupporting Actress\\n\\nLead Actress\\n\\nKit Harington\\n\\nJon Snow\\n\\nSupporting Actor\\n\\nLead Actor\\n\\nMaisie Williams\\n\\nArya Stark\\n\\nSupporting Actress\\n\\nMax von Sydow\\n\\nThree-eyed Raven\\n\\nGuest Actor\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\nSupporting Actor\\n\\nAlfie Allen\\n\\nTheon Greyjoy\\n\\nSupporting Actor\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\nSupporting Actress\\n\\nSophie Turner\\n\\nSansa Stark\\n\\nSupporting Actress\\n\\nCarice van Houten\\n\\nMelisandre\\n\\nGuest Actress\\n\\n==Significant Guild and Peer Awards==\\n\\n===AFI Awards===\\nThe AFI Award, created in 2000 by the American Film Institute, is given annually to ten films and ten television programs to acknowledge the \"most significant achievements in the art of the moving image\". \\'\\'Game of Thrones\\'\\' has been awarded seven times.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '9800c235de8975411a076a69d431f672'}>,\n",
+       "  <Document: {'content': '==Total nominations and awards for the cast==\\nPeter Dinklage is the series\\'s most successful cast member in terms of awards, having won eight awards and received 38 additional nominations\\n\\nActor\\n\\nCharacter\\n\\nTenure\\n\\nNominations\\n\\nAwards\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\n2011–2019\\n\\n47\\n\\n8\\n\\nLena Headey\\n\\nCersei Lannister\\n\\n2011–2019\\n\\n33\\n\\n6\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\n2011–2019\\n\\n32\\n\\n5\\n\\nKit Harington\\n\\nJon Snow\\n\\n2011–2019\\n\\n30\\n\\n5\\n\\nMaisie Williams\\n\\nArya Stark\\n\\n2011–2019\\n\\n22\\n\\n5\\n\\nSophie Turner\\n\\nSansa Stark\\n\\n2011–2019\\n\\n14\\n\\n4\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\n2013–2017\\n\\n9\\n\\n3\\n\\nSean Bean\\n\\nEddard \"Ned\" Stark\\n\\n2011\\n\\n8\\n\\n3\\n\\nPedro Pascal\\n\\nOberyn Martell\\n\\n2014\\n\\n5\\n\\n3\\n\\nJack Gleeson\\n\\nJoffrey Baratheon\\n\\n2011–2014\\n\\n12\\n\\n2\\n\\nNatalie Dormer\\n\\nMargaery Tyrell\\n\\n2012–2016\\n\\n4\\n\\n2\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\n2011–2019\\n\\n17\\n\\n1\\n\\nMichelle Fairley\\n\\nCatelyn Stark\\n\\n2011–2013\\n\\n8\\n\\n1\\n\\nLiam Cunningham\\n\\nDavos Seaworth\\n\\n2012–2019\\n\\n8\\n\\n1\\n\\nCharles Dance\\n\\nTywin Lannister\\n\\n2011–2015\\n\\n6\\n\\n1\\n\\nIain Glen\\n\\nJorah Mormont\\n\\n2011–2019\\n\\n7\\n\\n1\\n\\nIsaac Hempstead Wright\\n\\nBrandon \"Bran\" Stark\\n\\n2011–2014, 2016–2019\\n\\n7\\n\\n1\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\n2012–2019\\n\\n7\\n\\n1\\n\\nConleth Hill\\n\\nVarys\\n\\n2011–2019\\n\\n6\\n\\n1\\n\\nNathalie Emmanuel\\n\\nMissandei\\n\\n2013–2019\\n\\n6\\n\\n1\\n\\nRory McCann\\n\\nSandor \"The Hound\" Clegane\\n\\n2011–2014, 2016–2019\\n\\n6\\n\\n1\\n\\nIan McElhinney\\n\\nBarristan Selmy\\n\\n2011, 2013–2015\\n\\n5\\n\\n1\\n\\nJohn Bradley\\n\\nSamwell Tarly\\n\\n2011–2019\\n\\n5\\n\\n1\\n\\nJosef Altin\\n\\nPypar\\n\\n2011, 2013–2014\\n\\n4\\n\\n1\\n\\nJulian Glover\\n\\nPycelle\\n\\n2011–2016\\n\\n4\\n\\n1\\n\\nMark Stanley\\n\\nGrenn\\n\\n2011–2014\\n\\n4\\n\\n1\\n\\n==Emmy nominations and awards for the cast==\\n\\nActor\\n\\nCharacter\\n\\nCategory\\n\\nSeasons\\n\\n1\\n\\n2\\n\\n3\\n\\n4\\n\\n5\\n\\n6\\n\\n7\\n\\n8\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\nSupporting Actor\\n\\nLena Headey\\n\\nCersei Lannister\\n\\nSupporting Actress\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\nGuest Actress\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\nSupporting Actress\\n\\nLead Actress\\n\\nKit Harington\\n\\nJon Snow\\n\\nSupporting Actor\\n\\nLead Actor\\n\\nMaisie Williams\\n\\nArya Stark\\n\\nSupporting Actress\\n\\nMax von Sydow\\n\\nThree-eyed Raven\\n\\nGuest Actor\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\nSupporting Actor\\n\\nAlfie Allen\\n\\nTheon Greyjoy\\n\\nSupporting Actor\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\nSupporting Actress\\n\\nSophie Turner\\n\\nSansa Stark\\n\\nSupporting Actress\\n\\nCarice van Houten\\n\\nMelisandre\\n\\nGuest Actress\\n\\n==Significant Guild and Peer Awards==\\n\\n===AFI Awards===\\nThe AFI Award, created in 2000 by the American Film Institute, is given annually to ten films and ten television programs to acknowledge the \"most significant achievements in the art of the moving image\". \\'\\'Game of Thrones\\'\\' has been awarded seven times.\\n\\n+ AFI Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '29293b5ff165c913c7845ce0d331a8bc'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has been awarded seven times.\\n\\n+ AFI Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2012\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2013\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2014\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2015\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2016\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2017\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n===American Cinema Editors Awards===\\nTim Porter won one American Cinema Editors Award in 2017\\n\\nThe American Cinema Editors presents annual awards for outstanding achievements in film editing. ''Game of Thrones'' has won one out of six nominations.\\n\\n+ American Cinema Editors Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '14bba6039a3b427f0de310bdff44820e'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won one out of six nominations.\\n\\n+ American Cinema Editors Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nFrances Parker (for \"Baelor\")\\n\\n2014\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nOral Norrie Ottey (for \"The Rains of Castamere\")\\n\\n2016\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nKatie Weiland (for \"The Dance of Dragons\")\\n\\nTim Porter (for \"Hardhome\")\\n\\n2017\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\n2018\\n\\nBest Edited Drama Series for Non-Commercial Television\\n\\nTim Porter (for \"Beyond the Wall\")\\n\\n===American Society of Cinematographers===\\nThe American Society of Cinematographers presents annual awards for outstanding achievements in directors of photography and special effects for film and television. \\'\\'Game of Thrones\\'\\' has been nominated ten times, winning three times.\\n\\n+ American Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'c235915723db2a1ff1cc59a9bc75a34d'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated ten times, winning three times.\\n\\n+ American Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Achievement in Cinematography in One-Hour Episodic Television Series\\n\\nKramer Morgenthau (for \"The North Remembers\")\\n\\n2013\\n\\nOutstanding Achievement in Cinematography in One-Hour Episodic Television Series\\n\\nJonathan Freeman (for \"Valar Dohaeris\")\\n\\nAnette Haellmigk (for \"Kissed by Fire\")\\n\\n2014\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nAnette Haellmigk (for \"The Children\")\\n\\nFabian Wagner (for \"Mockingbird\")\\n\\n2015\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner (for \"Hardhome\")\\n\\n2016\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner (for \"Battle of the Bastards\")\\n\\nAnette Haellmigk (for \"Book of the Stranger\")\\n\\n2017\\n\\nOutstanding Achievement in Cinematography in Regular Series for Non-Commercial Television\\n\\nRobert McLachlan (for \"The Spoils of War\")\\n\\nGregory Middleton (for \"Dragonstone\")\\n\\n===Annie Awards===\\nFirst awarded in 1972, the Annie Awards are given exclusively for animation. \\'\\'Game of Thrones\\'\\' has been nominated twice.\\n\\n+ Annie Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '635c158c2c803ced211fd266c9142516'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated twice.\\n\\n+ Annie Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2017\\n\\nOutstanding Achievement, Character Animation in a Live Action Production\\n\\nNicholas Tripodi, Dean Elliott, James Hollingworth, Matt Weaver (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Achievement, Character Animation in a Live Action Production\\n\\nPaul Story, Todd Labonte, Matthew Muntean, Cajun Hylton, Georgy Arevshatov (for \"Beyond the Wall\")\\n\\n===Australian Production Design Guild===\\nThe Australian Production Design Guild Awards are awarded annually in recognition of excellence in production design. \\'\\'Game of Thrones\\'\\' has won three awards out of three nominations.\\n\\n+ Australian Production Design Guild Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n2016\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n3D Award for Visual Effects Design\\n\\nIloura (for \"Battle of the Bastards\")\\n\\n===Art Directors Guild Awards===\\nThe ADG Excellence in Production Design Award is presented each year by the American Art Directors Guild to recognize the best production design and art direction in the film and television industries. \\'\\'Game of Thrones\\'\\' has won five awards out of seven nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'b1a568875d2beef93c8fd2a14242023f'}>,\n",
+       "  <Document: {'content': '2015\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n2016\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n3D Award for Visual Effects Design\\n\\nIloura (for \"Battle of the Bastards\")\\n\\n===Art Directors Guild Awards===\\nThe ADG Excellence in Production Design Award is presented each year by the American Art Directors Guild to recognize the best production design and art direction in the film and television industries. \\'\\'Game of Thrones\\'\\' has won five awards out of seven nominations.\\n\\n+ Art Directors Guild Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '577b83c6de51c8b8032aa6de6b6bbee9'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won five awards out of seven nominations.\\n\\n+ Art Directors Guild Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOne-Hour Single Camera Television Series\\n\\nGemma Jackson (for \"A Golden Crown\")\\n\\n2013\\n\\nOne-Hour Single Camera Television Series\\n\\nGemma Jackson (for \"The Ghost of Harrenhal\")\\n\\n2014\\n\\nOne-Hour Single Camera Television Series\\n\\nGemma Jackson (for \"Valar Dohaeris\")\\n\\n2015\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley (for \"The Laws of Gods and Men\" and \"The Mountain and the Viper\")\\n\\n2016\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley (for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\")\\n\\n2017\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley (for \"Blood of My Blood\", \"The Broken Man\", and \"No One\")\\n\\n2018\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley (for \"Dragonstone\", \"The Queen\\'s Justice\", and \"Eastwatch\")\\n\\n===Artios Awards===\\nThe Casting Society of America awarded annually to honor the top works of casting directors in feature films, television and theatre. \\'\\'Game of Thrones\\'\\' has won one award out of seven nominations.\\n\\n+ Artios Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'c5bc0dc58418cf651deb19a4036fc30'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has won one award out of seven nominations.\\n\\n+ Artios Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nOutstanding Achievement in Casting – Television Pilot Drama\\n\\nNina Gold\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\n2012\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\n2013\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\n2014\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\n2015\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\n2016\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold, Robert Sterne, Carla Stronge\\n\\n===ASCAP Awards===\\nRamin Djawadi won two ASCAP Awards in 2012 and 2013\\n\\nThe ASCAP Awards are awarded annually to composers for outstanding achievements and contributions to the world of film and television music. ''Game of Thrones'' has won two awards out of two nominations.\\n\\n+ ASCAP Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nTop Television Series\\n\\nRamin Djawadi\\n\\n2013\\n\\nTop Television Series\\n\\nRamin Djawadi\\n\\n===ASTRA Awards===\\nThe ASTRA Awards awarded annually. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '5e5157f113ed75f63fd03725fed74567'}>,\n",
+       "  <Document: {'content': '+ ASCAP Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nTop Television Series\\n\\nRamin Djawadi\\n\\n2013\\n\\nTop Television Series\\n\\nRamin Djawadi\\n\\n===ASTRA Awards===\\nThe ASTRA Awards awarded annually. According to the Australian Subscription Television and Radio Association (ASTRA), the awards \"recognise the wealth of talent that drives the Australian subscription television industry and highlight the creativity, commitment and investment in production and broadcasting.\"\\'\\'Game of Thrones\\'\\' has won three out of four nominations.\\n\\n+ ASTRA Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2013\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===BAFTA Awards===\\nThe British Academy Television Awards are presented in an annual award show hosted by the British Academy of Film and Television Arts (BAFTA). They have been awarded annually since 1955. It is the British equivalent to the Emmy Awards in the United States. \\'\\'Game of Thrones\\'\\' has won four out of eight nominations. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '86e13cda0efba08cbbaf0c1acb9bed0d'}>,\n",
+       "  <Document: {'content': 'It is the British equivalent to the Emmy Awards in the United States. \\'\\'Game of Thrones\\'\\' has won four out of eight nominations. British Academy Television Awards recognize outstanding work in British television programming, while the British Academy Television Craft Awards are presented to honor technical and creative achievements, and include categories recognising work of art directors, lighting and costume designers, cinematographers, casting directors, and other production-based personnel.\\n\\n====British Academy Television Awards====\\n\\n+ British Academy Television Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2013\\n\\nBest International Programme\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2017\\n\\nMust-See Moment\\n\\n\"Battle of the Bastards\"\\n\\n2018\\n\\nMust-See Moment\\n\\n\"Viserion is Killed by the Night King\" (for \"Beyond the Wall\")\\n\\n====British Academy Television Craft Awards====\\n\\n+ British Academy Television Craft Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '2189ba9a90094f2a33e192a2b003c813'}>,\n",
+       "  <Document: {'content': '2013\\n\\nBest International Programme\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2017\\n\\nMust-See Moment\\n\\n\"Battle of the Bastards\"\\n\\n2018\\n\\nMust-See Moment\\n\\n\"Viserion is Killed by the Night King\" (for \"Beyond the Wall\")\\n\\n====British Academy Television Craft Awards====\\n\\n+ British Academy Television Craft Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2018\\n\\nCostume Design\\n\\nMichele Clapton\\n\\nProduction Design\\n\\nDeborah Riley, Rob Cameron\\n\\nSpecial Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===British Society of Cinematographers===\\nThe British Society of Cinematographers Awards are awarded annually to honor the top works of cinematographers in feature films and television. \\'\\'Game of Thrones\\'\\' has been nominated five times, and won once.\\n\\n+ British Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '39f27219ca58afa10dde46056a013a47'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated five times, and won once.\\n\\n+ British Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Cinematography in a Television Drama\\n\\nSam McCurdy (for \"Blackwater\")\\n\\n2015\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner (for \"Hardhome\")\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley (for \"Hardhome\")\\n\\n2016\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner (for \"The Winds of Winter\")\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nSean Savage, David Morgan & John Ferguson (for \"Battle of the Bastards\")\\n\\n===Canadian Society of Cinematographers===\\nThe Canadian Society of Cinematographers Awards are awarded annually to honor the top works of cinematographers in feature films and television. \\'\\'Game of Thrones\\'\\' has been nominated four times and won twice.\\n\\n+ Canadian Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '673b607ddf510678152b7fcf9ed2ea85'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated four times and won twice.\\n\\n+ Canadian Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nTV Series Cinematography\\n\\nRobert McLachlan (for \"Oathkeeper\")\\n\\n2016\\n\\nTV Series Cinematography\\n\\nRobert McLachlan (for \"The Dance of Dragons\")\\n\\nTV Series Cinematography\\n\\nGregory Middleton (for \"Unbowed, Unbent, Unbroken\")\\n\\n2017\\n\\nTV Series Cinematography\\n\\nGregory Middleton (for \"Home\")\\n\\n===Cinema Audio Society Awards===\\nThe Cinema Audio Society presents annual awards for outstanding achievements in sound mixing. \\'\\'Game of Thrones\\'\\' has won five out of seven nominations.\\n\\n+ Cinema Audio Society Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'be034083312ca857f5786e575ac55dde'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won five out of seven nominations.\\n\\n+ Cinema Audio Society Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Mark Taylor (for \"Baelor\")\\n\\n2013\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"Blackwater\")\\n\\n2014\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"The Rains of Castamere\")\\n\\n2015\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"The Children\")\\n\\n2016\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Hardhome\")\\n\\n2017\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Beyond the Wall\")\\n\\n===Costume Designers Guild Awards===\\nThe Costume Designers Guild presents annual awards for excellence in costume design in motion pictures, television, and commercials. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'b7a375381a96b588b1e69015e2c30557'}>,\n",
+       "  <Document: {'content': '2012\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Mark Taylor (for \"Baelor\")\\n\\n2013\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"Blackwater\")\\n\\n2014\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"The Rains of Castamere\")\\n\\n2015\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"The Children\")\\n\\n2016\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Hardhome\")\\n\\n2017\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Beyond the Wall\")\\n\\n===Costume Designers Guild Awards===\\nThe Costume Designers Guild presents annual awards for excellence in costume design in motion pictures, television, and commercials. \\'\\'Game of Thrones\\'\\' has won four out of seven nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '3a9ecaf8087915573fb9a7ac9d72d83'}>,\n",
+       "  <Document: {'content': '2012\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Mark Taylor (for \"Baelor\")\\n\\n2013\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"Blackwater\")\\n\\n2014\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"The Rains of Castamere\")\\n\\n2015\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"The Children\")\\n\\n2016\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Hardhome\")\\n\\n2017\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Beyond the Wall\")\\n\\n===Costume Designers Guild Awards===\\nThe Costume Designers Guild presents annual awards for excellence in costume design in motion pictures, television, and commercials. \\'\\'Game of Thrones\\'\\' has won four out of seven nominations.\\n\\n+ Costume Designers Guild Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '70ac9b35053586c73b03d87d23a04f1f'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won four out of seven nominations.\\n\\n+ Costume Designers Guild Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton\\n\\n2013\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton\\n\\n2014\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton\\n\\n2015\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton\\n\\n2016\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton\\n\\n2017\\n\\nOutstanding Fantasy Television Series\\n\\nMichele Clapton, April Ferry\\n\\n2018\\n\\nOutstanding Fantasy Television Series\\n\\nMichele Clapton\\n\\n===Directors Guild of America Awards===\\nDavid Nutter won one Directors Guild of America Award for \"Mother\\'s Mercy\" in 2015\\n\\nThe Directors Guild of America Awards are awarded annually by the Directors Guild of America. \\'\\'Game of Thrones\\'\\' has been nominated for eight awards for Outstanding Directorial Achievement in Dramatic Series, winning in 2015 and 2016.\\n\\n+ Directors Guild of America Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': '2417d670b79211a113d49ecaa075a8c5'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated for eight awards for Outstanding Directorial Achievement in Dramatic Series, winning in 2015 and 2016.\\n\\n+ Directors Guild of America Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nDramatic Series\\n\\nTim Van Patten (for \"Winter Is Coming\")\\n\\n2014\\n\\nDramatic Series\\n\\nDavid Nutter (for \"The Rains of Castamere\")\\n\\n2015\\n\\nDramatic Series\\n\\nAlex Graves (for \"The Children\")\\n\\n2016\\n\\nDramatic Series\\n\\nDavid Nutter (for \"Mother\\'s Mercy\")\\n\\n2017\\n\\nDramatic Series\\n\\nMiguel Sapochnik (for \"Battle of the Bastards\")\\n\\n2018\\n\\nDramatic Series\\n\\nJeremy Podeswa (for \"The Dragon and the Wolf\")\\n\\nMatt Shakman (for \"The Spoils of War\")\\n\\nAlan Taylor (for \"Beyond the Wall\")\\n\\n===Emmy Awards===\\nD. B. Weiss and David Benioff received the Primetime Emmy Award for Outstanding Writing for a Drama Series for \"Mother\\'s Mercy\" in 2015 and for \"Battle of the Bastards\" in 2016\\nLena Headey received her first Primetime Emmy Award nomination for her performance as Cersei Lannister in 2014\\n\\nThe Emmy Awards were established in 1949 in order to recognize excellence in the American television industry, and are bestowed by members of the Academy of Television Arts & Sciences. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': 'f2cf86c0b79e92e5152a4b4708ec39a1'}>,\n",
+       "  <Document: {'content': '2012\\n\\nDramatic Series\\n\\nTim Van Patten (for \"Winter Is Coming\")\\n\\n2014\\n\\nDramatic Series\\n\\nDavid Nutter (for \"The Rains of Castamere\")\\n\\n2015\\n\\nDramatic Series\\n\\nAlex Graves (for \"The Children\")\\n\\n2016\\n\\nDramatic Series\\n\\nDavid Nutter (for \"Mother\\'s Mercy\")\\n\\n2017\\n\\nDramatic Series\\n\\nMiguel Sapochnik (for \"Battle of the Bastards\")\\n\\n2018\\n\\nDramatic Series\\n\\nJeremy Podeswa (for \"The Dragon and the Wolf\")\\n\\nMatt Shakman (for \"The Spoils of War\")\\n\\nAlan Taylor (for \"Beyond the Wall\")\\n\\n===Emmy Awards===\\nD. B. Weiss and David Benioff received the Primetime Emmy Award for Outstanding Writing for a Drama Series for \"Mother\\'s Mercy\" in 2015 and for \"Battle of the Bastards\" in 2016\\nLena Headey received her first Primetime Emmy Award nomination for her performance as Cersei Lannister in 2014\\n\\nThe Emmy Awards were established in 1949 in order to recognize excellence in the American television industry, and are bestowed by members of the Academy of Television Arts & Sciences. Emmy Awards are given in different ceremonies presented annually; Primetime Emmy Awards recognize outstanding work in American primetime television programming, while the Creative Arts Emmy Awards are presented to honor technical and creative achievements, and include categories recognising work of art directors, lighting and costume designers, cinematographers, casting directors, and other production-based personnel. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': 'b22033eb317ee056988476b09ff8d7ae'}>,\n",
+       "  <Document: {'content': \"Emmy Awards are given in different ceremonies presented annually; Primetime Emmy Awards recognize outstanding work in American primetime television programming, while the Creative Arts Emmy Awards are presented to honor technical and creative achievements, and include categories recognising work of art directors, lighting and costume designers, cinematographers, casting directors, and other production-based personnel. The Emmy Award corresponds to the Academy Award (for film), the Tony Award (for theatre), and the Grammy Award (for music).\\n\\n''Game of Thrones'' has won 59 out of 161 nominations. Peter Dinklage has been nominated for Outstanding Supporting Actor in a Drama Series eight times in a row for playing Tyrion Lannister, with a win in 2011, 2015, 2018 and 2019. Showrunners David Benioff and D. B. Weiss have won twice for Writing for a Drama Series. ''Game of Thrones'' has eight nominations for Outstanding Drama Series, winning four, in 2015, 2016, 2018 and 2019. Both David Nutter and Miguel Sapochnik have won for Outstanding Directing for a Drama Series. ''Game of Thrones'' holds the Emmy Award record for most wins for a scripted television series, ahead of ''Frasier'' (which received 37).\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': '330ca8c61453e586b6e1bf8e8aeb404a'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' holds the Emmy Award record for most wins for a scripted television series, ahead of ''Frasier'' (which received 37).\\n\\nAt the 67th Primetime Emmy Awards the show's fifth season was nominated for 24 awards. The show ultimately won twelve awards, setting a new record for most Emmy wins by a series in a single year, replacing the previous holder ''The West Wing''s nine wins, and tying that show and ''Hill Street Blues'' for the most wins by a drama series throughout its run. At the nominee announcement for the 68th Primetime Emmy Awards, the show's sixth season received the most nominations of any show with 23. The show yet again won twelve awards, tying the same record for most Emmy wins by a series in a single year.\\n\\nAt the 70th Primetime Emmy Awards the seventh season was nominated for 22 awards, the most of the ceremony, including a first Emmy nomination for Nikolaj Coster-Waldau.\\n\\nAt the 71st Primetime Emmy Awards the eighth season was nominated for 32 awards, thus breaking the record for most nominations received by a regular series in a single year. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': 'a3fcdeec4181dc574c4ece08e42eb18d'}>,\n",
+       "  <Document: {'content': 'At the 71st Primetime Emmy Awards the eighth season was nominated for 32 awards, thus breaking the record for most nominations received by a regular series in a single year. Actors Alfie Allen, Gwendoline Christie, Sophie Turner and Carice van Houten all received their first ever Emmy Award nomination thanks to this season.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': 'a10d2e9f7df5d6bba17c7ec4245bf1d0'}>,\n",
+       "  <Document: {'content': 'Actors Alfie Allen, Gwendoline Christie, Sophie Turner and Carice van Houten all received their first ever Emmy Award nomination thanks to this season.\\n\\n====Primetime Emmy Awards====\\n\\n+ Primetime Emmy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\n2011\\n\\nOutstanding Drama Series\\n\\nVince Gerardis, Frank Doelger, Ralph Vicinanza, Mark Huffam, David Benioff, Carolyn Strauss, George R. R. Martin, Guymon Casady and D. B. Weiss\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Baelor\")\\n\\nOutstanding Directing for a Drama Series\\n\\nTim Van Patten (for \"Winter Is Coming\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Baelor\")\\n\\n2012\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Frank Doelger, Carolyn Strauss, George R. R. Martin, Vanessa Taylor, Alan Taylor, Guymon Casady, Vince Gerardis and Bernadette Caulfield\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Blackwater\")\\n\\n2013\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Vanessa Taylor, Chris Newman and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Second Sons\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Rains of Castamere\")\\n\\n2014\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Chris Newman and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"The Laws of Gods and Men\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey (for \"The Lion and the Rose\")\\n\\nOutstanding Directing for a Drama Series\\n\\nNeil Marshall (for \"The Watchers on the Wall\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Children\")\\n\\n2015\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Chris Newman, Greg Spence, Lisa McAtackney and Bryan Cogman\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Hardhome\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"The Dance of Dragons\")\\n\\nLena Headey (for \"Mother\\'s Mercy\")\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter (for \"Mother\\'s Mercy\")\\n\\nJeremy Podeswa (for \"Unbowed, Unbent, Unbroken\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Mother\\'s Mercy\")\\n\\n2016\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Vince Gerardis, Guymon Casady, George R. R. Martin, Bryan Cogman, Chris Newman, Greg Spence and Lisa McAtackney\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"No One\")\\n\\nKit Harington (for \"Battle of the Bastards\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"Book of the Stranger\")\\n\\nLena Headey (for \"The Winds of Winter\")\\n\\nMaisie Williams (for \"No One\")\\n\\nOutstanding Directing for a Drama Series\\n\\nMiguel Sapochnik (for \"Battle of the Bastards\")\\n\\nJack Bender (for \"The Door\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, George R. R. Martin, Guymon Casady, Vince Gerardis, Bryan Cogman, Chris Newman, Lisa McAtackney and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nNikolaj Coster-Waldau (for \"The Spoils of War\")\\n\\nPeter Dinklage (for \"The Dragon and the Wolf\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey (for \"The Dragon and the Wolf\")\\n\\nOutstanding Directing for a Drama Series\\n\\nAlan Taylor (for \"Beyond the Wall\")\\n\\nJeremy Podeswa (for \"The Dragon and the Wolf\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Dragon and the Wolf\")\\n\\n2019\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Bernadette Caulfield, Frank Doelger, David Nutter, Miguel Sapochnik, Vince Gerardis, Guymon Casady, George R. R. Martin, Bryan Cogman, Chris Newman, Greg Spence, Lisa McAtackney and Duncan Muggoch\\n\\nOutstanding Lead Actor in a Drama Series\\n\\nKit Harington (for \"The Iron Throne\")\\n\\nOutstanding Lead Actress in a Drama Series\\n\\nEmilia Clarke (for \"The Last of the Starks\")\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nAlfie Allen (for \"The Long Night\")\\n\\nNikolaj Coster-Waldau (for \"A Knight of the Seven Kingdoms\")\\n\\nPeter Dinklage (for \"The Iron Throne\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nGwendoline Christie (for \"A Knight of the Seven Kingdoms\")\\n\\nLena Headey (for \"The Bells\")\\n\\nSophie Turner (for \"Winterfell\")\\n\\nMaisie Williams (for \"The Long Night\")\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Iron Throne\")\\n\\nDavid Nutter (for \"The Last of the Starks\")\\n\\nMiguel Sapochnik (for \"The Long Night\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Iron Throne\")\\n\\n====Creative Arts Emmy Awards====\\n\\n+ Creative Arts Emmy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 28}, 'embedding': None, 'id': 'ccdfde748e2751f4885e8e327fb5182f'}>,\n",
+       "  <Document: {'content': '====Primetime Emmy Awards====\\n\\n+ Primetime Emmy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\n2011\\n\\nOutstanding Drama Series\\n\\nVince Gerardis, Frank Doelger, Ralph Vicinanza, Mark Huffam, David Benioff, Carolyn Strauss, George R. R. Martin, Guymon Casady and D. B. Weiss\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Baelor\")\\n\\nOutstanding Directing for a Drama Series\\n\\nTim Van Patten (for \"Winter Is Coming\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Baelor\")\\n\\n2012\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Frank Doelger, Carolyn Strauss, George R. R. Martin, Vanessa Taylor, Alan Taylor, Guymon Casady, Vince Gerardis and Bernadette Caulfield\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Blackwater\")\\n\\n2013\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Vanessa Taylor, Chris Newman and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Second Sons\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Rains of Castamere\")\\n\\n2014\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Chris Newman and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"The Laws of Gods and Men\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey (for \"The Lion and the Rose\")\\n\\nOutstanding Directing for a Drama Series\\n\\nNeil Marshall (for \"The Watchers on the Wall\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Children\")\\n\\n2015\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Chris Newman, Greg Spence, Lisa McAtackney and Bryan Cogman\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Hardhome\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"The Dance of Dragons\")\\n\\nLena Headey (for \"Mother\\'s Mercy\")\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter (for \"Mother\\'s Mercy\")\\n\\nJeremy Podeswa (for \"Unbowed, Unbent, Unbroken\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Mother\\'s Mercy\")\\n\\n2016\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Vince Gerardis, Guymon Casady, George R. R. Martin, Bryan Cogman, Chris Newman, Greg Spence and Lisa McAtackney\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"No One\")\\n\\nKit Harington (for \"Battle of the Bastards\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"Book of the Stranger\")\\n\\nLena Headey (for \"The Winds of Winter\")\\n\\nMaisie Williams (for \"No One\")\\n\\nOutstanding Directing for a Drama Series\\n\\nMiguel Sapochnik (for \"Battle of the Bastards\")\\n\\nJack Bender (for \"The Door\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, George R. R. Martin, Guymon Casady, Vince Gerardis, Bryan Cogman, Chris Newman, Lisa McAtackney and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nNikolaj Coster-Waldau (for \"The Spoils of War\")\\n\\nPeter Dinklage (for \"The Dragon and the Wolf\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey (for \"The Dragon and the Wolf\")\\n\\nOutstanding Directing for a Drama Series\\n\\nAlan Taylor (for \"Beyond the Wall\")\\n\\nJeremy Podeswa (for \"The Dragon and the Wolf\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Dragon and the Wolf\")\\n\\n2019\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Bernadette Caulfield, Frank Doelger, David Nutter, Miguel Sapochnik, Vince Gerardis, Guymon Casady, George R. R. Martin, Bryan Cogman, Chris Newman, Greg Spence, Lisa McAtackney and Duncan Muggoch\\n\\nOutstanding Lead Actor in a Drama Series\\n\\nKit Harington (for \"The Iron Throne\")\\n\\nOutstanding Lead Actress in a Drama Series\\n\\nEmilia Clarke (for \"The Last of the Starks\")\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nAlfie Allen (for \"The Long Night\")\\n\\nNikolaj Coster-Waldau (for \"A Knight of the Seven Kingdoms\")\\n\\nPeter Dinklage (for \"The Iron Throne\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nGwendoline Christie (for \"A Knight of the Seven Kingdoms\")\\n\\nLena Headey (for \"The Bells\")\\n\\nSophie Turner (for \"Winterfell\")\\n\\nMaisie Williams (for \"The Long Night\")\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Iron Throne\")\\n\\nDavid Nutter (for \"The Last of the Starks\")\\n\\nMiguel Sapochnik (for \"The Long Night\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Iron Throne\")\\n\\n====Creative Arts Emmy Awards====\\n\\n+ Creative Arts Emmy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton and Rachael Webb-Crozier (for \"The Pointy End\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander and Candice Banks (for \"A Golden Crown\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Hameed Shaukat, Kirk Shintani, and Robert Feng\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Winter Is Coming\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen and Conor O\\'Sullivan (for \"A Golden Crown\")\\n\\nOutstanding Sound Editing for a Series\\n\\nRobin Quinn, Steve Fanagan, Eoghan McDonnell, Jon Stevenson, Tim Hands, Stefan Henrix, Caoimhe Doyle, Michelle McCormack and Andy Kennedy (for \"A Golden Crown\")\\n\\nOutstanding Special Visual Effects\\n\\nRafael Morant, Adam McInnes, Graham Hills, Lucy Ainsworth-Taylor, Stuart Brisdon, Damien Macé, Henry Badgett and Angela Barson (for \"Fire and Blood\")\\n\\nOutstanding Stunt Coordination\\n\\nPaul Jennings for (The Wolf and the Lion\")\\n\\n2012\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Garden of Bones\", \"The Ghost of Harrenhal\", and \"A Man Without Honor\")  (shared with \\'\\'Boardwalk Empire\\'\\')\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"The Prince of Winterfell\")\\n\\nOutstanding Creative Achievement in Interactive Media\\n\\n\\'\\'Game of Thrones\\'\\' Season Two – Enhanced Digital Experience\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"The Old Gods and the New\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"The Old Gods and the New\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Morghulis\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"Blackwater\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One-Hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"Blackwater\")\\n\\nOutstanding Special Visual Effects\\n\\nRainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Morghulis\")\\n\\n2013\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Valar Dohaeris\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nRob McLachlan (for \"Mhysa\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"Walk of Punishment\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"Second Sons\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Season Three – Enhanced Digital Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Kissed by Fire\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Dohaeris\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nOral Ottey (for \"The Rains of Castamere\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Sound Mixing for a Drama Series (One hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Special Visual Effects\\n\\nDoug Campbell, Rainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Production Design for a Narrative Contemporary or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Laws of Gods and Men\", and \"The Mountain and the Viper\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nAnette Haellmigk (for \"The Lion and the Rose\")\\n\\nJonathan Freeman (for \"Two Swords\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Sheena Wichary, Alexander Fordham, and Nina Ayres (for \"The Lion and the Rose\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Lion and the Rose\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, and Nicola Mount (for \"The Lion and the Rose\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Premiere – Facebook Live and Instagram, by Sabrina Caluori, Paul Beddoe-Stephens, Jim Marsh, Michael McMorrow, and Michael McMillian\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Ann McEwan (for \"Oathkeeper\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Mountain and the Viper\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie, or Special\\n\\nThe Children\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Jed M. Dodge, Tim Hands, Paula Fairfield, David Klotz, Bradley C. Katona, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Watchers on the Wall\")\\n\\nOutstanding Sound Mixing for a Drama Series (One Hour)\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, and Mathew Waters (for \"The Watchers on the Wall\")\\n\\nOutstanding Special and Visual Effects\\n\\nJoe Bauer, Joern Grosshans, Steve Kullback, Adam Chazen, Eric Carney, Sabrina Gerhardt, Matthew Rouleau, Thomas H. Schelesny, and Robert Simon (for \"The Children\")\\n\\nOutstanding Stunt Coordination for a Drama Series\\n\\nPaul Herbert\\n\\n2015\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner (for \"Hardhome\")\\n\\nAnette Haellmigk (for \"Sons of the Harpy\")\\n\\nRob McLachlan (for \"The Dance of Dragons\")\\n\\nGregory Middleton (for \"Unbowed, Unbent, Unbroken\")\\n\\nOutstanding Costumes for a Period/Fantasy Series, Limited Series or Movie\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham (for \"The Dance of Dragons\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Gift\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount (for \"Mother\\'s Mercy\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews (for \"Mother\\'s Mercy\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower (for \"Hardhome\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Hardhome\")\\n\\nKatie Weiland (for \"The Dance of Dragons\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit (for \"Hardhome\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau (for \"The Dance of Dragons\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2016\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton (for \"Home\")\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary (for \"The Winds of Winter\")\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow (for \"The Door\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora (for \"The Door\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Main Titles 360 Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth (for \"Battle of the Bastards\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"Blood of My Blood\", \"The Broken Man\", and \"No One\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower (for \"The Door\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nKatie Weiland (for \"Oathbreaker\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit (for \"The Door\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Battle of the Bastards\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney, Sam Conway, Matthew Rouleau, Michelle Blok, Glenn Melenhorst (for \"Battle of the Bastards\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2018\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Alexander Fordham, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"Beyond the Wall\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Queen\\'s Justice\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Dragon and the Wolf\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Pamela Smyth, Kate Thompson, and Nicola Mathews (for \"The Dragon and the Wolf\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Dragon and the Wolf\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"Dragonstone\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nJane Walker, Paul Spateri, Emma Sheffield, and Barrie Gower (for \"Beyond the Wall\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Beyond the Wall\")\\n\\nCrispin Green (for \"The Spoils of War\")\\n\\nKatie Weiland (for \"The Dragon and the Wolf\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Paula Fairfield, Tim Hands, Paul Bercovitch, Bradley C. Katona, John Matter, Brett Voss, David Klotz, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Spoils of War\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Richard Dyer, and Ronan Hill (for \"Beyond the Wall\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Michelle Blok, Sam Conway, Ted Rae, David Ramos, Wayne Stables, and Derek Spears (for \"Beyond the Wall\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n2019\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nJonathan Freeman (for \"The Iron Throne\")\\n\\nOutstanding Creative Achievement in Interactive Media within a Scripted Program\\n\\n\"Fight for the Living: Beyond the Wall Virtual Reality Experience\"\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"The Bells\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nCarice van Houten (for \"The Long Night\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Long Night\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Kirk Shintani, Shahana Khan, Ian Ruhfass, and Rustam Hasanov\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Nicola Mathews, and Pamela Smyth (for \"The Long Night\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Long Night\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Bells\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie or Special\\n\\nEmma Faulkes, Paul Spateri, Chloe Muton-Phillips, Duncan Jarman, Patt Foad, John Eldred-Tooby, Barrie Gower, and Sarah Gower (for \"The Long Night\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nKatie Weiland (for \"The Iron Throne\")\\n\\nTim Porter (for \"The Long Night\")\\n\\nCrispin Green (for \"Winterfell\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Tim Hands, Paula Fairfield, Bradley C. Katona, Paul Bercovitch, John Matter, David Klotz, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Long Night\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Simon Kerr, Danny Crowley, and Ronan Hill (for \"The Long Night\")\\n\\nOutstanding Special Visual Effects\\n\\nJoe Bauer, Steve Kullback, Adam Chazen, Sam Conway, Mohsen Mousavi, Martin Hill, Ted Rae, Patrick Tiberius Gehlen, and Thomas Schelesny (for \"The Bells\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n===Golden Reel Awards===\\nThe Golden Reel Award presents awards annually to sound editors in the several categories for feature film and television. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 29}, 'embedding': None, 'id': 'd2e4cf6e4f3eb4dc11972025a11b460d'}>,\n",
+       "  <Document: {'content': '2011\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton and Rachael Webb-Crozier (for \"The Pointy End\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander and Candice Banks (for \"A Golden Crown\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Hameed Shaukat, Kirk Shintani, and Robert Feng\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Winter Is Coming\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen and Conor O\\'Sullivan (for \"A Golden Crown\")\\n\\nOutstanding Sound Editing for a Series\\n\\nRobin Quinn, Steve Fanagan, Eoghan McDonnell, Jon Stevenson, Tim Hands, Stefan Henrix, Caoimhe Doyle, Michelle McCormack and Andy Kennedy (for \"A Golden Crown\")\\n\\nOutstanding Special Visual Effects\\n\\nRafael Morant, Adam McInnes, Graham Hills, Lucy Ainsworth-Taylor, Stuart Brisdon, Damien Macé, Henry Badgett and Angela Barson (for \"Fire and Blood\")\\n\\nOutstanding Stunt Coordination\\n\\nPaul Jennings for (The Wolf and the Lion\")\\n\\n2012\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Garden of Bones\", \"The Ghost of Harrenhal\", and \"A Man Without Honor\")  (shared with \\'\\'Boardwalk Empire\\'\\')\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"The Prince of Winterfell\")\\n\\nOutstanding Creative Achievement in Interactive Media\\n\\n\\'\\'Game of Thrones\\'\\' Season Two – Enhanced Digital Experience\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"The Old Gods and the New\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"The Old Gods and the New\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Morghulis\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"Blackwater\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One-Hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"Blackwater\")\\n\\nOutstanding Special Visual Effects\\n\\nRainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Morghulis\")\\n\\n2013\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Valar Dohaeris\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nRob McLachlan (for \"Mhysa\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"Walk of Punishment\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"Second Sons\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Season Three – Enhanced Digital Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Kissed by Fire\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Dohaeris\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nOral Ottey (for \"The Rains of Castamere\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Sound Mixing for a Drama Series (One hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Special Visual Effects\\n\\nDoug Campbell, Rainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Production Design for a Narrative Contemporary or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Laws of Gods and Men\", and \"The Mountain and the Viper\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nAnette Haellmigk (for \"The Lion and the Rose\")\\n\\nJonathan Freeman (for \"Two Swords\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Sheena Wichary, Alexander Fordham, and Nina Ayres (for \"The Lion and the Rose\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Lion and the Rose\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, and Nicola Mount (for \"The Lion and the Rose\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Premiere – Facebook Live and Instagram, by Sabrina Caluori, Paul Beddoe-Stephens, Jim Marsh, Michael McMorrow, and Michael McMillian\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Ann McEwan (for \"Oathkeeper\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Mountain and the Viper\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie, or Special\\n\\nThe Children\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Jed M. Dodge, Tim Hands, Paula Fairfield, David Klotz, Bradley C. Katona, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Watchers on the Wall\")\\n\\nOutstanding Sound Mixing for a Drama Series (One Hour)\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, and Mathew Waters (for \"The Watchers on the Wall\")\\n\\nOutstanding Special and Visual Effects\\n\\nJoe Bauer, Joern Grosshans, Steve Kullback, Adam Chazen, Eric Carney, Sabrina Gerhardt, Matthew Rouleau, Thomas H. Schelesny, and Robert Simon (for \"The Children\")\\n\\nOutstanding Stunt Coordination for a Drama Series\\n\\nPaul Herbert\\n\\n2015\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner (for \"Hardhome\")\\n\\nAnette Haellmigk (for \"Sons of the Harpy\")\\n\\nRob McLachlan (for \"The Dance of Dragons\")\\n\\nGregory Middleton (for \"Unbowed, Unbent, Unbroken\")\\n\\nOutstanding Costumes for a Period/Fantasy Series, Limited Series or Movie\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham (for \"The Dance of Dragons\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Gift\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount (for \"Mother\\'s Mercy\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews (for \"Mother\\'s Mercy\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower (for \"Hardhome\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Hardhome\")\\n\\nKatie Weiland (for \"The Dance of Dragons\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit (for \"Hardhome\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau (for \"The Dance of Dragons\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2016\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton (for \"Home\")\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary (for \"The Winds of Winter\")\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow (for \"The Door\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora (for \"The Door\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Main Titles 360 Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth (for \"Battle of the Bastards\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"Blood of My Blood\", \"The Broken Man\", and \"No One\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower (for \"The Door\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nKatie Weiland (for \"Oathbreaker\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit (for \"The Door\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Battle of the Bastards\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney, Sam Conway, Matthew Rouleau, Michelle Blok, Glenn Melenhorst (for \"Battle of the Bastards\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2018\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Alexander Fordham, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"Beyond the Wall\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Queen\\'s Justice\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Dragon and the Wolf\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Pamela Smyth, Kate Thompson, and Nicola Mathews (for \"The Dragon and the Wolf\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Dragon and the Wolf\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"Dragonstone\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nJane Walker, Paul Spateri, Emma Sheffield, and Barrie Gower (for \"Beyond the Wall\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Beyond the Wall\")\\n\\nCrispin Green (for \"The Spoils of War\")\\n\\nKatie Weiland (for \"The Dragon and the Wolf\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Paula Fairfield, Tim Hands, Paul Bercovitch, Bradley C. Katona, John Matter, Brett Voss, David Klotz, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Spoils of War\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Richard Dyer, and Ronan Hill (for \"Beyond the Wall\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Michelle Blok, Sam Conway, Ted Rae, David Ramos, Wayne Stables, and Derek Spears (for \"Beyond the Wall\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n2019\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nJonathan Freeman (for \"The Iron Throne\")\\n\\nOutstanding Creative Achievement in Interactive Media within a Scripted Program\\n\\n\"Fight for the Living: Beyond the Wall Virtual Reality Experience\"\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"The Bells\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nCarice van Houten (for \"The Long Night\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Long Night\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Kirk Shintani, Shahana Khan, Ian Ruhfass, and Rustam Hasanov\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Nicola Mathews, and Pamela Smyth (for \"The Long Night\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Long Night\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Bells\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie or Special\\n\\nEmma Faulkes, Paul Spateri, Chloe Muton-Phillips, Duncan Jarman, Patt Foad, John Eldred-Tooby, Barrie Gower, and Sarah Gower (for \"The Long Night\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nKatie Weiland (for \"The Iron Throne\")\\n\\nTim Porter (for \"The Long Night\")\\n\\nCrispin Green (for \"Winterfell\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Tim Hands, Paula Fairfield, Bradley C. Katona, Paul Bercovitch, John Matter, David Klotz, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Long Night\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Simon Kerr, Danny Crowley, and Ronan Hill (for \"The Long Night\")\\n\\nOutstanding Special Visual Effects\\n\\nJoe Bauer, Steve Kullback, Adam Chazen, Sam Conway, Mohsen Mousavi, Martin Hill, Ted Rae, Patrick Tiberius Gehlen, and Thomas Schelesny (for \"The Bells\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n===Golden Reel Awards===\\nThe Golden Reel Award presents awards annually to sound editors in the several categories for feature film and television. \\'\\'Game of Thrones\\'\\' has received eleven awards out of twenty two nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 30}, 'embedding': None, 'id': 'bc2b3182f7c7fcb746b031b8c5727840'}>,\n",
+       "  <Document: {'content': '2011\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton and Rachael Webb-Crozier (for \"The Pointy End\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander and Candice Banks (for \"A Golden Crown\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Hameed Shaukat, Kirk Shintani, and Robert Feng\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Winter Is Coming\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen and Conor O\\'Sullivan (for \"A Golden Crown\")\\n\\nOutstanding Sound Editing for a Series\\n\\nRobin Quinn, Steve Fanagan, Eoghan McDonnell, Jon Stevenson, Tim Hands, Stefan Henrix, Caoimhe Doyle, Michelle McCormack and Andy Kennedy (for \"A Golden Crown\")\\n\\nOutstanding Special Visual Effects\\n\\nRafael Morant, Adam McInnes, Graham Hills, Lucy Ainsworth-Taylor, Stuart Brisdon, Damien Macé, Henry Badgett and Angela Barson (for \"Fire and Blood\")\\n\\nOutstanding Stunt Coordination\\n\\nPaul Jennings for (The Wolf and the Lion\")\\n\\n2012\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Garden of Bones\", \"The Ghost of Harrenhal\", and \"A Man Without Honor\")  (shared with \\'\\'Boardwalk Empire\\'\\')\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"The Prince of Winterfell\")\\n\\nOutstanding Creative Achievement in Interactive Media\\n\\n\\'\\'Game of Thrones\\'\\' Season Two – Enhanced Digital Experience\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"The Old Gods and the New\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"The Old Gods and the New\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Morghulis\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"Blackwater\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One-Hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"Blackwater\")\\n\\nOutstanding Special Visual Effects\\n\\nRainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Morghulis\")\\n\\n2013\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Valar Dohaeris\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nRob McLachlan (for \"Mhysa\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"Walk of Punishment\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"Second Sons\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Season Three – Enhanced Digital Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Kissed by Fire\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Dohaeris\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nOral Ottey (for \"The Rains of Castamere\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Sound Mixing for a Drama Series (One hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Special Visual Effects\\n\\nDoug Campbell, Rainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Production Design for a Narrative Contemporary or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Laws of Gods and Men\", and \"The Mountain and the Viper\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nAnette Haellmigk (for \"The Lion and the Rose\")\\n\\nJonathan Freeman (for \"Two Swords\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Sheena Wichary, Alexander Fordham, and Nina Ayres (for \"The Lion and the Rose\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Lion and the Rose\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, and Nicola Mount (for \"The Lion and the Rose\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Premiere – Facebook Live and Instagram, by Sabrina Caluori, Paul Beddoe-Stephens, Jim Marsh, Michael McMorrow, and Michael McMillian\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Ann McEwan (for \"Oathkeeper\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Mountain and the Viper\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie, or Special\\n\\nThe Children\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Jed M. Dodge, Tim Hands, Paula Fairfield, David Klotz, Bradley C. Katona, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Watchers on the Wall\")\\n\\nOutstanding Sound Mixing for a Drama Series (One Hour)\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, and Mathew Waters (for \"The Watchers on the Wall\")\\n\\nOutstanding Special and Visual Effects\\n\\nJoe Bauer, Joern Grosshans, Steve Kullback, Adam Chazen, Eric Carney, Sabrina Gerhardt, Matthew Rouleau, Thomas H. Schelesny, and Robert Simon (for \"The Children\")\\n\\nOutstanding Stunt Coordination for a Drama Series\\n\\nPaul Herbert\\n\\n2015\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner (for \"Hardhome\")\\n\\nAnette Haellmigk (for \"Sons of the Harpy\")\\n\\nRob McLachlan (for \"The Dance of Dragons\")\\n\\nGregory Middleton (for \"Unbowed, Unbent, Unbroken\")\\n\\nOutstanding Costumes for a Period/Fantasy Series, Limited Series or Movie\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham (for \"The Dance of Dragons\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Gift\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount (for \"Mother\\'s Mercy\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews (for \"Mother\\'s Mercy\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower (for \"Hardhome\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Hardhome\")\\n\\nKatie Weiland (for \"The Dance of Dragons\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit (for \"Hardhome\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau (for \"The Dance of Dragons\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2016\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton (for \"Home\")\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary (for \"The Winds of Winter\")\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow (for \"The Door\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora (for \"The Door\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Main Titles 360 Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth (for \"Battle of the Bastards\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"Blood of My Blood\", \"The Broken Man\", and \"No One\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower (for \"The Door\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nKatie Weiland (for \"Oathbreaker\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit (for \"The Door\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Battle of the Bastards\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney, Sam Conway, Matthew Rouleau, Michelle Blok, Glenn Melenhorst (for \"Battle of the Bastards\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2018\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Alexander Fordham, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"Beyond the Wall\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Queen\\'s Justice\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Dragon and the Wolf\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Pamela Smyth, Kate Thompson, and Nicola Mathews (for \"The Dragon and the Wolf\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Dragon and the Wolf\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"Dragonstone\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nJane Walker, Paul Spateri, Emma Sheffield, and Barrie Gower (for \"Beyond the Wall\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Beyond the Wall\")\\n\\nCrispin Green (for \"The Spoils of War\")\\n\\nKatie Weiland (for \"The Dragon and the Wolf\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Paula Fairfield, Tim Hands, Paul Bercovitch, Bradley C. Katona, John Matter, Brett Voss, David Klotz, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Spoils of War\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Richard Dyer, and Ronan Hill (for \"Beyond the Wall\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Michelle Blok, Sam Conway, Ted Rae, David Ramos, Wayne Stables, and Derek Spears (for \"Beyond the Wall\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n2019\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nJonathan Freeman (for \"The Iron Throne\")\\n\\nOutstanding Creative Achievement in Interactive Media within a Scripted Program\\n\\n\"Fight for the Living: Beyond the Wall Virtual Reality Experience\"\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"The Bells\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nCarice van Houten (for \"The Long Night\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Long Night\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Kirk Shintani, Shahana Khan, Ian Ruhfass, and Rustam Hasanov\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Nicola Mathews, and Pamela Smyth (for \"The Long Night\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Long Night\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Bells\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie or Special\\n\\nEmma Faulkes, Paul Spateri, Chloe Muton-Phillips, Duncan Jarman, Patt Foad, John Eldred-Tooby, Barrie Gower, and Sarah Gower (for \"The Long Night\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nKatie Weiland (for \"The Iron Throne\")\\n\\nTim Porter (for \"The Long Night\")\\n\\nCrispin Green (for \"Winterfell\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Tim Hands, Paula Fairfield, Bradley C. Katona, Paul Bercovitch, John Matter, David Klotz, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Long Night\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Simon Kerr, Danny Crowley, and Ronan Hill (for \"The Long Night\")\\n\\nOutstanding Special Visual Effects\\n\\nJoe Bauer, Steve Kullback, Adam Chazen, Sam Conway, Mohsen Mousavi, Martin Hill, Ted Rae, Patrick Tiberius Gehlen, and Thomas Schelesny (for \"The Bells\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n===Golden Reel Awards===\\nThe Golden Reel Award presents awards annually to sound editors in the several categories for feature film and television. \\'\\'Game of Thrones\\'\\' has received eleven awards out of twenty two nominations.\\n\\n+ Golden Reel Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 31}, 'embedding': None, 'id': '1bd86d105f4cb26e27172db045d78e6b'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has received eleven awards out of twenty two nominations.\\n\\n+ Golden Reel Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Cripples, Bastards, and Broken Things\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Winter is Coming\")\\n\\n2013\\n\\nBest Sound Editing – Long Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Long Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Music in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\n2014\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\nJed Dodge and Tim Hands (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Music\\n\\nDavid Klotz (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley\\n\\nTim Kimmel (for \"The Rains of Castamere\")\\n\\n2015\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Hardhome\")\\n\\n2017\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Battle of the Bastards\")\\n\\n2018\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Brett Voss and Jeffrey Wilhoit (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel, Paul Bercovitch and Tim Hands (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Beyond the Wall\")\\n\\n===Grammy Awards===\\nThe Grammy, is an honor awarded by The Recording Academy to recognize outstanding achievement in the music industry. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 32}, 'embedding': None, 'id': 'b8b0a05606eb00dd26cf693711a7a732'}>,\n",
+       "  <Document: {'content': '2012\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Cripples, Bastards, and Broken Things\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Winter is Coming\")\\n\\n2013\\n\\nBest Sound Editing – Long Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Long Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Music in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\n2014\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\nJed Dodge and Tim Hands (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Music\\n\\nDavid Klotz (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley\\n\\nTim Kimmel (for \"The Rains of Castamere\")\\n\\n2015\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Hardhome\")\\n\\n2017\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Battle of the Bastards\")\\n\\n2018\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Brett Voss and Jeffrey Wilhoit (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel, Paul Bercovitch and Tim Hands (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Beyond the Wall\")\\n\\n===Grammy Awards===\\nThe Grammy, is an honor awarded by The Recording Academy to recognize outstanding achievement in the music industry. \\'\\'Game of Thrones\\'\\' has one nomination.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 33}, 'embedding': None, 'id': '78caf7182c8e0792674684cf28416a6f'}>,\n",
+       "  <Document: {'content': '2012\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Cripples, Bastards, and Broken Things\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Winter is Coming\")\\n\\n2013\\n\\nBest Sound Editing – Long Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Long Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Music in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\n2014\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\nJed Dodge and Tim Hands (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Music\\n\\nDavid Klotz (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley\\n\\nTim Kimmel (for \"The Rains of Castamere\")\\n\\n2015\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Hardhome\")\\n\\n2017\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Battle of the Bastards\")\\n\\n2018\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Brett Voss and Jeffrey Wilhoit (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel, Paul Bercovitch and Tim Hands (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Beyond the Wall\")\\n\\n===Grammy Awards===\\nThe Grammy, is an honor awarded by The Recording Academy to recognize outstanding achievement in the music industry. \\'\\'Game of Thrones\\'\\' has one nomination.\\n\\n+ Grammy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 34}, 'embedding': None, 'id': 'a30aea6e14009f91dfa236e0b233d598'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has one nomination.\\n\\n+ Grammy Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2018\\n\\nBest Score Soundtrack for Visual Media\\n\\nRamin Djawadi (for ''Game of Thrones: Season 7'')\\n\\n===Hollywood Post Alliance===\\nThe Hollywood Post Alliance presents awards to individuals and organizations to recognize the achievement of post production talent. ''Game of Thrones'' has received seven awards out of nineteen nominations.\\n\\n+ Hollywood Post Alliance Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 35}, 'embedding': None, 'id': '3f4fd36ca63138cd64ba797af88b3d43'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has received seven awards out of nineteen nominations.\\n\\n+ Hollywood Post Alliance Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Finley (for \"The Prince of Winterfell\")\\n\\n2013\\n\\nOutstanding Color Grading – Television\\n\\nJoe Finley (for \"Kissed by Fire\")\\n\\nOutstanding Sound – Television\\n\\nPaula Fairfield, Brad Katona, Jed Dodge, Onnalee Blank and Mathew Waters (for \"The Climb\")\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Bauer and Jabbar Raisani, Jörn Grosshans and Sven Martin, and Doug Campbell (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Sound\\n\\nTim Kimmel, Onnalee Blank, Mathew Waters, Paula Fairfield, Brad Katona and Jed M. Dodge (for \"The Children\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Mockingbird\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Sven Martin, Jörn Grosshans, Thomas Schelesny, Matthew Rouleau (for \"The Children\")\\n\\n2015\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Hardhome\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Hardhome\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani (for \"The Dance of Dragons\")\\n\\n2016\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch (for \"Battle of the Bastards\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau (for \"Battle of the Bastards\")\\n\\n2017\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Dragonstone\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Stormborn\")\\n\\nJesse Parker (for \"The Queen\\'s Justice\")\\n\\nCrispin Green (for \"Dragonstone\")\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley C. Katona, Paul Bercovitch (for \"The Spoils of War\")\\n\\n===Irish Film & Television Awards===\\nMichelle Fairley won one Irish Film & Television Award in 2014\\n\\nThe Irish Film & Television Awards are presented in an annual award show hosted by the Irish Film & Television Academy. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 36}, 'embedding': None, 'id': 'a801bf81d43d46df4ce5998427fae0dd'}>,\n",
+       "  <Document: {'content': '2012\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Finley (for \"The Prince of Winterfell\")\\n\\n2013\\n\\nOutstanding Color Grading – Television\\n\\nJoe Finley (for \"Kissed by Fire\")\\n\\nOutstanding Sound – Television\\n\\nPaula Fairfield, Brad Katona, Jed Dodge, Onnalee Blank and Mathew Waters (for \"The Climb\")\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Bauer and Jabbar Raisani, Jörn Grosshans and Sven Martin, and Doug Campbell (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Sound\\n\\nTim Kimmel, Onnalee Blank, Mathew Waters, Paula Fairfield, Brad Katona and Jed M. Dodge (for \"The Children\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Mockingbird\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Sven Martin, Jörn Grosshans, Thomas Schelesny, Matthew Rouleau (for \"The Children\")\\n\\n2015\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Hardhome\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Hardhome\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani (for \"The Dance of Dragons\")\\n\\n2016\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch (for \"Battle of the Bastards\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau (for \"Battle of the Bastards\")\\n\\n2017\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Dragonstone\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Stormborn\")\\n\\nJesse Parker (for \"The Queen\\'s Justice\")\\n\\nCrispin Green (for \"Dragonstone\")\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley C. Katona, Paul Bercovitch (for \"The Spoils of War\")\\n\\n===Irish Film & Television Awards===\\nMichelle Fairley won one Irish Film & Television Award in 2014\\n\\nThe Irish Film & Television Awards are presented in an annual award show hosted by the Irish Film & Television Academy. \\'\\'Game of Thrones\\'\\' has received four awards out of twenty-eight nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 37}, 'embedding': None, 'id': 'fe4d5ad37a636bd4a7bb66d2461c15cd'}>,\n",
+       "  <Document: {'content': '2012\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Finley (for \"The Prince of Winterfell\")\\n\\n2013\\n\\nOutstanding Color Grading – Television\\n\\nJoe Finley (for \"Kissed by Fire\")\\n\\nOutstanding Sound – Television\\n\\nPaula Fairfield, Brad Katona, Jed Dodge, Onnalee Blank and Mathew Waters (for \"The Climb\")\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Bauer and Jabbar Raisani, Jörn Grosshans and Sven Martin, and Doug Campbell (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Sound\\n\\nTim Kimmel, Onnalee Blank, Mathew Waters, Paula Fairfield, Brad Katona and Jed M. Dodge (for \"The Children\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Mockingbird\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Sven Martin, Jörn Grosshans, Thomas Schelesny, Matthew Rouleau (for \"The Children\")\\n\\n2015\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Hardhome\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Hardhome\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani (for \"The Dance of Dragons\")\\n\\n2016\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch (for \"Battle of the Bastards\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau (for \"Battle of the Bastards\")\\n\\n2017\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Dragonstone\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Stormborn\")\\n\\nJesse Parker (for \"The Queen\\'s Justice\")\\n\\nCrispin Green (for \"Dragonstone\")\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley C. Katona, Paul Bercovitch (for \"The Spoils of War\")\\n\\n===Irish Film & Television Awards===\\nMichelle Fairley won one Irish Film & Television Award in 2014\\n\\nThe Irish Film & Television Awards are presented in an annual award show hosted by the Irish Film & Television Academy. \\'\\'Game of Thrones\\'\\' has received four awards out of twenty-eight nominations.\\n\\n+ Irish Film & Television Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 38}, 'embedding': None, 'id': '371baf8282f42b0aaa0e127b3246200a'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has received four awards out of twenty-eight nominations.\\n\\n+ Irish Film & Television Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Television Drama\\n\\nMark Huffam\\n\\nBest Director Television Drama\\n\\nBrian Kirk\\n\\nBest Actress – Television\\n\\nMichelle Fairley\\n\\nBest Supporting Actor – Television\\n\\nAidan Gillen\\n\\nBest Sound (Film/TV Drama)\\n\\nRonan Hill\\n\\nBest Sound\\n\\nRonan Hill\\n\\n2013\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nBest Director of Photography\\n\\nP.J. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 39}, 'embedding': None, 'id': 'abb68b07a1d213a7d6f15d391537ae66'}>,\n",
+       "  <Document: {'content': \"2012\\n\\nBest Television Drama\\n\\nMark Huffam\\n\\nBest Director Television Drama\\n\\nBrian Kirk\\n\\nBest Actress – Television\\n\\nMichelle Fairley\\n\\nBest Supporting Actor – Television\\n\\nAidan Gillen\\n\\nBest Sound (Film/TV Drama)\\n\\nRonan Hill\\n\\nBest Sound\\n\\nRonan Hill\\n\\n2013\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nBest Director of Photography\\n\\nP.J. Dillon\\n\\nBest Sound\\n\\nRonan Hill, Mervyn Moore\\n\\n2014\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nAidan Gillen\\n\\nActress in a Supporting Role – Television\\n\\nMichelle Fairley\\n\\nBest Sound\\n\\nRonan Hill\\n\\n2015\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n''Game of Thrones''\\n\\n2016\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\n2017\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\nRonan Hill, Onnalee Blank and Matthew Waters\\n\\nBest VFX\\n\\nEd Bruce & Nicholas Murphy\\n\\n2018\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nAidan Gillen\\n\\nBest Sound\\n\\nRonan Hill, Onnalee Blank and Matthew Waters\\n\\nBest VFX\\n\\nEd Bruce & Nicholas Murphy\\n\\n===Location Managers Guild Awards===\\nThe Location Managers Guild Awards are awarded annually by the Location Managers Guild International. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 40}, 'embedding': None, 'id': '14a3b06761b022c24be611015bb06d05'}>,\n",
+       "  <Document: {'content': \"Dillon\\n\\nBest Sound\\n\\nRonan Hill, Mervyn Moore\\n\\n2014\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nAidan Gillen\\n\\nActress in a Supporting Role – Television\\n\\nMichelle Fairley\\n\\nBest Sound\\n\\nRonan Hill\\n\\n2015\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n''Game of Thrones''\\n\\n2016\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\n2017\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\nRonan Hill, Onnalee Blank and Matthew Waters\\n\\nBest VFX\\n\\nEd Bruce & Nicholas Murphy\\n\\n2018\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nAidan Gillen\\n\\nBest Sound\\n\\nRonan Hill, Onnalee Blank and Matthew Waters\\n\\nBest VFX\\n\\nEd Bruce & Nicholas Murphy\\n\\n===Location Managers Guild Awards===\\nThe Location Managers Guild Awards are awarded annually by the Location Managers Guild International. ''Game of Thrones'' took the most awards, two in total, and claimed the top TV award at the inaugural LMGA Awards in 2014.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 41}, 'embedding': None, 'id': 'b9f624a69add74b8f3b75fd39bfc91e5'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' took the most awards, two in total, and claimed the top TV award at the inaugural LMGA Awards in 2014.\\n\\nIn 2016 the show became the first series to win multiple LMGI Awards, with Location Managers Robert Boake and Tate Araez taking home the award for Outstanding Locations in a Period Television Series. Nominated again in 2017 and 2018, ''Game of Thrones'' took back the period television throne from ''The Crown'' in 2018, and currently holds a record 4 LMGI Award wins out of 6 nominations.\\n\\nThe film locations featured in ''Game of Thrones'' have also proven to be winners themselves, as the show has been responsible for driving a significant increase in tourism to the host regions filmed by the series.\\n\\n+ Location Managers Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 42}, 'embedding': None, 'id': '4034681fedaa27a82e3ac845f7c97686'}>,\n",
+       "  <Document: {'content': \"The film locations featured in ''Game of Thrones'' have also proven to be winners themselves, as the show has been responsible for driving a significant increase in tourism to the host regions filmed by the series.\\n\\n+ Location Managers Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nOutstanding Achievement by a Location Professional in Television\\n\\nRobert Boake\\n\\nOutstanding use of Locations in Television\\n\\n''Game of Thrones''\\n\\n2016\\n\\nOutstanding Locations in Period Television\\n\\nRobert Boake and Tate Araez\\n\\n2017\\n\\nOutstanding Locations in Period Television\\n\\nMatt Jones and Naomi Liston\\n\\n2018\\n\\nOutstanding Locations in Period Television\\n\\nRobert Boake, Matt Jones, Pedro Tate Araez\\n\\n2019\\n\\nOutstanding Locations in a Period Television Series\\n\\nRobert Boake\\n\\n===Makeup Artist and Hair Stylist Guild Awards===\\nThe Make-Up Artists and Hair Stylists Guild are awarded annually to make-up artists and hair stylist in feature films, television programs, commercials, live network events and theatrical productions in the United States. ''Game of Thrones'' has won six out of nine nominations.\\n\\n+ Makeup Artist and Hair Stylist Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 43}, 'embedding': None, 'id': '1d8c192edb69ea72ef75b59647e3e1f8'}>,\n",
+       "  <Document: {'content': \"+ Makeup Artist and Hair Stylist Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nBest Period and/or Character Makeup – Television\\n\\nPaul Engelen, Melissa Lackersteen\\n\\n2016\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\n2017\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker, Kay Bilk\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nBest Special Makeup Effects – Television\\n\\nBarrie Gower, Sarah Gower\\n\\n2018\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker, Nicola Matthews\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nBest Special Makeup Effects – Television\\n\\nBarrie Gower, Sarah Gower\\n\\n===Producers Guild of America Awards===\\nThe Producers Guild of America Award is bestowed by the Producers Guild of America to honor the work of producers in film and television. ''Game of Thrones'' has been nominated for seven awards for Best Episodic Drama, winning in 2016.\\n\\n+ Producers Guild of America Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 44}, 'embedding': None, 'id': '232535d8d481b7b7e79289a743015598'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated for seven awards for Best Episodic Drama, winning in 2016.\\n\\n+ Producers Guild of America Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Frank Doelger, Mark Huffam, Carolyn Strauss, D. B. Weiss\\n\\n2013\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, D. B. Weiss\\n\\n2014\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Christopher Newman, Greg Spence, Carolyn Strauss, and D. B. Weiss\\n\\n2015\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Chris Newman, Greg Spence, Carolyn Strauss, D. B. Weiss\\n\\n2016\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2017\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2018\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n===Screen Actors Guild Awards===\\nThe Screen Actors Guild Award, given by the Screen Actors Guild‐American Federation of Television and Radio Artists (SAG-AFTRA), is an award dedicated to honor the best performances of actors in film and television. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 45}, 'embedding': None, 'id': 'f51d2b963b3631f80fb7fb22e5f7c657'}>,\n",
+       "  <Document: {'content': '2012\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Frank Doelger, Mark Huffam, Carolyn Strauss, D. B. Weiss\\n\\n2013\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, D. B. Weiss\\n\\n2014\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Christopher Newman, Greg Spence, Carolyn Strauss, and D. B. Weiss\\n\\n2015\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Chris Newman, Greg Spence, Carolyn Strauss, D. B. Weiss\\n\\n2016\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2017\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2018\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n===Screen Actors Guild Awards===\\nThe Screen Actors Guild Award, given by the Screen Actors Guild‐American Federation of Television and Radio Artists (SAG-AFTRA), is an award dedicated to honor the best performances of actors in film and television. \\'\\'Game of Thrones\\'\\' has received seven awards out of eighteen nominations. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 46}, 'embedding': None, 'id': 'e388df058a50bfb349ed340afe071950'}>,\n",
+       "  <Document: {'content': '2012\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Frank Doelger, Mark Huffam, Carolyn Strauss, D. B. Weiss\\n\\n2013\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, D. B. Weiss\\n\\n2014\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Christopher Newman, Greg Spence, Carolyn Strauss, and D. B. Weiss\\n\\n2015\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Chris Newman, Greg Spence, Carolyn Strauss, D. B. Weiss\\n\\n2016\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2017\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2018\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n===Screen Actors Guild Awards===\\nThe Screen Actors Guild Award, given by the Screen Actors Guild‐American Federation of Television and Radio Artists (SAG-AFTRA), is an award dedicated to honor the best performances of actors in film and television. \\'\\'Game of Thrones\\'\\' has received seven awards out of eighteen nominations. Dinklage was nominated for a Screen Actors Guild Award for Outstanding Performance by a Male Actor in a Drama Series in 2014, his first for the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 47}, 'embedding': None, 'id': '4a5b177b9745615c327b9ebbfebdaaf1'}>,\n",
+       "  <Document: {'content': \"Dinklage was nominated for a Screen Actors Guild Award for Outstanding Performance by a Male Actor in a Drama Series in 2014, his first for the series.\\n\\n+ Screen Actors Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 48}, 'embedding': None, 'id': '17dcba1b929a80f39318497c7365c73'}>,\n",
+       "  <Document: {'content': \"Dinklage was nominated for a Screen Actors Guild Award for Outstanding Performance by a Male Actor in a Drama Series in 2014, his first for the series.\\n\\n+ Screen Actors Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAmrita Acharia, Mark Addy, Alfie Allen, Josef Altin, Sean Bean, Susan Brown, Emilia Clarke, Nikolaj Coster-Waldau, Peter Dinklage, Ron Donachie, Michelle Fairley, Jerome Flynn, Elyes Gabel, Aidan Gillen, Jack Gleeson, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Richard Madden, Jason Momoa, Rory McCann, Ian McElhinney, Luke McEwan, Roxanne McKee, Dar Salim, Mark Stanley, Donald Sumpter, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\n''Game of Thrones''\\n\\n2013\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRob Cooper, Jamie Edgell, Dave Fisher, Dave Forman, Paul Herbert, Michelle McKeown, Sian Miline, Jimmy O'Dee, Domonkos Pardanyi, Marcus Shakesheff, CC Smiff, and Mark Southworth\\n\\n2014\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, John Bradley, Oona Chaplin, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Mackenzie Crook, Charles Dance, Joe Dempsie, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Michelle Fairley, Jack Gleeson, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Kristofer Hivju, Paul Kaye, Sibel Kekilli, Rose Leslie, Richard Madden, Rory McCann, Michael McElhatton, Ian McElhinney, Philip McGinley, Hannah Murray, Iwan Rheon, Sophie Turner, Carice Van Houten, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRachelle Beinart, Richard Bradshaw, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Jozsef Fodor, Dave Forman, Paul Herbert, Paul Howell, Daniel Naprous, Florian Robin, CC Smiff, and Roy Taylor\\n\\n2015\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nJosef Altin, Jacob Anderson, John Bradley, Dominic Carter, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Charles Dance, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nLucy Allen, Cole Armitage, Gary Arthurs, Rachelle Beinart, Ferenc Berecz, Richard Bradshaw, Andy Butcher, Michael Byrch, Neil Chapelhow, Nick Chopping, Jonathan Cohen, Joel Conlan, Gary Connery, James Cox, Tom Cox, Jason Curle, Nicholas Daines, Bill Davey, Kelly J. Dent, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Neil Finnighan, Jozsef Fodor, Dean Forster, David Garrick, James Grogan, Tim Halloran, Paul Heasman, Robert Hladik, Al Holland, Gergely Horpacsi, Paul Howell, Stewart James, Gary Kane, Ian Kay, Robbie Keane, George Kirby, Cristian Knight, Laszlo Kosa, Geza Kovacs, Norbert Kovacs, Guy List, Phil Lonergan, Russell Macleod, Tina Maskell, Adrian McGaw, Nick McKinless, Erol Mehmet, Andy Merchant, Sian Milne, Daniel Naprous, Chris Newton, Ray Nicholas, Bela Orsanyi, Sam Parham, Ian Pead, Justin Pearson, Martin Pemberton, Heather Phillips, Rashid Phoenix, Andy Pilgrim, Christopher Pocock, Curtis Rivers, Marcus Shakesheff, Matt Sherren, Anthony Skrimshire, Mark Slaughter, Karen Smithson, Mark Southworth, Helen Steinway Bailey, Shane Steyn, Matthew Stirling, John Street, Gaspar Szabo, Gabor Szeman, Roy Taylor, Gyula Toth, Tony Van Silva, Reg Wayment, Linda Weal, Richard J Wheeldon, Heron White, Maxine Whittaker, Simon Whyman, Donna C. Williams, Lou Wong, Annabel E. Wood, Liang Yang, and Steen Young\\n\\n2016\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Michiel Huisman, Hannah Murray, Brenock O'Connor, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams, and Tom Wlaschiha\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\n2017\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean-Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carice Van Houten, Gemma Whelan, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, and Leo Woodruff\\n\\n2018\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, James Faulkner, Jerome Flynn, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Kristofer Hivju, Tom Hopper, Anton Lesser, Rory McCann, Sophie Turner, Carice Van Houten, Gemma Whelan, Rupert Vansittart, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Mark Archer, Luis Miguel Arranz, Ferenc Berecz, Richard Bradshaw, Michael Byrch, Nick Chopping, Jonathan Cohen, Chris Cox, Jake Cox, James Cox, David Cronnelly, Matt Crook, Ricardo Cruz Jr., Jason Curle, Levan Doran, Daniel Euston, Bradley Farmer, Pete Ford, Vladimir Furdik, Eduardo Gago, Angel Gomez, David Grant, Lawrence Hansen, Richard Hansen, Nicklas Hansson, Rob Hayns, Bobby Holland Hanton, Paul Howell, Radoslav Ignatov, Rowley Irlam, Erol Ismail, Orsányi Iván, Sonny Louis, Leigh Maddern, Jonny McBride, Leona McCarron, Kim McGarrity, Trayan Milenov-Troy, Sian Milne, David Newton, Jason Oettle, Radoslav Parvanov, Ian Pead, Andy Pilgrim, Oleg Podobin, Marc Redmond, Andrej Riabokon, Florian Robin, Doug Robson, Fabio Santos, Stanislav Satko, Paul Shapcott, Mark Slaughter, CC Smiff, James Stewart, Jonny Stockwell, Ryan Stuart, Gáspár Szabó, Lukas Tomsik, Marek Toth, Teodor Tzolov, Raycho Vasilev, Calvin Warrington-Heasman, Annabel Wood, Leo Woodruff, and Lewis Young\\n\\n===Society of Camera Operators Awards===\\nThe Society of Camera Operators Awards are awarded annually to honor the creative contribution of the camera operator in the motion picture and television industries. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 49}, 'embedding': None, 'id': '366237a8680174d7c5eff334633ccc3c'}>,\n",
+       "  <Document: {'content': \"2012\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAmrita Acharia, Mark Addy, Alfie Allen, Josef Altin, Sean Bean, Susan Brown, Emilia Clarke, Nikolaj Coster-Waldau, Peter Dinklage, Ron Donachie, Michelle Fairley, Jerome Flynn, Elyes Gabel, Aidan Gillen, Jack Gleeson, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Richard Madden, Jason Momoa, Rory McCann, Ian McElhinney, Luke McEwan, Roxanne McKee, Dar Salim, Mark Stanley, Donald Sumpter, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\n''Game of Thrones''\\n\\n2013\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRob Cooper, Jamie Edgell, Dave Fisher, Dave Forman, Paul Herbert, Michelle McKeown, Sian Miline, Jimmy O'Dee, Domonkos Pardanyi, Marcus Shakesheff, CC Smiff, and Mark Southworth\\n\\n2014\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, John Bradley, Oona Chaplin, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Mackenzie Crook, Charles Dance, Joe Dempsie, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Michelle Fairley, Jack Gleeson, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Kristofer Hivju, Paul Kaye, Sibel Kekilli, Rose Leslie, Richard Madden, Rory McCann, Michael McElhatton, Ian McElhinney, Philip McGinley, Hannah Murray, Iwan Rheon, Sophie Turner, Carice Van Houten, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRachelle Beinart, Richard Bradshaw, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Jozsef Fodor, Dave Forman, Paul Herbert, Paul Howell, Daniel Naprous, Florian Robin, CC Smiff, and Roy Taylor\\n\\n2015\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nJosef Altin, Jacob Anderson, John Bradley, Dominic Carter, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Charles Dance, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nLucy Allen, Cole Armitage, Gary Arthurs, Rachelle Beinart, Ferenc Berecz, Richard Bradshaw, Andy Butcher, Michael Byrch, Neil Chapelhow, Nick Chopping, Jonathan Cohen, Joel Conlan, Gary Connery, James Cox, Tom Cox, Jason Curle, Nicholas Daines, Bill Davey, Kelly J. Dent, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Neil Finnighan, Jozsef Fodor, Dean Forster, David Garrick, James Grogan, Tim Halloran, Paul Heasman, Robert Hladik, Al Holland, Gergely Horpacsi, Paul Howell, Stewart James, Gary Kane, Ian Kay, Robbie Keane, George Kirby, Cristian Knight, Laszlo Kosa, Geza Kovacs, Norbert Kovacs, Guy List, Phil Lonergan, Russell Macleod, Tina Maskell, Adrian McGaw, Nick McKinless, Erol Mehmet, Andy Merchant, Sian Milne, Daniel Naprous, Chris Newton, Ray Nicholas, Bela Orsanyi, Sam Parham, Ian Pead, Justin Pearson, Martin Pemberton, Heather Phillips, Rashid Phoenix, Andy Pilgrim, Christopher Pocock, Curtis Rivers, Marcus Shakesheff, Matt Sherren, Anthony Skrimshire, Mark Slaughter, Karen Smithson, Mark Southworth, Helen Steinway Bailey, Shane Steyn, Matthew Stirling, John Street, Gaspar Szabo, Gabor Szeman, Roy Taylor, Gyula Toth, Tony Van Silva, Reg Wayment, Linda Weal, Richard J Wheeldon, Heron White, Maxine Whittaker, Simon Whyman, Donna C. Williams, Lou Wong, Annabel E. Wood, Liang Yang, and Steen Young\\n\\n2016\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Michiel Huisman, Hannah Murray, Brenock O'Connor, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams, and Tom Wlaschiha\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\n2017\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean-Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carice Van Houten, Gemma Whelan, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, and Leo Woodruff\\n\\n2018\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, James Faulkner, Jerome Flynn, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Kristofer Hivju, Tom Hopper, Anton Lesser, Rory McCann, Sophie Turner, Carice Van Houten, Gemma Whelan, Rupert Vansittart, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Mark Archer, Luis Miguel Arranz, Ferenc Berecz, Richard Bradshaw, Michael Byrch, Nick Chopping, Jonathan Cohen, Chris Cox, Jake Cox, James Cox, David Cronnelly, Matt Crook, Ricardo Cruz Jr., Jason Curle, Levan Doran, Daniel Euston, Bradley Farmer, Pete Ford, Vladimir Furdik, Eduardo Gago, Angel Gomez, David Grant, Lawrence Hansen, Richard Hansen, Nicklas Hansson, Rob Hayns, Bobby Holland Hanton, Paul Howell, Radoslav Ignatov, Rowley Irlam, Erol Ismail, Orsányi Iván, Sonny Louis, Leigh Maddern, Jonny McBride, Leona McCarron, Kim McGarrity, Trayan Milenov-Troy, Sian Milne, David Newton, Jason Oettle, Radoslav Parvanov, Ian Pead, Andy Pilgrim, Oleg Podobin, Marc Redmond, Andrej Riabokon, Florian Robin, Doug Robson, Fabio Santos, Stanislav Satko, Paul Shapcott, Mark Slaughter, CC Smiff, James Stewart, Jonny Stockwell, Ryan Stuart, Gáspár Szabó, Lukas Tomsik, Marek Toth, Teodor Tzolov, Raycho Vasilev, Calvin Warrington-Heasman, Annabel Wood, Leo Woodruff, and Lewis Young\\n\\n===Society of Camera Operators Awards===\\nThe Society of Camera Operators Awards are awarded annually to honor the creative contribution of the camera operator in the motion picture and television industries. ''Game of Thrones'' has been nominated one time.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 50}, 'embedding': None, 'id': '29228d89f11a4385f23f699a48af12e5'}>,\n",
+       "  <Document: {'content': \"2012\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAmrita Acharia, Mark Addy, Alfie Allen, Josef Altin, Sean Bean, Susan Brown, Emilia Clarke, Nikolaj Coster-Waldau, Peter Dinklage, Ron Donachie, Michelle Fairley, Jerome Flynn, Elyes Gabel, Aidan Gillen, Jack Gleeson, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Richard Madden, Jason Momoa, Rory McCann, Ian McElhinney, Luke McEwan, Roxanne McKee, Dar Salim, Mark Stanley, Donald Sumpter, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\n''Game of Thrones''\\n\\n2013\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRob Cooper, Jamie Edgell, Dave Fisher, Dave Forman, Paul Herbert, Michelle McKeown, Sian Miline, Jimmy O'Dee, Domonkos Pardanyi, Marcus Shakesheff, CC Smiff, and Mark Southworth\\n\\n2014\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, John Bradley, Oona Chaplin, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Mackenzie Crook, Charles Dance, Joe Dempsie, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Michelle Fairley, Jack Gleeson, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Kristofer Hivju, Paul Kaye, Sibel Kekilli, Rose Leslie, Richard Madden, Rory McCann, Michael McElhatton, Ian McElhinney, Philip McGinley, Hannah Murray, Iwan Rheon, Sophie Turner, Carice Van Houten, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRachelle Beinart, Richard Bradshaw, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Jozsef Fodor, Dave Forman, Paul Herbert, Paul Howell, Daniel Naprous, Florian Robin, CC Smiff, and Roy Taylor\\n\\n2015\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nJosef Altin, Jacob Anderson, John Bradley, Dominic Carter, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Charles Dance, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nLucy Allen, Cole Armitage, Gary Arthurs, Rachelle Beinart, Ferenc Berecz, Richard Bradshaw, Andy Butcher, Michael Byrch, Neil Chapelhow, Nick Chopping, Jonathan Cohen, Joel Conlan, Gary Connery, James Cox, Tom Cox, Jason Curle, Nicholas Daines, Bill Davey, Kelly J. Dent, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Neil Finnighan, Jozsef Fodor, Dean Forster, David Garrick, James Grogan, Tim Halloran, Paul Heasman, Robert Hladik, Al Holland, Gergely Horpacsi, Paul Howell, Stewart James, Gary Kane, Ian Kay, Robbie Keane, George Kirby, Cristian Knight, Laszlo Kosa, Geza Kovacs, Norbert Kovacs, Guy List, Phil Lonergan, Russell Macleod, Tina Maskell, Adrian McGaw, Nick McKinless, Erol Mehmet, Andy Merchant, Sian Milne, Daniel Naprous, Chris Newton, Ray Nicholas, Bela Orsanyi, Sam Parham, Ian Pead, Justin Pearson, Martin Pemberton, Heather Phillips, Rashid Phoenix, Andy Pilgrim, Christopher Pocock, Curtis Rivers, Marcus Shakesheff, Matt Sherren, Anthony Skrimshire, Mark Slaughter, Karen Smithson, Mark Southworth, Helen Steinway Bailey, Shane Steyn, Matthew Stirling, John Street, Gaspar Szabo, Gabor Szeman, Roy Taylor, Gyula Toth, Tony Van Silva, Reg Wayment, Linda Weal, Richard J Wheeldon, Heron White, Maxine Whittaker, Simon Whyman, Donna C. Williams, Lou Wong, Annabel E. Wood, Liang Yang, and Steen Young\\n\\n2016\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Michiel Huisman, Hannah Murray, Brenock O'Connor, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams, and Tom Wlaschiha\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\n2017\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean-Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carice Van Houten, Gemma Whelan, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, and Leo Woodruff\\n\\n2018\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, James Faulkner, Jerome Flynn, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Kristofer Hivju, Tom Hopper, Anton Lesser, Rory McCann, Sophie Turner, Carice Van Houten, Gemma Whelan, Rupert Vansittart, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Mark Archer, Luis Miguel Arranz, Ferenc Berecz, Richard Bradshaw, Michael Byrch, Nick Chopping, Jonathan Cohen, Chris Cox, Jake Cox, James Cox, David Cronnelly, Matt Crook, Ricardo Cruz Jr., Jason Curle, Levan Doran, Daniel Euston, Bradley Farmer, Pete Ford, Vladimir Furdik, Eduardo Gago, Angel Gomez, David Grant, Lawrence Hansen, Richard Hansen, Nicklas Hansson, Rob Hayns, Bobby Holland Hanton, Paul Howell, Radoslav Ignatov, Rowley Irlam, Erol Ismail, Orsányi Iván, Sonny Louis, Leigh Maddern, Jonny McBride, Leona McCarron, Kim McGarrity, Trayan Milenov-Troy, Sian Milne, David Newton, Jason Oettle, Radoslav Parvanov, Ian Pead, Andy Pilgrim, Oleg Podobin, Marc Redmond, Andrej Riabokon, Florian Robin, Doug Robson, Fabio Santos, Stanislav Satko, Paul Shapcott, Mark Slaughter, CC Smiff, James Stewart, Jonny Stockwell, Ryan Stuart, Gáspár Szabó, Lukas Tomsik, Marek Toth, Teodor Tzolov, Raycho Vasilev, Calvin Warrington-Heasman, Annabel Wood, Leo Woodruff, and Lewis Young\\n\\n===Society of Camera Operators Awards===\\nThe Society of Camera Operators Awards are awarded annually to honor the creative contribution of the camera operator in the motion picture and television industries. ''Game of Thrones'' has been nominated one time.\\n\\n+ Society of Camera Operators Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 51}, 'embedding': None, 'id': 'fa0383000cc031e9b5bf9310de392e2e'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has been nominated one time.\\n\\n+ Society of Camera Operators Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2017\\n\\nCamera Operator of the Year – Television\\n\\nSean Savage\\n\\n===Visual Effects Society===\\nVisual Effects Society honors visual effects in film, television, commercials, music videos and video games. At the 11th annual Visual Effects Society awards, the show won four awards, the most of the TV shows nominated. In 2014 the show was the most successful TV nominee at the Visual Effects Society awards for the second year in a row, winning three of its four nominations. In 2015 ''Game of Thrones'' was the most successful TV nominee at the Visual Effects Society awards for the third consecutive year, winning three of its five nominations. In 2016 the show was the most successful TV nominee at the Visual Effects Society awards for the fourth consecutive year, winning four of its nine nominations. ''Game of Thrones'' has received twenty six awards out of forty seven nominations.\\n\\n+ Visual Effects Society Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 52}, 'embedding': None, 'id': '551c2494a82a4f6daf5dd9031fafc7e8'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has received twenty six awards out of forty seven nominations.\\n\\n+ Visual Effects Society Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nHenry Badgett, Mark Brown, Rafael Morant, James Sutton (for \"Fire and Blood\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nMarkus Kuha, Damien Macé, Dante Harbridge Robinson, Fani Vassiadi (for \"The Icewall\")\\n\\nOutstanding Supporting Visual Effects in a Broadcast Program\\n\\nLucy Ainsworth-Taylor, Angela Barson, Ed Bruce, Adam McInnes (for \"Winter Is Coming\")\\n\\n2013\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nIrfan Celik, Florian Friedmann, Ingo Schachner, Chris Stenner (for \"Training the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nFalk Boje, Esther Engel, Alexey Kuchinsky, Klaus Wuchta (for \"White Walker Army\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nRene Borst, Thilo Ewers, Adam Figielski, Jonas Stuckenbrock (for \"Pyke\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nRainer Gombos, Steve Kullback, Sven Martin, Juri Stanossek (for \"Valar Morghulis\")\\n\\n2014\\n\\nOutstanding Animated Character in a Broadcast\\n\\nPhilip Meyer, Ingo Schachner, Travis Nobles, Florian Friedmann (for \"Raising the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nKirk Brillon, Steve Gordon, Geoff Sayer, Winston Lee (for \"The Climb\")\\n\\nOutstanding Created Environment in a Broadcast Program\\n\\nPatrick Zentis, Mayur Patel, Nitin Singh, Tim Alexander (for \"The Climb\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nSteve Kullback, Joe Bauer, Jörn Großhans, Sven Martin (for \"Valar Dohaeris\")\\n\\n2015\\n\\nOutstanding Performance of an Animated Character in a Commercial, Broadcast Program, or Video Game\\n\\nPhilip Meyer, Thomas Kutschera, Igor Majdandzic, and Mark Spindler (for \"Drogon\")\\n\\nOutstanding Visual Effects in a Visual Effects-Driven Photoreal/Live Action Broadcast Program\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nOutstanding Created Environment in a Commercial, Broadcast Program, or Video Game\\n\\nRene Borst, Christian Zilliken, Jan Burda, Steffen Metzner (for \"Braavos Establisher\")\\n\\nOutstanding Compositing in a Photoreal/Live Action Broadcast Program\\n\\nKeegan Douglas, Okan Ataman, Brian Fortune, David Lopez (for \"Wight Attack\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon (for \"The Dance of Dragons\")\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer (for \"Mother\\'s Mercy\" – \"Wounded Drogon\")\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen (for \"The Dance of Dragons\" – \"Drogon Arena Rescue\")\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur (for \"City of Volantis\")\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson (for \"Drogon Arena\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés (for \"Hardhome\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela (for \"Hardhome\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"Drogon Arena\")\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding (for \"Drogon Lair\")\\n\\n2017\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway (for \"Battle of the Bastards\")\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback (for \"Battle of the Bastards\" – \"Drogon\")\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben (for \"Home\" – \"Emaciated Dragon\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik (for \"The Winds of Winter\" – \"Citadel\")\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies (for \"Battle of the Bastards\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen (for \"Battle of the Bastards\")\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson (for \"Battle of the Bastards\" – \"Retaking Winterfell\")\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren (for \"The Door\" – \"Land of Always Winter\")\\n\\n2018\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Chris Baird, David Ramos, Sam Conway (for \"Beyond the Wall\")\\n\\nOutstanding Animated Character in an Episode or Real-Time Project\\n\\nPaul Story, Todd Labonte, Matthew Muntean, Nicholas Wilson (for \"Beyond the Wall\" – \"Zombie Polar Bear\")\\n\\nJonathan Symmonds, Thomas Kutschera, Philipp Winterstein, Andreas Krieg (for \"Eastwatch\" – \"Drogon Meets Jon\")\\n\\nMurray Stevenson, Jason Snyman, Jenn Taylor, Florian Friedmann (for \"The Spoils of War\" – \"Drogon Loot Train Attack\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDaniel Villalba, Antonio Lado, José Luis Barreiro, Isaac de la Pompa (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nPatrice Poissant, Deak Ferrand, Dominic Daigle, Gabriel Morin (for \"Eastwatch\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nManuel Ramírez, Óscar Márquez, Pablo Hernández, David Gacituaga (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Hullin, Dominik Kirouac, Sylvain Nouveau, Nathan Arbuckle (for \"The Dragon and the Wolf\" – \"Wall Destruction\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nÓscar Perea, Santiago Martos, David Esteve, Michael Crane (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Montminy Brodeur, Xavier Fourmond, Reuben Barkataki, Sébastien Raets (for \"Eastwatch\")\\n\\nDom Hellier, Thijs Noij, Edwin Holdsworth, Giacomo Matteucci (for \"The Spoils of War\" – \"Loot Train Attack\")\\n\\n===Writers Guild of America Awards===\\nFirst presented in 1949, the Writers Guild of America Award recognizes the work of film, television and radio screenwriters. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 53}, 'embedding': None, 'id': '87da6b4aee807e4ecdf9050051ad32a8'}>,\n",
+       "  <Document: {'content': '2012\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nHenry Badgett, Mark Brown, Rafael Morant, James Sutton (for \"Fire and Blood\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nMarkus Kuha, Damien Macé, Dante Harbridge Robinson, Fani Vassiadi (for \"The Icewall\")\\n\\nOutstanding Supporting Visual Effects in a Broadcast Program\\n\\nLucy Ainsworth-Taylor, Angela Barson, Ed Bruce, Adam McInnes (for \"Winter Is Coming\")\\n\\n2013\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nIrfan Celik, Florian Friedmann, Ingo Schachner, Chris Stenner (for \"Training the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nFalk Boje, Esther Engel, Alexey Kuchinsky, Klaus Wuchta (for \"White Walker Army\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nRene Borst, Thilo Ewers, Adam Figielski, Jonas Stuckenbrock (for \"Pyke\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nRainer Gombos, Steve Kullback, Sven Martin, Juri Stanossek (for \"Valar Morghulis\")\\n\\n2014\\n\\nOutstanding Animated Character in a Broadcast\\n\\nPhilip Meyer, Ingo Schachner, Travis Nobles, Florian Friedmann (for \"Raising the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nKirk Brillon, Steve Gordon, Geoff Sayer, Winston Lee (for \"The Climb\")\\n\\nOutstanding Created Environment in a Broadcast Program\\n\\nPatrick Zentis, Mayur Patel, Nitin Singh, Tim Alexander (for \"The Climb\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nSteve Kullback, Joe Bauer, Jörn Großhans, Sven Martin (for \"Valar Dohaeris\")\\n\\n2015\\n\\nOutstanding Performance of an Animated Character in a Commercial, Broadcast Program, or Video Game\\n\\nPhilip Meyer, Thomas Kutschera, Igor Majdandzic, and Mark Spindler (for \"Drogon\")\\n\\nOutstanding Visual Effects in a Visual Effects-Driven Photoreal/Live Action Broadcast Program\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nOutstanding Created Environment in a Commercial, Broadcast Program, or Video Game\\n\\nRene Borst, Christian Zilliken, Jan Burda, Steffen Metzner (for \"Braavos Establisher\")\\n\\nOutstanding Compositing in a Photoreal/Live Action Broadcast Program\\n\\nKeegan Douglas, Okan Ataman, Brian Fortune, David Lopez (for \"Wight Attack\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon (for \"The Dance of Dragons\")\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer (for \"Mother\\'s Mercy\" – \"Wounded Drogon\")\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen (for \"The Dance of Dragons\" – \"Drogon Arena Rescue\")\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur (for \"City of Volantis\")\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson (for \"Drogon Arena\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés (for \"Hardhome\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela (for \"Hardhome\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"Drogon Arena\")\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding (for \"Drogon Lair\")\\n\\n2017\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway (for \"Battle of the Bastards\")\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback (for \"Battle of the Bastards\" – \"Drogon\")\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben (for \"Home\" – \"Emaciated Dragon\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik (for \"The Winds of Winter\" – \"Citadel\")\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies (for \"Battle of the Bastards\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen (for \"Battle of the Bastards\")\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson (for \"Battle of the Bastards\" – \"Retaking Winterfell\")\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren (for \"The Door\" – \"Land of Always Winter\")\\n\\n2018\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Chris Baird, David Ramos, Sam Conway (for \"Beyond the Wall\")\\n\\nOutstanding Animated Character in an Episode or Real-Time Project\\n\\nPaul Story, Todd Labonte, Matthew Muntean, Nicholas Wilson (for \"Beyond the Wall\" – \"Zombie Polar Bear\")\\n\\nJonathan Symmonds, Thomas Kutschera, Philipp Winterstein, Andreas Krieg (for \"Eastwatch\" – \"Drogon Meets Jon\")\\n\\nMurray Stevenson, Jason Snyman, Jenn Taylor, Florian Friedmann (for \"The Spoils of War\" – \"Drogon Loot Train Attack\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDaniel Villalba, Antonio Lado, José Luis Barreiro, Isaac de la Pompa (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nPatrice Poissant, Deak Ferrand, Dominic Daigle, Gabriel Morin (for \"Eastwatch\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nManuel Ramírez, Óscar Márquez, Pablo Hernández, David Gacituaga (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Hullin, Dominik Kirouac, Sylvain Nouveau, Nathan Arbuckle (for \"The Dragon and the Wolf\" – \"Wall Destruction\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nÓscar Perea, Santiago Martos, David Esteve, Michael Crane (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Montminy Brodeur, Xavier Fourmond, Reuben Barkataki, Sébastien Raets (for \"Eastwatch\")\\n\\nDom Hellier, Thijs Noij, Edwin Holdsworth, Giacomo Matteucci (for \"The Spoils of War\" – \"Loot Train Attack\")\\n\\n===Writers Guild of America Awards===\\nFirst presented in 1949, the Writers Guild of America Award recognizes the work of film, television and radio screenwriters. \\'\\'Game of Thrones\\'\\' has been nominated ten times.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 54}, 'embedding': None, 'id': '4764a31fb6612f4aff3cf154a399f02b'}>,\n",
+       "  <Document: {'content': '2012\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nHenry Badgett, Mark Brown, Rafael Morant, James Sutton (for \"Fire and Blood\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nMarkus Kuha, Damien Macé, Dante Harbridge Robinson, Fani Vassiadi (for \"The Icewall\")\\n\\nOutstanding Supporting Visual Effects in a Broadcast Program\\n\\nLucy Ainsworth-Taylor, Angela Barson, Ed Bruce, Adam McInnes (for \"Winter Is Coming\")\\n\\n2013\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nIrfan Celik, Florian Friedmann, Ingo Schachner, Chris Stenner (for \"Training the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nFalk Boje, Esther Engel, Alexey Kuchinsky, Klaus Wuchta (for \"White Walker Army\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nRene Borst, Thilo Ewers, Adam Figielski, Jonas Stuckenbrock (for \"Pyke\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nRainer Gombos, Steve Kullback, Sven Martin, Juri Stanossek (for \"Valar Morghulis\")\\n\\n2014\\n\\nOutstanding Animated Character in a Broadcast\\n\\nPhilip Meyer, Ingo Schachner, Travis Nobles, Florian Friedmann (for \"Raising the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nKirk Brillon, Steve Gordon, Geoff Sayer, Winston Lee (for \"The Climb\")\\n\\nOutstanding Created Environment in a Broadcast Program\\n\\nPatrick Zentis, Mayur Patel, Nitin Singh, Tim Alexander (for \"The Climb\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nSteve Kullback, Joe Bauer, Jörn Großhans, Sven Martin (for \"Valar Dohaeris\")\\n\\n2015\\n\\nOutstanding Performance of an Animated Character in a Commercial, Broadcast Program, or Video Game\\n\\nPhilip Meyer, Thomas Kutschera, Igor Majdandzic, and Mark Spindler (for \"Drogon\")\\n\\nOutstanding Visual Effects in a Visual Effects-Driven Photoreal/Live Action Broadcast Program\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nOutstanding Created Environment in a Commercial, Broadcast Program, or Video Game\\n\\nRene Borst, Christian Zilliken, Jan Burda, Steffen Metzner (for \"Braavos Establisher\")\\n\\nOutstanding Compositing in a Photoreal/Live Action Broadcast Program\\n\\nKeegan Douglas, Okan Ataman, Brian Fortune, David Lopez (for \"Wight Attack\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon (for \"The Dance of Dragons\")\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer (for \"Mother\\'s Mercy\" – \"Wounded Drogon\")\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen (for \"The Dance of Dragons\" – \"Drogon Arena Rescue\")\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur (for \"City of Volantis\")\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson (for \"Drogon Arena\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés (for \"Hardhome\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela (for \"Hardhome\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"Drogon Arena\")\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding (for \"Drogon Lair\")\\n\\n2017\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway (for \"Battle of the Bastards\")\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback (for \"Battle of the Bastards\" – \"Drogon\")\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben (for \"Home\" – \"Emaciated Dragon\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik (for \"The Winds of Winter\" – \"Citadel\")\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies (for \"Battle of the Bastards\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen (for \"Battle of the Bastards\")\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson (for \"Battle of the Bastards\" – \"Retaking Winterfell\")\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren (for \"The Door\" – \"Land of Always Winter\")\\n\\n2018\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Chris Baird, David Ramos, Sam Conway (for \"Beyond the Wall\")\\n\\nOutstanding Animated Character in an Episode or Real-Time Project\\n\\nPaul Story, Todd Labonte, Matthew Muntean, Nicholas Wilson (for \"Beyond the Wall\" – \"Zombie Polar Bear\")\\n\\nJonathan Symmonds, Thomas Kutschera, Philipp Winterstein, Andreas Krieg (for \"Eastwatch\" – \"Drogon Meets Jon\")\\n\\nMurray Stevenson, Jason Snyman, Jenn Taylor, Florian Friedmann (for \"The Spoils of War\" – \"Drogon Loot Train Attack\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDaniel Villalba, Antonio Lado, José Luis Barreiro, Isaac de la Pompa (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nPatrice Poissant, Deak Ferrand, Dominic Daigle, Gabriel Morin (for \"Eastwatch\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nManuel Ramírez, Óscar Márquez, Pablo Hernández, David Gacituaga (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Hullin, Dominik Kirouac, Sylvain Nouveau, Nathan Arbuckle (for \"The Dragon and the Wolf\" – \"Wall Destruction\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nÓscar Perea, Santiago Martos, David Esteve, Michael Crane (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Montminy Brodeur, Xavier Fourmond, Reuben Barkataki, Sébastien Raets (for \"Eastwatch\")\\n\\nDom Hellier, Thijs Noij, Edwin Holdsworth, Giacomo Matteucci (for \"The Spoils of War\" – \"Loot Train Attack\")\\n\\n===Writers Guild of America Awards===\\nFirst presented in 1949, the Writers Guild of America Award recognizes the work of film, television and radio screenwriters. \\'\\'Game of Thrones\\'\\' has been nominated ten times.\\n\\nIn June 2013, the show was placed at number 40 on the list of the 101 best written shows of all time by the Writers Guild of America, assessing series from the previous 70 years. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 55}, 'embedding': None, 'id': '9dd81c3813c1ade5823899ab8fc42693'}>,\n",
+       "  <Document: {'content': \"In June 2013, the show was placed at number 40 on the list of the 101 best written shows of all time by the Writers Guild of America, assessing series from the previous 70 years. HBO had attempted to submit the show for a third consecutive Writers Guild Award for Best Drama Series, but a technical error during the process meant it was not submitted and the fault was discovered too late for the show to be entered.\\n\\n+ Writers Guild of America Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 56}, 'embedding': None, 'id': 'dbbb9043d2357a422802afaa1112346e'}>,\n",
+       "  <Document: {'content': 'HBO had attempted to submit the show for a third consecutive Writers Guild Award for Best Drama Series, but a technical error during the process meant it was not submitted and the fault was discovered too late for the show to be entered.\\n\\n+ Writers Guild of America Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\nNew Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\n2013\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, Vanessa Taylor, D. B. Weiss\\n\\n2015\\n\\nEpisodic Drama\\n\\nGeorge R. R. Martin (for \"The Lion and the Rose\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, D. B. Weiss\\n\\n2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"Mother\\'s Mercy\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2017\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"The Winds of Winter\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2018\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n==Significant critical awards==\\n\\n===Critics\\' Choice Television Awards===\\nKit Harington was nominated for a Critics\\' Choice Television Award in 2016\\n\\nThe Critics\\' Choice Television Award is an annual accolade given by the Broadcast Film Critics Association since 2011 in order to recognize the most significant achievements in television. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 57}, 'embedding': None, 'id': 'be835a5ab9536db84f3ed288a38d04f'}>,\n",
+       "  <Document: {'content': '2012\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\nNew Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\n2013\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, Vanessa Taylor, D. B. Weiss\\n\\n2015\\n\\nEpisodic Drama\\n\\nGeorge R. R. Martin (for \"The Lion and the Rose\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, D. B. Weiss\\n\\n2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"Mother\\'s Mercy\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2017\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"The Winds of Winter\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2018\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n==Significant critical awards==\\n\\n===Critics\\' Choice Television Awards===\\nKit Harington was nominated for a Critics\\' Choice Television Award in 2016\\n\\nThe Critics\\' Choice Television Award is an annual accolade given by the Broadcast Film Critics Association since 2011 in order to recognize the most significant achievements in television. \\'\\'Game of Thrones\\'\\' has won two out of twenty nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 58}, 'embedding': None, 'id': 'e54728989b7e39573a711df662755cfe'}>,\n",
+       "  <Document: {'content': '2012\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\nNew Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\n2013\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, Vanessa Taylor, D. B. Weiss\\n\\n2015\\n\\nEpisodic Drama\\n\\nGeorge R. R. Martin (for \"The Lion and the Rose\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, D. B. Weiss\\n\\n2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"Mother\\'s Mercy\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2017\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"The Winds of Winter\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2018\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n==Significant critical awards==\\n\\n===Critics\\' Choice Television Awards===\\nKit Harington was nominated for a Critics\\' Choice Television Award in 2016\\n\\nThe Critics\\' Choice Television Award is an annual accolade given by the Broadcast Film Critics Association since 2011 in order to recognize the most significant achievements in television. \\'\\'Game of Thrones\\'\\' has won two out of twenty nominations.\\n\\n+ Critics\\' Choice Television Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 59}, 'embedding': None, 'id': 'a3ab2ab1afebdd79aa5f43c2e6790ca1'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has won two out of twenty nominations.\\n\\n+ Critics' Choice Television Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\n2012\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n2014\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n2015\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nMost Bingeworthy Show\\n\\n2016\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nMost Bingeworthy Show\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\n2018\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\n===Golden Globe Awards===\\nPeter Dinklage won one Golden Globe Award in 2012\\n\\nThe Golden Globe Awards are awarded annually by the Hollywood Foreign Press Association to honor the best achievements in film and television. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 60}, 'embedding': None, 'id': 'c182243b8c591a6eca34ab4dc71c12f8'}>,\n",
+       "  <Document: {'content': \"2011\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\n2012\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n2014\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n2015\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nMost Bingeworthy Show\\n\\n2016\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nMost Bingeworthy Show\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\n2018\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\n===Golden Globe Awards===\\nPeter Dinklage won one Golden Globe Award in 2012\\n\\nThe Golden Globe Awards are awarded annually by the Hollywood Foreign Press Association to honor the best achievements in film and television. In 2012, The show was nominated for 2 Golden Globe awards including Best Drama Series and Best Supporting Actor, taken by Dinklage. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 61}, 'embedding': None, 'id': '7373e316c6957137bbb6cb354adfdb97'}>,\n",
+       "  <Document: {'content': \"In 2012, The show was nominated for 2 Golden Globe awards including Best Drama Series and Best Supporting Actor, taken by Dinklage. ''Game of Thrones'' has won one award out of seven nominations.\\n\\n+ Golden Globe Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Television Series – Drama\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest Television Series – Drama\\n\\n''Game of Thrones''\\n\\n2016\\n\\nBest Television Series – Drama\\n\\n''Game of Thrones''\\n\\n2017\\n\\nBest Television Series – Drama\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\n2018\\n\\nBest Television Series - Drama\\n\\n''Game of Thrones''\\n\\n===Guinness World Records===\\nThe ''Guinness World Records'' is a reference book published annually, containing a collection of world records. Game of Thrones currently holds six records.\\n\\n+ Guinness World Records received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 62}, 'embedding': None, 'id': '6b0f83b3850cd3682ceef5509fff7afb'}>,\n",
+       "  <Document: {'content': 'Game of Thrones currently holds six records.\\n\\n+ Guinness World Records received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nMost pirated TV program\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost viewers sharing a single torrent file simultaneously\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nLargest TV drama simulcast\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost \"in demand\" TV show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost Emmy Awards for a drama series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost VES Awards won by a TV series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===Hugo Awards===\\nGeorge R. R. Martin won a Hugo Award for \"Blackwater\", along with director Neil Marshall, in 2013\\n\\nThe Hugo Awards are a set of awards given annually for the best science fiction or fantasy works and achievements of the previous year. \\'\\'Game of Thrones\\'\\' has won three out of six nominations.\\n\\n+ Hugo Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 63}, 'embedding': None, 'id': 'a1ea9258aad19b9478de75bb0d75ac9f'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won three out of six nominations.\\n\\n+ Hugo Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Dramatic Presentation, Long Form\\n\\nDavid Benioff, D. B. Weiss, Bryan Cogman, Jane Espenson, George R. R. Martin, Tim Van Patten, Brian Kirk, Daniel Minahan, and Alan Taylor for \\'\\'Game of Thrones\\'\\' – Season 1\\n\\n2013\\n\\nBest Dramatic Presentation, Short Form\\n\\nNeil Marshall (director) and George R. R. Martin (writer) (for \"Blackwater\")\\n\\n2014\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and David Nutter (director) (for \"The Rains of Castamere\")\\n\\n2015\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Alex Graves (director) (for \"The Mountain and the Viper\")\\n\\n2017\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Miguel Sapochnik (director) (for \"Battle of the Bastards\")\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Jack Bender (director) (for \"The Door\")\\n\\n===IGN Awards===\\nSean Bean won an IGN Awards in 2011\\n\\nThe IGN Awards are chosen annually by the IGN editors, honoring the best in film, television, games, comics and anime. \\'\\'Game of Thrones\\'\\' has won nine out of thirty two nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 64}, 'embedding': None, 'id': '5af0f951ebf69983689f9b0946c09f0f'}>,\n",
+       "  <Document: {'content': '2012\\n\\nBest Dramatic Presentation, Long Form\\n\\nDavid Benioff, D. B. Weiss, Bryan Cogman, Jane Espenson, George R. R. Martin, Tim Van Patten, Brian Kirk, Daniel Minahan, and Alan Taylor for \\'\\'Game of Thrones\\'\\' – Season 1\\n\\n2013\\n\\nBest Dramatic Presentation, Short Form\\n\\nNeil Marshall (director) and George R. R. Martin (writer) (for \"Blackwater\")\\n\\n2014\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and David Nutter (director) (for \"The Rains of Castamere\")\\n\\n2015\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Alex Graves (director) (for \"The Mountain and the Viper\")\\n\\n2017\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Miguel Sapochnik (director) (for \"Battle of the Bastards\")\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Jack Bender (director) (for \"The Door\")\\n\\n===IGN Awards===\\nSean Bean won an IGN Awards in 2011\\n\\nThe IGN Awards are chosen annually by the IGN editors, honoring the best in film, television, games, comics and anime. \\'\\'Game of Thrones\\'\\' has won nine out of thirty two nominations.\\n\\n+ IGN Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 65}, 'embedding': None, 'id': '56d8fdb57d84d30b1f45483117237198'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won nine out of thirty two nominations.\\n\\n+ IGN Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Actor\\n\\nPeter Dinklage\\n\\nBest TV Actress\\n\\nEmilia Clarke\\n\\nBest TV Episode\\n\\n\"Baelor\"\\n\\nBest TV Twist\\n\\n\"Off with his head!\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 66}, 'embedding': None, 'id': '396d63b526bc6b31b874dcb3ba9f0075'}>,\n",
+       "  <Document: {'content': '2011\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Actor\\n\\nPeter Dinklage\\n\\nBest TV Actress\\n\\nEmilia Clarke\\n\\nBest TV Episode\\n\\n\"Baelor\"\\n\\nBest TV Twist\\n\\n\"Off with his head!\"Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===IGN People\\'s Choice Award===\\nJack Gleeson won an IGN People\\'s Choice Award in 2012 and 2013\\n\\nThe IGN People\\'s Choice Awards are voted on annually by the general public, they honor the best in film, television, games, comics and anime \\'\\'Game of Thrones\\'\\' has won sixteen out of thirty two nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 67}, 'embedding': None, 'id': '8cf42e9ea673a03428eb712bc08236cf'}>,\n",
+       "  <Document: {'content': 'Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===IGN People\\'s Choice Award===\\nJack Gleeson won an IGN People\\'s Choice Award in 2012 and 2013\\n\\nThe IGN People\\'s Choice Awards are voted on annually by the general public, they honor the best in film, television, games, comics and anime \\'\\'Game of Thrones\\'\\' has won sixteen out of thirty two nominations.\\n\\n+ IGN People\\'s Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 68}, 'embedding': None, 'id': '6814904214fb07d49d8ba5204d7e80d4'}>,\n",
+       "  <Document: {'content': 'Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===IGN People\\'s Choice Award===\\nJack Gleeson won an IGN People\\'s Choice Award in 2012 and 2013\\n\\nThe IGN People\\'s Choice Awards are voted on annually by the general public, they honor the best in film, television, games, comics and anime \\'\\'Game of Thrones\\'\\' has won sixteen out of thirty two nominations.\\n\\n+ IGN People\\'s Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Actor\\n\\nPeter Dinklage\\n\\nBest TV Actress\\n\\nEmilia Clarke\\n\\nBest TV Episode\\n\\n\"Baelor\"\\n\\nBest TV Twist\\n\\n\"Off with his head!\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 69}, 'embedding': None, 'id': '2fa45f370605d04e56cc43a521eb3be3'}>,\n",
+       "  <Document: {'content': '2011\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Actor\\n\\nPeter Dinklage\\n\\nBest TV Actress\\n\\nEmilia Clarke\\n\\nBest TV Episode\\n\\n\"Baelor\"\\n\\nBest TV Twist\\n\\n\"Off with his head!\"Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===National Television Awards===\\nThe National Television Awards are presented in an annual award show broadcast by the ITV network. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 70}, 'embedding': None, 'id': '2ec70cefca2bfadb30bd374b1ea4b711'}>,\n",
+       "  <Document: {'content': 'Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===National Television Awards===\\nThe National Television Awards are presented in an annual award show broadcast by the ITV network. The results are voted on by the general public. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 71}, 'embedding': None, 'id': 'a93c8eeedbbb1f89d9b21bd61f2bc7c1'}>,\n",
+       "  <Document: {'content': 'Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===National Television Awards===\\nThe National Television Awards are presented in an annual award show broadcast by the ITV network. The results are voted on by the general public. \\'\\'Game of Thrones\\'\\' has been nominated four times.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 72}, 'embedding': None, 'id': '68071bb369a1c3d877e809db47b2083f'}>,\n",
+       "  <Document: {'content': 'Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===National Television Awards===\\nThe National Television Awards are presented in an annual award show broadcast by the ITV network. The results are voted on by the general public. \\'\\'Game of Thrones\\'\\' has been nominated four times.\\n\\n+ National Television Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 73}, 'embedding': None, 'id': '76be57004db474749d2591e632975299'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has been nominated four times.\\n\\n+ National Television Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nMultichannel show\\n\\n''Game of Thrones''\\n\\n2016\\n\\nBest International Show\\n\\n''Game of Thrones''\\n\\n2017\\n\\nBest Drama\\n\\n''Game of Thrones''\\n\\n2018\\n\\nBest Drama\\n\\n''Game of Thrones''\\n\\n===People's Choice Awards===\\nThe People's Choice Awards recognize the people and the work of popular culture. ''Game of Thrones'' has been nominated twelve times.\\n\\n+ People's Choice Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 74}, 'embedding': None, 'id': 'cdaa1bcc4d83321d4e05d8ad41898e49'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated twelve times.\\n\\n+ People\\'s Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nFavorite Cable TV Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2013\\n\\nFavorite Thriller Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nFavorite Premium Cable TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite TV Anti-Hero\\n\\nNikolaj Coster-Waldau as Jaime Lannister\\n\\nFavorite Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n2015\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n2016\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\nFavorite Cable Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n2017\\n\\nFavorite Premium Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n===Peabody Awards===\\nNikolaj Coster-Waldau accepted the Peabody Award on behalf of the show in 2012\\n\\nThe Peabody Award recognizes excellence in various platforms of the media, including film, television, and radio. The award \"spotlights instances of how electronic media can teach, expand our horizons, defend the public interest, or encourage empathy with others\". \\'\\'Game of Thrones\\'\\' was honored with a Peabody Award in 2011 for its first season. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 75}, 'embedding': None, 'id': 'a25ee34f0e11030a2b5703bb0a2b144'}>,\n",
+       "  <Document: {'content': 'The award \"spotlights instances of how electronic media can teach, expand our horizons, defend the public interest, or encourage empathy with others\". \\'\\'Game of Thrones\\'\\' was honored with a Peabody Award in 2011 for its first season. The award association gave the following description of the show:\\n\\n+ Peabody Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nPeabody Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===Poppy Awards===\\nSophie Turner won one Poppy Award in 2016\\n\\nThe Poppy Awards, formerly known as the EWwys, honors the Emmy-snubbed shows and actors of the year. \\'\\'Game of Thrones\\'\\' has received six awards from ten nomination.\\n\\n+ Poppy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 76}, 'embedding': None, 'id': '74856b99c3b75572cacba18e876296b6'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has received six awards from ten nomination.\\n\\n+ Poppy Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Supporting Actress, Drama\\n\\nEmilia Clarke\\n\\nBest Actor, Drama\\n\\nSean Bean\\n\\n2012\\n\\nBest Supporting Actress, Drama\\n\\nLena Headey\\n\\n2013\\n\\nBest Supporting Actress, Drama\\n\\nNatalie Dormer\\n\\n2014\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nBest Supporting Actor, Drama\\n\\nCharles Dance\\n\\nBest Guest Actor, Drama\\n\\nPedro Pascal\\n\\n2015\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nSophie Turner\\n\\n2016\\n\\nBest Supporting Actress, Drama\\n\\nSophie Turner\\n\\n===Satellite Awards===\\nPeter Dinklage won one Satellite Award in 2011\\n\\nThe Satellite Award is given annually by the International Press Academy (IPA) to honor the best work in the entertainment industry. ''Game of Thrones'' has received three awards out of fourteen nominations.\\n\\nIn 2011, Dinklage received the Satellite Award for Best Supporting Actor – Series, Miniseries, or Television Film, and received three more nominations (2012, 2014–2015). In 2014, ''Game of Thrones'' won the Satellite Award for Best Television Series – Genre, while Nikolaj Coster-Waldau and Emilia Clarke received nominations for Best Supporting Actor and Best Supporting Actress, respectively.\\n\\n+ Satellite Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 77}, 'embedding': None, 'id': '4a636011e8a011bffdab64d74486b2b5'}>,\n",
+       "  <Document: {'content': \"In 2014, ''Game of Thrones'' won the Satellite Award for Best Television Series – Genre, while Nikolaj Coster-Waldau and Emilia Clarke received nominations for Best Supporting Actor and Best Supporting Actress, respectively.\\n\\n+ Satellite Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nEmilia Clarke\\n\\n2014\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2016\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nLena Headey\\n\\n2017\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\n===Saturn Awards===\\nMaisie Williams won Saturn Award in 2015 and 2019\\n\\nThe Saturn Awards are awarded annually by the Academy of Science Fiction, Fantasy, and Horror Films to honor the top works mainly in science fiction, fantasy, and horror in film, television, and home video. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 78}, 'embedding': None, 'id': '1f64651c40cfffc1346cd0bf30c8590e'}>,\n",
+       "  <Document: {'content': \"2011\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nEmilia Clarke\\n\\n2014\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2016\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nLena Headey\\n\\n2017\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\n===Saturn Awards===\\nMaisie Williams won Saturn Award in 2015 and 2019\\n\\nThe Saturn Awards are awarded annually by the Academy of Science Fiction, Fantasy, and Horror Films to honor the top works mainly in science fiction, fantasy, and horror in film, television, and home video. ''Game of Thrones'' has won six out of thirty-four nominations.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 79}, 'embedding': None, 'id': 'a936739ec4887f5ed91d02391f835839'}>,\n",
+       "  <Document: {'content': \"2011\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nEmilia Clarke\\n\\n2014\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2016\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nLena Headey\\n\\n2017\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\n===Saturn Awards===\\nMaisie Williams won Saturn Award in 2015 and 2019\\n\\nThe Saturn Awards are awarded annually by the Academy of Science Fiction, Fantasy, and Horror Films to honor the top works mainly in science fiction, fantasy, and horror in film, television, and home video. ''Game of Thrones'' has won six out of thirty-four nominations.\\n\\n+ Saturn Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 80}, 'embedding': None, 'id': '75dcd51931ca8ec695505307bb74e0f4'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has won six out of thirty-four nominations.\\n\\n+ Saturn Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Actor on Television\\n\\nSean Bean\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2013\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\n2014\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress on Television\\n\\nGwendoline Christie\\n\\nMichelle Fairley\\n\\nBest Performance by a Younger Actor on Television\\n\\nJack Gleeson\\n\\n2015\\n\\nBest Limited Run Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress on Television\\n\\nEmilia Clarke\\n\\nBest Performance by a Younger Actor on Television\\n\\nMaisie Williams\\n\\n2016\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O'Connor\\n\\n2017\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2018\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on a Television Series\\n\\nLena Headey\\n\\nBest Supporting Actor on a Television Series\\n\\nKit Harington\\n\\nNikolaj Coster-Waldau\\n\\n2019\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actor on a Television Series\\n\\nKit Harington\\n\\nBest Actress on a Television Series\\n\\nEmilia Clarke\\n\\nBest Supporting Actor on a Television Series\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Supporting Actress on a Television Series\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nSophie Turner\\n\\nBest Performance by a Younger Actor on a Television Series\\n\\nMaisie Williams\\n\\n===Scream Awards===\\nEmilia Clarke won one Scream Award in 2011\\n\\nThe Scream Awards were awarded annually to honor the top works mainly in the horror, sci-fi, and fantasy genres of feature films and television. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 81}, 'embedding': None, 'id': '13edc9e82300598b72526ce7944482ba'}>,\n",
+       "  <Document: {'content': \"2012\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Actor on Television\\n\\nSean Bean\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2013\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\n2014\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress on Television\\n\\nGwendoline Christie\\n\\nMichelle Fairley\\n\\nBest Performance by a Younger Actor on Television\\n\\nJack Gleeson\\n\\n2015\\n\\nBest Limited Run Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress on Television\\n\\nEmilia Clarke\\n\\nBest Performance by a Younger Actor on Television\\n\\nMaisie Williams\\n\\n2016\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O'Connor\\n\\n2017\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2018\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on a Television Series\\n\\nLena Headey\\n\\nBest Supporting Actor on a Television Series\\n\\nKit Harington\\n\\nNikolaj Coster-Waldau\\n\\n2019\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actor on a Television Series\\n\\nKit Harington\\n\\nBest Actress on a Television Series\\n\\nEmilia Clarke\\n\\nBest Supporting Actor on a Television Series\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Supporting Actress on a Television Series\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nSophie Turner\\n\\nBest Performance by a Younger Actor on a Television Series\\n\\nMaisie Williams\\n\\n===Scream Awards===\\nEmilia Clarke won one Scream Award in 2011\\n\\nThe Scream Awards were awarded annually to honor the top works mainly in the horror, sci-fi, and fantasy genres of feature films and television. ''Game of Thrones'' has won three out of eight nominations.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 82}, 'embedding': None, 'id': '24c6ede756058a9df83e287563bdda96'}>,\n",
+       "  <Document: {'content': \"2012\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Actor on Television\\n\\nSean Bean\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2013\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\n2014\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress on Television\\n\\nGwendoline Christie\\n\\nMichelle Fairley\\n\\nBest Performance by a Younger Actor on Television\\n\\nJack Gleeson\\n\\n2015\\n\\nBest Limited Run Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress on Television\\n\\nEmilia Clarke\\n\\nBest Performance by a Younger Actor on Television\\n\\nMaisie Williams\\n\\n2016\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O'Connor\\n\\n2017\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2018\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on a Television Series\\n\\nLena Headey\\n\\nBest Supporting Actor on a Television Series\\n\\nKit Harington\\n\\nNikolaj Coster-Waldau\\n\\n2019\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actor on a Television Series\\n\\nKit Harington\\n\\nBest Actress on a Television Series\\n\\nEmilia Clarke\\n\\nBest Supporting Actor on a Television Series\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Supporting Actress on a Television Series\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nSophie Turner\\n\\nBest Performance by a Younger Actor on a Television Series\\n\\nMaisie Williams\\n\\n===Scream Awards===\\nEmilia Clarke won one Scream Award in 2011\\n\\nThe Scream Awards were awarded annually to honor the top works mainly in the horror, sci-fi, and fantasy genres of feature films and television. ''Game of Thrones'' has won three out of eight nominations.\\n\\n+ Scream Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 83}, 'embedding': None, 'id': 'f05d0b0fd2f2c75f7499cc0de0cc2433'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won three out of eight nominations.\\n\\n+ Scream Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Fantasy Actor\\n\\nSean Bean\\n\\nBest Fantasy Actress\\n\\nLena Headey\\n\\nBest Supporting Actor\\n\\nPeter Dinklage\\n\\nBreakout Performance – Female\\n\\nEmilia Clarke\\n\\nBest Ensemble\\n\\nAmrita Acharia, Mark Addy, Alfie Allen, Josef Altin, Sean Bean, Susan Brown, Emilia Clarke, Nikolaj Coster-Waldau, Peter Dinklage, Ron Donachie, Michelle Fairley, Jerome Flynn, Elyes Gabel, Aidan Gillen, Jack Gleeson, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Richard Madden, Jason Momoa, Rory McCann, Ian McElhinney, Luke McEwan, Roxanne McKee, Dar Salim, Mark Stanley, Donald Sumpter, Sophie Turner and Maisie Williams\\n\\nMost Memorable Mutilation\\n\\n\"Head covered in molten gold\" (from \"A Golden Crown\")\\n\\nThe Ultimate Scream\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===Television Critics Association Awards===\\nThe TCA Awards are awarded annually by the Television Critics Association for outstanding achievements in television. \\'\\'Game of Thrones\\'\\' has won three out of sixteen nominations.\\n\\n+ Television Critics Association Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 84}, 'embedding': None, 'id': '6ee6d4fabf1b21e5e5ea9ead82956685'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has won three out of sixteen nominations.\\n\\n+ Television Critics Association Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\nOutstanding New Program\\n\\nIndividual Achievement in Drama\\n\\nPeter Dinklage\\n\\n2012\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\nIndividual Achievement in Drama\\n\\nPeter Dinklage\\n\\n2013\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\n2014\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\n2015\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\n2016\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\n2019\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\n==Other awards==\\n\\n===Dorian Awards===\\nThe Dorian Awards are awarded annually by the Gay and Lesbian Entertainment Critics Association. ''Game of Thrones'' has been nominated twice.\\n\\n+ Dorian Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2013\\n\\nTV Drama of the Year\\n\\n''Game of Thrones''\\n\\n2017\\n\\nTV Drama of the Year\\n\\n''Game of Thrones''\\n\\n===Dragon Awards===\\nThe Dragon Awards are awarded annually, winners are voted upon by the general public. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 85}, 'embedding': None, 'id': 'b399f5da100e0f13b9195457f18af303'}>,\n",
+       "  <Document: {'content': \"2013\\n\\nTV Drama of the Year\\n\\n''Game of Thrones''\\n\\n2017\\n\\nTV Drama of the Year\\n\\n''Game of Thrones''\\n\\n===Dragon Awards===\\nThe Dragon Awards are awarded annually, winners are voted upon by the general public. ''Game of Thrones'' has won one out of one nomination.\\n\\n+ Dragon Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nBest Science Fiction or Fantasy TV Series\\n\\n''Game of Thrones''\\n\\n===Empire Awards===\\nEmpire Award in 2015\\n\\nThe Empire Awards are awarded annually, winners are voted upon by the general public. ''Game of Thrones'' has won one out of three nominations.\\n\\n+ Empire Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nHero Award\\n\\nJosef Altin, Jacob Anderson, John Bradley, Thomas Brodie-Sangster, Dominic Carter, Dean-Charles Chapman, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Charles Dance, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead-Wright, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, Indira Varma and Maisie Williams\\n\\n2016\\n\\nBest TV Series\\n\\n''Game of Thrones''\\n\\n2017\\n\\nBest TV Series\\n\\n''Game of Thrones''\\n\\n===E! Online Best. Ever. TV. Awards===\\nThe E! \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 86}, 'embedding': None, 'id': 'e331add716751836c73624c57bc6cf9a'}>,\n",
+       "  <Document: {'content': \"2015\\n\\nHero Award\\n\\nJosef Altin, Jacob Anderson, John Bradley, Thomas Brodie-Sangster, Dominic Carter, Dean-Charles Chapman, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Charles Dance, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead-Wright, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, Indira Varma and Maisie Williams\\n\\n2016\\n\\nBest TV Series\\n\\n''Game of Thrones''\\n\\n2017\\n\\nBest TV Series\\n\\n''Game of Thrones''\\n\\n===E! Online Best. Ever. TV. Awards===\\nThe E! Online Best. Ever. TV. Awards are awarded annually. ''Game of Thrones'' has been nominated once.\\n\\n+ E! Online Best. Ever. TV. Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nOutstanding Drama Series\\n\\n''Game of Thrones''\\n\\n===GLAAD Media Awards===\\nThe GLAAD Media Awards are awarded annually by the Gay & Lesbian Alliance Against Defamation. ''Game of Thrones'' has been nominated once.\\n\\n+ GLAAD Media Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nOutstanding Drama Series\\n\\n''Game of Thrones''\\n\\n===Glamour Awards===\\nThe Glamour Awards are awarded annually. ''Game of Thrones'' has won two out of two nominations.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 87}, 'embedding': None, 'id': 'd96f9d0e907db127e17da80de9e72299'}>,\n",
+       "  <Document: {'content': \"2015\\n\\nOutstanding Drama Series\\n\\n''Game of Thrones''\\n\\n===Glamour Awards===\\nThe Glamour Awards are awarded annually. ''Game of Thrones'' has won two out of two nominations.\\n\\n+ Glamour Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\n2017\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\n===Gold Derby TV Awards===\\nDiana Rigg won a Gold Derby TV Awards in 2013, 2015 and 2018\\n\\nThe Gold Derby TV Awards are awarded annually to the best of television. ''Game of Thrones'' has won fifteen out of forty-seven nominations.\\n\\n+ Gold Derby TV Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 88}, 'embedding': None, 'id': '3a8607f5f865d9bc852397b34ac4a051'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won fifteen out of forty-seven nominations.\\n\\n+ Gold Derby TV Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBreakthrough Performer of the Year\\n\\nMaisie Williams\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2013\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nNikolaj Coster-Waldau\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nMichelle Fairley\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Rains of Castamere\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nCharles Dance\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nPedro Pascal\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Lion and the Rose\"\\n\\n\"The Watchers on the Wall\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\n2018\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Beyond the Wall\"\\n\\n\"The Spoils of War\"\\n\\n2019\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Episode\\n\\n\"A Knight of the Seven Kingdoms\"\\n\\n\"The Long Night\"\\n\\nBest Drama Actor\\n\\nKit Harington\\n\\nBest Drama Actress\\n\\nEmilia Clarke\\n\\nBest Drama Supporting Actor\\n\\nAlfie Allen\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nMaisie Williams\\n\\nBest Drama Guest Actress\\n\\nCarice van Houten\\n\\nBest Ensemble\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n===Golden Nymph Awards===\\nThe Golden Nymph Awards are awarded annually at the Monte-Carlo Television Festival. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 89}, 'embedding': None, 'id': 'c2241115284aa9e283bd3cb17c51b09f'}>,\n",
+       "  <Document: {'content': '2011\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBreakthrough Performer of the Year\\n\\nMaisie Williams\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2013\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nNikolaj Coster-Waldau\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nMichelle Fairley\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Rains of Castamere\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nCharles Dance\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nPedro Pascal\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Lion and the Rose\"\\n\\n\"The Watchers on the Wall\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\n2018\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Beyond the Wall\"\\n\\n\"The Spoils of War\"\\n\\n2019\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Episode\\n\\n\"A Knight of the Seven Kingdoms\"\\n\\n\"The Long Night\"\\n\\nBest Drama Actor\\n\\nKit Harington\\n\\nBest Drama Actress\\n\\nEmilia Clarke\\n\\nBest Drama Supporting Actor\\n\\nAlfie Allen\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nMaisie Williams\\n\\nBest Drama Guest Actress\\n\\nCarice van Houten\\n\\nBest Ensemble\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n===Golden Nymph Awards===\\nThe Golden Nymph Awards are awarded annually at the Monte-Carlo Television Festival. \\'\\'Game of Thrones\\'\\' has won one out of five nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 90}, 'embedding': None, 'id': 'c9fcc5ac92c2a1170ed620b3ff44e8c7'}>,\n",
+       "  <Document: {'content': '2011\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBreakthrough Performer of the Year\\n\\nMaisie Williams\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2013\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nNikolaj Coster-Waldau\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nMichelle Fairley\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Rains of Castamere\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nCharles Dance\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nPedro Pascal\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Lion and the Rose\"\\n\\n\"The Watchers on the Wall\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\n2018\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Beyond the Wall\"\\n\\n\"The Spoils of War\"\\n\\n2019\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Episode\\n\\n\"A Knight of the Seven Kingdoms\"\\n\\n\"The Long Night\"\\n\\nBest Drama Actor\\n\\nKit Harington\\n\\nBest Drama Actress\\n\\nEmilia Clarke\\n\\nBest Drama Supporting Actor\\n\\nAlfie Allen\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nMaisie Williams\\n\\nBest Drama Guest Actress\\n\\nCarice van Houten\\n\\nBest Ensemble\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n===Golden Nymph Awards===\\nThe Golden Nymph Awards are awarded annually at the Monte-Carlo Television Festival. \\'\\'Game of Thrones\\'\\' has won one out of five nominations.\\n\\n+ Golden Nymph Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 91}, 'embedding': None, 'id': 'b599cf66740ee48ccfcaee01adbe1e43'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has won one out of five nominations.\\n\\n+ Golden Nymph Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding International Producer\\n\\nDavid Benioff, Frank Doelger, Carolyn Strauss and D. B. Weiss\\n\\nOutstanding Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nOutstanding Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\n===Gracie Allen Awards===\\nThe Gracie Award was established in 1975 by the Alliance for Women in Media to recognize the best representation of women in the media. The award is given to various types of programming or individuals. ''Game of Thrones'' has won one out of one nomination.\\n\\n+ Gracie Allen Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Female Rising Star in a Drama Series or Special\\n\\nEmilia Clarke\\n\\n===Hollywood Music in Media Awards===\\nThe Hollywood Music in Media Awards are awarded annually to recognize the music of visual mediums such as film, television, movie trailers, video games and commercials. ''Game of Thrones'' has been nominated one time.\\n\\n+ Hollywood Music in Media Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 92}, 'embedding': None, 'id': 'a1eb905000f8068f5140d42fec632de1'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated one time.\\n\\n+ Hollywood Music in Media Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nBest Original Score – TV Show / Digital Streaming Series\\n\\nRamin Djawadi\\n\\n===Humanitas Prize===\\nThe Humanitas Prize are awarded annually to film and television writing intended to \"promote human dignity, meaning, and freedom\". \\'\\'Game of Thrones\\'\\' has been nominated one time.\\n\\n+ Humanitas Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2017\\n\\n60 Minute Network or Syndicated Television\\n\\nDavid Benioff, D. B. Weiss (for \"The Dragon and the Wolf\")\\n\\n===International Film Music Critics Association===\\nThe International Film Music Critics Association are awarded annually to film and television composers. \\'\\'Game of Thrones\\'\\' has been nominated four times, and won once.\\n\\n+ International Film Music Critics Association Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 93}, 'embedding': None, 'id': 'a218dfb36dc2998844508f0869dd8246'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated four times, and won once.\\n\\n+ International Film Music Critics Association Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\n2013\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\n2016\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\nFilm Music Composition of the Year\\n\\nRamin Djawadi for \"Light of the Seven\"\\n\\n===Jupiter Awards===\\nThe Jupiter Awards are awarded annually. \\'\\'Game of Thrones\\'\\' has won once.\\n\\n+ Jupiter Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nBest International TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===Kerrang! Awards===\\nThe Kerrang! Awards are awarded annually. \\'\\'Game of Thrones\\'\\' has won two out of two nominations.\\n\\n+ Kerrang! Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===MTV Fandom Awards===\\nThe MTV Fandom Awards are awarded annually, voted by the public. \\'\\'Game of Thrones\\'\\' has won one out of two nominations.\\n\\n+ MTV Fandom Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 94}, 'embedding': None, 'id': '7a616051f9c6b426d67f00782758ac6f'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has won one out of two nominations.\\n\\n+ MTV Fandom Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nOMG Moment of the Year\\n\\n''Game of Thrones'' – The Purple Wedding\\n\\n2016\\n\\nFan Freak Out of the Year\\n\\n''Game of Thrones'' – Resurrection of Jon Snow\\n\\n===MTV Millennial Awards===\\nThe MTV Millennial Awards are annual awards that celebrates the music, videos and digital culture of the Millennial Generation in Latin America. ''Game of Thrones'' has won one out of one nomination.\\n\\n+ MTV Millennial Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nKiller Serie del Año\\n\\n''Game of Thrones''\\n\\n===MTV Movie & TV Awards===\\nThe MTV Movie & TV Awards is an annual award show presented by MTV to honor outstanding achievements in film and television. Founded in 1992, the winners of the awards are decided online by the audience. ''Game of Thrones'' won one award from ten nominations.\\n\\n+ MTV Movie & TV Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 95}, 'embedding': None, 'id': 'ea46759de5c5835269313adc891cb0fd'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' won one award from ten nominations.\\n\\n+ MTV Movie & TV Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2017\\n\\nShow of the Year\\n\\n''Game of Thrones''\\n\\nBest Actor in a Show\\n\\nEmilia Clarke\\n\\nTearjerker\\n\\nHodor's (Kristian Nairn) death\\n\\n2018\\n\\nBest Show\\n\\n''Game of Thrones''\\n\\nBest Performance in a Show\\n\\nMaisie Williams\\n\\nBest Hero\\n\\nEmilia Clarke\\n\\n2019\\n\\nBest Show\\n\\n''Game of Thrones''\\n\\nBest Performance in a Show\\n\\nEmilia Clarke\\n\\nBest Hero\\n\\nMaisie Williams\\n\\nBest Fight\\n\\nArya Stark (Maisie Williams) vs. White Walkers\\n\\n===NewNowNext Awards===\\nThe NewNowNext Awards are awarded annually. ''Game of Thrones'' has been nominated three times.\\n\\n+ NewNowNext Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nTV You Betta Watch\\n\\n''Game of Thrones''\\n\\n2013\\n\\nCause You're Hot\\n\\nRichard Madden\\n\\n2014\\n\\nBest New Television Actor\\n\\nPedro Pascal\\n\\n===Portal Awards===\\nThe Portal Awards are awarded annually for genre television and movies. ''Game of Thrones'' has won seven out of sixteen nominations.\\n\\n+ Portal Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 96}, 'embedding': None, 'id': 'f2362c4de96f1414e7ac788c5b57df98'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won seven out of sixteen nominations.\\n\\n+ Portal Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actor\\n\\nSean Bean\\n\\nBest Actress\\n\\nLena Headey\\n\\nBest Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Episode\\n\\n\"Winter Is Coming\"\\n\\nBest Young Actor\\n\\nIsaac Hempstead-Wright\\n\\nMaisie Williams\\n\\n2012\\n\\nBest Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\nBest Actress\\n\\nLena Headey\\n\\nBest Supporting Actor\\n\\nAidan Gillen\\n\\nBest Supporting Actress\\n\\nMichelle Fairley\\n\\nMaisie Williams\\n\\nBest Episode\\n\\n\"The Ghost of Harrenhal\"\\n\\nBest Young Actor\\n\\nJack Gleeson\\n\\nMaisie Williams\\n\\n===Royal Television Society===\\nThe Royal Television Society presents annual awards honoring the best in television. In 2014, the show won the International Program Award at the in 2014. \\'\\'Game of Thrones\\'\\' has won one out of one nomination.\\n\\n+ Royal Television Society Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nInternational Program\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===Screenwriters Choice Awards===\\nThe Screenwriters Choice Awards are awarded annually. \\'\\'Game of Thrones\\'\\' has won one out of two nominations.\\n\\n+ Screenwriters Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 97}, 'embedding': None, 'id': '10ab32576d2db66e1993fb231c246831'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won one out of two nominations.\\n\\n+ Screenwriters Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===SFX Awards===\\nThe SFX Awards are awarded annually to celebrate achievements in science fiction and are voted on by the readers of the SFX magazine. \\'\\'Game of Thrones\\'\\' has won two out of thirteen nominations.\\n\\n+ SFX Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest New TV Show\\n\\nDavid Benioff and D. B. Weiss\\n\\nBest TV Show\\n\\nDavid Benioff and D. B. Weiss\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\nBest Villain\\n\\nCharles Dance as Tywin Lannister\\n\\nBiggest Disappointment\\n\\nNo Hodor in \\'\\'Game of Thrones\\'\\' season five\\n\\nBest TV Episode\\n\\n\"The Mountain and the Viper\"\\n\\n===Shorty Awards===\\nThe Shorty Awards are annual awards, recognizing the people and organizations producing real-time short form content across social media. \\'\\'Game of Thrones\\'\\' has been nominated twice.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 98}, 'embedding': None, 'id': '902f65135dd807bab2d2afb2589ad151'}>,\n",
+       "  <Document: {'content': '2012\\n\\nBest New TV Show\\n\\nDavid Benioff and D. B. Weiss\\n\\nBest TV Show\\n\\nDavid Benioff and D. B. Weiss\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\nBest Villain\\n\\nCharles Dance as Tywin Lannister\\n\\nBiggest Disappointment\\n\\nNo Hodor in \\'\\'Game of Thrones\\'\\' season five\\n\\nBest TV Episode\\n\\n\"The Mountain and the Viper\"\\n\\n===Shorty Awards===\\nThe Shorty Awards are annual awards, recognizing the people and organizations producing real-time short form content across social media. \\'\\'Game of Thrones\\'\\' has been nominated twice.\\n\\n+ Shorty Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGIF of The Year\\n\\n\\'\\'Game of Thrones\\'\\' Come at Me Bro\\n\\n===TV Choice Awards===\\nThe TV Choice Awards are awarded annually, the nominees are selected by the editors at TV Choice magazine and the winners are chosen by voting of the general public. \\'\\'Game of Thrones\\'\\' has won two out of two nominations.\\n\\n+ TV Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 99}, 'embedding': None, 'id': '8cdc6da5b572a1aa35ae405344b13187'}>,\n",
+       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won two out of two nominations.\\n\\n+ TV Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===USC Scripter Awards===\\nThe USC Scripter Awards are awarded annually by the USC Libraries Board of Councilors in recognition of the year\\'s best adaptation of the printed word into film. \\'\\'Game of Thrones\\'\\' has been nominated two times.\\n\\n+ USC Scripter Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss (for \"Hardhome\")\\n\\n2017\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss (for \"The Winds of Winter\")\\n\\n===Webby Awards===\\nThe Webby Awards are awards for excellence on the Internet presented annually by the International Academy of Digital Arts and Sciences. \\'\\'Game of Thrones\\'\\' has won six awards out of six nominations.\\n\\n+ Webby Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 100}, 'embedding': None, 'id': 'f2ac972a2dc05f3b5347871a5aa9d1e'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has won six awards out of six nominations.\\n\\n+ Webby Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nBest Overall Social Presence\\n\\n''Game of Thrones''\\n\\n2017\\n\\nBest Overall Social Presence\\n\\n''Game of Thrones''\\n\\nUnscripted (Branded)\\n\\nBattle of the Bastards Featurette\\n\\n2018\\n\\nBest Overall Social Presence\\n\\n''Game of Thrones''\\n\\nBest Trailer\\n\\n''Game of Thrones''\\n\\nBest Digital Campaign\\n\\n''Game of Thrones''\\n\\n===Women's Image Network Awards===\\nThe Women's Image Network Awards are annual awards presented to individuals who promote women and girls in the media. ''Game of Thrones'' has won one out of two nominations.\\n\\n+ Women's Image Network Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nActress Drama Series\\n\\nLena Headey\\n\\n2014\\n\\nActress Drama Series\\n\\nLena Headey\\n\\n===World Soundtrack Awards===\\nThe World Soundtrack Academy presents annual awards honoring the music professionals involved in film and television. ''Game of Thrones'' has one nomination.\\n\\n+ World Soundtrack Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nTelevision Composer of the Year\\n\\nRamin Djawadi\\n\\n===Young Artist Awards===\\nThe Young Artist Awards are awarded annually to young artists. ''Game of Thrones'' has been nominated three times.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 101}, 'embedding': None, 'id': '3ba7277c6e0ac268c5b8a69e71b8d1b2'}>,\n",
+       "  <Document: {'content': \"2016\\n\\nTelevision Composer of the Year\\n\\nRamin Djawadi\\n\\n===Young Artist Awards===\\nThe Young Artist Awards are awarded annually to young artists. ''Game of Thrones'' has been nominated three times.\\n\\n+ Young Artist Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2013\\n\\nBest Performance in a TV Series – Supporting Young Actor\\n\\nIsaac Hempstead-Wright\\n\\nBest Performance in a TV Series – Supporting Young Actress\\n\\nSophie Turner\\n\\nMaisie Williams\\n\\n===Young Hollywood Awards===\\nThe Young Hollywood Awards are bestowed annually to honor the achievements in pop music, film and television, sports, fashion and social media. ''Game of Thrones'' has won one out of three nominations.\\n\\n+ Young Hollywood Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2013\\n\\nActor of the Year\\n\\nKit Harington\\n\\n2014\\n\\nBingeworthy TV Show\\n\\n''Game of Thrones''\\n\\nWe Love to Hate You\\n\\nJack Gleeson\\n\\n===Zulu Awards===\\nThe Zulu Awards are awarded annually, nominees are selected by Danish broadcaster Zulu and the winners are voted upon by the general public. ''Game of Thrones'' has been nominated one time.\\n\\n+ Zulu Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 102}, 'embedding': None, 'id': '4c38962a5f1fc798fd84c4f5395c4bd1'}>,\n",
+       "  <Document: {'content': \"''Game of Thrones'' has been nominated one time.\\n\\n+ Zulu Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2017\\n\\nBest Actor\\n\\nNikolaj Coster-Waldau\\n\\n==Notes==\\n\\n==References==\\n\\n==External links==\\n*  Official U.S. website HBO\\n*\\n*\\n*  Official UK website HBO UK\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 103}, 'embedding': None, 'id': '5b13213f2edcdf99912fa5ef6e771a3b'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'\\'\\'Game of Thrones\\'\\' Theme\\'\\'\\'\", also referred to as \"\\'\\'\\'\\'\\'Game of Thrones\\'\\' Main Title Theme\\'\\'\\'\", is the theme music of HBO\\'s award-winning fantasy television series \\'\\'Game of Thrones\\'\\', and plays during the title sequence.  It  was composed by Ramin Djawadi in 2011, after series creator David Benioff and D. B. Weiss approached him requesting a theme.\\n\\nAsked to avoid flutes and violins, which the producers felt were overused in fantasy themes, Djawadi used the cello as the lead instrument.  The piece begins in a minor key, then switches between corresponding major and minor keys repeatedly.  Djawadi was shown a preliminary rendering of the title sequence before composing this music to accompany it.  Several artists have covered or parodied the music, sometimes adding lyrics to the originally instrumental work.\\n\\n==Composition==\\nRamin Djawadi is the composer of the \\'\\'Game of Thrones\\'\\' score.\\nRamin Djawadi began composing the music for the show after he had watched the first two episodes of the series that the showrunners David Benioff and D. B. Weiss sent him, and discussed the concepts of the show with them.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '58117c6592e4d9179ca1ce68843d0c23'}>,\n",
+       "  <Document: {'content': \"Ramin Djawadi began composing the music for the show after he had watched the first two episodes of the series that the showrunners David Benioff and D. B. Weiss sent him, and discussed the concepts of the show with them.  According to Djawadi, the show creators wanted the main title theme to be about a journey as there are many locations and characters in the show and the narrative involves much traveling.  After Djawadi was shown a preliminary animated ''Game of Thrones'' title sequence that the visual effect artists were still working on, he was inspired to write the piece. He said that he started humming what would become the theme tune in the car after seeing the visuals for the title sequence, and conceived of the idea for the theme on the drive back to his studio. The finished theme music was presented to the producer three days later.\\n\\nDjawadi said he intended to capture the overall impression of the show with the theme tune. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'ffd339d248058b6feaaccde1eaf0c116'}>,\n",
+       "  <Document: {'content': 'The finished theme music was presented to the producer three days later.\\n\\nDjawadi said he intended to capture the overall impression of the show with the theme tune. Cello is featured strongly as Benioff and Weiss wanted to avoid the flutes or solo vocals found in many other productions in the fantasy genre so as to give the show a distinctive sound, and Djawadi chose cello as the main instrument for the music as he thought it has a \"darker sound\" that suited the show.\\n\\nDjawadi started with a riff and he built the title theme around the riff.  The tune begins with the riff played on strings in a minor key, then changed to a major key after 2 bars, and back to minor again. Djawadi said that he wanted to reflect the \"backstabbing and conspiracy\" and the unpredictability of the show: \"... I thought it would be cool to kinda do the same play with the music. So even though the majority of the piece is in minor, there\\'s that little hint of major in there where it kinda switches and then it changes back again.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '65d2a9a984f946444314ccef3c4b6664'}>,\n",
+       "  <Document: {'content': 'So even though the majority of the piece is in minor, there\\'s that little hint of major in there where it kinda switches and then it changes back again.\"The main melody is then introduced with the cello, joined later by a solo violin that may suggest an interplay between different characters. The melody is then repeated with the entire orchestra. The next section introduces a change in melody, described by Djawadi as giving \"a sense of adventure\", and continues with a repeat that involves a choir of twenty female voices - recorded in Prague, like the instrumental parts. The title theme ends with a combination of dulcimer and kantele, producing a \"shimmery quality\" in its sound that Djawadi thought would give a sense of mystery and anticipation for the episode.\\n\\nThe title music is reprised as a global theme in the soundtracks for the series.  It may be played occasionally on its own in fragments, sometimes as part of the theme of individual characters or in combination with other pieces of music, and may also be played in large section during particularly important scenes.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '64dffc1c5ca50cbc8e23044c76419811'}>,\n",
+       "  <Document: {'content': 'It may be played occasionally on its own in fragments, sometimes as part of the theme of individual characters or in combination with other pieces of music, and may also be played in large section during particularly important scenes.\\n\\n==Chart positions==\\n===Weekly charts===\\n\\nChart (2014)\\n\\nPeakposition\\n\\nBelgium (Ultratop Back Catalogue Singles Wallonia)\\n\\n22\\n\\nChart (2015)\\n\\nPeakposition\\n\\nFrench Singles Sales Chart (Pure Charts)\\n\\n131\\n\\nChart (2019)\\n\\nPeakposition\\n\\nFrench Singles Sales Chart (Pure Charts)\\n\\n65\\n\\nFrench Downloads (SNEP)\\n\\n65\\n\\n==Cover versions and parodies==\\nThe main theme of \\'\\'Game of Thrones\\'\\' has inspired many tributes and cover versions, including a rendition by the electropop band Chvrches. Lyrics were added for the first time in 2014, when \"Weird Al\" Yankovic performed a parody version during the 66th Primetime Emmy Awards. In March 2015, FORTE added lyrics based on High Valyrian text for an operatic performance and music video. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'f9b9bbc56064cae530ad6109fb5fbd7e'}>,\n",
+       "  <Document: {'content': 'Lyrics were added for the first time in 2014, when \"Weird Al\" Yankovic performed a parody version during the 66th Primetime Emmy Awards. In March 2015, FORTE added lyrics based on High Valyrian text for an operatic performance and music video. Some of the cover and parody versions mentioned by news media include:\\n* an electric and acoustic cello version by Grammy nominated cellist Tina Guo\\n*a violin version by Jason Yang,\\n* a metal version by Roger Lima,\\n* an electric harp duet version by the \"Harp Twins\", Camille and Kennerly Kitt,\\n* a bleeping \"8-bit\" remix in the style of early video game music,\\n* a rendition in the noise of floppy disk drives,\\n* a violin and voice duet by Lindsey Stirling and Peter Hollens,\\n* a cello version by \\'\\'Break of Reality\\'\\',\\n* a cello version by 2Cellos\\n* a violin version by Ben Shapiro\\n* a ska interpretation by Pannonia Allstars Ska Orchestra,\\n* a chamber music version by Aston,\\n* a Ragtime Piano Rendition by Jonny May,\\n* a choral parody used in two episodes of \\'\\'South Park\\'\\', with lyrics solely about \"wieners\",\\n* an elaborate parody of the opening at the beginning of a 2012 episode of \\'\\'The Simpsons\\'\\', \"Exit Through the Kwik-E-Mart\",\\n*a vocal version performed by French musician and singer Luc Arbogast. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '558f2f445f03a227ee6dbf3fcd89e312'}>,\n",
+       "  <Document: {'content': 'Some of the cover and parody versions mentioned by news media include:\\n* an electric and acoustic cello version by Grammy nominated cellist Tina Guo\\n*a violin version by Jason Yang,\\n* a metal version by Roger Lima,\\n* an electric harp duet version by the \"Harp Twins\", Camille and Kennerly Kitt,\\n* a bleeping \"8-bit\" remix in the style of early video game music,\\n* a rendition in the noise of floppy disk drives,\\n* a violin and voice duet by Lindsey Stirling and Peter Hollens,\\n* a cello version by \\'\\'Break of Reality\\'\\',\\n* a cello version by 2Cellos\\n* a violin version by Ben Shapiro\\n* a ska interpretation by Pannonia Allstars Ska Orchestra,\\n* a chamber music version by Aston,\\n* a Ragtime Piano Rendition by Jonny May,\\n* a choral parody used in two episodes of \\'\\'South Park\\'\\', with lyrics solely about \"wieners\",\\n* an elaborate parody of the opening at the beginning of a 2012 episode of \\'\\'The Simpsons\\'\\', \"Exit Through the Kwik-E-Mart\",\\n*a vocal version performed by French musician and singer Luc Arbogast. The song peaked at number 125 on French Singles Sales Chart in 2014 and stayed one week there.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '9e4411def110ec648a62ba61809863b8'}>,\n",
+       "  <Document: {'content': 'Some of the cover and parody versions mentioned by news media include:\\n* an electric and acoustic cello version by Grammy nominated cellist Tina Guo\\n*a violin version by Jason Yang,\\n* a metal version by Roger Lima,\\n* an electric harp duet version by the \"Harp Twins\", Camille and Kennerly Kitt,\\n* a bleeping \"8-bit\" remix in the style of early video game music,\\n* a rendition in the noise of floppy disk drives,\\n* a violin and voice duet by Lindsey Stirling and Peter Hollens,\\n* a cello version by \\'\\'Break of Reality\\'\\',\\n* a cello version by 2Cellos\\n* a violin version by Ben Shapiro\\n* a ska interpretation by Pannonia Allstars Ska Orchestra,\\n* a chamber music version by Aston,\\n* a Ragtime Piano Rendition by Jonny May,\\n* a choral parody used in two episodes of \\'\\'South Park\\'\\', with lyrics solely about \"wieners\",\\n* an elaborate parody of the opening at the beginning of a 2012 episode of \\'\\'The Simpsons\\'\\', \"Exit Through the Kwik-E-Mart\",\\n*a vocal version performed by French musician and singer Luc Arbogast. The song peaked at number 125 on French Singles Sales Chart in 2014 and stayed one week there.\\n* a parody performed by \"Weird Al\" Yankovic at the 2014 Emmy awards,\\n* an orchestra version performed at Illich Steel and Iron Works by Mariupol orchestra \"Renaissance\",\\n* a remix by Armin van Buuren, KSHMR and The Golden Army.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '55947a871b4597540a5d70981874a7c1'}>,\n",
+       "  <Document: {'content': '* a parody performed by \"Weird Al\" Yankovic at the 2014 Emmy awards,\\n* an orchestra version performed at Illich Steel and Iron Works by Mariupol orchestra \"Renaissance\",\\n* a remix by Armin van Buuren, KSHMR and The Golden Army.\\n* a bluegrass cover version performed by the Tennessee-based band Flat Lonesome for SirusXM radio station.\\n* In 2017, Canadian band Barenaked Ladies opened and closed their end-of-show popular music medley/parody with the Game of Thrones Theme with lead singer Ed Robertson adding the lyrics \"\\'\\'Horses - tits and horses\\'\\' repeated \\'\\'and some dong!\\'\\'\", poking fun at the content of the series.\\n* In 2019, American band Our Last Night covered the song with a post-hardcore theme.\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n* Ramin Djawadi – composer, primary artist, producer\\n* David Benioff – liner notes\\n* D.B. Weiss – liner notes\\n\\n==See also==\\n* \\'\\'Game of Thrones\\'\\' title sequence\\n* Music of \\'\\'Game of Thrones\\'\\'\\n\\n==References==\\n\\n==External links==\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '5cf4cefc5e5e9741263d261aced1a1c2'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''''A Game of Thrones: The Card Game Second Edition''''' (or '''''AGoT''''', for short) is a Living Card Game (LCG) produced by Fantasy Flight Games. It is based on ''A Song of Ice and Fire'',  a series of novels written by George R. R. Martin.  The second edition of this LCG, it is not backwards compatible with the first edition that preceded it.\\n\\nIn the game, players assume the leadership of one of the great houses of Westeros vying for control of King's Landing and the Iron Throne. To accomplish this, players launch military attacks against their opponents, undermine their opponents’ plans with intrigues of their own, and make power plays to win the support of the realm.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '917652e775dbdf73b703bd2e44a7d73b'}>,\n",
+       "  <Document: {'content': \"To accomplish this, players launch military attacks against their opponents, undermine their opponents’ plans with intrigues of their own, and make power plays to win the support of the realm.\\n\\n==Sets and expansions==\\n\\n+ '''A Game of Thrones LCG Core Set and Deluxe Expansions'''\\n\\nSet\\n\\nRelease Date\\n\\nNote\\n\\nA Game of Thrones : Second Edition (Core Set)\\n\\nOctober 2015\\n\\nWolves of the North\\n\\nApril 2016\\n\\nLions of Casterly Rock\\n\\nNovember 2016\\n\\nWatchers on the Wall\\n\\nApril 2017\\n\\nHouse of Thorns\\n\\nOctober 2017\\n\\nSands of Dorne\\n\\nMarch 2018\\n\\nKings of the Isles\\n\\nDecember 2018\\n\\nFury of the Storm\\n\\nMay 2019\\n\\n+ '''A Game of Thrones LCG chapter packs'''\\n\\nCycle\\n\\nChapter Pack\\n\\nRelease Date\\n\\nNotes\\n\\nWesteros Cycle\\n\\nTaking the Black\\n\\nDecember 2015\\n\\nRoad to Winterfell\\n\\nJanuary 2016\\n\\nThe King's Peace\\n\\nFebruary 2016\\n\\nNo Middle Ground\\n\\nMarch 2016\\n\\nCalm Over Westeros\\n\\nApril 2016\\n\\nTrue Steel\\n\\nMay 2016\\n\\nWar of the Five Kings Cycle\\n\\nAcross the Seven Kingdoms\\n\\nJuly 2016\\n\\nCalled to Arms\\n\\nAugust 2016\\n\\nFor Family Honor\\n\\nOctober 2016\\n\\nThere is My Claim\\n\\nNovember 2016\\n\\nGhosts of Harrenhal\\n\\nJanuary 2017\\n\\nTyrion's Chain\\n\\nFebruary 2017\\n\\nBlood and Gold Cycle\\n\\nAll Men are Fools\\n\\nMarch 2017\\n\\nGuarding the Realm\\n\\nMay 2017\\n\\nThe Fall of Astapor\\n\\nJune 2017\\n\\nThe Red Wedding\\n\\nJuly 2017\\n\\nOberyn's Revenge\\n\\nAugust 2017\\n\\nThe Brotherhood Without Banners\\n\\nSeptember 2017\\n\\nFlight of Crows Cycle\\n\\nThe Archmaester's Key\\n\\nNovember 2017\\n\\nJourney to Oldtown\\n\\nDecember 2017\\n\\nKingsmoot\\n\\nJanuary 2018\\n\\nFavor of the Old Gods\\n\\nFebruary 2018\\n\\nThe Faith Militant\\n\\nApril 2018\\n\\nSomeone Always Tells\\n\\nMay 2018\\n\\nDance of Shadows Cycle\\n\\nThe Shadow City\\n\\nJune 2018\\n\\nThe March on Winterfell\\n\\nJuly 2018\\n\\nStreets of King's Landing\\n\\nAugust 2018\\n\\nMusic of Dragons\\n\\nSeptember 2018\\n\\nIn Daznak's Pit\\n\\nOctober 2018\\n\\nDaggers in the Dark\\n\\nNovember 2018\\n\\nKing's Landing Cycle\\n\\nAt the Gates\\n\\nApril 2019\\n\\nCity of Secrets\\n\\nJune 2019\\n\\nPit of Snakes\\n\\nJuly 2019\\n\\n==External links==\\n* A Game of Thrones: Second Edition - Official website\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '432a50b9ee1413291709dd1018cb90bb'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''''Game of Thrones: Seven Kingdoms''''' was a fantasy massively multiplayer online role-playing game (MMORPG) under development by Bigpoint and Artplant, in collaboration with HBO. The game was based on the HBO TV series ''Game of Thrones'', which is itself an adaptation of the ''A Song of Ice and Fire'' book series by George R. R. Martin.\\n\\nThe game was being built using the Unity platform, and will be playable in the browser using the Unity Web Player plugin.\\n\\n==Gameplay==\\n''Game of Thrones: Seven Kingdoms'' was to be set within the fictional realm of Westeros, and will use a third-person viewpoint. Gameplay was planned to be mainly based around player vs player (PvP) combat, which would involve small group combat, one on one duels and siege battles, large scale battles in which players must capture keeps, forts and castles. Player vs Environment (PvE) combat was planned to be available at launch, although this was not a major priority. Combat would be realtime, with movement controlled using the WASD keys.\\n\\nPlayers would be able to come together to form guilds, known as lesser houses. Lesser houses align themselves with one of the three major houses in-game: Baratheon, Stark or Lannister. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '4d9556c4d45b59ac85aa409daf50669c'}>,\n",
+       "  <Document: {'content': \"Players would be able to come together to form guilds, known as lesser houses. Lesser houses align themselves with one of the three major houses in-game: Baratheon, Stark or Lannister. Solo play will be possible, although group play will be encouraged.\\n\\nPlayers would be able to customize their characters by changing features such as hair and clothing.\\n\\n==Development==\\n''Game of Thrones: Seven Kingdoms'' was announced in February 2012, with the first look being unveiled at the Game Developers Conference in March 2012. The first gameplay footage was shown in a trailer released on July 12, 2012.\\n\\nThe game was under development by German videogame developer Bigpoint, and Norwegian developer Artplant. The studios have previously worked together on Battlestar Galactica Online, another browser based multiplayer game.\\n\\nThe game was being made in collaboration with HBO. Everything put in game is reviewed by HBO in advance, and Bigpoint is working with the HBO design teams. The look and feel of the weapons and armour, for example, are straight from the show.\\n\\nThe game was being built on the Unity platform.\\n\\n==References==\\n\\n==External links==\\n* Official page\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'f1b71b7f4be9ff02d89b6ae2844c7b2d'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Laws of Gods and Men\\'\\'\\'\" is the sixth episode of the fourth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 36th overall. The episode was written by Bryan Cogman, and directed by Alik Sakharov. It aired on May 11, 2014.\\n\\nThe episode\\'s title refers to Tyrion Lannister\\'s trial for Joffrey Baratheon\\'s murder, as well as the laws of the Seven Kingdoms of Westeros.\\n\\n==Plot==\\n\\n===In Braavos===\\nStannis and Davos arrive in Braavos and meet Tycho Nestoris at the Iron Bank. Nestoris agrees to Stannis’ request and grants him funds he needs. Davos tells Salladhor Saan that he has the money Saan was promised and that they will set sail the next day.\\n\\n===In Meereen===\\nDaenery\\'s dragon Drogon attacks a group of goats led by a farmer. The farmer presents\\xa0a bag of charred goat bones to Daenerys and she promises to pay him three times their worth. Hizdahr zo Loraq requests to bury his father, who was one of the Meereenese masters crucified on Daenerys\\'s orders and was against crucifixion of the slave children. Daenerys agrees.\\n\\n===At the Dreadfort===\\nYara and Ironborn soldiers attack the Dreadfort. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'fb353e098294f5f7985609eefabcc98b'}>,\n",
+       "  <Document: {'content': \"Hizdahr zo Loraq requests to bury his father, who was one of the Meereenese masters crucified on Daenerys's orders and was against crucifixion of the slave children. Daenerys agrees.\\n\\n===At the Dreadfort===\\nYara and Ironborn soldiers attack the Dreadfort. Yara finds Theon, but he refuses her help and says that his name is Reek, not Theon. Ramsay and his men corner the Ironborn, who return to their boats.\\n\\n===In King's Landing===\\nVarys tells Tywin of Daenerys' conquest of Meereen and Tywin decides to take measures to prevent her from launching an invasion. Tyrion's trial begins with Tywin, Mace and Oberyn as judges. Several witnesses are called including Meryn, Pycelle, Cersei and Varys, who all testify against Tyrion. At recess, Jaime tells Tywin that he will go to Casterly Rock and be his heir if Tywin spares Tyrion. He agrees and tells that Tyrion will be sent to Night's Watch after being found guilty of Joffrey's murder. Returning to trial, Shae is called as next witness. She falsely testifies that Tyrion and Sansa planned Joffrey's murder. Tyrion has an outburst against the people of King's Landing and his father and demands a trial by combat.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'e52ab4d524b08aff889d08f55403333c'}>,\n",
+       "  <Document: {'content': 'She falsely testifies that Tyrion and Sansa planned Joffrey\\'s murder. Tyrion has an outburst against the people of King\\'s Landing and his father and demands a trial by combat.\\n\\n==Production==\\n\\n===Writing===\\nSeries veteran Bryan Cogman wrote this episode.\\n\"The Laws of Gods and Men\" was written by Bryan Cogman, based upon the source material, Martin\\'s \\'\\'A Storm of Swords\\'\\'. Chapters adapted from \\'\\'A Storm of Swords\\'\\' to the episode were chapters 66 and 70 (Tyrion IX and Tyrion X).\\n\\n==Reception==\\n\\n===Ratings===\\n\"The Laws of Gods and Men\" was watched by an estimated 6.4 million people during its original broadcast. In the United Kingdom, the episode was viewed by 1.657 million viewers, making it the highest-rated broadcast that week. It also received 0.1 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode received universal acclaim reviews from critics, with most reviewers singling out Tyrion\\'s trial as the highlight of the episode, particularly praising Peter Dinklage\\'s performance. It has received a 95% rating on the review aggregator website Rotten Tomatoes from 38 reviews with an average score of 9 out of 10. Matt Fowler of IGN called the final scene \"one of the best things I’ve seen Tyrion do in a long while. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '2f5a24320d1fee905cb3233ecacbec24'}>,\n",
+       "  <Document: {'content': 'Matt Fowler of IGN called the final scene \"one of the best things I’ve seen Tyrion do in a long while. He let a lifetime of hate ooze out of him like never before.\"===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2014\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Art Direction for a Single-Camera Fantasy Series\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron\\n\\n2015\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '92110f669802c45f7141ea8f506dc22b'}>,\n",
+       "  <Document: {'content': '\\n\\'\\'\\'Rickon Stark\\'\\'\\' is a fictional character in the \\'\\'A Song of Ice and Fire\\'\\' series of fantasy novels by American author George R. R. Martin, and its television adaptation \\'\\'Game of Thrones\\'\\'.\\n\\nIntroduced in 1996\\'s \\'\\'A Game of Thrones\\'\\', Rickon is the youngest child of Eddard Stark, the honorable lord of Winterfell, an ancient fortress in the North of the fictional kingdom of Westeros. He subsequently appeared in Martin\\'s \\'\\'A Clash of Kings\\'\\' (1998). The \\'\\'Publishers Weekly\\'\\' review of \\'\\'A Game of Thrones\\'\\' noted, \"It is fascinating to watch Martin\\'s characters mature and grow, particularly Stark\\'s children, who stand at the center of the book.\"Rickon is portrayed by Irish actor Art Parkinson in the HBO television adaptation.\\n\\n== Character description ==\\nRickon is the fifth and youngest child of Eddard \"Ned\" Stark and his wife Catelyn, and has five siblings—Robb, Sansa, Arya, Bran, and his illegitimate half-brother Jon Snow. Like his brothers and sisters, Rickon is constantly accompanied by his direwolf, Shaggydog, with whom he shares a strong connection. Martin describes Rickon as favoring his mother in appearance. He is naturally aggressive, strong-willed, and violent: traits reflected in his direwolf companion \\'Shaggydog\\'.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'ebf640f2bff919a26b723cf4eaaaed31'}>,\n",
+       "  <Document: {'content': \"Martin describes Rickon as favoring his mother in appearance. He is naturally aggressive, strong-willed, and violent: traits reflected in his direwolf companion 'Shaggydog'.\\n\\n== Overview ==\\nRickon Stark is not a point of view character in the novels and is mostly a background character.  His actions are witnessed and interpreted through the eyes of his older brother Bran.\\n\\n== Storylines ==\\nCoat of arms of House Stark\\n\\n=== ''A Game of Thrones'' ===\\nIn ''A Game of Thrones'' (1996), Ned Stark departs Winterfell with his daughters to serve King Robert Baratheon in King's Landing, while his wife Catelyn remains behind with their sons, grief-stricken over their comatose son Bran. An overwhelmed young Rickon can only follow around his oldest brother Robb, crying. Upset when Robb prepares to leave himself, Rickon hides in the crypts of Winterfell. Shortly after Ned's death in King's Landing, both Bran and Rickon share a vision of their father's spirit in the crypts.\\n\\n=== ''A Clash of Kings'' ===\\nFollowing Ned's death, Rickon is largely unsupervised as Robb and Catelyn leave for war and his sisters are held captive at King's Landing in 1998's ''A Clash of Kings''. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '3e357fe290de771429f9d5717a908f13'}>,\n",
+       "  <Document: {'content': \"=== ''A Clash of Kings'' ===\\nFollowing Ned's death, Rickon is largely unsupervised as Robb and Catelyn leave for war and his sisters are held captive at King's Landing in 1998's ''A Clash of Kings''. Alone with his crippled brother Bran, Rickon develops an unruly, often violent temper. His fear and rage are reflected in Shaggydog, who turns largely feral and attacks several people before being restrained. Several times, Bran's direwolf Summer is forced to fight Shaggydog into submission. Bran and Rickon become Theon Greyjoy's hostages when Theon takes Winterfell by force. He later manages to escape and hide in the crypts below the castle with Bran, Hodor, the Reed siblings Meera and Jojen, and the wildling woman Osha. After Winterfell is sacked and burned by Ramsay Bolton, the group emerges to find Winterfell in ruins, and a mortally wounded Maester Luwin tells them that Bran and Rickon need to be separated and taken into hiding. Osha flees with Rickon in her care, and the others accompany Bran.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '7d713f4b60288d1ff6d2687d1f5fa38f'}>,\n",
+       "  <Document: {'content': \"After Winterfell is sacked and burned by Ramsay Bolton, the group emerges to find Winterfell in ruins, and a mortally wounded Maester Luwin tells them that Bran and Rickon need to be separated and taken into hiding. Osha flees with Rickon in her care, and the others accompany Bran.\\n\\n=== ''A Dance with Dragons'' ===\\nIn ''A Dance with Dragons'' (2011), most of Westeros believes Rickon is dead, but Lord Wyman Manderly hears from a survivor of the sack of Winterfell that Rickon is allegedly alive and has fled with a woman to the island of Skagos. Skagos is a large, northern island in the mouth of the Bay of Seals and is subject to the rule of House Stark. In return for pledging his loyalty to Stannis Baratheon instead of the Lannisters, Manderly tasks Ser Davos Seaworth with retrieving Rickon from Skagos so that they may reveal him to the Northern lords and inspire them to rally against Roose Bolton, Ramsay Bolton, and Walder Frey for betraying the Starks.\\n\\n=== Family tree of House Stark ===\\n\\n== TV adaptation ==\\nRickon Stark is played by Art Parkinson in the television adaption of the book series. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '9b3b3e615d615454bd8cccaac310296c'}>,\n",
+       "  <Document: {'content': '=== Family tree of House Stark ===\\n\\n== TV adaptation ==\\nRickon Stark is played by Art Parkinson in the television adaption of the book series. During an interview with Flicks and the City in 2014, Parkinson mentioned that he was not allowed by his parents to watch \\'\\'Game of Thrones\\'\\' due to its adult content, apart from a few scenes that were related to him. In a later interview published in the \\'\\'New Zealand Herald\\'\\' on 7 April 2015, Parkinson admitted that he was put off watching \\'\\'Game of Thrones\\'\\' after seeing a 3-year-old child getting his throat slit in one episode. In a later interview with IGN, he admitted that he had begun watching the series.\\n\\nIn an interview with IGN, Parkinson spoke about his return and death, saying: \"Whenever I was told that I was coming back for Season 6, before they sent me through the scripts and stuff, they sent me through a ring just to say, \\'Listen, so that you don\\'t get a shock whenever you read the scripts, just know that you die this season.\\'\"He continued, \"Whenever I came back, I was excited to come back, and the scenes all seemed pretty amazing. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '6417ac6be068cce83d9746d3c00cff57'}>,\n",
+       "  <Document: {'content': 'In an interview with IGN, Parkinson spoke about his return and death, saying: \"Whenever I was told that I was coming back for Season 6, before they sent me through the scripts and stuff, they sent me through a ring just to say, \\'Listen, so that you don\\'t get a shock whenever you read the scripts, just know that you die this season.\\'\"He continued, \"Whenever I came back, I was excited to come back, and the scenes all seemed pretty amazing. I was so happy to re-embrace the character.\"Parkinson has said that his fellow castmember Natalia Tena, who played Rickon\\'s travelling companion Osha, helped him with his acting. He also shared what he believes Rickon went through after the death of his father Ned Stark, saying \"After the death of his father, I think he became very independent and very angry, I think Rickon is a little bit more mature now. He\\'s been away for quite a long time at this point. I think he\\'s a bit more independent and stronger-willed.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '39635bb434c8c5b9e1edc64d76cf0718'}>,\n",
+       "  <Document: {'content': \"He's been away for quite a long time at this point. I think he's a bit more independent and stronger-willed.\\n\\nRemarking on the character's exit and death:\\n\\nIt was sad at first, but once I realized how I was going to die in it, then it was cool. It was a cool death, and it was always going to be a good death, so at the same time, I was pretty happy.\\n\\n=== Storylines ===\\nRickon Stark is Lord Eddard and Lady Catelyn's youngest child, naturally aggressive, and strong-willed. His black direwolf Shaggydog shares these qualities. When Theon Greyjoy captures Winterfell in Season 2, Rickon hides in the crypts with Bran, Hodor, and the Wildling woman Osha. After Theon can't seem to find where Rickon and Bran are, he has two farm boys killed and burned, to pass their charred bodies off as the Stark boys'. After Winterfell is sacked and burned, Rickon, Bran, Hodor, Osha, and the direwolves travel through the North. In Season 3 before they reach the Wall, Rickon, Osha, and Shaggydog split up from the rest of the group and head to Last Hearth, the seat house of the Umbers.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '63e818f17059fa51ef9681d607c63c6b'}>,\n",
+       "  <Document: {'content': 'In Season 3 before they reach the Wall, Rickon, Osha, and Shaggydog split up from the rest of the group and head to Last Hearth, the seat house of the Umbers.\\n\\n====Season 6====\\nFollowing Lord Greatjon Umber\\'s death, Rickon and Osha are betrayed by Greatjon\\'s heir Smalljon and handed over to the new Warden of the North, Ramsay Bolton, in order to secure an alliance with the Boltons against the wildlings Jon Snow has let through the Wall. To prove Rickon\\'s identity, Smalljon kills Shaggydog and presents his head to Ramsay. Ramsay kills Osha, locks Rickon in the dungeons, and sends a letter to Jon at Castle Black demanding the return of Sansa Stark (his wife) and threatening to kill Rickon if Jon does not comply. In retaliation, Jon gathers an army of Stark loyalists and marches on Winterfell, now occupied by House Bolton.\\n\\nAs the armies prepare for battle outside Winterfell, Ramsay brings out Rickon and orders him to run to Jon in one of his sadistic \"games\", shooting arrows at Rickon in order to lure Jon into the open. Jon charges out to save Rickon, but Rickon is shot through the heart and dies almost instantly. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'bb37903e542bdda6d6060751ea7e0918'}>,\n",
+       "  <Document: {'content': 'As the armies prepare for battle outside Winterfell, Ramsay brings out Rickon and orders him to run to Jon in one of his sadistic \"games\", shooting arrows at Rickon in order to lure Jon into the open. Jon charges out to save Rickon, but Rickon is shot through the heart and dies almost instantly. In the aftermath of the ensuing battle, Rickon\\'s body is recovered, and Jon buries him in the crypt, alongside Ned\\'s remains.\\n\\nBecause many Northern noble houses were plotting against the Boltons in the books, the show\\'s decision to have them betray Rickon led to fan theories that the direwolf head Smalljon Umber showed to Ramsay was fake and the Umbers would turn on the Boltons. Parkinson dispelled the rumours, confirming the direwolf head was intended to be real. Rickon\\'s death scene referenced his first appearance in the series pilot, where he laughs at Bran\\'s poor archery shot before handing off arrows to the archer and running to stand by the target.\\n\\n== References ==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '833e5c57a5237c075d7b8513f729436d'}>,\n",
+       "  <Document: {'content': '\\n\\nThe soundtrack album of the fourth season of HBO series \\'\\'Game of Thrones\\'\\', titled \\'\\'\\'\\'\\'Game of Thrones: Season 4\\'\\'\\'\\'\\' was released digitally on June 10, 2014, and on CD on July 1, 2014. Season 4 of \\'\\'Game of Thrones\\'\\' saw the Icelandic band Sigur Rós perform their rendition of \"The Rains of Castamere\" in a cameo appearance at King Joffrey\\'s wedding in the second episode, \"The Lion and the Rose\".\\n\\n==Reception==\\nThe soundtrack received mostly positive reviews from critics. The soundtrack was awarded a score of 4/5 by Heather Phares of AllMusic.\\n\\n==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* Sigur Rós – primary artist\\n* George R.R. Martin – lyricist\\n* D.B. Weiss – liner notes\\n\\n==Charts==\\n\\nChart (2014)\\n\\nPeak position\\n\\n==Awards and nominations==\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2014\\n\\n66th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Music Composition For A Series (Original Dramatic Score)\\n\\nEpisode: \"The Mountain and the Viper\"\\n\\nHollywood Music in Media Awards\\n\\nBest Original Score - TV Show/Digital Streaming Series\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '1e766074e8b99365ca04ae681660c66f'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''Souad Adel Faress''' (born 25 March 1948) is a Ghanaian stage, radio, television and film actress. She is best known for her portrayal of the solicitor Usha Gupta, in the long-running BBC Radio 4 serial ''The Archers'' and for her role as the High Priestess of the Dosh Khaleen in the sixth season of the HBO series ''Game of Thrones''.\\n\\n==Biography==\\nFaress was born in 1948 in Accra, Ghana, to Irish and Syrian parents. She studied drama at the Guildhall School of Music and Drama and lives in London. She has been exploring playwriting as a medium since before 2004. Faress completed a writing course at City Literary Institute adult education college. Her other skills include dancing, horse riding, cricket and yoga.\\n\\n===Career===\\nFaress appeared in two episodes of the BBC serial ''I, Claudius'' (1976) as a slave girl (shouting fire) and as a dancer who at Messalina's (second and bigamous) marriage party realises troops are coming to arrest them. In another early BBC appearance, she played the character Selma in the ''Blake's 7'' episode 'Horizon'. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '27ac53133aa6a56df7d08bb690f4f390'}>,\n",
+       "  <Document: {'content': '===Career===\\nFaress appeared in two episodes of the BBC serial \\'\\'I, Claudius\\'\\' (1976) as a slave girl (shouting fire) and as a dancer who at Messalina\\'s (second and bigamous) marriage party realises troops are coming to arrest them. In another early BBC appearance, she played the character Selma in the \\'\\'Blake\\'s 7\\'\\' episode \\'Horizon\\'. Since then, Faress has had substantial roles in films such as \\'\\'My Beautiful Laundrette\\'\\' (1985) and \\'\\'Sixth Happiness\\'\\' (1997), as well as much stage work around the UK.\\n\\nIn October 2001 she appeared briefly in \\'\\'Coronation Street\\'\\' as Dev Alahan\\'s mother Umila.\\n\\nSouad Faress also appeared as the Old Rani in \\'\\'The Sarah Jane Adventures\\'\\' serial \\'\\'The Mad Woman in the Attic\\'\\' in October 2009.\\n\\nIn October 2014, it was announced that Faress would perform in \\'\\'Hurried Steps\\'\\', Sharon Wood\\'s translation of \"Passi Affrettati by Dacia Maraini. The play\\'s narratives were mainly sourced from Amnesty International, the subject matter including rape, honour killings, sex trafficking and other issues of violence against women.\\n\\nBetween 2010 and 2011 she played Jay Faldren\\'s grandmother,  Maryam Shakiba, in \\'\\'Casualty\\'\\'.\\n\\nFaress appeared in an episode of popular nursing drama \\'\\'No Angels\\'\\' as lead character, Anji Mittel\\'s aunt Di. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'cbb88065bead8bfe14a6f2754b496c1e'}>,\n",
+       "  <Document: {'content': 'Between 2010 and 2011 she played Jay Faldren\\'s grandmother,  Maryam Shakiba, in \\'\\'Casualty\\'\\'.\\n\\nFaress appeared in an episode of popular nursing drama \\'\\'No Angels\\'\\' as lead character, Anji Mittel\\'s aunt Di. Faress appeared briefly as a hospital doctor in \"The Dead of Jericho\", the first episode of \\'\\'Inspector Morse\\'\\'.\\n\\nIn 2016 she joined the HBO series \\'\\'Game of Thrones\\'\\' in Season 6 as the High Priestess of the Dosh Khaleen. Faress will also be appearing in ITV drama \\'\\'Brief Encounters\\'\\', \"a loose adaptation of Ann Summers CEO Jacqueline Gold\\'s memoirs, \\'\\'Good Vibrations\\'\\'.\"==See also==\\n* List of Ghanaian actors and actresses\\n\\n==References==\\n\\n== External links ==\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'e03b86e6b2c36b851a4569ec18862d24'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Hardhome\\'\\'\\'\" is the eighth episode of the fifth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 48th overall. The episode was written by the series\\' creators David Benioff and D. B. Weiss, and directed by Miguel Sapochnik.\\n\\nThe episode features a climactic battle sequence at the episode\\'s eponymous Wildling village, a battle mentioned but not seen in the original source material. It has since been hailed by many reviewers and fans as one of the series\\' best episodes. Filming of the episode\\'s eponymous battle required nearly a month of shooting. In the United States, the episode achieved a viewership of 7.01 million in its initial broadcast. The episode earned \\'\\'Game of Thrones\\'\\' several nominations at the 67th Primetime Emmy Awards and was also Dinklage\\'s pick to support his nomination for Outstanding Supporting Actor.\\n\\nThis episode marks the only appearance for Birgitte Hjort Sørensen.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nSepta Unella visits Cersei in her cell and offers her water if she confesses her crimes, but Cersei refuses to confess. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '4d51426234fb91f256ed7b9f69481af9'}>,\n",
+       "  <Document: {'content': \"==Plot==\\n\\n===In King's Landing===\\nSepta Unella visits Cersei in her cell and offers her water if she confesses her crimes, but Cersei refuses to confess. Qyburn visits her and informs her that she is being charged for incest and Robert's murder and that Kevan returned from Casterly Rock and serves as Hand, at Pycelle's order.\\n\\n===In Braavos===\\nArya takes the identity of Lanna. Jaqen tells her that the family of one of the insurance salesman's clients hired the Faceless Men to kill him after he refused to pay when the client drowned at sea. He tells her to learn everything about the insurer and poison him.\\n\\n===At the Wall===\\nSam tells Olly that they have no chance of defeating the White Walkers without wildlings.\\n\\n===In Winterfell===\\nReek tells Sansa that there is no escape from Ramsay and admits that he didn't kill Bran and Rickon. Roose and Ramsay\\xa0plan for battle with Stannis' army. Ramsay asks Roose for 20 men.\\n\\n===In Meereen===\\nTyrion convinces Daenerys to allow him to advise her. Tyrion convinces her to spare Jorah's life and exile him. Tyrion tells her that she will not succeed in taking the Iron Throne without a powerful Westerosi house. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '3b8c966669c812b2cc6518098ade56cc'}>,\n",
+       "  <Document: {'content': \"Tyrion convinces her to spare Jorah's life and exile him. Tyrion tells her that she will not succeed in taking the Iron Throne without a powerful Westerosi house. Jorah returns to Yezzan and asks to fight in the fighting pits.\\n\\n===At Hardhome===\\nJon and Tormund arrive at Hardhome and meet the Lord of Bones, who is killed by Tormund after a tense standoff. Jon shares dragonglass with the wildlings and offers them to settle south of the Wall if they help the Night's Watch against the White Walkers. Jon and Tormund convince 5,000 wildlings to join them, but Thenns, led by Loboda, refuse. They are attacked by wights. A White Walker kills Loboda and forces Jon to flee without the dragonglass. Jon kills the White Walker with his Valyrian steel sword. Hardhome walls fall to the wights and Jon and his remaining allies are forced to flee. The Night King revives the dead as wights.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode was written by David Benioff and D. B. Weiss, the series' creators. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'fe75f2b47dbc61c2c6858865a789e20'}>,\n",
+       "  <Document: {'content': '==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode was written by David Benioff and D. B. Weiss, the series\\' creators. It contains content from George Martin\\'s novels \\'\\'A Feast for Crows\\'\\', chapters Cersei X and Cat of the Canals and \\'\\'A Dance with Dragons\\'\\', chapter The Ugly Little Girl.\\n\\nLike other episodes this season, \"Hardhome\" contained a large amount of original material that does not appear in Martin\\'s novels. This includes the battle scene in which the Wildlings and Night\\'s Watch are ambushed by the White Walkers and army of the undead, as well as the meeting of Daenerys Targaryen and Tyrion Lannister. According to Erik Kain of \\'\\'Forbes,\\'\\' \"We have now fully parted ways with the books. If the rest of Season 5 hadn\\'t convinced you that the show was forging its own path, this episode is the final nail in the coffin.\"===Casting===\\nBirgitte Hjort Sørensen was cast as Karsi, a wildling chieftain originally written as a male character. Zachary Baharov appeared as Loboda, a Thenn leader, and Ross O\\'Hennessy replaced Edward Dogliani as the Lord Of Bones in this episode. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '5ca2ea090b4d6172d332ed99267b66dd'}>,\n",
+       "  <Document: {'content': 'Zachary Baharov appeared as Loboda, a Thenn leader, and Ross O\\'Hennessy replaced Edward Dogliani as the Lord Of Bones in this episode. Ian Whyte, who had previously been cast as a White Walker, played the giant. Members of the metal band Mastodon acted as wildlings at Hardhome in a cameo appearance.\\n\\n===Filming===\\nMiguel Sapochnik served as director for the episode, his second episode for the series.\\n\"Hardhome\" was directed by Miguel Sapochnik. He joined the series as a director in the fifth season. He also directed the previous episode, \"The Gift\".\\nThe sequence at Hardhome was filmed at the Magheramorne quarry and took nearly a month to film.\\n\\n==Reception==\\n\\n===Ratings===\\n\"Hardhome\" was watched by an estimated 7.01 million American viewers during its first airing.\\n\\nWith Live+7 DVR viewing factored in, the episode had an overall rating of 9.94 million viewers, and a 5.1 in the 18–49 demographic. In the United Kingdom, the episode was viewed by 2.383 million viewers, making it the highest-rated broadcast that week. It also received 0.211 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode received universal acclaim from both critics and viewers, with critics calling it the best episode of the series. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'ce60ba055085051dd612a9fb26d3f61f'}>,\n",
+       "  <Document: {'content': '===Critical reception===\\nThe episode received universal acclaim from both critics and viewers, with critics calling it the best episode of the series. It received a 100% rating on the review aggregator Rotten Tomatoes from 35 reviews with an overall rating of 9.7 out of 10 and the critics\\' consensus reading \"Following several episodes of setup, \\'Hardhome\\' blends a sharp script with spectacular blockbuster action to offer viewers a powerful, game-changing payoff.\"\\'\\'The Atlantic\\'\\' named \"Hardhome\" one of the best television episodes of 2015. Erik Kain of \\'\\'Forbes\\'\\' called this \"one of the best, most exciting episodes I\\'ve seen in the entire show\\'s run, let alone this season,\" citing \"High action and a series of pretty crazy revelations.\"Matt Fowler of IGN gave the episode 10/10, labeling it a \"masterpiece\". He praised the Tyrion/Daenerys scenes but predominantly the final sequence, which he described as \"edge-of-your-seat exciting\" and also \"quite terrifying\". This made \"Hardhome\" the first episode in season 5 to receive a 10/10 rating from IGN. Matt Fowler also named it the best episode of the entire series. Both Myles McNutt and Erik Adams of \\'\\'The A.V. Club\\'\\' gave the episode the website\\'s highest grade, \"A\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '24cee4d10cd732fbaf40739eecb8b086'}>,\n",
+       "  <Document: {'content': 'Matt Fowler also named it the best episode of the entire series. Both Myles McNutt and Erik Adams of \\'\\'The A.V. Club\\'\\' gave the episode the website\\'s highest grade, \"A\". They called it \"a welcome reminder that the show is still unpredictable,\" with McNutt citing it as his favorite episode of the series thus far. Bridle Roman of SFX gave the episode a perfect five stars, and highlighted the character of Karsi, played by Birgitte Hjort Sørensen, as a great addition and \"her death hits hard\" even if \"we have only seen her for a few scenes\". The episode received praise even from some of its usual critics: Madeline Davies of \\'\\'Jezebel\\'\\' wrote, \"I feel like I haven\\'t said this in a long time, but last night\\'s \\'\\'Game of Thrones\\'\\' was ...cool?\"Davies cited the quality of the battle scene and unifying theme of hope in desperate situations as the episode\\'s key strengths, specifically that the abused and tormented Sansa learns that her brothers may still be alive and that though Jon faces \"the seemingly impossible task of defeating a constantly growing army of White Walkers, he at least possesses a sword that can kill them.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '3cbe777f9a780623dba6e75bd070c6a6'}>,\n",
+       "  <Document: {'content': 'Davies cited the quality of the battle scene and unifying theme of hope in desperate situations as the episode\\'s key strengths, specifically that the abused and tormented Sansa learns that her brothers may still be alive and that though Jon faces \"the seemingly impossible task of defeating a constantly growing army of White Walkers, he at least possesses a sword that can kill them.\"Kirsten Acuna of \\'\\'Business Insider\\'\\' reports that this is the single most popular episode to date as rated by fans, noting that the \"tremendous reaction is in complete juxtaposition with reactions to an episode that aired two weeks ago,\" which received one of the series\\' lowest fan ratings. Acuna credits this response to the quality of the battle scene, which featured \"a fighting army of the walking dead which would easily give the AMC series of the same name a run for its money.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '1c80d431610a34a4e95df8ec76671350'}>,\n",
+       "  <Document: {'content': 'Acuna credits this response to the quality of the battle scene, which featured \"a fighting army of the walking dead which would easily give the AMC series of the same name a run for its money.\"===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Episode\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters\\n\\nOutstanding Color Grading\\n\\nJoe Finley\\n\\nOutstanding Editing\\n\\nTim Porter\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\nIGN People\\'s Choice Awards\\n\\nBest TV Episode\\n\\n2016\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley\\n\\nShorty Awards\\n\\nGIF of The Year\\n\\nCome At Me Bro\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner\\n\\nAmerican Cinema Editors Awards\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D.B. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '4dea0e403b251f50e5e4d9c9550f28da'}>,\n",
+       "  <Document: {'content': \"===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Episode\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters\\n\\nOutstanding Color Grading\\n\\nJoe Finley\\n\\nOutstanding Editing\\n\\nTim Porter\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\nIGN People's Choice Awards\\n\\nBest TV Episode\\n\\n2016\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley\\n\\nShorty Awards\\n\\nGIF of The Year\\n\\nCome At Me Bro\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner\\n\\nAmerican Cinema Editors Awards\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D.B. Weiss\\n\\nVisual Effects Society Awards\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '70c83a920eb0d48271be8288bcfb2813'}>,\n",
+       "  <Document: {'content': '\\n\\n\\'\\'\\'Robert Baratheon\\'\\'\\' is a fictional character in the \\'\\'A Song of Ice and Fire\\'\\' series of epic fantasy novels by American author George R. R. Martin, and its television adaptation \\'\\'Game of Thrones\\'\\'.\\n\\nIntroduced in 1996\\'s \\'\\'A Game of Thrones\\'\\', Robert Baratheon is the eldest son and heir of Lord Steffon Baratheon and Lady Cassana Estermont of Storm\\'s End, the lord paramount of the Stormlands, one of the Seven Kingdoms of Westeros, and brother to Stannis and Renly. After his betrothed Lyanna Stark was kidnapped by Rhaegar Targaryen, Robert, along with the aid of her brother and his close friend, Eddard Stark, and their foster guardian Jon Arryn, initiated a bloody rebellion against the \"Mad King\" Aerys II Targaryen.  After crushing the Targaryen dynasty and winning the war, Robert took the Iron Throne and established the Baratheon dynasty as its first king.  However, due to Lyanna\\'s untimely death during the war, Robert instead married Tywin Lannister\\'s daughter Cersei to ensure political stability. Although Robert\\'s rule has been relatively peaceful, he proves to be an ineffective ruler and is unhappy in both his marriage to Cersei and his responsibilities as king, instead living a life of infidelity and wanton excess. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '2cbf41381f70f1d7efdc0dfbd28d0f29'}>,\n",
+       "  <Document: {'content': \"Although Robert's rule has been relatively peaceful, he proves to be an ineffective ruler and is unhappy in both his marriage to Cersei and his responsibilities as king, instead living a life of infidelity and wanton excess. He has fathered many bastards, unaware that his three children by Cersei were in fact fathered by her twin brother Jaime Lannister as products of incest.\\n\\nAlthough Robert is killed in the first novel, the legacy of his rebellion and rule continues to have great impact on the contemporary events of Westeros. His death creates a power vacuum in which both of his brothers, Cersei's eldest son Joffrey, and several other claimants fight for control of the Seven Kingdoms, known as the War of Five Kings.\\n\\nRobert is portrayed by English actor Mark Addy in the HBO television adaptation.\\n\\n== Character ==\\nRobert Baratheon is not a point of view character in the novels, so his actions are mainly witnessed and interpreted through the eyes of Ned Stark.  He only appears in the first book of the series ''A Game of Thrones'', though is mentioned numerous times in the later books by characters such as Cersei Lannister.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'b1eb34a53afeed839b99465096738b14'}>,\n",
+       "  <Document: {'content': 'He only appears in the first book of the series \\'\\'A Game of Thrones\\'\\', though is mentioned numerous times in the later books by characters such as Cersei Lannister.\\n\\n===Background===\\nBefore he became king, Robert Baratheon was the ward of Jon Arryn and was raised at the Eyrie alongside Eddard Stark.  Fifteen years before the novels, Robert was betrothed to Ned\\'s younger sister Lyanna, with whom he was madly (and unrequitedly) in love.  After Lyanna disappeared with Rhaegar Targaryen, Robert led a rebellion to overthrow the Targaryen dynasty and later killed Rhaegar in single combat.  He then married Cersei Lannister to ensure House Lannister\\'s support for his rule, but remained attached to the memory of Lyanna, resulting in an estranged marriage with Cersei.\\n\\n===Personality and description===\\nRobert is in his mid-thirties when the events of the books begin.  He has the classical Baratheon look: black hair and bright blue eyes, and is a very tall man, with Eddard Stark estimating him to be six and a half feet.  As a young adult, Robert was handsome, clean-shaven and muscled \"like a maiden\\'s fantasy\".  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'ac0023bae046927508cda64dcc0763b3'}>,\n",
+       "  <Document: {'content': 'He has the classical Baratheon look: black hair and bright blue eyes, and is a very tall man, with Eddard Stark estimating him to be six and a half feet.  As a young adult, Robert was handsome, clean-shaven and muscled \"like a maiden\\'s fantasy\".  However, after he won the Iron Throne, Robert becomes very obese due to excessive feasting and drinking, gaining weight, and turns into an often red-faced man with dark circles underneath his eyes and appears half-drunk and sweating when walking, with a wild, thick, fierce beard that hides his double chin.\\n\\nIn his youth, Robert is as strong as a bull and fearless in battle with a powerful voice, wielding a large spiked iron war hammer too heavy for Eddard Stark to lift, and was a formidable warrior well loved by soldiers.  Though headstrong, rash and impatient, Robert can be merciful towards his enemies as long as they are honest and brave, and can inspire loyalty and friendship in even enemies via charisma alone.  Although deconditioned after becoming king due to weight gain and frequent drinking, Jaime Lannister still believes that Robert is stronger than him.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '8b5c20002ea65b8ecd8bb9c55f47c4e6'}>,\n",
+       "  <Document: {'content': \"Although deconditioned after becoming king due to weight gain and frequent drinking, Jaime Lannister still believes that Robert is stronger than him.\\n\\nRobert is a jovial man of huge appetites and knows to indulge in pleasures.  He is quite promiscuous, having fathered multiple bastard children (17 according to the prophecy by Lannisport fortuneteller Maggy the Frog) with whores or any women he encounters, and his lusts are the subject of ribald drinking songs throughout the realms.  As king, Robert is known to impose upon the hospitality (voluntarily or not) of his subjects, but at the same time also possesses a rather careless generosity.  A proud man, Robert rarely back down on words spoken in a drunken bravado.  As king, Robert is no longer used to someone disagreeing with him, which makes him vulnerable to manipulation by others. Robert loathes his responsibilities as king and frequently expresses his preference to winning the throne in battle rather than sitting on it. He has never truly loved his wife and is unaware that none of his three children with her are his, but instead Jaime Lannister's. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '89b34d7eaa2578dc0c832c7451b2142b'}>,\n",
+       "  <Document: {'content': 'He has never truly loved his wife and is unaware that none of his three children with her are his, but instead Jaime Lannister\\'s. Under his reign, the realm has been bankrupted, and Robert is deeply in debt to his wife\\'s family. Tyrion Lannister considers Robert to be \"a great blustering oaf\", while Varys describes him as a fool.  His queen wife Cersei Lannister considers him to be an ignorant, dumb, slow-witted, drunken brute who does not have the ruthless streak she believes a king requires.  According to Petyr Baelish, Robert is practised at closing his eyes to things he would rather not see.\\n\\n== Storylines ==\\nCoat of arms of House Baratheon\\n\\n====\\'\\'A Game of Thrones\\'\\'====\\n\\nKing Robert has come to Winterfell to appoint his close friend Eddard Stark the Hand of The King, after the untimely death of Jon Arryn. He was unaware that Cersei\\'s three children were in fact fathered by her twin brother Jaime.  More interested in food, drink, and tourneys than in governance, Robert has squandered the royal treasury, leaving the crown heavily in debt.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'b79edfdc15086a1830792ef0025d6dec'}>,\n",
+       "  <Document: {'content': \"More interested in food, drink, and tourneys than in governance, Robert has squandered the royal treasury, leaving the crown heavily in debt.  After a hunting accident orchestrated by Cersei, Robert is mortally wounded and appoints Eddard as the regent for his son Joffrey.  After Robert's death, Eddard loses the political struggle against the Lannisters and is later publicly executed by beheading, and the kingdoms plunge into civil war known as the War of the Five Kings (which encompasses the entirety of the following two books) in which Robert's two brothers Renly and Stannis Baratheon both declare themselves the rightful kings. Eddard's vengeful son Robb and later Balon Greyjoy also secede and declare kingships.\\n\\n====''A Clash of Kings''====\\n\\nRobert briefly appears in ''A Clash of Kings'' in a flashback sequence.\\n\\n=== Family tree of House Baratheon ===\\n\\n==TV adaptation==\\nRobert Baratheon is played by Mark Addy in the television adaption of the series of books. Addy's audition for the role was according to showrunners David Benioff and D. B. Weiss the best they saw, him being the easiest actor to cast for the show.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '49b3f9add838d9b1b53dc5bc3056a719'}>,\n",
+       "  <Document: {'content': \"Addy's audition for the role was according to showrunners David Benioff and D. B. Weiss the best they saw, him being the easiest actor to cast for the show.\\n\\n==== Season 1 ====\\nRobert's storyline is the same in the show as it is in the novels, however, his wife Cersei reveals that she had one stillborn son by him, which is not the case in the novels, where Cersei uses guile and trickery to prevent him from ever actually engaging in sexual intercourse with her while drunk.\\n\\n==== Season 2 ====\\nAfter his death, Robert's bastards are ordered to be killed by Joffrey Baratheon, Robert's supposed legal heir and the new king. Gendry is subsequently forced to flee the capital.\\n\\n==== Seasons 6 & 7 ====\\nIn seasons six and seven, Bran Stark's gift of vision as the Three-Eyed Raven revealed to him the truth of Rhaegar and Lyanna's relationship and states that 'Robert's Rebellion was built on a lie'.\\n\\n==== Season 8 ====\\nDaenerys Targaryen legitimizes Gendry, naming him Robert's lawful son and Lord of Storm's End.\\n\\n== References ==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'e3d5907057730199e4ff332e47f5890d'}>,\n",
+       "  <Document: {'content': \"\\nThe soundtrack album of the third season of HBO series ''Game of Thrones'', titled '''''Game of Thrones: Season 3''''', was released digitally on June 4, 2013, and on CD on July 2, 2013. The album was composed by Ramin Djawadi.\\n\\n==Reception==\\nThe soundtrack received positive reviews from critics.\\n\\n==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* The Hold Steady – band, primary artist\\n\\n* George R.R. Martin – lyricist\\n* Kerry Ingram – primary artist\\n* D.B. Weiss – liner notes\\n\\n==Charts==\\n\\nChart (2013)\\n\\nPeak position\\n\\n==Awards and nominations==\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2013\\n\\nASCAP Awards\\n\\nTop Television Series\\n\\nInternational Film Music Critics Association\\n\\nBest Original Score for a Television Series\\n\\n==References==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '6ade481abd50ec0ee6d19db25479ae30'}>,\n",
+       "  <Document: {'content': '\\n\"\\'\\'\\'Breaker of Chains\\'\\'\\'\" is the third episode of the fourth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 33rd overall. The episode was written by series co-creators David Benioff and D. B. Weiss, and directed by Alex Graves. It aired on April 20, 2014.\\n\\nThe episode received critical praise overall, but triggered a public controversy as many interpreted the sexual encounter between Jaime and Cersei Lannister as rape. It marks the final appearance of Jack Gleeson (Joffrey Baratheon).\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nSansa flees with Dontos while Cersei and Tywin order a search for Sansa. Dontos leads Sansa to a ship, led by Petyr, who kills Dontos and tells Sansa that necklace Dontos gave her was fake and part of the plan. In the Sept, Tywin speaks to Tommen about becoming the king. Cersei grieves for Joffrey. Jaime arrives and Cersei asks him to kill Tyrion, but he refuses. Tywin suspects that Oberyn had a hand in Joffrey\\'s murder and Oberyn accuses Tywin of ordering the Mountain to murder\\xa0Elia. Tywin offers to have the Mountain speak with Oberyn in exchange for Oberyn serving as one of the three judges in Tyrion\\'s trial. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '9354a7c969a062927d53c4357dda45d6'}>,\n",
+       "  <Document: {'content': \"Tywin offers to have the Mountain speak with Oberyn in exchange for Oberyn serving as one of the three judges in Tyrion's trial. Podrick visits Tyrion in his cell. Tyrion asks Podrick to bring Jaime.\\n\\n===In the North===\\nSam takes Gilly to Mole's Town for her safety. Tormund's wildling group raids a village and Styr forces a young boy, Olly, to run to Castle Black and tell the Night's Watch what they have done. Edd Tollett and Grenn return to Castle Black and inform them that Karl's group is living at Craster's Keep. Jon remembers his lie to Tormund about having 1,000 men guarding the Wall and says they must kill Karl's group before Tormund finds out the truth about the Night's Watch forces.\\n\\n===At Dragonstone===\\nStannis informs Davos that Joffrey is dead and chastises him for\\xa0releasing Gendry. Davos has Shireen write a message to the Iron Bank of Braavos in Stannis' name.\\n\\n===In the Riverlands===\\nSandor and Arya continue their journey to the Vale.\\n\\n===Outside Meereen===\\nDaenerys’ army arrive at Meereen. Jorah informs Daenerys that she must send a champion to fight the Champion of Meereen. Daario is selected and he kills the Champion. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'be4feee0d7b384cb006759ad1c1e23d9'}>,\n",
+       "  <Document: {'content': 'Jorah informs Daenerys that she must send a champion to fight the Champion of Meereen. Daario is selected and he kills the Champion. Daenerys tells slaves from Meereen that she intends to bring them freedom.\\n\\n==Production==\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"Breaker of Chains\" was written by executive producers David Benioff and D. B. Weiss, based upon the source material, Martin\\'s \\'\\'A Storm of Swords\\'\\'. The controversial Jaime-Cersei scene was adapted from chapter 62 of \\'\\'A Storm of Swords\\'\\' (Jaime VII). Other content came from chapters Sansa V, Tyrion IX and Daenerys V.\\n\\n==Reception==\\n===Ratings===\\n\"Breaker of Chains\" was watched by an estimated 6.6 million people during the first hour. Another 1.6 million watched it on rerun. In the United Kingdom, the episode was viewed by 1.665 million viewers, making it the highest-rated broadcast that week. It also received 0.099 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode was well received by critics, with a 95% rating from 37 reviews on Rotten Tomatoes. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '2c4bc34fa1af0c75779b43c4bc07479'}>,\n",
+       "  <Document: {'content': 'It also received 0.099 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode was well received by critics, with a 95% rating from 37 reviews on Rotten Tomatoes. Matt Fowler of IGN wrote that the episode deals \"with the direct aftermath of King Joffrey\\'s violent demise, though it also feels like the most bereft of the first three episodes\" and that it \"ended with a big, grandiose Daenerys moment – though if one were to compare her launching canisters filled with broken shackles over the walls of Meereen to last year\\'s flambéing of Astapor and stealing off with an entire army (which came at the end of episode 4), this moment lacked \\'oomph.\\'\"====Rape scene====\\nThe episode attracted controversy for a scene where Jaime Lannister forces himself upon Cersei Lannister in the Great Sept of Baelor. In the episode, Cersei repeatedly tells Jaime \"no\", \"stop\", and \"it\\'s not right\" while attempting to push him away. In the source novel, however, Cersei initially rejects Jaime\\'s advances but changes her mind, saying \"yes\", though some readers interpret it as implying rape.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'f65de6f205501f58ed1f50fa68fc24f7'}>,\n",
+       "  <Document: {'content': 'In the source novel, however, Cersei initially rejects Jaime\\'s advances but changes her mind, saying \"yes\", though some readers interpret it as implying rape.\\n\\nAlan Sepinwall of HitFix stated that viewers interpreted the scene as a rape: \"Though there aren\\'t comments on these reviews at the moment, the reaction I\\'ve seen on Twitter, in emails and on other blogs suggests nobody is agreeing with director Alex Graves\\' interpretation of the scene and are viewing it as rape, plain and simple.\"Sandy Hingston of \\'\\'Philadelphia\\'\\' wrote that the scene had outraged many viewers, but had led others to make \"tentative attempts in comments sections to explain why maybe actually this wasn\\'t rape.\"Critics, including Erik Kain of \\'\\'Forbes\\'\\', Sonia Saraiya of \\'\\'The A.V. Club\\'\\', Megan Gibson of \\'\\'Time\\'\\', Amanda Marcotte of \\'\\'Slate\\'\\', and Alyssa Rosenberg of the \\'\\'Washington Post\\'\\', also viewed the scene as rape.\\n\\nThe episode\\'s director, Alex Graves, described the scene in an interview with \\'\\'The Hollywood Reporter\\'\\' as \"and then Jaime comes in and he rapes her\". In another interview with HitFix, Graves said \"it becomes consensual by the end, because anything for Jaime and Cersei ultimately results in a turn-on, especially a power struggle.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '486c66a1703efb7b6deb03b531d911b7'}>,\n",
+       "  <Document: {'content': 'In another interview with HitFix, Graves said \"it becomes consensual by the end, because anything for Jaime and Cersei ultimately results in a turn-on, especially a power struggle.\"In an interview with Vulture, Graves further elaborated: \"The consensual part of it was that she wraps her legs around him, and she\\'s holding on to the table, clearly not to escape but to get some grounding in what\\'s going on. And also, the other thing that I think is clear before they hit the ground is she starts to make out with him. ... before he rips her undergarment, she\\'s way into kissing him back.\"Graves stated that it was important for him and others involved with filming the scene to indicate to viewers that the sexual encounter was not completely one-sided, and that he hoped this aspect would not be overlooked. Noting that during filming, \"nobody really wanted to talk about what was going on between the two characters\", he described the scene as \"one of my favorite scenes I\\'ve ever done\".\\n\\nOthers involved with the series also commented on the scene. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '9a3245bd261de2b603c1ea407dfd02cf'}>,\n",
+       "  <Document: {'content': 'Noting that during filming, \"nobody really wanted to talk about what was going on between the two characters\", he described the scene as \"one of my favorite scenes I\\'ve ever done\".\\n\\nOthers involved with the series also commented on the scene. David Benioff, who co-wrote the episode and serves as showrunner with D. B. Weiss, described the interaction of the two siblings as \"a really kind of horrifying scene, because you see, obviously, Joffrey\\'s body right there, and you see that Cersei is resisting this. She\\'s saying no, and he\\'s forcing himself on her. So it was a really uncomfortable scene, and a tricky scene to shoot.\"George R.R. Martin, author of the novels from which the series is adapted, stated that the dynamic is different between Jaime and Cersei in the show because, as opposed to the books, \"Jaime has been back for weeks at the least, maybe longer.\"He stated that while the setting is the same, \"neither character is in the same place as in the books\" and that he surmises this \"may be why Dan Weiss & David Benioff played the sept out differently.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'bc0b2b5c16b3e2d432a5717087e24af2'}>,\n",
+       "  <Document: {'content': 'He stated that while the setting is the same, \"neither character is in the same place as in the books\" and that he surmises this \"may be why Dan Weiss & David Benioff played the sept out differently.\"Martin added that he never discussed the scene with them, and that the scene \"was always intended to be disturbing... but I regret if it has disturbed people for the wrong reasons.\"Nikolaj Coster-Waldau, who portrays Jaime, said in an interview that while many saw the scene as a brutal rape, \"that was obviously never intended. I understand that one can see it as that, but for us it was much more complex.\"Lena Headey, who portrays Cersei, declined to comment about whether she interpreted the sex as consensual, but said that \"it\\'s a very complicated moment for many reasons ... There was this need and it wasn\\'t right and yet it felt great and yet it wasn\\'t right and it played out the way it did. And I was really happy with the scene.\"To Saraiya, the series\\' choice to portray this scene as rape appeared to be an act of \"exploitation for shock value\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '2cde0197266754eacc01c3ad98aeaa75'}>,\n",
+       "  <Document: {'content': 'To Saraiya, the series\\' choice to portray this scene as rape appeared to be an act of \"exploitation for shock value\". Marcotte and Josh Wigler of MTV commented that the rape scene may have damaged Jaime\\'s character\\'s arc of redemption from his earlier crimes, and Marcotte wrote that it might never recover from the rape. Dustin Rowles of \\'\\'Salon\\'\\' noted that viewers who were previously able to sympathize with Jaime despite his earlier crimes of murder and incest now became angry at the series\\' creators \"for allowing a terrible person to do something more terrible than our minds will allow us to forgive.\"Andrew Romano of \\'\\'The Daily Beast\\'\\' opined that the scene \"wasn\\'t supposed to be a rape. It was supposed to look consensual. The filmmakers messed up.\"He cited the fact that \"neither the director of the scene nor the two actors who played it seem to think that Jaime raped Cersei—and the story itself is continuing to chug along as if the rape never happened and Jaime is still a character we\\'re supposed to root for.\"He suggested that viewers \"ignore the rape—at least from a narrative perspective\" because Benioff and Weiss had \"botched\" it. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '693b8260ec70f5f09187c18e099bc9fb'}>,\n",
+       "  <Document: {'content': 'He cited the fact that \"neither the director of the scene nor the two actors who played it seem to think that Jaime raped Cersei—and the story itself is continuing to chug along as if the rape never happened and Jaime is still a character we\\'re supposed to root for.\"He suggested that viewers \"ignore the rape—at least from a narrative perspective\" because Benioff and Weiss had \"botched\" it. In a broader context, Hingston credited the episode with furthering \"the furious debate over consent going on in our culture\". Laura Hudson of \\'\\'Wired\\'\\' described the scene and its appraisal by its director as \"one that encourages the most dangerous thinking about rape imaginable: that when a woman is held down on the ground, screaming for the man to stop, that deep down inside her she might still really want it.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '8ceed197475bf28e604c2e69110c63e8'}>,\n",
+       "  <Document: {'content': 'Laura Hudson of \\'\\'Wired\\'\\' described the scene and its appraisal by its director as \"one that encourages the most dangerous thinking about rape imaginable: that when a woman is held down on the ground, screaming for the man to stop, that deep down inside her she might still really want it.\"She considered Graves\\' lack of realization that he was filming a rape scene disturbing because, according to Hudson, his comments encouraged the thinking that a man\\'s persistence might \"\\'turn\\' a rape into something consensual\", and that it is a \"dysfunctional, dangerous way of looking at sex and consent, one that is based on the idea of forcing women to give it\".\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'eecca780997ea9dee149f3a1f458d167'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'A Man Without Honor\\'\\'\\'\" is the seventh episode of the second season of HBO\\'s medieval fantasy television series \\'\\'Game of Thrones\\'\\'.\\n\\nThe episode is written by series co-creators David Benioff and D. B. Weiss and directed, for the second time in this season, by David Nutter. It premiered on May 13, 2012.\\n\\nThe name of the episode comes from Catelyn Stark\\'s assessment of Ser Jaime Lannister: \"You are a man without honor,\" after he kills a member of his own family to attempt escape.\\n\\n==Plot==\\n===In King\\'s Landing===\\nSansa awakens from a nightmare to find she has had her first period, meaning she can bear Joffrey’s children. Shae tries to help conceal the blood, but discovers the Hound has seen the sheet. Cersei tells Sansa it will be impossible to love Joffrey.\\n\\nTyrion tells Cersei his doubts about Joffrey\\'s plan to repel Stannis’ fleet. Cersei confesses her belief that Joffrey is punishment for her incest with Jaime.\\n\\n===Beyond the Wall===\\nJon searches for his companions, and captive Ygritte tells him life would be better with the wildlings. She teases his sexual inexperience, but he rebuffs her advances. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '652e1b70d3e023bdb5db38b2fc7a7955'}>,\n",
+       "  <Document: {'content': '===Beyond the Wall===\\nJon searches for his companions, and captive Ygritte tells him life would be better with the wildlings. She teases his sexual inexperience, but he rebuffs her advances. Ygritte escapes, and Jon is unable to find her until she reveals he is surrounded by wildlings.\\n\\n===At Harrenhal===\\nTywin orders Ser Gregor \"The Mountain\" Clegane to find Lorch\\'s killer, believing the murder was an attempt on his own life. Dining with Arya, Tywin deduces she is highborn and advises her to hide her status more carefully.\\n\\n===In the Westerlands===\\nRobb hears Alton Lannister\\'s unfavorable reply to the peace terms brought to Cersei and, with his prisons full, orders Alton placed with Jaime. Talisa agrees to accompany Robb to the Crag for medical supplies.\\n\\nJaime kills Alton to draw the jailer, Torrhen Karstark, into the pen, strangling him before fleeing. Jaime is recaptured, and Lord Rickard Karstark demands his head for murdering his son, but Catelyn delays the execution until Robb’s return. Confronting Jaime, Catelyn calls him \"a man without honor\", and Jaime bitterly explains the contradictions he faced in his vows as a knight. He provokes Catelyn, who asks for Brienne\\'s sword.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '389adde09d94902f1826f078270568a0'}>,\n",
+       "  <Document: {'content': 'Confronting Jaime, Catelyn calls him \"a man without honor\", and Jaime bitterly explains the contradictions he faced in his vows as a knight. He provokes Catelyn, who asks for Brienne\\'s sword.\\n\\n===In Qarth===\\nDaenerys has difficulty trusting Xaro, who claims he did not take her dragons. Vowing to find them, Jorah confronts the shadowbinder Quaithe. At a meeting of the Thirteen, Pyat Pree tells Daenerys that he has her dragons in the “House of the Undying”. Xaro proclaims himself King of Qarth as Pree, having multiplied himself around the room, assassinates the remaining Thirteen, and Daenerys flees with Jorah and Kovarro.\\n\\n===At Winterfell===\\nRealizing Osha has fled with Bran, Rickon, and Hodor, Theon beats Lorren for challenging him and hunts for the fugitives against Maester Luwin’s wishes, following the trail of Bran and Rickon.\\n\\nReturning from the hunt, Theon reveals the charred remains of two children as a public warning. Luwin cries out in horror for Bran and Rickon, while Theon struggles to hide his guilt.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '20b4f7c6fe7ea26adcbc8e8e719897b3'}>,\n",
+       "  <Document: {'content': 'Luwin cries out in horror for Bran and Rickon, while Theon struggles to hide his guilt.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThe episode was written by producers David Benioff and D. B. Weiss, their fourth of six scripts for the second season. This episode adapts content from the second novel, \\'\\'A Clash of Kings\\'\\', chapters Theon IV, Sansa IV and parts of Catelyn V and Catelyn VII.\\n\\n== Reception ==\\n=== Ratings ===\\n\"A Man Without Honor\" was watched by 3.69 million American households in its initial telecast on HBO. In the United Kingdom, the episode was seen by 0.935 million viewers on Sky Atlantic, being the channel\\'s highest-rated broadcast that week.\\n\\n=== Critical reception ===\\nThe episode received positive reviews from critics. Review aggregator Rotten Tomatoes surveyed 12 reviews of the episode and judged 92% of them to be positive, with an average score of 8.85 out of 10. The website\\'s critical consensus reads, \"Quieter and more meditative than previous episodes, \\'A Man Without Honor\\' is bolstered by incredible performances and a welcome return to an unlikely but surprisingly satisfying onscreen duo.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '2442704f1f60cdc3404d499dcb062e44'}>,\n",
+       "  <Document: {'content': 'The website\\'s critical consensus reads, \"Quieter and more meditative than previous episodes, \\'A Man Without Honor\\' is bolstered by incredible performances and a welcome return to an unlikely but surprisingly satisfying onscreen duo.\"IGN\\'s Matt Fowler gave the episode a 9 out of 10, noting that \"While \\'\\'A Man Without Honor\\'\\' didn\\'t have as many big, striking scenes as some of the past few episodes, it made up for its lack of face-melting moments with some absolutely fantastic one-on-one scenes.\"The A.V. Club\\'s Emily VanDerWerff gave it an A-.\\n\\n== References ==\\n\\n== External links ==\\n\\n*  \"A Man Without Honor\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'e455fcecc23f8d83fc6ca2e23af5eb7b'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''''Thronecast''''' is a British television series on Sky Atlantic which is presented by Sue Perkins and Jamie East. It is an aftershow to ''Game of Thrones'', which also airs on the channel. Each episode is an hour in length and features interviews with members of the cast and crew, interaction with the audience and social media alongside analysis of the previous episode and a preview of the next episode of ''Game of Thrones''. It was the only official aftershow for the American series until HBO aired ''After the Thrones'' in 2016 – a show which was cancelled after only one series.\\n\\n''Thronecast'' was originally devised by Koink Productions Ltd for Sky Digital Entertainment. The series began airing online in April 2011 as a series of 10-minute podcasts presented by Geoff Lloyd to correspond with the first series of ''Game of Thrones''. Annabel Port joined Geoff for the second series, which corresponded with the broadcast of the second series of ''Game of Thrones''. For the third series, ''Thronecast'' became a 15-minute on-air show that was broadcast on Sky Atlantic following each episode of the third series of ''Game of Thrones'', and featured new guest presenter Grace Dent. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '795fd6d6964d9325b6ad7fc981cf98b0'}>,\n",
+       "  <Document: {'content': \"For the third series, ''Thronecast'' became a 15-minute on-air show that was broadcast on Sky Atlantic following each episode of the third series of ''Game of Thrones'', and featured new guest presenter Grace Dent. The fourth series was presented by Jamie East and Rachel Parris, and was 30 minutes long. The fifth series was presented by Sue Perkins and Jamie East and was 30 minutes long, except for the series finale which was an extended hour long special. Perkins and East continued to present the seventh and eighth series of the show which were all 60 minutes long with the exception of the final episode of the show, which was 70 minutes. Series 7 and 8 included pre-series episodes titled ''War Room'', and the eighth series also included a 90 minutes long gameshow titled ''Gameshow of Thrones'' and a 30-minute pre-finale episode titled ''Thronecast: The End is Coming''.\\n\\nEach series was also made available on Sky Go, On Demand (previously Sky Anytime and Sky Anytime+) and NOW TV. Series 1 to 4 of Thronecast were produced by Koink Productions Ltd for Sky Atlantic. Series 5 of Thronecast was produced by CPL Productions for Sky Atlantic.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '84eed040cfb7a35294d7e8f4dc0e7244'}>,\n",
+       "  <Document: {'content': \"Series 1 to 4 of Thronecast were produced by Koink Productions Ltd for Sky Atlantic. Series 5 of Thronecast was produced by CPL Productions for Sky Atlantic.  Series 6 and 7 of the show were produced by Sky's in-house production company, Sky Vision Productions. Series 8 of the show was produced by independent production company Znak & Co Ltd.\\n\\n==Episodes==\\n\\n===Series overview===\\n\\n===Series 1 (2011)===\\n\\n===Series 2 (2012)===\\nA special episode of Thronecast was filmed before the start of the second series, which featured interviews with Emilia Clarke (Daenerys Targaryen), Alfie Allen (Theon Greyjoy), Michelle Fairley (Catelyn Stark), Kit Harington (Jon Snow), Charles Dance (Tywin Lannister), Liam Cunningham (Davos Seaworth), and Nikolaj Coster-Waldau (Jaime Lannister).\\n\\n===Series 3 (2013)===\\n\\n===Series 4 (2014)===\\n\\n===Series 5 (2015)===\\nThe series finale was an extended one-hour episode and was viewed by 560,000 viewers, making it the second highest-rated broadcast that week, behind the actual episode. It also received 29,000 timeshifted viewers.\\n\\n===Series 6 (2016)===\\nThe series finale was an extended one-hour episode and was viewed by 587,000 viewers, making it the second highest-rated broadcast that week, behind the actual episode. It also received 45,000 timeshifted viewers.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'e96098c95c6c5136e35c8a03fda6a03a'}>,\n",
+       "  <Document: {'content': '===Series 6 (2016)===\\nThe series finale was an extended one-hour episode and was viewed by 587,000 viewers, making it the second highest-rated broadcast that week, behind the actual episode. It also received 45,000 timeshifted viewers.\\n\\n===Specials (2017)===\\n\\nA pre-series episode titled \"War Room\", filmed on location, was broadcast the week before the start of Series 7.\\n\\n===Series 7 (2017)===\\n\\n===Specials (2019)===\\n\\nTwo specials aired before Series 8: \"Gameshow of Thrones\" featuring a number of stars of the show with superfans, plus \"War Room\" featuring superfans Al Murray, Lauren Laverne and Jonathan Ross.\\n\\n===Series 8 (2019)===\\n\\nThe series finale includes a special 30-minute pre-show episode titled \"The End is Coming\", and a 70-minute post-show episode. These episodes incorporated an additional studio segment titled \"Littlefinger\\'s Establishment\", where Jamie East talked to stars of the show and superfans about their memories of working on the show.\\n\\nEpisode 3 of Series 8 was the most viewed episode of \\'\\'Thronecast\\'\\' according to BARB figures.\\n\\n==See also==\\n*\\'\\'After the Thrones\\'\\' – a similar talk show hosted by Andy Greenwald and Chris Ryan which also discusses episodes of \\'\\'Game of Thrones\\'\\'.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '6b7655e1da49844a355f46abd1fe9b23'}>,\n",
+       "  <Document: {'content': \"==See also==\\n*''After the Thrones'' – a similar talk show hosted by Andy Greenwald and Chris Ryan which also discusses episodes of ''Game of Thrones''.\\n*''Talking Dead'' – a similar talk show hosted by Chris Hardwick which discusses episodes of ''The Walking Dead'' and ''Fear the Walking Dead''.\\n*''Talking Bad'' – a similar talk show hosted by Hardwick which discussed episodes of ''Breaking Bad''s fifth series.\\n*''Talking Saul'' – a similar talk show hosted by Hardwick which discusses episodes of ''Better Call Saul''.\\n*''Talking Preacher'' – a similar talk show hosted by Hardwick which discusses episodes of ''Preacher''.\\n\\n== References ==\\n\\n==External links==\\n*\\n* Official Thronecast website at Sky Atlantic\\n* Official Game of Thrones website at Sky Atlantic\\n* Sue Perkins Twitter\\n* Jamie East\\n* CPL Productions\\n* Koink Productions Ltd\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'ce07875ca5bfe5b21ddf13d84045d57f'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Rains of Castamere\\'\\'\\'\" is the ninth and penultimate episode of the third season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 29th episode of the series. The episode was written by executive producers David Benioff and D. B. Weiss, and directed by David Nutter. It aired on .\\n\\nThe episode is centered on the wedding of Edmure Tully and Roslin Frey, one of the most memorable events of the book series, commonly called \"The Red Wedding\", during which Robb Stark and his banner-men are massacred. Other storylines include Bran Stark\\'s group\\'s having to separate, Jon Snow\\'s loyalties being tested, and Daenerys Targaryen\\'s plotting her invasion of the city of Yunkai. The title is a song belonging to the Lannister family, whose lyrics foreshadow the Red Wedding and which is played by the band at the wedding right before the slaughter begins.\\n\\nThis episode earned Benioff and Weiss a nomination for Primetime Emmy Award for Outstanding Writing for a Drama Series. This episode marks the final appearance of Richard Madden (Robb Stark), Oona Chaplin (Talisa Stark), and Michelle Fairley (Catelyn Stark).\\n\\n==Plot==\\n\\n===Beyond the Wall===\\nSam and Gilly continue their march south. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'fbba1d232a2731a95779b6bdc5b07e4b'}>,\n",
+       "  <Document: {'content': \"This episode marks the final appearance of Richard Madden (Robb Stark), Oona Chaplin (Talisa Stark), and Michelle Fairley (Catelyn Stark).\\n\\n==Plot==\\n\\n===Beyond the Wall===\\nSam and Gilly continue their march south. Sam tells Gilly he plans for them to cross the Wall using the entrance at the Nightfort, an abandoned castle along the Wall.\\n\\n===In the North===\\nBran and his group take shelter in an abandoned mill. They see a horse breeder captured by Jon's wildling group. Hodor, scared, begins yelling, which threatens to give away their location to the wildlings. Bran uses his warg abilities to enter Hodor's mind and knocks him out. Orell tells Jon to kill the old man to prove his loyalty, but Jon is unable to kill him and Ygritte kills him. Tormund realizes that Jon is not loyal to them and orders his men to kill Jon. Bran enters Summer's mind and helps Jon to defeat Tormund's men and Orell. Jon steals a horse and escapes. Bran asks Rickon to leave for Last Hearth, the home of the Umber family.\\n\\n===In Yunkai===\\nDaario, Jorah and Grey Worm enter the city and kill Yunkai's slave soldiers. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '5fabb59e866b5b5a964a510ad38a60e8'}>,\n",
+       "  <Document: {'content': \"Bran asks Rickon to leave for Last Hearth, the home of the Umber family.\\n\\n===In Yunkai===\\nDaario, Jorah and Grey Worm enter the city and kill Yunkai's slave soldiers. They tell Daenerys that she is now in control of the city.\\n\\n===At the Twins===\\nRobb's army arrive at the Twins. Robb apologizes to Walder and his daughters. Sandor, travels to the Twins with Arya, steals the cart of food. Edmure marries Roslin, Walder calls for the bedding ceremony and the couple are taken to their chamber. Talisa tells Robb that she wants to name their son Eddard. Freys begin playing a Lannister song The Rains of Castamere. Arya and Sandor arrive, but are turned away by the guards, but Arya sneaks in. Catelyn realizes that they have been betrayed by Roose Bolton. Frey orders his men to attack Stark men. Lothar kills Talisa. Arya witnesses Frey men kill Stark soldiers and Robb's direwolf, Grey Wind. She is saved by Sandor. Bolton kills Robb, Catelyn kills Walder's wife Joyeuse and Black Walder kills Catelyn.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'c84c1c0f6b1dd6c512753c3db007f32c'}>,\n",
+       "  <Document: {'content': 'Arya witnesses Frey men kill Stark soldiers and Robb\\'s direwolf, Grey Wind. She is saved by Sandor. Bolton kills Robb, Catelyn kills Walder\\'s wife Joyeuse and Black Walder kills Catelyn.\\n\\n==Production==\\n\\n===Writing===\\n\"The Rains of Castamere\" was written by executive producers David Benioff and D. B. Weiss, based on George R. R. Martin\\'s original work from his novel \\'\\'A Storm of Swords\\'\\'. The episode adapts content from chapters 41 to 43 and 50 to 53 (Bran III, Jon V, Daenerys IV, Catelyn VI, Arya X, Catelyn VII, and Arya XI).\\n\\nThe episode includes one of the most important plot turns of the series: the betrayal and assassination of the Stark forces during a marriage ceremony in what came to be known as the \"Red Wedding\". The event culminates in Roose Bolton delivering Jaime Lannister\\'s message from \"The Bear and the Maiden Fair\", before killing Robb. This tragic turn of events had a profound impact on Benioff and Weiss in their first read of the novels and it was the scene that convinced them to attempt to obtain the rights for a television series.\\nThe Red Wedding was inspired by the Black Dinner that took place in 1440 at Edinburgh Castle.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '89301da14a1ac3bd973e3eef7ec1d133'}>,\n",
+       "  <Document: {'content': 'This tragic turn of events had a profound impact on Benioff and Weiss in their first read of the novels and it was the scene that convinced them to attempt to obtain the rights for a television series.\\nThe Red Wedding was inspired by the Black Dinner that took place in 1440 at Edinburgh Castle.\\nGeorge R. R. Martin conceived The Red Wedding during the earliest stages of the planning of his saga, when he was envisioning a trilogy with The Red Wedding as one of the climactic events at the end of the first of the three books. Martin was inspired by a couple of events in Scottish history. One of them was the 15th century historical event known as the \"Black Dinner\", where the Scottish king invited the chieftains of the powerful Clan Douglas to a feast at Edinburgh Castle. A black bull\\'s head, the symbol of death, was served as the last course of the dinner while a single drum was playing in the background, and the Douglases were murdered. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'e84878e7f82fcf249a6ce4e5e070e626'}>,\n",
+       "  <Document: {'content': 'A black bull\\'s head, the symbol of death, was served as the last course of the dinner while a single drum was playing in the background, and the Douglases were murdered. Another event from which the author drew inspiration was the 1692 Massacre of Glencoe, where Clan MacDonald hosted the Campbell Clan who killed thirty-eight of their hosts overnight.\\n\\nMartin has said The Red Wedding was the hardest thing he has ever written. He explained that he always tries to put himself in the skin of his characters when writing from their perspective, and develops bonds with them. He even felt attached to the minor characters killed during the massacre. It was so painful for him that he skipped the chapter and continued writing, and only when the rest of the book was finished, he \"forced himself\" to come back to the dreaded scene. In 2012, at ComicCon he even joked that \"he will visit a country with no television when the episode goes on air\".\\n\\nMartin also said he killed off Robb because he believed the audience would assume that the story was about Ned Stark\\'s heir avenging his death and wished to keep them guessing. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'fe788ce98d10d3027b5bf2364630115d'}>,\n",
+       "  <Document: {'content': 'Martin also said he killed off Robb because he believed the audience would assume that the story was about Ned Stark\\'s heir avenging his death and wished to keep them guessing. Richard Madden suggested Talisa — whose counterpart Jeyne Westerling was not killed in the books — died so Robb\\'s heir could not avenge his death.\\n\\n===Casting===\\nWill Champion, the drummer and backing vocalist of the band Coldplay, has a cameo appearance as one of the musicians who play at the wedding.\\n\\n==Reception==\\n\\n===Ratings===\\n\"The Rains of Castamere\" premiered to 5.22 million viewers and received a 2.8 ratings in adults 18–49. The second airing was viewed by 1.08 million people, bringing total viewership for the night to 6.30 million. In the United Kingdom, the episode was viewed by 1.013 million viewers, making it the highest-rated broadcast that week. It also received 0.112 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode was widely praised by critics and cited as one of the best of the series. Rotten Tomatoes, a prominent review aggregator, surveyed 21 reviews of the installment and judged 100% of them to be positive with an average score of 9.9 out of 10. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '70b7759fa6448380e4886737d20e0af7'}>,\n",
+       "  <Document: {'content': 'Rotten Tomatoes, a prominent review aggregator, surveyed 21 reviews of the installment and judged 100% of them to be positive with an average score of 9.9 out of 10. The website\\'s critical consensus reads, \"The most unforgettable episode of Game of Thrones thus far, \\'The Rains of Castamere\\' (or as it shall forever be known, \\'The Red Wedding\\') packs a dramatic wallop that feels as exquisitely shocking as it does ultimately inevitable.\"The majority of the comments were directed at the massacre at the end of the episode, where praise was especially given to Michelle Fairley\\'s performance, leading to the disappointment of many critics when she was not nominated for the Primetime Emmy Award for Outstanding Supporting Actress in a Drama Series for the 65th Primetime Emmy Awards. IGN\\'s Matt Fowler gave the episode a perfect 10/10, calling it \"an exquisitely awful event that managed to out-do the unpredictable and horrifying death of Ned Stark back in Season 1\". Fowler also said he believed that the episode\\'s depiction of the Red Wedding was more powerful than its depiction in \\'\\'A Song of Ice and Fire\\'\\'.\\n\\nWriting for \\'\\'The A.V. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '53fde72fdbdb67914710af337636e2ab'}>,\n",
+       "  <Document: {'content': 'Fowler also said he believed that the episode\\'s depiction of the Red Wedding was more powerful than its depiction in \\'\\'A Song of Ice and Fire\\'\\'.\\n\\nWriting for \\'\\'The A.V. Club\\'\\', both David Sims and Emily VanDerWerff gave the episode an \"A\" grade. Sims (writing for people who have not read the novels) expressed shock at the deaths of several main characters, writing, \"I don’t think I’ve really processed what I just watched\". VanDerWerff, who reviews the episodes for people who have read the novels, wrote \"If the reader doesn’t terribly want to deal with the thought of the deaths of Catelyn and Robb, well, he or she can read that much more quickly. Or he or she can read that much more slowly if there’s a need to process the emotions more fully. On TV, you can\\'t really do that.\"Reviewing for \\'\\'Forbes\\'\\', Erik Kain called the episode \"one of the best episodes of HBO\\'s dark drama yet\", and noted \"there was a deeper sense of tragedy knowing Robb also lost his unborn child\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '4ed6e508e6ed59039413398caec3bac4'}>,\n",
+       "  <Document: {'content': 'Reviewing for \\'\\'Forbes\\'\\', Erik Kain called the episode \"one of the best episodes of HBO\\'s dark drama yet\", and noted \"there was a deeper sense of tragedy knowing Robb also lost his unborn child\". Sean Collins of the \\'\\'Rolling Stone\\'\\' also praised the episode, and commented on the unusual step the show took in ending one of its central conflicts. Sarah Hughes of \\'\\'The Guardian\\'\\' highlighted the decision to kill Talisa, writing that her \"heartbreaking end was unbearable\".\\n\\nFor its 65th anniversary, \\'\\'TV Guide\\'\\' picked \"The Rains of Castamere\" as the third best episode of the 21st century.\\n\\n===Viewer reception===\\nThe episode was also notable for the intense and emotional response it pulled from viewers, many of whom were unaware of what was about to transpire and had their reactions filmed by people who had read the book on which it was based. This led to George R. R. Martin giving his personal analysis of the reactions, which he stated were on par with the responses he received from readers of \\'\\'A Storm of Swords\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'c76bf9b76d7f84de2f9d679a9cdbea18'}>,\n",
+       "  <Document: {'content': 'This led to George R. R. Martin giving his personal analysis of the reactions, which he stated were on par with the responses he received from readers of \\'\\'A Storm of Swords\\'\\'. Madden said that he sobbed \"very loudly\" on an airplane flight right after filming the scene, and that he and Fairley were \"both in tears\" when they watched the episode.\\n\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2013\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D.B. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '79777c23748161db647f515238696edc'}>,\n",
+       "  <Document: {'content': '===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2013\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D.B. Weiss\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nOral Ottey\\n\\nGold Derby TV Awards 2013\\n\\nBest Drama Episode\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\nIGN People\\'s Choice Awards\\n\\n2014\\n\\nAmerican Cinema Editors\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nOral Norrie Ottey\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss\\n\\nDirectors Guild of America Award\\n\\nDramatic Series\\n\\nDavid Nutter\\n\\nGolden Reel Awards\\n\\nBest Sound Editing — Short Form Dialogue and ADR in Television\\n\\nJed Dodge\\n\\nBest Sound Editing — Short Form Music\\n\\nDavid Klotz\\n\\nBest Sound Editing — Short Form Sound Effects and Foley\\n\\nTim Kimmel\\n\\nHugo Awards\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff, David Nutter, and D. B. Weiss\\n\\n==References==\\n\\n==External links==\\n\\n*  \"The Rains of Castamere\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'e926d15ae3d4fe118317b735ca79e3de'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Red Woman\\'\\'\\'\" is the sixth season premiere episode of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 51st overall. The episode was written by series creators David Benioff and D. B. Weiss, and directed by Jeremy Podeswa.\\n\\nThe body of Jon Snow is discovered by Ser Davos Seaworth and loyal members of the Night\\'s Watch protect his lifeless body; Melisandre has begun to lose her faith in the Lord of Light; Sansa Stark and Theon Greyjoy flee Winterfell, Ellaria Sand and the Sand Snakes seize control of Dorne; Jaime Lannister returns to King\\'s Landing with the body of his dead daughter; and Daenerys Targaryen is taken prisoner by the khalasar of Khal Moro.\\n\\n\"The Red Woman\" was positively received by critics who found the episode to be a satisfactory launching point for the season, and praising the scenes involving Sansa and Brienne, as well as the closing reveal with Melisandre, although the Dorne storyline was criticized once again for feeling too abrupt and deviating greatly from the books. For filming of the episode\\'s closing reveal, the director used similar technique to the body double of Cersei Lannister in the previous episode. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '1bf93c7b6f62b21fae850873738b9165'}>,\n",
+       "  <Document: {'content': \"For filming of the episode's closing reveal, the director used similar technique to the body double of Cersei Lannister in the previous episode. The episode title is an allusion to the epithet used to describe the Red Priestess Melisandre. In the United States, the episode premiere achieved a viewership of 7.94 million in its initial broadcast, and a same-day total including the streaming services HBO Go and HBO Now of 10.7 million viewers, a record for the series.\\n\\n==Plot==\\n\\n===In the North===\\nAt Winterfell, Ramsay Bolton mourns the death of Myranda, but commands that her corpse be fed to the hounds. Roose Bolton warns him that, despite their victory, they could be facing the wrath of the Lannisters in the future, and as such, they must secure the loyalty of the North's other nobles. Roose blames Ramsay for losing Sansa Stark, who, as a child of Eddard Stark, could have been used as a figurehead for the North. Roose implies that he will disinherit Ramsay if he cannot retrieve her and if Roose's unborn child with Walda is a son.\\n\\nMeanwhile, Sansa and Theon Greyjoy flee through the nearby forests. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'f0afe745436da8557c3dc3f8e5eaad4d'}>,\n",
+       "  <Document: {'content': 'Roose implies that he will disinherit Ramsay if he cannot retrieve her and if Roose\\'s unborn child with Walda is a son.\\n\\nMeanwhile, Sansa and Theon Greyjoy flee through the nearby forests. Upon being cornered by a squad of Bolton soldiers, they are rescued by Brienne of Tarth and Podrick Payne, who kill the soldiers. Brienne again offers to pledge her loyalty to Sansa, who accepts.\\n\\n===In Dorne===\\nDoran Martell receives news of Myrcella Baratheon\\'s death and is aghast, but he and Areo Hotah are immediately murdered by Ellaria Sand and Tyene Sand, while the palace guards look on without intervening. Ellaria states that his people have become dissatisfied with Doran\\'s inaction against the Lannisters. As Doran dies, he pleads for his son Trystane\\'s life, but Ellaria vows that \"weak men\" will never rule Dorne again.\\n\\n===In King\\'s Landing===\\nJaime Lannister arrives in King\\'s Landing bearing Myrcella\\'s body, and returns to Cersei Lannister. Cersei recounts Maggy the Frog\\'s prophecy that all her children would die before her and she would be left with nothing. Jaime promises that they will have revenge against all who have wronged the Lannisters, heightening the importance of his and Cersei\\'s staying close.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'ee279e5240daf734ffe3e1cfed357479'}>,\n",
+       "  <Document: {'content': \"Jaime promises that they will have revenge against all who have wronged the Lannisters, heightening the importance of his and Cersei's staying close.\\n\\nAcross the city, Margaery Tyrell, who is still held captive by the Faith Militant, converses with the High Sparrow, but he refuses to give her information about her brother Loras.\\n\\nIn his ship, still docked in the harbor, Trystane is painting stones for Myrcella's funeral. Obara and Nymeria Sand board his ship and enter his cabin, stating their intent to kill him. Despite proclaiming that he does not want to fight his family, he attempts to defend himself from Nymeria but is stabbed through the back of the head by Obara.\\n\\n===In Meereen===\\nTyrion Lannister and Varys walk through the streets, which are largely empty because of Daenerys Targaryen' absence and fear of the Sons of the Harpy. Tyrion observes that Daenerys' enemies are growing, including former slaves and former masters. Varys promises that his spies will find the leader of the Sons of the Harpy. The two discover that someone has set fire to all of the ships in Meereen's harbor, and Tyrion realizes that Daenerys' forces will be unable to sail to Westeros.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '4563c705212d754d5e15932f5456f84d'}>,\n",
+       "  <Document: {'content': \"The two discover that someone has set fire to all of the ships in Meereen's harbor, and Tyrion realizes that Daenerys' forces will be unable to sail to Westeros.\\n\\n===In the Dothraki Sea===\\nDaario Naharis and Jorah Mormont continue to trace Daenerys' location. Jorah takes a moment to observe his worsening greyscale. They discover her ring in the middle of thousands of hoof-prints, deducing that she has been taken by the Dothraki.\\n\\nDaenerys is presented to Khal Moro, who has taken her prisoner. Moro initially wishes to rape her, but treats her with respect after Daenerys explains that she was Khal Drogo's wife. Daenerys asks Moro to escort her back to Meereen, but he refuses to let her leave, as widows of Khals must live out their lives in Vaes Dothrak, the Dothraki's sacred city.\\n\\n===In Braavos===\\nArya Stark, now blinded, is sent to beg on the streets. The Waif suddenly appears and forces Arya to duel her using a wooden staff. Arya fails because of her blindness, and the Waif promises that she will return the following day.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '35ededa05ad4782ad219f51965c41e16'}>,\n",
+       "  <Document: {'content': \"The Waif suddenly appears and forces Arya to duel her using a wooden staff. Arya fails because of her blindness, and the Waif promises that she will return the following day.\\n\\n===At the Wall===\\nFollowing the murder of Jon Snow, his direwolf Ghost begins to howl as the sun rises above Castle Black, attracting the attention of Davos Seaworth, Eddison Tollett, and a few other loyalists. They release Ghost from his nearby pen and take Jon's body into a store-room. Melisandre briefly visits and is troubled, as she had experienced a vision in the flames of Jon fighting at Winterfell. The loyalists become fearful and also lock themselves in from the mutineers, with the exception of Edd, who sneaks out of Castle Black to get assistance from the Wildlings against the mutineers.\\n\\nSer Alliser Thorne convenes the other black brothers and takes responsibility for Jon's murder. Several black brothers accuse Thorne and the other officers of treason, but Thorne wins them over by citing Jon's actions and stating that Jon would have destroyed the Night's Watch by letting in the Wildlings. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '54a70562b07691abd4865bc67c96e6b4'}>,\n",
+       "  <Document: {'content': 'Several black brothers accuse Thorne and the other officers of treason, but Thorne wins them over by citing Jon\\'s actions and stating that Jon would have destroyed the Night\\'s Watch by letting in the Wildlings. Thorne and the other mutineers then surround the store-room and promise Jon\\'s friends amnesty if they surrender, but threaten to attack if they don\\'t open the door by nightfall.\\n\\nElsewhere, Melisandre, who has been sullen following Stannis\\'s defeat and Jon\\'s death, goes to sleep in her bedroom. As she undresses, she removes her ruby necklace, which proves to be enchanted, as she is revealed to have a physical body many decades older than she normally appears.\\n\\n==Production==\\n\\n===Writing===\\nSeries co-creators David Benioff and D. B. Weiss wrote the episode.\\n\"The Red Woman\" was written by the series\\' creators David Benioff and D. B. Weiss. Some elements in the episode are based on the sixth novel in the \\'\\'A Song of Ice and Fire\\'\\' series, \\'\\'The Winds of Winter\\'\\', which author George R. R. Martin had hoped to have completed before the sixth season began airing. It also contains elements from the chapters \"The Sacrifice\" and \"The Blind Girl\" from \\'\\'A Dance with Dragons\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '54536e8ea2f527248d86d324d813bc09'}>,\n",
+       "  <Document: {'content': 'Some elements in the episode are based on the sixth novel in the \\'\\'A Song of Ice and Fire\\'\\' series, \\'\\'The Winds of Winter\\'\\', which author George R. R. Martin had hoped to have completed before the sixth season began airing. It also contains elements from the chapters \"The Sacrifice\" and \"The Blind Girl\" from \\'\\'A Dance with Dragons\\'\\'. With this episode, Jonathan Pryce (High Sparrow) is promoted to series regular. The episode has the introduction of new recurring cast member Joe Naufahu, who plays Khal Moro. It was the first episode followed by \\'\\'After the Thrones\\'\\', HBO\\'s after-show hosted by Andy Greenwald and Chris Ryan. The episode had a premiere at the TCL Chinese Theatre two weeks before the premier of the episode on HBO. There was a lot of speculation prior to the episode regarding whether the character Jon Snow would remain dead or return to life. Fans speculated that Melisandre would be the one to bring back Snow to life, after and before the title of the first episode was revealed to be \"The Red Woman\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'ffd82d20b15a41aaefc7ec2a2df0396e'}>,\n",
+       "  <Document: {'content': 'Fans speculated that Melisandre would be the one to bring back Snow to life, after and before the title of the first episode was revealed to be \"The Red Woman\".\\n\\nLiam Cunningham, who portrays Davos Seaworth, spoke about the writing of the episode following its airing, and revealed how he reacted to the scene, saying \"It was initially shocking. You know what I thought was my favorite bit, and I said it to David and Dan, it was at that moment when the reveal comes and you kind of go, \\'Oh my God,\\' it puts things into context with Melisandre. It doesn\\'t explain or blah, blah, blah. … It\\'s confirmed like, she\\'s a witch, but there was such a touch of humanity. When she went to the bed and got into the bed and covered herself up… it was like a really striking moment in this weird madness of humanity.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '91d8c93014637c650d33cebd1b8efad'}>,\n",
+       "  <Document: {'content': 'When she went to the bed and got into the bed and covered herself up… it was like a really striking moment in this weird madness of humanity.\"John Bradley, who portrays Samwell Tarly, also spoke about the scene, saying \"And what I like about that, and seeing her like that is you know then that her sexuality over the course of the last few seasons, we\\'ve seen her use as such a tool -- that\\'s all very deliberate. She presents herself in this way as this beautiful woman because she knows the effect that that can have on people. She knows the effect that that has on Stannis, and that can make men do unadvisable things. So the fact that she did that and she uses that power in that way to have this influence on people. That puts her into context as somebody who really does know what she\\'s doing.\"===Filming===\\nCarice van Houten portrays the titular character of the episode, the Red Woman Melisandre.\\n\"The Red Woman\" was directed by Jeremy Podeswa. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'fa0dfabb6976f49bb8cb77dc562fcc27'}>,\n",
+       "  <Document: {'content': '===Filming===\\nCarice van Houten portrays the titular character of the episode, the Red Woman Melisandre.\\n\"The Red Woman\" was directed by Jeremy Podeswa. Podeswa previously directed the fifth season episodes \"Kill the Boy\" and \"Unbowed, Unbent, Unbroken\", the latter of which received a Primetime Emmy Award nomination for Outstanding Directing for a Drama Series. The budget for the sixth season increased compared to the previous seasons as each episode had an average cost of over $10 million, totaling approximately $100 million for the full season, setting a new high for the series.\\n\\nFor the closing reveal involving Melisandre, the director of the episode stated that a similar technique to the body double of Cersei Lannister in \"Mother\\'s Mercy\" was used, with Carice van Houten wearing prosthetic makeup for the face that was then transposed onto the real body of an old woman. Podeswa stated, \"The idea is there\\'s an indefinite indeterminate quality that she could be ancient. We were limited by choosing to use a real person rather than a complete  creation. Because what does a 400-year-old person look like? We don\\'t know. So if you try to create that, then you\\'re creating something that looks beyond our known reality. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '84c02b376e133b1484570aae185a39d0'}>,\n",
+       "  <Document: {'content': 'We don\\'t know. So if you try to create that, then you\\'re creating something that looks beyond our known reality. Here you feel like she\\'s very old without putting a number on it.\"==Reception==\\n\\n===Ratings===\\n\"The Red Woman\" was viewed by 7.94 million American households on its first viewing, which is slightly lower than number of viewers for the fifth-season premiere, 8.00 million, marking the first time in the show\\'s history a season premiere received less ratings than the premiere and finale of the season that came before it. HBO notes that two replays later in the night and early figures from HBO Go and HBO Now push the total to 10.7 million viewers, a same-day record for the show, meaning that the episode received an increase in viewerships. The episode also acquired a 4.0 rating in the 18–49 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was watched by 2.19 million viewers according to overnight ratings (2.289 million viewers over 7 days and 2.554 over 28 days), a record for the pay channel Sky Atlantic. The UK viewing figures reached an all-time high. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '599dc47db957734ee5c73e0841341057'}>,\n",
+       "  <Document: {'content': 'In the United Kingdom, the episode was watched by 2.19 million viewers according to overnight ratings (2.289 million viewers over 7 days and 2.554 over 28 days), a record for the pay channel Sky Atlantic. The UK viewing figures reached an all-time high. The 2am simulcast attracted 60,000 viewers. Similarly it broke the Australian record as the most watched show on pay TV, with 721,000 viewers in the overnight figures and 1.1 million in the final tally. Over a million had downloaded the episode within 12 hours of airing, with Australia having the most illegal downloads of the episode by a single nation.\\n\\n===Critical reception===\\nReviews for \"The Red Woman\" were very positive. The episode was praised for its humor, Brienne\\'s reunion with Sansa and Theon, and the revelation regarding Melisandre\\'s true age. Rotten Tomatoes assembled 38 reviews and calculated an approval percentage of 86% with an average rating of 7.6/10, and the consensus reading: \"A solid season opener, The Red Woman balanced its ongoing mysteries with a bit of humor and featured Sansa\\'s touching reunion with Brienne of Tarth.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '23ccfad374485e23f520622b4d6fce67'}>,\n",
+       "  <Document: {'content': 'Rotten Tomatoes assembled 38 reviews and calculated an approval percentage of 86% with an average rating of 7.6/10, and the consensus reading: \"A solid season opener, The Red Woman balanced its ongoing mysteries with a bit of humor and featured Sansa\\'s touching reunion with Brienne of Tarth.\"James Hunt of \\'\\'What Culture\\'\\' wrote in his review of the episode; \"It was a good return to the Seven Kingdoms (and beyond), one that was filled with tension throughout, and set about re-establishing the pieces on the board.\"Ellen Gray of \\'\\'Philadelphia Daily News\\'\\' noted in her review of the episode; \"First episodes are always difficult. So many people to check in on. Or kill. Or swear vengeance upon. Still, things are off to several promising starts.\"Mark W. Pleiss of \\'\\'PopMatters\\'\\' wrote in his review of the episode; \"The most recent episode of HBO\\'s Game of Thrones largely evaded the two major questions from the previous chapter, and instead lined up its sixth season to gravitate around the heroics and cunning of its female protagonists.\"Tim Surette of \\'\\'TV.com\\'\\' wrote that Melisandre is one of his favorite characters.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '3dfb1c6e514b11baacb8847997a174a6'}>,\n",
+       "  <Document: {'content': 'Mark W. Pleiss of \\'\\'PopMatters\\'\\' wrote in his review of the episode; \"The most recent episode of HBO\\'s Game of Thrones largely evaded the two major questions from the previous chapter, and instead lined up its sixth season to gravitate around the heroics and cunning of its female protagonists.\"Tim Surette of \\'\\'TV.com\\'\\' wrote that Melisandre is one of his favorite characters.\\n\\nIn isolation from the rest of the episode, many critics were baffled by the drastic changes to the Dorne storyline. For io9, Charlie Jane Anders called it \"the absolute worst\" part of the episode, and criticized the Dorne storyline in general for giving Doran Martell \"a grand total of ten minutes\\' screentime\" before suddenly killing him off, given that he is alive in the novels and his major subplot from the books - that he was simply \\'\\'feigning\\'\\' a desire for peace while planning to betray the Lannisters - was not introduced at all before his departure. Alan Sepinwall from HitFix also disliked the Dorne scenes and summarized, \"I\\'ll settle for not needing to audibly groan every time the story returns to Dorne, frankly.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '5bd205926a58a1becb38489f93f0bc8b'}>,\n",
+       "  <Document: {'content': 'Alan Sepinwall from HitFix also disliked the Dorne scenes and summarized, \"I\\'ll settle for not needing to audibly groan every time the story returns to Dorne, frankly.\"For Vulture, Nate Jones wrote an op-ed piece specifically criticizing the Dorne storyline in seasons five and six, culminating in the changes in the season six premiere, which it felt to be bizarre and illogical - purely relying on shock value when the characters\\' actions don\\'t make sense upon closer analysis. Jones was also critical of how it was altering the female characters in Dorne, saying that they were changed from the novels to be \"the kind of violent, scantily clad women that emerge when creators want to pay lip service to feminism, but don\\'t have the time or inclination to create actual three-dimensional female characters.\"==References==\\n\\n==External links==\\n\\n*  \"The Red Woman\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'c12e287a4510acb0bdc91bd7a78b4471'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Two Swords\\'\\'\\'\" is the fourth season premiere episode of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 31st overall. The episode was written by series co-creators and showrunners David Benioff and D. B. Weiss, and directed by Weiss. It premiered on April 6, 2014.\\n\\nThe title of the episode refers to the two swords Tywin Lannister forges from Ice, Ned Stark\\'s large Valyrian steel sword, in the opening sequence.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nTywin melts down Stark\\'s greatsword Ice and forges it into two new swords. One is given to Jaime as the new Lord Commander of the Kingsguard. Tywin wants to send Jaime to rule Casterly Rock, but he refuses. Tyrion waits for the arrival of Prince Doran Martell, but his brother Oberyn comes in his stead. Oberyn tells Tyrion that he seeks vengeance for his sister Elia, who was married to Rhaeger and killed by Gregor. Dontos gives Sansa his mother\\'s necklace for saving his life. Shae tries to rebuff her romance with Tyrion, but is overheard by another Sansa\\'s handmaiden. Qyburn gives Jaime a gilded steel hand. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'b9996fe677a3249612d831a85fb23f64'}>,\n",
+       "  <Document: {'content': \"Shae tries to rebuff her romance with Tyrion, but is overheard by another Sansa's handmaiden. Qyburn gives Jaime a gilded steel hand. Brienne tries to convince Jaime to follow through on his vow to return the Stark girls to safety, but he argues the matter is now complicated following Catelyn's death. Brienne tells Margaery that she didn't kill Renly and vows to kill his killer, Stannis.\\n\\n===In the North===\\nTormund and Ygritte meet with a group of Thenns, led by Styr.\\n\\n===At Castle Black===\\nThorne, now Lord Commander and Janos Slynt, call for Jon to be executed for killing Qhorin, but Aemon releases him.\\n\\n===In Slaver's Bay===\\nDaenerys leads her army on the march to Meereen. They discover a dead slave girl pointing the route to Meereen. Jorah tells there is one dead slave child on every mile to Meereen and Daenerys declares that she will see every dead child's face, before burying them.\\n\\n===In the Riverlands===\\nSandor tells Arya that he will ransom her to Lysa. They stop at an inn, where they come across Gregor's men, including Polliver. Sandor kills others, while Arya reclaims Needle from Polliver and kills him.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'ca6e18cfdbe3452cedab8a37ce186501'}>,\n",
+       "  <Document: {'content': 'They stop at an inn, where they come across Gregor\\'s men, including Polliver. Sandor kills others, while Arya reclaims Needle from Polliver and kills him.\\n\\n==Production==\\n\"Two Swords\" was written and directed by producers D.B. Weiss and David Benioff.\\nThe episode was directed by the writing team itself, although to comply with the rules of the Directors Guild of America only Weiss is credited for directing. They previously co-directed the season three episode \"Walk of Punishment\", in which Benioff received sole directing credit.\\n\\n===Writing===\\n\"Two Swords\" was written by executive producers David Benioff and D. B. Weiss, based on George R. R. Martin\\'s original work from his novel \\'\\'A Clash of Kings\\'\\', chapters Sansa II and Sansa VIII, and \\'\\'A Storm of Swords\\'\\', chapters Jaime VII, Tyrion V, Tyrion IV, Jon VI, Jon IX, Daenerys V, and Arya XIII. Arya\\'s revenge against Lommy\\'s murderer using the same method of killing was depicted in \\'\\'The Winds of Winter\\'\\' chapter Mercy.\\n\\n===Casting===\\nWith this episode, Gwendoline Christie (Brienne of Tarth) and Kristofer Hivju (Tormund Giantsbane) are promoted to series regulars. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '2a5eb7ff0e3b5ed8a9c2ce7a66682e3b'}>,\n",
+       "  <Document: {'content': 'Arya\\'s revenge against Lommy\\'s murderer using the same method of killing was depicted in \\'\\'The Winds of Winter\\'\\' chapter Mercy.\\n\\n===Casting===\\nWith this episode, Gwendoline Christie (Brienne of Tarth) and Kristofer Hivju (Tormund Giantsbane) are promoted to series regulars. The episode has the introduction of new recurring cast members Pedro Pascal (Oberyn Martell) and Indira Varma (Ellaria Sand), while Michiel Huisman replaces Ed Skrein as the recurring character Daario Naharis. Owen Teale (Alliser Thorne), Dominic Carter (Janos Slynt) and Tony Way (Ser Dontos Hollard) make return appearances after an absence of several years (Teale in the first season, Carter and Way in the second).\\n\\n==Reception==\\n===Television ratings===\\n\"Two Swords\" broke the viewership record for \\'\\'Game of Thrones\\'\\', which was set by episode six the previous season. 6.64 million people watched the premiere airing, and when coupled with encore airings, that number rose to 8.2 million total viewers. This was at the time HBO\\'s highest ratings for any show since the finale episode of \\'\\'The Sopranos\\'\\', though it was shortly exceeded. In the UK on Sky Atlantic, \"Two Swords\" was top in the week with a viewership of 1.21 million.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '2a3ac16a894436824d22e6f27aedcfbf'}>,\n",
+       "  <Document: {'content': 'This was at the time HBO\\'s highest ratings for any show since the finale episode of \\'\\'The Sopranos\\'\\', though it was shortly exceeded. In the UK on Sky Atlantic, \"Two Swords\" was top in the week with a viewership of 1.21 million.\\n\\n===Critical reception===\\nThe season premiere obtained a 97% rating from 25 reviews with an average score of 9 out of 10 according to Rotten Tomatoes.\\n\\nReviewing it for IGN, Matt Fowler gave the episode a mark of 8.5 out of 10, and wrote that \"\\'Two Swords\\' was a solid GoT premiere, though it certainly didn\\'t hide the fact that the show, post-Red Wedding, is going to possibly meander more than people might enjoy or expect.\"Two reviews from \\'\\'The A.V. Club\\'\\' were written, with one intended for those who have read the novels and one for those who have not. Reviewing for the novel readers, Emily VanDerWerff gave the episode an \"A-\", while Erik Adams, who reviewed for the non-readers, rated the episode a \"B+\". Myles McNutt at \\'\\'Cultural Learnings\\'\\' also gave the episode a positive review, singling out the final scene with Arya and Sandor as the episode\\'s best sequence.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'c790a7eddfc25a921fdca7df135ebc49'}>,\n",
+       "  <Document: {'content': \"Myles McNutt at ''Cultural Learnings'' also gave the episode a positive review, singling out the final scene with Arya and Sandor as the episode's best sequence.\\n\\n===Accolades===\\nThe episode was nominated for Outstanding Cinematography for a Single-Camera Series at the 66th Primetime Creative Arts Emmy Awards.\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'a597fdcc43daf590f2ad5d33d4e75582'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''The High Sparrow''' is a fictional character in the ''A Song of Ice and Fire'' series of high fantasy novels by American author George R. R. Martin and its television adaptation ''Game of Thrones''.\\n\\nThe High Sparrow first appears in ''A Feast for Crows'' (2005) and subsequently appeared in Martin's ''A Dance with Dragons'' (2011). He is the de facto leader of the protest 'sparrow' movement that arises from the Faith of the Seven as a result of the carnage inflicted by the War of Five Kings. Although he appears to be humble and compassionate, his demeanor belies his shrewd and unrelenting fanaticism, which often borders on dangerous zealotry. He becomes of central importance to the shadow conflicts at court between Houses Lannister and Tyrell, and is open about his disgust with the corruption and impiety in the capital. His true name is unknown; his title was given mockingly by political opponents as a comparison to the formal leader of the Faith, the High Septon.\\n\\nThe character is portrayed by Welsh actor Jonathan Pryce in the HBO television adaptation.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'f12bbaf1e8a852af1a94dc85eb984ceb'}>,\n",
+       "  <Document: {'content': 'His true name is unknown; his title was given mockingly by political opponents as a comparison to the formal leader of the Faith, the High Septon.\\n\\nThe character is portrayed by Welsh actor Jonathan Pryce in the HBO television adaptation.\\n\\n==Character profile==\\nThe High Sparrow is a prominent member of the \"sparrows\", a religious movement formed during the War of the Five Kings, and a septon (priest) in the Faith of the Seven. He is a small, thin, hard-eyed, grey-haired old man with a heavily lined face.  He does not wear rich robes or any gold; instead, he wears a simple white wool tunic.\\n\\nThe High Sparrow is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, such as Cersei Lannister. The High Sparrow is mostly a background character in the novels.\\n\\n==Storylines==\\n\\n==== \\'\\'A Feast for Crows\\'\\' ====\\nIn the aftermath of the War of Five Kings, \"Sparrows\", religious folk seeking the protection of King Tommen Baratheon following a spate of atrocities committed against members of the Faith of the Seven, converge on King\\'s Landing. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '76e1abdb429353c0f0a0a414caad5d16'}>,\n",
+       "  <Document: {'content': '==Storylines==\\n\\n==== \\'\\'A Feast for Crows\\'\\' ====\\nIn the aftermath of the War of Five Kings, \"Sparrows\", religious folk seeking the protection of King Tommen Baratheon following a spate of atrocities committed against members of the Faith of the Seven, converge on King\\'s Landing. When the Most Devout convene to elect the new High Septon, the sparrows hijack the selection process and force the selection of their leader, who is dubbed the \"High Sparrow\" by the royal fool Moon Boy. Cersei Lannister permits the High Sparrow to re-establish the Faith Militant, the military arm of the Faith, in return for the Faith forgiving the Crown\\'s debt to it and blessing Tommen. The Faith Militant subsequently arrest Margaery Tyrell and several of her cousins when Cersei\\'s confidante Osney Kettleblack claims to having slept with the women. The High Sparrow is suspicious of Osney\\'s testimony (not unfairly, as Osney had been persuaded to perjure Margaery by Cersei to shore up her own power) and has him tortured until he confesses not only that Cersei arranged for his false testimony, but also that she ordered him to kill the previous High Septon. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'f6bb19dbc4be08093499769f2a526fc8'}>,\n",
+       "  <Document: {'content': \"The High Sparrow is suspicious of Osney's testimony (not unfairly, as Osney had been persuaded to perjure Margaery by Cersei to shore up her own power) and has him tortured until he confesses not only that Cersei arranged for his false testimony, but also that she ordered him to kill the previous High Septon. When Cersei visits the Great Sept of Baelor, the High Sparrow has her arrested and imprisoned.\\n\\n==== ''A Dance with Dragons'' ====\\nThe High Sparrow releases Margaery and her cousins into Randyll Tarly's custody due to the weakness of the evidence against them, but continues to hold Cersei prisoner. In order to obtain visitors, Cersei confesses to bedding the Kettleblack brothers as well as her cousin Lancel. The High Sparrow agrees to release her on condition of performing a walk of penance, naked, through the streets of King's Landing.\\n\\n==TV adaptation==\\nJonathan Pryce plays the role of The High Sparrow in the television series.\\nThe High Sparrow is played by the Welsh actor Jonathan Pryce in the television adaption of the series of books. Pryce admitted that one of the main reasons he took the role was because of how influential the character is plot-wise. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'c2fe8d0774a127789b54bcf1623b1bce'}>,\n",
+       "  <Document: {'content': 'Pryce admitted that one of the main reasons he took the role was because of how influential the character is plot-wise. While at first being quite sceptical about \"sword and sorcery\" shows, he later had a change of heart after his positive experiences on the \\'\\'Game of Thrones\\'\\' sets.\\n\\nRemarking on the character:\\n\\nI quite like the fact that people are going, \"Oh, he\\'s a horrible character!\"And I\\'m going, \"No! He\\'s one of the good people in Game of Thrones! He\\'s clearing out all the bad people!\\n\\n====Season 5====\\nThe High Sparrow arrives in King\\'s Landing after Tywin Lannister\\'s death to serve the poor, downtrodden, and infirm. He quickly amasses a large following, including Cersei Lannister\\'s cousin and former lover Lancel, who are dubbed the \"sparrows\". He comes to the attention of Cersei after Lancel and the other sparrows force the High Septon to walk naked through the streets as punishment for soliciting prostitutes. The High Septon demands the High Sparrow\\'s execution, but Cersei instead has the High Septon imprisoned and the High Sparrow appointed as his successor. To further gain his support, Cersei also reinstates the Faith Militant. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '2a83fa690ad001a2aa38c5c3ddd8d8bb'}>,\n",
+       "  <Document: {'content': \"The High Septon demands the High Sparrow's execution, but Cersei instead has the High Septon imprisoned and the High Sparrow appointed as his successor. To further gain his support, Cersei also reinstates the Faith Militant. Hoping to destabilise House Tyrell, Cersei arranges for the Faith Militant to arrest Loras Tyrell for being homosexual, with Margaery also arrested when she lies in an attempt to exonerate him. However, Lancel confesses to his affair with Cersei, as well as their role in Robert Baratheon's death. Cersei is subsequently arrested when she visits the Great Sept of Baelor. She eventually confesses to her adultery with Lancel to secure her release, and is ordered to walk naked through the streets of King's Landing as punishment.\\n\\n====Season 6====\\nThe High Sparrow is confronted by Jaime Lannister, who is furious at Cersei's treatment at his hands, though Jaime is forced to stand down when the Faith Militant surround him. When Tommen begins visiting the High Sparrow to negotiate visits with Margaery, he begins to fall under the High Sparrow's influence. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'd2f554cc0e499bd9c979cd10d8fac64a'}>,\n",
+       "  <Document: {'content': \"When Tommen begins visiting the High Sparrow to negotiate visits with Margaery, he begins to fall under the High Sparrow's influence. Jaime marches on the Great Sept with the Tyrell army to secure Margaery and Loras' release, but is thwarted when Tommen reveals that he has formed an alliance between the Crown and the Faith. The High Sparrow's thrall over Tommen leads him to abolish trial by combat, thwarting Cersei's hopes of being absolved by that means.\\n\\nTommen announces that Loras and Cersei's trials will be held on the same day, but on the day of the trial both Cersei and Tommen fail to appear. Margaery realises that Cersei has set a trap and tries to persuade the High Sparrow to evacuate the Great Sept, but he refuses and orders the Faith Militant to restrain the crowd. Moments later, wildfire primed by Cersei's subordinate Qyburn detonates beneath the Sept, with the High Sparrow instantly vaporised in the resulting explosion.\\n\\n==References==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '13d9ab540b962d1a4c1a0bbf7097bcd7'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''Oberyn Nymeros Martell''', nicknamed '''the Red Viper''', is a fictional character in the ''A Song of Ice and Fire'' series of epic fantasy novels by American author George R. R. Martin, and its television adaptation ''Game of Thrones''.\\n\\nIntroduced in 2000's ''A Storm of Swords'', Oberyn is the younger brother of Doran Martell, from the desert kingdom of Dorne. Unlike his sickly and pensive brother, Oberyn is notorious both for his dangerous and unpredictable nature as well as his affinity for poisons, for which he earned his nickname. He is subsequently sporadically mentioned in ''A Feast for Crows'' and ''A Dance with Dragons''. As his brother is sickly, he travels to King's Landing in order to claim the Dornish seat on the small council, as well as to seek vengeance for his sister Elia Martell's death at the hands of Ser Gregor Clegane, whom he suspects acted directly on orders from Tywin Lannister.\\n\\nOberyn is portrayed by Chilean-American actor Pedro Pascal in the HBO television adaptation.\\n\\n== Character description ==\\nOberyn Martell is the youngest brother of Prince Doran Martell, the ruler of the southern principality of Dorne.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '27336e1604b266d0b6f220231f3b2bfc'}>,\n",
+       "  <Document: {'content': '== Character description ==\\nOberyn Martell is the youngest brother of Prince Doran Martell, the ruler of the southern principality of Dorne.  He is a hot-headed, forceful, and lustful man with a quick wit and a barbed tongue.  He is a formidable fighter, and is nicknamed \"the Red Viper\" due to his preference for red clothing, as well as the rumor of him using poisoned weapons in duels.  He has travelled the world, served in the famous mercenary company Second Sons before founding a company of his own, and briefly studied poisons and dark arts as a novice at the Citadel, managing to obtain six links of the maester\\'s chain before quitting due to boredom.  He is bisexual and has eight illegitimate daughters, collectively known as the \"Sand Snakes\".  He was very close to his older sister Elia, and seeks revenge for her death.\\n\\nOberyn Martell is not a point of view character in the novels.  His actions are directly witnessed and interpreted through the eyes of Tyrion Lannister, and is later mentioned via the chapters of his niece Arianne Martell and Areo Hotah, the bodyguard of his brother Doran.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '119e7faee91ca9a4d42ca05047b3df7e'}>,\n",
+       "  <Document: {'content': \"His actions are directly witnessed and interpreted through the eyes of Tyrion Lannister, and is later mentioned via the chapters of his niece Arianne Martell and Areo Hotah, the bodyguard of his brother Doran.\\n\\n== Storylines ==\\nCoat of arms of House Martell\\n\\nIn ''A Storm of Swords'', Oberyn leads a Dornish envoy to King's Landing to claim the seat on the small council on Prince Doran Martell's behalf and obtain justice for his sister Elia Martell's murderer, as was agreed with the acting Hand of the King, Tyrion Lannister.  When Tyrion is accused of murdering King Joffrey Baratheon, Oberyn volunteers to champion for Tyrion in the trial by combat, so he can fight and seek revenge from the rival champion, Ser Gregor Clegane, who is Elia's murderer.  Though Oberyn manages to gain the upper hand in the duel and impale Clegane to the ground with his spear, Clegane manages to surprise him by foot sweeping him to the ground and bear-hugging him, then gouging his eyes, and finally killing him by repeatedly smashing his face with a mailed fist.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'd077e79078caa7479a4e6829bf6e1803'}>,\n",
+       "  <Document: {'content': \"Though Oberyn manages to gain the upper hand in the duel and impale Clegane to the ground with his spear, Clegane manages to surprise him by foot sweeping him to the ground and bear-hugging him, then gouging his eyes, and finally killing him by repeatedly smashing his face with a mailed fist.  However, it is later revealed that Oberyn has coated his spear in manticore venom to ensure that Clegane will die a slow and agonizing death.\\n\\nIn ''A Feast for Crows'', Doran Martell reveals to his daughter Arianne that Oberyn has actually been acting as Doran's enforcer to carry out his secret plan for House Lannister's downfall.  Three of Oberyn's bastard daughters, known as the Sand Snakes, are assigned missions to infiltrate the Iron Throne as sleeper agents.\\n\\n== TV adaptation ==\\nPedro Pascal plays the role of Oberyn Martell in the television series.\\n\\nOberyn Martell is played by Pedro Pascal in the television adaption of the series of books.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '76e15d51a05222b783add2cb9c36990a'}>,\n",
+       "  <Document: {'content': \"== TV adaptation ==\\nPedro Pascal plays the role of Oberyn Martell in the television series.\\n\\nOberyn Martell is played by Pedro Pascal in the television adaption of the series of books.\\n\\n=== Season 4 ===\\nOberyn arrives in King's Landing with his paramour, Ellaria Sand, to attend Joffrey's wedding in his brother's stead, and his meeting with Tyrion makes it clear that he has actually come to take revenge against the Lannisters for their role in the deaths of his sister, nephew and niece. At the wedding, Joffrey dies after being poisoned, and Tywin initially suspects Oberyn of having a hand in the murder since Oberyn has a past with poison chemistry, while Oberyn denies involvement and accuses Tywin of ordering Gregor Clegane to rape and murder Elia. The two reach a settlement when Tywin promises Oberyn a meeting with Clegane in exchange for Oberyn serving as one of the three judges at Tyrion's trial. At the trial, Oberyn implies that he is not convinced of Tyrion's guilt, and openly questions Cersei's testimony and asks Shae why Tyrion would tell her about all of his plans to murder Joffrey if he was the perpetrator.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'fa8bc255e3521f50335cc42e42612b2d'}>,\n",
+       "  <Document: {'content': \"At the trial, Oberyn implies that he is not convinced of Tyrion's guilt, and openly questions Cersei's testimony and asks Shae why Tyrion would tell her about all of his plans to murder Joffrey if he was the perpetrator.\\n\\nWhen Tyrion demands a trial by combat and Gregor Clegane is chosen as Cersei's champion, Oberyn volunteers to fight for Tyrion, proclaiming that he will exact his vengeance, starting with Ser Gregor. Martell valiantly fights Clegane, his superior speed making up for Clegane's size, and manages to wound him in the shoulder and the leg, flooring him. Refusing to kill him immediately, Oberyn furiously demands that Clegane admit to raping and killing Elia and her children, and that the order came from Tywin himself. Distracted for a moment, Oberyn is floored by Clegane, who – in a manner self-admittedly paraphrasing the murder of Elia – knocks out his teeth, straddles him and slowly gouges out his eyes, admitting to the rape and murder of Elia before crushing his skull. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'd842faacf49761e78b0b701c07c32d58'}>,\n",
+       "  <Document: {'content': \"Distracted for a moment, Oberyn is floored by Clegane, who – in a manner self-admittedly paraphrasing the murder of Elia – knocks out his teeth, straddles him and slowly gouges out his eyes, admitting to the rape and murder of Elia before crushing his skull. Tyrion is subsequently sentenced to death, but Oberyn's objective of vengeance was not in vain, since his spear is revealed to have been laced with the deadly venom of the manticore, which is slowly killing Clegane.\\n\\n=== Reception ===\\nThe Chilean-American actor Pedro Pascal has garnered positive reviews for his role as Oberyn Martell in the television show. For his performance, Pascal was awarded the Ewwy Award for Best Guest Actor – Drama, and was nominated for the NewNowNext Award for Best New Television Actor, and, as part of the ensemble cast, the Screen Actors Guild Award for Outstanding Performance by an Ensemble in a Drama Series.\\n\\n== Family tree of House Martell ==\\n\\n== References ==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '2203592c9e3b7d957bcfa3cfa9f8dc6f'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Dance of Dragons\\'\\'\\'\" is the ninth and penultimate episode of the fifth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 49th overall. The episode was written by the series\\' creators David Benioff and D. B. Weiss based on material primarily found on George R.R. Martin\\'s novel \\'\\'A Dance with Dragons\\'\\', from which the title of the episode is derived. It was directed by David Nutter, who also directed the season finale.\\n\\nIn the episode, Jon Snow retreats from Hardhome to the Wall defeated, accompanied by the surviving wildlings, much to the chagrin of some of the Night\\'s Watch. In the North, Stannis Baratheon reluctantly allows Melisandre to sacrifice his daughter Shireen after Ramsay Bolton sabotages his resources, damaging his army\\'s morale in the process. In Braavos, Arya Stark detours from her mission given by Jaqen H\\'ghar to reconnoiter Meryn Trant instead. In Dorne, Jaime Lannister secures Myrcella Baratheon\\'s release from Doran Martell\\'s court against an indignant Ellaria Sand. In Meereen, the Sons of the Harpy attack the stadium of Daznak\\'s Pit in an attempt to assassinate Daenerys Targaryen, who is rescued by Jorah Mormont and her firstborn dragon, Drogon. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '7d00c5a896e7249aeca4816896a30216'}>,\n",
+       "  <Document: {'content': 'In Meereen, the Sons of the Harpy attack the stadium of Daznak\\'s Pit in an attempt to assassinate Daenerys Targaryen, who is rescued by Jorah Mormont and her firstborn dragon, Drogon. Leaving Tyrion Lannister and her retainers behind with awe, Daenerys rides the dragon for the first time and flees the city.\\n\\n\"The Dance of Dragons\" received a positive response from critics, who lauded its conclusion on Daznak\\'s Pit but was polarized by the characterization of Stannis Baratheon. In the United States, it received a viewership of 7.14 million in its initial broadcast. It won two Primetime Creative Arts Emmy Awards for Outstanding Single-Camera Picture Editing for a Drama Series and Outstanding Special Visual Effects, and was submitted by actress Emilia Clarke (who portrayed Daenerys Targaryen) for her nomination for a Primetime Emmy Award for Outstanding Supporting Actress in a Drama Series.\\n\\nThis episode marks the final appearance of Joel Fry (Hizdahr Zo Loraq).\\n\\n==Plot==\\n\\n===In Braavos===\\nArya, still in the disguise of an oyster vendor, continues her mission to poison and kill the Thin Man. However, as she is on the way to her target, she sees Mace Tyrell and Meryn Trant arrive to negotiate with the Iron Bank. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '62886a6205bf3bc12a697bd326a3653'}>,\n",
+       "  <Document: {'content': \"However, as she is on the way to her target, she sees Mace Tyrell and Meryn Trant arrive to negotiate with the Iron Bank. Arya, recognising Trant, ignores her mission and follows Mace and Trant into the city. She eventually tracks Trant to a brothel, where she learns that he is sexually attracted to pubescent girls. Upon returning to Jaqen, Arya lies that the Thin Man was not hungry and did not buy her oysters, and asks to try again the next day. Jaqen allows this, though it is unclear whether he believes Arya or not.\\n\\n===At the Wall===\\nJon and Tormund return to Castle Black, accompanied by the surviving Night's Watch and wildlings from Hardhome. Thorne, despite his disagreements with Jon, ultimately orders the gate to be opened to let them pass. Jon believes that his mission was a failure, though Sam points out that he still saved thousands of wildlings that would have been killed by the White Walkers. Jon is also aware that many of his Night's Watch brothers are unhappy with wildlings being let past the Wall, and Thorne warns him that his compassion will get them all killed.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'aebd78054bf3aafb9846ac8c84fd4aa4'}>,\n",
+       "  <Document: {'content': \"Jon is also aware that many of his Night's Watch brothers are unhappy with wildlings being let past the Wall, and Thorne warns him that his compassion will get them all killed.\\n\\n===In Dorne===\\nJaime is brought before Doran to explain his actions. Jaime reveals that he was sent to rescue Myrcella due to a threatening message sent to them from Dorne. Doran quickly realizes that Ellaria was behind the threat, and apologizes. In order to avoid an escalation of hostilities, Doran grants Jaime's freedom and Myrcella's return to King's Landing in exchange for Trystane assuming Oberyn's position on the Small Council. Trystane also allows Bronn to go free under the condition that he allow Hotah to strike him in the face in revenge for being struck by Bronn earlier.\\n\\nAfter the meeting, Doran privately confronts Ellaria and presents her with an ultimatum: pledge loyalty to him or be executed. Ellaria tearfully pledges her loyalty in front of the Sand Snakes, and Doran warns her that this is the last chance he will ever give her. Ellaria then meets with Jaime and makes amends with him, conceding that it is neither his nor Myrcella's fault that Oberyn was killed.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '8af45a0b6d2ffb8feae0915a2de35d87'}>,\n",
+       "  <Document: {'content': 'Ellaria then meets with Jaime and makes amends with him, conceding that it is neither his nor Myrcella\\'s fault that Oberyn was killed.\\n\\n===In the North===\\nRamsay and his group of 20 men infiltrate Stannis\\'s camp and burn his army\\'s supplies. With his supplies gone, Stannis realizes that his army will not survive the winter. Stannis refuses to retreat to Castle Black as Davos suggests, and points out that they lack the supplies to make it back regardless. He orders Davos to return to Castle Black to request more supplies and reinforcements in return for Stannis fully manning the Wall with troops when he takes the Iron Throne. When Davos leaves, Stannis reluctantly allows Melisandre to burn Shireen at the stake as a sacrifice, believing that the \"king\\'s blood\" in her veins will grant them the Lord of Light\\'s favor. As Shireen screams for her parents, Selyse, initially supportive of Stannis\\'s decision, has a change of heart and attempts to stop the ceremony, but is restrained while her child is burned alive in front of Stannis\\'s horrified troops.\\n\\n===In Meereen===\\nDaenerys, accompanied by Tyrion, Missandei, Daario and Hizdahr, oversees the beginning of the fighting season in Daznak\\'s Pit. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '43b2960f24952b707b2904e26877be0b'}>,\n",
+       "  <Document: {'content': \"===In Meereen===\\nDaenerys, accompanied by Tyrion, Missandei, Daario and Hizdahr, oversees the beginning of the fighting season in Daznak's Pit. One of the fighters reveals himself to be Jorah. After successfully defeating the other combatants, Jorah suddenly picks up a spear and throws it towards the royal stage, killing an assassin sneaking up on Daenerys. Immediately, the Sons of the Harpy emerge from the crowd and begin killing guards and spectators, including Hizdahr. Daenerys and her retinue attempt to escape, but the Sons of the Harpy seal the exits, leaving the survivors surrounded in the center of the fighting ring. Daenerys seemingly accepts her fate, when Drogon suddenly appears, setting ablaze and killing many Sons of the Harpy and causing many others to flee. While a group of the Sons attack Drogon by throwing spears at it, Daenerys climbs on to Drogon's back, ordering the dragon to fly. The Unsullied drive the Sons of the Harpy away, while Drogon takes to the sky with Daenerys, with her allies left awe-struck as they watch her fly away.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '7e5b37dd8ffba6cca2d7956ad9cd7764'}>,\n",
+       "  <Document: {'content': 'The Unsullied drive the Sons of the Harpy away, while Drogon takes to the sky with Daenerys, with her allies left awe-struck as they watch her fly away.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode was written for television by David Benioff and D. B. Weiss, the series\\' creators. It contains content from George Martin\\'s novel \\'\\'A Dance with Dragons\\'\\', chapter \"The Sacrifice\", \"The Watcher\", and \"Daenerys IX\". It also contains material from the preview chapter \"Mercy\" from the forthcoming \\'\\'The Winds of Winter\\'\\'.\\nLike other episodes this season, \"The Dance of Dragons\" contains original content not found in Martin\\'s novels, but it is in this case notable for containing content that had not \\'\\'yet\\'\\' appeared in the novels: Executive Producers David Benioff and D.B. Weiss confirmed that George R.R. Martin told them that Shireen would be burned to death as a sacrifice in an unpublished future novel. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '81c10c63e6c19bdbc6b00e59e46c5394'}>,\n",
+       "  <Document: {'content': 'Weiss confirmed that George R.R. Martin told them that Shireen would be burned to death as a sacrifice in an unpublished future novel. Benioff said: \"When George first told us about this, it was one of those moments where I remember looking at Dan, it was just, like, god it\\'s so, so horrible, and it\\'s so good in a story sense, because it all comes together.\"Weiss says he believes the decision to kill Shireen in this way is, \"entirely narratively justified,\" questioning why \"we\\'re all highly selective about which characters deserve our empathy. Stannis has been burning people alive for seemingly trivial reasons since season 2.\"===Filming===\\n\"The Dance of Dragons\" was directed by David Nutter, he also directed the subsequent episode, \"Mother\\'s Mercy\".\\n\\n==Reception==\\n\\n===Ratings===\\n\"The Dance of Dragons\" was watched by an estimated 7.14 million American viewers during its first airing. With Live+7 DVR viewing factored in, the episode had an overall rating of 9.92 million viewers, and a 5.2 in the 18–49 demographic. In the United Kingdom, the episode was viewed by 2.473 million viewers, making it the highest-rated broadcast that week. It also received 0.141 million timeshift viewers.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'cce8ad1b2d3263ac0ea725295db0e4a7'}>,\n",
+       "  <Document: {'content': 'In the United Kingdom, the episode was viewed by 2.473 million viewers, making it the highest-rated broadcast that week. It also received 0.141 million timeshift viewers.\\n\\n===Critical reception===\\n\"The Dance of Dragons\" received highly positive reviews, with many critics praising the episode\\'s conclusion in Daznak\\'s Pit. The showrunners\\'s decision to have Stannis sacrifice Shireen, however, polarized critics, with some praising it as strong character development and others feeling it betrayed Stannis\\'s character. The episode received a rating of 88% on the review aggregator Rotten Tomatoes from 33 reviews with an average rating of 8.9 out of 10 and the critics consensus reading \"Underscored by an especially heartwrenching moment and a spectacular display of power, \\'\\'The Dance of Dragons\\'\\' successfully delivers the shock and awe audiences have come to expect from the series\\' penultimate episodes.\"Matt Fowler of IGN heavily praised the episode, awarding it 9.3/10, an \"amazing\" score. He especially praised the episode\\'s final act, stating \"And just the spectacle of Dany climbing on top of Drogon, with care, and then soaring up and off into the sky was remarkable. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'ef23017bbc392789288c9cfa4f544015'}>,\n",
+       "  <Document: {'content': 'He especially praised the episode\\'s final act, stating \"And just the spectacle of Dany climbing on top of Drogon, with care, and then soaring up and off into the sky was remarkable. It\\'s funny that a huge moment for her as a Targaryen and the possible future leader of Westeros came right during one of her most bloody and spectacular failures as a Queen.\"He summarized his review by saying \"GoT delivered another exciting (and unsettling) episode as Stannis made a big decision & Daenerys attended a tournament\" and labelled Stannis\\'s decision a strong point of the episode. Charlotte Runcie of \\'\\'The Daily Telegraph\\'\\' also reviewed the episode favorably: \"Hurling Hollywood-level budgets behind a multi-season TV show paid rich rewards this week, from the enormous \\'\\'Gladiator\\'\\'-style sweep of the crowded coliseum arena to thrillingly choreographed fights to the death before a huge, baying audience.\"Runcie was also impressed with the Shireen sacrifice scene, calling it \"one of the most upsetting scenes\" in season 5.\\n\\nWriting for \\'\\'The A.V. Club\\'\\', both writers responded positively to the episode. Writing for people who have not read the novels, Brandon Nowalk awarded the episode an A-, praising Stannis\\'s sacrifice of Shireen. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '54b62276a62ffa006a108a806e4e98ae'}>,\n",
+       "  <Document: {'content': 'Writing for people who have not read the novels, Brandon Nowalk awarded the episode an A-, praising Stannis\\'s sacrifice of Shireen. He said \"Stannis burning his daughter Shireen at the stake is the hardest \\'\\'Game of Thrones\\'\\' has hit since the Red Wedding, only this violence is completely drained of excitement. It’s not a shock moment, and there’s no gross-out gore as in Oberyn’s death. Instead it’s a long, cold death march. What’s most impressive is the pacing, which is not the season’s strongest suit.\"He also praised the episode\\'s conclusion in Meereen by saying \"The close-up of Dany taking Missandei’s hand is so vivid I’m practically getting misty now\", and further stated \"The CGI could use some polish, but nothing short of Shireen can turn my smile upside down.\"Writing for people who have read the novels, Myles McNutt awarded the episode a B+, praising the Meereen story by saying \"It’s a powerful image, rendered as effectively as it could be given the budgetary limitations of the series. Watching Dany soar about the stadium on Drogon is an iconic image, but it’s not one that any other characters could really experience, which is meaningful in and of itself.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '91fbbe7673222cb76f6bfc3b1944873e'}>,\n",
+       "  <Document: {'content': 'Watching Dany soar about the stadium on Drogon is an iconic image, but it’s not one that any other characters could really experience, which is meaningful in and of itself.\"Erik Kain of \\'\\'Forbes\\'\\' was more critical of the sacrifice scene. While he praised the episode overall as \"thrilling and tragic and intense,\" he called the sacrifice scene \"a horrible, no-good, very bad, infuriating way to ruin Stannis as a character\" and \"a monstrosity of a writing decision.\"He also said the scene was \"one of the most disturbing, baffling, and unnecessary departures from the books\" that the show has so far presented, though he noted that the sacrifice scene had actually been George R. R. Martin\\'s idea and that (with two installments of \\'\\'The Song of Ice and Fire\\'\\' still unpublished) it was unclear exactly how much Shireen\\'s death in the show would deviate from her story arc in the books.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'a010758b96d11a9067bcc9ba1d9827d6'}>,\n",
+       "  <Document: {'content': 'He also said the scene was \"one of the most disturbing, baffling, and unnecessary departures from the books\" that the show has so far presented, though he noted that the sacrifice scene had actually been George R. R. Martin\\'s idea and that (with two installments of \\'\\'The Song of Ice and Fire\\'\\' still unpublished) it was unclear exactly how much Shireen\\'s death in the show would deviate from her story arc in the books.\\n\\nFinding a middle ground, Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' agreed that the sacrifice scene was disturbing, but she found that David Nutter \"did a beautiful job\" directing it and believed that the scene wondrously developed Stannis as a character who is so fixated on \"his own chosenness\" that he will do anything to fulfill his claim to the throne. She also noted that the scene departed from the books so far, but would possibly be reconciled in one of the future installments.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'aece7a849275e9fd1c6ed4cec6932dc1'}>,\n",
+       "  <Document: {'content': 'She also noted that the scene departed from the books so far, but would possibly be reconciled in one of the future installments.\\n\\n===Accolades===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nRob McLachlan\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nKatie Weiland\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau\\n\\nHollywood Professional Alliance\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani\\n\\n2016\\n\\nAmerican Cinema Editors Awards 2016\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nKatie Weiland\\n\\nVisual Effects Society Awards 2015\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon for \"The Dance of Dragons\"\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen for \"Dance of Dragons\" - Drogon Arena Rescue\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson for \"Drogon Arena\"\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"Drogon Arena\"\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '9d4549eb79823ee570a740a8ba8c39c7'}>,\n",
+       "  <Document: {'content': \"\\n\\n'''Sansa Stark''' is a fictional character created by American author George R. R. Martin. She is a prominent character in Martin's award-winning ''A Song of Ice and Fire'' series.\\n\\nIntroduced in ''A Game of Thrones'' (1996), Sansa is the elder daughter and second child of Lord Eddard Stark and his wife Lady Catelyn Stark. She subsequently appeared in the following three novels: ''A Clash of Kings'' (1998), ''A Storm of Swords'' (2000), and ''A Feast for Crows'' (2005). While absent from the fifth novel ''A Dance with Dragons'', as the books are separated geographically, Sansa is confirmed to return in the forthcoming next book in the series, ''The Winds of Winter''.\\n\\nIn HBO's adaptation of the series, ''Game of Thrones'', Sansa is portrayed by English actress Sophie Turner. The character has received critical acclaim, including praise as the 4th greatest character in the series by ''Rolling Stone''. She and the rest of the cast were nominated for Screen Actors Guild Awards for Outstanding Performance by an Ensemble in a Drama Series in 2012, 2014, 2015 and 2016. Furthermore, Turner received a nomination for the Primetime Emmy Award for Outstanding Supporting Actress in a Drama Series in 2019.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '2f5bcc5a944e6d329f81a40ee7d4509d'}>,\n",
+       "  <Document: {'content': \"Furthermore, Turner received a nomination for the Primetime Emmy Award for Outstanding Supporting Actress in a Drama Series in 2019.\\n\\n==Character and appearances==\\nSansa Stark is the second child and elder daughter of Eddard Stark and Catelyn Stark. She was born and raised in Winterfell, until leaving with her father and sister at the beginning of the series. She was raised with a younger sister Arya Stark, two younger brothers Rickon Stark and Bran Stark, as well as an older brother Robb Stark, and an older illegitimate half-brother, Jon Snow.\\n\\nRaised as a lady, Sansa is traditionally feminine. Sansa's interests are music, poetry, and singing. She strives to become like the heroines of romantic tales by attempting to find a prince, knight, or gentleman to fall in love with. For a companion animal, she owned a direwolf named Lady. However, Lady was killed in place of Arya's direwolf, Nymeria, after Nymeria attacked the Crown Prince, Joffrey Baratheon, and later fled.\\n\\nSansa has been described as tall, slim, womanly, and beautiful, destined to be a lady or a queen. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '4120a29e784c7b5d795f113d155c83bc'}>,\n",
+       "  <Document: {'content': 'However, Lady was killed in place of Arya\\'s direwolf, Nymeria, after Nymeria attacked the Crown Prince, Joffrey Baratheon, and later fled.\\n\\nSansa has been described as tall, slim, womanly, and beautiful, destined to be a lady or a queen. She has blue eyes and thick auburn hair that she inherits from her mother, who came from House Tully in the Riverlands region prior to her marriage to Eddard Stark. She has her hair dyed dark brown later on while in the Vale, disguised as Alayne Stone, the bastard daughter of Petyr Baelish.  Sansa is 11 years old in \\'\\'A Game of Thrones\\'\\' and nearly 14 in \\'\\'A Feast for Crows\\'\\'. Arguably the most naive of the Stark children at the start of the series, Sansa often finds herself used as a pawn in the machinations of the other characters. However, as the story progresses, she matures and becomes more of a player of the game rather than a pawn for other characters. She is the most beautiful woman in Westeros at the time of the events of \"A Song of Ice and Fire\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '244e2a5926e7f89b5f267a9d99493c97'}>,\n",
+       "  <Document: {'content': 'She is the most beautiful woman in Westeros at the time of the events of \"A Song of Ice and Fire\".\\n\\n==Storylines==\\nCoat of arms of House Stark\\n\\n===\\'\\'A Game of Thrones\\'\\'===\\n\\nSansa Stark begins the novel by being betrothed to Crown Prince Joffrey Baratheon, believing Joffrey to be a gallant prince. While Joffrey and Sansa are walking through the woods, Joffrey notices Arya sparring with the butcher\\'s boy, Mycah. A fight breaks out and Joffrey is attacked by Nymeria (Arya\\'s direwolf) after Joffrey threatens to hurt Arya. Sansa lies to King Robert about the circumstances of the fight in order to protect both Joffrey and her sister Arya.  Since Arya ran off with her wolf to save it, Sansa\\'s wolf is killed instead, estranging the Stark daughters.\\n\\nDuring the Tourney of the Hand to honour her father Lord Eddard Stark, Sansa Stark is enchanted by the knights performing in the event.  At the request of his mother, Queen Cersei Lannister, Joffrey spends a portion of the tourney with Sansa, but near the end he commands his guard Sandor Clegane, better known as The Hound, to take her back to her quarters. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'a80ae4f1d1187bde6a34a29aeeb0837d'}>,\n",
+       "  <Document: {'content': 'At the request of his mother, Queen Cersei Lannister, Joffrey spends a portion of the tourney with Sansa, but near the end he commands his guard Sandor Clegane, better known as The Hound, to take her back to her quarters. Sandor explains how his older brother, Gregor, aka \"Mountain that Rides\" pushed his face into a brazier of hot coals, for playing with one of his wooden toys.\\n\\nAfter Eddard discovers the truth of Joffrey\\'s paternity, he tells Sansa that they will be heading back to Winterfell. Sansa is devastated and wishes to stay in King\\'s Landing, so she runs off to inform Queen Cersei of her father\\'s plans, unwittingly providing Cersei with the information needed to arrest her father. After Robert dies, Sansa begs Joffrey to show mercy on her father and he agrees, if Ned will swear an oath of loyalty, but executes him anyway, in front of Sansa.  Sansa is now effectively a hostage in King\\'s Landing and finally sees Joffrey\\'s true nature, after he forces her to look at the tarred head of her now-deceased father.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'c4ea66d17668dd910673351c384d29d5'}>,\n",
+       "  <Document: {'content': \"Sansa is now effectively a hostage in King's Landing and finally sees Joffrey's true nature, after he forces her to look at the tarred head of her now-deceased father.\\n\\n===''A Clash of Kings''===\\nSansa is a hostage in King's Landing, and has learned to be outwardly loyal to King Joffrey to avoid severe physical abuse.\\n\\nAt the celebration for Joffrey's name day, she ostensibly saves the life of Ser Dontos Hollard, when he shows up late and inebriated, begging Joffrey to spare him, in turn winning Dontos' loyalty. Sansa is routinely beaten by Joffrey's guards. After her brother has won a battle against Joffrey's maternal family, she is publicly beaten and has her clothes torn.  Tyrion Lannister intervenes on her behalf, and Sandor Clegane gives her his cloak to cover herself.  Later, Sandor Clegane saves Sansa from a riot in King's Landing.\\n\\nDuring the Battle of Blackwater Bay, all of the highborn ladies in King's Landing seek refuge within a doubly secure, moated chamber within the Red Keep. There, Queen Cersei drunkenly mocks and berates Sansa, then flees when the tide of the battle falls, while Sansa stays to give comfort to the ladies before retreating to her own quarters. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'a5cf07b8a1b8a3472a257d09c5daaf75'}>,\n",
+       "  <Document: {'content': 'There, Queen Cersei drunkenly mocks and berates Sansa, then flees when the tide of the battle falls, while Sansa stays to give comfort to the ladies before retreating to her own quarters. There she finds a drunken Sandor Clegane, who offers to take her away from any imminent danger and the Lannisters themselves. She declines, but he insists she sing for him, at dagger point, then rips his cloak off and gives it to her a second time before fleeing the city. When Sansa awakens, the battle is over. House Lannister has won, but her betrothal to Joffrey is soon ended, so as he may marry Margaery Tyrell whose family just helped turn the tide of battle. However, Joffrey informs Sansa that he still has use for her when married, and to \"expect a nightly visitor for a long while\".\\n\\n===\\'\\'A Storm of Swords\\'\\'===\\n\\nIn the third book, Sansa is invited to dine with Margaery and her grandmother, Olenna Redwyne.  The two women seek to learn the true nature of Joffrey Baratheon now that he and Margaery are betrothed. In turn, Olenna suggests that Sansa should marry her grandson Willas Tyrell. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '6f2d35910be97cbad64e649738ddefd7'}>,\n",
+       "  <Document: {'content': \"The two women seek to learn the true nature of Joffrey Baratheon now that he and Margaery are betrothed. In turn, Olenna suggests that Sansa should marry her grandson Willas Tyrell. Sansa reveals the marriage plan to Ser Dontos, who warns her of the Tyrells; however, Sansa develops a close friendship with Margaery and is excited about becoming a part of her family. However, when Tywin learns of the marriage plot he schemes to have Sansa's brother Robb Stark killed, knowing that would leave Sansa to inherit Winterfell and the North.  He then commands his son Tyrion Lannister to marry Sansa. Tyrion is initially opposed to the marriage, but is eventually enticed by the prospect of becoming Lord of Winterfell, and so agrees to marry her.\\n\\nSansa is shocked one morning to learn that she is being fitted for a gown to marry Tyrion that day. Joffrey taunts Sansa and acts on behalf of her father to give her away during the ceremony to add further insult.  Sansa ignores Tyrion and refuses to bend as he attempts to put his cloak around her, a marriage ceremony custom in Westeros. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'd80bb11bb158e59de63310bb6a179ff3'}>,\n",
+       "  <Document: {'content': \"Sansa ignores Tyrion and refuses to bend as he attempts to put his cloak around her, a marriage ceremony custom in Westeros. In turn, Joffrey commands Ser Dontos to act as a stool so that Tyrion can cloak his bride in spite of his short stature. At the banquet, Sansa dances with many lords, who offer words of comfort; however, Joffrey merely threatens to rape her. Tyrion intervenes and states a desire to castrate Joffrey. After the wedding ceremony, Tyrion chooses not to consummate the marriage due to Sansa's lack of desire in him. It is not long before many in King's Landing come to know that the marriage was never consummated.\\n\\nNot long after Sansa's marriage, Joffrey and Margaery are wed and afterward a grand feast. At Joffrey's wedding, Joffrey is poisoned, and Cersei orders both Tyrion and Sansa arrested. As Joffrey begins to choke to death, Sansa manages to flee during the chaos.  Once in her room, she gathers her belongings and notices that one of the amethysts from her hairnet, a gift from Ser Dontos, is missing. Sansa immediately realizes that the prince had been poisoned and starts to doubt Ser Dontos' rescue plan. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'd64d31fdbda2973ffbd76628071090dc'}>,\n",
+       "  <Document: {'content': \"Once in her room, she gathers her belongings and notices that one of the amethysts from her hairnet, a gift from Ser Dontos, is missing. Sansa immediately realizes that the prince had been poisoned and starts to doubt Ser Dontos' rescue plan. Understanding that she will be implicated in the murder of Joffrey, she chooses to flee King's Landing with the knight anyway. Ser Dontos is later killed by Petyr Baelish, who reveals that he is the mastermind behind nearly all of the capital intrigue. He reveals that he was the one who sent Dontos to her and that Olenna took the amethyst from Sansa's hairnet.\\n\\nBaelish smuggles Sansa to safety in the Vale of Arryn, where she poses as his bastard daughter Alayne Stone. She is taken to her aunt Lysa Arryn, now married to Baelish. Lysa declares that Sansa must marry her sickly boy Robert, heir to the Vale.  Petyr Baelish and Lysa are wed; however, Lysa becomes jealous when she witnesses Littlefinger kissing her niece. Lysa later attempts to murder Sansa, but she is saved once again by Baelish, who kills Lysa.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '9f65498b4695fd77f50f8009024ea390'}>,\n",
+       "  <Document: {'content': \"Petyr Baelish and Lysa are wed; however, Lysa becomes jealous when she witnesses Littlefinger kissing her niece. Lysa later attempts to murder Sansa, but she is saved once again by Baelish, who kills Lysa.\\n\\n===''A Feast for Crows''===\\nAfter Lysa's death, Sansa becomes mistress of the Eyrie and still pretends to be Baelish's illegitimate daughter, Alayne Stone. Baelish successfully pacifies the lords of the Vale, who suspected Baelish's hand in Lysa's death. Afterwards, Baelish reveals to Sansa his plans to eventually marry her to the heir to the Vale, Harrold Hardyng, and his long-range plans to reveal her true identity and reclaim the North. Sansa acts as a mother figure to Robert Arryn, caring for him after the death of Lysa. By now she has lost much of her naivety, as well as trust for Baelish.\\n\\n===Family tree of House Stark===\\n\\n==TV adaptation==\\n===Storylines===\\n====Season 1====\\nSansa is first seen with Arya at Winterfell, during their embroidery lesson with Septa Mordane. Following the arrival of Robert Baratheon and his escort at Winterfell, he insists to Ned that Joffrey and Sansa should be married in order to join their houses. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'dd379c0d358d1faa82aebdeb1c454a74'}>,\n",
+       "  <Document: {'content': 'Following the arrival of Robert Baratheon and his escort at Winterfell, he insists to Ned that Joffrey and Sansa should be married in order to join their houses. Sansa, who is desperate to leave Winterfell, begs Catelyn to make Ned agree to the engagement.\\n\\nJoffrey is bitten by Arya\\'s direwolf Nymeria while bullying Mycah the butcher\\'s boy and Arya. Sansa, an eyewitness, claims to be ignorant of the event at the inquest. As retribution for Joffrey\\'s injury, Cersei convinces Robert to have Sansa\\'s direwolf, Lady, killed in place of the now-missing Nymeria.\\n\\nAfter arriving at King\\'s Landing, Sansa attends the Hand\\'s Tourney where Petyr \"Littlefinger\" Baelish tells Sansa and Arya the story behind Sandor \"The Hound\" Clegane\\'s gruesome facial burns. As time passes, Sansa wears her hair like a southerner and is more flippant with Mordane, expressing fears she will fail to give Joffrey a male heir.\\n\\nFollowing Ned\\'s initial resignation as Hand of the King, Sansa is devastated to hear she must return to Winterfell. She likens Joffrey to a lion and says he is nothing like Robert Baratheon. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '3f34ba351fd2baf088e9122eba805693'}>,\n",
+       "  <Document: {'content': \"Following Ned's initial resignation as Hand of the King, Sansa is devastated to hear she must return to Winterfell. She likens Joffrey to a lion and says he is nothing like Robert Baratheon. This statement inspires Ned to investigate the Baratheon family line, prompting him to realise that Cersei's children are bastards fathered by her twin brother Jaime Lannister, not Robert Baratheon.\\n\\nFollowing Robert's death and Ned's arrest for treason, all Stark servants in King's Landing are executed. Cersei exhorts Sansa to write Robb and Catelyn, imploring them to swear fealty to Joffrey. At court, Sansa pleads for her father's life; all agree on the condition Ned confesses his treason and swears fealty. Sansa is present at the Great Sept of Baelor and is horrified when Joffrey orders Ned's execution, fainting as Ned is beheaded.\\n\\nGrieving the death of her father, Sansa is forced by Joffrey to look upon the spiked heads of both Ned and Septa Mordane. She begs to return home, but he informs Sansa that they are still to be married, and she will stay and obey. Joffrey promises to present Robb's head also, to which she retorts that Robb may give her Joffrey's head, instead. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '936f9740585bb6ed0390ae031034523a'}>,\n",
+       "  <Document: {'content': \"She begs to return home, but he informs Sansa that they are still to be married, and she will stay and obey. Joffrey promises to present Robb's head also, to which she retorts that Robb may give her Joffrey's head, instead. While on the catwalk, Sansa moves to push Joffrey to his death but is stopped by Sandor Clegane, who offers practical consoling advice.\\n\\n====Season 2====\\nAs the War of the Five Kings progresses, Sansa's position in King's Landing becomes increasingly perilous. On Joffrey's name day celebration, Sansa saves an inebriated Ser Dontos Hollard from execution, by convincing Joffrey to instead make Dontos his fool. While at the celebration, Tyrion offers his condolences for Ned's death, in response to which Sansa insists her family are all traitors, and she is loyal to Joffrey. Later on, when her eldest brother Robb wins a battle against the Lannisters, Sansa is publicly beaten and humiliated in front of the court by Joffrey and Ser Meryn (on Joffrey's orders), as payment for her brother's crimes.\\n\\nTyrion Lannister enters the court and rescues Sansa. Despite being a dwarf, Tyrion takes pity on Sansa's situation and offers to have the engagement called off. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '7fd562d9ed01e31618a75d66901c8e83'}>,\n",
+       "  <Document: {'content': \"Tyrion Lannister enters the court and rescues Sansa. Despite being a dwarf, Tyrion takes pity on Sansa's situation and offers to have the engagement called off. Sansa maintains her facade that she is loyal to Joffrey, which impresses Tyrion to the point he believes Sansa might just survive King's Landing.\\n\\nTyrion has his lover, Shae, positioned as Sansa's handmaiden. Sansa and Shae form a friendship in which Sansa is able to vent about her hatred of the Lannisters without fear of being betrayed. Sansa is present when the royal family bids farewell to Joffrey's sister, Myrcella, on her departure to Dorne to form an alliance between the Lannisters and the Martells. While returning to the Red Keep, a riot breaks out in the streets of King's Landing, amidst which Sansa finds herself caught in the fray. Three peasant men chase Sansa and attempt to rape her before she is rescued and returned to the castle by Sandor Clegane.\\n\\nThe following morning, Sansa has a nightmare of the incident and wakes up in a bloodstained bed. Sansa has her first period, thus meaning she can now bear Joffrey's children. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '85306cb112a6524d6d0c2ae8150afa1b'}>,\n",
+       "  <Document: {'content': \"The following morning, Sansa has a nightmare of the incident and wakes up in a bloodstained bed. Sansa has her first period, thus meaning she can now bear Joffrey's children. Sansa and Shae attempt to conceal this, which involves Shae's threatening to kill a witness handmaiden if she tells anyone. However, Sandor Clegane sees the blood, and both Cersei and Joffrey are informed.\\n\\nCersei invites Sansa to her chambers to share some of her wisdom and experience as a wife and a mother. Cersei reminisces that her husband Robert was not interested in her childbirth. Cersei explains to Sansa that while Sansa may never love Joffrey (and vice versa), she will love his children. Cersei warns Sansa that the more people she loves, the weaker she will be. Therefore, Sansa should only love her children, as it is the only love she has no choice in.\\n\\nBefore the Battle of the Blackwater, Joffrey forces Sansa to kiss the blade of his sword, while bragging he will kill Stannis himself. Sansa taunts Joffrey by remarking he must be battling in the vanguard, which he would not be. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '6fbfee99ca10835030ab399a15792ce'}>,\n",
+       "  <Document: {'content': \"Before the Battle of the Blackwater, Joffrey forces Sansa to kiss the blade of his sword, while bragging he will kill Stannis himself. Sansa taunts Joffrey by remarking he must be battling in the vanguard, which he would not be. Joffrey promises that she will lick Stannis's blood, and later her brother Robb's blood off his sword. During the battle, Sansa takes refuge with Cersei, Shae, and the other women and children of King's Landing. A drunk Cersei declares to Sansa and the women that if the city falls, they will all be raped while the city is sacked. Cersei continues her taunting by also telling Sansa that tears are not the only weapons women have; the greatest weapon they own is between their legs. Following Cersei's departure from the refuge with Tommen, Sansa leaves to hide in her bedchamber, where she finds Sandor Clegane. Sandor, about to leave King's Landing, offers to take Sansa home. Sansa declines, insisting Stannis would not harm her. After the battle is won by the Lannister-Tyrell forces, Loras Tyrell asks Joffrey to take his sister, Margaery, as his bride. Joffrey accepts the proposal, which annuls his marriage with Sansa. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '205810d38b0da175211929d37314e470'}>,\n",
+       "  <Document: {'content': \"After the battle is won by the Lannister-Tyrell forces, Loras Tyrell asks Joffrey to take his sister, Margaery, as his bride. Joffrey accepts the proposal, which annuls his marriage with Sansa. Sansa, while pretending to be devastated, is secretly delighted she no longer has to marry Joffrey. However, Petyr Baelish warns that while Sansa is no longer engaged, Joffrey would have greater rein to abuse Sansa, especially now she is a woman. Petyr assures Sansa he will help get her home, in which Sansa once again displays a facade, asserting King's Landing is her home. Petyr advises Sansa that everyone in King's Landing is a liar and that they are much more cunning than her.\\n\\n====Season 3====\\nWith her engagement to Joffrey annulled, Sansa does not have to worry about spending the rest of her life with him, but is with lesser protection from tormentors. Petyr Baelish, an old friend of her mother's with a reputation for being sadistic and cunning, tells her that he can smuggle her out of the city, but she is reluctant. Sansa finds a friend in Loras Tyrell, who is kind to her and whom she hopes will ask for her hand. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'a9ebd352935a8d1fe957c3a39d090d71'}>,\n",
+       "  <Document: {'content': \"Sansa finds a friend in Loras Tyrell, who is kind to her and whom she hopes will ask for her hand. His sister, Joffrey's new fiancé, Margaery, is also kind to her and takes her to dine with her grandmother, Olenna Tyrell, who asks her for her opinion of Joffrey. Sansa reveals Joffrey's true, cruel personality, but Margaery and Lady Olenna merely pass it by as a trivial matter, saying there is nothing to be done to change a man's character, especially a king's. Sansa's affection for Loras grows; she is unaware that Loras is gay and, while he likes her and enjoys spending time with her, he can never love her. Margaery proposes the idea that Loras will marry Sansa, meaning Sansa can leave King's Landing, which delights Sansa. However, when this plan is reported to the Lannisters, they fear that the Tyrells will pose an even greater threat with a member of House Stark as one of their allies and quickly end the idea of the engagement, by betrothing Loras to Cersei and engaging Sansa to Tyrion, which both Sansa and Shae are against. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'db60fa32adbed6d082e4315e191554dd'}>,\n",
+       "  <Document: {'content': 'However, when this plan is reported to the Lannisters, they fear that the Tyrells will pose an even greater threat with a member of House Stark as one of their allies and quickly end the idea of the engagement, by betrothing Loras to Cersei and engaging Sansa to Tyrion, which both Sansa and Shae are against. However, the day of the wedding, Tyrion promises not to harm her and, as she prepares to consummate the marriage, Tyrion realizes how unhappy Sansa is and tells her that she doesn\\'t have to consummate it unless she wants to. When Sansa asks Tyrion what would happen if she never wants him in her bed, he quips, \"And so my watch begins.\"Sansa and Tyrion do form a somewhat friendly relationship, as he is kind to her and treats her well, and she soon realizes there are worse Lannisters to be wed to. However, their cordial relationship suffers a crushing blow when Sansa receives news of Robb and Catelyn\\'s deaths at the Red Wedding, an event orchestrated by Tywin Lannister, Tyrion\\'s father.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'b8b3477904ca4209fd8b0d63ad837bda'}>,\n",
+       "  <Document: {'content': \"However, their cordial relationship suffers a crushing blow when Sansa receives news of Robb and Catelyn's deaths at the Red Wedding, an event orchestrated by Tywin Lannister, Tyrion's father.\\n\\n====Season 4====\\nSansa, still distraught over the death of Robb and Catelyn, is approached by Dontos Hollard, a former knight whom Sansa had convinced Joffrey to make his fool instead of executing him. Dontos gives her a necklace, claiming it was his mother's. However, the necklace turns out to be a fake; one of the gems contains the poison which Lady Olenna Tyrell uses to poison Joffrey at his wedding to Margaery Tyrell. In the commotion, Sansa is taken by Dontos, to Baelish's boat in Blackwater Bay. Baelish smuggles Sansa from King's Landing after revealing the nature of the necklace and has Dontos killed with a crossbow.\\n\\nLord Baelish, with Sansa posing as his niece Alayne Stone, pass through the Blood Gates to the Eyrie and to the keep of Lysa Arryn. Lysa initially invites them with open arms, revealing she knows exactly who her niece is, and they are welcome to be housed. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '8f155df13762cc2834114d9d14d27b2'}>,\n",
+       "  <Document: {'content': \"Lysa initially invites them with open arms, revealing she knows exactly who her niece is, and they are welcome to be housed. It is soon revealed however, Lysa mistrusts the relationship between Sansa and Baelish accusing Baelish of violating Sansa and accusing him of never loving her. Later in the keep, Sansa strikes Lysa's son Robin and Baelish appears. Baelish then proclaims his undying love for her deceased mother, Catelyn, and he shares a kiss with a stunned Sansa, with Lysa watching from above. Sansa is immediately summoned to Lysa's throne room, where she believes she had been summoned for striking her son. Lysa reveals she had observed the kiss, and though defending herself and Baelish's actions against her, Lysa holds her to the Moon Door, a trap door that leads hundreds of feet down into the mountains below. Baelish intervenes before she gets the chance to execute Sansa and pushes Lysa to her death instead as he proclaimed his love for her sister. Then Baelish later claimed to the lords of the Vale that she committed suicide. Sansa is called to give testimony, and although she reveals her true identity, she supports Baelish's story. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '7c62ef850448e4d10104525d1ca72006'}>,\n",
+       "  <Document: {'content': \"Then Baelish later claimed to the lords of the Vale that she committed suicide. Sansa is called to give testimony, and although she reveals her true identity, she supports Baelish's story. She then joins Baelish and her cousin Robin Arryn on a tour of the Vale.\\n\\n====Season 5====\\nBaelish brokers a marriage between Sansa and Ramsay Bolton, now the heir to the North after the death of Robb Stark. Though Sansa is reluctant to marry Ramsay, as his father Roose had personally murdered Robb, Baelish persuades her by claiming that the marriage will give her the opportunity to avenge her family. On the way to Winterfell, they encounter Brienne of Tarth, who had sworn to Catelyn Stark to take Sansa to safety and tries to convince Sansa to come with her; Baelish has her chased off by his men, but Brienne follows Sansa to Winterfell regardless. Though initially charming, Ramsay's sadistic nature becomes apparent when Sansa discovers that he has captured and enslaved Ned's former ward Theon Greyjoy, and forced him to assume the identity of his serving man, Reek. Sansa and Ramsay wed in front of the Godswood. That night, Ramsay rapes Sansa, and forces Reek to watch. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': 'd2b8de9143e2a49fbd7dc726c53e2e96'}>,\n",
+       "  <Document: {'content': \"Though initially charming, Ramsay's sadistic nature becomes apparent when Sansa discovers that he has captured and enslaved Ned's former ward Theon Greyjoy, and forced him to assume the identity of his serving man, Reek. Sansa and Ramsay wed in front of the Godswood. That night, Ramsay rapes Sansa, and forces Reek to watch. Over the next few days, Ramsay continues to rape and beat Sansa every night, and keeps her locked in her bedchamber. Sansa begs Reek to help her signal her northern allies by lighting a candle in the broken tower. Reek, wishing to spare Sansa from Bolton's wrath, instead tells Ramsay. He proceeds to flay the maid who had told Sansa of the signal, and forces Sansa to look at her corpse. Furious, Sansa confronts Reek, who admits that he had failed to capture Sansa's brothers Bran and Rickon, and killed two farm boys in their place. While the Boltons prepare to battle Stannis Baratheon's advancing forces, Sansa signals to Brienne, unaware that she has left to kill Stannis. When help does not come, Sansa attempts to return to her room but is caught by Ramsay's paramour Myranda, who threatens to mutilate Sansa. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': 'aef5de17b5a5e23fb8e8eddcea29097d'}>,\n",
+       "  <Document: {'content': \"When help does not come, Sansa attempts to return to her room but is caught by Ramsay's paramour Myranda, who threatens to mutilate Sansa. Finally snapping, Theon throws Myranda to her death, just as the Bolton forces return. Fearful of Ramsay's reaction, Theon and Sansa jump from Winterfell's battlements into the snow.\\n\\n====Season 6====\\nSansa and Theon are captured by Bolton soldiers in the forest outside Winterfell, but Brienne and her squire Podrick Payne arrive in time to rescue them and kill the Bolton soldiers. This time, Sansa accepts Brienne's loyalty. While Theon returns to the Iron Islands, Sansa, Brienne, and Podrick journey on to Castle Black, where she reunites with her half-brother Jon Snow, who has just resigned as Lord Commander of the Night's Watch. Sansa tries to persuade Jon to help her drive the Boltons out of Winterfell; although Jon initially refuses, he changes his mind after Ramsay sends a letter to Jon in which he gloats that he holds Rickon Stark captive and threatens to kill the Starks and the Wildlings Jon has let through the Wall if Sansa is not returned. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': 'daa882aa7e1fa27aea4564521f589c11'}>,\n",
+       "  <Document: {'content': 'Sansa tries to persuade Jon to help her drive the Boltons out of Winterfell; although Jon initially refuses, he changes his mind after Ramsay sends a letter to Jon in which he gloats that he holds Rickon Stark captive and threatens to kill the Starks and the Wildlings Jon has let through the Wall if Sansa is not returned. Before Jon and Sansa leave Castle Black, Baelish arranges a meeting with Sansa in Mole\\'s Town. He insists that he was unaware of Ramsay\\'s nature and offers the support of the Knights of the Vale, also mentioning that her great-uncle Brynden \"Blackfish\" Tully has captured Riverrun from House Frey. Sansa sends Baelish away, declaring that she never wants to see him again, but sends Brienne to the Riverlands to convince the Blackfish to aid the Starks.\\n\\nAlthough Jon and Sansa are only able to win over a handful of Northern lords, Jon insists that they must march on Winterfell, despite Sansa\\'s objections. Sansa sends a letter to Baelish asking for his aid, and the Vale forces arrive at Winterfell in time to defeat the Boltons. Ramsay is captured and Sansa has Ramsay fed to his hounds. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': '8a95bf1a490a7aa2a28e92edd3a60414'}>,\n",
+       "  <Document: {'content': \"Sansa sends a letter to Baelish asking for his aid, and the Vale forces arrive at Winterfell in time to defeat the Boltons. Ramsay is captured and Sansa has Ramsay fed to his hounds. In the aftermath of the battle, Sansa apologizes for not telling Jon about Baelish and the Knights of the Vale. Jon forgives her and asks that they trust each other completely from now on. They remember their father when Sansa says she received a white raven informing them that Winter is here. While in the godswood, Baelish tells Sansa the North will rally behind her and confesses his ambition to rule Westeros with Sansa at his side, but Sansa rebuffs his advances. Later, the Northmen and Valemen declare Jon the new King in the North. Sansa's smile dies when she notices Littlefinger watching her.\\n\\n====Season 7====\\nJon travels to Dragonstone to negotiate with Daenerys Targaryen for her support against the White Walkers, leaving Sansa as regent in his absence. Soon after, Bran and Arya return to Winterfell. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': 'd8390ef5cfc53957d2d8093534080ac7'}>,\n",
+       "  <Document: {'content': '====Season 7====\\nJon travels to Dragonstone to negotiate with Daenerys Targaryen for her support against the White Walkers, leaving Sansa as regent in his absence. Soon after, Bran and Arya return to Winterfell. Littlefinger seeks to drive a wedge between Sansa and Arya by letting Arya find Sansa\\'s letter to Robb asking him to bend the knee to Joffrey, causing Arya to confront Sansa. Sansa sneaks into Arya\\'s quarters and comes across the \"faces\" Arya has taken from the various people she has killed on her travels; Arya catches Sansa and tells Sansa of her ability to assume people\\'s identities with the faces before she threatens her.\\n\\nSansa later receives an invitation to King\\'s Landing, where Jon intends to present Cersei, who is now Queen of the Seven Kingdoms, with proof of the White Walkers\\' existence. Refusing to return to King\\'s Landing, she sends Brienne as her representative.\\n\\nSansa shows Littlefinger the letter she received from Jon following the meeting in King\\'s Landing, in which Jon states that he had pledged his support to Daenerys Targaryen. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': '1f598e072485a95ce65163cb569b286d'}>,\n",
+       "  <Document: {'content': 'Sansa shows Littlefinger the letter she received from Jon following the meeting in King\\'s Landing, in which Jon states that he had pledged his support to Daenerys Targaryen. Littlefinger continues his manipulation of Sansa by claiming that Jon has betrayed the North, Sansa should seize power from Jon, and suggests Arya intends to murder her to become Lady of Winterfell. Sansa summons Arya to the great hall and begins an accusation of treason and murder, before directing the accusation towards Littlefinger. With help from their brother Bran (now known as Three-Eyed Raven), Sansa and Arya reveal that they are aware of Baelish\\'s numerous crimes, including the murder of Lysa Arryn, orchestrating the murders of both Jon Arryn and Eddard Stark, and manipulating the Starks and Lannisters to war. Baelish tries to plead for his life, but Sansa refuses and sentences Littlefinger to death. Arya executes him. The Stark sisters later resolve their differences and acknowledge that the Starks must stand together to survive the winter. They remember their father telling them, \"The lone wolf dies, but the pack survives.\"====Season 8====\\nSansa welcomes Daenerys and her court, including Tyrion, to Winterfell. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 28}, 'embedding': None, 'id': '285133b6c632c99c80e035b8f587e624'}>,\n",
+       "  <Document: {'content': 'They remember their father telling them, \"The lone wolf dies, but the pack survives.\"====Season 8====\\nSansa welcomes Daenerys and her court, including Tyrion, to Winterfell. Tyrion declares that the Lannister troops will be marching north as well to defend against the dead, but Sansa is skeptical; her fears are confirmed when Jaime Lannister arrives in Winterfell and he reveals Cersei\\'s treachery. Sansa wishes Jaime dead for attacking Ned in King\\'s Landing, but spares him when Brienne vouches for him. Sansa suspects that Jon\\'s decision to pledge to Daenerys as queen is due to his love for Daenerys. Sansa is wary of Daenerys but when speaking with her in private, Daenerys assures Sansa that her love for Jon is also genuine and they reach common ground. However, the situation becomes tense when Sansa asks of the North\\'s independence after Daenerys has conquered Westeros. They are interrupted by Theon\\'s arrival, who has come to help defend Winterfell.\\n\\nSansa watches the battle against the dead from Winterfell\\'s battlements, but when the Dothraki are easily overrun, Arya orders Sansa to seek refuge in the crypts with the other non-combatants. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 29}, 'embedding': None, 'id': 'af36a5a185883769093daf9d45cd5e90'}>,\n",
+       "  <Document: {'content': \"Sansa watches the battle against the dead from Winterfell's battlements, but when the Dothraki are easily overrun, Arya orders Sansa to seek refuge in the crypts with the other non-combatants. The Night King reanimates the dead around Winterfell, including the Starks buried in the crypts, but the undead fall when Arya kills the Night King, allowing Sansa, Tyrion, and the others in the crypts to survive. Sansa is present for the funeral for those fallen in battle, including Theon, who was killed defending Bran. She places a Stark pin in Theon's armor before he is cremated to honor him as an ally of the Starks.\\n\\nSansa is reunited with the Hound at the victory feast. The Hound declares that Sansa would not have experienced the horrors she faced had she fled King's Landing with him, but Sansa acknowledges that her experiences made her wiser. At the war council, Sansa and Daenerys disagree over giving the Northern army time to recover before marching on King's Landing. After the council, Sansa and Arya tell Jon they don't trust Daenerys but Jon defends Daenerys. Jon confides he is the son of Rhaegar Targaryen and Lyanna Stark after swearing Sansa and Arya to secrecy. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 30}, 'embedding': None, 'id': '254c2c7fdcbab7b9bae4b26c16d68c5b'}>,\n",
+       "  <Document: {'content': \"After the council, Sansa and Arya tell Jon they don't trust Daenerys but Jon defends Daenerys. Jon confides he is the son of Rhaegar Targaryen and Lyanna Stark after swearing Sansa and Arya to secrecy. Sansa reveals this to Tyrion.\\n\\nDaenerys takes King's Landing, but lays waste to the surrendered populace during which Cersei is also killed. Jon tries but fails to dissuade Daenerys from further destruction and ultimately assassinates her. When Jon is arrested by the Unsullied, Sansa camps the Northern army outside the city and travels to King's Landing to convene a council to determine the fates of Jon, Tyrion, and Westeros. Tyrion suggests Bran be crowned king, to near-unanimous approval. Arya and Sansa abstain, and Sansa asserts to Bran that the North has sacrificed too much to remain part of the Seven Kingdoms, declaring the North's independence. Sansa, Arya, and Bran bid farewell to Jon, who is exiled. Sansa returns to Winterfell and is crowned Queen in the North.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 31}, 'embedding': None, 'id': '5b8e10a0ddb019eeb64026e0e2c4ac4c'}>,\n",
+       "  <Document: {'content': 'Sansa, Arya, and Bran bid farewell to Jon, who is exiled. Sansa returns to Winterfell and is crowned Queen in the North.\\n\\n===Reception===\\nSophie Turner plays the role of Sansa Stark in the television series\\n\\nAs her storyline has progressed, Sansa has received critical acclaim for the development of her character and her emergence from a naive young girl to a strong young woman. \\'\\'Rolling Stone\\'\\' ranked Sansa as No. 4 on a list of the \"Top 40 \\'\\'Game of Thrones\\'\\' Characters\", saying that Sansa is \"often overlooked in favor of her killer kid sister\", but that her \"quiet, innate political shrewedness and emotional strength have enabled her to survive\", and calling her \"the show\\'s best-kept secret\". In a ranking of the 48 best \\'\\'Game of Thrones\\'\\' characters listed in the main credits in the first five seasons on the website \\'\\'The Wrap\\'\\', Sansa was ranked at No. 4, ahead of the more popular Daenerys Targaryen, Jon Snow, and Tyrion Lannister, saying that \"Sansa has been kind of great in Season 6, turning into the sort of badass we always hoped but never thought she actually could become\".\\n\\nIn an article published on \\'\\'Mic.', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 32}, 'embedding': None, 'id': '870bd1eb81c0d7a99653d7c5b0197f01'}>,\n",
+       "  <Document: {'content': '4, ahead of the more popular Daenerys Targaryen, Jon Snow, and Tyrion Lannister, saying that \"Sansa has been kind of great in Season 6, turning into the sort of badass we always hoped but never thought she actually could become\".\\n\\nIn an article published on \\'\\'Mic.\\'\\'com, Julianne Ross says that \"the elder Stark daughter is often cited as one of the most reviled characters on \\'\\'Game of Thrones\\'\\'\", while also saying that \"not coincidentally, Sansa Stark is also one of the most classically feminine characters on the show.\"Ross criticized the heavy hatred for Sansa, particularly in contrast to \"her universally (and rightly) adored tomboy little sister Arya\", stating that Sansa \"arguably gets a disproportionate amount of fan hate because she doesn\\'t fit the narrow \\'strong female character\\' mold we\\'re used to rooting for.\"Blogger Rhiannon Thomas of \\'\\'Feminist Fiction\\'\\' wrote in 2012 that \"the focus on this sort of female character - the oft-cited \\'strong female character\\' - seems to suggest that femininity is still \\'\\'bad,\\'\\' and that women can only be strong by adopting stereotypically male roles and attitudes\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 33}, 'embedding': None, 'id': '673fd50ffc7840442e6eb3cb12e9fe47'}>,\n",
+       "  <Document: {'content': 'Blogger Rhiannon Thomas of \\'\\'Feminist Fiction\\'\\' wrote in 2012 that \"the focus on this sort of female character - the oft-cited \\'strong female character\\' - seems to suggest that femininity is still \\'\\'bad,\\'\\' and that women can only be strong by adopting stereotypically male roles and attitudes\". Thomas went on to say that \"in an abusive situation that would break so many people, Sansa survives\" and that she has a \"woman\\'s courage\" that \"keeps her alive and in the game where characters like Arya would not last five minutes\".\\n\\nIn an article published on MTV.com by Crystal Bell titled \"Sansa Stark is the Only \\'\\'Game of Thrones\\'\\' Hero Worth Rooting For\", Bell writes: \"Sansa is the most relatable character in George R.R. Martin\\'s canon. She\\'s often despised for having no agency, but the way I see it, Sansa is hated for being a woman. Unlike Brienne, Arya, Cersei, and Margaery -- models of the \"strong female character\" archetype—Sansa\\'s passivity denotes weakness. She doesn\\'t have cool swordplay skills like her sister Arya; she isn\\'t a smart seductress like Margaery Tyrell or a fierce queen like Cersei. She is the epitome of femininity on \\'\\'Game of Thrones\\'\\', and therefore, she is dismissed.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 34}, 'embedding': None, 'id': 'd7fc4d209e94f5fd6e7f01b4dd9e732e'}>,\n",
+       "  <Document: {'content': 'She doesn\\'t have cool swordplay skills like her sister Arya; she isn\\'t a smart seductress like Margaery Tyrell or a fierce queen like Cersei. She is the epitome of femininity on \\'\\'Game of Thrones\\'\\', and therefore, she is dismissed.\"Bell went on to say: \"However, Sansa\\'s greatest strength as a character has been her unwavering resilience. She was tortured and humiliated for seasons by the unhinged man-boys around her. She\\'s been the subject of everyday sexism and misogyny since day one. And yet, she survives, even as armor-clad heroes fall before her. She is the show\\'s survivor. She continuously \\'\\'endures\\'\\' the pain and humiliation of being a woman in Westeros. Just because Sansa doesn\\'t wield a sword as fiercely as Arya and Brienne, or command a horde of dragons like Daenerys Targaryen, doesn\\'t make her any less of a hero.\"Sansa received particular acclaim in Season 6 of the show, during which she began her quest to retake her family home and exact revenge on those who wronged her. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 35}, 'embedding': None, 'id': '8d1fa41215fd69d25630886e871375c1'}>,\n",
+       "  <Document: {'content': 'Sansa received particular acclaim in Season 6 of the show, during which she began her quest to retake her family home and exact revenge on those who wronged her. In an interview with \\'\\'The New York Times\\'\\', actress Sophie Turner said that \"she\\'s Sansa no longer a pawn in anyone\\'s game; she\\'s no longer a prisoner...she\\'s the one taking charge and doing her own thing, which is very exciting\". Megan Garber of \\'\\'The Atlantic\\'\\' praised the show\\'s decision to have Sansa be the one orchestrating Ramsay\\'s death in Battle of the Bastards, saying that \"In the end, it was Sansa and her abuser, alone again in a darkened chamber; in the end though, it was Sansa making the decisions about who would be the victim.\"On the scene, Turner said: \"It\\'s amazing. It\\'s Sansa\\'s first kill and it\\'s such a strong moment for her because all her life she\\'s been affected by these men who have just done such terrible things to her....\"\\n\\nFollowing the penultimate Season 6 episode, Bennett Madison of \\'\\'Vanity Fair\\'\\' wrote \"When Sansa icily reminds her dopey brother that \\'No one can protect you\\', it\\'s because she\\'s always been on her own. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 36}, 'embedding': None, 'id': '20efbb043768c1d919702547478f2056'}>,\n",
+       "  <Document: {'content': 'It\\'s Sansa\\'s first kill and it\\'s such a strong moment for her because all her life she\\'s been affected by these men who have just done such terrible things to her....\"\\n\\nFollowing the penultimate Season 6 episode, Bennett Madison of \\'\\'Vanity Fair\\'\\' wrote \"When Sansa icily reminds her dopey brother that \\'No one can protect you\\', it\\'s because she\\'s always been on her own. As far back as King\\'s Landing, Sansa\\'s between quietly protecting herself, working on her stitchery while taking cool measure of everything going on around her, learning how to game the system, and slithering through situations that would have gotten the best of the show\\'s more flashy or impulsive characters. In \\'Battle of the Bastards\\', she got to show a little flash of her own; by being defiantly, gloriously correct in her convictions, by saving the day with her foresight and savvy, and by feeding Ramsay to the dogs.\"Turner later told \\'\\'Time\\'\\' magazine about how gratifying it was to watch Sansa\\'s development during Season 6 and defended the show on its cruel treatment of women: \"In my opinion, \\'\\'Game of Thrones\\'\\' is not sexist, and it\\'s accurate to medieval time. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 37}, 'embedding': None, 'id': 'db30e0e58750158e9b28f8c09d045cb8'}>,\n",
+       "  <Document: {'content': 'Turner later told \\'\\'Time\\'\\' magazine about how gratifying it was to watch Sansa\\'s development during Season 6 and defended the show on its cruel treatment of women: \"In my opinion, \\'\\'Game of Thrones\\'\\' is not sexist, and it\\'s accurate to medieval time. The show puts social boundaries on the women, and they break out of these boundaries.\"====Controversy====\\nIn the episode \"Unbowed, Unbent, Unbroken\", Sansa\\'s rape was the main subject of controversy for the season\\'s deviations from the books. The majority of professional criticism concerned the decision to have Ramsay rape Sansa on their wedding night, with most critics describing the scene as gratuitous and artistically unnecessary. \"This grim scene was difficult for the show to justify,\" said Charlotte Runcie of \\'\\'The Daily Telegraph\\'\\'. Joanna Robinson of \\'\\'Vanity Fair\\'\\' added, \"this rape scene undercuts all the agency that’s been growing in Sansa since the end of last season. ... I’d never advocate that \\'\\'Game of Thrones\\'\\' (or any work of fiction) shy away from edgy plots out of fear of pushback or controversy. But edgy plots should always accomplish something above pure titillation or shock value and what, \\'\\'exactly\\'\\', was accomplished here?\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 38}, 'embedding': None, 'id': '22c5206819014916c0cf2bf42385c540'}>,\n",
+       "  <Document: {'content': 'Joanna Robinson of \\'\\'Vanity Fair\\'\\' added, \"this rape scene undercuts all the agency that’s been growing in Sansa since the end of last season. ... I’d never advocate that \\'\\'Game of Thrones\\'\\' (or any work of fiction) shy away from edgy plots out of fear of pushback or controversy. But edgy plots should always accomplish something above pure titillation or shock value and what, \\'\\'exactly\\'\\', was accomplished here?\"Christopher Orr wrote in \\'\\'The Atlantic\\'\\', \"I continue to be astonished that showrunners Benioff and Weiss still apparently believe that their tendency to ramp up the sex, violence, and—especially—sexual violence of George R.R. Martin’s source material is a strength rather than the defining weakness of their adaptation.\"Myles McNutt of \\'\\'The A.V. Club\\'\\' wrote, \"The issue with the show returning to rape as a trope is not simply because there have been thinkpieces speaking out against it, and is not solely driven by the rational concerns lying at the heart of those thinkpieces. It’s also that the show has lost my faith as a viewer.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 39}, 'embedding': None, 'id': '9b6363c4a07a8e17dac9e2689bcfade9'}>,\n",
+       "  <Document: {'content': 'Club\\'\\' wrote, \"The issue with the show returning to rape as a trope is not simply because there have been thinkpieces speaking out against it, and is not solely driven by the rational concerns lying at the heart of those thinkpieces. It’s also that the show has lost my faith as a viewer.\"Writers from \\'\\'Vanity Fair\\'\\', \\'\\'The Mary Sue\\'\\' and \\'\\'The Daily Beast\\'\\' all disapproved of the decision to use Sansa\\'s victimization as a motivating agent for Theon, saying that the scene undermined Sansa\\'s character development: \"Was it really important to make that scene about Theon\\'s pain?\"wrote Joanna Robinson of \\'\\'Vanity Fair\\'\\'.\\n\\nOther critics responded positively to the scene. Sean T. Collins of \\'\\'Rolling Stone\\'\\' wrote: \"By involving a multidimensional main character instead of one introduced primarily to suffer, the series has a chance to grant this story the gravity and seriousness it deserves. Sarah Hughes of \\'\\'The Guardian\\'\\' wrote: \"I have repeatedly made clear that I’m not a fan of rape as a plot device – but the story of Ramsay and Sansa’s wedding was more than that. ... The writers are walking a very fine line here. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 40}, 'embedding': None, 'id': 'e13805acc400132db056176ea6bef537'}>,\n",
+       "  <Document: {'content': 'Sarah Hughes of \\'\\'The Guardian\\'\\' wrote: \"I have repeatedly made clear that I’m not a fan of rape as a plot device – but the story of Ramsay and Sansa’s wedding was more than that. ... The writers are walking a very fine line here. They handled it well tonight, telling a gothic tale of innocence sacrificed\". Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' wrote that the scene \"managed to maintain a fine balance, employing a dignity and care for the experiences of victims that \\'\\'Game of Thrones\\'\\' has not always demonstrated.\"Some critics questioned why this scene in particular should generate outrage when similar scenes have not. Sara Stewart of the \\'\\'New York Post\\'\\' pointed out that the rape and sexual abuse of both female and male characters is typical for \\'\\'Game of Thrones\\'\\': \"Why are we suddenly so outraged about the rape of Sansa Stark, when this show has served up a steady diet of sexual assault and violence against women since its first season began?\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 41}, 'embedding': None, 'id': '9663511852c9cd49ae9261c1811d74d9'}>,\n",
+       "  <Document: {'content': 'Sara Stewart of the \\'\\'New York Post\\'\\' pointed out that the rape and sexual abuse of both female and male characters is typical for \\'\\'Game of Thrones\\'\\': \"Why are we suddenly so outraged about the rape of Sansa Stark, when this show has served up a steady diet of sexual assault and violence against women since its first season began?\"Cathy Young of \\'\\'Reason\\'\\' magazine, writing in \\'\\'Time\\'\\' noted what she calls a lack of complaint in response to the sexual mistreatment of male characters in earlier seasons, specifically the literal emasculation of Theon Greyjoy and the sexual assault of Gendry.\\n\\nCriticism of the scene has not extended to the quality of the acting. Joanna Robinson of \\'\\'Vanity Fair\\'\\' wrote, \"And if we can say one positive thing about that scene it\\'s that Allen nailed his performance. Theon\\'s horror mirrored our own and the camera—focusing on his reaction—let our minds fill in the blanks.\"Sophie Turner defended the scene as an artistic challenge for herself as an actor, saying, \"When I read that scene, I kinda loved it. I love the way Ramsay had Theon watching. It was all so messed up. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 42}, 'embedding': None, 'id': '2e8b973c15b469f7aca568e0e696e80c'}>,\n",
+       "  <Document: {'content': 'Sophie Turner defended the scene as an artistic challenge for herself as an actor, saying, \"When I read that scene, I kinda loved it. I love the way Ramsay had Theon watching. It was all so messed up. It\\'s also so daunting for me to do it. ... I think it\\'s going to be the most challenging season for me so far, just because it\\'s so emotional for her. It\\'s not just crying all the time, like seasons 2 or 3, it\\'s super messed up.\"Iwan Rheon (Ramsay Bolton) agreed, referring to Turner\\'s performances this season as \"absolutely amazing.\"Some viewers, including U.S. Senator Claire McCaskill, announced that they would stop watching the show because of this scene. According to \\'\\'Business Insider\\'\\', this scene and increased use of streaming services are likely reasons why ratings dropped from 6.2 million viewers for this episode to 5.4 million for the next episode, \"The Gift.\"===Recognition and awards===\\nSophie Turner has received several award nominations for her portrayal of Sansa including an Emmy nomination for Outstanding Supporting Actress in a Drama Series in 2019. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 43}, 'embedding': None, 'id': '5cd937c122eaf2594689bb4564d64942'}>,\n",
+       "  <Document: {'content': '===Recognition and awards===\\nSophie Turner has received several award nominations for her portrayal of Sansa including an Emmy nomination for Outstanding Supporting Actress in a Drama Series in 2019. For her performance in the series she earned the Glamour Award for Best UK TV Actress in 2016 and 2017, and an EWwy Award for Best Supporting Actress in a Drama Series in 2016. Other nominations include the Young Artist Award for Best Performance in a TV Series by a Supporting Young Actress in 2012, and the EWwy Award for Best Supporting Actress in a Drama Series in 2015.\\n\\n==References==\\n===Inline citations===\\n\\n===General references===\\n*\\n*\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 44}, 'embedding': None, 'id': 'bf0e06e8448aa75ce075c488dc4f3e35'}>,\n",
+       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Mother\\'s Mercy\\'\\'\\'\" is the fifth season finale episode of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 50th overall. The tenth and final episode of the fifth season, the episode was written by the series creators David Benioff and D. B. Weiss and directed by David Nutter.\\n\\n\"Mother\\'s Mercy\" garnered critical acclaim, with critics lauding Lena Headey\\'s performance, David Nutter\\'s direction and the writing of Benioff and Weiss. Particular praise was directed to Cersei Lannister\\'s walk of atonement, which involved a body double and the use of CGI. In the United States, the episode premiere achieved a viewership of 8.11 million, making it the show\\'s most watched episode, until it was surpassed by the season six finale, \"The Winds of Winter\". The episode won Emmy Awards for Writing in a Drama Series and Directing in a Drama Series for Benioff, Weiss, and Nutter.\\n\\nThis episode marks the final appearance for Stephen Dillane (Stannis Baratheon), Tara Fitzgerald (Selyse Baratheon), and Ian Beattie (Ser Meryn Trant).\\n\\n==Plot==\\n\\n===In the North===\\nThe weather conditions at Stannis\\' camp drastically improve, clearing the route towards Winterfell. Melisandre takes credit, claiming that the Lord of Light is rewarding Stannis for sacrificing Shireen. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'a4725872397d98c52ac8385fe4b1cfea'}>,\n",
+       "  <Document: {'content': '==Plot==\\n\\n===In the North===\\nThe weather conditions at Stannis\\' camp drastically improve, clearing the route towards Winterfell. Melisandre takes credit, claiming that the Lord of Light is rewarding Stannis for sacrificing Shireen. However, Stannis\\' officers inform him that Shireen\\'s sacrifice has also prompted a mass desertion of his troops, leaving him with only half his army and no cavalry, and driven Selyse to hang herself. Melisandre immediately flees to Castle Black. Despite all of these setbacks, Stannis orders his remaining troops to march forward towards Winterfell. As Stannis\\' army marches through the Wolfswood, Podrick sees them and warns Brienne, who leaves to take revenge on Stannis.\\n\\nAs Stannis prepares to besiege the castle, the Bolton cavalry, led by Ramsay, suddenly emerges. It becomes clear they greatly outnumber Stannis\\'s army; nonetheless, Stannis leads his men into battle. After a quick yet fierce battle, the Boltons emerge as the victors. Stannis survives the destruction of his army, but is severely wounded after fighting several Bolton soldiers. Brienne then arrives and announces her intentions to execute Stannis for the murder of Renly (\"The Ghost of Harrenhal\"). Stannis accepts his fate, and Brienne executes him.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'fcc519116e0df9a1b87056ba70f90f94'}>,\n",
+       "  <Document: {'content': 'Brienne then arrives and announces her intentions to execute Stannis for the murder of Renly (\"The Ghost of Harrenhal\"). Stannis accepts his fate, and Brienne executes him.\\n\\nMeanwhile, in Winterfell, Sansa escapes her room using the corkscrew she stole and lights a candle in the ruined tower to signal for help, but Brienne has already left. After witnessing the defeat of the Baratheon army, she tries to return to her room but is confronted by Myranda, who threatens to mutilate Sansa. Theon finally finds his inner strength and throws Myranda from the castle ramparts to her death, just as the victorious Bolton army returns to Winterfell. Theon and Sansa realize they have to escape at once, and jump from the castle walls into the deep snow below.\\n\\n===In Dorne===\\nJaime, Myrcella, Bronn and Trystane leave for King\\'s Landing. On the ship, Myrcella reveals that she knows Jaime is her real father and that she is glad that he is her father. However, Myrcella suddenly collapses, and it is revealed that Ellaria had secretly poisoned Myrcella by kissing her with poisoned lipstick.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '5708cc764e7470ddc59e82070e3b6f0c'}>,\n",
+       "  <Document: {'content': 'However, Myrcella suddenly collapses, and it is revealed that Ellaria had secretly poisoned Myrcella by kissing her with poisoned lipstick.\\n\\n===In Braavos===\\nArya infiltrates the brothel patronized by Trant by taking one of the faces from the House of Black and White. She stabs Trant multiple times, revealing her true identity and confronting him for the murder of Syrio Forel before cutting his throat. When she returns to the House of Black and White, she is confronted by Jaqen and the Waif. Jaqen berates Arya for taking a life not meant for her to take, and that another life must be taken in order to appease the Many Faced God. Jaqen then proceeds to drink from a vial and collapses, much to Arya\\'s shock. However, the Waif changes her face to that of Jaqen and points out to Arya that \"Jaqen\" never existed, and that she had been talking to \"No One\" the entire time. The Waif also warns Arya that putting on a new face if one is not \"No One\" is like poison. Because of this, Arya is struck blind as punishment.\\n\\n===In Meereen===\\nDaario and Jorah decide to leave Meereen to search for Daenerys. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '727e4445d1ae38eb1b462ccb6ca83869'}>,\n",
+       "  <Document: {'content': \"Because of this, Arya is struck blind as punishment.\\n\\n===In Meereen===\\nDaario and Jorah decide to leave Meereen to search for Daenerys. Tyrion wishes to join them, but Daario points out that he is better suited to govern Meereen in Daenerys' absence, assisted by Missandei and the still-recovering Grey Worm. As Tyrion watches Jorah and Daario leave and ponders his next move, Varys suddenly appears. Varys compares Meereen's current state to that of King's Landing when it was threatened by Stannis, and offers use of his spy network to aid Tyrion.\\n\\n===In the Dothraki Sea===\\nDrogon brings Daenerys back to his lair. Daenerys orders Drogon to take her back to Meereen, but Drogon, still exhausted and wounded, ignores her and goes to sleep. Daenerys wanders off on her own in search of food, eventually spotting a Dothraki horde advancing in the distance. Daenerys drops a ring to the ground before being surrounded.\\n\\n===In King's Landing===\\nCersei confesses to committing adultery with Lancel to the High Sparrow, but denies having slept with Jaime or conspiring to have Robert Baratheon killed. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '86b74581e114fea88507444b9436dcf'}>,\n",
+       "  <Document: {'content': '===In King\\'s Landing===\\nCersei confesses to committing adultery with Lancel to the High Sparrow, but denies having slept with Jaime or conspiring to have Robert Baratheon killed. The High Sparrow decides to grant the \"Mother\\'s Mercy\" and let Cersei return to the Red Keep, but says she must return to face trial for the other sins she has not confessed to, and must undergo an \"atonement\". Cersei is stripped, her hair cut short, and she is forced to walk naked the entire way from the Great Sept of Baelor to the Red Keep. As she walks, she is relentlessly harassed, jeered and pelted with garbage by the angry populace, ultimately breaking down in tears when she finally returns to the Red Keep. She is covered up by Qyburn, who reveals to her the newest member of the Kingsguard, a re-animated Gregor \"the Mountain\" Clegane.\\n\\n===At the Wall===\\nJon sends Samwell with Gilly and her baby to Oldtown to become a maester, acknowledging (and even somewhat endorsing) the romantic relationship blossoming between Samwell and Gilly. Davos arrives shortly after to ask for supplies and Wildling reinforcements, but Jon refuses, reminding Davos that the Wildlings will never fight for Stannis. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'bed59e431632e3ba9668af2b13ba60f8'}>,\n",
+       "  <Document: {'content': 'Davos arrives shortly after to ask for supplies and Wildling reinforcements, but Jon refuses, reminding Davos that the Wildlings will never fight for Stannis. Melisandre arrives soon after; her demeanour leads Jon and Davos to realize that Stannis has been defeated and that Shireen and Selyse are dead.\\n\\nAt night, Olly tells Jon that a wildling had recently seen his uncle Benjen Stark, and takes him to speak to him. Led outside to the courtyard, he only finds Thorne, Bowen Marsh, Othell Yarwyck and several other men, along with a grave-marker that reads \"Traitor\". As Jon realizes that he has been betrayed, Thorne, Marsh, Yarwyck, Olly, and the others take turns stabbing Jon, each uttering \"For the Watch\". The men leave Jon as he dies alone in the snow.\\n\\n==Production==\\n\\n===Writing===\\n\"Mother\\'s Mercy\" was written by the series\\' creators David Benioff and D. B. Weiss. It primarily covers elements from George Martin\\'s novels \\'\\'A Feast for Crows\\'\\', chapters \"Samwell I\" and \"Cat of the Canals\" and \\'\\'A Dance with Dragons\\'\\', chapters \"Jon II\", \"Theon I\", \"Daenerys X\", \"Cersei I\", \"Cersei II\" and \"Jon XIII\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'bf7fc088ba953929b61ade782a1f72d3'}>,\n",
+       "  <Document: {'content': 'It primarily covers elements from George Martin\\'s novels \\'\\'A Feast for Crows\\'\\', chapters \"Samwell I\" and \"Cat of the Canals\" and \\'\\'A Dance with Dragons\\'\\', chapters \"Jon II\", \"Theon I\", \"Daenerys X\", \"Cersei I\", \"Cersei II\" and \"Jon XIII\". It also contains material from Martin\\'s upcoming sixth novel in the series, \\'\\'The Winds of Winter\\'\\', chapter \"Mercy\".\\n\\n===Filming===\\n\"Mother\\'s Mercy\" was directed by David Nutter, He also directed the previous episode, \"The Dance of Dragons\". It was photographed by Robert McLachlan and was edited by Tim Porter, one of the show\\'s main editors, under the supervision of Nutter.\\n\\n===Cersei\\'s walk of atonement===\\nActress Lena Headey portrays Cersei Lannister in the series.\\nFor Cersei Lannister\\'s nude \"walk of atonement\" through King\\'s Landing, HBO employed actress Rebecca Van Cleave as a body double for Lena Headey. While filming the scene in Dubrovnik, Croatia in October 2014, Van Cleave performed fully nude while Headey wore a simple beige shift. Both performances were then merged by the show\\'s editing team, combining close-ups of Headey\\'s facial expressions and long shots of Van Cleave\\'s nude walk through the city. In some instances CGI was used to put Headey\\'s face on Van Cleave\\'s body.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '813c60620dd13cf09aa1de16f3b11615'}>,\n",
+       "  <Document: {'content': 'Both performances were then merged by the show\\'s editing team, combining close-ups of Headey\\'s facial expressions and long shots of Van Cleave\\'s nude walk through the city. In some instances CGI was used to put Headey\\'s face on Van Cleave\\'s body.\\n\\nHeadey says that the showrunners decided to cast a body double for \"several reasons.\"Headey says she was glad to use a body double for the scene because she wanted to focus on the character\\'s emotions. Another actor noted that Headey\\'s \"extensive tattoos\" may have been another reason. Months later, Headey announced that she was pregnant with her second child, though it is unclear if she was pregnant when this scene had to be filmed. Sarah Buchanan of \\'\\'Express\\'\\' notes the strangeness of casting a much younger actress to play the middle-aged Cersei when the key outcome of the walk of atonement in \\'\\'A Dance with Dragons\\'\\' is that when the people see that Cersei\\'s body is not beautiful, she loses a large part of her mystique. The 27-year-old Van Cleave did not show any of the specific signs of aging or past pregnancy for which the Cersei of the books was mocked by the crowd.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '4a550f5e2a1203d8d22db3212ccc68d8'}>,\n",
+       "  <Document: {'content': 'The 27-year-old Van Cleave did not show any of the specific signs of aging or past pregnancy for which the Cersei of the books was mocked by the crowd.\\n\\nWhen casting for the role, HBO received applications from more than  actresses to act as Headey\\'s body double for the scene. A selection of seven finalists, including Van Cleave, was then flown to Belfast, Northern Ireland to audition before the show\\'s producers. Headey also opted out of the casting process, but director David Nutter said finding the right double was one of the most important parts of making the finale. \"We needed somebody who could do The Walk of Shame physically, somebody who could match Lena\\'s integrity, intensity, and sensibility. We found a tremendous actress in Rebecca; she was a godsend. The courage of this girl, who\\'s never done anything like this in her life, who understood what was important about this...once I had her I felt like I could accomplish anything.\"Van Cleave herself described the scene as \"one of the scariest, most wonderful, most gratifying experiences I could have imagined ... ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'a137a6c5c80a57fcec082cd6d7b779f3'}>,\n",
+       "  <Document: {'content': 'Van Cleave herself described the scene as \"one of the scariest, most wonderful, most gratifying experiences I could have imagined ... I never in a million years would have thought I would be in Dubrovnik surrounded by hundreds of extras and crew members throwing food at me, but it was amazing\" and \"I hope the next thing I do will have my head in it.\"She also praised Headey\\'s willingness to help her, describing the two of them as a \"tag-team\"; Headey would walk behind Van Cleave during the shoot, coaching her on the character\\'s responses.\\n\\nWhen the showrunners first announced the plans of shooting a nude scene in Dubrovnik, the city\\'s Catholic Church of St. Nicholas strongly opposed the idea, due to it being immoral to walk their sacred grounds in the nude. Eventually, both parties came to the agreement that the shooting could commence, provided no nude scenes would be filmed in a place of worship. During the three-day shoot, Headey walked Van Cleave through each shot, giving insight into all the emotions Cersei was feeling. The six-minute scene was filmed using 500 extras.\\n\\n===Fate of Jon Snow===\\n\\nD.B. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'efd8bd56faee8305a0b915e230d51ac8'}>,\n",
+       "  <Document: {'content': 'During the three-day shoot, Headey walked Van Cleave through each shot, giving insight into all the emotions Cersei was feeling. The six-minute scene was filmed using 500 extras.\\n\\n===Fate of Jon Snow===\\n\\nD.B. Weiss discussed the decision to kill Jon Snow by saying that he felt the visual medium did not give him the freedom to leave Jon\\'s fate open: \"In a book, you can present that kind of ambiguity. In a show, everybody sees it for what it is. It’s that rule: \\'If we don\\'t see the body then they’re not really dead.\\'Like when we cut Ned’s head off, we didn’t want a gory Monty Python geyser of blood, but we needed to see the blade enter his neck and cut out on the frame where the blade was mid-neck. ... we needed Ned\\'s death to be totally unambiguous.\"Actor Kit Harington said of the scene, \"I loved it. I loved how they brought Olly in to be the person who kills me. I love how the storyline with Thorne was wrapped up.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'adbc8679d21fad07b830618ee5894073'}>,\n",
+       "  <Document: {'content': 'I loved how they brought Olly in to be the person who kills me. I love how the storyline with Thorne was wrapped up.\"Although Weiss meant the scene to be unambiguous, the episode left many viewers uncertain as to whether Jon Snow had been killed or merely injured (his fate in the novels is left unconfirmed as of \\'\\'A Dance with Dragons\\'\\', with much debate about Martin\\'s future intentions). Some of the confusion may be because, as reviewers have noted, Jon was killed in what can be seen as the middle of his story arc and his death does not have an obvious narrative purpose. For example, Nate Jones of \\'\\'Vulture\\'\\' notes, \"It\\'s easy to see what other characters\\' deaths meant for the series\\' sprawling narrative: Ned\\'s execution sent the Stark kids adrift in a universe where there was nobody looking out for them, while Robb\\'s murder was the final death knell for the hopes that the saga would ever have a traditional \\'happy\\' ending. What would be accomplished, narratively, by getting rid of Jon permanently right now?\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'c6fcb975e9ecc5aac9e316adfcbe4a8e'}>,\n",
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+       "  'data/build_a_scalable_question_answering_system/31.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/25.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/19.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/118.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/130.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/124.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/125.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/131.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/119.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/18.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/24.txt',\n",
+       "  'data/build_a_scalable_question_answering_system/30.txt'],\n",
+       " 'node_id': 'DocumentStore'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "files_to_index = [doc_dir + \"/\" + f for f in os.listdir(doc_dir)]\n",
+    "indexing_pipeline.run_batch(file_paths=files_to_index)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The code in this tutorial uses Game of Thrones data, but you can also supply your own `.txt` files and index them in the same way.\n",
+    "\n",
+    "As an alternative, you can cast you text data into [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore using [`DocumentStore.write_documents()`](https://docs.haystack.deepset.ai/reference/document-store-api#basedocumentstorewrite_documents)."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the Documents are in the DocumentStore, let's initialize the nodes we want to use in our query pipeline."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the Retriever\n",
+    "\n",
+    "Our query pipeline is going to use a Retriever, so we need to initialize it. A Retriever sifts through all the Documents and returns only those that are relevant to the question. This tutorial uses the BM25Retriever. This is the recommended Retriever for a question answering system like the one we're creating. For more Retriever options, see [Retriever](https://docs.haystack.deepset.ai/docs/retriever)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import BM25Retriever\n",
+    "\n",
+    "retriever = BM25Retriever(document_store=document_store)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The BM25Retriever is initialized and ready for the pipeline."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the Reader\n",
+    "\n",
+    "Our query pipeline also needs a Reader, so we'll initialize it next. A Reader scans the texts it received from the Retriever and extracts the top answer candidates. Readers are based on powerful deep learning models but are much slower than Retrievers at processing the same amount of text. This tutorials uses a FARMReader with a base-sized RoBERTa question answering model called [`deepset/roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2). It's a good all-round model to start with. To find a model that's best for your use case, see [Models](https://docs.haystack.deepset.ai/docs/reader#models)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import FARMReader\n",
+    "\n",
+    "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the Retriever-Reader Pipeline\n",
+    "\n",
+    "You can combine the Reader and Retriever in a querying pipeline using the `Pipeline` class. The combination of the two speeds up processing because the Reader only processes the Documents that it received from the Retriever. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize the `Pipeline` object and add the Retriever and Reader as nodes. You should provide the `name` or `name`s of preceding nodes as the input argument. Note that in a querying pipeline, the input to the first node is `Query`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "\n",
+    "querying_pipeline = Pipeline()\n",
+    "querying_pipeline.add_node(component=retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
+    "querying_pipeline.add_node(component=reader, name=\"Reader\", inputs=[\"Retriever\"])\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's it! Your pipeline's ready to answer your questions!"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asking a Question\n",
+    "\n",
+    "1. Use the pipeline's `run()` method to ask a question. The query argument is where you type your question. Additionally, you can set the number of documents you want the Reader and Retriever to return using the `top-k` parameter. To learn more about setting arguments, see [Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments). To understand the importance of the `top-k` parameter, see [Choosing the Right top-k Values](https://docs.haystack.deepset.ai/docs/optimization#choosing-the-right-top-k-values).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Inferencing Samples: 100%|██████████| 1/1 [00:23<00:00, 23.77s/ Batches]\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = querying_pipeline.run(\n",
+    "    query=\"Who is the father of Arya Stark?\",\n",
+    "    params={\n",
+    "        \"Retriever\": {\"top_k\": 10},\n",
+    "        \"Reader\": {\"top_k\": 5}\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are some questions you could try out:\n",
+    "- Who is the father of Arya Stark?\n",
+    "- Who created the Dothraki vocabulary?\n",
+    "- Who is the sister of Sansa?"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Print out the answers the pipeline returns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'answers': [<Answer {'answer': 'Eddard', 'type': 'extractive', 'score': 0.9933727979660034, 'context': \"s Nymeria after a legendary warrior queen. She travels with her father, Eddard, to King's Landing when he is made Hand of the King. Before she leaves,\", 'offsets_in_document': [{'start': 207, 'end': 213}], 'offsets_in_context': [{'start': 72, 'end': 78}], 'document_id': '9e3c863097d66aeed9992e0b6bf1f2f4', 'meta': {'_split_id': 4}}>,\n",
+      "             <Answer {'answer': 'Ned', 'type': 'extractive', 'score': 0.975361168384552, 'context': \"k in the television series.\\n\\n====Season 1====\\nArya accompanies her father Ned and her sister Sansa to King's Landing. Before their departure, Arya's h\", 'offsets_in_document': [{'start': 630, 'end': 633}], 'offsets_in_context': [{'start': 74, 'end': 77}], 'document_id': '7d3360fa29130e69ea6b2ba5c5a8f9c8', 'meta': {'_split_id': 13}}>,\n",
+      "             <Answer {'answer': 'Lord Eddard Stark', 'type': 'extractive', 'score': 0.9566042423248291, 'context': 'rk daughters.\\n\\nDuring the Tourney of the Hand to honour her father Lord Eddard Stark, Sansa Stark is enchanted by the knights performing in the event.', 'offsets_in_document': [{'start': 804, 'end': 821}], 'offsets_in_context': [{'start': 67, 'end': 84}], 'document_id': 'a80ae4f1d1187bde6a34a29aeeb0837d', 'meta': {'_split_id': 3}}>,\n",
+      "             <Answer {'answer': 'Joffrey', 'type': 'extractive', 'score': 0.6413302421569824, 'context': \"Mycah, sparring in the woods with broomsticks.  Arya defends Mycah from Joffrey's torments and her direwolf Nymeria helps Arya fight off Joffrey, woun\", 'offsets_in_document': [{'start': 634, 'end': 641}], 'offsets_in_context': [{'start': 72, 'end': 79}], 'document_id': '9e3c863097d66aeed9992e0b6bf1f2f4', 'meta': {'_split_id': 4}}>,\n",
+      "             <Answer {'answer': 'King Robert', 'type': 'extractive', 'score': 0.5891151428222656, 'context': \"en refuses to yield Gendry, who is actually a bastard son of the late King Robert, to the Lannisters.  The Night's Watch convoy is overrun and massacr\", 'offsets_in_document': [{'start': 820, 'end': 831}], 'offsets_in_context': [{'start': 70, 'end': 81}], 'document_id': '87e8469dcf7354fd2a25fbd2ba07c543', 'meta': {'_split_id': 5}}>],\n",
+      " 'documents': [<Document: {'content': '== Storylines ==\\n=== Novels ===\\n==== \\'\\'A Game of Thrones\\'\\' ====\\nCoat of arms of House Stark\\n\\nArya adopts a direwolf cub, which she names Nymeria after a legendary warrior queen. She travels with her father, Eddard, to King\\'s Landing when he is made Hand of the King. Before she leaves, her half-brother Jon Snow has a smallsword made for her as a parting gift, which she names \"Needle\" after her least favorite ladylike activity.\\n\\nWhile taking a walk together, Prince Joffrey and her sister Sansa happen upon Arya and her friend, the low-born butcher apprentice Mycah, sparring in the woods with broomsticks.  Arya defends Mycah from Joffrey\\'s torments and her direwolf Nymeria helps Arya fight off Joffrey, wounding his arm in the process.  Knowing that Nymeria will likely be killed in retribution, Arya chases her wolf away; but Sansa\\'s direwolf Lady is killed in Nymeria\\'s stead and Mycah is hunted down and killed by Sandor Clegane, Joffrey\\'s bodyguard.\\n\\nIn King\\'s Landing, her father discovers Arya\\'s possession of Needle, but instead of confiscating it he arranges for fencing lessons under the Braavosi swordmaster Syrio Forel, who teaches her the style of fighting known as \"water dancing\".  ', 'content_type': 'text', 'score': 0.7704076817912666, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '9e3c863097d66aeed9992e0b6bf1f2f4'}>,\n",
+      "               <Document: {'content': \"She was also the future bride of Prince Joffrey, and thus the future Queen of the Seven Kingdoms as well. She names her direwolf Lady; she is the smallest of the pack and the first to die, sentenced to death by Cersei after Arya's direwolf, Nymeria, bit a violent Joffrey.\\n\\n===Arya Stark===\\nMaisie Williams\\n\\n'''Arya Stark''' portrayed by Maisie Williams. Arya Stark of House Stark is the younger daughter and third child of Lord Eddard and Catelyn Stark of Winterfell. Ever the tomboy, Arya would rather be training to use weapons than sewing with a needle. She names her direwolf Nymeria, after a legendary warrior queen.\\n\\n===Robb Stark===\\nRichard Madden\\n\\n'''Robb Stark''' (seasons 1–3) portrayed by Richard Madden. Robb Stark of House Stark is the eldest son of Eddard and Catelyn Stark and the heir to Winterfell. His dire wolf is called Grey Wind. Robb becomes involved in the war against the Lannisters after his father, Ned Stark, is arrested for treason. Robb summons his bannermen for war against House Lannister and marches to the Riverlands. Eventually, crossing the river at the Twins becomes strategically necessary. \", 'content_type': 'text', 'score': 0.7546451298652186, 'meta': {'_split_id': 22}, 'embedding': None, 'id': '726b0655306246943c29202b25bd4733'}>,\n",
+      "               <Document: {'content': 'When Rafford begs for a healer, Arya cuts his throat in the same fashion as he had killed Lommy and throws his corpse into a canal.  She heads back to perform the play, knowing this murder will most likely ruin her Mercedene identity.\\n\\n=== Family tree of House Stark ===\\n\\n=== Television series ===\\n\\nArya Stark is portrayed by English actress Maisie Williams in the television adaption of the book series, this being Williams\\' first role as an actress. Williams was chosen from among 300 actresses across England.\\nMaisie Williams plays the role of Arya Stark in the television series.\\n\\n====Season 1====\\nArya accompanies her father Ned and her sister Sansa to King\\'s Landing. Before their departure, Arya\\'s half-brother Jon Snow gifts Arya a sword which she dubs \"Needle\". On the Kingsroad, Arya is sparring with a butcher\\'s boy, Mycah, when Sansa\\'s betrothed Prince Joffrey Baratheon attacks Mycah, prompting Arya\\'s direwolf Nymeria to bite Joffrey. Arya shoos Nymeria away so she is not killed, but is furious when Sansa later refuses to support her version of events. Mycah is later killed by Joffrey\\'s bodyguard Sandor \"The Hound\" Clegane, earning him Arya\\'s hatred. ', 'content_type': 'text', 'score': 0.7499777030244019, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '7d3360fa29130e69ea6b2ba5c5a8f9c8'}>,\n",
+      "               <Document: {'content': '* \\'\\'\\'Lothar Frey\\'\\'\\' (seasons 3, 6) portrayed by Tom Brooke in season 3, and by Daniel Tuite in season 6. One of Lord Walder Frey\\'s many sons, nicknamed “Lame Lothar” because of his twisted leg. He and his half-brother Black Walder are sent by their father to Riverrun to propose a marriage between Lord Edmure Tully and Roslin Frey as terms for House Frey rejoining Robb Stark\\'s campaign against the Lannisters. He is one of the first to commence the \"Red Wedding\", stabbing Talisa Stark in the womb several times and killing her and her unborn child. In the sixth season, he is ordered by Walder to retake Riverrun from Brynden Tully. Though they succeed with Lannister help, he is killed by Arya Stark, who subsequently bakes him into a pie.\\n* \\'\\'\\'Black Walder Rivers\\'\\'\\' (seasons 3, 6) portrayed by Tim Plester. One of Lord Walder Frey\\'s many bastard sons, nicknamed “Black Walder” for his dark demeanor. He and his half-brother Lame Lothar are sent by their father to Riverrun to propose a marriage between Lord Edmure Tully and Roslin Frey as terms for House Frey rejoining Robb Stark\\'s campaign against the Lannister. ', 'content_type': 'text', 'score': 0.7489998506563029, 'meta': {'_split_id': 76}, 'embedding': None, 'id': 'ef7ad089df4cc85d00e671a3f32153a4'}>,\n",
+      "               <Document: {'content': 'She is the most beautiful woman in Westeros at the time of the events of \"A Song of Ice and Fire\".\\n\\n==Storylines==\\nCoat of arms of House Stark\\n\\n===\\'\\'A Game of Thrones\\'\\'===\\n\\nSansa Stark begins the novel by being betrothed to Crown Prince Joffrey Baratheon, believing Joffrey to be a gallant prince. While Joffrey and Sansa are walking through the woods, Joffrey notices Arya sparring with the butcher\\'s boy, Mycah. A fight breaks out and Joffrey is attacked by Nymeria (Arya\\'s direwolf) after Joffrey threatens to hurt Arya. Sansa lies to King Robert about the circumstances of the fight in order to protect both Joffrey and her sister Arya.  Since Arya ran off with her wolf to save it, Sansa\\'s wolf is killed instead, estranging the Stark daughters.\\n\\nDuring the Tourney of the Hand to honour her father Lord Eddard Stark, Sansa Stark is enchanted by the knights performing in the event.  At the request of his mother, Queen Cersei Lannister, Joffrey spends a portion of the tourney with Sansa, but near the end he commands his guard Sandor Clegane, better known as The Hound, to take her back to her quarters. ', 'content_type': 'text', 'score': 0.7477537651604484, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'a80ae4f1d1187bde6a34a29aeeb0837d'}>,\n",
+      "               <Document: {'content': 'In King\\'s Landing, her father discovers Arya\\'s possession of Needle, but instead of confiscating it he arranges for fencing lessons under the Braavosi swordmaster Syrio Forel, who teaches her the style of fighting known as \"water dancing\".  After her father\\'s arrest, Syrio is killed protecting her and Arya narrowly escapes capture.  She later witnesses the public execution of her father before falling under the protection of the Night\\'s Watch recruiter Yoren.\\n\\n==== \\'\\'A Clash of Kings\\'\\' ====\\nArya escapes King\\'s Landing with Yoren and his party of recruits; and on the road, she clashes with the other Night\\'s Watch child recruits Lommy, Gendry, and Hot Pie but eventually befriends them. On the way, the party is attacked by Amory Lorch when Yoren refuses to yield Gendry, who is actually a bastard son of the late King Robert, to the Lannisters.  The Night\\'s Watch convoy is overrun and massacred, but Arya and the other children escape through a tunnel.  Before escaping, she rescues three prisoners locked in a wagon cage, among them a mysterious man named Jaqen H\\'ghar.\\n\\nArya and her friends are later captured by Ser Gregor Clegane and taken to Harrenhal as slave laborers.  ', 'content_type': 'text', 'score': 0.7445102113414457, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '87e8469dcf7354fd2a25fbd2ba07c543'}>,\n",
+      "               <Document: {'content': 'Tyrion is consigned to the Eyrie\\'s \"sky cells\" while Lysa prepares to pass judgment on him as an accomplice in her husband\\'s murder.\\n\\n===In the North===\\nAt Winterfell, Theon Greyjoy grows jealous of Tyrion after his favorite prostitute Ros taunts him. To take Bran\\'s mind off his paralysis and his mother\\'s departure, Maester Luwin teaches him the Dothraki art of horseback archery.\\n\\n===In King\\'s Landing===\\nAfter Ned Stark convinces King Robert not to join the tourney, the crowd watches the fearsome Ser Gregor \"The Mountain\" Clegane joust with Ser Loras Tyrell, the \"Knight of Flowers\", who wins by riding a mare in heat, distracting Clegane\\'s stallion. Clegane beheads his horse and attempts to kill Loras, but Sandor \"The Hound\" Clegane intervenes.\\n\\nVarys reveals to Ned that Jon Arryn was killed by a poison called the \"Tears of Lys\", and suggests that Arryn\\'s slain squire Ser Hugh of the Vale was the poisoner.\\n\\nIn training, Arya chases a cat through the Red Keep and overhears a conversation between Varys and Illyrio, who appear to be plotting against the throne. Arya tries to warn her father but is unable to identify the plotters. ', 'content_type': 'text', 'score': 0.7395308845606815, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '1d850d17893da5959cd677e4d82c144'}>,\n",
+      "               <Document: {'content': '\"Asha\" is portrayed as a fairly provocative and independent woman, a captain of thirty ships, as opposed to her television counterpart Yara, who did not retain \"Asha\\'s\" traits, although her rivalry with Theon remained intact. Their father Balon Greyjoy was played by Patrick Malahide. Many of the characters involved in the Greyjoys\\' storyline weren\\'t introduced, most notably Balon\\'s brother Aeron Greyjoy. Nonetheless, the storyline received enormous praise, with the alteration of Yara\\'s name and persona being the only criticism.\\n\\nTom Wlaschiha is cast as Jaqen H\\'ghar, a mysterious prisoner who develops a murderous relationship with young Arya Stark (Maisie Williams). Wlaschiha\\'s pronunciation of his character\\'s name, \\'\\'Jack-in\\'\\', was adopted for use in the show. Natalie Dormer, best known for her portrayal as seductive Anne Boleyn in Showtime\\'s \\'\\'The Tudors\\'\\', was cast in a similar role as Margaery Tyrell, a noblewoman and the wife of the third claimant to the throne, Renly Baratheon. Gwendoline Christie played, to much praise, Brienne of Tarth, a female warrior who joins Renly Baratheon\\'s guard, but later becomes a follower of Catelyn Stark. To prepare for the role, Christie took up an intense training regimen, adding over a stone (6.4\\xa0kg) of muscle mass. ', 'content_type': 'text', 'score': 0.7392356266647196, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '937a3c260025a30112a7a29da8d1d242'}>,\n",
+      "               <Document: {'content': \"===On the Iron Islands===\\nReturning to his homeland, Theon Greyjoy tries to seduce a young woman, Yara. At Pyke, Theon presents his father Balon with Robb's offer that will make Balon King of the Iron Islands. Balon refuses, wishing to take his crown with Yara, revealed to be Theon's sister, at the helm of his fleet. Theon realises Balon's intention is to take the North for himself.\\n\\n===In the Red Waste===\\nRakharo's horse returns to Daenerys Targaryen's camp carrying his severed head, which Ser Jorah Mormont explains is a message from another khal, and Daenerys vows revenge.\\n\\n===On the Kingsroad===\\nCity Watchmen search the caravan for Gendry but are turned away by Yoren. Gendry tells Arya Stark that he knows she is a girl, and she reveals she is actually Arya Stark after learning that her father met Gendry before he was executed.\\n\\n===At Craster's Keep===\\nSamwell Tarly asks Jon Snow about taking Gilly, one of Craster's daughter-wives, with them but Jon refuses. Gilly is pregnant, and Jon wonders what happens to Craster's sons. That night, Jon follows Craster taking a newborn child into the woods, and sees a White Walker retrieve the baby, but Craster knocks Jon unconscious.\\n\\n\", 'content_type': 'text', 'score': 0.7388862068321547, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '8a495456ded3ab7e15cd66c6d3ca82ad'}>,\n",
+      "               <Document: {'content': \"Decades before the events of ''A Game of Thrones'', he was sent to the Citadel in Oldtown on the orders of his grandfather Daeron II, who felt there were too many Targaryens, and became a Maester (one of an order of scholars and healers). He was later offered the throne of the Seven Kingdoms after the death of his father Maekar, but ceded the rule to Aegon and joined the Night's Watch. By ''A Game of Thrones'', he is elderly and blind, but provides guidance to the men of the Watch. In ''A Feast for Crows'', Jon Snow sends him to the Citadel by sea, but Maester Aemon dies on the voyage between Braavos and Oldtown.\\n\\nIn the HBO television adaptation, he is portrayed by Peter Vaughan.\\n\\n=== Yoren ===\\nYoren is a recruiter of the Night's Watch. In ''A Game of Thrones'' Yoren travels with Tyrion Lannister from the Wall to King's Landing, and is present when Tyrion is arrested by Catelyn Stark. He then races to King's Landing to inform Eddard Stark. During Lord Eddard's execution, he finds Arya Stark and shields her from seeing her father's death. \", 'content_type': 'text', 'score': 0.7351640317180352, 'meta': {'_split_id': 91}, 'embedding': None, 'id': '1a134f1ce82ea118cb873dfd9ba090e2'}>],\n",
+      " 'no_ans_gap': 11.405375480651855,\n",
+      " 'node_id': 'Reader',\n",
+      " 'params': {'Reader': {'top_k': 5}, 'Retriever': {'top_k': 10}},\n",
+      " 'query': 'Who is the father of Arya Stark?',\n",
+      " 'root_node': 'Query'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "pprint(prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. Simplify the printed answers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.utils import print_answers\n",
+    "\n",
+    "print_answers(\n",
+    "    prediction,\n",
+    "    details=\"minimum\" ## Choose from `minimum`, `medium` and `all`\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And there you have it! Congratulations on building a scalable machine learning based question answering system!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# Next Steps\n",
+    "\n",
+    "To learn how to improve the performance of the Reader, see [Fine-Tune a Reader](https://haystack.deepset.ai/tutorials/03_fine_tune_a_reader)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## About us\n",
+    "\n",
+    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
+    "\n",
+    "We bring NLP to the industry via open source!  \n",
+    "Our focus: Industry specific language models & large scale QA systems.  \n",
+    "  \n",
+    "Some of our other work: \n",
+    "- [German BERT](https://deepset.ai/german-bert)\n",
+    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
+    "\n",
+    "Get in touch:\n",
+    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
+    "\n",
+    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3fd1520721e0740ccf655b87fc1a15949508d880e32fa0665a9483d3c4c5735c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 6bedcc80f2e1b10b9c457b708b1cbc1fda0c2814 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Wed, 11 Jan 2023 16:29:08 +0100
Subject: [PATCH 027/206] the url for the next tutorial was wrong (#100)

* the url for the next tutorial was wrong

* fixing code block
---
 markdowns/01_Basic_QA_Pipeline.md    |  7 ++-----
 tutorials/01_Basic_QA_Pipeline.ipynb | 12 +++++-------
 2 files changed, 7 insertions(+), 12 deletions(-)

diff --git a/markdowns/01_Basic_QA_Pipeline.md b/markdowns/01_Basic_QA_Pipeline.md
index c3407da6..dc359eb0 100644
--- a/markdowns/01_Basic_QA_Pipeline.md
+++ b/markdowns/01_Basic_QA_Pipeline.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb
 toc: True
 title: "Build Your First Question Answering System"
-last_updated: 2023-01-02
+last_updated: 2023-01-11
 level: "beginner"
 weight: 10
 description: Get Started by creating a Retriever Reader pipeline.
@@ -14,7 +14,7 @@ download: "/downloads/01_Basic_QA_Pipeline.ipynb"
     
 
 
-> We've modified this first tutorial to make it simpler to start with. If you're looking for a Question Answering tutorial that uses a DocumentStore such as Elasticsearch, go to our new [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_Scalable_QA_System) tutorial.
+> We've modified this first tutorial to make it simpler to start with. If you're looking for a Question Answering tutorial that uses a DocumentStore such as Elasticsearch, go to our new [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_scalable_qa_system) tutorial.
 
 - **Level**: Beginner
 - **Time to complete**: 15 minutes
@@ -47,10 +47,7 @@ pip install --upgrade pip
 pip install farm-haystack[colab]
 ```
 
-
-```python
 Set the logging level to INFO:
-```
 
 
 ```python
diff --git a/tutorials/01_Basic_QA_Pipeline.ipynb b/tutorials/01_Basic_QA_Pipeline.ipynb
index 1f3261c4..396910dd 100644
--- a/tutorials/01_Basic_QA_Pipeline.ipynb
+++ b/tutorials/01_Basic_QA_Pipeline.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Tutorial: Build Your First Question Answering System\n",
     "\n",
-    "> We've modified this first tutorial to make it simpler to start with. If you're looking for a Question Answering tutorial that uses a DocumentStore such as Elasticsearch, go to our new [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_Scalable_QA_System) tutorial.\n",
+    "> We've modified this first tutorial to make it simpler to start with. If you're looking for a Question Answering tutorial that uses a DocumentStore such as Elasticsearch, go to our new [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_scalable_qa_system) tutorial.\n",
     "\n",
     "- **Level**: Beginner\n",
     "- **Time to complete**: 15 minutes\n",
@@ -63,10 +63,8 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
     "Set the logging level to INFO:"
    ]
@@ -383,7 +381,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.8.12 ('haystack_py38')",
    "language": "python",
    "name": "python3"
   },
@@ -397,11 +395,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]"
+   "version": "3.8.12"
   },
   "vscode": {
    "interpreter": {
-    "hash": "3fd1520721e0740ccf655b87fc1a15949508d880e32fa0665a9483d3c4c5735c"
+    "hash": "85ea2c107d7945555de8e73270cf8a4d668bafec7aac344fa62e3415dc7bf5ec"
    }
   }
  },

From 6859c614e99a82c6973420cc846382c572625380 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Thu, 12 Jan 2023 11:08:17 +0100
Subject: [PATCH 028/206] fixing next steps url (#101)

---
 markdowns/03_Scalable_QA_System.md    |    4 +-
 tutorials/03_Scalable_QA_System.ipynb | 1249 +------------------------
 2 files changed, 10 insertions(+), 1243 deletions(-)

diff --git a/markdowns/03_Scalable_QA_System.md b/markdowns/03_Scalable_QA_System.md
index 1e307c6c..93a37435 100644
--- a/markdowns/03_Scalable_QA_System.md
+++ b/markdowns/03_Scalable_QA_System.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb
 toc: True
 title: "Build a Scalable Question Answering System"
-last_updated: 2023-01-02
+last_updated: 2023-01-12
 level: "beginner"
 weight: 15
 description: Create a scalable Retriever Reader pipeline that uses an ElasticsearchDocumentStore.
@@ -277,7 +277,7 @@ And there you have it! Congratulations on building a scalable machine learning b
 
 # Next Steps
 
-To learn how to improve the performance of the Reader, see [Fine-Tune a Reader](https://haystack.deepset.ai/tutorials/03_fine_tune_a_reader).
+To learn how to improve the performance of the Reader, see [Fine-Tune a Reader](https://haystack.deepset.ai/tutorials/02_finetune_a_model_on_your_data).
 
 ## About us
 
diff --git a/tutorials/03_Scalable_QA_System.ipynb b/tutorials/03_Scalable_QA_System.ipynb
index a10f5b8b..fa85d74a 100644
--- a/tutorials/03_Scalable_QA_System.ipynb
+++ b/tutorials/03_Scalable_QA_System.ipynb
@@ -305,1215 +305,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Converting files: 100%|██████████| 183/183 [00:00<00:00, 708.99it/s]\n",
-      "Preprocessing:   0%|          | 0/183 [00:00<?, ?docs/s]WARNING:haystack.nodes.preprocessor.preprocessor:We found one or more sentences whose word count is higher than the split length.\n",
-      "Preprocessing: 100%|██████████| 183/183 [00:00<00:00, 419.58docs/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'documents': [<Document: {'content': '\\n\\n\\'\\'\\'\\'\\'A Game of Thrones\\'\\'\\'\\'\\' is the first novel in \\'\\'A Song of Ice and Fire\\'\\', a series of fantasy novels by the American author George R. R. Martin.  It was first published on August 1, 1996.  The novel won the 1997 Locus Award and was nominated for both the 1997 Nebula Award and the 1997 World Fantasy Award. The novella \\'\\'Blood of the Dragon\\'\\', comprising the Daenerys Targaryen chapters from the novel, won the 1997 Hugo Award for Best Novella. In January 2011, the novel became a \\'\\'New York Times\\'\\' Bestseller and reached #1 on the list in July 2011.\\n\\nIn the novel, recounting events from various points of view,  Martin introduces the plot-lines of the noble houses of Westeros, the Wall, and the Targaryens. The novel has inspired several spin-off works, including several games. It is also the namesake and basis for the first season of \\'\\'Game of Thrones\\'\\', an HBO television series that premiered in April 2011. A March 2013 paperback TV tie-in re-edition was also titled \\'\\'Game of Thrones\\'\\', excluding the indefinite article \"A\".\\n\\n==Plot==\\n\\n\\'\\'A Game of Thrones\\'\\' follows three principal storylines simultaneously.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '6ad052f1c1a94fdddf96cc27be2fba13'}>,\n",
-       "  <Document: {'content': 'A March 2013 paperback TV tie-in re-edition was also titled \\'\\'Game of Thrones\\'\\', excluding the indefinite article \"A\".\\n\\n==Plot==\\n\\n\\'\\'A Game of Thrones\\'\\' follows three principal storylines simultaneously.\\n\\n===In the Seven Kingdoms===\\nAt the beginning of the story, Eddard \"Ned\" Stark, the Warden of the North in the Seven Kingdoms, executes a deserter from the Night\\'s Watch, the military order that guards the Wall on the Seven Kingdoms\\' northern border. On the way home to his castle Winterfell, he discovers six orphaned direwolf pups, which are adopted by his six children. Upon the death of Lord Jon Arryn, the principal advisor to King Robert Baratheon, Robert recruits Ned to replace Arryn as \"Hand of the King\", and betroth Ned\\'s daughter Sansa to Robert\\'s son Joffrey. Ned accepts the position when he learns that Arryn\\'s widow Lysa believes Queen Cersei Lannister and her family poisoned Arryn. Shortly thereafter, Ned\\'s son Bran discovers Cersei having sex with her twin brother Jaime Lannister, who throws Bran from the tower to conceal their affair, paralyzing his legs.\\n\\nNed and his daughters Sansa and Arya depart for the capital city, King\\'s Landing. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '5a135d20e69aa364c65e8704c2a54dfc'}>,\n",
-       "  <Document: {'content': 'Shortly thereafter, Ned\\'s son Bran discovers Cersei having sex with her twin brother Jaime Lannister, who throws Bran from the tower to conceal their affair, paralyzing his legs.\\n\\nNed and his daughters Sansa and Arya depart for the capital city, King\\'s Landing. During the journey south, an altercation between Arya and Joffrey leads to Arya\\'s direwolf being driven away and Sansa\\'s direwolf being executed to appease the Lannisters. Upon arriving in King\\'s Landing to take his post as Hand, Ned finds that Robert is an ineffective king whose only interests are hunting, drinking and womanizing, leaving his Small Council to govern the realm.\\n\\nAt Winterfell, an assassin attempts to kill Bran, and Ned\\'s wife Catelyn travels to King\\'s Landing to bring word to Ned. Catelyn\\'s childhood friend, Petyr \"Littlefinger\" Baelish, implicates Tyrion Lannister, the dwarf brother of Cersei and Jaime, in the assassination attempt. En route back to Winterfell, Catelyn encounters Tyrion by chance, arrests him, and takes him to stand trial for the attempt on Bran\\'s life. In retaliation for Tyrion\\'s abduction, his father Lord Tywin Lannister sends soldiers to raid the Riverlands, Catelyn\\'s home region. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '94c21b1014caac9012ccdb53de3479f3'}>,\n",
-       "  <Document: {'content': \"En route back to Winterfell, Catelyn encounters Tyrion by chance, arrests him, and takes him to stand trial for the attempt on Bran's life. In retaliation for Tyrion's abduction, his father Lord Tywin Lannister sends soldiers to raid the Riverlands, Catelyn's home region. Tyrion regains his freedom by recruiting a mercenary named Bronn to defend him in trial by combat.\\n\\nNed investigates Jon Arryn's death and eventually discovers that Robert's legal heirs, including Joffrey, are in fact Cersei's children by Jaime, and that Jon Arryn was killed to conceal his discovery of their incest. Ned offers Cersei a chance to flee before he informs Robert, but she uses this chance to arrange Robert's death in a hunting accident and install Joffrey on the throne.  Ned enlists Littlefinger's help to challenge Joffrey's claim; but Littlefinger betrays him, resulting in Ned's arrest. Arya escapes the castle, but Sansa is taken hostage by the Lannisters.\\n\\nNed's eldest son Robb marches his army south in response to his father's arrest, and in order to relieve the threat on the Riverlands. To secure a strategically necessary bridge crossing, Catelyn negotiates a marital alliance between Robb and the notoriously unreliable House Frey. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '31938ace0bef496e0b2a764fbe5e9316'}>,\n",
-       "  <Document: {'content': 'To secure a strategically necessary bridge crossing, Catelyn negotiates a marital alliance between Robb and the notoriously unreliable House Frey. Robb defeats a Lannister army in the Riverlands, capturing Jaime. Tywin sends Tyrion back to King\\'s Landing to act as Hand of the King to Joffrey. When Ned is executed, Robb\\'s followers declare the North\\'s independence from the Seven Kingdoms, proclaiming Robb \"King in the North\".\\n\\n===On the Wall ===\\nThe prologue of the novel introduces the Wall: an ancient barrier of stone, ice, and magic, hundreds of feet high and hundreds of miles long, shielding the Seven Kingdoms from the northern wilderness. The Wall is manned by the Night\\'s Watch: an order of warriors sworn to serve there for life, defending the realm from the fabled Others, an ancient and hostile inhuman race, as well as from the human \"wildlings\" who live north of the Wall.\\n\\nJon Snow, Ned’s bastard son, is inspired by his uncle, Benjen Stark, to join the Night\\'s Watch, but becomes disillusioned when he discovers that its primary function is as a penal colony. Jon unites his fellow recruits against their harsh instructor and protects the cowardly but good-natured and intelligent Samwell Tarly. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '6d4dbfa626d0497853a1c10298824920'}>,\n",
-       "  <Document: {'content': 'Jon Snow, Ned’s bastard son, is inspired by his uncle, Benjen Stark, to join the Night\\'s Watch, but becomes disillusioned when he discovers that its primary function is as a penal colony. Jon unites his fellow recruits against their harsh instructor and protects the cowardly but good-natured and intelligent Samwell Tarly. Jon is appointed steward to the leader of the Watch, Lord Commander Jeor Mormont, making him a potential successor to Mormont. Benjen fails to return from an expedition north of the Wall. Six months later, the dead bodies of two men from his party are recovered; these re-animate as undead wights before being dispatched by Jon.\\n\\nWhen word of his father\\'s execution reaches Jon, he attempts to join Robb against the Lannisters, but is persuaded to remain loyal to the Watch. Mormont then declares his intention to march north to find Benjen, dead or alive, and to investigate rumors of a \"King-Beyond-the-Wall\" uniting the wildlings.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'a1bc114deb532c8199d2d03120b07fb4'}>,\n",
-       "  <Document: {'content': 'Mormont then declares his intention to march north to find Benjen, dead or alive, and to investigate rumors of a \"King-Beyond-the-Wall\" uniting the wildlings.\\n\\n===Across the narrow sea===\\nAcross the sea to the east of Westeros, the exiled prince Viserys Targaryen, son of the late \"mad king\" and rightful heir to House Targaryen, the royal house of Westeros before it was overthrown by Robert Baratheon, betroths his 13-year-old sister Daenerys to Khal Drogo, a warlord of the nomadic Dothraki, in exchange for the use of Drogo\\'s army to reclaim the throne of Westeros. Illyrio, a wealthy merchant who has been supporting the penniless Targaryens, gives Daenerys three petrified dragon eggs as a wedding gift. Jorah Mormont, a knight exiled from Westeros, joins Viserys as an adviser. Initially terrified of her new husband and his people, Daenerys eventually embraces her role as Drogo\\'s queen. Drogo, however, shows little interest in conquering Westeros before the birth of his child with Daenerys, and an impatient Viserys tries to browbeat his sister into coercing Drogo. When Viserys publicly threatens Daenerys, Drogo executes him by pouring molten gold on his head.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'a447290ba61e322277ffbdbddc315bd5'}>,\n",
-       "  <Document: {'content': \"Drogo, however, shows little interest in conquering Westeros before the birth of his child with Daenerys, and an impatient Viserys tries to browbeat his sister into coercing Drogo. When Viserys publicly threatens Daenerys, Drogo executes him by pouring molten gold on his head.\\n\\nAn assassin seeking King Robert's favor attempts to poison Daenerys and her unborn child, finally convincing Drogo to conquer Westeros. While sacking villages to fund the invasion of Westeros, Drogo is badly wounded, and Daenerys commands a captive folk healer to save him. The healer, angered by the Dothraki raids against her people, sacrifices Daenerys' unborn child to power the spell to save Drogo's life, which restores Drogo's physical health but leaves him in a persistent vegetative state.\\n\\nWith Drogo completely incapacitated and unable to lead, much of the Dothraki army departs to follow a new Khal. Daenerys smothers Drogo with a pillow, and the healer responsible for his condition is tied to Drogo's funeral pyre on her orders. She places her three dragon eggs on the pyre and enters it herself; she soon emerges, unburned, with three newly hatched dragons. Awe-struck, Jorah and the remaining Dothraki swear allegiance to her.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '80cf938890f2375f800efaed2ece2810'}>,\n",
-       "  <Document: {'content': 'She places her three dragon eggs on the pyre and enters it herself; she soon emerges, unburned, with three newly hatched dragons. Awe-struck, Jorah and the remaining Dothraki swear allegiance to her.\\n\\n== Themes ==\\nThroughout the novel, characters are often faced with decisions that match one redeemable trait against another. \\'\\'The Guardian\\'\\' outlines characters who are frequently \"forced to choose between their love for those close to them and the greater interests of honour, duty and the realm.\"In Westeros, Ned ultimately decides to venture south with Robert, leaving much of his family in Winterfell. At the Wall, Jon wrestles with the predicament of joining his half-brother Robb in rebellion or staying with his sworn brothers in the Night\\'s Watch. Daenerys has issue with the Dothraki treatment of those they conquered in Essos. These conflicts characters encounter oftentimes reflect inconsistent decision making. Catelyn initially is overwhelmed by grief and does not leave Bran\\'s bedside while he is comatose, ignoring her political responsibilities, choosing family over duty. But soon after, Catelyn leaves Bran and her family for Kings Landing to inform Ned of potential Lannister treason, effectively displaying a more duty fulfilling role. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '5ff68b1b8bb556e89df86c90b07ed6bb'}>,\n",
-       "  <Document: {'content': 'But soon after, Catelyn leaves Bran and her family for Kings Landing to inform Ned of potential Lannister treason, effectively displaying a more duty fulfilling role. Family, duty, and honor play major roles in conflicts that arise in the story arc, and qualities traditionally categorized as noble oppose each other in resolution. Character decision conflicts and consequence analysis are particular to how Martin wants to portray fantasy.\\n\\nMartin characteristically deviates from the traditional fantasy model and clear-cut lines of good versus evil. Martin reflects: \"I think the battle between good and evil is fought largely within the individual human heart, by the decisions that we make. It\\'s not like evil dresses up in black clothing and you know, they\\'re really ugly\". This viewpoint characterizes the book and is evident in the actions of several different families which frequently have conflicts with each other. The Starks\\' and Lannisters\\' conflict is a central component of the novel, and the reader receives points of view from both sides. Likewise, Daenerys\\' storyline develops around the Targaryen\\'s upheaval in Westeros, in which the Starks played a significant role. Martin argues: Having multiple viewpoints is crucial to the grayness of the characters. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'd37bf82e67b6f11a3694bccebafd5207'}>,\n",
-       "  <Document: {'content': 'Likewise, Daenerys\\' storyline develops around the Targaryen\\'s upheaval in Westeros, in which the Starks played a significant role. Martin argues: Having multiple viewpoints is crucial to the grayness of the characters. You have to be able to see the struggle from both sides, because real human beings in a war have all these processes of self-justification, telling ourselves why what we\\'re doing is the right thing.\\n\\n==Viewpoint characters==\\nEach chapter concentrates on the third person limited point of view of a single character; the book presents the perspective of eight main characters. Additionally, a minor character provides the prologue. Chapter headings indicate the perspective.\\n* Prologue: Will, a man of the Night\\'s Watch.\\n* Lord Eddard \"Ned\" Stark, Warden of the North and Lord of Winterfell, Hand of the King.\\n* Lady Catelyn Stark, of House Tully, wife of Eddard Stark.\\n* Sansa Stark, elder daughter of Eddard and Catelyn Stark.\\n* Arya Stark, younger daughter of Eddard and Catelyn Stark.\\n* Bran Stark, middle son of Eddard and Catelyn Stark.\\n* Jon Snow, illegitimate son of Eddard Stark.\\n* Tyrion Lannister, a dwarf, brother of the twins Queen Cersei and Jaime, son of Lord Tywin Lannister.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'da61d85f2e6b4653ccf41793d12c74f1'}>,\n",
-       "  <Document: {'content': \"* Jon Snow, illegitimate son of Eddard Stark.\\n* Tyrion Lannister, a dwarf, brother of the twins Queen Cersei and Jaime, son of Lord Tywin Lannister.\\n* Princess Daenerys Targaryen, Stormborn, the Princess of Dragonstone and heiress to the Targaryen throne after her older brother Viserys Targaryen.\\n\\nIn the later books, certain viewpoint characters are added while others are removed.\\n\\n==Writing==\\nMartin acknowledges several authors who lent their time and expertise during the writing of the novel: Sage Walker, Martin Wright, Melinda Snodgrass, Carl Keim, Bruce Baugh, Tim O'Brien, Roger Zelazny, Jane Lindskold, and Laura Mixon.\\n\\n==Editions==\\n\\nAccording to author George R.R. Martin, the Bantam 1996 edition was the true first. The HarperCollins/ Voyager 1996 edition was the British first edition. Its official publication date was earlier than that of the US Bantam edition, but Bantam went to print several months earlier to hand out copies at the American Boksellers Association (ABA). The novel has been translated into many languages and published in multiple editions in hardcover, paperback, e-book, and audio book form. In different languages, the number of books may not be the same. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'fa60b05c6c85a6a2c168bacbc90c6202'}>,\n",
-       "  <Document: {'content': 'The novel has been translated into many languages and published in multiple editions in hardcover, paperback, e-book, and audio book form. In different languages, the number of books may not be the same. In June 2000, Meisha Merlin published a limited edition of the book, fully illustrated by Jeffrey Jones.\\n\\n==Adaptations==\\n\\n\\'\\'A Game of Thrones\\'\\' and the subsequent novels in the \\'\\'A Song of Ice and Fire\\'\\' series have been adapted in a HBO television series, a comics series, several card, board and video games, and other media.\\n\\n==Reception==\\n\\'\\'A Game of Thrones\\'\\' has received critical acclaim. Lauren K. Nathan of the Associated Press wrote that the book \"grips the reader from Page One\" and is set in a \"magnificent\" fantasy world that is \"mystical, but still believable.\"Steve Perry told readers of \\'\\'The Oregonian\\'\\' that the plot is \"complex and fascinating\" and the book is \"rich and colorful\" with \"all the elements of a great fantasy novel\". Writing in \\'\\'The Washington Post\\'\\', John H. Riskind commented that \"many fans of sword-and-sorcery will enjoy the epic scope of this book\" but felt that the book \"suffers from one-dimensional characters and less than memorable imagery.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '3e6af0a9016329a642445f1d1a24ae5c'}>,\n",
-       "  <Document: {'content': 'Writing in \\'\\'The Washington Post\\'\\', John H. Riskind commented that \"many fans of sword-and-sorcery will enjoy the epic scope of this book\" but felt that the book \"suffers from one-dimensional characters and less than memorable imagery.\"Phyllis Eisenstein of the \\'\\'Chicago Sun-Times\\'\\' wrote that, although the book uses many generic fantasy tropes, Martin\\'s approach is \"so refreshingly human and intimate that it transcends them.\"She described it as \"an absorbing combination of the mythic, the sweepingly historical, and the intensely personal.\"John Prior, writing in the \\'\\'San Diego Union-Tribune\\'\\', called Martin\\'s writing \"strong and imaginative, with plenty of Byzantine intrigue and dynastic struggle\", and compared it to Robert Jordan\\'s \\'\\'Wheel of Time\\'\\' books, \"though much darker, with no comedy or romance to relieve the nastiness.\"On November 5, 2019, the \\'\\'BBC News\\'\\' listed \\'\\'A Game of Thrones\\'\\' on its list of the 100 most influential novels.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '75c3d8eda5fd7a1c24c5f9ea4e4029c'}>,\n",
-       "  <Document: {'content': \"On November 5, 2019, the ''BBC News'' listed ''A Game of Thrones'' on its list of the 100 most influential novels.\\n\\n==Awards and nominations==\\n* Locus Award – Best Novel (Fantasy) (Won) – (1997)\\n* World Fantasy Award – Best Novel (Nominated) – (1997)\\n* Hugo Award – Best Novella for ''Blood of the Dragon'' (Won) – (1997)\\n* Nebula Award – Best Novel (Nominated) – (1997)\\n* Ignotus Award – Best Novel (Foreign) (Won) – (2003)\\n\\n==References==\\n\\n==External links==\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '2c3df17510e19c515b32315c751d6d7d'}>,\n",
-       "  <Document: {'content': \"\\nThe soundtrack album of the fifth season of HBO series ''Game of Thrones'', titled '''''Game of Thrones: Season 5''''', was released digitally on June 9, 2015, and on CD on July 17, 2015. The album was composed by Ramin Djawadi.\\n\\n==Reception==\\nThe soundtrack received positive reviews from critics.\\n\\n==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* Czech Film Orchestra and Choir – primary artist\\n* Bradley Hanan Carter – featured artist\\n* D.B. Weiss – liner notes\\n\\n==References==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '4ee95a362a4f7e7272ad5031e523748f'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Blood of My Blood\\'\\'\\'\" is the sixth episode of the sixth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 56th overall. The episode was written by Bryan Cogman, and directed by Jack Bender.\\n\\nBran Stark and Meera Reed are rescued from the White Walkers by Benjen Stark. Samwell Tarly returns to his family\\'s home in Horn Hill, accompanied by Gilly and little Sam; Jaime Lannister attempts to rescue the Queen, Margaery Tyrell; Arya Stark defies the Faceless Men; and Daenerys Targaryen rides on Drogon and emboldens her newly acquired khalasar.\\n\\n\"Blood of My Blood\" was positively received by critics who praised the return of several notable characters, including Benjen Stark, Walder Frey and Edmure Tully. Further praise was given to other plot points, such as Samwell\\'s return to Horn Hill, and Arya\\'s decision to return to being a Stark rather than a disciple of the Many-Faced God. The episode title is a reference to a famous Dothraki saying used between a Khal and his bloodriders. Filming of Bran\\'s visions was put together precisely and also very carefully chosen. In the United States, the episode achieved a viewership of 6.71 million in its initial broadcast.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'b86de13b285afdab87d78db42e7cc63f'}>,\n",
-       "  <Document: {'content': \"Filming of Bran's visions was put together precisely and also very carefully chosen. In the United States, the episode achieved a viewership of 6.71 million in its initial broadcast.\\n\\n==Plot==\\n\\n===Beyond the Wall===\\nBran and Meera continue to flee the wights. Bran, still in his visions, witnesses several events: Jaime Lannister killing King Aerys Targaryen, a dragon flying over the Red Keep, his fall from Winterfell which left him paralyzed, the Night King transforming Craster's last son into a White Walker, the massacre at Hardhome, Ned Stark's beheading, the murder of Catelyn and Robb Stark at the Red Wedding, and wildfire exploding beneath King's Landing. As the wights close in, a black-clad rider appears and pulls Meera and Bran onto his horse, allowing them to escape.\\n\\nBran awakens to find the rider is his uncle, Benjen Stark, who had gone missing beyond the Wall. Benjen explains that he was stabbed by a White Walker during a ranging and was left to die and become another wight, but was saved by the Children of the Forest by being impaled with dragonglass. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'f402fc193d2f589b95e28dfd798144fd'}>,\n",
-       "  <Document: {'content': \"Benjen explains that he was stabbed by a White Walker during a ranging and was left to die and become another wight, but was saved by the Children of the Forest by being impaled with dragonglass. He tells Bran that he needs to become the Three-Eyed Raven and control his skinchanging before the Night King comes south.\\n\\n===At Horn Hill===\\nSamwell, Gilly, and Little Sam arrive at Horn Hill, the seat of House Tarly. Sam warns Gilly not to mention that she is a Wildling, due to his father Randyll's hatred of Wildlings. They are warmly greeted by Sam's mother Melessa and sister Talla.\\n\\nAt dinner, Randyll insults Sam's bookishness, weight, and lack of fighting prowess. Gilly defends Sam, mentioning that Sam killed a Thenn and a White Walker. When Sam's brother Dickon insists that White Walkers don't exist, Gilly declares that she saw the act herself, but in doing so reveals her Wildling heritage. Disgusted, Randyll further insults Sam and Gilly, prompting Melessa and Talla to leave the room with Gilly in anger. Randyll tells Sam that Gilly and Little Sam can remain at Horn Hill, but that he must never set foot in Horn Hill again. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '6d1806499a5317d563e14f6a21bb8ea8'}>,\n",
-       "  <Document: {'content': \"Randyll tells Sam that Gilly and Little Sam can remain at Horn Hill, but that he must never set foot in Horn Hill again. Sam bids farewell to Gilly, but then changes his mind and decides to bring her and Little Sam with him to the Citadel. As they leave, Sam takes House Tarly's ancestral Valyrian steel sword, Heartsbane, as well.\\n\\n===In Braavos===\\nArya returns to watch the play featuring Lady Crane. She sneaks backstage during the last act and poisons Lady Crane's rum. As she attempts to leave, Lady Crane stops her, and they discuss acting and Lady Crane's early life. Back in the dressing room, Arya stops Lady Crane from drinking her rum, and warns her that her rival Bianca wants her dead. The scene is witnessed by the Waif, who returns to tell Jaqen of Arya's failure. Jaqen gives the Waif permission to kill Arya, on the condition that Arya doesn't suffer. Meanwhile, Arya retrieves her sword, Needle, from the rocks where she had hidden it and goes into hiding.\\n\\n===In King's Landing===\\nThe High Sparrow, along with Tommen, prepares for Margaery's walk of atonement. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'bef29c74ce910360f883d2f9c9901c2f'}>,\n",
-       "  <Document: {'content': \"Meanwhile, Arya retrieves her sword, Needle, from the rocks where she had hidden it and goes into hiding.\\n\\n===In King's Landing===\\nThe High Sparrow, along with Tommen, prepares for Margaery's walk of atonement. The High Sparrow allows Tommen to visit his wife, where he discovers that Margaery has become a devout follower of the Faith of the Seven and repented for her sins.\\n\\nJaime and Mace Tyrell lead a company of Tyrell soldiers to the Great Sept of Baelor, where the High Sparrow is presenting Margaery to the people of King's Landing. Jaime orders the High Sparrow to release Margaery and Loras, threatening force against the Faith Militant. The High Sparrow declares that Margaery will not have to perform a walk of atonement, and instead presents Tommen, who announces that he has agreed to unite the Faith and the Crown. When Mace asks Olenna what this means, she bitterly replies it means the High Sparrow has won.\\n\\nIn the throne room, Tommen relieves Jaime of his duties on the Kingsguard as punishment for taking up arms against the Faith, to Jaime's dismay. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '87f2839ee49182eac93874f0c903af27'}>,\n",
-       "  <Document: {'content': \"In the throne room, Tommen relieves Jaime of his duties on the Kingsguard as punishment for taking up arms against the Faith, to Jaime's dismay. While speaking with Cersei, Jaime reveals that he has been given orders to oust the Blackfish from Riverrun, but that he would rather massacre the Faith Militant to release Tommen from the High Sparrow's influence. Cersei warns him that, if he does so, he will be killed, thus defeating the purpose of liberating Tommen. She counsels him to instead lead the Lannister army on Riverrun as a show of force to their enemies. Cersei expresses no concern about her upcoming trial, as it will be a trial by combat and she has chosen The Mountain as her champion. Cersei and Jaime then passionately kiss.\\n\\n===At the Twins===\\nLord Walder Frey receives word that Riverrun has been retaken by the Blackfish, and chastises his sons Lothar Frey and Black Walder Rivers, who had been ordered to hold the castle. Walder demands that the Tully stronghold be taken back, refusing to be humiliated by not being able to hold a single castle. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '2ceaf82547449ad8e91b756cc7ca081b'}>,\n",
-       "  <Document: {'content': 'Walder demands that the Tully stronghold be taken back, refusing to be humiliated by not being able to hold a single castle. He orders his men to bring in Edmure Tully, held as a prisoner of the Freys since the Red Wedding, and declares that they will use him to retake Riverrun.\\n\\n===In the Dothraki Sea===\\nWhile riding, Daenerys is discussing with Daario how many ships she needs for her army to cross the Narrow Sea when she notices an unusual gust of wind and rides out alone to investigate. Daario grows impatient, but as he prepares to follow her, Drogon flies over the khalasar, with Daenerys riding on his back. Daenerys lands nearby and rallies the Dothraki by stating that she chooses them all to be her bloodriders, rather than the traditional three chosen by khals. She then asks if they will cross the Narrow Sea with her and help her retake the Seven Kingdoms, which they unanimously agree to do.\\n\\n==Production==\\n\\n===Writing===\\nSeries veteran Bryan Cogman wrote the episode, his first of two episodes of the season.\\n\"Blood of My Blood\" was written by Bryan Cogman. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '356a021f86ca70b3feca20446b119017'}>,\n",
-       "  <Document: {'content': '==Production==\\n\\n===Writing===\\nSeries veteran Bryan Cogman wrote the episode, his first of two episodes of the season.\\n\"Blood of My Blood\" was written by Bryan Cogman. Cogman has been a writer for the series since its beginning, previously writing seven other episodes, as well as the subsequent episode. The title of the episode, \"Blood of My Blood\", is a reference to the famous Dothraki saying between a Khal and his bloodriders.\\n\\nIn an interview with \\'\\'Entertainment Weekly\\'\\', Cogman described some of the thought process he had when writing the reunion between Samwell and his family, saying, \"As much as Sam has gone through, I love exploring those family dynamics. His mother and sister and brother are all fundamentally decent people but his father is just a cold hearted bastard when it comes to his distant son. There\\'s a painful part of the scene where his father just unloads on him and tells him every hateful thing he ever thought about him and Sam can\\'t defend himself. We found that fascinating – Sam has killed a man, he\\'s killed a White Walker, he\\'s emerged as such a great hero, but he still can\\'t stand up to his dad.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '6c09038292a2a072f19a9193b19e972'}>,\n",
-       "  <Document: {'content': 'We found that fascinating – Sam has killed a man, he\\'s killed a White Walker, he\\'s emerged as such a great hero, but he still can\\'t stand up to his dad.\"Cogman noted, about the Arya storyline in the same interview, that he comes from a theater background, and that \"being able to comment on the show and the reactions to the show through the players were so much fun. The show is often accused of being gratuitous in all kinds of way – the violence and the bigness of the characters. It\\'s a huge operatic story. We\\'re able to lovingly spoof ourselves but also play with ideas about how audiences view the show, good and bad, and how a perspective of a story changes. Plus there’s the dramatic deliciousness of Arya watching her own life play out on stage.\"Weiss also spoke about Arya, saying \"We were excited to do the play within the play, and it\\'s a distorted fun house mirror representation of things we\\'ve already seen.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'b4e2ef245af6e0ef778fbbf8c9196558'}>,\n",
-       "  <Document: {'content': 'Weiss also spoke about Arya, saying \"We were excited to do the play within the play, and it\\'s a distorted fun house mirror representation of things we\\'ve already seen.\"Benioff continued, \"Part of Arya\\'s amusement is just that she knows that they\\'re getting so many details wrong, but she always regretted that she didn\\'t have a chance to watch Joffrey die, and now she gets to. It\\'s obviously a comic version of it, but that gives her great pleasure.\"Weiss also noted, \"Arya is slowly getting seduced by these performances, and Lady Crane, the actress that she\\'s charged with killing, this is somebody who like her has taken as her life\\'s work the job of becoming other people.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'c80ee8a91bab228230dffead911aa666'}>,\n",
-       "  <Document: {'content': 'Weiss also noted, \"Arya is slowly getting seduced by these performances, and Lady Crane, the actress that she\\'s charged with killing, this is somebody who like her has taken as her life\\'s work the job of becoming other people.\"In regards to the final scene of the episode with Daenerys Targaryen riding Drogon and emboldening her newly acquired khalasar, Benioff stated in the \"Inside the Episode\" featurette that the scene is a reflection of the speech that Khal Drogo gave before his death, with Benioff saying \"One of our favorite moments from season one was watching Khal Drogo deliver a speech to his gathered khalasar, that speech clearly lingered in Daenerys\\'s mind, and she\\'s echoing almost the exact same language when she\\'s talking to the Dothraki now. She\\'s basically telling them the promise that one of the great Khals had made years before and saying now is the time to live up to that promise and fulfill it.\"===Casting===\\nActor David Bradley (\\'\\'pictured\\'\\'), as well as Joseph Mawle and Tobias Menzies returned to the series after a nearly three-year absence.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '3049a32bc7d240cba229a5732506eb45'}>,\n",
-       "  <Document: {'content': '===Casting===\\nActor David Bradley (\\'\\'pictured\\'\\'), as well as Joseph Mawle and Tobias Menzies returned to the series after a nearly three-year absence.\\nThe episode saw the return of several characters from previous seasons and the introduction of new characters that had either been mentioned or had some connection to established characters. Joseph Mawle, who was previously cast to play Benjen Stark and was featured in three episodes in the show\\'s first season before disappearing (as he does in the \\'\\'A Song of Ice and Fire\\'\\' series that the show is based on). In an interview with \\'\\'Entertainment Weekly\\'\\', Bryan Cogman spoke of the re-introduction of Benjen and Mawle to the show saying \"It was great to have Joe Mawle back with us – it must have been a trip for him to step back into the character after so long – but he\\'s also decidedly not the Benjen of season 1. So that was fun to explore.\"Mawle had been interviewed in 2013, where he expressed his desire to return to the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'f0437239170ccbf1337fa74579c973df'}>,\n",
-       "  <Document: {'content': 'So that was fun to explore.\"Mawle had been interviewed in 2013, where he expressed his desire to return to the series.\\n\\nAnother return involved the story of the Riverlands with actor David Bradley returning to the show as Walder Frey, who last appeared in the aftermath of the Red Wedding, as well as Tobias Menzies as Edmure Tully, who also had not appeared since the Red Wedding episode \"The Rains of Castamere\", in the third season. Tim Plester, who plays one of Walder\\'s sons also returned but his other son, Lothar, was re-cast, with Daniel Tuite taking over the role.\\n\\nSeveral new actors were cast as Samwell Tarly\\'s family in Horn Hill. Samwell\\'s father, Randyll Tarly, had been referenced several times and had been described as \"cruel\" in his treatment of his oldest son. In an interview with \\'\\'The Hollywood Reporter\\'\\', John Bradley (Samwell Tarly) spoke about the introduction of his family to the series, stating, \"When you see Sam with his father and mother and brother especially, he starts to make sense. The character is contextualized. Why is his psyche the way it is? Why does he behave the way he does? ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'ffefd025cac423cb89b6637e69106dd3'}>,\n",
-       "  <Document: {'content': 'The character is contextualized. Why is his psyche the way it is? Why does he behave the way he does? Ever since you first saw him, he comes from this very sincere and heartfelt maternal love, and then there\\'s this monster. You can see why he\\'s so damaged. His emotional life has been pulled in so many different directions. He\\'s so incredibly confused.\"Actor James Faulkner was cast in the role of Randyll, with Samantha Spiro portraying Samwell\\'s mother and Freddie Stroma and Rebecca Benson portraying Samwell\\'s brother Dickon and sister Talla.\\n\\nFor Bran\\'s brief vision sequence at the beginning of the episode, actor David Rintoul was cast as King Aerys II Targaryen in a scene that had only been described to that point. It depicts the murder of Aerys, who is repeatedly yelling \"burn them all\", at the hands of Jaime Lannister.\\n\\n===Filming===\\nThe Cathedral in Girona, Spain stood in for the Great Sept, with some extending CGI altering the building.\\n\"Blood of My Blood\" was directed by Jack Bender, who also directed the previous episode \"The Door\", his directorial debut for the series. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '7594d8407c94d8aa8a30a429af61acb1'}>,\n",
-       "  <Document: {'content': '\"Blood of My Blood\" was directed by Jack Bender, who also directed the previous episode \"The Door\", his directorial debut for the series. Bender had been approached to direct for the series but declined due to the extensive time commitment involved in shooting, which he noted in an interview as having to commit to \"four-and-a-half to six months because of the enormity of the episodes\".\\n\\nFor the primary King\\'s Landing scene at the Great Sept, with Margaery being presented to the city by the High Sparrow, the grand staircase of the Girona Cathedral in Girona, Spain was used. The cathedral was constructed in the 11th century, and continued its expansion throughout the 12th and 13th centuries, as well as the 18th century. According to prior reports, filming took place over the course of approximately two weeks, with many different challenges involved in shooting at the location, including \"extras requiring medical attention due to exhaustion and dehydration,\" as well as the need for extensive security to close off certain areas, during filming, from the public. Parts of Braavos were also filmed in Girona. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '349ec6ab5d7c19e7de1f3d8b631562cb'}>,\n",
-       "  <Document: {'content': 'According to prior reports, filming took place over the course of approximately two weeks, with many different challenges involved in shooting at the location, including \"extras requiring medical attention due to exhaustion and dehydration,\" as well as the need for extensive security to close off certain areas, during filming, from the public. Parts of Braavos were also filmed in Girona. In nearby Canet de Mar, Catalonia, Spain, the Castell de Santa Florentina, an 11th-century medieval castle, was utilized for the castle of House Tarly.\\n\\nThe director of the episode, Bender, conducted an interview with \\'\\'The Wall Street Journal\\'\\' following the airing of the episode, and spoke about filming the play with Arya in Braavos, stating \"I staged the whole play, we did it, and the producers came in to watch the rehearsal, including all the fart jokes, all that stuff, some of which was written. So, after we watched the rehearsal, and the guys laughed a lot, I said, \\'My only concern is, am I mocking your brilliant show too much?\\'And they said, \\'No, do it more!\\'They\\'re completely unpretentious, David and Dan, and they loved it.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '7d94c51e0c58f9dcc51332b48b8c8a81'}>,\n",
-       "  <Document: {'content': 'So, after we watched the rehearsal, and the guys laughed a lot, I said, \\'My only concern is, am I mocking your brilliant show too much?\\'And they said, \\'No, do it more!\\'They\\'re completely unpretentious, David and Dan, and they loved it.\"He also noted that several other extra scenes were filmed, but were cut from the final version of the episode, noting that they will likely be released as deleted scenes on the DVD for the season.\\n\\nIn the behind the scenes video published by HBO following the airing of the fifth and sixth episodes, Benioff described Bran\\'s visions at the beginning of the episode as being something that was put together very precisely and purposefully, noting \"Even though some of those images flash by in just a fraction of a second each of them was very carefully chosen. And the Mad King was probably the most dramatic of those because we\\'ve been hearing about the Mad King from the very beginning of the show, but he\\'s never appeared on screen before. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'f82a15076e9cbe4feb7d8f517da31b93'}>,\n",
-       "  <Document: {'content': 'And the Mad King was probably the most dramatic of those because we\\'ve been hearing about the Mad King from the very beginning of the show, but he\\'s never appeared on screen before. And shooting it, you know, spending a lot of time on what ended up being maybe like a second and a half, couple seconds, of screen time.\"The new scenes of the Mad King, Aerys II Targaryen, are interspersed with previously shown footage of the White Walkers, as well as wildfire explosions, and other landmark moments from throughout the series, such as Ned Stark\\'s beheading, the Red Wedding, and Bran falling from the Broken Tower in the series premiere. In the \\'\\'A Song of Ice and Fire\\'\\' series by George R. R. Martin, the Mad King is described as having long, unkempt hair and beard, with nine inch long fingernails. The show\\'s version portrayed his look in a more tidy fashion, with David Rintoul playing the role of the Mad King.\\n\\nEllie Kendrick, who portrays Meera Reed in the series, in an interview with \\'\\'The Hollywood Reporter\\'\\' spoke about the re-introduction of Benjen Stark, or Coldhands Benjen, and working with Joseph Mawle, saying \"It was so fantastic. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'df31215869b9895304451c9f53b5dc25'}>,\n",
-       "  <Document: {'content': 'Ellie Kendrick, who portrays Meera Reed in the series, in an interview with \\'\\'The Hollywood Reporter\\'\\' spoke about the re-introduction of Benjen Stark, or Coldhands Benjen, and working with Joseph Mawle, saying \"It was so fantastic. And it\\'s interesting. If you\\'ve read the books, then you know about the Coldhands character, who was cut out of the Bran, Meera and Jojen storyline on the show. It was always interesting, having read the books, watching this amalgamation of story lines happen. Benjen is sort of like the Coldhands character in that he\\'s a slightly suspicious guy who is half-dead and half-alive with blue hands. I thought that was very cool, the way it happened. It was a nice marriage between the book and TV revelations rolled into one. I was very excited having him on set. I loved working with Joseph Mawle, and I loved having another member of our rapidly dwindling gang.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'ba355e741833c625b3f2ec048e87aaeb'}>,\n",
-       "  <Document: {'content': 'I was very excited having him on set. I loved working with Joseph Mawle, and I loved having another member of our rapidly dwindling gang.\"==Reception==\\n\\n===Ratings===\\n\"Blood of My Blood\" was viewed by 6.71 million American households on its initial viewing on HBO, which was a modest decrease from the previous week\\'s rating of 7.89 million viewers for the episode \"The Door\", likely due to the Memorial Day holiday in the United States. The episode also acquired a 3.25 rating in the 18–49 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was viewed by 2.684 million viewers on Sky Atlantic, making it the highest-rated broadcast that week on its channel. It also received 0.126 million timeshift viewers.\\n\\n===Critical reception===\\n\"Blood of My Blood\" was positively received by critics who praised the return of several notable characters from past seasons, including Benjen Stark and Walder Frey, as well as Samwell Tarly\\'s return home to Horn Hill and Arya\\'s decision to return to being a Stark, abandoning the teachings of the Faceless Men. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '149cd13e61259f937e90b3aaba28e002'}>,\n",
-       "  <Document: {'content': '===Critical reception===\\n\"Blood of My Blood\" was positively received by critics who praised the return of several notable characters from past seasons, including Benjen Stark and Walder Frey, as well as Samwell Tarly\\'s return home to Horn Hill and Arya\\'s decision to return to being a Stark, abandoning the teachings of the Faceless Men. It has received a 88% rating on the review aggregator website Rotten Tomatoes from 44 reviews with an average score of 7.6/10, a decrease over the previous episode. The site\\'s consensus reads \"Crucial power dynamics are reassessed and significant characters return in the skillfully plotted and gratifying \"Blood of My Blood\".\"Matt Fowler of IGN wrote in his review of the episode, \"Some sluggish stories took better turns this week – albeit sometimes by just ending things for now – while a book character many thought would never appear on the series made a notable splash, answering a big mystery that the novels hadn’t even revealed yet in the process. There were some great moments in \"Blood of My Blood\", but mostly the big returns made it possible to salivate for the payoffs (and possible characters) to come.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '1a8ec0584b9dc59c76360554c2f514d9'}>,\n",
-       "  <Document: {'content': 'There were some great moments in \"Blood of My Blood\", but mostly the big returns made it possible to salivate for the payoffs (and possible characters) to come.\"He also continued by noting, \"this installment probably speaks, more than any other episode so far this season, to the accelerated rate of payoffs we\\'re now getting that the show isn\\'t directly following the books.\"Fowler gave the episode an 8.6 out of 10. Laura Prudom of \\'\\'Variety\\'\\' also praised the pacing of the episode as well as the season\\'s payoffs, writing \"There\\'s a clarity of purpose in Season 6 that certainly gives the impression that we\\'re barreling towards a conclusion instead of just meandering through Dorne or killing time in Qarth.\"Emily VanDerWerff of \\'\\'Vox\\'\\' likewise praised the structure of the season, noting the recent absence of Ramsay Bolton as a positive, but continuing, \"Though it wasn\\'t as good as the last two episodes, \"Blood of My Blood\" continued the general upswing in quality \\'\\'Game of Thrones\\'\\' season six has undergone.\"VanDerWerff also wrote in her review, \"\"Blood of My Blood\" is another exciting hour of television, even if it\\'s a bit more piece move-y than, say, \"The Door\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '1f3af391340906369877fef03fcb52c1'}>,\n",
-       "  <Document: {'content': 'VanDerWerff also wrote in her review, \"\"Blood of My Blood\" is another exciting hour of television, even if it\\'s a bit more piece move-y than, say, \"The Door\". The revelations are milder, the characters mostly talk about what they\\'re going to do next, and the stories go from Point A to Point B, instead of Point X, Y, or Z. But there\\'s still plenty going on that\\'s worth checking out.\"Comparing the episode to \"Book of the Stranger\", Jeremy Egner of \\'\\'The New York Times\\'\\' said, \"The problem with emerging naked from a flaming temple is that it\\'s hard to top. It\\'s hard to top, that is, unless you happen to have a dragon with a flair for dramatic timing.\"Aaron Riccio of \\'\\'Slant Magazine\\'\\' criticized the episode, writing in his review \"The largest problem with tonight\\'s episode is that it either changes course so abruptly or restates certain theses so redundantly that it feels like a bit of a tease, especially to those not invested in Samwell Tarly\\'s storyline.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': '1da4c40cf5379069697fa967a2ed5ba3'}>,\n",
-       "  <Document: {'content': 'Aaron Riccio of \\'\\'Slant Magazine\\'\\' criticized the episode, writing in his review \"The largest problem with tonight\\'s episode is that it either changes course so abruptly or restates certain theses so redundantly that it feels like a bit of a tease, especially to those not invested in Samwell Tarly\\'s storyline.\"===Accolades===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron\\n\\n2017\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Blood of My Blood\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': '6ea833652206dd2b5f2492dc0c16a66f'}>,\n",
-       "  <Document: {'content': \"\\n\\nThe fifth season of the fantasy drama television series ''Game of Thrones'' premiered on HBO on April 12, and concluded on June 14, 2015. It was broadcast on Sunday at 9:00 pm in the United States, consisting of 10 episodes, each running approximately 50–60 minutes. The season primarily adapts material from ''A Feast for Crows'' and ''A Dance with Dragons'', the fourth and fifth novels in George R. R. Martin's ''A Song of Ice and Fire'' series, though it also uses elements from the third novel, ''A Storm of Swords'', as well as the upcoming sixth novel ''The Winds of Winter''. It also contains original content not found in Martin's novels. The series is adapted for television by David Benioff and D. B. Weiss.\\n\\nLike previous seasons in ''Game of Thrones'', the fifth season continues storylines primarily set within the fictional land of Westeros, whereas one storyline is set in another continent, Essos. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'c2626c5cc086558e8ec18b730be32f67'}>,\n",
-       "  <Document: {'content': \"Like previous seasons in ''Game of Thrones'', the fifth season continues storylines primarily set within the fictional land of Westeros, whereas one storyline is set in another continent, Essos. The season continues the aftermath of the deaths of King Joffrey Baratheon and Tywin Lannister, as Tywin's son Tyrion, now on the run after killing his father, travel to Essos, where he crosses paths with Daenerys Targaryen, who is struggling to balance her political ambitions with her dragons. Erstwhile, the Lannister family continues to struggle coping with Tywin and Joffrey's deaths while encountering new enemies; Jon Snow and the Night's Watch now serve under Stannis Baratheon; the remaining Stark children face new hardships in their respective journeys.\\n\\nHBO ordered the fifth season on April 8, 2014, together with the sixth season, which began filming in July 2014. The season was filmed primarily in Ireland, Northern Ireland, Croatia and Spain.\\n\\n''Game of Thrones'' features a large ensemble cast, including Peter Dinklage, Nikolaj Coster-Waldau, Lena Headey, Emilia Clarke, and Kit Harington. The season introduced a number of new cast members, including Jonathan Pryce and Alexander Siddig.\\n\\nCritics praised the show's production values and cast, giving specific accolades for Dinklage's portrayal of Tyrion Lannister. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '5173233b73ccd2eda66dda4b4430705'}>,\n",
-       "  <Document: {'content': \"The season introduced a number of new cast members, including Jonathan Pryce and Alexander Siddig.\\n\\nCritics praised the show's production values and cast, giving specific accolades for Dinklage's portrayal of Tyrion Lannister. Viewership yet again rose compared to the previous season. This season set a Guinness World Record for winning the highest number of Emmy Awards for a series in a single season and year, winning 12 out of 24 nominations, including Outstanding Drama Series.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '397228d322efd561c1e2e556c81797'}>,\n",
-       "  <Document: {'content': 'This season set a Guinness World Record for winning the highest number of Emmy Awards for a series in a single season and year, winning 12 out of 24 nominations, including Outstanding Drama Series.\\n\\n==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n* Peter Dinklage as Tyrion Lannister\\n* Nikolaj Coster-Waldau as Jaime Lannister\\n* Lena Headey as Cersei Lannister\\n* Emilia Clarke as Daenerys Targaryen\\n* Kit Harington as Jon Snow\\n* Aidan Gillen as Petyr \"Littlefinger\" Baelish\\n* Charles Dance as Tywin Lannister\\n* Natalie Dormer as Margaery Tyrell\\n* Stephen Dillane as Stannis Baratheon\\n* Liam Cunningham as Davos Seaworth\\n* Carice van Houten as Melisandre\\n* John Bradley as Samwell Tarly\\n* Sophie Turner as Sansa Stark\\n* Kristofer Hivju as Tormund Giantsbane\\n\\n* Hannah Murray as Gilly\\n* Conleth Hill as Varys\\n* Gwendoline Christie as Brienne of Tarth\\n* Michiel Huisman as Daario Naharis\\n* Nathalie Emmanuel as Missandei\\n* Dean-Charles Chapman as Tommen Baratheon\\n* Indira Varma as Ellaria Sand\\n* Maisie Williams as Arya Stark\\n* Jerome Flynn as Bronn\\n* Tom Wlaschiha as Jaqen H\\'ghar\\n* Alfie Allen as Theon Greyjoy / \"Reek\"\\n* Michael McElhatton as Roose Bolton\\n* Iwan Rheon as Ramsay Bolton\\n* Iain Glen as Jorah Mormont\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 5. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '3efbd816e07bb84d7b114611c529693f'}>,\n",
-       "  <Document: {'content': '==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n* Peter Dinklage as Tyrion Lannister\\n* Nikolaj Coster-Waldau as Jaime Lannister\\n* Lena Headey as Cersei Lannister\\n* Emilia Clarke as Daenerys Targaryen\\n* Kit Harington as Jon Snow\\n* Aidan Gillen as Petyr \"Littlefinger\" Baelish\\n* Charles Dance as Tywin Lannister\\n* Natalie Dormer as Margaery Tyrell\\n* Stephen Dillane as Stannis Baratheon\\n* Liam Cunningham as Davos Seaworth\\n* Carice van Houten as Melisandre\\n* John Bradley as Samwell Tarly\\n* Sophie Turner as Sansa Stark\\n* Kristofer Hivju as Tormund Giantsbane\\n\\n* Hannah Murray as Gilly\\n* Conleth Hill as Varys\\n* Gwendoline Christie as Brienne of Tarth\\n* Michiel Huisman as Daario Naharis\\n* Nathalie Emmanuel as Missandei\\n* Dean-Charles Chapman as Tommen Baratheon\\n* Indira Varma as Ellaria Sand\\n* Maisie Williams as Arya Stark\\n* Jerome Flynn as Bronn\\n* Tom Wlaschiha as Jaqen H\\'ghar\\n* Alfie Allen as Theon Greyjoy / \"Reek\"\\n* Michael McElhatton as Roose Bolton\\n* Iwan Rheon as Ramsay Bolton\\n* Iain Glen as Jorah Mormont\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 5. They are listed by the region in which they first appear:\\n\\n====At and beyond the Wall====\\n* Peter Vaughan as Maester Aemon\\n* Owen Teale as Alliser Thorne\\n* Brian Fortune as Othell Yarwyck\\n* Michael Condron as Bowen Marsh\\n* Dominic Carter as Janos Slynt\\n* Ben Crompton as Eddison Tollett\\n* J. J. Murphy as Denys Mallister\\n* Will O\\'Connell as Todder\\n* Brenock O\\'Connor as Olly\\n* Ciarán Hinds as Mance Rayder\\n* Birgitte Hjort Sørensen as Karsi\\n* Zahary Baharov as Loboda\\n* Ross O\\'Hennessy as the Lord of Bones\\n* Murray McArthur as Dim Dalba\\n* Ian Whyte as Wun Wun\\n* Ali Lyons as Johnna\\n* Richard Brake as the Night King\\n\\n====In the North====\\n* Elizabeth Webster as Walda Bolton\\n* Tara Fitzgerald as Selyse Florent\\n* Kerry Ingram as Shireen Baratheon\\n* Charlotte Hope as Myranda\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Rupert Vansittart as Yohn Royce\\n* Daniel Portman as Podrick Payne\\n\\n====In Dorne====\\n* Alexander Siddig as Doran Martell\\n* Toby Sebastian as Trystane Martell\\n* Keisha Castle-Hughes as Obara Sand\\n* Rosabell Laurenti Sellers as Tyene Sand\\n* Jessica Henwick as Nymeria Sand\\n* Nell Tiger Free as Myrcella Baratheon\\n* DeObia Oparei as Areo Hotah\\n\\n====In King\\'s Landing====\\n* Julian Glover as Grand Maester Pycelle\\n* Anton Lesser as Qyburn\\n* Ian Gelder as Kevan Lannister\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Finn Jones as Loras Tyrell\\n* Diana Rigg as Olenna Tyrell\\n* Eugene Simon as Lancel Lannister\\n* Ian Beattie as Meryn Trant\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Paul Bentley as the High Septon\\n* Jonathan Pryce as the High Sparrow\\n* Hannah Waddingham as Septa Unella\\n* Will Tudor as Olyvar\\n* Josephine Gillan as Marei\\n\\n====In Braavos====\\n* Mark Gatiss as Tycho Nestoris\\n* Gary Oliver as Ternesio Terys\\n* Oengus MacNamara as the thin man\\n* Faye Marsay as the Waif\\n* Sarine Sofair as Lhara\\n* Hattie Gotobed as Ghita\\n\\n====In Slaver\\'s Bay====\\n* Ian McElhinney as Barristan Selmy\\n* Jacob Anderson as Grey Worm\\n* Reece Noi as Mossador\\n* Joel Fry as Hizdahr zo Loraq\\n* Enzo Cilenti as Yezzan zo Qaggaz\\n* Adewale Akinnuoye-Agbaje as Malko\\n* Meena Rayann as Vala\\n\\n==Production==\\n\\n===Crew===\\nThe production team during a location scouting at Klis Fortress, Croatia prior to season 5.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8116723ea144970ce89242830c72b80a'}>,\n",
-       "  <Document: {'content': \"They are listed by the region in which they first appear:\\n\\n====At and beyond the Wall====\\n* Peter Vaughan as Maester Aemon\\n* Owen Teale as Alliser Thorne\\n* Brian Fortune as Othell Yarwyck\\n* Michael Condron as Bowen Marsh\\n* Dominic Carter as Janos Slynt\\n* Ben Crompton as Eddison Tollett\\n* J. J. Murphy as Denys Mallister\\n* Will O'Connell as Todder\\n* Brenock O'Connor as Olly\\n* Ciarán Hinds as Mance Rayder\\n* Birgitte Hjort Sørensen as Karsi\\n* Zahary Baharov as Loboda\\n* Ross O'Hennessy as the Lord of Bones\\n* Murray McArthur as Dim Dalba\\n* Ian Whyte as Wun Wun\\n* Ali Lyons as Johnna\\n* Richard Brake as the Night King\\n\\n====In the North====\\n* Elizabeth Webster as Walda Bolton\\n* Tara Fitzgerald as Selyse Florent\\n* Kerry Ingram as Shireen Baratheon\\n* Charlotte Hope as Myranda\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Rupert Vansittart as Yohn Royce\\n* Daniel Portman as Podrick Payne\\n\\n====In Dorne====\\n* Alexander Siddig as Doran Martell\\n* Toby Sebastian as Trystane Martell\\n* Keisha Castle-Hughes as Obara Sand\\n* Rosabell Laurenti Sellers as Tyene Sand\\n* Jessica Henwick as Nymeria Sand\\n* Nell Tiger Free as Myrcella Baratheon\\n* DeObia Oparei as Areo Hotah\\n\\n====In King's Landing====\\n* Julian Glover as Grand Maester Pycelle\\n* Anton Lesser as Qyburn\\n* Ian Gelder as Kevan Lannister\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Finn Jones as Loras Tyrell\\n* Diana Rigg as Olenna Tyrell\\n* Eugene Simon as Lancel Lannister\\n* Ian Beattie as Meryn Trant\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Paul Bentley as the High Septon\\n* Jonathan Pryce as the High Sparrow\\n* Hannah Waddingham as Septa Unella\\n* Will Tudor as Olyvar\\n* Josephine Gillan as Marei\\n\\n====In Braavos====\\n* Mark Gatiss as Tycho Nestoris\\n* Gary Oliver as Ternesio Terys\\n* Oengus MacNamara as the thin man\\n* Faye Marsay as the Waif\\n* Sarine Sofair as Lhara\\n* Hattie Gotobed as Ghita\\n\\n====In Slaver's Bay====\\n* Ian McElhinney as Barristan Selmy\\n* Jacob Anderson as Grey Worm\\n* Reece Noi as Mossador\\n* Joel Fry as Hizdahr zo Loraq\\n* Enzo Cilenti as Yezzan zo Qaggaz\\n* Adewale Akinnuoye-Agbaje as Malko\\n* Meena Rayann as Vala\\n\\n==Production==\\n\\n===Crew===\\nThe production team during a location scouting at Klis Fortress, Croatia prior to season 5.\\nThe writing staff for the fifth season includes executive producers and showrunners David Benioff and D. B. Weiss, producer Bryan Cogman, and Dave Hill, who was promoted to staff writer this season after previously working as an assistant to Benioff and Weiss. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'a37ecb4dae71ca4540112309b8b72f0e'}>,\n",
-       "  <Document: {'content': \"The writing staff for the fifth season includes executive producers and showrunners David Benioff and D. B. Weiss, producer Bryan Cogman, and Dave Hill, who was promoted to staff writer this season after previously working as an assistant to Benioff and Weiss. Author George R. R. Martin, who had written one episode for each of the first four seasons, did not write an episode for the fifth season as he was working to finish writing the sixth novel of the series, ''The Winds of Winter''. The directing staff for the fifth season is Michael Slovis (episodes 1 and 2), Mark Mylod (episodes 3 and 4), Jeremy Podeswa (episodes 5 and 6), Miguel Sapochnik (episodes 7 and 8), and David Nutter (episodes 9 and 10). Nutter is the only returning director with the rest being first-time ''Game of Thrones'' directors.\\n\\n===Writing===\\nThis season features more original material than previous seasons. The deviations from Martin's novels are in part attributable to the way the television series has covered most of the novel series' published material and in part to the directors' opinions of the actors' abilities. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '4c03fc99666517ccf8f9e9abb957c198'}>,\n",
-       "  <Document: {'content': 'The deviations from Martin\\'s novels are in part attributable to the way the television series has covered most of the novel series\\' published material and in part to the directors\\' opinions of the actors\\' abilities. For example, Sansa Stark arrives at Winterfell to marry Ramsay Bolton in \"High Sparrow,\" a plotline that had been given to a minor character in the novels. In an interview, show writer David Benioff explains that Sophie Turner\\'s development as an actress was one of the reasons that they decided to give her character more dramatic scenes, saying, \"Even if child actors come in and do a great audition, it’s so hard to know if they’re going to quite literally grow into the parts. With Sansa and Arya in particular, their storylines have become quite dark. It was such a gamble and the fact that they’ve both become such great wonderful actresses is a bit of a miracle.\"Bryan Cogman added that it made more sense to give the Winterfell storyline to a proven actress who was already popular with viewers than to bring in a new character.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '1d37a4128a872e3bcf5990cd3cc0b976'}>,\n",
-       "  <Document: {'content': 'Bryan Cogman added that it made more sense to give the Winterfell storyline to a proven actress who was already popular with viewers than to bring in a new character.\\n\\nOther changes include the portrayal of Tommen as old enough to interact with Margaery and the Faith Militant as an adult, the speeding up or streamlining of several subplots, and the introduction of fewer new characters. According to Benioff and Weiss, \"We felt we\\'d capsize the show if we put in every single character from the books.\"Critics particularly liked the decision to have Tyrion actually meet Daenerys at the end of \"The Gift,\" which has yet to happen as of \\'\\'A Dance with Dragons\\'\\'. David Benioff cited the television adaptation\\'s faster pace as part of the rationale behind this decision.\\n\\n===Filming===\\nThe Alcázar of Seville stands in for the Water Gardens of Dorne.\\nFilming for the fifth season began in July 2014 in Belfast and ended in December 2014. Locations in Northern Ireland included Titanic Studios, Belfast and the cliff edge of Binevenagh Mountain. The Winterfell sets were in the village of Moneyglass.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'd9e9e9bcccfe9680ddd3eb3c0d3214d8'}>,\n",
-       "  <Document: {'content': 'Locations in Northern Ireland included Titanic Studios, Belfast and the cliff edge of Binevenagh Mountain. The Winterfell sets were in the village of Moneyglass.\\n\\nSome of the scenes that take place in the principality of Dorne were filmed in Spain, beginning in October 2014. Locations explored for the production included the Alcázar of Seville and the University of Osuna. On October 14, some scenes were filmed on the Roman bridge of Córdoba. Benioff and Weiss said that season 5 would include flashbacks, which they had previously avoided, and that Córdoba would represent the city of Volantis.\\n\\nCersei\\'s \"walk of atonement\" from \\'\\'A Dance with Dragons\\'\\' was filmed in Dubrovnik, Croatia, in the Stradun street between the Dubrovnik Cathedral and the Sponza Palace in early October 2014. The production reportedly employed a body double for Lena Headey for part of the scene in which Cersei appears naked.\\nMeereen scenes were once again shot in Diocletian\\'s Palace in Split, and on Klis Fortress north of Split.\\n\\nA town on the coast of the Bay of Kaštela in Croatia, the 16th century Kaštel Gomilica stood in for some parts of Braavos.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '2a5c668cf8f966adc4de33bb07f7cd4c'}>,\n",
-       "  <Document: {'content': \"A town on the coast of the Bay of Kaštela in Croatia, the 16th century Kaštel Gomilica stood in for some parts of Braavos.\\n\\nA very small portion of one episode was filmed in Calgary, Alberta, Canada: the scenes featuring Jon Snow's wolf Ghost (played by animal actor Quigly) who also appears in season 6.\\n\\n===Casting===\\nJonathan Pryce plays the High Sparrow.\\nThe fifth season adds previously recurring actors Indira Varma (Ellaria Sand), Michiel Huisman (Daario Naharis), Nathalie Emmanuel (Missandei), Dean-Charles Chapman (Tommen Baratheon), Tom Wlaschiha (Jaqen H'ghar) and Michael McElhatton (Roose Bolton) to the series' main cast.\\n\\nIn the fifth season, the region of Dorne is introduced as a location. Alexander Siddig joins the cast as Doran Martell, the ruling Prince of Dorne, and elder brother of Oberyn Martell, while his son Trystane Martell is portrayed by Toby Sebastian. The role of Cersei's daughter Myrcella Baratheon, who is Doran's ward and betrothed to Trystane is played by Nell Tiger Free. The role was portrayed by Aimee Richardson in the first two seasons. DeObia Oparei portrays Areo Hotah, the captain of the Dornish palace guard. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'bcdc1b81e723d7a8ec7dff371cec96fc'}>,\n",
-       "  <Document: {'content': 'The role was portrayed by Aimee Richardson in the first two seasons. DeObia Oparei portrays Areo Hotah, the captain of the Dornish palace guard. The three eldest bastard daughters of Oberyn Martell (collectively known as the \"Sand Snakes\") are portrayed by Keisha Castle-Hughes (Obara Sand), Jessica Henwick (Nymeria Sand), and Rosabell Laurenti Sellers (Tyene Sand).\\n\\nIn King\\'s Landing, Jonathan Pryce joins the cast as the High Sparrow, the leader of the militant faithful in King\\'s Landing, while Hannah Waddingham portrays Septa Unella, one of the \"Most Devout\", the ruling council of the Faith of the Seven. Nell Williams  was cast as a young Cersei Lannister seen in flashbacks, while Jodhi May was cast as Maggy the Frog, a fortune-teller.\\n\\nAcross the Narrow Sea, Enzo Cilenti joins as Yezzan, a Yunkish nobleman and slave trader, while Adewale Akinnuoye-Agbaje was cast as Malko, a slaver, who does not appear in the novels. At Castle Black and beyond the Wall, new cast members include Michael Condron as Bowen Marsh, First Steward of the Night\\'s Watch. J. J. Murphy, who was cast as Night\\'s Watch officer Denys Mallister, died in August 2014 shortly after filming his first scenes; his role was not recast. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '6d5dbb71ef48dd9838d4828f15f23bf6'}>,\n",
-       "  <Document: {'content': 'J. J. Murphy, who was cast as Night\\'s Watch officer Denys Mallister, died in August 2014 shortly after filming his first scenes; his role was not recast. One role that was recast for season 5 was Ross O\\'Hennessy as the Lord of Bones. O\\'Hennessy replaces Edward Dogliani who was last seen in the season 3 premiere \"Valar Dohaeris\". Charles Dance, whose character died in the previous season, returned for one episode to portray Tywin Lannister\\'s corpse.  Isaac Hempstead-Wright (Bran Stark), Kristian Nairn (Hodor) and Ellie Kendrick (Meera Reed) are absent this season because their part in the story has reached the end of \\'\\'A Dance with Dragons\\'\\'.\\n\\n===Music===\\n\\nThe soundtrack for the season was released digitally on June 9, 2015, and on CD on July 17, 2015.\\n\\n==Reception==\\n\\n===Critical response===\\nOn Metacritic, the season (based on the first four episodes) has a score of 91 out of 100 based on 29 reviews, indicating \"universal acclaim\". On Rotten Tomatoes, the fifth season has a 93% approval rating from 50 critics with an average rating of 8.61 out of 10. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '787d472cb3c90791ae12e65f7cc245c3'}>,\n",
-       "  <Document: {'content': 'On Rotten Tomatoes, the fifth season has a 93% approval rating from 50 critics with an average rating of 8.61 out of 10. The site\\'s critical consensus reads, \"Bloody action and extreme power plays return full throttle, as \\'\\'Game of Thrones\\'\\' enjoys a new-found liberation from the world of the source material, resulting in more unexpected thrills.\"One notable exception is the sixth episode, \"Unbowed, Unbent, Unbroken\", which was heavily criticized due to the showrunners\\' decision to have a well-liked character suffer a sexual assault. It received a rating of 54% on Rotten Tomatoes, lower than any previous episode of the show. The eighth episode, \"Hardhome\", received significant praise and was immediately regarded as one of the best episodes in the series. According to \\'\\'Business Insider\\'\\', \"Despite the attacks, \\'\\'Game of Thrones\\'\\' remains a steady hit.\"Season 5 featured more significant changes from the novels. Some of the changes were more well-received than others (the sexual assault scene was heavily criticized and caused great controversy). The various plotlines for the season also differed significantly in reception. The Dornish subplot received an overwhelmingly negative reception. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'c2f7ec7b363203870aa5d82ab00b5ea4'}>,\n",
-       "  <Document: {'content': 'Some of the changes were more well-received than others (the sexual assault scene was heavily criticized and caused great controversy). The various plotlines for the season also differed significantly in reception. The Dornish subplot received an overwhelmingly negative reception. In particular, the hyped Sand Snakes were maligned by critics and fans alike, as many found them cheesy and unlikeable. In contrast, Tyrion\\'s storyline was highly praised. Many fans felt that his subplot progressed overly slowly in the novels, and his meeting with Daenerys was particularly lauded.\\n\\n===Ratings===\\n\\nThe gross viewing figure per episode for the show, which include streaming, DVR recordings and repeat showings, averaged at 20 million this season.\\n\\nLive +7 ratings were not available, so Live +3 ratings have been used instead.\\n\\n\\'\\'Business Insider\\'\\' noted a significant difference in the ratings between this season and previous seasons. The typical pattern involves \"Solid premiere viewings followed by a slow but steady gain of momentum through to the finale. Historically, the finale episodes of each season have beaten the premiere for total number of viewers\". However, this season showed a significant decline from 8 million viewers at the premiere to 5.4 million at the seventh episode, \"The Gift\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'ea75daf400fa7ece48a757c12602ef01'}>,\n",
-       "  <Document: {'content': 'However, this season showed a significant decline from 8 million viewers at the premiere to 5.4 million at the seventh episode, \"The Gift\". \\'\\'Business Insider\\'\\' cites two likely reasons for the lower ratings: backlash over the rape scene in \"Unbowed, Unbent, Unbroken\", one of many deviations from the novels throughout the season that \"have upset fans,\" and increased online streaming through HBO Now. It also acknowledged a possible effect of the first four episodes leaking online before the season premiere and the decision to air on Memorial Day weekend in the United States (a weekend that had been skipped in previous seasons). \\'\\'Business Insider\\'\\' reached out to HBO for comment, HBO responded that it is seeing an increase in overall viewer numbers this season taking into account streaming services, which are not included in Nielsen ratings. The dip in ratings did not extend to the eighth and ninth episodes, \"Hardhome,\" and \"The Dance of Dragons\", which were both seen by over 7 million viewers. The season finale, \"Mother\\'s Mercy\" was seen by 8.11 million people, setting a new record and making it the most watched episode of the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'd25ac3839fb3c8335bb167138d352853'}>,\n",
-       "  <Document: {'content': 'The season finale, \"Mother\\'s Mercy\" was seen by 8.11 million people, setting a new record and making it the most watched episode of the series.\\n\\n===Accolades===\\n\\nFor the 5th Critics\\' Choice Television Awards, the series was nominated for Best Drama Series. For the 31st TCA Awards, the series was nominated for Program of the Year and Outstanding Achievement in Drama. For the 67th Primetime Emmy Awards, the series received 24 nominations, the most of any series. It won 12 awards, including Outstanding Drama Series, Peter Dinklage for Outstanding Supporting Actor in a Drama Series, David Benioff and D. B. Weiss for Outstanding Writing for a Drama Series for \"Mother\\'s Mercy\", and David Nutter for Outstanding Directing for a Drama Series for \"Mother\\'s Mercy\". Nominations included Lena Headey and Emilia Clarke both for Outstanding Supporting Actress in a Drama Series, Diana Rigg for Outstanding Guest Actress in a Drama Series, and Jeremy Podeswa for Outstanding Directing for a Drama Series for \"Unbowed, Unbent, Unbroken\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'dba7f59b098f34d9642d65ca2088b371'}>,\n",
-       "  <Document: {'content': 'Nominations included Lena Headey and Emilia Clarke both for Outstanding Supporting Actress in a Drama Series, Diana Rigg for Outstanding Guest Actress in a Drama Series, and Jeremy Podeswa for Outstanding Directing for a Drama Series for \"Unbowed, Unbent, Unbroken\".\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n5th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n31st TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nSophie Turner\\n\\n67th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter for \"Mother\\'s Mercy\"\\n\\nJeremy Podeswa for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\n67th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\nAnette Haellmigk for \"Sons of the Harpy\"\\n\\nRob McLachlan for \"The Dance of Dragons\"\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham for \"The Dance of Dragons\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount for Mother\\'s Mercy\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews for \"Mother\\'s Mercy\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower for \"Hardhome\"\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \"Hardhome\"\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit for \"Hardhome\"\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau for \"The Dance of Dragons\"\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nE! ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '7391e0412687b3fa706579ba1a90485c'}>,\n",
-       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n5th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n31st TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nSophie Turner\\n\\n67th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter for \"Mother\\'s Mercy\"\\n\\nJeremy Podeswa for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\n67th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\nAnette Haellmigk for \"Sons of the Harpy\"\\n\\nRob McLachlan for \"The Dance of Dragons\"\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham for \"The Dance of Dragons\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount for Mother\\'s Mercy\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews for \"Mother\\'s Mercy\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower for \"Hardhome\"\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \"Hardhome\"\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit for \"Hardhome\"\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau for \"The Dance of Dragons\"\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nE! Online Best. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'a65be222eee4271207bf1323ee7636d7'}>,\n",
-       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n5th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n31st TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nSophie Turner\\n\\n67th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter for \"Mother\\'s Mercy\"\\n\\nJeremy Podeswa for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\n67th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\nAnette Haellmigk for \"Sons of the Harpy\"\\n\\nRob McLachlan for \"The Dance of Dragons\"\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham for \"The Dance of Dragons\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount for Mother\\'s Mercy\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews for \"Mother\\'s Mercy\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower for \"Hardhome\"\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \"Hardhome\"\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit for \"Hardhome\"\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau for \"The Dance of Dragons\"\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nE! Online Best. Ever. TV. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '859d3ca71dd4fd11a3186d84f79ce774'}>,\n",
-       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n5th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n31st TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nSophie Turner\\n\\n67th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter for \"Mother\\'s Mercy\"\\n\\nJeremy Podeswa for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\n67th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\nAnette Haellmigk for \"Sons of the Harpy\"\\n\\nRob McLachlan for \"The Dance of Dragons\"\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham for \"The Dance of Dragons\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount for Mother\\'s Mercy\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews for \"Mother\\'s Mercy\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower for \"Hardhome\"\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \"Hardhome\"\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit for \"Hardhome\"\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau for \"The Dance of Dragons\"\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nE! Online Best. Ever. TV. Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Post Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Color Grading\\n\\nJoe Finley for \"Hardhome\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Hardhome\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani for \"The Dance of Dragons\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"Hardhome\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley for \"Hardhome\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n20th Satellite Awards\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\nIGN People\\'s Choice Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nGuinness World Records\\n\\nLargest TV drama simulcast\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost viewers sharing a single torrent file simultaneously\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n42nd People\\'s Choice Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\nShorty Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGIF of the Year\\n\\n\\'\\'Game of Thrones\\'\\' Come At Me Bro\\n\\nEmpire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nArt Directors Guild Awards 2015\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley for  \"High Sparrow\", \"Unbowed, Unbent, Unbroken\" and \"Hardhome\"\\n\\nScreenwriters Choice Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nLocation Managers Guild Awards\\n\\nOutstanding Locations in Period Television\\n\\nRobert Boake and Tate Araez\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss for \"Hardhome\"\\n\\nAmerican Cinema Editors Awards 2016\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nTim Porter for \"Hardhome\"\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton for \\'\\'Game of Thrones\\'\\'\\n\\n68th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nDavid Nutter for \"Mother’s Mercy\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\n73rd Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n13th Irish Film & Television Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nMTV Millennial Awards\\n\\nKiller Series of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n21st National Television Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProducers Guild of America Awards 2015\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n42nd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O\\'Connor\\n\\n22nd Screen Actors Guild Awards\\n\\nOutstanding Action Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aiden Gillen, Ian Glen, Kit Harington, Lena Headey, Michiel Huisman, Brenock O\\'Conner, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams and Tom Wlaschiha\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nGlamour Awards 2016\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Hardhome\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"Hardhome\"\\n\\nVisual Effects Society Awards 2015\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon for \"The Dance of Dragons\"\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer\\' for \"Mother\\'s Mercy\" - Wounded Drogon\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen for \"Dance of Dragons\" - Drogon Arena Rescue\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur for \"City of Volantis\"\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson for \"Drogon Arena\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés for \"Hardhome\"\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela for \"Hardhome\"\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"Drogon Arena\"\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding for \"Drogon Lair\"\\n\\nWriters Guild of America Awards 2015\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan for \"The Dance of Dragons\"\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast to 170 countries by HBO and its broadcast partners. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '7f8a95fcae771c61b52a5010f3302dec'}>,\n",
-       "  <Document: {'content': 'Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Post Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Color Grading\\n\\nJoe Finley for \"Hardhome\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Hardhome\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani for \"The Dance of Dragons\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"Hardhome\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley for \"Hardhome\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n20th Satellite Awards\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\nIGN People\\'s Choice Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nGuinness World Records\\n\\nLargest TV drama simulcast\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost viewers sharing a single torrent file simultaneously\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n42nd People\\'s Choice Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\nShorty Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGIF of the Year\\n\\n\\'\\'Game of Thrones\\'\\' Come At Me Bro\\n\\nEmpire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nArt Directors Guild Awards 2015\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley for  \"High Sparrow\", \"Unbowed, Unbent, Unbroken\" and \"Hardhome\"\\n\\nScreenwriters Choice Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nLocation Managers Guild Awards\\n\\nOutstanding Locations in Period Television\\n\\nRobert Boake and Tate Araez\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss for \"Hardhome\"\\n\\nAmerican Cinema Editors Awards 2016\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nTim Porter for \"Hardhome\"\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton for \\'\\'Game of Thrones\\'\\'\\n\\n68th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nDavid Nutter for \"Mother’s Mercy\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\n73rd Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n13th Irish Film & Television Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nMTV Millennial Awards\\n\\nKiller Series of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n21st National Television Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProducers Guild of America Awards 2015\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n42nd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O\\'Connor\\n\\n22nd Screen Actors Guild Awards\\n\\nOutstanding Action Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aiden Gillen, Ian Glen, Kit Harington, Lena Headey, Michiel Huisman, Brenock O\\'Conner, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams and Tom Wlaschiha\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nGlamour Awards 2016\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Hardhome\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"Hardhome\"\\n\\nVisual Effects Society Awards 2015\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon for \"The Dance of Dragons\"\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer\\' for \"Mother\\'s Mercy\" - Wounded Drogon\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen for \"Dance of Dragons\" - Drogon Arena Rescue\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur for \"City of Volantis\"\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson for \"Drogon Arena\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés for \"Hardhome\"\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela for \"Hardhome\"\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"Drogon Arena\"\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding for \"Drogon Lair\"\\n\\nWriters Guild of America Awards 2015\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan for \"The Dance of Dragons\"\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast to 170 countries by HBO and its broadcast partners. In some countries, it aired the day after its first release. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '4a53b2aadbe92075acdc7ca63001422b'}>,\n",
-       "  <Document: {'content': 'Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Post Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters for \"Hardhome\"\\n\\nOutstanding Color Grading\\n\\nJoe Finley for \"Hardhome\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Hardhome\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani for \"The Dance of Dragons\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"Hardhome\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley for \"Hardhome\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n20th Satellite Awards\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\nIGN People\\'s Choice Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nGuinness World Records\\n\\nLargest TV drama simulcast\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost viewers sharing a single torrent file simultaneously\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n42nd People\\'s Choice Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\nShorty Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGIF of the Year\\n\\n\\'\\'Game of Thrones\\'\\' Come At Me Bro\\n\\nEmpire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nArt Directors Guild Awards 2015\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley for  \"High Sparrow\", \"Unbowed, Unbent, Unbroken\" and \"Hardhome\"\\n\\nScreenwriters Choice Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nLocation Managers Guild Awards\\n\\nOutstanding Locations in Period Television\\n\\nRobert Boake and Tate Araez\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss for \"Hardhome\"\\n\\nAmerican Cinema Editors Awards 2016\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nKatie Weiland for \"The Dance of Dragons\"\\n\\nTim Porter for \"Hardhome\"\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton for \\'\\'Game of Thrones\\'\\'\\n\\n68th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nDavid Nutter for \"Mother’s Mercy\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Hardhome\"\\n\\n73rd Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n13th Irish Film & Television Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nMTV Millennial Awards\\n\\nKiller Series of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n21st National Television Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProducers Guild of America Awards 2015\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n42nd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O\\'Connor\\n\\n22nd Screen Actors Guild Awards\\n\\nOutstanding Action Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aiden Gillen, Ian Glen, Kit Harington, Lena Headey, Michiel Huisman, Brenock O\\'Conner, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams and Tom Wlaschiha\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nGlamour Awards 2016\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel for \"Hardhome\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Hardhome\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"Hardhome\"\\n\\nVisual Effects Society Awards 2015\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon for \"The Dance of Dragons\"\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer\\' for \"Mother\\'s Mercy\" - Wounded Drogon\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen for \"Dance of Dragons\" - Drogon Arena Rescue\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur for \"City of Volantis\"\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson for \"Drogon Arena\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés for \"Hardhome\"\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela for \"Hardhome\"\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"Drogon Arena\"\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding for \"Drogon Lair\"\\n\\nWriters Guild of America Awards 2015\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"Mother\\'s Mercy\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan for \"The Dance of Dragons\"\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Unbowed, Unbent, Unbroken\"\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast to 170 countries by HBO and its broadcast partners. In some countries, it aired the day after its first release. Sky Atlantic, the network serving the United Kingdom and Ireland, aired the premiere the day after HBO, but joined the simulcast for the rest of the season.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': 'd55e5cef91a9778a472621b5344ef06d'}>,\n",
-       "  <Document: {'content': \"Sky Atlantic, the network serving the United Kingdom and Ireland, aired the premiere the day after HBO, but joined the simulcast for the rest of the season.\\n\\n===Marketing===\\nA half-hour documentary, ''Game of Thrones: A Day in the Life'', aired on HBO on February 8, 2015. It covered one day of production of season 5 on three sets in Belfast, Dubrovnik and Osuna from the viewpoint of key crew members. The first official trailer for season 5 was released on January 30, 2015, and the season's second trailer was released on March 9, 2015. The world premiere of the first episode of the fifth season was held at the Tower of London on March 18, 2015.\\n\\n===Home media===\\nThe season was released on Blu-ray and DVD on March 15, 2016, in region 1 and March 14, 2016, in region 2.\\n\\n===Illegal distribution===\\nOn April 11, prior to the airing of the season's first episode, screener copies of the first four episodes were leaked to several file sharing sites. According to TorrentFreak, 18 million different IP addresses downloaded the leaked episodes, totaling 32 million downloads during the first week. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': '74951450a7bdf08c9250fa9ba28fed44'}>,\n",
-       "  <Document: {'content': \"According to TorrentFreak, 18 million different IP addresses downloaded the leaked episodes, totaling 32 million downloads during the first week. The fifth season of ''Game of Thrones'' was the most-pirated TV series in 2015.\\n\\n==References==\\n\\n==External links==\\n*  – official US site\\n*  – official UK site\\n*  ''Game of Thrones'' – The Viewers Guide on HBO.com\\n*  Making ''Game of Thrones'' on HBO.com\\n*\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': '3ad0560ccfad8ba389bb6d683f9107ad'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Baelor\\'\\'\\'\" is the ninth and penultimate episode of the first season of the HBO medieval fantasy television series \\'\\'Game of Thrones\\'\\'. First aired on June 12, 2011, it was written by the show\\'s creators and executive producers David Benioff and D. B. Weiss, and directed by Alan Taylor, his directorial debut for the series.\\n\\nThe plot depicts Eddard Stark (Sean Bean), imprisoned and accused of high treason, struggling with the decision whether to falsely confess to save his daughters, and he is ultimately beheaded by King Joffrey Baratheon (Jack Gleeson). Stark\\'s wife Catelyn (Michelle Fairley) negotiates with Lord Walder Frey (David Bradley) for the use of a strategic river crossing as their son Robb (Richard Madden) fights his first battle in the war against the Lannisters. Meanwhile, Jon Snow (Kit Harington) discovers a secret about Maester Aemon (Peter Vaughan), and Daenerys Targaryen (Emilia Clarke) stands up to Dothraki soldier Qotho (Dar Salim) and challenges Dothraki traditions to care for Khal Drogo (Jason Momoa). The title refers to the great Sept (church) in King\\'s Landing where Ned meets his fate.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'ef4990d40c60ff4a9853a71ced4698dd'}>,\n",
-       "  <Document: {'content': 'Meanwhile, Jon Snow (Kit Harington) discovers a secret about Maester Aemon (Peter Vaughan), and Daenerys Targaryen (Emilia Clarke) stands up to Dothraki soldier Qotho (Dar Salim) and challenges Dothraki traditions to care for Khal Drogo (Jason Momoa). The title refers to the great Sept (church) in King\\'s Landing where Ned meets his fate.\\n\\nThe episode received great acclaim among critics, who cited the final scene with Eddard Stark\\'s beheading as a highlight for the series, calling it a \"daring, tragic finish.\"In the United States, the episode achieved a viewership of 2.66 million in its initial broadcast. The episode was nominated for an Emmy Award for Outstanding Writing for a Drama Series and Peter Dinklage won the award for Outstanding Supporting Actor in a Drama Series for his performance.\\n\\n==Plot==\\n===In the Riverlands===\\nThe Stark army reaches the Twins, a bridge stronghold controlled by Walder Frey, who agrees to allow the army to cross the river and to commit his troops in return for Robb and Arya Stark marrying two of his children.\\n\\nTyrion Lannister suspects his father Tywin, who decides Tyrion and his barbarians will fight in the vanguard, wants him killed. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'e4a82c0744b78cedc52768fa911d5d29'}>,\n",
-       "  <Document: {'content': \"Tyrion Lannister suspects his father Tywin, who decides Tyrion and his barbarians will fight in the vanguard, wants him killed. As Tyrion, Bronn, and the prostitute Shae swap stories, Tyrion reveals he was married to a woman his father revealed was a prostitute, and made Tyrion watch as his guardsmen raped her.\\n\\nAs a Stark force approaches, Tyrion is trampled in the rush and regains consciousness to find the battle over. Tywin discovers the Stark host was only 2,000 men, not the 20,000 he was led to expect.\\n\\nRobb, having divided his forces, defeats Jaime Lannister's army with his remaining 18,000 men and captures Jaime.\\n\\n===At the Wall===\\nJeor Mormont gives Jon Snow his ancestral Valyrian steel sword Longclaw. Jon is upset he cannot join Robb against the Lannisters.\\n\\nMaester Aemon reveals to Jon that he is Aemon Targaryen, the Mad King Aerys's uncle and Daenerys Targaryen's great-uncle, and advises Jon that the choice he must make between the Night's Watch and his family will haunt him the rest of his life.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '49955c729238d425a1dc43f963adff96'}>,\n",
-       "  <Document: {'content': 'Maester Aemon reveals to Jon that he is Aemon Targaryen, the Mad King Aerys\\'s uncle and Daenerys Targaryen\\'s great-uncle, and advises Jon that the choice he must make between the Night\\'s Watch and his family will haunt him the rest of his life.\\n\\n===In Lhazar===\\nKhal Drogo is weakened by his infected wound, and Jorah warns that if Drogo dies, his bloodriders will fight to be his successor and kill Daenerys and her unborn child. Daenerys refuses to abandon her husband, and encourages Mirri Maz Duur to use blood magic. She prepares a spell, warning that no one may enter the tent, and Jorah kills Qotho when he tries to intervene. Daenerys goes into premature labor, but the Dothraki midwives refuse to help. Desperate, Jorah carries Daenerys into Drogo\\'s tent to seek Mirri\\'s help.\\n\\n===In King\\'s Landing===\\nAfter Varys tells him that Sansa Stark\\'s life is also at stake, Eddard \"Ned\" Stark agrees to make a false confession and swear loyalty to King Joffrey Baratheon.\\n\\nArya Stark finds a crowd gathering to watch her father be judged, and climbs onto the statue of Baelor the Blessed. Ned notices Arya and alerts Night\\'s Watch recruiter Yoren. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'ec5c1df06f24c1f79006b017887c5b60'}>,\n",
-       "  <Document: {'content': 'Arya Stark finds a crowd gathering to watch her father be judged, and climbs onto the statue of Baelor the Blessed. Ned notices Arya and alerts Night\\'s Watch recruiter Yoren. Before Sansa, Cersei Lannister, Joffrey and the Small Council, Ned confesses to treason and swears fealty to Joffrey. Instead of sparing Ned as promised, Joffrey orders him to be executed. Seeing that Arya has been rescued by Yoren, Ned accepts his fate and is beheaded.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by the showrunners David Benioff and D. B. Weiss, based on the original book by George R. R. Martin.\\n\\nThe title of the episode refers to the Great Sept of Baelor, the main religious building in King\\'s Landing, where the episode\\'s pivotal scene takes place. In the world created by George R. R. Martin, Baelor I Targaryen was a king during a previous century, revered as a patron and supporter of the Faith of the Seven. \"Baelor\" includes the content of the book\\'s chapters Eddard XV, Catelyn IX, Jon VIII, Tyrion VIII, Catelyn X, Daenerys VIII and Arya V (59–61 and 63–66).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '189b6dcd64bb6151a205559ad128b6e2'}>,\n",
-       "  <Document: {'content': '\"Baelor\" includes the content of the book\\'s chapters Eddard XV, Catelyn IX, Jon VIII, Tyrion VIII, Catelyn X, Daenerys VIII and Arya V (59–61 and 63–66).\\n\\nThe scene with the drinking game between Tyrion, Bronn and Shae was written specifically for the episode, but the story of Tyrion\\'s ill-fated marriage to Tysha was taken from a previous chapter of the books. Shae\\'s background was changed from Westerosi to foreign to accommodate Kekilli\\'s accent. Other notable divergence from the books include the modification of the whole strategy of Robb Stark when dividing his forces, and a change to the Targaryen genealogy as explained by Maester Aemon: in the TV adaptation the Mad King is described as the son, rather than the grandson, of Aegon V (thus eliminating Jaehaerys II from the succession of kings).\\n\\n===Casting===\\nSibel Kekilli plays the role of the prostitute Shae.\\n\"Baelor\" marks the first appearance of the German actress Sibel Kekilli, in the role of the prostitute Shae. Executive producer George R. R. Martin commented that she was extraordinary in her audition, in which she read the scene where Shae meets Tyrion in a tent the night before the battle of the Green Fork. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '55dfeee8abf3a9f7d01cdfa9d630f52b'}>,\n",
-       "  <Document: {'content': 'Executive producer George R. R. Martin commented that she was extraordinary in her audition, in which she read the scene where Shae meets Tyrion in a tent the night before the battle of the Green Fork. According to Martin, \"a lot of beautiful young women read for Shae. ... But there\\'s another dimension to Shae as well. She\\'s not as practiced and hardened at this as a more seasoned pro. There\\'s still a girl next door quality to her, a sense of vulnerability, playfulness, and, yes, innocence. ... All of our Shaes were hot as hell. But only a handful of them captured that other quality, maybe three out of twenty, and Sibel was the standout. ... Watching those auditions, any red-blooded male would want to take every one of our Shae candidates to bed. But Sibel made you fall in love with her as well.\"Also introduced in this episode was the English actor David Bradley, playing the role of Walder Frey.\\n\\nThe scenes at the Great Sept of Baelor were filmed at Fort Manoel, in Malta.\\n\\n===Filming locations===\\nThe interiors of the episode were filmed at the Paint Hall studios, close to Belfast. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '131a352ee8805186bf1e8fb8d5989e11'}>,\n",
-       "  <Document: {'content': 'The scenes at the Great Sept of Baelor were filmed at Fort Manoel, in Malta.\\n\\n===Filming locations===\\nThe interiors of the episode were filmed at the Paint Hall studios, close to Belfast. The area of the Castle Ward estate, also in Northern Ireland, was used to film on location the Stark and Lannister camps, the Crossing, and the battlefields of the Green Fork and the Whispering Woods.\\n\\nThe climactic scene before the Great Sept of Baelor was shot at Fort Manoel, in the Maltese town of Gżira. The filming took place in the last week of October 2010.\\n\\n===Execution===\\n\\nYears later, showrunner D. B. Weiss commented that the explicit style of the execution scene was selected in part to make it clear to the viewers that Eddard Stark, despite being the arguable protagonist of season one, actually was dead: \"It’s that rule: \\'If you don\\'t see the body then they’re not really dead.\\'Like when we cut Ned’s head off, we didn’t want a gory Monty Python geyser of blood, but we needed to see the blade enter his neck and cut out on the frame where the blade was mid-neck. ... we needed Ned\\'s death to be totally unambiguous.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '5bac2ec9e3eabd9f251bb0ecfd600694'}>,\n",
-       "  <Document: {'content': 'Like when we cut Ned’s head off, we didn’t want a gory Monty Python geyser of blood, but we needed to see the blade enter his neck and cut out on the frame where the blade was mid-neck. ... we needed Ned\\'s death to be totally unambiguous.\"==Reception==\\n\\n===Ratings===\\n\"Baelor\" gathered 2.7 million viewers in its premiere telecast in the United States, equaling the season high reached with the previous week\\'s episode. The total for the night, including the repeat, was slightly lower, with 3.4\\xa0million viewers. In the United Kingdom, the episode was viewed by 1.043 million viewers, making it the highest-rated broadcast that week.\\n\\n===Critical response===\\nThe episode received great acclaim among the critics. Review aggregator Rotten Tomatoes surveyed 19 reviews of the episode and judged 100% of them to be positive with an average score of 9.5 out of 10. The website\\'s critical consensus reads, \"Veering slightly from the source material, \"Baelor\" sports excellent performances and a bold, unprecedented climax.\"From the reviewers of \\'\\'The A.V. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'b6a29640af1da0dee7d5333394e40547'}>,\n",
-       "  <Document: {'content': 'The website\\'s critical consensus reads, \"Veering slightly from the source material, \"Baelor\" sports excellent performances and a bold, unprecedented climax.\"From the reviewers of \\'\\'The A.V. Club\\'\\', where it was rated  with an A, Emily VanDerWerff called it \"unquestionably the finest episode of \\'\\'Game Of Thrones\\'\\' yet,\" and David Sims found it \"terrific\" and with a conclusion that would be \"sure to blow the minds (and break the hearts)\" of the watchers. Matt Fowler of IGN TV gave the episode a perfect \"10\" saying that it was a \"clean and epic entry with a daring, tragic finish\" that had \"an admirable undercurrent of audience contempt.\"The focus of most reviews was in the climactic final scene, whose directing and acting is universally acclaimed by critics. Writing for Cultural Learnings, Myles McNutt stated: \"the final shot, with Arya looking to the sky as everything goes to silence and all she sees is the birds flying was just wonderfully haunting. Alan Taylor\\'s direction sold both the chaos and the resignation of that moment.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '1261bd910a505cf8a0d0b7705f425be8'}>,\n",
-       "  <Document: {'content': 'Writing for Cultural Learnings, Myles McNutt stated: \"the final shot, with Arya looking to the sky as everything goes to silence and all she sees is the birds flying was just wonderfully haunting. Alan Taylor\\'s direction sold both the chaos and the resignation of that moment.\"HitFix\\'s Alan Sepinwall felt that \"that final scene was so gorgeously shot, and the weariness of Bean\\'s performance and the horror of Maisie Williams\\' so perfectly conveyed the emotions of it, even as things seemed so chaotic.\"The emotional charge of the scene hit home for many reviewers: Scott Meslow of \\'\\'The Atlantic\\'\\' called it \"an absolutely nightmarish scene\" and labelled Eddard\\'s death \"horrific in its indignity.\"Jace Lacob from Televisionary and Maureen Ryan from AOL TV admitted having shed tears at the episode\\'s dramatic conclusion. The latter found the scene \"masterful\" and felt that the visual medium and Alan Taylor\\'s excellent work had made it more powerful than the book\\'s original version.\\n\\nBesides the final scene, other aspects were discussed: Garcia noted the acting of Richard Madden and how the Freys had been introduced. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '7d9ec4ac665b197b041c1f8c57e8c45e'}>,\n",
-       "  <Document: {'content': 'Besides the final scene, other aspects were discussed: Garcia noted the acting of Richard Madden and how the Freys had been introduced. Ryan praised the wide range of emotions used by Emilia Clarke while playing Daenerys, and how Peter Dinklage played Tyrion\\'s frustration and confusion during the episode. Both she and McNutt were glad that Tyrion\\'s exposition scene in the tent with Bronn and Shae did not use sex to keep viewers, as was done in past episodes.\\n\\nThere was debate about the merits of the producer\\'s decision to avoid depicting the two battles between the Starks and Lannisters. Ryan criticized it and confessed being \"a little disappointed that many of the major characters are caught up in a war and we\\'re not seeing it.\"Sims regretted not seeing the fight, and although he falsely claimed to understand the budget constrictions, he felt that \"all this off-screen fighting is just getting my blood rushing for some on-screen fighting.\"Sepinwall concludes: \"Ideally, we\\'d get a few epic, \\'\\'Braveheart\\'\\'-level battle scenes at some point, but I also respect the demands of time and budget here. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'acb6b08a4bafcdc92117a9b59b6c2aa3'}>,\n",
-       "  <Document: {'content': 'Sepinwall concludes: \"Ideally, we\\'d get a few epic, \\'\\'Braveheart\\'\\'-level battle scenes at some point, but I also respect the demands of time and budget here. Those kinds of sequences cost a fortune, and they eat up a lot of screen time, and I think ultimately I\\'d have rather had the time, say, that we spent in Tyrion\\'s tent the night before the battle, with the mortifying story of his ex-wife, and then whatever it cost to make the execution sequence look as good as it did, than for the episode to have given us one or two long fight scenes.\"In 2013, \\'\\'TV Guide\\'\\' ranked the final scene as the second greatest twist of all time.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'c4736dc6d7490fbc64e64470bae7fec0'}>,\n",
-       "  <Document: {'content': 'Those kinds of sequences cost a fortune, and they eat up a lot of screen time, and I think ultimately I\\'d have rather had the time, say, that we spent in Tyrion\\'s tent the night before the battle, with the mortifying story of his ex-wife, and then whatever it cost to make the execution sequence look as good as it did, than for the episode to have given us one or two long fight scenes.\"In 2013, \\'\\'TV Guide\\'\\' ranked the final scene as the second greatest twist of all time.\\n\\n==== Awards and nominations ====\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2011\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\nBest TV Twist\\n\\nIGN People\\'s Choice Awards\\n\\nBest TV Episode\\n\\nBest TV Twist\\n\\n2012\\n\\nAmerican Cinema Editors\\n\\nBest Edited One-Hour Series for Non-Commercial Television\\n\\nFrances Parker\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Mark Taylor\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Baelor\" at HBO\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '7cbefa35692834f37b14f01a173fd3b5'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Fire and Blood\\'\\'\\'\" is the tenth and final episode of the first season of the HBO medieval fantasy television series \\'\\'Game of Thrones\\'\\'. First aired on June 19, 2011, it was written by the show\\'s creators and executive producers David Benioff and D. B. Weiss, and directed by Alan Taylor.\\n\\nThe title of the episode is the motto of House Targaryen, and alludes to the aftermath of the previous episode\\'s climactic events. The episode\\'s action revolves around the Starks\\' reactions to Eddard Stark\\'s execution: Sansa is taken hostage, Arya flees in disguise, Robb and Catelyn lead an army against the Lannisters, and Jon Snow struggles with his divided loyalty. Across the narrow sea, Daenerys must deal with the blood magic that has robbed her of her husband, her son, and her army.\\n\\nThe episode was well received by critics, who singled out the closing scene as a particularly strong way to end the first season. In the United States, the episode achieved a viewership of 3.04\\xa0million in its initial broadcast. This episode was nominated for the Primetime Emmy Award for Outstanding Special Visual Effects for a Series.\\n\\n==Plot==\\n===At Winterfell===\\nMaester Luwin informs Bran and Rickon of Ned\\'s execution.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '9745dd6b3af224033b1d9e2624d3de87'}>,\n",
-       "  <Document: {'content': 'This episode was nominated for the Primetime Emmy Award for Outstanding Special Visual Effects for a Series.\\n\\n==Plot==\\n===At Winterfell===\\nMaester Luwin informs Bran and Rickon of Ned\\'s execution.\\n\\n===In the Riverlands===\\nAt the Stark army camp, Robb vows revenge on the Lannisters after Ned\\'s death, but Catelyn says they must first rescue Arya and Sansa. The Starks followers now support Northern independence, proclaiming Robb the \"King in the North\", rather than support Stannis or Renly Baratheon, who have both claimed the Iron Throne. Jaime tells Catelyn he pushed Bran out of the tower window, but does not explain why.\\n\\nAt the Lannister army camp, Tywin, unable to sue for peace with the Starks after Ned\\'s execution, orders Tyrion to go to King\\'s Landing in his stead as \"Hand of the King\" to keep Joffrey under control. Against his father\\'s orders, Tyrion brings Shae with him.\\n\\n===In King\\'s Landing===\\nJoffrey forces Sansa to look at Ned and his household staff\\'s severed heads on spikes. When Sansa says she wishes to see Joffrey\\'s head mounted there after Joffrey says Robb\\'s head will be, Joffrey has Ser Meryn slap her. The Hound advises Sansa to obey Joffrey for her own safety.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '132911597e419d813963709f1f97f19d'}>,\n",
-       "  <Document: {'content': 'When Sansa says she wishes to see Joffrey\\'s head mounted there after Joffrey says Robb\\'s head will be, Joffrey has Ser Meryn slap her. The Hound advises Sansa to obey Joffrey for her own safety.\\n\\nArya, rescued by Night\\'s Watch recruiter Yoren, escapes with him under the alias of \"Arry\", a boy, to the Wall with his new recruits, including Lommy, Hot Pie, and Gendry, the late Robert\\'s unknowing bastard son.\\n\\n===At the Wall===\\nJon attempts to desert the Night\\'s Watch to join Robb and avenge Ned, but Sam, Pyp, and Grenn convince him to return. The next morning, Jeor, despite knowing Jon attempted to desert, orders him to join him in an expedition beyond the Wall, intended to counter the threats of the wildlings and the White Walkers, and to find Benjen Stark.\\n\\n===In Lhazar===\\nDaenerys learns that her unborn son died due to Mirri\\'s spell. Furthermore, although Drogo\\'s life was saved, he has fallen into a catatonic state, causing most of his followers to abandon him. Mirri reveals that she caused this to avenge the destruction of her village and her people. Daenerys mercy kills Drogo.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '6ef2546140c8f92f2097d36addf8e4e0'}>,\n",
-       "  <Document: {'content': \"Mirri reveals that she caused this to avenge the destruction of her village and her people. Daenerys mercy kills Drogo.\\n\\nDaenerys has Mirri tied to Drogo's funeral pyre, with her dragons eggs placed on top. Daenerys declares herself queen of a new khalasar and steps into the lit pyre. By daybreak, Jorah and her remaining loyalists find her unharmed in the ashes, carrying three dragon hatchlings, the first born in decades. Amazed, they bow to Daenerys.\\n\\n==Production==\\n===Writing===\\nThe episode was written by showrunners David Benioff and D. B. Weiss. Like the rest of the first season, it adapts the plot of ''A Game of Thrones,'' the first novel in the ''A Song of Ice and Fire'' series by George R. R. Martin. The episode covers the novel's chapters 66 to 73, that is, Arya V, Bran VII, Sansa VI, Daenerys IX, Tyrion IX, Jon IX, Catelyn XI, and Daenerys X. It also covers part of the second novel, ''A Clash of Kings'': Arya I (chapter 2) and part of Catelyn VII (chapter 55). \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '5c3628f383b2adf27f47a1d483a65d7b'}>,\n",
-       "  <Document: {'content': 'It also covers part of the second novel, \\'\\'A Clash of Kings\\'\\': Arya I (chapter 2) and part of Catelyn VII (chapter 55). Scenes added for the adaptation include Catelyn and Robb receiving news of Eddard\\'s death, the revelation of Cersei and Lancel Lannister\\'s relationship, as well as interactions between Grand Maester Pycelle, the prostitute Ros, Varys, and Littlefinger.\\n\\n===Filming===\\nThe dragons featured in the episode\\'s finale were implemented by BlueBolt, the lead VFX agency for the first season. VFX supervisor Angela Barson confirmed that the CGI dragons were among the most stressful effects, prompting sleepless nights. Commenting on the episode\\'s climactic scene where the hatchling dragons are revealed, actress Emilia Clarke told VH1, \"You see the relationship that Dany has with her eggs, and you see that grow and grow and grow and kind of the intuitive connection she has with them, you see that develop really beautifully\". Clarke also hinted that she expected to \"get to play with some more dragons!\"in the second season, based on her conversations with book author and executive producer George R.R. Martin. \"VFX Data Wrangler\" Naill McEvoy later confirmed that dragon presence would be increasing in season two.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'a54b9dd9233f5b82efdda34d6aae98be'}>,\n",
-       "  <Document: {'content': 'in the second season, based on her conversations with book author and executive producer George R.R. Martin. \"VFX Data Wrangler\" Naill McEvoy later confirmed that dragon presence would be increasing in season two.\\n\\nIn the scene where Joffrey forces Sansa to view the heads of Ned and his entourage on spikes, one of the prosthetic heads briefly seen in profile is that of former U.S. president George W. Bush. In their commentary on the DVD release of season 1, David Benioff and D.B. Weiss explained that this was not meant as a political statement, but rather because the production used the prosthetic heads that happened to be at hand. Following media reports in June 2012, HBO apologized for this shot, which their statement described as \"unacceptable, disrespectful and in very bad taste\". The statement also said that the shot would be edited for any future home video production and TV broadcasts. HBO removed the episode from digital download services until the scene was edited. In the edited scene, the head bears no resemblance to Bush.\\n\\n==Reception==\\n===Airings and ratings===\\n\"Fire and Blood\" was first aired on HBO in the U.S. and Canada on June 19, 2011. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'cef83c00803182da9354b0e231b356a2'}>,\n",
-       "  <Document: {'content': '==Reception==\\n===Airings and ratings===\\n\"Fire and Blood\" was first aired on HBO in the U.S. and Canada on June 19, 2011. The episode was the most watched episode of the season and was viewed by an estimated 3.041\\xa0million viewers and received a 1.4\\xa0share among adults between the ages of 18 and 49. This means that it was seen by 1.4% of all 18- to 49-year-olds at the time of the broadcast. With repeats, the episode brought in 3.9\\xa0million total viewers. In the United Kingdom, the episode was viewed by 1.314\\xa0million viewers, making it the highest-rated broadcast that week.\\n\\n===Critical response===\\n\"Fire and Blood\" received positive reviews, and much critical acclaim for the closing scene.\\n\\nReview aggregator Rotten Tomatoes surveyed 32 reviews of the episode and judged 100% of them to be positive with an average score of 9.2 out of 10. The website\\'s critical consensus reads, \"\"Fire and Blood\" creatively provides closure after a shocking penultimate episode while strategically setting up arcs for season two.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'ae7230698942c89dd28faf7ce8d14c32'}>,\n",
-       "  <Document: {'content': 'The website\\'s critical consensus reads, \"\"Fire and Blood\" creatively provides closure after a shocking penultimate episode while strategically setting up arcs for season two.\"Matt Fowler of IGN wrote that \" \\'Fire and Blood\\' wasn\\'t exactly a powerful roar of an episode, but that book fans would definitely appreciate the small parts of the second book, \\'\\'A Clash of Kings\\'\\', that got included to help set up season two next year\". He rated the episode 8.5 out of 10.\\n\\nEmily VanDerWerff of \\'\\'The A.V. Club\\'\\' gave it an \"A-\", commenting:\\n\\nDavid Sims, also writing for \\'\\'The A.V. Club\\'\\', called it a fitting end to the season, \"leaving absolutely everyone salivating for season two\". Writing for the \\'\\'Star-Ledger\\'\\', Jenifer Braun praised the episode for its set dressing (\"I have to say, it\\'s a pleasure just to look at all the shiny stuff the HBO set dressers came up with for Tywin Lannister\\'s tent\") and the authenticity of the baby dragons (\"And wow, HBO, seamless special effects here. Baby Dragon looks every bit as real as the series\\' dogs\"). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '437fdbcb05b8a9f9107c30a0e631f431'}>,\n",
-       "  <Document: {'content': 'Writing for the \\'\\'Star-Ledger\\'\\', Jenifer Braun praised the episode for its set dressing (\"I have to say, it\\'s a pleasure just to look at all the shiny stuff the HBO set dressers came up with for Tywin Lannister\\'s tent\") and the authenticity of the baby dragons (\"And wow, HBO, seamless special effects here. Baby Dragon looks every bit as real as the series\\' dogs\"). HitFix said it \"Wrapped up its terrific first season...it was damned entertaining along the way--with the finale as possibly the most entertaining so far--and we know that at least one more season is coming. And if the creative team can keep up this level of quality, it\\'s hard to imagine HBO shutting things down anytime soon, even with a budget that only figures to get higher. Dragons aren\\'t cheap, but they\\'re also amazing\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '9e04c09757d7eaed4367f7e069ffa1c8'}>,\n",
-       "  <Document: {'content': 'And if the creative team can keep up this level of quality, it\\'s hard to imagine HBO shutting things down anytime soon, even with a budget that only figures to get higher. Dragons aren\\'t cheap, but they\\'re also amazing\".\\n\\n==== Awards and nominations ====\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2011\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Special Visual Effects\\n\\nRafael Morant, Adam McInnes, Graham Hills, Lucy Ainsworth-Taylor, Stuart Brisdon, Damien Macé, Henry Badgett, and Angela Barson\\n\\n===George W. Bush controversy and removal===\\nThe use of an effigy of George W. Bush\\'s head on a spike caused HBO to issue an apology and to edit the scene.\\nThe episode contains a scene in which Joffrey forces Sansa to look at her father and family household staff beheaded on spikes. After a scene of the disfigured heads is shot, one decapitated head resembled former president of the United States of America George W. Bush. HBO quickly began removing and editing the scene as it sparked political outrage on social media. The beheaded prop was edited to resemble an ordinary head prop.\\n\\nA small amount of politicians began calling for a boycott of the series, describing the scene as \\'despicable\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '854ccc4941ece25d20e4bf10a3cb3e97'}>,\n",
-       "  <Document: {'content': 'The beheaded prop was edited to resemble an ordinary head prop.\\n\\nA small amount of politicians began calling for a boycott of the series, describing the scene as \\'despicable\\'. Creators David Benioff and D.B. Weiss both apologized for the filming of the prop, stating:\\n\\n== References ==\\n\\n== External links ==\\n\\n*  \"Fire and Blood\" at HBO\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '4971acff41e8779287becea809c606ee'}>,\n",
-       "  <Document: {'content': '\\n\\nThe sixth season of the fantasy drama television series \\'\\'Game of Thrones\\'\\' premiered on HBO on April 24, 2016, and concluded on June 26, 2016. It consists of ten episodes, each of approximately 50–60 minutes long, largely of original content not found in George R. R. Martin\\'s \\'\\'A Song of Ice and Fire\\'\\' series. Some story elements were derived from the novels and from information Martin revealed to the show-runners. The series was adapted for television by David Benioff and D. B. Weiss.  HBO ordered the season on April 8, 2014, together with the fifth season, which began filming in July 2015 primarily in Northern Ireland, Spain, Croatia, Iceland and Canada. The entire season cost over $100 million to produce.\\n\\nThe season follows the continuing struggle between the Starks and other noble families of Westeros for the Iron Throne. The Starks defeat the Bolton forces in the \"Battle of the Bastards\", culminating with Sansa Stark feeding her tormenting husband Ramsay Bolton — who had earlier murdered his father Roose and stepfamily — to his starving hounds, and Jon Snow is proclaimed the King in the North. Tyrion attempts to rule Meereen while Daenerys is held captive by a Dothraki tribe.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'e05ffbe9d51f347cb542b6974e79234c'}>,\n",
-       "  <Document: {'content': 'The Starks defeat the Bolton forces in the \"Battle of the Bastards\", culminating with Sansa Stark feeding her tormenting husband Ramsay Bolton — who had earlier murdered his father Roose and stepfamily — to his starving hounds, and Jon Snow is proclaimed the King in the North. Tyrion attempts to rule Meereen while Daenerys is held captive by a Dothraki tribe.  At King\\'s Landing, the Tyrell army attempts to liberate Margaery and Loras, but Margaery capitulates to the High Sparrow, who becomes more powerful by influencing King Tommen. At her trial, Cersei burns the Great Sept — killing her rivals — and is later crowned Queen of the Seven Kingdoms, while Tommen commits suicide. Ellaria Sand and three of Oberyn Martell\\'s daughters kill Doran and Trystane Martell and seize control of Dorne.  In Essos, Daenerys Targaryen is captured by Khal Moro who takes her before the khals; she burns them alive and takes command of the Dothraki. Olenna and the Dornish ally with Daenerys.\\n\\n\\'\\'Game of Thrones\\'\\' features a large ensemble cast, including Peter Dinklage, Nikolaj Coster-Waldau, Lena Headey, Emilia Clarke and Kit Harington. The season introduced new cast members, including Max von Sydow, Pilou Asbæk and Essie Davis.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '789201589c816766066e3d6cbb44a248'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' features a large ensemble cast, including Peter Dinklage, Nikolaj Coster-Waldau, Lena Headey, Emilia Clarke and Kit Harington. The season introduced new cast members, including Max von Sydow, Pilou Asbæk and Essie Davis.\\n\\nCritics praised its production values, writing, plot development, and cast. ''Game of Thrones'' received most nominations for the 68th Primetime Emmy Awards, with 23 nominations, and won twelve, including that for Outstanding Drama Series for the second year in a row. U.S. viewership rose compared to the previous season, and by approximately 13% over its course, from 7.9 million to 8.9 million by the finale.\\n\\n==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 6. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'ef6b5b817406a726f5083ee59c6925be'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' received most nominations for the 68th Primetime Emmy Awards, with 23 nominations, and won twelve, including that for Outstanding Drama Series for the second year in a row. U.S. viewership rose compared to the previous season, and by approximately 13% over its course, from 7.9 million to 8.9 million by the finale.\\n\\n==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 6. They are listed by the region in which they first appear:\\n\\n====In the North, including the Wall====\\n* Daniel Portman as Podrick Payne\\n* Natalia Tena as Osha\\n* Art Parkinson as Rickon Stark\\n* Owen Teale as Alliser Thorne\\n* Ben Crompton as Eddison Tollett\\n* Brenock O\\'Connor as Olly\\n* Charlotte Hope as Myranda\\n* Elizabeth Webster as Walda Bolton\\n* Paul Rattray as Harald Karstark\\n* Dean Jagger as Smalljon Umber\\n* Tim McInnerny as Robett Glover\\n* Bella Ramsey as Lyanna Mormont\\n* Sean Blowers as Wyman Manderly\\n* Tom Varey as Cley Cerwyn\\n* Richard Rycroft as Maester Wolkan\\n* Michael Condron as Bowen Marsh\\n* Brian Fortune as Othell Yarwyck\\n* Ian Whyte as Wun Wun\\n* Murray McArthur as Dim Dalba\\n\\n====Beyond the Wall====\\n* Max von Sydow as the Three-eyed Raven\\n* Ellie Kendrick as Meera Reed\\n* Kristian Nairn as Hodor\\n* Joseph Mawle as Benjen Stark\\n* Kae Alexander as Leaf\\n* Vladimir \"Furdo\" Furdik as the Night King\\n\\n====In the Riverlands====\\n* David Bradley as Walder Frey\\n* Clive Russell as Brynden Tully\\n* Tobias Menzies as Edmure Tully\\n* Richard Dormer as Beric Dondarrion\\n* Paul Kaye as Thoros of Myr\\n* Tim Plester as Black Walder Rivers\\n* Daniel Tuite as Lothar Frey\\n* Jóhannes Haukur Jóhannesson as Lem\\n* Ricky Champ as Gatins\\n* Ian Davies as Morgan\\n* Ian McShane as Brother Ray\\n\\n====On the Iron Islands====\\n* Gemma Whelan as Yara Greyjoy\\n* Patrick Malahide as Balon Greyjoy\\n* Pilou Asbæk as Euron Greyjoy\\n* Michael Feast as Aeron Greyjoy\\n\\n====In Dorne====\\n* Alexander Siddig as Doran Martell\\n* Toby Sebastian as Trystane Martell\\n* Jessica Henwick as Nymeria Sand\\n* Keisha Castle-Hughes as Obara Sand\\n* Rosabell Laurenti Sellers as Tyene Sand\\n* DeObia Oparei as Areo Hotah\\n\\n====In King\\'s Landing====\\n* Diana Rigg as Olenna Tyrell\\n* Julian Glover as Grand Maester Pycelle\\n* Finn Jones as Loras Tyrell\\n* Anton Lesser as Qyburn\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Eugene Simon as Lancel Lannister\\n* Ian Gelder as Kevan Lannister\\n* Hannah Waddingham as Septa Unella\\n* Nell Tiger Free as Myrcella Baratheon\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Josephine Gillan as Marei\\n* Nathanael Saleh as Arthur\\n* Annette Hannah as Frances\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Rupert Vansittart as Yohn Royce\\n\\n====In Braavos====\\n* Faye Marsay as the Waif\\n* Richard E. Grant as Izembaro\\n* Essie Davis as Lady Crane\\n* Leigh Gill as Bobono\\n* Eline Powell as Bianca\\n* Rob Callender as Clarenzo\\n* Kevin Eldon as Camello\\n\\n====In Meereen====\\n* Jacob Anderson as Grey Worm\\n* George Georgiou as Razdal mo Eraz\\n* Eddie Jackson as Belicho Paenymion\\n* Enzo Cilenti as Yezzan zo Qaggaz\\n* Ania Bukstein as Kinvara\\n* Gerald Lepkowski as Zanrush\\n* Meena Rayann as Vala\\n\\n====In Vaes Dothrak====\\n* Joe Naufahu as Khal Moro\\n* Andrei Claude as Khal Rhalko\\n* Tamer Hassan as Khal Forzho\\n* Staz Nair as Qhono\\n* Chuku Modu as Aggo\\n* Deon Lee-Williams as Iggo\\n* Souad Faress as the High Priestess\\n* Hannah John-Kamen as Ornella\\n\\n====In the Reach====\\n* James Faulkner as Randyll Tarly\\n* Samantha Spiro as Melessa Tarly\\n* Freddie Stroma as Dickon Tarly\\n* Rebecca Benson as Talla Tarly\\n\\n====In flashbacks====\\n* Robert Aramayo & Sebastian Croft as Eddard Stark\\n* Matteo Elezi as Benjen Stark\\n* Aisling Franciosi & Cordelia Hill as Lyanna Stark\\n* Wayne Foskett as Rickard Stark\\n* Fergus Leathem as Rodrik Cassel\\n* Annette Tierney as Old Nan\\n* Sam Coleman as Hodor\\n* Leo Woodruff as Howland Reed\\n* Luke Roberts as Arthur Dayne\\n* Eddie Eyre as Gerold Hightower\\n* David Rintoul as Aerys II Targaryen\\n\\n==Production==\\n\\n===Crew===\\nThe writing staff for the sixth season includes executive producers and showrunners David Benioff and D. B. Weiss, producer Bryan Cogman and Dave Hill. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'ecdf0bb81b76c5fc9bed10781d5264be'}>,\n",
-       "  <Document: {'content': 'They are listed by the region in which they first appear:\\n\\n====In the North, including the Wall====\\n* Daniel Portman as Podrick Payne\\n* Natalia Tena as Osha\\n* Art Parkinson as Rickon Stark\\n* Owen Teale as Alliser Thorne\\n* Ben Crompton as Eddison Tollett\\n* Brenock O\\'Connor as Olly\\n* Charlotte Hope as Myranda\\n* Elizabeth Webster as Walda Bolton\\n* Paul Rattray as Harald Karstark\\n* Dean Jagger as Smalljon Umber\\n* Tim McInnerny as Robett Glover\\n* Bella Ramsey as Lyanna Mormont\\n* Sean Blowers as Wyman Manderly\\n* Tom Varey as Cley Cerwyn\\n* Richard Rycroft as Maester Wolkan\\n* Michael Condron as Bowen Marsh\\n* Brian Fortune as Othell Yarwyck\\n* Ian Whyte as Wun Wun\\n* Murray McArthur as Dim Dalba\\n\\n====Beyond the Wall====\\n* Max von Sydow as the Three-eyed Raven\\n* Ellie Kendrick as Meera Reed\\n* Kristian Nairn as Hodor\\n* Joseph Mawle as Benjen Stark\\n* Kae Alexander as Leaf\\n* Vladimir \"Furdo\" Furdik as the Night King\\n\\n====In the Riverlands====\\n* David Bradley as Walder Frey\\n* Clive Russell as Brynden Tully\\n* Tobias Menzies as Edmure Tully\\n* Richard Dormer as Beric Dondarrion\\n* Paul Kaye as Thoros of Myr\\n* Tim Plester as Black Walder Rivers\\n* Daniel Tuite as Lothar Frey\\n* Jóhannes Haukur Jóhannesson as Lem\\n* Ricky Champ as Gatins\\n* Ian Davies as Morgan\\n* Ian McShane as Brother Ray\\n\\n====On the Iron Islands====\\n* Gemma Whelan as Yara Greyjoy\\n* Patrick Malahide as Balon Greyjoy\\n* Pilou Asbæk as Euron Greyjoy\\n* Michael Feast as Aeron Greyjoy\\n\\n====In Dorne====\\n* Alexander Siddig as Doran Martell\\n* Toby Sebastian as Trystane Martell\\n* Jessica Henwick as Nymeria Sand\\n* Keisha Castle-Hughes as Obara Sand\\n* Rosabell Laurenti Sellers as Tyene Sand\\n* DeObia Oparei as Areo Hotah\\n\\n====In King\\'s Landing====\\n* Diana Rigg as Olenna Tyrell\\n* Julian Glover as Grand Maester Pycelle\\n* Finn Jones as Loras Tyrell\\n* Anton Lesser as Qyburn\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Eugene Simon as Lancel Lannister\\n* Ian Gelder as Kevan Lannister\\n* Hannah Waddingham as Septa Unella\\n* Nell Tiger Free as Myrcella Baratheon\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Josephine Gillan as Marei\\n* Nathanael Saleh as Arthur\\n* Annette Hannah as Frances\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Rupert Vansittart as Yohn Royce\\n\\n====In Braavos====\\n* Faye Marsay as the Waif\\n* Richard E. Grant as Izembaro\\n* Essie Davis as Lady Crane\\n* Leigh Gill as Bobono\\n* Eline Powell as Bianca\\n* Rob Callender as Clarenzo\\n* Kevin Eldon as Camello\\n\\n====In Meereen====\\n* Jacob Anderson as Grey Worm\\n* George Georgiou as Razdal mo Eraz\\n* Eddie Jackson as Belicho Paenymion\\n* Enzo Cilenti as Yezzan zo Qaggaz\\n* Ania Bukstein as Kinvara\\n* Gerald Lepkowski as Zanrush\\n* Meena Rayann as Vala\\n\\n====In Vaes Dothrak====\\n* Joe Naufahu as Khal Moro\\n* Andrei Claude as Khal Rhalko\\n* Tamer Hassan as Khal Forzho\\n* Staz Nair as Qhono\\n* Chuku Modu as Aggo\\n* Deon Lee-Williams as Iggo\\n* Souad Faress as the High Priestess\\n* Hannah John-Kamen as Ornella\\n\\n====In the Reach====\\n* James Faulkner as Randyll Tarly\\n* Samantha Spiro as Melessa Tarly\\n* Freddie Stroma as Dickon Tarly\\n* Rebecca Benson as Talla Tarly\\n\\n====In flashbacks====\\n* Robert Aramayo & Sebastian Croft as Eddard Stark\\n* Matteo Elezi as Benjen Stark\\n* Aisling Franciosi & Cordelia Hill as Lyanna Stark\\n* Wayne Foskett as Rickard Stark\\n* Fergus Leathem as Rodrik Cassel\\n* Annette Tierney as Old Nan\\n* Sam Coleman as Hodor\\n* Leo Woodruff as Howland Reed\\n* Luke Roberts as Arthur Dayne\\n* Eddie Eyre as Gerold Hightower\\n* David Rintoul as Aerys II Targaryen\\n\\n==Production==\\n\\n===Crew===\\nThe writing staff for the sixth season includes executive producers and showrunners David Benioff and D. B. Weiss, producer Bryan Cogman and Dave Hill. Author George R. R. Martin, who had written one episode for each of the first four seasons, did not write an episode for the sixth season, as he was working to finish writing the sixth \\'\\'A Song of Ice and Fire\\'\\' novel, \\'\\'The Winds of Winter\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'b1fa85846d5ab04543d5dbb44d1aa9f0'}>,\n",
-       "  <Document: {'content': 'Author George R. R. Martin, who had written one episode for each of the first four seasons, did not write an episode for the sixth season, as he was working to finish writing the sixth \\'\\'A Song of Ice and Fire\\'\\' novel, \\'\\'The Winds of Winter\\'\\'. The directing staff for the sixth season was Jeremy Podeswa (episodes 1 and 2), Daniel Sackheim (episodes 3 and 4), Jack Bender (episodes 5 and 6), Mark Mylod (episodes 7 and 8), and Miguel Sapochnik (episodes 9 and 10). Sackheim and Bender were first-time \\'\\'Game of Thrones\\'\\' directors, with the rest each having directed two episodes in the previous season.\\n\\n===Writing===\\nWith the end of the fifth season, the content of the show has reached the plot of the latest novel in Martin\\'s \\'\\'A Song of Ice and Fire\\'\\' series, \\'\\'A Dance with Dragons\\'\\'. Season 6 director Jeremy Podeswa said in August 2015: \"Right now in season six, what we\\'re shooting currently isn\\'t based on anything in the book. It\\'s fully based on discussions the writers have had with George Martin, because the series has now surpassed the books in terms of what\\'s available.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '5e55fa91468a1bfcceb8dad27f3e0108'}>,\n",
-       "  <Document: {'content': 'It\\'s fully based on discussions the writers have had with George Martin, because the series has now surpassed the books in terms of what\\'s available.\"Actress Natalie Dormer, who plays Margaery Tyrell, later added that the show\\'s writers \"know where it\\'s got to go and what George Martin\\'s intentions for the characters are. But they are just filling in the gaps.\"The season premiere starts off right where the fifth season ended. A plot from \\'\\'The Winds of Winter\\'\\' regarding a traveling theater troupe located in Braavos that stages a play called \"The Bloody Hand\", about the events that have taken place in King\\'s Landing since the beginning of the series, is included in the sixth season.\\n\\n===Filming===\\nCastle of Zafra in Guadalajara, Spain is among the filming locations.\\nFilming for season six began in July 2015, and ended on December 17, 2015. The budget for the sixth season increased compared to the previous seasons as each episode did cost over $10 million per episode, totaling over $100 million for the full season and setting a new high for the series. The season filmed in five different countries, Northern Ireland, Spain, Croatia, Iceland and Canada.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'ec1f7824869a2f33cd589de75289112f'}>,\n",
-       "  <Document: {'content': 'The budget for the sixth season increased compared to the previous seasons as each episode did cost over $10 million per episode, totaling over $100 million for the full season and setting a new high for the series. The season filmed in five different countries, Northern Ireland, Spain, Croatia, Iceland and Canada.\\n\\nLike the previous seasons, a large amount of production took place in Northern Ireland, mainly in Belfast and on the Causeway Coast, including film locations in the Binevenagh, Magilligan area which was used to film scenes for the Dothraki Grasslands, and Larrybane Quarry and Ballintoy Harbour, both used for scenes in the Iron Islands. The small village of Corbet was also used, for the siege of Riverrun. As in previous seasons, some of Castle Black was set at the abandoned Magheramorne quarry.\\n\\nFrom September 3 to October 23, 2015 the show also filmed in Spain, specifically in Girona, Navarre, Peniscola, and Almería. Some filming locations in Spain included the Castle of Zafra in Guadalajara, the Bardenas Reales Natural Park in Navarre, the Alcazaba in Almería, and the Castle of Santa Florentina in Canet de Mar.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'f75f7275559d42a3a3a7b4cd96eaea4e'}>,\n",
-       "  <Document: {'content': \"Some filming locations in Spain included the Castle of Zafra in Guadalajara, the Bardenas Reales Natural Park in Navarre, the Alcazaba in Almería, and the Castle of Santa Florentina in Canet de Mar.\\n\\nIn August 2015, HBO announced that for the first time since season 1, the show would not be filming any scenes in Croatia. The Croatian city of Dubrovnik has stood in for King's Landing since the beginning of season 2; nearby cities such as Klis, Split and Šibenik have been used to depict various other locations. Contradicting the statement by HBO, cast of the show were seen in costume in Dubrovnik in October 2015.\\n\\nOnly a very small portion of the season was filmed in Canada (north of Calgary, Alberta): the scenes featuring Jon Snow's wolf Ghost (played by animal actor Quigly). However, some of the special effects were created at Montreal's Rodeo FX studios which has won Emmy Awards previously for its work on the series.\\n\\n===Casting===\\nPilou Asbæk plays Euron Greyjoy.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '96a6421d0c5818c4e13431d2137d8e5d'}>,\n",
-       "  <Document: {'content': 'However, some of the special effects were created at Montreal\\'s Rodeo FX studios which has won Emmy Awards previously for its work on the series.\\n\\n===Casting===\\nPilou Asbæk plays Euron Greyjoy.\\nThe sixth season saw the return of Isaac Hempstead Wright as Bran Stark, Kristian Nairn as Hodor, Ellie Kendrick as Meera Reed, Gemma Whelan as Yara Greyjoy, and Rory McCann as Sandor \"The Hound\" Clegane,  who did not appear in the fifth season. Clive Russell, Tobias Menzies, Patrick Malahide, Richard Dormer and Paul Kaye also returned to the show as Brynden Tully, Edmure Tully, Balon Greyjoy, Beric Dondarrion and Thoros of Myr after not appearing since the third season. Jonathan Pryce as the High Sparrow was added to the series main cast after appearing in a recurring role in the previous season.\\n\\nAcross the Narrow Sea, Melanie Liburd plays a Red priestess from Asshai who is in R\\'hllor\\'s service. At the Reach, House Tarly is introduced, with Freddie Stroma joining the cast as Samwell Tarly\\'s brother Dickon Tarly, a character who was mentioned in the novels but has yet to appear in them. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '75f0ca86e21a8b0bf403c9ba51a6c148'}>,\n",
-       "  <Document: {'content': 'At the Reach, House Tarly is introduced, with Freddie Stroma joining the cast as Samwell Tarly\\'s brother Dickon Tarly, a character who was mentioned in the novels but has yet to appear in them. Other members of House Tarly that were introduced were Randyll Tarly, played by James Faulkner; Melessa Tarly, played by Samantha Spiro and Talla Tarly, who was portrayed by Rebecca Benson.\\n\\nVeteran actor Max von Sydow was cast to play the Three-Eyed-Raven, who is training Bran. The character was previously played by Struan Rodger in the fourth-season finale \"The Children\". David Bradley confirmed in August 2015, that he would be returning to the show as Walder Frey after last appearing in the third-season finale \"Mhysa\", but he did not confirm when he would be returning. After the second official trailer was released, it was confirmed that Bradley would appear in the sixth season. Danish actor Pilou Asbæk joins the show as Theon Greyjoy\\'s uncle, pirate captain Euron Greyjoy. Members of the Icelandic band Of Monsters and Men made cameo appearances. Ricky Champ played Gatins, an outlaw who is a part of a band using religion to justify extorting the people of the countryside. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '92ea8991c9f4e2782ff0e3e418719ed6'}>,\n",
-       "  <Document: {'content': 'Ricky Champ played Gatins, an outlaw who is a part of a band using religion to justify extorting the people of the countryside. A young Ned Stark was portrayed by Sebastian Croft in a flashback scene.\\n\\nThe sixth season also included a traveling theater troupe located in Braavos that stages a play called \"The Bloody Hand\", about the events that have taken place in King\\'s Landing since the beginning of the series. Essie Davis and Kevin Eldon joined the cast in this theater troupe, portraying actors playing Cersei Lannister and Ned Stark, respectively, while Richard E. Grant was cast as the troupe\\'s manager. Members of Icelandic indie band Of Monsters and Men appear as the musicians of the Braavos theatre group.\\n\\n===Music===\\n\\nThe soundtrack for the season was digitally released on June 24, 2016, and was released on CD on July 29, 2016. The album reached  on the \\'\\'Billboard\\'\\' 200,  on Soundtrack chart, and  on the Canadian Albums chart on its digital release, with the track from the season finale \"Light of the Seven\" reaching  on \\'\\'Billboard\\'\\'s Spotify Viral 50 chart.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'fadd1f462bf41db27526acb2702b238a'}>,\n",
-       "  <Document: {'content': 'The album reached  on the \\'\\'Billboard\\'\\' 200,  on Soundtrack chart, and  on the Canadian Albums chart on its digital release, with the track from the season finale \"Light of the Seven\" reaching  on \\'\\'Billboard\\'\\'s Spotify Viral 50 chart.\\n\\n==Reception==\\n\\n===Critical response===\\nThe season received highly positive reviews, with special praise directed at the episodes \"The Door\", \"Battle of the Bastards\" and \"The Winds of Winter\". On Metacritic, the season (based on the first episode) has a score of 73 out of 100 based on 9 reviews, indicating \"generally favorable reviews\". On Rotten Tomatoes, the sixth season has a 94% approval rating from 34 critics with an average rating of 8.28 out of 10. The site\\'s critical consensus reads, \"Bloody and captivating as always, \\'\\'Game of Thrones\\'\\' plunges back into the midst of a world touched by grief, dread, and precarious sexuality.\"===Ratings===\\n\\nThe season finale had 8.89 million viewers on its initial airing on HBO, up ten percent from the previous season\\'s finale, which was the most-watched episode prior to this episode. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '847eb1b9bb597a52dc7dcc0aeb05b20b'}>,\n",
-       "  <Document: {'content': \"===Ratings===\\n\\nThe season finale had 8.89 million viewers on its initial airing on HBO, up ten percent from the previous season's finale, which was the most-watched episode prior to this episode. The average gross viewing figure per episode for the show, which include streaming, DVR recordings and repeat showings, reached over 25 million this season, and it was described as the last consensus show on television. The figure went up by 25% compared to previous year, and viewing figures of the show this season on its on demand services HBO Now and HBO Go went up by over 90%, which were new records for HBO.  Almost 40% of viewers of the show watched this season on HBO digital platforms.  The show also broke records on pay television channels in 2016 in the United Kingdom with an average audience of more than five million across all platforms, and in Australia with a cumulative average audience of 1.2 million viewers.\\n\\nLive +7 ratings were not available, so Live +3 ratings have been used instead.\\n\\n===Accolades===\\n\\nFor the 32nd TCA Awards, the series was nominated for Program of the Year and Outstanding Achievement in Drama. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '9b8075e44053267903360baad2841316'}>,\n",
-       "  <Document: {'content': '===Accolades===\\n\\nFor the 32nd TCA Awards, the series was nominated for Program of the Year and Outstanding Achievement in Drama. For the 68th Primetime Emmy Awards, the series received 23 nominations, the most of any series. It won 12 awards, including Outstanding Drama Series, David Benioff and D. B. Weiss for Outstanding Writing for a Drama Series for \"Battle of the Bastards\", and Miguel Sapochnik for Outstanding Directing for a Drama Series for \"Battle of the Bastards\". For the 7th Critics\\' Choice Television Awards, the series won for Best Drama Series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'cbdd17245301ced34323d02f16867a68'}>,\n",
-       "  <Document: {'content': 'It won 12 awards, including Outstanding Drama Series, David Benioff and D. B. Weiss for Outstanding Writing for a Drama Series for \"Battle of the Bastards\", and Miguel Sapochnik for Outstanding Directing for a Drama Series for \"Battle of the Bastards\". For the 7th Critics\\' Choice Television Awards, the series won for Best Drama Series.\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n32nd TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nTV Choice Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nDragon Awards\\n\\nBest Science Fiction or Fantasy TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold, Robert Sterne, Carla Stronge\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nSophie Turner\\n\\n68th Primetime Emmy Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nKit Harington as Jon Snow\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\nMaisie Williams as Arya Stark\\n\\nOutstanding Directing for a Drama Series\\n\\nJack Bender for \"The Door\"\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Battle of the Bastards\"\\n\\n68th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow as Three-Eyed Raven\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton for \\'\\'Home\\'\\'\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary for \\'\\'The Winds of Winter\\'\\'\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora for \\'\\'The Door\\'\\'\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth for \"Battle of the Bastards\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \\'\\'Blood of My Blood\\'\\', \\'\\'The Broken Man\\'\\', and \\'\\'No One\\'\\'\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower for \\'\\'The Door\\'\\'\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \\'\\'Battle of the Bastards\\'\\'\\n\\nKatie Weiland for \\'\\'Oathbreaker\\'\\'\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit for \\'\\'The Door\\'\\'\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney,  Sam Conway,  Matthew Rouleau,  Michelle Blok, Glenn Melenhorst for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Main Titles 360 Experience\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch  for \"Battle of the Bastards\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau  for \"Battle of the Bastards\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n3D Award for Visual Effects Design\\n\\nIloura for \"Battle of the Bastards\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"The Winds of Winter\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nSean Savage, David Morgan & John Ferguson for \"Battle of the Bastards\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Battle of the Bastards\"\\n\\nAnette Haellmigk for \"Book of the Stranger\"\\n\\n7th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN People\\'s Choice Award\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMTV Fandom Awards\\n\\nFan Freak Out of the Year\\n\\n\\'\\'Game of Thrones\\'\\' – Resurrection of Jon Snow\\n\\nInternational Film Music Critics Association\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\nFilm Music Composition Of The Year\\n\\nRamin Djawadi for \"Light of the Seven\"\\n\\nWorld Soundtrack Awards\\n\\nTelevision Composer of the Year\\n\\nRamin Djawadi\\n\\n2017\\n\\n43rd People\\'s Choice Awards\\n\\nFavorite Premium Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n44th Annie Awards\\n\\nOutstanding Achievement, Character Animation in a Live Action Production\\n\\nNicholas Tripodi, Dean Elliott, James Hollingworth, Matt Weaver for \"Battle of the Bastards\"\\n\\n74th Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\n21st Satellite Awards\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWriters Guild of America Awards 2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n23rd Screen Actors Guild Awards\\n\\nOutstanding Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, Leo Woodruff\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunnungham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carcie Van Houten, Gemma Whelan, Maisie Williams,\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nAmerican Cinema Editors Awards 2017\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nArt Directors Guild Awards 2016\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley for  \"Blood of My Blood\", \"The Broken Man\" and \"No One\"\\n\\nProducers Guild of America Awards 2016\\n\\nThe Norman Felton Award for Outstanding Producer of Episodic Television, Drama\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\nVisual Effects Society Awards 2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway for \"Battle of the Bastards\"\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback for \"Battle of the Bastards\" – Drogon\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben for \"Home\" – Emaciated Dragon\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta for \"Battle of the Bastards\" – Meereen City\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik for \"The Winds of Winter\" – Citadel\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies for \"Battle of the Bastards\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen for \"Battle of the Bastards\"\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond for \"Battle of the Bastards\" – Meereen City\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar for \"Battle of the Bastards\" – Meereen City\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson for \"Battle of the Bastards\" – Retaking Winterfell\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren for \"The Door\" – Land of Always Winter\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss for \"Battle of the Bastards\"\\n\\n69th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker, Kay Bilk\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nBest Special Makeup Effects – Television\\n\\nBarrie Gower, Sarah Gower\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Fantasy Television Series\\n\\nMichele Clapton, April Ferry\\n\\nDorian Awards\\n\\nTV Drama of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n22nd National Television Awards\\n\\nBest Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Battle of the Bastards\"\\n\\nSociety of Camera Operators Awards\\n\\nCamera Operator of the Year – Television\\n\\nSean Savage\\n\\nZulu Awards\\n\\nBest Actor\\n\\nNikolaj Coster-Waldau\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Home\"\\n\\nLocation Managers Guild Awards\\n\\nLMGI Award for Outstanding Locations in Period Television\\n\\nMatt Jones and Naomi Liston\\n\\n43rd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\n22nd Empire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIrish Film & Television Academy\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest VFX\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nUnscripted (Branded)\\n\\n\"Battle of the Bastards\" Featurette\\n\\nHugo Award\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Miguel Sapochnik (director) for \"Battle of the Bastards\"\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Jack Bender (director) for \"The Door\"\\n\\n2017 MTV Movie & TV Awards\\n\\nShow Of The Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actor In A Show\\n\\nEmilia Clarke\\n\\nTearjerker\\n\\nHodor\\'s (Kristian Nairn) Death\\n\\n2017 British Academy Television Awards\\n\\nMust-See Moment\\n\\n\"Battle of the Bastards\"\\n\\nGlamour Awards\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast around the world by HBO and its broadcast partners. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '8cde72af54363c7dbdbcbadef960ae0f'}>,\n",
-       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n32nd TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nTV Choice Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nDragon Awards\\n\\nBest Science Fiction or Fantasy TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold, Robert Sterne, Carla Stronge\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nSophie Turner\\n\\n68th Primetime Emmy Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nKit Harington as Jon Snow\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\nMaisie Williams as Arya Stark\\n\\nOutstanding Directing for a Drama Series\\n\\nJack Bender for \"The Door\"\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Battle of the Bastards\"\\n\\n68th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow as Three-Eyed Raven\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton for \\'\\'Home\\'\\'\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary for \\'\\'The Winds of Winter\\'\\'\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora for \\'\\'The Door\\'\\'\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth for \"Battle of the Bastards\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \\'\\'Blood of My Blood\\'\\', \\'\\'The Broken Man\\'\\', and \\'\\'No One\\'\\'\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower for \\'\\'The Door\\'\\'\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \\'\\'Battle of the Bastards\\'\\'\\n\\nKatie Weiland for \\'\\'Oathbreaker\\'\\'\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit for \\'\\'The Door\\'\\'\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney,  Sam Conway,  Matthew Rouleau,  Michelle Blok, Glenn Melenhorst for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Main Titles 360 Experience\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch  for \"Battle of the Bastards\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau  for \"Battle of the Bastards\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n3D Award for Visual Effects Design\\n\\nIloura for \"Battle of the Bastards\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"The Winds of Winter\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nSean Savage, David Morgan & John Ferguson for \"Battle of the Bastards\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Battle of the Bastards\"\\n\\nAnette Haellmigk for \"Book of the Stranger\"\\n\\n7th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN People\\'s Choice Award\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMTV Fandom Awards\\n\\nFan Freak Out of the Year\\n\\n\\'\\'Game of Thrones\\'\\' – Resurrection of Jon Snow\\n\\nInternational Film Music Critics Association\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\nFilm Music Composition Of The Year\\n\\nRamin Djawadi for \"Light of the Seven\"\\n\\nWorld Soundtrack Awards\\n\\nTelevision Composer of the Year\\n\\nRamin Djawadi\\n\\n2017\\n\\n43rd People\\'s Choice Awards\\n\\nFavorite Premium Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n44th Annie Awards\\n\\nOutstanding Achievement, Character Animation in a Live Action Production\\n\\nNicholas Tripodi, Dean Elliott, James Hollingworth, Matt Weaver for \"Battle of the Bastards\"\\n\\n74th Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\n21st Satellite Awards\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWriters Guild of America Awards 2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n23rd Screen Actors Guild Awards\\n\\nOutstanding Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, Leo Woodruff\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunnungham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carcie Van Houten, Gemma Whelan, Maisie Williams,\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nAmerican Cinema Editors Awards 2017\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nArt Directors Guild Awards 2016\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley for  \"Blood of My Blood\", \"The Broken Man\" and \"No One\"\\n\\nProducers Guild of America Awards 2016\\n\\nThe Norman Felton Award for Outstanding Producer of Episodic Television, Drama\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\nVisual Effects Society Awards 2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway for \"Battle of the Bastards\"\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback for \"Battle of the Bastards\" – Drogon\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben for \"Home\" – Emaciated Dragon\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta for \"Battle of the Bastards\" – Meereen City\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik for \"The Winds of Winter\" – Citadel\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies for \"Battle of the Bastards\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen for \"Battle of the Bastards\"\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond for \"Battle of the Bastards\" – Meereen City\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar for \"Battle of the Bastards\" – Meereen City\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson for \"Battle of the Bastards\" – Retaking Winterfell\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren for \"The Door\" – Land of Always Winter\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss for \"Battle of the Bastards\"\\n\\n69th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker, Kay Bilk\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nBest Special Makeup Effects – Television\\n\\nBarrie Gower, Sarah Gower\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Fantasy Television Series\\n\\nMichele Clapton, April Ferry\\n\\nDorian Awards\\n\\nTV Drama of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n22nd National Television Awards\\n\\nBest Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Battle of the Bastards\"\\n\\nSociety of Camera Operators Awards\\n\\nCamera Operator of the Year – Television\\n\\nSean Savage\\n\\nZulu Awards\\n\\nBest Actor\\n\\nNikolaj Coster-Waldau\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Home\"\\n\\nLocation Managers Guild Awards\\n\\nLMGI Award for Outstanding Locations in Period Television\\n\\nMatt Jones and Naomi Liston\\n\\n43rd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\n22nd Empire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIrish Film & Television Academy\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest VFX\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nUnscripted (Branded)\\n\\n\"Battle of the Bastards\" Featurette\\n\\nHugo Award\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Miguel Sapochnik (director) for \"Battle of the Bastards\"\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Jack Bender (director) for \"The Door\"\\n\\n2017 MTV Movie & TV Awards\\n\\nShow Of The Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actor In A Show\\n\\nEmilia Clarke\\n\\nTearjerker\\n\\nHodor\\'s (Kristian Nairn) Death\\n\\n2017 British Academy Television Awards\\n\\nMust-See Moment\\n\\n\"Battle of the Bastards\"\\n\\nGlamour Awards\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast around the world by HBO and its broadcast partners. While in some countries, it aired the day after its first release.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '1369e11a77cb0c1daca95a47877eff03'}>,\n",
-       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n32nd TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nTV Choice Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nDragon Awards\\n\\nBest Science Fiction or Fantasy TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold, Robert Sterne, Carla Stronge\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nSophie Turner\\n\\n68th Primetime Emmy Awards\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nKit Harington as Jon Snow\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nLena Headey as Cersei Lannister\\n\\nMaisie Williams as Arya Stark\\n\\nOutstanding Directing for a Drama Series\\n\\nJack Bender for \"The Door\"\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"Battle of the Bastards\"\\n\\n68th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow as Three-Eyed Raven\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton for \\'\\'Home\\'\\'\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary for \\'\\'The Winds of Winter\\'\\'\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora for \\'\\'The Door\\'\\'\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth for \"Battle of the Bastards\"\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron for \\'\\'Blood of My Blood\\'\\', \\'\\'The Broken Man\\'\\', and \\'\\'No One\\'\\'\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower for \\'\\'The Door\\'\\'\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nTim Porter for \\'\\'Battle of the Bastards\\'\\'\\n\\nKatie Weiland for \\'\\'Oathbreaker\\'\\'\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit for \\'\\'The Door\\'\\'\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney,  Sam Conway,  Matthew Rouleau,  Michelle Blok, Glenn Melenhorst for \\'\\'Battle of the Bastards\\'\\'\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Main Titles 360 Experience\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch  for \"Battle of the Bastards\"\\n\\nOutstanding Editing\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau  for \"Battle of the Bastards\"\\n\\nAustralian Production Design Guild\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n3D Award for Visual Effects Design\\n\\nIloura for \"Battle of the Bastards\"\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner for \"The Winds of Winter\"\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nSean Savage, David Morgan & John Ferguson for \"Battle of the Bastards\"\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner for \"Battle of the Bastards\"\\n\\nAnette Haellmigk for \"Book of the Stranger\"\\n\\n7th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nMost Bingeworthy Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN People\\'s Choice Award\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMTV Fandom Awards\\n\\nFan Freak Out of the Year\\n\\n\\'\\'Game of Thrones\\'\\' – Resurrection of Jon Snow\\n\\nInternational Film Music Critics Association\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\nFilm Music Composition Of The Year\\n\\nRamin Djawadi for \"Light of the Seven\"\\n\\nWorld Soundtrack Awards\\n\\nTelevision Composer of the Year\\n\\nRamin Djawadi\\n\\n2017\\n\\n43rd People\\'s Choice Awards\\n\\nFavorite Premium Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n44th Annie Awards\\n\\nOutstanding Achievement, Character Animation in a Live Action Production\\n\\nNicholas Tripodi, Dean Elliott, James Hollingworth, Matt Weaver for \"Battle of the Bastards\"\\n\\n74th Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\n21st Satellite Awards\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWriters Guild of America Awards 2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n23rd Screen Actors Guild Awards\\n\\nOutstanding Performance by a Stunt Ensemble in a Drama Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, Leo Woodruff\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunnungham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carcie Van Houten, Gemma Whelan, Maisie Williams,\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nAmerican Cinema Editors Awards 2017\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter for \"Battle of the Bastards\"\\n\\nArt Directors Guild Awards 2016\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley for  \"Blood of My Blood\", \"The Broken Man\" and \"No One\"\\n\\nProducers Guild of America Awards 2016\\n\\nThe Norman Felton Award for Outstanding Producer of Episodic Television, Drama\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\nVisual Effects Society Awards 2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway for \"Battle of the Bastards\"\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback for \"Battle of the Bastards\" – Drogon\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben for \"Home\" – Emaciated Dragon\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta for \"Battle of the Bastards\" – Meereen City\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik for \"The Winds of Winter\" – Citadel\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies for \"Battle of the Bastards\"\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen for \"Battle of the Bastards\"\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond for \"Battle of the Bastards\" – Meereen City\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar for \"Battle of the Bastards\" – Meereen City\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson for \"Battle of the Bastards\" – Retaking Winterfell\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren for \"The Door\" – Land of Always Winter\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss for \"Battle of the Bastards\"\\n\\n69th Directors Guild of America Awards\\n\\nDramatic Series\\n\\nMiguel Sapochnik for \"Battle of the Bastards\"\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss for \"The Winds of Winter\"\\n\\nHollywood Makeup Artist and Hair Stylist Guild Awards\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker, Kay Bilk\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nBest Special Makeup Effects – Television\\n\\nBarrie Gower, Sarah Gower\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Fantasy Television Series\\n\\nMichele Clapton, April Ferry\\n\\nDorian Awards\\n\\nTV Drama of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n22nd National Television Awards\\n\\nBest Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands for \"Battle of the Bastards\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz for \"Battle of the Bastards\"\\n\\nSociety of Camera Operators Awards\\n\\nCamera Operator of the Year – Television\\n\\nSean Savage\\n\\nZulu Awards\\n\\nBest Actor\\n\\nNikolaj Coster-Waldau\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nGregory Middleton for \"Home\"\\n\\nLocation Managers Guild Awards\\n\\nLMGI Award for Outstanding Locations in Period Television\\n\\nMatt Jones and Naomi Liston\\n\\n43rd Saturn Awards\\n\\nBest Fantasy Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\n22nd Empire Awards\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIrish Film & Television Academy\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest VFX\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nWebby Award\\n\\nBest Overall Social Presence\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nUnscripted (Branded)\\n\\n\"Battle of the Bastards\" Featurette\\n\\nHugo Award\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Miguel Sapochnik (director) for \"Battle of the Bastards\"\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Jack Bender (director) for \"The Door\"\\n\\n2017 MTV Movie & TV Awards\\n\\nShow Of The Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actor In A Show\\n\\nEmilia Clarke\\n\\nTearjerker\\n\\nHodor\\'s (Kristian Nairn) Death\\n\\n2017 British Academy Television Awards\\n\\nMust-See Moment\\n\\n\"Battle of the Bastards\"\\n\\nGlamour Awards\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\n==Release==\\n\\n===Broadcast===\\nThe season was simulcast around the world by HBO and its broadcast partners. While in some countries, it aired the day after its first release.\\n\\n===Marketing===\\nOn November 23, 2015, a teaser poster displaying Jon Snow was released on the official \\'\\'Game of Thrones\\'\\' Twitter account. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '74d1dd3a5a526756cb9339cb8c20247d'}>,\n",
-       "  <Document: {'content': '===Marketing===\\nOn November 23, 2015, a teaser poster displaying Jon Snow was released on the official \\'\\'Game of Thrones\\'\\' Twitter account. A 41-second teaser trailer was released on December 3, featuring Jon Snow from the fifth season episode \"Hardhome\", as well as many of the previous seasons\\' highlights, and a voice-over from Max von Sydow as the Three-Eyed Raven and Isaac Hempstead Wright as Bran Stark. The first footage from the season was revealed in a new promotional video released by HBO highlighting its new and returning original shows for the coming year on December 6, 2015, showcasing scenes involving Daenerys Targaryen, Ramsay Bolton, Cersei Lannister, and Tommen Baratheon. On December 28, 2015, \\'\\'Entertainment Weekly\\'\\' released its \"Exclusive First Look\" issue, featuring an image of Bran Stark, who was noticeably older from his last appearance in season four, and with shorter hair.\\n\\nOn January 22, 2016, three teaser trailers were released, with each teaser depicting the banners of the houses Targaryen, Lannister and Stark, and included voice-overs by Iwan Rheon as Ramsay Bolton, Jonathan Pryce as The High Sparrow and an unknown character speaking in Dothraki. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'a4f6ae0c6f538be668e7c5a38c26050f'}>,\n",
-       "  <Document: {'content': 'On January 22, 2016, three teaser trailers were released, with each teaser depicting the banners of the houses Targaryen, Lannister and Stark, and included voice-overs by Iwan Rheon as Ramsay Bolton, Jonathan Pryce as The High Sparrow and an unknown character speaking in Dothraki. On February 11, 2016, HBO released 28 exclusive photos from the sixth season, picturing several of the main characters during the season and confirming the fates of Theon, Sansa and Myrcella, while Jon Snow was notably absent. HBO released a teaser trailer on February 14, 2016, that shows the faces of a number of living as well as deceased characters such as Ned Stark, Robb Stark, Catelyn Stark, Joffrey Baratheon, Tywin Lannister, Stannis Baratheon, Ygritte and, controversially, Jon Snow in the House of Black and White. On February 24, 2016, HBO released 16 character posters of both deceased and alive characters, and two official posters featuring various characters.\\n\\nA behind-the-scenes video of the sixth season was released on February 29, 2016, focusing on camera operators working on the show. The first official trailer for season 6 was released on March 8, 2016. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'e065b32375821ff343ced7fb6bd680cf'}>,\n",
-       "  <Document: {'content': 'A behind-the-scenes video of the sixth season was released on February 29, 2016, focusing on camera operators working on the show. The first official trailer for season 6 was released on March 8, 2016. Another behind-the-scenes video was released on March 22, 2016, focusing on the creative process of prosthetics, specifically the White Walkers. The video also contained new footage of White Walkers from the sixth season. On March 24, \\'\\'Entertainment Weekly\\'\\' revealed a series of new issues titled \"Dame of Thrones\", featuring six of the female lead characters from the series and focus on the sixth season. On March 26, 2016, new photos from season six, in addition to a new promo named \"March Madness\" with new footage, was released by HBO. After the screening of the season premiere \"The Red Woman\", HBO released a second official trailer. As well, in July 2016, HBO released a \"blooper reel\" online, a video of outtakes from season 6.\\n\\n===\\'\\'After the Thrones\\'\\'===\\n\\n\\'\\'After the Thrones\\'\\', a live aftershow in which hosts Andy Greenwald and Chris Ryan discuss episodes of the series, airs on the stand-alone streaming service HBO Now on the Monday following each episode of the show\\'s sixth season.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '4954dfb4c6112141cbe16b6056ad615c'}>,\n",
-       "  <Document: {'content': \"===''After the Thrones''===\\n\\n''After the Thrones'', a live aftershow in which hosts Andy Greenwald and Chris Ryan discuss episodes of the series, airs on the stand-alone streaming service HBO Now on the Monday following each episode of the show's sixth season.\\n\\n===Home media===\\nThe season was released on Blu-ray and DVD on November 15, 2016. The set includes extra background, behind-the-scenes material and deleted scenes.\\n\\n===Copyright infringement===\\nThe sixth season of ''Game of Thrones'' was the most-pirated TV series in 2016.\\n\\n==References==\\n\\n==External links==\\n*  – official US site\\n*  – official UK site\\n*  ''Game of Thrones'' – The Viewers Guide on HBO.com\\n*  Making ''Game of Thrones'' on HBO.com\\n*\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '8ba14dcb1ecae81f513f6b84ef6be329'}>,\n",
-       "  <Document: {'content': '\\n\\nThe \\'\\'\\'Night King\\'\\'\\' is a fictional character appearing in the HBO high fantasy television series \\'\\'Game of Thrones\\'\\', based on George R. R. Martin\\'s novel series \\'\\'A Song of Ice and Fire\\'\\'. He is depicted as the leader and the first of the White Walkers, having existed since the age of the First Men, and is the most dangerous and powerful of his race. The Night King is an original creation of the television adaptation, thus far having no counterpart in the novels upon which the show is based.\\n\\nThe Night King was portrayed by British-American actor Richard Brake in seasons 4 and 5 and then by Slovakian actor and stuntman Vladimir Furdik in seasons 6 to 8.\\n\\n==Description==\\nIn \\'\\'Game of Thrones\\'\\', the Night King is physically distinguished from the other White Walkers by his \"crown\" of pointy, frosty horns.\\n\\n==The Night\\'s King in the novels==\\nThe \"Night King\", as presented in the show, has not appeared in \\'\\'A Song of Ice and Fire\\'\\'. In the novels, the title \"Night\\'s King\" is given to the long-ago legendary 13th Lord Commander of the Night\\'s Watch, who supposedly married a female White Walker and led the Night\\'s Watch to commit atrocities. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'e1b5cdbb7677a5cb341a2fbd288a9b80'}>,\n",
-       "  <Document: {'content': 'In the novels, the title \"Night\\'s King\" is given to the long-ago legendary 13th Lord Commander of the Night\\'s Watch, who supposedly married a female White Walker and led the Night\\'s Watch to commit atrocities. Regarding whether the character in the television series is the same as the one in the novels, Martin said, \"As for the Night\\'s King (the form I prefer), in the books he is a legendary figure, akin to Lann the Clever and Brandon the Builder, and no more likely to have survived to the present day than they have.\"==Development==\\n\\'\\'\\'Top:\\'\\'\\' Vladimír Furdík as the Night King with only practical effects and prosthetics.\\'\\'\\'Bottom:\\'\\'\\' Added VFX in post-production, with altered eyes and an icy layer.\\nIn order to create the Night King, according to Barrie Gower, it is primarily practical prosthetic, but also incorporates some visual effects (VFX) to create a more icy look and feel, saying about the eyes, \"The effects department alters the eyes in post-production. They give them that blue-glowy hue to them, which we can\\'t really achieve with contact lenses.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'ba218d9615a9c20a4f372d9fb9a34e90'}>,\n",
-       "  <Document: {'content': 'In order to create the Night King, according to Barrie Gower, it is primarily practical prosthetic, but also incorporates some visual effects (VFX) to create a more icy look and feel, saying about the eyes, \"The effects department alters the eyes in post-production. They give them that blue-glowy hue to them, which we can\\'t really achieve with contact lenses.\"In regards to the overall prosthetic, \"they\\'ve added this sort of icy layer over the top of him to create this—it\\'s something incredibly difficult to achieve practically, prosthetics are cast in a translucent rubber, which can only give you so much of that icy quality, so visual effects help augment it a little bit further to give it more dimension.\"In the fourth and fifth season, the Night King was portrayed by Richard Brake, with a head mold of Brake being created in order to accurately mold the prosthetic to his face. In the sixth season he was portrayed by Vladimir Furdik. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '4dae655e6fc817b7e99b8f4bf7d6c58f'}>,\n",
-       "  <Document: {'content': 'In the fourth and fifth season, the Night King was portrayed by Richard Brake, with a head mold of Brake being created in order to accurately mold the prosthetic to his face. In the sixth season he was portrayed by Vladimir Furdik. The White Walker army was first shot in front of a green screen in Magheramorne quarry, and according to a piece in \\'\\'The Hollywood Reporter\\'\\' \"A scan was taken from a drone and used as the basis for a CG model of the location, which was augmented with VFX and joined with volcanic hills that were photographed in Iceland.\"Crowd replication was used to create the 1,000-man army, with special effects supervisor Joe Bauer saying \"It\\'s scans of those actors in the makeup and costumes, with variations we used to make a digital army that extends up onto the hills.\"Bauer also noted that VFX were used to create the weather conditions in the scene, noting \"It\\'s wind, mist, fog and heavy atmosphere that you\\'d get in a marine layer. The dramatic value is to say something\\'s wrong; it\\'s a mystery what they are facing. The temperature drops, and our characters can see their breath. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '548595e95ad3dd7eb0d3d72f18fe24e1'}>,\n",
-       "  <Document: {'content': 'The dramatic value is to say something\\'s wrong; it\\'s a mystery what they are facing. The temperature drops, and our characters can see their breath. The weather obscures their vision.\"Spain-based El Ranchito, one of several special effects companies used by \\'\\'Game of Thrones\\'\\', was responsible for the White Walker army shots.\\n\\n==Storylines==\\n\\n===Season 4===\\nThe Night King\\'s first appearance is through a vision Bran Stark has with a Weirwood Heart tree. He experiences a flood of images from the past, present, and future, many of which he was not physically present for. He does not comprehend what all of these images are but, in retrospect, one of them is an image of the Night King picking up the last of Craster\\'s sons off an ice altar.\\n\\nAfter Rast leaves Craster\\'s final son in the Haunted Forest, a White Walker retrieves the child. The Walker travels to the White Walkers\\' fortress in the Lands of Always Winter and lays the child on an altar made of ice. The Night King approaches and places his finger on the boy, transforming him into a White Walker.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '672dfe2066974c9d39f2fe6c11379c83'}>,\n",
-       "  <Document: {'content': \"The Walker travels to the White Walkers' fortress in the Lands of Always Winter and lays the child on an altar made of ice. The Night King approaches and places his finger on the boy, transforming him into a White Walker.\\n\\n===Season 5===\\nThe Night King appears again when Jon Snow and Tormund Giantsbane are coordinating the evacuation of Hardhome. While loading the ships with wildlings, Hardhome is attacked by an army of wights. While assisting in the defense of Hardhome, Jon and Tormund see the Night King observing the battle along with his generals. The Night King also observes Jon destroying a White Walker with his Valyrian steel sword Longclaw. Jon and his allies are forced to flee Hardhome after the walls fall to the army of the dead; as they sail to safety, they witness the Night King reviving the dead as wights.\\n\\n===Season 6===\\nThe Night King appears in a vision being observed by Bran and the Three-eyed Raven, where they witness him as a human being forcibly transformed into the first White Walker by the Children of the Forest by impaling him with a dragonglass dagger. Bran subsequently confronts Leaf about creating the White Walkers. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '9a2de49f0677a93aae89b1ae732b243f'}>,\n",
-       "  <Document: {'content': \"===Season 6===\\nThe Night King appears in a vision being observed by Bran and the Three-eyed Raven, where they witness him as a human being forcibly transformed into the first White Walker by the Children of the Forest by impaling him with a dragonglass dagger. Bran subsequently confronts Leaf about creating the White Walkers. She explains that they were at war with the First Men and had no choice.\\n\\nLater on, Bran decides to observe a vision without the Three-Eyed Raven. He witnesses a massive army of wights led by the Night King, who touches him whilst in the vision. Bran awakens to find a mark where he was touched, and the Three-Eyed Raven warns that he must leave, as the Night King is now able to bypass the magic protecting the Three-Eyed Raven's lair. The Night King and his army quickly arrive and the Night King kills the Three-Eyed Raven, but Bran and Meera Reed are able to escape.\\n\\n===Season 7===\\nThe Night King is shown leading his army south. Through ravens' eyes, Bran locates the Night King's army beyond the Wall. When the Night King looks up, the ravens disperse, and Bran is pulled out of the warging. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '6c8239e7e402c69f509d3eb84d6ed5e4'}>,\n",
-       "  <Document: {'content': \"Through ravens' eyes, Bran locates the Night King's army beyond the Wall. When the Night King looks up, the ravens disperse, and Bran is pulled out of the warging. He requests that ravens be sent throughout the Seven Kingdoms to warn of the threat.\\n\\nThe Night King is present when the wights and White Walkers battle Jon Snow at the Wight Hunt, who traveled beyond the Wall with the Hound, Jorah, Beric, Thoros, Gendry, Tormund and several other Wildlings to attempt to capture a wight to use as proof for the gathered high lords of Westeros. Remembering their encounter from Hardhome, the Night King keeps his gaze on Jon for some time. Beric Dondarrion suggests trying to kill the Night King; as they know, killing a White Walker kills any and all wights it raised, so killing the Night King might destroy every such monster under his command, putting an end to the Great War before it truly begins. Jon dismisses the plan, however, arguing that trying to fight their way through the wights to reach the Night King would be suicide. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'fbe0d7b656deaed268fd6d44f08bc4a9'}>,\n",
-       "  <Document: {'content': \"Jon dismisses the plan, however, arguing that trying to fight their way through the wights to reach the Night King would be suicide. When Daenerys Targaryen arrives and her dragons burn many wights, the men try to evacuate on Drogon but the wights continue their attack; using an ice javelin, the Night King kills Viserion. The Night King keeps his gaze on an enraged Jon, who appears on the verge of calling him down to fight directly, but his lieutenant hands him another spear. The Night King throws it at Drogon, but the dragon takes flight and dodges in time. Jon stays on the ground to cover the others' departure, but is pulled into the water, and the others are forced to flee on Drogon. Jon emerges from the water and is saved by Benjen Stark, who gives Jon his horse to ride to Eastwatch while sacrificing himself to hold off the wights. After their battle is over, the Night King reanimates Viserion.\\n\\nThe undead army arrives at Eastwatch; when the Night King appears, riding Viserion, Tormund orders the defenders to evacuate. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '8bb1498629489392fa41a1e82c72b532'}>,\n",
-       "  <Document: {'content': \"After their battle is over, the Night King reanimates Viserion.\\n\\nThe undead army arrives at Eastwatch; when the Night King appears, riding Viserion, Tormund orders the defenders to evacuate. Breathing blue fire, Viserion destroys Eastwatch and a section of the Wall, allowing the White Walkers and their wight army to finally invade the Seven Kingdoms  while the Night King flies overhead towards the North.\\n\\n===Season 8===\\nLed by the Night King, the dead quickly advance south and attack Last Hearth, massacring the population and adding them to their army, before marching on Winterfell. Bran, knowing that the Night King plans to destroy him, plans to hide in Winterfell's Godswood as bait, with Jon and Daenerys lying in wait with the dragons to ambush him. However, Jon and Daenerys leave their position to attack the wights when they begin overwhelming Winterfell's defenders, and the Night King engages them with Viserion, resulting in Jon and the Night King being dismounted. Daenerys attempts to burn the Night King, not realizing that he is immune to fire, and flees when the Night King attempts to kill her dragon Drogon. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '74dfb5a396729b155f3d67f739a5e9bd'}>,\n",
-       "  <Document: {'content': 'Daenerys attempts to burn the Night King, not realizing that he is immune to fire, and flees when the Night King attempts to kill her dragon Drogon. Jon attempts to fight the Night King, but the Night King reanimates those killed in battle to protect him. The Night King and the White Walkers head to the Godswood for Bran, and are confronted by Theon Greyjoy, whom the Night King kills. However, before he can kill Bran, he is ambushed by Arya Stark, who destroys him by plunging a dagger made out of Valyrian steel into the Night King\\'s chest. With the Night King dead, the White Walkers shatter and the Army of the Dead collapses.\\n\\n==Merchandising==\\nDark Horse produced a Night King bust in March 2016, which was followed by an 8-inch Night King figure by Dark Horse Deluxe in late 2016. Funko also released a \"Night King\" POP! figure that same year.\\n\\n== References ==\\n* 50px Content in this article was copied from  Night King at Game of Thrones Wiki, which is licensed under the  Creative Commons Attribution-Share Alike 3.0 (Unported) (CC-BY-SA 3.0) license.', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'c3aa18c2dec1317a864aceb134ae33b0'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Mhysa\\'\\'\\'\" is the third season finale of the American medieval epic fantasy television series \\'\\'Game of Thrones\\'\\', and its 30th episode overall. Written by executive producers David Benioff and D. B. Weiss, and directed by David Nutter, it originally aired on  on HBO in the United States.\\n\\nThe episode revolves on the aftermath of the events instigated by \"The Red Wedding\", in which Tywin Lannister is revealed to be the mastermind behind the massacre — with Walder Frey and Roose Bolton having conspired with the Lannisters against the Starks. As a result, House Frey receives the Seat of Riverrun and Roose Bolton is appointed the new \"Warden of the North\". Elsewhere, House Greyjoy begins a new military campaign. In the North, Maester Aemon sends out ravens to alert the whole of Westeros about the arrival of the White Walkers. And across the narrow sea, the freed slaves of Yunkai hail Daenerys as their \"mhysa\", the Ghiscari language\\'s word for \"mother\".\\n\\nAccording to Nielsen Media Research, \"Mhysa\" was seen by 5.4 million household viewers in the United States, a twenty-eight percent increase compared to the second season finale, \"Valar Morghulis\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '93865fe1862df748dc3e381c4941dc52'}>,\n",
-       "  <Document: {'content': 'According to Nielsen Media Research, \"Mhysa\" was seen by 5.4 million household viewers in the United States, a twenty-eight percent increase compared to the second season finale, \"Valar Morghulis\". After its broadcast, the episode received generally positive reviews from television critics, with some of them addressing its anti-climactic closure of the series\\' third season, and its establishment of potential storylines for the fourth season, such as through the final scene\\'s \"glimmer of hope\". The episode received a nomination for a Primetime Emmy Award for Outstanding Cinematography for a Single-Camera Series at the 65th Primetime Creative Arts Emmy Awards.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nTyrion learns of the deaths of Robb and Catelyn during the Red Wedding. Tyrion tells Tywin that northerners will never forget the role the Lannisters played in the Red Wedding. Varys gives Shae a sack of diamonds to sail for Essos, but she refuses. Jaime, Brienne, and Qyburn arrive in King\\'s Landing.\\n\\n===At the Twins===\\nSandor and Arya find a group of Frey men. Arya kills one, who claimed to have desecrated Robb\\'s body and Sandor kills others. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '9e7166ef4d952cc33fa2a51e70669cb3'}>,\n",
-       "  <Document: {'content': \"===At the Twins===\\nSandor and Arya find a group of Frey men. Arya kills one, who claimed to have desecrated Robb's body and Sandor kills others. Walder and Bolton discuss the escape of Brynden and their new oppositions as Lord of Riverrun and Warden of the North.\\n\\n===In the North===\\nBran and his group arrive at the Nightfort and meet Sam and Gilly. Sam realizes that Bran is Jon's brother. Bran asks Sam to take them north of the Wall. He takes them through the passage and gives them his dragonglass weapons. Sam and Gilly arrive at Castle Black and meet Aemon, who, after hearing of White Walkers, sends all ravens with messages warning the return of White Walkers. Ygritte finds Jon, but before he escapes, Ygritte shoots three arrows into him. Jon arrives at Castle Black, where his wounds are treated. At the Dreadfort, Theon's torturer, revealed to be Bolton's bastard Ramsay Snow, nicknames Theon Reek.\\n\\n===On the Iron Islands===\\nBalon reads letter from Ramsay, demanding to have the Ironborn soldiers withdraw from the North. Balon allows Theon's torture to continue, but Yara takes 50 best Ironborn men to attack the Dreadfort.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'e2f34a69ff1601a1ef9856c0587c4dd7'}>,\n",
-       "  <Document: {'content': '===On the Iron Islands===\\nBalon reads letter from Ramsay, demanding to have the Ironborn soldiers withdraw from the North. Balon allows Theon\\'s torture to continue, but Yara takes 50 best Ironborn men to attack the Dreadfort.\\n\\n===At Dragonstone===\\nStannis informs Davos of Robb\\'s death and Melisandre plans to sacrifice Gendry to the Lord of Light. Davos gives Gendry a boat to escape Dragonstone. Stannis orders Davos’ execution, but after he shows Stannis a letter from the Wall, Melisandre tells Stannis that only he can save the North from the White Walkers and that he will need Davos\\' help, preventing his execution.\\n\\n===Outside Yunkai===\\nDaenerys frees slaves from Yunkai, who begin to shout mhysa, which Missandei tells Daenerys is Ghiscari for mother.\\n\\n==Production==\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\\n===Writing===\\n\"Mhysa\" was written by executive producers David Benioff and D. B. Weiss, based on George R. R. Martin\\'s original work from his novel \\'\\'A Storm of Swords\\'\\'. Chapters adapted from \\'\\'A Storm of Swords\\'\\' to the episode were chapters 43, 49, 53 to 55, 57, 63, and 64 (Daenerys IV, Jon VI, Arya XI, Tyrion VI, Davos V, Bran IV, Jaime VII, Davos VI).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '6d1cf2d41eed8182323309040fb9a485'}>,\n",
-       "  <Document: {'content': 'Chapters adapted from \\'\\'A Storm of Swords\\'\\' to the episode were chapters 43, 49, 53 to 55, 57, 63, and 64 (Daenerys IV, Jon VI, Arya XI, Tyrion VI, Davos V, Bran IV, Jaime VII, Davos VI).\\n\\n===Casting===\\nAfter being absent for the entire second season, Peter Vaughan returns as Maester Aemon and Josef Altin returns as Pypar. This episode also marks the return, after a long absence, of Patrick Malahide as Balon Greyjoy and Gemma Whelan as Yara Greyjoy.\\n\\n==Reception==\\n\\n===Ratings===\\nIn its original American broadcast in HBO, \"Mhysa\" was seen by an estimated 5.4 million household viewers, according to Nielsen Media Research. It marked a year-to-year increase in viewership of 28 percent compared to the second season finale, \"Valar Morghulis\", which was seen by 4.2 million. The second broadcast of the \"Mhysa\" during the night was viewed by 900,000 viewers, bringing its total viewership for to 6.30 million. According to analysts, the success of the episode significantly helped \\'\\'Game of Thrones\\'\\' to surpass \\'\\'True Blood\\'\\' as the second most-watched series on HBO, after \\'\\'The Sopranos\\'\\'. In the United Kingdom, the episode was viewed by 1.154 million viewers, making it the highest-rated broadcast that week. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '6d39a052364b087c5e4cf62c180246bb'}>,\n",
-       "  <Document: {'content': 'According to analysts, the success of the episode significantly helped \\'\\'Game of Thrones\\'\\' to surpass \\'\\'True Blood\\'\\' as the second most-watched series on HBO, after \\'\\'The Sopranos\\'\\'. In the United Kingdom, the episode was viewed by 1.154 million viewers, making it the highest-rated broadcast that week. It also received 0.110 million timeshift viewers.\\n\\n===Critical reception===\\n\"Mhysa\" received generally positive reviews from television critics, with some of them addressing the finale\\'s anti-climactic closure of the third season and for establishing new storylines for the fourth. Review aggregator Rotten Tomatoes surveyed 20 reviews of the episode and deemed 100% of them to be positive with an average score of 8.5 out of 10. The website\\'s critical consensus reads, \"\\'Mhysa\\' wraps up several of season 3\\'s lingering storylines while subtly setting the table for season 4.\"James Poniewozik of \\'\\'Time\\'\\' wrote in his review, \"The end of season 3, then, spent some time among the winners and losers in post-Red-Wedding Westeros, giving the audience a chance to soak in the shock, seethe at the winners\\' glee, and get a reminder of the larger forces – White Walkers, dragons – well beyond the war between the Lannisters and the Starks. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'b47c44b02fb421d246d64d4a1cc83089'}>,\n",
-       "  <Document: {'content': 'James Poniewozik of \\'\\'Time\\'\\' wrote in his review, \"The end of season 3, then, spent some time among the winners and losers in post-Red-Wedding Westeros, giving the audience a chance to soak in the shock, seethe at the winners\\' glee, and get a reminder of the larger forces – White Walkers, dragons – well beyond the war between the Lannisters and the Starks. If the Red Wedding seemed to kill hope, \\'Mhysa\\' made clear that it didn\\'t end anything. And it weaved together the many, many threads of \\'\\'GoTs\\'\\' tapestry by returning to a recurring theme: that \\'\\'Game of Thrones\\'\\' is ultimately about family.\"Matt Fowler of IGN described that the finale had \"nicely set up a lot of cool stuff for Season 4, but it was also lacking some of the power and majesty of previous finales. Especially the ending with Dany, who herself had a better ending back in \\'And Now His Watch is Ended\\' when her dragons torched Astapor and she left with a full army.\"He also praised the scene where Arya killed a Frey soldier. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'd3ebe29866be25b4f27bff5b912ea77'}>,\n",
-       "  <Document: {'content': 'Especially the ending with Dany, who herself had a better ending back in \\'And Now His Watch is Ended\\' when her dragons torched Astapor and she left with a full army.\"He also praised the scene where Arya killed a Frey soldier. Writing for \\'\\'Today\\'\\', Drusilla Moorhouse remarked that \"After last week\\'s shocking massacre, most fans braced for more tragic deaths in season three\\'s finale. Instead, the blow was softened with poignant reunions and surprising saves, setting the stage for an explosive fourth season.\"In her review for Zap2it, Terri Schwartz wrote that \"Nothing can ever quite redeem the deaths of Robb and Catelyn Stark, but at least larger forces are taking shape that are propelling this series into Season 4. Daenerys is as powerful as she\\'s ever been, Jon Snow returns to the Wall while Bran heads north of it and the Greyjoys ready an assault to finally save Theon from his captor. Then there\\'s the fact that Stannis decides to sail north to the Wall to aid the Night\\'s Watch in their fight against the White Walkers, which seems like it\\'s going to end up being the greater, global conflict in the future of \\'Game of Thrones\\'.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'e81eb41ca3fcb4ae4c04929df94c5127'}>,\n",
-       "  <Document: {'content': 'Then there\\'s the fact that Stannis decides to sail north to the Wall to aid the Night\\'s Watch in their fight against the White Walkers, which seems like it\\'s going to end up being the greater, global conflict in the future of \\'Game of Thrones\\'.\"Writing for \\'\\'The A.V. Club\\'\\', David Sims gave \"Mhysa\" an \"A-\" rating, while Emily VanDerWerff gave it a \"B+\". Sims, writing for audiences who have not read the novels, described the episode as lacking \"a lot of serious plot movement or major twists and may have fans gnashing their teeth a little bit as they wait nine months for season four. The previous season finales have also had that quality, but they each ended on a barnstorming note. \\'Mhysa\\', not quite so much.\"VanDerWerff, writing for audiences who have read the novels, wrote that \"On a plot level, not a lot happens in \\'Mhysa\\'\", but praised the episode in addressing the series\\' \"value of one human life is in the face of a kingdom.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '1c9580cd48a950550ba738ce741cd4b3'}>,\n",
-       "  <Document: {'content': 'VanDerWerff, writing for audiences who have read the novels, wrote that \"On a plot level, not a lot happens in \\'Mhysa\\'\", but praised the episode in addressing the series\\' \"value of one human life is in the face of a kingdom.\"He also mildly criticized the series\\' template of when \"something terrible happens in Westeros, Dany offers a glimmer of hope over in her story line\", referring to it as its \"Achilles\\' heel\". Kevin Fitzpatrick of ScreenCrush wrote in his review, \"All in all, the air (or blood) of the season had mostly been let out by \\'The Rains of Castamere\\', as even a returning Jaime’s most interesting moments arrived much earlier in the season, but \\'Mhysa\\' gave us a good course-heading for season 4, with some much-needed catharsis along the way.\"The episode\\'s final scene, in which Daenerys, \"the blondest possible savior figure\", appears with \"uncharacterized brown people\" as \"being lifted up as their messiah and praising her for saving them from bondage\", was criticized by at least four commentators as having colonialist or even racist undertones. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'f8bf54b4d5d7bc96341bbedb8f48c04f'}>,\n",
-       "  <Document: {'content': 'The episode\\'s final scene, in which Daenerys, \"the blondest possible savior figure\", appears with \"uncharacterized brown people\" as \"being lifted up as their messiah and praising her for saving them from bondage\", was criticized by at least four commentators as having colonialist or even racist undertones. They asked why the series chose to portray the Yunkish as nearly uniformly dark-skinned, rather than as ethnically diverse as in the source novels, to which George R. R. Martin replied that this was because the scene was shot in Morocco with local extras. Commentators also criticized, more broadly, that the series\\'s inclusion of people of color was limited to only a small number of characters.\\n\\n== References ==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '8a9390fc1041619d81dfcaa56ec6524c'}>,\n",
-       "  <Document: {'content': \"\\n\\nThe fictional world in which the ''A Song of Ice and Fire'' novels by George R. R. Martin take place is divided into several continents, known collectively as The Known World. Some unofficial fan groups call it Planetos.\\n\\nMost of the story takes place on the continent of Westeros and in a large political entity known as the Seven Kingdoms. Those kingdoms are spread across nine regions: the North, the Iron Islands, the Riverlands, the Vale, the Westerlands, the Stormlands, the Reach, the Crownlands, and Dorne. A massive wall of ice and old magic separates the Seven Kingdoms from the largely unmapped area to the north. The vast continent of Essos is located east of Westeros, across the Narrow Sea. The closest foreign nations to Westeros are the Free Cities, which is a collection of independent city-states along the western edge of Essos. The lands along the southern coastline of Essos are called the Lands of the Summer Sea and include Slaver's Bay and the ruins of Valyria. The latter is the former home of the ancestors of House Targaryen. To the south of Essos are the continents of Sothoryos and Ulthos, which in the narrative are largely unexplored.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '3099b7d06df8807e298e579c084a5338'}>,\n",
-       "  <Document: {'content': 'The latter is the former home of the ancestors of House Targaryen. To the south of Essos are the continents of Sothoryos and Ulthos, which in the narrative are largely unexplored.\\n\\nThe planet experiences erratic seasons of unpredictable duration that can last for many years. At the beginning of \\'\\'A Song of Ice and Fire\\'\\', Westeros has enjoyed a decade-long summer, and many feared that an even longer and harsher winter would follow.\\n\\nGeorge R. R. Martin set the \\'\\'Ice and Fire\\'\\' story in an alternative world of Earth, a \"secondary world\", such as that which J. R. R. Tolkien pioneered with Middle-earth. Martin has also suggested that world may be larger than the real world planet Earth. The \\'\\'Ice and Fire\\'\\' narrative is set in a post-magic world where people no longer believe in supernatural things such as the Others. Although the characters understand the natural aspects of their world, they do not know or understand its magical elements. Religion, though, has a significant role in the life of people, and the characters practice many different religions.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'a5d3affa8a2e9403e92aac8664bfd89b'}>,\n",
-       "  <Document: {'content': 'Although the characters understand the natural aspects of their world, they do not know or understand its magical elements. Religion, though, has a significant role in the life of people, and the characters practice many different religions.\\n\\n==Maps==\\n\\nMap\\n\\n\\'\\'Game\\'\\'\\n\\n\\'\\'Clash\\'\\'\\n\\n\\'\\'Storm\\'\\'\\n\\n\\'\\'Feast\\'\\'\\n\\n\\'\\'Dance\\'\\'\\n\\n(\\'\\'Lands\\'\\')\\n\\nThe North of Westeros\\n\\nUS\\n\\nThe South of Westeros\\n\\nUS\\n\\nKing\\'s Landing city map\\n\\nBeyond the Wall\\n\\nSlaver\\'s Bay, Valyria, and Sothoryos\\n\\nThe Iron Islands\\n\\nThe Free Cities\\n\\n\\'\\'A Game of Thrones\\'\\', the first installment of the \\'\\'A Song of Ice and Fire\\'\\' series, has two maps of Westeros. Each new book has added one or two maps so that, as of \\'\\'A Dance with Dragons\\'\\', seven maps of the fictional world are available in the books. Martin said in 2003 that complete world maps were not made available so that readers may better identify with people of the real Middle Ages who were uneducated about distant places. He also did not \"subscribe to the theory put forth in \\'\\'The Tough Guide To Fantasyland\\'\\' ... that eventually the characters must visit every place shown on The Map.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'fd3dfe1e99f0b62df361e46d88d79be8'}>,\n",
-       "  <Document: {'content': 'He also did not \"subscribe to the theory put forth in \\'\\'The Tough Guide To Fantasyland\\'\\' ... that eventually the characters must visit every place shown on The Map.\"He conceded, however, that readers may be able to piece together a world map by the end of the series. He was intentionally vague about the size of the \\'\\'Ice and Fire\\'\\' world, omitting a scale on the maps to discourage prediction of travel lengths based on measured distances. A new map artist was used in \\'\\'A Dance with Dragons\\'\\' so that the maps are available in two versions by James Sinclair and Jeffrey L. Ward, depending on the book. The old maps were redone to match the style of the new ones.\\n\\nA set of foldout maps was published on October 30, 2012 as \\'\\'The Lands of Ice and Fire\\'\\' (). The illustrator and cartographer Jonathan Roberts drew the maps, based on drafts by Martin. The twelve maps in the set are entitled \"The Known World\", \"The West\", \"Central Essos\", \"The East\", \"Westeros\", \"Beyond The Wall\", \"The Free Cities\", \"Slaver\\'s Bay\", \"The Dothraki Sea\", \"King\\'s Landing\", \"Braavos\", and \"Journeys\". The latter tracks the paths taken by the novels\\' characters.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '5ac45046c1eb62939c277c51fb22b622'}>,\n",
-       "  <Document: {'content': 'The twelve maps in the set are entitled \"The Known World\", \"The West\", \"Central Essos\", \"The East\", \"Westeros\", \"Beyond The Wall\", \"The Free Cities\", \"Slaver\\'s Bay\", \"The Dothraki Sea\", \"King\\'s Landing\", \"Braavos\", and \"Journeys\". The latter tracks the paths taken by the novels\\' characters.\\n\\n==Westeros ==\\n\\nThe story takes place primarily on a continent called \\'\\'\\'Westeros\\'\\'\\', which is roughly the size of South America. The continent is home to the Seven Kingdoms and lands beyond the great Wall, which makes up a large chunk of Westeros, being roughly the size of Canada, with most of it largely unmapped and unexplored.\\n\\nAt the novel\\'s beginning, the majority of Westeros is united under the rule of one king, with each of nine regions controlled by a different major house. Martin here drew inspiration from medieval European history, in particular the Hundred Years\\' War, the Crusades, the Albigensian Crusade, and the Wars of the Roses.\\n\\nThe first inhabitants of the continent were the Children of the Forest, a nature-worshipping anthropoid species who carved the faces of their gods in weirwood trees. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '7e8ae98911f2f73f7012006975356f4a'}>,\n",
-       "  <Document: {'content': 'The first inhabitants of the continent were the Children of the Forest, a nature-worshipping anthropoid species who carved the faces of their gods in weirwood trees. Some time later, the First Men\\'s attempts at cultivating the land led to a war with the Children of the Forest that eventually was settled by an agreement known as \"The Pact\". This was the beginning of the Age of Heroes. During that time, the First Men adopted the gods of the Children of the Forest. Those gods later became known in Westeros as the Old Gods.\\n\\nEight thousand years before the events of the novels, an enigmatic species called the Others emerged from the furthermost north during the decades-long winter known as \"The Long Night\". The Children of the Forest and the First Men jointly repelled the Others and then built a massive wall barring passage from the far north.\\n\\nSometime later, the Andals invaded Westeros and established the Faith of the Seven, writing, and steel. Only the North remained unconquered. The Children of the Forest disappeared from Andal lands. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '940c3cc431cbb154149fc67a5d1dd200'}>,\n",
-       "  <Document: {'content': 'Sometime later, the Andals invaded Westeros and established the Faith of the Seven, writing, and steel. Only the North remained unconquered. The Children of the Forest disappeared from Andal lands. Over time, \\'\\'\\'seven kingdoms\\'\\'\\' were forged across Westeros: The North, The Iron Islands (inclusive of the riverlands and parts of the crownlands), The Vale, The Westerlands, The Stormlands (inclusive of the remaining parts of the crownlands), The Reach, and Dorne. The Seven Kingdoms were constantly at war with one another, and no kingdom remained dominant for long.\\n\\nThree hundred years before the novels begin, Aegon the Conqueror and his two Targaryen sister-wives came from Dragonstone and landed at present-day King\\'s Landing. The three assembled a temporary bastion, known as \"Aegon\\'s Fort\", which grew into the capital city, later known as King\\'s Landing. Their powerful dragons overwhelmed six of the Seven Kingdoms through conquest or treaty, with Dorne remaining independent for another two hundred years until it was absorbed through a marriage-alliance. The Targaryens built the Iron Throne, which consists of the swords of defeated rulers, fused together by dragonfire. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '7c2d706d37a59f49e75e2a8e35aead95'}>,\n",
-       "  <Document: {'content': \"Their powerful dragons overwhelmed six of the Seven Kingdoms through conquest or treaty, with Dorne remaining independent for another two hundred years until it was absorbed through a marriage-alliance. The Targaryens built the Iron Throne, which consists of the swords of defeated rulers, fused together by dragonfire. They also established King's Landing as their capital city and consolidated parts of surrounding regions of the riverlands and stormlands into the crownlands region. They remained the ruling power on the continent until deposed by a rebellion led by Robert Baratheon.\\n\\n===The North ===\\nThe North consists of the northern half of the Seven Kingdoms and is ruled by House Stark from their castle at Winterfell. The North is sparsely populated, but it is nearly as big as the other six kingdoms combined. Martin compared the North to Scotland. The climate is cold overall, with hard winters and mild snows common regardless of season. The region's northern border is the New Gift, which is a stretch of land 50 leagues wide and in possession of the Night's Watch. An isthmus of swampland named The Neck separates the North from the South. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '156a3750949be37c7c81a342fd0caf52'}>,\n",
-       "  <Document: {'content': \"The region's northern border is the New Gift, which is a stretch of land 50 leagues wide and in possession of the Night's Watch. An isthmus of swampland named The Neck separates the North from the South. It is home to small, marsh-dwelling crannogmen and is ruled by House Reed of Greywater Watch, bannermen of House Stark. The Neck's narrowness, difficult terrain, and almost impenetrable Moat Cailin help protect the North from invasion. The city of White Harbor is a thriving port. Illegitimate children born of a noble parent in the North are given the surname Snow.\\n\\n====Winterfell====\\n\\nWinterfell is the ancestral castle of House Stark and was built over a natural hot spring. Scalding water runs through the castle walls and warms its halls and rooms. There are several open pools where heated water collects within the godswood. The hot spring also prevents the ground from freezing. The castle has deep catacombs where bodies of the Starks are entombed behind statues in their likeness with a direwolf at their feet and their swords left in their hands. The tombs have been used since the old kings of the North, known as the Kings of Winter, were in power. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '6a97eacf1e096f420efa42518b868dcc'}>,\n",
-       "  <Document: {'content': 'The tombs have been used since the old kings of the North, known as the Kings of Winter, were in power. They ruled before Aegon the Conqueror and the arrival of the Andals before that.\\n\\nTo depict Winterfell, both the pilot and season 1 of the television adaptation used the 16th century clock tower and ancient courtyard of the Clearsky Adventure Centre located at Castle Ward in County Down, Northern Ireland, Doune Castle in Stirling, Scotland, which previously featured as Castle Anthrax in the film \\'\\'Monty Python and the Holy Grail\\'\\', was also used for exterior scenes. Saintfield Estates stood in as Winterfell\\'s godswood, which is an enclosed wooded area where characters can worship the old gods beside trees with faces carved in their bark. A car park stood in for Winterfell\\'s courtyard, and a wine cellar was used to depict the Stark family crypt. Tollymore Forest featured prominently in the prologue of the pilot episode and in the pivotal scene where the Starks first find the direwolves. Cairncastle, meanwhile, served as the location where Ned Stark beheads the deserter Will.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '25496ce841ae3d3821b8998032cb3d18'}>,\n",
-       "  <Document: {'content': 'Tollymore Forest featured prominently in the prologue of the pilot episode and in the pivotal scene where the Starks first find the direwolves. Cairncastle, meanwhile, served as the location where Ned Stark beheads the deserter Will.\"The interior of Winterfell, such as the Tower of the First Keep, the Great Hall, and Catelyn\\'s bedchamber, were filmed at The Paint Hall studio. Set designer Gemma Jackson said, \"Winterfell was based on a Scottish castle.\"====The Wall====\\nThe Wall in the \\'\\'Ice and Fire\\'\\' series was inspired by Hadrian\\'s Wall in the North of England.\\nThe Wall is a huge structure of stone, ice, and magic on the northern border of the Seven Kingdoms. It is home to the Night\\'s Watch, a brotherhood sworn to protect the realms of men from the threats beyond the Wall.\\n\\nThe Wall was inspired by Martin\\'s visit to Hadrian\\'s Wall, in the North of England close to the border with Scotland. Looking out over the hills, Martin wondered what a Roman centurion from the Mediterranean would feel, not knowing what threats might come from the north. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'fc52e412a86dd53e8edd75a265cc5d67'}>,\n",
-       "  <Document: {'content': 'Looking out over the hills, Martin wondered what a Roman centurion from the Mediterranean would feel, not knowing what threats might come from the north. This experience was so profound that a decade later, in 1991, he wanted to \"write a story about the people guarding the end of the world\", and ultimately \"the things that come out of the fictional north are a good deal more terrifying than Scotsmen or Picts\".\\n\\nMartin adjusted the size, length, and magical nature of the Wall for genre demands; Jon Snow\\'s chapters describe it as approximately  long and  high in general, rising up to a perceived  in spots due to huge foundation blocks. The top is wide enough for a dozen mounted knights to ride abreast (approximately 30\\xa0ft or 10\\xa0m), while the base is so thick that the Wall\\'s gates are more like tunnels through the ice.\\n\\nThe novels\\' legends claim that the First Men, or more specifically Brandon the Builder with the possible help of children of the forest and giants, constructed the Wall some 8,000 years before the events of the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'bf0656124f6eb60462cb3711d2c08141'}>,\n",
-       "  <Document: {'content': 'The novels\\' legends claim that the First Men, or more specifically Brandon the Builder with the possible help of children of the forest and giants, constructed the Wall some 8,000 years before the events of the series.\\n\\nThe Wall has since been maintained by the Night\\'s Watch to guard the realms of men against the threats from beyond, originally the Others, and later against wildling raids.\\n\\nA strip of land known as \"the Gift\", now stretching 50 leagues (about ) south of the wall, was given to them in perpetuity thousands of years ago for cultivation. In \\'\\'A Game of Thrones\\'\\', out of the nineteen castles built along the wall, only three are still manned: Castle Black with 600 men, and the Shadow Tower and Eastwatch-by-the-Sea with 200 men each. Parts of Castle Black have fallen into ruin.\\n\\nThe TV series\\' Castle Black and the Wall were filmed in the abandoned Magheramorne Quarry near Belfast, Northern Ireland, whereas the scenes shot atop the wall were filmed inside Paint Hall Studios. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'c52e692f8751898eaba439a9017cf6a2'}>,\n",
-       "  <Document: {'content': 'The TV series\\' Castle Black and the Wall were filmed in the abandoned Magheramorne Quarry near Belfast, Northern Ireland, whereas the scenes shot atop the wall were filmed inside Paint Hall Studios. The composite set (with both exteriors and interiors) consisted of a large section of Castle Black including the courtyard, the ravenry, the mess hall, and the barracks, and used the stone wall of the quarry as the basis for the ice wall that protects Westeros from the dangers that dwell beyond. They also made a functional elevator to lift the rangers to the top of the Wall. A castle with real rooms and a working elevator were built near a cliff  high. \"Working construction lifts were discovered at a nearby work site and rise 18 feet; CGI fills in the rest to make the wall appear 700 feet high.\"The area around the elevator was painted white to make it look like ice. Martin was surprised by the height and thought: \"Oh I may have made the wall too big!\"Martin observed: \"It\\'s a pretty spectacular, yet miserable location. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'cd9a96a6a62d9f7feac5b331d9efae67'}>,\n",
-       "  <Document: {'content': 'Martin was surprised by the height and thought: \"Oh I may have made the wall too big!\"Martin observed: \"It\\'s a pretty spectacular, yet miserable location. It is wet and rainy, and the mud is thick....it really gets the actors in the mood of being at the end of the world in all of this cold and damp and chill.\"====Beyond the Wall====\\nSeason 2 of the TV adaptation featured scenes set north of the Wall that were filmed on the Vatnajökull glacier in Iceland.\\n\\'\\'A Clash of Kings\\'\\' takes the story to the lands Beyond the Wall, although the first five books do not explore \"what lies really north ... but we will in the last two books\". The TV adaptation used Iceland as filming location for the lands Beyond the Wall. Martin, who has never been to Iceland, said Beyond the Wall was \"considerably larger than Iceland and the area closest to my Wall is densely forested, so in that sense it\\'s more like Canada Hudson Bay or the Canadian forests just north of Michigan. And then as you get further and further north, it changes. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '6dacf4321149864e6eaba4fb7c23726a'}>,\n",
-       "  <Document: {'content': 'Martin, who has never been to Iceland, said Beyond the Wall was \"considerably larger than Iceland and the area closest to my Wall is densely forested, so in that sense it\\'s more like Canada Hudson Bay or the Canadian forests just north of Michigan. And then as you get further and further north, it changes. You get into tundra and ice fields and it becomes more of an arctic environment. You have plains on one side and a very high range of mountains on the other. Of course, once again this is fantasy, so my mountains are more like the Himalayas.\"In an HBO featurette, Martin stated the lands beyond the wall make up a big part of Westeros, being roughly the size of Canada. The Valley of Thenn is one such location beyond the Wall, and north of that is the Lands of Always Winter, where the Others come from.\\n\\nDuring the first season, the HBO team used places that they could decorate with artificial snow for the north of the Wall, but a bigger landscape was chosen for Season 2. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '569f7a02b01af13e0e4d01202eb8ae13'}>,\n",
-       "  <Document: {'content': 'During the first season, the HBO team used places that they could decorate with artificial snow for the north of the Wall, but a bigger landscape was chosen for Season 2. \"Primary filming for these scenes, which encompass both the Frostfangs and the Fist of the First Men, occurred at the Svínafellsjökull calving glacier in Skaftafell, Iceland, followed by shooting near Smyrlabjörg and Vík í Mýrdal on Höfðabrekkuheiði. Benioff said, \"We always knew we wanted something shatteringly beautiful and barren and brutal for this part of Jon\\'s journey, because he\\'s in the true North now. It\\'s all real. It\\'s all in camera. We\\'re not doing anything in postproduction to add mountains or snow or anything.\"===The Iron Islands===\\nThe Iron Islands are a group of seven islands to the west of Westeros – Pyke, Great Wyk, Old Wyk, Harlaw, Saltcliffe, Blacktyde, and Orkmont – in Ironman\\'s Bay off the west coast of the continent. Ruled by House Greyjoy of Pyke, the isles are described as bare and barren, and the local weather as \"windy and cold, and damp\". The members of this seafaring nation are known in the rest of Westeros as Ironmen, and to themselves as Ironborn. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'd13f0b626e4b6aaa78183107e7c6d0bb'}>,\n",
-       "  <Document: {'content': 'The members of this seafaring nation are known in the rest of Westeros as Ironmen, and to themselves as Ironborn. Illegitimate children born in the Iron Islands are given the surname Pyke.\\n\\nFor fierce raids, the Ironmen are titled the \"terror of the seas\". They worship the Drowned God, who \"had made them to reave and rape, to carve out kingdoms and write their names in fire and blood and song\". The appendix of \\'\\'A Game of Thrones\\'\\' summarizes that the Ironmen once ruled over the Riverlands and much of the western coast of Westeros. When Aegon the Conqueror extinguished Harren the Black\\'s line, he chose House Greyjoy as the new rulers of the Ironmen.\\n\\n====Pyke====\\nFor the TV adaptation, the harbour of Ballintoy in Northern Ireland was redressed as the port of Pyke.\\nPyke is the seat of House Greyjoy. The television adaptation filmed the scenes of Pyke\\'s port at Lordsport Harbour in Ballintoy Harbour, in Northern Ireland\\'s County Antrim. The sea has worn away much of the rock on which Pyke originally stood, so the castle now consists mostly of a main keep on the main island and smaller towers perched on rocks.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'eed75de86f4fb709d4d9d4fc097c920d'}>,\n",
-       "  <Document: {'content': 'The sea has worn away much of the rock on which Pyke originally stood, so the castle now consists mostly of a main keep on the main island and smaller towers perched on rocks.\\n\\n====Old Wyk====\\nOld Wyk is the smallest and holiest island in the Iron Islands. It is where Kingsmoots are held, and where the Grey King slew Nagga, a sea dragon, and made a court of his bones.\\n\\n===The Riverlands===\\nThe Riverlands are the populous and fertile areas surrounding the forks of the river Trident on Westeros. While they form one of the nine regions of Westeros, the Riverlands\\' central location and geographic features made the region an inter-kingdom battle zone that changed hands rather than becoming its own \\'eighth\\' kingdom of the Seven Kingdoms. Centrally located between the Westerlands, the Crownlands, the Vale, and the North and lacking the natural defenses of other regions, they have seen frequent warfare. The first ruler to unite the Riverlands was Benedict Justman, but the Justman dynasty died out three centuries later. The Durrandons conquered the Riverlands, but lost rule of it to Harwyn \"Hardhand\" Hoare, King of the Iron Islands. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '50c201ad37fa165432cac4bb59945aa0'}>,\n",
-       "  <Document: {'content': 'The first ruler to unite the Riverlands was Benedict Justman, but the Justman dynasty died out three centuries later. The Durrandons conquered the Riverlands, but lost rule of it to Harwyn \"Hardhand\" Hoare, King of the Iron Islands. At the time of Aegon\\'s conquest, the Riverlands were ruled by Harwyn\\'s grandson, Harren the Black, king of the Iron Islands, and the Tullys were local nobles who rebelled against him by joining Aegon the Conqueror. As with Westerosi customs to give bastards a surname showing their origins, illegitimate children born in the Riverlands are given the surname Rivers.\\n\\n====Harrenhal====\\nHarrenhal is an enormous ruined castle and is the site of many important events in the novels. Harrenhal was built by Harren the Black, after his conquest of the Riverlands, intending to make it the largest fortification ever built in Westeros. The castle has been described as so large that an entire army was needed to garrison it. The Great Hall had 35 hearths and seated thousands. Shortly after the castle was completed, Aegon the Conqueror\\'s dragon slew Harren, his sons, and his entire army by setting the castle alight.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '38252a3e8803beb451e1ff6744190182'}>,\n",
-       "  <Document: {'content': 'Shortly after the castle was completed, Aegon the Conqueror\\'s dragon slew Harren, his sons, and his entire army by setting the castle alight.\\n\\nSince then, the ruins of the castle have been occupied by a variety of houses, all of which eventually became extinct. As a result, the people of Westeros believe the castle is cursed. The logistical and economic difficulties inherent in keeping such an enormous castle maintained and garrisoned has made it something of a white elephant. At the start of the War of the Five Kings, the castle is in ruin, with only a fraction of it habitable, and held by Lady Shella Whent, the last of her House, who is stripped of Harrenhal when the Lannisters seize her castle. The castle changes hands repeatedly over the course of the novels, many of those holding it meeting unpleasant ends.\\n\\n====Riverrun====\\nRiverrun is the ancestral stronghold of House Tully. The castle is located along one of the \"forks\" of the Trident and controls access to the interior of Westeros. The castle is bordered on two sides by the Tumblestone River and the Red Fork. The third side fronts on a massive manmade ditch. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '63f7bd7aae70514770d935c5d12fbb42'}>,\n",
-       "  <Document: {'content': \"The castle is bordered on two sides by the Tumblestone River and the Red Fork. The third side fronts on a massive manmade ditch. It was built by Ser Axel Tully on land he received from the Andal King Armistead Vance.\\n\\nThe castle is the location of Robb Stark's great victory over House Lannister and the site of his crowning. By the end of the ''A Feast for Crows'', Brynden Tully surrenders the castle to Jaime Lannister to spare further bloodshed. Riverrun then passed into the hands of Emmon Frey, an ally of House Lannister.\\n\\n====The Twins====\\nThe Twins is the seat of House Frey, which has grown wealthy by charging a toll of all those who cross for the past six centuries. Because the Freys are both wealthy and numerous, theirs is one of the most powerful houses sworn to House Tully. The castle's strategic position gives House Frey enormous importance in times of war.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '6a7cd3744b304ac9fe5dbfb641cd24ef'}>,\n",
-       "  <Document: {'content': 'Because the Freys are both wealthy and numerous, theirs is one of the most powerful houses sworn to House Tully. The castle\\'s strategic position gives House Frey enormous importance in times of war.\\n\\nWhen Robb Stark goes to The Twins to repair his alliance with House Frey, the Freys massacre him, his mother, and his army (and in the TV adaptation, his wife): an event known as \"The Red Wedding\", which violates native customs of guest right and incurs enmity throughout the Seven Kingdoms, especially in the Riverlands and North.\\n\\n===The Vale of Arryn===\\nFor the TV adaptation, images of the Greek rock formations of Meteora were used for the composite views of the Vale.\\nThe Vale is the area surrounded almost completely by the Mountains of the Moon in the east of Westeros. The Vale is under the rulership of House Arryn, one of the oldest lines of Andal nobility and formerly Kings of Mountain and Vale. Their seat, the Eyrie, is a castle high in the mountains, small but considered unassailable. The only way to reach the Vale is by a mountain road teeming with animals called \\'shadowcats\\', rock slides, and dangerous mountain clans. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': 'f1c06e65a87d676e6a3e886754dbbe3'}>,\n",
-       "  <Document: {'content': \"The only way to reach the Vale is by a mountain road teeming with animals called 'shadowcats', rock slides, and dangerous mountain clans. The mountain road ends at the Vale's sole entrance, the Bloody Gate: a pair of twin watchtowers, connected by a covered bridge, on the rocky mountain slopes over a very narrow path. The protection of the surrounding mountains gives the Vale itself a temperate climate, fertile meadows, and woods. The snowmelt from the mountains and a constant waterfall that never freezes, named Alyssa's Tears, provide plentiful water. The Vale has rich black soil, wide slow-moving rivers, and hundreds of small lakes. Illegitimate children born in the Vale are given the surname Stone.\\n\\n====The Eyrie====\\ncastle of Neuschwanstein, on which the Eyrie is based\\nBased on the German castle of Neuschwanstein, the Eyrie is the seat of House Arryn. It is situated on the Giant's Lance and reachable only by a narrow mule trail, guarded by the Gates of the Moon and three small castles, titled Stone, Snow, and Sky. Travelers must enter the Gates of the Moon and its upper bailey before reaching the narrow path up the mountain. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': 'caf447419074e577ef8e711f659525b2'}>,\n",
-       "  <Document: {'content': \"Travelers must enter the Gates of the Moon and its upper bailey before reaching the narrow path up the mountain. The steps up the Giant's Lance starts directly behind the Gates of the Moon. The Eyrie clings to the mountain and is six hundred feet above Sky. The last part of the climb to the Eyrie is something of a cross between a chimney and a stone ladder, which leads to the Eyrie's cellar entrance. Due to the Mountains of the Moon's harsh winters, travel to and from the Eyrie is possible through the mountains only in summer.\\n\\nThe Eyrie is the smallest of the great castles in the story, consisting of seven slim towers bunched tightly together. It has no stables, kennels, or smithies, but the towers can house 500 men, and the granary can sustain a small household for a year or more. The Eyrie does not keep livestock on hand; all dairy produce, meats, fruits, vegetables, etc., must be brought from the Vale below. Its cellars hold six great winches with long iron chains to draw supplies and occasionally guests from below, with oxen are used to raise and lower them. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': '7bc6ae2d3db3c17b320c382be33ecd7c'}>,\n",
-       "  <Document: {'content': 'Its cellars hold six great winches with long iron chains to draw supplies and occasionally guests from below, with oxen are used to raise and lower them. Winter snows can make supplying the fortress impossible. The Eyrie\\'s dungeons, known as \"sky cells\", are left open to the sky on one side and have sloping floors that put prisoners in danger of slipping or rolling off the edge. Executions in the Eyrie are carried out via the Moon Door, which opens from the high hall onto a 600-foot drop.\\n\\nThe Eyrie is made of pale stone and primarily decorated with the blue and white colors of House Arryn. Elegant details provide warmth and comfort through plentiful fireplaces, carpets, and luxurious fabrics. Many of the chambers have been described to be warm and comfortable, with magnificent views of the Vale, the Mountains of the Moon, or the waterfall. The Maiden\\'s Tower is the easternmost of the seven slender towers, so all the Vale can be seen from its windows and balconies. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': '1cbb6ff63ad37c7f17a502fd72e6d75a'}>,\n",
-       "  <Document: {'content': \"The Maiden's Tower is the easternmost of the seven slender towers, so all the Vale can be seen from its windows and balconies. The apartments of the Lady of the Eyrie open over a small garden planted with blue flowers and ringed by white towers, containing grass and scattered statuary, with the central statue of a weeping woman believed to be Alyssa Arryn, around low, flowering shrubs. The lord's chambers have doors of solid oak, and plush velvet curtains covering windows of small rhomboid panes of glass. The High Hall has a blue silk carpet leading to the carved weirwood thrones of the Lord and Lady Arryn. The floors and walls are of milk-white marble veined with blue. Daylight enters down through high narrow arched windows along the eastern wall, and there are some fifty high iron sconces where torches may be lit.\\n\\nThe Eyrie was held by Lord Jon Arryn, who fostered Ned Stark and Robert Baratheon prior to Robert's Rebellion. After the war, Lord Arryn served as King Robert I Baratheon's Hand of the King (prime minister). After Lord Arryn was assassinated, his wife, Lady Lysa Arryn, took her sickly child, Robin, and fled to the Eyrie. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': '857c852fbc99c426461a22b580d8599d'}>,\n",
-       "  <Document: {'content': 'After Lord Arryn was assassinated, his wife, Lady Lysa Arryn, took her sickly child, Robin, and fled to the Eyrie. Lysa refused to align herself with any of the claimants during the War of the Five Kings, but eventually pretends to a possible alliance with House Lannister after Lord Petyr Baelish agrees to marry her. Later Baelish kills Lysa after she attempts to murder her niece, Sansa Stark. As of \\'\\'Feast for Crows\\'\\', Baelish rules in the Eyrie as the Lord Protector and Regent for the sickly, epileptic Lord Robert \"Robin\" Arryn, and plans for Sansa to marry Harold Harding, who will become heir to the Eyrie and the Vale in the event of young Robin Arryn\\'s death.\\n\\nFor the CGI compositions of the Vale of Arryn in the TV series, as seen in the establishing shot of the Eyrie and from the sky cells, the visual effects team used images and textures from the Greek rock formations of Meteora. Initially they had been considering the Zhangjiajie Mountains in China, but because the landscape base plates were shot in Northern Ireland, using Meteora resulted a better option. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': '836e529a330e67d0e499b4fabeaee2a3'}>,\n",
-       "  <Document: {'content': 'Initially they had been considering the Zhangjiajie Mountains in China, but because the landscape base plates were shot in Northern Ireland, using Meteora resulted a better option. Set designer Gemma Jackson said, \"A lot of the mosaics in the Eyrie were based on a beautiful chapel I visited in Rome.\"The interior of the High Hall of the Arryns was filmed at The Paint Hall, occupying one of the four soundstages there. Martin acknowledged that the set differed significantly from its presentation in the books: \"In the books, the room is long and rectangular. But The Paint Hall soundstage had essentially a square space, which they chose to put a round hall in, with a staircase curving up to a throne that was high above.\"===The Westerlands===\\nThe Westerlands are the Westerosi lands to the west of the Riverlands and north of the Reach. They are ruled by House Lannister of Casterly Rock, formerly Kings of the Rock. People of this region are often called \"Westermen.\"Lannisport, lying hard by Casterly Rock, is the chief town of the region and one of the great ports and cities of Westeros. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 28}, 'embedding': None, 'id': '85fb05953b90664ad418db8c47363110'}>,\n",
-       "  <Document: {'content': 'Lannisport, lying hard by Casterly Rock, is the chief town of the region and one of the great ports and cities of Westeros. The Westerlands are rich in precious metals, mostly gold, which is the source of their wealth. As with Westerosi customs to give bastards a surname showing their origins, illegitimate children born in the Westerlands are given the surname Hill.\\n\\n====Casterly Rock====\\nCasterly Rock was inspired by the Rock of Gibraltar (pictured).\\nA stronghold carved from a mountain overlooking the harbor city of Lannisport and the sea beyond, Casterly Rock is the ancestral seat of House Lannister. According to popular legend, the hero known as Lann the Clever tricked the Casterlys into giving up the Rock, and took it for himself. The Rock is renowned as the wealthiest region due to its abundance of gold mining resources, and it is one of the strongest castles of the Seven Kingdoms. It has never been taken in battle, despite attacks by the Iron Islanders and the plans of Robb Stark in the War of the Five Kings. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 29}, 'embedding': None, 'id': 'e65129a611873d2f8f71505068375167'}>,\n",
-       "  <Document: {'content': \"It has never been taken in battle, despite attacks by the Iron Islanders and the plans of Robb Stark in the War of the Five Kings. It was held by Lord Tywin Lannister before the War of the Five Kings, but after his death, Queen Regent Cersei Lannister made one of her cousins castellan of the castle. As of ''A Dance with Dragons'', the narrative has not actually taken place in Casterly Rock, yet descriptions of it have been offered by the Lannisters in the POV chapters.\\n\\nWest of Casterly Rock is the coastal city of Lannisport. A busy port under the governance of the Lannisters of Casterly Rock, Lannisport thrives as a protected and wealthy city. The city is also home to many lesser Lannisters and other people with similar surnames, such as Lannys.\\n\\nGeorge R. R. Martin stated on his blog that he drew inspiration for Casterly Rock from the Rock of Gibraltar.\\n\\n===The Reach===\\nThe Castle of Almodóvar del Río in Córdoba, Spain, which represented Highgarden in ''Game of Thrones''\\nThe Reach is the most lush and fertile region of Westeros, ruled by House Tyrell from Highgarden. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 30}, 'embedding': None, 'id': '750837ad0de8f0c643e20b99b9a67de1'}>,\n",
-       "  <Document: {'content': \"===The Reach===\\nThe Castle of Almodóvar del Río in Córdoba, Spain, which represented Highgarden in ''Game of Thrones''\\nThe Reach is the most lush and fertile region of Westeros, ruled by House Tyrell from Highgarden. The Tyrells were stewards to House Gardener, the Kings of the Reach before Aegon's conquest. After the last Gardener King was killed on the Field of Fire, the Tyrells surrendered Highgarden to Aegon and were rewarded with both the castle and the position of overlords of the Reach. The wealth and power of the Reach comes from their bountiful harvests of the most sought-after wines and foods. During times of war, the lengthy distance of the Reach and its abundance of foods protects their inhabitants from initial famine and sickness. In a significant political maneuver during the civil war in Westeros and the War of the Five Kings, House Tyrell provides the starving populace of King's Landing with hundreds of carts of food, ensuring the positive image of House Tyrell foremost, and the alliance for the Iron Throne with House Baratheon as secondary. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 31}, 'embedding': None, 'id': '195705a56943dad4c9709def7c22e3e8'}>,\n",
-       "  <Document: {'content': \"In a significant political maneuver during the civil war in Westeros and the War of the Five Kings, House Tyrell provides the starving populace of King's Landing with hundreds of carts of food, ensuring the positive image of House Tyrell foremost, and the alliance for the Iron Throne with House Baratheon as secondary. However, the Tyrells were responsible for the starvation in the first place, as part of their plan to help Renly usurp the Iron Throne. The most prominent city in the Reach is Oldtown. It is the oldest city in Westeros, home to the Maester's Citadel, and the previous seat of the Faith of the Seven. Illegitimate children born in the Reach are given the surname Flowers.\\n\\n====Oldtown====\\nThe Hightower of Oldtown bears similarities to the Lighthouse of Alexandria (3D reconstruction pictured)\\nOldtown is one of the largest cities in Westeros and is by far the oldest, built by the First Men before the Andal Invasion. It survived the invasion by welcoming the Andals rather than resisting them. The city is located in the southwestern part of Westeros, at the mouth of the River Honeywine, where it opens onto Whispering Sound and the Sunset Sea beyond.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 32}, 'embedding': None, 'id': '40cd5a64f1820aa97c5f66f6d19208d6'}>,\n",
-       "  <Document: {'content': \"The city is located in the southwestern part of Westeros, at the mouth of the River Honeywine, where it opens onto Whispering Sound and the Sunset Sea beyond.\\n\\nOldtown is primarily known as the location of the Citadel, home of the order of Maesters who serve as councillors, doctors, scientists, and postmasters for the Seven Kingdoms. The city's Starry Sept was the seat of the Faith of the Seven until the construction of the Great Sept of Baelor in King's Landing. Aegon the Conqueror's reign is dated from his entrance into the city of Oldtown and his acknowledgment as king by the High Septon.\\n\\nOldtown is the second most important port in the Seven Kingdoms after King's Landing: trading ships from the Summer Islands, the Free Cities, the eastern cities, and the rest of Westeros constantly crowd into its harbors. The city itself is described as stunningly beautiful. Many rivers and canals crisscross its cobbled streets, and breathtaking stone mansions are common. The city lacks the squalor of King's Landing, which usurped its position as the preeminent city of Westeros.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 33}, 'embedding': None, 'id': 'b3d569ee3160a9b11bf515192426448f'}>,\n",
-       "  <Document: {'content': \"Many rivers and canals crisscross its cobbled streets, and breathtaking stone mansions are common. The city lacks the squalor of King's Landing, which usurped its position as the preeminent city of Westeros.\\n\\nThe largest structure in the city, and the tallest structure in Westeros, is the Hightower, a massive stepped lighthouse which extends some  into the sky and is topped by a huge beacon which can be seen for many miles out to sea. Oldtown is ruled from the Hightower by House Hightower. Originally kings in their own right, they later swore fealty to the Gardeners of Highgarden, and became vassals of the Tyrells after the Conquest. The Hightowers are known for their loyalty and stalwartness. The current ruler of the city is Lord Leyton Hightower.\\n\\nOldtown remained aloof from the War of the Five Kings, but late in the war the Ironborn under King Euron Greyjoy launched a massive raid along the coast, conquering the Shield Islands and parts of the Arbor before trying to blockade the mouth of the Honeywine. An attempt to attack the city harbor was repulsed by the city's defenders. Oldtown remains under threat from the Ironborn.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 34}, 'embedding': None, 'id': '820b4e33089e35fc93a11d9c910689cc'}>,\n",
-       "  <Document: {'content': \"An attempt to attack the city harbor was repulsed by the city's defenders. Oldtown remains under threat from the Ironborn.\\n\\n===The Stormlands===\\nThe Stormlands are the Westerosi areas between King's Landing and the Sea of Dorne. In the east they are bordered by Shipbreaker Bay and the Dornish Sea to the south. Before Aegon's conquest they were ruled by the Storm Kings, and afterwards by House Baratheon, bastard relatives to the Targaryens. The Dornish Marches are located within this region, and were common battlegrounds between the Stormlands and Dorne until Dorne joined the Seven Kingdoms. Illegitimate children born in the Stormlands are given the surname Storm.\\n\\n====Storm's End====\\nStorm's End is the seat of House Baratheon and, before them, the ancestral seat of the Storm Kings extending back many thousands of years. According to legend, the first Storm King in the age of the First Men was Durran, who married Elenei, the daughter of the sea god and the goddess of the wind. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 35}, 'embedding': None, 'id': '906bfca1f0f3a5afdabf4937cf9d8de7'}>,\n",
-       "  <Document: {'content': \"According to legend, the first Storm King in the age of the First Men was Durran, who married Elenei, the daughter of the sea god and the goddess of the wind. In a rage her parents sent vast storms to shatter his keep and kill his wedding guests and family; whereupon Durran declared war against the gods and raised several castles over Shipbreaker Bay, each larger and more formidable than the last. Finally, the seventh castle stayed in place and resisted the storms. Some believe the Children of the Forest took a hand in its construction; others suggest that Brandon Stark, the builder of the Wall, advised Durran on its construction. The truth of the matter is unknown.\\n\\nStorm's End has never fallen to either siege or storm. Its outer defenses consist of a huge curtain wall,  tall and  thick on its thinnest side, nearly  thick on its seaward side. The wall consists of a double course of stones with an inner core of sand and rubble. The wall is smooth and curving, the stones so well placed that the wind cannot enter. On the seaward side, there is a  drop below the wall into the sea.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 36}, 'embedding': None, 'id': '94807bc04de1a3975af5e8d28a56deff'}>,\n",
-       "  <Document: {'content': \"The wall is smooth and curving, the stones so well placed that the wind cannot enter. On the seaward side, there is a  drop below the wall into the sea.\\n\\nThe castle itself consists of one huge drum tower crowned with formidable battlements, and so large that it can comfortably contain stables, barracks, armory and lord's chambers in the same structure. Although never taken in battle, Storm's End has endured several sieges and battles in recent history. The last Storm King, Argilac the Arrogant, abandoned his impressive defenses to meet the Targaryen commander, Orys Baratheon, in open battle during Aegon Targaryen's War of Conquest, and lost. This led to Orys Baratheon marrying Argilac's daughter and becoming Lord of Storm's End.\\n\\nDuring the War of the Usurper, Storm's End was besieged for a year by the host of Lord Mace Tyrell, who commanded the landward forces, while Paxter Redwyne's fleet of the Arbor kept the castle cut off by sea. Stannis Baratheon, commanding the defense, refused to yield and his men were reduced to eating rats. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 37}, 'embedding': None, 'id': 'f9ac7b97e524ac8d8da78666d4b0f881'}>,\n",
-       "  <Document: {'content': \"During the War of the Usurper, Storm's End was besieged for a year by the host of Lord Mace Tyrell, who commanded the landward forces, while Paxter Redwyne's fleet of the Arbor kept the castle cut off by sea. Stannis Baratheon, commanding the defense, refused to yield and his men were reduced to eating rats. A smuggler named Davos ran the blockade to resupply the castle and Stannis rewarded him by knighting him and giving him lands, thus founding House Seaworth, but he also cut off the fingertips of his left hand as punishment for all his previous smuggling. After the war, Stannis was furious when his brother Robert, now king, gave the castle to their younger brother Renly and placed Stannis in command of Dragonstone. This led to many years of bitterness on Stannis' part.\\n\\nDuring the War of the Five Kings, Storm's End supported Renly when he attempted to usurp the crown, and was besieged by Stannis. When the castellan, Cortnay Penrose, refused to yield even after Renly's death, he was killed by Stannis' ally, the priestess Melisandre, and the castle surrendered. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 38}, 'embedding': None, 'id': 'f85e2dbd6be8e73c3bbc5ade3da44147'}>,\n",
-       "  <Document: {'content': \"When the castellan, Cortnay Penrose, refused to yield even after Renly's death, he was killed by Stannis' ally, the priestess Melisandre, and the castle surrendered. Later, the castle was besieged by a strong army under Mace Tyrell, but he abandoned the siege after a few weeks to return to King's Landing after the arrest of his daughter Margaery by the High Septon. As of ''A Dance with Dragons'', the castle remains in the hands of Stannis Baratheon.\\n\\nAt the end of ''A Dance with Dragons'' an army lands in the Stormlands led by Jon Connington and a young man claiming to be Aegon Targaryen, the son of Rhaegar Targaryen and Elia Martell and heir to the Iron Throne. To attract support, Aegon plans to conquer Storm's End and raise the banner of House Targaryen above the battlements.\\n\\nIn the TV adaptation, scenes in the Stormlands were filmed in Larrybane, Northern Ireland. The scene where Stannis' red priestess Melisandre gave birth to a shadow creature was filmed in the Cushendun Caves, also in Northern Ireland.\\n\\n===The Crownlands===\\nThe Crownlands are the lands in Westeros surrounding King's Landing, ruled directly by the crown of the Iron Throne. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 39}, 'embedding': None, 'id': '62307ac791b1b40e0e52dd7a98520cc4'}>,\n",
-       "  <Document: {'content': \"===The Crownlands===\\nThe Crownlands are the lands in Westeros surrounding King's Landing, ruled directly by the crown of the Iron Throne. The Targaryen kings consolidated this as one of the nine regions of Westeros, after their conquest of the Seven Kingdoms, from sparsely populated pieces of the Riverlands and Stormlands. The Crownlands form the entire coastline of Blackwater Bay, and include the original Targaryen homeland on the island of Dragonstone, at the Narrow Sea entrance to Blackwater Bay. Besides King's Landing, which is the largest city in Westeros, the Crownlands include many towns and castles. The illegitimate children born in the Crownlands are given the surname Waters.\\n\\n====Dragonstone====\\n\\nDragonstone was once the westernmost outpost of the ancient Freehold of Valyria. A century before the Doom, the Targaryen family moved to Dragonstone. When the Doom came upon Valyria, House Targaryen survived along with the last of the Valyrian dragons. Another century later, Aegon Targaryen and his sisters Rhaenys and Visenya launched a massive campaign of conquest from the island and eventually conquered all of Westeros except for Dorne, and North of the Wall. Aegon's progeny reigned as kings of the Seven Kingdoms for centuries.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 40}, 'embedding': None, 'id': '47b9d96203246378d1ee6d3b88561e18'}>,\n",
-       "  <Document: {'content': \"Another century later, Aegon Targaryen and his sisters Rhaenys and Visenya launched a massive campaign of conquest from the island and eventually conquered all of Westeros except for Dorne, and North of the Wall. Aegon's progeny reigned as kings of the Seven Kingdoms for centuries.\\n\\nDragonstone is a massive, forbidding fortress, taking up a large portion of the island of the same name. The castle is unique in that the builders and sorcerers of Valyria carved its towers and keeps into the shapes of dragons and made ferocious gargoyles to cover its walls using both magic and masonry. The castle's lower levels are warmed by residual volcanic activity deep below the keep. There is a small port and town outside of the castle.\\n\\nDuring the War of the Usurper, before the sack of King's Landing, the Targaryen Queen Rhaella, who was pregnant, and her son Viserys were sent to Dragonstone along with part of the Targaryen fleet and a garrison of loyal soldiers. But after King's Landing fell, Robert Baratheon dispatched his brother Stannis to take the island stronghold. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 41}, 'embedding': None, 'id': '302417250b252cf9a076639c19070528'}>,\n",
-       "  <Document: {'content': \"During the War of the Usurper, before the sack of King's Landing, the Targaryen Queen Rhaella, who was pregnant, and her son Viserys were sent to Dragonstone along with part of the Targaryen fleet and a garrison of loyal soldiers. But after King's Landing fell, Robert Baratheon dispatched his brother Stannis to take the island stronghold. After a storm destroyed the royalist fleet, the Targaryen garrison tried to betray Viserys and his newborn sister, Daenerys, to Stannis (the queen had died in childbirth). But Targaryen loyalists led by Ser Willem Darry took the children away. Stannis conquered Dragonstone easily, and King Robert granted him ownership of the castle. Stannis took this a slight because his younger brother Renly then inherited Storm's End, the ancient seat of House Baratheon. Ser Axell Florent, one of the uncles of Stannis' wife Selyse Florent, acted as castellan.\\n\\nUpon Robert's death, Stannis declared himself King of Westeros and condemned the queen's children as bastards born of incest, as he had discovered with Jon Arryn. Dragonstone became his main seat. He returned there after the disastrous Battle of the Blackwater. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 42}, 'embedding': None, 'id': '806be06bae089790a25f6164519257ce'}>,\n",
-       "  <Document: {'content': 'Upon Robert\\'s death, Stannis declared himself King of Westeros and condemned the queen\\'s children as bastards born of incest, as he had discovered with Jon Arryn. Dragonstone became his main seat. He returned there after the disastrous Battle of the Blackwater. His councilor, the red priestess Melisandre of Asshai, tried to convince him to let her raise the \"stone dragon\" of the castle through blood magic, but Lord Davos Seaworth convinced Stannis to go north to the Wall to help the Night\\'s Watch instead. After Stannis abandoned Dragonstone, leaving the Bastard of Nightsong Rolland Storm as castellan, Queen Regent Cersei Lannister dispatched a fleet to barricade it. However, Ser Loras Tyrell, impatient to free the fleet to protect his home castle of Highgarden, attacked Dragonstone directly. He took the castle but lost a thousand men and was himself reportedly gravely wounded. As of \\'\\'A Dance with Dragons\\'\\', Dragonstone is now controlled by troops loyal to House Tyrell, and theoretically, once again under control of the Iron Throne.\\n\\nOne scene set at Dragonstone, in which Stannis burns wooden sculptures of the Seven gods, was filmed at the beach of Downhill Strand. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 43}, 'embedding': None, 'id': 'e07789bcc07701794577ab3250029c0'}>,\n",
-       "  <Document: {'content': \"One scene set at Dragonstone, in which Stannis burns wooden sculptures of the Seven gods, was filmed at the beach of Downhill Strand. In Season 7 of the show, filming for Dragonstone took place at several locations in the Basque region of Spain: the islet of Gaztelugatxe in Bermeo, Itzurun Beach in Zumaia, and Muriola Beach in Barrika.\\n\\n====King's Landing====\\n\\nKing's Landing is the royal capital of Westeros and the Seven Kingdoms. King's Landing has an estimated population of 1 million people, making it the most populous city in Westeros. It is situated on the Blackwater river on the spot where Aegon the Conqueror landed in Westeros to begin his conquest. The main city is surrounded by a wall, which is manned by the City Watch of King's Landing, which is nicknamed the gold cloaks, after the cloaks they wear. Within the walls, the city's natural landscape is dominated by three hills, named after Aegon and his two sister-wives Rhaenys and Visenya. Poorer smallfolk (commoners) build shanty settlements outside the city. King's Landing is described as extremely populous but unsightly and dirty. The stench of the city's waste can be smelled far beyond its walls.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 44}, 'embedding': None, 'id': '93d766a13e41412d6b7509b125d18170'}>,\n",
-       "  <Document: {'content': 'King\\'s Landing is described as extremely populous but unsightly and dirty. The stench of the city\\'s waste can be smelled far beyond its walls.\\n\\nThe royal castle, called the Red Keep, sits on Aegon\\'s Hill. It is the seat of the royal court. The Keep holds the Iron Throne. Aegon commissioned the throne\\'s construction from the swords of his defeated enemies. According to legend, he kept the blades sharp because he believed that no ruler should ever sit comfortably. Centuries later, kings still cut themselves on the throne. It is a common belief that one who cuts himself on the throne has been \"rejected\" by the throne and is therefore not fit to rule.\\n\\nThe city also holds the Great Sept of Baelor, where the Most Devout convene with the High Septon. It is the holiest sept of the Seven. The slums of King\\'s Landing are called Flea Bottom, where residents are so poor they regularly subsist on \"bowls of brown\", a mystery stew that can include the meat of puppies and murder victims.\\n\\nMartin compared King\\'s Landing to medieval Paris or London. It was inspired by the view of Staten Island from his childhood home in Bayonne, New Jersey.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 45}, 'embedding': None, 'id': 'cb15aeef4a87f16bf8f22ec04665d79a'}>,\n",
-       "  <Document: {'content': 'Martin compared King\\'s Landing to medieval Paris or London. It was inspired by the view of Staten Island from his childhood home in Bayonne, New Jersey.\\n\\nThe first season of the TV adaptation used Malta\\'s former capital Mdina to represent King\\'s Landing. \"Like King\\'s Landing, Mdina is a walled medieval city built upon a hill, but unlike King\\'s Landing, Mdina is an inland city so the production was limited to interior shots such as side streets and the town gate, which can be seen when Ned Stark arrives. Nearby Fort Manoel doubled as the great Sept of Baelor,\" which can be seen when Ned Stark is executed. Various other locations around Malta represent the Red Keep, \"including the real-life residence of the president of Malta, San Anton Palace. The gates of Fort Ricasoli doubled as the Red Keep\\'s gates; Fort St. Angelo was used for the scenes of Arya Stark chasing cats; and St. Dominic monastery stood in for the scene where Ned Stark confronts Cersei Lannister in the godswood.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 46}, 'embedding': None, 'id': '217639e74ebd43cd506b71389651342c'}>,\n",
-       "  <Document: {'content': 'The gates of Fort Ricasoli doubled as the Red Keep\\'s gates; Fort St. Angelo was used for the scenes of Arya Stark chasing cats; and St. Dominic monastery stood in for the scene where Ned Stark confronts Cersei Lannister in the godswood.\"\"In season two, filming for King\\'s Landing and the Red Keep shifted from Malta to the historic parts of Dubrovnik and the Minčeta, Bokar, and Lovrijenac fortresses in Croatia, which allowed for more exterior shots of an authentic walled medieval city.\"Parts of Season three were filmed there, too, as well as in nearby Trsteno. \"Known as \\'\\'the Pearl of the Adriatic\\'\\', the city proved to share many characteristics with the fictional capital: it had a well-preserved medieval look, with high walls and the sea at its side. According to David Benioff, executive producer of the show, \"King\\'s Landing might be the single most important location in the entire show, and it has to look right\", and \"The minute we started walking around the city walls we knew that was it. You read the descriptions in the book and you come to Dubrovnik and that\\'s what the actual city is. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 47}, 'embedding': None, 'id': '6657448ebdf308e050a5edd0aa4c27e9'}>,\n",
-       "  <Document: {'content': 'According to David Benioff, executive producer of the show, \"King\\'s Landing might be the single most important location in the entire show, and it has to look right\", and \"The minute we started walking around the city walls we knew that was it. You read the descriptions in the book and you come to Dubrovnik and that\\'s what the actual city is. It has the sparkling sea, sun and beautiful architecture.\"Co-Executive Producer D.B. Weiss added \"To find a full-on, immaculately preserved medieval walled city that actually looks uncannily like King\\'s Landing where the bulk of our show is set, that was in and of itself such an amazing find\". The Tourney of the Hand in season 1 was filmed in Shane\\'s Castle, Northern Ireland.\\n\\nThe Red Keep interior are filmed at Belfast\\'s studio The Paint Hall. Set designer Gemma Jackson said, \"When I was thinking about King\\'s Landing, the whole red aspect of it, that immediately made me think of Rajasthan. The floor at King\\'s Landing was from the Pantheon in Rome.\"Martin said that \"Our throne room is a spectacular throne room – we actually redressed a throne room built for another film. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 48}, 'embedding': None, 'id': '937f3d4f5a707285881a04404ff1e943'}>,\n",
-       "  <Document: {'content': 'Martin said that \"Our throne room is a spectacular throne room – we actually redressed a throne room built for another film. And again, it occupied a quarter of the Paint Hall, so it\\'s very big, but in my mind in the books, it\\'s Westminster Abbey, it\\'s St. Paul\\'s Cathedral.\\n\\n===Dorne===\\nCastle of Zafra in Guadalajara, Spain, which portrayed the Tower of Joy in Dorne in \\'\\'Game of Thrones\\'\\'\\nDorne is the southernmost and least populated land of Westeros. The capital, Sunspear, is the seat of the ruling House Martell. As of the first five books, Doran Nymeros Martell is the Prince of Dorne and Lord of Sunspear. Doran\\'s sister, Princess Elia, was married in a political alliance to Prince Rhaegar Targaryen, the Prince of Dragonstone and heir to the Iron Throne. They had two children, a daughter, Rhaenys, and a son, Aegon. During the Sack of King\\'s Landing at the end of Robert\\'s Rebellion, Princess Elia was raped and murdered by Gregor Clegane, a House Lannister bannerman (vassal). Her children were also killed in front of her. Prince Doran and his wife, Princess Mellaria, have three children, Arianne, Quentyn and Trystane. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 49}, 'embedding': None, 'id': '35025602a80d9980a33e53d2599455ce'}>,\n",
-       "  <Document: {'content': \"Her children were also killed in front of her. Prince Doran and his wife, Princess Mellaria, have three children, Arianne, Quentyn and Trystane. During the War of the Five Kings, Tyrion Lannister, as Hand of the King, turns the historical enmity of House Martell and Dorne into an alliance by sending King Joffrey's middle sibling and sister, Myrcella Baratheon, as the betrothed future bride to Trystane, the youngest child of Prince Doran, who is about her own age. The eldest child of Prince Doran, Arianne, is heir to House Martell, Sunspear and the rule of Dorne. The wealth of Dorne comes from their famous Sand Steeds, purebred horses of endurance, speed, and grace, and from spices, wines, fishing, fabrics, and textiles.\\n\\nDorne is bordered by the Sea of Dorne to the north, the islands known as the Stepstones to the east, and stretches from the high mountains of the Dornish marches, the Red Mountains, separating Dorne from the remainder of the Seven Kingdoms by land. The two major passes though the Red Mountains that connect Dorne with the rest of the continent are the Stone Way Pass and the Prince's Pass. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 50}, 'embedding': None, 'id': '828d474089cfda7e94cd605664cfb64f'}>,\n",
-       "  <Document: {'content': \"The two major passes though the Red Mountains that connect Dorne with the rest of the continent are the Stone Way Pass and the Prince's Pass. The Prince's Pass leads to the Reach, while the Stone Way exits the mountains near Summerhall. The southern coast of the continent is bordered by the Summer Sea. Described as tropical in climate by George R. R. Martin, Dorne has the highest temperatures kingdom in Westeros, is arid, with a rocky, mountainous, terrain that includes the only desert on the continent. Its rivers provide some fertile lands and during a long summer there is enough rain and other supplies of water to keep Dorne habitable. Inland water is almost as valuable as gold, and wells are jealously guarded. Notable locations of Dorne are Starfall, the seat of House Dayne, and Yronwood, the seat of House Yronwood, the most powerful of the Martell bannermen. Planky Town is a trade port town at the mouth of the River Greenblood.\\n\\nDornishmen have a reputation for hot-bloodedness. They differ both culturally and ethnically from other Westerosi due to the historical mass immigration of Rhoynish people. They have adopted many Rhoynish customs as well, including equal primogeniture. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 51}, 'embedding': None, 'id': '57301cd897971eaa0fd2d392d640936b'}>,\n",
-       "  <Document: {'content': 'They differ both culturally and ethnically from other Westerosi due to the historical mass immigration of Rhoynish people. They have adopted many Rhoynish customs as well, including equal primogeniture. Dorne was the only kingdom in Westeros to successfully resist Aegon\\'s conquest, even killing one of his dragons during the war. It was conquered by Daeron I over a century after the Targaryen invasion, but rose against him leading to his death. Finally under Daeron\\'s cousin Daeron II they joined through marriage. This accomplishment has allowed Dorne to retain a measure of independence. Lords of the ruling House Martell still style themselves \"Prince\" and \"Princess\" in the Rhoynish fashion. Unlike most of the rest of Westeros, illegitimate children born in Dorne are treated nearly the same as legal offspring and given the surname Sand, as with Westerosi customs to give bastards a surname showing their origins.\\n\\nAccording to \\'\\'A Storm of Swords\\'\\', \"There were three sorts of Dornishmen .... There were the salty Dornishmen who lived along the coasts, the sandy Dornishmen of the deserts and long river valleys, and the stony Dornishmen who made their fastnesses in the passes and heights of the Red Mountains. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 52}, 'embedding': None, 'id': '696888dffd6297f9202827d15602f1df'}>,\n",
-       "  <Document: {'content': 'There were the salty Dornishmen who lived along the coasts, the sandy Dornishmen of the deserts and long river valleys, and the stony Dornishmen who made their fastnesses in the passes and heights of the Red Mountains. The salty Dornishmen had the most Rhoynish blood, the stony Dornishmen the least. All three sorts seemed well represented in Doran’s retinue. The salty Dornishmen were lithe and dark, with smooth olive skin and long black hair streaming in the wind. The sandy Dornishmen were even darker, their faces burned brown by the hot Dornish sun. They wound long bright scarfs around their helms to ward off sunstroke. The stony Dornishmen were biggest and fairest, sons of the Andals and the First Men, brownhaired or blond, with faces that freckled or burned in the sun instead of browning.\"In the show, Dornish scenes were filmed in the Alcázar of Seville, Seville, Spain.\\n\\n==Summer Sea==\\n====Basilisk Isles====\\nEast of Naath, the Basilisk Isles have been a festering sore of the Summer Sea, and a safe haven for pirates, slavers, sellswords, and outlaws. Ruins have been found on the Isle of Tears, the Isle of Toads, and Ax Island. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 53}, 'embedding': None, 'id': '8a7419519e38b5884a350718c6b54e13'}>,\n",
-       "  <Document: {'content': \"==Summer Sea==\\n====Basilisk Isles====\\nEast of Naath, the Basilisk Isles have been a festering sore of the Summer Sea, and a safe haven for pirates, slavers, sellswords, and outlaws. Ruins have been found on the Isle of Tears, the Isle of Toads, and Ax Island. The Isle of Tears is the largest island, with steep valleys and black bogs. It was conquered by the Ghiscari and it was called Gorgai for two centuries, until the dragonlords of Valyria captured it and renamed it Gorgossos. It was used as a prison by the Freehold, a place where they sent their most despicable criminals.\\n\\n====Naath====\\nNaath, also known as the Isle of Butterflies, is an island off the north-west coast of Sothoryos that lies west of the Basilisk Isles. The Naathi people have dark skin and golden eyes. They practice extreme pacifism, making music instead of war and refusing to eat meat, only fruit. This makes them especially vulnerable to slavers from Essos. Daenerys' interpreter Missandei is from Naath.\\n\\n====Summer Islands ====\\nAs indicated on a map in ''A Storm of Swords'', the Summer Islands are situated to the south of Westeros, with a local fauna of talking birds, apes, and monkeys. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 54}, 'embedding': None, 'id': '6ac7f9a61205c42eeb859fd23c1a05ec'}>,\n",
-       "  <Document: {'content': '====Summer Islands ====\\nAs indicated on a map in \\'\\'A Storm of Swords\\'\\', the Summer Islands are situated to the south of Westeros, with a local fauna of talking birds, apes, and monkeys. The novels describe the island natives as dark-skinned people who speak their own language. They wear colored feathery clothes and live on fruit and fish. From their port city named Tall Trees Town, the Summer Isles export rare goods to Westeros such as wine, spices, feathers, but also a special kind of wood from which bows are made that have a longer range than most others. People of the Seven Kingdoms call the Summer Islanders\\' great vessels \\'\\'swan ships\\'\\', \"for their billowing white sails and for their figureheads, most of which depicted birds\". Samwell Tarly, who spends two chapters in \\'\\'A Feast for Crows\\'\\' aboard a swan ship, describes the Summer Islander women as wanton, and their gods as strange; they \"revered the elderly and celebrated their dead\" through sexual intercourse. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 55}, 'embedding': None, 'id': 'e2b89ff3d8cfac49f3e1ab1234aba89d'}>,\n",
-       "  <Document: {'content': 'Samwell Tarly, who spends two chapters in \\'\\'A Feast for Crows\\'\\' aboard a swan ship, describes the Summer Islander women as wanton, and their gods as strange; they \"revered the elderly and celebrated their dead\" through sexual intercourse. As a prostitute explains to Tyrion in \\'\\'A Clash of Kings\\'\\', the Summer Islanders regard their sexuality as the gods\\' gift to worship them through mating, and hence many of their highborn youths and maidens serve in pleasure houses for a few years to honor the gods.\\n\\n==Essos==\\nPart of the narrative in \\'\\'A Song of Ice and Fire\\'\\' lies across the Narrow Sea from Westeros, an area comprising the large eastern continent named Essos. Being roughly the size of Eurasia, Essos has geography and climate that vary greatly. The western coastline is characterized by green rolling hills, the massive Forest of Qohor, and extensive island chains such as Braavos and Lys. The middle of the continent is covered by the flat grasslands of the Dothraki Sea and the arid lands known as the Red Waste to the east. Beyond the Red Waste lies the city of Qarth. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 56}, 'embedding': None, 'id': '2239e950ed1e0465b3b2aa569738020a'}>,\n",
-       "  <Document: {'content': \"The middle of the continent is covered by the flat grasslands of the Dothraki Sea and the arid lands known as the Red Waste to the east. Beyond the Red Waste lies the city of Qarth. The south is dominated by dry rolling hills and has a Mediterranean climate, with a coastline along the Summer Sea and Slaver's Bay. The north coast of the mainland is separated from the polar cap by the Shivering Sea. To the south, across the Summer Sea, lies the uncharted jungle continent of Sothoryos.\\n\\nMuch of the fictional history of Essos relates to Valyria, a city located on a peninsula in southern Essos and the origin of House Targaryen before the destruction of the Valyrian Empire in an unspecified cataclysm. After the destruction of Valyria, the cities of Astapor, Yunkai, and Meereen regained independence and ruled their respective areas as city-states. The area is known in the books as Slaver's Bay.\\n\\n===Free Cities and vicinity ===\\nAcross the Narrow sea on the western side of Essos lie the nine Free Cities, independent city-states that are mostly on islands or along the coast. They are Lys, Myr, Pentos, Braavos, Lorath, Norvos, Qohor, Volantis and Tyrosh. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 57}, 'embedding': None, 'id': 'ea7a8debaa388e127d1cd3ada29c3ed'}>,\n",
-       "  <Document: {'content': '===Free Cities and vicinity ===\\nAcross the Narrow sea on the western side of Essos lie the nine Free Cities, independent city-states that are mostly on islands or along the coast. They are Lys, Myr, Pentos, Braavos, Lorath, Norvos, Qohor, Volantis and Tyrosh. Although most Free Cities are named early in the first novel, the books only provide a map of this region in \\'\\'A Dance with Dragons\\'\\'. Mountains to the east separate the coast from the plains of the Dothraki Sea, though gaps in the mountain range provide the Dothraki people some access to the Free Cities. The Free Cities were colonies built by the ancient Valyrian Freehold, and later declared independence after the Doom of Valyria. An exception to this is Braavos, which was founded by refugees fleeing Valyrian expansion, escaped slaves and other rabble. The languages of the Free Cities are derivatives of High Valyrian.\\n\\nThe Free Cities span an area characterized by the river Rhoyne, which the local character Yandry describes as \"the greatest river in the world\". Its banks are the homeland of the Rhoynar, who worship the river as \"Mother Rhoyne\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 58}, 'embedding': None, 'id': '4a60313c1e5ef7d022daa3880e50c3ac'}>,\n",
-       "  <Document: {'content': 'The Free Cities span an area characterized by the river Rhoyne, which the local character Yandry describes as \"the greatest river in the world\". Its banks are the homeland of the Rhoynar, who worship the river as \"Mother Rhoyne\". As mapped in \\'\\'A Dance with Dragons\\'\\', the Rhoyne originates from the conjunction of two of its tributaries, the Upper Rhoyne and the Little Rhoyne, southeast of the ruins of Ghoyan Drohe. The headwaters of the Upper Rhoyne lie in Andalos, the homeland of the Andals between Braavos and Pentos. The Rhoyne\\'s course runs southeast to turn due south after Dagger Lake, where river pirates hide on and around the many lake islands. The Rhoyne gains in width considerably as it gets fed by more tributaries, until it opens into the Summer Sea in a delta near the Free City of Volantis.\\n\\n====Braavos====\\nUnique among the Free Cities, Braavos was not a Valyrian colony, but a secret refuge from Valyrian expansion. It is a city spread over hundreds of tiny islands in a lagoon on the northwestern end of Essos where the Narrow Sea and Shivering Sea meet. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 59}, 'embedding': None, 'id': '326a13ac37c0cdff51889ec92511446f'}>,\n",
-       "  <Document: {'content': \"It is a city spread over hundreds of tiny islands in a lagoon on the northwestern end of Essos where the Narrow Sea and Shivering Sea meet. Braavos is home to the 'Iron Bank', one of the wealthiest banks in the known world . Braavos is also known for its swordsmen known as 'bravos', and its mysterious assassins, the Faceless Men. It is also famed for the Titan of Braavos, both a fortress and a statue. The ruler of Braavos is known as the Sealord and it is from the sea that the city's power and wealth flows. The hulls of Braavosi ships are painted purple and their merchant ships sail to many distant lands and bring their trade and wealth back home. Braavos has many moneylenders and the Iron Bank of Braavos lends money to foreign nations, especially The Crown, which has borrowed millions.\\n\\nBraavosi dress in flashy colors while the very rich and powerful dress in black and in blues that are almost black. Officials of Braavos, called keyholders and justiciars, wear drab coats of brown or grey. The city is also renowned worldwide for its courtesans. Every courtesan has her own barge and servants to work them. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 60}, 'embedding': None, 'id': '550ed103d12f11b119707751e1ba2bff'}>,\n",
-       "  <Document: {'content': \"The city is also renowned worldwide for its courtesans. Every courtesan has her own barge and servants to work them. The beauty of famed courtesans has inspired many a song. They are showered with gifts from goldsmiths and craftsmen beg for their custom. Nobility and rich merchants pay the courtesans large amounts of money to appear alongside them at events, and bravos are known to kill each other in their names. The character Syrio Forel, former first sword of the Sealord of Braavos, introduces Arya Stark to a unique form of Braavosi sword fighting, called Water Dancing. The style is a refined form of fencing in which the practitioner stands sideways and wields a slender blade. Pugnacious bravos fill the city, frequently dueling to display their skill.\\n\\nBraavos was inspired by Venice, Italy. It was filmed in Croatian towns of Šibenik, and Kaštel Gomilica in the TV series.\\n\\n====Pentos====\\nThe TV series filmed Illyrio's mansion in Pentos at Verdala Palace in Malta.\\nPentos is a major trading port on a bay of the western coast. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 61}, 'embedding': None, 'id': 'eb8b974aed88027b218c60f895433009'}>,\n",
-       "  <Document: {'content': '====Pentos====\\nThe TV series filmed Illyrio\\'s mansion in Pentos at Verdala Palace in Malta.\\nPentos is a major trading port on a bay of the western coast. Dominated by an architecture of square brick towers, it is headed by a Prince who is chosen by the de facto rulers of the city, known as Magisters. Khalasars occasionally make their way this far from the Dothraki Sea, but the Pentoshi are spared much of the raiding and invasions by paying tribute to their khals. Men from Pentos wear dyed and forked beards. As in many Free Cities, slavery is outlawed, but the wealthy and powerful members of the city have the ability to flout these laws by keeping servants collared in bronze.\\n\\nDaenerys\\'s scenes in the pilot episode were filmed in Morocco. The television adaptation re-used the Jerusalem sets of \\'\\'Kingdom of Heaven\\'\\' near Ouarzazate, Morocco. \"One small portion of the Jerusalem set, redressed and repainted, became the courtyard of Illyrio\\'s manse where Dany first meets Khal Drogo.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 62}, 'embedding': None, 'id': '2372d61234bcb758211fe08cec212b6d'}>,\n",
-       "  <Document: {'content': '\"One small portion of the Jerusalem set, redressed and repainted, became the courtyard of Illyrio\\'s manse where Dany first meets Khal Drogo.\"\"When the pilot was delivered, HBO asked for extensive reshoots, including the scrapping of all the footage shot in a landlocked part of Morocco which was supposed to take place in Pentos, a fictional port city and filming it again in Malta.\"The exterior scenes at Illyrio\\'s mansion in Pentos were shot at Verdala Palace, the 16th century summer palace of the president of Malta. \"One of Malta\\'s most spectacular natural attractions, the Azure Window on the island of Gozo, stood in for the location of Daenerys Targaryen\\'s wedding to Khal Drogo.\"When Pentos reappeared in Season 5, it was filmed in Croatia.\\n\\n====Volantis====\\nVolantis is a port on the southern coast of Essos, and is the oldest and proudest of the Free Cities. A fortification known as the Black Wall protects the oldest parts of the city. The city is ruled by three triarchs, who are elected every year by free landholders of Volantis, and defended by slave soldiers called the \"Tiger cloaks\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 63}, 'embedding': None, 'id': '48ff3c494a478684830645acff61096d'}>,\n",
-       "  <Document: {'content': 'The city is ruled by three triarchs, who are elected every year by free landholders of Volantis, and defended by slave soldiers called the \"Tiger cloaks\". Volantis is incredibly important to the slave market, and in the city there are five slaves to every free man. All Volantene slaves have facial tattoos denoting their profession: for instance, sex slaves have tears tattooed on their faces, and the tiger cloaks have tiger stripes. The worship of R\\'hllor is the most influential religion of Volantis, especially among slaves.\\n\\nThe TV adaptation used locations in Córdoba, Spain.\\n\\n====Other Free Cities ====\\n* \\'\\'\\'Lorath\\'\\'\\' is a port city on a group of northern islands. The character Jaqen H\\'ghar poses as a Lorathi in \\'\\'A Clash of Kings\\'\\', wearing long hair dyed red on one side and white on the other.\\n* \\'\\'\\'Lys\\'\\'\\' sits astride a series of southern islands. The Lysene have a tendency to be tall and fair of skin and eyes, unlike most inhabitants of the Free Cities. Lys is well known for its pleasure houses, training slaves in the arts of love and selling them as concubines and bed-slaves. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 64}, 'embedding': None, 'id': '9ec31a8c6f97dfbcf33d7bfd5015ec39'}>,\n",
-       "  <Document: {'content': \"Lys is well known for its pleasure houses, training slaves in the arts of love and selling them as concubines and bed-slaves. Lys also frequently fights over control of the Stepstones and the Disputed Lands. There appears to be a love goddess whose worship is peculiar to Lys. Dany's handmaiden Doreah and the pirate Salladhor Saan are Lysene.\\n* '''Myr''' is a coastal city renowned for their master lenscrafters, intricate lace, and fine carpets. The dark eyed and dark skinned Myrmen are similar to Norvosi and Pentoshi in that they are ruled by magisters that are known to pay tribute to passing Dothraki khalasars. Myr is a hub of trade in both slaves and their signature green nectar wines. Myr frequently fights over control of the Disputed Lands.\\n* '''Norvos''' sits on the main continent in two parts, one atop a high hill and the other beside a low river. The city has three large bells, each with its own name and distinctive voice, that are rung frequently. The surrounding area is a land of rolling hills, terraced farms, and white-stucco villages. The climate is fairly mild. Norvosi can be recognized by their dyed and upswept mustaches. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 65}, 'embedding': None, 'id': 'c763cfd05425776aca1f270bd7574d3f'}>,\n",
-       "  <Document: {'content': \"The surrounding area is a land of rolling hills, terraced farms, and white-stucco villages. The climate is fairly mild. Norvosi can be recognized by their dyed and upswept mustaches. The city is run by a council of magisters that are known to pay tribute to passing Dothraki khalasars. It is also home to a group of bearded priests that train elite guardsmen. These guardsmen swear oaths of duty and consider themselves wedded to their distinctive long axes.\\n* '''Qohor''' is situated on the main continent, in the vast Forest of Qohor. It is known for its fine tapestries and its smiths, who have the rare ability to reforge Valyrian steel, even directly infusing the metal with a variety of different colors. The Black Goat is a prominent god in the city. Qohor's city guard has been composed solely of Unsullied eunuch slave soldiers ever since the Battle of the Three Thousand, when 3,000 Unsullied soldiers successfully defended the city against over 25,000 Dothraki horsemen. Guardsmen tie braids of human hair to their spears to commemorate the Dothraki cutting their braids in salute to Qohor's defenders.\\n* '''Tyrosh''', a coastal city-state ruled by an Archon, is infamous for its avarice. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 66}, 'embedding': None, 'id': '386fda3da94f686a50dad8b62a834bd3'}>,\n",
-       "  <Document: {'content': \"Guardsmen tie braids of human hair to their spears to commemorate the Dothraki cutting their braids in salute to Qohor's defenders.\\n* '''Tyrosh''', a coastal city-state ruled by an Archon, is infamous for its avarice. Traders deal extensively in slaves and Tyroshi pear brandy. The city features an abundance of pleasure houses, but they are not as highly regarded as those in Lys. Tyroshi master armorsmiths make intricate armor in fantastic shapes. Tyrosh is a popular center for the hiring of sellswords. The city is often drawn into the ongoing conflicts over the Disputed Lands and the Stepstones. The Tyroshi often wear forked beards and pointed mustaches dyed in bright colors. The character Daario Naharis is from Tyrosh.\\n\\n===Central Essos===\\nThis section covers the Essos locations east of the Free Cities that Daenerys Targeryen passes through on her travels in ''A Game of Thrones'' and ''A Clash of Kings'' before moving on to Slaver's Bay.\\n\\n==== Valyria ====\\nValyria is a peninsula in South-Central Essos, west of Slaver's Bay. Before the Doom of Valyria, it was the seat of the Valyrian Freehold, a massive empire thousands of years old. The Valyrians are characterized by their silver hair and violet eyes. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 67}, 'embedding': None, 'id': 'e80cc224b4a21801d32a278719fc84c3'}>,\n",
-       "  <Document: {'content': 'Before the Doom of Valyria, it was the seat of the Valyrian Freehold, a massive empire thousands of years old. The Valyrians are characterized by their silver hair and violet eyes. Valyria was called The Freehold because every man who owned land was allowed to vote for their leaders. The Valyrians also used slaves to mine the Fourteen Flames, a series of volcanoes rich with ore. They subjugated the Ghiscari and the Rhoynar and established all of the Free Cities, save Braavos. They did this through their knowledge of dragonlore. Many Valyrians rode dragons. However, hundreds of years ago, an event known as the Doom of Valyria, apparently involving a violent eruption of the Fourteen Flames, destroyed the Freehold and made Valyria an archipelago. The Targaryens are of the blood of old Valyria, who escaped before The Doom.\\n\\n====Dothraki Sea ====\\nThe Dothraki Sea is a vast, flat grassland on Essos. It is inhabited by the Dothraki people, a copper-skinned race of warlike nomads with their own language and unique culture. The Dothraki live in hordes called khalasars, each led by a chief called a khal. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 68}, 'embedding': None, 'id': '91432015dec34f013c9953a198fef70d'}>,\n",
-       "  <Document: {'content': 'It is inhabited by the Dothraki people, a copper-skinned race of warlike nomads with their own language and unique culture. The Dothraki live in hordes called khalasars, each led by a chief called a khal. Khalasars are broken into groups, called khas, which are each led by one of the khal\\'s captains, called kos. Each khal and his khalasar owe fealty to a ruling council of royal priestesses, called the dosh khaleen, whose members are each a former khal\\'s consort, called a khaleesi during the reign of her husband, one who became part of the dosh khaleen following his death.\\n\\nDothraki are expert riders and their horses are of prime importance in their culture, used for food, transportation, raw materials, warfare, and establishing social standing. They regularly raid other peoples.\\n\\nGeorge R. R. Martin said \"The Dothraki were actually fashioned as an amalgam of a number of steppe and plains cultures ... Mongols and Huns, certainly, but also Alans, Sioux, Cheyenne, and various other Amerindian tribes ... seasoned with a dash of pure fantasy. So any resemblance to Arabs or Turks is coincidental. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 69}, 'embedding': None, 'id': '98f883df13eef8fc57f73fb82e2fcaea'}>,\n",
-       "  <Document: {'content': 'George R. R. Martin said \"The Dothraki were actually fashioned as an amalgam of a number of steppe and plains cultures ... Mongols and Huns, certainly, but also Alans, Sioux, Cheyenne, and various other Amerindian tribes ... seasoned with a dash of pure fantasy. So any resemblance to Arabs or Turks is coincidental. Well, except to the extent that the Turkic peoples|historic Turks were also originally horsemen of the steppes, not unlike the Alans, Huns, and the rest.\"However, he also noted that \"In general, though, while I do draw inspiration from history, I try to avoid direct one-for-one transplants, so it would not be correct to say that the Dothraki are Mongols.\"The Dothraki have only one permanent city, called Vaes Dothrak, which serves as their capital. The dosh khaleen hold the city as their seat. It is filled with statues stolen from other cities the Dothraki conquered or raided. There is a law that no Dothraki may shed blood within the boundaries of Vaes Dothrak and that those who do are cursed. Two gigantic bronze stallions, whose hooves meet midair, form an arch above the entryway to the city. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 70}, 'embedding': None, 'id': 'f33f75c341a4f29fe09c75ee6f8dc892'}>,\n",
-       "  <Document: {'content': 'There is a law that no Dothraki may shed blood within the boundaries of Vaes Dothrak and that those who do are cursed. Two gigantic bronze stallions, whose hooves meet midair, form an arch above the entryway to the city. For the first season of the TV adaptation, Sandy Brae in the Mourne Mountains of northern Ireland was chosen to stand in for Vaes Dothrak; the bronze stallions making up the Horse Gate as the main entrance of Vaes Dothrak were later C.G.I.ed on two pedestals erected on location.\\n\\n====Lhazar====\\nLhazar is an area of the semi-arid lands south of the Dothraki Sea. A region of pastures and hills, it is inhabited by the Lhazareen, a peaceful people with bronze skin, flat faces, and almond eyes. They are predominantly shepherds, called the Lamb Men by the Dothraki, who frequently prey on them. They worship a god called the Great Shepherd and believe that all of humanity is part of a single flock. The scenes at the village of the Lamb Men that is sacked by the Dothraki were filmed in Malta, at the farming town of Manikata.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 71}, 'embedding': None, 'id': '1d3845d7384dd23f3298afc784518da2'}>,\n",
-       "  <Document: {'content': \"The scenes at the village of the Lamb Men that is sacked by the Dothraki were filmed in Malta, at the farming town of Manikata.\\n\\n===Slaver's Bay ===\\nSlaver's Bay is a marginal sea of the Summer Sea, lying to the south of the Dothraki Sea, to the west of Lhazar and thousands of leagues to the east of the Free Cities. The climate is very hot. After a first mention in ''A Game of Thrones'' in relation to slavery, Daenerys Targaryen conquers the three great Slaver's Bay port city-states Astapor, Yunkai and Meereen in ''A Storm of Swords''. She stays in Meereen throughout most of ''A Dance with Dragons''.\\n\\nThe cities were built from the rubble of Old Ghis, an ancient rival of Valyria that was crushed by Valyria thousands of years before the series' events. The economies of the cities are largely based on slave labor and the slave trade. Treatment of slaves is often harsh, while citizens live in relative luxury. Professional soldiers of all three cities wear outlandish costumes and hairstyles that limit their usefulness in battle. The cities' militaries are highly dependent on additional slave and mercenary armies for the actual fighting.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 72}, 'embedding': None, 'id': '71c1337ba2650b567443d8e107461ca3'}>,\n",
-       "  <Document: {'content': 'Professional soldiers of all three cities wear outlandish costumes and hairstyles that limit their usefulness in battle. The cities\\' militaries are highly dependent on additional slave and mercenary armies for the actual fighting.\\n\\nPresent inhabitants of the bay are a mixed race that no longer speak the old Ghiscari tongue but variations of High Valyrian with a characteristic growl. The ancient folk of Ghis, who name themselves the harpy\\'s sons in Astapor, are said to have bristly red-black hair. The Good Masters of Astapor all appear alike to Daenerys as \"thick fleshy men with amber skin, broad noses, dark eyes. Their wiry hair was black or a dark red, or that queer mixture of red and black that was peculiar to Ghiscari\". Only the freeborn men of Astapor are permitted to wear garments called tokars, whose fringes display their status. Many Astapori women veil their face for the dust. The Astapori are drenched in sweet perfumes.\\n\\n====Astapor====\\nAstapor lies on the banks of the Worm River, a wide, slow and crooked stream with wooded islands. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 73}, 'embedding': None, 'id': '67ceb92305bd51d67a4d467c27ff4021'}>,\n",
-       "  <Document: {'content': 'The Astapori are drenched in sweet perfumes.\\n\\n====Astapor====\\nAstapor lies on the banks of the Worm River, a wide, slow and crooked stream with wooded islands. Entering Astapor at the beginning of \\'\\'A Storm of Swords\\'\\', Daenerys experiences it as an ancient and dilapidated city that has long passed its glory days. The city is dominated by its red brick architecture, and Arstan Whitebeard explains to Daenerys that the saying \"Brick and blood built Astapor, ... and brick and blood her people\" refers to the slaves who make the bricks. Astapor\\'s stepped pyramids, its fighting pits, streets, the surrounding walls and the Plaza of Pride are all made of red bricks. The so-called Plaza of Punishment at Astapor\\'s main gates is even larger than the Plaza of Pride.\\n\\nThe Plaza of Pride, which has a red-brick fountain and a huge bronze harpy statue in its center, serves as an open air slave market and a marshaling area for the Unsullied, elite eunuch spearmen with a renown for discipline and effectiveness. Astapor is the only city to sell Unsullied, but also sells bed slaves, fieldhands, scribes, craftsmen and tutors. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 74}, 'embedding': None, 'id': '1189d958a6e71d2bd4c34ba996d2b410'}>,\n",
-       "  <Document: {'content': \"The Plaza of Pride, which has a red-brick fountain and a huge bronze harpy statue in its center, serves as an open air slave market and a marshaling area for the Unsullied, elite eunuch spearmen with a renown for discipline and effectiveness. Astapor is the only city to sell Unsullied, but also sells bed slaves, fieldhands, scribes, craftsmen and tutors. The Unsullied require a huge investment in both time and money by the Astapori who raise and train them, but they earn the most profitable of returns for the Good Masters of Astapor. The Unsullied wear spiked bronze hats, and they obey at all costs, even if it demands their death. They are given new slave names each day to be reminded of their worthlessness. In times of attack, unsold Unsullied are deployed to the massive, crumbling red-brick walls that the Astapori no longer man.\\n\\nDaenerys decides to buy all of Astapor's trained and untrained Unsullied, over 8600 in number, and tells them to kill all adult Astapori slavers and soldiers when she leaves the city. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 75}, 'embedding': None, 'id': '23d708300322e675f1f2f2478471d248'}>,\n",
-       "  <Document: {'content': \"Daenerys decides to buy all of Astapor's trained and untrained Unsullied, over 8600 in number, and tells them to kill all adult Astapori slavers and soldiers when she leaves the city. She gives the power over Astapor to a council of former slaves led by a healer, a scholar and a priest, and tens of thousands of former slaves join her on her travels to Yunkai. A former butcher named Cleon fends off a scheme to have the Good Masters re-established, and was crowned as the King of Astapor in reward.\\n\\nThe TV show used the coastal town of Essaouira, Morocco to film scenes in Astapor.\\n\\n====Yunkai====\\nThe smallest of the three cities, Yunkai, like Meereen, does not trade in Unsullied but is known for its fighting pits and its pleasure houses, both of which turn out slaves at a brisk pace. The city is similar to Astapor in architecture except for its smaller size and its use of yellow brick in its buildings instead of red. The slavers of Yunkai are known as the Wise Masters. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 76}, 'embedding': None, 'id': '16a92112fa0ec03346df1525ab5161f7'}>,\n",
-       "  <Document: {'content': \"The city is similar to Astapor in architecture except for its smaller size and its use of yellow brick in its buildings instead of red. The slavers of Yunkai are known as the Wise Masters. Because of the city's lack of Unsullied, it relies on a mixed professional and slave army of approximately 4,000 with at least 1,000 mercenaries. Typical for Ghiscari, Yunkai soldiers wear impractical armor and oiled hair teased into enormous shapes, limiting their effectiveness.\\n\\nYunkish scenes were filmed in Aït Benhaddou, Morocco in the TV show.\\n\\n====Meereen====\\nThe largest of the three slaver cities, Meereen has a population equaling that of Astapor and Yunkai combined. The city has architecture similar to that of its neighbors, but it is made of bricks of many colors. Its landscape is dominated by a massive pyramid, named the Great Pyramid, and the Temple of Graces, which is capped by a golden dome. Meereen is unique among the Ghiscari cities in that it is filled with many temples and pyramids. The slavers of Meereen are known as the Great Masters. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 77}, 'embedding': None, 'id': 'dc4dc2e4a874026ebad708231aab1678'}>,\n",
-       "  <Document: {'content': \"Meereen is unique among the Ghiscari cities in that it is filled with many temples and pyramids. The slavers of Meereen are known as the Great Masters. They field a force of lancers equipped in traditionally extravagant Ghiscari fashion with scales of copper and lances as long as fourteen feet. It is built on the banks of the river Skahadhazan.\\n\\nFor the HBO television series, many of the scenes in Meereen were filmed in Split and the Fortress of Klis, Croatia. In Season 5, Daznak's Pit in the city was shot in the Plaza de Toros in Osuna, Spain.\\n\\n=== Eastern Essos ===\\n====Red Waste ====\\nThe Red Waste is a great desert-like area in the eastern part of Essos. Not much is known about it, since it was only briefly seen in ''A Clash of Kings'' when Daenerys Targaryen and her ''khalasar'' crossed it. The only known settlement in the region, Vaes Tolorro, is in ruins.\\n\\n====Qarth====\\nFirst mentioned in ''A Game of Thrones'', the city of Qarth has not yet appeared on any maps in the books. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 78}, 'embedding': None, 'id': 'cff7fbfa6e205cda09503fff1c5a8ad6'}>,\n",
-       "  <Document: {'content': \"====Qarth====\\nFirst mentioned in ''A Game of Thrones'', the city of Qarth has not yet appeared on any maps in the books. However, the HBO ''Viewer's Guide'' world map and the opening titles of the TV series' second season show Qarth located at a strait between the Summer Sea and the Jade Sea in the south-east of Essos. Upon Daenerys' first visit to Qarth in ''A Clash of Kings'', the warlock Pyat Pree describes his city as the center of the world and as a gateway of commerce and culture between the east and west, and the north and south. The reader learns through Daenerys's eyes that the city is surrounded by three graded walls of thirty to fifty feet in height, respectively engraved with portraits of animals, war, and lovemaking. The city's buildings are of many colors, including rose, violet, and umber. Slender towers rise throughout the city, fountains adorn every square, and thousands of colored birds, blooming trees and flowers fill the city. The TV adaptation filmed Qarth on the island of Lokrum near Dubrovnik and constructed a set at the Dubac quarry in Croatia to double for the gates of Qarth.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 79}, 'embedding': None, 'id': 'fb1fb5cf36925de01d23016d5b30d72f'}>,\n",
-       "  <Document: {'content': 'The TV adaptation filmed Qarth on the island of Lokrum near Dubrovnik and constructed a set at the Dubac quarry in Croatia to double for the gates of Qarth.\\n\\nThe Qartheen are described as \"tall pale folk in linen and samite and tiger fur\", with the women wearing gowns that leave one breast bare, while the men sport beaded silk skirts. Daenerys perceives them as \"nothing if not polite\". Slaves serve their needs. The Pureborn, descendants of the city\\'s ancient kings and queens, govern Qarth and also command the city\\'s defenses. Three principal merchant groups battle amongst themselves and against the Pureborn for dominance of the city: the Thirteen, the Ancient Guild of Spicers, and the Tourmaline Brotherhood. Qarth\\'s warlocks, whose lips are turned blue from a potion called \"the shade of the evening\", are said to brood over these factions; they are still feared although their power and prestige have waned over the years. Qarth is also home to the Sorrowful Men, a guild of assassins named so for whispering \"I am so sorry,\" before killing their victims. Daenerys leaves Qarth again at the end of \\'\\'A Clash of Kings\\'\\'.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 80}, 'embedding': None, 'id': '651da7952515faf0c48a723e9714e35e'}>,\n",
-       "  <Document: {'content': 'Qarth is also home to the Sorrowful Men, a guild of assassins named so for whispering \"I am so sorry,\" before killing their victims. Daenerys leaves Qarth again at the end of \\'\\'A Clash of Kings\\'\\'.\\n\\n=== Unvisited lands  ===\\n\\n====Asshai and the Shadow Lands ====\\nAsshai and the Shadow Lands are mysterious locations in the \\'\\'Ice and Fire\\'\\' world. They are first mentioned in \\'\\'A Game of Thrones\\'\\' and were first mapped in \\'\\'The Lands of Ice and Fire\\'\\', lying on the far east of the known world. Martin is unsure if the books will ever take the readers to Asshai, but said that readers may learn more through the POV character Melisandre (who originates from Asshai) or through the memories and mentions of other characters. Jorah Mormont describes Asshai as a port city far to the south of the Dothraki sea, at the end of the known world. Asshai exports such goods as black amethysts, amber and dragonglass. At another time, Jorah Mormont tells Daenerys of great kingdoms to the east of the Red Waste, and lists Asshai by the Shadow as one of the cities full of wonders there. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 81}, 'embedding': None, 'id': '3382bb3d242e13298dc059696ded2b17'}>,\n",
-       "  <Document: {'content': 'At another time, Jorah Mormont tells Daenerys of great kingdoms to the east of the Red Waste, and lists Asshai by the Shadow as one of the cities full of wonders there. According to Martin, all ship travels between Westeros and Asshai go via the Summer Sea and the Jade Sea through the straits at Qarth, and that the common folk still believe the world to be flat. However, according to Martin, \"Asshai is not nearly important to trade as Yi Ti, and the rich port cities of Yi Ti (and Leng) and more easily reached via Qarth.\"Quaithe of the Shadow prophesies Daenerys in Qarth that \"To go north, you must journey south. To reach the west, you must go east ... and to touch the light you must pass beneath the shadow.\"When Daenerys interprets this to mean she must go to Asshai, Quaithe says she would find the truth there.\\n\\nThere are many tales about the Shadow Lands, though how much truth they hold is unclear. The Dothraki believe that ghost grass covers the Shadow Lands, with stalks that glow in the dark and grow taller than a man on horseback. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 82}, 'embedding': None, 'id': 'bc65dec1382cb91ee09decfcc7499d51'}>,\n",
-       "  <Document: {'content': 'The Dothraki believe that ghost grass covers the Shadow Lands, with stalks that glow in the dark and grow taller than a man on horseback. Daenerys heard that \"spellsingers, warlocks, and aeromancers practiced their arts openly in Asshai, while shadowbinders and bloodmages worked terrible sorceries in the black of night\". There are also Westerosi maesters in Asshai. The mages of Asshai teach others their healing powers, but also their spells requiring blood sacrifice. Ancient books of Asshai record the Azor Ahai prophecy followed by members of the R\\'hllor faith. Daenerys heard that dragons themselves originated from the Shadow Lands beyond Asshai and the islands of the Jade Sea, and they possibly still live there. Bran dreams of flying Dragons in Asshai. The petrified dragon eggs Illyrio gives to Daenerys are said to come from the Shadow Lands. The \"dour and frightening\" Shadow Men cover their bodies in tattoos and wear lacquered wooden masks, and the appearance of the Asshai\\'i is described as dark and solemn. The Dothraki believe the Asshai\\'i to be the spawn of shadows. The Asshai\\'i have a language of their own.\\n\\n====Ibben====\\nIbben is a collection of islands north of Essos in the Bay of Whales. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 83}, 'embedding': None, 'id': '8c90de32defc3f936e3df2bb795b8229'}>,\n",
-       "  <Document: {'content': 'The Asshai\\'i have a language of their own.\\n\\n====Ibben====\\nIbben is a collection of islands north of Essos in the Bay of Whales. The largest of these islands is Ib, which contains the cities Port of Ibben and Ib Nor. Until the Doom of Valyria, Ibben was ruled by a God-King. Now power is held by the Shadow Council, which is made up of nobles, priests, and wealthy guildsmen. Ibben is first mentioned in \\'\\'A Game of Thrones\\'\\', where Tyrion talks of rumors that mammoths \"roam the cold wastes beyond the Port of Ibben\". In 2002, Martin said the narrative would \"probably not\" take readers to Ibben, which he described as a \"cold, mountainous, Iceland-sized island\" (i.e. 40,000 square miles) in the Shivering Sea, with the Port of Ibben as the major city; some Ibbenese also live on smaller islands nearby or in colonies on Essos. Ibben is unmapped in the books as of \\'\\'A Dance with Dragons\\'\\', but similar to Martin\\'s descriptions, the HBO \\'\\'Viewer\\'s Guide\\'\\' world map gives the island\\'s location as to the north-east of Essos. Martin said that due to a large whale population in the Shivering sea, many of the Ibbenses were whalers. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 84}, 'embedding': None, 'id': 'ba997e482a3ddb30b71ed72ece872c45'}>,\n",
-       "  <Document: {'content': 'Ibben is unmapped in the books as of \\'\\'A Dance with Dragons\\'\\', but similar to Martin\\'s descriptions, the HBO \\'\\'Viewer\\'s Guide\\'\\' world map gives the island\\'s location as to the north-east of Essos. Martin said that due to a large whale population in the Shivering sea, many of the Ibbenses were whalers. The Ibbenses are known to chew whale blubber in order to maintain their metabolism in the cold climate. Several characters see Ibbenese whalers and cogs at the ports of King\\'s Landing, Braavos, Maidenpool, Eastwatch-by-the-Sea, and the Iron Islands. The novels describe the people of Ibben as squat and hairy; Arya even meets an Ibbenese woman with a mustache. Tyrion and Varys meet foul-smelling Ibbenese,  who \"were as fond of axes as they were of each other\". Arya sees \"a dark brutal axeman from Ib\" in her dreams. The Ibbenese are said to speak with low, raspy voices and to have their own language.\\n\\n====Yi Ti====\\nThe novels repeatedly describe Yi Ti as a city full of wonders, lying in the far east. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 85}, 'embedding': None, 'id': '747446a6b16a268de6bddd9a74fc9290'}>,\n",
-       "  <Document: {'content': 'The Ibbenese are said to speak with low, raspy voices and to have their own language.\\n\\n====Yi Ti====\\nThe novels repeatedly describe Yi Ti as a city full of wonders, lying in the far east. As of \\'\\'A Dance with Dragons\\'\\', Yi Ti has not appeared on any maps in the books, but Martin specified that \"Yi Ti is to the south east of Qarth, generally, across the Jade Sea.\"The city is first mentioned in \\'\\'A Game of Thrones\\'\\', talking of rumors that \"basilisks infested the jungles of Yi Ti\". Sailor stories presented in \\'\\'A Feast for Crows\\'\\' mention that a grey plague has hit Yi Ti. The god of the people of Yi Ti is called the Lion of Night. Daenerys sees people of Yi Ti as bright-eyed men in monkey-tail hats in the markets of Vaes Dothrak. Martin is unsure \"to what extent those peoples like of Yi Ti will ever enter this present story, however... their lands are very far away.\"====Plains of Jogos Nhai====\\nNorth of Yi Ti, the Plain of Jogos Nhai are windswept, with rolling hills. They are dominated by a race of mounted warriors called the Jogos Nhai. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 86}, 'embedding': None, 'id': '8839dd9ea1ca89dbef082b8bb6c9d675'}>,\n",
-       "  <Document: {'content': \"====Plains of Jogos Nhai====\\nNorth of Yi Ti, the Plain of Jogos Nhai are windswept, with rolling hills. They are dominated by a race of mounted warriors called the Jogos Nhai. The Jogos Nhai live in yurts and tents, and are a nomadic people. They are short, squat, and have large heads and small faces. Men and women both have pointed skulls, a result of their custom of binding the heads of newborns. They also ride zorses, a striped mount that can withstand much more than average horse. The Jogos Nhai do not fight between themselves, and live in small clans bound by blood. They live in a state of perpetual war with outsiders. Each tribe is commanded by a ''jhat'', or war chief, and a moonsinger, who is a priestess, healer, and judge. Moonsingers are generally female, and ''jhats'' are mostly male. (Paraphrased from ''The World of Ice and Fire)''\\n\\n==Sothoryos ==\\nTo the south of Essos is the continent of Sothoryos (mistakenly spelled Sothoros in early novels). Sothoryos is the third continent of the known world, and is vast, plague-ridden, covered in jungles, and largely unexplored. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 87}, 'embedding': None, 'id': '2a96da3d0d5c66ba77e5d63a805ba64b'}>,\n",
-       "  <Document: {'content': '(Paraphrased from \\'\\'The World of Ice and Fire)\\'\\'\\n\\n==Sothoryos ==\\nTo the south of Essos is the continent of Sothoryos (mistakenly spelled Sothoros in early novels). Sothoryos is the third continent of the known world, and is vast, plague-ridden, covered in jungles, and largely unexplored. It is reported to be as large as Essos and described as a \"land without end\" by Jaenara Belaerys, a Valyrian dragonlord from before the Doom of Valyria.\\n\\nThe continent is first named on a map in \\'\\'A Storm of Swords\\'\\' (2000), showing the cities of Yeen and Zamettar on it. The narrative itself first refers to the continent in \\'\\'A Feast for Crows\\'\\' (2005). Martin had described Sothoryos in 2002 as \"the southern continent, roughly equivalent to Africa, jungly, plague-ridden, and largely unexplored\". The novels provide little other information. The swampy nature of Sothoryos is briefly referenced by Victarion in \\'\\'A Dance with Dragons\\'\\', and teak from Sothoryos is said to be used to build ships. A corsair\\'s road runs along the continent\\'s northern coast. \\'\\'A Dance with Dragons\\'\\' refers to the diseases on Sothoryos in regards to the wealthy but sick Yunkai slave trader Yezzan zo Qaggaz. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 88}, 'embedding': None, 'id': 'f1e25fd970686b4eaaf80361e97e3b20'}>,\n",
-       "  <Document: {'content': '\\'\\'A Dance with Dragons\\'\\' refers to the diseases on Sothoryos in regards to the wealthy but sick Yunkai slave trader Yezzan zo Qaggaz. Victarion describes some people as \"squat and hairy as the apes of Sothoros\", and some people fighting in Daznak\\'s Pit for Daenerys\\'s entertainment in \\'\\'A Dance with Dragons\\'\\' are described as \"brindle-skinned half-men from the jungles of Sothoros\". Martin said that, unlike other peoples in the novels, the brindled men of Sothoryos were pure fantasy constructs.\\n\\n==Ulthos ==\\nThe map collection \\'\\'The Lands of Ice and Fire\\'\\' also shows the north tip of a landmass named \"Ulthos\" to the south of Essos and east of Sothoryos. Asked whether this was another continent, Martin replied, \"Well, it\\'s a large landmass. I am a little unclear on the formal definition of \\'continent\\' as opposed to \\'big island.\\'Also on the size of Ulthos, which after all sits at the edge of the known world. Terra incognita and all that.\"==References==\\n===Secondary sources===\\n\\n===Primary sources===\\n\\n===Bibliography===\\n*\\n*\\n*\\n*\\n*\\n*\\n*\\n*\\n\\n==External links==\\n*  Wiki of Ice and Fire, Geography', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 89}, 'embedding': None, 'id': 'a29e5dbffa6c4e655177a2e39a370d6a'}>,\n",
-       "  <Document: {'content': '\\nThe soundtrack album of the sixth season of HBO series \\'\\'Game of Thrones\\'\\', titled \\'\\'\\'\\'\\'Game of Thrones: Season 6\\'\\'\\'\\'\\', was released digitally on June 24, 2016, and later released on CD on July 29, 2016. The album reached No. 1 on the \\'\\'Billboard\\'\\'s Soundtracks chart, and the track from the season finale \"Light of the Seven\" reached No. 1 on \\'\\'Billboard\\'\\'s Spotify Viral 50 chart. The \"Light of the Seven\" is the first time piano is used in the music for \\'\\'Game of Thrones\\'\\'. The album was composed by Ramin Djawadi. The soundtrack has received favorable reviews and peaked at #1 on the U.S. Soundtrack Albums chart and #27 on the U.S. Billboard 200. It won an International Film Music Critics Association for \"Best Original Score for a Television Series\".\\n\\n==Reception==\\nThe soundtrack received positive reviews from critics.\\n\\n==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* D.B. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'a7f0a201dd811abaf7c491ab5b28e623'}>,\n",
-       "  <Document: {'content': \"==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* D.B. Weiss – liner notes\\n\\n==Charts==\\n\\nChart (2016)\\n\\nPeak position\\n\\nUK Soundtrack Albums (OCC)\\n\\n2\\n\\nUS Soundtrack Albums (''Billboard'')\\n\\n1\\n\\nUS Digital Albums (''Billboard'')\\n\\n5\\n\\nUS Top Album Sales (''Billboard'')\\n\\n12\\n\\n==Awards and nominations==\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nInternational Film Music Critics Association\\n\\nBest Original Score for a Television Series\\n\\nWorld Soundtrack Awards\\n\\nTelevision Composer of the Year\\n\\n==Notes==\\n\\n==References==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '6d8be95995f0206a44d17bca174ff57c'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''Renly Baratheon''' is a fictional character in the ''A Song of Ice and Fire'' series of fantasy novels by American author George R. R. Martin, and its television adaptation ''Game of Thrones''.\\n\\nIntroduced in 1996's ''A Game of Thrones'', Renly is the youngest of the three sons of Lord Steffon Baratheon and Cassana Estermont, and the younger brother of Robert and Stannis Baratheon. He is Lord of Storm's End and Lord Paramount of the Stormlands. He served as master of laws in his eldest brother's small council, before crowning himself king in the wake of Robert's death with the support of the Reach and the Stormlands, an act that helps kick off the War of Five Kings. This brings him into conflict with his older brother Stannis, whose claim is greater.\\n\\nRenly's homosexual relationship with Loras Tyrell is alluded to in the books and is made clearly evident in the television show. Both men are among Martin's most prominent LGBTQ characters, although Renly and Loras' adapted relationship and the show's portrayal of the latter has received mixed criticism.\\n\\nRenly Baratheon is portrayed by Gethin Anthony in the HBO television adaptation.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '8734113490279f03dfe70ea47b2278c8'}>,\n",
-       "  <Document: {'content': \"Both men are among Martin's most prominent LGBTQ characters, although Renly and Loras' adapted relationship and the show's portrayal of the latter has received mixed criticism.\\n\\nRenly Baratheon is portrayed by Gethin Anthony in the HBO television adaptation.\\n\\n== Character description ==\\nRenly Baratheon is the younger brother of King Robert  and of King Stannis. He is a handsome and charismatic man that wins friends easily, which makes him popular with the smallfolk. He is thought to look extremely similar to his older brother Robert, albeit smaller and slimmer, inheriting the Baratheon height and long black hair. Although he is well-liked and charismatic, many powerful lords at court secretly consider him to be vain and frivolous. He is described as disdainful of reading, although he enjoys hunting and jousting. Renly is also a closeted homosexual engaged in a relationship with Loras Tyrell.\\n\\nRenly is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, such as Ned and Catelyn Stark. He is also often mentioned and remembered by Brienne of Tarth, who had fallen in love with him at a young age. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'c67d8a963e1205bd37f2a52478cbccdd'}>,\n",
-       "  <Document: {'content': \"He is also often mentioned and remembered by Brienne of Tarth, who had fallen in love with him at a young age. Renly is mostly a background character in the novels.\\n\\n== Storylines ==\\n\\nleft\\nRenly Baratheon is the youngest of the Baratheon brothers and Lord of Storm's End. He is described as handsome and charismatic, winning friends easily. Renly serves on Robert's council as Master of Laws. After Robert dies, Renly declares himself King of the Seven Kingdoms in ''A Clash of Kings'', wins the support of the Baratheon bannermen as he is their Lord Paramount, and seals an alliance with House Tyrell by marrying Margaery Tyrell. Before he can march on the capital though, he hears Stannis is besieging Storm's End. Renly marches there, intending to kill his brother in battle, and turning down an offer to become Stannis' heir. Before the battle he is assassinated by a shadow conjured by Melisandre, though it is unclear if Stannis is aware of this or not.\\n\\n==TV adaptation==\\nBanner of arms of King Renly Baratheon in ''Game of Thrones''\\nRenly Baratheon is played by the British actor Gethin Anthony in the television adaption of the series of books.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '3e5d106df8df16ca298ee34e12161fa3'}>,\n",
-       "  <Document: {'content': '==TV adaptation==\\nBanner of arms of King Renly Baratheon in \\'\\'Game of Thrones\\'\\'\\nRenly Baratheon is played by the British actor Gethin Anthony in the television adaption of the series of books.\\n\\nGethin Anthony on his character: \"The reason why I like playing him is I think he\\'s a fantastic man who believes in Westeros being a cultured and enlightened place. His major advantage -- and why he gets on well in the world -- is that he gets on well with people.\"====Season 1====\\nRenly Baratheon, Lord of Storm\\'s End, is the youngest brother of King Robert and Master of Laws in the Small Council. He is popular with the people because he is handsome, jovial, and throws extravagant balls and masquerades. He is not fond of fighting or bloodshed, and would rather make friends than kill enemies. He is secretly the lover of Ser Loras Tyrell, the Knight of the Flowers, who convinces him that those qualities make him better ruling material than either of his older brothers. While Robert lies dying, Renly attempts to convince Ned of this, and that the two of them should kidnap Joffrey and rule the realm themselves. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '4ed57eb30a37b00966edc6077a2dc68c'}>,\n",
-       "  <Document: {'content': \"While Robert lies dying, Renly attempts to convince Ned of this, and that the two of them should kidnap Joffrey and rule the realm themselves. However, Ned refuses, so Renly, Loras, and their followers flee south. Once Joffrey becomes King and has Ned executed, Renly challenges his alleged nephew's claim to the throne.\\n\\n====Season 2====\\nRenly declares himself King of the Seven Kingdoms during Season 2 and wins the support of the Baratheon bannermen and the support of other houses, despite Stannis' better claim. He also seals an alliance with the powerful House Tyrell and its bannermen (including Randyll Tarly, father of Samwell Tarly) by marrying Margaery Tyrell. He leads his massive army slowly through southern Westeros, biding his time. Catelyn Stark tries to convince Renly and Stannis to put aside their differences and unite against the Lannisters but it fails as both brothers refuse to give their claim for the throne. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8457d8c15418d17a65ac518fd9990072'}>,\n",
-       "  <Document: {'content': 'Catelyn Stark tries to convince Renly and Stannis to put aside their differences and unite against the Lannisters but it fails as both brothers refuse to give their claim for the throne. On the night before a battle between his and Stannis\\'s forces, Renly agrees to Catelyn to allow Robb Stark to keep the title \"King in the North\" and rule the North and Riverlands, but on the condition that he swear fealty to Renly as King on the Iron Throne. Before Catelyn can offer a real negotiation, Renly is assassinated by Melisandre, who gives birth to a shadow demon and sends it to kill Renly in order to remove him from Stannis\\' path. Stannis is initially unaware of the exact nature of Melisandre\\'s crime, and is later visibly shocked and saddened of the role he played in his brother\\'s death, which he visibly regrets.\\n\\n====Season 5====\\nRenly is later avenged in the Season 5 finale when Brienne of Tarth executes his brother Stannis after the battle outside Winterfell against the Boltons, spitefully telling Stannis that Renly was the rightful King.\\n\\n=== Family tree of House Baratheon ===\\n\\n== References ==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '9881897fca71bbdb3afa6a55b3d2cb81'}>,\n",
-       "  <Document: {'content': '\\n\\nA \\'\\'\\'White Walker\\'\\'\\' is a humanoid creature from the HBO television series \\'\\'Game of Thrones\\'\\', and the George R. R. Martin novel series \\'\\'A Song of Ice and Fire\\'\\' on which it is based. Primarily referred to as \\'\\'\\'the Others\\'\\'\\' in the novels, White Walkers are a supernatural threat to humankind who dwell north of The Wall in Westeros. \\'\\'The Verge\\'\\' named them among \"the most visually iconic creatures on the show\". White Walkers are also featured in the show\\'s merchandising.\\n\\n==Description==\\nMartin introduces the Others in the prologue of \\'\\'A Game of Thrones\\'\\' (1996), describing them as \"Tall ... and gaunt and hard as old bones, with flesh pale as milk\" with eyes \"deeper and bluer than any human eyes, a blue that burned like ice\". Accompanied by intense cold, they wear armor that \"seemed to change color as it moved\", and wield thin crystal swords capable of shattering steel. The Others move silently, and they speak their own language; Martin writes that their voices are \"like the cracking of ice on a winter lake\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '3bd02732b5daab9adcf1c4c2bd3d9a48'}>,\n",
-       "  <Document: {'content': 'The Others move silently, and they speak their own language; Martin writes that their voices are \"like the cracking of ice on a winter lake\". In \\'\\'A Storm of Swords\\'\\' (2000), they are shown to be vulnerable to weapons made of dragonglass (obsidian), as Samwell Tarly kills one this way:\\n\\nIn \\'\\'A Dance with Dragons\\'\\' (2011), Sam uncovers ancient record fragments which suggest that the Others are also vulnerable to something called \"dragonsteel\", which he and Jon Snow surmise is another term for Valyrian steel.\\n\\nCreatures killed by the Others soon reanimate as wights: undead with pallid skin, black hands, and glowing blue eyes similar to the Others\\'. Dragonglass has no effect on them. Wights may be physically injured, but even dismembered parts remain animated, so they must be destroyed by fire. The humans who live in the north beyond the Wall—called \"wildlings\" by the inhabitants of Westeros—burn their dead so they will not become wights.\\n\\n==Novels==\\nIn 2012, Chris Lackner wrote in \\'\\'Dose\\'\\', \"Fans of the novels are eagerly awaiting Martin\\'s final two installments of the seven-part series. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '4f6edb34639955903421e2113915bd8'}>,\n",
-       "  <Document: {'content': '==Novels==\\nIn 2012, Chris Lackner wrote in \\'\\'Dose\\'\\', \"Fans of the novels are eagerly awaiting Martin\\'s final two installments of the seven-part series. In particular, they are eager to learn more about the White Walkers—or The Others—a mysterious, undead race seemingly bent on humanity\\'s destruction.\"===Backstory===\\nIn the novels and the 2014 companion book \\'\\'The World of Ice & Fire\\'\\', Martin establishes that, millennia before the events of \\'\\'A Song of Ice and Fire\\'\\' series, a pact existed between the Children of the Forest (elf-like creatures) and the First Men (mankind). This was weakened by the emergence of the Others, an enigmatic and malevolent nonhuman species who inflicted a night that lasted a generation and a winter that lasted decades. After the Others were pushed back, the Children and the First Men raised The Wall, a vast barrier of stone, ice and magic from one coast of northern Westeros to the other, to bar the passage of the Others south.\\n\\n===\\'\\'A Game of Thrones\\'\\'===\\nAs \\'\\'A Game of Thrones\\'\\' (1996) begins, the general belief across Westeros is that the Others are a legend to scare children, or else \"gone eight thousand years\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '50770e83e0a0fe099c7931d338490858'}>,\n",
-       "  <Document: {'content': '===\\'\\'A Game of Thrones\\'\\'===\\nAs \\'\\'A Game of Thrones\\'\\' (1996) begins, the general belief across Westeros is that the Others are a legend to scare children, or else \"gone eight thousand years\". But the Others have re-emerged, and are gaining power—and creating wights—beyond the Wall. In the Prologue, a ranging party from the Night\\'s Watch come face to face with a group of the Others, who kill Ser Waymar Royce. Reanimated as a wight, he then kills the ranger Will. Out ranging beyond the Wall, Jon Snow and the Night\\'s Watch find the corpses of two of their fallen brothers. Brought home to Castle Black, the dead men rise and kill several of their living fellows before they are destroyed.\\n\\n===\\'\\'A Storm of Swords\\'\\'===\\nSam Tarly kills an Other with a dragonglass dagger in \\'\\'A Storm of Swords\\'\\' (2000). His former comrade Small Paul is killed and reanimated as a wight. The undead Paul is unfazed by the dragonglass, but Sam is able to put him down with fire. Bran Stark recalls the story of the Night\\'s King, a Stark and the 13th Lord Commander of the Night\\'s Watch who had been seduced by a female White Walker. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '1b629dce89a5636ecc0366d7b060d40a'}>,\n",
-       "  <Document: {'content': 'Bran Stark recalls the story of the Night\\'s King, a Stark and the 13th Lord Commander of the Night\\'s Watch who had been seduced by a female White Walker. The Night\\'s King and his queen enslaved the brothers of the Watch until the Starks and the wildlings joined forces to defeat him.\\n\\n===\\'\\'The Winds of Winter\\'\\'===\\nMartin said in 2012 that readers will see more of the Others in his forthcoming novel \\'\\'The Winds of Winter\\'\\'. He noted in a 2012 \\'\\'Dose\\'\\' interview, \"We\\'ll learn more about their history, certainly, but I don\\'t know about culture ... I don\\'t know if they have a culture\".\\n\\n==TV adaptation==\\nThe White Walkers portrayed on HBO\\'s \\'\\'Game of Thrones\\'\\' differ slightly in appearance from their literary counterparts, but Aaron Souppouris of \\'\\'The Verge\\'\\' named them among \"the most visually iconic creatures on the show\". In the TV series, the primary White Walker has been portrayed by Ross Mullan. Their apparent leader is the Night King, portrayed by Richard Brake and Vladimir Furdik, who first appeared in the episode \"Oathkeeper\", in which he places his hand on Craster\\'s baby son, transforming him into a White Walker. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8f3023cc341da8053820472f2aa29c49'}>,\n",
-       "  <Document: {'content': 'Their apparent leader is the Night King, portrayed by Richard Brake and Vladimir Furdik, who first appeared in the episode \"Oathkeeper\", in which he places his hand on Craster\\'s baby son, transforming him into a White Walker. In \"Hardhome\", the effectiveness of Valyrian steel against the White Walkers is proven as Jon shatters one to pieces with a single stroke of his ancient sword Longclaw. Unlike in the novels, the TV series has established that wights can be destroyed by dragonglass.\\n\\nIn the season 6 episode \"The Door\" (May 2016), Bran Stark experiences a vision of the creation of the Night King by Leaf, one of the Children of the Forest, by stabbing a human prisoner in the chest with a dragonglass dagger. Leaf explains to an incredulous Bran that the Children were at war with the First Men at the time of the Night King\\'s creation. In season 7, when Jon kills a White Walker, the wights under its control are also destroyed. The Night King kills Daenerys Targaryen\\'s dragon Viserion in the seventh season episode \"Beyond the Wall\", and then reanimates him as a wight. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'be6b0beed55ab4b83b35c8e59316e6c3'}>,\n",
-       "  <Document: {'content': 'The Night King kills Daenerys Targaryen\\'s dragon Viserion in the seventh season episode \"Beyond the Wall\", and then reanimates him as a wight. In the season finale episode \"The Dragon and the Wolf\", the Night King uses the dragon to breach the Wall.\\n\\nIn the eighth season premiere, \"Winterfell\", a group of survivors from the Wall\\'s destruction discover that Last Hearth, the home of House Umber, has been attacked by the Army of the Dead in their conquest of the North, with all of its inhabitants killed. The Night King has also left a message in the form of the deceased Ned Umber impaled on a wall and surrounded by a spiral of severed limbs. In the second episode \"A Knight of the Seven Kingdoms\", the inhabitants of Winterfell make plans for defending the castle against the Army of the Dead. Bran states that the Night King is seeking an \"endless night\", as he plans to cleanse the world of all life and memory and intends to kill Bran as a major step towards this goal. The White Walkers are later seen viewing Winterfell from a distance as the living prepare to fight them. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'efc2864183898d7c5f977b6e839619c4'}>,\n",
-       "  <Document: {'content': 'Bran states that the Night King is seeking an \"endless night\", as he plans to cleanse the world of all life and memory and intends to kill Bran as a major step towards this goal. The White Walkers are later seen viewing Winterfell from a distance as the living prepare to fight them. In the third episode \"The Long Night\", the Army of the Dead marches on Winterfell and nearly wipes out the combined armies of the living. The Night King eventually reaches the Godswood, kills Theon Greyjoy, and prepares to strike down Bran. However, the Night King is ambushed and killed by Arya Stark with the Valyrian steel dagger that Bran had previously given her (\"The Spoils of War\"), which causes both him and the other White Walkers to shatter and results in the complete obliteration of the Army of the Dead.\\n\\n===Merchandising===\\nIn 2012, Funko released a White Walker figure as part of their POP! Television line, which are 4.5 inch vinyl figures in the Japanese super deformed style. The company later produced a Mystery Mini Blind Box figurine of a stylized White Walker. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '97e07ff2570008971773944f828f8afc'}>,\n",
-       "  <Document: {'content': 'Television line, which are 4.5 inch vinyl figures in the Japanese super deformed style. The company later produced a Mystery Mini Blind Box figurine of a stylized White Walker. Dark Horse released a 9-inch White Walker bust statue in 2013, and later a 9-inch full figure statue. In 2014, Funko released a 6.5 inch articulated White Walker action figure (with spear accessory) as part of their HBO-licensed Legacy Collection line, which features \"some of the series’ most popular characters\".\\n\\n==See also==\\n* Themes in \\'\\'A Song of Ice and Fire\\'\\'\\n\\n==References==\\n\\n==Further reading==\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '2297c34014b4a3799961a56ec978a990'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'No One\\'\\'\\'\" is the eighth episode of the sixth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 58th episode overall. It was written by series co-creators David Benioff and D. B. Weiss, and directed by Mark Mylod.\\n\\nArya Stark fights for her life and chooses her destiny; Jaime Lannister returns Edmure Tully to Riverrun; Cersei Lannister is thwarted; Daenerys Targaryen returns to Meereen; and Sandor Clegane tracks down the Brotherhood Without Banners.\\n\\n\"No One\" received mostly positive reviews from critics, who listed the conclusion of Arya\\'s story with the Faceless Men, the reintroduction of the Brotherhood Without Banners, and Jaime\\'s scheme to retake Riverrun as high points of the episode, but also received some criticism as being anticlimactic. Filming of the episode\\'s foot chase scene between Arya and the Waif required a month of practice in Belfast to get the choreography right. In the United States, the episode achieved a viewership of 7.60 million in its initial broadcast. The episode was Peter Dinklage and Maisie Williams\\' selection for the 68th Primetime Emmy Awards to support their nominations.\\n\\nThe episode marked the final appearance of Tom Wlaschiha (Jaqen H\\'ghar), Faye Marsay (the Waif), and Clive Russell (Brynden Tully).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '380ec6032289b8430c9845c888c8cf84'}>,\n",
-       "  <Document: {'content': \"The episode marked the final appearance of Tom Wlaschiha (Jaqen H'ghar), Faye Marsay (the Waif), and Clive Russell (Brynden Tully).\\n\\n==Plot==\\n\\n===In the Riverlands===\\nSandor tracks down some of the Brotherhood Without Banners and brutally kills them. He then comes across Beric Dondarrion and Thoros of Myr, who are preparing to hang the rest of the men responsible for the attack on the village. Sandor bargains with Beric, who allows him to personally hang two of the men. Beric and Thoros try to recruit Sandor into the Brotherhood, explaining that they intend to head north to fight the White Walkers and need his strength.\\n\\nBrienne and Podrick arrive at Riverrun, where they reunite with Jaime and Bronn. Brienne explains that she has come to recruit the Blackfish and the Tully army for Sansa, but Jaime points out that he is currently besieged. Brienne proposes that if she can convince the Blackfish to surrender, then Jaime will allow him and the Tully army safe passage North. Jaime agrees to the plan, but only gives Brienne until nightfall. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '52a67ba3106ed4a0b25a337f102b087f'}>,\n",
-       "  <Document: {'content': \"Brienne proposes that if she can convince the Blackfish to surrender, then Jaime will allow him and the Tully army safe passage North. Jaime agrees to the plan, but only gives Brienne until nightfall. She attempts to return Oathkeeper to Jaime, stating that she has completed her mission, but Jaime tells her that it is hers and hers alone. Brienne notes that should she be inside Riverrun for a siege she would be bound to fight alongside the Blackfish, and against Jaime.   She attempts to negotiate with the Blackfish, but, despite being sympathetic to Sansa's plight, he refuses to abandon Riverrun.\\n\\nMeanwhile, Jaime talks with Edmure, trying to win his cooperation by tempting him with seeing his newborn son, and offering to send him and his family to Casterly Rock, away from the Freys. Edmure insults Jaime, and asks him how he can live with himself. Jaime responds that he wants to take Riverrun to be back with Cersei and is willing to do anything to achieve it, even if it means killing every Tully he finds. Edmure, cowed by this threat, is sent by Jaime to parley with the Blackfish. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'f59bf0ccaab17da57d4072061b660654'}>,\n",
-       "  <Document: {'content': \"Jaime responds that he wants to take Riverrun to be back with Cersei and is willing to do anything to achieve it, even if it means killing every Tully he finds. Edmure, cowed by this threat, is sent by Jaime to parley with the Blackfish. Despite the Blackfish's protests, the Tully soldiers allow Edmure entry into the castle. Edmure then orders his men to surrender Riverrun, and also orders the Blackfish's capture. The Blackfish helps Brienne and Podrick escape, but remains to fight to the end.\\n\\nJaime is informed about the death of the Blackfish, and sees Brienne and Pod escaping on the river. Jaime and Brienne mournfully wave each other farewell.\\n\\n===In Meereen===\\nWith the Red Priests on their side and spreading pro-Daenerys propaganda, Tyrion and Varys see Meereen returning to life. After warning Tyrion not to trust the Red Priests, Varys leaves for Westeros on a secret mission to find more allies. Tyrion then has a drink with Grey Worm and Missandei, but they are interrupted by the arrival of a fleet sent by the Masters to attack the city. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '636e4bee88dcea044a3b47c4471500f'}>,\n",
-       "  <Document: {'content': \"Tyrion then has a drink with Grey Worm and Missandei, but they are interrupted by the arrival of a fleet sent by the Masters to attack the city. As the fleet bombards Meereen, Tyrion and Grey Worm are discussing their strategy when Daenerys returns to the city with Drogon.\\n\\n===In King's Landing===\\nThe Faith Militant, led by Lancel Lannister and with Tommen's blessing, arrive to take Cersei to see the High Sparrow (whom they now refer to as the High Septon). Cersei refuses to go with them and when one of the Faith Militant attacks, he is brutally killed by Gregor Clegane. Seeing the rest of the Faith Militant soldiers intimidated, Cersei remarks that the High Sparrow is free to come to the Red Keep to see her. She then attends a royal announcement by Tommen in the throne room, but Kevan Lannister, her uncle and Hand of the King, refuses to let her stand by his side. Tommen announces the date that Cersei and Loras' trials will be held, and that, in consultation with the High Sparrow, he has decided to end the practice of trial by combat as a means of resolving conflicts. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '589ef3301e3907f069debc91b7af25c3'}>,\n",
-       "  <Document: {'content': 'Tommen announces the date that Cersei and Loras\\' trials will be held, and that, in consultation with the High Sparrow, he has decided to end the practice of trial by combat as a means of resolving conflicts. The prospect of a trial by seven septons instead clearly worries Cersei. Qyburn then reports to Cersei about a \"rumor\" that she ordered him to investigate, and remarks that it is \"more, much more\".\\n\\n===In Braavos===\\nLady Crane returns to her chambers to find a wounded Arya hiding inside, and helps stitch her wounds. She tells Arya that, thanks to her warning, she mutilated her would-be killer Bianca\\'s face before kicking her out of the acting company. She then offers to have Arya join them, but she refuses, saying that she intends to travel west of Westeros to see the edge of the world. As Arya recovers, the Waif arrives and kills Lady Crane, intending to kill Arya as well. Arya flees through the streets of Braavos, but during the chase, Arya\\'s wounds reopen and she limps back to her hideout with the Waif in pursuit. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'c01a12e266292b0cd276840e8a75cfb3'}>,\n",
-       "  <Document: {'content': 'Arya flees through the streets of Braavos, but during the chase, Arya\\'s wounds reopen and she limps back to her hideout with the Waif in pursuit. As the Waif closes in, Arya extinguishes the candle lighting the room; having trained while blinded for several weeks, Arya has the upper hand.\\n\\nAt the House of Black and White, Jaqen follows a bloodtrail to the Hall of Faces, where he finds the Waif\\'s face before being held at sword-point by Arya. Jaqen congratulates Arya for finally becoming No One. However, she rejects the title, asserting her identity as Arya Stark before turning and leaving, announcing that she is \"going home.\"Jaqen proudly watches on as she leaves.\\n\\n==Production==\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"No One\" was written by the series\\' creators David Benioff and D. B. Weiss.\\n\\nIn the \"Inside the Episode\" featurette published by HBO following the airing of the episode, David Benioff spoke about the revelation in King\\'s Landing, and Tommen\\'s decision to abolish \"trial by combat,\" stating \"This moment is a grievous blow for Cersei because she\\'s been counting on trial by combat really since the end of season five. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'b402865903c28f6e18285cf8e968e430'}>,\n",
-       "  <Document: {'content': 'In the \"Inside the Episode\" featurette published by HBO following the airing of the episode, David Benioff spoke about the revelation in King\\'s Landing, and Tommen\\'s decision to abolish \"trial by combat,\" stating \"This moment is a grievous blow for Cersei because she\\'s been counting on trial by combat really since the end of season five. This was always her ace in the hole, the Mountain was never going to lose a trial by combat there\\'s no one out there who could beat him. And then Tommen takes it away from her and knowing really what he\\'s doing. He might be a weak King, but he\\'s not an idiot, he understands that this move is going to mean that she\\'s probably not going to fare that well in this trial. So it\\'s a pretty devastating moment for Cersei, and you see that in her reaction, and partly because it just bodes so ill for this coming trial, and partly because it\\'s her own son who\\'s really betrayed her.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '4daf0b5c3e9ea68c780c43651a352676'}>,\n",
-       "  <Document: {'content': 'So it\\'s a pretty devastating moment for Cersei, and you see that in her reaction, and partly because it just bodes so ill for this coming trial, and partly because it\\'s her own son who\\'s really betrayed her.\"Speaking about the siege of Riverrun, Weiss noted about Brienne and Jaime\\'s reunion that \"Jaime didn\\'t expect to see Brienne necessarily ever again, he certainly didn\\'t expect to see her marching into his military camp. And his relationship is obviously very complicated, and fraught, and has undercurrents that he\\'s uncomfortable with feeling.\"Benioff continued about Brienne\\'s escape, \"It\\'s these two who are not quite sure how they feel about the other, and he lets her go. He\\'d be well within his rights to send his men after her, to capture her, and it wouldn\\'t be that hard to capture her since she\\'s being slowly rowed away by Podrick, but he doesn\\'t. She might technically be an enemy now that she\\'s serving Sansa Stark, who is still obviously a suspect in Joffrey\\'s murder, but she\\'s not Jaime\\'s enemy.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '1094e23be35baadb144fbd49bbbe14cb'}>,\n",
-       "  <Document: {'content': 'She might technically be an enemy now that she\\'s serving Sansa Stark, who is still obviously a suspect in Joffrey\\'s murder, but she\\'s not Jaime\\'s enemy.\"Clive Russell, who portrayed Brynden \"Blackfish\" Tully in the series, spoke about his character\\'s encounter with Brienne of Tarth, in an interview with IGN, noting \"Brienne represented for him his young self, a person of integrity, a soldier of integrity, trying to do the right thing. That added to the conflict he felt of what was the right thing to do: whether to send men to Sansa or not.\"He continued by saying about their relationship to each other, \"She spoke very clearly to power. She was talking to an older man who\\'s, initially, very irritated by her being there, but eventually takes her very, very seriously. There\\'s a touching moment where he acknowledges that to her face. It\\'s very touching and sad, and I think that\\'s what it\\'s about. He\\'s really recognizing the next generation of what I once was.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'e4aa912de93daf59b03a39f9a31a4059'}>,\n",
-       "  <Document: {'content': 'It\\'s very touching and sad, and I think that\\'s what it\\'s about. He\\'s really recognizing the next generation of what I once was.\"In the conclusion of the \"Inside the Episode\" segment Weiss spoke about Arya\\'s story, saying \"Arya is in danger, she\\'s got an open wound in her stomach, and the one person who has protected her to this point unfortunately gets murdered. The fact that the Many-Faced God gets the people who have been promised to him really makes you think that there\\'s almost an inevitability about what happens to people who run afoul of the Faceless Men, as Arya has done.\"Weiss continued about the final part of the sequence, \"Arya\\'s telling Jaqen by putting the face on the wall that \\'this account is settled, and we\\'re good here, and now I\\'m going to walk away,\\' and I think she knows what the answer\\'s going to be. The implication, obviously, is that Jaqen was, on some level, rooting for the outcome that he got. He may be \"no one,\" but there\\'s still enough of a person left in him to respect, and admire who this girl is and what she\\'s become. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'cc260cdccd05e8e6bf5941fd9cdc7278'}>,\n",
-       "  <Document: {'content': 'He may be \"no one,\" but there\\'s still enough of a person left in him to respect, and admire who this girl is and what she\\'s become. Arya finally tells us something that we\\'ve kind of known all along, that she\\'s not no one, she\\'s Arya Stark of Winterfell.\"===Casting===\\nRichard Dormer (\\'\\'pictured\\'\\') and Paul Kaye returned to the series as Beric Dondarrion and Thoros of Myr, respectively.\\n\"No One\" saw the reintroduction of Richard Dormer and Paul Kaye, who portrayed Beric Dondarrion and Thoros of Myr, respectively, in the third season, last appearing in the episode \"The Bear and the Maiden Fair\", before returning for the sixth season. Dormer was previously asked about returning to \\'\\'Game of Thrones\\'\\' in an interview in February 2015, saying at the time that he was doubtful about coming back to the series, stating \"they haven\\'t told me about it, so I don\\'t think so.\"He also noted about his portrayal of the character, \"Beric was a noble character, a leader of men. Kind of like Robin Hood. So I thought, that\\'s how I\\'ll play the guy.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '4bb670e316e0f838e71ddc1f235ecd6a'}>,\n",
-       "  <Document: {'content': 'He also noted about his portrayal of the character, \"Beric was a noble character, a leader of men. Kind of like Robin Hood. So I thought, that\\'s how I\\'ll play the guy.\"Paul Kaye\\'s participation in the series was confirmed in mid-April 2016, with his agency posting about the casting shortly before the premiere of the sixth season.\\n\\nThe episode also featured the deaths of several recurring characters in the series. Faye Marsay, who portrayed the Waif, spoke about her participation in the episode, and leaving \\'\\'Game of Thrones\\'\\' after several seasons, saying \"As much as I\\'m sad that I\\'m now off of the show, I think that storyline needed to be concluded that way. I\\'m rooting for Arya and Maisie as well. Even though I won\\'t be able to hang out with everyone again, I think it was done right and done properly. I think Arya deserved to rip off her face and stick it on a wall.\"Recurring guest actors Clive Russell as Blackfish, and Essie Davis as Lady Crane portrayed their characters for the last time in the series, as they were killed off on screen.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '61cd164e98ee05fbe643298d4412dfb9'}>,\n",
-       "  <Document: {'content': 'Recurring guest actors Clive Russell as Blackfish, and Essie Davis as Lady Crane portrayed their characters for the last time in the series, as they were killed off on screen.\\n\\nIn the first scene featuring Sandor \"the Hound\" Clegane, comedian Steve Love was cast as one of the Brotherhood members killed by the Hound. Love previously had become known for his impressions of various \\'\\'Game of Thrones\\'\\' characters, uploading the video to YouTube, which ultimately resulted in his appearing on \\'\\'Jimmy Kimmel Live!\\'\\'. Following the appearance, Love stated that he had received an email from Benioff offering him a part in the show, saying \"I broached the subject by sending them an email back saying \\'Listen I don\\'t know what your plans are for me but it would really mean a lot to me if I could have one of those trademark \\'\\'Game of Thrones\\'\\' gruesome death scenes, and they wrote back saying \\'Actually Steve, that was exactly what we had in mind for you.\\'\"===Filming===\\nFilming of the exterior chase scenes throughout Braavos took place in Girona, Spain.\\n\"No One\" was directed by Mark Mylod. Mylod previously directed the fifth season episodes \"High Sparrow\" and \"Sons of the Harpy\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '608d16084551f64d5ba6847032a4a887'}>,\n",
-       "  <Document: {'content': '\"No One\" was directed by Mark Mylod. Mylod previously directed the fifth season episodes \"High Sparrow\" and \"Sons of the Harpy\". Mylod also directed the previous episode, \"The Broken Man\", for the sixth season. Mylod stated in an interview that the most physically challenging scene to direct was the chase scene, while the scene he was most scared to shoot was the conversation between Jaime and Edmure, stating that he felt the writing was \"so freaking good that I was scared about fucking it up.\"Mylod additionally spoke about filming the final scenes of the episode, the foot chase between Arya and the Waif, in the behind the scenes featurette published by HBO after the airing of the episode. Mylod stated that he did extensive research in order to create the foot chase, saying \"I devoured every foot chase in the history of cinema, but trying to take from that something that would fit into the blueprint of the \\'\\'Game of Thrones\\'\\' visual style, which is not necessarily the same kind of kinetic, handheld feel that you find in many contemporary thrillers. It\\'s a much more classic frame.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '59f5c1b3b5113fc7b5e0175c9877d160'}>,\n",
-       "  <Document: {'content': 'Mylod stated that he did extensive research in order to create the foot chase, saying \"I devoured every foot chase in the history of cinema, but trying to take from that something that would fit into the blueprint of the \\'\\'Game of Thrones\\'\\' visual style, which is not necessarily the same kind of kinetic, handheld feel that you find in many contemporary thrillers. It\\'s a much more classic frame.\"He continued, \"Everything hinged from finding this very lovely, steep stairwell and thinking \\'Wouldn\\'t it be great if she leapt off that, and then tumbled down the stairs?\\'I had this image in my head that I wanted lots of oranges rolling down, not quite sure where that comes from, but it looked pretty.\"Mylod also stated in a separate interview that it was a conscious decision not to show the final fight between Arya and the Waif, given that it took place in the dark, noting \"when the character dies in the dark, unless we switch on night-vision goggles, we\\'re not going to be able to see that, and we\\'re not going to stick around to hear a scream in the dark anyway. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'e6bc7cd0ce3ae23dc19506bc16c18bae'}>,\n",
-       "  <Document: {'content': 'Mylod also stated in a separate interview that it was a conscious decision not to show the final fight between Arya and the Waif, given that it took place in the dark, noting \"when the character dies in the dark, unless we switch on night-vision goggles, we\\'re not going to be able to see that, and we\\'re not going to stick around to hear a scream in the dark anyway. It was actually a conscious choice in the writing and I think, for me, a very elegant storytelling beat as to get that reveal a few seconds later.\"In an interview, Maisie Williams described the thought process behind Arya\\'s scenes, as well as the process of filming saying \"We wanted people to think this could be the end, or the start of the end. Like maybe her wound is going to fester – like The Hound.\"She continued, \"There was this constant spectrum of conversation with Mark Mylod during the chase of about how petrified she needs to look, but also how safe she is. Arya\\'s been very lucky with the people she\\'s encountered so far. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'ecffd34599624110ee85f97fb59dc4d2'}>,\n",
-       "  <Document: {'content': 'She continued, \"There was this constant spectrum of conversation with Mark Mylod during the chase of about how petrified she needs to look, but also how safe she is. Arya\\'s been very lucky with the people she\\'s encountered so far. The whole time she was with The Hound she took a back seat because he was really good. So I wanted her to look like she was struggling.\"Williams also stated that she gave input on how the scene should take place, stating \"I didn\\'t want the chase stunt to be unnecessary or superhuman. I got on set and they were going to have Arya rolling around, and diving, and I was like, \"That looks amazing, but no.\"I\\'d be like, \"Why would she run over there? She\\'d just duck under here and just get out.\"It doesn\\'t look quite as cinematic, maybe, but they\\'ll have to find something else if they want cinematic. And I felt awful because the job of the stunt guys is to make everything look as crazy and cool as possible. But I know Arya now. In the beginning it was a lot of guesswork and now I\\'ve figured her out.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'a6457e9683c1a742ff405885962df1c6'}>,\n",
-       "  <Document: {'content': 'But I know Arya now. In the beginning it was a lot of guesswork and now I\\'ve figured her out.\"Faye Marsay, who portrayed the Waif, spoke about her scenes and filming with Williams, saying, \"I think we did a month in Belfast before we started shooting. We went into the stunt tent and lived there. It was choreographed like a dance. Maisie is so well coordinated and good at what she does. I\\'m a little bit less coordinated and easily pissed off. I would drop the stick down and go, \"I can\\'t do it!\"And she would encourage me. She\\'s so supportive. We did a month, and then the day before the cameras turned on, we would go through the moves a couple of times so we wouldn\\'t hurt each other. There were a few times we clipped each other. I remember getting her in the stomach really bad one time, and she nearly cut my ear off at one point. It was fun. It was a lot of fun, but we were exhausted as well.\"The castle face, and drawbridge were physically built, with the rest added in post-production with special effects.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '1a7a26a3486d12b0a7c4a982b8183ba3'}>,\n",
-       "  <Document: {'content': 'It was a lot of fun, but we were exhausted as well.\"The castle face, and drawbridge were physically built, with the rest added in post-production with special effects.\\nFilming of the Riverrun sequence began in October 2015, and led to some complaints by locals living in the area following the construction of a portion of the Tully castle. The shooting of the scenes took place in Corbet, County Down, Northern Ireland. Due to some concerns over the size of the structure, filming was unable to take place until local inspectors were able to determine if the production unit followed what had been agreed upon in the \"planning application.\"Construction of a portion of the castle began in September, with tents and wooden additions being added shortly after. In addition, the production unit also created an actual working drawbridge for the scenes, rather than relying on CGI to create the set piece. Visual effects were added to create the rest of the castle.\\n\\nClive Russell, who portrayed Blackfish, spoke about working with Gwendoline Christie as Brienne of Tarth, noting \"There\\'s something very curious, by the way, I\\'m probably about 6\\'6\" tall. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '32208598d0fe12d1822599c1fe9f7ccd'}>,\n",
-       "  <Document: {'content': 'Clive Russell, who portrayed Blackfish, spoke about working with Gwendoline Christie as Brienne of Tarth, noting \"There\\'s something very curious, by the way, I\\'m probably about 6\\'6\" tall. I think she\\'s about 6\\'3\", but all the time I worked with her, I felt like she was taller than me. Now I don\\'t really know what that has to do with it. It might be something to do with being confronted by a physically charismatic very tall woman, or maybe it\\'s that she\\'s caught the impressiveness of the character of that woman, that soldier. She\\'s also an extraordinarily hilarious woman. We had great fun together. It was great fun to play.\"Gwendoline Christie also spoke about filming with Nikolaj Coster-Waldau, who portrays Jaime Lannister, again, saying \"I was excited, It was really fun to be with Nikolaj again, and I thought the way of which their reunion happens is not really expected. I just loved that it was so formal, because within the confines of such formality, and having to negotiate with each other, there are so many other stories begging to be told in those moments. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '715bbec3b4bcf9c97647cdc4263608f0'}>,\n",
-       "  <Document: {'content': 'I just loved that it was so formal, because within the confines of such formality, and having to negotiate with each other, there are so many other stories begging to be told in those moments. There\\'s a slow process of creeping familiarity among two people who haven\\'t been together for a long time; it was allowed to build.\"Coster-Waldau also spoke about their first reunion scene, stating \"It\\'s a great scene. These characters are so much about holding their cards so close to their chest and they don\\'t want to reveal how they\\'re feeling. But we know there\\'s history between them, that this is more than two knights meeting. But they would never acknowledge that.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '3921e81a3ee52427c4094786fb7283b4'}>,\n",
-       "  <Document: {'content': 'But we know there\\'s history between them, that this is more than two knights meeting. But they would never acknowledge that.\"Mylod, spoke about the decision to show Blackfish\\'s death off screen, saying \"there was a lot of tonal discussion about this, and I think I can safely speak for the writers that the choice was a conscious one in the writing not to see his actual death, because the emotional focus was really about the final moments between him and Brienne -- two warriors, two samurais, facing each other -- and Brienne\\'s acknowledgment that this fellow samurai has chosen his moment, and this is his moment of dignity and choice of his death. So it was really about that moment of choice and the dignity and acceptance between these two great warriors, rather than the actual detail of his actual death. That was actually a tonal choice as to how we saw that character.\"==Reception==\\n===Ratings===\\n\"No One\" was viewed by 7.60 million American households on its initial viewing on HBO, which was slightly less than the previous week\\'s rating of 7.80 million viewers for the episode \"The Broken Man\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': '999699120efa16322198b13a7e741804'}>,\n",
-       "  <Document: {'content': '==Reception==\\n===Ratings===\\n\"No One\" was viewed by 7.60 million American households on its initial viewing on HBO, which was slightly less than the previous week\\'s rating of 7.80 million viewers for the episode \"The Broken Man\". The episode also acquired a 3.9 rating in the 18–49 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was viewed by 2.436 million viewers on Sky Atlantic, making it the highest-rated broadcast that week on its channel. It also received 0.113 million timeshift viewers.\\n\\n===Critical reception===\\n\"No One\" was received mostly positively by critics, who listed the conclusion of Arya\\'s story with the Faceless Men, the reintroduction of the Brotherhood Without Banners, and Jaime\\'s scheme to retake Riverrun as high points of the episode, but some critics described sequences in the episode as being anticlimactic. It has received an 85% rating on the review aggregator website Rotten Tomatoes from 47 reviews with an average score of 7.2/10. The site\\'s consensus reads \"\"No One\" sees some fan favorites back in action and moves the final few pieces into place for a momentous – and long-awaited – battle.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': '191a39f5b9a2deac301e5139e2c1ec87'}>,\n",
-       "  <Document: {'content': 'The site\\'s consensus reads \"\"No One\" sees some fan favorites back in action and moves the final few pieces into place for a momentous – and long-awaited – battle.\"Matt Fowler of IGN wrote in his review for the episode, \"\"No One\" may have given us a bloodless siege, but there were plenty of bloody moments, namely from the brothers Clegane, who were both awesomely brutal this week. Though not against one another, as it looks like, for now, the Cleganebowl\\'s been canceled. I loved the end of Arya\\'s arc along with Jaime\\'s scheme to take Riverrun - though I\\'m curious to see what comes of the Tully angle. Brienne failed to get an army for Sansa and Jaime\\'s just going to head back home now? It seems like there should be more to this that him just winning one for the Lannister name. Though regardless of what the payoff might be, it was still good to see how so much of this story tied back to the first three seasons and Jaime\\'s time as Catelyn\\'s prisoner.\"He gave the episode an 8.2 out of 10. Myles McNutt of \\'\\'The A.V. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': 'e154a433c1e64043941ff7d8ddc8d604'}>,\n",
-       "  <Document: {'content': 'Though regardless of what the payoff might be, it was still good to see how so much of this story tied back to the first three seasons and Jaime\\'s time as Catelyn\\'s prisoner.\"He gave the episode an 8.2 out of 10. Myles McNutt of \\'\\'The A.V. Club\\'\\' criticized the episode\\'s momentum within the season, writing \"The lack of momentum driving these stories is surprising to me given that the season has largely been doing a fine job with internal momentum.\"McNutt continued, \"The show may have run into an unfortunate convergence of so many anti-climactic storylines in a single episode, but their very existence is a necessary byproduct of a narrative that is preparing to shed its skin and move forward with a new lease on life.\"Despite this, he gave the episode a grade of B-. Erik Kain of \\'\\'Forbes\\'\\' wrote about the episode, \"Not the greatest episode of the season, but I still didn\\'t want it to end. Sometimes I realize that even an episode I have lots of complaints about manages to be some of the best television out there. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': 'f94b8ecb1c0d57c5ebe8a63e3e1b0c28'}>,\n",
-       "  <Document: {'content': 'Sometimes I realize that even an episode I have lots of complaints about manages to be some of the best television out there. \\'\\'Game of Thrones\\'\\' isn\\'t always on the money, but even an off night can be surprisingly good.\"James Hibberd of \\'\\'Entertainment Weekly\\'\\' summarized the episode in his review by saying \"Cheers, chills, and the sound of online fan theories imploding. \"No One\" delivered Jaime Lannister\\'s finest scene in years, a thrilling chase, and set one Stark on an exciting new path.\"Jeremy Egner of \\'\\'The New York Times\\'\\' criticized the episode in his review, writing, \"It\\'s just not all that satisfying. After endless weeks of stick-beating and the events of last week, it all seemed a little too tidy,\" and additionally called Arya\\'s story \"anticlimactic.\"However, Egner also praised the episode\\'s continuation with the King\\'s Landing story, as well as the follow up with Sandor Clegane and the Brotherhood Without Banners. \\'\\'TV Guide\\'\\'s Damian Holbrook criticized as implausible the fact that Arya, after being stabbed multiple times in the previous episode by the Waif, was able to outrun and kill her faster, stronger nemesis in this episode. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': 'def2b6f1ddefea690ce1e7ea000e6b74'}>,\n",
-       "  <Document: {'content': '\\'\\'TV Guide\\'\\'s Damian Holbrook criticized as implausible the fact that Arya, after being stabbed multiple times in the previous episode by the Waif, was able to outrun and kill her faster, stronger nemesis in this episode. Hanh Nguyen of Indiewire also felt the Riverrun sequence ended in an anticlimactic fashion as well.\\n\\n===Accolades===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nMaisie Williams as Arya Stark\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron\\n\\n2017\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley\\n\\n==References==\\n\\n==External links==\\n\\n*  \"No One\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': 'b0f60389984113044ee451bd2a9b1fc6'}>,\n",
-       "  <Document: {'content': '\\n\\'\\'\\'\\'\\'For the Throne: Music Inspired by the HBO Series Game of Thrones\\'\\'\\'\\'\\' is a soundtrack inspired by the television series \\'\\'Game of Thrones\\'\\', released by Columbia Records on April 26, 2019. The album features the SZA, The Weeknd and Travis Scott collaborative single, \"Power Is Power\", which was released on April 18, 2019, as well as the two promotional singles, \"Kingdom of One\" by Maren Morris and \"Nightshade\" by The Lumineers, which were released simultaneously on April 12, 2019.\\n\\n==Reception==\\n\\nAllMusic\\'s Stephen Thomas Erlewine rated the album 2.5 out of 5 stars.\\n\\n==Track listing==\\nCredits adapted from Tidal.\\n\\n\\'\\'\\'Notes\\'\\'\\'\\n*  signifies a co-producer\\n*  signifies a miscellaneous producer\\n* \"Nightshade\" features background vocals by Lauren Jacobsen\\n* \"Turn on Me\" features background vocals by Arone Dyer and Kyle Resnick\\n\\n==Charts==\\n\\nChart (2019)\\n\\nPeakposition\\n\\nAustralian Digital Albums (ARIA)\\n\\n23\\n\\n==See also==\\n* Music of \\'\\'Game of Thrones\\'\\'\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '455fa4e148d221665b8e91e4eff8c3fc'}>,\n",
-       "  <Document: {'content': '\\n\\nThe soundtrack album of the seventh season of HBO series \\'\\'Game of Thrones\\'\\', titled \\'\\'\\'\\'\\'Game of Thrones: Season 7\\'\\'\\'\\'\\', was released digitally on August 25, 2017 on CD on September 29, 2017.\\n\\n==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* D.B. Weiss – liner notes\\n\\n==Charts==\\n\\nChart (2017)\\n\\nPeak position\\n\\nNew Zealand Heatseekers Albums (RMNZ)\\n\\n4\\n\\nSpanish Albums (PROMUSICAE)\\n\\n85\\n\\n==Awards and nominations==\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2018\\n\\n60th Annual Grammy Awards\\n\\nBest Score Soundtrack for Visual Media\\n\\nRamin Djawadi\\n\\n70th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Music Composition for a Series\\n\\nEpisode: \"The Dragon and the Wolf\"\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'a357d25e0d539ec9d52454586340c73b'}>,\n",
-       "  <Document: {'content': \"\\n\\nThe title sequence of the HBO fantasy television series ''Game of Thrones'' that introduces every episode serves as a guide to the physical landscape of the world of the series. It changes depending on the locations visited in the particular episode it introduces.  The title sequence was created by Elastic for HBO, and is accompanied by a theme tune composed by Ramin Djawadi.\\n\\nThe sequence depicts a three-dimensional map of the series' fictional world, projected onto a concave earth, and lit by a small sun contained within an armilla (or spherical astrolabe) that metaphorically depicts major events in the history of the fictional world at the sphere's center. As the camera swoops across the map and focuses on the locations in which the episode's events take place, complicated clockwork mechanisms cause buildings and other structures to emerge from the map and unfold. Meanwhile, the names of the principal cast (with the symbols of the characters' families next to the names) and creative staff are displayed. The sequence concludes after about one-and-a-half minutes with the title card and brief opening credits indicating the episode's writers and directors.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '1eda7e619e4122ce8ece54663206274e'}>,\n",
-       "  <Document: {'content': \"The sequence concludes after about one-and-a-half minutes with the title card and brief opening credits indicating the episode's writers and directors.\\n\\nCreative director Angus Wall, art director Robert Feng, animator Kirk Shintani and designer Hameed Shaukat received the 2011 Primetime Emmy Award for Outstanding Main Title Design for their work on the sequence. The title sequence has been ranked one of the best TV title sequences of all time.\\n\\n==Description==\\nThe title sequence consists of a three-dimensional map of the world, with the continents of Westeros and Essos located on the inner surface of a sphere, which is rendered in the style of a fantasy role-playing map used as a game board by participants for their battle plans. The maps used are those of Westeros and Essos that precede the novels in the book series. At the center of the sphere is a heliocentric armillary sphere. The title sequence serves as a guide to the physical landscape of the world of ''Game of Thrones'', and details of the title sequence change each week depending on the locations visited, and new locations may be added in each season.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '3d6dee7567c7e62be320cd7b7fd303ae'}>,\n",
-       "  <Document: {'content': \"The title sequence serves as a guide to the physical landscape of the world of ''Game of Thrones'', and details of the title sequence change each week depending on the locations visited, and new locations may be added in each season.\\n\\nThe sequence of every episode opens with a close-up of the sun and the sphere surrounding it. Relief depictions of the fictional's world's history are visible on the sphere, such as the Doom of Valyria, Aegon's Conquest and the rise of House Baratheon, which appear at varying points in the sequence.  The camera then pans to different parts of the map, on which different locations in the fictional world are shown.  Many of the cities and buildings on these locations appear out of the ground using clockwork mechanisms. Other elements, such as the weirwood tree at Winterfell and the Horse Gate at Vaes Dothrak, are also added at the various locations. The locations shown vary depending on the locations visited in that particular episode, and three or four variations of the title sequence are shown in each season.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '2f2487ca34b95d6660bb731a1d147e03'}>,\n",
-       "  <Document: {'content': \"The locations shown vary depending on the locations visited in that particular episode, and three or four variations of the title sequence are shown in each season.  However, due to the limitation on time for the title sequence, no more than six locations may be shown in any episode. Also, because of their importance in the show, every episode features King's Landing, Winterfell, The Wall, and wherever Daenerys may be regardless of whether or not any of these locations is featured in that episode.\\n\\nThe sigils of the reigning families are added to each location; for example, the Baratheon stag sigil on King's Landing and the Stark dire wolf at Winterfell. The names of the cast are also shown together with the corresponding sigil of the character they portray. The sigil may change depending on the storyline, for example the flayed man sigil is displayed over Winterfell when it is taken by the Boltons, but it reverts to the dire wolf sigil after it is recaptured by the Starks. Other events in the show are also reflected in the title sequence. For example, after Winterfell is burned, smoke is depicted billowing out from it. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '12a6c15da9508d4ddbab9bfb286efdfc'}>,\n",
-       "  <Document: {'content': \"Other events in the show are also reflected in the title sequence. For example, after Winterfell is burned, smoke is depicted billowing out from it. The ''Game of Thrones'' logo appears over the armilla at the end.\\n\\nHBO released an interactive 360-degree video of the title sequence in season 6.\\n\\nWhile the locations featured change from episode to episode, the general design and route of the sequence remain roughly similar. However, in the final season, the title sequence received a major revamp to denote the change in season and shift in storyline. In this new version, the first location shown is the breached wall instead of King's Landing, and moves down to the cities south of the wall following the path of the army of the dead. The interiors of two major locations are shown for the first time: the crypts of Winterfell and the Red Keep at King's Landing, as well as the latter's throne room. The appearance of the armilla and the events depicted on it also change to reflect event of the season 7 finale, such as the fall of the Wall.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '1c1f3c313403065a4abc0624b500736e'}>,\n",
-       "  <Document: {'content': \"The appearance of the armilla and the events depicted on it also change to reflect event of the season 7 finale, such as the fall of the Wall.\\n\\n==Conception and production==\\nThe title sequence was created by three teams: its design, which forms the bulk of the project, was done by Elastic, the computer graphics by a52, and the title sequence editing by Rock Paper Scissors. Elastic had previously created the title sequences for ''Rome'', ''Big Love'', and ''Carnivale'' for HBO, and they were approached by Carolyn Strauss of HBO, with whom they had worked on these shows, to create the title sequence for ''Game of Thrones''.  Angus Wall, the head of title design firm Elastic, met with Strauss, the showrunners David Benioff and Dan Weiss, and producer Greg Spence to discuss the project around a year before the show debut.\\n\\nWhen the pilot was first made, it was felt that the geography of the imaginary world of Westeros and Essos might confuse the viewers, and that maps could be useful as navigational guides for the viewers. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'e29772576e56fb6290b2d36c33690f81'}>,\n",
-       "  <Document: {'content': 'When the pilot was first made, it was felt that the geography of the imaginary world of Westeros and Essos might confuse the viewers, and that maps could be useful as navigational guides for the viewers. An early suggestion was to use animated maps as a transition between scenes to orient viewers, but that idea was dropped as it would interrupt the narrative flow. The idea of the map was therefore moved into the title sequence.  For the original pilot, the showrunners Benioff and Weiss initially wrote the title sequence as a crow\\'s flight from King\\'s Landing to Winterfell;  however, the production team at Elastic thought the idea was too flat and devised the idea of using 3-D models within a sphere that represents the world of the show. The sphere was used to obviate the question of what might lie beyond the horizon of the map, and the whole sphere is lit by the sun in the middle. According to Angus Wall, the title sequence had \"a concrete function in the world of the show, in that it serves as a legend the way the map at the beginning of a fantasy book orients you.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '8d3373db4fc3bafe33bb250df2ff851b'}>,\n",
-       "  <Document: {'content': 'According to Angus Wall, the title sequence had \"a concrete function in the world of the show, in that it serves as a legend the way the map at the beginning of a fantasy book orients you.\"The title sequence informs the viewers of the locations of the show relevant to each episode, and changes to reflect the storylines of the show and changes within this world.\\n\\nOn the use of an armillary and models with moving parts, producer Greg Spence explained that Angus Wall at Elastic came up with \"a vision of a mad monk, in a tower somewhere,\" who was somehow keeping track of all this action \"and creating as he went. He would then fashion little automatons out of the materials that would be available in his world. They would be stone, or tin, or wood, and everything would feel very hand-crafted.\"The idea is, therefore, that everything in the title sequence could be created with hammer, saw, and chisel, and operated with gears and cogs. The turning gears and cogs were meant to be reminiscent of Leonardo da Vinci\\'s inventions.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'd2593a65f16235a960027fe30012ba5a'}>,\n",
-       "  <Document: {'content': 'The idea is, therefore, that everything in the title sequence could be created with hammer, saw, and chisel, and operated with gears and cogs. The turning gears and cogs were meant to be reminiscent of Leonardo da Vinci\\'s inventions.\\n\\nThe design team were given a list of the locations where the action may take place in each episode after the shooting had completed, and they had around 3 months to create title sequences. The concepts, including details such as the machinery used, were first sketched by hand, and the models were then created with computer graphics.\\n\\n==\\'\\'Game of Thrones\\'\\' theme==\\n\\nThe theme music that accompanies the title sequence was composed by Ramin Djawadi. The production team showed the title sequence they were working on to Djawadi, who was then inspired to create the music for the \"\\'\\'Game of Thrones\\'\\' Theme\" and finished the theme music three days later. Djawadi said the showrunners Benioff and Weiss wanted the theme music to be about a journey that reflects the variety of locations and characters in the show.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'ff7402efad573e27661e1de7319ed287'}>,\n",
-       "  <Document: {'content': \"Djawadi said the showrunners Benioff and Weiss wanted the theme music to be about a journey that reflects the variety of locations and characters in the show.\\n\\n==List of elements==\\nThe following table lists the locations shown in each episode's title sequence, in the order of their appearance in the sequence as determined by the first episode in which they are shown. The locations King's Landing, Winterfell, and The Wall are featured in every title sequence, as well as the most recent location in Essos (in the first 6 seasons), even if they are not present in that particular episode. It is also noteworthy that the appearance of Winterfell has changed over the seasons: in Seasons 3 and 4 Winterfell was covered in smoke, referencing its burning at the end of Season 2, and in Season 5 the smoke was removed but the Bolton sigil replaced the Stark sigil, reflecting how the Boltons moved to Winterfell during that season. In episode 6.10, the Stark sigil returned, the result of Jon Snow and Sansa Stark's reclaiming Winterfell from the Boltons at the end of the previous episode.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '153133224a368436e12a23cf7360d78a'}>,\n",
-       "  <Document: {'content': 'In episode 6.10, the Stark sigil returned, the result of Jon Snow and Sansa Stark\\'s reclaiming Winterfell from the Boltons at the end of the previous episode.\\n\\n===Summary===\\n\\nS. 1\\n\\nS. 2\\n\\nS. 3\\n\\nS. 4\\n\\nS. 5\\n\\nS. 6\\n\\nS. 7\\n\\nS. 8\\n\\nTotal Count\\n\\n\\'\\'\\'Locations\\'\\'\\'\\n\\nKing\\'s Landing\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nWinterfell\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nThe Wall\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nMeereen\\n\\n10\\n\\n10\\n\\n10\\n\\n30\\n\\nBraavos\\n\\n5\\n\\n9\\n\\n7\\n\\n21\\n\\nDragonstone\\n\\n3\\n\\n4\\n\\n5\\n\\n7\\n\\n19\\n\\nPyke\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nVaes Dothrak\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nHarrenhal\\n\\n7\\n\\n8\\n\\n15\\n\\nRiverrun\\n\\n6\\n\\n3\\n\\n9\\n\\nDorne\\n\\n7\\n\\n2\\n\\n9\\n\\nThe Eyrie\\n\\n4\\n\\n2\\n\\n1\\n\\n7\\n\\nThe Dreadfort\\n\\n7\\n\\n7\\n\\nQarth\\n\\n7\\n\\n7\\n\\nOldtown\\n\\n7\\n\\n7\\n\\nYunkai\\n\\n6\\n\\n6\\n\\nLast Hearth\\n\\n6\\n\\n6\\n\\nThe Twins\\n\\n1\\n\\n2\\n\\n1\\n\\n1\\n\\n5\\n\\nMoat Cailin\\n\\n3\\n\\n1\\n\\n4\\n\\nAstapor\\n\\n4\\n\\n4\\n\\nEastwatch\\n\\n3\\n\\n3\\n\\nPentos\\n\\n1\\n\\n1\\n\\n2\\n\\n==Homage==\\n\\'\\'The Simpsons\\'\\' episode \"Exit Through the Kwik-E-Mart\" features a homage to the \\'\\'Game of Thrones\\'\\' title sequence, with famous buildings in the town of Springfield rising through the ground as characters watch on, dressed in \\'\\'Game of Thrones\\'\\'-style costumes. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '4a9ee1b73895ad98e7f84395d99d0908'}>,\n",
-       "  <Document: {'content': '===Summary===\\n\\nS. 1\\n\\nS. 2\\n\\nS. 3\\n\\nS. 4\\n\\nS. 5\\n\\nS. 6\\n\\nS. 7\\n\\nS. 8\\n\\nTotal Count\\n\\n\\'\\'\\'Locations\\'\\'\\'\\n\\nKing\\'s Landing\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nWinterfell\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nThe Wall\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nMeereen\\n\\n10\\n\\n10\\n\\n10\\n\\n30\\n\\nBraavos\\n\\n5\\n\\n9\\n\\n7\\n\\n21\\n\\nDragonstone\\n\\n3\\n\\n4\\n\\n5\\n\\n7\\n\\n19\\n\\nPyke\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nVaes Dothrak\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nHarrenhal\\n\\n7\\n\\n8\\n\\n15\\n\\nRiverrun\\n\\n6\\n\\n3\\n\\n9\\n\\nDorne\\n\\n7\\n\\n2\\n\\n9\\n\\nThe Eyrie\\n\\n4\\n\\n2\\n\\n1\\n\\n7\\n\\nThe Dreadfort\\n\\n7\\n\\n7\\n\\nQarth\\n\\n7\\n\\n7\\n\\nOldtown\\n\\n7\\n\\n7\\n\\nYunkai\\n\\n6\\n\\n6\\n\\nLast Hearth\\n\\n6\\n\\n6\\n\\nThe Twins\\n\\n1\\n\\n2\\n\\n1\\n\\n1\\n\\n5\\n\\nMoat Cailin\\n\\n3\\n\\n1\\n\\n4\\n\\nAstapor\\n\\n4\\n\\n4\\n\\nEastwatch\\n\\n3\\n\\n3\\n\\nPentos\\n\\n1\\n\\n1\\n\\n2\\n\\n==Homage==\\n\\'\\'The Simpsons\\'\\' episode \"Exit Through the Kwik-E-Mart\" features a homage to the \\'\\'Game of Thrones\\'\\' title sequence, with famous buildings in the town of Springfield rising through the ground as characters watch on, dressed in \\'\\'Game of Thrones\\'\\'-style costumes. The Wall is replaced by the monolithic \"Couch\" at the end of the sequence.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'bff802af02ff186a131ecee9f8af32c5'}>,\n",
-       "  <Document: {'content': '===Summary===\\n\\nS. 1\\n\\nS. 2\\n\\nS. 3\\n\\nS. 4\\n\\nS. 5\\n\\nS. 6\\n\\nS. 7\\n\\nS. 8\\n\\nTotal Count\\n\\n\\'\\'\\'Locations\\'\\'\\'\\n\\nKing\\'s Landing\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nWinterfell\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nThe Wall\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n10\\n\\n7\\n\\n6\\n\\n73\\n\\nMeereen\\n\\n10\\n\\n10\\n\\n10\\n\\n30\\n\\nBraavos\\n\\n5\\n\\n9\\n\\n7\\n\\n21\\n\\nDragonstone\\n\\n3\\n\\n4\\n\\n5\\n\\n7\\n\\n19\\n\\nPyke\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nVaes Dothrak\\n\\n9\\n\\n3\\n\\n3\\n\\n15\\n\\nHarrenhal\\n\\n7\\n\\n8\\n\\n15\\n\\nRiverrun\\n\\n6\\n\\n3\\n\\n9\\n\\nDorne\\n\\n7\\n\\n2\\n\\n9\\n\\nThe Eyrie\\n\\n4\\n\\n2\\n\\n1\\n\\n7\\n\\nThe Dreadfort\\n\\n7\\n\\n7\\n\\nQarth\\n\\n7\\n\\n7\\n\\nOldtown\\n\\n7\\n\\n7\\n\\nYunkai\\n\\n6\\n\\n6\\n\\nLast Hearth\\n\\n6\\n\\n6\\n\\nThe Twins\\n\\n1\\n\\n2\\n\\n1\\n\\n1\\n\\n5\\n\\nMoat Cailin\\n\\n3\\n\\n1\\n\\n4\\n\\nAstapor\\n\\n4\\n\\n4\\n\\nEastwatch\\n\\n3\\n\\n3\\n\\nPentos\\n\\n1\\n\\n1\\n\\n2\\n\\n==Homage==\\n\\'\\'The Simpsons\\'\\' episode \"Exit Through the Kwik-E-Mart\" features a homage to the \\'\\'Game of Thrones\\'\\' title sequence, with famous buildings in the town of Springfield rising through the ground as characters watch on, dressed in \\'\\'Game of Thrones\\'\\'-style costumes. The Wall is replaced by the monolithic \"Couch\" at the end of the sequence.  A version of the title sequence has also been recreated with Oreo cookies.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'c89565387052fff9c59472b422ad9ea5'}>,\n",
-       "  <Document: {'content': 'The Wall is replaced by the monolithic \"Couch\" at the end of the sequence.  A version of the title sequence has also been recreated with Oreo cookies.\\n\\n==Awards==\\nThe design team behind the title sequence, Angus Wall, art director Rob Feng, designer Hameed Shaukat, and C.G. supervisor Kirk Shintani, won a Creative Arts Emmy Award on September 10, 2011.\\n\\n=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2011\\n\\n63rd Primetime Creative Arts Emmy Awards\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Hameed Shaukat, Kirk Shintani and Robert Feng\\n\\n2016\\n\\n68th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Main Titles 360 Experience\\n\\n==References==\\n\\n== External links ==\\n* Perkins, Will; Albinson, Ian (June 29, 2011).  \"Game of Thrones (2011)\". Art of the Title. May 11, 2011.\\n* Appelo, Tim (April 19, 2011).  \"Secrets Behind \\'Game of Thrones\\' Opening Credits (Video)\". \\'\\'The Hollywood Reporter\\'\\'.', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '87ff72fefc766c030ce7be5f0b02b184'}>,\n",
-       "  <Document: {'content': '\\n\"\\'\\'\\'Power Is Power\\'\\'\\'\" is a song by American singer SZA, Canadian singer The Weeknd and American rapper Travis Scott. It was released on April 18, 2019, as a single from the \\'\\'Game of Thrones\\'\\' companion soundtrack, \\'\\'For the Throne\\'\\'. The song is the second of the two collaborative singles released by The Weeknd and Scott in 2019, being preceded by \"Wake Up\".\\n\\n==Background and release==\\nFollowing the release of the collaboration \"Wake Up\" by Travis Scott featuring The Weeknd, as the fourth single from Scott\\'s \\'\\'Astroworld\\'\\' on March 26, 2019. In early April of that year, rumours regarding the possibility of a \\'\\'Game of Thrones\\'\\' inspired track by SZA, The Weeknd, and Travis Scott began to circulate around the web. The artists\\' involvement on the \\'\\'For the Throne\\'\\' soundtrack was then later confirmed on April 8, via an announcement of the soundtrack by Columbia Records. Following the release of the promotional singles \"Kingdom of One\" by Maren Morris and \"Nightshade\" by The Lumineers on April 12, SZA then later shared the cover art and release date of the song, on her Instagram profile on April 16.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '6a962445cf0cb6f26890dff6704d2022'}>,\n",
-       "  <Document: {'content': 'Following the release of the promotional singles \"Kingdom of One\" by Maren Morris and \"Nightshade\" by The Lumineers on April 12, SZA then later shared the cover art and release date of the song, on her Instagram profile on April 16.\\n\\n==Lyrics==\\nThe title of the song is a line spoken by the character Cersei Lannister in the HBO TV series \\'\\'Game of Thrones\\'\\', the song\\'s lyric however revolve around the character Jon Snow. Writers at the time interpreted the lyrics as possibly foreshadowing the aforementioned character winning out the conclusion of the show.\\n\\n==Music video==\\nTeasement for the music video first began on April 20, 2019, the video features a medieval theme, reminiscent of its source show \\'\\'Game of Thrones\\'\\'. It was directed by Anthony Mandler and released on May 5, 2019.\\n\\n==Charts==\\n\\nChart (2019)\\n\\nPeakposition\\n\\nAustralia (ARIA)\\n\\n30\\n\\nChina Airplay/FL (\\'\\'Billboard\\'\\')\\n\\n22\\n\\nFrance Digital Tracks (SNEP)\\n\\n140\\n\\nIreland (IRMA)\\n\\n31\\n\\nNew Zealand Hot Singles (RMNZ)\\n\\n3\\n\\nNorway (VG-lista)\\n\\n33\\n\\nSweden (Sverigetopplistan)\\n\\n41\\n\\n==Release history==\\n\\nRegion\\n\\nDate\\n\\nFormat\\n\\nLabel(s)\\n\\nUnited States\\n\\nApril  18, 2019\\n\\nApril 23, 2019\\n\\nContemporary hit radio\\n\\nRhythmic contemporary\\n\\nItaly\\n\\nMay 3, 2019\\n\\nContemporary hit radio\\n\\nSony\\n\\n==References==\\n\\n==External links==\\n*\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'd5fa8511ac704de276cc348c3184cbd'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Bells\\'\\'\\'\" is the fifth and penultimate episode of the eighth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 72nd and penultimate episode of the series. It was written by David Benioff and D. B. Weiss, and directed by Miguel Sapochnik. It aired on May 12, 2019.\\n\\n\"The Bells\" features the final battle for control of the Iron Throne, with Daenerys Targaryen\\'s forces commencing their assault on Cersei Lannister\\'s forces at King\\'s Landing.\\n\\nThe episode received criticism from critics and audiences alike. Critics praised the episode as visually impressive and commended the acting and direction, but criticized the pacing and logic of the story, as well as its handling of the character arcs of Tyrion, Jaime, Cersei, Grey Worm, Varys, and particularly Daenerys. It received three Primetime Emmy Award nominations and was also picked by Lena Headey to support her nomination for Outstanding Supporting Actress in a Drama Series. It eventually was awarded the prizes for Outstanding Fantasy/Sci-Fi Costumes and Outstanding Special Visual Effects.\\n\\n==Plot==\\n===At Dragonstone===\\nVarys writes a letter about Jon Snow\\'s true heritage when a girl informs him that Daenerys Targaryen continues to refuse food. He tells her to \"try again at supper\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '8fc5971192395101d381df9216d208dd'}>,\n",
-       "  <Document: {'content': '==Plot==\\n===At Dragonstone===\\nVarys writes a letter about Jon Snow\\'s true heritage when a girl informs him that Daenerys Targaryen continues to refuse food. He tells her to \"try again at supper\". Varys implores Jon to take the Iron Throne, but Jon refuses to betray Daenerys. Tyrion Lannister informs Daenerys of Varys\\' plot; she executes Varys. When Daenerys says she does not have the people\\'s love in Westeros, Jon assures Daenerys he loves her. However, when he stops her kiss, Daenerys resigns herself to fear. Tyrion implores Daenerys to spare the common folk in King\\'s Landing if the city bells ring, and she nods toward Grey Worm. She informs Tyrion that Jaime Lannister has been captured on his way to King\\'s Landing and warns Tyrion not to fail her again.\\n\\n===At King\\'s Landing===\\nTyrion arrives at the Unsullied camp and frees Jaime so Jaime can take Cersei Lannister away from King\\'s Landing to Pentos. Jaime makes for the Red Keep.\\n\\nThe next day, Arya Stark and Sandor Clegane enter the Red Keep. The battle begins with Daenerys and Drogon burning Euron Greyjoy\\'s Iron Fleet, the Golden Company, and the city\\'s defenses. Grey Worm kills Harry Strickland. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'e6bb5a6d08b709df9476f750debef0cb'}>,\n",
-       "  <Document: {'content': \"The battle begins with Daenerys and Drogon burning Euron Greyjoy's Iron Fleet, the Golden Company, and the city's defenses. Grey Worm kills Harry Strickland. When Daenerys' army storms King's Landing, the remaining Lannister forces drop their weapons and the city surrenders. Daenerys refuses to accept the surrender and, atop Drogon, lays waste to the city, while her army, led by Grey Worm, massacre the unarmed Lannister soldiers. Jon unsuccessfully tries to stop his men from attacking, kills one who attempts rape, and eventually, he and Davos Seaworth have the rest fall back as the city burns.\\n\\nJaime fights Euron, who badly wounds him, but Jamie is able to mortally wound Euron, leaving him to die. Sandor convinces Arya to give up her desire for revenge against Cersei and he and Arya part ways as friends. As Cersei and Qyburn attempt to escape, Sandor arrives and kills the Queensguard in order to fight his brother, Gregor Clegane. Gregor kills Qyburn and Cersei flees. Gregor resists all attacks but when Sandor tackles him through a wall, the brothers fall to their deaths in the fire below. Jaime reunites with Cersei, but find their escape passage is blocked by rubble from the attack. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'f7f222e145f4dc8c377986442056e833'}>,\n",
-       "  <Document: {'content': \"Gregor resists all attacks but when Sandor tackles him through a wall, the brothers fall to their deaths in the fire below. Jaime reunites with Cersei, but find their escape passage is blocked by rubble from the attack. They resign themselves to their fate and embrace as the Keep's cellar ceiling collapses on them, killing them and their unborn child.\\n\\nArya experiences the chaos in King's Landing and tries to help the smallfolk she encounters but is knocked unconscious when a bell tower falls near her. When she wakes up, she discovers the burned corpses of the smallfolk on the street. She encounters a white horse coated in blood and rides it away.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by David Benioff and D. B. Weiss.\\n\\n=== Filming ===\\nThe episode was directed by Miguel Sapochnik. This was his final episode of the series.\\n\\nFor the filming of the episode, the city of Dubrovnik (the stand-in for King's Landing) was recreated on the backlot of their Belfast set. Filming Varys's death scene ultimately ended up taking seven months as rain kept postponing the shoot.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '58d862f5ee9c824dbc9c17f0a0f1462e'}>,\n",
-       "  <Document: {'content': 'For the filming of the episode, the city of Dubrovnik (the stand-in for King\\'s Landing) was recreated on the backlot of their Belfast set. Filming Varys\\'s death scene ultimately ended up taking seven months as rain kept postponing the shoot.\\n\\nIn a promotional photo of the scene in which Jaime and Cersei embrace at the Red Keep, his right hand is visible instead of the metallic prosthetic the character received in season 4. In the actual episode, only the metallic hand is seen. The error in the image garnered attention in light of the previous episode\\'s coffee cup mistake.\\n\\n===Casting===\\nLaura Elphinstone was cast as one of the inhabitants of King\\'s Landing who helped Arya Stark during the dragon attack on the city. The American football quarterback Aaron Rodgers appears in a cameo in this episode.\\n\\n== Reception ==\\n=== Ratings ===\\nThe episode was viewed by 12.48 million viewers on its initial live broadcast on HBO, surpassing \"The Dragon and the Wolf\" as the most-watched episode of the series. An additional 5.9 million viewers watched on streaming platforms, for a total of 18.4 million viewers.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'f6841f0213ff2f336d536cbd5b0767e6'}>,\n",
-       "  <Document: {'content': '== Reception ==\\n=== Ratings ===\\nThe episode was viewed by 12.48 million viewers on its initial live broadcast on HBO, surpassing \"The Dragon and the Wolf\" as the most-watched episode of the series. An additional 5.9 million viewers watched on streaming platforms, for a total of 18.4 million viewers.\\n\\n===Critical response===\\n====General====\\n\\nOn the review aggregator Rotten Tomatoes, the episode has an approval rating of 49% based on 107 reviews, and an average rating of 6.32/10, with the critical consensus stating, \"Death, destruction, and the deterioration of Daenerys\\' sanity make \\'The Bells\\' an episode for the ages; but too much plot in too little time muddles the story and may leave some viewers feeling its conclusions are unearned.\"It is the second lowest-rated episode on Rotten Tomatoes, behind \"The Iron Throne.\"Lenika Cruz of \\'\\'The Atlantic\\'\\' wrote that although she found the special effects stunning and the acting spectacular, this was \"the worst \\'\\'Game of Thrones\\'\\' episode ever\" because the plot was either too obvious or illogical, with the massacre seeming \"an unearned negation of the identity Daenerys had spent years building for herself\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '5edb5e76fb60bbfcfb25e02ed9fcf6d0'}>,\n",
-       "  <Document: {'content': 'Lenika Cruz of \\'\\'The Atlantic\\'\\' wrote that although she found the special effects stunning and the acting spectacular, this was \"the worst \\'\\'Game of Thrones\\'\\' episode ever\" because the plot was either too obvious or illogical, with the massacre seeming \"an unearned negation of the identity Daenerys had spent years building for herself\". Emily VanDerWerff of \\'\\'Vox\\'\\' found the episode grim and absent of sense, but credited director Miguel Sapochnik for \"the gorgeous visuals that extended beyond the battle scenes.\"VanDerWerff praised the acting of the cast such as Maisie Williams, Lena Headey, Peter Dinklage, and Emilia Clarke; the latter two made their characters\\' actions believable even when they were illogical. Alex McLevy of \\'\\'The A.V. Club\\'\\' wrote that the episode successfully demonstrated that \"the chaos of war makes villains and victims of us all\", with the \"progression from exhilarating hope to tragic denouement being skillfully executed by director Miguel Sapochnik\" with better large-scale choreography than in the \"The Long Night\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'c1103c4daa45c0e2577059f7e98adf96'}>,\n",
-       "  <Document: {'content': 'Club\\'\\' wrote that the episode successfully demonstrated that \"the chaos of war makes villains and victims of us all\", with the \"progression from exhilarating hope to tragic denouement being skillfully executed by director Miguel Sapochnik\" with better large-scale choreography than in the \"The Long Night\".\\n\\nHugh Montgomery of BBC Culture wrote that characters such as Tyrion, Jaime, Daenerys, and Cersei were \"definitively sacrificed to the show\\'s scrambled plotting\", and that \\'\\'Game of Thrones\\'\\' was previously \"a show that intelligently delineated a senseless world\", but that it was now nonsense. VanDerWerff stated that Cersei and Jaime\\'s deaths were not given much thought by the writers and were instead simply something to check off a list. Kelly Lawler of \\'\\'USA Today\\'\\', saying that the episode lacked substance and was an \"absolute disaster\", argued that Grey Worm, Daenerys and Varys took actions which were wildly out of character.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '37005f7bda29653b14f2cfa4238f648e'}>,\n",
-       "  <Document: {'content': 'Kelly Lawler of \\'\\'USA Today\\'\\', saying that the episode lacked substance and was an \"absolute disaster\", argued that Grey Worm, Daenerys and Varys took actions which were wildly out of character.\\n\\nAlan Sepinwall of \\'\\'Rolling Stone\\'\\' wrote that the episode\\'s \"technical genius\" and \"the visual clarity only made it easier to see how muddled the show has been, from both a narrative and character standpoint, in this home stretch\", with the scorpions turning from accurate to useless, Euron surviving Drogon to have a \"pointless fight with Jaime\", the setting of Cleganebowl being invulnerable while the castle fell, Jaime easily entering the gated Red Keep, and the appearance of \"seemingly hundreds of Dothraki\" despite most of them apparently dying in \\'The Long Night\\'. Lawler also said that the writers \"threw out their own rule book (suddenly the scorpions don\\'t work and Drogon can burn everything?)to pursue gross spectacle\".\\n\\nSeveral critics criticized the pacing of events; Lawler wrote that the pacing started off rushed, while Spencer Kornhaber of \\'\\'The Atlantic\\'\\' said that the show was \"sprinting through plot check marks\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'c6511ae05914710a0cbf5e999990a906'}>,\n",
-       "  <Document: {'content': 'Several critics criticized the pacing of events; Lawler wrote that the pacing started off rushed, while Spencer Kornhaber of \\'\\'The Atlantic\\'\\' said that the show was \"sprinting through plot check marks\". Will Bedingfield of \\'\\'Wired\\'\\' also felt that the show rushed storylines, seeing the death of Varys as being without the climactic buildup previous seasons had. Sarah Hughes of \\'\\'The Guardian\\'\\' attributed season eight\\'s pacing problems to season seven having had pacing issues that ensured that the rest of the series would feel \"breathless and rushed.\"====Daenerys\\'s arc====\\nCritics generally considered Daenerys Targaryen\\'s (Emilia Clarke) villainous turn in this episode unearned.\\nDaenerys\\'s turn from hero to villain was especially criticized, with many reviewers and fans feeling that, because she was built up as a morally good protagonist for seven seasons, it made no narrative sense, or that it was not executed properly within the provided timescale. Mike Hogan of \\'\\'Vanity Fair\\'\\' said that although the show had been clear that Daenerys has a temper, \"we have seen her balance that violence with mercy, kindness, and above all shrewdness.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'a5c0fc006c867debc2174ad32c6c23b7'}>,\n",
-       "  <Document: {'content': 'Mike Hogan of \\'\\'Vanity Fair\\'\\' said that although the show had been clear that Daenerys has a temper, \"we have seen her balance that violence with mercy, kindness, and above all shrewdness.\"Zack Beauchamp of \\'\\'Vox\\'\\' argued that Daenerys\\'s previous cruelties were somewhat logical because she killed people who \"committed crimes deserving of punishment\" and Samwell Tarly\\'s father and brother because they \"refused to submit to her rule,\" but that there was no reason to go after harmless civilians. He said although the show had been \"building toward Daenerys becoming the Mad Queen\" and this route could have worked, \"its execution was sloppy and rushed\" and \"it felt as if Daenerys had become a monster simply because the show needed her to become a monster, not because it was paying off a thoughtfully developed character turn.\"\\'\\'Slate\\'\\'\\'s Sam Adams said \"the show spent far more time making Dany a hero\" than building her up as the Mad Queen.\\n\\n\\'\\'The Atlantic\\'\\' staff opined, \"\\'\\'Game of Thrones\\'\\' could have easily demonstrated the nasty reality of Daenerys\\'s fight for Westeros without putting the choice to massacre innocents directly on her shoulders. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'fd29e403f9eaf0ff8b33873cabcb29ca'}>,\n",
-       "  <Document: {'content': '\\'\\'The Atlantic\\'\\' staff opined, \"\\'\\'Game of Thrones\\'\\' could have easily demonstrated the nasty reality of Daenerys\\'s fight for Westeros without putting the choice to massacre innocents directly on her shoulders. Instead, \\'The Bells\\' ended up painting one of the most pivotal plot points in the final season as an emotional lashing-out from a tired, lonely, paranoid young woman.\"Kathryn VanArendonk of \\'\\'Vulture\\'\\' also criticized the episode for \"ultimately hinging on a trope as painfully stale as \\'and then the scary powerful woman goes crazy\\'.\"Eliana Dockterman of \\'\\'Time\\'\\' criticized the analyses of Daenerys\\'s advisors as also playing \"into the terrible trope of the crazed, power-hungry woman\" and Tyrion\\'s advice as having kept Daenerys from taking the Iron Throne much earlier and without as much bloodshed. She said she understood the idea of power corrupting \"a heroic figure like Daenerys\" and Daenerys falling prey to a messiah complex, but that the show needed to evolve her to that point like it evolved other characters to their points. Alan Sepinwall of \\'\\'Rolling Stone\\'\\' stated that Daenerys\\'s descent into madness is the kind of development that requires \"at least another half of a regular-length GoT season to feel earned.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '8b14195a9ca1b7e48e1589c267d61e2a'}>,\n",
-       "  <Document: {'content': 'Alan Sepinwall of \\'\\'Rolling Stone\\'\\' stated that Daenerys\\'s descent into madness is the kind of development that requires \"at least another half of a regular-length GoT season to feel earned.\"Myles McNutt of \\'\\'The A.V. Club\\'\\' said that Daenerys\\'s final arc comes down to two aspects\\xa0– story development and character development. He said Daenerys burning down King\\'s Landing is a logical and effective way of ending the story on a thematic level, adding that how \"it reshapes the rest of the episode is a striking reframing of the violence that has defined the show.\"Character-wise, he felt that the choice was \"undeniably trickier\" because one might be led to believe that Daenerys\\'s choice was purely reflexive when it was instead due to \"a collection of life experiences that left her believing that ruling with fear was the only path ahead of her.\"He said \"the writers failed to create the necessary structure for the villainous turn\" and they could have better highlighted Daenerys\\'s destiny as a villain by referencing things such as her prophecy in Qarth. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '35aac4ce23167aec964eac239acff1a1'}>,\n",
-       "  <Document: {'content': 'He said \"the writers failed to create the necessary structure for the villainous turn\" and they could have better highlighted Daenerys\\'s destiny as a villain by referencing things such as her prophecy in Qarth. \\'\\'Variety\\'\\'\\'s Daniel D\\'Addario also argued that it made sense that Daenerys would burn the city because her \"tactics have always been more deeply rooted in dominance than in empathy.\"Arguing that the change in Daenerys\\'s arc has likely been planned by George R. R. Martin from the beginning, Vox\\'s Andrew Prokop stated, \"If \\'\\'Game of Thrones\\'\\' ended with a triumphant Daenerys Targaryen heroically taking the Iron Throne, it wouldn\\'t be \\'\\'Game of Thrones\\'\\'. This is the show of Ned Stark\\'s death. This is the show of the Red Wedding. This is the ending it was headed toward all along.\"Screen Rant\\'s Alexandra August felt that \"ultimately it comes down to your interpretation of Dany\\'s arc and how much that interpretation was diluted by pacing, execution and cultural context, if at all.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '839828d5cb0048cfa33f534445fc57b0'}>,\n",
-       "  <Document: {'content': 'Screen Rant\\'s Alexandra August felt that \"ultimately it comes down to your interpretation of Dany\\'s arc and how much that interpretation was diluted by pacing, execution and cultural context, if at all.\"She pondered different ways the show could have pulled off the arc better, such as Daenerys only choosing to kill Cersei, but concluded that there would eventually be someone else to push her over the edge regardless. Also stating that \"there are many ways to interpret\" Daenerys\\'s decision to burn the city, Megan Garber of \\'\\'The Atlantic\\'\\' said that maybe it was the Targaryen madness having settled in, or her ruthlessness taking over, or a \"crazy edit\" by the writers, or a decision \"that some innocents must die in the present so that many more can live peacefully in the future,\" or maybe that Daenerys \"having recently lost her second dragon and the apparent loyalty of those left in her orbit, she simply made a blunt calculation about power and what will be required to attain it.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '28eb61fd0a5d61efe71d295d8f669146'}>,\n",
-       "  <Document: {'content': 'Also stating that \"there are many ways to interpret\" Daenerys\\'s decision to burn the city, Megan Garber of \\'\\'The Atlantic\\'\\' said that maybe it was the Targaryen madness having settled in, or her ruthlessness taking over, or a \"crazy edit\" by the writers, or a decision \"that some innocents must die in the present so that many more can live peacefully in the future,\" or maybe that Daenerys \"having recently lost her second dragon and the apparent loyalty of those left in her orbit, she simply made a blunt calculation about power and what will be required to attain it.\"Garber said that \"ambiguity is a powerful tool in storytelling,\" but that \"it is also a difficult one to wield well,\" and that \"Dany is a savior, and Dany is a monster, and it is impossible to know where one ends and the other begins.\"To Garber, this uncertainty made Daenerys\\'s actions all the more horrific because logic will not always win and \"justice won\\'t always save the day.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'eb7e9b331c2a1e7f96dea6ddd03e8da4'}>,\n",
-       "  <Document: {'content': 'To Garber, this uncertainty made Daenerys\\'s actions all the more horrific because logic will not always win and \"justice won\\'t always save the day.\"Showrunners David Benioff and D. B. Weiss attributed Daenerys\\'s decision to burn the city to having lost almost all of her friends and advisors, no longer trusting Jon Snow, and wanting to reclaim the home that her family built. Benioff said that Jon also being unable to return her affections because they are related played a factor. Weiss stated, \"I think that when she says, \\'Let it be fear,\\' she\\'s resigning herself to the fact that she may have to get things done in a way that isn\\'t pleasant.\"Benioff said that the ruthless side to Daenerys had always been there and that \"if Cersei hadn\\'t betrayed her, if Cersei hadn\\'t executed Missandei, if Jon hadn\\'t told her the truth ... if all of these things had happened in any different way, then I don\\'t think we\\'d be seeing this side of Daenerys Targaryen.\"Emilia Clarke said, \"Every single thing that\\'s led her to this point, and there she is, alone.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '6f62fbd8037e398d407605328036eccf'}>,\n",
-       "  <Document: {'content': 'Benioff said that the ruthless side to Daenerys had always been there and that \"if Cersei hadn\\'t betrayed her, if Cersei hadn\\'t executed Missandei, if Jon hadn\\'t told her the truth ... if all of these things had happened in any different way, then I don\\'t think we\\'d be seeing this side of Daenerys Targaryen.\"Emilia Clarke said, \"Every single thing that\\'s led her to this point, and there she is, alone.\"VanArendonk expressed disappointment that Weiss and Benioff explained Daenerys\\'s actions as due to her emotions, instead of some \"twisted, empathetic logic\". Emma Baty of \\'\\'Cosmopolitan\\'\\' also criticized the showrunners\\' response, stating, \"Totally, totally. A woman is definitely prone to ending thousands of innocent lives just because her dad did the same, even though in the entire series, she\\'s been insistent on not becoming that person. That checks out!\"===Audience response===\\n\\'\\'People\\'\\' magazine\\'s Aurelie Corinthios said, \"Despite the major plot developments, viewers were left wanting more from a series that has spent the last eight seasons meticulously building up complex characters.\"She added that \"others defended the episode, arguing that while the action was definitely \\'rushed,\\' the main elements still made sense. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'ef357233fb73fa0d3396b9911577e2db'}>,\n",
-       "  <Document: {'content': 'She added that \"others defended the episode, arguing that while the action was definitely \\'rushed,\\' the main elements still made sense. Plus, didn\\'t we always know we wouldn\\'t get a happy ending?\"Lauren Hill of \\'\\'Chicago Tribune\\'\\' relayed that the episode \"had fans very divided. Some are upset by the character arcs of certain fan favorites while others saw this coming for quite some time.\"Reviewers made note of the negative fan reactions, especially to Daenerys\\'s arc. Estelle Tang of \\'\\'Elle\\'\\' stated that many \"commented how unbelievable it was for Dany to turn into a violent vengeance-seeker this far into the show\\'s run.\"\\'\\'Esquire\\'\\'\\'s Gabrielle Bruney said viewers felt that the writers threw out her character development. \\'\\'The Hollywood Reporter\\'\\' staff stated that \"many \\'\\'Game of Thrones\\'\\' viewers see Daenerys\\' acts as nothing short of a character assassination, laying blame for the treatment of the Dragon Queen, Cersei and the series\\' other powerful women at the feet of creators David Benioff and Dan Weiss.\"Mehera Bonner of \\'\\'Cosmopolitan\\'\\' wrote that \"fans are absolutely devastated\" by the episode and that they bemoaned \"how the show has been completely ruined by sloppy writing and terrible decisions.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '7e51d840ebfb5756ccd355d34dc7c852'}>,\n",
-       "  <Document: {'content': 'Mehera Bonner of \\'\\'Cosmopolitan\\'\\' wrote that \"fans are absolutely devastated\" by the episode and that they bemoaned \"how the show has been completely ruined by sloppy writing and terrible decisions.\"She said, \"While you could argue that we should have seen a lot of this coming, many fans were... I don\\'t know... just hoping for better? Better than Daenerys becoming a Mad Queen just because it\\'s in her family history and better than Jaime reverting to his season 1 personality just because it’s the easy way out.\"A petition to HBO for a remake of the eighth season of \\'\\'Game of Thrones\\'\\' \"that makes sense\" was started on Change.org after the fourth episode \"The Last of the Starks\" aired, but went viral after the fifth episode \"The Bells\" aired. The petition described showrunners David Benioff and D. B. Weiss as \"woefully incompetent writers\". As of May 18, 2019 (i.e., the evening before the series finale), it has amassed over 1 million signatures. The petition was labelled as \"ridiculous\" by actor Isaac Hempstead Wright (who plays Bran Stark), and \"rude\" by actor Jacob Anderson (who plays Grey Worm). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '92a9c686c0e07bcc055923ac17784b42'}>,\n",
-       "  <Document: {'content': 'The petition was labelled as \"ridiculous\" by actor Isaac Hempstead Wright (who plays Bran Stark), and \"rude\" by actor Jacob Anderson (who plays Grey Worm). Sophie Turner (who plays Sansa) said that \"there\\'s always been crazy twists and turns\" on \\'\\'Game of Thrones\\'\\' and \"so Daenerys becoming something of the Mad Queen — it shouldn\\'t be such a negative thing for fans. It\\'s a shock for sure, but I think it\\'s just because it hasn\\'t gone their way.\"She argued, \"All of these petitions and things like that — I think it\\'s disrespectful to the crew, and the writers, and the filmmakers who have worked tirelessly over 10 years, and for 11 months shooting the last season.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '57a5a788ed4a715ec616717c1be4408b'}>,\n",
-       "  <Document: {'content': 'She argued, \"All of these petitions and things like that — I think it\\'s disrespectful to the crew, and the writers, and the filmmakers who have worked tirelessly over 10 years, and for 11 months shooting the last season.\"=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2019\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Emma O\\'Loughlin, and Kate O\\'Farrell\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron\\n\\nOutstanding Special Visual Effects\\n\\nJoe Bauer, Steve Kullback, Adam Chazen, Sam Conway, Mohsen Mousavi, Martin Hill, Ted Rae, Patrick Tiberius Gehlen, and Thomas Schelesny\\n\\n==References==\\n\\n== External links ==\\n* \"The Bells\" at HBO.com\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '22108e4ec739168f148d959c9f8361ab'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Iron Throne\\'\\'\\'\" is the series finale of the American fantasy drama television series \\'\\'Game of Thrones\\'\\'. It is the sixth episode of the eighth season and the 73rd overall episode of the series. Written and directed by the series creators David Benioff and D. B. Weiss, it aired on HBO in the United States and Canada on May 19, 2019. This is the only episode of the season that does not feature the \"Game Revealed\" and \"Inside the Episode\" behind-the-scenes specials, which were recorded yet unreleased for unknown reasons.\\n\\n\"The Iron Throne\" features the characters dealing with the aftermath of Daenerys Targaryen\\'s (Emilia Clarke) devastation of King\\'s Landing and determining who will finally rule Westeros. Some commentators took issue with the episode\\'s story arcs, pacing and tone, while others deemed it a satisfying conclusion to the series. Benioff and Weiss received directing and writing nominations for the episode at the 71st Primetime Emmy Awards, whilst Kit Harington and Peter Dinklage selected the episodes to support their nominations for Outstanding Lead Actor in a Drama Series and Outstanding Supporting Actor in a Drama Series, respectively.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '10b24f0a1bc598a504c5093ee7d64aef'}>,\n",
-       "  <Document: {'content': 'Benioff and Weiss received directing and writing nominations for the episode at the 71st Primetime Emmy Awards, whilst Kit Harington and Peter Dinklage selected the episodes to support their nominations for Outstanding Lead Actor in a Drama Series and Outstanding Supporting Actor in a Drama Series, respectively.\\n\\n== Plot ==\\nJon and Davos survey the destruction that Daenerys Targaryen wrought on King\\'s Landing after she took the city from Cersei Lannister, Queen of the Seven Kingdoms. Tyrion finds the crushed corpses of Cersei and Jaime in the ruins of the Red Keep and mourns them. Grey Worm executes Lannister soldiers, telling Jon that Daenerys ordered it. Daenerys declares to the Unsullied and Dothraki that they \"liberated\" the people of King\\'s Landing, and she will \"liberate\" the entire world. Tyrion publicly resigns as Daenerys’ Hand in protest and is arrested for treason.\\n\\nArya tells Jon that because of his Targaryen heritage, Daenerys will kill him as a threat to her rule. Jon visits Tyrion in captivity, who tells Jon that despite Jon\\'s love for Daenerys, it is Jon\\'s duty to kill her for being the people\\'s greatest threat. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '42749e49c5071efaeed4f3f82685533'}>,\n",
-       "  <Document: {'content': 'Jon visits Tyrion in captivity, who tells Jon that despite Jon\\'s love for Daenerys, it is Jon\\'s duty to kill her for being the people\\'s greatest threat. Tyrion also warns that Arya and her sister Sansa will not bend the knee to Daenerys, putting all of House Stark in danger.\\n\\nIn the throne room, Jon confronts Daenerys, who blames the deaths of civilians on Cersei for using them as human shields. She refuses to forgive Tyrion or the Lannister prisoners, arguing that their executions\\xa0– and a continued \"liberation\" campaign\\xa0– are necessary to establish her vision of a good world. Unable to dissuade her, a conflicted Jon reaffirms his fealty to her and fatally stabs her as they kiss. As he grieves, Drogon arrives. After discovering that Daenerys has been murdered, Drogon melts the Iron Throne and carries Daenerys\\'s body away to the east.\\n\\nSome time later, the lords and ladies of the Seven Kingdoms convene to discuss the fates of Tyrion and Jon. Tyrion, still held prisoner by Grey Worm, suggests that future monarchs be chosen by a council, instead of inheriting the crown. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '1db90b7dc33a694f6d5d575bf6cf7379'}>,\n",
-       "  <Document: {'content': \"Tyrion, still held prisoner by Grey Worm, suggests that future monarchs be chosen by a council, instead of inheriting the crown. This suggestion is agreed upon by the lords and ladies present, and Tyrion nominates Bran to be the new king of Westeros, dubbing him Bran the Broken. Everyone agrees with the nomination except for Sansa, who declares the North's independence from the Seven Kingdoms. Bran accepts, appoints Tyrion as his Hand, and sentences Jon to rejoin the Night's Watch to appease Grey Worm and the Unsullied, who then depart with the Dothraki for Naath, the homeland of his deceased lover Missandei.\\n\\nDays later, Tyrion convenes a new Small Council consisting of Bronn (now Lord of Highgarden), Brienne, Davos, and Sam. As they begin planning to rebuild King's Landing, Bran, King of the Six Kingdoms, briefly meets with the council and tasks himself with finding Drogon.\\n\\nArya sets sail to explore the uncharted seas west of Westeros. Sansa is crowned Queen in the North by the Northern lords. Jon returns to Castle Black and reunites with Tormund, Ghost and the rest of the Wildlings, and leads them to return to the lands beyond the Wall.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '20d5c03d30ebc3e6665098d10a09f9c5'}>,\n",
-       "  <Document: {'content': 'Jon returns to Castle Black and reunites with Tormund, Ghost and the rest of the Wildlings, and leads them to return to the lands beyond the Wall.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by David Benioff and D. B. Weiss. Isaac Hempstead-Wright stated that Benioff and Weiss told him Bran becoming king came from George R. R. Martin\\'s outline for future novels.\\n\\n=== Filming ===\\nThe episode was directed by David Benioff and D. B. Weiss.\\n\\nDuring filming of the Dragonpit scene in Seville, Spain, actors Vladimir Furdik (The Night King), Faye Marsay (The Waif), Tom Wlaschiha (Jaqen H\\'ghar), and Kit Harington (Jon Snow) were brought by HBO to the city as decoys to hide plot points. In the same scene, two plastic water bottles were spotted by the audience behind John Bradley and Liam Cunningham\\'s feet.\\n\\n== Reception ==\\n\\n=== Ratings ===\\nThe episode was viewed by 13.61 million viewers on its initial live broadcast on HBO, making it the most watched episode of the series, surpassing the preceding episode \"The Bells\", as well as the most-watched HBO telecast ever, surpassing the 13.4 million viewers of \\'\\'The Sopranos\\'\\' episode \"For All Debts Public and Private\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '3c1def87a747b6338556d5e7793fceb0'}>,\n",
-       "  <Document: {'content': '== Reception ==\\n\\n=== Ratings ===\\nThe episode was viewed by 13.61 million viewers on its initial live broadcast on HBO, making it the most watched episode of the series, surpassing the preceding episode \"The Bells\", as well as the most-watched HBO telecast ever, surpassing the 13.4 million viewers of \\'\\'The Sopranos\\'\\' episode \"For All Debts Public and Private\". An additional 5.7 million viewers watched on streaming platforms, for a total of 19.3 million viewers.\\n\\n=== Critical response ===\\nOn review aggregator Rotten Tomatoes, the episode has an approval rating of 48% based on 133 reviews, with an average rating of 6.32 out of 10. The website\\'s critical consensus reads: \"If nothing else, \\'The Iron Throne\\' is a bittersweet\\xa0– if unfortunately bland\\xa0– series finale that ensures \\'\\'Game of Thrones\\'\\' fans will linger on the fate of their favorite characters for some time. Will they ever be satisfied by the show\\'s conclusions? Ask us again in 10 years.\"As of 9 August 2019, it is the lowest rated episode of the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'bdade211303f88d1f4439abf2be3c8bd'}>,\n",
-       "  <Document: {'content': 'Ask us again in 10 years.\"As of 9 August 2019, it is the lowest rated episode of the series.\\n\\nJames Poniewozik of \\'\\'The New York Times\\'\\' felt the story insufficiently explained why Daenerys burned King\\'s Landing and how she arrived at her final character point, stating that \"a woman, abused and traded like chattel, becomes so caught up in her zeal to do good that she sees anything but blind adoration as evil\" was a solid idea, but that viewers were not taken \"inside her perspective to make that change seem real and inevitable.\"Poniewozik said \"small character moments\" like Tyrion straightening chairs, Arya sailing west and Jon rejoining the Wildlings \"made emotional sense\". Hank Stuever of \\'\\'The Washington Post\\'\\', who watched with lowered expectations, said the series \"sailed (and trotted) off to a noble and perhaps anticlimactic end...It was everything nobody wanted, but it was still quite a thing: adequately just, narratively symmetrical and sufficiently poignant. It went long on swelling imagery and somewhat short on dialogue.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'fad3883d3d680c534f377ea0fea8b42f'}>,\n",
-       "  <Document: {'content': 'It was everything nobody wanted, but it was still quite a thing: adequately just, narratively symmetrical and sufficiently poignant. It went long on swelling imagery and somewhat short on dialogue.\"Laura Prudom of \\'\\'IGN\\'\\' wrote that the finale was \"not a disaster\", but also \"not quite the dream of spring we might\\'ve hoped for\", \"struggling to resolve many of the show\\'s lingering plot threads in a satisfying and coherent conclusion, and once again falling victim to the season\\'s needlessly truncated episode order.\"Critics took issue with the episode\\'s pacing and final revelations. Spencer Kornhaber of \\'\\'The Atlantic\\'\\' wrote that the finale was \"tonally odd, logically strained, and emotionally thin\" and a \"drama turned into a sitcom\". Lenika Cruz, also writing for \\'\\'The Atlantic\\'\\', wrote that the episode had \"pacing issues, rushed character development, tonal dissonance, the lack of attention to detail, unexplained reversals, and weak dialogue\". Kelly Lawler of \\'\\'USA Today\\'\\' wrote, \"Tragedy and injustice were as baked into the series\\' identity as dragons and battles,\" but the finale was \"unrecognizable. It was hacky; it was cliched. Every character left standing received a saccharine coda...It didn\\'t gracefully swerve into another lane, it careened off a cliff.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '20f52580ff32c019c3db8182305c990f'}>,\n",
-       "  <Document: {'content': 'Every character left standing received a saccharine coda...It didn\\'t gracefully swerve into another lane, it careened off a cliff.\"Inkoo Kang of \\'\\'Slate\\'\\' wrote that \"We know governance is complicated, and the show’s depiction of those complications is one of the reasons why it initially felt so refreshingly relatable\\xa0– but the finale’s argument that an abdication of responsibility is the best we can hope for in a leader\\xa0– feels lazy and false.\"Conversely, \\'\\'TV Guide\\'\\'s consensus concluded that the Game of Thrones finale \"finished strong,\" ranking it #33 on the list of all 73 episodes. Richard Roeper, writing for the \\'\\'Chicago Sun-Times\\'\\', wrote that \"over all, the finale was a solid and largely satisfying wrap-up to one of the most exciting and enthralling TV series ever,\" and was \"melancholy, bittersweet, twist-filled and at times surprisingly humorous.\"Hugh Montgomery of BBC also rated the episode 4/5, writing that the finale largely satisfies \"on the terms the creators have previously set out\" in the \"ruinous\" penultimate episode. Bran becoming king was \"true to the show’s sense of realpolitik\" as a \"contingently happy ending\", whereas the show \"provides an efficient, if disappointingly uncontroversial, ending\" for Jon, Arya and Sansa. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '67fcffdcc643eff3656bd6796aa9951d'}>,\n",
-       "  <Document: {'content': 'Bran becoming king was \"true to the show’s sense of realpolitik\" as a \"contingently happy ending\", whereas the show \"provides an efficient, if disappointingly uncontroversial, ending\" for Jon, Arya and Sansa. Karl Quinn of \\'\\'The Age\\'\\' wrote that the show \"may have resolved itself\" too quickly in dramatic terms, but Bran\\'s ascension \"made perfect sense\" thematically according to the show\\'s \"anti-war and anti-despot themes...After all the bloodshed, butchery and burning, \\'\\'Game of Thrones\\'\\' ended not with a bang but with a ballot.\"Lucy Mangan of \\'\\'The Guardian\\'\\' gave a 4/5 rating, stating that \"the finale just about delivered. It was true to the series\\' overall subject\\xa0– war, and the pity of war\\xa0– and, after doing a lot of wrong to several protagonists last week, did right by those left standing.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'e15606f2c630a4b68cbdda57297e4cad'}>,\n",
-       "  <Document: {'content': 'It was true to the series\\' overall subject\\xa0– war, and the pity of war\\xa0– and, after doing a lot of wrong to several protagonists last week, did right by those left standing.\"=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2019\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss\\n\\nOutstanding Lead Actor in a Drama Series\\n\\nKit Harington\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Cinematography for a Single-Camera Series (One Hour)\\n\\nJonathan Freeman\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nKatie Weiland\\n\\n== References ==\\n\\n== External links ==\\n* \"The Iron Throne\" at HBO.com\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'c9695141a2397fa7b8b56a51f5c766b3'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'A Knight of the Seven Kingdoms\\'\\'\\'\" is the second episode of the eighth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 69th overall. It was written by Bryan Cogman, and directed by David Nutter. It aired on April 21, 2019.\\n\\nThe episode takes place entirely in Winterfell and is dedicated to the buildup before the battle between the living and the dead. It has been likened to a bottle episode, though it does not meet the definition of the term.\\n\"A Knight of the Seven Kingdoms\" received positive reception from critics, with many highlighting the episode\\'s balance of the show\\'s longtime characters and citing it as one of the best episodes of the series. Gwendoline Christie (Brienne of Tarth) submitted the episode to support her Primetime Emmy Award nomination for Outstanding Supporting Actress in a Drama Series. Nikolaj Coster-Waldau (Jaime Lannister) later selected the episode to support his nomination for Outstanding Supporting Actor in a Drama Series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'a588c0c8589a8a6e5820928e353674f6'}>,\n",
-       "  <Document: {'content': 'Nikolaj Coster-Waldau (Jaime Lannister) later selected the episode to support his nomination for Outstanding Supporting Actor in a Drama Series.\\n\\nThe title is a reference to the title bestowed on Brienne of Tarth after Jaime Lannister knights her and to the collection of stories of the same name by George R. R. Martin, the author of the \\'\\'A Song of Ice and Fire\\'\\' novels on which \\'\\'Game of Thrones\\'\\' is based.\\n\\n==Plot==\\nDaenerys and Sansa both contemplate the fate of Jaime Lannister, who reveals Cersei was lying about sending her army. They let Jaime fight for them after Brienne vouches for him. Jaime speaks with Bran and apologizes for trying to kill him (\"Winter Is Coming\"), but Bran holds no anger. Meanwhile, Daenerys is angry at Tyrion for believing Cersei, but is mollified by Jorah.\\n\\nSansa and Daenerys attempt to clear the air between them based on their mutual love of Jon, but Daenerys doesn\\'t have an answer when Sansa asks about the fate of the North once Daenerys takes the Iron Throne. Theon returns wishing to fight for the Starks. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '33c2920f811875dda23b96d7fd66e7b2'}>,\n",
-       "  <Document: {'content': 'Sansa and Daenerys attempt to clear the air between them based on their mutual love of Jon, but Daenerys doesn\\'t have an answer when Sansa asks about the fate of the North once Daenerys takes the Iron Throne. Theon returns wishing to fight for the Starks. Tormund, Beric, and Edd arrive and tell Jon that the Army of the Dead will arrive before the following morning.\\n\\nAt the war council, Bran persuades them to let him act as bait to lure the Night King, who wishes to kill him. Theon volunteers to defend Bran with the Ironborn, and Jon and Daenerys plan to ambush the Night King when he reveals himself to destroy him.\\n\\nArya talks to the Hound, and asks him why he came North since he only fought for himself, and he retorts that he had fought for her (\"The Children\"). Arya then visits Gendry. He reveals he is a bastard of Robert Baratheon. Wanting to lose her virginity before the battle, Arya seduces and sleeps with Gendry.\\n\\nTyrion, Jaime, Brienne, Podrick, Davos, and Tormund gather in the meeting hall to drink before the battle. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'd2d015a10c58eb883daa86e5faff4af6'}>,\n",
-       "  <Document: {'content': 'Wanting to lose her virginity before the battle, Arya seduces and sleeps with Gendry.\\n\\nTyrion, Jaime, Brienne, Podrick, Davos, and Tormund gather in the meeting hall to drink before the battle. The conversation turns to why women cannot be knighted; in response to Tormund\\'s remark that he would knight Brienne without hesitation, Jaime knights an emotional Brienne.\\n\\nJorah fails at attempting to stop Lyanna Mormont from fighting in the battle. Sam approaches Jorah and gives him House Tarly\\'s Valyrian sword Heartsbane, out of gratitude for the influence Jorah\\'s father Jeor had on him.\\n\\nDaenerys visits Jon in the crypts and he reveals his true parentage to Daenerys but Daenerys is skeptical about the reliability of the information. They are interrupted by horn blasts signalling the approach of the Army of the Dead.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by \"series veteran\" Bryan Cogman. This was his eleventh and final script of the series overall. For him \"the biggest challenge was not writing a Wikipedia page.\"The episode adapts material from the unpublished novels \\'\\'The Winds of Winter\\'\\' and \\'\\'A Dream of Spring\\'\\', along with original material not published in George R. R. Martin\\'s novels.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '40f23a534ff560155ec04ed0c9530903'}>,\n",
-       "  <Document: {'content': 'The episode adapts material from the unpublished novels \\'\\'The Winds of Winter\\'\\' and \\'\\'A Dream of Spring\\'\\', along with original material not published in George R. R. Martin\\'s novels.\\n\\n===Filming===\\nThe episode was directed by David Nutter, his second of three episodes this season. The outdoor Winterfell scenes were filmed at sets in Moneyglass and Magheramorne in Northern Ireland, with indoor scenes filmed at Paint Hall studios in Belfast. Executive producers David Benioff and D. B. Weiss let Maisie Williams decide how much of her body to show on camera during her sex scene; she didn\\'t believe that Arya\\'s nakedness was central to the narrative, and she \"kept herself pretty private.\"===Music===\\nThe episode features a song called \"Jenny\\'s Song\", sung by Podrick while many of the characters were drinking by the fire before the upcoming battle. The first line of the song appeared in the book \\'\\'A Storm of Swords\\'\\'; the writers wrote the rest of the lyrics while series composer Ramin Djawadi set it to music. Another version of the song, titled \"Jenny of Oldstones\", was sung by the band Florence and the Machine and played over the ending credits.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8538c2ca08b346848ad5a4ac3f44c2d6'}>,\n",
-       "  <Document: {'content': 'Another version of the song, titled \"Jenny of Oldstones\", was sung by the band Florence and the Machine and played over the ending credits.\\n\\n==Reception==\\n\\n===Ratings===\\nThe episode was viewed by 10.29 million viewers on its initial live broadcast on HBO, and an additional 5.61 million viewers on streaming platforms, for a total 15.9 million viewers.\\n\\n===Critical response===\\nThe episode received largely positive reviews from critics and acclaim from many longtime commentators of the series. It has an 87% rating on the review aggregator site Rotten Tomatoes based on 94 reviews, with an average rating of 8.12/10. The site\\'s critics\\' consensus states: \"What \\'A Knight of the Seven Kingdoms\\' lacks in forward narrative momentum it makes up for in cryptic callbacks, intimate moments, and the promise of imminent battle – though some fans may find their patience tested. The things we do for love.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '47344b86693fcc0094f12a50724ea10a'}>,\n",
-       "  <Document: {'content': 'The site\\'s critics\\' consensus states: \"What \\'A Knight of the Seven Kingdoms\\' lacks in forward narrative momentum it makes up for in cryptic callbacks, intimate moments, and the promise of imminent battle – though some fans may find their patience tested. The things we do for love.\"Jeremy Egner of \\'\\'The New York Times\\'\\' believed that the episode did a good job with building up the story before the eventual climax at Winterfell and wrote, \"In this last week before the big White Walker clash and the presumable carnage and loss of beloved characters it will entail, it was a reminder that the things we do for love can be heroic, too.\"David Sims of \\'\\'The Atlantic\\'\\' suggested that \"audiences didn\\'t need most of last week\\'s dire episode,\" adding that smooshing the episodes together would result in \"a good hour of fan service,\" before the potential death of some of the characters in the next episode. Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' believed that the episode did a good job of distinguishing between the two sides of the upcoming battle by showing \"the warm bodies and warmer conversations between our tragically, beautifully human characters.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '209ab0cd7de3e156499ceb5e934a6f55'}>,\n",
-       "  <Document: {'content': 'Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' believed that the episode did a good job of distinguishing between the two sides of the upcoming battle by showing \"the warm bodies and warmer conversations between our tragically, beautifully human characters.\"Rosenberg went on to analyze that the apocalyptic situation facing the characters served as a meta commentary for the impending end of the series, writing, \"It’s a sly note to those of us who have spent a decade or longer with George R.R. Martin’s characters, an admonition that even when this hugely absorbing series ends in a few weeks, its best moments will live on in those of us who have loved it and challenged it. And in the hands of writer and co-executive producer Bryan Cogman — who has always, to my mind, better understood what made “Game of Thrones” wonderful than even the men who created and ran the darn thing\"  Author Stephen King shared praise about the episode on Twitter, writing \"As a long-time storyteller, I\\'m in awe of how perfectly the minds behind this show brought all the major characters together at Winterfell. They made it look easy. Constant Readers, it is not.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'fd5df7f89b236cee2af23d8b72e0f678'}>,\n",
-       "  <Document: {'content': 'And in the hands of writer and co-executive producer Bryan Cogman — who has always, to my mind, better understood what made “Game of Thrones” wonderful than even the men who created and ran the darn thing\"  Author Stephen King shared praise about the episode on Twitter, writing \"As a long-time storyteller, I\\'m in awe of how perfectly the minds behind this show brought all the major characters together at Winterfell. They made it look easy. Constant Readers, it is not.\"Among the negative reviews, Liz Shannon Miller of \\'\\'IndieWire\\'\\' criticized the pace of the episode and unnecessary confrontations between the characters and added, \"The issues surrounding \"A Knight of the Seven Kingdoms\" would be lesser if a) Episode 1 hadn\\'t also been so devoted to moving pieces into place and b) Season 8 wasn\\'t going to be only six episodes.\"=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2019\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting  Actor in a Drama Series\\n\\nNikolaj Coster-Waldau\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nGwendoline Christie\\n\\n==References==\\n\\n==External links==\\n*  \"A Knight of the Seven Kingdoms\" at HBO.com\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '7c4fc9e6b42ea5b9c5e5f1467dab6dd1'}>,\n",
-       "  <Document: {'content': \"\\n\\nThe fire maps show the locations of actively burning fires around the world on a monthly basis, based on observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite. The colors are based on a count of the number (not size) of fires observed within a 1,000-square-kilometer area. White pixels show the high end of the count—as many as 100 fires in a 1,000-square-kilometer area per day. Yellow pixels show as many as 10 fires, orange shows as many as 5 fires, and red areas as few as 1 fire per day.\\n\\n'''Fire''' is the rapid oxidation of a material in the exothermic chemical process of combustion, releasing heat, light, and various reaction products.  Slower oxidative processes like rusting or digestion are not included by this definition.\\n\\nFire is hot because the conversion of the weak double bond in molecular oxygen, O2, to the stronger bonds in the combustion products carbon dioxide and water releases energy (418 kJ per 32 g of O2); the bond energies of the fuel play only a minor role here.  At a certain point in the combustion reaction, called the ignition point, flames are produced.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'f11e1a02e6f564970b14a3c7b6d1ad5f'}>,\n",
-       "  <Document: {'content': \"Fire is hot because the conversion of the weak double bond in molecular oxygen, O2, to the stronger bonds in the combustion products carbon dioxide and water releases energy (418 kJ per 32 g of O2); the bond energies of the fuel play only a minor role here.  At a certain point in the combustion reaction, called the ignition point, flames are produced.  The ''flame'' is the visible portion of the fire. Flames consist primarily of carbon dioxide, water vapor, oxygen and nitrogen.  If hot enough, the gases may become ionized to produce plasma. Depending on the substances alight, and any impurities outside, the color of the flame and the fire's intensity will be different.\\n\\nFire in its most common form can result in conflagration, which has the potential to cause physical damage through burning.  Fire is an important process that affects ecological systems around the globe. The positive effects of fire include stimulating growth and maintaining various ecological systems.\\n\\nThe negative effects of fire include hazard to life and property, atmospheric pollution, and water contamination. If fire removes protective vegetation, heavy rainfall may lead to an increase in soil erosion by water. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '83ebe99b0f112e76eabd9f52c8f85f4c'}>,\n",
-       "  <Document: {'content': 'The negative effects of fire include hazard to life and property, atmospheric pollution, and water contamination. If fire removes protective vegetation, heavy rainfall may lead to an increase in soil erosion by water. Also, when vegetation is burned, the nitrogen it contains is released into the atmosphere, unlike elements such as potassium and phosphorus which remain in the ash and are quickly recycled into the soil. This loss of nitrogen caused by a fire produces a long-term reduction in the fertility of the soil, which only slowly recovers as nitrogen is \"fixed\" from the atmosphere by lightning and by leguminous plants such as clover.\\n\\nFire has been used by humans in rituals, in agriculture for clearing land, for cooking, generating heat and light, for signaling, propulsion purposes, smelting, forging, incineration of waste, cremation, and as a weapon or mode of destruction.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'f4f40f1cc3c90c7fdb09ed5429efa4ca'}>,\n",
-       "  <Document: {'content': 'Fire has been used by humans in rituals, in agriculture for clearing land, for cooking, generating heat and light, for signaling, propulsion purposes, smelting, forging, incineration of waste, cremation, and as a weapon or mode of destruction.\\n\\n==Physical properties==\\n\\n===Chemistry===\\n\\nThe fire tetrahedron\\nFires start when a flammable or a combustible material, in combination with a sufficient quantity of an oxidizer such as oxygen gas or another oxygen-rich compound (though non-oxygen oxidizers exist), is exposed to a source of heat or ambient temperature above the flash point for the fuel/oxidizer mix, and is able to sustain a rate of rapid oxidation that produces a chain reaction. This is commonly called the fire tetrahedron. Fire cannot exist without all of these elements in place and in the right proportions. For example, a flammable liquid will start burning only if the fuel and oxygen are in the right proportions. Some fuel-oxygen mixes may require a catalyst, a substance that is not consumed, when added, in any chemical reaction during combustion, but which enables the reactants to combust more readily.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '7fc52dfa8407c0beeaae65e4449c723'}>,\n",
-       "  <Document: {'content': 'Some fuel-oxygen mixes may require a catalyst, a substance that is not consumed, when added, in any chemical reaction during combustion, but which enables the reactants to combust more readily.\\n\\nOnce ignited, a chain reaction must take place whereby fires can sustain their own heat by the further release of heat energy in the process of combustion and may propagate, provided there is a continuous supply of an oxidizer and fuel.\\n\\nIf the oxidizer is oxygen from the surrounding air, the presence of a force of gravity, or of some similar force caused by acceleration, is necessary to produce convection, which removes combustion products and brings a supply of oxygen to the fire. Without gravity, a fire rapidly surrounds itself with its own combustion products and non-oxidizing gases from the air, which exclude oxygen and extinguish the fire. Because of this, the risk of fire in a spacecraft is small when it is coasting in inertial flight. This does not apply if oxygen is supplied to the fire by some process other than thermal convection.\\n\\nFire can be extinguished by removing any one of the elements of the fire tetrahedron. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '5f67057e3cd47adf446c8458eeec9344'}>,\n",
-       "  <Document: {'content': 'This does not apply if oxygen is supplied to the fire by some process other than thermal convection.\\n\\nFire can be extinguished by removing any one of the elements of the fire tetrahedron. Consider a natural gas flame, such as from a stove-top burner. The fire can be extinguished by any of the following:\\n* turning off the gas supply, which removes the fuel source;\\n* covering the flame completely, which smothers the flame as the combustion both uses the available oxidizer (the oxygen in the air) and displaces it from the area around the flame with CO2;\\n* application of water, which removes heat from the fire faster than the fire can produce it (similarly, blowing hard on a flame will displace the heat of the currently burning gas from its fuel source, to the same end), or\\n* application of a retardant chemical such as Halon to the flame, which retards the chemical reaction itself until the rate of combustion is too slow to maintain the chain reaction.\\n\\nIn contrast, fire is intensified by increasing the overall rate of combustion. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '84808fa3ce6e77a5f9377a13b5e60d97'}>,\n",
-       "  <Document: {'content': \"The fire can be extinguished by any of the following:\\n* turning off the gas supply, which removes the fuel source;\\n* covering the flame completely, which smothers the flame as the combustion both uses the available oxidizer (the oxygen in the air) and displaces it from the area around the flame with CO2;\\n* application of water, which removes heat from the fire faster than the fire can produce it (similarly, blowing hard on a flame will displace the heat of the currently burning gas from its fuel source, to the same end), or\\n* application of a retardant chemical such as Halon to the flame, which retards the chemical reaction itself until the rate of combustion is too slow to maintain the chain reaction.\\n\\nIn contrast, fire is intensified by increasing the overall rate of combustion. Methods to do this include balancing the input of fuel and oxidizer to stoichiometric proportions, increasing fuel and oxidizer input in this balanced mix, increasing the ambient temperature so the fire's own heat is better able to sustain combustion, or providing a catalyst, a non-reactant medium in which the fuel and oxidizer can more readily react.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'a89a72d29430684d324284409cd90815'}>,\n",
-       "  <Document: {'content': 'Methods to do this include balancing the input of fuel and oxidizer to stoichiometric proportions, increasing fuel and oxidizer input in this balanced mix, increasing the ambient temperature so the fire\\'s own heat is better able to sustain combustion, or providing a catalyst, a non-reactant medium in which the fuel and oxidizer can more readily react.\\n\\n===Flame===\\n\\nA candle\\'s flame\\nNorthwest Crown Fire Experiment, Canada\\nPhoto of a fire taken with a 1/4000th of a second exposure\\nISS\\nA flame is a mixture of reacting gases and solids emitting visible, infrared, and sometimes ultraviolet light, the frequency spectrum of which depends on the chemical composition of the burning material and intermediate reaction products. In many cases, such as the burning of organic matter, for example wood, or the incomplete combustion of gas, incandescent solid particles called soot produce the familiar red-orange glow of \"fire\". This light has a continuous spectrum. Complete combustion of gas has a dim blue color due to the emission of single-wavelength radiation from various electron transitions in the excited molecules formed in the flame. Usually oxygen is involved, but hydrogen burning in chlorine also produces a flame, producing hydrogen chloride (HCl). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '41a0b2341e11444f8f495c43b1a6f289'}>,\n",
-       "  <Document: {'content': 'Complete combustion of gas has a dim blue color due to the emission of single-wavelength radiation from various electron transitions in the excited molecules formed in the flame. Usually oxygen is involved, but hydrogen burning in chlorine also produces a flame, producing hydrogen chloride (HCl). Other possible combinations producing flames, amongst many, are fluorine and hydrogen, and hydrazine and nitrogen tetroxide. Hydrogen and hydrazine/UDMH flames are similarly pale blue, while burning boron and its compounds, evaluated in mid-20th century as a high energy fuel for jet and rocket engines, emits intense green flame, leading to its informal nickname of \"Green Dragon\".\\n\\nThe glow of a flame is complex. Black-body radiation is emitted from soot, gas, and fuel particles, though the soot particles are too small to behave like perfect blackbodies. There is also photon emission by de-excited atoms and molecules in the gases. Much of the radiation is emitted in the visible and infrared bands. The color depends on temperature for the black-body radiation, and on chemical makeup for the emission spectra. The dominant color in a flame changes with temperature. The photo of the forest fire in Canada is an excellent example of this variation. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'ea906689fb967fe4df221dc7c392b475'}>,\n",
-       "  <Document: {'content': 'The dominant color in a flame changes with temperature. The photo of the forest fire in Canada is an excellent example of this variation. Near the ground, where most burning is occurring, the fire is white, the hottest color possible for organic material in general, or yellow. Above the yellow region, the color changes to orange, which is cooler, then red, which is cooler still. Above the red region, combustion no longer occurs, and the uncombusted carbon particles are visible as black smoke.\\n\\nThe common distribution of a flame under normal gravity conditions depends on convection, as soot tends to rise to the top of a general flame, as in a candle in normal gravity conditions, making it yellow. In micro gravity or zero gravity, such as an environment in outer space, convection no longer occurs, and the flame becomes spherical, with a tendency to become more blue and more efficient (although it may go out if not moved steadily, as the CO2 from combustion does not disperse as readily in micro gravity, and tends to smother the flame). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'c776a92731920cdf861a7b90c7155a5d'}>,\n",
-       "  <Document: {'content': 'In micro gravity or zero gravity, such as an environment in outer space, convection no longer occurs, and the flame becomes spherical, with a tendency to become more blue and more efficient (although it may go out if not moved steadily, as the CO2 from combustion does not disperse as readily in micro gravity, and tends to smother the flame). There are several possible explanations for this difference, of which the most likely is that the temperature is sufficiently evenly distributed that soot is not formed and complete combustion occurs. Experiments by NASA reveal that diffusion flames in micro gravity allow more soot to be completely oxidized after they are produced than diffusion flames on Earth, because of a series of mechanisms that behave differently in micro gravity when compared to normal gravity conditions. These discoveries have potential applications in applied science and industry, especially concerning fuel efficiency.\\n\\nIn combustion engines, various steps are taken to eliminate a flame. The method depends mainly on whether the fuel is oil, wood, or a high-energy fuel such as jet fuel.\\n\\n===Flame temperatures===\\n\\n====Temperatures of flames by appearance====\\nIt is true that objects at specific temperatures do radiate visible light. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'bf751b26cdf23b75a3c613400fd62b02'}>,\n",
-       "  <Document: {'content': 'The method depends mainly on whether the fuel is oil, wood, or a high-energy fuel such as jet fuel.\\n\\n===Flame temperatures===\\n\\n====Temperatures of flames by appearance====\\nIt is true that objects at specific temperatures do radiate visible light. Objects whose surface is at a temperature above approximately  will glow, emitting light at a color that indicates the temperature of that surface. See the section on red heat for more about this effect. It is a misconception that one can judge the temperature of a fire by the color of its flames or the sparks in the flames. For many reasons, chemically and optically, these colors may not match the red/orange/yellow/white heat temperatures on the chart. Barium nitrate burns a bright green, for instance, and this is not present on the heat chart.\\n\\n====Typical temperatures of flames====\\n\\nThe \"adiabatic flame temperature\" of a given fuel and oxidizer pair indicates the temperature at which the gases achieve stable combustion.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '34af782daab33c3eb48fce3dd8f58dae'}>,\n",
-       "  <Document: {'content': '====Typical temperatures of flames====\\n\\nThe \"adiabatic flame temperature\" of a given fuel and oxidizer pair indicates the temperature at which the gases achieve stable combustion.\\n* Oxy–dicyanoacetylene\\n* Oxy–acetylene\\n* Oxyhydrogen\\n* Air–acetylene\\n* Blowtorch (air–MAPP gas)\\n* Bunsen burner (air–natural gas)\\n* Candle (air–paraffin)\\n* Smoldering cigarette:\\n** Temperature without drawing: side of the lit portion; ; middle of the lit portion:\\n** Temperature during drawing: middle of the lit portion:\\n** Always hotter in the middle.\\n\\n==Fire ecology==\\n\\nEvery natural ecosystem has its own fire regime, and the organisms in those ecosystems are adapted to or dependent upon that fire regime. Fire creates a mosaic of different habitat patches, each at a different stage of succession. Different species of plants, animals, and microbes specialize in exploiting a particular stage, and by creating these different types of patches, fire allows a greater number of species to exist within a landscape.\\n\\n==Fossil record==\\n\\n==Human control==\\n\\nBushman starting a fire in Namibia\\nProcess of ignition of a match\\nThe ability to control fire was a dramatic change in the habits of early humans. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'daee9108d9a3a17ffe3c739f67794cc8'}>,\n",
-       "  <Document: {'content': '==Fossil record==\\n\\n==Human control==\\n\\nBushman starting a fire in Namibia\\nProcess of ignition of a match\\nThe ability to control fire was a dramatic change in the habits of early humans. Making fire to generate heat and light made it possible for people to cook food, simultaneously increasing the variety and availability of nutrients and reducing disease by killing organisms in the food. The heat produced would also help people stay warm in cold weather, enabling them to live in cooler climates. Fire also kept nocturnal predators at bay. Evidence of cooked food is found from , although there is a theory that fire could have been used in a controlled fashion about 1 million years ago.  Evidence becomes widespread around 50 to 100 thousand years ago, suggesting regular use from this time; resistance to air pollution started to evolve in human populations at a similar point in time.  The use of fire became progressively more sophisticated, with it being used to create charcoal and to control wildlife from tens of thousands of years ago.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '704e8bdbee03dd3e72bd0a27d9edba2a'}>,\n",
-       "  <Document: {'content': 'The use of fire became progressively more sophisticated, with it being used to create charcoal and to control wildlife from tens of thousands of years ago.\\n\\nFire has also been used for centuries as a method of torture and execution, as evidenced by death by burning as well as torture devices such as the iron boot, which could be filled with water, oil, or even lead and then heated over an open fire to the agony of the wearer.\\n\\nBy the Neolithic Revolution, during the introduction of grain-based agriculture, people all over the world used fire as a tool in landscape management. These fires were typically controlled burns or \"cool fires\", as opposed to uncontrolled \"hot fires\", which damage the soil. Hot fires destroy plants and animals, and endanger communities. This is especially a problem in the forests of today where traditional burning is prevented in order to encourage the growth of timber crops. Cool fires are generally conducted in the spring and autumn. They clear undergrowth, burning up biomass that could trigger a hot fire should it get too dense. They provide a greater variety of environments, which encourages game and plant diversity. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'd267ee84428adb053223e63db390c027'}>,\n",
-       "  <Document: {'content': 'They clear undergrowth, burning up biomass that could trigger a hot fire should it get too dense. They provide a greater variety of environments, which encourages game and plant diversity. For humans, they make dense, impassable forests traversable. Another human use for fire in regards to landscape management is its use to clear land for agriculture. Slash-and-burn agriculture is still common across much of tropical Africa, Asia and South America. \"For small farmers, it is a convenient way to clear overgrown areas and release nutrients from standing vegetation back into the soil\", said Miguel Pinedo-Vasquez, an ecologist at the  Earth Institute’s Center for Environmental Research and Conservation. However this useful strategy is also problematic. Growing population, fragmentation of forests and warming climate are making the earth\\'s surface more prone to ever-larger escaped fires. These harm ecosystems and human infrastructure, cause health problems, and send up spirals of carbon and soot that may encourage even more warming of the atmosphere – and thus feed back into more fires. Globally today, as much as 5 million square kilometres – an area more than half the size of the United States – burns in a given year.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'c1345cdf370d5aa364f232d81b4ca27a'}>,\n",
-       "  <Document: {'content': 'Globally today, as much as 5 million square kilometres – an area more than half the size of the United States – burns in a given year.\\n\\nThere are numerous modern applications of fire. In its broadest sense, fire is used by nearly every human being on earth in a controlled setting every day. Users of internal combustion vehicles employ fire every time they drive. Thermal power stations provide electricity for a large percentage of humanity.\\nHamburg after four fire-bombing raids in July 1943, which killed an estimated 50,000 people\\nThe use of fire in warfare has a long history. Fire was the basis of all early thermal weapons. Homer detailed the use of fire by Greek soldiers who hid in a wooden horse to burn Troy during the Trojan war. Later the Byzantine fleet used Greek fire to attack ships and men. In the First World War, the first modern flamethrowers were used by infantry, and were successfully mounted on armoured vehicles in the Second World War. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'cd7b7e40a1288daf5aff21869790f7e5'}>,\n",
-       "  <Document: {'content': \"In the First World War, the first modern flamethrowers were used by infantry, and were successfully mounted on armoured vehicles in the Second World War. In the latter war, incendiary bombs were used by Axis and Allies alike, notably on Tokyo, Rotterdam, London, Hamburg and, notoriously, at Dresden; in the latter two cases firestorms were deliberately caused in which a ring of fire surrounding each city was drawn inward by an updraft caused by a central cluster of fires. The United States Army Air Force also extensively used incendiaries against Japanese targets in the latter months of the war, devastating entire cities constructed primarily of wood and paper houses. The use of napalm was employed in July 1944, towards the end of the Second World War; although its use did not gain public attention until the Vietnam War. Molotov cocktails were also used.\\n\\n===Use as fuel===\\ncoal-fired power station in the People's Republic of China\\nDisability-adjusted life year for fires per 100,000\\xa0inhabitants in 2004\\n\\nSetting fuel aflame releases usable energy. Wood was a prehistoric fuel, and is still viable today. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '28dc57283fd2463638d0f6814d5190a8'}>,\n",
-       "  <Document: {'content': \"===Use as fuel===\\ncoal-fired power station in the People's Republic of China\\nDisability-adjusted life year for fires per 100,000\\xa0inhabitants in 2004\\n\\nSetting fuel aflame releases usable energy. Wood was a prehistoric fuel, and is still viable today. The use of fossil fuels, such as petroleum, natural gas, and coal, in power plants supplies the vast majority of the world's electricity today; the International Energy Agency states that nearly 80% of the world's power came from these sources in 2002. The fire in a power station is used to heat water, creating steam that drives turbines. The turbines then spin an electric generator to produce electricity. Fire is also used to provide mechanical work directly, in both external and internal combustion engines.\\n\\nThe unburnable solid remains of a combustible material left after a fire is called ''clinker'' if its melting point is below the flame temperature, so that it fuses and then solidifies as it cools, and ''ash'' if its melting point is above the flame temperature.\\n\\n==Protection and prevention==\\n\\nThis visualization shows fires detected in the United States from July 2002 through July 2011. Look for fires that reliably burn each year in western states and across the Southeast.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '114b639ac00e7e99e48ea485e21e29a1'}>,\n",
-       "  <Document: {'content': \"==Protection and prevention==\\n\\nThis visualization shows fires detected in the United States from July 2002 through July 2011. Look for fires that reliably burn each year in western states and across the Southeast.\\n\\nWildfire prevention programs around the world may employ techniques such as ''wildland fire use'' and ''prescribed or controlled burns''. ''Wildland fire use'' refers to any fire of natural causes that is monitored but allowed to burn. ''Controlled burns'' are fires ignited by government agencies under less dangerous weather conditions.\\n\\nFire fighting services are provided in most developed areas to extinguish or contain uncontrolled fires. Trained firefighters use fire apparatus, water supply resources such as water mains and fire hydrants or they might use A and B class foam depending on what is feeding the fire.\\n\\nFire prevention is intended to reduce sources of ignition.  Fire prevention also includes education to teach people how to avoid causing fires. Buildings, especially schools and tall buildings, often conduct fire drills to inform and prepare citizens on how to react to a building fire. Purposely starting destructive fires constitutes arson and is a crime in most jurisdictions.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'bf7f8f032e7e7024b7a923f78859fa8e'}>,\n",
-       "  <Document: {'content': 'Buildings, especially schools and tall buildings, often conduct fire drills to inform and prepare citizens on how to react to a building fire. Purposely starting destructive fires constitutes arson and is a crime in most jurisdictions.\\n\\nModel building codes require passive fire protection and active fire protection systems to minimize damage resulting from a fire. The most common form of active fire protection is fire sprinklers. To maximize passive fire protection of buildings, building materials and furnishings in most developed countries are tested for fire-resistance, combustibility and flammability. Upholstery, carpeting and plastics used in vehicles and vessels are also tested.\\n\\nWhere fire prevention and fire protection have failed to prevent damage, fire insurance can mitigate the financial impact.\\n\\n==Restoration==\\nDifferent restoration methods and measures are used depending on the type of fire damage that occurred. Restoration after fire damage can be performed by property management teams, building maintenance personnel, or by the homeowners themselves; however, contacting a certified professional fire damage restoration specialist is often regarded as the safest way to restore fire damaged property due to their training and extensive experience. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '7bd4af8c709e620cfb057c38b93e2bdf'}>,\n",
-       "  <Document: {'content': 'Restoration after fire damage can be performed by property management teams, building maintenance personnel, or by the homeowners themselves; however, contacting a certified professional fire damage restoration specialist is often regarded as the safest way to restore fire damaged property due to their training and extensive experience. Most are usually listed under \"Fire and Water Restoration\" and they can help speed repairs, whether for individual homeowners or for the largest of institutions.\\n\\nFire and Water Restoration companies are regulated by the appropriate state\\'s Department of Consumer Affairs – usually the state contractors license board. In California, all Fire and Water Restoration companies must register with the California Contractors State License Board. Presently, the California Contractors State License Board has no specific classification for \"water and fire damage restoration.\"Hence, the Contractor\\'s State License Board requires both an asbestos certification (ASB) as well as a demolition classification (C-21) in order to perform Fire and Water Restoration work.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': 'f5b58f828986cf4114e6b2ef0c35c409'}>,\n",
-       "  <Document: {'content': \"Hence, the Contractor's State License Board requires both an asbestos certification (ASB) as well as a demolition classification (C-21) in order to perform Fire and Water Restoration work.\\n\\n==See also==\\n\\n* Aodh (given name)\\n* Colored fire\\n* Control of fire by early humans\\n* Deflagration\\n* Fire (classical element)\\n* Fire investigation\\n* Fire lookout\\n* Fire lookout tower\\n* Fire making\\n* Fire pit\\n* Fire safety\\n* Fire triangle\\n* Fire whirl\\n* Fire worship\\n* Flame test\\n* Life Safety Code\\n* List of fires\\n* List of light sources\\n* Phlogiston theory\\n* Piano burning\\n* Prometheus, the Greek mythological figure who gave mankind fire\\n* Pyrokinesis\\n* Pyrolysis\\n* Pyromania\\n* Self-immolation\\n* ''The Chemical History of a Candle''\\n\\n==References==\\n\\n==Bibliography==\\n*\\n* Haung, Kai. 2009. Population and Building Factors That Impact Residential Fire Rates in Large U.S. Cities. Applied Research Project. Texas State University.  TXstate.edu\\n*\\n* Kosman, Admiel:  Sacred fire. In: Thu, January 13, 2011.\\n\\n==External links==\\n\\n*  How Fire Works at HowStuffWorks\\n*  What exactly is fire? \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': 'd287f03ce4a38564c5e884be219ef21d'}>,\n",
-       "  <Document: {'content': \"Texas State University.  TXstate.edu\\n*\\n* Kosman, Admiel:  Sacred fire. In: Thu, January 13, 2011.\\n\\n==External links==\\n\\n*  How Fire Works at HowStuffWorks\\n*  What exactly is fire? from The Straight Dope\\n*  On Fire, an Adobe Flash-based science tutorial from the NOVA (TV series)\\n*  20 Things You Didn't Know About... Fire from ''Discover'' magazine\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': '1858abe9d71d119d917fa91e5051ec'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Climb\\'\\'\\'\" is the sixth episode of the third season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 26th episode of the series. Directed by Alik Sakharov and written by David Benioff and D. B. Weiss, it aired on May 5, 2013.\\n\\nThe episode\\'s title comes from climbing of the wall by Jon Snow and Ygritte, and also the references from dialogue between Lord Petyr Baelish and Lord Varys.\\n\\nThe episode marks the final appearance of Esmé Bianco.\\n\\n==Plot==\\n===In King\\'s Landing===\\nMeeting with Olenna, Tywin threatens to appoint Loras to the Kingsguard, thereby renouncing his right to inheritance and marriage and leaving House Tyrell without a male heir, and Olenna consents to Loras and Cersei\\'s marriage.\\n\\nTyrion accuses Cersei of trying to have him killed during the Battle of the Blackwater, but deduces it was Joffrey who ordered his death and Cersei tells him he is not in danger now that Tywin is the Hand. Tyrion informs Sansa that she will not wed Loras, but himself.\\n\\nBaelish tells Varys he has given Ros to Joffrey to kill for his pleasure. Sansa watches tearfully as Baelish departs by ship, losing her chance at leaving the capital.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'f61131d2cd3f7e8f48321941d66d216d'}>,\n",
-       "  <Document: {'content': 'Baelish tells Varys he has given Ros to Joffrey to kill for his pleasure. Sansa watches tearfully as Baelish departs by ship, losing her chance at leaving the capital.\\n\\n===In the Riverlands===\\nArriving at the Brotherhood\\'s hideout, Melisandre is shocked to learn of Beric\\'s six resurrections, and takes Gendry into her custody. Arya unnerves Melisandre, who declares Arya will \"shut many eyes forever\" and they will meet again.\\n\\nAt Riverrun, Robb and his advisors discuss an alliance with Lame Lothar Frey and Black Walder Rivers. Lord Walder Frey\\'s demands include a formal apology from Robb, the castle Harrenhal, and for Edmure to marry one of his daughters.\\n\\n===At Harrenhal===\\nRoose Bolton agrees to send Jaime to King\\'s Landing if Jaime assures Tywin that Bolton had nothing to do with his maiming, but keeps Brienne under arrest for abetting treason.\\n\\n===In the North===\\nBran defuses tensions between Osha and Meera, and Jojen tells Bran his vision of Jon surrounded by enemies.\\n\\nTorturing Theon, the boy threatens to sever his finger if he cannot guess the boy\\'s identity and their location. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '5dad6d33498e41e70474c82df21ad9a9'}>,\n",
-       "  <Document: {'content': \"===In the North===\\nBran defuses tensions between Osha and Meera, and Jojen tells Bran his vision of Jon surrounded by enemies.\\n\\nTorturing Theon, the boy threatens to sever his finger if he cannot guess the boy's identity and their location. After his finger is flayed for several wrong guesses, Theon guesses the boy is a Karstark and he is being tortured at the Karhold for betraying Robb. The boy pretends Theon was correct before continuing to flay his finger, admitting his torture is solely for his amusement. Theon finally begs his torturer to remove his finger.\\n\\n===Beyond the Wall===\\nEn route to the Wall, Sam shows Gilly the dragonglass dagger he found and tells her about Castle Black.\\n\\nYgritte reveals she is aware Jon remains loyal to the Night's Watch, but tells him their loyalty to each other is greater. As Tormund's party climbs the wall, Ygritte inadvertently causes an avalanche that kills some wildlings and leaves her and Jon hanging by their rope. Before Orell cuts the rope, Jon secures himself to the Wall and reaches the top with Ygritte, where they embrace.\\n\\n==Production==\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '8db59acae29596ddfc361782af79b0e3'}>,\n",
-       "  <Document: {'content': 'Before Orell cuts the rope, Jon secures himself to the Wall and reaches the top with Ygritte, where they embrace.\\n\\n==Production==\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"The Climb\" is the sixth episode of the season written by showrunners David Benioff and D. B. Weiss, and 18th overall. It is based upon George R. R. Martin\\'s novel \\'\\'A Storm of Swords\\'\\', namely, chapters 30, 35, 37, and 48 (Jon IV, Catelyn IV, Jaime V, and Samwell III).\\n\\nGilly\\'s baby, appearing in the fourth episode and in \"The Climb\"\\'s first scene, was played by ten months-old Arya Hasson – named after Arya Stark – from the Waterside in Derry.\\n\\n==Reception==\\n\\n===Ratings===\\n\"The Climb\" set a new record for \\'\\'Game of Thrones\\'\\' in ratings, the fourth consecutive episode to establish a new series high. 5.5 million viewers watched the premiere airing, with 1.27 million additional viewers watching the second airing. The episode also set a new series high in viewers aged 18–49, with a rating of 2.9. In the United Kingdom, the episode was seen by 0.926 million viewers on Sky Atlantic, being the channel\\'s highest-rated broadcast that week.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '8438149f1b17e47789c50b2f998fdd5e'}>,\n",
-       "  <Document: {'content': 'In the United Kingdom, the episode was seen by 0.926 million viewers on Sky Atlantic, being the channel\\'s highest-rated broadcast that week.\\n\\n===Critical reception===\\n\"The Climb\" was met with positive reviews from critics. Review aggregator Rotten Tomatoes surveyed 21 reviews of the episode and judged 90% of them to be positive with an average score of 7.9 out of 10. The website\\'s critical consensus reads \"Tywin Lannister and Olenna Tyrell steal the show with their exquisite negotiation as \\'The Climb\\' builds towards the Wildlings\\' death-defying scaling of The Wall.\"Writing for IGN, Matt Fowler gave the episode an 8.8/10, writing \"This week\\'s Game of Thrones started off slow, but then built to a roaring crescendo.\"Two reviews were published by The A.V. Club. David Sims gave the episode a \"B\" rating for people new to the series, while Emily VanDerWerff rated the episode a \"B+\" for people who have read the novels.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8c34f280bc83c66bad0aa4d5ae05e207'}>,\n",
-       "  <Document: {'content': 'David Sims gave the episode a \"B\" rating for people new to the series, while Emily VanDerWerff rated the episode a \"B+\" for people who have read the novels.\\n\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2013\\n\\nHollywood Post Alliance Awards\\n\\nOutstanding Sound – Television\\n\\nPaula Fairfield, Brad Katona, Jed Dodge, Onnalee Blank and Mathew Waters\\n\\n2014\\n\\nVisual Effects Society\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nKirk Brillon, Steve Gordon, Geoff Sayer, Winston Lee\\n\\nOutstanding Created Environment in a Broadcast Program\\n\\nPatrick Zentis, Mayur Patel, Nitin Singh, Tim Alexander\\n\\n== References ==\\n\\n== External links ==\\n\\n*  \"The Climb\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '710be7a1e297c8bfad9146db0ca6bd8d'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Light of the Seven\\'\\'\\'\" is a piece in the HBO\\'s series \\'\\'Game of Thrones\\'\\', the television series adaptation of \\'\\'A Song of Ice and Fire\\'\\' by George R. R. Martin. It first played during the season six finale of the show and was composed by Ramin Djawadi in 2016. The \"Light of the Seven\" is the first time piano is used in the music for \\'\\'Game of Thrones\\'\\'. It was nominated by the International Film Music Critics Association for Film Music Composition of the Year.\\n\\n==Background==\\nIn an interview, Djawadi spoke about \"Light of the Seven\", which largely consisted of piano, something unusual for the series. Djawadi stated, \"The interesting thing to me was the use of the piano. When we started the season showrunners David Benioff and Dan Weiss, and Miguel Sapochnik, the director of the episode, reached out to me and said, \\'There\\'s something coming up in episode 10.\\'We talked about the \\'Light of the Seven\\', and how it needed to be a new piece of music. Any kind of character theme could tip it, and we didn\\'t want to tip the audience. Miguel brought it up: \\'What about the piano?\\'We discussed it. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'bce0334d76f8b513f70e1e07c9a85fc9'}>,\n",
-       "  <Document: {'content': 'Any kind of character theme could tip it, and we didn\\'t want to tip the audience. Miguel brought it up: \\'What about the piano?\\'We discussed it. The piano is not really in the language of the \\'\\'Game of Thrones\\'\\' score.\"Djawadi had tried other instruments, including playing the whole tune with harp, but none of them sounded right, eventually he settled on the colder sound of the piano.\\n\\nDjawadi said: \"It all felt like a perfect fit. What\\'s great about the scene, too, is there\\'s hardly any dialogue. It\\'s nine minutes long. I knew I had to start minimal and give it space. Let notes ring, then give it space, and build up the anticipation from there, without tipping in either direction.\"Djawadi stated that he refrained from using the typical Lannister theme, \"The Rains of Castamere\", in order to create more of a mystery. The piece also featured vocals by two young boys singing in unison, and Djawadi instructed the boys to sing in such a way that although it is \"not out of tune, but you get that feeling of Something’s wrong.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '977c3b65c1be94ddf8fa65b0bce87690'}>,\n",
-       "  <Document: {'content': 'The piece also featured vocals by two young boys singing in unison, and Djawadi instructed the boys to sing in such a way that although it is \"not out of tune, but you get that feeling of Something’s wrong.\"Djawadi describing how he pieced all of the separate pieces of the music together by saying \"The boys I recorded completely separate. The strings I recorded all together. Even the solo instruments, I recorded them separately — the solo violins and solo cellists were recorded separately. The piano, I played. And the organ as well.\"In another interview, Djawadi talked about the process, saying, \"That was the big guidance for me, in how I wanted to build this piece, It\\'s a different instrument, and I put it in an upper register, but the idea is that it\\'s building something that stays the same but changes over time. Of course, now that I say that, people might be like, \\'No, it\\'s not really that.\\'And it\\'s not staying true to the form. Obviously the picture is guiding me, so I have to pull back and break away from it. I couldn\\'t keep it as a passacaglia all the way through. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'de1125d197f81839dbd845901bea7414'}>,\n",
-       "  <Document: {'content': 'Obviously the picture is guiding me, so I have to pull back and break away from it. I couldn\\'t keep it as a passacaglia all the way through. But there are definitely moments where it defaults to that.\"==Composition==\\n\"Light of the Seven\" has a duration of approximately ten minutes. When Djawadi originally composed the piece, he intended at first to make it a passacaglia. The instrumentation of \"Light of the Seven\" consists of piano, organ, strings and two boy soloists. On why he decided to use two young soloists instead of a full choir, Djawadi said, \"I felt that two of them were more haunting than using a full choir, because it\\'s a smaller environment, when they\\'re running around in the catacombs.\"==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* Ramin Djawadi – composer, primary artist, producer\\n\\n* David Benioff – liner notes\\n* D.B. Weiss – liner notes\\n\\n==Live performances==\\nDjawadi performing \"Light of the Seven\" at the \\'\\'Game of Thrones Live Concert Experience\\'\\'.\\nDjawadi has performed the piece with a live orchestra at the Game of Thrones Live Concert Experience, which consisted of 24 dates in cities across the United States and Canada.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '988fc448e24d85bd87dcf5ea3572e82b'}>,\n",
-       "  <Document: {'content': 'Djawadi has performed the piece with a live orchestra at the Game of Thrones Live Concert Experience, which consisted of 24 dates in cities across the United States and Canada.\\n\\n==Reception==\\nThe piece received universal praise from critics and fans, with Lili Loofbourow of \\'\\'The Week\\'\\' calling it the \"real winner\" of the season finale. Djawadi responded to the universal praise the composition got, saying, \"I never would have thought that would happen, It\\'s so exciting because it\\'s such a special finale.\"==Aftermath==\\nA remixed version of \"Light of the Seven\" was used in a season 7 trailer. The final motif from this piece is used in the season 6 track \"Hear Me Roar\", and is also used from season 7 onwards in tracks involving Cersei Lannister and her machinations, such as \"The Long Farewell\" or \"No One Walks Away from Me\", acting as an alternate theme to \"The Rains of Castamere\" for her character.\\n\\n==Charts==\\n\\nChart (2016)\\n\\nPeakposition\\n\\n==Accolades==\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2016\\n\\nInternational Film Music Critics Association\\n\\nFilm Music Composition of the Year\\n\\nRamin Djawadi\\n\\n==See also==\\n*\\'\\'Game of Thrones\\'\\' Theme\\n*Music of Game of Thrones\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'd6b637002ac38897140234e82a4f7144'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''Varys''' is a fictional character in the ''A Song of Ice and Fire'' series of fantasy novels by American author George R. R. Martin, and its television adaptation ''Game of Thrones''.\\n\\nIntroduced in 1996's ''A Game of Thrones'', Varys is a former slave eunuch from the fictional city of Lys and the master of whisperers in King's Landing. He subsequently appeared in Martin's books ''A Clash of Kings'' (1998), ''A Storm of Swords'' (2000), and ''A Dance with Dragons'' (2011). He proves to be a key ally to Ned Stark and Tyrion Lannister at court, but his true motives remain shrouded in mystery to those who employ his services.\\n\\nVarys is portrayed by Conleth Hill in the HBO television adaptation.\\n\\n==Character overview==\\nVarys is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, such as Ned Stark, Tyrion and Cersei Lannister.\\n\\n===Background===\\nVarys was born as a slave in the Free City of Lys and joined a travelling acting troupe. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '76e3a8a5c2d1ad872842f8301b933800'}>,\n",
-       "  <Document: {'content': \"==Character overview==\\nVarys is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, such as Ned Stark, Tyrion and Cersei Lannister.\\n\\n===Background===\\nVarys was born as a slave in the Free City of Lys and joined a travelling acting troupe. While the troupe was performing in Myr, a sorcerer bought the young Varys from the troupe's leader, drugged Varys before removing his genitals and burning them in a brazier in a blood magic ritual, and afterwards cast him into the streets. Varys turned to begging, prostitution, and ultimately theft to survive, but soon became known in Myr and fled to Pentos. There he befriended a poor sellsword, Illyrio Mopatis, with whom he teamed up to steal valuables from other thieves and return them to their owners for a fee. Varys eventually realised that there was more to gain from stealing secrets instead of valuables and trained his spies to copy information from the wealthy and powerful. Varys and Illyrio became rich, and Varys' reputation reached the ears of the King of Westeros, Aerys II Targaryen, who appointed Varys as his Master of Whisperers. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '411f6808d892a1aa0739081093e19a16'}>,\n",
-       "  <Document: {'content': \"Varys and Illyrio became rich, and Varys' reputation reached the ears of the King of Westeros, Aerys II Targaryen, who appointed Varys as his Master of Whisperers. Jaime claims that Aerys saw traitors everywhere, and Varys was quick to point out any he missed. Apparently, when Rhaegar Targaryen intended to use a Tourney at Harrenhal to call a Great Council to deal with his father's instability, and possibly dethrone him, Varys warned Aerys, and they attended the tourney. Sometime prior to the Sack of King's Landing, Varys had Rhaegar's infant son Aegon swapped with a lowborn baby and smuggled to Essos to be raised in hiding by Rhaegar's friend Jon Connington, who was exiled by the Mad King for failing to defeat Robert. Varys remains Master of Whisperers after Robert Baratheon seizes the Iron Throne but secretly remains loyal to House Targaryen.\\n\\n===Description===\\nVarys, also called the Spider, is a eunuch and courtier who serves as Master of Whisperers, the spymaster for the king of the Seven Kingdoms at the royal court in King's Landing. As Master of Whisperers he is on the privy council. He is feared by nobles and common people alike. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '1d268e3eb3193136f5b7305e76bb660a'}>,\n",
-       "  <Document: {'content': '===Description===\\nVarys, also called the Spider, is a eunuch and courtier who serves as Master of Whisperers, the spymaster for the king of the Seven Kingdoms at the royal court in King\\'s Landing. As Master of Whisperers he is on the privy council. He is feared by nobles and common people alike. He knows all of the secret passages in the royal castle, and his spies are found everywhere. He is a skilled manipulator and commands a network of informants across two continents. He often puts on the public persona of being nothing more than a bald, pudgy man well suited to the pleasantries of court life; humble, obsequious, fawning, and a little effeminate. This is simply a facade that Varys has developed, which often leads those who do not know him well to underestimate him as a cheerful and vapid flatterer. In reality he is a cunning and ruthless manipulator of court politics, on par with Master of Coin, Petyr Baelish (\"Littlefinger\"), with whom he frequently spars. Unlike Littlefinger, Varys insists that his goals are to achieve what he honestly feels is best for the realm.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'bf1b376c53d6df00400a93d9f9279f1c'}>,\n",
-       "  <Document: {'content': 'In reality he is a cunning and ruthless manipulator of court politics, on par with Master of Coin, Petyr Baelish (\"Littlefinger\"), with whom he frequently spars. Unlike Littlefinger, Varys insists that his goals are to achieve what he honestly feels is best for the realm.\\n\\n==Storylines==\\n\\n===In the books===\\n====\\'\\'A Game of Thrones\\'\\'====\\nSoon after Eddard Stark\\'s arrival in King\\'s Landing, Varys warns him that Robert\\'s wife Cersei Lannister intends to kill Robert and offers his help to Eddard in investigating the Lannisters. He later meets in secret with Illyrio to discuss stalling a war between Houses Stark and Lannister until the right moment; this is overheard by Arya Stark, but she is unable to identify the men. When Robert is ultimately killed, Varys remains spymaster for his heir Joffrey and suggests that Ser Barristan Selmy be blamed for Robert\\'s death. Selmy is removed from the Kingsguard and ultimately defects to Daenerys Targaryen, which may have been Varys\\' plan all along. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8d8cf01e8087566f2ab9489074319837'}>,\n",
-       "  <Document: {'content': \"Selmy is removed from the Kingsguard and ultimately defects to Daenerys Targaryen, which may have been Varys' plan all along. Varys visits Eddard in captivity and convinces him to plead guilty to treason and join the Night's Watch in order to save the life of his daughters Sansa and Arya, which would also avert war between the Starks and Lannisters, and Ned eventually agrees. Varys is horrified, however, when Joffrey has Ned executed anyway and tries in vain to dissuade the King whilst Littlefinger smugly looks on. In the aftermath, Varys arranges for Robert's bastard Gendry to join the Night's Watch, to avoid being killed by Cersei.\\n\\n====''A Clash of Kings''====\\nVarys is the first to learn that Tyrion Lannister has brought his mistress Shae with him to King's Landing, and tells Tyrion of a route that he can use to visit her. He forms an uneasy alliance with Tyrion to share the information gathered by his spy network.\\n\\n====''A Storm of Swords''====\\nVarys continues to facilitate Tyrion and Shae's trysts, but testifies against Tyrion when Tyrion is tried for Joffrey's murder. After Tyrion is sentenced to death, Jaime Lannister forces Varys to help Tyrion escape. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '5d6ce185e969b6b8637750c5c0712955'}>,\n",
-       "  <Document: {'content': \"====''A Storm of Swords''====\\nVarys continues to facilitate Tyrion and Shae's trysts, but testifies against Tyrion when Tyrion is tried for Joffrey's murder. After Tyrion is sentenced to death, Jaime Lannister forces Varys to help Tyrion escape. As Varys and Tyrion escape through the secret passages of the Red Keep, Tyrion decides to seek out his father Tywin's chambers. Varys protests but gives Tyrion the exact directions to Tywin's room. It is likely Varys took advantage of the situation to have Tywin killed, weakening a chief rival of House Targaryen's.\\n\\n====''A Feast for Crows''====\\nAfter helping Tyrion escape, Varys disappears. A Red Keep turnkey, Rugen, disappears at the same time, and a Tyrell coin is found in his chambers. This cements Cersei's distrust of House Tyrell, although she is unaware Rugen is merely a disguise of Varys'.\\n\\n====''A Dance with Dragons''====\\nVarys sneaks into the Red Keep to kill Grand Maester Pycelle. He also lures Lord Regent Kevan Lannister into Pycelle's chambers and mortally wounds him. As Kevan dies, Varys explains that his death was necessary to destabilise the Seven Kingdoms in preparation for Aegon Targaryen's invasion, before ordering his little birds to finish Kevan off.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '99fa8267c79a8eb3d9b0fd75df84102f'}>,\n",
-       "  <Document: {'content': 'As Kevan dies, Varys explains that his death was necessary to destabilise the Seven Kingdoms in preparation for Aegon Targaryen\\'s invasion, before ordering his little birds to finish Kevan off.\\n\\n===In the show===\\nConleth Hill plays the role of Varys in the television series\\n\\nCoat of arms of House Targaryen\\n\\n==== Season 1 & 2 ====\\nVarys\\' storyline remains, for the most part, identical between the first two seasons of the show and the books.\\n\\n====Season 3====\\nAfter Littlefinger\\'s confidante Ros is severely beaten by Joffrey\\'s guards and Littlefinger fails to intervene, Varys takes Ros into his service as a spy. Littlefinger finds out and sells her to the sadist Joffrey, taunting the eunuch that Varys could not protect her. Varys affirms his course as a means to prevent chaos befalling the realm, claiming that Littlefinger \"would see the Seven Kingdoms burn, if he could be king over the ashes.\"====Season 4====\\nVarys informs Tyrion that Cersei has discovered his relationship with Shae. Although Varys claims he will not lie for him, he implores Tyrion to send Shae away for her own safety. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '87353708e5785390cf802029dfad3b71'}>,\n",
-       "  <Document: {'content': \"Although Varys claims he will not lie for him, he implores Tyrion to send Shae away for her own safety. Varys later testifies against Tyrion at his trial for murdering Joffrey, but when Tyrion is convicted, Jaime enlists Varys' help in smuggling Tyrion out of King's Landing to Essos. As Varys prepares to return to the Red Keep, he hears the tolling bells, making him realise that Tyrion's escape has been discovered (along with his murder of Tywin), and he joins Tyrion on the voyage to Essos\\n\\n====Season 5====\\nVarys and Tyrion arrive in Pentos, where Varys reveals his allegiance to House Targaryen and convinces Tyrion to travel with him to Meereen and aid Daenerys Targaryen in retaking the Iron Throne. While in Volantis, Tyrion is kidnapped by Daenerys' former advisor Jorah Mormont. Varys journeys on to Meereen, where he arrives to discover Tyrion in control of the city in Daenerys' absence. He offers Tyrion the use of his spy network to maintain order in the city.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '30347b7d7d68b0b2fc8b53d47ec89904'}>,\n",
-       "  <Document: {'content': \"Varys journeys on to Meereen, where he arrives to discover Tyrion in control of the city in Daenerys' absence. He offers Tyrion the use of his spy network to maintain order in the city.\\n\\n====Season 6====\\nVarys discovers that the Meereenese insurgency, the Sons of the Harpy, are funded by the masters of Yunkai and Astapor and the slavers of Volantis, and brokers a meeting between Tyrion and representatives of those cities. The slavers agree to a truce with Tyrion, and Meereen begins to prosper. Varys departs for the Seven Kingdoms, telling Tyrion that he will seek out allies for Daenerys. His ultimate destination is Dorne, where Ellaria Sand has killed Doran Martell in anger at his inaction against the Lannisters and seized power. There he meets and forms alliances with Ellaria and Olenna Tyrell, who also seeks vengeance against Cersei, as Cersei's machinations have caused the death of all the other Tyrells. Varys returns to Meereen with ships from Dorne and the Reach, and sets sail for Westeros with Daenerys and her army.\\n\\n====Season 7====\\n\\nVarys, along with Daenerys and her entire army, reaches Dragonstone. While in Dragonstone, Varys helps in planning the attack against Cersei. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '3cc2a29968936f397cab201d9ca5bacf'}>,\n",
-       "  <Document: {'content': \"====Season 7====\\n\\nVarys, along with Daenerys and her entire army, reaches Dragonstone. While in Dragonstone, Varys helps in planning the attack against Cersei. Daenerys thanks him for getting the support of The Reach and Dorne, but confronts Varys for his past allegiances. Varys defends himself by saying that he doesn't serve any King/Queen but serves the people. Daenerys forgives him but warns Varys not to betray her, or else she would burn him alive. Varys then tells Daenerys of Euron Greyjoy's attack as well as defeat of Highgarden against the Lannister army. While talking with Tyrion, Varys criticizes Daenerys for burning Randyll and Dickon Tarly alive. He also reads the raven sent from Winterfell before informing Jon Snow. Varys accompanies Daenerys, Jon, and others to King's Landing to discuss a truce with Cersei.\\n\\n====Season 8====\\nDuring the White Walkers' attack on Winterfell, Varys shelters in the crypts with the other non-combatants, and survives the Night King's reanimation of those buried in the crypts. In the aftermath of the battle, Varys observes Daenerys' dismay at not being lauded by the Northerners. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '1fc0a3bf324a7e9eeb58bd6cc38c3239'}>,\n",
-       "  <Document: {'content': \"====Season 8====\\nDuring the White Walkers' attack on Winterfell, Varys shelters in the crypts with the other non-combatants, and survives the Night King's reanimation of those buried in the crypts. In the aftermath of the battle, Varys observes Daenerys' dismay at not being lauded by the Northerners. He accompanies her court back to Dragonstone to prepare to attack King's Landing, and is told by Tyrion that Jon Snow is secretly Rhaegar's son and thus the rightful heir to the Iron Throne. The fleet is ambushed by Euron Greyjoy's Iron Fleet and Missandei is captured and executed. After failing to convince Tyrion to help him install Jon as king, or (it is implied) to poison Daenerys, Varys openly approaches Jon and suggests that he become king. Jon rebuffs him, and Tyrion reveals Varys' betrayal to Daenerys. Daenerys has Varys brought before her and has Drogon burn him alive.\\n\\n==TV adaptation==\\nVarys is played by the Northern Irish actor Conleth Hill in the television adaption of the series of books. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '3b61736919a1a97f3e44aebadba35979'}>,\n",
-       "  <Document: {'content': '==TV adaptation==\\nVarys is played by the Northern Irish actor Conleth Hill in the television adaption of the series of books. Author George R. R. Martin wrote about the casting of Hill as Varys, \"Hill, like Varys, is quite a chameleon, an actor who truly disappears inside the characters he portrays, more than capable not only of bringing the slimy, simpering eunuch to life.\"===Recognition and awards===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nResult\\n\\n2011\\n\\nScream Awards\\n\\nBest Ensemble\\n\\n2012\\n\\nScreen Actors Guild Award\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\n2015\\n\\nScreen Actors Guild Award\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nEmpire Award\\n\\nEmpire Hero Award (Ensemble)\\n\\n2017\\n\\nScreen Actors Guild Award\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '5289aa061bbd9270e6d91201b4fd6469'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''Stannis Baratheon''' is a fictional character in the ''A Song of Ice and Fire'' series of epic fantasy novels by American author George R. R. Martin, and its television adaptation ''Game of Thrones''. He is the second son of Steffon Baratheon, the lord of Storm's End, and his wife Lady Cassana Estermont, and brother to King Robert and Renly. He is the Lord of Dragonstone and a claimant to the Iron Throne after his elder brother's death. Stannis's goals are frequently impeded by his lack of manpower and resources, owing to his unpopularity with other noble houses. He must therefore rely on the counsel of the foreign priestess Melisandre and his right-hand man, lowborn smuggler Davos Seaworth, who later becomes his Hand of the King. Stannis often struggles to escape the shadow of his two more overtly charismatic brothers, particularly Robert.\\n\\nThough first mentioned in 1996's ''A Game of Thrones'', Stannis formally appeared in ''A Clash of Kings'' (1998), ''A Storm of Swords'' (2000) and ''A Dance with Dragons'' (2011).  In December 2011, Martin posted a sample chapter from the yet-unfinished ''The Winds of Winter'', told from Theon Greyjoy's viewpoint, which confirmed Stannis' return in the sixth book.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '6d51432d5a55c90a7bb92fdb5215be75'}>,\n",
-       "  <Document: {'content': \"In December 2011, Martin posted a sample chapter from the yet-unfinished ''The Winds of Winter'', told from Theon Greyjoy's viewpoint, which confirmed Stannis' return in the sixth book.\\n\\nStannis is portrayed by English actor Stephen Dillane in the HBO television adaptation of the series, who has received significant critical praise for his performance. Stannis is a divisive character among fans of the books and television show alike, enjoying both great popularity for his dedication to justice and dark horse status, as well as opposition for his unrelenting attitude towards the Iron Throne. He has earned particular attention for the differences in his characterization between the novels and show, particularly during season five.\\n\\n== Character description ==\\nStannis Baratheon is the younger brother of King Robert and older brother of Renly. He is portrayed as a brooding and humorless man with a harsh but fair sense of duty and justice whose dour demeanor is often off-putting in comparison to his two more charismatic brothers. He is renowned as a skilled and prescient military commander. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '522d9ecfddc59c4ac7b7c858ea6bbea'}>,\n",
-       "  <Document: {'content': \"He is portrayed as a brooding and humorless man with a harsh but fair sense of duty and justice whose dour demeanor is often off-putting in comparison to his two more charismatic brothers. He is renowned as a skilled and prescient military commander. Although he is initially said to be extremely stubborn and inflexible, in later books he has shown some ability to use diplomacy and deception to achieve his goals.\\n\\nStannis is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, predominantly Davos Seaworth and Jon Snow, and later Asha and Theon Greyjoy.\\n\\n===Background===\\nStannis was born as the second of Steffon Baratheon and Cassana Estermont's three sons, and in his youth he was constantly overshadowed by his older brother Robert.  When Stannis was fourteen he witnessed his parents' death in a shipwreck off the coast of House Baratheon's castle of Storm's End, and subsequently lost his faith in the Seven Gods.\\n\\nDuring Robert's Rebellion, a teenage Stannis holds Storm's End in Robert's absence, but is besieged by Mace Tyrell and Paxter Redwyne for the best part of the year. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '742aad591829819999c419a8245199ad'}>,\n",
-       "  <Document: {'content': \"During Robert's Rebellion, a teenage Stannis holds Storm's End in Robert's absence, but is besieged by Mace Tyrell and Paxter Redwyne for the best part of the year. Stannis' garrison only avoids starvation thanks to the smuggler Davos Seaworth, who evades the blockade by the Redwyne fleet to bring the Baratheon soldiers a cargo of onions and fish.  When Eddard Stark arrives to lift the siege, Stannis knights Davos as reward for his aid, but also insists that Davos have four fingers removed as punishment for his years of illegal smuggling; Davos agrees, on the condition that Stannis removes the fingers himself.  Stannis is subsequently tasked with building a new royal fleet to assault Dragonstone and capture the island, but arrives to find that Aerys II Targaryen's children Viserys and the newborn Daenerys had fled. Robert names Stannis Lord of Dragonstone, giving him control over the islands of Blackwater Bay and the nearby peninsula of Massey's Hook - but Stannis feels slighted, as their younger brother Renly is named Lord of Storm's End, giving him control over the entirety of the Stormlands. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '5d9fde6af2fb69b7ad9e773552ad355f'}>,\n",
-       "  <Document: {'content': \"Robert names Stannis Lord of Dragonstone, giving him control over the islands of Blackwater Bay and the nearby peninsula of Massey's Hook - but Stannis feels slighted, as their younger brother Renly is named Lord of Storm's End, giving him control over the entirety of the Stormlands. It is suggested that this was because Robert was following the custom of granting Dragonstone to the king's heir, but Stannis perceives it as an insult.\\n\\nSeveral years later, Stannis is married to Lady Selyse Florent, but Robert soiled their wedding bed by deflowering Selyse's cousin Delena, further insulting Stannis. Stannis and Selyse have a single daughter together, Shireen, who nearly dies in infancy after contracting greyscale and is left disfigured.  Stannis, who is named Robert's Master of Ships, destroys the Iron Fleet under the command of Victarion Greyjoy at Fair Isle during the Greyjoy Rebellion and leads the conquest of Great Wyk, the largest of the Iron Islands, adding to his significant military resume.\\n\\nAt the beginning of the first novel ''A Game of Thrones'', Stannis comes to suspect that Robert's children with Cersei Lannister are not actually his, and reveals his suspicions to Robert's Hand of the King, Lord Jon Arryn. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '4d623cbd3c5c071b1a0c43b0099464a4'}>,\n",
-       "  <Document: {'content': 'At the beginning of the first novel \\'\\'A Game of Thrones\\'\\', Stannis comes to suspect that Robert\\'s children with Cersei Lannister are not actually his, and reveals his suspicions to Robert\\'s Hand of the King, Lord Jon Arryn. The two discover several of Robert\\'s bastard children in King\\'s Landing, and begin to gather proof, before Jon\\'s sudden, untimely death.\\n\\n===Appearance and personality===\\nStannis is frequently described as a large and sinewy man that towers over others, such as Davos Seaworth and Jon Snow, a Baratheon trait. He lacks the long black hair of his brothers, and is instead balding, although he keeps a close-cropped beard of the signature Baratheon black. His face is described as \\'tight like cured leather\\' with hollow, gaunt cheeks. His eyes are described by Asha Greyjoy as \\'deep, sunken pits\\', with a powerful stare that suggests an \\'iron ferocity\\'.\\n\\nStannis\\' most prominent characteristics are his flinty and austere demeanor, unrelenting stubbornness, and powerful sense of duty and justice, the latter of which he is notorious for throughout Westeros. He rarely forgives a slight; Jon Snow once comments that \"Stannis Baratheon with a grievance was like a mastiff with a bone; he gnawed it down to splinters.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '3e2e88445d30b30cae600d21579699cf'}>,\n",
-       "  <Document: {'content': 'He rarely forgives a slight; Jon Snow once comments that \"Stannis Baratheon with a grievance was like a mastiff with a bone; he gnawed it down to splinters.\"His signature tic is grinding his teeth, often in the face of unpleasant or unforeseen circumstances. He is a renowned commander, sailor, and warrior, although he is a better tactician than fighter. Stannis is known for his brusqueness and lack of tact in social situations and finds himself uncomfortable around women, including his own wife. He abhors brothels and once tried to have them banned from King\\'s Landing, which made him unpopular with the smallfolk there. He dresses plainly in dark clothing and is rarely seen without his sword and dagger. Stannis is plagued with bitterness at the lack of respect and affection he has been shown by his older brother King Robert, and even in childhood was described by the Baratheon family maester as \"the most unloved of the three\"; a \"solemn and joyless\" child \"mature beyond his years\". Stannis is an atheist, despite claims that he is being manipulated by the red priestess Melisandre.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'add8f357b6a5fc72e2daa111fc8c72ef'}>,\n",
-       "  <Document: {'content': 'Stannis is plagued with bitterness at the lack of respect and affection he has been shown by his older brother King Robert, and even in childhood was described by the Baratheon family maester as \"the most unloved of the three\"; a \"solemn and joyless\" child \"mature beyond his years\". Stannis is an atheist, despite claims that he is being manipulated by the red priestess Melisandre.\\n\\nAlthough Stannis suffers from a lack of support and resources during the War of Five Kings, he is frequently described by his adversaries, including Tywin Lannister, as the most dangerous rival claimant. Above all he is characterized by his fearless and uncompromising pursuit of justice and duty - he is described by Varys in this way: \"His claim is the true one, he is known for his prowess as a battle commander, and he is utterly without mercy. There is no creature on earth half so terrifying as a truly just man.\"== Storylines ==\\nPersonal coat of arms of Stannis Baratheon\\n\\n==== \\'\\'A Game of Thrones\\'\\' ====\\n\\nWhen Robert travels to Winterfell to name Eddard as his new Hand, Stannis flees to Dragonstone with his forces, including most of the royal navy. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '87fa3ebb96aeb4fa258c954ca8796fab'}>,\n",
-       "  <Document: {'content': \"== Storylines ==\\nPersonal coat of arms of Stannis Baratheon\\n\\n==== ''A Game of Thrones'' ====\\n\\nWhen Robert travels to Winterfell to name Eddard as his new Hand, Stannis flees to Dragonstone with his forces, including most of the royal navy. Stannis suspects Cersei's involvement in Jon's death. It is later revealed that the true culprit was Jon's wife Lysa, who was persuaded to poison her husband by her lover, Petyr Baelish. Eddard continues Stannis and Jon's investigations, and discovers that Cersei's children are the products of her incestuous affair with her twin brother Jaime Lannister. After Robert's death, Eddard tries to replace Robert's presumed heir Joffrey Baratheon with Stannis, but loses the political struggle against the Lannisters and is executed.\\n\\n==== ''A Clash of Kings'' ====\\n\\nStannis comes under the influence of the red priestess Melisandre, who believes that Stannis is the reincarnation of Azor Ahai, a messianic figure in her faith. Stannis declares himself the one true King; however, most of the Baratheon bannermen support the claim of his younger and much more charismatic brother, Renly, as does the powerful House Tyrell due to Renly's marriage to Margaery Tyrell. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'b282f0ffcf4a1ac92ff13bf79afe67df'}>,\n",
-       "  <Document: {'content': \"Stannis declares himself the one true King; however, most of the Baratheon bannermen support the claim of his younger and much more charismatic brother, Renly, as does the powerful House Tyrell due to Renly's marriage to Margaery Tyrell. Stannis tries to negotiate with Renly, offering him the chance to become his heir, which offer Renly rejects, planning to have Stannis killed the next day. Renly is subsequently assassinated by a shadow conjured by Melisandre using Stannis' life force, and many of Renly's bannermen immediately swear allegiance to Stannis, although the Tyrells side with the Lannisters due to their offer to have Margaery marry Joffrey.\\n\\nStannis assaults King's Landing by sea, but many of his men are killed when Tyrion Lannister detonates wildfire in the River Blackwater. Stannis' forces still nearly claim victory, but Lannister and Tyrell reinforcements arrive in time to drive Stannis' men away from King's Landing. However Rolland Storm, the Bastard of Nightsong, commands his forces well enough that Stannis is able to escape.\\n\\n==== ''A Storm of Swords'' ====\\n\\nStannis retreats to Dragonstone with what is left of his army. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '4dc3c67e373d9665ac02b922422add50'}>,\n",
-       "  <Document: {'content': \"However Rolland Storm, the Bastard of Nightsong, commands his forces well enough that Stannis is able to escape.\\n\\n==== ''A Storm of Swords'' ====\\n\\nStannis retreats to Dragonstone with what is left of his army. He imprisons his Hand Lord Alester Florent, one of his wife's uncles, for trying to offer terms of surrender to the Lannisters. Another of Selyses' uncles, Ser Axell Florent, castellan of Dragonstone, aims to become Stannis' Hand of the King. He suggests a plan to attack Claw Isle in retaliation for Lord Celtigar's bending the knee to Joffrey, but Davos derides an attack on this defenseless island as evil. For this honest counsel Stannis names Davos as his new Hand. Melisandre tells Stannis to burn Edric Storm, one of Robert's bastards, as a sacrifice to raise dragons from Dragonstone. Stannis is conflicted about burning his nephew but uses Edric's blood collected by three leeches to curse the rival kings: Balon Greyjoy, Joffrey Baratheon, and Robb Stark. After Joffrey's, Balon's, and Robb's deaths seem to prove the power of king's blood, Stannis considers sacrificing Edric. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '198ec749acf329d298b8000dbd824c7a'}>,\n",
-       "  <Document: {'content': \"Stannis is conflicted about burning his nephew but uses Edric's blood collected by three leeches to curse the rival kings: Balon Greyjoy, Joffrey Baratheon, and Robb Stark. After Joffrey's, Balon's, and Robb's deaths seem to prove the power of king's blood, Stannis considers sacrificing Edric. Davos smuggles Edric off Dragonstone to the Free Cities, and then persuades Stannis to sail to the North and save the Night's Watch from the wildling army. After Melisandre burns Alester Florent as a sacrifice to R'hllor to ensure favorable winds, Stannis takes most of his remaining army and sails north to crush the wildling assault, managing to rout the whole host with few casualties. He remains at the Night's Watch headquarters at Castle Black to negotiate a settlement with the wildlings, and offers to legitimise Eddard's bastard Jon Snow if Jon will fight with Stannis' army. Jon, who has been elected Lord Commander of the Night's Watch, declines, as he has made an oath binding him to serve the Night's Watch.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '88b34d3211ff38e893eee061feee413b'}>,\n",
-       "  <Document: {'content': \"Jon, who has been elected Lord Commander of the Night's Watch, declines, as he has made an oath binding him to serve the Night's Watch.\\n\\n==== ''A Feast for Crows'' and ''A Dance with Dragons'' ====\\n\\nStannis sends ravens to the Northern houses naming himself as king and asking for their support, but only House Karstark and a faction of House Umber swear allegiance. Stannis has the King-beyond-the-Wall Mance Rayder burned at the stake, and most of the wildlings bend the knee to Stannis, although Stannis is unaware that Melisandre had used glamours to switch Rayder and his lieutenant, the Lord of Bones. To rally the North, Stannis intends to attack House Bolton's castle The Dreadfort with the help of Arnolf Karstark, castellan of Karhold. In reality Arnolf is working with the Boltons, hoping to entrap Stannis and have his hostage great-nephew Harrion Karstark executed so his branch of the family can take Karhold. On Jon's advice, Stannis instead rallies the northern mountain clans and attacks Deepwood Motte, ousting the Ironborn holding the castle and taking Asha Greyjoy captive. He restores the castle to House Glover, thus winning their support and that of House Mormont. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '6ec701757df6488227eefd4d3f2187b7'}>,\n",
-       "  <Document: {'content': \"On Jon's advice, Stannis instead rallies the northern mountain clans and attacks Deepwood Motte, ousting the Ironborn holding the castle and taking Asha Greyjoy captive. He restores the castle to House Glover, thus winning their support and that of House Mormont. He marches on Winterfell to confront the Boltons, joined by the men of Arnolf Karstark and Mors Umber (one of the castellans of the Last Hearth), but his host is waylaid by heavy snows and forced to stop at a crofter's village three days away from Winterfell. A letter is later sent to Jon Snow by Ramsay Bolton claiming that Stannis has been defeated and killed, though it is unknown how much of the letter is true. George R. R. Martin confirmed to a fan in 2015 that Stannis is still alive in the books.\\n\\n==== ''The Winds of Winter'' ====\\n\\nIn December 2011, Martin posted a sample chapter from the yet-unfinished sixth book ''The Winds of Winter'', told from Theon Greyjoy's viewpoint, where Stannis is actively and efficiently preparing for the looming battle against the Bolton-Frey-Karstark alliance.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'fdc35f52b4166a28b0ba6879636940b6'}>,\n",
-       "  <Document: {'content': '==== \\'\\'The Winds of Winter\\'\\' ====\\n\\nIn December 2011, Martin posted a sample chapter from the yet-unfinished sixth book \\'\\'The Winds of Winter\\'\\', told from Theon Greyjoy\\'s viewpoint, where Stannis is actively and efficiently preparing for the looming battle against the Bolton-Frey-Karstark alliance.  He secures a loan by signing a blood contract with the Braavosi banker Tycho Nestoris, whose convoy managed to find Stannis\\' army, and plans to immediately send Tycho back to the Wall for safety.  Tycho also brought a message from Jon Snow informing Stannis of Arnolf Karstark\\'s planned treachery, after which Stannis arrested Arnolf, his son Arthor and his three grandsons and plans to execute them. He also subdued Tybald, the Dreadfort maester, who out of fear confesses that he has already revealed Stannis\\'s position to the Boltons in Winterfell.\\n\\nStannis then sends Ser Justin Massey to escort Tycho and the escaped \"Arya Stark\" back to Castle Black to reunite her with Jon Snow, in gratitude for Jon\\'s counsel of gathering the northern mountain clans. Massey is to then travel to Braavos and use a loan from the Iron Bank to hire twenty thousand sellswords for Stannis.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'c57e1237f2969da02215bcc017da0da4'}>,\n",
-       "  <Document: {'content': \"Massey is to then travel to Braavos and use a loan from the Iron Bank to hire twenty thousand sellswords for Stannis.  Stannis tells Massey that he may hear of Stannis's death in Braavos, but even if that news is true, Massey is to follow the orders as to seat his daughter Shireen on the Iron Throne.\\n\\nStannis then interrogates Theon for information regarding the Boltons' military strength. When Theon taunts Stannis for not taking Ramsay seriously, Stannis confidently reveals that he already has a battle plan to utilize the terrain against the incoming Frey army. Stannis next receives Asha Greyjoy, and informs her that her brother Theon must be executed, as showing him mercy would mean losing all of the northmen. To spare Theon the agony of death by burning, Asha pleas Stannis to personally execute Theon by beheading in the tradition of the northmen.\\n\\n=== Family tree of House Baratheon ===\\n\\n== TV adaptation ==\\nStephen Dillane plays the role of Stannis Baratheon in the television series.\\nStannis Baratheon is played by Stephen Dillane in the television adaption of the series of books.\\n\\n==== Season 2 ====\\nStannis' personal banner in the TV series.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '9cd3810dd4d90563c9160220b7f44ee1'}>,\n",
-       "  <Document: {'content': \"Stannis Baratheon is played by Stephen Dillane in the television adaption of the series of books.\\n\\n==== Season 2 ====\\nStannis' personal banner in the TV series.\\nStannis falls under the influence of Melisandre, a priestess of R'hllor who believes Stannis is the reincarnation of a legendary hero from her religion. After Robert's death, Stannis claims himself the true heir to the Iron Throne as Cersei's children are bastards born of incest. However, most of the Baratheon bannermen support the claim of the younger but far more charismatic Renly. Stannis confronts Renly and offers to make him his heir if he supports him, but Renly refuses, only using the negotiations as an opportunity to mock his brother, and intending to kill Stannis in battle the next day. Melisandre, who had seduced Stannis, gives birth to a shadow baby bearing Stannis' face that kills Renly, and many of Renly's bannermen immediately swear allegiance to Stannis. He then attacks King's Landing by sailing up Blackwater Bay. Stannis' force breaches the walls, but due to Tyrion Lannister's use of wildfire and the arrival of last-minute Lannister and Tyrell reinforcements, he is defeated. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '74ce08c3bc3be3722a97543574435753'}>,\n",
-       "  <Document: {'content': \"Stannis' force breaches the walls, but due to Tyrion Lannister's use of wildfire and the arrival of last-minute Lannister and Tyrell reinforcements, he is defeated. Nonetheless, he is convinced to continue fighting by Melisandre, and is further convinced by her magic as she shows him a vision of a battle in the flames.\\n\\n==== Season 3 ====\\nStannis acquires one of Robert's bastards, Gendry, from the Brotherhood Without Banners and plans to sacrifice him to further his quest for the Iron Throne. After Davos Seaworth questions his course of action, Stannis has three leeches drawn with Gendry's blood and throws them into a fire, calling for the death of Robb Stark, Balon Greyjoy and Joffrey Baratheon. Upon the news of Robb's death, Davos releases Gendry to prevent him from being sacrificed. Stannis subsequently sentences Davos to death but is swayed by Melisandre, who encourages him to travel North to aid the Night's Watch against the White Walkers emerging from beyond the Wall.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '67f1cb5a4e64706416522848011580c'}>,\n",
-       "  <Document: {'content': \"Stannis subsequently sentences Davos to death but is swayed by Melisandre, who encourages him to travel North to aid the Night's Watch against the White Walkers emerging from beyond the Wall.\\n\\n==== Season 4 ====\\nStannis hears of Joffrey's death and chastises Davos again for releasing Gendry and not finding him a suitable army, prompting Davos to write to the Iron Bank of Braavos in Stannis' name to help pay for an army. The Iron Bank nearly refuses Stannis' request, but Davos manages to convince them that Stannis is the only one they can turn to since Tywin Lannister is nearing old age. Stannis and his army arrive at the Wall shortly after the Battle of Castle Black, interrupting a parley between Jon Snow and Mance Rayder and crushing the wildling force. Stannis learns that Jon is Ned Stark's son, and on Jon's advice, he places Mance and his men under arrest. He is later present at the funeral of the Night's Watch brothers who died during the Battle for Castle Black.\\n\\n==== Season 5 ====\\nStannis begins plans to retake the North from Roose Bolton, hoping to recruit Mance's wildling army, if Mance will bend the knee to him. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '15fd7a5820d20d0709f88a166ccf6e55'}>,\n",
-       "  <Document: {'content': \"==== Season 5 ====\\nStannis begins plans to retake the North from Roose Bolton, hoping to recruit Mance's wildling army, if Mance will bend the knee to him. Mance refuses, and Stannis has him burnt at the stake. Stannis offers to legitimise Jon as a Stark to win the loyalty of the Northerners who refuse to recognise Stannis as their king, but Jon decides to remain loyal to his vows to the Night's Watch. Stannis marches on Winterfell, but his army is delayed by a large snowstorm. In the chaos Ramsay Bolton and his men infiltrate Stannis's camp, and destroy all supplies and horses. Melisandre persuades a reluctant Stannis to sacrifice Shireen to ensure victory. Although the snowstorm lifts, half of Stannis's army deserts him, Selyse commits suicide out of guilt, and Melisandre flees to Castle Black. Stannis decides to complete the march on foot. As his army arrives at Winterfell, it is swiftly defeated by a cavalry charge led by Ramsay Bolton. Stannis survives the battle, but is confronted by Brienne of Tarth, a former member of Renly's Kingsguard. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'da7ca350e93f3b86674f23e9c401067c'}>,\n",
-       "  <Document: {'content': 'As his army arrives at Winterfell, it is swiftly defeated by a cavalry charge led by Ramsay Bolton. Stannis survives the battle, but is confronted by Brienne of Tarth, a former member of Renly\\'s Kingsguard. Stannis confesses to killing Renly with blood magic, and Brienne executes him, telling Stannis she is killing him in the name of the \\'rightful\\' King Renly.\\n\\n=== Critical reception ===\\nThe English actor Stephen Dillane has received positive reviews for his performance as Stannis Baratheon, especially in the fifth season. On her review for \"Sons of the Harpy\", Sarah Hughes of \\'\\'The Guardian\\'\\' wrote, \"Stephen Dillane has always been wonderful at showing us the core of this rigid, complex man.\"His performance on the same episode also received positive reviews from \\'\\'Vulture\\'\\'.\\n\\nHarri Sargeant of \\'\\'Hypable\\'\\' wrote, \"Stephen Dillane has always been one of the classiest actors on the show, and his last moments as the King Who Almost Was saw as commanding a performance as ever.\"In her review for \\'\\'Mother\\'s Mercy\\'\\', Meghan O\\'Keefe of \\'\\'Decider\\'\\' wrote, \"Whether you like Stannis or not, you have to admit that Stephen Dillane delivered a monumental performance this season.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '47ada5ae629cc8d2082fd73d87ac3e8f'}>,\n",
-       "  <Document: {'content': 'In her review for \\'\\'Mother\\'s Mercy\\'\\', Meghan O\\'Keefe of \\'\\'Decider\\'\\' wrote, \"Whether you like Stannis or not, you have to admit that Stephen Dillane delivered a monumental performance this season.\"Cindy Davis of \\'\\'Pajiba\\'\\' wrote \"After Shireen’s seemingly pointless death, it’s a testament to Stephen Dillane’s phenomenal skills that we could feel anything at all for a man who watched his daughter burned alive.\"Nick Steinberg of \\'\\'Goliath\\'\\'  wrote, \"Credit has to go to Stephen Dillane for his performance, especially in these last few episodes where he’s had to convey a form of grim tragedy that’s effectively conveyed the character’s downfall.  For his part, Dillane expressed misgivings about his performance, telling \\'\\'The Times\\'\\' newspaper, \"I didn’t know what I was doing until we’d finished filming and it was too late. The damage had been done. I thought no one would believe in me and I was rather disheartened by the end. I felt I’d built the castle on non-existent foundations.”\\'\\'\\'\\'\\'\\n\\n== References ==\\n\\n==Sources==\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': 'd009bfb76568ef6498116374a60a1da4'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Winterfell\\'\\'\\'\" is the eighth season premiere episode of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 68th overall. It was written by Dave Hill and directed by David Nutter. It aired on April 14, 2019.\\n\\nThe episode centres on Daenerys Targaryen and Jon Snow arriving in Winterfell with Unsullied and Dothraki forces, after Jon Snow has pledged his allegiance to her. It also reunites Jon with his Stark siblings Bran and Arya for the first time since the first season\\'s second episode.\\n\\n\"Winterfell\" received positive reception from critics, who listed Jon Snow and Daenerys\\'s arrival at Winterfell, Jon\\'s long-awaited reunion with Arya, the destruction of Last Hearth, Sam\\'s revelation to Jon Snow about his true lineage, Jaime and Bran\\'s tense encounter, and the performances of Sophie Turner and John Bradley as highlights of the episode. The episode received a Primetime Emmy Award nomination for Outstanding Single-Camera Picture Editing for a Drama Series and was Sophie Turner\\'s pick to support her nomination for Outstanding Supporting Actress in a Drama Series.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nQyburn informs Cersei Lannister that the White Walkers have breached the Wall, which pleases Cersei. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '789ae3bc1c0c5aefc1746d80e14ff3fa'}>,\n",
-       "  <Document: {'content': \"The episode received a Primetime Emmy Award nomination for Outstanding Single-Camera Picture Editing for a Drama Series and was Sophie Turner's pick to support her nomination for Outstanding Supporting Actress in a Drama Series.\\n\\n==Plot==\\n\\n===In King's Landing===\\nQyburn informs Cersei Lannister that the White Walkers have breached the Wall, which pleases Cersei. Euron Greyjoy arrives with Yara Greyjoy as his prisoner and with the Golden Company. Euron uses his deeds to convince Cersei to start their relationship, and the pair have sex. Euron insists that he will impregnate Cersei, who appears to be visibly upset when he leaves the room.\\n\\nQyburn visits Bronn and presents him with the crossbow Tyrion used to assassinate his father Tywin Lannister. Should Jaime and Tyrion survive the war against the dead, Cersei wants Bronn to assassinate them.\\n\\n===In the Narrow Sea===\\nTheon Greyjoy secretly boards Euron's flagship in the night and frees his sister Yara. Yara decides to retake the Iron Islands while Euron is distracted by matters in King's Landing, whereas Theon decides to journey to Winterfell to fight the undead with Daenerys and the Starks.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '88431eaea9002ed2b0de8739091ac042'}>,\n",
-       "  <Document: {'content': \"Yara decides to retake the Iron Islands while Euron is distracted by matters in King's Landing, whereas Theon decides to journey to Winterfell to fight the undead with Daenerys and the Starks.\\n\\n===At Last Hearth===\\nTormund Giantsbane and Beric Dondarrion, having survived the fall of Eastwatch, lead their party into House Umber's seat of Last Hearth, already sacked by the dead. They encounter Eddison Tollett, who had evacuated the Night's Watch from Castle Black. They find young Lord Ned Umber, who had been evacuating Last Hearth's inhabitants, dead and impaled to a wall. Tormund declares that they must reach Winterfell before the Night King. Ned reanimates as a wight, and Beric burns him with his flaming sword.\\n\\n===At Winterfell===\\nDaenerys Targaryen arrives at Winterfell alongside Jon Snow, Tyrion Lannister, Jorah Mormont, Varys and Missandei, with Grey Worm leading her Unsullied and Dothraki armies. Jon reunites with Bran and Daenerys is greeted by Sansa Stark, the townspeople, and the Northern lords, though the pleasantries are cut short when Bran Stark reveals to Daenerys and Jon that the Night King has reanimated Viserion and breached the Wall. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '224868eb8735b122bec3f7b1edd5768e'}>,\n",
-       "  <Document: {'content': \"Jon reunites with Bran and Daenerys is greeted by Sansa Stark, the townspeople, and the Northern lords, though the pleasantries are cut short when Bran Stark reveals to Daenerys and Jon that the Night King has reanimated Viserion and breached the Wall. Sansa has ordered the Northern lords to retreat to Winterfell to make the stand against the undead, but at council, Lyanna Mormont and the other lords express displeasure towards Jon for abandoning his position as King in the North by swearing fealty to a Targaryen. This tension is magnified when Tyrion declares that the Lannister army will soon march north too; Sansa fears that the combined forces might overstretch their supplies, but in private implies to Tyrion that she does not believe Cersei will send her army.\\n\\nArya reunites with Jon, Gendry (who is working in Winterfell's forges crafting dragonglass weapons), and the Hound. Davos Seaworth suggests to Tyrion and Varys that the Northerners could be won over by betrothing Jon and Daenerys. Jon and Daenerys ride the dragons, deepening their bond. Later, Sansa accuses Jon of bending the knee out of love for Daenerys.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '80e86822114c852613ac35bca52d3158'}>,\n",
-       "  <Document: {'content': 'Jon and Daenerys ride the dragons, deepening their bond. Later, Sansa accuses Jon of bending the knee out of love for Daenerys.\\n\\nSamwell Tarly meets Daenerys, who thanks him for curing Jorah of his greyscale, but after discovering his identity admits that she executed his father, Randyll, and his brother, Dickon. Bran instructs a heartbroken Samwell to tell Jon about his true Targaryen heritage, the news of which leaves Jon stunned.\\n\\nJaime Lannister arrives at Winterfell and is shocked when he encounters Bran in the courtyard, waiting for him.\\n\\n== Production ==\\n=== Writing ===\\nThe episode was written by Dave Hill, his final script of the series. The episode adapts material from the unpublished novels \\'\\'The Winds of Winter\\'\\' and \\'\\'A Dream of Spring\\'\\', among them the released sample chapter \"The Forsaken\", in which Euron Greyjoy converses with a captive member of his family aboard his ship.\\n\\n=== Casting ===\\nThe episode saw the introduction of Marc Rissmann as Harry Strickland, the commander of the Golden Company. On preparing for his role in the season, Rissmann said, \"I did some research into who this person was, where he comes from, and the books are quite precise. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '43455d19d7a5d9b8d244e1cd156213f5'}>,\n",
-       "  <Document: {'content': 'On preparing for his role in the season, Rissmann said, \"I did some research into who this person was, where he comes from, and the books are quite precise. And then you see in the scripts what is there, what are the similarities, what are the differences. So I did a bit of research on that, especially since this world was so properly thought through. It\\'s a functioning world, and that\\'s why it\\'s actually so appealing.\"Jacob Anderson (Grey Worm) is listed as a series regular in the opening credits for the first time in this episode; he has previously appeared as a supporting/guest actor. Actors Rob McElhenney and Martin Starr and writer Dave Hill made cameo appearances as Ironborn soldiers killed when Theon rescues Yara.\\n\\n=== Filming ===\\nThe episode was directed by David Nutter who had previously directed two episodes each in the second, third and fifth season. The outdoor Winterfell scenes were filmed at sets in Moneyglass and Magheramorne in Northern Ireland, with indoor scenes filmed at Paint Hall studios in Belfast.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '5b111e706df0efb674492f03a48b5c8c'}>,\n",
-       "  <Document: {'content': 'The outdoor Winterfell scenes were filmed at sets in Moneyglass and Magheramorne in Northern Ireland, with indoor scenes filmed at Paint Hall studios in Belfast.\\n\\n== Reception ==\\n\\n=== Ratings ===\\n\"Winterfell\" was viewed by 11.76 million viewers on its initial live broadcast on HBO, and an additional 5.6 million viewers on streaming platforms, for a total 17.4 million viewers.\\n\\n=== Critical response ===\\nThe episode received largely positive reviews from critics, and is the best reviewed episode of the season. It has a 92% approval rating on the review aggregator website Rotten Tomatoes, based on 107 reviews with an average score of 7.72 out of 10. The site\\'s consensus reads, \"Though surprisingly bloodless, hard-fought reunions, bone-chilling surprises, and a welcome dose of humor help \"Winterfell\" set the stage for what should be an epic final season.\"Sarah Hughes of \\'\\'The Guardian\\'\\' wrote, \"This was a thrilling episode with its pedal to the floor, in which new alliances were made, old ones tested and long-awaited reunions occurred (hurrah for Jon and Arya\\'s tenderly scripted meeting)\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'c0aca0cb3791a8c7c78acb9a66de872a'}>,\n",
-       "  <Document: {'content': 'Sarah Hughes of \\'\\'The Guardian\\'\\' wrote, \"This was a thrilling episode with its pedal to the floor, in which new alliances were made, old ones tested and long-awaited reunions occurred (hurrah for Jon and Arya\\'s tenderly scripted meeting)\". Todd VanDerWiff of \\'\\'Vox\\'\\' praised the episode\\'s visual quality, stating, \"For as much as I grouse about \\'\\'Game of Thrones\\'\\', it\\'s often casually stunning in a way that no other TV show comes close to pulling off\". Ron Hogan of Den of Geek similarly highlighted the episode\\'s pacing, while also praising John Bradley who \"runs through the entire gamut of emotions in a very short time on screen\".\\n\\nAmong the negative reviews, Willa Paskin of \\'\\'Slate\\'\\' criticized the show\\'s pace, stating \"Momentum, the idea that we are hurtling toward some conclusion that will explain it all, has been so encoded into the \\'\\'Game of Thrones\\'\\' experience that in the absence of any forward motion, the show is ... kind of dull.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '9a58a549c08d02b72f2eff8c97abffec'}>,\n",
-       "  <Document: {'content': 'Among the negative reviews, Willa Paskin of \\'\\'Slate\\'\\' criticized the show\\'s pace, stating \"Momentum, the idea that we are hurtling toward some conclusion that will explain it all, has been so encoded into the \\'\\'Game of Thrones\\'\\' experience that in the absence of any forward motion, the show is ... kind of dull.\"=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2019\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nSophie Turner\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nCrispin Green\\n\\n==References==\\n\\n==External links==\\n*  \"Winterfell\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'b008e340d0eae5f90f4f6be27985740f'}>,\n",
-       "  <Document: {'content': '\\n\"\\'\\'\\'The Queen\\'s Justice\\'\\'\\'\" is the third episode of the seventh season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 63rd overall. The episode was written by series co-creators David Benioff and D. B. Weiss, and directed by Mark Mylod.\\n\\nAt Dragonstone, Jon Snow and Davos Seaworth meet with Daenerys Targaryen, from whom Jon asks for aid in defeating the White Walkers, but Daenerys iterates her desire to take the Iron Throne instead. In King\\'s Landing, Euron Greyjoy returns bearing gifts to Cersei Lannister, in the form of Ellaria Sand and Tyene Sand. At the Citadel, Samwell Tarly and Archmaester Ebrose examine a now healed Jorah Mormont. In Winterfell, Sansa Stark is reunited with her brother Bran Stark. At Casterly Rock, the Unsullied infiltrate and capture the castle, but Euron\\'s Iron Fleet arrives, and begins destroying their ships, leaving them stranded. Meanwhile, Jaime Lannister leads the Lannister army to take Highgarden.\\n\\nThe title of the episode refers to Cersei Lannister exacting vengeance, her own way of justice, on both the Sand Snakes and Olenna Tyrell. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '5af9d0cdc1b4906a8284be277cb7777b'}>,\n",
-       "  <Document: {'content': 'The title of the episode refers to Cersei Lannister exacting vengeance, her own way of justice, on both the Sand Snakes and Olenna Tyrell. \"The Queen\\'s Justice\" received positive reviews from critics, who considered the long-awaited meeting between Daenerys and Jon, the agonizing fates of Ellaria and Tyene Sand, Sansa and Bran\\'s reunion, the bait-and-switch scene concerning Casterly Rock and Highgarden, and the final fates of Ollena Tyrell and Ellaria Sand as highlights of the episode. In the United States, it achieved a viewership of 9.25 million in its initial broadcast. It was also Diana Rigg\\'s pick to support her nomination at the 70th Primetime Emmy Awards for Outstanding Guest Actress.\\n\\n\"The Queen\\'s Justice\" marked the final appearances of Diana Rigg (Olenna Tyrell), Rosabell Laurenti Sellers (Tyene Sand) and Indira Varma (Ellaria Sand).\\n\\n==Plot==\\n===On Dragonstone===\\nJon and Davos arrive at Dragonstone with their soldiers. They agree Daenerys is not responsible for her father\\'s crimes, but Daenerys denies Jon\\'s assertion he should not be held by his ancestors\\' oath to hers. Jon explains the threat of the White Walkers and wights, but Daenerys wants to claim the Iron Throne before considering other threats. Jon is forced to remain at Dragonstone.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '141d83cbbc04da67378f7f0fa211e387'}>,\n",
-       "  <Document: {'content': \"Jon explains the threat of the White Walkers and wights, but Daenerys wants to claim the Iron Throne before considering other threats. Jon is forced to remain at Dragonstone.\\n\\nVarys asks Melisandre why she hides from Jon; she admits they parted on bad terms due to her mistakes. She plans to return to Volantis, and Varys urges her to stay overseas forever, but she predicts that both of them will die in Westeros.\\n\\nSpeaking with Jon, Tyrion concedes that Jon's willingness to meet with Daenerys has convinced him that the White Walkers are real. Tyrion explains that Daenerys' followers are loyal because Daenerys dedicates herself to protecting others from threats she understands. Tyrion relays Jon's request to mine dragonglass on Dragonstone; Daenerys accepts Tyrion's recommendation to agree as a gesture of goodwill.\\n\\n===In the Narrow Sea===\\nOne of the few remaining ships of Yara's fleet rescue Theon. The Ironborn disbelieve his claim that he tried to rescue Yara from Euron.\\n\\n===In King's Landing===\\nEuron parades his captives Ellaria, Tyene, and Yara through abusive crowds in the streets of King's Landing. He presents the Sands as his gift to Cersei. Ellaria and Tyene are bound and gagged in the dungeon. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '6cf213e07395b2cfdc7fcf5253486c19'}>,\n",
-       "  <Document: {'content': '===In King\\'s Landing===\\nEuron parades his captives Ellaria, Tyene, and Yara through abusive crowds in the streets of King\\'s Landing. He presents the Sands as his gift to Cersei. Ellaria and Tyene are bound and gagged in the dungeon. Cersei kisses Tyene with the same poison Ellaria used to kill Myrcella, and tells Ellaria she will be kept alive to watch her daughter die and decompose. Tycho Nestoris of the Iron Bank comes to collect repayment of the Lannisters\\' loans. Cersei denigrates Daenerys as a possible investment, and secures a fortnight to pay the debt.\\n\\n===At Winterfell===\\nSansa competently manages Winterfell, preparing it as an emergency refuge for all Northerners. Littlefinger counsels Sansa to \"fight every battle\" mentally. Bran and Meera arrive, and Sansa is reunited with her brother. In the Godswood, Sansa is confused by Bran\\'s revelation that he is the Three-Eyed Raven, and troubled by his specific knowledge of her suffering (\"Unbowed, Unbent, Unbroken\").\\n\\n===In Oldtown===\\nArchmaester Ebrose proclaims that Jorah is healed of greyscale and releases him. Jorah plans to return to Daenerys. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '57445d745395501e3be1887d806a22c2'}>,\n",
-       "  <Document: {'content': '===In Oldtown===\\nArchmaester Ebrose proclaims that Jorah is healed of greyscale and releases him. Jorah plans to return to Daenerys. Sam admits that he administered the forbidden treatment; Ebrose praises his skill, but punishes his disobedience with an assignment to copy a large number of old documents.\\n\\n===At Casterly Rock===\\nThe Unsullied infiltrate Casterly Rock via its sewers, which Tyrion designed. They capture the castle, finding far fewer defenders than expected. Meanwhile, the Iron Fleet arrives and destroys Daenerys\\' ships, stranding Grey Worm and his army.\\n\\n===At Highgarden===\\nJaime, Randyll, and their armies take Highgarden, having abandoned Casterly Rock to trick Daenerys into committing her Unsullied forces to a strategically useless position. Jaime grants Olenna the mercy of a quick and painless death by poison, over Cersei\\'s original plans to execute her. After drinking the poison, Olenna confesses to murdering Joffrey and asks Jaime to tell Cersei. Incensed, Jaime leaves Olenna to die alone.\\n\\n==Production==\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"The Queen\\'s Justice\" was written by the series\\' creators, David Benioff and D. B. Weiss, and directed by Mark Mylod, his second of two episodes for this season. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'e96340339305bc444c448eeda5d384c'}>,\n",
-       "  <Document: {'content': '\"The Queen\\'s Justice\" was written by the series\\' creators, David Benioff and D. B. Weiss, and directed by Mark Mylod, his second of two episodes for this season. Mylod joined the series as a director in the fifth season, working on \"High Sparrow\" and \"Sons of the Harpy\".\\n\\nIndira Varma made her final appearance as Ellaria Sand.\\n\"The Queen\\'s Justice\" was the final episode for actress Indira Varma, who had played Ellaria Sand since the fourth season. \"The Queen\\'s Justice\" was also the last episode for recurring cast members Diana Rigg and Rosabell Laurenti Sellers, as Olenna Tyrell and Tyene Sand were killed. The episode featured the return of Mark Gatiss as Tycho Nestoris, who was last seen in season 5\\'s \"The Dance of Dragons\".\\n\\nKit Harington spoke about the scene he shot with Peter Dinklage in Spain, saying \"It was a beautiful location with 50 mph winds and I was wearing a cape next to a cliff—There was a danger of me being blown off! I\\'m not sure it\\'s the way I would have wanted to go.\"Indira Varma and Rosabell Laurenti Sellers\\'s last scene was technically difficult for the actresses, because they were shackled. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '8e238d23589f156ea70ebb9ec22dfeea'}>,\n",
-       "  <Document: {'content': 'I\\'m not sure it\\'s the way I would have wanted to go.\"Indira Varma and Rosabell Laurenti Sellers\\'s last scene was technically difficult for the actresses, because they were shackled. The shackles were felt-lined but tight, and Varma and Sellers ended up \"bruised and battered\" due to the physical intensity of their acting. Varma had to be cut from the shackles at the end of the day.\\n\\n==Analysis==\\nD. B. Weiss talked about the return of Bran to Winterfell, saying \"One of the things we loved about Game of Thrones from the very first book is it\\'s not a world where magic is the primary driver of the story, it\\'s a world where human psychology and behavior and desire are drivers of the story. We try very hard to make sure it stays that way because that\\'s a lot more relatable to the vast majority of the audience than magic powers—as much fun as those are. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'f03d536a95af335ca33eeb1bcba33768'}>,\n",
-       "  <Document: {'content': 'We try very hard to make sure it stays that way because that\\'s a lot more relatable to the vast majority of the audience than magic powers—as much fun as those are. So with Bran, ideally you want to use him in a way that adds to the story and enriches the story and not in a way that\\'s a magic bullet to conveniently deal with things that you haven\\'t come up with a better way to deal with. So it was a balancing act to account for who Bran is now without letting that overtake the story.\"Isaac Hempstead Wright explained how the magical conversion has altered the characters personality, saying \"It\\'s like imagining you have all of space and time in your head—Bran is existing in thousands of planes of existence at any one time. So it\\'s quite difficult for Bran to have any kind of semblance of personality anymore because he\\'s really like a giant computer\". He went on to add that \"Bran really at this stage is not the Three-Eyed Raven. He\\'s got the title but hasn\\'t had thousands of years of sitting in a cave looking through time. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '6f906c9ff2d637410b2d63ad266b1291'}>,\n",
-       "  <Document: {'content': 'He went on to add that \"Bran really at this stage is not the Three-Eyed Raven. He\\'s got the title but hasn\\'t had thousands of years of sitting in a cave looking through time. Somebody put in front of him a massive encyclopedia of all of time and he\\'s only opened page one. He can look stuff up but doesn\\'t have this all-knowing all-seeing capability just yet.\"Indira Varma expected the seventh season would be her character\\'s last, reasoning that a \"less important character\" like Ellaria would have to be phased out to make room for the story\\'s climax. She expected audiences to be \"more invested in Cersei,\" who had more screen time than Ellaria, but she hoped positive fan reception for Pedro Pascal as Oberyn Martell would carry over to sympathy for Ellaria. Varma had hoped for her character to die on screen, but found the fate Weiss and Benioff crafted for Ellaria to be \"really clever\" and beyond \"every parent\\'s worst nightmare.\"Weiss praised Diana Rigg\\'s performance in her final scene as Olenna Tyrell, for contributing to the character uniquely \"winning her own death scene.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'f8f5dbceb543ad8ae5625ed159fb280d'}>,\n",
-       "  <Document: {'content': 'Weiss praised Diana Rigg\\'s performance in her final scene as Olenna Tyrell, for contributing to the character uniquely \"winning her own death scene.\"==Reception==\\n===Ratings===\\n\"The Queen\\'s Justice\" was viewed by 9.25 million total viewers on its initial viewing on HBO, which was slightly less than the previous week\\'s rating of 9.27 million viewers for the episode \"Stormborn\". The episode also acquired a 4.3 rating in the 18–49 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was viewed by 3.272 million viewers on Sky Atlantic during its Simulcast, making it the highest-rated broadcast that week.\\n\\n===Critical reception===\\n\"The Queen\\'s Justice\" received positive reactions from critics. Review aggregator Rotten Tomatoes surveyed 40 reviews of the episode and judged 89% of them to be positive, with an average rating of 7.9 out of 10. The website\\'s consensus for the episode stated \"\\'The Queens Justice\\' saw the much-anticipated meeting between Jon Snow and Daenerys Targaryen -- but had much larger surprises in store before its shocking end.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '4b1f47097920809b3400c7a18a95c4f1'}>,\n",
-       "  <Document: {'content': 'The website\\'s consensus for the episode stated \"\\'The Queens Justice\\' saw the much-anticipated meeting between Jon Snow and Daenerys Targaryen -- but had much larger surprises in store before its shocking end.\"Matt Fowler of IGN described the episode as \"amazing\", saying \"\"The Queen\\'s Justice\" did justice to the long-awaited coming together of Jon Snow and Daenerys Targaryen. With a devilishly good script and some pointed action (along with a few twists and turns), this episode contained a ton of long scenes, but no fat. - This was prime Thrones.\"He gave the episode a 9.5 out of 10. Shane Ryan of \\'\\'Paste Magazine\\'\\' wrote \"It was, by far, the best episode of the season. I\\'ve always contended that the show does best when it pushes the drama and the narrative ahead by means of two-person scenes, and the examples in \"The Queen\\'s Justice\" were phenomenal\". Glen Weldon of NPR also praised the episode and Diana Rigg\\'s performance, writing \"It\\'s a hell of a way to go out, but a character like Olenna -- and an actress like Rigg, who always let you see the danger flashing behind those eyes\\xa0– deserves nothing less.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'a2e6fae2c93b5be21c2e0a3ec47ac064'}>,\n",
-       "  <Document: {'content': 'Glen Weldon of NPR also praised the episode and Diana Rigg\\'s performance, writing \"It\\'s a hell of a way to go out, but a character like Olenna -- and an actress like Rigg, who always let you see the danger flashing behind those eyes\\xa0– deserves nothing less.\"Bennett Madison of \\'\\'Vanity Fair\\'\\' wrote, \"\\'\\'Game of Thrones\\'\\' has spent so much time building up tension and drawing out plots that many of its ongoing story lines have literally been brewing for years\\xa0... With this episode, \\'The Queen\\'s Justice\\', we\\'re finally starting to see some real payoff.\"Andy Hartup of GamesRadar similarly gave praise to the episode for granting victories to antagonists like Cersei and Euron, while criticizing the character dynamics in scenes taking place at Winterfell and Dragonstone. Sean T. Collins of \\'\\'Rolling Stone\\'\\' wrote of the episode, \"As befits its title, this week\\'s installment asks us to consider what it means to be a ruler\\xa0... and what it means to seek justice.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'fd73abb51f62030095b8f8ac5cfe6b89'}>,\n",
-       "  <Document: {'content': 'Sean T. Collins of \\'\\'Rolling Stone\\'\\' wrote of the episode, \"As befits its title, this week\\'s installment asks us to consider what it means to be a ruler\\xa0... and what it means to seek justice.\"Kevin Yeoman of Screen Rant praised the final scene of Olenna Tyrell, writing \"As much as a momentous encounter between two major characters grabbed the spotlight, \\'\\'Game of Thrones\\'\\' proved that a character\\'s last words could be just as important as their first.\"Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' praised the performances of Rigg, Coster-Waldau and Dinklage, but was critical of Emilia Clarke and Kit Harington\\'s scenes together, writing that \"in the Dragonstone scenes, Clarke and Harington mostly served to bring out each other\\'s most wooden line readings\". She also addressed the way the episode approached themes of female empowerment, writing that \"if any show has been a cautionary tale about the difference between female empowerment and true social change, \\'\\'Game of Thrones\\'\\' has been it\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '5663620d3c943eb8543a977de024197e'}>,\n",
-       "  <Document: {'content': 'She also addressed the way the episode approached themes of female empowerment, writing that \"if any show has been a cautionary tale about the difference between female empowerment and true social change, \\'\\'Game of Thrones\\'\\' has been it\".\\n\\n===Accolades===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2017\\n\\nHollywood Post Alliance\\n\\nOutstanding Editing\\n\\nJesse Parker\\n\\n2018\\n\\nArt Directors Guild Awards\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley\\n\\n2018\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\n==References==\\n\\n==External links==\\n\\n*  \"The Queen\\'s Justice\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'a41431dcca659d6ac4a444cea676f4ea'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Last of the Starks\\'\\'\\'\" is the fourth episode of the eighth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 71st overall. It was written by David Benioff and D. B. Weiss, and directed by David Nutter. It aired on May 5, 2019.\\n\\n\"The Last of the Starks\" shows the aftermath of the battle against the Army of the Dead while setting the stage for the final confrontation, with Daenerys, Jon, and their remaining forces going towards King\\'s Landing to confront Cersei and demand her surrender.\\n\\nThe episode received mixed reviews. Critics praised its return to the political intrigue of earlier \\'\\'Game of Thrones\\'\\' episodes, but criticized the episode\\'s writing. It received a Primetime Emmy Award nomination for Outstanding Directing for a Drama Series and was picked by Emilia Clarke to support her nomination for Outstanding Lead Actress in a Drama Series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '94742c832df92c2bff9b5e2bfd74560b'}>,\n",
-       "  <Document: {'content': \"It received a Primetime Emmy Award nomination for Outstanding Directing for a Drama Series and was picked by Emilia Clarke to support her nomination for Outstanding Lead Actress in a Drama Series.\\n\\nThis episode marks the final appearance of Nathalie Emmanuel (Missandei), and Hannah Murray (Gilly), as well as the final appearances of six actors whose characters died in the previous episode, but were seen as corpses: Alfie Allen (Theon Greyjoy), Iain Glen (Ser Jorah Mormont), Bella Ramsey (Lyanna Mormont), Richard Dormer (Beric Dondarrion), Ben Crompton (Eddison Tollett), and Staz Nair (Qhono).\\n\\n==Plot==\\n===At Winterfell===\\nJon leads a mass funeral cremation for the dead. At a feast, Daenerys legitimizes Gendry as a Baratheon and makes him Lord of Storm's End, the ancestral home of House Baratheon. Gendry proposes to Arya, but she declines. Brienne leaves a drinking game in discomfort when Tyrion guesses she is a virgin. Jaime goes to Brienne's room and they have sex. Daenerys is uncomfortable at the acclaim the wildlings give Jon and, in private, begs him to never reveal his true parentage. Jon reassures Daenerys that he's renounced his claim for hers but insists he must tell Arya and Sansa the truth.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'a44c33ee8d93324bc5663eacf226ae8a'}>,\n",
-       "  <Document: {'content': \"Jon reassures Daenerys that he's renounced his claim for hers but insists he must tell Arya and Sansa the truth.\\n\\nDaenerys plans to immediately storm King's Landing, but Sansa argues the soldiers need rest. Ultimately, they agree Jon will lead the army on foot while Daenerys and her fleet will sail to Dragonstone. Afterwards, Arya and Sansa tell Jon that they distrust Daenerys but Jon defends her. After swearing Sansa and Arya to secrecy, Jon has Bran tell Arya and Sansa of his true parentage.\\n\\nBronn arrives to kill Jaime and Tyrion, but accepts their offer of Highgarden in exchange for their lives. Arya and Sandor Clegane ride for King's Landing. Sansa reveals Jon's parentage to Tyrion, who then tells Varys. Jon gives Ghost to Tormund, who plans to return north of the Wall with the wildlings; Tormund suggests that Jon join them. Jon also bids farewell to Sam and a pregnant Gilly.\\n\\nAfter learning of Cersei's capture of Missandei and Sansa suggesting Daenerys will execute Cersei for this, Jaime leaves for King's Landing during the night in hopes of saving her, despite Brienne's tearful plea that he stay with her.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '93891fd493a911edb3abdeb11e43f69'}>,\n",
-       "  <Document: {'content': \"After learning of Cersei's capture of Missandei and Sansa suggesting Daenerys will execute Cersei for this, Jaime leaves for King's Landing during the night in hopes of saving her, despite Brienne's tearful plea that he stay with her.\\n\\n===At Dragonstone===\\nEuron's Iron Fleet ambushes Daenerys's fleet and kills Rhaegal. Missandei is captured. Daenerys is convinced to talk to Cersei first instead of attacking King's Landing. Varys fears for Daenerys' mental stability and tells Tyrion he believes Jon would be a better ruler, but Tyrion stands by her.\\n\\n===At King's Landing===\\nEuron reports his success to Cersei, who claims to be pregnant with his child. She orders the gates of the Red Keep remain open, making her subjects human shields to deter an all-out assault on the city.\\n\\nDaenerys and her army arrive at the walls of King's Landing. Cersei and Daenerys demand each other's surrender, with Cersei threatening to kill Missandei. Tyrion attempts to appeal to Cersei's humanity to get her to surrender. Cersei refuses and has Gregor Clegane behead Missandei, horrifying and enraging Grey Worm and Daenerys.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by David Benioff and D. B. Weiss.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'a7a5c6b32df1f35ca19ce6f95f21c452'}>,\n",
-       "  <Document: {'content': 'Cersei refuses and has Gregor Clegane behead Missandei, horrifying and enraging Grey Worm and Daenerys.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by David Benioff and D. B. Weiss.\\n\\n=== Filming ===\\nThe episode was directed by David Nutter. This was his final episode of the series overall.\\n\\nDuring filming of the banquet scenes, a disposable coffee cup was accidentally left on the set; it was briefly visible in the original broadcast of the episode, but was digitally erased two days later.\\n\\nActress Nathalie Emmanuel was digitally inserted into the long shots of her own death scene, because filming took place at a great physical height and was exposed to photographers. To keep the plot secret, Emmanuel did not participate in those takes, while her close-ups were shot separately on a sound stage.\\n\\n== Reception ==\\n=== Ratings ===\\nThe episode was viewed by 11.8 million viewers on its initial live broadcast on HBO. An additional 5.4 million viewers watched on streaming platforms, for a total of 17.2 million viewers.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '79eea431733099ce5d08c48dc5268c5a'}>,\n",
-       "  <Document: {'content': '== Reception ==\\n=== Ratings ===\\nThe episode was viewed by 11.8 million viewers on its initial live broadcast on HBO. An additional 5.4 million viewers watched on streaming platforms, for a total of 17.2 million viewers.\\n\\n=== Critical response ===\\nThe episode received mixed to negative reviews; on the review aggregator Rotten Tomatoes it has an approval rating of 58%, based on 107 reviews and an average rating of 7.01/10, with the critics consensus stating: \"\\'The Last of the Starks\\' strains to set the board for \\'\\'Game of Thrones\\'\\' conclusion, but serves up enough political intrigue and touching character interactions to satisfy\".\\n\\nAmong the positive reviews, Spencer Kornhaber of \\'\\'The Atlantic\\'\\' wrote, \"despite all of the logical head-smack moments ... I\\'m not brimming with complaints. The episode recaptured some classic \\'\\'Thrones\\'\\' qualities, really.\"Chris Barton of \\'\\'Los Angeles Times\\'\\' praised the storytelling and the interaction between characters and believed that \"the bulk of this episode was filled with the sort of palace intrigue and character dynamics that made the show more than the sum of its dragons.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '3756b0d98b2d49dcdca42ab764d670bc'}>,\n",
-       "  <Document: {'content': 'Chris Barton of \\'\\'Los Angeles Times\\'\\' praised the storytelling and the interaction between characters and believed that \"the bulk of this episode was filled with the sort of palace intrigue and character dynamics that made the show more than the sum of its dragons.\"Sean T. Collins of \\'\\'Rolling Stone\\'\\' believed that the episode did a good job of showing the conflicts between humans and wrote, \"It hasn\\'t missed a step thematically, moving from humanity\\'s need to stop killing itself and face a common threat to its compulsion to annihilation even after seeing what it can accomplish as a united front.\"Sarah Hughes of \\'\\'The Guardian\\'\\' wrote in her review that the pace of the episode was just right and added, \"Where a lesser show would have hurried the action, moving us swiftly on to the battle with Cersei, The Last of the Starks took time to show what the true cost of the Battle of Winterfell was.\"Among the negative reviews, Shirley Li of \\'\\'The Atlantic\\'\\' wrote that she wasn\\'t impressed by \"The Last of the Starks\" and criticized the \"nonsensical storytelling\" in this episode. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '37e3d660cb1b56737f377542948b81b5'}>,\n",
-       "  <Document: {'content': 'Among the negative reviews, Shirley Li of \\'\\'The Atlantic\\'\\' wrote that she wasn\\'t impressed by \"The Last of the Starks\" and criticized the \"nonsensical storytelling\" in this episode. David Malitz of \\'\\'The Washington Post\\'\\' also questioned the logic in this episode, and, referring to the confrontation between Daenerys and Euron, wrote, \"Maybe it was just good luck on those initial kill shots, though, because the next few dozen arrows that fly toward Daenerys and Drogon all miss their mark. Every single one of them. Yep.\"Steve Johnson of \\'\\'Chicago Tribune\\'\\' believed that the show had lost its exciting storyline and, referring to the downfall of the Night King in the last episode, wrote, \"How are we supposed to get thrilled about fighting mere Cersei again when we\\'ve already bested the greatest enemy of all time?\"Alan Sepinwall of \\'\\'Rolling Stone\\'\\' labeled the episode as a \"structural oddity\", and wrote, \"It never feels solid enough. The script races through the transition, piling one personality-altering event on top of the next so that none of them gets to breathe.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'c198b1d50e1825f1fe9d5f43359f7120'}>,\n",
-       "  <Document: {'content': 'The script races through the transition, piling one personality-altering event on top of the next so that none of them gets to breathe.\"Longtime \\'\\'Game of Thrones\\'\\' reviewer, Erik Kain of \\'\\'Forbes\\'\\', stated that \"so much happened and so little of it felt right, that today I\\'m left feeling letdown more than anything, and dreading the next two episodes instead of eagerly awaiting them.\"On the \"Inside the Episode\" featurette for \"The Last of the Starks\", showrunner David Benioff explained that Daenerys lost Rhaegal and her fleet because \"Dany kind of forgot about the Iron Fleet and Euron\\'s forces\". Amy Jones of \\'\\'The Daily Telegraph\\'\\' described the explanation as \"so stupid\", while fans ridiculed Benioff by turning the statement into a meme.\\n\\n=== Awards and nominations ===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2019\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter\\n\\nOutstanding Lead Actress in a Drama Series\\n\\nEmilia Clarke\\n\\n== References ==\\n\\n== External links ==\\n*  \"The Last of the Starks\" at HBO.com\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '3ba5ab4e5bb7ee00e967eb818df716c0'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''''A Dance with Dragons''''' is the fifth novel, of seven planned, in the epic fantasy series ''A Song of Ice and Fire'' by American author George R. R. Martin.  In some areas, the paperback edition was published in two parts, titled ''Dreams and Dust'' and ''After the Feast''. It was the only novel in the series to be published during the eight-season run of the HBO adaptation of the series, ''Game of Thrones'', and runs to 1,040 pages with a word count of almost 415,000.\\n\\nThe US hardcover was officially published on July 12, 2011, and a few weeks later went to No. 1 on both ''Publishers Weekly'' and ''USA Today'' bestsellers lists.  The novel has been adapted for television as the fifth season of ''Game of Thrones'', although elements of the book have also appeared in the series' third, fourth and sixth seasons.\\n\\n==Plot summary==\\n\\n===The Wall and beyond===\\nStannis Baratheon, a claimant to the Iron Throne of Westeros, occupies the Wall at the realm's northern border, having helped to repel an invasion of wildlings from the northern wilderness.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'bb8cef06dc7ee7058437fe50740433a'}>,\n",
-       "  <Document: {'content': \"==Plot summary==\\n\\n===The Wall and beyond===\\nStannis Baratheon, a claimant to the Iron Throne of Westeros, occupies the Wall at the realm's northern border, having helped to repel an invasion of wildlings from the northern wilderness.  Stannis apparently executes Mance Rayder, the leader of the wildlings, for refusing to submit to him, and marches south with his army to seek support in his bid for the throne.\\n\\nJon Snow, the newly elected Lord Commander of the Night's Watch, the order that defends the Wall, brokers a truce with the wildlings, allowing them through the Wall in exchange for their assistance in defending the Wall against the hostile otherworldly creatures known as the Others. This creates unrest among the Night's Watch, who have considered the wildlings their enemies for centuries.\\n\\nStannis's advisor, the sorceress Melisandre, warns Jon that a Northern girl is in trouble, who Jon assumes is his half-sister, Arya Stark. Mance is revealed to be alive thanks to Melisandre's magical trickery, and he is sent to the Starks' ancestral castle Winterfell, now occupied by the enemy Boltons, to rescue Arya. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '3ac61694d8bc5cecd532dced3b6ec89'}>,\n",
-       "  <Document: {'content': \"Mance is revealed to be alive thanks to Melisandre's magical trickery, and he is sent to the Starks' ancestral castle Winterfell, now occupied by the enemy Boltons, to rescue Arya. However, the girl in Melisandre's visions turns out to be Alys Karstark, a young noblewoman fleeing to the Wall to escape her treacherous uncle. To protect Alys and aid the wildlings' integration into Westerosi society, Jon arranges for Alys to marry Sigorn of Thenn, a wildling leader.\\n\\nJon receives a taunting letter from Ramsay Bolton, who claims to have crushed Stannis's army at Winterfell. Ramsay demands that Jon hand over to him Stannis' wife and daughter or be killed, and also insists that Jon deliver Arya and his foster-brother Theon Greyjoy, neither of whom Jon has seen in years. Jon instead decides that he will seek out and kill Ramsay himself, asking for volunteers from the Watch to accompany him. For this perceived betrayal of the Watch's principle of neutrality, Jon is betrayed and stabbed by several of his own men.\\n\\nMeanwhile, Jon's crippled half-brother Bran Stark, traveling north of the Wall, is led to the last surviving Children of the Forest, the non-human natives of Westeros. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'd7563c9f6a8722264fd8ef5501291e26'}>,\n",
-       "  <Document: {'content': 'Meanwhile, Jon\\'s crippled half-brother Bran Stark, traveling north of the Wall, is led to the last surviving Children of the Forest, the non-human natives of Westeros. They introduce Bran and his companions to the \"last greenseer\", an ancient man intertwined with the roots of a weirwood tree. He helps Bran learn clairvoyant \"greensight\", enabling Bran to witness the past and present through the eyes of the weirwood trees that grow throughout the North.\\n\\n===Across the Narrow Sea===\\n\\n====In the Free Cities====\\nHaving killed his father Tywin, the Hand of the King, the dwarf Tyrion Lannister is smuggled out of Westeros to the city of Pentos by the spymaster Varys, where he is sheltered by the aristocrat Illyrio Mopatis. Tyrion is sent south with a party ostensibly to aid the exiled princess Daenerys Targaryen, who controls the only living dragons, in claiming the Iron Throne; on the journey, he learns that Varys and Illyrio have hidden the presumed-dead Aegon Targaryen, son of Daenerys\\'s late brother Prince Rhaegar, intending to eventually install him as king of Westeros with Daenerys\\'s aid. Tyrion persuades Aegon to invade Westeros immediately, without waiting for Daenerys. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'ce79d99c0500588db63b14222ca5f492'}>,\n",
-       "  <Document: {'content': 'Tyrion is sent south with a party ostensibly to aid the exiled princess Daenerys Targaryen, who controls the only living dragons, in claiming the Iron Throne; on the journey, he learns that Varys and Illyrio have hidden the presumed-dead Aegon Targaryen, son of Daenerys\\'s late brother Prince Rhaegar, intending to eventually install him as king of Westeros with Daenerys\\'s aid. Tyrion persuades Aegon to invade Westeros immediately, without waiting for Daenerys. Soon afterward, Tyrion is kidnapped by Daenerys\\'s disgraced advisor Jorah Mormont, who intends to deliver him to Daenerys in order to regain her favor.\\n\\nIn the Free City of Braavos, Arya is an acolyte of the guild of assassins known as the Faceless Men. Temporarily afflicted with blindness at their hands, she develops her sense of hearing, and realizes that she can \"see\" through cats the same way she could with her pet direwolf, Nymeria. After her sight is restored, she is tasked with assassinating a corrupt merchant while magically disguised with another girl\\'s face. After doing so, Arya is declared an apprentice of the Faceless Men, to be sent to another assassin to continue her training.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'def69d1880e03d47f414c28d3756969f'}>,\n",
-       "  <Document: {'content': 'After doing so, Arya is declared an apprentice of the Faceless Men, to be sent to another assassin to continue her training.\\n\\n====Slaver\\'s Bay====\\nDaenerys has conquered the city of Meereen and banned slavery, but struggles to maintain peace within the city while also trying to prevent military defeat at the hands of the neighboring city of Yunkai. A plague known as the \"pale mare\" (dysentery) ravages the city. Daenerys\\' dragons have become increasingly dangerous, and she reluctantly confines them in a dungeon for the safety of her people. Drogon, the largest, evades capture and flies off. Despite her sexual relationship with the mercenary Daario Naharis, Daenerys marries the Meereenese nobleman Hizdahr zo Loraq to secure an alliance that will hopefully appease the Sons of the Harpy, a violent Meereenese resistance group.\\n\\nQuentyn Martell, the son of the Prince of Dorne in southern Westeros, arrives in Meereen to seek to reestablish the allegiance between Daenerys\\'s family and his, but is unable to maintain her attention. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '36e68a459f060b1726eb88a61362590b'}>,\n",
-       "  <Document: {'content': 'Quentyn Martell, the son of the Prince of Dorne in southern Westeros, arrives in Meereen to seek to reestablish the allegiance between Daenerys\\'s family and his, but is unable to maintain her attention. Jorah and Tyrion are shipwrecked, kidnapped, and sold to a Yunkish slave trader; at Meereen, Tyrion escapes in the mass confusion of the plague ravaging the Yunkish army, joins the Second Sons mercenary group, and secures their support for Daenerys. Meanwhile, another Westerosi, Victarion Greyjoy, the brother of the king of the Ironborn, sails for Meereen, intending to marry Daenerys and use her dragons to overthrow his brother.\\n\\nAt Hizdahr\\'s insistence, Daenerys reopens the city\\'s \"fighting pits\" for gladiatorial combat, but the noise and carnage attract Drogon. Two hundred people are killed in the resulting panic, at which point Daenerys climbs on Drogon to calm him. He flies off with Daenerys on his back. Hizdahr is soon implicated in an attempt to poison Daenerys, and her advisor Barristan Selmy removes Hizdahr from power and prepares for battle with the armies outside Meereen. Quentyn attempts to prove his worth by riding one of the remaining dragons, but is killed, releasing both dragons in the process.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'bc532897c5904b9db75548e195cbfd90'}>,\n",
-       "  <Document: {'content': \"Quentyn attempts to prove his worth by riding one of the remaining dragons, but is killed, releasing both dragons in the process.\\n\\nDrogon flies Daenerys to the Dothraki Sea, the grassland controlled by the nomadic Dothraki people. After several days, Daenerys encounters a Dothraki horde led by Khal Jhaqo.\\n\\n===In the Seven Kingdoms===\\n\\n====The North====\\nIn the North, Roose Bolton has assumed authority after conspiring with House Frey to overthrow the Stark family, the North's longtime overlords; meanwhile, much of the region is occupied by the Ironborn. Stannis wins the support of the northern lords and hill clans by pledging support for the Starks and fighting off the Ironborn; while doing so, he captures Asha Greyjoy, Victarion's niece. He marches his forces toward Winterfell to attack the Boltons, but his army becomes snowbound and cannot advance.\\n\\nStannis's advisor Davos Seaworth is sent to win the support of the wealthy lord Wyman Manderly, who pretends to execute Davos to curry favor with the Lannister regime that controls the Iron Throne. In a secret meeting, Davos is told the Manderlys and other Northern vassals intend to feign submission to the Boltons and Lannisters while plotting revenge for Robb Stark's death. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '46c70a8b4172f52ef3959b253412eeb4'}>,\n",
-       "  <Document: {'content': 'In a secret meeting, Davos is told the Manderlys and other Northern vassals intend to feign submission to the Boltons and Lannisters while plotting revenge for Robb Stark\\'s death. Revealing to Davos that young Rickon Stark is in hiding on the remote island of Skagos, Manderly pledges to support Stannis if Davos can retrieve Rickon and unite the Starks\\' supporters around him.\\n\\nTheon Greyjoy is a prisoner of the Boltons, mutilated and driven nearly insane by torture at the hands of Roose\\'s sadistic son Ramsay, who renames him \"Reek\". To cement his rule over the North, Roose Bolton has Ramsay apparently married to Arya Stark; his bride is actually Jeyne Poole, a friend of Arya\\'s sister Sansa forced to impersonate Arya. Ramsay abuses Jeyne physically and sexually. Mance Rayder arrives at Winterfell in disguise and enlists Theon to help him free the false Arya. Mance and his spearwives are left behind while Theon and Jeyne escape, leaping from the castle wall into the snow to be captured by Stannis\\'s forces.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '66ce3b17b025800618e8253f258db5a6'}>,\n",
-       "  <Document: {'content': \"Mance and his spearwives are left behind while Theon and Jeyne escape, leaping from the castle wall into the snow to be captured by Stannis's forces.\\n\\n====The South====\\n\\nJaime Lannister, the uncle (and, secretly, father) of the young king Tommen Baratheon, negotiates the surrender of the last of the late Robb Stark's allies, nominally putting an end to the Stark–Lannister war in the Riverlands. Brienne of Tarth, whom Jaime had sent to search for Sansa Stark (and who unbeknownst to Jaime was captured by Sansa's undead, vengeful mother Catelyn), finds Jaime and tells him Sansa is in danger; he follows after her.\\n\\nAfter Tyrion convinces Aegon to attack Westeros, Aegon and his forces capture several castles in the Stormlands with little resistance. Meanwhile, Aegon's foster father Jon Connington is secretly succumbing to greyscale, a deadly and infectious disease.\\n\\nDoran Martell, the prince of Dorne, sends three of his nieces north to the capital city of King's Landing to infiltrate the Faith and the government and work in Dorne's interests.\\n\\nCersei Lannister, Tommen's mother and queen regent, has been arrested by the Faith on charges of fornication and conspiracy. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '562d27e377501054bbac611e014134dd'}>,\n",
-       "  <Document: {'content': 'Doran Martell, the prince of Dorne, sends three of his nieces north to the capital city of King\\'s Landing to infiltrate the Faith and the government and work in Dorne\\'s interests.\\n\\nCersei Lannister, Tommen\\'s mother and queen regent, has been arrested by the Faith on charges of fornication and conspiracy. In order to gain release from her imprisonment, she confesses to several of the lesser charges against her, but does not confess to having murdered her husband King Robert Baratheon, nor that her children are the product of incest. As a condition of her release, she is forced to walk naked across the city as the smallfolk pelt her with rotten vegetables and insults. Meanwhile, Cersei\\'s ally, the ex-maester Qyburn, has created \"Ser Robert Strong\", an eight-foot-tall figure encased in armor, to be an unbeatable champion in Cersei\\'s upcoming trial by combat.\\n\\nHaving taken control of the government, Cersei\\'s uncle Kevan Lannister and the Grand Maester Pycelle attempt to undo the damage caused by Cersei\\'s misrule. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '29b313e5b03cffdef524eb2cc7542e12'}>,\n",
-       "  <Document: {'content': 'Having taken control of the government, Cersei\\'s uncle Kevan Lannister and the Grand Maester Pycelle attempt to undo the damage caused by Cersei\\'s misrule. However, Varys returns to King\\'s Landing and murders both Kevan and Pycelle, revealing that he has been plotting for years for the Lannisters to destroy themselves so that Aegon Targaryen can take the throne, having been raised to be an ideal ruler. The book ends with Varys sending his \"little birds\" (child spies) to finish Kevan off.\\n\\n===Extras===\\nIn addition to the maps published in previous books, the book includes a new map of the previously-unmapped area of the Free Cities on the eastern continent. Like the previous four volumes in the \\'\\'Ice and Fire\\'\\' series, the book includes an appendix with a complete list of characters.\\n\\n==Characters==\\nThe story is narrated from the point of view of 18 different characters, including two minor one-off point-of-view (POV) characters featured in the prologue and epilogue. All but two POV characters were identified before the book\\'s release.\\n\\nIn the North:\\n* Prologue: Varamyr Sixskins, a skinchanger and one of the surviving wildlings north of the Wall.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '7468bec9ae06bbaf5977cb45f5077efa'}>,\n",
-       "  <Document: {'content': \"All but two POV characters were identified before the book's release.\\n\\nIn the North:\\n* Prologue: Varamyr Sixskins, a skinchanger and one of the surviving wildlings north of the Wall.\\n* Jon Snow, the 998th Lord Commander of the Night's Watch and bastard son of Eddard Stark.\\n* Bran Stark, rightful heir to his brother, the late Robb Stark. Seeking an old power beyond the Wall, believed dead by his own family.\\n* Davos Seaworth, former smuggler and Hand of the King to Stannis Baratheon. Has recently learned to read, sent to negotiate with northern houses.\\n* Reek, the Prince of Winterfell, the Turncloak, a Ghost in Winterfell: Theon Greyjoy, presumed-dead son of recently deceased King Balon Greyjoy of the Iron Islands, a captive of Ramsay Bolton and now tortured, starved and barely sane.\\n* The Wayward Bride, the King's Prize, the Sacrifice: Asha Greyjoy, niece of King Euron Greyjoy of the Iron Islands, fled the Iron Islands after her uncle's coronation.\\n* Lady Melisandre, a shadow-binder from Asshai and a devoted priestess to the red god R'hllor, advisor to Stannis.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'f96b47d4980a02f5475eb75a593f32af'}>,\n",
-       "  <Document: {'content': \"* The Wayward Bride, the King's Prize, the Sacrifice: Asha Greyjoy, niece of King Euron Greyjoy of the Iron Islands, fled the Iron Islands after her uncle's coronation.\\n* Lady Melisandre, a shadow-binder from Asshai and a devoted priestess to the red god R'hllor, advisor to Stannis.\\n\\nIn the eastern continent of Essos:\\n* Daenerys Targaryen, heir to the Targaryen dynasty which ruled Westeros for 300 years until their deposition 15 years before the first novel. Self-proclaimed Queen of Westeros, she now rules the city of Meereen.\\n* Tyrion Lannister, dwarf and uncle to the King Tommen of Westeros, a fugitive wanted for kinslaying and regicide. Recently fled the Seven Kingdoms.\\n* The Merchant's Man, the Windblown, the Spurned Suitor, the Dragontamer: Quentyn Martell, eldest son of Prince Doran Martell of Dorne, traveling into the East on a mission for his father.\\n* The Lost Lord, the Griffin Reborn: Jon Connington, one of the former Hands of the King to Aerys Targaryen and one of Prince Rhaegar's closest friends. Exiled, and falsely believed dead. His identity as a narrator was kept secret throughout the book's pre-release process.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '4d56eeea24e7683f857141101ddb5308'}>,\n",
-       "  <Document: {'content': '* The Lost Lord, the Griffin Reborn: Jon Connington, one of the former Hands of the King to Aerys Targaryen and one of Prince Rhaegar\\'s closest friends. Exiled, and falsely believed dead. His identity as a narrator was kept secret throughout the book\\'s pre-release process.\\n* The Queensguard, the Discarded Knight, the Kingbreaker, the Queen\\'s Hand: Ser Barristan Selmy, the former Lord Commander of Robert Baratheon\\'s Kingsguard, and the first of Daenerys\\'s Queensguard.\\n* The Iron Suitor: Victarion Greyjoy, Captain of the Iron fleet, recently gone on a quest to find Daenerys and use her for the Ironborn\\'s own ends.\\n* The Blind Girl, the Ugly Little Girl: Arya Stark, hiding in the Free City of Braavos, where she has taken on the identity of the \"Cat of the Canals\" and continues her training as an assassin by the House of Black and White (The Faceless Men).\\n\\nIn the South:\\n* The Watcher: Areo Hotah, Captain of Doran Martell\\'s guard.\\n* Ser Jaime Lannister, the Kingslayer, Lord Commander of the Kingsguard; currently occupying the lands around Riverrun.\\n* Cersei Lannister, the Queen Regent, currently imprisoned in a tower cell, awaiting trial.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '26e5b3d4b44783405c5a01bff045c450'}>,\n",
-       "  <Document: {'content': \"* Ser Jaime Lannister, the Kingslayer, Lord Commander of the Kingsguard; currently occupying the lands around Riverrun.\\n* Cersei Lannister, the Queen Regent, currently imprisoned in a tower cell, awaiting trial.\\n* Epilogue: Ser Kevan Lannister, head of House Lannister in light of his brother's death, and current regent to King Tommen. His identity as a narrator was kept secret throughout the book's pre-release process.\\n\\nChapters for several POVs, which may include Sansa Stark, Samwell Tarly, Aeron Damphair, Arianne Martell, and Brienne of Tarth were written for the book, but they will instead tentatively appear in the next book, ''The Winds of Winter''.\\n\\n==Writing==\\n\\n===Early development===\\n\\n''A Dance with Dragons'' was originally intended to be the title of the second novel in the sequence, when Martin still envisioned the series as a trilogy. Some early US editions of ''A Game of Thrones'' (1996) list ''A Dance of Dragons'' as the forthcoming second volume in the series. The 1998 anthology ''Legends'', which features the novella ''The Hedge Knight'' from the same universe, listed ''A Dance of Dragons'' as the third installment of a four-book series.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'e4b46b846dc5a9670a1a11792410204d'}>,\n",
-       "  <Document: {'content': 'The 1998 anthology \\'\\'Legends\\'\\', which features the novella \\'\\'The Hedge Knight\\'\\' from the same universe, listed \\'\\'A Dance of Dragons\\'\\' as the third installment of a four-book series.\\n\\nIn May 2005 Martin announced that the \"sheer size\" of his still-unfinished manuscript for \\'\\'A Feast for Crows\\'\\' had led him and his publishers to split the narrative into two books. Rather than divide the text in half chronologically, Martin opted to instead split the material by character and location, resulting in \"two novels taking place simultaneously\" with different casts of characters. Published in 2005, \\'\\'A Feast for Crows\\'\\' is narrated primarily by characters in the South of the Seven Kingdoms and in the new locations of the Iron Islands and Dorne. \\'\\'A Dance with Dragons\\'\\' features characters in the North and across the narrow sea, although Jaime Lannister, Cersei Lannister, Arya Stark, Areo Hotah and Victarion and Asha Greyjoy appear in both volumes.\\n\\nApproximately one-third of the published \\'\\'A Dance with Dragons\\'\\' consists of material that had been written for the pre-split \\'\\'A Feast for Crows\\'\\', although much of this has been rewritten by Martin.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '7bbd3364b026fa497256948cc4edb618'}>,\n",
-       "  <Document: {'content': 'Approximately one-third of the published \\'\\'A Dance with Dragons\\'\\' consists of material that had been written for the pre-split \\'\\'A Feast for Crows\\'\\', although much of this has been rewritten by Martin.\\n\\nIn 2009, Martin confirmed that, contrary to earlier statements, Sansa Stark would not appear in \\'\\'A Dance with Dragons\\'\\'; Sansa chapters initially slated for the novel have instead been pushed back to \\'\\'The Winds of Winter\\'\\', which is planned to be the sixth book in the series. In early 2010, Martin noted that his intent for \\'\\'A Dance with Dragons\\'\\' was for the first 800 manuscript pages to cover the alternate characters in the same time span as \\'\\'A Feast for Crows\\'\\', and that \"Everything that follows is post-\\'\\'Feast\\'\\', so that\\'s where some of the cast from the last book start popping up again.\"Stating that \"I wanted to resolve at least a few of the cliffhangers from \\'\\'Feast\\'\\',\" Martin also mentioned the possibility that some of his finished chapters might get pushed to the next novel, \\'\\'The Winds of Winter,\\'\\' depending on the length of the finished manuscript for \\'\\'A Dance with Dragons\\'\\'.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '7fcd0f70d70e02ec7566967374fc75f0'}>,\n",
-       "  <Document: {'content': 'Stating that \"I wanted to resolve at least a few of the cliffhangers from \\'\\'Feast\\'\\',\" Martin also mentioned the possibility that some of his finished chapters might get pushed to the next novel, \\'\\'The Winds of Winter,\\'\\' depending on the length of the finished manuscript for \\'\\'A Dance with Dragons\\'\\'.\\n\\n===Road to publication===\\nDespite original, optimistic predictions of possible completion in late 2006, Martin completed the novel in April 2011, nearly five years later. During this period, Martin\\'s blog featured sporadic updates on his progress, and in January 2008 he posted an update affirming his vigilant commitment to finishing the novel. In early 2008, publisher Spectra Books (a division of Random House) announced that \\'\\'A Dance with Dragons\\'\\' would be released on September 30, 2008, but Martin stated this would only be possible if he finished writing by the end of June, before his trip to Spain and Portugal; he did not meet his goal.\\n\\nOn February 19, 2009, Martin posted on his website: \"I am trying to finish the book by June. I think I can do that. If I do, \\'\\'A Dance with Dragons\\'\\' will likely be published in September or October.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '51bade9a1c320667378355bfaec8484e'}>,\n",
-       "  <Document: {'content': 'I think I can do that. If I do, \\'\\'A Dance with Dragons\\'\\' will likely be published in September or October.\"On June 22, 2009, the author expressed \"guarded optimism\" with respect to his progress on the novel, while still not confirming a publication date. When asked in a July 2009 interview with \\'\\'FREE! Magazine\\'\\' how the book was going, Martin stated, \"It is going pretty well, actually. I am hoping to finish it by September or October; that is my goal.\"On October 6, 2009, Martin said that his working manuscript for \\'\\'A Dance With Dragons\\'\\' had just exceeded 1,100 pages of completed chapters, plus \"considerably more in partials, fragments, and roughs.\"He noted that this made the upcoming novel longer than his earlier books \\'\\'A Game of Thrones\\'\\' and \\'\\'A Feast for Crows\\'\\', and nearly as long as \\'\\'A Clash of Kings\\'\\'.\\n\\nOn March 2, 2010, Martin remarked that he had reached 1,311 manuscript pages, making \\'\\'Dance\\'\\' the second-longest novel in the series at that point, behind only the 1,521-page manuscript of \\'\\'A Storm of Swords\\'\\'. On July 8, 2010, Martin spoke at a conference and confirmed the current length of the book to be 1,400 manuscript pages. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': '83adaa6e0bef262766b272dd7a40a368'}>,\n",
-       "  <Document: {'content': \"On July 8, 2010, Martin spoke at a conference and confirmed the current length of the book to be 1,400 manuscript pages. He expressed his disappointment that he was unable to completely finish the novel by the conference, although he would not speculate how soon the book would be completed after his return home on July 11. At the same conference, Martin also confirmed that he has written one Sansa, one Arya, and two Arianne chapters for the planned sixth novel, ''Winds of Winter'', and had transferred two Cersei chapters from that book into ''A Dance with Dragons''. On August 7, 2010, Martin confirmed that he had completed eight POVs, excluding the prologue and epilogue.\\n\\nAt the New York Comic Con on  October 10, 2010, Spectra senior editor Anne Groell announced that Martin had only five chapters remaining to finish, with sections of the chapters already completed. She stated her desire to have the manuscript completed by December. In a December 2010 interview with Bear Swarm, Martin stated that he almost had ''A Dance With Dragons'' completed.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '3799a9a03559e11e96d4d88b6f5114d7'}>,\n",
-       "  <Document: {'content': 'She stated her desire to have the manuscript completed by December. In a December 2010 interview with Bear Swarm, Martin stated that he almost had \\'\\'A Dance With Dragons\\'\\' completed.\\n\\nOn March 3, 2011, the publisher announced that the novel, though at that point still not completed, would be officially published on July 12, 2011. Martin claimed this July 2011 publication date was different from the previous publication dates mentioned, in that this was \"real\", as opposed to earlier \"wishful thinking, boundless optimism, cockeyed dreams, and honest mistakes\". On March 12, he revealed that the unfinished manuscript had exceeded \\'\\'A Storm of Swords\\'\\' in length, making it the longest volume in the entire series. On March 27, he announced that the manuscript had exceeded 1,600 pages. On April 27, 2011, it was announced that Martin had completed \\'\\'A Dance with Dragons\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': 'caa9ea1a48ec51dc54502d06058b5243'}>,\n",
-       "  <Document: {'content': 'On March 27, he announced that the manuscript had exceeded 1,600 pages. On April 27, 2011, it was announced that Martin had completed \\'\\'A Dance with Dragons\\'\\'. After incorporating requested changes made by his book editor and her staff copy-editors, suggested final draft notes from trusted friends, and his own final \"sweat\" (a line-by-line reread done to tighten and eliminate any unnecessary \"fat\" remaining in the manuscript), the final draft had been reduced to 1,510 pages; this made \\'\\'Dragons\\'\\' the second-longest novel, by a narrow margin, to volume three, \\'\\'A Storm of Swords\\'\\'. The novel was now ready to meet its July 12, 2011 release date.\\n\\nOn June 29, 2011, Amazon Germany had mistakenly released 180 copies of the novel early. Martin requested that those who held copies not spoil the book for fans who had to wait. Several \\'\\'A Song of Ice and Fire\\'\\' websites put an embargo in place on their forums with the same intent.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': 'ad3565f907a294979bdfab9f1cd4f757'}>,\n",
-       "  <Document: {'content': 'Several \\'\\'A Song of Ice and Fire\\'\\' websites put an embargo in place on their forums with the same intent.\\n\\n==Editions==\\n\\'\\'\\'Foreign-language editions\\'\\'\\'\\n* Bulgarian: Бард: \"Танц с Дракони\" (\"Dance with Dragons\")\\n* Catalan: Alfaguara: \"Dansa amb dracs\" (\"Dance with Dragons\")\\n* Chinese (traditional): Three volumes, \"與龍共舞\" 上, 中 and 下\\n* Croatian: Two volumes, Algoritam: \"Ples zmajeva\" (\"A Dance of Dragons\")\\n* Czech: Talpress: \"Tanec s draky\" (\"A Dance with Dragons\")\\n* Danish: \"En dans med drager\" (\"A Dance with Dragons\")\\n* Dutch: Two volumes, Luitingh-Sijthoff: \"Oude vetes, Nieuwe strijd\", \"Zwaarden tegen draken\" (\"Old Feuds, New Battle\", \"Swords Against Dragons\")\\n* Estonian: Two volumes, hardcover : Varrak \"Tants Lohedega\" (\"A Dance with Dragons\") book 1 (09/23/2016) & book 2 (05/18/2017)\\n* Finnish: Two volumes: \"Lohikäärmetanssi\" (\"Dragondance\") 1 and 2\\n* French: Three volumes, Pygmalion: \"Le Bûcher d\\'un roi\", \"Les dragons de Meereen\", \"Une danse avec les dragons\" (\"The Stake/Pyre of a King\", \"The Dragons of Meereen\", \"A Dance with Dragons\")\\n* German: FanPro (2012): \"Drachenreigen\" (\"Dragons\\' Round\"). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': 'd5e7dbae4119df4c7e7139c9d949cee7'}>,\n",
-       "  <Document: {'content': '==Editions==\\n\\'\\'\\'Foreign-language editions\\'\\'\\'\\n* Bulgarian: Бард: \"Танц с Дракони\" (\"Dance with Dragons\")\\n* Catalan: Alfaguara: \"Dansa amb dracs\" (\"Dance with Dragons\")\\n* Chinese (traditional): Three volumes, \"與龍共舞\" 上, 中 and 下\\n* Croatian: Two volumes, Algoritam: \"Ples zmajeva\" (\"A Dance of Dragons\")\\n* Czech: Talpress: \"Tanec s draky\" (\"A Dance with Dragons\")\\n* Danish: \"En dans med drager\" (\"A Dance with Dragons\")\\n* Dutch: Two volumes, Luitingh-Sijthoff: \"Oude vetes, Nieuwe strijd\", \"Zwaarden tegen draken\" (\"Old Feuds, New Battle\", \"Swords Against Dragons\")\\n* Estonian: Two volumes, hardcover : Varrak \"Tants Lohedega\" (\"A Dance with Dragons\") book 1 (09/23/2016) & book 2 (05/18/2017)\\n* Finnish: Two volumes: \"Lohikäärmetanssi\" (\"Dragondance\") 1 and 2\\n* French: Three volumes, Pygmalion: \"Le Bûcher d\\'un roi\", \"Les dragons de Meereen\", \"Une danse avec les dragons\" (\"The Stake/Pyre of a King\", \"The Dragons of Meereen\", \"A Dance with Dragons\")\\n* German: FanPro (2012): \"Drachenreigen\" (\"Dragons\\' Round\"). Two volumes, Blanvalet (2012): \"Der Sohn des Greifen\", \"Ein Tanz mit Drachen\" (\"The Son of the Griffin\", \"A Dance with Dragons\")\\n* Greek: Two volumes, Anubis: \"Το Κάλεσμα της Φλόγας\", \"Το Σπαθί στο Σκοτάδι\" (\"Call of the Flame\", \"The Sword in the Darkness\")\\n* Hebrew: \"ריקוד עם דרקונים חלק א – חלומות ואבק, ריקוד עם דרקונים חלק ב – לאחר המשתה\" (\"Dance with Dragons Part I – Dreams and Dust, Dancing with Dragons Part II – After the Feast\")\\n* Hungarian: Alexandra Könyvkiadó: \"Sárkányok tánca\" (\"Dance of Dragons\")\\n* Italian: Three volumes, Arnoldo Mondadori Editore (2011, 2012): \"I guerrieri del ghiaccio\", \"I fuochi di Valyria\", \"La danza dei draghi\" (\"The Warriors of Ice\", \"The Fires of Valyria\", \"The Dance of Dragons\")\\n* Japanese: Hardcover : Three volumes, Hayakawa (2013): \"竜との舞踏\" (\"Dance with Dragons\") I, II and III, paperback : Hayakawa (2016), I, II and III\\n* Latvian: \"Deja ar Pūķiem\" (\"A Dance with Dragons\")\\n* Lithuanian: Two volumes: \"Šokis su Drakonais: Sapnai ir Dulkės\", \"Šokis su Drakonais: Po Puotos\" (\"Dance with Dragons: Dreams and the Dust\", \"Dance with Dragons: After the Feast\")\\n* Norwegian: Two volumes, Vendetta: \"Drømmer og støv\", \"Dragenes dans\" (\"Dreams and Dust\", \"Dance of the Dragons\")\\n* Polish: \"Taniec ze smokami\" (\"A Dance with Dragons\")\\n* Brazilian Portuguese: Leya: \"A Dança dos Dragões\" (\"The Dance of Dragons\")\\n* European Portuguese: Two volumes, Saída de Emergência: \"A Dança dos Dragões\", \"Os Reinos do Caos\" (\"A Dance of Dragons\", \"The Kingdom of Chaos\")\\n* Romanian: Nemira: \"Dansul dragonilor\" (\"The Dance of the Dragons\")\\n* Russian: AST: \"Танец с драконами\" (\"Dance with Dragons\")\\n* Serbian: Two volumes, Лагуна: \"Плес са змајевима Део први: Снови и прах\", \"Плес са змајевима Део други: После гозбе\" (\"A Dance with Dragons Part I: Dreams and Dust\", \"A Dance with Dragons Part II: After the Feast\")\\n* Slovenian: \"Ples z zmaji\" (\"A Dance with Dragons\")\\n* Spanish: Ediciones Gigamesh: \"Danza de dragones\" (\"Dance of Dragons\")\\n* Swedish: \"Drakarnas dans\" (\"Dance of the Dragons\")\\n* Turkish: \"Ejderhaların Dansı\" (\"Dance of Dragons\")\\n* Ukrainian: One volume, KM Publishing (2018): \"Танок драконів\" (\"A Dance of Dragons\")\\n* Vietnamese: Three volumes: \"Trò Chơi Vương Quyền 5A: Đánh thức Rồng Thiêng\", \"Trò Chơi Vương Quyền 5B: Trấn thủ thành Mereen\", \"Trò Chơi Vương Quyền 5C: Vũ điệu Rồng Thiêng\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': 'e79ec14d7dbc21c9b8384b9642b69448'}>,\n",
-       "  <Document: {'content': 'Two volumes, Blanvalet (2012): \"Der Sohn des Greifen\", \"Ein Tanz mit Drachen\" (\"The Son of the Griffin\", \"A Dance with Dragons\")\\n* Greek: Two volumes, Anubis: \"Το Κάλεσμα της Φλόγας\", \"Το Σπαθί στο Σκοτάδι\" (\"Call of the Flame\", \"The Sword in the Darkness\")\\n* Hebrew: \"ריקוד עם דרקונים חלק א – חלומות ואבק, ריקוד עם דרקונים חלק ב – לאחר המשתה\" (\"Dance with Dragons Part I – Dreams and Dust, Dancing with Dragons Part II – After the Feast\")\\n* Hungarian: Alexandra Könyvkiadó: \"Sárkányok tánca\" (\"Dance of Dragons\")\\n* Italian: Three volumes, Arnoldo Mondadori Editore (2011, 2012): \"I guerrieri del ghiaccio\", \"I fuochi di Valyria\", \"La danza dei draghi\" (\"The Warriors of Ice\", \"The Fires of Valyria\", \"The Dance of Dragons\")\\n* Japanese: Hardcover : Three volumes, Hayakawa (2013): \"竜との舞踏\" (\"Dance with Dragons\") I, II and III, paperback : Hayakawa (2016), I, II and III\\n* Latvian: \"Deja ar Pūķiem\" (\"A Dance with Dragons\")\\n* Lithuanian: Two volumes: \"Šokis su Drakonais: Sapnai ir Dulkės\", \"Šokis su Drakonais: Po Puotos\" (\"Dance with Dragons: Dreams and the Dust\", \"Dance with Dragons: After the Feast\")\\n* Norwegian: Two volumes, Vendetta: \"Drømmer og støv\", \"Dragenes dans\" (\"Dreams and Dust\", \"Dance of the Dragons\")\\n* Polish: \"Taniec ze smokami\" (\"A Dance with Dragons\")\\n* Brazilian Portuguese: Leya: \"A Dança dos Dragões\" (\"The Dance of Dragons\")\\n* European Portuguese: Two volumes, Saída de Emergência: \"A Dança dos Dragões\", \"Os Reinos do Caos\" (\"A Dance of Dragons\", \"The Kingdom of Chaos\")\\n* Romanian: Nemira: \"Dansul dragonilor\" (\"The Dance of the Dragons\")\\n* Russian: AST: \"Танец с драконами\" (\"Dance with Dragons\")\\n* Serbian: Two volumes, Лагуна: \"Плес са змајевима Део први: Снови и прах\", \"Плес са змајевима Део други: После гозбе\" (\"A Dance with Dragons Part I: Dreams and Dust\", \"A Dance with Dragons Part II: After the Feast\")\\n* Slovenian: \"Ples z zmaji\" (\"A Dance with Dragons\")\\n* Spanish: Ediciones Gigamesh: \"Danza de dragones\" (\"Dance of Dragons\")\\n* Swedish: \"Drakarnas dans\" (\"Dance of the Dragons\")\\n* Turkish: \"Ejderhaların Dansı\" (\"Dance of Dragons\")\\n* Ukrainian: One volume, KM Publishing (2018): \"Танок драконів\" (\"A Dance of Dragons\")\\n* Vietnamese: Three volumes: \"Trò Chơi Vương Quyền 5A: Đánh thức Rồng Thiêng\", \"Trò Chơi Vương Quyền 5B: Trấn thủ thành Mereen\", \"Trò Chơi Vương Quyền 5C: Vũ điệu Rồng Thiêng\". (\"Game of Thrones 5A: Awake Dragon\", \"Game of Thrones 5B: Defense on Mereen\", \"Game of Thrones 5C: Dance of Dragons\")\\n\\n==Reception==\\n\\n===Critical response===\\n\\'\\'The Atlantic\\'\\' Rachael Brown found \\'\\'A Dance with Dragons\\'\\' \"infinitely more satisfying than its predecessor, 2005\\'s bleak and plodding \\'\\'A Feast for Crows\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': '3eb84fabefc2ca973ba5310c5b158e02'}>,\n",
-       "  <Document: {'content': '(\"Game of Thrones 5A: Awake Dragon\", \"Game of Thrones 5B: Defense on Mereen\", \"Game of Thrones 5C: Dance of Dragons\")\\n\\n==Reception==\\n\\n===Critical response===\\n\\'\\'The Atlantic\\'\\' Rachael Brown found \\'\\'A Dance with Dragons\\'\\' \"infinitely more satisfying than its predecessor, 2005\\'s bleak and plodding \\'\\'A Feast for Crows\\'\\'. The aspects of Martin\\'s work that have endeared him to fans are abundant here – rich world building, narrative twists and turns, and gritty depictions of the human struggle for power. Characters who were sorely missed in \\'\\'Feast\\'\\' – Daenerys Targaryen, Tyrion Lannister, and Jon Snow – make up more than a third of the novel, and Martin is wise enough to give us at least a chapter from (almost) everyone else. Weaknesses that have plagued Martin\\'s previous books are also present: too much repetition, unexceptional prose, and characters who use the same idioms (and have sex in exactly the same manner) no matter their ethnicity, social class, or continent. But while \\'\\'A Dance with Dragons\\'\\' cries out for better editing, it remains entirely engrossing. Martin has hidden so many clues and red herrings throughout his previous volumes that it is a thrill to see certain pieces fall into place.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': '2267265af380de430821f99fcef960d3'}>,\n",
-       "  <Document: {'content': 'Martin has hidden so many clues and red herrings throughout his previous volumes that it is a thrill to see certain pieces fall into place.\"Remy Verhoeve of \\'\\'The Huffington Post\\'\\' said, \"\\'\\'A Dance with Dragons\\'\\' is just a book, of course. It is not the Second Coming or anything. And I understand that the author must feel a lot of pressure concerning this one (he should). It\\'s late and it has to repair some of the damage done by \\'\\'A Feast for Crows\\'\\', which frankly felt as if it was written by a ghost writer at times. Finally it is here, and some of the things we\\'ve been wondering about for more than a decade are actually revealed (not everything, but at least some things). It has the same structural problems as the previous book; it is sprawling and incoherent at times, but at least the characters are more interesting than in the previous installment. It does feel like I\\'m reading a bunch of separate stories within the same setting–the chapters are told through the eyes of various characters–but that doesn\\'t really bother me as I love the setting and like to see it through various points of view. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': 'f907563ae7d212560f1b927c3b903095'}>,\n",
-       "  <Document: {'content': 'It does feel like I\\'m reading a bunch of separate stories within the same setting–the chapters are told through the eyes of various characters–but that doesn\\'t really bother me as I love the setting and like to see it through various points of view. Theories that have been bandied about online for the last ten years mostly come true in this book, so in that respect the novel isn\\'t shocking but neither is it disappointing. Martin also manages to put in a few twists, but ends the book much like he did the previous one with cliffhangers instead of wrapping things up a little better so the next long wait won\\'t hurt so much.\"David Orr of \\'\\'The New York Times\\'\\' said, \"\\'\\'A Dance With Dragons\\'\\' comes in at roughly 9,574,622,012 pages, and smart money says the final two books in the series will make this one look like \\'The Old Man and the Sea\\'. Such length isn\\'t necessary, and it hurts Martin\\'s prose and his plot mechanics. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 28}, 'embedding': None, 'id': '3a4a01e6868032b989327ddec01385b1'}>,\n",
-       "  <Document: {'content': 'David Orr of \\'\\'The New York Times\\'\\' said, \"\\'\\'A Dance With Dragons\\'\\' comes in at roughly 9,574,622,012 pages, and smart money says the final two books in the series will make this one look like \\'The Old Man and the Sea\\'. Such length isn\\'t necessary, and it hurts Martin\\'s prose and his plot mechanics. Tyrion \\'waddles\\' at least 12 times here, and even if we suppose the unflattering word reflects Tyrion\\'s contempt for his own awkward gait, it seems unlikely he would indulge this contempt when he\\'s, say, fighting for his life. Similarly, when your novel\\'s terrain stretches across hundreds of miles and your world lacks jet propulsion, as an author you face some basic problems of transportation that can result in conveyance via Rube Goldberg.\"\\'\\'Time\\'\\' James Poniewozik found a possible \"weakness to the early Meereen sections by marking time (and making Dany uncharacteristically indecisive) to allow time for all the pieces to fall into place\" and said, \"All this makes for a thousand-page book that feels half as long, that moves dextrously, answers key questions and gobsmacks you with convincing feints and change-ups. As in \\'\\'AFFC\\'\\', there are sections that feel like they could have used an editor. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 29}, 'embedding': None, 'id': 'e44ce1c07cc87ef89b4e08d9fd23cd30'}>,\n",
-       "  <Document: {'content': '\\'\\'Time\\'\\' James Poniewozik found a possible \"weakness to the early Meereen sections by marking time (and making Dany uncharacteristically indecisive) to allow time for all the pieces to fall into place\" and said, \"All this makes for a thousand-page book that feels half as long, that moves dextrously, answers key questions and gobsmacks you with convincing feints and change-ups. As in \\'\\'AFFC\\'\\', there are sections that feel like they could have used an editor. In some chapters you suddenly find yourself in a strange land with a character you have little attachment to, wondering where this thread is going, as if you had stayed too long at a party after the friends you came with have left.\\n\\n\\'\\'The Washington Post\\'\\' Bill Sheehan said, \"Filled with vividly rendered set pieces, unexpected turnings, assorted cliffhangers and moments of appalling cruelty, \\'\\'A Dance With Dragons\\'\\' is epic fantasy as it should be written: passionate, compelling, convincingly detailed and thoroughly imagined. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 30}, 'embedding': None, 'id': '2adea5c9147399c3a414768eba117875'}>,\n",
-       "  <Document: {'content': '\\'\\'The Washington Post\\'\\' Bill Sheehan said, \"Filled with vividly rendered set pieces, unexpected turnings, assorted cliffhangers and moments of appalling cruelty, \\'\\'A Dance With Dragons\\'\\' is epic fantasy as it should be written: passionate, compelling, convincingly detailed and thoroughly imagined. Despite a number of overtly fantastic elements (dragons, seers, shape shifters and sorcerers), the book — and the series as a whole — feels grounded in the brutal reality of medieval times and has more in common with the Wars of the Roses than it does with \\'\\'The Lord of the Rings\\'\\'. The result is a complex summer blockbuster with brains and heart, a book with rare — and potentially enormous — appeal.\\n\\nMegan Wasson of \\'\\'The Christian Science Monitor\\'\\' said, \"\\'\\'A Dance with Dragons\\'\\' may well be one of the best books in the five-book series so far. Martin\\'s prose is concise but pithy, begging to be devoured over and over again. All the fans\\' favorite characters make an appearance, unlike in the last book. But what truly sets this book above some of the others in the series is Martin\\'s ability to keep his readers on their toes and the edges of their seats. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 31}, 'embedding': None, 'id': 'b51cf08c9cc513e4532e9e6bd28ffea2'}>,\n",
-       "  <Document: {'content': 'But what truly sets this book above some of the others in the series is Martin\\'s ability to keep his readers on their toes and the edges of their seats. No character, no matter how likeable and seemingly important, is ever safe from Martin\\'s pen (remember Ned Stark?), and on the other hand, no one can ever be truly pronounced dead. What you thought was going to happen after reading Books No. 1, 2, and even 3 and 4, now clearly will not happen, and that\\'s where Martin\\'s strength lies.\"===Awards===\\nIn April 2012, \\'\\'A Dance with Dragons\\'\\' was nominated for the 2012 Hugo Award for Best Novel.  In May 2012, it was nominated for the 2012 Locus Award for Best Fantasy Novel and won it in June 2012. In August 2012, the novel was nominated for a World Fantasy Award for Best Novel.\\n\\n==References==\\n\\n==External links==\\n*  of author George R. R. Martin\\n*\\n*\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 32}, 'embedding': None, 'id': 'a3736b25be33ef08206fb25e75a485f3'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Mountain and the Viper\\'\\'\\'\" is the eighth episode of the fourth season of HBO\\'s acclaimed fantasy television series \\'\\'Game of Thrones\\'\\', and the 38th overall. The episode was written by series co-creators David Benioff and D. B. Weiss, and directed by Alex Graves. It aired on June 1, 2014.\\n\\nThis episode marks the final appearance of Pedro Pascal (Oberyn Martell).\\n\\n==Plot==\\n\\n===At the Wall===\\nMole\\'s Town is attacked by wildlings, but Ygritte spares Gilly and her son\\n\\n===At Moat Cailin===\\nTheon enters Moat Cailin and gives the terms for their surrender to Ralf Kenning. Ralf declines, but is murdered by one of his men, who agrees. However, after the Ironborn open the Moat\\'s gates, Ramsay\\'s army slaughters them. As a reward for retaking the Moat, Roose legitimizes Ramsay as a true Bolton. Bolton\\'s army and Reek depart for their new seat at Winterfell.\\n\\n===In Meereen===\\nBarristan receives a letter with the Hand of the King\\'s seal. The letter is Jorah\\'s royal pardon signed by Robert for spying on Daenerys. Jorah admits his spying on Daenerys and she exiles him.\\n\\n===In the Vale===\\nPetyr is interrogated by the nobility of the Vale about Lysa\\'s death. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '43967ec76b2a3cf2ffc1fd0313d559eb'}>,\n",
-       "  <Document: {'content': \"Jorah admits his spying on Daenerys and she exiles him.\\n\\n===In the Vale===\\nPetyr is interrogated by the nobility of the Vale about Lysa's death. He claims that Lysa committed suicide. Lord Yohn Royce demands to speak with Sansa, who reveals her identity, corroborates Petyr's story and convinces them of his innocence. Petyr, Sansa and Robin leave the Eyrie to tour the Vale. Sansa dyes her hair black to hide her identity. Sandor and Arya arrive at Bloody Gate, only to be informed of Lysa's death, by Donnel.\\n\\n===In King's Landing===\\nTyrion's trial by combat begins. Oberyn knocks Gregor to the ground, but instead of finishing him, Oberyn screams at him to confess that Tywin gave him the order to kill Elia and her children. Gregor knocks Oberyn down, confessing to the murder of Elia and her children and crushing Oberyn's skull with his hands. Tywin sentences Tyrion to death for\\xa0regicide.\\n\\n==Production==\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'f8fee3ecd82a6e80bd72375ac20926dd'}>,\n",
-       "  <Document: {'content': 'Tywin sentences Tyrion to death for\\xa0regicide.\\n\\n==Production==\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode contains content from three of George Martin\\'s novels: \\'\\'A Storm of Swords\\'\\', chapters Daenerys V, Daenerys VI, and Tyrion X; \\'\\'A Feast for Crows\\'\\', chapters Alayne I and Alayne II; and \\'\\'A Dance with Dragons\\'\\', chapter Reek II.\\n\\n==Reception==\\n=== Ratings ===\\n\"The Mountain and the Viper\" was watched by an estimated 7.17 million people during its first airing. In the United Kingdom, the episode was viewed by 1.811 million viewers, making it the highest-rated broadcast that week. It also received 0.062 million timeshift viewers.\\n\\n=== Critical reception ===\\nThe episode received high praise from critics and audiences alike, with the fight between Prince Oberyn and the Mountain being hailed as the episode\\'s highlight. On Rotten Tomatoes it obtained a 97% score, based on 32 reviews, with an average rating of 9/10. The website\\'s critical consensus reads, \"With one of the most gruesome scenes to date, \\'The Mountain and the Viper\\' delivers a tense, twisty final scene well worth the wait.\"Writing for \\'\\'The A.V. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '639758a024fa6ba71a91be123ecb8aba'}>,\n",
-       "  <Document: {'content': 'The website\\'s critical consensus reads, \"With one of the most gruesome scenes to date, \\'The Mountain and the Viper\\' delivers a tense, twisty final scene well worth the wait.\"Writing for \\'\\'The A.V. Club\\'\\', Emily VanDerWerff gave the episode an A- and praised the staging of the final fight by director Alex Graves. Erik Adams, also writing for the \\'\\'A.V. Club\\'\\' gave the episode an A. Terri Schwartz, writing for Zap2it.com, wrote the episode was \"one of the strongest \\'\\'Game of Thrones\\'\\' episodes to date, and that\\'s just the type of episode that the death of Oberyn Martell deserves.\"===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2014\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Art Direction for a Single-Camera Fantasy Series\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi\\n\\n2015\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley\\n\\nSFX Awards\\n\\nBest TV Episode\\n\\nHugo Awards\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff, Alex Graves, and D. B. Weiss\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '82d8a7d59bb3d017cd7f597b0901f800'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Second Sons\\'\\'\\'\" is the eighth episode of the third season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 28th episode of the series. The episode was written by executive producers David Benioff and D. B. Weiss, and directed by Michelle MacLaren. It aired on .\\n\\nThe episode is centered on the wedding of Tyrion Lannister and Sansa Stark in King\\'s Landing, Gendry\\'s arrival at Dragonstone and Daenerys\\'s meeting with the mercenary company of the Second Sons before the walls of Yunkai.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nTyrion visits Sansa to ease her apprehension at the prospect of being his wife. In the Sept of Baelor, Cersei threatens Margaery with the story of House Reyne, former Lannister vassals whom Tywin exterminated when they rebelled against him. After arriving at the Sept, Sansa is walked down the aisle by Joffrey.\\n\\nAt their wedding feast, Tyrion gets drunk. Joffrey, after threatening to rape Sansa, calls for the traditional bedding ceremony to begin, but his plan is thwarted when Tyrion threatens to castrate an outraged Joffrey. Tywin defuses the situation. Tyrion leaves the feast with Sansa, and he tells Sansa he will not share her bed until she wants him to.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'cca00568aee4e0899cdab10f267cc5da'}>,\n",
-       "  <Document: {'content': \"Tyrion leaves the feast with Sansa, and he tells Sansa he will not share her bed until she wants him to.\\n\\n===At Dragonstone===\\nMelisandre returns to Dragonstone with Gendry and takes him to see Stannis, who recognizes Gendry as one of Robert's bastards. When Gendry is taken to his chamber, Stannis and Melisandre discuss what they intend to do with him.\\n\\nIn the dungeons, Davos continues to learn to read. Stannis visits him to discuss Melisandre's plan to sacrifice Gendry. Davos objects to the plan, but Stannis remains resolved. He then makes Davos swear to never raise his hand to Melisandre again, and frees him.\\n\\nLater, Melisandre visits Gendry and seduces him, tying him to a bed in the process. She lays three leeches on him to draw his royal blood. Stannis ritually burns the leeches, speaking the names of the usurpers to his throne: Robb Stark, Balon Greyjoy, and Joffrey Baratheon.\\n\\n===In the Riverlands===\\nArya tries to kill the Hound while he is sleeping, but he is revealed to be awake, and thwarts her attempt on his life. They depart their camp and head for the Twins, where the Hound intends to ransom Arya to Robb.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '7f509021deeb21d914524674d7103725'}>,\n",
-       "  <Document: {'content': \"===In the Riverlands===\\nArya tries to kill the Hound while he is sleeping, but he is revealed to be awake, and thwarts her attempt on his life. They depart their camp and head for the Twins, where the Hound intends to ransom Arya to Robb.\\n\\n===At Yunkai===\\nJorah tells Daenerys that Yunkai has employed a mercenary group called the Second Sons. Daenerys meets with Mero, his co-captain Prendahl na Ghezn and his lieutenant Daario Naharis. She attempts to bribe Mero to fight for her, and gives him two days to make a decision.\\n\\nAfter nightfall, Daario enters Daenerys' camp, disguised as an Unsullied soldier. He enters her tent and shows her and Missandei the severed heads of Mero and Prendahl, admitting that he is smitten by Daenerys.\\n\\n===Beyond the Wall===\\nSam and Gilly continue their journey to the Wall. They stop at an abandoned hut for the night. When they hear a murder of crows cawing nearby, Sam leaves the hut to investigate. Soon after, he is attacked by a White Walker. The Walker intents on taking Gilly's son, but Sam stabs it with his dragonglass dagger, causing the Walker to disintegrate.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'e2c90efbcc4092d92464264f681371f8'}>,\n",
-       "  <Document: {'content': 'The Walker intents on taking Gilly\\'s son, but Sam stabs it with his dragonglass dagger, causing the Walker to disintegrate.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"Second Sons\" was written by the show creators and executive producers David Benioff and D. B. Weiss, based on material from George R. R. Martin\\'s novel \\'\\'A Storm of Swords\\'\\'. The episode adapts parts of the book\\'s chapters 19, 29, 37, 43, 47 and 48 (Samwell I, Sansa III, Davos IV, Daenerys IV, Samwell III and Arya IX).\\n\\n===Casting===\\nThe episode introduced the mercenary captains in service of Yunkai: Mark Killeen was cast as Mero (known as the Titan\\'s Bastard), Ed Skrein the recurring role of Daario Naharis, and Ramon Tikaram the part of Prendahl na Ghezn. Tikaram is mistakenly credited as \"Ramon Tikrum\" in the closing credits.\\n\\n===Filming locations===\\nMost of the scenes of the episode were shot in the Belfast studios of The Paint Hall, including the wedding of Tyrion and Sansa that was filmed at the huge semicircular set of the Great Sept of Baelor in mid September 2012. For this scene, a few hundred extras were recruited.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'acb061ea5521482ac9a142d5036eb80f'}>,\n",
-       "  <Document: {'content': '===Filming locations===\\nMost of the scenes of the episode were shot in the Belfast studios of The Paint Hall, including the wedding of Tyrion and Sansa that was filmed at the huge semicircular set of the Great Sept of Baelor in mid September 2012. For this scene, a few hundred extras were recruited.\\n\\n==Reception==\\n===Ratings===\\nThe episode received 5.1 million viewers, an increase from the previous week, with an 18-49 demographic of 2.6. In the United Kingdom, the episode was seen by 0.907 million viewers on Sky Atlantic, being the channel\\'s highest-rated broadcast that week.\\n\\n===Critical reception===\\n\"Second Sons\" received critical acclaim. Review aggregator Rotten Tomatoes surveyed 20 reviews of the episode and judged 100% of them to be positive with an average score of 8 out of 10. The website\\'s critical consensus reads, \" \\'Second Sons\\' shines through efficient storytelling -- and a comparatively low number of storylines to keep track of this week.\"Writing for IGN, Matt Fowler rated the episode a 9.0/10, and wrote \"This week\\'s well-crafted and wonderfully acted Game of Thrones gave us a cold wedding, a hot bath and a blood-letting.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '665447811e009cc2cb8021eeed498a1'}>,\n",
-       "  <Document: {'content': 'Writing for IGN, Matt Fowler rated the episode a 9.0/10, and wrote \"This week\\'s well-crafted and wonderfully acted Game of Thrones gave us a cold wedding, a hot bath and a blood-letting.\"He especially praised the scenes between Sansa and Tyrion and between Ser Davos and Stannis. David Sims and Emily VanDerWerff, both writing for The A.V. Club, gave the episode \"B\" ratings. Sims was frustrated by the episode\\'s meandering pace, but praised the end of the episode, with Sam killing the white walker, as \"the most crucial, fascinating, electric moment of the night\". VanDerWerff praised the use of nudity in the episode, writing \"...I actually think Game Of Thrones has gotten quite a bit better at utilizing nudity and sex in the midst of everything else as a method of telling its story. It’s come a long way from the \\'sexposition\\' days of season one, when it sometimes seemed like the series would toss some breasts into the background of a scene just in case we got bored of hearing somebody talk at length.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'c07114fdc35dbf9a678f1edd7ae67bc6'}>,\n",
-       "  <Document: {'content': 'It’s come a long way from the \\'sexposition\\' days of season one, when it sometimes seemed like the series would toss some breasts into the background of a scene just in case we got bored of hearing somebody talk at length.\"===Accolades===\\nDue to his nomination, Peter Dinklage submitted this episode for consideration for the Primetime Emmy Award for Outstanding Supporting Actor in a Drama Series for the 65th Primetime Emmy Awards.\\n\\nAt the 65th Primetime Creative Arts Emmy Awards, the episode was nominated for Outstanding Hairstyling for a Single-Camera Series.\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Second Sons\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'da645181d022b5ee7f7e4cee304aab71'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''Roose Bolton''' is a fictional character in the ''A Song of Ice and Fire'' series of fantasy novels by American author George R. R. Martin, and its television adaptation ''Game of Thrones''.\\n\\nIntroduced in 1996's ''A Game of Thrones'', Roose, a Northern lord with his seat at the Dreadfort, is a retainer of Lord Eddard Stark. His family is notorious for their cruelty and custom of flaying their enemies; he frequently has himself leeched, leading him to be known as '''the Leech Lord''' across Westeros. He later appears in ''A Clash of Kings'' (1998), ''A Storm of Swords'' (2000), and ''A Dance with Dragons'' (2011).\\n\\nRoose joins Robb Stark's rebellion as one of his chief lieutenants. With the help of the Brave Companions, he takes and holds Harrenhal until rejoining Robb Stark to help retake the Neck occupied by the Greyjoys. In truth he is a chief orchestrator of the Red Wedding alongside Tywin Lannister and Lord Walder Frey, receiving the title of Warden of the North from the former after personally slaying Robb. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '51ff18519f3d4cb58207312ecb2ce6b9'}>,\n",
-       "  <Document: {'content': \"In truth he is a chief orchestrator of the Red Wedding alongside Tywin Lannister and Lord Walder Frey, receiving the title of Warden of the North from the former after personally slaying Robb. His rule as Warden in the North is punctuated by unrest, and several forces conspire to unseat him and House Frey and restore the Starks to power.\\n\\nRoose is portrayed by Irish actor Michael McElhatton in the HBO television adaptation.\\n\\n==Character description==\\nLord Roose Bolton is a significant vassal of Lord Eddard Stark. His seat is the Dreadfort and his sigil is a flayed man, an homage to the ancient Bolton tradition of flaying enemies. He is nicknamed '''the Leech Lord''' for regular leechings meant to improve his health.\\n\\nRoose Bolton is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, such as Catelyn Stark, Arya Stark and Theon Greyjoy/Reek.\\n\\n===Background===\\nRoose practiced an ancient and illicitly banned tradition called the first night, in which a lord had the right to bed a commoner's wife. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '745b2a3c05ee31c800411a2d2a6f274f'}>,\n",
-       "  <Document: {'content': \"===Background===\\nRoose practiced an ancient and illicitly banned tradition called the first night, in which a lord had the right to bed a commoner's wife. One day, prior to Robert's Rebellion, Roose was fox hunting along the Weeping Water when he came across a young woman washing clothes in the stream, whom was married to the old miller without Roose's knowledge. Desiring the woman, and angered for not being informed of the marriage, Roose had the miller hanged and violently raped the woman beneath the tree where her husband still hung.\\n\\nA year later, the woman came to the Dreadfort with a newborn boy whom she claimed was Roose's bastard, named Ramsay. Roose considered having the woman whipped and the baby killed before realizing that the baby is indeed his, owing to its pale, cold eyes. He gave the woman money and annual supplies as payment to raise Ramsay, and had the tongue of the miller's brother cut out to prevent Lord Rickard Stark from being informed of his illegal activities. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '444b51afbd73ccf9592fc5d8c3851ea1'}>,\n",
-       "  <Document: {'content': \"He gave the woman money and annual supplies as payment to raise Ramsay, and had the tongue of the miller's brother cut out to prevent Lord Rickard Stark from being informed of his illegal activities. He sends his odious and depraved servant Reek to the young Ramsay and his mother after the latter demands a servant to help raise the boy, an act he considers to be highly amusing. Roose later participates in Robert's Rebellion and (according to a semi-canonical source) the Greyjoy Rebellion.\\n\\nDespite Roose's orders, Roose's only trueborn son, Domeric, seeks out Ramsay. Domeric dies soon after, and Roose suspects that Ramsay poisoned Domeric to become his heir. Roose, left without a trueborn heir, brings Ramsay to the Dreadfort, where he later serves as the fortress's castellan and has thus far refused to legitimize or otherwise acknowledge him as his true heir. He considers his bastard son foolhardy and tactless, with 'tainted blood that would poison even the leeches'.\\n\\n===Appearance and personality===\\nRoose is most often described as an oddly average and unassuming-looking man, clean-shaven with pallid skin. He is often described as ageless, with an apathetic demeanor that betrays nothing. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '396d6c8ff3b352d157e042d31f48c12d'}>,\n",
-       "  <Document: {'content': '===Appearance and personality===\\nRoose is most often described as an oddly average and unassuming-looking man, clean-shaven with pallid skin. He is often described as ageless, with an apathetic demeanor that betrays nothing. His most prominent feature are his strangely pale and cold eyes, so light as to almost blend with the sclera.\\n\\nRoose appears to be unassuming and mild-mannered, although in truth he is remorseless and unforgiving, possessed of the same cruelty and ruthlessness his house is notorious for. Theon Greyjoy believes that he is even more cruel and menacing than his bastard son, despite Ramsay\\'s more evident depravity. While his voice is small and soft, he does not need to raise it in order to inspire silence and attention – Jaime Lannister and Robb Stark both remark that even just his silence is threatening. His personal motto is \"A peaceful land, a quiet people\", a lesson he has thus far failed to instill into his bastard son. He often dresses in a pale pink fur cloak embroidered in blood red to symbolize his family\\'s custom of flaying.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '7e06a08a4012eecac8344a51b97de4b7'}>,\n",
-       "  <Document: {'content': 'He often dresses in a pale pink fur cloak embroidered in blood red to symbolize his family\\'s custom of flaying.\\n\\n== Storylines ==\\n\\n====\\'\\'A Game of Thrones\\'\\'====\\n\\nRoose is amongst the lords who travel to Winterfell to aid Robb Stark in his campaign against the Lannisters. His intelligence and caution sees him given command of part of the Northern host when the army splits up at the Twins, and he leads the attack on Tywin Lannister\\'s army in the Battle of the Green Fork. The battle ends in a Lannister victory and Roose retreats with the survivors to the causeway of Moat Cailin.\\n\\n====\\'\\'A Clash of Kings\\'\\'====\\n\\nTo form an alliance with House Frey, Roose is offered by Lord Walder Frey one of his female family members hand in marriage, as well as offering the prospective bride\\'s weight in silver for her dowry. Roose chooses \"Fat\" Walda Frey, the fattest female member of House Frey. He also makes an alliance with the Brave Companions, Essosi sellswords employed by Tywin, to help the Northerners capture Harrenhal from the Lannister force occupying it. After capturing Harrenhal Roose takes Arya Stark as a servant, mistaking her for a commoner.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '51ae48fc8837184b0dfcf3a088c92fc7'}>,\n",
-       "  <Document: {'content': \"He also makes an alliance with the Brave Companions, Essosi sellswords employed by Tywin, to help the Northerners capture Harrenhal from the Lannister force occupying it. After capturing Harrenhal Roose takes Arya Stark as a servant, mistaking her for a commoner.\\n\\n====''A Storm of Swords''====\\n\\nHoat brings Jaime Lannister to Harrenhal, having cut off Jaime's hand in hope of blaming Roose and preventing the Boltons allying with the Lannisters. Roose has Jaime sent back to King's Landing after Jaime assures Roose that he will not blame him. Roose then travels to the Twins for Edmure Tully's wedding to Roslin Frey, but at the wedding the Freys turn on the Starks and Roose personally kills Robb Stark. It is revealed that Roose had conspired with the Freys and Tywin Lannister to betray the Starks. As reward for his service, Tywin names Roose the new Warden of the North\\n\\n====''A Feast for Crows'' and ''A Dance With Dragons''====\\n\\nRoose returns North with his forces, joined by two thousand Frey men. Meeting with Ramsay (now legitimised as a Bolton) and a captive Theon Greyjoy, the Boltons travel to Barrowton for Ramsay's wedding to Jeyne Poole, forced to assume the identity of Arya Stark. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'bd8b1cd3a9313df0a9a3c4783bbcc829'}>,\n",
-       "  <Document: {'content': \"Meeting with Ramsay (now legitimised as a Bolton) and a captive Theon Greyjoy, the Boltons travel to Barrowton for Ramsay's wedding to Jeyne Poole, forced to assume the identity of Arya Stark. After hearing that Stannis Baratheon has captured Deepwood Motte, Roose decides to move the wedding to Winterfell to bait Stannis out. The Boltons and their Northern allies (many of whom are only grudgingly pledging fealty to the Boltons, or plan to betray them) remain at Winterfell after the wedding in anticipation of Stannis' attack. Tensions are high during the wedding due to the anger of the Northmen at the Freys. Three of the Freys who had been travelling with Lord Wyman Manderly of White Harbor, who lost his younger son Ser Wendel Manderly at the Red Wedding, have disappeared, and are heavily implied to have been put in pies which Wyman gives to the Freys and Boltons, eating some himself. Lady Barbrey Dustin of Barrowton, the younger sister of Bethany Ryswell, tells Theon that Roose has no feelings and plays with people for amusement. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'ba328e225fd5be9f13c2a06c937a39f6'}>,\n",
-       "  <Document: {'content': \"Lady Barbrey Dustin of Barrowton, the younger sister of Bethany Ryswell, tells Theon that Roose has no feelings and plays with people for amusement. When one of Walder Frey's grandsons, Little Walder Frey, is found murdered (possibly by his cousin Big Walder Frey), their uncle Ser Hosteen Frey attacks Wyman, leading to a fight in which White Harbor and Frey men are killed. Roose is forced to send them both out of Winterfell to encounter Stannis.\\n\\n=== Family tree of House Bolton ===\\n\\n;Notes\\n\\n== TV adaptation ==\\n\\nRoose Bolton is played by Michael McElhatton in the HBO television adaption of the series of books. He and the rest of the cast were nominated for Screen Actors Guild Awards for Outstanding Performance by an Ensemble in a Drama Series in 2014.\\n\\n====Second season====\\nRoose declares for King in the North Robb Stark and serves as a chief member of his war council, although Robb sternly admonishes Roose when he advocates flaying Lannister prisoners to obtain information. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'c65e7f49a87247559de4a8cc61297d9d'}>,\n",
-       "  <Document: {'content': \"====Second season====\\nRoose declares for King in the North Robb Stark and serves as a chief member of his war council, although Robb sternly admonishes Roose when he advocates flaying Lannister prisoners to obtain information. After Theon Greyjoy betrays the Starks and seizes Winterfell, Roose brings the news to Robb and offers to send his bastard son Ramsay Snow with a force of Dreadfort men to oust Theon and the Ironborn from Winterfell.\\n\\n====Third season====\\nThe dagger with which Roose Bolton kills Robb Stark, and the knife with which Catelyn Stark kills Walder Frey's wife.\\nFollowing the Northern army's arrival at Harrenhal, Roose presents a letter from Ramsay claiming that the Ironborn sacked Winterfell before fleeing. Robb orders Roose and the Bolton forces to hold Harrenhal while the rest of his army rides to Riverrun. One of Roose's man-at-arms, Locke, captures the escaped Jaime Lannister and his escort Brienne of Tarth, cutting off Jaime's swordhand in the process, before bringing the two to Harrenhal. Roose agrees to let Jaime go, but keeps Brienne as a hostage, though Jaime later returns to secure her release. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'c3521d80f1b1e66b5dd21e7e09da68f6'}>,\n",
-       "  <Document: {'content': \"Roose agrees to let Jaime go, but keeps Brienne as a hostage, though Jaime later returns to secure her release. He then meets up with the Stark army at the Twins for the wedding of Edmure Tully and Roslin Frey. However, it is revealed that Roose has conspired with Lord Walder Frey to betray the Starks, and after the wedding the Freys and Bolton slaughter the Stark forces, with Roose personally killing Robb. As part of the Bolton-Frey alliance, Roose agrees to marry Walder's daughter Walda - Walder offers him the bride's weight in silver as dowry, so Roose decides to marry the fattest of Walder's daughters. In the aftermath of the massacre, Roose hints to Walder that his betrayal of Robb was motivated by resentment at having his advice ignored by Robb. Roose also reveals that Winterfell was actually sacked by his bastard Ramsay, who subsequently flayed the Ironborn garrison there and took Theon prisoner, for his own amusement. As reward for his defection, Tywin Lannister names Roose the Warden of the North.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '605f1edf10fcac7c39cc2f9597075f03'}>,\n",
-       "  <Document: {'content': 'Roose also reveals that Winterfell was actually sacked by his bastard Ramsay, who subsequently flayed the Ironborn garrison there and took Theon prisoner, for his own amusement. As reward for his defection, Tywin Lannister names Roose the Warden of the North.\\n\\n====Fourth season====\\nWith the Ironborn holding Moat Cailin - the fortification barring passage between the North and the rest of Westeros - Roose is forced to smuggle himself back into the North. Upon his return to the Dreadfort, he chastises Ramsay for having gelded Theon and sending terms of surrender to the Greyjoys without his approval, while reminding Ramsay of his bastard parentage. Insulted, Ramsay demonstrates how effectively he has broken Theon (whom he has since renamed \"Reek\") by having Reek shave him, even after revealing Roose\\'s murder of Robb, while also coaxing Reek into revealing he faked the deaths of Bran and Rickon Stark. After Ramsay points out that the other Northerners will turn on the Boltons if it is revealed that there is a living male Stark, Roose tasks Locke with hunting down Bran and Rickon and killing Jon Snow, Robb\\'s bastard half-brother. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'd5414b94206122329c63d346a536cd87'}>,\n",
-       "  <Document: {'content': 'After Ramsay points out that the other Northerners will turn on the Boltons if it is revealed that there is a living male Stark, Roose tasks Locke with hunting down Bran and Rickon and killing Jon Snow, Robb\\'s bastard half-brother. Roose also sends Ramsay and Reek to lift the siege of Moat Cailin; when Ramsay is successful, Roose presents him with a royal decree of legitimisation as a trueborn Bolton. Roose subsequently moves to rebuild and occupy Winterfell.\\n\\n====Fifth season====\\nIn the aftermath of Tywin Lannister\\'s death and Ramsay\\'s murder of a disobedient vassal and his family, Roose seeks to secure House Bolton\\'s position by arranging to have Ramsay marry Sansa Stark, supposedly the last trueborn Stark alive. In doing so, Roose seemingly secures an alliance with the forces of the Vale and its Lord Protector Petyr \"Littlefinger\" Baelish (unaware that Baelish intends on having the Bolton army decimated by the approaching Baratheon army before defeating the victor with the Vale\\'s army). After Ramsay torments Sansa by having Reek serve them at dinner, Roose announces that he and Walda are expecting a son. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '760f1800a03332ada6f6e2b7d738e335'}>,\n",
-       "  <Document: {'content': \"After Ramsay torments Sansa by having Reek serve them at dinner, Roose announces that he and Walda are expecting a son. However, later Roose privately reassures Ramsay of his position as his heir, and asks him to assist in defeating Stannis Baratheon's army. To this end, Roose permits Ramsay and his men to launch a sneak attack on Stannis' camp, destroying the army's supplies. With the supplies destroyed and most of Stannis' army subsequently deserting him, the Boltons easily defeat the Baratheons when they attempt to lay siege to Winterfell, but in the aftermath of the battle Reek and Sansa manage to escape, severely jeopardizing House Bolton's rule in the North.\\n\\n====Sixth season====\\nDespite their victory over Stannis, Roose warns Ramsay that the North will someday have to face the Lannisters, and chastises him for allowing Sansa and Theon to escape, as Sansa was crucial to unifying the North. He implies that if Sansa is not recovered, Ramsay's position as heir may be usurped by Walda's baby. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '744ba06402a9fb3808b3be6be973f032'}>,\n",
-       "  <Document: {'content': \"====Sixth season====\\nDespite their victory over Stannis, Roose warns Ramsay that the North will someday have to face the Lannisters, and chastises him for allowing Sansa and Theon to escape, as Sansa was crucial to unifying the North. He implies that if Sansa is not recovered, Ramsay's position as heir may be usurped by Walda's baby. Soon afterwards, it is announced that Walda has given birth to a boy; Ramsay immediately kills Roose by stabbing him in the stomach, before setting his dogs upon Walda and the baby, severely jeopardizing House Bolton and leaving Ramsay as the last remaining Bolton. Ramsay is ultimately killed when Jon Snow retakes Winterfell in the Battle of the Bastards, leaving House Bolton extinct.\\n\\n== References ==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'e93b46ebe3293699bd24ab071f3bef3c'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''''Rani Mahal''''' is an upcoming Indian television series, which will air on Sony Entertainment Television (India) and Sony Entertainment Television Asia. Many news sources claimed that this show is an Indian adaptation of American fantasy drama series ''Game of Thrones'' which based on best-selling novel ''A Song of Ice and Fire'', which has been denied by Sony Entertainment Television (India) and Lost Boy Productions.\\n\\n== Cast ==\\n*Yuvraj Thakur\\n*Anita Hassanandani\\n*Ankita Lokhande\\n*VJ Bani\\n*Reena Roy as Raj Mata\\n\\n==References==\\n\\n==External links==\\n*  Official website\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '5e997c167670b17cfc7479c9de62b8d5'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Winter Is Coming\\'\\'\\'\" is the series premiere episode of the HBO medieval fantasy television series \\'\\'Game of Thrones\\'\\'. The first episode of the first season, it was written by the show creators David Benioff and D. B. Weiss, in a faithful adaptation of the first chapters of George R. R. Martin\\'s book \\'\\'A Game of Thrones\\'\\'. The episode was directed by Tim Van Patten, redoing the work done by director Tom McCarthy in an unaired pilot.\\n\\nAs the first episode of the series, it introduces the setting and the main characters of the show. The episode centers on the Stark family, and how its lord, Eddard \"Ned\" Stark, gets involved in the court politics after the king chooses Eddard to replace his recently deceased chief administrator (\"Hand of the King\"). The episode received largely positive reviews, and was seen initially by 2.2 million viewers. A week before the episode first aired, HBO made the first 15 minutes available as an Internet preview.\\n\\nThe title of the episode is the motto (referred to as \"House Words\" in-universe) of House Stark, which is spoken several times in the episode and in the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '1250c7f4363788ddac725fb5b9a8dcda'}>,\n",
-       "  <Document: {'content': 'The title of the episode is the motto (referred to as \"House Words\" in-universe) of House Stark, which is spoken several times in the episode and in the series.\\n\\n==Plot==\\n===Beyond the Wall===\\nOn the continent of Westeros, rangers of the Night\\'s Watch scout wildling movements beyond the Wall, the massive ice barrier to the north, and discover demonic White Walkers and wildlings turned to undead wights. Will, the sole surviving ranger, flees South.\\n\\n===In King\\'s Landing===\\nWatching as the corpse of Jon Arryn, the Hand of the King, is tended to, Jaime Lannister assures his twin sister, Queen Cersei Lannister, that if Arryn had spoken to anyone about them, they would already have been executed.\\n\\n===In Pentos===\\nExiled prince Viserys Targaryen plots to reclaim his father\\'s throne from King Robert Baratheon, and brokers a marriage between his sister Daenerys and Dothraki warlord Khal Drogo. As wedding gifts, Daenerys is given books of the Seven Kingdoms from Ser Jorah Mormont, an exiled knight loyal to the Targaryens, and three petrified dragon eggs from Magister Illyrio Mopatis, who helped arrange the marriage.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '4028b00ef0f8cef8ffc5e645872687af'}>,\n",
-       "  <Document: {'content': 'As wedding gifts, Daenerys is given books of the Seven Kingdoms from Ser Jorah Mormont, an exiled knight loyal to the Targaryens, and three petrified dragon eggs from Magister Illyrio Mopatis, who helped arrange the marriage.\\n\\n===In the North===\\nThe Starks of Winterfell are introduced: Lord Eddard \"Ned\" Stark, his wife Lady Catelyn, their children – heir Robb, elder daughter Sansa, younger daughter Arya, ten-year-old son Bran, youngest son, Rickon, and Ned\\'s bastard son Jon Snow and ward Theon Greyjoy.\\n\\nNed takes his sons to witness Will\\'s execution for desertion. Ignoring Will\\'s warning of the White Walkers, Ned beheads him, insisting Walkers are long extinct. The Starks find a dead stag, sigil of House Baratheon, and a dead direwolf, sigil of the Starks, whose pups are taken in by the children.\\n\\nNews arrives of the death of Lord Arryn, Eddard\\'s friend and Catelyn\\'s brother-in-law. Winterfell receives the royal court, including King Robert, his wife Queen Cersei, their children – heir Prince Joffrey, Princess Myrcella, and Prince Tommen – as well as Jaime, a member of the Kingsguard, and his and Cersei\\'s younger brother Tyrion Lannister, a dwarf known as \"The Imp\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '4774695f19422618065b13e85f906e82'}>,\n",
-       "  <Document: {'content': 'Winterfell receives the royal court, including King Robert, his wife Queen Cersei, their children – heir Prince Joffrey, Princess Myrcella, and Prince Tommen – as well as Jaime, a member of the Kingsguard, and his and Cersei\\'s younger brother Tyrion Lannister, a dwarf known as \"The Imp\". Robert pays respects to Lyanna Stark, his late fiancée and Ned\\'s sister, appoints Ned the new Hand of the King, and suggests Sansa be betrothed to Joffrey. Catelyn receives a message from her sister Lysa, Arryn\\'s widow, who suspects her husband was murdered by the Lannisters. Ned reluctantly accepts the position of Hand of the King.\\n\\nBran climbs an abandoned tower and stumbles upon Cersei and Jaime having sex. To keep the incestuous relationship a secret, Jaime shoves Bran out of the window.\\n\\n==Production==\\n===Conception and development===\\nA number of Hollywood studios had contacted George R.\\xa0R. Martin about possibly adapting his book series \\'\\'A Song of Ice and Fire\\'\\' into a film. However, Martin expressed the opinion the books could not be made into a film as too much would have to be cut from the books, but thought it could be made into a television series. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '6edf0e1a6b877e8d7082ee3e5d33bdae'}>,\n",
-       "  <Document: {'content': 'However, Martin expressed the opinion the books could not be made into a film as too much would have to be cut from the books, but thought it could be made into a television series. In January 2006, David Benioff happened to speak to Martin\\'s literary agent, and the agent sent the first four books of \\'\\'A Song of Ice and Fire\\'\\' to David Benioff. Benioff read a few hundred pages of the first book in the series, \\'\\'A Game of Thrones\\'\\', called D. B. Weiss and said: \"Maybe I\\'m crazy, but I haven\\'t had this much fun reading anything in about 20 years. So take a look because I think it might make a great HBO series.\"Weiss, who then read the first book in two days, was very enthusiastic about a possible television project based on the books. They arranged a meeting with Martin, who asked them if they knew who Jon Snow\\'s real mother might be, and was satisfied with their answer.\\n\\nIn March 2006, a few weeks after meeting Martin, Benioff and Weiss pitched the show to Showtime and then Carolyn Strauss of HBO, the latter of whom accepted their proposal. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '280451609aea7df792bc798da26464a0'}>,\n",
-       "  <Document: {'content': 'In March 2006, a few weeks after meeting Martin, Benioff and Weiss pitched the show to Showtime and then Carolyn Strauss of HBO, the latter of whom accepted their proposal. HBO acquired the rights to the novels to turn them into a television series, with Benioff and Weiss as writers and executive producers of the series. The series went into development in January 2007. The series would begin with the first book from 1996, \"A Game of Thrones\", with the intention that each novel in the series would form the basis for a season\\'s worth of episodes. However, Benioff and Weiss had to resubmit a proposal after Carolyn Strauss stepped down as president of HBO in 2008. The first and second drafts of the pilot script, written by Benioff and Weiss, were submitted in August 2007 and June 2008 respectively. While HBO found both drafts to their liking, a pilot was not ordered until November 2008.\\n\\n===Writing===\\nSeries co-creators David Benioff and D. B. Weiss wrote the episode.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '53a7b9909310c3b97b45c6a00d4d4721'}>,\n",
-       "  <Document: {'content': \"While HBO found both drafts to their liking, a pilot was not ordered until November 2008.\\n\\n===Writing===\\nSeries co-creators David Benioff and D. B. Weiss wrote the episode.\\nScripted by the show creators David Benioff and D. B. Weiss, the first episode includes the plot of the book's chapters 1–9 and 12 (Prologue, Bran I, Catelyn I, Daenerys I, Eddard I, Jon I, Catelyn II, partial Arya I, Bran II, Daenerys II). Changes in the adaptation include the sequence of events in the prologue (in the books it is Gared and not Will who survives and is beheaded by Eddard afterwards, and Arya's material is set before the arrival of the royal family), new scenes showing the Lannister twins' perspective, and Daenerys's wedding night showing Drogo not waiting for her to consent to sex.\\n\\n===Filming===\\nSean Bean portrays Ned Stark in the series.\\nTom McCarthy was chosen to direct the pilot episode, shot between October 24 and November 19, 2009 on location in Northern Ireland, Scotland and Morocco. However, the pilot was deemed unsatisfactory and had to be reshot.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'e43b90f1a955a85bc30eeb0c125a2a4c'}>,\n",
-       "  <Document: {'content': 'Tom McCarthy was chosen to direct the pilot episode, shot between October 24 and November 19, 2009 on location in Northern Ireland, Scotland and Morocco. However, the pilot was deemed unsatisfactory and had to be reshot.\\n\\nThe new pilot episode was filmed in 2010 by new director Tim Van Patten, and several actors appearing in the original pilot did not return for the series. Tamzin Merchant was replaced as Daenerys Targaryen by Emilia Clarke, and Jennifer Ehle was replaced as Catelyn Stark by Michelle Fairley. Additionally, Ian McNeice was replaced as Magister Illyrio by Roger Allam, Richard Ridings as Gared by Dermot Keaney, and Jamie Campbell Bower as Ser Waymar Royce by Rob Ostlere.\\n\\nAnother difference is that the original pilot featured scenes shot in Scotland and scenes in Pentos were shot in Morocco, but in the aired series, Winterfell was filmed in a combination of locations in Northern Ireland, while scenes from Pentos were from Malta. The Doune Castle in Scotland was originally used to recreate Winterfell, and its great hall was used for some interior shots. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '5d0873156b7bac605db51b824a0ccdc5'}>,\n",
-       "  <Document: {'content': \"The Doune Castle in Scotland was originally used to recreate Winterfell, and its great hall was used for some interior shots. Some scenes survived, but as it was not practical to return to Scotland for the reshoot, an exact replica of Doune's great hall was recreated in the soundstage in Belfast for the series. Castle Ward in Northern Ireland was used in the reshoot to film King Robert's entourage entry into Winterfell castle. A car park stood in for the Winterfell castle's courtyard and a wine cellar for the Stark family crypt. The Tollymore Forest Park was used for the opening scene of the encounter with the White Walkers.\\n\\nAll the scenes shot in Morocco were reshot in Malta. The original pilot reused the sets of ''Kingdom of Heaven'' in Morocco to stand in for Pentos and the site of Drogo and Daenerys's wedding. In Malta, the Verdala Palace, the 16th century summer palace of the president of Malta, was used for the exterior scenes at Illyrio's mansion. The Azure Window was used as the backdrop for the wedding. Filming at the Azure Window, however, caused some controversy when a protected ecosystem was damaged by a subcontractor.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '59899cbfc7bd437516d083be6407253b'}>,\n",
-       "  <Document: {'content': 'The Azure Window was used as the backdrop for the wedding. Filming at the Azure Window, however, caused some controversy when a protected ecosystem was damaged by a subcontractor.\\n\\nIn the sex scene, the then-pregnant Lena Headey was substituted by a body double; the production hid her pregnancy for the rest of the season. In the scene in which the Starks encounter a stag killed by a dire wolf as they return from the execution, an actual animal was used rather than a prop. As the stag had been dead for two days, it stank so much that the actors had to take much care not to let it show on their faces. Some scenes filmed were never aired, for example a flashback to the death of Eddard Stark\\'s brother, and the death of Jon Arryn.\\n\\n===Original pilot===\\n\\nThe original pilot from 2009 was poorly received in a private viewing with friends, one of whom, Craig Mazin, said to Benioff and Weiss, \"You guys have a massive problem\", and said \"change everything\" when asked for ideas. It was so disliked that Kit Harington joked that when he annoys Benioff and Weiss, they threaten to release the episode on YouTube. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '88464f38b40fc667ff430464ee1172f1'}>,\n",
-       "  <Document: {'content': 'It was so disliked that Kit Harington joked that when he annoys Benioff and Weiss, they threaten to release the episode on YouTube. Weiss said of the viewing: \"Watching them watch the pilot was a deeply humiliating, painful experience, because these are very smart individuals, and it just clearly wasn’t working for any of them on a very basic level.\"For example, it was never established that the two major characters, Jaime and Cersei Lannister were in fact brother and sister, a major plot point.\\n\\nHBO did not make a decision for four months after the pilot was delivered. In March 2010, HBO\\'s decision to greenlight the series including the pilot was announced, with the production of the series scheduled to start June 2010. HBO however demanded extensive reshoot of the pilot, and wanted all the scenes from Morocco scrapped. A cameo appearance by George RR Martin as a Pentoshi nobleman at Daenerys\\'s wedding filmed in Morocco was therefore also cut. In all 90 percent of the pilot was re-shot in 2010, with some cast changes and a different director.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'a929b4426ffbd58e8b7885528bc9b1f7'}>,\n",
-       "  <Document: {'content': 'A cameo appearance by George RR Martin as a Pentoshi nobleman at Daenerys\\'s wedding filmed in Morocco was therefore also cut. In all 90 percent of the pilot was re-shot in 2010, with some cast changes and a different director.\\n\\n===Aired episode===\\nThe original pilot remained unaired, although some footage from the original pilot was used in the first aired episode. This includes Sansa\\'s scenes with Catelyn (Michelle Fairley\\'s footage as Catelyn was inserted over Jennifer Ehle\\'s performance), Will\\'s ride through the woods (retained though also portrayed by a different actor), most of the feast at Winterfell, and Ned and Robert\\'s scene in the crypt. That scene is one of a few to be filmed on 35 mm film, and consequently slight film grain can be seen in the HD version of the episode.\\n\\n==Reception==\\n\\n===Preview===\\nOn April 3, 2011, two weeks before the series premiere aired, the first 15 minutes of \"Winter Is Coming\" were released as a preview on HBO\\'s website. \\'\\'Wired\\'\\'\\'s Dave Banks called the preview \"much better than anticipated.\"Scott Stinson of Toronto\\'s National Post noted that \"you know you aren’t watching a network drama when there have been two beheadings in the first 15 minutes.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '21bc16cfe1a75859a189717d10f204be'}>,\n",
-       "  <Document: {'content': 'Scott Stinson of Toronto\\'s National Post noted that \"you know you aren’t watching a network drama when there have been two beheadings in the first 15 minutes.\"===Ratings===\\nThe first episode of \\'\\'Game of Thrones\\'\\' obtained 2.2 million viewers in its premiere airing, with an additional 2 million viewers in the reruns aired during the same night. The day after the premiere HBO aired the episode six additional times, adding another 1.2 million to the viewer\\'s figures. Reruns aired during the following week upped the total viewership to 6.8 million.\\n\\n===International===\\nThe show premiered on HBO Canada at the same time as its U.S. premiere. On April 18, 2011, the show premiered in United Kingdom and Ireland through Sky Atlantic, gathering 750,000 viewers, a ratings record for the network. The series was broadcast throughout Latin America beginning on May 8, 2011. New Zealand\\'s \\'\\'Dominion Post\\'\\' noted in an article on copyright laws that the popular series was downloaded via file sharing service regularly before its release to that market. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '107fc4ce2c24eb3a5190741bc70b6256'}>,\n",
-       "  <Document: {'content': 'New Zealand\\'s \\'\\'Dominion Post\\'\\' noted in an article on copyright laws that the popular series was downloaded via file sharing service regularly before its release to that market. In Australia, the July 17 premiere of the series was largely overshadowed by the release of \\'\\'A Dance with Dragons\\'\\', but according to \\'\\'The Sydney Morning Herald\\'\\' was successful \"especially with women, who aren\\'t seen as a target market for sword-fighting sagas\".\\n\\n===Critical response===\\n\\nThe critical response to the first episode of the series was positive. Review aggregator Rotten Tomatoes surveyed 10 reviews of the episode and judged 100% of them to be positive with an average score of 8.5 out of 10. The website\\'s critical consensus reads, Winter is Coming\\' is an introduction to a wonderfully bleak journey that honors its source material with stellar execution and an impressive cast.\"James Poniewozik from \\'\\'Time\\'\\' considered it an \"epic win\", and Jace Lacob from \\'\\'The Daily Beast\\'\\' deemed it \"unforgettable\". HitFix\\'s Alan Sepinwall wrote that while it was too early to say if \\'\\'Game of Thrones\\'\\' belonged to the HBO pantheon with shows like \\'\\'The Sopranos\\'\\' or \\'\\'The Wire\\'\\', it had many things in common with those shows. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '568b50b6550e93a2cb84e685fbb78e51'}>,\n",
-       "  <Document: {'content': 'HitFix\\'s Alan Sepinwall wrote that while it was too early to say if \\'\\'Game of Thrones\\'\\' belonged to the HBO pantheon with shows like \\'\\'The Sopranos\\'\\' or \\'\\'The Wire\\'\\', it had many things in common with those shows. IGN\\'s Matt Fowler wrote that the pilot \"effortlessly takes us along, faithfully, through the book, but it also manages to capture the majestically morbid spirit of Martin\\'s pages and turn them into thrilling television\".\\n\\nMuch praise was given to the production values and the acting: Scott Meslow from \\'\\'The Atlantic\\'\\' states that \"the show\\'s immense cast is almost universally strong, and the fantasy land of Westeros feels lived-in, and looks terrific\". Alan Sepinwall also qualifies the casting as \"really exceptional\", and states that the show is \"feast for the eyes\", with all the different locations having their own memorable looks. The opening sequence, with an aerial view of the world where the series takes place with the different settings emerging from it, was also acclaimed.\\n\\nOn April 19, less than two days after the initial airing, HBO announced that the series had been renewed for a second season. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '9b4fe4076d73e9f04606fe6d3a4fc107'}>,\n",
-       "  <Document: {'content': 'On April 19, less than two days after the initial airing, HBO announced that the series had been renewed for a second season. In a press teleconference, HBO executives announced their satisfaction with initial ratings, which they compared favorably to \\'\\'True Blood\\'\\'.\\n\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominees\\n\\nResult\\n\\n2011\\n\\nPortal Award\\n\\nBest Episode\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nTim Van Patten\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Make-up for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen\\n\\n2012\\n\\nGolden Reel Awards\\n\\nBest Sound Editing — Short Form Sound Effects and Foley in Television\\n\\nDirectors Guild of America Awards\\n\\nDramatic Series\\n\\nTim Van Patten\\n\\nVisual Effects Society\\n\\nOutstanding Supporting Visual Effects in a Broadcast Program\\n\\nLucy Ainsworth-Taylor, Angela Barson, Ed Bruce and Adam McInnes\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Winter Is Coming\" at HBO\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'f17fb53c0b0e9826cdce881ed78d5adb'}>,\n",
-       "  <Document: {'content': '\\n\"\\'\\'\\'Valar Dohaeris\\'\\'\\'\" is the third season premiere episode of the HBO fantasy television series \\'\\'Game of Thrones\\'\\'. Written by executive producers David Benioff and D. B. Weiss, and directed by Daniel Minahan, it aired on March 31, 2013.\\n\\nThe premiere continued where the second season left off, with the Lannisters consolidating their power at King\\'s Landing in the aftermath of the Battle of the Blackwater. Meanwhile, Jon Snow meets the \"King beyond the Wall,\" and Daenerys leaves Qarth for Slaver\\'s Bay.\\n\\nThe episode\\'s title translates to \"all men must serve\" in the High Valyrian language in George R. R. Martin\\'s \\'\\'A Song of Ice and Fire\\'\\' novels, which the series adapts. The title mirrors that of season 2\\'s finale, \"Valar Morghulis\" – \"all men must die.\"The episode received mostly positive reviews, set a new ratings record for the series, and was one of the most torrented episodes for an HBO series.\\n\\n==Plot==\\n===Beyond the Wall===\\nThe White Walkers’ attack leaves few Night\\'s Watch survivors. Samwell Tarly is saved by the direwolf Ghost and Lord Commander Jeor Mormont, who reprimands Sam for failing to warn of the approaching army and orders the survivors back to the Wall.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'bb33c4d6d17b41496fad7f380fc3d556'}>,\n",
-       "  <Document: {'content': 'Samwell Tarly is saved by the direwolf Ghost and Lord Commander Jeor Mormont, who reprimands Sam for failing to warn of the approaching army and orders the survivors back to the Wall.\\n\\nAt the wildling camp, Jon Snow mistakes Tormund Giantsbane for the King-beyond-the-Wall, but Mance Rayder reveals himself and questions Jon\\'s motives for deserting the Night\\'s Watch. Jon earns Mance\\'s approval, declaring that he wants \"to fight for the side that fights for the living\".\\n\\n===In King\\'s Landing===\\nNewly knighted Ser Bronn returns to the service of Tyrion Lannister, whose father Tywin, new Hand of the King, agrees to recognize Tyrion\\'s accomplishments during the Battle of Blackwater but refuses to name him heir to Casterly Rock and viciously insults him.\\n\\nPetyr Baelish offers to smuggle Sansa Stark out of King\\'s Landing. Ros tells Shae to look out for Sansa, especially when dealing with Baelish.\\n\\nKing Joffrey Baratheon and his betrothed Lady Margaery Tyrell travel through Flea Bottom, when Margaery exits her litter to visit an orphanage as Joffrey hides. At dinner, Joffrey trades insults with his mother, in contrast to the harmonious Tyrells.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'b77178260b72dca465c9447d70f7ff1b'}>,\n",
-       "  <Document: {'content': 'King Joffrey Baratheon and his betrothed Lady Margaery Tyrell travel through Flea Bottom, when Margaery exits her litter to visit an orphanage as Joffrey hides. At dinner, Joffrey trades insults with his mother, in contrast to the harmonious Tyrells.\\n\\n===In Blackwater Bay===\\nStranded Davos Seaworth is rescued by the pirate Salladhor Saan, and recounts seeing his son Matthos die. Salladhor reveals he is leaving the service of Stannis, who is in seclusion at Dragonstone and will only speak with Melisandre, who is burning people alive. Davos, intending to kill Melisandre, persuades Salladhor to bring him to Stannis.\\n\\n===At Dragonstone===\\nDiscovering Stannis is indifferent to his survival and that Melisandre believes him responsible for Stannis’ defeat, Davos tries to attack Melisandre but is thrown in the dungeons.\\n\\n===At Harrenhal===\\nRobb Stark and his forces discover Gregor Clegane has abandoned Harrenhal and slaughtered the prisoners. Lord Roose Bolton sympathizes with Lord Rickard Karstark\\'s frustration that Catelyn Stark let Jaime Lannister go, assuring Karstark that his best hunter is after Jaime. Robb finds a survivor, Qyburn.\\n\\n===Across the Narrow Sea===\\nReaching Astapor in Slaver\\'s Bay with her fast-growing dragons, Daenerys Targaryen considers buying an army of the \"Unsullied\", renowned eunuch soldiers. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '9be46d3e912d5402a15828dd05e0e6ee'}>,\n",
-       "  <Document: {'content': '===Across the Narrow Sea===\\nReaching Astapor in Slaver\\'s Bay with her fast-growing dragons, Daenerys Targaryen considers buying an army of the \"Unsullied\", renowned eunuch soldiers. A warlock masked as a young girl attempts to assassinate Daenerys with a scorpion-like creature, but is thwarted by Ser Barristan Selmy, Kingsguard to Daenerys\\' father, who swears his allegiance to her.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by showrunners David Benioff and D. B. Weiss. It was based mainly on the first chapters of \\'\\'A Storm of Swords\\'\\', the third novel in George R. R. Martin\\'s \\'\\'A Song of Ice and Fire\\'\\' series. Specifically, it adapts material from chapters Samwell I, Jon I, Davos I, Davos II, Tyrion I, Daenerys I, Davos III, and Daenerys II from \\'\\'A Storm of Swords\\'\\' and chapter Daenerys V of \\'\\'A Clash of Kings\\'\\'.\\n\\nSome of the twists that open the third book were used in the previous season\\'s finale (mainly Robb\\'s marriage and the White Walkers attacking the Night\\'s Watch). Conversely, Barristan Selmy saving Daenerys from the manticore was borrowed from her last chapter in the second book \\'\\'A Clash of Kings\\'\\'.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'f9d05a282d7b85808d6f073b6afa47b3'}>,\n",
-       "  <Document: {'content': 'Conversely, Barristan Selmy saving Daenerys from the manticore was borrowed from her last chapter in the second book \\'\\'A Clash of Kings\\'\\'.\\n\\n===Casting===\\nCiarán Hinds joins the series as Mance Rayder\\n\"Valar Dohaeris\" introduces the Irish actor Ciarán Hinds as the Wildling leader and Night\\'s Watch deserter Mance Rayder, one of the latest Season 3 roles to be cast. The producers explained that casting Mance was a great challenge because he was someone who had become \"King beyond the Wall\" not by birthright but by convincing all the tribes to unite under his leadership. They had to find an actor with the charisma required to portray this. Unusual for a production such as \\'\\'Game of Thrones\\'\\', the first scene Hinds filmed was the first one where his character appears: the meeting with Jon Snow at his tent.\\n\\nThe season premiere also marks the first appearance of guest stars Kristofer Hivju as the Wildling Tormund Giantsbane, Nathalie Emmanuel as the slave Missandei, and Anton Lesser as the wounded prisoner Qyburn. The three castings were announced at San Diego Comic-Con in July 2012.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'da99838e256a6e49dceea240cabc92a1'}>,\n",
-       "  <Document: {'content': 'The season premiere also marks the first appearance of guest stars Kristofer Hivju as the Wildling Tormund Giantsbane, Nathalie Emmanuel as the slave Missandei, and Anton Lesser as the wounded prisoner Qyburn. The three castings were announced at San Diego Comic-Con in July 2012.\\n\\nWith this episode, Rose Leslie (Ygritte) and Oona Chaplin (Talisa) are promoted to series regulars, after guest starring in the second season. After being absent for the entire second season, Ian McElhinney returns as Ser Barristan Selmy. Actor Ian Whyte, previously cast as a White Walker in the first season, was recast as the huge Ser Gregor Clegane in the second and appears in the episode as the giant seen in the wildlings camp.\\n\\n===Valyrian===\\nDavid J. Peterson, who created the Dothraki language for the first season of the show, was entrusted by the producers to design a new constructed language to depict Valyrian, the tongue of the fallen Valyrian Empire. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '43df89192697da27dff3c80fdd519cc6'}>,\n",
-       "  <Document: {'content': \"===Valyrian===\\nDavid J. Peterson, who created the Dothraki language for the first season of the show, was entrusted by the producers to design a new constructed language to depict Valyrian, the tongue of the fallen Valyrian Empire. After immersing himself in the fictional background, Peterson ended devising two languages: High Valyrian, the oldest form that was spoken at the height of the Empire and that in its purest form still exists as a language of scholarship and refinement, and the Slaver's Bay variety of Low Valyrian, a creolized version that is spoken in local dialects around the Slaver's Bay. The relationship between the two languages would be similar to the one between Classical Latin and Vulgar Latin.\\n\\nTo translate sentences in Low Valyrian such as the ones spoken by Kraznys mo Nakloz and Missandei during the episode, Peterson would first write them into High Valyrian, and then apply a series of phonological, semantic and grammatical changes to the text.\\n\\n===Filming locations===\\nThe episode used locations in four different countries: The Paint Hall in Belfast, the City Walls of Dubrovnik, Mývatn and Vinderbelgjarfjall, and the Old Fort at Essaouira.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'e9eee465a78da0d0f4be4d91c116f6bd'}>,\n",
-       "  <Document: {'content': \"===Filming locations===\\nThe episode used locations in four different countries: The Paint Hall in Belfast, the City Walls of Dubrovnik, Mývatn and Vinderbelgjarfjall, and the Old Fort at Essaouira.\\nThe production continued to use the Paint Hall studios in Belfast for most interior shoots. The scene of Daenerys at sea was filmed at the Linen Mill Film & Television Studios at Banbridge, using the same ship built for Theon's arrival in Pyke and used as Stannis' flagship in season 2. The beach of Downhill Strand returned as the island of Dragonstone.\\n\\nThe old city of Dubrovnik was again used for the exterior shots of the capital, King's Landing. Tyrion and Bronn are seen walking on the famed city walls, and the scene where Lord Baelish visits Sansa at the docks was filmed in the old port between Fort Lovrijenac and the Pila Gates.\\n\\nAlso repeating from last season, the scenes beyond the Wall were filmed in Iceland. The Wildling's camp was built in a new location, on a lava field in the Mývatn Lake region in the North, a few hours from the town of Akureyri. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '2a341235dcd97eca4f897784a9f1a933'}>,\n",
-       "  <Document: {'content': \"The Wildling's camp was built in a new location, on a lava field in the Mývatn Lake region in the North, a few hours from the town of Akureyri. Building the camp took months of work from the art department and weeks of construction by the local crew. Although the scenes at the Icelandic exteriors were filmed at minus 11 degrees Celsius, the interior of Mance's tent was filmed on one of the soundstages at the Paint Hall. The fires burning inside the tent made the set very hot, and the actors suffered the heat wearing heavy furs designed for Arctic climates.\\n\\nTo depict the slaver city of Astapor, the production used the Moroccan city of Essaouira. The Unsullied are introduced in the city ramparts of ''Skala de la Ville'', the 18th-century sea bastion that runs along the northern cliffs, and during the closing scene when Barristan reveals himself, the Genoese-built citadel by the harbour can be clearly seen.\\n\\n===Dedication===\\nThe episode is dedicated to the memory of cinematographer Martin Kenzie, who had worked in the photography department and died of cancer while the third season was being filmed on July 16, 2012.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '42cb29307c35b598758d701e43ec17d4'}>,\n",
-       "  <Document: {'content': '===Dedication===\\nThe episode is dedicated to the memory of cinematographer Martin Kenzie, who had worked in the photography department and died of cancer while the third season was being filmed on July 16, 2012.\\n\\n==Reception==\\n\\n===Ratings===\\n\"Valar Dohaeris\"\\'s first airing was seen by 4.4 million viewers; the total rose to 6.7 million viewers once the two repeats of the night are taken into account. Both figures represented a viewership record for the show. In the United Kingdom, the episode was seen by 1.173 million viewers on Sky Atlantic, being the channel\\'s highest-rated broadcast that week.\\n\\n===Critical reception===\\nThe episode received critical acclaim. Review aggregator Rotten Tomatoes surveyed 21 reviews of the episode and judged 100% of them to be positive with an average score of 7.5 out of 10. The website\\'s critical consensus reads, \"\\'Valar Dohaeris\\' overextends itself trying to reintroduce a multitude of characters and plotlines, but is strengthened by its brilliantly acted two-person scenes (and three growing baby dragons).\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'e604d36a7f5c832eec1cb69e95b3039'}>,\n",
-       "  <Document: {'content': 'The website\\'s critical consensus reads, \"\\'Valar Dohaeris\\' overextends itself trying to reintroduce a multitude of characters and plotlines, but is strengthened by its brilliantly acted two-person scenes (and three growing baby dragons).\"In an advance review for \\'\\'The Daily Beast\\'\\', Jace Lacob wrote that the season premiere lacks \"energy and intensity, but provides a necessary foundation,\" and that the season, like the novel it is based on, \"takes a little to get going.\"Matt Fowler, writing for IGN, gave the episode an 8.6/10, writing that \"while understandably not showing us everyone, Game of Thrones returned in fine form with dragons, zombies and giants.\"Neela Dabnath of \\'\\'The Independent\\'\\' felt that \"Valar Dohaeris\" was a strong start to Season 3 and \"deftly picked up the various story strands from the last season ... even if it was just a series of brief check-in.\"She also praised the show for taking \"creative liberties which always pay off in bucket loads,\" commenting on how this can \"add to Martin’s world and flesh it out in new ways.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '75d03b7916a60d2211ca4986ce6f5d41'}>,\n",
-       "  <Document: {'content': 'She also praised the show for taking \"creative liberties which always pay off in bucket loads,\" commenting on how this can \"add to Martin’s world and flesh it out in new ways.\"Forbes.com writer Erik Kain stated he felt \"Valar Dohaeris\" did \"exactly what it needed to do,\" by bringing the viewers \"back up to speed on the broader conflict and the various minor character conflicts and positioning for power. The ball hasn’t really been moved forward at all, but our feet are on sturdy ground to move forward.\"He did however express some disappointment over how some elements of the episode differed from its source material in regards to the reintroduction of the character Barristan Selmy and the apparent absence of the character Strong Belwas. Reviewing the episode for \\'\\'The Guardian\\'\\', Sarah Hughes felt that \"Valar Dohaeris\" \"didn\\'t really feel like a season opener,\" and that the opening scene was \"a little disorientating.\"However she enjoyed the scenes in King\\'s Landing and lauded Stephen Dillane\\'s brief performance. Commenting on the Daenerys\\' plot, she felt her storyline was advancing quite quickly. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'c91e130c9ff0f781dd54ca36da667b7'}>,\n",
-       "  <Document: {'content': 'However she enjoyed the scenes in King\\'s Landing and lauded Stephen Dillane\\'s brief performance. Commenting on the Daenerys\\' plot, she felt her storyline was advancing quite quickly. She also felt that the Barristan Selmy reveal was handled well as; \"there are certain conventions that work better on page than screen and the hidden identity trope is one of them. By getting the reveal out of the way early, Benioff and Weiss can concentrate on the potential rivalry between Barristan and Jorah.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'e4de1db6a90e213adbd0df291f53a21d'}>,\n",
-       "  <Document: {'content': 'By getting the reveal out of the way early, Benioff and Weiss can concentrate on the potential rivalry between Barristan and Jorah.\"===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2013\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton\\n\\nOutstanding Special Visual Effects\\n\\nDoug Campbell, Rainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen\\n\\nHollywood Post Alliance Awards\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Bauer and Jabbar Raisani, Jörn Grosshans and Sven Martin, and Doug Campbell\\n\\n2014\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Television Series\\n\\nGemma Jackson\\n\\nAmerican Society of Cinematographers\\n\\nOne-Hour Episodic Television Series\\n\\nJonathan Freeman\\n\\nVisual Effects Society\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nSteve Kullback, Joe Bauer, Jörn Großhans, Sven Martin\\n\\n== References ==\\n\\n== External links ==\\n\\n*  \"Valar Dohaeris\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '4cc439bdc13daf40e039ed827359ba89'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Gift\\'\\'\\'\" is the seventh episode of the fifth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 47th overall. The episode was written by the series\\' creators David Benioff and D. B. Weiss, and directed by Miguel Sapochnik, his directorial debut for the series.\\n\\nThis episode marks the final appearance for Peter Vaughan (Maester Aemon).\\n\\n==Plot==\\n\\n===At the Wall===\\nJon leaves for Hardhome with Tormund and some rangers, in spite of Ser Alliser\\'s complaints. Before they leave, Sam gives Jon a bag of dragonglass weapons, reminding him of their use against the White Walkers. Sam and Gilly visit Maester Aemon, who has fallen ill and dies at nightfall. In the morning, Sam eulogizes him before lighting his funeral pyre. Afterward, Gilly is attacked in the dining hall by two brothers. Sam attempts to intervene and is badly beaten before Jon\\'s wolf, Ghost, scares off the brothers. Gilly tends to Sam and the two have sex.\\n\\n===At Winterfell===\\nReek brings food to Sansa, who has been imprisoned in her bedchamber by Ramsay. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '7c11f0f5ca7ce507ad5a790554d6d961'}>,\n",
-       "  <Document: {'content': \"Gilly tends to Sam and the two have sex.\\n\\n===At Winterfell===\\nReek brings food to Sansa, who has been imprisoned in her bedchamber by Ramsay. She begs for him to help, asking him to light a candle at the top of the broken tower, the aim of which is to send a signal to Stark sympathizers to rescue her. Instead, Reek tells Ramsay, who reacts by flaying and killing the old lady from whom Sansa learned the signal. As Ramsay is talking to Sansa, however, she discreetly steals a corkscrew from the top of a barrel. Meanwhile, Brienne and Podrick wait out in the snow for the signal, to no avail.\\n\\n===In the North===\\nStannis' troops have made a camp and are trapped by the snowstorm. Davos Seaworth meets with Stannis and tells him that supplies, as well as horses, have been lost to the winter. The Stormcrows, a group of 500 sellswords, have abandoned the camp during the night. Davos suggests returning to Castle Black, but Stannis replies that doing so could delay them for years, since winters in Westeros can last for years at a time. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '2d87585b0673d1299e6b33f3c1664b9a'}>,\n",
-       "  <Document: {'content': \"Davos suggests returning to Castle Black, but Stannis replies that doing so could delay them for years, since winters in Westeros can last for years at a time. After Davos leaves, Stannis asks Melisandre if she is sure of his victory. Melisandre assures him that she saw a vision of Stannis winning, but she asks for permission to sacrifice his daughter Shireen to the Red God. Stannis is disgusted and refuses, and dismisses Melisandre.\\n\\n===In Meereen===\\nJorah is sold by the slave traders to a Meereenese man, Yezzan zo Qaggaz, who is looking for fighters for the upcoming games at Meereen. Before Yezzan leaves, Tyrion convinces Yezzan to purchase him as well by showing fighting prowess by attacking with a chain a slave herder who had been whipping Tyrion.\\n\\nDaenerys and Daario discuss her pending marriage to Hizdahr zo Loraq. Daario advises her to kill all of Meereen's nobles when the fighting pits get re-opened, but she declines.\\n\\nYezzan takes his fighters to Meereen, where they fight in front of Daenerys. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '869ff69f7f3f51d0231580cb5f1c94c6'}>,\n",
-       "  <Document: {'content': \"Daario advises her to kill all of Meereen's nobles when the fighting pits get re-opened, but she declines.\\n\\nYezzan takes his fighters to Meereen, where they fight in front of Daenerys. When Jorah realizes that Daenerys is present, he enters the pits and easily defeats the other fighters before revealing his identity to Daenerys, who orders he be taken out of her sight. Jorah tells her he brought her a gift, and Tyrion enters the arena, where he reveals his identity to a stunned Daenerys.\\n\\n===In Dorne===\\nMyrcella is brought to speak with Jaime, and asks why he came to take her back to King's Landing, when what she wants is to stay and marry Trystane. In the cells, Bronn is taunted by Tyene Sand exposing her breasts to him, before learning that her daggers, with which he'd been cut during their fight, had been coated with a slow-acting poison that would activate when his heart rate increased. He slowly succumbs to the poison, but Tyene gives him the antidote after he gives in to her request that he call her the most beautiful woman in the world.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'bb6d8ccb47b9354db01261a3a1cc851a'}>,\n",
-       "  <Document: {'content': \"He slowly succumbs to the poison, but Tyene gives him the antidote after he gives in to her request that he call her the most beautiful woman in the world.\\n\\n===In King's Landing===\\nIn the Sept of Baelor, Olenna meets with the High Sparrow to discuss the imprisonment of her grandchildren, Loras and Margaery. She demands that he release them, but he declines, stating they will be punished for their crimes. The High Sparrow tells her that the laws of the Faith must be applied to everyone equally. Olenna first tries to bribe him, but upon seeing that he cannot be bought, threatens to end the Tyrell support for the capital by stopping shipments of food. The High Sparrow remains unfazed. He counters that she and the rest of the nobility are outnumbered by the smallfolk, and tells her to consider what will come when the smallfolk stop fearing the nobility. On her way out, Olenna receives a letter from Petyr Baelish.\\n\\nIn the Red Keep, Tommen anguishes over his inability to help Margaery. Cersei offers to speak with the High Sparrow for him, and advocate for Margaery and Loras's release.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'f0039cdc3473f0bd455469a618c96a98'}>,\n",
-       "  <Document: {'content': \"In the Red Keep, Tommen anguishes over his inability to help Margaery. Cersei offers to speak with the High Sparrow for him, and advocate for Margaery and Loras's release.\\n\\nPetyr meets with Olenna in his ransacked brothel, where they share a tense conversation. Olenna reminds Petyr of the role they both played in Joffrey's murder, and warns that, should her house fall, she won't keep his involvement a secret. In the Sept, Cersei meets with Margaery and brings her food, but Margaery refuses and angrily orders her mother-in-law to leave, knowing full well that she was responsible for imprisoning both her and Loras. Cersei then meets with the High Sparrow, and after a brief discussion on Margaery and Loras, he reveals that her cousin Lancel has informed against her regarding a sin she has committed. Cersei tries to leave but is stopped by a tall septa, who proceeds to lock Cersei in a cell.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode was written by David Benioff and D. B. Weiss, the series' creators. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '5a2e0b488b27783cca9da1b0b3ab5727'}>,\n",
-       "  <Document: {'content': '==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode was written by David Benioff and D. B. Weiss, the series\\' creators. It contains content from George R. R. Martin\\'s novels \\'\\'A Feast for Crows\\'\\', chapters Samwell II, Samwell IV, and Cersei X and \\'\\'A Dance with Dragons\\'\\', chapters The King\\'s Prize, Tyrion X, and Daenerys IV.\\n\\nLike other episodes in season 5, this episode differed considerably from Martin\\'s novels in places. Aemon\\'s death takes place at Castle Black rather than at sea, which Myles McNutt of \\'\\'A.V. Club\\'\\' notes, \"establishes a shifting of the guard at Castle Black and marking Sam’s reemergence as a more significant character.\"In what Sara Stewart of the \\'\\'New York Post\\'\\' calls \"the biggest departure yet from the books,\" Tyrion actually meets Daenerys at the end of the episode, which has yet to happen as of \\'\\'A Dance with Dragons\\'\\'. The decision was largely approved by critics from \\'\\'The Atlantic\\'\\' and other publications. David Benioff cited the television adaptation\\'s faster pace as part of the rationale behind this decision.\\n\\n===Filming===\\nMiguel Sapochnik served as director for the episode, his first episode for the series.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '5a633ce359afee5eefab39e3b96024a2'}>,\n",
-       "  <Document: {'content': 'David Benioff cited the television adaptation\\'s faster pace as part of the rationale behind this decision.\\n\\n===Filming===\\nMiguel Sapochnik served as director for the episode, his first episode for the series.\\n\"The Gift\" was directed by Miguel Sapochnik. It was his first time being a director for \\'\\'Game of Thrones\\'\\'; he also directed the subsequent episode, \"Hardhome\".\\n\\n==Reception==\\n\\n===Ratings===\\n\"The Gift\" was watched by 5.5 million and a 2.5 in the key 18–49 demographic during the its first airing. This is significantly fewer viewers than the previous episode, \"Unbowed, Unbent, Unbroken\", which had an audience of 6.2 million. According to \\'\\'Business Insider\\'\\', likely causes for the lower ratings include backlash from the rape scene in \"Unbowed, Unbent, Unbroken\", in response to which many fans announced they would stop watching the show, and increased use of streaming through HBO Now. Rebecca Martin of \\'\\'Wetpaint\\'\\', however, maintains that the Memorial Day weekend air date was probably the only reason for the decrease in ratings. The season two episode \"Blackwater\", which also aired on Memorial Day weekend, also suffered a notable drop in ratings. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'ea7d673b456066e7094c8466f2162935'}>,\n",
-       "  <Document: {'content': 'Rebecca Martin of \\'\\'Wetpaint\\'\\', however, maintains that the Memorial Day weekend air date was probably the only reason for the decrease in ratings. The season two episode \"Blackwater\", which also aired on Memorial Day weekend, also suffered a notable drop in ratings. Whatever the reason, the ratings for the episode immediately after \"The Gift\", \"Hardhome\", were higher than those of either \"The Gift\" or \"Unbowed, Unbent, Unbroken\". With Live+7 DVR viewing factored in, the episode had an overall rating of 8.87 million viewers, and a 4.5 in the 18–49 demographic.\\n\\nIn the United Kingdom, the episode was viewed by 2.293 million viewers, making it the highest-rated broadcast that week. It also received 0.176 million timeshift viewers.\\n\\n===Critical reviews===\\nThe episode received very positive reviews. It scored 91% on Rotten Tomatoes from 32 reviews with a rating average of 7.9 out of 10 and the consensus reading: \"\\'The Gift\\' is a jam-packed installment that delivers long-awaited plot turns in a satisfying manner, even though it continues a disturbing theme from the previous episode.\"Erik Kain of \\'\\'Forbes\\'\\' called this a \"terrific, exciting, tense episode,\" though, like Myles McNutt of \\'\\'The A.V. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '67c19e788a02040c517c0a76dfeb5ad8'}>,\n",
-       "  <Document: {'content': 'It scored 91% on Rotten Tomatoes from 32 reviews with a rating average of 7.9 out of 10 and the consensus reading: \"\\'The Gift\\' is a jam-packed installment that delivers long-awaited plot turns in a satisfying manner, even though it continues a disturbing theme from the previous episode.\"Erik Kain of \\'\\'Forbes\\'\\' called this a \"terrific, exciting, tense episode,\" though, like Myles McNutt of \\'\\'The A.V. Club\\'\\', he questioned both the \"anemic\" Dorne storyline and the gratuitousness of Tyene exposing her breasts in the prison scene.\\n\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg as Lady Olenna Tyrell\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'ed25aa9e7b544cf6dea5a04921684e28'}>,\n",
-       "  <Document: {'content': '\\n\"\\'\\'\\'Unbowed, Unbent, Unbroken\\'\\'\\'\" is the sixth episode of the fifth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 46th overall. The episode was written by Bryan Cogman, and directed by Jeremy Podeswa.\\n\\nThe episode polarized critics and viewers for its ending, depicting a violent sexual assault. It received a rating of 54% on Rotten Tomatoes, and was the lowest-rated \\'\\'Game of Thrones\\'\\' episode until \"The Bells\" in season 8. Nonetheless, director Jeremy Podeswa received an Emmy Award nomination for Outstanding Directing for a Drama Series for this episode.\\n\\nThe name of the episode comes from the House Martell motto \\'\\'Unbowed, Unbent, Unbroken\\'\\', words about strength that are put in contrast with the fates of several main characters, especially women, as these take a turn for the worse.\\n\\n==Plot==\\n\\n===In Braavos===\\nThe Waif tells Arya that to pass the Game of Faces she must be able to convincingly lie. When a man brings her sick daughter to the temple, so that she can die in peace, Arya lies to her that she was ill like her in the past and gives her the temple\\'s poisoned water to end her suffering. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '7e9c78679cae8600462c3e8bad1b476d'}>,\n",
-       "  <Document: {'content': \"When a man brings her sick daughter to the temple, so that she can die in peace, Arya lies to her that she was ill like her in the past and gives her the temple's poisoned water to end her suffering. Jaqen takes Arya to a chamber where the Faceless Men store the faces of all the people that have died in the temple and tells her that she is not yet ready to become no one, but she is ready to become someone else.\\n\\n===In Dorne===\\nBronn and Jaime disguise as Dornish guards and infiltrate the Water Gardens to take Myrcella, but are attacked by Sand Snakes. Dornish guards, led by Areo Hotah, arrive and arrest both groups and Ellaria Sand\\n\\n===On the Valyrian peninsula===\\nTyrion tells Jorah that his father Jeor is dead. They are captured by slavers, led by Malko. After hearing that Daenerys reopened the fighting pits, Tyrion convinces the slavers to take them to Meereen, saying that Jorah is an accomplished warrior.\\n\\n===In King's Landing===\\nPetyr Baelish arrives in King's Landing and tells Cersei that Sansa will marry Ramsay at Winterfell. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '67c48a205860bf5a87eea50cb79390c0'}>,\n",
-       "  <Document: {'content': \"After hearing that Daenerys reopened the fighting pits, Tyrion convinces the slavers to take them to Meereen, saying that Jorah is an accomplished warrior.\\n\\n===In King's Landing===\\nPetyr Baelish arrives in King's Landing and tells Cersei that Sansa will marry Ramsay at Winterfell. He asks her to lead the Knights of the Vale to destroy the victor, after Stannis’ attack on the Boltons and be named Warden of the North. She agrees. Olenna arrives and tells Cersei that her actions have put the Lannister-Tyrell alliance in peril, but Cersei tells her that she had nothing to do with Loras’ arrest. At Loras' holy inquest, the High Sparrow interrogates Loras and Margaery, who both deny that Loras is homosexual. Olyvar testifies against Loras and the Faith Militant arrests Loras and Margaery.\\n\\n===In Winterfell===\\nTheon takes Sansa to the Godswood, where she is married to Ramsay. At his chambers, Ramsay orders Sansa to undress and rapes her, to Theon's horror.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series producer Bryan Cogman.\\nThis episode was written by the series producer Bryan Cogman, who has written at least one episode in every season of the show. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '295727c3dc37252d6adca6754e113e9b'}>,\n",
-       "  <Document: {'content': 'This episode was written by the series producer Bryan Cogman, who has written at least one episode in every season of the show. It contains some content from George Martin\\'s novel \\'\\'A Feast for Crows\\'\\', chapters Arya II, The Queenmaker, Cat of the Canals, and Cersei X and \\'\\'A Dance with Dragons\\'\\', chapters the Ugly Little Girl, Tyrion X, and the Prince of Winterfell, though series consultant Elio Garcia describes the portrayal of some of these events as \"vastly different\" from the original.\\n\\nLike other episodes this season, it also included content and storylines written specifically for the television adaptation.  Myles McNutt of \\'\\'A.V. Club\\'\\' points out that this changes the way the viewers interpret the showrunner\\'s decisions. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '82fd8609fe2385b35c47c882857c1023'}>,\n",
-       "  <Document: {'content': 'Like other episodes this season, it also included content and storylines written specifically for the television adaptation.  Myles McNutt of \\'\\'A.V. Club\\'\\' points out that this changes the way the viewers interpret the showrunner\\'s decisions. When describing his opinion of the decision to show Sansa raped by Ramsay on their wedding night (a storyline given to a different character, Jeyne Poole, in the books), he compares the scene to a similar one between Daenerys and Drogo in season one (which was consensual in the novels): \"While we could frame the shifted events of Dany and Khal Drogo’s wedding night in light of where we knew Dany’s story was going, here we have no idea what this does to Sansa’s storyline.\"Most critics questioned the decision to show Sansa raped on her wedding night, but, as \\'\\'Business Insider\\'\\' pointed out, \"The book version of this scene was much, much worse,\" with Theon ordered, graphically, to participate in Jeyne\\'s mistreatment. In an interview with \\'\\'Entertainment Weekly\\'\\', show writer Bryan Cogman was asked about the decision to decrease the level of violence, responding, \"Lord no. \\'\\'No-no-no-no-no.\\'\\'No. It’s still a shared form of abuse that they have to endure, Sansa and Theon. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'a42c54102e1f606d3764390aeb08255e'}>,\n",
-       "  <Document: {'content': 'In an interview with \\'\\'Entertainment Weekly\\'\\', show writer Bryan Cogman was asked about the decision to decrease the level of violence, responding, \"Lord no. \\'\\'No-no-no-no-no.\\'\\'No. It’s still a shared form of abuse that they have to endure, Sansa and Theon. But it’s not the extreme torture and humiliation that scene in the book is.\"However, in other ways, the episode veers back to book canon: \"Whereas Loras’ arrest suggested the show was replacing Margaery\\'s alleged dalliances with his homosexuality, here the show gradually builds to Margaery\\'s arrest for lying on her brother\\'s behalf.\"===Filming===\\n\"Unbowed, Unbent, Unbroken\" was directed by Jeremy Podeswa. He also directed the previous episode, \"Kill the Boy\".\\n\\n==Reception==\\n\\n===Ratings===\\n\"Unbowed, Unbent, Unbroken\" was watched by 6.24 million American viewers during its first airing. With Live+7 DVR viewing factored in, the episode had an overall rating of 8.79 million viewers, and a 4.5 in the 18-49 demographic. In the United Kingdom, the episode was viewed by 2.285 million viewers, making it the highest-rated broadcast that week. It also received 0.126 million timeshift viewers.\\n\\n===Critical reviews and controversy===\\nThe episode received polarized reviews from critics. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'a26c9b8dc110e9d6ac78bf883d1a844'}>,\n",
-       "  <Document: {'content': 'In the United Kingdom, the episode was viewed by 2.285 million viewers, making it the highest-rated broadcast that week. It also received 0.126 million timeshift viewers.\\n\\n===Critical reviews and controversy===\\nThe episode received polarized reviews from critics. On Rotten Tomatoes, the episode received a 54% approval rating from 50 critics with a rating average of 7.55 out of 10, the lowest of any episode in the series at that time. The critical consensus states: \"Unbalanced storytelling and unnecessary, excessive brutality add up to disturbing viewing, although \\'Unbowed, Unbent, Unbroken\\' still includes enough plot revelations to offer hope for future episodes.\"The majority of professional criticism concerned the decision to have Ramsay rape Sansa on their wedding night, with most critics describing the scene as gratuitous and artistically unnecessary. \"This grim scene was difficult for the show to justify,\" said Charlotte Runcie of \\'\\'The Daily Telegraph\\'\\'. Joanna Robinson of \\'\\'Vanity Fair\\'\\' added, \"this rape scene undercuts all the agency that’s been growing in Sansa since the end of last season. ... I’d never advocate that \\'\\'Game of Thrones\\'\\' (or any work of fiction) shy away from edgy plots out of fear of pushback or controversy. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '199cac14d101c3dab4eb7f4127a05b78'}>,\n",
-       "  <Document: {'content': 'Joanna Robinson of \\'\\'Vanity Fair\\'\\' added, \"this rape scene undercuts all the agency that’s been growing in Sansa since the end of last season. ... I’d never advocate that \\'\\'Game of Thrones\\'\\' (or any work of fiction) shy away from edgy plots out of fear of pushback or controversy. But edgy plots should always accomplish something above pure titillation or shock value and what, \\'\\'exactly\\'\\', was accomplished here?\"Christopher Orr wrote in \\'\\'The Atlantic\\'\\', \"I continue to be astonished that showrunners Benioff and Weiss still apparently believe that their tendency to ramp up the sex, violence, and—especially—sexual violence of George R.R. Martin’s source material is a strength rather than the defining weakness of their adaptation.\"Myles McNutt of \\'\\'The A.V. Club\\'\\' wrote, \"The issue with the show returning to rape as a trope is not simply because there have been thinkpieces speaking out against it, and is not solely driven by the rational concerns lying at the heart of those thinkpieces. It’s also that the show has lost my faith as a viewer.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '9b6363c4a07a8e17dac9e2689bcfade9'}>,\n",
-       "  <Document: {'content': 'Club\\'\\' wrote, \"The issue with the show returning to rape as a trope is not simply because there have been thinkpieces speaking out against it, and is not solely driven by the rational concerns lying at the heart of those thinkpieces. It’s also that the show has lost my faith as a viewer.\"Writers from \\'\\'Vanity Fair\\'\\', \\'\\'The Mary Sue\\'\\' and \\'\\'The Daily Beast\\'\\' all disapproved of the decision to use Sansa\\'s victimization as a motivating agent for Theon, saying that the scene undermined Sansa\\'s character development: \"Was it really important to make that scene about Theon\\'s pain?\"wrote Joanna Robinson of \\'\\'Vanity Fair\\'\\'.\\n\\nOther critics responded positively to the scene. Sean T. Collins of \\'\\'Rolling Stone\\'\\' wrote: \"By involving a multidimensional main character instead of one introduced primarily to suffer, the series has a chance to grant this story the gravity and seriousness it deserves. Sarah Hughes of \\'\\'The Guardian\\'\\' wrote: \"I have repeatedly made clear that I’m not a fan of rape as a plot device – but the story of Ramsay and Sansa’s wedding was more than that. ... The writers are walking a very fine line here. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'e13805acc400132db056176ea6bef537'}>,\n",
-       "  <Document: {'content': 'Sarah Hughes of \\'\\'The Guardian\\'\\' wrote: \"I have repeatedly made clear that I’m not a fan of rape as a plot device – but the story of Ramsay and Sansa’s wedding was more than that. ... The writers are walking a very fine line here. They handled it well tonight, telling a gothic tale of innocence sacrificed\". Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' wrote that the scene \"managed to maintain a fine balance, employing a dignity and care for the experiences of victims that \\'\\'Game of Thrones\\'\\' has not always demonstrated.\"Some critics questioned why this scene in particular should generate outrage when similar scenes have not. Sara Stewart of the \\'\\'New York Post\\'\\' pointed out that the rape and sexual abuse of both female and male characters is typical for \\'\\'Game of Thrones\\'\\': \"Why are we suddenly so outraged about the rape of Sansa Stark, when this show has served up a steady diet of sexual assault and violence against women since its first season began?\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '9663511852c9cd49ae9261c1811d74d9'}>,\n",
-       "  <Document: {'content': 'Sara Stewart of the \\'\\'New York Post\\'\\' pointed out that the rape and sexual abuse of both female and male characters is typical for \\'\\'Game of Thrones\\'\\': \"Why are we suddenly so outraged about the rape of Sansa Stark, when this show has served up a steady diet of sexual assault and violence against women since its first season began?\"Cathy Young of \\'\\'Reason\\'\\' magazine, writing in \\'\\'Time\\'\\' noted what she calls a lack of complaint in response to the sexual mistreatment of male characters in earlier seasons, specifically the literal emasculation of Theon Greyjoy and the sexual assault of Gendry.\\n\\nCriticism of the scene has not extended to the quality of the acting. Joanna Robinson of \\'\\'Vanity Fair\\'\\' wrote, \"And if we can say one positive thing about that scene it\\'s that Allen nailed his performance. Theon\\'s horror mirrored our own and the camera—focusing on his reaction—let our minds fill in the blanks.\"Sophie Turner defended the scene as an artistic challenge for herself as an actor, saying, \"When I read that scene, I kinda loved it. I love the way Ramsay had Theon watching. It was all so messed up. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '2e8b973c15b469f7aca568e0e696e80c'}>,\n",
-       "  <Document: {'content': 'Sophie Turner defended the scene as an artistic challenge for herself as an actor, saying, \"When I read that scene, I kinda loved it. I love the way Ramsay had Theon watching. It was all so messed up. It’s also so daunting for me to do it. ... I think it\\'s going to be the most challenging season for me so far, just because it\\'s so emotional for her. It’s not just crying all the time, like seasons 2 or 3, it’s super messed up.\"She had since gone on to interview for the DailyMail about it and that the scene had inspired her to work for organizations to stop violence against women and to help underprivileged girls and women in Africa in similar situations. Iwan Rheon (Ramsay Bolton) agreed, referring to Turner\\'s performances this season as \"absolutely amazing.\"Some viewers, including U.S. Senator Claire McCaskill, announced that they would stop watching the show because of this scene. According to \\'\\'Business Insider\\'\\', this scene and increased use of streaming services are likely reasons why ratings dropped from 6.2 million viewers for this episode to 5.4 million for the next episode, \"The Gift.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'bd62e1fce6209a5be2f05bea149d0467'}>,\n",
-       "  <Document: {'content': 'According to \\'\\'Business Insider\\'\\', this scene and increased use of streaming services are likely reasons why ratings dropped from 6.2 million viewers for this episode to 5.4 million for the next episode, \"The Gift.\"However, there is some question as to how much of this drop is attributable to its Memorial Day weekend air date. Rebecca Martin of \\'\\'Wetpaint\\'\\' maintains that the air date was probably the only reason for the decrease in ratings. The season two episode \"Blackwater,\" which also aired on Memorial Day, also suffered a notable drop in ratings. No episode was aired on Memorial Day weekend in either season three or season four. Whatever the reason, the ratings for the episode after \"The Gift,\" \"Hardhome,\" were higher than those of both previous episodes.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '6ac7521c4fa4251fb974766a65c9bfd2'}>,\n",
-       "  <Document: {'content': 'Whatever the reason, the ratings for the episode after \"The Gift,\" \"Hardhome,\" were higher than those of both previous episodes.\\n\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nPrimetime Emmy Award\\n\\nOutstanding Directing for a Drama Series\\n\\nJeremy Podeswa\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron\\n\\n2016\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nGregory Middleton\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Unbowed, Unbent, Unbroken\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'd3c7107628afe50d21b2fc73db35723b'}>,\n",
-       "  <Document: {'content': '\\nalt=Game of Thrones logo\\n\\'\\'Game of Thrones\\'\\' is an American fantasy drama television series created by David Benioff and D. B. Weiss. The series is based on George R. R. Martin\\'s series of fantasy novels, \\'\\'A Song of Ice and Fire\\'\\'. The series takes place on the fictional continents of Westeros and Essos, and chronicles the power struggles among noble families as they fight for control of the Iron Throne of the Seven Kingdoms. The series starts when House Stark, led by Lord Eddard \"Ned\" Stark (Sean Bean), is drawn into schemes surrounding King Robert Baratheon (Mark Addy).\\n\\nThe series premiered on April 17, 2011, on HBO. David Benioff and D. B. Weiss both serve as executive producers, along with Carolyn Strauss, Frank Doelger, Bernadette Caulfield, and George R. R. Martin. Filming for the series has taken place in a number of locations, including Croatia, Ireland, Iceland, and Spain. Episodes were broadcast on Sunday at 9:00 pm Eastern Time, and the episodes are between 50 and 82 minutes in length. The first seven seasons are available on DVD and Blu-ray.\\n\\nThe series concluded with its eighth season, which premiered on April 14, 2019, and consisted of six episodes. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'b1c5ee918ebf752f590490ee19c10b16'}>,\n",
-       "  <Document: {'content': 'The first seven seasons are available on DVD and Blu-ray.\\n\\nThe series concluded with its eighth season, which premiered on April 14, 2019, and consisted of six episodes. The show\\'s episodes have won numerous awards including four Primetime Emmy Award for Outstanding Drama Series.\\n\\n== Series overview ==\\n\\n== Episodes ==\\n\\n=== Season 1 (2011) ===\\n\\nIn the continent of Westeros, Warden of the North Eddard \"Ned\" Stark becomes \"Hand of the King\" (chief adviser) to King Robert Baratheon. When Ned\\'s son Bran stumbles upon Queen Cersei having sex with her twin brother Jaime Lannister, Jaime cripples Bran. After moving to King\\'s Landing, the capital of the Seven Kingdoms, Ned discovers that Prince Joffrey and his siblings were incestuously fathered by Jaime. When King Robert dies, Ned does not recognize Joffrey as the rightful heir and is executed. Ned\\'s bastard son Jon Snow joins the Night\\'s Watch, an ancient brotherhood sworn to watch over the massive Wall in the North, which separates the Seven Kingdoms from hostile tribes known as \"Wildlings\" and mythological humanoid creatures known as \"White Walkers\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '49fefb606d65b7dc11919b1e197a3542'}>,\n",
-       "  <Document: {'content': 'Ned\\'s bastard son Jon Snow joins the Night\\'s Watch, an ancient brotherhood sworn to watch over the massive Wall in the North, which separates the Seven Kingdoms from hostile tribes known as \"Wildlings\" and mythological humanoid creatures known as \"White Walkers\". In the continent of Essos, the exiled Viserys Targaryen, son of King Aerys who was deposed by King Robert, marries off his sister Daenerys to the Dothraki warlord Khal Drogo, in exchange for an army to return to and take over Westeros. Eventually Drogo kills Viserys and Drogo later dies himself. After Drogo dies, Daenerys manages to hatch three dragons.\\n\\n=== Season 2 (2012) ===\\n\\nNed\\'s oldest son Robb has declared war on the Lannisters and holds Jaime prisoner. However, his mother Catelyn secretly frees Jaime to ransom her daughters, Sansa and Arya, believed to be captives in King\\'s Landing. Sansa is being regularly abused by her betrothed King Joffrey, while a disguised Arya escapes Lannister forces in Harrenhal. Ned\\'s ward, Theon Greyjoy sides with his father and seizes Winterfell, the Starks\\' ancestral home, though Bran and his brother Rickon escape. Meanwhile, Stannis and Renly Baratheon, brothers of the deceased King Robert, claim the throne for themselves. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'c87e2928c3b8a596170a02209669c55b'}>,\n",
-       "  <Document: {'content': \"Ned's ward, Theon Greyjoy sides with his father and seizes Winterfell, the Starks' ancestral home, though Bran and his brother Rickon escape. Meanwhile, Stannis and Renly Baratheon, brothers of the deceased King Robert, claim the throne for themselves. Stannis has the support of Melisandre, a Red Priestess of the Lord of Light. Stannis kills Renly and attacks King's Landing, whose defense is successfully led by Tyrion Lannister, the acting Hand of the King and Queen Cersei's younger brother. On a ranging beyond the Wall, Jon Snow manages to gain the trust of Wildlings, while other members of the Watch come under attack by an army of White Walkers and their reanimated corpses. In Essos, Daenerys and her khalasar find refuge in Qarth. After foiling an attempt to steal her dragons, Daenerys leaves the city.\\n\\n=== Season 3 (2013) ===\\n\\nAfter Robb breaks his vow to marry one of Lord Walder Frey's daughters, Frey arranges the massacre of Robb, his mother, his wife, his unborn child and his bannermen, during a wedding feast. The northern House Bolton, which had liberated Winterfell from Theon at Robb's request, also betrays the Starks. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '2137caaa92082a70e9e55b2ecce87ae8'}>,\n",
-       "  <Document: {'content': '=== Season 3 (2013) ===\\n\\nAfter Robb breaks his vow to marry one of Lord Walder Frey\\'s daughters, Frey arranges the massacre of Robb, his mother, his wife, his unborn child and his bannermen, during a wedding feast. The northern House Bolton, which had liberated Winterfell from Theon at Robb\\'s request, also betrays the Starks. After Robb\\'s death, Lord Roose Bolton is appointed the new Warden of the North by Lord Tywin Lannister, the head of House Lannister and new Hand of the King. In Dreadfort, the Boltons\\' ancestral castle, Theon is tortured by Roose Bolton\\'s bastard son Ramsay Snow. Further north, Jon Snow climbs the Wall with a Wildling party and ends up on its south side, but then betrays them. In the capital, King Joffrey has decided to marry Margaery of House Tyrell, setting Sansa aside. Lord Tywin, however, arranges Sansa\\'s marriage with his son Tyrion. Jaime reaches King\\'s Landing, having had his dominant hand cut off. In Essos, Daenerys acquires \"The Unsullied\", an army of eunuch slave soldiers. She also joins forces with the \"Second Sons\", a company of mercenaries, and takes over two cities.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '804139beaa9467a2c45b62343fcc4ed2'}>,\n",
-       "  <Document: {'content': 'In Essos, Daenerys acquires \"The Unsullied\", an army of eunuch slave soldiers. She also joins forces with the \"Second Sons\", a company of mercenaries, and takes over two cities.\\n\\n=== Season 4 (2014) ===\\n\\nIn King\\'s Landing, Lady Olenna secretly poisons King Joffrey at his wedding with her granddaughter Margaery, but Tyrion is falsely accused of the murder by his family, and is found guilty. However, Jaime and Varys conspire to smuggle Tyrion to Essos. Tyrion kills his father Tywin before leaving. Petyr Baelish smuggles Sansa into the Vale, ruled by her aunt and his lover, the widowed Lysa Arryn. Baelish marries Lysa but later kills her. After attempting to reunite with her family, Arya takes a ship bound for Braavos, in Essos. Having returned to the Night\\'s Watch, Jon Snow defends Castle Black against an army of Wildlings, who are superior in strength. The Watch is rescued by the arrival of Stannis Baratheon and his forces. A crippled Bran with newfound powers travels north beyond the Wall with a few companions. Beneath a weirwood tree, he finds the Three-eyed Raven, an old man with the ability to perceive future and past. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'bd1f6776b78b687b9d3493ad9086f752'}>,\n",
-       "  <Document: {'content': \"Beneath a weirwood tree, he finds the Three-eyed Raven, an old man with the ability to perceive future and past. In Essos, Daenerys takes control of Meereen and abolishes slavery. When she discovers that her trusted advisor, Ser Jorah Mormont, had spied on her for Robert Baratheon, she exiles him.\\n\\n=== Season 5 (2015) ===\\n\\nIn King's Landing, Margaery marries the new King Tommen Baratheon, Joffrey's younger brother. The Sparrows, a group of religious fanatics, impose their views upon the city, imprisoning Margaery, her brother Loras, and Cersei for committing various sins. Jaime travels to Dorne to take back Myrcella Baratheon. However, Oberyn Martell's lover, Ellaria and his bastard daughters kill Myrcella as revenge for Oberyn's death. In Winterfell, the new seat of House Bolton, Baelish arranges Sansa's marriage with the now-legitimized son of Roose Bolton, the sadist Ramsay. Stannis's unsuccessful march on Winterfell, which leads to his death, allows Sansa the opportunity to escape with Theon. At the Wall, as the newly elected Lord Commander of the Night's Watch, Jon Snow forms an alliance with the Wildlings to save them from the White Walkers and their army of reanimated corpses. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '206495c95288ae2b36bd62912fea912f'}>,\n",
-       "  <Document: {'content': \"At the Wall, as the newly elected Lord Commander of the Night's Watch, Jon Snow forms an alliance with the Wildlings to save them from the White Walkers and their army of reanimated corpses. However, Jon is stabbed to death by some brothers who see him as a traitor. Arya arrives in Braavos, where she finds Jaqen H'ghar whom she had previously helped escape, and begins training with the Faceless Men, a guild of assassins. In Essos, Tyrion becomes an advisor to Daenerys. Ser Jorah saves the life of Daenerys against a revolt of slavers, who flees Meereen on Drogon's back.\\n\\n=== Season 6 (2016) ===\\n\\nAt the Wall, Melisandre resurrects Jon. Jon reunites with Sansa and leaves the Night's Watch. Aided by the Wildlings, loyalists and the Knights of the Vale, they defeat the Boltons and Jon is proclaimed the King in the North. Beyond the Wall, Bran trains with the Three-eyed Raven but they come under attack by the White Walkers. The Three-eyed Raven is killed and succeeded by Bran, who escapes with the help of Hodor, who dies in the process. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '6e99db0a7535f617b62798eda470ebc'}>,\n",
-       "  <Document: {'content': \"The Three-eyed Raven is killed and succeeded by Bran, who escapes with the help of Hodor, who dies in the process. Bran realizes Jon is in fact the son of his deceased aunt Lyanna Stark and Crown Prince Rhaegar Targaryen. In King's Landing, Cersei kills the High Sparrow, Margaery, Loras and many others by blowing up the Great Sept with wildfire. Tommen kills himself after witnessing the events, and Cersei is crowned Queen. In the Iron Islands, Euron Greyjoy usurps leadership by killing his brother and Theon's father, Balon. Ellaria seizes control of Dorne, and joins Olenna Tyrell in an alliance with Daenerys, who forgives Ser Jorah, takes control of the Dothraki and defeats the slavers. Daenerys sails for Westeros, joined by Theon and his sister Yara. In Braavos, Arya continues her training with the Faceless Men, but eventually leaves them.\\n\\n=== Season 7 (2017) ===\\n\\nDaenerys arrives in Westeros and takes over Dragonstone. She plans to overthrow Cersei, but Jon arrives to instead save Westeros from the White Walkers. The Night King, leader of the White Walkers, kills and reanimates Daenerys' dragon Viserion. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '1312c397699daa8c4f6070bc68b2b4bb'}>,\n",
-       "  <Document: {'content': \"She plans to overthrow Cersei, but Jon arrives to instead save Westeros from the White Walkers. The Night King, leader of the White Walkers, kills and reanimates Daenerys' dragon Viserion. Jon and Daenerys attempt to persuade Cersei to join their cause by showing the existence of a wight they had captured, but she has her own plans to increase control over the continent. At Winterfell, Sansa is reunited with her siblings, Arya and Bran. When Lord Protector of the Vale Petyr Baelish begins to turn the Stark children against one another, they manage to have him executed. In a vision, Bran sees that his aunt, Lyanna, was, in fact, married to Prince Rhaegar, and that Jon's real name is Aegon Targaryen, making him the true heir to the Iron Throne. After his sister Yara is kidnapped by his uncle Euron, Theon sets out to save her. The Night King demolishes a section of The Wall with the help of the reanimated Viserion, allowing the White Walkers and Army of the Dead to pass into the Seven Kingdoms.\\n\\n=== Season 8 (2019) ===\\n\\nJon and Daenerys learn the Army of the Dead has breached the Wall. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '377eb73b649cabbe04e43eae3728c45d'}>,\n",
-       "  <Document: {'content': \"The Night King demolishes a section of The Wall with the help of the reanimated Viserion, allowing the White Walkers and Army of the Dead to pass into the Seven Kingdoms.\\n\\n=== Season 8 (2019) ===\\n\\nJon and Daenerys learn the Army of the Dead has breached the Wall. Theon rescues Yara, then returns to Winterfell. Sam reveals to Jon that he is actually Aegon Targaryen. Jaime arrives at Winterfell, revealing Cersei won't help defeat the Army of the Dead. Jon reveals his Targaryen lineage to Daenerys, who wants it kept a secret. After a battle, Arya kills the Night King, destroying the Army of the Dead. Euron's navy kills Rhaegal and Cersei executes Missandei who had been captured, enraging Daenerys, who, after her army takes King's Landing, destroys much of the city indiscriminately. Cersei and Jaime, who returned to Cersei, are killed. Tyrion denounces Daenerys and is imprisoned for treason to await execution. Jon, unable to stop her, kills Daenerys. Bran Stark is proclaimed king, allowing the North to secede as an independent kingdom. Bran appoints Tyrion as his Hand. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'a85383bf487f01f1214a67cc90fa5b12'}>,\n",
-       "  <Document: {'content': \"Bran Stark is proclaimed king, allowing the North to secede as an independent kingdom. Bran appoints Tyrion as his Hand. Jon is sentenced to the Night's Watch, Sansa is crowned Queen in the North, and Arya sets sail to explore west of Westeros.\\n\\n==Specials==\\n\\n==Home media release==\\n\\nSeason\\n\\nEpisodes\\n\\nDVD and Blu-ray release date\\n\\nRegion 1\\n\\nRegion 2\\n\\nRegion 4\\n\\n1\\n\\n10\\n\\nMarch 6, 2012\\n\\nMarch 5, 2012\\n\\nAugust 10, 2012\\n\\n2\\n\\n10\\n\\nFebruary 19, 2013\\n\\nMarch 4, 2013\\n\\nMarch 6, 2013\\n\\n3\\n\\n10\\n\\nFebruary 18, 2014\\n\\nFebruary 17, 2014\\n\\nFebruary 19, 2014\\n\\n4\\n\\n10\\n\\nFebruary 17, 2015\\n\\nFebruary 16, 2015\\n\\nFebruary 18, 2015\\n\\n5\\n\\n10\\n\\nMarch 15, 2016\\n\\nMarch 14, 2016\\n\\nMarch 16, 2016\\n\\n6\\n\\n10\\n\\nNovember 15, 2016\\n\\nNovember 14, 2016\\n\\nNovember 16, 2016\\n\\n7\\n\\n7\\n\\nDecember 12, 2017\\n\\nDecember 11, 2017\\n\\nDecember 11, 2017\\n\\n8\\n\\n6\\n\\nDecember 3, 2019\\n\\nDecember 2, 2019\\n\\nDecember 4, 2019\\n\\n== Ratings ==\\n\\n== References ==\\n\\n== External links ==\\n*\\n*  ''Game of Thrones''\\xa0– The Viewers Guide on HBO.com\\n*  Making ''Game of Thrones'' on HBO.com\\n*\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '3270f7f2226aac27bc9cf95c4cfd87b7'}>,\n",
-       "  <Document: {'content': \"\\n\\nThe fourth season of the fantasy drama television series ''Game of Thrones'' premiered in the United States on HBO on April 6, 2014, and concluded on June 15, 2014. It was broadcast on Sunday at 9:00 pm in the United States, consisting of 10 episodes, each running approximately 50–60 minutes. The season is adapted primarily from the second half of ''A Storm of Swords'', along with elements of ''A Feast for Crows'' and ''A Dance with Dragons'', all novels from the ''A Song of Ice and Fire'' series by George R. R. Martin. The series is adapted for television by David Benioff and D. B. Weiss. HBO ordered the fourth season on April 2, 2013, which began filming in July 2013. The season was filmed primarily in Ireland, Northern Ireland and Croatia.\\n\\nThe story takes place in a fictional world, primarily upon a continent called Westeros, with one storyline occurring on another continent to the east known as Essos. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '8a5a88aa17764db954febe097dca8ee2'}>,\n",
-       "  <Document: {'content': \"The story takes place in a fictional world, primarily upon a continent called Westeros, with one storyline occurring on another continent to the east known as Essos. Like the novel and previous seasons, the fourth season mainly centres around the war of the five kings; after the death of Robb Stark at The Red Wedding, all three remaining kings in Westeros believe they have a claim to the Iron Throne. Meanwhile, at the wall, Jon Snow and the Night's Watch get ready for the battle against the free folk.\\n\\n''Game of Thrones'' features a large ensemble cast, including Peter Dinklage, Nikolaj Coster-Waldau, Lena Headey, Emilia Clarke and Kit Harington. The season introduced a number of new cast members, including Pedro Pascal, Indira Varma, Michiel Huisman and Dean-Charles Chapman.\\n\\nCritics praised the show's production values and cast, with specific accolades for Dinklage's portrayal of Tyrion Lannister. Viewership yet again rose compared to the previous season. It won 4 of the 19 Emmy Awards for which it was nominated.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'a3131856516010ddbd5192a5767693e'}>,\n",
-       "  <Document: {'content': 'Viewership yet again rose compared to the previous season. It won 4 of the 19 Emmy Awards for which it was nominated.\\n\\n==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n* Peter Dinklage as Tyrion Lannister\\n* Nikolaj Coster-Waldau as Jaime Lannister\\n* Lena Headey as Cersei Lannister\\n* Emilia Clarke as Daenerys Targaryen\\n* Kit Harington as Jon Snow\\n* Charles Dance as Tywin Lannister\\n* Natalie Dormer as Margaery Tyrell\\n* Jack Gleeson as Joffrey Baratheon\\n* Sophie Turner as Sansa Stark\\n* Maisie Williams as Arya Stark\\n* John Bradley as Samwell Tarly\\n* Rose Leslie as Ygritte\\n* Kristofer Hivju as Tormund Giantsbane\\n* Rory McCann as Sandor \"The Hound\" Clegane\\n\\n* Gwendoline Christie as Brienne of Tarth\\n* Jerome Flynn as Bronn\\n* Sibel Kekilli as Shae\\n* Iain Glen as Jorah Mormont\\n* Liam Cunningham as Davos Seaworth\\n* Stephen Dillane as Stannis Baratheon\\n* Carice van Houten as Melisandre\\n* Alfie Allen as Theon Greyjoy / \"Reek\"\\n* Isaac Hempstead Wright as Bran Stark\\n* Iwan Rheon as Ramsay Snow / Ramsay Bolton\\n* Conleth Hill as Varys\\n* Aidan Gillen as Petyr \"Littlefinger\" Baelish\\n* Hannah Murray as Gilly\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 4. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'b81940c805f56762728d0d85321b82fa'}>,\n",
-       "  <Document: {'content': '==Episodes==\\n\\n==Cast==\\n\\n===Main cast===\\n\\n* Peter Dinklage as Tyrion Lannister\\n* Nikolaj Coster-Waldau as Jaime Lannister\\n* Lena Headey as Cersei Lannister\\n* Emilia Clarke as Daenerys Targaryen\\n* Kit Harington as Jon Snow\\n* Charles Dance as Tywin Lannister\\n* Natalie Dormer as Margaery Tyrell\\n* Jack Gleeson as Joffrey Baratheon\\n* Sophie Turner as Sansa Stark\\n* Maisie Williams as Arya Stark\\n* John Bradley as Samwell Tarly\\n* Rose Leslie as Ygritte\\n* Kristofer Hivju as Tormund Giantsbane\\n* Rory McCann as Sandor \"The Hound\" Clegane\\n\\n* Gwendoline Christie as Brienne of Tarth\\n* Jerome Flynn as Bronn\\n* Sibel Kekilli as Shae\\n* Iain Glen as Jorah Mormont\\n* Liam Cunningham as Davos Seaworth\\n* Stephen Dillane as Stannis Baratheon\\n* Carice van Houten as Melisandre\\n* Alfie Allen as Theon Greyjoy / \"Reek\"\\n* Isaac Hempstead Wright as Bran Stark\\n* Iwan Rheon as Ramsay Snow / Ramsay Bolton\\n* Conleth Hill as Varys\\n* Aidan Gillen as Petyr \"Littlefinger\" Baelish\\n* Hannah Murray as Gilly\\n\\n===Guest cast===\\nThe recurring actors listed here are those who appeared in season 4. They are listed by the region in which they first appear:\\n\\n====At and beyond the Wall====\\n* Owen Teale as Alliser Thorne\\n* Peter Vaughan as Maester Aemon\\n* Brian Fortune as Othell Yarwyck\\n* Dominic Carter as Janos Slynt\\n* Ben Crompton as Eddison Tollett\\n* Noah Taylor as Locke\\n* Mark Stanley as Grenn\\n* Josef Altin as Pypar\\n* Luke Barnes as Rast\\n* Burn Gorman as Karl Tanner\\n* Brenock O\\'Connor as Olly\\n* Lu Corfield as the Mole\\'s Town madam\\n* Lois Winstone as a Mole\\'s Town prostitute\\n* Ciarán Hinds as Mance Rayder\\n* Yuri Kolokolnikov as Styr\\n* Ian Whyte as Dongo\\n* Joseph Gatt as a Thenn warg\\n* Deirdre Monaghan as Morag\\n* Jane McGrath as Sissy\\n* Thomas Brodie-Sangster as Jojen Reed\\n* Ellie Kendrick as Meera Reed\\n* Kristian Nairn as Hodor\\n* Struan Rodger as the Three-Eyed Raven\\n* Octavia Alexandru as Leaf\\n* Richard Brake as the Night King\\n* Ross Mullan as White Walkers\\n\\n====In the North====\\n* Michael McElhatton as Roose Bolton\\n* Elizabeth Webster as Walda Bolton\\n* Gemma Whelan as Yara Greyjoy\\n* Charlotte Hope as Myranda\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Kate Dickie as Lysa Arryn\\n* Rupert Vansittart as Yohn Royce\\n* Alisdair Simpson as Donnel Waynwood\\n\\n====In King\\'s Landing====\\n* Dean-Charles Chapman as Tommen Baratheon\\n* Julian Glover as Grand Maester Pycelle\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Finn Jones as Loras Tyrell\\n* Diana Rigg as Olenna Tyrell\\n* Pedro Pascal as Oberyn Martell\\n* Indira Varma as Ellaria Sand\\n* Ian Beattie as Meryn Trant\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Daniel Portman as Podrick Payne\\n* Tony Way as Dontos Hollard\\n* Paul Bentley as the High Septon\\n* Anton Lesser as Qyburn\\n* Will Tudor as Olyvar\\n* Josephine Gillan as Marei\\n* Pixie Le Knot as Kayla\\n\\n====On Dragonstone====\\n* Tara Fitzgerald as Selyse Florent\\n* Kerry Ingram as Shireen Baratheon\\n\\n====In the Riverlands====\\n* Gary Oliver as Ternesio Terys\\n* Andy Kellegher as Polliver\\n* Andy Beckwith as Rorge\\n* Gerard Jordan as Biter\\n* Ben Hawkey as Hot Pie\\n\\n====In Braavos====\\n* Mark Gatiss as Tycho Nestoris\\n* Lucian Msamati as Salladhor Saan\\n* Sarine Sofair as Lhara\\n\\n====In Slaver\\'s Bay====\\n* Michiel Huisman as Daario Naharis\\n* Ian McElhinney as Barristan Selmy\\n* Nathalie Emmanuel as Missandei\\n* Jacob Anderson as Grey Worm\\n* Joel Fry as Hizdahr zo Loraq\\n* Reece Noi as Mossador\\n\\n==Production==\\nOn April 2, 2013, HBO announced it had renewed the series for a fourth season, to consist of 10 episodes.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '31c50d7c99ec669fa9f4e39e2ea05a4c'}>,\n",
-       "  <Document: {'content': \"They are listed by the region in which they first appear:\\n\\n====At and beyond the Wall====\\n* Owen Teale as Alliser Thorne\\n* Peter Vaughan as Maester Aemon\\n* Brian Fortune as Othell Yarwyck\\n* Dominic Carter as Janos Slynt\\n* Ben Crompton as Eddison Tollett\\n* Noah Taylor as Locke\\n* Mark Stanley as Grenn\\n* Josef Altin as Pypar\\n* Luke Barnes as Rast\\n* Burn Gorman as Karl Tanner\\n* Brenock O'Connor as Olly\\n* Lu Corfield as the Mole's Town madam\\n* Lois Winstone as a Mole's Town prostitute\\n* Ciarán Hinds as Mance Rayder\\n* Yuri Kolokolnikov as Styr\\n* Ian Whyte as Dongo\\n* Joseph Gatt as a Thenn warg\\n* Deirdre Monaghan as Morag\\n* Jane McGrath as Sissy\\n* Thomas Brodie-Sangster as Jojen Reed\\n* Ellie Kendrick as Meera Reed\\n* Kristian Nairn as Hodor\\n* Struan Rodger as the Three-Eyed Raven\\n* Octavia Alexandru as Leaf\\n* Richard Brake as the Night King\\n* Ross Mullan as White Walkers\\n\\n====In the North====\\n* Michael McElhatton as Roose Bolton\\n* Elizabeth Webster as Walda Bolton\\n* Gemma Whelan as Yara Greyjoy\\n* Charlotte Hope as Myranda\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Kate Dickie as Lysa Arryn\\n* Rupert Vansittart as Yohn Royce\\n* Alisdair Simpson as Donnel Waynwood\\n\\n====In King's Landing====\\n* Dean-Charles Chapman as Tommen Baratheon\\n* Julian Glover as Grand Maester Pycelle\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Finn Jones as Loras Tyrell\\n* Diana Rigg as Olenna Tyrell\\n* Pedro Pascal as Oberyn Martell\\n* Indira Varma as Ellaria Sand\\n* Ian Beattie as Meryn Trant\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Daniel Portman as Podrick Payne\\n* Tony Way as Dontos Hollard\\n* Paul Bentley as the High Septon\\n* Anton Lesser as Qyburn\\n* Will Tudor as Olyvar\\n* Josephine Gillan as Marei\\n* Pixie Le Knot as Kayla\\n\\n====On Dragonstone====\\n* Tara Fitzgerald as Selyse Florent\\n* Kerry Ingram as Shireen Baratheon\\n\\n====In the Riverlands====\\n* Gary Oliver as Ternesio Terys\\n* Andy Kellegher as Polliver\\n* Andy Beckwith as Rorge\\n* Gerard Jordan as Biter\\n* Ben Hawkey as Hot Pie\\n\\n====In Braavos====\\n* Mark Gatiss as Tycho Nestoris\\n* Lucian Msamati as Salladhor Saan\\n* Sarine Sofair as Lhara\\n\\n====In Slaver's Bay====\\n* Michiel Huisman as Daario Naharis\\n* Ian McElhinney as Barristan Selmy\\n* Nathalie Emmanuel as Missandei\\n* Jacob Anderson as Grey Worm\\n* Joel Fry as Hizdahr zo Loraq\\n* Reece Noi as Mossador\\n\\n==Production==\\nOn April 2, 2013, HBO announced it had renewed the series for a fourth season, to consist of 10 episodes.\\n\\n===Crew===\\nDavid Benioff and D. B. Weiss serve as main writers and showrunners for the fourth season. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '31dd5205833aa60a6482f6329b02e6b'}>,\n",
-       "  <Document: {'content': \"They are listed by the region in which they first appear:\\n\\n====At and beyond the Wall====\\n* Owen Teale as Alliser Thorne\\n* Peter Vaughan as Maester Aemon\\n* Brian Fortune as Othell Yarwyck\\n* Dominic Carter as Janos Slynt\\n* Ben Crompton as Eddison Tollett\\n* Noah Taylor as Locke\\n* Mark Stanley as Grenn\\n* Josef Altin as Pypar\\n* Luke Barnes as Rast\\n* Burn Gorman as Karl Tanner\\n* Brenock O'Connor as Olly\\n* Lu Corfield as the Mole's Town madam\\n* Lois Winstone as a Mole's Town prostitute\\n* Ciarán Hinds as Mance Rayder\\n* Yuri Kolokolnikov as Styr\\n* Ian Whyte as Dongo\\n* Joseph Gatt as a Thenn warg\\n* Deirdre Monaghan as Morag\\n* Jane McGrath as Sissy\\n* Thomas Brodie-Sangster as Jojen Reed\\n* Ellie Kendrick as Meera Reed\\n* Kristian Nairn as Hodor\\n* Struan Rodger as the Three-Eyed Raven\\n* Octavia Alexandru as Leaf\\n* Richard Brake as the Night King\\n* Ross Mullan as White Walkers\\n\\n====In the North====\\n* Michael McElhatton as Roose Bolton\\n* Elizabeth Webster as Walda Bolton\\n* Gemma Whelan as Yara Greyjoy\\n* Charlotte Hope as Myranda\\n\\n====In the Vale====\\n* Lino Facioli as Robin Arryn\\n* Kate Dickie as Lysa Arryn\\n* Rupert Vansittart as Yohn Royce\\n* Alisdair Simpson as Donnel Waynwood\\n\\n====In King's Landing====\\n* Dean-Charles Chapman as Tommen Baratheon\\n* Julian Glover as Grand Maester Pycelle\\n* Roger Ashton-Griffiths as Mace Tyrell\\n* Finn Jones as Loras Tyrell\\n* Diana Rigg as Olenna Tyrell\\n* Pedro Pascal as Oberyn Martell\\n* Indira Varma as Ellaria Sand\\n* Ian Beattie as Meryn Trant\\n* Hafþór Júlíus Björnsson as Gregor Clegane\\n* Daniel Portman as Podrick Payne\\n* Tony Way as Dontos Hollard\\n* Paul Bentley as the High Septon\\n* Anton Lesser as Qyburn\\n* Will Tudor as Olyvar\\n* Josephine Gillan as Marei\\n* Pixie Le Knot as Kayla\\n\\n====On Dragonstone====\\n* Tara Fitzgerald as Selyse Florent\\n* Kerry Ingram as Shireen Baratheon\\n\\n====In the Riverlands====\\n* Gary Oliver as Ternesio Terys\\n* Andy Kellegher as Polliver\\n* Andy Beckwith as Rorge\\n* Gerard Jordan as Biter\\n* Ben Hawkey as Hot Pie\\n\\n====In Braavos====\\n* Mark Gatiss as Tycho Nestoris\\n* Lucian Msamati as Salladhor Saan\\n* Sarine Sofair as Lhara\\n\\n====In Slaver's Bay====\\n* Michiel Huisman as Daario Naharis\\n* Ian McElhinney as Barristan Selmy\\n* Nathalie Emmanuel as Missandei\\n* Jacob Anderson as Grey Worm\\n* Joel Fry as Hizdahr zo Loraq\\n* Reece Noi as Mossador\\n\\n==Production==\\nOn April 2, 2013, HBO announced it had renewed the series for a fourth season, to consist of 10 episodes.\\n\\n===Crew===\\nDavid Benioff and D. B. Weiss serve as main writers and showrunners for the fourth season. They co-wrote seven out of ten episodes. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'e50e3f8a5e9d361d8258ff33e00aa518'}>,\n",
-       "  <Document: {'content': '===Crew===\\nDavid Benioff and D. B. Weiss serve as main writers and showrunners for the fourth season. They co-wrote seven out of ten episodes. The remaining three episodes were written by Bryan Cogman (two episodes), and the author of \\'\\'A Song of Ice and Fire\\'\\', George R. R. Martin (one episode).\\n\\nBenioff and Weiss co-directed the season premiere after making their directorial debut in season 3, although only Weiss is credited as Benioff received credit for their previous directed episode; Alex Graves, who directed two episodes in season 3, returned and directed episodes 2, 3, 8 and 10; Michelle MacLaren, who also directed two episodes in season 3, returned to direct episodes 4 and 5; former series cinematographer Alik Sakharov, who directed in seasons 2 and 3, returned to direct episodes 6 and 7; and Neil Marshall directed episode 9 after previously directing \"Blackwater\", the ninth episode of season 2.\\n\\n===Casting===\\nPedro Pascal plays Oberyn Martell.\\nThe fourth season adds previously recurring actors Gwendoline Christie (Brienne of Tarth), Iwan Rheon (Ramsay Snow), Kristofer Hivju (Tormund Giantsbane) and Hannah Murray (Gilly) to the series\\' main cast.  Iain Glen\\'s credit is moved last in the rotation and given the \"With\" moniker.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '714763f36d96b95788b41cdd506fe616'}>,\n",
-       "  <Document: {'content': 'The fourth season adds previously recurring actors Gwendoline Christie (Brienne of Tarth), Iwan Rheon (Ramsay Snow), Kristofer Hivju (Tormund Giantsbane) and Hannah Murray (Gilly) to the series\\' main cast.  Iain Glen\\'s credit is moved last in the rotation and given the \"With\" moniker.\\n\\nPrince Oberyn Martell, nicknamed \"The Red Viper\", is played by Chilean-American actor Pedro Pascal. \"This was a tough one\", said showrunners David Benioff and Dan Weiss about the casting. \"The Red Viper is sexy and charming, yet believably dangerous; intensely likable, yet driven by hate. The boys love him, the girls love him, and he loves them all back. Unless your last name is Lannister. We found a fellow who can handle the job description and make it seem effortless. He wasn\\'t easy to find and he won\\'t be easy to stop\". Martin commented on the casting by saying: \"I wasn\\'t present for Pedro Pascal\\'s audition, but I understand that he really killed it with his reading. And since his casting was announced, the producer of another TV show on which he appeared recently has written me to say how terrific Pascal is, and to congratulate us on the casting. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '779106dc599370f078b26784bf62c4cf'}>,\n",
-       "  <Document: {'content': 'And since his casting was announced, the producer of another TV show on which he appeared recently has written me to say how terrific Pascal is, and to congratulate us on the casting. So I suspect that he will turn out to be a wonderful Red Viper\". Actress Indira Varma was cast as Ellaria Sand, Prince Oberyn\\'s paramour.\\n\\nRoger Ashton-Griffiths joins the cast in the role of Mace Tyrell. \"The lord oaf of Highgarden\", as his mother describes him, is otherwise known as father to Margaery and Loras. His casting was confirmed by George R. R. Martin, who introduced the actor as the solution to a riddle he\\'d set fans with the following message, \"Yes, it\\'s the fine British character actor Roger Ashton-Griffiths, who has been cast in the role of Mace Tyrell, son to the Queen of Thorns, and father of Loras and Margaery\".\\n\\nMark Gatiss plays Tycho Nestoris, a representative of the Iron Bank of Braavos, to whom the Iron Throne owes millions in borrowed gold. The role of Hizdahr zo Loraq is played by young British actor Joel Fry. Hizdahr is the young scion of an ancient Meereenese family who crosses paths with Daenerys Targaryen in Meereen. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '543612958de3cea51f08d24facf699d8'}>,\n",
-       "  <Document: {'content': 'The role of Hizdahr zo Loraq is played by young British actor Joel Fry. Hizdahr is the young scion of an ancient Meereenese family who crosses paths with Daenerys Targaryen in Meereen. Elizabeth Webster was cast as Fat Walda Frey. Walda Frey is a granddaughter of Lord Walder Frey. She is the new wife of Roose Bolton, the Lord of the Dreadfort. During the wedding feast of Edmure Tully and Roslin Frey, Lord Bolton recounts to Catelyn Stark and Ser Brynden \"Blackfish\" Tully how Lord Walder Frey proposed him to marry one of his granddaughters and offered her weight in silver as dowry. Lord Bolton then adds he chose the fattest bride available and she has made him very rich.\\n\\nPaola Dionisotti and Rupert Vansittart were cast as Lady Anya Waynwood and Bronze Yohn Royce. They are the heads of House Waynwood and House Royce of Runestone: two powerful vassal houses of House Arryn. Yuri Kolokolnikov plays Styr: One of Mance Rayder\\'s lieutenants and the Magnar – the name of the first ever Lord of Thenn which is now a title – of the Thenn people, a wildling clan. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '77692212c9127505e38185cc647c8fbb'}>,\n",
-       "  <Document: {'content': 'Yuri Kolokolnikov plays Styr: One of Mance Rayder\\'s lieutenants and the Magnar – the name of the first ever Lord of Thenn which is now a title – of the Thenn people, a wildling clan. Two mysterious characters from Bran\\'s storyline have also been cast: the Three-Eyed Crow, who is played by Struan Rodger, and a Child of the Forest, played by Octavia Alexandru.\\n\\nRoles that were recast for season 4 include Michiel Huisman as Daario Naharis. Huisman replaces Ed Skrein, who portrayed the character in season 3. Dean-Charles Chapman plays the role of Tommen Baratheon, King Joffrey\\'s younger brother. Tommen was played by Callum Wharry in seasons 1 and 2. Chapman appeared as Martyn Lannister in two episodes of season 3 (\"Walk of Punishment\" and \"Kissed by Fire\"). Hafþór Júlíus Björnsson was cast as Ser Gregor Clegane, called \"The Mountain\". Hafþór replaces Ian Whyte, who portrayed the character in season 2.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '5b164512c3c47bda4c16593dad411fe9'}>,\n",
-       "  <Document: {'content': 'Hafþór Júlíus Björnsson was cast as Ser Gregor Clegane, called \"The Mountain\". Hafþór replaces Ian Whyte, who portrayed the character in season 2.\\n\\nAfter an absence of at least a season, Owen Teale returns as Alliser Thorne, Kate Dickie as Lysa Arryn, Dominic Carter as Janos Slynt, Tony Way as Dontos Hollard, Andy Beckwith as Rorge, Gerard Jordan as Biter, Andy Kellegher as Polliver, Lino Facioli as Robin Arryn and Brian Fortune as Othell Yarwyck.\\n\\n===Filming===\\nFilming for the season began on July 8, 2013, in Northern Ireland. The series also returned to Iceland and Dubrovnik for filming.\\nNew locations in Croatia include Diocletian\\'s Palace in Split, Klis Fortress north of Split, Perun quarry east of Split, Mosor mountain, and Baška Voda further down to the south. In the commentary for episode 2, \"The Lion and the Rose,\" the showrunners revealed that parts of Joffrey\\'s death scene had been filmed in California. The Thingvellir National Park in Iceland was used as the location for the fight between Brienne and The Hound.\\n\\nFilming for the season lasted 136 days and was completed on November 21, 2013.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '127e6cce9d13a12f13d8e22bb48ed4b0'}>,\n",
-       "  <Document: {'content': 'The Thingvellir National Park in Iceland was used as the location for the fight between Brienne and The Hound.\\n\\nFilming for the season lasted 136 days and was completed on November 21, 2013.\\n\\n===Music===\\n\\nThe Icelandic post-rock band Sigur Rós appears in the second episode, as a group of musicians serenading the royal couple at their wedding reception with \"The Rains of Castamere.\"This continues the series\\'s tradition of employing noted indie bands, begun in season 2 with The National and continued in season 3 with The Hold Steady.\\n\\nThe soundtrack for the season was released digitally on June 10, 2014, and on CD on July 1, 2014.\\n\\n==Reception==\\n\\n===Critical response===\\nThe fourth season was acclaimed by critics. However, the third episode attracted criticism for the inclusion of a scene in which Jaime Lannister appears to be raping his sister and lover Cersei in the Great Sept of Baelor. In the source novel, Cersei verbally consents to the sexual encounter, but does not in the television portrayal. The final episode was also criticised for the omission of the events of the epilogue of \\'\\'A Storm of Swords\\'\\' which was expected by fans to be the final scene.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'cf8a414ecc4694daabb84201ff8f2175'}>,\n",
-       "  <Document: {'content': 'The final episode was also criticised for the omission of the events of the epilogue of \\'\\'A Storm of Swords\\'\\' which was expected by fans to be the final scene.\\n\\nThe review aggregator website Metacritic gave season 4 a score of 94 out of 100 based on 29 reviews, signifying \"universal acclaim\". On Rotten Tomatoes, the fourth season has a 97% approval rating from 44 critics with an average rating of 8.89 out of 10. The site\\'s critical consensus reads, \"\\'\\'Game of Thrones\\'\\' continues to be one of the best shows on TV, combining meticulously-plotted character arcs with the spectacular design of the Seven Kingdoms.\"===Ratings===\\n\\nSeason 4 obtained the strongest viewer numbers of all seasons aired up until that point, with a series high of 7.20 million viewers of the first airing of the seventh episode. With its fourth season, \\'\\'Game of Thrones\\'\\' has become the most-watched HBO series in history (surpassing the fourth season of \\'\\'The Sopranos\\'\\' which had a gross audience of 18.2 million viewers), averaging 18.4 million viewers across multiple platforms, including live viewing, encores, DVR views, HBO GO and On Demand views.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '314f1336f6374d195055c5c9221a70d1'}>,\n",
-       "  <Document: {'content': 'With its fourth season, \\'\\'Game of Thrones\\'\\' has become the most-watched HBO series in history (surpassing the fourth season of \\'\\'The Sopranos\\'\\' which had a gross audience of 18.2 million viewers), averaging 18.4 million viewers across multiple platforms, including live viewing, encores, DVR views, HBO GO and On Demand views.\\n\\n===Accolades===\\n\\nFor the 30th TCA Awards, the series was nominated for Outstanding Achievement in Drama and Program of the Year. For the 4th Critics\\' Choice Television Awards, the series was nominated for Best Drama Series and Diana Rigg received a nomination for Best Guest Performer in a Drama Series. For the 66th Primetime Emmy Awards, the series received 19 nominations, including Outstanding Drama Series, Peter Dinklage for Outstanding Supporting Actor in a Drama Series, Lena Headey for Outstanding Supporting Actress in a Drama Series, Diana Rigg for Outstanding Guest Actress in a Drama Series, David Benioff and D. B. Weiss for Outstanding Writing for a Drama Series for \"The Children\", and Neil Marshall for Outstanding Directing for a Drama Series for \"The Watchers on the Wall\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': 'ee07e11bfe4636bf2058a4f8978c6c49'}>,\n",
-       "  <Document: {'content': 'For the 66th Primetime Emmy Awards, the series received 19 nominations, including Outstanding Drama Series, Peter Dinklage for Outstanding Supporting Actor in a Drama Series, Lena Headey for Outstanding Supporting Actress in a Drama Series, Diana Rigg for Outstanding Guest Actress in a Drama Series, David Benioff and D. B. Weiss for Outstanding Writing for a Drama Series for \"The Children\", and Neil Marshall for Outstanding Directing for a Drama Series for \"The Watchers on the Wall\". For the 67th Writers Guild of America Awards, the series was nominated for Best Drama Series and George R. R. Martin was nominated for Best Episodic Drama for \"The Lion and the Rose\". For the 21st Screen Actors Guild Awards, the cast was nominated for Best Drama Ensemble, Peter Dinklage was nominated for Best Drama Actor, and the series won for Best Stunt Team. For the 72nd Golden Globe Awards, the series was nominated for Best Television Series – Drama. For the 67th Directors Guild of America Awards, Alex Graves was nominated for Outstanding Directing – Drama Series for the episode \"The Children\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'c3dea0d40d3d602c80624b57ca429f71'}>,\n",
-       "  <Document: {'content': 'For the 67th Directors Guild of America Awards, Alex Graves was nominated for Outstanding Directing – Drama Series for the episode \"The Children\".\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2014\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nTV Choice Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nYoung Hollywood Awards\\n\\nWe Love to Hate You\\n\\nJack Gleeson\\n\\nBingeworthy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nBest Supporting Actor, Drama\\n\\nCharles Dance\\n\\nBest Guest Actor, Drama\\n\\nPedro Pascal\\n\\n66th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nNeil Marshall for \"The Watchers on the Wall\"\\n\\nOutstanding Drama Series\\n\\n\"The Lion and the Rose\", \"Breaker of Chains\", \"First of His Name\", \"The Laws of Gods and Men\", \"The Watchers on the Wall\", and \"The Children\"\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"The Children\"\\n\\n66th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Art Direction for a Single-Camera Fantasy Series\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron for \"The Laws of Gods and Men\" and \"The Mountain and the Viper\"\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nAnette Haellmigk for \"The Lion and the Rose\"\\n\\nJonathan Freeman for \"Two Swords\"\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Sheena Wichary, Alexander Fordham, and Nina Ayres for \"The Lion and the Rose\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, and Nicola Mount for \"The Lion and the Rose\"\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Premiere – Facebook Live and Instagram, by Sabrina Caluori, Paul Beddoe-Stephens, Jim Marsh, Michael McMorrow, Michael McMillian\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Ann McEwan for \"Oathkeeper\"\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi for \"The Mountain and the Viper\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker and Barrie Gower for \"The Children\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Jed M. Dodge, Tim Hands, Paula Fairfield, David Klotz, Bradley C. Katona, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit for \"The Watchers on the Wall\"\\n\\nOutstanding Sound Mixing for a Drama Series (One Hour)\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, and Mathew Waters for \"The Watchers on the Wall\"\\n\\nOutstanding Special and Visual Effects\\n\\nJoe Bauer, Joern Grosshans, Steve Kullback, Adam Chazen, Eric Carney, Sabrina Gerhardt, Matthew Rouleau, Thomas H. Schelesny, and Robert Simon for \"The Children\"\\n\\nOutstanding Stunt Coordination for a Drama Series\\n\\nPaul Herbert\\n\\nWomen\\'s Image Network Awards\\n\\nActress Drama Series\\n\\nLena Headey\\n\\n19th Satellite Awards\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n4th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n30th TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2014\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nCharles Dance\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nPedro Pascal\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Lion and the Rose\"\\n\\n\"The Watchers on the Wall\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nHollywood Music in Media Awards\\n\\nBest Original Score - TV Show/Digital Streaming Series\\n\\nRamin Djawadi\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Onnalee Blank, Mathew Waters, Paula Fairfield, Brad Katona and Jed M. Dodge for \"The Children\"\\n\\nOutstanding Color Grading\\n\\nJoe Finley for \"Mockingbird\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Sven Martin, Jörn Grosshans, Thomas Schelesny, Matthew Rouleau for \"The Children\"\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN People\\'s Choice Award\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nJupiter Award\\n\\nBest International TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n1st MTV Fandom Awards\\n\\nOMG Moment of the Year\\n\\n\\'\\'Game of Thrones\\'\\' - The Purple Wedding\\n\\n2015\\n\\nGuinness World Records\\n\\nMost pirated TV program\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nPeople\\'s Choice Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley for  \"The Laws of Gods and Men\" and \"The Mountain and the Viper\"\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss for \"The Children\"\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan for \"Oathkeeper\"\\n\\n12th Irish Film & Television Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nSFX Awards\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\nBest Villain\\n\\nCharles Dance as Tywin Lannister\\n\\nBiggest Disappointment\\n\\nNo Hodor in Game Of Thrones season five\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\\'\\'The Mountain and the Viper\\'\\'\\n\\nScreenwriters Choice Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nNewNowNext Awards\\n\\nBest New Television Actor\\n\\nPedro Pascal\\n\\nAstra Awards\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nAnette Haellmigk for \"The Children\"\\n\\nFabian Wagner for \"Mockingbird\"\\n\\n20th Empire Awards\\n\\nHero Award\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBritish Academy Television Awards\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton for \\'\\'Game of Thrones\\'\\'\\n\\nDirectors Guild of America Award\\n\\nDramatic Series\\n\\nAlex Graves for \"The Children\"\\n\\nGLAAD Media Award\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGolden Reel Award\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Watchers on the Wall\"\\n\\n72nd Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHugo Awards\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff, Alex Graves, and D. B. Weiss for \"The Mountain and The Viper\"\\n\\nNational Television Awards\\n\\nMultichannel show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProducers Guild Awards\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Chris Newman, Greg Spence, Carolyn Strauss, D. B. Weiss\\n\\nSaturn Award\\n\\nBest Limited Run Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Performance by a Younger Actor on Television\\n\\nMaisie Williams\\n\\nBest Supporting Actress on Television\\n\\nEmilia Clarke\\n\\n21st Screen Actors Guild Awards\\n\\nOutstanding Action Performance by a Stunt Ensemble in a Drama Series\\n\\nLucy Allen, Cole Armitage, Gary Arthurs, Rachelle Beinart, Ferenc Berecz, Richard Bradshaw, Andy Butcher, Michael Byrch, Neil Chapelhow, Nick Chopping, Jonathan Cohen, Joel Conlan, Gary Connery, James Cox, Tom Cox, Jason Curle, Nicholas Daines, Bill Davey, Kelly J. Dent, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Neil Finnighan, Jozsef Fodor, Dean Forster, David Garrick, James Grogan, Tim Halloran, Paul Heasman, Robert Hladik, Al Holland, Gergely Horpacsi, Paul Howell, Stewart James, Gary Kane, Ian Kay, Robbie Keane, George Kirby, Cristian Knight, Laszlo Kosa, Geza Kovacs, Norbert Kovacs, Guy List, Phil Lonergan, Russell Macleod, Tina Maskell, Adrian McGaw, Nick McKinless, Erol Mehmet, Andy Merchant, Sian Milne, Daniel Naprous, Chris Newton, Ray Nicholas, Bela Orsanyi, Sam Parham, Ian Pead, Justin Pearson, Martin Pemberton, Heather Phillips, Rashid Phoenix, Andy Pilgrim, Christopher Pocock, Curtis Rivers, Marcus Shakesheff, Matt Sherren, Anthony Skrimshire, Mark Slaughter, Karen Smithson, Mark Southworth, Helen Steinway Bailey, Shane Steyn, Matthew Stirling, John Street, Gaspar Szabo, Gabor Szeman, Roy Taylor, Gyula Toth, Tony Van Silva, Reg Wayment, Linda Weal, Richard J Wheeldon, Heron White, Maxine Whittaker, Simon Whyman, Donna C. Williams, Lou Wong, Annabel E. Wood, Liang Yang, Steen Young\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nJosef Altin, Jacob Anderson, John Bradley, Dominic Carter, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Charles Dance, Peter Dinklage, Natalie Dormer, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nVisual Effects Society\\n\\nOutstanding Performance of an Animated Character in a Commercial, Broadcast Program, or Video Game\\n\\nPhilip Meyer, Thomas Kutschera, Igor Majdandzic, and Mark Spindler for \"Drogon\"\\n\\nOutstanding Visual Effects in a Visual Effects-Driven Photoreal/Live Action Broadcast Program\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nOutstanding Created Environment in a Commercial, Broadcast Program, or Video Game\\n\\nRene Borst, Christian Zilliken, Jan Burda, Steffen Metzner for \"Braavos Establisher\"\\n\\nOutstanding Compositing in a Photoreal/Live Action Broadcast Program\\n\\nKeegan Douglas, Okan Ataman, Brian Fortune, David Lopez for \"Wight Attack\"\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"The Watchers on the Wall\"\\n\\nWriters Guild of America Awards\\n\\nEpisodic Drama\\n\\nGeorge R. R. Martin for \"The Lion and the Rose\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, D. B. Weiss\\n\\n==Release==\\n\\n===Home media===\\nThe fourth season of \\'\\'Game of Thrones\\'\\' was released on DVD and Blu-ray in region 1 on .\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '25d97fdfb5d033c825e91fcced3521ad'}>,\n",
-       "  <Document: {'content': 'Year\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2014\\n\\nAFI Awards\\n\\nAFI TV Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nTV Choice Awards\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nYoung Hollywood Awards\\n\\nWe Love to Hate You\\n\\nJack Gleeson\\n\\nBingeworthy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nArtios Awards\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\nEWwy Award\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nBest Supporting Actor, Drama\\n\\nCharles Dance\\n\\nBest Guest Actor, Drama\\n\\nPedro Pascal\\n\\n66th Primetime Emmy Awards\\n\\nOutstanding Directing for a Drama Series\\n\\nNeil Marshall for \"The Watchers on the Wall\"\\n\\nOutstanding Drama Series\\n\\n\"The Lion and the Rose\", \"Breaker of Chains\", \"First of His Name\", \"The Laws of Gods and Men\", \"The Watchers on the Wall\", and \"The Children\"\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss for \"The Children\"\\n\\n66th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Art Direction for a Single-Camera Fantasy Series\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron for \"The Laws of Gods and Men\" and \"The Mountain and the Viper\"\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nAnette Haellmigk for \"The Lion and the Rose\"\\n\\nJonathan Freeman for \"Two Swords\"\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Sheena Wichary, Alexander Fordham, and Nina Ayres for \"The Lion and the Rose\"\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, and Nicola Mount for \"The Lion and the Rose\"\\n\\nOutstanding Interactive Program\\n\\nGame of Thrones Premiere – Facebook Live and Instagram, by Sabrina Caluori, Paul Beddoe-Stephens, Jim Marsh, Michael McMorrow, Michael McMillian\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Ann McEwan for \"Oathkeeper\"\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi for \"The Mountain and the Viper\"\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker and Barrie Gower for \"The Children\"\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Jed M. Dodge, Tim Hands, Paula Fairfield, David Klotz, Bradley C. Katona, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit for \"The Watchers on the Wall\"\\n\\nOutstanding Sound Mixing for a Drama Series (One Hour)\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, and Mathew Waters for \"The Watchers on the Wall\"\\n\\nOutstanding Special and Visual Effects\\n\\nJoe Bauer, Joern Grosshans, Steve Kullback, Adam Chazen, Eric Carney, Sabrina Gerhardt, Matthew Rouleau, Thomas H. Schelesny, and Robert Simon for \"The Children\"\\n\\nOutstanding Stunt Coordination for a Drama Series\\n\\nPaul Herbert\\n\\nWomen\\'s Image Network Awards\\n\\nActress Drama Series\\n\\nLena Headey\\n\\n19th Satellite Awards\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\nBest Television Series – Genre\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n4th Critics\\' Choice Television Awards\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n30th TCA Awards\\n\\nOutstanding Achievement in Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProgram of the Year\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGold Derby TV Awards 2014\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nCharles Dance\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nPedro Pascal\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Lion and the Rose\"\\n\\n\"The Watchers on the Wall\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nHollywood Music in Media Awards\\n\\nBest Original Score - TV Show/Digital Streaming Series\\n\\nRamin Djawadi\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Onnalee Blank, Mathew Waters, Paula Fairfield, Brad Katona and Jed M. Dodge for \"The Children\"\\n\\nOutstanding Color Grading\\n\\nJoe Finley for \"Mockingbird\"\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Sven Martin, Jörn Grosshans, Thomas Schelesny, Matthew Rouleau for \"The Children\"\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nIGN People\\'s Choice Award\\n\\nBest TV Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nJupiter Award\\n\\nBest International TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n1st MTV Fandom Awards\\n\\nOMG Moment of the Year\\n\\n\\'\\'Game of Thrones\\'\\' - The Purple Wedding\\n\\n2015\\n\\nGuinness World Records\\n\\nMost pirated TV program\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nPeople\\'s Choice Awards\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley for  \"The Laws of Gods and Men\" and \"The Mountain and the Viper\"\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss for \"The Children\"\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan for \"Oathkeeper\"\\n\\n12th Irish Film & Television Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nSFX Awards\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\nBest Villain\\n\\nCharles Dance as Tywin Lannister\\n\\nBiggest Disappointment\\n\\nNo Hodor in Game Of Thrones season five\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\\'\\'The Mountain and the Viper\\'\\'\\n\\nScreenwriters Choice Awards\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nNewNowNext Awards\\n\\nBest New Television Actor\\n\\nPedro Pascal\\n\\nAstra Awards\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nAnette Haellmigk for \"The Children\"\\n\\nFabian Wagner for \"Mockingbird\"\\n\\n20th Empire Awards\\n\\nHero Award\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBritish Academy Television Awards\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nCostume Designers Guild Awards\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton for \\'\\'Game of Thrones\\'\\'\\n\\nDirectors Guild of America Award\\n\\nDramatic Series\\n\\nAlex Graves for \"The Children\"\\n\\nGLAAD Media Award\\n\\nOutstanding Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGolden Reel Award\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Watchers on the Wall\"\\n\\n72nd Golden Globe Awards\\n\\nBest Television Series – Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nHugo Awards\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff, Alex Graves, and D. B. Weiss for \"The Mountain and The Viper\"\\n\\nNational Television Awards\\n\\nMultichannel show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nProducers Guild Awards\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Chris Newman, Greg Spence, Carolyn Strauss, D. B. Weiss\\n\\nSaturn Award\\n\\nBest Limited Run Television Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Performance by a Younger Actor on Television\\n\\nMaisie Williams\\n\\nBest Supporting Actress on Television\\n\\nEmilia Clarke\\n\\n21st Screen Actors Guild Awards\\n\\nOutstanding Action Performance by a Stunt Ensemble in a Drama Series\\n\\nLucy Allen, Cole Armitage, Gary Arthurs, Rachelle Beinart, Ferenc Berecz, Richard Bradshaw, Andy Butcher, Michael Byrch, Neil Chapelhow, Nick Chopping, Jonathan Cohen, Joel Conlan, Gary Connery, James Cox, Tom Cox, Jason Curle, Nicholas Daines, Bill Davey, Kelly J. Dent, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Neil Finnighan, Jozsef Fodor, Dean Forster, David Garrick, James Grogan, Tim Halloran, Paul Heasman, Robert Hladik, Al Holland, Gergely Horpacsi, Paul Howell, Stewart James, Gary Kane, Ian Kay, Robbie Keane, George Kirby, Cristian Knight, Laszlo Kosa, Geza Kovacs, Norbert Kovacs, Guy List, Phil Lonergan, Russell Macleod, Tina Maskell, Adrian McGaw, Nick McKinless, Erol Mehmet, Andy Merchant, Sian Milne, Daniel Naprous, Chris Newton, Ray Nicholas, Bela Orsanyi, Sam Parham, Ian Pead, Justin Pearson, Martin Pemberton, Heather Phillips, Rashid Phoenix, Andy Pilgrim, Christopher Pocock, Curtis Rivers, Marcus Shakesheff, Matt Sherren, Anthony Skrimshire, Mark Slaughter, Karen Smithson, Mark Southworth, Helen Steinway Bailey, Shane Steyn, Matthew Stirling, John Street, Gaspar Szabo, Gabor Szeman, Roy Taylor, Gyula Toth, Tony Van Silva, Reg Wayment, Linda Weal, Richard J Wheeldon, Heron White, Maxine Whittaker, Simon Whyman, Donna C. Williams, Lou Wong, Annabel E. Wood, Liang Yang, Steen Young\\n\\nOutstanding Performance by An Ensemble in a Drama Series\\n\\nJosef Altin, Jacob Anderson, John Bradley, Dominic Carter, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Charles Dance, Peter Dinklage, Natalie Dormer, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nVisual Effects Society\\n\\nOutstanding Performance of an Animated Character in a Commercial, Broadcast Program, or Video Game\\n\\nPhilip Meyer, Thomas Kutschera, Igor Majdandzic, and Mark Spindler for \"Drogon\"\\n\\nOutstanding Visual Effects in a Visual Effects-Driven Photoreal/Live Action Broadcast Program\\n\\n\\'\\'Game of Thrones\\'\\' for \"The Children\"\\n\\nOutstanding Created Environment in a Commercial, Broadcast Program, or Video Game\\n\\nRene Borst, Christian Zilliken, Jan Burda, Steffen Metzner for \"Braavos Establisher\"\\n\\nOutstanding Compositing in a Photoreal/Live Action Broadcast Program\\n\\nKeegan Douglas, Okan Ataman, Brian Fortune, David Lopez for \"Wight Attack\"\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"The Watchers on the Wall\"\\n\\nWriters Guild of America Awards\\n\\nEpisodic Drama\\n\\nGeorge R. R. Martin for \"The Lion and the Rose\"\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, D. B. Weiss\\n\\n==Release==\\n\\n===Home media===\\nThe fourth season of \\'\\'Game of Thrones\\'\\' was released on DVD and Blu-ray in region 1 on .\\n\\n===IMAX===\\nBetween January 30 and February 5, 2015, the last two episodes of season four were shown in 205 IMAX theaters in the U.S. \\'\\'Game of Thrones\\'\\' is the first TV series released in this format. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '75b350d201851f889480daefe56e7fd8'}>,\n",
-       "  <Document: {'content': \"===IMAX===\\nBetween January 30 and February 5, 2015, the last two episodes of season four were shown in 205 IMAX theaters in the U.S. ''Game of Thrones'' is the first TV series released in this format. The show earned $686,000 in its opening day at the box office and $1.5 million during its opening weekend. The one-week release grossed $1,896,092.\\n\\n===Copyright infringement===\\nThe fourth season of ''Game of Thrones'' was the most-pirated TV series in 2014.\\n\\n==References==\\n\\n==External links==\\n*  – official US site\\n*  – official UK site\\n*  ''Game of Thrones'' – The Viewers Guide on HBO.com\\n*  Making ''Game of Thrones'' on HBO.com\\n*\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'd5f237e8b356c3910826640c9c355375'}>,\n",
-       "  <Document: {'content': '\\n\"\\'\\'\\'Stormborn\\'\\'\\'\" is the second episode of the seventh season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 62nd overall. The episode was written by Bryan Cogman, and directed by Mark Mylod. The title of the episode refers to both Daenerys Targaryen, who was born during a terrible storm, and Euron Greyjoy, who declares himself to be \"the storm\".\\n\\nThe episode\\'s main plot focuses on Daenerys planning her conquest of Westeros; at the behest of Melisandre, she orders Tyrion to summon Jon Snow, who agrees to meet with her despite the objections of Sansa and several other supporters. In King\\'s Landing, Cersei Lannister warns her bannermen about Daenerys\\'s impending invasion. Meanwhile, in the Narrow Sea, the Greyjoys and Sand Snakes are attacked by Euron Greyjoy\\'s Iron Fleet, with Yara Greyjoy and Ellaria Sand being taken as hostages.\\n\\n\"Stormborn\" received praise from critics, who considered Euron Greyjoy\\'s raid on Yara\\'s Iron Fleet, the assembly of Daenerys\\' allies at Dragonstone, and Arya\\'s reunion with her direwolf Nymeria as highlights of the episode. In the United States, it achieved a viewership of 9.27 million in its initial broadcast.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '572b9f20f0bf562a07b54095994bb4a1'}>,\n",
-       "  <Document: {'content': '\"Stormborn\" received praise from critics, who considered Euron Greyjoy\\'s raid on Yara\\'s Iron Fleet, the assembly of Daenerys\\' allies at Dragonstone, and Arya\\'s reunion with her direwolf Nymeria as highlights of the episode. In the United States, it achieved a viewership of 9.27 million in its initial broadcast.\\n\\nThis episode marks the final appearance of Keisha Castle-Hughes (Obara Sand) and Jessica Henwick (Nymeria Sand).\\n\\n==Plot==\\n===On Dragonstone===\\nDaenerys confronts Varys over his previously shifting allegiances. Varys declares he is loyal to the people, and Daenerys secures his oath to tell her directly if she fails the people, threatening to execute him if he betrays her. Daenerys grants an audience to Melisandre, who urges her to meet Jon Snow. Tyrion vouches for Jon and recommends the Starks as allies. Daenerys instructs Tyrion to summon Jon to bend the knee. Yara Greyjoy and Ellaria Sand support an overwhelming offensive on King\\'s Landing. Daenerys doesn\\'t wish to destroy the territory she plans on ruling, but instead adopts Tyrion\\'s plan to have her Westerosi armies besiege King\\'s Landing, while the Unsullied will take Casterly Rock.\\n\\nBefore departing to lead his army, Grey Worm reveals to Missandei he never feared before loving her. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'b34f4964c63ef570a1bd179ffe04da87'}>,\n",
-       "  <Document: {'content': \"Daenerys doesn't wish to destroy the territory she plans on ruling, but instead adopts Tyrion's plan to have her Westerosi armies besiege King's Landing, while the Unsullied will take Casterly Rock.\\n\\nBefore departing to lead his army, Grey Worm reveals to Missandei he never feared before loving her. Missandei is moved by his words and they make love.\\n\\n===In Oldtown===\\nArchmaester Ebrose tells Sam that Jorah's greyscale is now untreatable, Jorah has about six months of sanity left, and suicide is his only alternative to exile among the Stone Men of Valyria. Sam discovers a treatment; Ebrose tells him it is forbidden because it risks transmission, but Sam performs the agonizing procedure in secret.\\n\\n===At Winterfell===\\nJon receives Daenerys' message. Sansa and Davos advocate refusal, but the latter notes that dragonfire can stop the undead. After receiving Sam's information about dragonglass on Dragonstone, Jon decides to go, despite uniform opposition from his loyalists. Jon names Sansa as regent, surprising her, and threatens Littlefinger to stop pursuing Sansa when he tries to ingratiate himself with Jon.\\n\\n===In the Riverlands===\\nArya encounters Hot Pie in an inn and learns that Jon has retaken Winterfell; she turns north. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'dafcb65590a6d1168a6bfcc535577185'}>,\n",
-       "  <Document: {'content': '===In the Riverlands===\\nArya encounters Hot Pie in an inn and learns that Jon has retaken Winterfell; she turns north. She\\'s beset by a wolfpack, the alpha being her direwolf Nymeria, having driven her off years earlier (\"The Kingsroad\") Arya invites her north but Nymeria instead leaves her.\\n\\n===In King\\'s Landing===\\nCersei appeals to lords, including House Tyrell\\'s bannermen, for support against Daenerys, portraying her as a dangerous foreign invader. Jaime offers to make Randyll Tarly Warden of the South if he becomes Jaime\\'s second-in-command. Qyburn demonstrates a scorpion, a projectile weapon able to pierce dragon skulls.\\n\\n===In the Narrow Sea===\\nEuron\\'s fleet attacks Yara\\'s navy. Euron kills Obara and Nymeria Sand; his men abduct Ellaria and Tyene Sand, while he takes Yara prisoner. Theon, triggered by the violence, jumps overboard, abandoning Yara.\\n\\n==Production==\\n===Writing===\\nBryan Cogman wrote the episode.\\n\"Stormborn\" was written by Bryan Cogman. Cogman has been a writer for the series since its beginning, previously writing nine other episodes. The title of the episode, \"Stormborn\", is a reference to Daenerys Targaryen, who was born in the midst of a great storm, earning her that nickname. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'f10871a8318faeebd7a37c704e6b088e'}>,\n",
-       "  <Document: {'content': 'The title of the episode, \"Stormborn\", is a reference to Daenerys Targaryen, who was born in the midst of a great storm, earning her that nickname. In the \"Inside the Episode\" segment published by HBO following the initial airing of the episode, co-creators and executive producers of the series David Benioff and D. B. Weiss were interviewed, saying \"We last saw Nymeria when Arya made her run away because she wanted to save Nymeria\\'s life, - She knew Cersei was going to kill her if she found her, and when she finally finds Nymeria again — or Nymeria finds her — she of course wants Nymeria to come back home with her and be her loyal companion again. But Nymeria\\'s found her own life.\"They continued noting that the line \"That\\'s not you\" was a direct reference to what she said to Ned Stark back in the first season, when he was telling her that she will be a \"lady of a castle and marrying some lord and wearing a nice frilly dress\", which Arya responded by saying \"That\\'s not me\". Weiss continued, \"Arya\\'s not domesticated. It makes total sense the wolf wouldn\\'t be either. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '916440f3061f0b47ecb4130f4b1581cd'}>,\n",
-       "  <Document: {'content': 'They continued noting that the line \"That\\'s not you\" was a direct reference to what she said to Ned Stark back in the first season, when he was telling her that she will be a \"lady of a castle and marrying some lord and wearing a nice frilly dress\", which Arya responded by saying \"That\\'s not me\". Weiss continued, \"Arya\\'s not domesticated. It makes total sense the wolf wouldn\\'t be either. Once the wolf walks away, at first she\\'s heartbroken to have come this close, but then she realizes the wolf is doing exactly what she would do if she was that wolf.\"===Casting===\\nThe episode featured the return of Ben Hawkey as Hot Pie, who was last seen in season 4\\'s \"Mockingbird\". Hawkey spoke about his return to \\'\\'Entertainment Weekly\\'\\', saying that he did not expect to ever come back. Prior to getting the script and to avoid the return of character to be leaked, the producers gave Hawkey a code name when communicating by email. He continued by saying that the scene was \"really nice\" and was a \"perfect little Hot Pie scene\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'd144f6ae2e5eb03aa504f4d1118b62f0'}>,\n",
-       "  <Document: {'content': 'Prior to getting the script and to avoid the return of character to be leaked, the producers gave Hawkey a code name when communicating by email. He continued by saying that the scene was \"really nice\" and was a \"perfect little Hot Pie scene\". \"Stormborn\" was also the last episode for Keisha Castle-Hughes and Jessica Henwick, as Obara and Nymeria Sand were killed. The episode introduced new recurring cast member Tom Hopper as Dickon Tarly, replacing Freddie Stroma, who briefly portrayed the character in Season 6.\\n\\n===Filming===\\n\"Stormborn\" was directed by Mark Mylod, his first of two episodes for this season. He joined the series as a director in the fifth season, his first episode being \"High Sparrow\", which was followed by \"Sons of the Harpy\". John Bradley spoke about what went into shooting the Jorah Mormont greyscale scene with actor Iain Glen. Before filming Iain had to sit on the prosthetics trailer for around five hours while the make up department \"applied these really detailed and intricate greyscale prosthetics, piece by piece.\"He continued \"I was basically peeling the plastic latex prosthetic off of Iain\\'s actual body. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'a83812ab8fb3d6b534776beb5b5337db'}>,\n",
-       "  <Document: {'content': 'Before filming Iain had to sit on the prosthetics trailer for around five hours while the make up department \"applied these really detailed and intricate greyscale prosthetics, piece by piece.\"He continued \"I was basically peeling the plastic latex prosthetic off of Iain\\'s actual body. He was kind of in a suit - It was the same as pulling away a prosthetic. It was a very, very big technical job for the prosthetics department. There were about five or six guys on set that day that you can\\'t see but were just out of the camera line, there with pumps and buckets of pus.\"Nathalie Emmanuel discussed her character\\'s love scene with Grey Worm, saying that the characters have shown \"interest in each other\", we have seen them \"express it for each other, but not really say it - This point is the climax and they physically act upon it\", much due to them facing \"the prospect of never seeing each other again\". Emmanuel went on to praise the relationship that has developed between these two characters, stating that it\\'s \"something sweet and pure and beautiful\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '7c77ccf972fe3aaf153f01f4a16d77ae'}>,\n",
-       "  <Document: {'content': 'Emmanuel went on to praise the relationship that has developed between these two characters, stating that it\\'s \"something sweet and pure and beautiful\".\\n\\nGemma Whelan said in an interview with \\'\\'Entertainment Weekly\\'\\' that the kissing scene by Yara Greyjoy and Ellaria Sand before the battle sequence at the end was improvised. She stated that \"It wasn’t directed that we would kiss, - It just seemed like something we should do\". The scenes with the Sand Snakes was originally planned to be a more \"drawn out storyline\", but due to Jessica Henwick\\'s schedule filming her scenes as Colleen Wing in \\'\\'Iron Fist\\'\\' and \\'\\'The Defenders\\'\\', she would have had to go back and forth to shoot both shows. Speaking about the battle sequence, Henwick said \"normally there’s a lot of CG and you watch it on screen and you see a massive epic battle, but when you’re filming it’s all quite tame by comparison. For this, the audience can’t feel the heat on their face from the pyrotechnics going off or feel the wave machine trying to knock us off our feet, or the sweat dripping off our faces.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '31206879f89ba2e72bbe55e50282d945'}>,\n",
-       "  <Document: {'content': 'For this, the audience can’t feel the heat on their face from the pyrotechnics going off or feel the wave machine trying to knock us off our feet, or the sweat dripping off our faces.\"Mylod also discussed his direction for the battle sequence, saying that \"We agreed that the violence should be brutal and feel un-choreographed, that it shouldn\\'t feel structured\". Stunt coordinator Rowley Irlam also stated that they were inspired by riots when it was being choreographed. Mylod decided to put much more of the focus to Theon, Yara and Euron Greyjoy, rather than people around them.\\n\\n==Reception==\\n\\n===Ratings===\\n\"Stormborn\" was viewed by 9.27 million total viewers on its initial viewing on HBO. The episode also acquired a 4.33 rating in the 18–49 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was viewed by 2.770 million viewers on Sky Atlantic during its Simulcast, making it the highest-rated broadcast that week.\\n\\n===Critical reception===\\n\\n\"Stormborn\" has received high praise from critics. It has a 96% rating on the review aggregator website Rotten Tomatoes from 38 reviews with an average score of 8.2 out of 10. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '25f383aa2f70f8e1704a3febe89739e0'}>,\n",
-       "  <Document: {'content': 'It has a 96% rating on the review aggregator website Rotten Tomatoes from 38 reviews with an average score of 8.2 out of 10. The site\\'s consensus reads \"While necessarily setting up the events for the season, \"Stormborn\" features compelling strategic discussion, and a cracking action sequence to end the episode with a bang.\"Ed Power of \\'\\'The Daily Telegraph\\'\\' said \"It\\'s been a cautious start to \\'Game of Thrones\\' season seven. But, following further, patience-testing re-arranging of the chess pieces, episode two went out in a literal blaze of glory.\"Matt Fowler of IGN described the episode as \"amazing\", saying \"With Daenerys now in Westeros, war is imminent and worlds were colliding in this week\\'s big episode.\"He gave the episode a 9 out of 10. Ben Philippe of \\'\\'New York Observer\\'\\' also gave a positive review, saying \"\\'Stormborn,\\' proves that the table setting always pays off in Game of Thrones by giving us a sample platter of everything that the show does best.\"Nina Shen Rastogi of \\'\\'New York Magazine\\'\\' similarly gave praise to the episode, writing \"All of this prologue is what made the final scene land so hard, and so well. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '3f79bd7e6969d2b7a8a94ff896f1e941'}>,\n",
-       "  <Document: {'content': 'Nina Shen Rastogi of \\'\\'New York Magazine\\'\\' similarly gave praise to the episode, writing \"All of this prologue is what made the final scene land so hard, and so well. The shipboard battle between the Greyjoys was very good GOT, in my mind: dramatic, unexpected, and authentic on a character level.\"Sean T. Collins of \\'\\'Rolling Stone\\'\\' praised the battle sequence at the end, writing \"It ends with a naval battle as grandiose as any we\\'ve seen in the series. But the strong second chapter of Game of Thrones\\' seventh season - \\'Stormborn\\' - navigates even more treacherous waters.\"Erik Kain of \\'\\'Forbes\\'\\' gave a mixed review, writing that he enjoyed Arya\\'s scenes but criticizing the Dragonstone scenes. Kain also criticized the plot hole of Ellaria Sand being in control of Dorne.\\n\\n===Accolades===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2017\\n\\nHollywood Post Alliance\\n\\nOutstanding Editing\\n\\nTim Porter\\n\\n==References==\\n\\n==External links==\\n\\n*  \"Stormborn\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '886ddb817ecb9403145f57f21468cc4f'}>,\n",
-       "  <Document: {'content': '\\n\\n\\'\\'\\'Eddard\\'\\'\\' \"\\'\\'\\'Ned\\'\\'\\'\" \\'\\'\\'Stark\\'\\'\\' is a fictional character in the 1996 fantasy novel \\'\\'A Game of Thrones\\'\\' by George R. R. Martin, and \\'\\'Game of Thrones\\'\\', HBO\\'s adaptation of Martin\\'s \\'\\'A Song of Ice and Fire\\'\\' series. In the storyline, Ned is the lord of Winterfell, an ancient fortress in the North of the fictional continent of Westeros. Though the character is established as the main character in the novel and the first season of the TV adaptation, a plot twist involving Ned at the end of the novel and the end of the first season shocked both readers of the book and viewers of the TV series.\\n\\nNed is portrayed by Sean Bean in the first season of \\'\\'Game of Thrones\\'\\', as a child by Sebastian Croft in the sixth season and as a young adult by Robert Aramayo in the sixth and seventh seasons. Bean was nominated for a Saturn Award for Best Actor on Television and a Scream Award for Best Fantasy Actor for the role. He and the rest of the cast were nominated for Screen Actors Guild Awards for Outstanding Performance by an Ensemble in a Drama Series in 2011.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '184ca22544285a03c5a3be806ec9ca40'}>,\n",
-       "  <Document: {'content': \"He and the rest of the cast were nominated for Screen Actors Guild Awards for Outstanding Performance by an Ensemble in a Drama Series in 2011.\\n\\n== Character ==\\n=== Description ===\\nIn ''A Game of Thrones'' (1996), Ned Stark is introduced as the virtuous and honorable patriarch of House Stark and the father of six children. The moral compass of the story, he is content to remain far from courtly intrigues and is unwavering in his view of loyalty and honor. His family name, Stark, serves as an indication of his resistance to moral compromise, but his boundaries are increasingly tested over the course of the novel. Finding himself a key player in the escalating political intrigue of King's Landing, Ned struggles as his own sense of honor draws him into corrupt goings-on at court. As the story progresses, he begins to see the importance of moral and practical compromises to achieve a just end, and is ultimately forced to choose between the safety of his family and doing what is right.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '7dd478a5e17391528a8ecef97d4d1dc5'}>,\n",
-       "  <Document: {'content': 'As the story progresses, he begins to see the importance of moral and practical compromises to achieve a just end, and is ultimately forced to choose between the safety of his family and doing what is right.\\n\\nSean Bean said of the character, \"he’s a good man trying to do his best in the middle of this corruption, he’s a fish out of water, he’s used to being up north in Winterfell where people are pretty straight and pragmatic, and he comes down to a place where people are playing games and backstabbing … he’s a principled man who tries to hold things together. This is a journey that he makes where ultimately his loyalty causes his downfall.\"=== Development and overview ===\\n\\'\\'Publishers Weekly\\'\\' noted in 1996 that, despite the honest Ned Stark\\'s intervention in court politics, \"no amount of heroism or good intentions can keep the realm under control.\"From his very first introduction, Ned is portrayed as a noble hero and set up to be the heart of the story. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'dcda6328625198daf5d3e89bde279411'}>,\n",
-       "  <Document: {'content': \"From his very first introduction, Ned is portrayed as a noble hero and set up to be the heart of the story. With fifteen chapters devoted to his point of view, more than any single character in the novel, he is presented as a primary character in the series, and the main storyline of ''A Game of Thrones'', the drama in King's Landing, is told entirely from his perspective. In the ''London Review of Books'', John Lanchester writes that everything about Ned is designed to gain audience sympathy, from his strong sense of honor and moral compass to his compassion towards his wife and children. Readers are led to believe that Ned will be the main character of the series, but ultimately he is, from a literary perspective, a classic decoy protagonist. After struggling to keep himself and the kingdom on a moral path for the entire novel, the only option that remains to save his family is to put aside his honor; he does so, but is betrayed anyway. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'b6224ae5673e6bc2d374345adb0e5f16'}>,\n",
-       "  <Document: {'content': 'After struggling to keep himself and the kingdom on a moral path for the entire novel, the only option that remains to save his family is to put aside his honor; he does so, but is betrayed anyway. Calling Ned\\'s execution \"shocking\", \\'\\'The New York Times\\'\\' noted in 2011 that the novel was \"famous for dispatching a thoroughly admirable major character with whom readers have been identifying for most of the book\". In an interview for \\'\\'Entertainment Weekly\\'\\', author George R. R. Martin commented on this misdirection:\\n\\nDavid Benioff, executive producer and writer of the HBO adaptation, told \\'\\'Entertainment Weekly\\'\\' that when he read the novel:\\n\\nIn a review of the \\'\\'Game of Thrones\\'\\' TV episode \"Baelor\", James Poniewozik wrote in \\'\\'Time\\'\\' that \"the execution of Eddard Stark is crucial to the story and its themes and everything that follows, but it\\'s also a meta-message to the reader: don\\'t take anything for granted here\". James Hibberd of \\'\\'Entertainment Weekly\\'\\' stated that tricking the audience into thinking Ned is the hero and then killing him makes the series\\' story better. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'f689e00d50e9a75e78323d87d0a84c5'}>,\n",
-       "  <Document: {'content': 'James Hibberd of \\'\\'Entertainment Weekly\\'\\' stated that tricking the audience into thinking Ned is the hero and then killing him makes the series\\' story better. Writing that \"the big twist here isn\\'t that Ned Stark dies, but who the true protagonists of \\'\\'Game of Thrones\\'\\' are\", Hibberd pointed out that the series\\' focus proves to be the \"new generation\" of leaders, in particular the Stark children but also Daenerys and even Tyrion. He noted:\\n\\n== Storylines ==\\nCoat of arms of House Stark\\n\\n=== Background ===\\nAs established in \\'\\'A Game of Thrones\\'\\', Eddard \"Ned\" Stark is the second son of Rickard Stark, the Lord of Winterfell. Years before the events of the novel, the quiet and shy young Ned is fostered in the Vale by Jon Arryn. During this time Ned becomes close friends with Robert Baratheon, heir to the Stormlands and another ward of Arryn\\'s. Robert is eventually betrothed to Ned\\'s sister Lyanna, but before he can marry her, Crown Prince Rhaegar Targaryen absconds with Lyanna. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'aa3334298acedafa649abb4cb72bae14'}>,\n",
-       "  <Document: {'content': 'Robert is eventually betrothed to Ned\\'s sister Lyanna, but before he can marry her, Crown Prince Rhaegar Targaryen absconds with Lyanna. Ned\\'s father and older brother Brandon go to King Aerys II Targaryen and demand that Lyanna be freed; the so-called \"Mad King\" executes both Rickard and Brandon, convinced that the Starks seek to usurp him. Ned, Robert, and Arryn then rise in revolt, securing the support of House Tully with Ned marrying Catelyn Tully. Ned leaves for war the next morning.\\n\\nAt the decisive Battle of the Trident, Robert manages to kill Prince Rhaegar and scatter the Targaryen army. Robert is injured in the battle, so Ned takes command and marches on the capital. In King\\'s Landing, Ned finds that House Lannister has — through treachery — already sacked the city, and murdered Aerys and the royal family. Disgusted by the dishonorable massacre, Ned departs to lift the siege of the Baratheon stronghold Storm\\'s End, and later attempts to rescue Lyanna but finds his sister dying; her last words are \"Promise me Ned.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '6e3891080b1d24b07f9e0d57eaad2ac5'}>,\n",
-       "  <Document: {'content': 'Disgusted by the dishonorable massacre, Ned departs to lift the siege of the Baratheon stronghold Storm\\'s End, and later attempts to rescue Lyanna but finds his sister dying; her last words are \"Promise me Ned.\"Ned returns to Winterfell with his bastard son Jon Snow in tow; meanwhile Catelyn has delivered their son and Ned\\'s heir, Robb, conceived on their wedding night.\\n\\nSix years after the end of Robert\\'s Rebellion, Balon Greyjoy, the Lord of the Iron Islands, declares independence from the Iron Throne. Ned aids King Robert in putting down \"Greyjoy\\'s Rebellion\". Balon surrenders, and his sole surviving heir, Theon, is taken back to Winterfell as Stark\\'s ward and hostage. Ned rules the North for nine more years before the events of the novel.\\n\\n=== \\'\\'A Game of Thrones\\'\\' ===\\nAs \\'\\'A Game of Thrones\\'\\' begins, Catelyn informs Ned that his mentor Jon Arryn has died, and that King Robert intends to offer Ned Jon\\'s old position as Hand of the King. Content to be far from court intrigue, Ned is reluctant to accept the offer until he receives a letter from Arryn\\'s widow, who believes that her husband had been poisoned by the Lannisters. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '3560a3078881efd297fdc32781fbd7bb'}>,\n",
-       "  <Document: {'content': 'Content to be far from court intrigue, Ned is reluctant to accept the offer until he receives a letter from Arryn\\'s widow, who believes that her husband had been poisoned by the Lannisters. Ned subsequently agrees to the appointment to protect Robert. He then travels south to King\\'s Landing with his daughters Sansa and Arya. Catelyn later comes to the capital in secret, under the protection of her childhood friend Petyr \"Littlefinger\" Baelish, to tell Ned of an assassination attempt on their young son Bran. Ned\\'s longstanding mistrust of the Lannisters is further fueled by Littlefinger\\'s confirmation that the dagger used by the would-be assassin once belonged to Tyrion Lannister. Increasingly disgusted by the political intrigues at court, Ned finally resigns his position when Robert insists on having Aerys\\' only surviving child, the young Daenerys Targaryen, assassinated in exile. Meanwhile, Catelyn has taken Tyrion hostage at the Eyrie, and in retaliation Jaime Lannister attacks and seriously injures Ned in the street before he and his daughters can depart King\\'s Landing. Visiting a wounded Ned, Robert reappoints him as Hand.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '94d6cb1b308087b51e09557f03d95fde'}>,\n",
-       "  <Document: {'content': 'Meanwhile, Catelyn has taken Tyrion hostage at the Eyrie, and in retaliation Jaime Lannister attacks and seriously injures Ned in the street before he and his daughters can depart King\\'s Landing. Visiting a wounded Ned, Robert reappoints him as Hand.\\n\\nNed eventually concludes that all of Robert\\'s heirs with his wife Cersei are illegitimate, the product of her incest with her twin brother Jaime. Further, Ned suspects that Arryn had been poisoned to conceal the truth. Ned confronts Cersei with his discovery and gives her the chance to escape with her children into exile. Before Ned can tell the king, Robert is fatally wounded while hunting and names Ned Protector of the Realm, to function as regent until his \"son\" Joffrey comes of age. Ned alters Robert\\'s will, replacing Joffrey\\'s name with \"my rightful heir\" to make the succession ambiguous. With the palace in chaos, Ned rebuffs multiple offers to increase his own power, instead opting to crown Robert\\'s brother, Stannis Baratheon, as king. Cersei, however, outmaneuvers Ned, and the duplicitous Littlefinger has the city watch arrest Ned instead of Cersei. With all of his entourage slaughtered, Sansa a hostage and Arya escaped but alone, Ned is charged with treason. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '618f0222ae7467fb7c4ed165b3a0086d'}>,\n",
-       "  <Document: {'content': 'Cersei, however, outmaneuvers Ned, and the duplicitous Littlefinger has the city watch arrest Ned instead of Cersei. With all of his entourage slaughtered, Sansa a hostage and Arya escaped but alone, Ned is charged with treason. A deal is struck in which Ned will be spared and sent into exile if he declares Joffrey the rightful king. Fearing for his daughters, Ned makes a public confession of his \"treason\". The sadistic Joffrey, however, has Ned executed anyway for his own amusement.\\n\\n=== Later novels ===\\nNed\\'s arrest pits the Stark and Lannister forces against each other, and his execution causes a civil war later called the War of Five Kings. Tyrion Lannister eventually returns Ned\\'s bones to Catelyn, but it is not confirmed whether they ever arrived at Winterfell. He is mentioned in a flashback along with Robert Baratheon in \\'\\'A Clash of Kings\\'\\'.\\n\\n=== Family tree of House Stark ===\\n\\n== TV adaptation ==\\nSean Bean plays the role of Ned Stark in the television series.\\nIn January 2007 HBO secured the rights to adapt Martin\\'s series for television. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'fa230ac65a46b44f0f57617e859fc889'}>,\n",
-       "  <Document: {'content': '=== Family tree of House Stark ===\\n\\n== TV adaptation ==\\nSean Bean plays the role of Ned Stark in the television series.\\nIn January 2007 HBO secured the rights to adapt Martin\\'s series for television. When the pilot went into production in 2009, one of the first casting announcements was Sean Bean as the \"lead\" Eddard Stark. As the show premiered in 2011, the \\'\\'Los Angeles Times\\'\\' called Bean\\'s Ned \"the strong and brooding headliner of the series\".\\n\\nAs in the source novel, Ned is beheaded in the ninth episode of season 1, \"Baelor\". Though praising the character\\'s demise for its role in propelling the story, James Hibberd of \\'\\'Entertainment Weekly\\'\\' later noted that:\\n\\nHibberd echoed the show\\'s producers\\' statement that \"the move lays down a dramatic precedent for the show: Nobody is safe\". He called it a \"risky\" move that would probably lose the show viewers who had tuned in for Bean, but would hopefully attract others impressed by the boldness of it. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '9b77bd69c8835c274da60c6f36e6e41b'}>,\n",
-       "  <Document: {'content': 'He called it a \"risky\" move that would probably lose the show viewers who had tuned in for Bean, but would hopefully attract others impressed by the boldness of it. Executive producer and writer D. B. Weiss told \\'\\'Entertainment Weekly\\'\\' in 2011 that when he and Benioff pitched the series to HBO, the fact that \"main character\" Ned was slated to die \"was a selling point for them\". Noting that the network has killed off characters in other successful series, he said that this sense of jeopardy \"completely ups the ante for any moment when a character is in a dire situation if you know another character didn\\'t survive a similar situation\". HBO programming president Sue Naegle concurred, saying that Ned\\'s death made the show creatively more attractive, adding that \"The book series was filled with unexpected twists and turns. I loved this idea we\\'d bring together the group of characters, then once you started to believe all the tropes of heroes, you pull the rug out from under them. It\\'s the opposite of feeling manipulated\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '28677c484dadb8f6ca7f9e95a665d51a'}>,\n",
-       "  <Document: {'content': 'I loved this idea we\\'d bring together the group of characters, then once you started to believe all the tropes of heroes, you pull the rug out from under them. It\\'s the opposite of feeling manipulated\". Noting that the story and world of the series is bigger than any one character, Naegle said, \"Sean brings a giant following, but \\'\\'Thrones\\'\\' is not just about the promise you\\'re going to see one of your favorite actors week in and week out. The star is the story\". Bean noted that Ned\\'s death \"was as much a surprise to me as anyone\" and called it \"a very courageous move for a television company\".\\n\\nThe image of Bean as Ned Stark sitting in the Iron Throne is featured on the covers of the 2011 Season 1 DVD and Blu-ray Disc sets, released in March 2012.\\n\\nThe character makes a return in the sixth season, under a recurring capacity, via flashback visions of his youth and childhood seen by Bran and the Three-Eyed Raven using Greensight.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'ec0806f8eed370d4ba83227cb8f9f06d'}>,\n",
-       "  <Document: {'content': 'The character makes a return in the sixth season, under a recurring capacity, via flashback visions of his youth and childhood seen by Bran and the Three-Eyed Raven using Greensight.\\n\\n===Storylines===\\n====Season 1====\\nIn season 1, King Robert Baratheon asks his old friend Eddard \"Ned\" Stark, Lord of Winterfell and Warden of the North, to assume the position of Hand of the King after the sudden death of their mentor, Jon Arryn. Not interested in politics or the intrigues of the court, Ned accepts out of duty, as well as to discover how Arryn died. Robert is killed just as Ned discovers that his three children by Cersei were actually fathered by the queen\\'s twin, Jaime. Before Ned is able to neutralize Cersei and place Robert\\'s brother Stannis on the throne, he is betrayed by Littlefinger, and the queen has him imprisoned for treason. Ned makes a public confession to save his daughters from Cersei\\'s wrath, but Joffrey has Ned beheaded anyway.\\n\\n====Seasons 6 and 7====\\nIn the season 6 episode \"Home\", Bran sees a vision of Ned (Sebastian Croft) as a child with his sister and brother, Lyanna and Benjen. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '326f2850a3449cc7ae1169f2c101f541'}>,\n",
-       "  <Document: {'content': '====Seasons 6 and 7====\\nIn the season 6 episode \"Home\", Bran sees a vision of Ned (Sebastian Croft) as a child with his sister and brother, Lyanna and Benjen. In the following episode, \"Oathbreaker\", Bran witnesses the battle between a young Ned (Robert Aramayo) and the knight Ser Arthur Dayne at the Tower of Joy. In the episode \"The Door\", Bran watches Ned bid Benjen and his father, Rickard, farewell as he departs for his fosterage at the Vale. In the following episode, \"Blood of My Blood\", Bran briefly glimpses Ned\\'s hand amongst someone\\'s blood, revealed in the season finale, \"The Winds of Winter\", as belonging to Lyanna (Aisling Franciosi), dying of childbirth in the tower. She makes him swear to protect her son - Jon Snow. Bran revisits this moment in the season 7 finale, \"The Dragon and the Wolf\", where Lyanna whispers Jon\\'s true name to Ned - Aegon Targaryen.\\n\\n===Recognition and awards===\\nBean was nominated for a Saturn Award for Best Actor on Television, a Scream Award for Best Fantasy Actor, and an EWwy Award for Best Actor, Drama for the role. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'd3f0c299bebbbf1c60c26659fbb5974a'}>,\n",
-       "  <Document: {'content': '===Recognition and awards===\\nBean was nominated for a Saturn Award for Best Actor on Television, a Scream Award for Best Fantasy Actor, and an EWwy Award for Best Actor, Drama for the role. IGN named Ned its Best TV Hero of 2011, and Bean won the Portal Award for Best Actor.\\n\\n== References ==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '8b393ae6dbf5bd1bb3f9461170bb4645'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''''Game of Thrones''''' is an American  fantasy drama television series created by David Benioff and D. B. Weiss for HBO. It is an adaptation of ''A Song of Ice and Fire'', George R. R. Martin's series of fantasy novels, the first of which is ''A Game of Thrones''. The show was both produced and filmed in Belfast and elsewhere in the United Kingdom. Filming locations also included Canada, Croatia, Iceland, Malta, Morocco, and Spain. The series premiered on HBO in the United States on April 17, 2011, and concluded on May 19, 2019, with 73 episodes broadcast over eight seasons.\\n\\nSet on the fictional continents of Westeros and Essos, ''Game of Thrones'' has several plots and a large ensemble cast and follows several story arcs. One arc is about the Iron Throne of the Seven Kingdoms and follows a web of alliances and conflicts among the noble dynasties either vying to claim the throne or fighting for independence from it. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'faa34b7c837f5024230c016c4a1d0f92'}>,\n",
-       "  <Document: {'content': \"One arc is about the Iron Throne of the Seven Kingdoms and follows a web of alliances and conflicts among the noble dynasties either vying to claim the throne or fighting for independence from it. Another focuses on the last descendant of the realm's deposed ruling dynasty, who has been exiled and is plotting a return to the throne, while another story arc follows the Night's Watch, a brotherhood defending the realm against the fierce peoples and legendary creatures of the North.\\n\\n''Game of Thrones'' attracted a record viewership on HBO and has a broad, active, and international fan base. The series was acclaimed by critics for its acting, complex characters, story, scope, and production values, although its frequent use of nudity and violence (including sexual violence) was criticized; the final season received further criticism for its condensed story and creative decisions, with many considering it a disappointing conclusion. The series received 58 Primetime Emmy Awards, the most by a drama series, including Outstanding Drama Series in 2015, 2016, 2018, 2019. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '3aa78ebbfef7d960faa751bd853e64d2'}>,\n",
-       "  <Document: {'content': \"The series received 58 Primetime Emmy Awards, the most by a drama series, including Outstanding Drama Series in 2015, 2016, 2018, 2019. Its other awards and nominations include three Hugo Awards for Best Dramatic Presentation (2012–2014), a 2011 Peabody Award, and five nominations for the Golden Globe Award for Best Television Series – Drama (2012 and 2015–2018). In 2019, the show's final season established a new record for most Emmy nominations received in the same year by any regular series with 32, breaking the 25 years long record of 26 nominations established by ''NYPD Blue'' in 1994; it also established ''Game of Thrones'' as the drama series with the most overall Emmy nominations, with a total of 161.\\n\\nOf the ensemble cast, Peter Dinklage has won four Primetime Emmy Awards for Outstanding Supporting Actor in a Drama Series (2011, 2015, 2018, and 2019) and the Golden Globe Award for Best Supporting Actor – Series, Miniseries or Television Film (2012) for his performance as Tyrion Lannister. Alfie Allen, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Lena Headey, Kit Harington, Sophie Turner, Diana Rigg, Carice van Houten, Max von Sydow, and Maisie Williams have also received Primetime Emmy Award nominations for their performances.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '2cd5b13890980c040707eedd6d9a42a9'}>,\n",
-       "  <Document: {'content': 'Alfie Allen, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Lena Headey, Kit Harington, Sophie Turner, Diana Rigg, Carice van Houten, Max von Sydow, and Maisie Williams have also received Primetime Emmy Award nominations for their performances.\\n\\n==Background==\\n\\nPower and violence are central themes of \\'\\'Game of Thrones\\'\\', and the number of weapons made for the series (some of which are shown here) reflects this.\\n\\n===Setting===\\n\\n\\'\\'Game of Thrones\\'\\' is roughly based on the storylines of \\'\\'A Song of Ice and Fire\\'\\', set in the fictional Seven Kingdoms of Westeros and the continent of Essos. The series chronicles the violent dynastic struggles among the realm\\'s noble families for the Iron Throne, while other families fight for independence from it. It opens with additional threats in the icy North and Essos in the east.\\n\\nShowrunner David Benioff jokingly suggested \"\\'\\'The Sopranos\\'\\' in Middle-earth\" as \\'\\'Game of Thrones\\'\\' tagline, referring to its intrigue-filled plot and dark tone in a fantasy setting of magic and dragons. In a 2012 study, out of 40 recent television drama shows, \\'\\'Game of Thrones\\'\\' ranked second in deaths per episode, averaging 14 deaths.\\n\\n===Themes===\\n\\nThe series is generally praised for what is perceived as a sort of medieval realism. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '1be9a1a353d1ef1860fcf45b3d64ff5c'}>,\n",
-       "  <Document: {'content': 'In a 2012 study, out of 40 recent television drama shows, \\'\\'Game of Thrones\\'\\' ranked second in deaths per episode, averaging 14 deaths.\\n\\n===Themes===\\n\\nThe series is generally praised for what is perceived as a sort of medieval realism. George R.R. Martin set out to make the story feel more like historical fiction than contemporary fantasy, with less emphasis on magic and sorcery and more on battles, political intrigue, and the characters, believing that magic should be used moderately in the epic fantasy genre. Martin has stated that \"the true horrors of human history derive not from orcs and Dark Lords, but from ourselves.\"A common theme in the fantasy genre is the battle between good and evil, which Martin says does not mirror the real world. Just like people\\'s capacity for good and for evil in real life, Martin explores the questions of redemption and character change. The series allows the audience to view different characters from their perspective, unlike in many other fantasies, and thus the supposed villains can provide their side of the story. Benioff said, \"George brought a measure of harsh realism to high fantasy. He introduced gray tones into a black-and-white universe.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '8011c8d05e1a913e59055eb5a523f31e'}>,\n",
-       "  <Document: {'content': 'Benioff said, \"George brought a measure of harsh realism to high fantasy. He introduced gray tones into a black-and-white universe.\"In early seasons, under the influence of the \\'\\'A Song of Ice and Fire\\'\\' books, main characters were regularly killed off, and this was credited with developing tension among viewers. In later seasons, critics pointed out that certain characters had developed \"plot armor\" to survive in unlikely circumstances, and attributed this to \\'\\'Game of Thrones\\'\\' deviating from the novels to become more of a traditional television series. The series also reflects the substantial death rates in war.\\n\\n===Inspirations and derivations===\\nAlthough the first season closely follows the events of the first novel, later seasons have made significant changes. According to David Benioff, the series is \"about adapting the series as a whole and following the map George laid out for us and hitting the major milestones, but not necessarily each of the stops along the way\".\\n\\nThe novels and their adaptations base aspects of their settings, characters, and plot on events in European history. Most of Westeros is reminiscent of high medieval Europe, from lands and cultures, to the palace intrigue, feudal system, castles, and knightly tournaments. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '82cbeed68e0c87bb01e5f35a386cea8a'}>,\n",
-       "  <Document: {'content': 'Most of Westeros is reminiscent of high medieval Europe, from lands and cultures, to the palace intrigue, feudal system, castles, and knightly tournaments. A principal inspiration for the novels is the English Wars of the Roses (1455–1485) between the houses of Lancaster and York, reflected in Martin\\'s houses of Lannister and Stark. The scheming Cersei Lannister evokes Isabella, the \"she-wolf of France\" (1295–1358). She and her family, as portrayed in Maurice Druon\\'s historical novel series, \\'\\'The Accursed Kings\\'\\', were a main inspiration of Martin\\'s.\\n\\nOther historical antecedents of series elements include Hadrian\\'s Wall (which becomes Martin\\'s Wall), the Roman Empire, and the legend of Atlantis (ancient Valyria), Byzantine Greek fire (\"wildfire\"), Icelandic sagas of the Viking Age (the Ironborn), the Mongol hordes (the Dothraki), the Hundred Years\\' War, and the Italian Renaissance. The series\\' popularity has been attributed, in part, to Martin\\'s skill at fusing these elements into a seamless, credible version of alternate history. Martin acknowledges, \"I take history and I file off the serial numbers and I turn it up to 11.\"==Cast and characters==\\n\\n\\'\\'Game of Thrones\\'\\' has an ensemble cast estimated to be the largest on television; during its third season, 257 cast names were recorded. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '5f2128f83bcf624c2f3283f26b3e96f2'}>,\n",
-       "  <Document: {'content': '==Cast and characters==\\n\\n\\'\\'Game of Thrones\\'\\' has an ensemble cast estimated to be the largest on television; during its third season, 257 cast names were recorded. In 2014, several actor contracts were renegotiated to include a seventh-season option, with raises which reportedly made them among the highest-paid performers on cable television. In 2016, several actor contracts were again renegotiated, reportedly increasing the salary of five of the main cast members to $1 million per episode for the last two seasons, which would make them the highest paid actors on television. The main cast is listed below.\\n\\nLord Eddard \"Ned\" Stark (Sean Bean) is the head of House Stark, whose members are involved in plotlines throughout most of the series. He and his wife, Catelyn Tully (Michelle Fairley), have five children: Robb (Richard Madden), the eldest; followed by Sansa (Sophie Turner), Arya (Maisie Williams), Bran (Isaac Hempstead-Wright), and Rickon (Art Parkinson). Ned\\'s illegitimate son Jon Snow (Kit Harington) and his friend Samwell Tarly (John Bradley) serve in the Night\\'s Watch under Lord Commander Jeor Mormont (James Cosmo). The Wildlings living north of the Wall include the young Gilly (Hannah Murray), and the warriors Tormund Giantsbane (Kristofer Hivju) and Ygritte (Rose Leslie).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '62cb2cde2478995175657f0eb914e68b'}>,\n",
-       "  <Document: {'content': 'The Wildlings living north of the Wall include the young Gilly (Hannah Murray), and the warriors Tormund Giantsbane (Kristofer Hivju) and Ygritte (Rose Leslie).\\n\\nOthers associated with House Stark include Ned\\'s ward Theon Greyjoy (Alfie Allen), his vassal Roose Bolton (Michael McElhatton), and Bolton\\'s bastard son Ramsay Snow (Iwan Rheon). Robb falls in love with the healer Talisa Maegyr (Oona Chaplin), and Arya befriends the blacksmith\\'s apprentice Gendry (Joe Dempsie) and the assassin Jaqen H\\'ghar (Tom Wlaschiha). The tall warrior Brienne of Tarth (Gwendoline Christie) serves Catelyn and, later, Sansa.\\n\\nIn King\\'s Landing, the capital, Ned\\'s friend King Robert Baratheon (Mark Addy), shares a loveless marriage with Cersei Lannister (Lena Headey), who has taken her twin brother, the \"Kingslayer\" Ser Jaime Lannister (Nikolaj Coster-Waldau), as her lover. She loathes her younger brother, the dwarf Tyrion Lannister (Peter Dinklage), who is attended by his mistress Shae (Sibel Kekilli) and the sellsword Bronn (Jerome Flynn). Cersei\\'s father is Lord Tywin Lannister (Charles Dance), and she also has two young sons: Joffrey (Jack Gleeson) and Tommen (Dean-Charles Chapman). Joffrey is guarded by the scar-faced warrior Sandor \"the Hound\" Clegane (Rory McCann).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '89cd9ee20955ce149f0039cc39e70818'}>,\n",
-       "  <Document: {'content': 'Cersei\\'s father is Lord Tywin Lannister (Charles Dance), and she also has two young sons: Joffrey (Jack Gleeson) and Tommen (Dean-Charles Chapman). Joffrey is guarded by the scar-faced warrior Sandor \"the Hound\" Clegane (Rory McCann).\\n\\nThe king\\'s Small Council of advisors includes the crafty Master of Coin Lord Petyr \"Littlefinger\" Baelish (Aidan Gillen) and the eunuch spymaster Lord Varys (Conleth Hill). Robert\\'s brother Stannis Baratheon (Stephen Dillane) is advised by foreign priestess Melisandre (Carice van Houten) and former smuggler Ser Davos Seaworth (Liam Cunningham). The wealthy Tyrell family is represented at court by Margaery Tyrell (Natalie Dormer). The High Sparrow (Jonathan Pryce) is the capital\\'s religious leader. In the southern principality of Dorne, Ellaria Sand (Indira Varma) seeks vengeance against the Lannisters.\\n\\nAcross the Narrow Sea, siblings Viserys (Harry Lloyd) and Daenerys Targaryen (Emilia Clarke) – the exiled children of the last king of the original ruling dynasty, who was overthrown by Robert Baratheon – are running for their lives and trying to win back the throne. Daenerys marries Khal Drogo (Jason Momoa), the leader of the nomadic Dothraki. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '60e7af960748238e8d2f8eaf37c8215a'}>,\n",
-       "  <Document: {'content': \"Across the Narrow Sea, siblings Viserys (Harry Lloyd) and Daenerys Targaryen (Emilia Clarke) – the exiled children of the last king of the original ruling dynasty, who was overthrown by Robert Baratheon – are running for their lives and trying to win back the throne. Daenerys marries Khal Drogo (Jason Momoa), the leader of the nomadic Dothraki. Her retinue includes the exiled knight Ser Jorah Mormont (Iain Glen), her aide Missandei (Nathalie Emmanuel), the sellsword Daario Naharis (Michiel Huisman), and Grey Worm (Jacob Anderson), who leads Daenerys' army of elite eunuch-warriors, the Unsullied.\\n\\n==Production==\\n===Conception and development===\\nShowrunners D. B. Weiss and David Benioff created the series, wrote most of its episodes and directed several.\\n\\nIn January 2006, David Benioff had a phone conversation with George R. R. Martin's literary agent about the books he represented and became interested in ''A Song of Ice and Fire'', as he had been a fan of fantasy fiction when young but had not read the books before.  The literary agent then sent Benioff the series' first four books. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '6a242df0509d905effa4b61456aebe3f'}>,\n",
-       "  <Document: {'content': 'In January 2006, David Benioff had a phone conversation with George R. R. Martin\\'s literary agent about the books he represented and became interested in \\'\\'A Song of Ice and Fire\\'\\', as he had been a fan of fantasy fiction when young but had not read the books before.  The literary agent then sent Benioff the series\\' first four books. Benioff read a few hundred pages of the first novel, \\'\\'A Game of Thrones\\'\\', shared his enthusiasm with D. B. Weiss, and suggested that they adapt Martin\\'s novels into a television series; Weiss finished the first novel in \"maybe 36 hours\". They pitched the series to HBO after a five-hour meeting with Martin (himself a veteran screenwriter) in a restaurant on Santa Monica Boulevard. According to Benioff, they won Martin over with their answer to his question, \"Who is Jon Snow\\'s mother?\"Before being approached by Benioff and Weiss, Martin had had other meetings with other scriptwriters, most of whom wanted to adapt the series as a feature film. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '84393501b1d1d39ae0be38edfb82b865'}>,\n",
-       "  <Document: {'content': 'Before being approached by Benioff and Weiss, Martin had had other meetings with other scriptwriters, most of whom wanted to adapt the series as a feature film. Martin, however, deemed it \"unfilmable\" and impossible to be done as a feature film, stating that the size of one of his novels is as long as \\'\\'The Lord of the Rings\\'\\', which had been adapted as three feature films. Similarly, Benioff also said that it would be impossible to turn the novels into a feature film as the scale of the novels is too big for a feature film, and dozens of characters would have to be discarded. Benioff added, \"a fantasy movie of this scope, financed by a major studio, would almost certainly need a PG-13 rating. That means no sex, no blood, no profanity. Fuck that.\"Martin himself was pleased with the suggestion that they adapt it as an HBO series, saying that he \"never imagined it anywhere else\". \"I knew it couldn\\'t be done as a network television series. It\\'s too adult. The level of sex and violence would never have gone through.\"The series began development in January 2007. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '4ec14c6db4e25fddcacbb973727570a5'}>,\n",
-       "  <Document: {'content': 'It\\'s too adult. The level of sex and violence would never have gone through.\"The series began development in January 2007. HBO acquired the television rights to the novels, with Benioff and Weiss as its executive producers, and Martin as a co-executive producer. The intention was for each novel to yield a season\\'s worth of episodes. Initially, Martin would write one episode per season while Benioff and Weiss would write the rest of the episodes. Jane Espenson and Bryan Cogman were later added to write one episode apiece the first season.\\n\\nThe first and second drafts of the pilot script by Benioff and Weiss were submitted in August 2007 and June 2008, respectively. Although HBO liked both drafts, a pilot was not ordered until November 2008; the 2007–2008 Writers Guild of America strike may have delayed the process. The pilot episode, \"Winter Is Coming\", was first shot in 2009; after a poor reception in a private viewing, HBO demanded an extensive re-shoot (about 90 percent of the episode, with cast and directorial changes).\\n\\nThe pilot reportedly cost HBO US$5–10million to produce, while the first season\\'s budget was estimated at $50–60million. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '9d21f98a10bdf5be8dbed72d7232c7c9'}>,\n",
-       "  <Document: {'content': 'The pilot episode, \"Winter Is Coming\", was first shot in 2009; after a poor reception in a private viewing, HBO demanded an extensive re-shoot (about 90 percent of the episode, with cast and directorial changes).\\n\\nThe pilot reportedly cost HBO US$5–10million to produce, while the first season\\'s budget was estimated at $50–60million. In the second season, the series received a 15-percent budget increase for the climactic battle in \"Blackwater\" (which had an $8million budget). Between 2012 and 2015, the average budget per episode increased from $6million to \"at least\" $8million. The sixth-season budget was over $10million per episode, for a season total of over $100million and a series record.\\n\\n===Casting===\\nNina Gold and Robert Sterne are the series\\' primary casting directors. Through a process of auditions and readings, the main cast was assembled. The only exceptions were Peter Dinklage and Sean Bean, whom the writers wanted from the start; they were announced as joining the pilot in 2009. Other actors signed for the pilot were Kit Harington as Jon Snow, Jack Gleeson as Joffrey Baratheon, Harry Lloyd as Viserys Targaryen, and Mark Addy as Robert Baratheon. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '9dbd4f909d641a07ad390a9ec69be57d'}>,\n",
-       "  <Document: {'content': \"Other actors signed for the pilot were Kit Harington as Jon Snow, Jack Gleeson as Joffrey Baratheon, Harry Lloyd as Viserys Targaryen, and Mark Addy as Robert Baratheon. Addy was, according to showrunners Benioff and Weiss, the easiest actor to cast for the series, due to his audition performance. Some of the characters in the pilot were recast for the first season: Catelyn Stark was initially played by Jennifer Ehle, but the role was recast with Michelle Fairley. Daenerys Targaryen was also recast, with Emilia Clarke replacing Tamzin Merchant. The rest of the first season's cast was filled in the second half of 2009.\\n\\nAlthough many of the cast returned after the first season, the producers had many new characters to cast for each of the following seasons. Due to the large number of new characters, Benioff and Weiss postponed the introduction of several key characters in the second season and merged several characters into one or assigned plot functions to different characters.  Some recurring characters were recast over the years; for example, Gregor Clegane was played by three different actors, while Dean-Charles Chapman who played Tommen Baratheon also played a minor Lannister character.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'bdd6a200c2576db4385148e13ae94513'}>,\n",
-       "  <Document: {'content': \"Some recurring characters were recast over the years; for example, Gregor Clegane was played by three different actors, while Dean-Charles Chapman who played Tommen Baratheon also played a minor Lannister character.\\n\\n===Writing===\\nGeorge R. R. Martin, author of ''A Song of Ice and Fire'', is a series co-executive producer and wrote one episode for each of the first four seasons.\\n\\n''Game of Thrones'' used seven writers in six seasons. Series creators David Benioff and D. B. Weiss, the showrunners, write most of the episodes each season.\\n\\n''A Song of Ice and Fire'' author George R. R. Martin wrote one episode in each of the first four seasons. Martin did not write an episode for the later seasons, since he wanted to focus on completing the sixth novel (''The Winds of Winter''). Jane Espenson co-wrote one first-season episode as a freelance writer.\\n\\nBryan Cogman, initially a script coordinator for the series, was promoted to producer for the fifth season. Cogman, who wrote at least one episode for the first five seasons, is the only other writer in the writers' room with Benioff and Weiss. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': 'bd97d194ed9fb9ea7662c57a5875075b'}>,\n",
-       "  <Document: {'content': \"Cogman, who wrote at least one episode for the first five seasons, is the only other writer in the writers' room with Benioff and Weiss. Before his promotion, Vanessa Taylor (a writer during the second and third seasons) worked closely with Benioff and Weiss. Dave Hill joined the writing staff for the fifth season after working as an assistant to Benioff and Weiss. Although Martin is not in the writers' room, he reads the script outlines and makes comments.\\n\\nBenioff and Weiss sometimes assign characters to particular writers; for example, Cogman was assigned to Arya Stark for the fourth season. The writers spend several weeks writing a character outline, including what material from the novels to use and the overarching themes. After these individual outlines are complete, they spend another two to three weeks discussing each main character's individual arc and arranging them episode by episode.\\n\\nA detailed outline is created, with each of the writers working on a portion to create a script for each episode. Cogman, who wrote two episodes for the fifth season, took a month and a half to complete both scripts. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'd8ea78984d3c4240bbf854167b8b6919'}>,\n",
-       "  <Document: {'content': \"A detailed outline is created, with each of the writers working on a portion to create a script for each episode. Cogman, who wrote two episodes for the fifth season, took a month and a half to complete both scripts. They are then read by Benioff and Weiss, who make notes, and parts of the script are rewritten. All ten episodes are written before filming begins since they are filmed out of order with two units in different countries.\\n\\nBenioff and Weiss write each of their episodes together, with one of them writing the first half of the script and the other the second half. After that they begin with passing the drafts back and forth to make notes and rewrite parts of it.\\n\\n===Adaptation schedule and episodes===\\n\\nAfter ''Game of Thrones'' began outpacing the published novels in the sixth season, the series was based on a plot outline of the future novels provided by Martin and original content. In April 2016, the showrunners' plan was to shoot 13 more episodes after the sixth season: seven episodes in the seventh season and six episodes in the eighth. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': '2d99a5c25513938aaac93d75f4cd8c67'}>,\n",
-       "  <Document: {'content': \"In April 2016, the showrunners' plan was to shoot 13 more episodes after the sixth season: seven episodes in the seventh season and six episodes in the eighth. Later that month, the series was renewed for a seventh season with a seven-episode order. Eight seasons were ordered and filmed, adapting the novels at a rate of about 48 seconds per page for the first three seasons.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'f54f8ddbb48ddcb0f7c44db94062b05a'}>,\n",
-       "  <Document: {'content': \"Eight seasons were ordered and filmed, adapting the novels at a rate of about 48 seconds per page for the first three seasons.\\n\\nSeason\\n\\nOrdered\\n\\nFilming\\n\\nFirst aired\\n\\nLast aired\\n\\nNovel(s) adapted\\n\\nRefs\\n\\nSeason 1\\n\\nMarch 2, 2010\\n\\nSecond half of 2010\\n\\nApril 17, 2011\\n\\nJune 19, 2011\\n\\n''A Game of Thrones''\\n\\nSeason 2\\n\\nApril 19, 2011\\n\\nSecond half of 2011\\n\\nApril 1, 2012\\n\\nJune 3, 2012\\n\\n''A Clash of Kings'' and some early chapters from ''A Storm of Swords''\\n\\nSeason 3\\n\\nApril 10, 2012\\n\\nJuly – November 2012\\n\\nMarch 31, 2013\\n\\nJune 9, 2013\\n\\nAbout the first two-thirds of ''A Storm of Swords''\\n\\nSeason 4\\n\\nApril 2, 2013\\n\\nJuly – November 2013\\n\\nApril 6, 2014\\n\\nJune 15, 2014\\n\\nThe remaining one-third of ''A Storm of Swords'' and some elements from ''A Feast for Crows'' and ''A Dance with Dragons''\\n\\nSeason 5\\n\\nApril 8, 2014\\n\\nJuly – December 2014\\n\\nApril 12, 2015\\n\\nJune 14, 2015\\n\\n''A Feast for Crows'', ''A Dance with Dragons'' and original content, with some late chapters from ''A Storm of Swords'' and elements from ''The Winds of Winter''\\n\\nSeason 6\\n\\nApril 8, 2014\\n\\nJuly – December 2015\\n\\nApril 24, 2016\\n\\nJune 26, 2016\\n\\nOriginal content and outline from ''The Winds of Winter'', with some late elements from ''A Feast for Crows'' and ''A Dance with Dragons''\\n\\nSeason 7\\n\\nApril 21, 2016\\n\\nAugust 2016 – February 2017\\n\\nJuly 16, 2017\\n\\nAugust 27, 2017\\n\\nOriginal content and outline from ''The Winds of Winter'' and ''A Dream of Spring''\\n\\nSeason 8\\n\\nJuly 30, 2016\\n\\nOctober 2017 – July 2018\\n\\nApril 14, 2019\\n\\nMay 19, 2019\\n\\nOriginal content and outline from ''The Winds of Winter'' and ''A Dream of Spring''\\n\\nThe first two seasons adapted one novel each. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '22c1a724f4443038e260e2184bfa1879'}>,\n",
-       "  <Document: {'content': \"Season\\n\\nOrdered\\n\\nFilming\\n\\nFirst aired\\n\\nLast aired\\n\\nNovel(s) adapted\\n\\nRefs\\n\\nSeason 1\\n\\nMarch 2, 2010\\n\\nSecond half of 2010\\n\\nApril 17, 2011\\n\\nJune 19, 2011\\n\\n''A Game of Thrones''\\n\\nSeason 2\\n\\nApril 19, 2011\\n\\nSecond half of 2011\\n\\nApril 1, 2012\\n\\nJune 3, 2012\\n\\n''A Clash of Kings'' and some early chapters from ''A Storm of Swords''\\n\\nSeason 3\\n\\nApril 10, 2012\\n\\nJuly – November 2012\\n\\nMarch 31, 2013\\n\\nJune 9, 2013\\n\\nAbout the first two-thirds of ''A Storm of Swords''\\n\\nSeason 4\\n\\nApril 2, 2013\\n\\nJuly – November 2013\\n\\nApril 6, 2014\\n\\nJune 15, 2014\\n\\nThe remaining one-third of ''A Storm of Swords'' and some elements from ''A Feast for Crows'' and ''A Dance with Dragons''\\n\\nSeason 5\\n\\nApril 8, 2014\\n\\nJuly – December 2014\\n\\nApril 12, 2015\\n\\nJune 14, 2015\\n\\n''A Feast for Crows'', ''A Dance with Dragons'' and original content, with some late chapters from ''A Storm of Swords'' and elements from ''The Winds of Winter''\\n\\nSeason 6\\n\\nApril 8, 2014\\n\\nJuly – December 2015\\n\\nApril 24, 2016\\n\\nJune 26, 2016\\n\\nOriginal content and outline from ''The Winds of Winter'', with some late elements from ''A Feast for Crows'' and ''A Dance with Dragons''\\n\\nSeason 7\\n\\nApril 21, 2016\\n\\nAugust 2016 – February 2017\\n\\nJuly 16, 2017\\n\\nAugust 27, 2017\\n\\nOriginal content and outline from ''The Winds of Winter'' and ''A Dream of Spring''\\n\\nSeason 8\\n\\nJuly 30, 2016\\n\\nOctober 2017 – July 2018\\n\\nApril 14, 2019\\n\\nMay 19, 2019\\n\\nOriginal content and outline from ''The Winds of Winter'' and ''A Dream of Spring''\\n\\nThe first two seasons adapted one novel each. For the later seasons, its creators see ''Game of Thrones'' as an adaptation of ''A Song of Ice and Fire'' as a whole rather than the individual novels; this enables them to move events across novels, according to screen-adaptation requirements.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '9b9c2bfc39d03ec4c7f1b7ff6051d23a'}>,\n",
-       "  <Document: {'content': \"For the later seasons, its creators see ''Game of Thrones'' as an adaptation of ''A Song of Ice and Fire'' as a whole rather than the individual novels; this enables them to move events across novels, according to screen-adaptation requirements.\\n\\n===Filming===\\nThe Azure Window at Ras-id-Dwerja, on Gozo, was the site of the Dothraki wedding in season one.\\n\\nPrincipal photography for the first season was scheduled to begin on July 26, 2010, and the primary location was the Paint Hall Studios in Belfast, Northern Ireland. Exterior scenes in Northern Ireland were filmed at Sandy Brae in the Mourne Mountains (standing in for Vaes Dothrak), Castle Ward (Winterfell), Saintfield Estates (the Winterfell godswood), Tollymore Forest (outdoor scenes), Cairncastle (the execution site), the Magheramorne quarry (Castle Black), and Shane's Castle (the tourney grounds). Doune Castle in Stirling, Scotland, was also used in the original pilot episode for scenes at Winterfell. The producers initially considered filming the whole series in Scotland, but decided on Northern Ireland because of the availability of studio space.\\n\\nThe first season's southern scenes were filmed in Malta, a change in location from the  pilot episode's Moroccan sets. The city of Mdina was used for King's Landing. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': '1924f2a363b7fd1a7bc5838a1e94025d'}>,\n",
-       "  <Document: {'content': \"The first season's southern scenes were filmed in Malta, a change in location from the  pilot episode's Moroccan sets. The city of Mdina was used for King's Landing. Filming was also done at Fort Manoel (representing the Sept of Baelor), at the Azure Window on the island of Gozo (the Dothraki wedding site) and at San Anton Palace, Fort Ricasoli, Fort St. Angelo and St. Dominic monastery (all used for scenes in the Red Keep).\\n\\nThe walled city of Dubrovnik became King's Landing in season two.\\nFilming of the second season's southern scenes shifted from Malta to Croatia, where the city of Dubrovnik and nearby locations allowed exterior shots of a walled, coastal medieval city. The Walls of Dubrovnik and Fort Lovrijenac were used for scenes in King's Landing, though, exteriors of some local buildings, for example, the Red Keep and the Sept of Baelor, are computer-generated. The island of Lokrum, the St. Dominic monastery in the coastal town of Trogir, the Rector's Palace in Dubrovnik, and the Dubac quarry (a few kilometers east) were used for scenes set in Qarth. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': '7c25cd8bf9ac99445d60b60bdfa11de1'}>,\n",
-       "  <Document: {'content': \"The island of Lokrum, the St. Dominic monastery in the coastal town of Trogir, the Rector's Palace in Dubrovnik, and the Dubac quarry (a few kilometers east) were used for scenes set in Qarth. Scenes set north of the Wall, in the Frostfangs and at the Fist of the First Men, were filmed in November 2011 in Iceland: on the Vatnajökull glacier near Smyrlabjörg, the Svínafellsjökull glacier near Skaftafell and the Mýrdalsjökull glacier near Vik on Höfðabrekkuheiði.\\n\\nThird-season production returned to Dubrovnik, with the Walls of Dubrovnik, Fort Lovrijenac and nearby locations again used for scenes in King's Landing and the Red Keep. Trsteno Arboretum, a new location, is the garden of the Tyrells in King's Landing. The third season also returned to Morocco (including the city of Essaouira) to film Daenerys' scenes in Essos. Dimmuborgir and the Grjótagjá cave in Iceland were used as well. One scene, with a live bear, was filmed in Los Angeles. The production used three units (Dragon, Wolf and Raven) filming in parallel, six directing teams, 257 cast members and 703 crew members.\\n\\nBallintoy Harbour was Lordsport on the Iron Islands.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': '673de9797339e660df0e92430a735775'}>,\n",
-       "  <Document: {'content': \"The production used three units (Dragon, Wolf and Raven) filming in parallel, six directing teams, 257 cast members and 703 crew members.\\n\\nBallintoy Harbour was Lordsport on the Iron Islands.\\nThe fourth season returned to Dubrovnik and included new locations, including Diocletian's Palace in Split, Klis Fortress north of Split, Perun quarry east of Split, the Mosor mountain range, and Baška Voda further south. Thingvellir National Park in Iceland was used for the fight between Brienne and the Hound. Filming took 136 days and ended on November 21, 2013. The fifth season added Seville, Spain, used for scenes of Dorne, as well as Córdoba.\\n\\nThe sixth season, which began filming in July 2015, returned to Spain and filmed in Navarra, Guadalajara, Seville, Almeria, Girona and Peniscola. Filming also returned to Dubrovnik, Croatia.\\n\\nFilming of the seven episodes of season 7 began on August 31, 2016, at Titanic Studios in Belfast, with other filming in Iceland, Northern Ireland and many locations in Spain, including Seville, Cáceres, Almodovar del Rio, Santiponce, Zumaia and Bermeo. The series also filmed in Dubrovnik, which is used for location of King's Landing. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': '2f6f738f1cc790608b37951cc0a8d55e'}>,\n",
-       "  <Document: {'content': 'Filming of the seven episodes of season 7 began on August 31, 2016, at Titanic Studios in Belfast, with other filming in Iceland, Northern Ireland and many locations in Spain, including Seville, Cáceres, Almodovar del Rio, Santiponce, Zumaia and Bermeo. The series also filmed in Dubrovnik, which is used for location of King\\'s Landing. Filming continued until the end of February 2017 as necessary to ensure winter weather in some of the European locations.\\n\\n===Directing===\\nEach ten-episode season of \\'\\'Game of Thrones\\'\\' has four to six directors, who usually direct back-to-back episodes. Alan Taylor has directed seven episodes, the most episodes of the series. Alex Graves, David Nutter, Mark Mylod, and Jeremy Podeswa have directed six each. Daniel Minahan directed five episodes, and Michelle MacLaren, Alik Sakharov, and Miguel Sapochnik directed four each; MacLaren is also the only female director in the entire series\\' run. Brian Kirk directed three episodes during the first season, and Tim Van Patten directed the series\\' first two episodes. Neil Marshall directed two episodes, both with large battle scenes: \"Blackwater\" and \"The Watchers on the Wall\". Other directors have been Jack Bender, David Petrarca, Daniel Sackheim, Michael Slovis and Matt Shakman. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': '10734fb1d5590c3ed4b25eb4a37be345'}>,\n",
-       "  <Document: {'content': 'Neil Marshall directed two episodes, both with large battle scenes: \"Blackwater\" and \"The Watchers on the Wall\". Other directors have been Jack Bender, David Petrarca, Daniel Sackheim, Michael Slovis and Matt Shakman. David Benioff and D. B. Weiss have directed two episodes together but only credited one each episode, which was determined after a coin toss.\\n\\n===Technical aspects===\\nAlik Sakharov was the pilot\\'s cinematographer. The series has had a number of cinematographers, and has received seven Primetime Emmy Award for Outstanding Cinematography for a Single-Camera Series nominations.\\n\\nOral Norrey Ottey, Frances Parker, Martin Nicholson, Crispin Green, Tim Porter and Katie Weiland have edited the series for a varying number of episodes. Weiland received a Primetime Emmy Award for Outstanding Single-Camera Picture Editing for a Drama Series in 2015.\\n\\n===Costumes===\\nThe costumes of Ygritte, Jon Snow and Tormund Giantsbane reflect the harsh climate in which they are worn.\\nDresses worn at the royal court in King\\'s Landing indicate their wearers\\' wealth and status.\\nFunctional weapons and armor, like Brienne of Tarth\\'s (left), were manufactured for the series.\\nMichele Clapton was costume designer for \\'\\'Game of Thrones\\'\\' first five seasons before she was replaced by April Ferry. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': '6aa4a0cc4cd64424b8b7b3aa4d4668c'}>,\n",
-       "  <Document: {'content': \"Functional weapons and armor, like Brienne of Tarth's (left), were manufactured for the series.\\nMichele Clapton was costume designer for ''Game of Thrones'' first five seasons before she was replaced by April Ferry. Clapton returned to the series as costume designer for the seventh season.\\n\\nThe costumes used in the series drew inspiration from a number of sources, such as Japanese and Persian armor. Dothraki dress resembles that of the Bedouin (one was made out of fish skins to resemble dragon scales), and the Wildlings wear animal skins like the Inuit. Wildling bone armor is made from molds of actual bones, and is assembled with string and latex resembling catgut. Although the extras who play Wildlings and the Night's Watch often wear hats (normal in a cold climate), members of the principal cast usually do not so viewers can recognize the main characters. Björk's Alexander McQueen high-neckline dresses inspired Margaery Tyrell's funnel-neck outfit, and prostitutes' dresses are designed for easy removal. All clothing used is aged for two weeks so it appears realistic on high-definition television.\\n\\nAbout two dozen wigs are used for the actresses. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 28}, 'embedding': None, 'id': '8f6b14fc066e489340cc9c9cbdcc7d8f'}>,\n",
-       "  <Document: {'content': \"All clothing used is aged for two weeks so it appears realistic on high-definition television.\\n\\nAbout two dozen wigs are used for the actresses. Made of human hair and up to  in length, they cost up to $7,000 each and are washed and styled like real hair. Applying the wigs is time-consuming; Emilia Clarke, for example, requires about two hours to style her brunette hair with a platinum-blonde wig and braids. Other actors, such as Jack Gleeson and Sophie Turner, receive frequent hair coloring. For characters such as Daenerys (Clarke) and her Dothraki, their hair, wigs and costumes are processed to appear as if they have not been washed for weeks.\\n\\n===Makeup===\\nFor the first three seasons, Paul Engelen was ''Game of Thrones'' main makeup designer and prosthetic makeup artist with Melissa Lackersteen, Conor O'Sullivan, and Rob Trenton. At the beginning of the fourth season Engelen's team was replaced by Jane Walker and her crew, composed of Ann McEwan and Barrie and Sarah Gower.\\n\\n===Visual effects===\\nFor the series' large number of visual effects, HBO hired British-based BlueBolt and Irish-based Screen Scene for season one. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 29}, 'embedding': None, 'id': '6d4c2894f02c06419f4ce5a58ca23a7b'}>,\n",
-       "  <Document: {'content': '===Visual effects===\\nFor the series\\' large number of visual effects, HBO hired British-based BlueBolt and Irish-based Screen Scene for season one. Most of the environment builds were done as 2.5D projections, giving viewers perspective while keeping the programming from being overwhelming. In 2011, the season-one finale, \"Fire and Blood\", was nominated for a Primetime Emmy Award for Outstanding Special Visual Effects.\\n\\nBecause the effects became more complex in subsequent seasons (including CGI creatures, fire, and water), German-based Pixomondo became the lead visual-effects producer; nine of its twelve facilities contributed to the project for season two, with Stuttgart the lead.  Scenes were also produced by British-based Peanut FX, Canadian-based Spin VFX, and US-based Gradient Effects.  \"Valar Morghulis\" and \"Valar Dohaeris\" earned Pixomondo Primetime Emmy Awards for Outstanding Special Visual Effects in 2012 and 2013, respectively.\\n\\nFor season four, HBO added German-based Mackevision to the project. The season-four finale, \"The Children\", won the 2014 Emmy Award for Visual Effects. Additional producers for season four included Canadian-based Rodeo FX, German-based Scanline VFX and US-based BAKED FX.  The muscle and wing movements of the adolescent dragons in seasons four and five were based largely on those of a chicken.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 30}, 'embedding': None, 'id': '98076683556cf8a96d99c9ca1d3277ea'}>,\n",
-       "  <Document: {'content': \"The muscle and wing movements of the adolescent dragons in seasons four and five were based largely on those of a chicken.  Pixomondo retained a team of 22 to 30 people which focused solely on visualizing Daenerys Targaryen's dragons, with the average production time per season of 20 to 22 weeks.  For the fifth season, HBO added Canadian-based Image Engine and US-based Crazy Horse Effects to its list of main visual-effects producers.\\n\\n===Sound===\\nUnusual for a television series, the sound team receives a rough cut of a full season and approaches it as a ten-hour feature film. Although seasons one and two had different sound teams, one team has been in charge of sound since then. For the series' blood-and-gore sounds, the team often uses a chamois. For dragon screams, mating tortoises, dolphin, seal, lion and bird sounds have been used.\\n\\n===Title sequence===\\n\\nThe series' title sequence was created by production studio Elastic for HBO. Creative director Angus Wall and his collaborators received the 2011 Primetime Emmy Award for Main Title Design for the sequence, which depicts a three-dimensional map of the series' fictional world. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 31}, 'embedding': None, 'id': 'c9f359bc9234f18b4624747d4d3a86e'}>,\n",
-       "  <Document: {'content': \"Creative director Angus Wall and his collaborators received the 2011 Primetime Emmy Award for Main Title Design for the sequence, which depicts a three-dimensional map of the series' fictional world. The map is projected on the inside of a sphere which is centrally lit by a small sun in an armillary sphere. As the camera moves across the map, focusing on the locations of the episode's events, clockwork mechanisms intertwine and allow buildings and other structures to emerge from the map. Accompanied by the title music, the names of the principal cast and creative staff appear. The sequence concludes after about 90 seconds with the title card and brief opening credits indicating the episode's writer(s) and director. Its composition changes as the story progresses, with new locations replacing those featuring less prominently or not at all.\\n\\n===Music===\\n\\nRamin Djawadi composed the ''Game of Thrones'' score.\\n\\nThe music for the series was composed by Ramin Djawadi. The first season's soundtrack, written in about ten weeks before the premiere, was published by Varèse Sarabande in June 2011. Soundtrack albums for subsequent seasons have been released, with tracks by the National, the Hold Steady, and Sigur Rós. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 32}, 'embedding': None, 'id': '767145c9015dfd43a598c63635c66ccc'}>,\n",
-       "  <Document: {'content': \"The first season's soundtrack, written in about ten weeks before the premiere, was published by Varèse Sarabande in June 2011. Soundtrack albums for subsequent seasons have been released, with tracks by the National, the Hold Steady, and Sigur Rós. Djawadi has composed themes for each of the major houses and also for some of the main characters. The themes may evolve over time, as Daenerys Targaryen's theme started small and then became more powerful after each season. Her theme started first with a single instrument, a cello, and Djawadi later incorporated more instruments for it.\\n\\n===Language===\\nThe Westerosi characters of ''Game of Thrones'' speak British-accented English, often (but not consistently) with the accent of the English region corresponding to the character's Westerosi region. The Northerner Eddard Stark speaks in actor Sean Bean's native northern accent, and the southern lord Tywin Lannister speaks with a southern accent, while characters from Dorne speak English with a Spanish accent. Characters foreign to Westeros often have a non-British accent.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 33}, 'embedding': None, 'id': '9ec7bcd7ec3b543fa209876ae02c525a'}>,\n",
-       "  <Document: {'content': \"The Northerner Eddard Stark speaks in actor Sean Bean's native northern accent, and the southern lord Tywin Lannister speaks with a southern accent, while characters from Dorne speak English with a Spanish accent. Characters foreign to Westeros often have a non-British accent.\\n\\nAlthough the common language of Westeros is represented as English, the producers charged linguist David J. Peterson with constructing Dothraki and Valyrian languages based on the few words in the novels; Dothraki and Valyrian dialogue is often subtitled in English. It has been reported that during the series these fictional languages have been heard by more people than the Welsh, Irish, and Scots Gaelic languages combined.\\n\\n===Effect on location===\\n''Game of Thrones'' is funded by Northern Ireland Screen, a UK government agency financed by Invest NI and the European Regional Development Fund. , Northern Ireland Screen gave the series £9.25million ($14.37million); according to government estimates, this has benefited the Northern Ireland economy by £65million ($100.95million).\\n\\nTourism Ireland has a ''Game of Thrones''-themed marketing campaign similar to New Zealand's Tolkien-related advertising. Invest NI and the Northern Ireland Tourist Board also expect the series to generate tourism revenue. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 34}, 'embedding': None, 'id': 'c62b4b2ce92aab8f7d10b9a8d1701a68'}>,\n",
-       "  <Document: {'content': \"Tourism Ireland has a ''Game of Thrones''-themed marketing campaign similar to New Zealand's Tolkien-related advertising. Invest NI and the Northern Ireland Tourist Board also expect the series to generate tourism revenue. According to Arlene Foster, the series has given Northern Ireland the most non-political publicity in its history. The production of ''Game of Thrones'' and other TV series also boosted Northern Ireland's creative industries, contributing to an estimated 12.4-percent growth in arts, entertainment, and recreation jobs between 2008 and 2013 (compared with 4.3percent in the rest of the UK during the same period). In September 2018, after the filming had finished, HBO announced plans to convert its filming locations in Northern Ireland into tourist attractions to be opened in 2019.\\n\\nTourism organizations elsewhere reported increases in bookings after their locations appeared in ''Game of Thrones''. In 2012, bookings through LateRooms.com increased by 28 percent in Dubrovnik and 13 percent in Iceland. The following year, bookings doubled in Ouarzazate, Morocco (the location of Daenerys' season-three scenes). ''Game of Thrones'' has been attributed as a significant factor in the boom of tourism in Iceland that had a strong impact on its economy. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 35}, 'embedding': None, 'id': 'cb8520768babd78b75a24e0747df35d6'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been attributed as a significant factor in the boom of tourism in Iceland that had a strong impact on its economy. Tourist numbers increased by 30% in 2015, followed by another 40% in 2016, with a final figure of 2.4million visitors expected for 2016, which is around seven times the population of the country.  However, the increase in tourism to Dubrovnik, with \\'\\'Game of Thrones\\'\\' estimated to be responsible for half of its annual increase over many years, had led to concerns on \"over-tourism\" and its mayor to impose limits on tourist number in the city.\\n\\n==Availability==\\n===Broadcast===\\n\\'\\'Game of Thrones\\'\\' is broadcast by HBO in the United States and by its local subsidiaries or other pay television services in other countries, at the same time as in the US or weeks (or months) later. The series\\' broadcast in China on CCTV, begun in 2014, was heavily edited to remove scenes of sex and violence, in accordance with a Chinese practice of censoring Western television series to prevent what the \\'\\'People\\'s Daily\\'\\' called \"negative effects and hidden security dangers\". This resulted in viewer complaints about the incoherence of what remained. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 36}, 'embedding': None, 'id': '8acb6bbc32fb08237ab5911d22354493'}>,\n",
-       "  <Document: {'content': 'The series\\' broadcast in China on CCTV, begun in 2014, was heavily edited to remove scenes of sex and violence, in accordance with a Chinese practice of censoring Western television series to prevent what the \\'\\'People\\'s Daily\\'\\' called \"negative effects and hidden security dangers\". This resulted in viewer complaints about the incoherence of what remained. Broadcasters carrying \\'\\'Game of Thrones\\'\\' include Showcase in Australia; HBO Canada, Super Écran, and Showcase in Canada; HBO Latin America in Latin America; SoHo and Prime in New Zealand, and Sky Atlantic in the United Kingdom and Ireland.\\n\\n===Home media===\\nThe ten episodes of the first season of \\'\\'Game of Thrones\\'\\' were released as a DVD and Blu-ray box set on March 6, 2012. The box set includes extra background and behind-the-scenes material but no deleted scenes, since nearly all the footage shot for the first season was used. The box set sold over 350,000 copies in the first week after release, the largest first-week DVD sales ever for an HBO series, and the series set an HBO-series record for digital-download sales. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 37}, 'embedding': None, 'id': '3fe45b81f64f545dba47cefb080f0bf0'}>,\n",
-       "  <Document: {'content': \"The box set sold over 350,000 copies in the first week after release, the largest first-week DVD sales ever for an HBO series, and the series set an HBO-series record for digital-download sales. A collector's-edition box set was released in November 2012, combining the DVD and Blu-ray versions of the first season with the first episode of season two. A paperweight in the shape of a dragon egg is included in the set.\\n\\nDVD-Blu-ray box sets and digital downloads of the second season became available on February 19, 2013. First-day sales broke HBO records, with 241,000 box sets sold and 355,000 episodes downloaded. The third season was made available for purchase as a digital download on the Australian iTunes Store, parallel to the US premiere, and was released on DVD and Blu-ray in region 1 on February 18, 2014. The fourth season was released on DVD and Blu-ray on February 17, 2015, and the fifth season on March 15, 2016. The sixth season was released on Blu-ray and DVD on November 15, 2016. The seventh season was released on Blu-ray and DVD on December 12, 2017. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 38}, 'embedding': None, 'id': 'c654abf486276c5c3af19628c1fc8acb'}>,\n",
-       "  <Document: {'content': \"The sixth season was released on Blu-ray and DVD on November 15, 2016. The seventh season was released on Blu-ray and DVD on December 12, 2017. Beginning in 2016, HBO began issuing Steelbook Blu-ray sets which include both Dolby TrueHD 7.1 and Dolby Atmos audio options. In 2018, the first season was released in 4K HDR on Ultra HD Blu-ray.\\n\\n===Copyright infringement===\\n''Game of Thrones'' has been widely pirated, primarily outside the US. According to the file-sharing news website TorrentFreak, ''Game of Thrones'' has been the most pirated television series since 2012, which means it has held the record for six years in a row. Illegal downloads increased to about seven million in the first quarter of 2015, up 45 percent from 2014. An unnamed episode was downloaded about 4,280,000 times through public BitTorrent trackers in 2012, roughly equal to its number of broadcast viewers. Piracy rates were particularly high in Australia, and US Ambassador to Australia Jeff Bleich issued a statement condemning Australian piracy of the series in 2013.\\n\\nDelays in availability apart from HBO and its affiliates before 2015 and the cost of subscriptions to these services have been cited as causes of the series' illegal distribution. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 39}, 'embedding': None, 'id': 'c6f6ac95d648c64a5e5a0913306a16f9'}>,\n",
-       "  <Document: {'content': 'Delays in availability apart from HBO and its affiliates before 2015 and the cost of subscriptions to these services have been cited as causes of the series\\' illegal distribution. According to \\'\\'TorrentFreak\\'\\', a subscription to a service for \\'\\'Game of Thrones\\'\\' would cost up to $25 per month in the United States, up to £26 per episode in the UK and up to $52 per episode in Australia.\\n\\nFor \"combating piracy\", HBO said in 2013 that it intended to make its content more widely available within a week of the US premiere (including HBO Go). In 2015, the fifth season was simulcast to 170 countries and to HBO Now users. On April 11, the day before the season premiere, screener copies of the first four episodes of the fifth season leaked to a number of file-sharing websites. Within a day of the leak, the files were downloaded over 800,000 times; in one week the illegal downloads reached 32million, with the season-five premiere alone (\"The Wars to Come\") pirated 13million times. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 40}, 'embedding': None, 'id': 'c060f8159c2619b821944a9a76563793'}>,\n",
-       "  <Document: {'content': 'Within a day of the leak, the files were downloaded over 800,000 times; in one week the illegal downloads reached 32million, with the season-five premiere alone (\"The Wars to Come\") pirated 13million times. The season-five finale (\"Mother\\'s Mercy\") was the most simultaneously shared file in the history of the BitTorrent filesharing protocol, with over 250,000 simultaneous sharers and over 1.5million downloads in eight hours. For the sixth season, HBO did not send screeners to the press, to prevent the spread of unlicensed copies and spoilers.\\n\\nObservers, including series director David Petrarca and Time Warner CEO Jeff Bewkes, said that illegal downloads did not hurt the series\\' prospects; it benefited from \"buzz\" and social commentary, and the high piracy rate did not significantly translate to lost subscriptions. According to \\'\\'Polygon\\'\\', HBO\\'s relaxed attitude towards piracy and the sharing of login credentials amounted to a premium-television \"free-to-play\" model. At a 2015 Oxford Union debate, series co-creator David Benioff said that he was just glad that people watched the series; illegally downloaded episodes sometimes interested viewers enough to buy a copy, especially in countries where \\'\\'Game of Thrones\\'\\' was not televised. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 41}, 'embedding': None, 'id': 'd76af9472bb9160b57723dfe04f5df8a'}>,\n",
-       "  <Document: {'content': 'At a 2015 Oxford Union debate, series co-creator David Benioff said that he was just glad that people watched the series; illegally downloaded episodes sometimes interested viewers enough to buy a copy, especially in countries where \\'\\'Game of Thrones\\'\\' was not televised. Co-creator D. B. Weiss had mixed feelings, saying that the series was expensive to produce and \"if it doesn\\'t make the money back, then it ceases to exist\". However, he was pleased that so many people \"enjoy the show so much they can\\'t wait to get their hands on it.\"In 2015, \\'\\'Guinness World Records\\'\\' called \\'\\'Game of Thrones\\'\\' the most pirated television program.\\n\\n===IMAX===\\nBeginning on January 23, 2015, the last two episodes of season four were shown in 205 IMAX theaters across the United States; \\'\\'Game of Thrones\\'\\' is the first television series shown in this format. The show earned $686,000 at the box office on its opening day and $1.5million during its opening weekend; the week-long release grossed $1,896,092.\\n\\n==Reception and achievements==\\n\\'\\'Game of Thrones\\'\\' was highly anticipated by fans before its premiere, and has become a critical and commercial success. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 42}, 'embedding': None, 'id': 'f8a80ae14df4e1efa3470830ee217a5e'}>,\n",
-       "  <Document: {'content': '==Reception and achievements==\\n\\'\\'Game of Thrones\\'\\' was highly anticipated by fans before its premiere, and has become a critical and commercial success. According to \\'\\'The Guardian\\'\\', by 2014 it was \"the biggest drama\" and \"the most talked about show\" on television. A 2015 \\'\\'The Hollywood Reporter\\'\\' survey of 2,800 actors, producers, directors, and other industry people named \\'\\'Game of Thrones\\'\\' as their #4 favorite show.\\n\\n===Cultural influence===\\nAlthough \\'\\'Game of Thrones\\'\\' was dismissed by some critics, its success has been credited with an increase in the popularity of the fantasy genre. On the eve of the second season\\'s premiere, according to CNN, \"after this weekend, you may be hard pressed to find someone who isn\\'t a fan of some form of epic fantasy\" and cited Ian Bogost as saying that the series continues a trend of successful screen adaptations beginning with Peter Jackson\\'s 2001 \\'\\'The Lord of the Rings\\'\\' film trilogy and the \\'\\'Harry Potter\\'\\' films establishing fantasy as a mass-market genre; they are \"gateway drugs to fantasy fan culture\". The success of the show led to a number of fantasy series being commissioned on television, including a retelling of the \\'\\'Lord of the Rings\\'\\' by Amazon Studios. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 43}, 'embedding': None, 'id': 'b2b668d09942a8207cba78926e24e285'}>,\n",
-       "  <Document: {'content': \"The success of the show led to a number of fantasy series being commissioned on television, including a retelling of the ''Lord of the Rings'' by Amazon Studios. According to Neil Gaiman, whose works ''Good Omens'' and ''American Gods'' were adapted for TV, ''Game of Thrones'' did help change attitudes towards fantasy on television, but mainly it made big budgets for fantasy series more acceptable. The success of the genre had been attributed by writers to a longing for escapism in popular culture, frequent female nudity and a skill in balancing lighthearted and serious topics (dragons and politics, for example) which provided it with a prestige enjoyed by conventional, top-tier drama series.\\n\\nThe series' popularity increased sales of the ''A Song of Ice and Fire'' novels (republished in tie-in editions), which remained at the top of bestseller lists for months. According to ''The Daily Beast'', ''Game of Thrones'' was a favorite of sitcom writers and the series has been referred to in other TV series. With other fantasy series, it has been cited as a reason for an increase in the purchase (and abandonment) of huskies and other wolf-like dogs.\\n\\n''Game of Thrones'' has added to the popular vocabulary. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 44}, 'embedding': None, 'id': '4935ed59f770366985d14c5517ec8b1'}>,\n",
-       "  <Document: {'content': 'With other fantasy series, it has been cited as a reason for an increase in the purchase (and abandonment) of huskies and other wolf-like dogs.\\n\\n\\'\\'Game of Thrones\\'\\' has added to the popular vocabulary. A first season scene in which Petyr Baelish explains his motives (or background) while prostitutes have sex in the background gave rise to the word \"sexposition\" for providing exposition with sex and nudity. \"Dothraki\", the series\\' nomadic horsemen, was ranked fourth in a September 2012 Global Language Monitor list of words from television most used on the Internet. In 2012, the media used \"Game of Thrones\" as a figure of speech or comparison for situations of intense conflict and deceit, such as US healthcare politics, the Syrian Civil War and the ousting of Bo Xilai from the Chinese government.\\n\\nIn 2019, singer-songwriter Taylor Swift told \\'\\'Entertainment Weekly\\'\\' that several songs on her 2017 album \\'\\'Reputation\\'\\' were inspired by characters and plots of \\'\\'Game of Thrones\\'\\'.\\n\\n\"Khaleesi\" became more popular as a name for baby girls in the United States. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 45}, 'embedding': None, 'id': 'f409bf8752b365d213b20d962a2ba0b2'}>,\n",
-       "  <Document: {'content': 'In 2019, singer-songwriter Taylor Swift told \\'\\'Entertainment Weekly\\'\\' that several songs on her 2017 album \\'\\'Reputation\\'\\' were inspired by characters and plots of \\'\\'Game of Thrones\\'\\'.\\n\\n\"Khaleesi\" became more popular as a name for baby girls in the United States. In the novels and the TV series, \"khaleesi\" is not a name, but the title of the wife of a khal (warlord) in the Dothraki language, held by Daenerys Targaryen.\\n\\n\\'\\'Game of Thrones\\'\\' has also become a subject of scientific inquiry. In 2016, researchers published a paper analyzing emotional sentiment in online public discourse associated with the unfolding storyline during the fourth season. The analysis purported to be able to distinguish discussions about the storyline of an episode from media critiques or assessments of a specific actor\\'s performance. In 2018, Australian scientists conducted a survival analysis and examined the mortality among 330 important characters during the first seven seasons of \\'\\'Game of Thrones\\'\\'. In 2019, the Australian branch of the Red Cross conducted a study using international human rights laws to determine which of the \\'\\'Game of Thrones\\'\\' characters had committed the most war crimes during the first seven seasons of the show.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 46}, 'embedding': None, 'id': '39575949d0404d2dc1232b192c12c22c'}>,\n",
-       "  <Document: {'content': 'In 2019, the Australian branch of the Red Cross conducted a study using international human rights laws to determine which of the \\'\\'Game of Thrones\\'\\' characters had committed the most war crimes during the first seven seasons of the show.\\n\\n===Critical response===\\n====General====\\n\\nSeason\\n\\nCritical response\\n\\nRotten Tomatoes\\n\\nMetacritic\\n\\n1\\n\\n90% (40 reviews)\\n\\n80 (28 reviews)\\n\\n2\\n\\n96% (37 reviews)\\n\\n90 (26 reviews)\\n\\n3\\n\\n96% (45 reviews)\\n\\n91 (25 reviews)\\n\\n4\\n\\n97% (44 reviews)\\n\\n94 (29 reviews)\\n\\n5\\n\\n93% (50 reviews)\\n\\n91 (29 reviews)\\n\\n6\\n\\n94% (34 reviews)\\n\\n73 (9 reviews)\\n\\n7\\n\\n93% (51 reviews)\\n\\n77 (12 reviews)\\n\\n8\\n\\n56% (13 reviews)\\n\\n74 (12 reviews)\\n\\n\\'\\'Game of Thrones\\'\\', particularly the first seven seasons, received critical acclaim, although the series\\' frequent use of nudity and violence has been criticized. Its seasons have appeared on annual \"best of\" lists published by \\'\\'The Washington Post\\'\\' (2011), \\'\\'Time\\'\\' (2011 and 2012) and \\'\\'The Hollywood Reporter\\'\\' (2012).\\n\\nThe performances of the cast were praised. Peter Dinklage\\'s \"charming, morally ambiguous, and self-aware\" Tyrion, who earned him Emmy and Golden Globe awards, was particularly noted. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 47}, 'embedding': None, 'id': 'f3f52d575a7f3a38a578a3a98718a87f'}>,\n",
-       "  <Document: {'content': 'The performances of the cast were praised. Peter Dinklage\\'s \"charming, morally ambiguous, and self-aware\" Tyrion, who earned him Emmy and Golden Globe awards, was particularly noted. \"In many ways, \\'\\'Game of Thrones\\'\\' belongs to Dinklage\", wrote Mary McNamara of the \\'\\'Los Angeles Times\\'\\' before Tyrion became the series\\' central figure in season two. Several critics highlighted performances by actresses and children. Fourteen-year-old Maisie Williams, noted in the first season for her debut as Arya Stark, was singled out for her season-two work with veteran actor Charles Dance (Tywin Lannister). Stephen Dillane has received positive reviews for his performance as Stannis Baratheon, especially in the fifth season, with one critic noting \"Whether you like Stannis or not, you have to admit that Stephen Dillane delivered a monumental performance this season.\"The series has a rating of 89 percent on Rotten Tomatoes.\\n\\nFirst-season reviewers said the series had high production values, a fully realized world and compelling characters. According to \\'\\'Variety\\'\\', \"There may be no show more profitable to its network than \\'Game of Thrones\\' is to HBO. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 48}, 'embedding': None, 'id': 'f8d178557cfe98681a7ebc058f4e31b7'}>,\n",
-       "  <Document: {'content': 'According to \\'\\'Variety\\'\\', \"There may be no show more profitable to its network than \\'Game of Thrones\\' is to HBO. Fully produced by the pay cabler and already a global phenomenon after only one season, the fantasy skein was a gamble that has paid off handsomely\". The second season was also well received. \\'\\'Entertainment Weekly\\'\\' praised its \"vivid, vital, and just plain fun\" storytelling and, according to \\'\\'The Hollywood Reporter\\'\\', the series made a \"strong case for being one of TV\\'s best series\"; its seriousness made it the only drama comparable to \\'\\'Mad Men\\'\\' or \\'\\'Breaking Bad\\'\\'. \\'\\'The New York Times\\'\\' gave the series a mixed review, criticizing its number of characters, their lack of complexity and a meandering plot.\\n\\nThe third season was extremely well received by critics, with Metacritic giving it a score of 91 out of 100 (indicating \"universal acclaim\"). The fourth season was similarly praised; Metacritic gave it a score of 94 out of 100 based on 29 reviews, again indicating \"universal acclaim\". The fifth season was also well received by critics and has a score of 91 out of 100 (based on 29 reviews) on Metacritic. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 49}, 'embedding': None, 'id': '6261cb48cfbc770d966850d1539d27f5'}>,\n",
-       "  <Document: {'content': 'The fifth season was also well received by critics and has a score of 91 out of 100 (based on 29 reviews) on Metacritic. The sixth season was praised by critics, though not as highly as its predecessors. It has a score of 73 on Metacritic (based on nine reviews), indicating \"generally favorable reviews\". The seventh season scored 77 out of 100 (based on twelve reviews) and was praised for its action sequences and focused central characters, but received criticism for its breakneck pace and plot developments that \"defied logic.\"Darren Franich of \\'\\'Entertainment Weekly\\'\\' gave the series a \\'B\\' rating, stating that it was ultimately \"okay\", with both \"transcendent moments\" and \"miserable phases\", it was \"beloved enough to be criticized by everyone for something\". Franich described seasons 3 and 4 as \"relentless\", seasons 6\\'s ending having a \"killer one-two punch\", while seasons 7 and 8 were \"indifferent\".\\n\\n====Sex and violence====\\nDespite its otherwise enthusiastic reception by critics, some have criticized the show for the amount of female nudity, violence, and sexual violence it depicts, and for the manner in which it depicts these themes. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 50}, 'embedding': None, 'id': 'e6db372e90d294df3020e5a3fa59dffa'}>,\n",
-       "  <Document: {'content': '====Sex and violence====\\nDespite its otherwise enthusiastic reception by critics, some have criticized the show for the amount of female nudity, violence, and sexual violence it depicts, and for the manner in which it depicts these themes. \\'\\'The Atlantic\\'\\' called the series\\' \"tendency to ramp up the sex, violence, and—especially—sexual violence\" of the source material \"the defining weakness\" of the adaptation. George R. R. Martin responded that he feels obliged to be truthful about history and human nature, and that rape and sexual violence are common in war; and that omitting them from the narrative would have rung false and undermined one of his novels\\' themes, its historical realism. HBO said that they \"fully support the vision and artistry of Dan and David\\'s exceptional work and we feel this work speaks for itself.\"The amount of sex and nudity in the series, especially in scenes that are incidental to the plot, was the focus of much of the criticism aimed at the series in its first and second seasons. Stephen Dillane, who portrays Stannis Baratheon, likened the series\\' frequent explicit scenes to \"German porn from the 1970s\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 51}, 'embedding': None, 'id': '6abc1f0e4c577fe646d94b6b78127ac1'}>,\n",
-       "  <Document: {'content': 'The amount of sex and nudity in the series, especially in scenes that are incidental to the plot, was the focus of much of the criticism aimed at the series in its first and second seasons. Stephen Dillane, who portrays Stannis Baratheon, likened the series\\' frequent explicit scenes to \"German porn from the 1970s\". Charlie Jane Anders wrote in io9 that while the first season was replete with light-hearted \"sexposition\", the second season appeared to focus on distasteful, exploitative, and dehumanizing sex with little informational content.\\n\\nAccording to \\'\\'The Washington Post\\'\\'s Anna Holmes, the nude scenes appeared to be aimed mainly at titillating heterosexual men, right down to the Brazilian waxes sported by the women in the series\\' faux-medieval setting, which made these scenes alienating to other viewers. \\'\\'The Huffington Post\\'\\'s Maureen Ryan likewise noted that \\'\\'Game of Thrones\\'\\' mostly presented women naked, rather than men, and that the excess of \"random boobage\" undercut any aspirations the series might have to address the oppression of women in a feudal society. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 52}, 'embedding': None, 'id': '4a2db830b5b7906faf90ee22d765da25'}>,\n",
-       "  <Document: {'content': '\\'\\'The Huffington Post\\'\\'s Maureen Ryan likewise noted that \\'\\'Game of Thrones\\'\\' mostly presented women naked, rather than men, and that the excess of \"random boobage\" undercut any aspirations the series might have to address the oppression of women in a feudal society. \\'\\'Saturday Night Live\\'\\' parodied this aspect of the adaptation in a sketch that portrayed the series as retaining a 13-year-old boy as a consultant whose main concern was showing as many breasts as possible.\\n\\nIn the third season, which saw Theon Greyjoy lengthily tortured and eventually emasculated, the series was also criticized for its use of torture. \\'\\'New York\\'\\' magazine called the scene \"torture porn.\"Madeleine Davies of \\'\\'Jezebel\\'\\' agreed, saying, \"it\\'s not uncommon that \\'\\'Game of Thrones\\'\\' gets accused of being torture porn—senseless, objectifying violence combined with senseless, objectifying sexual imagery.\"According to Davies, although the series\\' violence tended to serve a narrative purpose, Theon\\'s torture in \"The Bear and the Maiden Fair\" was excessive.\\n\\nA scene in the fourth season\\'s episode \"Breaker of Chains\", in which Jaime Lannister rapes his sister and lover Cersei, triggered a broad public discussion about the series\\' depiction of sexual violence against women. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 53}, 'embedding': None, 'id': '737b24f5478a7e2e4d6a83cda8a0088a'}>,\n",
-       "  <Document: {'content': 'A scene in the fourth season\\'s episode \"Breaker of Chains\", in which Jaime Lannister rapes his sister and lover Cersei, triggered a broad public discussion about the series\\' depiction of sexual violence against women. According to Dave Itzkoff of \\'\\'The New York Times\\'\\', the scene caused outrage, in part because of comments by director Alex Graves that the scene became \"consensual by the end\". Itzkoff also wrote that critics fear that \"rape has become so pervasive in the drama that it is almost background noise: a routine and unshocking occurrence\". Sonia Saraiya of \\'\\'The A.V. Club\\'\\' wrote that the series\\' choice to portray this sexual act, and a similar one between Daenerys Targaryen and Khal Drogo in the first season—both described as consensual in the source novels—as a rape appeared to be an act of \"exploitation for shock value\".\\n\\nIn the fifth season\\'s episode \"Unbowed, Unbent, Unbroken\", Sansa Stark is raped by Ramsay Bolton. Most reviewers, including those from \\'\\'Vanity Fair\\'\\', \\'\\'Salon\\'\\', \\'\\'The Atlantic\\'\\', and \\'\\'The Daily Beast\\'\\', found the scene gratuitous and artistically unnecessary. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 54}, 'embedding': None, 'id': '202ca00aadfbc0f5d7869f75bbd84168'}>,\n",
-       "  <Document: {'content': 'Most reviewers, including those from \\'\\'Vanity Fair\\'\\', \\'\\'Salon\\'\\', \\'\\'The Atlantic\\'\\', and \\'\\'The Daily Beast\\'\\', found the scene gratuitous and artistically unnecessary. For example, Joanna Robinson, writing for \\'\\'Vanity Fair\\'\\', said that the scene \"undercuts all the agency that\\'s been growing in Sansa since the end of last season.\"In contrast, Sara Stewart of the \\'\\'New York Post\\'\\' wondered why viewers were not similarly upset about the many background and minor characters who\\'d undergone similar or worse treatment. In response to the scene, pop culture website \\'\\'The Mary Sue\\'\\' announced that it would cease coverage of the series because of the repeated use of rape as a plot device, and US Senator Claire McCaskill said that she would no longer watch it.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 55}, 'embedding': None, 'id': '9ba68eee11519345359d3338eacdbf32'}>,\n",
-       "  <Document: {'content': 'In response to the scene, pop culture website \\'\\'The Mary Sue\\'\\' announced that it would cease coverage of the series because of the repeated use of rape as a plot device, and US Senator Claire McCaskill said that she would no longer watch it.\\n\\nAs the sixth and seventh seasons saw Daenerys, Sansa, and Cersei assume ruling positions, Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' noted that the series could be seen as a \"long-arc revenge fantasy about what happens when women who have been brutalized and raped gain power\"—namely, that their past leaves them too broken to do anything but commit brutal acts in their own turn, and that their personal liberation does not effect the social change needed to protect others from suffering. \\'\\'Time\\'\\' reported before the seventh season that \"Even if Benioff and Weiss don\\'t always admit it, the show has changed. Scenes in which exposition is delivered in one brothel or another, for example, have been pared back\".\\n\\n====Lighting issues====\\nThe lighting, or lack of light, in darker scenes has been a recurring point of criticism since season 6 of the series. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 56}, 'embedding': None, 'id': '9fec7800720ec4de807168e2a6d0c400'}>,\n",
-       "  <Document: {'content': '====Lighting issues====\\nThe lighting, or lack of light, in darker scenes has been a recurring point of criticism since season 6 of the series. In 2016, \\'\\'Bustle\\'\\'s Caitlyn Callegari listed 31 examples of scenes where the lighting caused viewers problems ranging from not being able to tell a character\\'s hair color to not being able to see what was going on. Some reviewers have noted this is part of a wider trend among shows that are made by people who have experience working primarily on films, suggesting they \"haven\\'t grasped the nuances (or lack thereof)\" of television as a medium, especially the differences between watching a scene on a television screen versus watching it on the big screen in a movie theater.\\n\\nIn a 2017 interview, Robert McLachlan, a cinematographer working on the show, explained the lack of lighting as an artistic choice: \"we\\'re trying to be as naturalistic as possible\". The criticism reached a high point during \"The Long Night\", the third episode of season 8. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 57}, 'embedding': None, 'id': 'b1bbdea9b789a5344354f2974d1c9c8f'}>,\n",
-       "  <Document: {'content': 'In a 2017 interview, Robert McLachlan, a cinematographer working on the show, explained the lack of lighting as an artistic choice: \"we\\'re trying to be as naturalistic as possible\". The criticism reached a high point during \"The Long Night\", the third episode of season 8. Barely minutes into the episode, viewers took to social media sites such as Twitter to express their discontent about the fact that they were having severe difficulties watching the battle and trying to figure out what was going on.\\n\\n===Fandom===\\n\\nIn this manipulated image published by the White House in 2014, US president Barack Obama (a fan of the series) sits on the Iron Throne in the Oval Office with the king\\'s crown on his lap.\\n\\nThe popularity of the show in the country, coupled with its filming locations in Northern Ireland, led to the creation of a show-specific exhibit in the National Wax Museum Plus in Dublin.\\n\\n\\'\\'A Song of Ice and Fire\\'\\' and \\'\\'Game of Thrones\\'\\' have a broad, active international fan base. In 2012 \\'\\'Vulture.com\\'\\' ranked the series\\' fans as the most devoted in popular culture, more so than Lady Gaga\\'s, Justin Bieber\\'s, \\'\\'Harry Potter\\'\\'s or \\'\\'Star Wars\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 58}, 'embedding': None, 'id': '19f0b294819a866763463fd7f870ddc0'}>,\n",
-       "  <Document: {'content': 'In 2012 \\'\\'Vulture.com\\'\\' ranked the series\\' fans as the most devoted in popular culture, more so than Lady Gaga\\'s, Justin Bieber\\'s, \\'\\'Harry Potter\\'\\'s or \\'\\'Star Wars\\'\\'. Fans include political leaders such as former US president Barack Obama, former British prime minister David Cameron, former Australian prime minister Julia Gillard and Dutch foreign minister Frans Timmermans, who framed European politics in quotes from Martin\\'s novels in a 2013 speech.\\n\\nBBC News said in 2013 that \"the passion and the extreme devotion of fans\" had created a phenomenon unlike anything related to other popular TV series, manifesting itself in fan fiction, \\'\\'Game of Thrones\\'\\'-themed burlesque routines and parents naming their children after series characters; writers quoted attributed this success to the rich detail, moral ambiguity, sexual explicitness and epic scale of the series and novels. The previous year, \"Arya\" was the fastest-rising girl\\'s name in the US after it jumped in popularity from 711th to 413th place.\\n\\n, about 58 percent of series viewers were male and 42 percent female, and the average male viewer was 41 years old. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 59}, 'embedding': None, 'id': '59bc20096f0f99987c5c3d638a316b50'}>,\n",
-       "  <Document: {'content': ', about 58 percent of series viewers were male and 42 percent female, and the average male viewer was 41 years old. According to SBS Broadcasting Group marketing director Helen Kellie, \\'\\'Game of Thrones\\'\\' has a high fan-engagement rate; 5.5 percent of the series\\' 2.9million Facebook fans talked online about the series in 2012, compared to 1.8percent of the more than ten million fans of \\'\\'True Blood\\'\\' (HBO\\'s other fantasy series). Vulture.com cited Westeros.org and WinterIsComing.net (news and discussion forums), ToweroftheHand.com (which organizes communal readings of the novels) and Podcastoficeandfire.com as fan sites dedicated to the TV and novel series; and podcasts cover \\'\\'Game of Thrones\\'\\'.\\n\\n===Awards and accolades===\\n\\n\\'\\'Game of Thrones\\'\\' has won numerous of awards since it debuted as a series, including 58 Primetime Emmy Awards, 5 Screen Actors Guild Award, and a Peabody Award; it holds the Emmy-award records for both most wins for a scripted television series, surpassing the record of 37 wins held by \\'\\'Frasier\\'\\' since 2004, and for most nominations for a drama with 161. In 2013 the Writers Guild of America listed \\'\\'Game of Thrones\\'\\' as the 40th \"best written\" series in television history. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 60}, 'embedding': None, 'id': '5bd32b253fc174de0daa4e3c205fdaa'}>,\n",
-       "  <Document: {'content': 'In 2013 the Writers Guild of America listed \\'\\'Game of Thrones\\'\\' as the 40th \"best written\" series in television history. In 2015 \\'\\'The Hollywood Reporter\\'\\' placed it at number four on their \"best TV shows ever\" list, while in 2016 the series was placed seventh on \\'\\'Empire\\'\\' \"The 50 best TV shows ever\". The same year \\'\\'Rolling Stone\\'\\' named it the twelfth \"greatest TV Show of all time\".\\n\\nThe 2011 first season received 13 Emmy nominations (including Outstanding Drama Series), and won for Outstanding Supporting Actor in a Drama Series (given to Peter Dinklage for his portrayal of Tyrion Lannister) and Outstanding Main Title Design. Other nominations included Outstanding Directing (\"Winter Is Coming\") and Outstanding Writing (\"Baelor\"). Dinklage was also named Best Supporting Actor at the Golden Globe, Satellite and Scream Awards.\\n\\nIn 2012, the second season received six Emmy Awards from 11 nominations, including Outstanding Drama Series and Outstanding Supporting Actor in a Drama Series (Dinklage).\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 61}, 'embedding': None, 'id': 'c9c7528828d83d20f09a7b0dcbaa5b91'}>,\n",
-       "  <Document: {'content': 'In 2012, the second season received six Emmy Awards from 11 nominations, including Outstanding Drama Series and Outstanding Supporting Actor in a Drama Series (Dinklage).\\n\\nThe 2013 third season received 16 Emmy nominations, including Outstanding Drama Series, Outstanding Supporting Actor in a Drama Series (Dinklage), Outstanding Supporting Actress in a Drama Series (Emilia Clarke), Outstanding Guest Actress in a Drama Series (Diana Rigg) and Outstanding Writing (\"The Rains of Castamere\"), winning two Creative Arts Emmys.\\n\\nIn 2014, the fourth season received four Emmys from 19 nominations, which included Outstanding Drama Series, Outstanding Supporting Actor in a Drama Series (Dinklage), Outstanding Supporting Actress in a Drama Series (Lena Headey), Outstanding Guest Actress in a Drama Series (Rigg), Outstanding Directing (\"The Watchers on the Wall\") and Outstanding Writing (\"The Children\").\\n\\nThe 2015 fifth season won the most Primetime Emmy Awards for a series in a year (12 awards from 24 nominations), including Outstanding Drama Series; other wins included Outstanding Supporting Actor in a Drama Series (Dinklage), Outstanding Directing (\"Mother\\'s Mercy\") and Outstanding Writing (\"Mother\\'s Mercy\"), and eight were Creative Arts Emmy Awards.\\n\\nIn 2016, the sixth season received the most nominations for the 68th Primetime Emmy Awards (23). ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 62}, 'embedding': None, 'id': 'b1630ac6dd6411e653bf6bd82419c69b'}>,\n",
-       "  <Document: {'content': 'The 2015 fifth season won the most Primetime Emmy Awards for a series in a year (12 awards from 24 nominations), including Outstanding Drama Series; other wins included Outstanding Supporting Actor in a Drama Series (Dinklage), Outstanding Directing (\"Mother\\'s Mercy\") and Outstanding Writing (\"Mother\\'s Mercy\"), and eight were Creative Arts Emmy Awards.\\n\\nIn 2016, the sixth season received the most nominations for the 68th Primetime Emmy Awards (23). It won for Outstanding Drama Series, Outstanding Directing (\"Battle of the Bastards\"), Outstanding Writing (\"Battle of the Bastards\"), and nine Creative Arts Emmys. Nominations included Outstanding Supporting Actor in a Drama Series (Dinklage and Kit Harington), Outstanding Supporting Actress in a Drama Series (Clarke, Headey and Maisie Williams), Outstanding Guest Actor in a Drama Series (Max von Sydow) and Outstanding Directing (\"The Door\").\\n\\nIn 2018, the seventh season received the most nominations at the 70th Primetime Emmy Awards (22). It won for Outstanding Drama Series, Outstanding Supporting Actor in a Drama Series (Peter Dinklage), and seven Creative Arts Emmys. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 63}, 'embedding': None, 'id': 'c809980f9d8ca909e5808fbc61595ce7'}>,\n",
-       "  <Document: {'content': 'It won for Outstanding Drama Series, Outstanding Supporting Actor in a Drama Series (Peter Dinklage), and seven Creative Arts Emmys. Nominations included Outstanding Supporting Actress in a Drama Series (Lena Headey), Outstanding Supporting Actor in a Drama Series (Nikolaj Coster-Waldau), Outstanding Guest Actress in a Drama Series (Diana Rigg), Outstanding Writing for a Drama Series (David Benioff and D. B. Weiss for \"The Dragon and the Wolf\"), and Outstanding Directing for a Drama Series (Jeremy Podeswa for \"The Dragon and the Wolf\" and Alan Taylor for \"Beyond the Wall\").\\n\\nIn 2019, the final season established a new record for most Emmy nominations received in the same year by any regular series with 32, breaking the 25 years long record previously held by \\'\\'NYPD Blue\\'\\', which had scored 26 nominations for its first season in 1994. Those nominations included Outstanding Drama Series, three nominations for Outstanding Directing for a Drama Series, Outstanding Writing for a Drama Series and ten nominations for acting. Emilia Clarke and Kit Harington in the lead acting categories: Alfie Allen, Peter Dinklage, Nikolaj Coster-Waldau, Gwendoline Christie, Lena Headey, Sophie Turner, and Maisie Williams in the supporting acting categories; and Carice van Houten in the guest acting category. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 64}, 'embedding': None, 'id': 'fd73ab59dc89992efcd6e74795c30490'}>,\n",
-       "  <Document: {'content': \"Emilia Clarke and Kit Harington in the lead acting categories: Alfie Allen, Peter Dinklage, Nikolaj Coster-Waldau, Gwendoline Christie, Lena Headey, Sophie Turner, and Maisie Williams in the supporting acting categories; and Carice van Houten in the guest acting category. The series eventually won twelve awards, becoming the first series to win Outstanding Drama Series for an eighth season and tying its own record (previously achieved for season five) for most Emmys won by a series in a single season. It also tied the record for most Outstanding Drama Series wins, becoming the fifth series to reach four wins after ''Hill Street Blues'', ''L.A. Law'', ''The West Wing'', and  ''Mad Men''; unlike all the other record holders, which had won for their first four seasons, ''Game of Thrones'' achieved the record with its final four seasons. Peter Dinklage broke the record for most wins in the Outstanding Supporting Actor in a Drama Series category with his fourth win for the series; his nomination had also extended his record for most nominations in the category to eight (one for every season of the show).\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 65}, 'embedding': None, 'id': '6938ac26792161e2e0e835431b33cb70'}>,\n",
-       "  <Document: {'content': 'Peter Dinklage broke the record for most wins in the Outstanding Supporting Actor in a Drama Series category with his fourth win for the series; his nomination had also extended his record for most nominations in the category to eight (one for every season of the show).\\n\\n===Viewer numbers===\\nThe first season averaged 2.5million viewers for its first Sunday-night screenings and a gross audience (including repeats and on-demand viewings) of 9.3million viewers per episode. For its second season, the series had an average gross audience of 11.6million viewers. The third season was seen by 14.2million viewers, making \\'\\'Game of Thrones\\'\\' the second-most-viewed HBO series (after \\'\\'The Sopranos\\'\\'). For the fourth season, HBO said that its average gross audience of 18.4million viewers (later adjusted to 18.6million) had passed \\'\\'The Sopranos\\'\\' for the record.\\n\\nBy the sixth season the average per-episode gross viewing figure had increased to over 25million, with nearly 40 percent of viewers watching on HBO digital platforms. In 2016, a \\'\\'New York Times\\'\\' study of the 50 TV shows with the most Facebook likes found that \\'\\'Game of Thrones\\'\\' was \"much more popular in cities than in the countryside, probably the only show involving zombies that is\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 66}, 'embedding': None, 'id': '9a7fa95b1d6121aca12dcacfe5c9983a'}>,\n",
-       "  <Document: {'content': 'In 2016, a \\'\\'New York Times\\'\\' study of the 50 TV shows with the most Facebook likes found that \\'\\'Game of Thrones\\'\\' was \"much more popular in cities than in the countryside, probably the only show involving zombies that is\". By season seven, average viewer numbers had increased to 32.8million per episode across all platforms.\\n\\nThe series set records on pay-television channels in the United Kingdom (with a 2016 average audience of more than five million on all platforms) and Australia (with a cumulative average audience of 1.2million).\\n\\n==Other media and products==\\n\\n===Video games===\\n\\nThe series has inspired several video games based on the TV series and novels. The strategy game \\'\\'Game of Thrones Ascent\\'\\' ties into the HBO series, making characters and settings available to players as they appear on television. Behaviour Interactive is developing a free-to-play strategy game based on the series for mobile devices. \\'\\'Reigns: Game of Thrones\\'\\', a spin-off of the \\'\\'Reigns\\'\\' strategy video game series, is in development by Nerial, published by Devolver Digital, and set to release in October 2018.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 67}, 'embedding': None, 'id': 'cf99680dad336ae9d1582a6c59f879a7'}>,\n",
-       "  <Document: {'content': \"''Reigns: Game of Thrones'', a spin-off of the ''Reigns'' strategy video game series, is in development by Nerial, published by Devolver Digital, and set to release in October 2018.\\n\\n===Merchandise and exhibition===\\n''Game of Thrones'' merchandise in HBO's New York City store\\n\\nHBO has licensed a variety of merchandise based on ''Game of Thrones'', including games, replica weapons and armor, jewelry, bobblehead dolls by Funko, beer by Ommegang and apparel. High-end merchandise includes a $10,500 Ulysse Nardin wristwatch and a $30,000 resin replica of the Iron Throne. In 2013 and 2014, a traveling exhibition of costumes, props, armor and weapons from the series visited major cities in Europe and the Americas.\\n\\n===Accompanying material===\\n''Thronecast: The Official Guide to Game of Thrones'', a series of podcasts presented by Geoff Lloyd and produced by Koink, has been released on the Sky Atlantic website and the UK iTunes store during the series' run; a new podcast, with analysis and cast interviews, is released after each episode. In 2014 and 2015 HBO commissioned ''Catch the Throne'', two rap albums about the series.\\n\\nA companion book, ''Inside HBO's Game of Thrones'' () by series writer Bryan Cogman, was published on September 27, 2012. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 68}, 'embedding': None, 'id': '1cd91c08aa173ed8e645bb8fa9d1d323'}>,\n",
-       "  <Document: {'content': \"A companion book, ''Inside HBO's Game of Thrones'' () by series writer Bryan Cogman, was published on September 27, 2012. The 192-page book, illustrated with concept art and behind-the-scenes photographs, covers the creation of the series' first two seasons and its principal characters and families.\\n\\n''After the Thrones'' is a live aftershow in which hosts Andy Greenwald and Chris Ryan discuss episodes of the series. It airs on HBO Now the Monday after each sixth-season episode. The Game of Thrones Live Concert Experience, a North American 28-city orchestral tour which performed the series' soundtrack with composer Ramin Djawadi, began February 2017 and concluded in April 2017. A second tour occurred in 2018 across cities in Europe and North America.\\n\\nEach season's Blu-ray and DVD set contains several short animated sequences narrated by the cast as their characters as they detail events in the history of Westeros. For the seventh season, this is to include the animated prequel series ''Game of Thrones: Conquest & Rebellion'', illustrated in a different animation style than previous videos. The series focuses on Aegon Targaryen's conquest of the Seven Kingdoms of Westeros.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 69}, 'embedding': None, 'id': '428db9ea328c532228326c0439b815ea'}>,\n",
-       "  <Document: {'content': 'For the seventh season, this is to include the animated prequel series \\'\\'Game of Thrones: Conquest & Rebellion\\'\\', illustrated in a different animation style than previous videos. The series focuses on Aegon Targaryen\\'s conquest of the Seven Kingdoms of Westeros.\\n\\n===Successor series===\\nIn May 2017, after years of speculation about possible successor series, HBO commissioned Max Borenstein, Jane Goldman, Brian Helgeland, Carly Wray, and Bryan Cogman to develop individual \\'\\'Game of Thrones\\'\\' successor series; all of the writers were to be working individually with George R. R. Martin, who also co-wrote two of the scripts. D. B. Weiss and David Benioff said that they would not be involved with any of the projects.\\n\\nMartin said that all the concepts under discussion were prequels, although he believes the term \"successor show\" applies better to these projects, as they are not \\'\\'Game of Thrones\\'\\' spin-offs in the traditional sense. He ruled out Robert\\'s Rebellion (the overthrow of Daenerys\\' father by Robert Baratheon) as a possible idea and revealed that some may be set outside Westeros. Later, he stated: \"at least two of them are solidly based on material in \\'\\'Fire and Blood\\'\\'.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 70}, 'embedding': None, 'id': '22833aa49d7348675e5460bb4833f917'}>,\n",
-       "  <Document: {'content': 'He ruled out Robert\\'s Rebellion (the overthrow of Daenerys\\' father by Robert Baratheon) as a possible idea and revealed that some may be set outside Westeros. Later, he stated: \"at least two of them are solidly based on material in \\'\\'Fire and Blood\\'\\'.\"In September 2018, regarding the other four projects, HBO president of programming Casey Bloys said that some of them have been abandoned completely, while others remain as possibilities for the future. In May 2019, Martin stated that two other projects were still in the script stage, but are \"edging closer\". In April 2019, Cogman confirmed his prequel would not be moving forward.\\n\\n==== \\'\\'Bloodmoon\\'\\' ====\\nOn June 8, 2018, HBO commissioned a pilot to a \\'\\'Game of Thrones\\'\\' prequel series from Goldman as showrunner and Martin as co-creator. The prequel was to take place in the Age of Heroes, a period that begins roughly 10,000 years before the events of \\'\\'Game of Thrones\\'\\'. Notable events of that period include the foundation of powerful Houses, the Long Night when the White Walkers first descended upon Westeros, and the Andal Invasion when the Andals invaded from Essos and conquered most of Westeros. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 71}, 'embedding': None, 'id': '3e116ce5d8ac9d0b591a79822b76144b'}>,\n",
-       "  <Document: {'content': 'Notable events of that period include the foundation of powerful Houses, the Long Night when the White Walkers first descended upon Westeros, and the Andal Invasion when the Andals invaded from Essos and conquered most of Westeros. Martin suggested \\'\\'The Long Night\\'\\' as a title for the series. On May 13, 2019, it was reported that the show had already begun filming in Belfast, under the working title \\'\\'Bloodmoon\\'\\'. S. J. Clarkson was announced to direct and executive produce the pilot, while Naomi Watts was cast as the female lead playing \"a charismatic socialite hiding a dark secret.\"Other series regulars were to include: Josh Whitehouse, Toby Regbo, Ivanno Jeremiah, Georgie Henley, Naomi Ackie, Denise Gough, Jamie Campbell Bower, Sheila Atim, Alex Sharp, Miranda Richardson, Marquis Rodriguez, John Simm, Richard McCabe, John Heffernan, and Dixie Egerickx. In September 2019, Martin claimed the pilot was in post-production but in October 2019, it was announced that HBO had decided to not move forward with Goldman\\'s prequel series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 72}, 'embedding': None, 'id': 'ab2113356c789cb362acbbe755c4a323'}>,\n",
-       "  <Document: {'content': 'In September 2019, Martin claimed the pilot was in post-production but in October 2019, it was announced that HBO had decided to not move forward with Goldman\\'s prequel series.\\n\\n==== \\'\\'House of the Dragon\\'\\' ====\\nIn September 2019, Nellie Andreeva of \\'\\'Deadline Hollywood\\'\\' reported that a second prequel from Martin and Ryan Condal that \"tracks the beginning of the end for House Targaryen\" was close to receiving a pilot order from HBO; the project is not considered an original sixth script, as it builds upon Cogman\\'s idea from 2017. This prequel, titled \\'\\'House of the Dragon\\'\\', was picked up straight to series on October 29, 2019. The 10-episode series is to be based on material from \\'\\'Fire and Blood\\'\\', executive produced by Martin, Vince Gerardis, Condal, and Miguel Sapochnik; the latter two are to be its showrunners as well.\\n\\n==References==\\n\\n==External links==\\n*\\n*\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 73}, 'embedding': None, 'id': 'abe94170f4131433e9ef7e719c46697d'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''Jorah Mormont''' is a fictional character in the ''A Song of Ice and Fire'' series of fantasy novels by American author George R. R. Martin and its television adaptation ''Game of Thrones''.\\n\\nIntroduced in ''A Game of Thrones'' (1996), Jorah is a knight in exile, the disgraced former lord of Bear Island and the only son of Jeor Mormont, the honorable lord commander of the Night's Watch. Jorah subsequently appeared in Martin's ''A Clash of Kings'' (1998), ''A Storm of Swords'' (2000) and ''A Dance with Dragons'' (2011). After fleeing Westeros, Jorah pledges fealty to Daenerys Targaryen and over the course of both the novels and the television show becomes her closest and most loyal companion; Jorah's passionate yet unrequited love of Daenerys is central to the character's arc in both the novels and television show. He is portrayed as a skilled warrior whose knowledge of the peoples and customs of Essos proves invaluable to Daenerys' journeys.\\n\\nJorah is portrayed by the Scottish actor Iain Glen in the HBO television adaptation.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '89eee1cbee4aceaae32cba0f2b44a06f'}>,\n",
-       "  <Document: {'content': \"He is portrayed as a skilled warrior whose knowledge of the peoples and customs of Essos proves invaluable to Daenerys' journeys.\\n\\nJorah is portrayed by the Scottish actor Iain Glen in the HBO television adaptation.\\n\\n==Character==\\n===Background===\\nSer Jorah Mormont is the only child of the Night's Watch's Lord Commander Jeor Mormont, who abdicated shortly before Robert's Rebellion to join the Night's Watch and let Jorah assume the lordship of Bear Island. At some point Jorah married a lady of House Glover, who died from miscarriage after ten years of marriage. Jorah fought in Greyjoy's Rebellion, distinguishing himself by being one of the first to enter the fray during the siege of Pyke and was knighted by King Robert Baratheon. At a tourney at Lannisport celebrating the Baratheon victory, Jorah fell in love with the beautiful Lynesse Hightower (aunt of Margaery Tyrell). He named her Queen of Love and Beauty after winning the tourney and asked her father for her hand in marriage, which he accepted. However, Lynesse found herself ill-suited to the rough life on Bear Island, having grown up as a member of the wealthy House Hightower. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'd9c513ac3ea589e189f0035b8a7c3d44'}>,\n",
-       "  <Document: {'content': 'However, Lynesse found herself ill-suited to the rough life on Bear Island, having grown up as a member of the wealthy House Hightower. Jorah bankrupted himself trying to provide her with luxuries and eventually sold poachers found on his lands to a Tyroshi slaver to fund her lifestyle. His liege lord, Eddard Stark, condemned Jorah to death, but he and Lynesse fled to Lys. Jorah then made his living as a sellsword, but was still unable to provide enough to satisfy Lynesse, and eventually returned from a campaign to find that Lynesse had become a concubine of a wealthy Lysene merchant. The merchant warned Jorah that he would be enslaved to settle his debts if he remained in Lys and Jorah was forced to flee again. Afterwards he drifted among the Free Cities and the Dothraki, before becoming a spy for Varys.\\n\\nJorah Mormont is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of Daenerys Targaryen and Tyrion Lannister.  Jorah mostly serves to provide Daenerys with background information such as Westerosi history and the various cultures of Essos.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'f920976ec731533d578e5bb7a28ee9d5'}>,\n",
-       "  <Document: {'content': \"Jorah Mormont is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of Daenerys Targaryen and Tyrion Lannister.  Jorah mostly serves to provide Daenerys with background information such as Westerosi history and the various cultures of Essos.\\n\\n==Storylines==\\nCoat of arms of House Mormont\\n\\n===''A Game of Thrones''===\\n\\nIn Pentos, Jorah enters the service of House Targaryen during Daenerys Targaryen's wedding to Khal Drogo, although he is actually spying on behalf of Robert's Master of Whisperers Varys in hopes of earning a royal pardon. Although Jorah holds Daenerys' brother Viserys in contempt, he comes to admire Daenerys for her bravery and strength of character, and eventually falls in love with her. In Vaes Dothrak, Jorah is warned by Illyrio Mopatis that Robert has ordered Daenerys' assassination and saves her from being poisoned. After Drogo's death, Jorah is the first to pledge fealty to Daenerys and is astounded when she emerges from Drogo's funeral pyre with three dragon hatchlings.\\n\\n===''A Clash of Kings''===\\n\\nJorah accompanies Daenerys and the remnants of her khalasar to Qarth. There, Jorah stops sending reports to Varys, having truly fallen in love with Daenerys. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '19932e957aa990bd4baaf85227f9800e'}>,\n",
-       "  <Document: {'content': \"===''A Clash of Kings''===\\n\\nJorah accompanies Daenerys and the remnants of her khalasar to Qarth. There, Jorah stops sending reports to Varys, having truly fallen in love with Daenerys. Daenerys discovers his love after Jorah notes her physical similarity to Lynesse, but does not tell Jorah that she knows. At Qarth's docks, Jorah and Daenerys encounter the former pit fighter Strong Belwas and his squire Arstan Whitebeard. Arstan claims to know Jorah; Jorah finds him familiar, but does not recognise him. Despite Jorah's mistrust, Daenerys accepts the duo into her service.\\n\\n===''A Storm of Swords''===\\n\\nJorah confesses his love to Daenerys, who rejects his advances. Jorah recommends that they sail to Astapor to buy an army of Unsullied, and commands the army when they overthrow the masters of Yunkai. As they arrive at Meereen, Arstan is revealed to be Ser Barristan Selmy, former Kingsguard to Aerys II Targaryen and Robert, and he announces Jorah's former status as a spy. Daenerys orders Jorah and Selmy to infiltrate Meereen and release slaves. Upon their return, Jorah refuses to beg forgiveness, insisting that Daenerys owes him forgiveness as reward for his service. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'b6395ea820367ae193b0f332abff227c'}>,\n",
-       "  <Document: {'content': \"Daenerys orders Jorah and Selmy to infiltrate Meereen and release slaves. Upon their return, Jorah refuses to beg forgiveness, insisting that Daenerys owes him forgiveness as reward for his service. Daenerys agrees but notes that she cannot grant it without undermining her authority, and banishes Jorah.\\n\\n===''A Dance With Dragons''===\\n\\nJorah encounters the exiled Tyrion Lannister in a brothel in Selhorys and abducts him, hoping to return to Daenerys' graces by presenting a Lannister captive as a gift.  In Volantis they encounter the dwarf Penny, whom Tyrion takes pity on and is allowed by Jorah to accompany them.  During their voyage to Meereen, their ship is disabled by a storm and later seized by slavers, and Jorah is badly beaten and branded trying to fend them off.  In Meereen the trio are sold to the Yunkish master Yezzan zo Qaggaz.  Jorah becomes despondent after learning that Daenerys has taken the Meereenese nobleman Hizdhar zo Loraq as her husband.  When Yezzan dies from dysentery, Jorah and Tyrion escape to the camp of the sellsword company Second Sons, formerly in service to Daenerys before defecting to the slavers, and Jorah rejoins the company along with Tyrion.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '83cf21ee6c65ff4e60660099fd967ec3'}>,\n",
-       "  <Document: {'content': 'When Yezzan dies from dysentery, Jorah and Tyrion escape to the camp of the sellsword company Second Sons, formerly in service to Daenerys before defecting to the slavers, and Jorah rejoins the company along with Tyrion.  Jorah realises that the Yunkai\\'i will lose the coming battle with Meereen and tells Tyrion that they need to convince the company to defect again.\\n\\n===Family tree of House Mormont===\\n\\n;Notes\\n\\n==TV adaptation==\\nIain Glen plays the role of Jorah Mormont in the television series.\\nJorah Mormont is portrayed by Scottish actor Iain Glen in the television adaption of the series of books.\\n\\n====Season 1====\\nSer Jorah Mormont is an exiled knight in the service of Daenerys Targaryen and the son of Jeor Mormont of the Night\\'s Watch. To fund his wife\\'s extravagant lifestyle, he sold poachers on his land to slave traders, which is illegal in the Seven Kingdoms. Rather than face punishment by Lord Stark, he fled to Essos and learned the lifestyle of the Dothraki, who embrace him as one of their own and know him as \"Jorah the Andal\". Jorah serves as an adviser to the Targaryens on both political and cultural matters regarding the Seven Kingdoms and Essos. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'bd2c1f52ffb3969f2a16c7dcc1761e8e'}>,\n",
-       "  <Document: {'content': 'Jorah serves as an adviser to the Targaryens on both political and cultural matters regarding the Seven Kingdoms and Essos. Jorah is actually spying on the Targaryens for Lord Varys in exchange for a pardon from Robert, but after learning more about Daenerys, it appears that Jorah is falling for her; even preventing her poisoning, when Robert & Varys so order it.\\n\\n====Season 2====\\nAfter Daenerys is widowed, Jorah travels with her to Qarth and becomes the first knight of her Queensguard. He serves Daenerys as an adviser and protector throughout Season 2. When a mysterious, masked, female seuthsayer (privately) questions his fealty, he affirms that he will no longer help (Varys or) anyone betray (Daenerys). He explains to Daenerys that he respects her birthright claim to the Seven Kingdoms, as she \"has a kind heart\", the key to being a just Queen.\\n\\n====Season 3====\\nIn Season 3, Jorah plays an important part in the capture of Yunkai, together with Unsullied commander Grey Worm and Daario Naharis, Lieutenant and leader of the Second Sons. Led by Daario, the trio infiltrates the city and kills a number of Yunkish guards to open a gate, ensuring the capture of Yunkai. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'aa391edac5696472fadc8ac9dcd619c2'}>,\n",
-       "  <Document: {'content': 'Led by Daario, the trio infiltrates the city and kills a number of Yunkish guards to open a gate, ensuring the capture of Yunkai. Jorah later assists Daenerys and her army in conquering Meereen, and later informs her of Joffrey\\'s death, but at the same time dissuades her from invading King\\'s Landing, since she is not strong enough to take all of Westeros yet. When Daenerys appears to begin a romantic relationship with Daario, Jorah voices his disapproval.\\n\\n====Season 4====\\nIn Season 4, following the Conquering of Meereen, Daenerys discovers that Jorah\\'s original mission was to spy on her for the \"usurper\" Robert Baratheon, which almost led to her poisoning by the wine merchant in Vaes Dothrak. She exiles Jorah from Meereen on the threat of death. Jorah subsequently departs alone.\\n\\n====Season 5====\\nJorah encounters Tyrion Lannister in Volantis and kidnaps him with the intention of delivering him to Daenerys. Sailing through the ruins of Valyria, the duo is assaulted by \"stone men\" – humans turned insane by the disease greyscale – and Jorah is infected in the struggle. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '3e6565f93c04cc69ffda151262c71f51'}>,\n",
-       "  <Document: {'content': 'Sailing through the ruins of Valyria, the duo is assaulted by \"stone men\" – humans turned insane by the disease greyscale – and Jorah is infected in the struggle. As they make their way to Meereen on foot, Jorah learns from Tyrion that Jeor was killed in a mutiny beyond the Wall. Jorah and Tyrion are captured by slavers, who are convinced by Tyrion to sell them to the fighting pits in Meereen. At a demonstration of pit fighters, Jorah encounters Daenerys, who orders him freed but refuses to let him return to her service. With nowhere else left to go, Jorah returns to the fighting pits. At the reopening of the fighting pits, Jorah foils an assassination attempt on Daenerys and protects her from the insurgent Sons of the Harpy in the resulting confrontation. After Daenerys flies away on Drogon, Jorah and Daario Naharis leave Meereen to look for her.\\n\\n====Season 6====\\nJorah and Daario discover Daenerys\\' intentionally discarded ring in a grass plain, and Jorah deduces that she has been captured by a Dothraki horde and taken to Vaes Dothrak. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'ca55464fb081ccccceff6e5f7731faed'}>,\n",
-       "  <Document: {'content': \"====Season 6====\\nJorah and Daario discover Daenerys' intentionally discarded ring in a grass plain, and Jorah deduces that she has been captured by a Dothraki horde and taken to Vaes Dothrak. Upon arriving there, they are reunited with Daenerys, who rejects their offer to flee the city and instead asks them to help her with her own plan, to which they reluctantly agree. The next evening, Jorah and Daario watch as Daenerys, after trapping and burning all the Khals alive in the temple building, emerges from it completely unharmed, and they join the rest of the Dothraki in kneeling before her in awe. As Daenerys and her entourage prepare to leave Vaes Dothrak, Jorah reveals his greyscale to Daenerys and his intention to end his life before his illness overtakes him. Daenerys instead orders him to find a cure for his condition and return to her, declaring that she will need his counsel after conquering Westeros. Jorah then departs alone.\\n\\n====Season 7====\\nJorah returns to Westeros, seeking aid at the Citadel in Oldtown. By this time his greyscale has drastically progressed, consuming his entire arm and part of his torso. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '2868c73fa876dc4cfad221eb575a9619'}>,\n",
-       "  <Document: {'content': \"====Season 7====\\nJorah returns to Westeros, seeking aid at the Citadel in Oldtown. By this time his greyscale has drastically progressed, consuming his entire arm and part of his torso. Archmaester Ebrose diagnoses Jorah's greyscale as untreatably advanced, and informs Jorah that he has six months of sanity left and will be exiled from the Citadel the next day. Samwell Tarly, who formerly served under Jeor in the Night's Watch, rediscovers a cure for greyscale. Despite being forbidden to administer it, because whoever treats the patient could become infected, he treats Jorah in secret and successfully heals Jorah's greyscale. Ebrose, although unimpressed by Jorah's attempts to conceal Sam's treatment, discharges Jorah. Jorah then returns to Daenerys, who is happy to see him and accepts him into her service once again. He then joins the expedition led by Jon Snow beyond the Wall to capture a wight and prove its existence to Cersei Lannister. He and Jon talk about their respective fathers, and Jon expresses his relief that Jorah was not executed. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '890bad138e944514c4f1382a24c8a2f'}>,\n",
-       "  <Document: {'content': \"He then joins the expedition led by Jon Snow beyond the Wall to capture a wight and prove its existence to Cersei Lannister. He and Jon talk about their respective fathers, and Jon expresses his relief that Jorah was not executed. Jon attempts to return Longclaw to Jorah, on the basis that it is still the family sword of House Mormont, but Jorah refuses, claiming that he forfeited his right to claim the sword and that Jeor ''gave'' the weapon to Jon, making it his. Jorah survives the expedition and returns to Dragonstone.\\n\\n====Season 8====\\nJorah travels with Jon, Daenerys, and her forces to Winterfell to make a stand against the White Walkers. Daenerys is displeased with Tyrion when she learns that his sister Cersei Lannister is not sending her army, but she is mollified after Jorah speaks up in support of him. Samwell grants Jorah House Tarly's ancestral sword Heartsbane out of gratitude for Jeor's formative influence on him. During the Battle of Winterfell, Jorah is mortally wounded defending Daenerys from certain death at the hands of the undead, though he does survive long enough to see the army felled.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '74f98f30f781d66af5ecd8e544a49cb5'}>,\n",
-       "  <Document: {'content': 'During the Battle of Winterfell, Jorah is mortally wounded defending Daenerys from certain death at the hands of the undead, though he does survive long enough to see the army felled.  Jorah dies in the arms of a sobbing Daenerys, encircled by the wings of her dragon, Drogon.\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': 'a6c0538d5e0a05112372ef47c0810939'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''David Benioff''' (; né '''Friedman''' ; September 25, 1970) is an American screenwriter and television producer, writer, and director. Along with his collaborator D. B. Weiss, he is best known as co-creator, showrunner, and writer of ''Game of Thrones'' (2011–2019), the HBO adaptation of George R. R. Martin's series of books ''A Song of Ice and Fire''. He is also known for writing ''Troy'' (2004) and co-writing ''X-Men Origins: Wolverine'' (2009).\\n\\n==Early life==\\nBenioff was born David Friedman in New York City, to a Jewish family who emigrated from Austria, Romania, Germany, Poland and Russia. He is the son of Barbara (Benioff) and Stephen Friedman, who is a former head of Goldman Sachs. He is a distant cousin of Salesforce founder Marc Benioff. As an adult, he uses the last name Benioff, his mother's maiden name, to avoid confusion with other writers named David Friedman. He is the youngest of three children (Suzy, Caroline, and David) and grew up in Manhattan, first in Peter Cooper Village, then on 86th Street where he spent most of his childhood, before eventually moving near the U.N. headquarters when he was 16.\\n\\nBenioff is an alumnus of The Collegiate School and of Dartmouth College. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '6474302a93b75672c830a5fbf09ad2ce'}>,\n",
-       "  <Document: {'content': \"He is the youngest of three children (Suzy, Caroline, and David) and grew up in Manhattan, first in Peter Cooper Village, then on 86th Street where he spent most of his childhood, before eventually moving near the U.N. headquarters when he was 16.\\n\\nBenioff is an alumnus of The Collegiate School and of Dartmouth College. While at Dartmouth he was a member of Phi Delta Alpha Fraternity and the Sphinx Senior Society. After graduating in 1992, he worked in a number of jobs: for a time as a club bouncer in San Francisco, and as a high school English teacher at Poly Prep in Brooklyn, New York City for two years, where he served as the school's wrestling coach.\\n\\nBenioff became interested in pursuing an academic career and went to Trinity College Dublin in 1995, for a one-year program to study Irish literature. While in Dublin he met D. B. Weiss, who would later become his collaborator. Benioff wrote a thesis on Samuel Beckett while at Trinity College, but decided against a career in academia after writing the thesis. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '8b4b61b16e6a0504edbd61c63fa6c8b9'}>,\n",
-       "  <Document: {'content': \"Benioff wrote a thesis on Samuel Beckett while at Trinity College, but decided against a career in academia after writing the thesis. He worked as a radio DJ in Moose, Wyoming for a year—mostly as a side job, which he mainly accepted to take a year in the countryside at a writer's retreat. He then applied to join the Creative Writing Program at the University of California, Irvine after reading ''The Mysteries of Pittsburgh'' by Michael Chabon (an alumnus there), and received a Master of Fine Arts degree in creative writing there in 1999.\\n\\nIn 2001, ''People'' magazine included Benioff on its list of America's Top 50 Most Eligible Bachelors.\\n\\n==Career==\\n===Writing career===\\nBenioff spent two years writing his first published novel ''The 25th Hour'', originally titled ''Fireman Down'', and completed the book as his thesis for his master's degree at Irvine. He was asked to adapt the book into a screenplay after Tobey Maguire read a preliminary trade copy and became interested in making a film of the book. The film adaptation, titled ''25th Hour'' and starring Edward Norton, was directed by Spike Lee. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '5fa701327394f5101b66c7050c2d3376'}>,\n",
-       "  <Document: {'content': 'He was asked to adapt the book into a screenplay after Tobey Maguire read a preliminary trade copy and became interested in making a film of the book. The film adaptation, titled \\'\\'25th Hour\\'\\' and starring Edward Norton, was directed by Spike Lee. Benioff then wrote a collection of short stories titled \\'\\'When the Nines Roll Over (And Other Stories)\\'\\' (2004).\\n\\nHe drafted a screenplay of the mythological epic \\'\\'Troy\\'\\' (2004), for which Warner Bros. pictures paid him $2.5 million. He also wrote the script for the psychological thriller \\'\\'Stay\\'\\' (2005), which was directed by Marc Forster, and stars Ewan McGregor and Naomi Watts. His screenplay for \\'\\'The Kite Runner\\'\\' (2007), adapted from the novel of the same name, marked his second collaboration with director Marc Forster.\\n\\nBenioff was hired in 2004 to write the screenplay for the \\'\\'X-Men\\'\\' spin-off \\'\\'X-Men Origins: Wolverine\\'\\' (2009). He based his script on Barry Windsor-Smith\\'s \"Weapon X\" story, Chris Claremont and Frank Miller\\'s 1982 limited series on the character, as well as the 2001 limited series \\'\\'Origin\\'\\'. Hugh Jackman collaborated on the script, which he wanted to be more of a character piece compared with the previous \\'\\'X-Men\\'\\' films. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'f9a75b204b1e025e71aa48cb2c69cf60'}>,\n",
-       "  <Document: {'content': 'Hugh Jackman collaborated on the script, which he wanted to be more of a character piece compared with the previous \\'\\'X-Men\\'\\' films. Skip Woods was later hired by Fox to revise and rewrite Benioff\\'s script. Benioff had aimed for a \"darker and a bit more brutal\" story, writing it with an R rating in mind, although he acknowledged the film\\'s final tone would rest with the producers and director.\\n\\nIn 2006, Benioff became interested in adapting George R.R. Martin\\'s novel series, \\'\\'A Song of Ice and Fire\\'\\', and began working with D. B. Weiss on a proposed television series, \\'\\'Game of Thrones\\'\\'. The pilot, \"Winter Is Coming\", was put into development by HBO in 2007 and the series greenlit in 2010. Benioff and Weiss acted as the executive producers, showrunners, and writers of the show, which began airing on HBO in 2011. Benioff and Weiss had previously worked together on a script for a horror film titled \\'\\'The Headmaster\\'\\', but it was never made.\\n\\nIn October 2007, Universal Pictures hired Benioff to write an adapted screenplay of the Charles R. Cross biography of Kurt Cobain, but the screenplay was not used. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '7c0bbac4fca315d3624b3588e22ecaa7'}>,\n",
-       "  <Document: {'content': 'In October 2007, Universal Pictures hired Benioff to write an adapted screenplay of the Charles R. Cross biography of Kurt Cobain, but the screenplay was not used. In 2008, Benioff\\'s second novel, \\'\\'City of Thieves\\'\\', was published.\\n\\nOn April 10, 2014, Benioff announced he and Weiss had taken on their first feature film project to write, produce, and direct \\'\\'Dirty White Boys\\'\\', a novel by the Pulitzer prize-winning author Stephen Hunter. On July 19, 2017, Benioff announced that he and Weiss would produce another HBO series, titled \\'\\'Confederate\\'\\', after the final season of \\'\\'Game of Thrones\\'\\'. Benioff and Weiss said, \"We have discussed \\'\\'Confederate\\'\\' for years, originally as a concept for a feature film, but our experience on \\'\\'Thrones\\'\\' has convinced us that no one provides a bigger, better storytelling canvas than HBO.\"The announcement of \\'\\'Confederate\\'\\' was met with public animosity, and as of August 2019 (when Benioff and Weiss\\' deal with Netflix deal was announced) would not be moving forward.\\n\\nOn February 6, 2018, Disney announced that both Benioff and Weiss would write and produce a new series of \\'\\'Star Wars\\'\\' films after the final season of \\'\\'Game of Thrones\\'\\' is completed in 2019.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '1c29a73fcfe46cda0ce12e9e7d1627b8'}>,\n",
-       "  <Document: {'content': 'On February 6, 2018, Disney announced that both Benioff and Weiss would write and produce a new series of \\'\\'Star Wars\\'\\' films after the final season of \\'\\'Game of Thrones\\'\\' is completed in 2019.\\n\\nTowards the end of the final season of \\'\\'Game of Thrones\\'\\', a petition to HBO was started on Change.org. It described showrunners Benioff and Weiss as \"woefully incompetent writers\", and thus demanded \"competent writers\" to remake the eighth season of \\'\\'Game of Thrones\\'\\' in a manner \"that makes sense\". The petition eventually amassed over 1.5 million signatures. Richard Roeper, writing for the \\'\\'Chicago Sun Times\\'\\', described that the backlash to the eighth season was so great that in his 25+ years of reviewing movies and television, he doubts that he has \"ever seen the level of fan (and to a lesser degree, critical) vitriol leveled at\" \\'\\'Game of Thrones\\'\\'.\\n\\nIn early August 2019, Benioff and Weiss negotiated an exclusive multi-year film and television deal with Netflix worth US$200 million. Due to their commitments to Netflix, Benioff and Weiss exited their contract to produce \\'\\'Star Wars\\'\\' films for Disney and Lucasfilm.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'c886e3fb2c2a45820cd4b236faa71cab'}>,\n",
-       "  <Document: {'content': 'Due to their commitments to Netflix, Benioff and Weiss exited their contract to produce \\'\\'Star Wars\\'\\' films for Disney and Lucasfilm.\\n\\n===Directing career===\\nBenioff and Weiss together directed two episodes of \\'\\'Game of Thrones\\'\\', but used a coin-flip to decide who would get the credit on the show. Benioff was given the credit for season 3 episode 3, \"Walk of Punishment\", while Weiss was credited with season 4 episode 1, \"Two Swords\". Benioff and Weiss co-directed the series finale, which was widely panned.\\n\\n==Personal life==\\nOn September 30, 2006, Benioff married actress Amanda Peet in New York City. Together they have three children. The family lives in Manhattan and Beverly Hills.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '4b39a3e287ad1ae5b3f2f9a4e42e04fb'}>,\n",
-       "  <Document: {'content': '==Personal life==\\nOn September 30, 2006, Benioff married actress Amanda Peet in New York City. Together they have three children. The family lives in Manhattan and Beverly Hills.\\n\\n==Bibliography==\\n\\n===Author===\\n\\nTitle\\n\\nYear\\n\\nType\\n\\nNote\\n\\n\\'\\'The 25th Hour\\'\\'\\n\\n2001\\n\\nNovel\\n\\nPaperback: 224 pagesPublisher: Plume; Reissue edition (January 29, 2002)Language: English\\n\\n\\'\\'When the Nines Roll Over (and Other Stories)\\'\\'\\n\\n2004\\n\\nShort Story Collection\\n\\nHardcover: 223 pagesPublisher: Viking Books (August 19, 2004)Language: English\\n\\n\\'\\'City of Thieves\\'\\'\\n\\n2008\\n\\nNovel\\n\\nHardcover: 281 pagesPublisher: Viking Books (May 15, 2008)Language: English\\n\\n==Filmography==\\n===Film===\\n\\nYear\\n\\nTitle\\n\\nWriter\\n\\nProducer\\n\\nDirector\\n\\nNotes\\n\\n2002\\n\\n\\'\\'25th Hour\\'\\'\\n\\nSpike Lee\\n\\nNominated—Boston Society of Film Critics Award for Best Screenplay (2002)\\n\\n2004\\n\\n\\'\\'Troy\\'\\'\\n\\nWolfgang Petersen\\n\\n2005\\n\\n\\'\\'Stay\\'\\'\\n\\nMarc Forster\\n\\n\\'\\'When the Nines Roll Over\\'\\'\\n\\nHimself\\n\\nShort film based on a story from \\'\\'When the Nines Roll Over\\'\\'\\n\\n2007\\n\\n\\'\\'The Kite Runner\\'\\'\\n\\nMarc Forster\\n\\nChristopher Award for Best Feature Film (2007)Nominated—Golden Globe Award for Best Foreign Language Film (2008)Nominated—BAFTA Award for Best Adapted Screenplay (2008)Nominated—Satellite Award for Best Adapted Screenplay (2007)\\n\\n2009\\n\\n\\'\\'X-Men Origins: Wolverine\\'\\'\\n\\nGavin Hood\\n\\nCo-wrote with Skip Woods\\n\\n\\'\\'Brothers\\'\\'\\n\\nJim Sheridan\\n\\n2019\\n\\n\\'\\'Gemini Man\\'\\'\\n\\nAng Lee\\n\\nCo-wrote with Billy Ray and Darren Lemke\\n\\n===Television===\\n\\nYear\\n\\nTitle\\n\\nWriter\\n\\nProducer\\n\\nDirector\\n\\nNotes\\n\\n2011–2019\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nCo-creatorExecutive producerDirected and wrote episode: \"Walk of Punishment\"Directed and wrote episode (with D. B. Weiss):\"The Iron Throne\"Wrote: 45 episodesPrimetime Emmy Award for Outstanding Drama Series (2015-2016, 2018)Primetime Emmy Award for Outstanding Writing for a Drama Series (2015-2016)Hugo Award for Best Dramatic Presentation, Long Form (2012)Hugo Award for Best Dramatic Presentation, Short Form (2013-2014)Producers Guild of America Award for Best Episodic Drama (2015)Golden Nymph Awards for Outstanding International Producer (2012)Nominated—Primetime Emmy Award for Outstanding Drama Series (2011-2014)Nominated—Primetime Emmy Award for Outstanding Writing for a Drama Series (2011-2014)Nominated—Producers Guild of America Award for Best Episodic Drama (2011-2014, 2016, 2018)Nominated—BAFTA for Best International Programme (2013)Nominated—Writers Guild of America Award for Dramatic Series (2011-2012, 2014-2016, 2018)Nominated—Writers Guild of America Award for Episodic Drama (2015-2016)Nominated—Writers Guild of America Award for New Series (2011)Nominated—Hugo Award for Best Dramatic Presentation, Short Form (2015, 2017)Nominated—USC Scripter Award for Best Adapted Screenplay (2016-2017)Nominated—Humanitas Prize for 60 Minute Network or Syndicated Television (2017)\\n\\n2013\\n\\n\\'\\'It\\'s Always Sunny in Philadelphia\\'\\'\\n\\nWrote episode: \"Flowers for Charlie\"Bored Lifeguard #1 (cameo in \"The Gang Goes to a Water Park\")\\n\\n==See also==\\n\\n* List of awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\n==References==\\n\\n==External links==\\n\\n*\\n*\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'c02e1b9ca5d2af705d0351f933ec45bd'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'First of His Name\\'\\'\\'\" is the fifth episode of the fourth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 35th overall. The episode was written by series co-creators David Benioff and D. B. Weiss, and directed by Michelle MacLaren. It aired on May 4, 2014.\\n\\nThe title of the episode refers to a phrase used during Tommen Baratheon\\'s coronation as king. A similar style is used by Daenerys Targaryen styling herself Queen in Meereen. Both are the first of their respective names to lay claim to the Throne of the Seven Kingdoms.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nTommen is crowned as king. Cersei decides that Tommen will marry Margaery in fortnight. Tywin tells Cersei that the gold mines in the Westerlands have run dry and that the crown is deeply indebted to the Iron Bank of Braavos, but the union of the houses of Lannister and Tyrell will help rectify this problem. Cersei asks Oberyn to send a ship back to Sunspear as a gift for Myrcella.\\n\\n===In the Vale===\\nPetyr and Sansa arrive in the Vale. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'f7755f202b1dbd8979457b383f8fb825'}>,\n",
-       "  <Document: {'content': \"Cersei asks Oberyn to send a ship back to Sunspear as a gift for Myrcella.\\n\\n===In the Vale===\\nPetyr and Sansa arrive in the Vale. At the Bloody Gate, Petyr tells Ser Donnel Waynwood that Sansa's name is Alayne, hiding her true identity as she is still wanted in King's Landing for Joffrey's murder. Lysa reveals that she poisoned Jon\\xa0Arryn and accused the Lannisters in her letter to Catelyn for Petyr. Lysa tells Sansa that once Tyrion is executed, she will marry Robin.\\n\\n===In Meereen===\\nDaario tells Daenerys that his forces have taken the Meereenese navy. Jorah tells her that Astapor and Yunkai have fallen back into old habits. Daenerys tells Jorah that instead of sailing to Westeros, she intends to stay for now and rule Slaver's Bay.\\n\\n===On the Kingsroad===\\nBrienne and Podrick ride toward the Wall, believing that Sansa is there with Jon.\\n\\n===In the Riverlands===\\nArya reveals that Syrio was killed by Meryn.\\n\\n===Beyond the Wall===\\nJon's group attack Karl's group at Craster's Keep. Locke tries to kidnap Bran, but Bran enters Hodor's mind and kills Locke. Hodor frees Jojen and Meera. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '767c0b9233079393f2b8f28188509688'}>,\n",
-       "  <Document: {'content': '===Beyond the Wall===\\nJon\\'s group attack Karl\\'s group at Craster\\'s Keep. Locke tries to kidnap Bran, but Bran enters Hodor\\'s mind and kills Locke. Hodor frees Jojen and Meera. Bran wishes to reunite with Jon, but Jojen tells him that Jon will stop their journey. Jon kills Karl. Counting the dead, Edd notices Rast is missing. Rast, having fled to the woods, is killed by Ghost. Jon burns Craster\\'s Keep, at the suggestion of Craster\\'s wives.\\n\\n==Production==\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"First of His Name\" was written by executive producers David Benioff and D. B. Weiss, based upon the source material, Martin\\'s \\'\\'A Storm of Swords\\'\\'. Chapters adapted from \\'\\'A Storm of Swords\\'\\' to the episode were chapters 68 and 71 (Sansa VI and Daenerys VI). It also covers part of the fourth novel, \\'\\'A Feast of Crows\\'\\': namely chapter 14 (Brienne III).\\n\\n===Casting===\\nKate Dickie (Lysa Arryn) and Lino Facioli (Robin Arryn) make return appearances after an absence of several years (since the first season).\\n\\n==Reception==\\n===Ratings===\\n\"First of His Name\" established a new series high in ratings, with 7.16 million people watching it during its first airing. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '8a0cbafe585b20fcf4c2f5fae85a7d99'}>,\n",
-       "  <Document: {'content': '==Reception==\\n===Ratings===\\n\"First of His Name\" established a new series high in ratings, with 7.16 million people watching it during its first airing. In the United Kingdom, the episode was viewed by 1.643 million viewers, making it the highest-rated broadcast that week. It also received 0.082 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode received universal acclaim from critics. Rotten Tomatoes reported that 100% of the episode\\'s 40 reviews on the site were positive with an average score of 8.25 out of 10. Its consensus reads: \"Only slow by \\'\\'Game of Thrones\\'\\' standards, \"First of His Name\" demonstrates how carefully constructed this show has been since season one.\"Matt Fowler of \\'\\'IGN\\'\\' gave the episode a 9/10 writing that the episode \"gave us a much-needed look back at some past events on the series.\"He highlighted the revelation that Littlefinger\\'s actions \"put the story of the entire series into motion.\"Erik Adams of \\'\\'The A.V. Club\\'\\' gave the episode an A and praised the writers for their focus on the female characters.\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '309e2b21d1d7304639f4223658a304dd'}>,\n",
-       "  <Document: {'content': \"\\n\\n+ '''List of awards won by ''Game of Thrones'''''\\n\\n250px\\n\\nAwards and nominations\\n\\n'''Award'''\\n\\n'''Won'''\\n\\n'''Nominated'''\\n\\n;Art Directors Guild\\n\\n;American Cinema Editors\\n\\n;AFI Awards\\n\\n;Annie Awards\\n\\n;British Academy Television Awards\\n\\n;American Society of Cinematographers\\n\\n;Artios Awards\\n\\n;Cinema Audio Society\\n\\n;Costume Designers Guild\\n\\n;Critics' Choice Television Awards\\n\\n;Directors Guild of America Awards\\n\\n;Primetime Emmy Awards\\n\\n;GLAAD Media Awards\\n\\n;Golden Globe Awards\\n\\n;Grammy Award\\n\\n;Golden Nymph Awards\\n\\n;Golden Reel Awards\\n\\n;Hollywood Post Alliance\\n\\n;Irish Film & Television Academy\\n\\n;Kerrang! Awards\\n\\n;Location Managers Guild Awards\\n\\n;National Television Awards\\n\\n;Peabody Awards\\n\\n;People's Choice Awards\\n\\n;Producers Guild of America Awards\\n\\n;Royal Television Society\\n\\n;Satellite Awards\\n\\n;Saturn Awards\\n\\n;Scream Awards\\n\\n;Screen Actors Guild Awards\\n\\n;TCA Awards\\n\\n;Visual Effects Society\\n\\n;Writers Guild of America Awards\\n\\n;Young Artist Awards\\n\\n; '''Total number of wins and nominations'''\\n\\nReferences\\n\\n''Game of Thrones'' is an American  fantasy drama television series created for HBO by David Benioff and D. B. Weiss. It is an adaptation of ''A Song of Ice and Fire'', George R. R. Martin's series of fantasy novels. The story takes place on the fictional continents of Westeros and Essos, it has several plot lines and a large ensemble cast. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '70f34057da7241ef86034cff1c89660a'}>,\n",
-       "  <Document: {'content': \"The story takes place on the fictional continents of Westeros and Essos, it has several plot lines and a large ensemble cast. The first story arc follows a dynastic conflict among competing claimants for succession to the Iron Throne of the Seven Kingdoms, with other noble families fighting for independence from the throne. The second covers attempts to reclaim the throne by the exiled last scion of the realm's deposed ruling dynasty; the third chronicles the threat of the impending winter and the legendary creatures and fierce peoples of the North.\\n\\nThe series, mostly written by Benioff, Weiss and Martin, has been nominated for many awards, including six Golden Globe Awards (one win), ten Writers Guild of America Awards, seven Producers Guild of America Awards (one win), eight Directors Guild of America Awards (two wins), eight Art Directors Guild Awards (five wins), twenty one Saturn Awards (two wins), fourteen Satellite Awards (three wins), and one Peabody Award. The series has received 160 Primetime Emmy Award nominations, including eight consecutive Outstanding Drama Series nominations, with 59 wins to date. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '40ae02c65ebb8b11e035beadf26d919'}>,\n",
-       "  <Document: {'content': \"The series has received 160 Primetime Emmy Award nominations, including eight consecutive Outstanding Drama Series nominations, with 59 wins to date. ''Game of Thrones'' received many nominations, with awards recognizing various aspects of the series such as directing, writing, cast, visual effects, or overall quality.\\n\\nPeter Dinklage is the most awarded member of the cast, with recognitions such as the Primetime Emmy Award and Golden Globe for Best Supporting Actor in a Series. In addition, cast members Lena Headey, Emilia Clarke, Kit Harington, Maisie Williams, Nikolaj Coster-Waldau, Alfie Allen, Sophie Turner, Gwendoline Christie, Carice van Houten, Diana Rigg, and Max von Sydow received Primetime Emmy Award nominations for their performances in the series. The rest of the cast was also very praised, many receiving various awards nominations, including six Screen Actors Guild Award for Outstanding Performance by an Ensemble in a Drama Series nominations rewarding all of the main cast for seasons 1, 3, 4, 5, 6 and 7. In 2015, it set a record for winning the most number of Primetime Emmy Awards for a series in a single year, with 12 wins out of 24 nominations. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '377121bc397a9560ce24d004128b2f0a'}>,\n",
-       "  <Document: {'content': 'In 2015, it set a record for winning the most number of Primetime Emmy Awards for a series in a single year, with 12 wins out of 24 nominations. In 2016, it became the most awarded series in Emmy Awards history, with a total of 38 wins. To date, \\'\\'Game of Thrones\\'\\' has won 308 awards out of 628 nominations.\\n\\n\\'\\'Game of Thrones\\'\\' also holds six world records from the Guinness Book of World Records, \"Most pirated TV program\" and \"Largest TV drama simulcast\" among others.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '70e2207dc3015a9f55472c58dc980a44'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' also holds six world records from the Guinness Book of World Records, \"Most pirated TV program\" and \"Largest TV drama simulcast\" among others.\\n\\n==Total nominations and awards for the cast==\\nPeter Dinklage is the series\\'s most successful cast member in terms of awards, having won eight awards and received 38 additional nominations\\n\\nActor\\n\\nCharacter\\n\\nTenure\\n\\nNominations\\n\\nAwards\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\n2011–2019\\n\\n47\\n\\n8\\n\\nLena Headey\\n\\nCersei Lannister\\n\\n2011–2019\\n\\n33\\n\\n6\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\n2011–2019\\n\\n32\\n\\n5\\n\\nKit Harington\\n\\nJon Snow\\n\\n2011–2019\\n\\n30\\n\\n5\\n\\nMaisie Williams\\n\\nArya Stark\\n\\n2011–2019\\n\\n22\\n\\n5\\n\\nSophie Turner\\n\\nSansa Stark\\n\\n2011–2019\\n\\n14\\n\\n4\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\n2013–2017\\n\\n9\\n\\n3\\n\\nSean Bean\\n\\nEddard \"Ned\" Stark\\n\\n2011\\n\\n8\\n\\n3\\n\\nPedro Pascal\\n\\nOberyn Martell\\n\\n2014\\n\\n5\\n\\n3\\n\\nJack Gleeson\\n\\nJoffrey Baratheon\\n\\n2011–2014\\n\\n12\\n\\n2\\n\\nNatalie Dormer\\n\\nMargaery Tyrell\\n\\n2012–2016\\n\\n4\\n\\n2\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\n2011–2019\\n\\n17\\n\\n1\\n\\nMichelle Fairley\\n\\nCatelyn Stark\\n\\n2011–2013\\n\\n8\\n\\n1\\n\\nLiam Cunningham\\n\\nDavos Seaworth\\n\\n2012–2019\\n\\n8\\n\\n1\\n\\nCharles Dance\\n\\nTywin Lannister\\n\\n2011–2015\\n\\n6\\n\\n1\\n\\nIain Glen\\n\\nJorah Mormont\\n\\n2011–2019\\n\\n7\\n\\n1\\n\\nIsaac Hempstead Wright\\n\\nBrandon \"Bran\" Stark\\n\\n2011–2014, 2016–2019\\n\\n7\\n\\n1\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\n2012–2019\\n\\n7\\n\\n1\\n\\nConleth Hill\\n\\nVarys\\n\\n2011–2019\\n\\n6\\n\\n1\\n\\nNathalie Emmanuel\\n\\nMissandei\\n\\n2013–2019\\n\\n6\\n\\n1\\n\\nRory McCann\\n\\nSandor \"The Hound\" Clegane\\n\\n2011–2014, 2016–2019\\n\\n6\\n\\n1\\n\\nIan McElhinney\\n\\nBarristan Selmy\\n\\n2011, 2013–2015\\n\\n5\\n\\n1\\n\\nJohn Bradley\\n\\nSamwell Tarly\\n\\n2011–2019\\n\\n5\\n\\n1\\n\\nJosef Altin\\n\\nPypar\\n\\n2011, 2013–2014\\n\\n4\\n\\n1\\n\\nJulian Glover\\n\\nPycelle\\n\\n2011–2016\\n\\n4\\n\\n1\\n\\nMark Stanley\\n\\nGrenn\\n\\n2011–2014\\n\\n4\\n\\n1\\n\\n==Emmy nominations and awards for the cast==\\n\\nActor\\n\\nCharacter\\n\\nCategory\\n\\nSeasons\\n\\n1\\n\\n2\\n\\n3\\n\\n4\\n\\n5\\n\\n6\\n\\n7\\n\\n8\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\nSupporting Actor\\n\\nLena Headey\\n\\nCersei Lannister\\n\\nSupporting Actress\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\nGuest Actress\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\nSupporting Actress\\n\\nLead Actress\\n\\nKit Harington\\n\\nJon Snow\\n\\nSupporting Actor\\n\\nLead Actor\\n\\nMaisie Williams\\n\\nArya Stark\\n\\nSupporting Actress\\n\\nMax von Sydow\\n\\nThree-eyed Raven\\n\\nGuest Actor\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\nSupporting Actor\\n\\nAlfie Allen\\n\\nTheon Greyjoy\\n\\nSupporting Actor\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\nSupporting Actress\\n\\nSophie Turner\\n\\nSansa Stark\\n\\nSupporting Actress\\n\\nCarice van Houten\\n\\nMelisandre\\n\\nGuest Actress\\n\\n==Significant Guild and Peer Awards==\\n\\n===AFI Awards===\\nThe AFI Award, created in 2000 by the American Film Institute, is given annually to ten films and ten television programs to acknowledge the \"most significant achievements in the art of the moving image\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'df1f37224dbe8f0205e855aaa9b1e4f2'}>,\n",
-       "  <Document: {'content': '==Total nominations and awards for the cast==\\nPeter Dinklage is the series\\'s most successful cast member in terms of awards, having won eight awards and received 38 additional nominations\\n\\nActor\\n\\nCharacter\\n\\nTenure\\n\\nNominations\\n\\nAwards\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\n2011–2019\\n\\n47\\n\\n8\\n\\nLena Headey\\n\\nCersei Lannister\\n\\n2011–2019\\n\\n33\\n\\n6\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\n2011–2019\\n\\n32\\n\\n5\\n\\nKit Harington\\n\\nJon Snow\\n\\n2011–2019\\n\\n30\\n\\n5\\n\\nMaisie Williams\\n\\nArya Stark\\n\\n2011–2019\\n\\n22\\n\\n5\\n\\nSophie Turner\\n\\nSansa Stark\\n\\n2011–2019\\n\\n14\\n\\n4\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\n2013–2017\\n\\n9\\n\\n3\\n\\nSean Bean\\n\\nEddard \"Ned\" Stark\\n\\n2011\\n\\n8\\n\\n3\\n\\nPedro Pascal\\n\\nOberyn Martell\\n\\n2014\\n\\n5\\n\\n3\\n\\nJack Gleeson\\n\\nJoffrey Baratheon\\n\\n2011–2014\\n\\n12\\n\\n2\\n\\nNatalie Dormer\\n\\nMargaery Tyrell\\n\\n2012–2016\\n\\n4\\n\\n2\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\n2011–2019\\n\\n17\\n\\n1\\n\\nMichelle Fairley\\n\\nCatelyn Stark\\n\\n2011–2013\\n\\n8\\n\\n1\\n\\nLiam Cunningham\\n\\nDavos Seaworth\\n\\n2012–2019\\n\\n8\\n\\n1\\n\\nCharles Dance\\n\\nTywin Lannister\\n\\n2011–2015\\n\\n6\\n\\n1\\n\\nIain Glen\\n\\nJorah Mormont\\n\\n2011–2019\\n\\n7\\n\\n1\\n\\nIsaac Hempstead Wright\\n\\nBrandon \"Bran\" Stark\\n\\n2011–2014, 2016–2019\\n\\n7\\n\\n1\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\n2012–2019\\n\\n7\\n\\n1\\n\\nConleth Hill\\n\\nVarys\\n\\n2011–2019\\n\\n6\\n\\n1\\n\\nNathalie Emmanuel\\n\\nMissandei\\n\\n2013–2019\\n\\n6\\n\\n1\\n\\nRory McCann\\n\\nSandor \"The Hound\" Clegane\\n\\n2011–2014, 2016–2019\\n\\n6\\n\\n1\\n\\nIan McElhinney\\n\\nBarristan Selmy\\n\\n2011, 2013–2015\\n\\n5\\n\\n1\\n\\nJohn Bradley\\n\\nSamwell Tarly\\n\\n2011–2019\\n\\n5\\n\\n1\\n\\nJosef Altin\\n\\nPypar\\n\\n2011, 2013–2014\\n\\n4\\n\\n1\\n\\nJulian Glover\\n\\nPycelle\\n\\n2011–2016\\n\\n4\\n\\n1\\n\\nMark Stanley\\n\\nGrenn\\n\\n2011–2014\\n\\n4\\n\\n1\\n\\n==Emmy nominations and awards for the cast==\\n\\nActor\\n\\nCharacter\\n\\nCategory\\n\\nSeasons\\n\\n1\\n\\n2\\n\\n3\\n\\n4\\n\\n5\\n\\n6\\n\\n7\\n\\n8\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\nSupporting Actor\\n\\nLena Headey\\n\\nCersei Lannister\\n\\nSupporting Actress\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\nGuest Actress\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\nSupporting Actress\\n\\nLead Actress\\n\\nKit Harington\\n\\nJon Snow\\n\\nSupporting Actor\\n\\nLead Actor\\n\\nMaisie Williams\\n\\nArya Stark\\n\\nSupporting Actress\\n\\nMax von Sydow\\n\\nThree-eyed Raven\\n\\nGuest Actor\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\nSupporting Actor\\n\\nAlfie Allen\\n\\nTheon Greyjoy\\n\\nSupporting Actor\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\nSupporting Actress\\n\\nSophie Turner\\n\\nSansa Stark\\n\\nSupporting Actress\\n\\nCarice van Houten\\n\\nMelisandre\\n\\nGuest Actress\\n\\n==Significant Guild and Peer Awards==\\n\\n===AFI Awards===\\nThe AFI Award, created in 2000 by the American Film Institute, is given annually to ten films and ten television programs to acknowledge the \"most significant achievements in the art of the moving image\". \\'\\'Game of Thrones\\'\\' has been awarded seven times.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '9800c235de8975411a076a69d431f672'}>,\n",
-       "  <Document: {'content': '==Total nominations and awards for the cast==\\nPeter Dinklage is the series\\'s most successful cast member in terms of awards, having won eight awards and received 38 additional nominations\\n\\nActor\\n\\nCharacter\\n\\nTenure\\n\\nNominations\\n\\nAwards\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\n2011–2019\\n\\n47\\n\\n8\\n\\nLena Headey\\n\\nCersei Lannister\\n\\n2011–2019\\n\\n33\\n\\n6\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\n2011–2019\\n\\n32\\n\\n5\\n\\nKit Harington\\n\\nJon Snow\\n\\n2011–2019\\n\\n30\\n\\n5\\n\\nMaisie Williams\\n\\nArya Stark\\n\\n2011–2019\\n\\n22\\n\\n5\\n\\nSophie Turner\\n\\nSansa Stark\\n\\n2011–2019\\n\\n14\\n\\n4\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\n2013–2017\\n\\n9\\n\\n3\\n\\nSean Bean\\n\\nEddard \"Ned\" Stark\\n\\n2011\\n\\n8\\n\\n3\\n\\nPedro Pascal\\n\\nOberyn Martell\\n\\n2014\\n\\n5\\n\\n3\\n\\nJack Gleeson\\n\\nJoffrey Baratheon\\n\\n2011–2014\\n\\n12\\n\\n2\\n\\nNatalie Dormer\\n\\nMargaery Tyrell\\n\\n2012–2016\\n\\n4\\n\\n2\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\n2011–2019\\n\\n17\\n\\n1\\n\\nMichelle Fairley\\n\\nCatelyn Stark\\n\\n2011–2013\\n\\n8\\n\\n1\\n\\nLiam Cunningham\\n\\nDavos Seaworth\\n\\n2012–2019\\n\\n8\\n\\n1\\n\\nCharles Dance\\n\\nTywin Lannister\\n\\n2011–2015\\n\\n6\\n\\n1\\n\\nIain Glen\\n\\nJorah Mormont\\n\\n2011–2019\\n\\n7\\n\\n1\\n\\nIsaac Hempstead Wright\\n\\nBrandon \"Bran\" Stark\\n\\n2011–2014, 2016–2019\\n\\n7\\n\\n1\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\n2012–2019\\n\\n7\\n\\n1\\n\\nConleth Hill\\n\\nVarys\\n\\n2011–2019\\n\\n6\\n\\n1\\n\\nNathalie Emmanuel\\n\\nMissandei\\n\\n2013–2019\\n\\n6\\n\\n1\\n\\nRory McCann\\n\\nSandor \"The Hound\" Clegane\\n\\n2011–2014, 2016–2019\\n\\n6\\n\\n1\\n\\nIan McElhinney\\n\\nBarristan Selmy\\n\\n2011, 2013–2015\\n\\n5\\n\\n1\\n\\nJohn Bradley\\n\\nSamwell Tarly\\n\\n2011–2019\\n\\n5\\n\\n1\\n\\nJosef Altin\\n\\nPypar\\n\\n2011, 2013–2014\\n\\n4\\n\\n1\\n\\nJulian Glover\\n\\nPycelle\\n\\n2011–2016\\n\\n4\\n\\n1\\n\\nMark Stanley\\n\\nGrenn\\n\\n2011–2014\\n\\n4\\n\\n1\\n\\n==Emmy nominations and awards for the cast==\\n\\nActor\\n\\nCharacter\\n\\nCategory\\n\\nSeasons\\n\\n1\\n\\n2\\n\\n3\\n\\n4\\n\\n5\\n\\n6\\n\\n7\\n\\n8\\n\\nPeter Dinklage\\n\\nTyrion Lannister\\n\\nSupporting Actor\\n\\nLena Headey\\n\\nCersei Lannister\\n\\nSupporting Actress\\n\\nDiana Rigg\\n\\nOlenna Tyrell\\n\\nGuest Actress\\n\\nEmilia Clarke\\n\\nDaenerys Targaryen\\n\\nSupporting Actress\\n\\nLead Actress\\n\\nKit Harington\\n\\nJon Snow\\n\\nSupporting Actor\\n\\nLead Actor\\n\\nMaisie Williams\\n\\nArya Stark\\n\\nSupporting Actress\\n\\nMax von Sydow\\n\\nThree-eyed Raven\\n\\nGuest Actor\\n\\nNikolaj Coster-Waldau\\n\\nJaime Lannister\\n\\nSupporting Actor\\n\\nAlfie Allen\\n\\nTheon Greyjoy\\n\\nSupporting Actor\\n\\nGwendoline Christie\\n\\nBrienne of Tarth\\n\\nSupporting Actress\\n\\nSophie Turner\\n\\nSansa Stark\\n\\nSupporting Actress\\n\\nCarice van Houten\\n\\nMelisandre\\n\\nGuest Actress\\n\\n==Significant Guild and Peer Awards==\\n\\n===AFI Awards===\\nThe AFI Award, created in 2000 by the American Film Institute, is given annually to ten films and ten television programs to acknowledge the \"most significant achievements in the art of the moving image\". \\'\\'Game of Thrones\\'\\' has been awarded seven times.\\n\\n+ AFI Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '29293b5ff165c913c7845ce0d331a8bc'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has been awarded seven times.\\n\\n+ AFI Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2012\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2013\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2014\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2015\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2016\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n2017\\n\\nTop 10 TV Programs of the Year\\n\\n''Game of Thrones''\\n\\n===American Cinema Editors Awards===\\nTim Porter won one American Cinema Editors Award in 2017\\n\\nThe American Cinema Editors presents annual awards for outstanding achievements in film editing. ''Game of Thrones'' has won one out of six nominations.\\n\\n+ American Cinema Editors Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '14bba6039a3b427f0de310bdff44820e'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won one out of six nominations.\\n\\n+ American Cinema Editors Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nFrances Parker (for \"Baelor\")\\n\\n2014\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nOral Norrie Ottey (for \"The Rains of Castamere\")\\n\\n2016\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nKatie Weiland (for \"The Dance of Dragons\")\\n\\nTim Porter (for \"Hardhome\")\\n\\n2017\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\n2018\\n\\nBest Edited Drama Series for Non-Commercial Television\\n\\nTim Porter (for \"Beyond the Wall\")\\n\\n===American Society of Cinematographers===\\nThe American Society of Cinematographers presents annual awards for outstanding achievements in directors of photography and special effects for film and television. \\'\\'Game of Thrones\\'\\' has been nominated ten times, winning three times.\\n\\n+ American Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'c235915723db2a1ff1cc59a9bc75a34d'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated ten times, winning three times.\\n\\n+ American Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Achievement in Cinematography in One-Hour Episodic Television Series\\n\\nKramer Morgenthau (for \"The North Remembers\")\\n\\n2013\\n\\nOutstanding Achievement in Cinematography in One-Hour Episodic Television Series\\n\\nJonathan Freeman (for \"Valar Dohaeris\")\\n\\nAnette Haellmigk (for \"Kissed by Fire\")\\n\\n2014\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nAnette Haellmigk (for \"The Children\")\\n\\nFabian Wagner (for \"Mockingbird\")\\n\\n2015\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner (for \"Hardhome\")\\n\\n2016\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner (for \"Battle of the Bastards\")\\n\\nAnette Haellmigk (for \"Book of the Stranger\")\\n\\n2017\\n\\nOutstanding Achievement in Cinematography in Regular Series for Non-Commercial Television\\n\\nRobert McLachlan (for \"The Spoils of War\")\\n\\nGregory Middleton (for \"Dragonstone\")\\n\\n===Annie Awards===\\nFirst awarded in 1972, the Annie Awards are given exclusively for animation. \\'\\'Game of Thrones\\'\\' has been nominated twice.\\n\\n+ Annie Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '635c158c2c803ced211fd266c9142516'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated twice.\\n\\n+ Annie Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2017\\n\\nOutstanding Achievement, Character Animation in a Live Action Production\\n\\nNicholas Tripodi, Dean Elliott, James Hollingworth, Matt Weaver (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Achievement, Character Animation in a Live Action Production\\n\\nPaul Story, Todd Labonte, Matthew Muntean, Cajun Hylton, Georgy Arevshatov (for \"Beyond the Wall\")\\n\\n===Australian Production Design Guild===\\nThe Australian Production Design Guild Awards are awarded annually in recognition of excellence in production design. \\'\\'Game of Thrones\\'\\' has won three awards out of three nominations.\\n\\n+ Australian Production Design Guild Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n2016\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n3D Award for Visual Effects Design\\n\\nIloura (for \"Battle of the Bastards\")\\n\\n===Art Directors Guild Awards===\\nThe ADG Excellence in Production Design Award is presented each year by the American Art Directors Guild to recognize the best production design and art direction in the film and television industries. \\'\\'Game of Thrones\\'\\' has won five awards out of seven nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'b1a568875d2beef93c8fd2a14242023f'}>,\n",
-       "  <Document: {'content': '2015\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n2016\\n\\nProduction Design for a Television Drama\\n\\nDeborah Riley\\n\\n3D Award for Visual Effects Design\\n\\nIloura (for \"Battle of the Bastards\")\\n\\n===Art Directors Guild Awards===\\nThe ADG Excellence in Production Design Award is presented each year by the American Art Directors Guild to recognize the best production design and art direction in the film and television industries. \\'\\'Game of Thrones\\'\\' has won five awards out of seven nominations.\\n\\n+ Art Directors Guild Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '577b83c6de51c8b8032aa6de6b6bbee9'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won five awards out of seven nominations.\\n\\n+ Art Directors Guild Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOne-Hour Single Camera Television Series\\n\\nGemma Jackson (for \"A Golden Crown\")\\n\\n2013\\n\\nOne-Hour Single Camera Television Series\\n\\nGemma Jackson (for \"The Ghost of Harrenhal\")\\n\\n2014\\n\\nOne-Hour Single Camera Television Series\\n\\nGemma Jackson (for \"Valar Dohaeris\")\\n\\n2015\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley (for \"The Laws of Gods and Men\" and \"The Mountain and the Viper\")\\n\\n2016\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley (for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\")\\n\\n2017\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley (for \"Blood of My Blood\", \"The Broken Man\", and \"No One\")\\n\\n2018\\n\\nOne-Hour Single Camera Period Or Fantasy Television Series\\n\\nDeborah Riley (for \"Dragonstone\", \"The Queen\\'s Justice\", and \"Eastwatch\")\\n\\n===Artios Awards===\\nThe Casting Society of America awarded annually to honor the top works of casting directors in feature films, television and theatre. \\'\\'Game of Thrones\\'\\' has won one award out of seven nominations.\\n\\n+ Artios Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'c5bc0dc58418cf651deb19a4036fc30'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has won one award out of seven nominations.\\n\\n+ Artios Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nOutstanding Achievement in Casting – Television Pilot Drama\\n\\nNina Gold\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\n2012\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\n2013\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\n2014\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\n2015\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold\\n\\n2016\\n\\nOutstanding Achievement in Casting – Television Series Drama\\n\\nNina Gold, Robert Sterne, Carla Stronge\\n\\n===ASCAP Awards===\\nRamin Djawadi won two ASCAP Awards in 2012 and 2013\\n\\nThe ASCAP Awards are awarded annually to composers for outstanding achievements and contributions to the world of film and television music. ''Game of Thrones'' has won two awards out of two nominations.\\n\\n+ ASCAP Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nTop Television Series\\n\\nRamin Djawadi\\n\\n2013\\n\\nTop Television Series\\n\\nRamin Djawadi\\n\\n===ASTRA Awards===\\nThe ASTRA Awards awarded annually. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '5e5157f113ed75f63fd03725fed74567'}>,\n",
-       "  <Document: {'content': '+ ASCAP Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nTop Television Series\\n\\nRamin Djawadi\\n\\n2013\\n\\nTop Television Series\\n\\nRamin Djawadi\\n\\n===ASTRA Awards===\\nThe ASTRA Awards awarded annually. According to the Australian Subscription Television and Radio Association (ASTRA), the awards \"recognise the wealth of talent that drives the Australian subscription television industry and highlight the creativity, commitment and investment in production and broadcasting.\"\\'\\'Game of Thrones\\'\\' has won three out of four nominations.\\n\\n+ ASTRA Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2013\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nFavourite Program – International Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===BAFTA Awards===\\nThe British Academy Television Awards are presented in an annual award show hosted by the British Academy of Film and Television Arts (BAFTA). They have been awarded annually since 1955. It is the British equivalent to the Emmy Awards in the United States. \\'\\'Game of Thrones\\'\\' has won four out of eight nominations. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '86e13cda0efba08cbbaf0c1acb9bed0d'}>,\n",
-       "  <Document: {'content': 'It is the British equivalent to the Emmy Awards in the United States. \\'\\'Game of Thrones\\'\\' has won four out of eight nominations. British Academy Television Awards recognize outstanding work in British television programming, while the British Academy Television Craft Awards are presented to honor technical and creative achievements, and include categories recognising work of art directors, lighting and costume designers, cinematographers, casting directors, and other production-based personnel.\\n\\n====British Academy Television Awards====\\n\\n+ British Academy Television Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2013\\n\\nBest International Programme\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2017\\n\\nMust-See Moment\\n\\n\"Battle of the Bastards\"\\n\\n2018\\n\\nMust-See Moment\\n\\n\"Viserion is Killed by the Night King\" (for \"Beyond the Wall\")\\n\\n====British Academy Television Craft Awards====\\n\\n+ British Academy Television Craft Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '2189ba9a90094f2a33e192a2b003c813'}>,\n",
-       "  <Document: {'content': '2013\\n\\nBest International Programme\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nRadio Times Audience Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2017\\n\\nMust-See Moment\\n\\n\"Battle of the Bastards\"\\n\\n2018\\n\\nMust-See Moment\\n\\n\"Viserion is Killed by the Night King\" (for \"Beyond the Wall\")\\n\\n====British Academy Television Craft Awards====\\n\\n+ British Academy Television Craft Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2018\\n\\nCostume Design\\n\\nMichele Clapton\\n\\nProduction Design\\n\\nDeborah Riley, Rob Cameron\\n\\nSpecial Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===British Society of Cinematographers===\\nThe British Society of Cinematographers Awards are awarded annually to honor the top works of cinematographers in feature films and television. \\'\\'Game of Thrones\\'\\' has been nominated five times, and won once.\\n\\n+ British Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '39f27219ca58afa10dde46056a013a47'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated five times, and won once.\\n\\n+ British Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Cinematography in a Television Drama\\n\\nSam McCurdy (for \"Blackwater\")\\n\\n2015\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner (for \"Hardhome\")\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley (for \"Hardhome\")\\n\\n2016\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner (for \"The Winds of Winter\")\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nSean Savage, David Morgan & John Ferguson (for \"Battle of the Bastards\")\\n\\n===Canadian Society of Cinematographers===\\nThe Canadian Society of Cinematographers Awards are awarded annually to honor the top works of cinematographers in feature films and television. \\'\\'Game of Thrones\\'\\' has been nominated four times and won twice.\\n\\n+ Canadian Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': '673b607ddf510678152b7fcf9ed2ea85'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated four times and won twice.\\n\\n+ Canadian Society of Cinematographers Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nTV Series Cinematography\\n\\nRobert McLachlan (for \"Oathkeeper\")\\n\\n2016\\n\\nTV Series Cinematography\\n\\nRobert McLachlan (for \"The Dance of Dragons\")\\n\\nTV Series Cinematography\\n\\nGregory Middleton (for \"Unbowed, Unbent, Unbroken\")\\n\\n2017\\n\\nTV Series Cinematography\\n\\nGregory Middleton (for \"Home\")\\n\\n===Cinema Audio Society Awards===\\nThe Cinema Audio Society presents annual awards for outstanding achievements in sound mixing. \\'\\'Game of Thrones\\'\\' has won five out of seven nominations.\\n\\n+ Cinema Audio Society Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'be034083312ca857f5786e575ac55dde'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won five out of seven nominations.\\n\\n+ Cinema Audio Society Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Mark Taylor (for \"Baelor\")\\n\\n2013\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"Blackwater\")\\n\\n2014\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"The Rains of Castamere\")\\n\\n2015\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"The Children\")\\n\\n2016\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Hardhome\")\\n\\n2017\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Beyond the Wall\")\\n\\n===Costume Designers Guild Awards===\\nThe Costume Designers Guild presents annual awards for excellence in costume design in motion pictures, television, and commercials. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'b7a375381a96b588b1e69015e2c30557'}>,\n",
-       "  <Document: {'content': '2012\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Mark Taylor (for \"Baelor\")\\n\\n2013\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"Blackwater\")\\n\\n2014\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"The Rains of Castamere\")\\n\\n2015\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"The Children\")\\n\\n2016\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Hardhome\")\\n\\n2017\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Beyond the Wall\")\\n\\n===Costume Designers Guild Awards===\\nThe Costume Designers Guild presents annual awards for excellence in costume design in motion pictures, television, and commercials. \\'\\'Game of Thrones\\'\\' has won four out of seven nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '3a9ecaf8087915573fb9a7ac9d72d83'}>,\n",
-       "  <Document: {'content': '2012\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Mark Taylor (for \"Baelor\")\\n\\n2013\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"Blackwater\")\\n\\n2014\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss (for \"The Rains of Castamere\")\\n\\n2015\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"The Children\")\\n\\n2016\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Hardhome\")\\n\\n2017\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, Richard Dyer, Brett Voss (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Achievement in Sound Mixing – Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss (for \"Beyond the Wall\")\\n\\n===Costume Designers Guild Awards===\\nThe Costume Designers Guild presents annual awards for excellence in costume design in motion pictures, television, and commercials. \\'\\'Game of Thrones\\'\\' has won four out of seven nominations.\\n\\n+ Costume Designers Guild Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '70ac9b35053586c73b03d87d23a04f1f'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won four out of seven nominations.\\n\\n+ Costume Designers Guild Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton\\n\\n2013\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton\\n\\n2014\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton\\n\\n2015\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton\\n\\n2016\\n\\nOutstanding Period/Fantasy Television Series\\n\\nMichele Clapton\\n\\n2017\\n\\nOutstanding Fantasy Television Series\\n\\nMichele Clapton, April Ferry\\n\\n2018\\n\\nOutstanding Fantasy Television Series\\n\\nMichele Clapton\\n\\n===Directors Guild of America Awards===\\nDavid Nutter won one Directors Guild of America Award for \"Mother\\'s Mercy\" in 2015\\n\\nThe Directors Guild of America Awards are awarded annually by the Directors Guild of America. \\'\\'Game of Thrones\\'\\' has been nominated for eight awards for Outstanding Directorial Achievement in Dramatic Series, winning in 2015 and 2016.\\n\\n+ Directors Guild of America Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': '2417d670b79211a113d49ecaa075a8c5'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated for eight awards for Outstanding Directorial Achievement in Dramatic Series, winning in 2015 and 2016.\\n\\n+ Directors Guild of America Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nDramatic Series\\n\\nTim Van Patten (for \"Winter Is Coming\")\\n\\n2014\\n\\nDramatic Series\\n\\nDavid Nutter (for \"The Rains of Castamere\")\\n\\n2015\\n\\nDramatic Series\\n\\nAlex Graves (for \"The Children\")\\n\\n2016\\n\\nDramatic Series\\n\\nDavid Nutter (for \"Mother\\'s Mercy\")\\n\\n2017\\n\\nDramatic Series\\n\\nMiguel Sapochnik (for \"Battle of the Bastards\")\\n\\n2018\\n\\nDramatic Series\\n\\nJeremy Podeswa (for \"The Dragon and the Wolf\")\\n\\nMatt Shakman (for \"The Spoils of War\")\\n\\nAlan Taylor (for \"Beyond the Wall\")\\n\\n===Emmy Awards===\\nD. B. Weiss and David Benioff received the Primetime Emmy Award for Outstanding Writing for a Drama Series for \"Mother\\'s Mercy\" in 2015 and for \"Battle of the Bastards\" in 2016\\nLena Headey received her first Primetime Emmy Award nomination for her performance as Cersei Lannister in 2014\\n\\nThe Emmy Awards were established in 1949 in order to recognize excellence in the American television industry, and are bestowed by members of the Academy of Television Arts & Sciences. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': 'f2cf86c0b79e92e5152a4b4708ec39a1'}>,\n",
-       "  <Document: {'content': '2012\\n\\nDramatic Series\\n\\nTim Van Patten (for \"Winter Is Coming\")\\n\\n2014\\n\\nDramatic Series\\n\\nDavid Nutter (for \"The Rains of Castamere\")\\n\\n2015\\n\\nDramatic Series\\n\\nAlex Graves (for \"The Children\")\\n\\n2016\\n\\nDramatic Series\\n\\nDavid Nutter (for \"Mother\\'s Mercy\")\\n\\n2017\\n\\nDramatic Series\\n\\nMiguel Sapochnik (for \"Battle of the Bastards\")\\n\\n2018\\n\\nDramatic Series\\n\\nJeremy Podeswa (for \"The Dragon and the Wolf\")\\n\\nMatt Shakman (for \"The Spoils of War\")\\n\\nAlan Taylor (for \"Beyond the Wall\")\\n\\n===Emmy Awards===\\nD. B. Weiss and David Benioff received the Primetime Emmy Award for Outstanding Writing for a Drama Series for \"Mother\\'s Mercy\" in 2015 and for \"Battle of the Bastards\" in 2016\\nLena Headey received her first Primetime Emmy Award nomination for her performance as Cersei Lannister in 2014\\n\\nThe Emmy Awards were established in 1949 in order to recognize excellence in the American television industry, and are bestowed by members of the Academy of Television Arts & Sciences. Emmy Awards are given in different ceremonies presented annually; Primetime Emmy Awards recognize outstanding work in American primetime television programming, while the Creative Arts Emmy Awards are presented to honor technical and creative achievements, and include categories recognising work of art directors, lighting and costume designers, cinematographers, casting directors, and other production-based personnel. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': 'b22033eb317ee056988476b09ff8d7ae'}>,\n",
-       "  <Document: {'content': \"Emmy Awards are given in different ceremonies presented annually; Primetime Emmy Awards recognize outstanding work in American primetime television programming, while the Creative Arts Emmy Awards are presented to honor technical and creative achievements, and include categories recognising work of art directors, lighting and costume designers, cinematographers, casting directors, and other production-based personnel. The Emmy Award corresponds to the Academy Award (for film), the Tony Award (for theatre), and the Grammy Award (for music).\\n\\n''Game of Thrones'' has won 59 out of 161 nominations. Peter Dinklage has been nominated for Outstanding Supporting Actor in a Drama Series eight times in a row for playing Tyrion Lannister, with a win in 2011, 2015, 2018 and 2019. Showrunners David Benioff and D. B. Weiss have won twice for Writing for a Drama Series. ''Game of Thrones'' has eight nominations for Outstanding Drama Series, winning four, in 2015, 2016, 2018 and 2019. Both David Nutter and Miguel Sapochnik have won for Outstanding Directing for a Drama Series. ''Game of Thrones'' holds the Emmy Award record for most wins for a scripted television series, ahead of ''Frasier'' (which received 37).\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': '330ca8c61453e586b6e1bf8e8aeb404a'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' holds the Emmy Award record for most wins for a scripted television series, ahead of ''Frasier'' (which received 37).\\n\\nAt the 67th Primetime Emmy Awards the show's fifth season was nominated for 24 awards. The show ultimately won twelve awards, setting a new record for most Emmy wins by a series in a single year, replacing the previous holder ''The West Wing''s nine wins, and tying that show and ''Hill Street Blues'' for the most wins by a drama series throughout its run. At the nominee announcement for the 68th Primetime Emmy Awards, the show's sixth season received the most nominations of any show with 23. The show yet again won twelve awards, tying the same record for most Emmy wins by a series in a single year.\\n\\nAt the 70th Primetime Emmy Awards the seventh season was nominated for 22 awards, the most of the ceremony, including a first Emmy nomination for Nikolaj Coster-Waldau.\\n\\nAt the 71st Primetime Emmy Awards the eighth season was nominated for 32 awards, thus breaking the record for most nominations received by a regular series in a single year. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': 'a3fcdeec4181dc574c4ece08e42eb18d'}>,\n",
-       "  <Document: {'content': 'At the 71st Primetime Emmy Awards the eighth season was nominated for 32 awards, thus breaking the record for most nominations received by a regular series in a single year. Actors Alfie Allen, Gwendoline Christie, Sophie Turner and Carice van Houten all received their first ever Emmy Award nomination thanks to this season.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': 'a10d2e9f7df5d6bba17c7ec4245bf1d0'}>,\n",
-       "  <Document: {'content': 'Actors Alfie Allen, Gwendoline Christie, Sophie Turner and Carice van Houten all received their first ever Emmy Award nomination thanks to this season.\\n\\n====Primetime Emmy Awards====\\n\\n+ Primetime Emmy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\n2011\\n\\nOutstanding Drama Series\\n\\nVince Gerardis, Frank Doelger, Ralph Vicinanza, Mark Huffam, David Benioff, Carolyn Strauss, George R. R. Martin, Guymon Casady and D. B. Weiss\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Baelor\")\\n\\nOutstanding Directing for a Drama Series\\n\\nTim Van Patten (for \"Winter Is Coming\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Baelor\")\\n\\n2012\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Frank Doelger, Carolyn Strauss, George R. R. Martin, Vanessa Taylor, Alan Taylor, Guymon Casady, Vince Gerardis and Bernadette Caulfield\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Blackwater\")\\n\\n2013\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Vanessa Taylor, Chris Newman and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Second Sons\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Rains of Castamere\")\\n\\n2014\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Chris Newman and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"The Laws of Gods and Men\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey (for \"The Lion and the Rose\")\\n\\nOutstanding Directing for a Drama Series\\n\\nNeil Marshall (for \"The Watchers on the Wall\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Children\")\\n\\n2015\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Chris Newman, Greg Spence, Lisa McAtackney and Bryan Cogman\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Hardhome\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"The Dance of Dragons\")\\n\\nLena Headey (for \"Mother\\'s Mercy\")\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter (for \"Mother\\'s Mercy\")\\n\\nJeremy Podeswa (for \"Unbowed, Unbent, Unbroken\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Mother\\'s Mercy\")\\n\\n2016\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Vince Gerardis, Guymon Casady, George R. R. Martin, Bryan Cogman, Chris Newman, Greg Spence and Lisa McAtackney\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"No One\")\\n\\nKit Harington (for \"Battle of the Bastards\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"Book of the Stranger\")\\n\\nLena Headey (for \"The Winds of Winter\")\\n\\nMaisie Williams (for \"No One\")\\n\\nOutstanding Directing for a Drama Series\\n\\nMiguel Sapochnik (for \"Battle of the Bastards\")\\n\\nJack Bender (for \"The Door\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, George R. R. Martin, Guymon Casady, Vince Gerardis, Bryan Cogman, Chris Newman, Lisa McAtackney and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nNikolaj Coster-Waldau (for \"The Spoils of War\")\\n\\nPeter Dinklage (for \"The Dragon and the Wolf\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey (for \"The Dragon and the Wolf\")\\n\\nOutstanding Directing for a Drama Series\\n\\nAlan Taylor (for \"Beyond the Wall\")\\n\\nJeremy Podeswa (for \"The Dragon and the Wolf\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Dragon and the Wolf\")\\n\\n2019\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Bernadette Caulfield, Frank Doelger, David Nutter, Miguel Sapochnik, Vince Gerardis, Guymon Casady, George R. R. Martin, Bryan Cogman, Chris Newman, Greg Spence, Lisa McAtackney and Duncan Muggoch\\n\\nOutstanding Lead Actor in a Drama Series\\n\\nKit Harington (for \"The Iron Throne\")\\n\\nOutstanding Lead Actress in a Drama Series\\n\\nEmilia Clarke (for \"The Last of the Starks\")\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nAlfie Allen (for \"The Long Night\")\\n\\nNikolaj Coster-Waldau (for \"A Knight of the Seven Kingdoms\")\\n\\nPeter Dinklage (for \"The Iron Throne\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nGwendoline Christie (for \"A Knight of the Seven Kingdoms\")\\n\\nLena Headey (for \"The Bells\")\\n\\nSophie Turner (for \"Winterfell\")\\n\\nMaisie Williams (for \"The Long Night\")\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Iron Throne\")\\n\\nDavid Nutter (for \"The Last of the Starks\")\\n\\nMiguel Sapochnik (for \"The Long Night\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Iron Throne\")\\n\\n====Creative Arts Emmy Awards====\\n\\n+ Creative Arts Emmy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 28}, 'embedding': None, 'id': 'ccdfde748e2751f4885e8e327fb5182f'}>,\n",
-       "  <Document: {'content': '====Primetime Emmy Awards====\\n\\n+ Primetime Emmy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\n2011\\n\\nOutstanding Drama Series\\n\\nVince Gerardis, Frank Doelger, Ralph Vicinanza, Mark Huffam, David Benioff, Carolyn Strauss, George R. R. Martin, Guymon Casady and D. B. Weiss\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Baelor\")\\n\\nOutstanding Directing for a Drama Series\\n\\nTim Van Patten (for \"Winter Is Coming\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Baelor\")\\n\\n2012\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Frank Doelger, Carolyn Strauss, George R. R. Martin, Vanessa Taylor, Alan Taylor, Guymon Casady, Vince Gerardis and Bernadette Caulfield\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Blackwater\")\\n\\n2013\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Vanessa Taylor, Chris Newman and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Second Sons\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Rains of Castamere\")\\n\\n2014\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Chris Newman and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"The Laws of Gods and Men\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey (for \"The Lion and the Rose\")\\n\\nOutstanding Directing for a Drama Series\\n\\nNeil Marshall (for \"The Watchers on the Wall\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Children\")\\n\\n2015\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Guymon Casady, Vince Gerardis, George R. R. Martin, Chris Newman, Greg Spence, Lisa McAtackney and Bryan Cogman\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"Hardhome\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"The Dance of Dragons\")\\n\\nLena Headey (for \"Mother\\'s Mercy\")\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Nutter (for \"Mother\\'s Mercy\")\\n\\nJeremy Podeswa (for \"Unbowed, Unbent, Unbroken\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Mother\\'s Mercy\")\\n\\n2016\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, Vince Gerardis, Guymon Casady, George R. R. Martin, Bryan Cogman, Chris Newman, Greg Spence and Lisa McAtackney\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage (for \"No One\")\\n\\nKit Harington (for \"Battle of the Bastards\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke (for \"Book of the Stranger\")\\n\\nLena Headey (for \"The Winds of Winter\")\\n\\nMaisie Williams (for \"No One\")\\n\\nOutstanding Directing for a Drama Series\\n\\nMiguel Sapochnik (for \"Battle of the Bastards\")\\n\\nJack Bender (for \"The Door\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"Battle of the Bastards\")\\n\\n2018\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Frank Doelger, Bernadette Caulfield, George R. R. Martin, Guymon Casady, Vince Gerardis, Bryan Cogman, Chris Newman, Lisa McAtackney and Greg Spence\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nNikolaj Coster-Waldau (for \"The Spoils of War\")\\n\\nPeter Dinklage (for \"The Dragon and the Wolf\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nLena Headey (for \"The Dragon and the Wolf\")\\n\\nOutstanding Directing for a Drama Series\\n\\nAlan Taylor (for \"Beyond the Wall\")\\n\\nJeremy Podeswa (for \"The Dragon and the Wolf\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Dragon and the Wolf\")\\n\\n2019\\n\\nOutstanding Drama Series\\n\\nDavid Benioff, D. B. Weiss, Carolyn Strauss, Bernadette Caulfield, Frank Doelger, David Nutter, Miguel Sapochnik, Vince Gerardis, Guymon Casady, George R. R. Martin, Bryan Cogman, Chris Newman, Greg Spence, Lisa McAtackney and Duncan Muggoch\\n\\nOutstanding Lead Actor in a Drama Series\\n\\nKit Harington (for \"The Iron Throne\")\\n\\nOutstanding Lead Actress in a Drama Series\\n\\nEmilia Clarke (for \"The Last of the Starks\")\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nAlfie Allen (for \"The Long Night\")\\n\\nNikolaj Coster-Waldau (for \"A Knight of the Seven Kingdoms\")\\n\\nPeter Dinklage (for \"The Iron Throne\")\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nGwendoline Christie (for \"A Knight of the Seven Kingdoms\")\\n\\nLena Headey (for \"The Bells\")\\n\\nSophie Turner (for \"Winterfell\")\\n\\nMaisie Williams (for \"The Long Night\")\\n\\nOutstanding Directing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Iron Throne\")\\n\\nDavid Nutter (for \"The Last of the Starks\")\\n\\nMiguel Sapochnik (for \"The Long Night\")\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D. B. Weiss (for \"The Iron Throne\")\\n\\n====Creative Arts Emmy Awards====\\n\\n+ Creative Arts Emmy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton and Rachael Webb-Crozier (for \"The Pointy End\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander and Candice Banks (for \"A Golden Crown\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Hameed Shaukat, Kirk Shintani, and Robert Feng\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Winter Is Coming\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen and Conor O\\'Sullivan (for \"A Golden Crown\")\\n\\nOutstanding Sound Editing for a Series\\n\\nRobin Quinn, Steve Fanagan, Eoghan McDonnell, Jon Stevenson, Tim Hands, Stefan Henrix, Caoimhe Doyle, Michelle McCormack and Andy Kennedy (for \"A Golden Crown\")\\n\\nOutstanding Special Visual Effects\\n\\nRafael Morant, Adam McInnes, Graham Hills, Lucy Ainsworth-Taylor, Stuart Brisdon, Damien Macé, Henry Badgett and Angela Barson (for \"Fire and Blood\")\\n\\nOutstanding Stunt Coordination\\n\\nPaul Jennings for (The Wolf and the Lion\")\\n\\n2012\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Garden of Bones\", \"The Ghost of Harrenhal\", and \"A Man Without Honor\")  (shared with \\'\\'Boardwalk Empire\\'\\')\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"The Prince of Winterfell\")\\n\\nOutstanding Creative Achievement in Interactive Media\\n\\n\\'\\'Game of Thrones\\'\\' Season Two – Enhanced Digital Experience\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"The Old Gods and the New\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"The Old Gods and the New\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Morghulis\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"Blackwater\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One-Hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"Blackwater\")\\n\\nOutstanding Special Visual Effects\\n\\nRainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Morghulis\")\\n\\n2013\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Valar Dohaeris\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nRob McLachlan (for \"Mhysa\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"Walk of Punishment\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"Second Sons\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Season Three – Enhanced Digital Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Kissed by Fire\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Dohaeris\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nOral Ottey (for \"The Rains of Castamere\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Sound Mixing for a Drama Series (One hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Special Visual Effects\\n\\nDoug Campbell, Rainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Production Design for a Narrative Contemporary or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Laws of Gods and Men\", and \"The Mountain and the Viper\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nAnette Haellmigk (for \"The Lion and the Rose\")\\n\\nJonathan Freeman (for \"Two Swords\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Sheena Wichary, Alexander Fordham, and Nina Ayres (for \"The Lion and the Rose\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Lion and the Rose\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, and Nicola Mount (for \"The Lion and the Rose\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Premiere – Facebook Live and Instagram, by Sabrina Caluori, Paul Beddoe-Stephens, Jim Marsh, Michael McMorrow, and Michael McMillian\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Ann McEwan (for \"Oathkeeper\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Mountain and the Viper\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie, or Special\\n\\nThe Children\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Jed M. Dodge, Tim Hands, Paula Fairfield, David Klotz, Bradley C. Katona, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Watchers on the Wall\")\\n\\nOutstanding Sound Mixing for a Drama Series (One Hour)\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, and Mathew Waters (for \"The Watchers on the Wall\")\\n\\nOutstanding Special and Visual Effects\\n\\nJoe Bauer, Joern Grosshans, Steve Kullback, Adam Chazen, Eric Carney, Sabrina Gerhardt, Matthew Rouleau, Thomas H. Schelesny, and Robert Simon (for \"The Children\")\\n\\nOutstanding Stunt Coordination for a Drama Series\\n\\nPaul Herbert\\n\\n2015\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner (for \"Hardhome\")\\n\\nAnette Haellmigk (for \"Sons of the Harpy\")\\n\\nRob McLachlan (for \"The Dance of Dragons\")\\n\\nGregory Middleton (for \"Unbowed, Unbent, Unbroken\")\\n\\nOutstanding Costumes for a Period/Fantasy Series, Limited Series or Movie\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham (for \"The Dance of Dragons\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Gift\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount (for \"Mother\\'s Mercy\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews (for \"Mother\\'s Mercy\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower (for \"Hardhome\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Hardhome\")\\n\\nKatie Weiland (for \"The Dance of Dragons\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit (for \"Hardhome\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau (for \"The Dance of Dragons\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2016\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton (for \"Home\")\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary (for \"The Winds of Winter\")\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow (for \"The Door\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora (for \"The Door\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Main Titles 360 Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth (for \"Battle of the Bastards\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"Blood of My Blood\", \"The Broken Man\", and \"No One\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower (for \"The Door\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nKatie Weiland (for \"Oathbreaker\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit (for \"The Door\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Battle of the Bastards\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney, Sam Conway, Matthew Rouleau, Michelle Blok, Glenn Melenhorst (for \"Battle of the Bastards\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2018\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Alexander Fordham, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"Beyond the Wall\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Queen\\'s Justice\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Dragon and the Wolf\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Pamela Smyth, Kate Thompson, and Nicola Mathews (for \"The Dragon and the Wolf\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Dragon and the Wolf\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"Dragonstone\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nJane Walker, Paul Spateri, Emma Sheffield, and Barrie Gower (for \"Beyond the Wall\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Beyond the Wall\")\\n\\nCrispin Green (for \"The Spoils of War\")\\n\\nKatie Weiland (for \"The Dragon and the Wolf\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Paula Fairfield, Tim Hands, Paul Bercovitch, Bradley C. Katona, John Matter, Brett Voss, David Klotz, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Spoils of War\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Richard Dyer, and Ronan Hill (for \"Beyond the Wall\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Michelle Blok, Sam Conway, Ted Rae, David Ramos, Wayne Stables, and Derek Spears (for \"Beyond the Wall\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n2019\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nJonathan Freeman (for \"The Iron Throne\")\\n\\nOutstanding Creative Achievement in Interactive Media within a Scripted Program\\n\\n\"Fight for the Living: Beyond the Wall Virtual Reality Experience\"\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"The Bells\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nCarice van Houten (for \"The Long Night\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Long Night\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Kirk Shintani, Shahana Khan, Ian Ruhfass, and Rustam Hasanov\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Nicola Mathews, and Pamela Smyth (for \"The Long Night\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Long Night\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Bells\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie or Special\\n\\nEmma Faulkes, Paul Spateri, Chloe Muton-Phillips, Duncan Jarman, Patt Foad, John Eldred-Tooby, Barrie Gower, and Sarah Gower (for \"The Long Night\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nKatie Weiland (for \"The Iron Throne\")\\n\\nTim Porter (for \"The Long Night\")\\n\\nCrispin Green (for \"Winterfell\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Tim Hands, Paula Fairfield, Bradley C. Katona, Paul Bercovitch, John Matter, David Klotz, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Long Night\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Simon Kerr, Danny Crowley, and Ronan Hill (for \"The Long Night\")\\n\\nOutstanding Special Visual Effects\\n\\nJoe Bauer, Steve Kullback, Adam Chazen, Sam Conway, Mohsen Mousavi, Martin Hill, Ted Rae, Patrick Tiberius Gehlen, and Thomas Schelesny (for \"The Bells\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n===Golden Reel Awards===\\nThe Golden Reel Award presents awards annually to sound editors in the several categories for feature film and television. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 29}, 'embedding': None, 'id': 'd2e4cf6e4f3eb4dc11972025a11b460d'}>,\n",
-       "  <Document: {'content': '2011\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton and Rachael Webb-Crozier (for \"The Pointy End\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander and Candice Banks (for \"A Golden Crown\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Hameed Shaukat, Kirk Shintani, and Robert Feng\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Winter Is Coming\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen and Conor O\\'Sullivan (for \"A Golden Crown\")\\n\\nOutstanding Sound Editing for a Series\\n\\nRobin Quinn, Steve Fanagan, Eoghan McDonnell, Jon Stevenson, Tim Hands, Stefan Henrix, Caoimhe Doyle, Michelle McCormack and Andy Kennedy (for \"A Golden Crown\")\\n\\nOutstanding Special Visual Effects\\n\\nRafael Morant, Adam McInnes, Graham Hills, Lucy Ainsworth-Taylor, Stuart Brisdon, Damien Macé, Henry Badgett and Angela Barson (for \"Fire and Blood\")\\n\\nOutstanding Stunt Coordination\\n\\nPaul Jennings for (The Wolf and the Lion\")\\n\\n2012\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Garden of Bones\", \"The Ghost of Harrenhal\", and \"A Man Without Honor\")  (shared with \\'\\'Boardwalk Empire\\'\\')\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"The Prince of Winterfell\")\\n\\nOutstanding Creative Achievement in Interactive Media\\n\\n\\'\\'Game of Thrones\\'\\' Season Two – Enhanced Digital Experience\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"The Old Gods and the New\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"The Old Gods and the New\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Morghulis\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"Blackwater\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One-Hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"Blackwater\")\\n\\nOutstanding Special Visual Effects\\n\\nRainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Morghulis\")\\n\\n2013\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Valar Dohaeris\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nRob McLachlan (for \"Mhysa\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"Walk of Punishment\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"Second Sons\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Season Three – Enhanced Digital Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Kissed by Fire\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Dohaeris\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nOral Ottey (for \"The Rains of Castamere\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Sound Mixing for a Drama Series (One hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Special Visual Effects\\n\\nDoug Campbell, Rainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Production Design for a Narrative Contemporary or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Laws of Gods and Men\", and \"The Mountain and the Viper\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nAnette Haellmigk (for \"The Lion and the Rose\")\\n\\nJonathan Freeman (for \"Two Swords\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Sheena Wichary, Alexander Fordham, and Nina Ayres (for \"The Lion and the Rose\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Lion and the Rose\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, and Nicola Mount (for \"The Lion and the Rose\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Premiere – Facebook Live and Instagram, by Sabrina Caluori, Paul Beddoe-Stephens, Jim Marsh, Michael McMorrow, and Michael McMillian\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Ann McEwan (for \"Oathkeeper\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Mountain and the Viper\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie, or Special\\n\\nThe Children\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Jed M. Dodge, Tim Hands, Paula Fairfield, David Klotz, Bradley C. Katona, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Watchers on the Wall\")\\n\\nOutstanding Sound Mixing for a Drama Series (One Hour)\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, and Mathew Waters (for \"The Watchers on the Wall\")\\n\\nOutstanding Special and Visual Effects\\n\\nJoe Bauer, Joern Grosshans, Steve Kullback, Adam Chazen, Eric Carney, Sabrina Gerhardt, Matthew Rouleau, Thomas H. Schelesny, and Robert Simon (for \"The Children\")\\n\\nOutstanding Stunt Coordination for a Drama Series\\n\\nPaul Herbert\\n\\n2015\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner (for \"Hardhome\")\\n\\nAnette Haellmigk (for \"Sons of the Harpy\")\\n\\nRob McLachlan (for \"The Dance of Dragons\")\\n\\nGregory Middleton (for \"Unbowed, Unbent, Unbroken\")\\n\\nOutstanding Costumes for a Period/Fantasy Series, Limited Series or Movie\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham (for \"The Dance of Dragons\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Gift\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount (for \"Mother\\'s Mercy\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews (for \"Mother\\'s Mercy\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower (for \"Hardhome\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Hardhome\")\\n\\nKatie Weiland (for \"The Dance of Dragons\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit (for \"Hardhome\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau (for \"The Dance of Dragons\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2016\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton (for \"Home\")\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary (for \"The Winds of Winter\")\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow (for \"The Door\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora (for \"The Door\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Main Titles 360 Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth (for \"Battle of the Bastards\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"Blood of My Blood\", \"The Broken Man\", and \"No One\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower (for \"The Door\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nKatie Weiland (for \"Oathbreaker\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit (for \"The Door\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Battle of the Bastards\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney, Sam Conway, Matthew Rouleau, Michelle Blok, Glenn Melenhorst (for \"Battle of the Bastards\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2018\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Alexander Fordham, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"Beyond the Wall\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Queen\\'s Justice\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Dragon and the Wolf\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Pamela Smyth, Kate Thompson, and Nicola Mathews (for \"The Dragon and the Wolf\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Dragon and the Wolf\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"Dragonstone\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nJane Walker, Paul Spateri, Emma Sheffield, and Barrie Gower (for \"Beyond the Wall\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Beyond the Wall\")\\n\\nCrispin Green (for \"The Spoils of War\")\\n\\nKatie Weiland (for \"The Dragon and the Wolf\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Paula Fairfield, Tim Hands, Paul Bercovitch, Bradley C. Katona, John Matter, Brett Voss, David Klotz, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Spoils of War\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Richard Dyer, and Ronan Hill (for \"Beyond the Wall\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Michelle Blok, Sam Conway, Ted Rae, David Ramos, Wayne Stables, and Derek Spears (for \"Beyond the Wall\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n2019\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nJonathan Freeman (for \"The Iron Throne\")\\n\\nOutstanding Creative Achievement in Interactive Media within a Scripted Program\\n\\n\"Fight for the Living: Beyond the Wall Virtual Reality Experience\"\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"The Bells\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nCarice van Houten (for \"The Long Night\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Long Night\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Kirk Shintani, Shahana Khan, Ian Ruhfass, and Rustam Hasanov\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Nicola Mathews, and Pamela Smyth (for \"The Long Night\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Long Night\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Bells\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie or Special\\n\\nEmma Faulkes, Paul Spateri, Chloe Muton-Phillips, Duncan Jarman, Patt Foad, John Eldred-Tooby, Barrie Gower, and Sarah Gower (for \"The Long Night\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nKatie Weiland (for \"The Iron Throne\")\\n\\nTim Porter (for \"The Long Night\")\\n\\nCrispin Green (for \"Winterfell\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Tim Hands, Paula Fairfield, Bradley C. Katona, Paul Bercovitch, John Matter, David Klotz, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Long Night\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Simon Kerr, Danny Crowley, and Ronan Hill (for \"The Long Night\")\\n\\nOutstanding Special Visual Effects\\n\\nJoe Bauer, Steve Kullback, Adam Chazen, Sam Conway, Mohsen Mousavi, Martin Hill, Ted Rae, Patrick Tiberius Gehlen, and Thomas Schelesny (for \"The Bells\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n===Golden Reel Awards===\\nThe Golden Reel Award presents awards annually to sound editors in the several categories for feature film and television. \\'\\'Game of Thrones\\'\\' has received eleven awards out of twenty two nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 30}, 'embedding': None, 'id': 'bc2b3182f7c7fcb746b031b8c5727840'}>,\n",
-       "  <Document: {'content': '2011\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton and Rachael Webb-Crozier (for \"The Pointy End\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander and Candice Banks (for \"A Golden Crown\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Hameed Shaukat, Kirk Shintani, and Robert Feng\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Winter Is Coming\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen and Conor O\\'Sullivan (for \"A Golden Crown\")\\n\\nOutstanding Sound Editing for a Series\\n\\nRobin Quinn, Steve Fanagan, Eoghan McDonnell, Jon Stevenson, Tim Hands, Stefan Henrix, Caoimhe Doyle, Michelle McCormack and Andy Kennedy (for \"A Golden Crown\")\\n\\nOutstanding Special Visual Effects\\n\\nRafael Morant, Adam McInnes, Graham Hills, Lucy Ainsworth-Taylor, Stuart Brisdon, Damien Macé, Henry Badgett and Angela Barson (for \"Fire and Blood\")\\n\\nOutstanding Stunt Coordination\\n\\nPaul Jennings for (The Wolf and the Lion\")\\n\\n2012\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Garden of Bones\", \"The Ghost of Harrenhal\", and \"A Man Without Honor\")  (shared with \\'\\'Boardwalk Empire\\'\\')\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"The Prince of Winterfell\")\\n\\nOutstanding Creative Achievement in Interactive Media\\n\\n\\'\\'Game of Thrones\\'\\' Season Two – Enhanced Digital Experience\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"The Old Gods and the New\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"The Old Gods and the New\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Morghulis\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"Blackwater\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One-Hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"Blackwater\")\\n\\nOutstanding Special Visual Effects\\n\\nRainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Morghulis\")\\n\\n2013\\n\\nOutstanding Art Direction for a Single-Camera Series\\n\\nGemma Jackson, Frank Walsh, and Tina Jones (for \"Valar Dohaeris\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nRob McLachlan (for \"Mhysa\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Alexander Fordham, and Chloe Aubry (for \"Walk of Punishment\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, and Gary Machin (for \"Second Sons\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Season Three – Enhanced Digital Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nPaul Engelen and Melissa Lackersteen (for \"Kissed by Fire\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nPaul Engelen, Conor O\\'Sullivan, and Rob Trenton (for \"Valar Dohaeris\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nOral Ottey (for \"The Rains of Castamere\")\\n\\nOutstanding Sound Editing for a Series\\n\\nPeter Brown, Kira Roessler, Tim Hands, Paul Aulicino, Stephen P. Robinson, Vanessa Lapato, Brett Voss, James Moriana, Jeffrey Wilhoit, and David Klotz (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Sound Mixing for a Drama Series (One hour)\\n\\nMatthew Waters, Onnalee Blank, Ronan Hill, and Mervyn Moore (for \"And Now His Watch Is Ended\")\\n\\nOutstanding Special Visual Effects\\n\\nDoug Campbell, Rainer Gombos, Juri Stanossek, Sven Martin, Steve Kullback, Jan Fiedler, Chris Stenner, Tobias Mannewitz, Thilo Ewers, and Adam Chazen (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Production Design for a Narrative Contemporary or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Laws of Gods and Men\", and \"The Mountain and the Viper\")\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold and Robert Sterne\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nAnette Haellmigk (for \"The Lion and the Rose\")\\n\\nJonathan Freeman (for \"Two Swords\")\\n\\nOutstanding Costumes for a Series\\n\\nMichele Clapton, Sheena Wichary, Alexander Fordham, and Nina Ayres (for \"The Lion and the Rose\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Lion and the Rose\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, and Nicola Mount (for \"The Lion and the Rose\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Premiere – Facebook Live and Instagram, by Sabrina Caluori, Paul Beddoe-Stephens, Jim Marsh, Michael McMorrow, and Michael McMillian\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Ann McEwan (for \"Oathkeeper\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Mountain and the Viper\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie, or Special\\n\\nThe Children\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Jed M. Dodge, Tim Hands, Paula Fairfield, David Klotz, Bradley C. Katona, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Watchers on the Wall\")\\n\\nOutstanding Sound Mixing for a Drama Series (One Hour)\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, and Mathew Waters (for \"The Watchers on the Wall\")\\n\\nOutstanding Special and Visual Effects\\n\\nJoe Bauer, Joern Grosshans, Steve Kullback, Adam Chazen, Eric Carney, Sabrina Gerhardt, Matthew Rouleau, Thomas H. Schelesny, and Robert Simon (for \"The Children\")\\n\\nOutstanding Stunt Coordination for a Drama Series\\n\\nPaul Herbert\\n\\n2015\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner (for \"Hardhome\")\\n\\nAnette Haellmigk (for \"Sons of the Harpy\")\\n\\nRob McLachlan (for \"The Dance of Dragons\")\\n\\nGregory Middleton (for \"Unbowed, Unbent, Unbroken\")\\n\\nOutstanding Costumes for a Period/Fantasy Series, Limited Series or Movie\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham (for \"The Dance of Dragons\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Gift\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Rosalia Culora, Gary Machin, Laura Pollock, Nicola Mount (for \"Mother\\'s Mercy\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker and Nicola Matthews (for \"Mother\\'s Mercy\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"High Sparrow\", \"Unbowed, Unbent, Unbroken\", and \"Hardhome\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower (for \"Hardhome\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Hardhome\")\\n\\nKatie Weiland (for \"The Dance of Dragons\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit (for \"Hardhome\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau (for \"The Dance of Dragons\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2016\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nGregory Middleton (for \"Home\")\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Chloe Aubry, Sheena Wichary (for \"The Winds of Winter\")\\n\\nOutstanding Guest Actor in a Drama Series\\n\\nMax von Sydow (for \"The Door\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, Laura Pollock, Gary Machin, Rosalia Culora (for \"The Door\")\\n\\nOutstanding Interactive Program\\n\\n\\'\\'Game of Thrones\\'\\' Main Titles 360 Experience\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kate Thompson, Nicola Mathews, Kay Bilk, Marianna Kyriacou, Pamela Smyth (for \"Battle of the Bastards\")\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron (for \"Blood of My Blood\", \"The Broken Man\", and \"No One\")\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Sarah Gower, Emma Sheffield, Tristan Versluis, Barrie Gower (for \"The Door\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nKatie Weiland (for \"Oathbreaker\")\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Tim Hands, Paul Bercovitch, Paula Fairfield, Bradley C. Katona, Michael Wabro, David Klotz, Brett Voss, Jeffrey Wilhoit, Dylan Tuomy-Wilhoit (for \"The Door\")\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters (for \"Battle of the Bastards\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Derek Spears, Eric Carney, Sam Conway, Matthew Rouleau, Michelle Blok, Glenn Melenhorst (for \"Battle of the Bastards\")\\n\\nOutstanding Stunt Coordination for a Series\\n\\nRowley Irlam\\n\\n2018\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Alexander Fordham, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"Beyond the Wall\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nDiana Rigg (for \"The Queen\\'s Justice\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Dragon and the Wolf\")\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Pamela Smyth, Kate Thompson, and Nicola Mathews (for \"The Dragon and the Wolf\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Dragon and the Wolf\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"Dragonstone\")\\n\\nOutstanding Prosthetic Makeup for a Series, Miniseries, Movie or a Special\\n\\nJane Walker, Paul Spateri, Emma Sheffield, and Barrie Gower (for \"Beyond the Wall\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter (for \"Beyond the Wall\")\\n\\nCrispin Green (for \"The Spoils of War\")\\n\\nKatie Weiland (for \"The Dragon and the Wolf\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Paula Fairfield, Tim Hands, Paul Bercovitch, Bradley C. Katona, John Matter, Brett Voss, David Klotz, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Spoils of War\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Richard Dyer, and Ronan Hill (for \"Beyond the Wall\")\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Michelle Blok, Sam Conway, Ted Rae, David Ramos, Wayne Stables, and Derek Spears (for \"Beyond the Wall\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n2019\\n\\nOutstanding Casting for a Drama Series\\n\\nNina Gold, Robert Sterne, and Carla Stronge\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nJonathan Freeman (for \"The Iron Throne\")\\n\\nOutstanding Creative Achievement in Interactive Media within a Scripted Program\\n\\n\"Fight for the Living: Beyond the Wall Virtual Reality Experience\"\\n\\nOutstanding Fantasy/Sci-Fi Costumes\\n\\nMichele Clapton, Emma O\\'Loughlin, and Kate O\\'Farrell (for \"The Bells\")\\n\\nOutstanding Guest Actress in a Drama Series\\n\\nCarice van Houten (for \"The Long Night\")\\n\\nOutstanding Hairstyling for a Single-Camera Series\\n\\nKevin Alexander, Candice Banks, Nicola Mount, and Rosalia Culora (for \"The Long Night\")\\n\\nOutstanding Main Title Design\\n\\nAngus Wall, Kirk Shintani, Shahana Khan, Ian Ruhfass, and Rustam Hasanov\\n\\nOutstanding Makeup for a Single-Camera Series (Non-Prosthetic)\\n\\nJane Walker, Kay Bilk, Marianna Kyriacou, Nicola Mathews, and Pamela Smyth (for \"The Long Night\")\\n\\nOutstanding Music Composition for a Series (Original Dramatic Score)\\n\\nRamin Djawadi (for \"The Long Night\")\\n\\nOutstanding Production Design for a Narrative Period or Fantasy Program (One Hour or More)\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron (for \"The Bells\")\\n\\nOutstanding Prosthetic Makeup for a Series, Limited Series, Movie or Special\\n\\nEmma Faulkes, Paul Spateri, Chloe Muton-Phillips, Duncan Jarman, Patt Foad, John Eldred-Tooby, Barrie Gower, and Sarah Gower (for \"The Long Night\")\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nKatie Weiland (for \"The Iron Throne\")\\n\\nTim Porter (for \"The Long Night\")\\n\\nCrispin Green (for \"Winterfell\")\\n\\nOutstanding Sound Editing for a Comedy or Drama Series (One Hour)\\n\\nTim Kimmel, Tim Hands, Paula Fairfield, Bradley C. Katona, Paul Bercovitch, John Matter, David Klotz, Brett Voss, Jeffrey Wilhoit, and Dylan T. Wilhoit (for \"The Long Night\")\\n\\nOutstanding Sound Mixing for a Comedy or Drama Series (One Hour)\\n\\nOnnalee Blank, Mathew Waters, Simon Kerr, Danny Crowley, and Ronan Hill (for \"The Long Night\")\\n\\nOutstanding Special Visual Effects\\n\\nJoe Bauer, Steve Kullback, Adam Chazen, Sam Conway, Mohsen Mousavi, Martin Hill, Ted Rae, Patrick Tiberius Gehlen, and Thomas Schelesny (for \"The Bells\")\\n\\nOutstanding Stunt Coordination for a Drama Series, Limited Series or Movie\\n\\nRowley Irlam\\n\\n===Golden Reel Awards===\\nThe Golden Reel Award presents awards annually to sound editors in the several categories for feature film and television. \\'\\'Game of Thrones\\'\\' has received eleven awards out of twenty two nominations.\\n\\n+ Golden Reel Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 31}, 'embedding': None, 'id': '1bd86d105f4cb26e27172db045d78e6b'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has received eleven awards out of twenty two nominations.\\n\\n+ Golden Reel Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Cripples, Bastards, and Broken Things\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Winter is Coming\")\\n\\n2013\\n\\nBest Sound Editing – Long Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Long Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Music in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\n2014\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\nJed Dodge and Tim Hands (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Music\\n\\nDavid Klotz (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley\\n\\nTim Kimmel (for \"The Rains of Castamere\")\\n\\n2015\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Hardhome\")\\n\\n2017\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Battle of the Bastards\")\\n\\n2018\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Brett Voss and Jeffrey Wilhoit (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel, Paul Bercovitch and Tim Hands (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Beyond the Wall\")\\n\\n===Grammy Awards===\\nThe Grammy, is an honor awarded by The Recording Academy to recognize outstanding achievement in the music industry. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 32}, 'embedding': None, 'id': 'b8b0a05606eb00dd26cf693711a7a732'}>,\n",
-       "  <Document: {'content': '2012\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Cripples, Bastards, and Broken Things\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Winter is Coming\")\\n\\n2013\\n\\nBest Sound Editing – Long Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Long Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Music in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\n2014\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\nJed Dodge and Tim Hands (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Music\\n\\nDavid Klotz (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley\\n\\nTim Kimmel (for \"The Rains of Castamere\")\\n\\n2015\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Hardhome\")\\n\\n2017\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Battle of the Bastards\")\\n\\n2018\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Brett Voss and Jeffrey Wilhoit (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel, Paul Bercovitch and Tim Hands (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Beyond the Wall\")\\n\\n===Grammy Awards===\\nThe Grammy, is an honor awarded by The Recording Academy to recognize outstanding achievement in the music industry. \\'\\'Game of Thrones\\'\\' has one nomination.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 33}, 'embedding': None, 'id': '78caf7182c8e0792674684cf28416a6f'}>,\n",
-       "  <Document: {'content': '2012\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Cripples, Bastards, and Broken Things\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Winter is Coming\")\\n\\n2013\\n\\nBest Sound Editing – Long Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Long Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Valar Morghulis\")\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Music in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley in Television\\n\\n\\'\\'Game of Thrones\\'\\' (for \"Blackwater\")\\n\\n2014\\n\\nBest Sound Editing – Short Form Dialogue and ADR in Television\\n\\nJed Dodge and Tim Hands (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Music\\n\\nDavid Klotz (for \"The Rains of Castamere\")\\n\\nBest Sound Editing – Short Form Sound Effects and Foley\\n\\nTim Kimmel (for \"The Rains of Castamere\")\\n\\n2015\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel (for \"Hardhome\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Hardhome\")\\n\\n2017\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Brett Voss, John Matter, Jeffrey Wilhoit, Dylan Wilhoit, Paula Fairfield and Bradley Katona (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel and Tim Hands (for \"Battle of the Bastards\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Battle of the Bastards\")\\n\\n2018\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Brett Voss and Jeffrey Wilhoit (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel, Paul Bercovitch and Tim Hands (for \"The Spoils of War\")\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz (for \"Beyond the Wall\")\\n\\n===Grammy Awards===\\nThe Grammy, is an honor awarded by The Recording Academy to recognize outstanding achievement in the music industry. \\'\\'Game of Thrones\\'\\' has one nomination.\\n\\n+ Grammy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 34}, 'embedding': None, 'id': 'a30aea6e14009f91dfa236e0b233d598'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has one nomination.\\n\\n+ Grammy Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2018\\n\\nBest Score Soundtrack for Visual Media\\n\\nRamin Djawadi (for ''Game of Thrones: Season 7'')\\n\\n===Hollywood Post Alliance===\\nThe Hollywood Post Alliance presents awards to individuals and organizations to recognize the achievement of post production talent. ''Game of Thrones'' has received seven awards out of nineteen nominations.\\n\\n+ Hollywood Post Alliance Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 35}, 'embedding': None, 'id': '3f4fd36ca63138cd64ba797af88b3d43'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has received seven awards out of nineteen nominations.\\n\\n+ Hollywood Post Alliance Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Finley (for \"The Prince of Winterfell\")\\n\\n2013\\n\\nOutstanding Color Grading – Television\\n\\nJoe Finley (for \"Kissed by Fire\")\\n\\nOutstanding Sound – Television\\n\\nPaula Fairfield, Brad Katona, Jed Dodge, Onnalee Blank and Mathew Waters (for \"The Climb\")\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Bauer and Jabbar Raisani, Jörn Grosshans and Sven Martin, and Doug Campbell (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Sound\\n\\nTim Kimmel, Onnalee Blank, Mathew Waters, Paula Fairfield, Brad Katona and Jed M. Dodge (for \"The Children\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Mockingbird\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Sven Martin, Jörn Grosshans, Thomas Schelesny, Matthew Rouleau (for \"The Children\")\\n\\n2015\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Hardhome\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Hardhome\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani (for \"The Dance of Dragons\")\\n\\n2016\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch (for \"Battle of the Bastards\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau (for \"Battle of the Bastards\")\\n\\n2017\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Dragonstone\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Stormborn\")\\n\\nJesse Parker (for \"The Queen\\'s Justice\")\\n\\nCrispin Green (for \"Dragonstone\")\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley C. Katona, Paul Bercovitch (for \"The Spoils of War\")\\n\\n===Irish Film & Television Awards===\\nMichelle Fairley won one Irish Film & Television Award in 2014\\n\\nThe Irish Film & Television Awards are presented in an annual award show hosted by the Irish Film & Television Academy. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 36}, 'embedding': None, 'id': 'a801bf81d43d46df4ce5998427fae0dd'}>,\n",
-       "  <Document: {'content': '2012\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Finley (for \"The Prince of Winterfell\")\\n\\n2013\\n\\nOutstanding Color Grading – Television\\n\\nJoe Finley (for \"Kissed by Fire\")\\n\\nOutstanding Sound – Television\\n\\nPaula Fairfield, Brad Katona, Jed Dodge, Onnalee Blank and Mathew Waters (for \"The Climb\")\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Bauer and Jabbar Raisani, Jörn Grosshans and Sven Martin, and Doug Campbell (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Sound\\n\\nTim Kimmel, Onnalee Blank, Mathew Waters, Paula Fairfield, Brad Katona and Jed M. Dodge (for \"The Children\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Mockingbird\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Sven Martin, Jörn Grosshans, Thomas Schelesny, Matthew Rouleau (for \"The Children\")\\n\\n2015\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Hardhome\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Hardhome\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani (for \"The Dance of Dragons\")\\n\\n2016\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch (for \"Battle of the Bastards\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau (for \"Battle of the Bastards\")\\n\\n2017\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Dragonstone\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Stormborn\")\\n\\nJesse Parker (for \"The Queen\\'s Justice\")\\n\\nCrispin Green (for \"Dragonstone\")\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley C. Katona, Paul Bercovitch (for \"The Spoils of War\")\\n\\n===Irish Film & Television Awards===\\nMichelle Fairley won one Irish Film & Television Award in 2014\\n\\nThe Irish Film & Television Awards are presented in an annual award show hosted by the Irish Film & Television Academy. \\'\\'Game of Thrones\\'\\' has received four awards out of twenty-eight nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 37}, 'embedding': None, 'id': 'fe4d5ad37a636bd4a7bb66d2461c15cd'}>,\n",
-       "  <Document: {'content': '2012\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Finley (for \"The Prince of Winterfell\")\\n\\n2013\\n\\nOutstanding Color Grading – Television\\n\\nJoe Finley (for \"Kissed by Fire\")\\n\\nOutstanding Sound – Television\\n\\nPaula Fairfield, Brad Katona, Jed Dodge, Onnalee Blank and Mathew Waters (for \"The Climb\")\\n\\nOutstanding Visual Effects – Television\\n\\nJoe Bauer and Jabbar Raisani, Jörn Grosshans and Sven Martin, and Doug Campbell (for \"Valar Dohaeris\")\\n\\n2014\\n\\nOutstanding Sound\\n\\nTim Kimmel, Onnalee Blank, Mathew Waters, Paula Fairfield, Brad Katona and Jed M. Dodge (for \"The Children\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Mockingbird\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Sven Martin, Jörn Grosshans, Thomas Schelesny, Matthew Rouleau (for \"The Children\")\\n\\n2015\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters (for \"Hardhome\")\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Hardhome\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Hardhome\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani (for \"The Dance of Dragons\")\\n\\n2016\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley Katona, Paul Bercovitch (for \"Battle of the Bastards\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Battle of the Bastards\")\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Eric Carney, Derek Spears, Glenn Melenhorst, Matthew Rouleau (for \"Battle of the Bastards\")\\n\\n2017\\n\\nOutstanding Color Grading\\n\\nJoe Finley (for \"Dragonstone\")\\n\\nOutstanding Editing\\n\\nTim Porter (for \"Stormborn\")\\n\\nJesse Parker (for \"The Queen\\'s Justice\")\\n\\nCrispin Green (for \"Dragonstone\")\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Mathew Waters, Onnalee Blank, Bradley C. Katona, Paul Bercovitch (for \"The Spoils of War\")\\n\\n===Irish Film & Television Awards===\\nMichelle Fairley won one Irish Film & Television Award in 2014\\n\\nThe Irish Film & Television Awards are presented in an annual award show hosted by the Irish Film & Television Academy. \\'\\'Game of Thrones\\'\\' has received four awards out of twenty-eight nominations.\\n\\n+ Irish Film & Television Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 38}, 'embedding': None, 'id': '371baf8282f42b0aaa0e127b3246200a'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has received four awards out of twenty-eight nominations.\\n\\n+ Irish Film & Television Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Television Drama\\n\\nMark Huffam\\n\\nBest Director Television Drama\\n\\nBrian Kirk\\n\\nBest Actress – Television\\n\\nMichelle Fairley\\n\\nBest Supporting Actor – Television\\n\\nAidan Gillen\\n\\nBest Sound (Film/TV Drama)\\n\\nRonan Hill\\n\\nBest Sound\\n\\nRonan Hill\\n\\n2013\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nBest Director of Photography\\n\\nP.J. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 39}, 'embedding': None, 'id': 'abb68b07a1d213a7d6f15d391537ae66'}>,\n",
-       "  <Document: {'content': \"2012\\n\\nBest Television Drama\\n\\nMark Huffam\\n\\nBest Director Television Drama\\n\\nBrian Kirk\\n\\nBest Actress – Television\\n\\nMichelle Fairley\\n\\nBest Supporting Actor – Television\\n\\nAidan Gillen\\n\\nBest Sound (Film/TV Drama)\\n\\nRonan Hill\\n\\nBest Sound\\n\\nRonan Hill\\n\\n2013\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nBest Director of Photography\\n\\nP.J. Dillon\\n\\nBest Sound\\n\\nRonan Hill, Mervyn Moore\\n\\n2014\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nAidan Gillen\\n\\nActress in a Supporting Role – Television\\n\\nMichelle Fairley\\n\\nBest Sound\\n\\nRonan Hill\\n\\n2015\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n''Game of Thrones''\\n\\n2016\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\n2017\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\nRonan Hill, Onnalee Blank and Matthew Waters\\n\\nBest VFX\\n\\nEd Bruce & Nicholas Murphy\\n\\n2018\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nAidan Gillen\\n\\nBest Sound\\n\\nRonan Hill, Onnalee Blank and Matthew Waters\\n\\nBest VFX\\n\\nEd Bruce & Nicholas Murphy\\n\\n===Location Managers Guild Awards===\\nThe Location Managers Guild Awards are awarded annually by the Location Managers Guild International. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 40}, 'embedding': None, 'id': '14a3b06761b022c24be611015bb06d05'}>,\n",
-       "  <Document: {'content': \"Dillon\\n\\nBest Sound\\n\\nRonan Hill, Mervyn Moore\\n\\n2014\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nAidan Gillen\\n\\nActress in a Supporting Role – Television\\n\\nMichelle Fairley\\n\\nBest Sound\\n\\nRonan Hill\\n\\n2015\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\n''Game of Thrones''\\n\\n2016\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\n2017\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nBest Sound\\n\\nRonan Hill, Onnalee Blank and Matthew Waters\\n\\nBest VFX\\n\\nEd Bruce & Nicholas Murphy\\n\\n2018\\n\\nBest Television Drama\\n\\n''Game of Thrones''\\n\\nActor in a Supporting Role – Television\\n\\nLiam Cunningham\\n\\nAidan Gillen\\n\\nBest Sound\\n\\nRonan Hill, Onnalee Blank and Matthew Waters\\n\\nBest VFX\\n\\nEd Bruce & Nicholas Murphy\\n\\n===Location Managers Guild Awards===\\nThe Location Managers Guild Awards are awarded annually by the Location Managers Guild International. ''Game of Thrones'' took the most awards, two in total, and claimed the top TV award at the inaugural LMGA Awards in 2014.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 41}, 'embedding': None, 'id': 'b9f624a69add74b8f3b75fd39bfc91e5'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' took the most awards, two in total, and claimed the top TV award at the inaugural LMGA Awards in 2014.\\n\\nIn 2016 the show became the first series to win multiple LMGI Awards, with Location Managers Robert Boake and Tate Araez taking home the award for Outstanding Locations in a Period Television Series. Nominated again in 2017 and 2018, ''Game of Thrones'' took back the period television throne from ''The Crown'' in 2018, and currently holds a record 4 LMGI Award wins out of 6 nominations.\\n\\nThe film locations featured in ''Game of Thrones'' have also proven to be winners themselves, as the show has been responsible for driving a significant increase in tourism to the host regions filmed by the series.\\n\\n+ Location Managers Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 42}, 'embedding': None, 'id': '4034681fedaa27a82e3ac845f7c97686'}>,\n",
-       "  <Document: {'content': \"The film locations featured in ''Game of Thrones'' have also proven to be winners themselves, as the show has been responsible for driving a significant increase in tourism to the host regions filmed by the series.\\n\\n+ Location Managers Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nOutstanding Achievement by a Location Professional in Television\\n\\nRobert Boake\\n\\nOutstanding use of Locations in Television\\n\\n''Game of Thrones''\\n\\n2016\\n\\nOutstanding Locations in Period Television\\n\\nRobert Boake and Tate Araez\\n\\n2017\\n\\nOutstanding Locations in Period Television\\n\\nMatt Jones and Naomi Liston\\n\\n2018\\n\\nOutstanding Locations in Period Television\\n\\nRobert Boake, Matt Jones, Pedro Tate Araez\\n\\n2019\\n\\nOutstanding Locations in a Period Television Series\\n\\nRobert Boake\\n\\n===Makeup Artist and Hair Stylist Guild Awards===\\nThe Make-Up Artists and Hair Stylists Guild are awarded annually to make-up artists and hair stylist in feature films, television programs, commercials, live network events and theatrical productions in the United States. ''Game of Thrones'' has won six out of nine nominations.\\n\\n+ Makeup Artist and Hair Stylist Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 43}, 'embedding': None, 'id': '1d8c192edb69ea72ef75b59647e3e1f8'}>,\n",
-       "  <Document: {'content': \"+ Makeup Artist and Hair Stylist Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nBest Period and/or Character Makeup – Television\\n\\nPaul Engelen, Melissa Lackersteen\\n\\n2016\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\n2017\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker, Kay Bilk\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nBest Special Makeup Effects – Television\\n\\nBarrie Gower, Sarah Gower\\n\\n2018\\n\\nBest Period and/or Character Makeup – Television\\n\\nJane Walker, Nicola Matthews\\n\\nBest Period and/or Character Hair Styling – Television\\n\\nKevin Alexander, Candice Banks\\n\\nBest Special Makeup Effects – Television\\n\\nBarrie Gower, Sarah Gower\\n\\n===Producers Guild of America Awards===\\nThe Producers Guild of America Award is bestowed by the Producers Guild of America to honor the work of producers in film and television. ''Game of Thrones'' has been nominated for seven awards for Best Episodic Drama, winning in 2016.\\n\\n+ Producers Guild of America Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 44}, 'embedding': None, 'id': '232535d8d481b7b7e79289a743015598'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated for seven awards for Best Episodic Drama, winning in 2016.\\n\\n+ Producers Guild of America Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Frank Doelger, Mark Huffam, Carolyn Strauss, D. B. Weiss\\n\\n2013\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, D. B. Weiss\\n\\n2014\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Christopher Newman, Greg Spence, Carolyn Strauss, and D. B. Weiss\\n\\n2015\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Chris Newman, Greg Spence, Carolyn Strauss, D. B. Weiss\\n\\n2016\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2017\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2018\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n===Screen Actors Guild Awards===\\nThe Screen Actors Guild Award, given by the Screen Actors Guild‐American Federation of Television and Radio Artists (SAG-AFTRA), is an award dedicated to honor the best performances of actors in film and television. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 45}, 'embedding': None, 'id': 'f51d2b963b3631f80fb7fb22e5f7c657'}>,\n",
-       "  <Document: {'content': '2012\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Frank Doelger, Mark Huffam, Carolyn Strauss, D. B. Weiss\\n\\n2013\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, D. B. Weiss\\n\\n2014\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Christopher Newman, Greg Spence, Carolyn Strauss, and D. B. Weiss\\n\\n2015\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Chris Newman, Greg Spence, Carolyn Strauss, D. B. Weiss\\n\\n2016\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2017\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2018\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n===Screen Actors Guild Awards===\\nThe Screen Actors Guild Award, given by the Screen Actors Guild‐American Federation of Television and Radio Artists (SAG-AFTRA), is an award dedicated to honor the best performances of actors in film and television. \\'\\'Game of Thrones\\'\\' has received seven awards out of eighteen nominations. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 46}, 'embedding': None, 'id': 'e388df058a50bfb349ed340afe071950'}>,\n",
-       "  <Document: {'content': '2012\\n\\n\"Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Frank Doelger, Mark Huffam, Carolyn Strauss, D. B. Weiss\\n\\n2013\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, D. B. Weiss\\n\\n2014\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Christopher Newman, Greg Spence, Carolyn Strauss, and D. B. Weiss\\n\\n2015\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, Bernadette Caulfield, Frank Doelger, Chris Newman, Greg Spence, Carolyn Strauss, D. B. Weiss\\n\\n2016\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2017\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n2018\\n\\n\"The Norman Felton Award for Outstanding Producer of Episodic Television, Drama\"\\n\\nDavid Benioff, D. B. Weiss, Bernadette Caulfield, Frank Doelger, Carolyn Strauss, Bryan Cogman, Lisa McAtackney, Chris Newman, Greg Spence\\n\\n===Screen Actors Guild Awards===\\nThe Screen Actors Guild Award, given by the Screen Actors Guild‐American Federation of Television and Radio Artists (SAG-AFTRA), is an award dedicated to honor the best performances of actors in film and television. \\'\\'Game of Thrones\\'\\' has received seven awards out of eighteen nominations. Dinklage was nominated for a Screen Actors Guild Award for Outstanding Performance by a Male Actor in a Drama Series in 2014, his first for the series.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 47}, 'embedding': None, 'id': '4a5b177b9745615c327b9ebbfebdaaf1'}>,\n",
-       "  <Document: {'content': \"Dinklage was nominated for a Screen Actors Guild Award for Outstanding Performance by a Male Actor in a Drama Series in 2014, his first for the series.\\n\\n+ Screen Actors Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 48}, 'embedding': None, 'id': '17dcba1b929a80f39318497c7365c73'}>,\n",
-       "  <Document: {'content': \"Dinklage was nominated for a Screen Actors Guild Award for Outstanding Performance by a Male Actor in a Drama Series in 2014, his first for the series.\\n\\n+ Screen Actors Guild Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAmrita Acharia, Mark Addy, Alfie Allen, Josef Altin, Sean Bean, Susan Brown, Emilia Clarke, Nikolaj Coster-Waldau, Peter Dinklage, Ron Donachie, Michelle Fairley, Jerome Flynn, Elyes Gabel, Aidan Gillen, Jack Gleeson, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Richard Madden, Jason Momoa, Rory McCann, Ian McElhinney, Luke McEwan, Roxanne McKee, Dar Salim, Mark Stanley, Donald Sumpter, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\n''Game of Thrones''\\n\\n2013\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRob Cooper, Jamie Edgell, Dave Fisher, Dave Forman, Paul Herbert, Michelle McKeown, Sian Miline, Jimmy O'Dee, Domonkos Pardanyi, Marcus Shakesheff, CC Smiff, and Mark Southworth\\n\\n2014\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, John Bradley, Oona Chaplin, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Mackenzie Crook, Charles Dance, Joe Dempsie, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Michelle Fairley, Jack Gleeson, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Kristofer Hivju, Paul Kaye, Sibel Kekilli, Rose Leslie, Richard Madden, Rory McCann, Michael McElhatton, Ian McElhinney, Philip McGinley, Hannah Murray, Iwan Rheon, Sophie Turner, Carice Van Houten, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRachelle Beinart, Richard Bradshaw, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Jozsef Fodor, Dave Forman, Paul Herbert, Paul Howell, Daniel Naprous, Florian Robin, CC Smiff, and Roy Taylor\\n\\n2015\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nJosef Altin, Jacob Anderson, John Bradley, Dominic Carter, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Charles Dance, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nLucy Allen, Cole Armitage, Gary Arthurs, Rachelle Beinart, Ferenc Berecz, Richard Bradshaw, Andy Butcher, Michael Byrch, Neil Chapelhow, Nick Chopping, Jonathan Cohen, Joel Conlan, Gary Connery, James Cox, Tom Cox, Jason Curle, Nicholas Daines, Bill Davey, Kelly J. Dent, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Neil Finnighan, Jozsef Fodor, Dean Forster, David Garrick, James Grogan, Tim Halloran, Paul Heasman, Robert Hladik, Al Holland, Gergely Horpacsi, Paul Howell, Stewart James, Gary Kane, Ian Kay, Robbie Keane, George Kirby, Cristian Knight, Laszlo Kosa, Geza Kovacs, Norbert Kovacs, Guy List, Phil Lonergan, Russell Macleod, Tina Maskell, Adrian McGaw, Nick McKinless, Erol Mehmet, Andy Merchant, Sian Milne, Daniel Naprous, Chris Newton, Ray Nicholas, Bela Orsanyi, Sam Parham, Ian Pead, Justin Pearson, Martin Pemberton, Heather Phillips, Rashid Phoenix, Andy Pilgrim, Christopher Pocock, Curtis Rivers, Marcus Shakesheff, Matt Sherren, Anthony Skrimshire, Mark Slaughter, Karen Smithson, Mark Southworth, Helen Steinway Bailey, Shane Steyn, Matthew Stirling, John Street, Gaspar Szabo, Gabor Szeman, Roy Taylor, Gyula Toth, Tony Van Silva, Reg Wayment, Linda Weal, Richard J Wheeldon, Heron White, Maxine Whittaker, Simon Whyman, Donna C. Williams, Lou Wong, Annabel E. Wood, Liang Yang, and Steen Young\\n\\n2016\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Michiel Huisman, Hannah Murray, Brenock O'Connor, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams, and Tom Wlaschiha\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\n2017\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean-Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carice Van Houten, Gemma Whelan, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, and Leo Woodruff\\n\\n2018\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, James Faulkner, Jerome Flynn, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Kristofer Hivju, Tom Hopper, Anton Lesser, Rory McCann, Sophie Turner, Carice Van Houten, Gemma Whelan, Rupert Vansittart, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Mark Archer, Luis Miguel Arranz, Ferenc Berecz, Richard Bradshaw, Michael Byrch, Nick Chopping, Jonathan Cohen, Chris Cox, Jake Cox, James Cox, David Cronnelly, Matt Crook, Ricardo Cruz Jr., Jason Curle, Levan Doran, Daniel Euston, Bradley Farmer, Pete Ford, Vladimir Furdik, Eduardo Gago, Angel Gomez, David Grant, Lawrence Hansen, Richard Hansen, Nicklas Hansson, Rob Hayns, Bobby Holland Hanton, Paul Howell, Radoslav Ignatov, Rowley Irlam, Erol Ismail, Orsányi Iván, Sonny Louis, Leigh Maddern, Jonny McBride, Leona McCarron, Kim McGarrity, Trayan Milenov-Troy, Sian Milne, David Newton, Jason Oettle, Radoslav Parvanov, Ian Pead, Andy Pilgrim, Oleg Podobin, Marc Redmond, Andrej Riabokon, Florian Robin, Doug Robson, Fabio Santos, Stanislav Satko, Paul Shapcott, Mark Slaughter, CC Smiff, James Stewart, Jonny Stockwell, Ryan Stuart, Gáspár Szabó, Lukas Tomsik, Marek Toth, Teodor Tzolov, Raycho Vasilev, Calvin Warrington-Heasman, Annabel Wood, Leo Woodruff, and Lewis Young\\n\\n===Society of Camera Operators Awards===\\nThe Society of Camera Operators Awards are awarded annually to honor the creative contribution of the camera operator in the motion picture and television industries. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 49}, 'embedding': None, 'id': '366237a8680174d7c5eff334633ccc3c'}>,\n",
-       "  <Document: {'content': \"2012\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAmrita Acharia, Mark Addy, Alfie Allen, Josef Altin, Sean Bean, Susan Brown, Emilia Clarke, Nikolaj Coster-Waldau, Peter Dinklage, Ron Donachie, Michelle Fairley, Jerome Flynn, Elyes Gabel, Aidan Gillen, Jack Gleeson, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Richard Madden, Jason Momoa, Rory McCann, Ian McElhinney, Luke McEwan, Roxanne McKee, Dar Salim, Mark Stanley, Donald Sumpter, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\n''Game of Thrones''\\n\\n2013\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRob Cooper, Jamie Edgell, Dave Fisher, Dave Forman, Paul Herbert, Michelle McKeown, Sian Miline, Jimmy O'Dee, Domonkos Pardanyi, Marcus Shakesheff, CC Smiff, and Mark Southworth\\n\\n2014\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, John Bradley, Oona Chaplin, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Mackenzie Crook, Charles Dance, Joe Dempsie, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Michelle Fairley, Jack Gleeson, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Kristofer Hivju, Paul Kaye, Sibel Kekilli, Rose Leslie, Richard Madden, Rory McCann, Michael McElhatton, Ian McElhinney, Philip McGinley, Hannah Murray, Iwan Rheon, Sophie Turner, Carice Van Houten, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRachelle Beinart, Richard Bradshaw, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Jozsef Fodor, Dave Forman, Paul Herbert, Paul Howell, Daniel Naprous, Florian Robin, CC Smiff, and Roy Taylor\\n\\n2015\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nJosef Altin, Jacob Anderson, John Bradley, Dominic Carter, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Charles Dance, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nLucy Allen, Cole Armitage, Gary Arthurs, Rachelle Beinart, Ferenc Berecz, Richard Bradshaw, Andy Butcher, Michael Byrch, Neil Chapelhow, Nick Chopping, Jonathan Cohen, Joel Conlan, Gary Connery, James Cox, Tom Cox, Jason Curle, Nicholas Daines, Bill Davey, Kelly J. Dent, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Neil Finnighan, Jozsef Fodor, Dean Forster, David Garrick, James Grogan, Tim Halloran, Paul Heasman, Robert Hladik, Al Holland, Gergely Horpacsi, Paul Howell, Stewart James, Gary Kane, Ian Kay, Robbie Keane, George Kirby, Cristian Knight, Laszlo Kosa, Geza Kovacs, Norbert Kovacs, Guy List, Phil Lonergan, Russell Macleod, Tina Maskell, Adrian McGaw, Nick McKinless, Erol Mehmet, Andy Merchant, Sian Milne, Daniel Naprous, Chris Newton, Ray Nicholas, Bela Orsanyi, Sam Parham, Ian Pead, Justin Pearson, Martin Pemberton, Heather Phillips, Rashid Phoenix, Andy Pilgrim, Christopher Pocock, Curtis Rivers, Marcus Shakesheff, Matt Sherren, Anthony Skrimshire, Mark Slaughter, Karen Smithson, Mark Southworth, Helen Steinway Bailey, Shane Steyn, Matthew Stirling, John Street, Gaspar Szabo, Gabor Szeman, Roy Taylor, Gyula Toth, Tony Van Silva, Reg Wayment, Linda Weal, Richard J Wheeldon, Heron White, Maxine Whittaker, Simon Whyman, Donna C. Williams, Lou Wong, Annabel E. Wood, Liang Yang, and Steen Young\\n\\n2016\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Michiel Huisman, Hannah Murray, Brenock O'Connor, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams, and Tom Wlaschiha\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\n2017\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean-Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carice Van Houten, Gemma Whelan, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, and Leo Woodruff\\n\\n2018\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, James Faulkner, Jerome Flynn, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Kristofer Hivju, Tom Hopper, Anton Lesser, Rory McCann, Sophie Turner, Carice Van Houten, Gemma Whelan, Rupert Vansittart, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Mark Archer, Luis Miguel Arranz, Ferenc Berecz, Richard Bradshaw, Michael Byrch, Nick Chopping, Jonathan Cohen, Chris Cox, Jake Cox, James Cox, David Cronnelly, Matt Crook, Ricardo Cruz Jr., Jason Curle, Levan Doran, Daniel Euston, Bradley Farmer, Pete Ford, Vladimir Furdik, Eduardo Gago, Angel Gomez, David Grant, Lawrence Hansen, Richard Hansen, Nicklas Hansson, Rob Hayns, Bobby Holland Hanton, Paul Howell, Radoslav Ignatov, Rowley Irlam, Erol Ismail, Orsányi Iván, Sonny Louis, Leigh Maddern, Jonny McBride, Leona McCarron, Kim McGarrity, Trayan Milenov-Troy, Sian Milne, David Newton, Jason Oettle, Radoslav Parvanov, Ian Pead, Andy Pilgrim, Oleg Podobin, Marc Redmond, Andrej Riabokon, Florian Robin, Doug Robson, Fabio Santos, Stanislav Satko, Paul Shapcott, Mark Slaughter, CC Smiff, James Stewart, Jonny Stockwell, Ryan Stuart, Gáspár Szabó, Lukas Tomsik, Marek Toth, Teodor Tzolov, Raycho Vasilev, Calvin Warrington-Heasman, Annabel Wood, Leo Woodruff, and Lewis Young\\n\\n===Society of Camera Operators Awards===\\nThe Society of Camera Operators Awards are awarded annually to honor the creative contribution of the camera operator in the motion picture and television industries. ''Game of Thrones'' has been nominated one time.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 50}, 'embedding': None, 'id': '29228d89f11a4385f23f699a48af12e5'}>,\n",
-       "  <Document: {'content': \"2012\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAmrita Acharia, Mark Addy, Alfie Allen, Josef Altin, Sean Bean, Susan Brown, Emilia Clarke, Nikolaj Coster-Waldau, Peter Dinklage, Ron Donachie, Michelle Fairley, Jerome Flynn, Elyes Gabel, Aidan Gillen, Jack Gleeson, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Richard Madden, Jason Momoa, Rory McCann, Ian McElhinney, Luke McEwan, Roxanne McKee, Dar Salim, Mark Stanley, Donald Sumpter, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\n''Game of Thrones''\\n\\n2013\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRob Cooper, Jamie Edgell, Dave Fisher, Dave Forman, Paul Herbert, Michelle McKeown, Sian Miline, Jimmy O'Dee, Domonkos Pardanyi, Marcus Shakesheff, CC Smiff, and Mark Southworth\\n\\n2014\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, John Bradley, Oona Chaplin, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Mackenzie Crook, Charles Dance, Joe Dempsie, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Michelle Fairley, Jack Gleeson, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Kristofer Hivju, Paul Kaye, Sibel Kekilli, Rose Leslie, Richard Madden, Rory McCann, Michael McElhatton, Ian McElhinney, Philip McGinley, Hannah Murray, Iwan Rheon, Sophie Turner, Carice Van Houten, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nRachelle Beinart, Richard Bradshaw, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Jozsef Fodor, Dave Forman, Paul Herbert, Paul Howell, Daniel Naprous, Florian Robin, CC Smiff, and Roy Taylor\\n\\n2015\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nJosef Altin, Jacob Anderson, John Bradley, Dominic Carter, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Charles Dance, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nLucy Allen, Cole Armitage, Gary Arthurs, Rachelle Beinart, Ferenc Berecz, Richard Bradshaw, Andy Butcher, Michael Byrch, Neil Chapelhow, Nick Chopping, Jonathan Cohen, Joel Conlan, Gary Connery, James Cox, Tom Cox, Jason Curle, Nicholas Daines, Bill Davey, Kelly J. Dent, Ben Dimmock, Levan Doran, Jamie Edgell, Bradley Farmer, Neil Finnighan, Jozsef Fodor, Dean Forster, David Garrick, James Grogan, Tim Halloran, Paul Heasman, Robert Hladik, Al Holland, Gergely Horpacsi, Paul Howell, Stewart James, Gary Kane, Ian Kay, Robbie Keane, George Kirby, Cristian Knight, Laszlo Kosa, Geza Kovacs, Norbert Kovacs, Guy List, Phil Lonergan, Russell Macleod, Tina Maskell, Adrian McGaw, Nick McKinless, Erol Mehmet, Andy Merchant, Sian Milne, Daniel Naprous, Chris Newton, Ray Nicholas, Bela Orsanyi, Sam Parham, Ian Pead, Justin Pearson, Martin Pemberton, Heather Phillips, Rashid Phoenix, Andy Pilgrim, Christopher Pocock, Curtis Rivers, Marcus Shakesheff, Matt Sherren, Anthony Skrimshire, Mark Slaughter, Karen Smithson, Mark Southworth, Helen Steinway Bailey, Shane Steyn, Matthew Stirling, John Street, Gaspar Szabo, Gabor Szeman, Roy Taylor, Gyula Toth, Tony Van Silva, Reg Wayment, Linda Weal, Richard J Wheeldon, Heron White, Maxine Whittaker, Simon Whyman, Donna C. Williams, Lou Wong, Annabel E. Wood, Liang Yang, and Steen Young\\n\\n2016\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Ian Beattie, John Bradley, Gwendoline Christie, Emilia Clarke, Michael Condron, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Stephen Dillane, Peter Dinklage, Nathalie Emmanuel, Tara Fitzgerald, Jerome Flynn, Brian Fortune, Joel Fry, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Michiel Huisman, Hannah Murray, Brenock O'Connor, Daniel Portman, Iwan Rheon, Owen Teale, Sophie Turner, Carice Van Houten, Maisie Williams, and Tom Wlaschiha\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Richard Bradshaw, Jonathan Cohen, Christopher Cox, Jacob Cox, Matt Crook, Rob DeGroot, Levan Doran, Clint Elvy, James Embree, Bradley Farmer, Richard Hansen, Bobby Holland-Hanton, Radoslav Ignatov, Borislav Iliev, Rowley Irlam, Erol Ismail, Milen Kaleychev, Paul Lowe, Jonathan McBride, Sian Milne, David Newton, Radoslav Parvanov, Ian Pead, Jan Petrina, Rashid Phoenix, Andy Pilgrim, Dominic Preece, Marc Redmond, Paul Shapcott, Ryan Stuart, Pablo Verdejo, Calvin Warrington-Heasman, Annabel E. Wood, Danko Yordanov, and Lewis Young\\n\\n2017\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Dean-Charles Chapman, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, Kit Harington, Lena Headey, Conleth Hill, Kristofer Hivju, Michiel Huisman, Faye Marsay, Jonathan Pryce, Sophie Turner, Carice Van Houten, Gemma Whelan, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Kristina Baskett, Rachelle Beinart, Richard Bradshaw, Michael Byrch, Nick Chopping, Christopher Cox, Jake Cox, David Cronnelly, Matt Crook, Levan Doran, Bradley Farmer, Vladimir Furdik, Richard Hansen, Rob Hayns, Paul Howell, Rowley Irlam, Erol Ismail, Milen Kaleychev, Leigh Maddern, Jonathan McBride, Leona McCarron, Kim McGarrity, Richard Mead, Casey Michaels, Sian Milne, David Newton, Jason Otelle, Radoslav Parvanov, Ian Pead, Rashid Phoenix, Andy Pilgrim, Marc Redmond, Paul Shapcott, Jonny Stockwell, Ryan Stuart, Edward Upcott, and Leo Woodruff\\n\\n2018\\n\\nOutstanding Performance by a Male Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nOutstanding Performance by an Ensemble in a Drama Series\\n\\nAlfie Allen, Jacob Anderson, Emilia Clarke, Nikolaj Coster-Waldau, Liam Cunningham, Peter Dinklage, Nathalie Emmanuel, James Faulkner, Jerome Flynn, Aidan Gillen, Iain Glen, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Kristofer Hivju, Tom Hopper, Anton Lesser, Rory McCann, Sophie Turner, Carice Van Houten, Gemma Whelan, Rupert Vansittart, and Maisie Williams\\n\\nOutstanding Performance by a Stunt Ensemble in a Television Series\\n\\nBoian Anev, Mark Archer, Luis Miguel Arranz, Ferenc Berecz, Richard Bradshaw, Michael Byrch, Nick Chopping, Jonathan Cohen, Chris Cox, Jake Cox, James Cox, David Cronnelly, Matt Crook, Ricardo Cruz Jr., Jason Curle, Levan Doran, Daniel Euston, Bradley Farmer, Pete Ford, Vladimir Furdik, Eduardo Gago, Angel Gomez, David Grant, Lawrence Hansen, Richard Hansen, Nicklas Hansson, Rob Hayns, Bobby Holland Hanton, Paul Howell, Radoslav Ignatov, Rowley Irlam, Erol Ismail, Orsányi Iván, Sonny Louis, Leigh Maddern, Jonny McBride, Leona McCarron, Kim McGarrity, Trayan Milenov-Troy, Sian Milne, David Newton, Jason Oettle, Radoslav Parvanov, Ian Pead, Andy Pilgrim, Oleg Podobin, Marc Redmond, Andrej Riabokon, Florian Robin, Doug Robson, Fabio Santos, Stanislav Satko, Paul Shapcott, Mark Slaughter, CC Smiff, James Stewart, Jonny Stockwell, Ryan Stuart, Gáspár Szabó, Lukas Tomsik, Marek Toth, Teodor Tzolov, Raycho Vasilev, Calvin Warrington-Heasman, Annabel Wood, Leo Woodruff, and Lewis Young\\n\\n===Society of Camera Operators Awards===\\nThe Society of Camera Operators Awards are awarded annually to honor the creative contribution of the camera operator in the motion picture and television industries. ''Game of Thrones'' has been nominated one time.\\n\\n+ Society of Camera Operators Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 51}, 'embedding': None, 'id': 'fa0383000cc031e9b5bf9310de392e2e'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has been nominated one time.\\n\\n+ Society of Camera Operators Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2017\\n\\nCamera Operator of the Year – Television\\n\\nSean Savage\\n\\n===Visual Effects Society===\\nVisual Effects Society honors visual effects in film, television, commercials, music videos and video games. At the 11th annual Visual Effects Society awards, the show won four awards, the most of the TV shows nominated. In 2014 the show was the most successful TV nominee at the Visual Effects Society awards for the second year in a row, winning three of its four nominations. In 2015 ''Game of Thrones'' was the most successful TV nominee at the Visual Effects Society awards for the third consecutive year, winning three of its five nominations. In 2016 the show was the most successful TV nominee at the Visual Effects Society awards for the fourth consecutive year, winning four of its nine nominations. ''Game of Thrones'' has received twenty six awards out of forty seven nominations.\\n\\n+ Visual Effects Society Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 52}, 'embedding': None, 'id': '551c2494a82a4f6daf5dd9031fafc7e8'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has received twenty six awards out of forty seven nominations.\\n\\n+ Visual Effects Society Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nHenry Badgett, Mark Brown, Rafael Morant, James Sutton (for \"Fire and Blood\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nMarkus Kuha, Damien Macé, Dante Harbridge Robinson, Fani Vassiadi (for \"The Icewall\")\\n\\nOutstanding Supporting Visual Effects in a Broadcast Program\\n\\nLucy Ainsworth-Taylor, Angela Barson, Ed Bruce, Adam McInnes (for \"Winter Is Coming\")\\n\\n2013\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nIrfan Celik, Florian Friedmann, Ingo Schachner, Chris Stenner (for \"Training the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nFalk Boje, Esther Engel, Alexey Kuchinsky, Klaus Wuchta (for \"White Walker Army\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nRene Borst, Thilo Ewers, Adam Figielski, Jonas Stuckenbrock (for \"Pyke\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nRainer Gombos, Steve Kullback, Sven Martin, Juri Stanossek (for \"Valar Morghulis\")\\n\\n2014\\n\\nOutstanding Animated Character in a Broadcast\\n\\nPhilip Meyer, Ingo Schachner, Travis Nobles, Florian Friedmann (for \"Raising the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nKirk Brillon, Steve Gordon, Geoff Sayer, Winston Lee (for \"The Climb\")\\n\\nOutstanding Created Environment in a Broadcast Program\\n\\nPatrick Zentis, Mayur Patel, Nitin Singh, Tim Alexander (for \"The Climb\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nSteve Kullback, Joe Bauer, Jörn Großhans, Sven Martin (for \"Valar Dohaeris\")\\n\\n2015\\n\\nOutstanding Performance of an Animated Character in a Commercial, Broadcast Program, or Video Game\\n\\nPhilip Meyer, Thomas Kutschera, Igor Majdandzic, and Mark Spindler (for \"Drogon\")\\n\\nOutstanding Visual Effects in a Visual Effects-Driven Photoreal/Live Action Broadcast Program\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nOutstanding Created Environment in a Commercial, Broadcast Program, or Video Game\\n\\nRene Borst, Christian Zilliken, Jan Burda, Steffen Metzner (for \"Braavos Establisher\")\\n\\nOutstanding Compositing in a Photoreal/Live Action Broadcast Program\\n\\nKeegan Douglas, Okan Ataman, Brian Fortune, David Lopez (for \"Wight Attack\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon (for \"The Dance of Dragons\")\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer (for \"Mother\\'s Mercy\" – \"Wounded Drogon\")\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen (for \"The Dance of Dragons\" – \"Drogon Arena Rescue\")\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur (for \"City of Volantis\")\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson (for \"Drogon Arena\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés (for \"Hardhome\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela (for \"Hardhome\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"Drogon Arena\")\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding (for \"Drogon Lair\")\\n\\n2017\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway (for \"Battle of the Bastards\")\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback (for \"Battle of the Bastards\" – \"Drogon\")\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben (for \"Home\" – \"Emaciated Dragon\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik (for \"The Winds of Winter\" – \"Citadel\")\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies (for \"Battle of the Bastards\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen (for \"Battle of the Bastards\")\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson (for \"Battle of the Bastards\" – \"Retaking Winterfell\")\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren (for \"The Door\" – \"Land of Always Winter\")\\n\\n2018\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Chris Baird, David Ramos, Sam Conway (for \"Beyond the Wall\")\\n\\nOutstanding Animated Character in an Episode or Real-Time Project\\n\\nPaul Story, Todd Labonte, Matthew Muntean, Nicholas Wilson (for \"Beyond the Wall\" – \"Zombie Polar Bear\")\\n\\nJonathan Symmonds, Thomas Kutschera, Philipp Winterstein, Andreas Krieg (for \"Eastwatch\" – \"Drogon Meets Jon\")\\n\\nMurray Stevenson, Jason Snyman, Jenn Taylor, Florian Friedmann (for \"The Spoils of War\" – \"Drogon Loot Train Attack\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDaniel Villalba, Antonio Lado, José Luis Barreiro, Isaac de la Pompa (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nPatrice Poissant, Deak Ferrand, Dominic Daigle, Gabriel Morin (for \"Eastwatch\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nManuel Ramírez, Óscar Márquez, Pablo Hernández, David Gacituaga (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Hullin, Dominik Kirouac, Sylvain Nouveau, Nathan Arbuckle (for \"The Dragon and the Wolf\" – \"Wall Destruction\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nÓscar Perea, Santiago Martos, David Esteve, Michael Crane (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Montminy Brodeur, Xavier Fourmond, Reuben Barkataki, Sébastien Raets (for \"Eastwatch\")\\n\\nDom Hellier, Thijs Noij, Edwin Holdsworth, Giacomo Matteucci (for \"The Spoils of War\" – \"Loot Train Attack\")\\n\\n===Writers Guild of America Awards===\\nFirst presented in 1949, the Writers Guild of America Award recognizes the work of film, television and radio screenwriters. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 53}, 'embedding': None, 'id': '87da6b4aee807e4ecdf9050051ad32a8'}>,\n",
-       "  <Document: {'content': '2012\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nHenry Badgett, Mark Brown, Rafael Morant, James Sutton (for \"Fire and Blood\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nMarkus Kuha, Damien Macé, Dante Harbridge Robinson, Fani Vassiadi (for \"The Icewall\")\\n\\nOutstanding Supporting Visual Effects in a Broadcast Program\\n\\nLucy Ainsworth-Taylor, Angela Barson, Ed Bruce, Adam McInnes (for \"Winter Is Coming\")\\n\\n2013\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nIrfan Celik, Florian Friedmann, Ingo Schachner, Chris Stenner (for \"Training the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nFalk Boje, Esther Engel, Alexey Kuchinsky, Klaus Wuchta (for \"White Walker Army\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nRene Borst, Thilo Ewers, Adam Figielski, Jonas Stuckenbrock (for \"Pyke\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nRainer Gombos, Steve Kullback, Sven Martin, Juri Stanossek (for \"Valar Morghulis\")\\n\\n2014\\n\\nOutstanding Animated Character in a Broadcast\\n\\nPhilip Meyer, Ingo Schachner, Travis Nobles, Florian Friedmann (for \"Raising the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nKirk Brillon, Steve Gordon, Geoff Sayer, Winston Lee (for \"The Climb\")\\n\\nOutstanding Created Environment in a Broadcast Program\\n\\nPatrick Zentis, Mayur Patel, Nitin Singh, Tim Alexander (for \"The Climb\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nSteve Kullback, Joe Bauer, Jörn Großhans, Sven Martin (for \"Valar Dohaeris\")\\n\\n2015\\n\\nOutstanding Performance of an Animated Character in a Commercial, Broadcast Program, or Video Game\\n\\nPhilip Meyer, Thomas Kutschera, Igor Majdandzic, and Mark Spindler (for \"Drogon\")\\n\\nOutstanding Visual Effects in a Visual Effects-Driven Photoreal/Live Action Broadcast Program\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nOutstanding Created Environment in a Commercial, Broadcast Program, or Video Game\\n\\nRene Borst, Christian Zilliken, Jan Burda, Steffen Metzner (for \"Braavos Establisher\")\\n\\nOutstanding Compositing in a Photoreal/Live Action Broadcast Program\\n\\nKeegan Douglas, Okan Ataman, Brian Fortune, David Lopez (for \"Wight Attack\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon (for \"The Dance of Dragons\")\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer (for \"Mother\\'s Mercy\" – \"Wounded Drogon\")\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen (for \"The Dance of Dragons\" – \"Drogon Arena Rescue\")\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur (for \"City of Volantis\")\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson (for \"Drogon Arena\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés (for \"Hardhome\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela (for \"Hardhome\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"Drogon Arena\")\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding (for \"Drogon Lair\")\\n\\n2017\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway (for \"Battle of the Bastards\")\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback (for \"Battle of the Bastards\" – \"Drogon\")\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben (for \"Home\" – \"Emaciated Dragon\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik (for \"The Winds of Winter\" – \"Citadel\")\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies (for \"Battle of the Bastards\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen (for \"Battle of the Bastards\")\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson (for \"Battle of the Bastards\" – \"Retaking Winterfell\")\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren (for \"The Door\" – \"Land of Always Winter\")\\n\\n2018\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Chris Baird, David Ramos, Sam Conway (for \"Beyond the Wall\")\\n\\nOutstanding Animated Character in an Episode or Real-Time Project\\n\\nPaul Story, Todd Labonte, Matthew Muntean, Nicholas Wilson (for \"Beyond the Wall\" – \"Zombie Polar Bear\")\\n\\nJonathan Symmonds, Thomas Kutschera, Philipp Winterstein, Andreas Krieg (for \"Eastwatch\" – \"Drogon Meets Jon\")\\n\\nMurray Stevenson, Jason Snyman, Jenn Taylor, Florian Friedmann (for \"The Spoils of War\" – \"Drogon Loot Train Attack\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDaniel Villalba, Antonio Lado, José Luis Barreiro, Isaac de la Pompa (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nPatrice Poissant, Deak Ferrand, Dominic Daigle, Gabriel Morin (for \"Eastwatch\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nManuel Ramírez, Óscar Márquez, Pablo Hernández, David Gacituaga (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Hullin, Dominik Kirouac, Sylvain Nouveau, Nathan Arbuckle (for \"The Dragon and the Wolf\" – \"Wall Destruction\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nÓscar Perea, Santiago Martos, David Esteve, Michael Crane (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Montminy Brodeur, Xavier Fourmond, Reuben Barkataki, Sébastien Raets (for \"Eastwatch\")\\n\\nDom Hellier, Thijs Noij, Edwin Holdsworth, Giacomo Matteucci (for \"The Spoils of War\" – \"Loot Train Attack\")\\n\\n===Writers Guild of America Awards===\\nFirst presented in 1949, the Writers Guild of America Award recognizes the work of film, television and radio screenwriters. \\'\\'Game of Thrones\\'\\' has been nominated ten times.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 54}, 'embedding': None, 'id': '4764a31fb6612f4aff3cf154a399f02b'}>,\n",
-       "  <Document: {'content': '2012\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nHenry Badgett, Mark Brown, Rafael Morant, James Sutton (for \"Fire and Blood\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nMarkus Kuha, Damien Macé, Dante Harbridge Robinson, Fani Vassiadi (for \"The Icewall\")\\n\\nOutstanding Supporting Visual Effects in a Broadcast Program\\n\\nLucy Ainsworth-Taylor, Angela Barson, Ed Bruce, Adam McInnes (for \"Winter Is Coming\")\\n\\n2013\\n\\nOutstanding Animated Character in a Commercial or Broadcast Program\\n\\nIrfan Celik, Florian Friedmann, Ingo Schachner, Chris Stenner (for \"Training the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nFalk Boje, Esther Engel, Alexey Kuchinsky, Klaus Wuchta (for \"White Walker Army\")\\n\\nOutstanding Created Environment in a Commercial or Broadcast Program\\n\\nRene Borst, Thilo Ewers, Adam Figielski, Jonas Stuckenbrock (for \"Pyke\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nRainer Gombos, Steve Kullback, Sven Martin, Juri Stanossek (for \"Valar Morghulis\")\\n\\n2014\\n\\nOutstanding Animated Character in a Broadcast\\n\\nPhilip Meyer, Ingo Schachner, Travis Nobles, Florian Friedmann (for \"Raising the Dragons\")\\n\\nOutstanding Compositing in a Broadcast Program\\n\\nKirk Brillon, Steve Gordon, Geoff Sayer, Winston Lee (for \"The Climb\")\\n\\nOutstanding Created Environment in a Broadcast Program\\n\\nPatrick Zentis, Mayur Patel, Nitin Singh, Tim Alexander (for \"The Climb\")\\n\\nOutstanding Visual Effects in a Broadcast Program\\n\\nSteve Kullback, Joe Bauer, Jörn Großhans, Sven Martin (for \"Valar Dohaeris\")\\n\\n2015\\n\\nOutstanding Performance of an Animated Character in a Commercial, Broadcast Program, or Video Game\\n\\nPhilip Meyer, Thomas Kutschera, Igor Majdandzic, and Mark Spindler (for \"Drogon\")\\n\\nOutstanding Visual Effects in a Visual Effects-Driven Photoreal/Live Action Broadcast Program\\n\\n\\'\\'Game of Thrones\\'\\' (for \"The Children\")\\n\\nOutstanding Created Environment in a Commercial, Broadcast Program, or Video Game\\n\\nRene Borst, Christian Zilliken, Jan Burda, Steffen Metzner (for \"Braavos Establisher\")\\n\\nOutstanding Compositing in a Photoreal/Live Action Broadcast Program\\n\\nKeegan Douglas, Okan Ataman, Brian Fortune, David Lopez (for \"Wight Attack\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"The Watchers on the Wall\")\\n\\n2016\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon (for \"The Dance of Dragons\")\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nFlorian Friedmann, Jonathan Symmonds, Sven Skoczylas, Sebastian Lauer (for \"Mother\\'s Mercy\" – \"Wounded Drogon\")\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen (for \"The Dance of Dragons\" – \"Drogon Arena Rescue\")\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nDominic Piche, Christine Leclerc, Patrice Poissant, Thomas Montminy-Brodeur (for \"City of Volantis\")\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson (for \"Drogon Arena\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés (for \"Hardhome\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela (for \"Hardhome\")\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn (for \"Drogon Arena\")\\n\\nTravis Nobles, Mark Spindler, Max Riess, Nadja Ding (for \"Drogon Lair\")\\n\\n2017\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Glenn Melenhorst, Matthew Rouleau, Sam Conway (for \"Battle of the Bastards\")\\n\\nOutstanding Animated Performance in an Episode or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Matt Derksen, Joseph Hoback (for \"Battle of the Bastards\" – \"Drogon\")\\n\\nSebastian Lauer, Jonathan Symmonds, Thomas Kutschera, Anthony Sieben (for \"Home\" – \"Emaciated Dragon\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDeak Ferrand, Dominic Daigle, François Croteau, Alexandru Banuta (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nEdmond Engelbrecht, Tomoka Matsumura, Edwin Holdsworth, Cheri Fojtik (for \"The Winds of Winter\" – \"Citadel\")\\n\\nOutstanding Virtual Cinematography in a Photoreal Project\\n\\nPatrick Tiberius Gehlen, Michelle Blok, Christopher Baird, Drew Wood-Davies (for \"Battle of the Bastards\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nKevin Blom, Sasmit Ranadive, Wanghua Huang, Ben Andersen (for \"Battle of the Bastards\")\\n\\nThomas Hullin, Dominik Kirouac, James Dong, Xavier Fourmond (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nThomas Montminy-Brodeur, Patrick David, Michael Crane, Joe Salazar (for \"Battle of the Bastards\" – \"Meereen City\")\\n\\nDominic Hellier, Morgan Jones, Thijs Noij, Caleb Thompson (for \"Battle of the Bastards\" – \"Retaking Winterfell\")\\n\\nEduardo Díaz, Aníbal Del Busto, Angel Rico, Sonsoles López-Aranguren (for \"The Door\" – \"Land of Always Winter\")\\n\\n2018\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Chris Baird, David Ramos, Sam Conway (for \"Beyond the Wall\")\\n\\nOutstanding Animated Character in an Episode or Real-Time Project\\n\\nPaul Story, Todd Labonte, Matthew Muntean, Nicholas Wilson (for \"Beyond the Wall\" – \"Zombie Polar Bear\")\\n\\nJonathan Symmonds, Thomas Kutschera, Philipp Winterstein, Andreas Krieg (for \"Eastwatch\" – \"Drogon Meets Jon\")\\n\\nMurray Stevenson, Jason Snyman, Jenn Taylor, Florian Friedmann (for \"The Spoils of War\" – \"Drogon Loot Train Attack\")\\n\\nOutstanding Created Environment in an Episode, Commercial or Real-Time Project\\n\\nDaniel Villalba, Antonio Lado, José Luis Barreiro, Isaac de la Pompa (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nPatrice Poissant, Deak Ferrand, Dominic Daigle, Gabriel Morin (for \"Eastwatch\")\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nManuel Ramírez, Óscar Márquez, Pablo Hernández, David Gacituaga (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Hullin, Dominik Kirouac, Sylvain Nouveau, Nathan Arbuckle (for \"The Dragon and the Wolf\" – \"Wall Destruction\")\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nÓscar Perea, Santiago Martos, David Esteve, Michael Crane (for \"Beyond the Wall\" – \"Frozen Lake\")\\n\\nThomas Montminy Brodeur, Xavier Fourmond, Reuben Barkataki, Sébastien Raets (for \"Eastwatch\")\\n\\nDom Hellier, Thijs Noij, Edwin Holdsworth, Giacomo Matteucci (for \"The Spoils of War\" – \"Loot Train Attack\")\\n\\n===Writers Guild of America Awards===\\nFirst presented in 1949, the Writers Guild of America Award recognizes the work of film, television and radio screenwriters. \\'\\'Game of Thrones\\'\\' has been nominated ten times.\\n\\nIn June 2013, the show was placed at number 40 on the list of the 101 best written shows of all time by the Writers Guild of America, assessing series from the previous 70 years. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 55}, 'embedding': None, 'id': '9dd81c3813c1ade5823899ab8fc42693'}>,\n",
-       "  <Document: {'content': \"In June 2013, the show was placed at number 40 on the list of the 101 best written shows of all time by the Writers Guild of America, assessing series from the previous 70 years. HBO had attempted to submit the show for a third consecutive Writers Guild Award for Best Drama Series, but a technical error during the process meant it was not submitted and the fault was discovered too late for the show to be entered.\\n\\n+ Writers Guild of America Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 56}, 'embedding': None, 'id': 'dbbb9043d2357a422802afaa1112346e'}>,\n",
-       "  <Document: {'content': 'HBO had attempted to submit the show for a third consecutive Writers Guild Award for Best Drama Series, but a technical error during the process meant it was not submitted and the fault was discovered too late for the show to be entered.\\n\\n+ Writers Guild of America Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\nNew Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\n2013\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, Vanessa Taylor, D. B. Weiss\\n\\n2015\\n\\nEpisodic Drama\\n\\nGeorge R. R. Martin (for \"The Lion and the Rose\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, D. B. Weiss\\n\\n2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"Mother\\'s Mercy\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2017\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"The Winds of Winter\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2018\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n==Significant critical awards==\\n\\n===Critics\\' Choice Television Awards===\\nKit Harington was nominated for a Critics\\' Choice Television Award in 2016\\n\\nThe Critics\\' Choice Television Award is an annual accolade given by the Broadcast Film Critics Association since 2011 in order to recognize the most significant achievements in television. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 57}, 'embedding': None, 'id': 'be835a5ab9536db84f3ed288a38d04f'}>,\n",
-       "  <Document: {'content': '2012\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\nNew Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\n2013\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, Vanessa Taylor, D. B. Weiss\\n\\n2015\\n\\nEpisodic Drama\\n\\nGeorge R. R. Martin (for \"The Lion and the Rose\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, D. B. Weiss\\n\\n2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"Mother\\'s Mercy\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2017\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"The Winds of Winter\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2018\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n==Significant critical awards==\\n\\n===Critics\\' Choice Television Awards===\\nKit Harington was nominated for a Critics\\' Choice Television Award in 2016\\n\\nThe Critics\\' Choice Television Award is an annual accolade given by the Broadcast Film Critics Association since 2011 in order to recognize the most significant achievements in television. \\'\\'Game of Thrones\\'\\' has won two out of twenty nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 58}, 'embedding': None, 'id': 'e54728989b7e39573a711df662755cfe'}>,\n",
-       "  <Document: {'content': '2012\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\nNew Series\\n\\nDavid Benioff, Bryan Cogman, Jane Espenson, George R. R. Martin, D. B. Weiss\\n\\n2013\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, Vanessa Taylor, D. B. Weiss\\n\\n2015\\n\\nEpisodic Drama\\n\\nGeorge R. R. Martin (for \"The Lion and the Rose\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, George R. R. Martin, D. B. Weiss\\n\\n2016\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"Mother\\'s Mercy\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2017\\n\\nEpisodic Drama\\n\\nDavid Benioff and D. B. Weiss (for \"The Winds of Winter\")\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n2018\\n\\nTelevision Drama Series\\n\\nDavid Benioff, Bryan Cogman, Dave Hill, D. B. Weiss\\n\\n==Significant critical awards==\\n\\n===Critics\\' Choice Television Awards===\\nKit Harington was nominated for a Critics\\' Choice Television Award in 2016\\n\\nThe Critics\\' Choice Television Award is an annual accolade given by the Broadcast Film Critics Association since 2011 in order to recognize the most significant achievements in television. \\'\\'Game of Thrones\\'\\' has won two out of twenty nominations.\\n\\n+ Critics\\' Choice Television Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 59}, 'embedding': None, 'id': 'a3ab2ab1afebdd79aa5f43c2e6790ca1'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has won two out of twenty nominations.\\n\\n+ Critics' Choice Television Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\n2012\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n2014\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n2015\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nMost Bingeworthy Show\\n\\n2016\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nMost Bingeworthy Show\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\n2018\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\n===Golden Globe Awards===\\nPeter Dinklage won one Golden Globe Award in 2012\\n\\nThe Golden Globe Awards are awarded annually by the Hollywood Foreign Press Association to honor the best achievements in film and television. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 60}, 'embedding': None, 'id': 'c182243b8c591a6eca34ab4dc71c12f8'}>,\n",
-       "  <Document: {'content': \"2011\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\n2012\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n2014\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Guest Performer in a Drama Series\\n\\nDiana Rigg\\n\\n2015\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nMost Bingeworthy Show\\n\\n2016\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nMost Bingeworthy Show\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\n2018\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nBest Supporting Actress in a Drama Series\\n\\nEmilia Clarke\\n\\n===Golden Globe Awards===\\nPeter Dinklage won one Golden Globe Award in 2012\\n\\nThe Golden Globe Awards are awarded annually by the Hollywood Foreign Press Association to honor the best achievements in film and television. In 2012, The show was nominated for 2 Golden Globe awards including Best Drama Series and Best Supporting Actor, taken by Dinklage. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 61}, 'embedding': None, 'id': '7373e316c6957137bbb6cb354adfdb97'}>,\n",
-       "  <Document: {'content': \"In 2012, The show was nominated for 2 Golden Globe awards including Best Drama Series and Best Supporting Actor, taken by Dinklage. ''Game of Thrones'' has won one award out of seven nominations.\\n\\n+ Golden Globe Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Television Series – Drama\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries or Television Film\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest Television Series – Drama\\n\\n''Game of Thrones''\\n\\n2016\\n\\nBest Television Series – Drama\\n\\n''Game of Thrones''\\n\\n2017\\n\\nBest Television Series – Drama\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress – Series, Miniseries or Television Film\\n\\nLena Headey\\n\\n2018\\n\\nBest Television Series - Drama\\n\\n''Game of Thrones''\\n\\n===Guinness World Records===\\nThe ''Guinness World Records'' is a reference book published annually, containing a collection of world records. Game of Thrones currently holds six records.\\n\\n+ Guinness World Records received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 62}, 'embedding': None, 'id': '6b0f83b3850cd3682ceef5509fff7afb'}>,\n",
-       "  <Document: {'content': 'Game of Thrones currently holds six records.\\n\\n+ Guinness World Records received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nMost pirated TV program\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost viewers sharing a single torrent file simultaneously\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nLargest TV drama simulcast\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost \"in demand\" TV show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost Emmy Awards for a drama series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nMost VES Awards won by a TV series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===Hugo Awards===\\nGeorge R. R. Martin won a Hugo Award for \"Blackwater\", along with director Neil Marshall, in 2013\\n\\nThe Hugo Awards are a set of awards given annually for the best science fiction or fantasy works and achievements of the previous year. \\'\\'Game of Thrones\\'\\' has won three out of six nominations.\\n\\n+ Hugo Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 63}, 'embedding': None, 'id': 'a1ea9258aad19b9478de75bb0d75ac9f'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won three out of six nominations.\\n\\n+ Hugo Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Dramatic Presentation, Long Form\\n\\nDavid Benioff, D. B. Weiss, Bryan Cogman, Jane Espenson, George R. R. Martin, Tim Van Patten, Brian Kirk, Daniel Minahan, and Alan Taylor for \\'\\'Game of Thrones\\'\\' – Season 1\\n\\n2013\\n\\nBest Dramatic Presentation, Short Form\\n\\nNeil Marshall (director) and George R. R. Martin (writer) (for \"Blackwater\")\\n\\n2014\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and David Nutter (director) (for \"The Rains of Castamere\")\\n\\n2015\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Alex Graves (director) (for \"The Mountain and the Viper\")\\n\\n2017\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Miguel Sapochnik (director) (for \"Battle of the Bastards\")\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Jack Bender (director) (for \"The Door\")\\n\\n===IGN Awards===\\nSean Bean won an IGN Awards in 2011\\n\\nThe IGN Awards are chosen annually by the IGN editors, honoring the best in film, television, games, comics and anime. \\'\\'Game of Thrones\\'\\' has won nine out of thirty two nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 64}, 'embedding': None, 'id': '5af0f951ebf69983689f9b0946c09f0f'}>,\n",
-       "  <Document: {'content': '2012\\n\\nBest Dramatic Presentation, Long Form\\n\\nDavid Benioff, D. B. Weiss, Bryan Cogman, Jane Espenson, George R. R. Martin, Tim Van Patten, Brian Kirk, Daniel Minahan, and Alan Taylor for \\'\\'Game of Thrones\\'\\' – Season 1\\n\\n2013\\n\\nBest Dramatic Presentation, Short Form\\n\\nNeil Marshall (director) and George R. R. Martin (writer) (for \"Blackwater\")\\n\\n2014\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and David Nutter (director) (for \"The Rains of Castamere\")\\n\\n2015\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Alex Graves (director) (for \"The Mountain and the Viper\")\\n\\n2017\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Miguel Sapochnik (director) (for \"Battle of the Bastards\")\\n\\nDavid Benioff (writer), D. B. Weiss (writer), and Jack Bender (director) (for \"The Door\")\\n\\n===IGN Awards===\\nSean Bean won an IGN Awards in 2011\\n\\nThe IGN Awards are chosen annually by the IGN editors, honoring the best in film, television, games, comics and anime. \\'\\'Game of Thrones\\'\\' has won nine out of thirty two nominations.\\n\\n+ IGN Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 65}, 'embedding': None, 'id': '56d8fdb57d84d30b1f45483117237198'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won nine out of thirty two nominations.\\n\\n+ IGN Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Actor\\n\\nPeter Dinklage\\n\\nBest TV Actress\\n\\nEmilia Clarke\\n\\nBest TV Episode\\n\\n\"Baelor\"\\n\\nBest TV Twist\\n\\n\"Off with his head!\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 66}, 'embedding': None, 'id': '396d63b526bc6b31b874dcb3ba9f0075'}>,\n",
-       "  <Document: {'content': '2011\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Actor\\n\\nPeter Dinklage\\n\\nBest TV Actress\\n\\nEmilia Clarke\\n\\nBest TV Episode\\n\\n\"Baelor\"\\n\\nBest TV Twist\\n\\n\"Off with his head!\"Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===IGN People\\'s Choice Award===\\nJack Gleeson won an IGN People\\'s Choice Award in 2012 and 2013\\n\\nThe IGN People\\'s Choice Awards are voted on annually by the general public, they honor the best in film, television, games, comics and anime \\'\\'Game of Thrones\\'\\' has won sixteen out of thirty two nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 67}, 'embedding': None, 'id': '8cf42e9ea673a03428eb712bc08236cf'}>,\n",
-       "  <Document: {'content': 'Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===IGN People\\'s Choice Award===\\nJack Gleeson won an IGN People\\'s Choice Award in 2012 and 2013\\n\\nThe IGN People\\'s Choice Awards are voted on annually by the general public, they honor the best in film, television, games, comics and anime \\'\\'Game of Thrones\\'\\' has won sixteen out of thirty two nominations.\\n\\n+ IGN People\\'s Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 68}, 'embedding': None, 'id': '6814904214fb07d49d8ba5204d7e80d4'}>,\n",
-       "  <Document: {'content': 'Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===IGN People\\'s Choice Award===\\nJack Gleeson won an IGN People\\'s Choice Award in 2012 and 2013\\n\\nThe IGN People\\'s Choice Awards are voted on annually by the general public, they honor the best in film, television, games, comics and anime \\'\\'Game of Thrones\\'\\' has won sixteen out of thirty two nominations.\\n\\n+ IGN People\\'s Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Actor\\n\\nPeter Dinklage\\n\\nBest TV Actress\\n\\nEmilia Clarke\\n\\nBest TV Episode\\n\\n\"Baelor\"\\n\\nBest TV Twist\\n\\n\"Off with his head!\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 69}, 'embedding': None, 'id': '2fa45f370605d04e56cc43a521eb3be3'}>,\n",
-       "  <Document: {'content': '2011\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Actor\\n\\nPeter Dinklage\\n\\nBest TV Actress\\n\\nEmilia Clarke\\n\\nBest TV Episode\\n\\n\"Baelor\"\\n\\nBest TV Twist\\n\\n\"Off with his head!\"Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===National Television Awards===\\nThe National Television Awards are presented in an annual award show broadcast by the ITV network. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 70}, 'embedding': None, 'id': '2ec70cefca2bfadb30bd374b1ea4b711'}>,\n",
-       "  <Document: {'content': 'Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===National Television Awards===\\nThe National Television Awards are presented in an annual award show broadcast by the ITV network. The results are voted on by the general public. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 71}, 'embedding': None, 'id': 'a93c8eeedbbb1f89d9b21bd61f2bc7c1'}>,\n",
-       "  <Document: {'content': 'Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===National Television Awards===\\nThe National Television Awards are presented in an annual award show broadcast by the ITV network. The results are voted on by the general public. \\'\\'Game of Thrones\\'\\' has been nominated four times.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 72}, 'embedding': None, 'id': '68071bb369a1c3d877e809db47b2083f'}>,\n",
-       "  <Document: {'content': 'Best TV Hero\\n\\nSean Bean as Ned Stark\\n\\nKit Harington as Jon Snow\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\n2012\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Blackwater\"\\n\\nBest TV Villain\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete first season on Blu-ray\\n\\n2013\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Rains of Castamere\"\\n\\nBest TV Villain\\n\\nDavid Bradley as Walder Frey\\n\\nJack Gleeson as Joffrey Baratheon\\n\\nBest TV DVD or Blu-ray\\n\\nFor the complete second season on Blu-ray\\n\\n2014\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Children\"\\n\\n2015\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"Hardhome\"\\n\\nBest TV Villain\\n\\nIwan Rheon as Ramsay Bolton\\n\\n2016\\n\\nBest TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Drama Series\\n\\nBest TV Episode\\n\\n\"The Winds of Winter\"\\n\\n2017\\n\\nBest Action Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest TV Episode\\n\\n\"The Spoils of War\"\\n\\n===National Television Awards===\\nThe National Television Awards are presented in an annual award show broadcast by the ITV network. The results are voted on by the general public. \\'\\'Game of Thrones\\'\\' has been nominated four times.\\n\\n+ National Television Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 73}, 'embedding': None, 'id': '76be57004db474749d2591e632975299'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has been nominated four times.\\n\\n+ National Television Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nMultichannel show\\n\\n''Game of Thrones''\\n\\n2016\\n\\nBest International Show\\n\\n''Game of Thrones''\\n\\n2017\\n\\nBest Drama\\n\\n''Game of Thrones''\\n\\n2018\\n\\nBest Drama\\n\\n''Game of Thrones''\\n\\n===People's Choice Awards===\\nThe People's Choice Awards recognize the people and the work of popular culture. ''Game of Thrones'' has been nominated twelve times.\\n\\n+ People's Choice Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 74}, 'embedding': None, 'id': 'cdaa1bcc4d83321d4e05d8ad41898e49'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated twelve times.\\n\\n+ People\\'s Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nFavorite Cable TV Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2013\\n\\nFavorite Thriller Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nFavorite Premium Cable TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite TV Anti-Hero\\n\\nNikolaj Coster-Waldau as Jaime Lannister\\n\\nFavorite Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n2015\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\n2016\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Cable Sci-Fi/Fantasy TV Show\\n\\nFavorite Cable Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n2017\\n\\nFavorite Premium Sci-Fi/Fantasy TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nFavorite Sci-Fi/Fantasy TV Actress\\n\\nEmilia Clarke\\n\\n===Peabody Awards===\\nNikolaj Coster-Waldau accepted the Peabody Award on behalf of the show in 2012\\n\\nThe Peabody Award recognizes excellence in various platforms of the media, including film, television, and radio. The award \"spotlights instances of how electronic media can teach, expand our horizons, defend the public interest, or encourage empathy with others\". \\'\\'Game of Thrones\\'\\' was honored with a Peabody Award in 2011 for its first season. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 75}, 'embedding': None, 'id': 'a25ee34f0e11030a2b5703bb0a2b144'}>,\n",
-       "  <Document: {'content': 'The award \"spotlights instances of how electronic media can teach, expand our horizons, defend the public interest, or encourage empathy with others\". \\'\\'Game of Thrones\\'\\' was honored with a Peabody Award in 2011 for its first season. The award association gave the following description of the show:\\n\\n+ Peabody Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nPeabody Award\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===Poppy Awards===\\nSophie Turner won one Poppy Award in 2016\\n\\nThe Poppy Awards, formerly known as the EWwys, honors the Emmy-snubbed shows and actors of the year. \\'\\'Game of Thrones\\'\\' has received six awards from ten nomination.\\n\\n+ Poppy Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 76}, 'embedding': None, 'id': '74856b99c3b75572cacba18e876296b6'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has received six awards from ten nomination.\\n\\n+ Poppy Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Supporting Actress, Drama\\n\\nEmilia Clarke\\n\\nBest Actor, Drama\\n\\nSean Bean\\n\\n2012\\n\\nBest Supporting Actress, Drama\\n\\nLena Headey\\n\\n2013\\n\\nBest Supporting Actress, Drama\\n\\nNatalie Dormer\\n\\n2014\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nBest Supporting Actor, Drama\\n\\nCharles Dance\\n\\nBest Guest Actor, Drama\\n\\nPedro Pascal\\n\\n2015\\n\\nBest Supporting Actress, Drama\\n\\nMaisie Williams\\n\\nSophie Turner\\n\\n2016\\n\\nBest Supporting Actress, Drama\\n\\nSophie Turner\\n\\n===Satellite Awards===\\nPeter Dinklage won one Satellite Award in 2011\\n\\nThe Satellite Award is given annually by the International Press Academy (IPA) to honor the best work in the entertainment industry. ''Game of Thrones'' has received three awards out of fourteen nominations.\\n\\nIn 2011, Dinklage received the Satellite Award for Best Supporting Actor – Series, Miniseries, or Television Film, and received three more nominations (2012, 2014–2015). In 2014, ''Game of Thrones'' won the Satellite Award for Best Television Series – Genre, while Nikolaj Coster-Waldau and Emilia Clarke received nominations for Best Supporting Actor and Best Supporting Actress, respectively.\\n\\n+ Satellite Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 77}, 'embedding': None, 'id': '4a636011e8a011bffdab64d74486b2b5'}>,\n",
-       "  <Document: {'content': \"In 2014, ''Game of Thrones'' won the Satellite Award for Best Television Series – Genre, while Nikolaj Coster-Waldau and Emilia Clarke received nominations for Best Supporting Actor and Best Supporting Actress, respectively.\\n\\n+ Satellite Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nEmilia Clarke\\n\\n2014\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2016\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nLena Headey\\n\\n2017\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\n===Saturn Awards===\\nMaisie Williams won Saturn Award in 2015 and 2019\\n\\nThe Saturn Awards are awarded annually by the Academy of Science Fiction, Fantasy, and Horror Films to honor the top works mainly in science fiction, fantasy, and horror in film, television, and home video. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 78}, 'embedding': None, 'id': '1f64651c40cfffc1346cd0bf30c8590e'}>,\n",
-       "  <Document: {'content': \"2011\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nEmilia Clarke\\n\\n2014\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2016\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nLena Headey\\n\\n2017\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\n===Saturn Awards===\\nMaisie Williams won Saturn Award in 2015 and 2019\\n\\nThe Saturn Awards are awarded annually by the Academy of Science Fiction, Fantasy, and Horror Films to honor the top works mainly in science fiction, fantasy, and horror in film, television, and home video. ''Game of Thrones'' has won six out of thirty-four nominations.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 79}, 'embedding': None, 'id': 'a936739ec4887f5ed91d02391f835839'}>,\n",
-       "  <Document: {'content': \"2011\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nEmilia Clarke\\n\\n2014\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor – Series, Miniseries, or Television Film\\n\\nPeter Dinklage\\n\\n2016\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress – Series, Miniseries, or Television Film\\n\\nLena Headey\\n\\n2017\\n\\nBest Television Series – Genre\\n\\n''Game of Thrones''\\n\\n===Saturn Awards===\\nMaisie Williams won Saturn Award in 2015 and 2019\\n\\nThe Saturn Awards are awarded annually by the Academy of Science Fiction, Fantasy, and Horror Films to honor the top works mainly in science fiction, fantasy, and horror in film, television, and home video. ''Game of Thrones'' has won six out of thirty-four nominations.\\n\\n+ Saturn Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 80}, 'embedding': None, 'id': '75dcd51931ca8ec695505307bb74e0f4'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has won six out of thirty-four nominations.\\n\\n+ Saturn Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Actor on Television\\n\\nSean Bean\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2013\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\n2014\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress on Television\\n\\nGwendoline Christie\\n\\nMichelle Fairley\\n\\nBest Performance by a Younger Actor on Television\\n\\nJack Gleeson\\n\\n2015\\n\\nBest Limited Run Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress on Television\\n\\nEmilia Clarke\\n\\nBest Performance by a Younger Actor on Television\\n\\nMaisie Williams\\n\\n2016\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O'Connor\\n\\n2017\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2018\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on a Television Series\\n\\nLena Headey\\n\\nBest Supporting Actor on a Television Series\\n\\nKit Harington\\n\\nNikolaj Coster-Waldau\\n\\n2019\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actor on a Television Series\\n\\nKit Harington\\n\\nBest Actress on a Television Series\\n\\nEmilia Clarke\\n\\nBest Supporting Actor on a Television Series\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Supporting Actress on a Television Series\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nSophie Turner\\n\\nBest Performance by a Younger Actor on a Television Series\\n\\nMaisie Williams\\n\\n===Scream Awards===\\nEmilia Clarke won one Scream Award in 2011\\n\\nThe Scream Awards were awarded annually to honor the top works mainly in the horror, sci-fi, and fantasy genres of feature films and television. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 81}, 'embedding': None, 'id': '13edc9e82300598b72526ce7944482ba'}>,\n",
-       "  <Document: {'content': \"2012\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Actor on Television\\n\\nSean Bean\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2013\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\n2014\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress on Television\\n\\nGwendoline Christie\\n\\nMichelle Fairley\\n\\nBest Performance by a Younger Actor on Television\\n\\nJack Gleeson\\n\\n2015\\n\\nBest Limited Run Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress on Television\\n\\nEmilia Clarke\\n\\nBest Performance by a Younger Actor on Television\\n\\nMaisie Williams\\n\\n2016\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O'Connor\\n\\n2017\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2018\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on a Television Series\\n\\nLena Headey\\n\\nBest Supporting Actor on a Television Series\\n\\nKit Harington\\n\\nNikolaj Coster-Waldau\\n\\n2019\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actor on a Television Series\\n\\nKit Harington\\n\\nBest Actress on a Television Series\\n\\nEmilia Clarke\\n\\nBest Supporting Actor on a Television Series\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Supporting Actress on a Television Series\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nSophie Turner\\n\\nBest Performance by a Younger Actor on a Television Series\\n\\nMaisie Williams\\n\\n===Scream Awards===\\nEmilia Clarke won one Scream Award in 2011\\n\\nThe Scream Awards were awarded annually to honor the top works mainly in the horror, sci-fi, and fantasy genres of feature films and television. ''Game of Thrones'' has won three out of eight nominations.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 82}, 'embedding': None, 'id': '24c6ede756058a9df83e287563bdda96'}>,\n",
-       "  <Document: {'content': \"2012\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Actor on Television\\n\\nSean Bean\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2013\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\n2014\\n\\nBest Television Presentation\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nNikolaj Coster-Waldau\\n\\nBest Supporting Actress on Television\\n\\nGwendoline Christie\\n\\nMichelle Fairley\\n\\nBest Performance by a Younger Actor on Television\\n\\nJack Gleeson\\n\\n2015\\n\\nBest Limited Run Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actress on Television\\n\\nEmilia Clarke\\n\\nBest Performance by a Younger Actor on Television\\n\\nMaisie Williams\\n\\n2016\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\nBest Supporting Actress on Television\\n\\nLena Headey\\n\\nBest Performance by a Younger Actor in a Television Series\\n\\nMaisie Williams\\n\\nBrenock O'Connor\\n\\n2017\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on Television\\n\\nLena Headey\\n\\nBest Supporting Actor on Television\\n\\nKit Harington\\n\\n2018\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actress on a Television Series\\n\\nLena Headey\\n\\nBest Supporting Actor on a Television Series\\n\\nKit Harington\\n\\nNikolaj Coster-Waldau\\n\\n2019\\n\\nBest Fantasy Television Series\\n\\n''Game of Thrones''\\n\\nBest Actor on a Television Series\\n\\nKit Harington\\n\\nBest Actress on a Television Series\\n\\nEmilia Clarke\\n\\nBest Supporting Actor on a Television Series\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Supporting Actress on a Television Series\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nSophie Turner\\n\\nBest Performance by a Younger Actor on a Television Series\\n\\nMaisie Williams\\n\\n===Scream Awards===\\nEmilia Clarke won one Scream Award in 2011\\n\\nThe Scream Awards were awarded annually to honor the top works mainly in the horror, sci-fi, and fantasy genres of feature films and television. ''Game of Thrones'' has won three out of eight nominations.\\n\\n+ Scream Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 83}, 'embedding': None, 'id': 'f05d0b0fd2f2c75f7499cc0de0cc2433'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won three out of eight nominations.\\n\\n+ Scream Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Fantasy Actor\\n\\nSean Bean\\n\\nBest Fantasy Actress\\n\\nLena Headey\\n\\nBest Supporting Actor\\n\\nPeter Dinklage\\n\\nBreakout Performance – Female\\n\\nEmilia Clarke\\n\\nBest Ensemble\\n\\nAmrita Acharia, Mark Addy, Alfie Allen, Josef Altin, Sean Bean, Susan Brown, Emilia Clarke, Nikolaj Coster-Waldau, Peter Dinklage, Ron Donachie, Michelle Fairley, Jerome Flynn, Elyes Gabel, Aidan Gillen, Jack Gleeson, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead Wright, Conleth Hill, Richard Madden, Jason Momoa, Rory McCann, Ian McElhinney, Luke McEwan, Roxanne McKee, Dar Salim, Mark Stanley, Donald Sumpter, Sophie Turner and Maisie Williams\\n\\nMost Memorable Mutilation\\n\\n\"Head covered in molten gold\" (from \"A Golden Crown\")\\n\\nThe Ultimate Scream\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===Television Critics Association Awards===\\nThe TCA Awards are awarded annually by the Television Critics Association for outstanding achievements in television. \\'\\'Game of Thrones\\'\\' has won three out of sixteen nominations.\\n\\n+ Television Critics Association Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 84}, 'embedding': None, 'id': '6ee6d4fabf1b21e5e5ea9ead82956685'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has won three out of sixteen nominations.\\n\\n+ Television Critics Association Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\nOutstanding New Program\\n\\nIndividual Achievement in Drama\\n\\nPeter Dinklage\\n\\n2012\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\nIndividual Achievement in Drama\\n\\nPeter Dinklage\\n\\n2013\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\n2014\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\n2015\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\n2016\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\nOutstanding Achievement in Drama\\n\\n2019\\n\\nProgram of the Year\\n\\n''Game of Thrones''\\n\\n==Other awards==\\n\\n===Dorian Awards===\\nThe Dorian Awards are awarded annually by the Gay and Lesbian Entertainment Critics Association. ''Game of Thrones'' has been nominated twice.\\n\\n+ Dorian Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2013\\n\\nTV Drama of the Year\\n\\n''Game of Thrones''\\n\\n2017\\n\\nTV Drama of the Year\\n\\n''Game of Thrones''\\n\\n===Dragon Awards===\\nThe Dragon Awards are awarded annually, winners are voted upon by the general public. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 85}, 'embedding': None, 'id': 'b399f5da100e0f13b9195457f18af303'}>,\n",
-       "  <Document: {'content': \"2013\\n\\nTV Drama of the Year\\n\\n''Game of Thrones''\\n\\n2017\\n\\nTV Drama of the Year\\n\\n''Game of Thrones''\\n\\n===Dragon Awards===\\nThe Dragon Awards are awarded annually, winners are voted upon by the general public. ''Game of Thrones'' has won one out of one nomination.\\n\\n+ Dragon Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nBest Science Fiction or Fantasy TV Series\\n\\n''Game of Thrones''\\n\\n===Empire Awards===\\nEmpire Award in 2015\\n\\nThe Empire Awards are awarded annually, winners are voted upon by the general public. ''Game of Thrones'' has won one out of three nominations.\\n\\n+ Empire Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nHero Award\\n\\nJosef Altin, Jacob Anderson, John Bradley, Thomas Brodie-Sangster, Dominic Carter, Dean-Charles Chapman, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Charles Dance, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead-Wright, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, Indira Varma and Maisie Williams\\n\\n2016\\n\\nBest TV Series\\n\\n''Game of Thrones''\\n\\n2017\\n\\nBest TV Series\\n\\n''Game of Thrones''\\n\\n===E! Online Best. Ever. TV. Awards===\\nThe E! \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 86}, 'embedding': None, 'id': 'e331add716751836c73624c57bc6cf9a'}>,\n",
-       "  <Document: {'content': \"2015\\n\\nHero Award\\n\\nJosef Altin, Jacob Anderson, John Bradley, Thomas Brodie-Sangster, Dominic Carter, Dean-Charles Chapman, Gwendoline Christie, Emilia Clarke, Nikolaj Coster-Waldau, Ben Crompton, Liam Cunningham, Charles Dance, Peter Dinklage, Natalie Dormer, Nathalie Emmanuel, Iain Glen, Julian Glover, Kit Harington, Lena Headey, Isaac Hempstead-Wright, Conleth Hill, Rory McCann, Ian McElhinney, Pedro Pascal, Daniel Portman, Mark Stanley, Sophie Turner, Indira Varma and Maisie Williams\\n\\n2016\\n\\nBest TV Series\\n\\n''Game of Thrones''\\n\\n2017\\n\\nBest TV Series\\n\\n''Game of Thrones''\\n\\n===E! Online Best. Ever. TV. Awards===\\nThe E! Online Best. Ever. TV. Awards are awarded annually. ''Game of Thrones'' has been nominated once.\\n\\n+ E! Online Best. Ever. TV. Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nOutstanding Drama Series\\n\\n''Game of Thrones''\\n\\n===GLAAD Media Awards===\\nThe GLAAD Media Awards are awarded annually by the Gay & Lesbian Alliance Against Defamation. ''Game of Thrones'' has been nominated once.\\n\\n+ GLAAD Media Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nOutstanding Drama Series\\n\\n''Game of Thrones''\\n\\n===Glamour Awards===\\nThe Glamour Awards are awarded annually. ''Game of Thrones'' has won two out of two nominations.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 87}, 'embedding': None, 'id': 'd96f9d0e907db127e17da80de9e72299'}>,\n",
-       "  <Document: {'content': \"2015\\n\\nOutstanding Drama Series\\n\\n''Game of Thrones''\\n\\n===Glamour Awards===\\nThe Glamour Awards are awarded annually. ''Game of Thrones'' has won two out of two nominations.\\n\\n+ Glamour Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\n2017\\n\\nBest UK TV Actress\\n\\nSophie Turner\\n\\n===Gold Derby TV Awards===\\nDiana Rigg won a Gold Derby TV Awards in 2013, 2015 and 2018\\n\\nThe Gold Derby TV Awards are awarded annually to the best of television. ''Game of Thrones'' has won fifteen out of forty-seven nominations.\\n\\n+ Gold Derby TV Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 88}, 'embedding': None, 'id': '3a8607f5f865d9bc852397b34ac4a051'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won fifteen out of forty-seven nominations.\\n\\n+ Gold Derby TV Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBreakthrough Performer of the Year\\n\\nMaisie Williams\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2013\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nNikolaj Coster-Waldau\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nMichelle Fairley\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Rains of Castamere\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nCharles Dance\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nPedro Pascal\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Lion and the Rose\"\\n\\n\"The Watchers on the Wall\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\n2018\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Beyond the Wall\"\\n\\n\"The Spoils of War\"\\n\\n2019\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Episode\\n\\n\"A Knight of the Seven Kingdoms\"\\n\\n\"The Long Night\"\\n\\nBest Drama Actor\\n\\nKit Harington\\n\\nBest Drama Actress\\n\\nEmilia Clarke\\n\\nBest Drama Supporting Actor\\n\\nAlfie Allen\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nMaisie Williams\\n\\nBest Drama Guest Actress\\n\\nCarice van Houten\\n\\nBest Ensemble\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n===Golden Nymph Awards===\\nThe Golden Nymph Awards are awarded annually at the Monte-Carlo Television Festival. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 89}, 'embedding': None, 'id': 'c2241115284aa9e283bd3cb17c51b09f'}>,\n",
-       "  <Document: {'content': '2011\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBreakthrough Performer of the Year\\n\\nMaisie Williams\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2013\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nNikolaj Coster-Waldau\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nMichelle Fairley\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Rains of Castamere\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nCharles Dance\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nPedro Pascal\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Lion and the Rose\"\\n\\n\"The Watchers on the Wall\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\n2018\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Beyond the Wall\"\\n\\n\"The Spoils of War\"\\n\\n2019\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Episode\\n\\n\"A Knight of the Seven Kingdoms\"\\n\\n\"The Long Night\"\\n\\nBest Drama Actor\\n\\nKit Harington\\n\\nBest Drama Actress\\n\\nEmilia Clarke\\n\\nBest Drama Supporting Actor\\n\\nAlfie Allen\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nMaisie Williams\\n\\nBest Drama Guest Actress\\n\\nCarice van Houten\\n\\nBest Ensemble\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n===Golden Nymph Awards===\\nThe Golden Nymph Awards are awarded annually at the Monte-Carlo Television Festival. \\'\\'Game of Thrones\\'\\' has won one out of five nominations.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 90}, 'embedding': None, 'id': 'c9fcc5ac92c2a1170ed620b3ff44e8c7'}>,\n",
-       "  <Document: {'content': '2011\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\n2012\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBreakthrough Performer of the Year\\n\\nMaisie Williams\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2013\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nNikolaj Coster-Waldau\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nMichelle Fairley\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Rains of Castamere\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nCharles Dance\\n\\nBest Drama Supporting Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nPedro Pascal\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"The Lion and the Rose\"\\n\\n\"The Watchers on the Wall\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2015\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Hardhome\"\\n\\n\"Mother\\'s Mercy\"\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nKit Harington\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actor\\n\\nIan McShane\\n\\nMax von Sydow\\n\\nBest Drama Episode\\n\\n\"Battle of the Bastards\"\\n\\n\"The Winds of Winter\"\\n\\n2018\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nEnsemble of the Year\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nLena Headey\\n\\nBest Drama Guest Actress\\n\\nDiana Rigg\\n\\nBest Drama Episode\\n\\n\"Beyond the Wall\"\\n\\n\"The Spoils of War\"\\n\\n2019\\n\\nBest Drama Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Drama Episode\\n\\n\"A Knight of the Seven Kingdoms\"\\n\\n\"The Long Night\"\\n\\nBest Drama Actor\\n\\nKit Harington\\n\\nBest Drama Actress\\n\\nEmilia Clarke\\n\\nBest Drama Supporting Actor\\n\\nAlfie Allen\\n\\nNikolaj Coster-Waldau\\n\\nPeter Dinklage\\n\\nBest Drama Supporting Actress\\n\\nGwendoline Christie\\n\\nLena Headey\\n\\nMaisie Williams\\n\\nBest Drama Guest Actress\\n\\nCarice van Houten\\n\\nBest Ensemble\\n\\nThe cast of \\'\\'Game of Thrones\\'\\'\\n\\n===Golden Nymph Awards===\\nThe Golden Nymph Awards are awarded annually at the Monte-Carlo Television Festival. \\'\\'Game of Thrones\\'\\' has won one out of five nominations.\\n\\n+ Golden Nymph Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 91}, 'embedding': None, 'id': 'b599cf66740ee48ccfcaee01adbe1e43'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has won one out of five nominations.\\n\\n+ Golden Nymph Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding International Producer\\n\\nDavid Benioff, Frank Doelger, Carolyn Strauss and D. B. Weiss\\n\\nOutstanding Actress in a Drama Series\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nOutstanding Actor in a Drama Series\\n\\nPeter Dinklage\\n\\nKit Harington\\n\\n===Gracie Allen Awards===\\nThe Gracie Award was established in 1975 by the Alliance for Women in Media to recognize the best representation of women in the media. The award is given to various types of programming or individuals. ''Game of Thrones'' has won one out of one nomination.\\n\\n+ Gracie Allen Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nOutstanding Female Rising Star in a Drama Series or Special\\n\\nEmilia Clarke\\n\\n===Hollywood Music in Media Awards===\\nThe Hollywood Music in Media Awards are awarded annually to recognize the music of visual mediums such as film, television, movie trailers, video games and commercials. ''Game of Thrones'' has been nominated one time.\\n\\n+ Hollywood Music in Media Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 92}, 'embedding': None, 'id': 'a1eb905000f8068f5140d42fec632de1'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated one time.\\n\\n+ Hollywood Music in Media Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nBest Original Score – TV Show / Digital Streaming Series\\n\\nRamin Djawadi\\n\\n===Humanitas Prize===\\nThe Humanitas Prize are awarded annually to film and television writing intended to \"promote human dignity, meaning, and freedom\". \\'\\'Game of Thrones\\'\\' has been nominated one time.\\n\\n+ Humanitas Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2017\\n\\n60 Minute Network or Syndicated Television\\n\\nDavid Benioff, D. B. Weiss (for \"The Dragon and the Wolf\")\\n\\n===International Film Music Critics Association===\\nThe International Film Music Critics Association are awarded annually to film and television composers. \\'\\'Game of Thrones\\'\\' has been nominated four times, and won once.\\n\\n+ International Film Music Critics Association Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 93}, 'embedding': None, 'id': 'a218dfb36dc2998844508f0869dd8246'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has been nominated four times, and won once.\\n\\n+ International Film Music Critics Association Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\n2013\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\n2016\\n\\nBest Original Score for a Television Series\\n\\nRamin Djawadi\\n\\nFilm Music Composition of the Year\\n\\nRamin Djawadi for \"Light of the Seven\"\\n\\n===Jupiter Awards===\\nThe Jupiter Awards are awarded annually. \\'\\'Game of Thrones\\'\\' has won once.\\n\\n+ Jupiter Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nBest International TV Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===Kerrang! Awards===\\nThe Kerrang! Awards are awarded annually. \\'\\'Game of Thrones\\'\\' has won two out of two nominations.\\n\\n+ Kerrang! Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2014\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===MTV Fandom Awards===\\nThe MTV Fandom Awards are awarded annually, voted by the public. \\'\\'Game of Thrones\\'\\' has won one out of two nominations.\\n\\n+ MTV Fandom Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 94}, 'embedding': None, 'id': '7a616051f9c6b426d67f00782758ac6f'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has won one out of two nominations.\\n\\n+ MTV Fandom Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nOMG Moment of the Year\\n\\n''Game of Thrones'' – The Purple Wedding\\n\\n2016\\n\\nFan Freak Out of the Year\\n\\n''Game of Thrones'' – Resurrection of Jon Snow\\n\\n===MTV Millennial Awards===\\nThe MTV Millennial Awards are annual awards that celebrates the music, videos and digital culture of the Millennial Generation in Latin America. ''Game of Thrones'' has won one out of one nomination.\\n\\n+ MTV Millennial Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nKiller Serie del Año\\n\\n''Game of Thrones''\\n\\n===MTV Movie & TV Awards===\\nThe MTV Movie & TV Awards is an annual award show presented by MTV to honor outstanding achievements in film and television. Founded in 1992, the winners of the awards are decided online by the audience. ''Game of Thrones'' won one award from ten nominations.\\n\\n+ MTV Movie & TV Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 95}, 'embedding': None, 'id': 'ea46759de5c5835269313adc891cb0fd'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' won one award from ten nominations.\\n\\n+ MTV Movie & TV Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2017\\n\\nShow of the Year\\n\\n''Game of Thrones''\\n\\nBest Actor in a Show\\n\\nEmilia Clarke\\n\\nTearjerker\\n\\nHodor's (Kristian Nairn) death\\n\\n2018\\n\\nBest Show\\n\\n''Game of Thrones''\\n\\nBest Performance in a Show\\n\\nMaisie Williams\\n\\nBest Hero\\n\\nEmilia Clarke\\n\\n2019\\n\\nBest Show\\n\\n''Game of Thrones''\\n\\nBest Performance in a Show\\n\\nEmilia Clarke\\n\\nBest Hero\\n\\nMaisie Williams\\n\\nBest Fight\\n\\nArya Stark (Maisie Williams) vs. White Walkers\\n\\n===NewNowNext Awards===\\nThe NewNowNext Awards are awarded annually. ''Game of Thrones'' has been nominated three times.\\n\\n+ NewNowNext Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nTV You Betta Watch\\n\\n''Game of Thrones''\\n\\n2013\\n\\nCause You're Hot\\n\\nRichard Madden\\n\\n2014\\n\\nBest New Television Actor\\n\\nPedro Pascal\\n\\n===Portal Awards===\\nThe Portal Awards are awarded annually for genre television and movies. ''Game of Thrones'' has won seven out of sixteen nominations.\\n\\n+ Portal Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 96}, 'embedding': None, 'id': 'f2362c4de96f1414e7ac788c5b57df98'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won seven out of sixteen nominations.\\n\\n+ Portal Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nBest Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actor\\n\\nSean Bean\\n\\nBest Actress\\n\\nLena Headey\\n\\nBest Supporting Actor\\n\\nPeter Dinklage\\n\\nBest Episode\\n\\n\"Winter Is Coming\"\\n\\nBest Young Actor\\n\\nIsaac Hempstead-Wright\\n\\nMaisie Williams\\n\\n2012\\n\\nBest Series\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\nBest Actress\\n\\nLena Headey\\n\\nBest Supporting Actor\\n\\nAidan Gillen\\n\\nBest Supporting Actress\\n\\nMichelle Fairley\\n\\nMaisie Williams\\n\\nBest Episode\\n\\n\"The Ghost of Harrenhal\"\\n\\nBest Young Actor\\n\\nJack Gleeson\\n\\nMaisie Williams\\n\\n===Royal Television Society===\\nThe Royal Television Society presents annual awards honoring the best in television. In 2014, the show won the International Program Award at the in 2014. \\'\\'Game of Thrones\\'\\' has won one out of one nomination.\\n\\n+ Royal Television Society Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nInternational Program\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===Screenwriters Choice Awards===\\nThe Screenwriters Choice Awards are awarded annually. \\'\\'Game of Thrones\\'\\' has won one out of two nominations.\\n\\n+ Screenwriters Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 97}, 'embedding': None, 'id': '10ab32576d2db66e1993fb231c246831'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won one out of two nominations.\\n\\n+ Screenwriters Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2015\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nBest Television Drama\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===SFX Awards===\\nThe SFX Awards are awarded annually to celebrate achievements in science fiction and are voted on by the readers of the SFX magazine. \\'\\'Game of Thrones\\'\\' has won two out of thirteen nominations.\\n\\n+ SFX Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2012\\n\\nBest New TV Show\\n\\nDavid Benioff and D. B. Weiss\\n\\nBest TV Show\\n\\nDavid Benioff and D. B. Weiss\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\nBest Villain\\n\\nCharles Dance as Tywin Lannister\\n\\nBiggest Disappointment\\n\\nNo Hodor in \\'\\'Game of Thrones\\'\\' season five\\n\\nBest TV Episode\\n\\n\"The Mountain and the Viper\"\\n\\n===Shorty Awards===\\nThe Shorty Awards are annual awards, recognizing the people and organizations producing real-time short form content across social media. \\'\\'Game of Thrones\\'\\' has been nominated twice.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 98}, 'embedding': None, 'id': '902f65135dd807bab2d2afb2589ad151'}>,\n",
-       "  <Document: {'content': '2012\\n\\nBest New TV Show\\n\\nDavid Benioff and D. B. Weiss\\n\\nBest TV Show\\n\\nDavid Benioff and D. B. Weiss\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\n2013\\n\\nBest Actress\\n\\nEmilia Clarke\\n\\nLena Headey\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\n2015\\n\\nBest TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nBest Actress\\n\\nMaisie Williams\\n\\nBest Actor\\n\\nPeter Dinklage\\n\\nBest Villain\\n\\nCharles Dance as Tywin Lannister\\n\\nBiggest Disappointment\\n\\nNo Hodor in \\'\\'Game of Thrones\\'\\' season five\\n\\nBest TV Episode\\n\\n\"The Mountain and the Viper\"\\n\\n===Shorty Awards===\\nThe Shorty Awards are annual awards, recognizing the people and organizations producing real-time short form content across social media. \\'\\'Game of Thrones\\'\\' has been nominated twice.\\n\\n+ Shorty Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nFavorite TV Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\nGIF of The Year\\n\\n\\'\\'Game of Thrones\\'\\' Come at Me Bro\\n\\n===TV Choice Awards===\\nThe TV Choice Awards are awarded annually, the nominees are selected by the editors at TV Choice magazine and the winners are chosen by voting of the general public. \\'\\'Game of Thrones\\'\\' has won two out of two nominations.\\n\\n+ TV Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 99}, 'embedding': None, 'id': '8cdc6da5b572a1aa35ae405344b13187'}>,\n",
-       "  <Document: {'content': '\\'\\'Game of Thrones\\'\\' has won two out of two nominations.\\n\\n+ TV Choice Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2014\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n2016\\n\\nBest International Show\\n\\n\\'\\'Game of Thrones\\'\\'\\n\\n===USC Scripter Awards===\\nThe USC Scripter Awards are awarded annually by the USC Libraries Board of Councilors in recognition of the year\\'s best adaptation of the printed word into film. \\'\\'Game of Thrones\\'\\' has been nominated two times.\\n\\n+ USC Scripter Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss (for \"Hardhome\")\\n\\n2017\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D. B. Weiss (for \"The Winds of Winter\")\\n\\n===Webby Awards===\\nThe Webby Awards are awards for excellence on the Internet presented annually by the International Academy of Digital Arts and Sciences. \\'\\'Game of Thrones\\'\\' has won six awards out of six nominations.\\n\\n+ Webby Awards and nominations received by \\'\\'Game of Thrones\\'\\'\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 100}, 'embedding': None, 'id': 'f2ac972a2dc05f3b5347871a5aa9d1e'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has won six awards out of six nominations.\\n\\n+ Webby Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nBest Overall Social Presence\\n\\n''Game of Thrones''\\n\\n2017\\n\\nBest Overall Social Presence\\n\\n''Game of Thrones''\\n\\nUnscripted (Branded)\\n\\nBattle of the Bastards Featurette\\n\\n2018\\n\\nBest Overall Social Presence\\n\\n''Game of Thrones''\\n\\nBest Trailer\\n\\n''Game of Thrones''\\n\\nBest Digital Campaign\\n\\n''Game of Thrones''\\n\\n===Women's Image Network Awards===\\nThe Women's Image Network Awards are annual awards presented to individuals who promote women and girls in the media. ''Game of Thrones'' has won one out of two nominations.\\n\\n+ Women's Image Network Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2011\\n\\nActress Drama Series\\n\\nLena Headey\\n\\n2014\\n\\nActress Drama Series\\n\\nLena Headey\\n\\n===World Soundtrack Awards===\\nThe World Soundtrack Academy presents annual awards honoring the music professionals involved in film and television. ''Game of Thrones'' has one nomination.\\n\\n+ World Soundtrack Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2016\\n\\nTelevision Composer of the Year\\n\\nRamin Djawadi\\n\\n===Young Artist Awards===\\nThe Young Artist Awards are awarded annually to young artists. ''Game of Thrones'' has been nominated three times.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 101}, 'embedding': None, 'id': '3ba7277c6e0ac268c5b8a69e71b8d1b2'}>,\n",
-       "  <Document: {'content': \"2016\\n\\nTelevision Composer of the Year\\n\\nRamin Djawadi\\n\\n===Young Artist Awards===\\nThe Young Artist Awards are awarded annually to young artists. ''Game of Thrones'' has been nominated three times.\\n\\n+ Young Artist Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2013\\n\\nBest Performance in a TV Series – Supporting Young Actor\\n\\nIsaac Hempstead-Wright\\n\\nBest Performance in a TV Series – Supporting Young Actress\\n\\nSophie Turner\\n\\nMaisie Williams\\n\\n===Young Hollywood Awards===\\nThe Young Hollywood Awards are bestowed annually to honor the achievements in pop music, film and television, sports, fashion and social media. ''Game of Thrones'' has won one out of three nominations.\\n\\n+ Young Hollywood Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2013\\n\\nActor of the Year\\n\\nKit Harington\\n\\n2014\\n\\nBingeworthy TV Show\\n\\n''Game of Thrones''\\n\\nWe Love to Hate You\\n\\nJack Gleeson\\n\\n===Zulu Awards===\\nThe Zulu Awards are awarded annually, nominees are selected by Danish broadcaster Zulu and the winners are voted upon by the general public. ''Game of Thrones'' has been nominated one time.\\n\\n+ Zulu Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 102}, 'embedding': None, 'id': '4c38962a5f1fc798fd84c4f5395c4bd1'}>,\n",
-       "  <Document: {'content': \"''Game of Thrones'' has been nominated one time.\\n\\n+ Zulu Awards and nominations received by ''Game of Thrones''\\n\\nYear\\n\\nCategory\\n\\nRecipient\\n\\nResult\\n\\nRef.\\n\\n2017\\n\\nBest Actor\\n\\nNikolaj Coster-Waldau\\n\\n==Notes==\\n\\n==References==\\n\\n==External links==\\n*  Official U.S. website HBO\\n*\\n*\\n*  Official UK website HBO UK\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 103}, 'embedding': None, 'id': '5b13213f2edcdf99912fa5ef6e771a3b'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'\\'\\'Game of Thrones\\'\\' Theme\\'\\'\\'\", also referred to as \"\\'\\'\\'\\'\\'Game of Thrones\\'\\' Main Title Theme\\'\\'\\'\", is the theme music of HBO\\'s award-winning fantasy television series \\'\\'Game of Thrones\\'\\', and plays during the title sequence.  It  was composed by Ramin Djawadi in 2011, after series creator David Benioff and D. B. Weiss approached him requesting a theme.\\n\\nAsked to avoid flutes and violins, which the producers felt were overused in fantasy themes, Djawadi used the cello as the lead instrument.  The piece begins in a minor key, then switches between corresponding major and minor keys repeatedly.  Djawadi was shown a preliminary rendering of the title sequence before composing this music to accompany it.  Several artists have covered or parodied the music, sometimes adding lyrics to the originally instrumental work.\\n\\n==Composition==\\nRamin Djawadi is the composer of the \\'\\'Game of Thrones\\'\\' score.\\nRamin Djawadi began composing the music for the show after he had watched the first two episodes of the series that the showrunners David Benioff and D. B. Weiss sent him, and discussed the concepts of the show with them.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '58117c6592e4d9179ca1ce68843d0c23'}>,\n",
-       "  <Document: {'content': \"Ramin Djawadi began composing the music for the show after he had watched the first two episodes of the series that the showrunners David Benioff and D. B. Weiss sent him, and discussed the concepts of the show with them.  According to Djawadi, the show creators wanted the main title theme to be about a journey as there are many locations and characters in the show and the narrative involves much traveling.  After Djawadi was shown a preliminary animated ''Game of Thrones'' title sequence that the visual effect artists were still working on, he was inspired to write the piece. He said that he started humming what would become the theme tune in the car after seeing the visuals for the title sequence, and conceived of the idea for the theme on the drive back to his studio. The finished theme music was presented to the producer three days later.\\n\\nDjawadi said he intended to capture the overall impression of the show with the theme tune. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'ffd339d248058b6feaaccde1eaf0c116'}>,\n",
-       "  <Document: {'content': 'The finished theme music was presented to the producer three days later.\\n\\nDjawadi said he intended to capture the overall impression of the show with the theme tune. Cello is featured strongly as Benioff and Weiss wanted to avoid the flutes or solo vocals found in many other productions in the fantasy genre so as to give the show a distinctive sound, and Djawadi chose cello as the main instrument for the music as he thought it has a \"darker sound\" that suited the show.\\n\\nDjawadi started with a riff and he built the title theme around the riff.  The tune begins with the riff played on strings in a minor key, then changed to a major key after 2 bars, and back to minor again. Djawadi said that he wanted to reflect the \"backstabbing and conspiracy\" and the unpredictability of the show: \"... I thought it would be cool to kinda do the same play with the music. So even though the majority of the piece is in minor, there\\'s that little hint of major in there where it kinda switches and then it changes back again.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '65d2a9a984f946444314ccef3c4b6664'}>,\n",
-       "  <Document: {'content': 'So even though the majority of the piece is in minor, there\\'s that little hint of major in there where it kinda switches and then it changes back again.\"The main melody is then introduced with the cello, joined later by a solo violin that may suggest an interplay between different characters. The melody is then repeated with the entire orchestra. The next section introduces a change in melody, described by Djawadi as giving \"a sense of adventure\", and continues with a repeat that involves a choir of twenty female voices - recorded in Prague, like the instrumental parts. The title theme ends with a combination of dulcimer and kantele, producing a \"shimmery quality\" in its sound that Djawadi thought would give a sense of mystery and anticipation for the episode.\\n\\nThe title music is reprised as a global theme in the soundtracks for the series.  It may be played occasionally on its own in fragments, sometimes as part of the theme of individual characters or in combination with other pieces of music, and may also be played in large section during particularly important scenes.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '64dffc1c5ca50cbc8e23044c76419811'}>,\n",
-       "  <Document: {'content': 'It may be played occasionally on its own in fragments, sometimes as part of the theme of individual characters or in combination with other pieces of music, and may also be played in large section during particularly important scenes.\\n\\n==Chart positions==\\n===Weekly charts===\\n\\nChart (2014)\\n\\nPeakposition\\n\\nBelgium (Ultratop Back Catalogue Singles Wallonia)\\n\\n22\\n\\nChart (2015)\\n\\nPeakposition\\n\\nFrench Singles Sales Chart (Pure Charts)\\n\\n131\\n\\nChart (2019)\\n\\nPeakposition\\n\\nFrench Singles Sales Chart (Pure Charts)\\n\\n65\\n\\nFrench Downloads (SNEP)\\n\\n65\\n\\n==Cover versions and parodies==\\nThe main theme of \\'\\'Game of Thrones\\'\\' has inspired many tributes and cover versions, including a rendition by the electropop band Chvrches. Lyrics were added for the first time in 2014, when \"Weird Al\" Yankovic performed a parody version during the 66th Primetime Emmy Awards. In March 2015, FORTE added lyrics based on High Valyrian text for an operatic performance and music video. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'f9b9bbc56064cae530ad6109fb5fbd7e'}>,\n",
-       "  <Document: {'content': 'Lyrics were added for the first time in 2014, when \"Weird Al\" Yankovic performed a parody version during the 66th Primetime Emmy Awards. In March 2015, FORTE added lyrics based on High Valyrian text for an operatic performance and music video. Some of the cover and parody versions mentioned by news media include:\\n* an electric and acoustic cello version by Grammy nominated cellist Tina Guo\\n*a violin version by Jason Yang,\\n* a metal version by Roger Lima,\\n* an electric harp duet version by the \"Harp Twins\", Camille and Kennerly Kitt,\\n* a bleeping \"8-bit\" remix in the style of early video game music,\\n* a rendition in the noise of floppy disk drives,\\n* a violin and voice duet by Lindsey Stirling and Peter Hollens,\\n* a cello version by \\'\\'Break of Reality\\'\\',\\n* a cello version by 2Cellos\\n* a violin version by Ben Shapiro\\n* a ska interpretation by Pannonia Allstars Ska Orchestra,\\n* a chamber music version by Aston,\\n* a Ragtime Piano Rendition by Jonny May,\\n* a choral parody used in two episodes of \\'\\'South Park\\'\\', with lyrics solely about \"wieners\",\\n* an elaborate parody of the opening at the beginning of a 2012 episode of \\'\\'The Simpsons\\'\\', \"Exit Through the Kwik-E-Mart\",\\n*a vocal version performed by French musician and singer Luc Arbogast. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '558f2f445f03a227ee6dbf3fcd89e312'}>,\n",
-       "  <Document: {'content': 'Some of the cover and parody versions mentioned by news media include:\\n* an electric and acoustic cello version by Grammy nominated cellist Tina Guo\\n*a violin version by Jason Yang,\\n* a metal version by Roger Lima,\\n* an electric harp duet version by the \"Harp Twins\", Camille and Kennerly Kitt,\\n* a bleeping \"8-bit\" remix in the style of early video game music,\\n* a rendition in the noise of floppy disk drives,\\n* a violin and voice duet by Lindsey Stirling and Peter Hollens,\\n* a cello version by \\'\\'Break of Reality\\'\\',\\n* a cello version by 2Cellos\\n* a violin version by Ben Shapiro\\n* a ska interpretation by Pannonia Allstars Ska Orchestra,\\n* a chamber music version by Aston,\\n* a Ragtime Piano Rendition by Jonny May,\\n* a choral parody used in two episodes of \\'\\'South Park\\'\\', with lyrics solely about \"wieners\",\\n* an elaborate parody of the opening at the beginning of a 2012 episode of \\'\\'The Simpsons\\'\\', \"Exit Through the Kwik-E-Mart\",\\n*a vocal version performed by French musician and singer Luc Arbogast. The song peaked at number 125 on French Singles Sales Chart in 2014 and stayed one week there.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '9e4411def110ec648a62ba61809863b8'}>,\n",
-       "  <Document: {'content': 'Some of the cover and parody versions mentioned by news media include:\\n* an electric and acoustic cello version by Grammy nominated cellist Tina Guo\\n*a violin version by Jason Yang,\\n* a metal version by Roger Lima,\\n* an electric harp duet version by the \"Harp Twins\", Camille and Kennerly Kitt,\\n* a bleeping \"8-bit\" remix in the style of early video game music,\\n* a rendition in the noise of floppy disk drives,\\n* a violin and voice duet by Lindsey Stirling and Peter Hollens,\\n* a cello version by \\'\\'Break of Reality\\'\\',\\n* a cello version by 2Cellos\\n* a violin version by Ben Shapiro\\n* a ska interpretation by Pannonia Allstars Ska Orchestra,\\n* a chamber music version by Aston,\\n* a Ragtime Piano Rendition by Jonny May,\\n* a choral parody used in two episodes of \\'\\'South Park\\'\\', with lyrics solely about \"wieners\",\\n* an elaborate parody of the opening at the beginning of a 2012 episode of \\'\\'The Simpsons\\'\\', \"Exit Through the Kwik-E-Mart\",\\n*a vocal version performed by French musician and singer Luc Arbogast. The song peaked at number 125 on French Singles Sales Chart in 2014 and stayed one week there.\\n* a parody performed by \"Weird Al\" Yankovic at the 2014 Emmy awards,\\n* an orchestra version performed at Illich Steel and Iron Works by Mariupol orchestra \"Renaissance\",\\n* a remix by Armin van Buuren, KSHMR and The Golden Army.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '55947a871b4597540a5d70981874a7c1'}>,\n",
-       "  <Document: {'content': '* a parody performed by \"Weird Al\" Yankovic at the 2014 Emmy awards,\\n* an orchestra version performed at Illich Steel and Iron Works by Mariupol orchestra \"Renaissance\",\\n* a remix by Armin van Buuren, KSHMR and The Golden Army.\\n* a bluegrass cover version performed by the Tennessee-based band Flat Lonesome for SirusXM radio station.\\n* In 2017, Canadian band Barenaked Ladies opened and closed their end-of-show popular music medley/parody with the Game of Thrones Theme with lead singer Ed Robertson adding the lyrics \"\\'\\'Horses - tits and horses\\'\\' repeated \\'\\'and some dong!\\'\\'\", poking fun at the content of the series.\\n* In 2019, American band Our Last Night covered the song with a post-hardcore theme.\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n* Ramin Djawadi – composer, primary artist, producer\\n* David Benioff – liner notes\\n* D.B. Weiss – liner notes\\n\\n==See also==\\n* \\'\\'Game of Thrones\\'\\' title sequence\\n* Music of \\'\\'Game of Thrones\\'\\'\\n\\n==References==\\n\\n==External links==\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '5cf4cefc5e5e9741263d261aced1a1c2'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''''A Game of Thrones: The Card Game Second Edition''''' (or '''''AGoT''''', for short) is a Living Card Game (LCG) produced by Fantasy Flight Games. It is based on ''A Song of Ice and Fire'',  a series of novels written by George R. R. Martin.  The second edition of this LCG, it is not backwards compatible with the first edition that preceded it.\\n\\nIn the game, players assume the leadership of one of the great houses of Westeros vying for control of King's Landing and the Iron Throne. To accomplish this, players launch military attacks against their opponents, undermine their opponents’ plans with intrigues of their own, and make power plays to win the support of the realm.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '917652e775dbdf73b703bd2e44a7d73b'}>,\n",
-       "  <Document: {'content': \"To accomplish this, players launch military attacks against their opponents, undermine their opponents’ plans with intrigues of their own, and make power plays to win the support of the realm.\\n\\n==Sets and expansions==\\n\\n+ '''A Game of Thrones LCG Core Set and Deluxe Expansions'''\\n\\nSet\\n\\nRelease Date\\n\\nNote\\n\\nA Game of Thrones : Second Edition (Core Set)\\n\\nOctober 2015\\n\\nWolves of the North\\n\\nApril 2016\\n\\nLions of Casterly Rock\\n\\nNovember 2016\\n\\nWatchers on the Wall\\n\\nApril 2017\\n\\nHouse of Thorns\\n\\nOctober 2017\\n\\nSands of Dorne\\n\\nMarch 2018\\n\\nKings of the Isles\\n\\nDecember 2018\\n\\nFury of the Storm\\n\\nMay 2019\\n\\n+ '''A Game of Thrones LCG chapter packs'''\\n\\nCycle\\n\\nChapter Pack\\n\\nRelease Date\\n\\nNotes\\n\\nWesteros Cycle\\n\\nTaking the Black\\n\\nDecember 2015\\n\\nRoad to Winterfell\\n\\nJanuary 2016\\n\\nThe King's Peace\\n\\nFebruary 2016\\n\\nNo Middle Ground\\n\\nMarch 2016\\n\\nCalm Over Westeros\\n\\nApril 2016\\n\\nTrue Steel\\n\\nMay 2016\\n\\nWar of the Five Kings Cycle\\n\\nAcross the Seven Kingdoms\\n\\nJuly 2016\\n\\nCalled to Arms\\n\\nAugust 2016\\n\\nFor Family Honor\\n\\nOctober 2016\\n\\nThere is My Claim\\n\\nNovember 2016\\n\\nGhosts of Harrenhal\\n\\nJanuary 2017\\n\\nTyrion's Chain\\n\\nFebruary 2017\\n\\nBlood and Gold Cycle\\n\\nAll Men are Fools\\n\\nMarch 2017\\n\\nGuarding the Realm\\n\\nMay 2017\\n\\nThe Fall of Astapor\\n\\nJune 2017\\n\\nThe Red Wedding\\n\\nJuly 2017\\n\\nOberyn's Revenge\\n\\nAugust 2017\\n\\nThe Brotherhood Without Banners\\n\\nSeptember 2017\\n\\nFlight of Crows Cycle\\n\\nThe Archmaester's Key\\n\\nNovember 2017\\n\\nJourney to Oldtown\\n\\nDecember 2017\\n\\nKingsmoot\\n\\nJanuary 2018\\n\\nFavor of the Old Gods\\n\\nFebruary 2018\\n\\nThe Faith Militant\\n\\nApril 2018\\n\\nSomeone Always Tells\\n\\nMay 2018\\n\\nDance of Shadows Cycle\\n\\nThe Shadow City\\n\\nJune 2018\\n\\nThe March on Winterfell\\n\\nJuly 2018\\n\\nStreets of King's Landing\\n\\nAugust 2018\\n\\nMusic of Dragons\\n\\nSeptember 2018\\n\\nIn Daznak's Pit\\n\\nOctober 2018\\n\\nDaggers in the Dark\\n\\nNovember 2018\\n\\nKing's Landing Cycle\\n\\nAt the Gates\\n\\nApril 2019\\n\\nCity of Secrets\\n\\nJune 2019\\n\\nPit of Snakes\\n\\nJuly 2019\\n\\n==External links==\\n* A Game of Thrones: Second Edition - Official website\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '432a50b9ee1413291709dd1018cb90bb'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''''Game of Thrones: Seven Kingdoms''''' was a fantasy massively multiplayer online role-playing game (MMORPG) under development by Bigpoint and Artplant, in collaboration with HBO. The game was based on the HBO TV series ''Game of Thrones'', which is itself an adaptation of the ''A Song of Ice and Fire'' book series by George R. R. Martin.\\n\\nThe game was being built using the Unity platform, and will be playable in the browser using the Unity Web Player plugin.\\n\\n==Gameplay==\\n''Game of Thrones: Seven Kingdoms'' was to be set within the fictional realm of Westeros, and will use a third-person viewpoint. Gameplay was planned to be mainly based around player vs player (PvP) combat, which would involve small group combat, one on one duels and siege battles, large scale battles in which players must capture keeps, forts and castles. Player vs Environment (PvE) combat was planned to be available at launch, although this was not a major priority. Combat would be realtime, with movement controlled using the WASD keys.\\n\\nPlayers would be able to come together to form guilds, known as lesser houses. Lesser houses align themselves with one of the three major houses in-game: Baratheon, Stark or Lannister. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '4d9556c4d45b59ac85aa409daf50669c'}>,\n",
-       "  <Document: {'content': \"Players would be able to come together to form guilds, known as lesser houses. Lesser houses align themselves with one of the three major houses in-game: Baratheon, Stark or Lannister. Solo play will be possible, although group play will be encouraged.\\n\\nPlayers would be able to customize their characters by changing features such as hair and clothing.\\n\\n==Development==\\n''Game of Thrones: Seven Kingdoms'' was announced in February 2012, with the first look being unveiled at the Game Developers Conference in March 2012. The first gameplay footage was shown in a trailer released on July 12, 2012.\\n\\nThe game was under development by German videogame developer Bigpoint, and Norwegian developer Artplant. The studios have previously worked together on Battlestar Galactica Online, another browser based multiplayer game.\\n\\nThe game was being made in collaboration with HBO. Everything put in game is reviewed by HBO in advance, and Bigpoint is working with the HBO design teams. The look and feel of the weapons and armour, for example, are straight from the show.\\n\\nThe game was being built on the Unity platform.\\n\\n==References==\\n\\n==External links==\\n* Official page\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'f1b71b7f4be9ff02d89b6ae2844c7b2d'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Laws of Gods and Men\\'\\'\\'\" is the sixth episode of the fourth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 36th overall. The episode was written by Bryan Cogman, and directed by Alik Sakharov. It aired on May 11, 2014.\\n\\nThe episode\\'s title refers to Tyrion Lannister\\'s trial for Joffrey Baratheon\\'s murder, as well as the laws of the Seven Kingdoms of Westeros.\\n\\n==Plot==\\n\\n===In Braavos===\\nStannis and Davos arrive in Braavos and meet Tycho Nestoris at the Iron Bank. Nestoris agrees to Stannis’ request and grants him funds he needs. Davos tells Salladhor Saan that he has the money Saan was promised and that they will set sail the next day.\\n\\n===In Meereen===\\nDaenery\\'s dragon Drogon attacks a group of goats led by a farmer. The farmer presents\\xa0a bag of charred goat bones to Daenerys and she promises to pay him three times their worth. Hizdahr zo Loraq requests to bury his father, who was one of the Meereenese masters crucified on Daenerys\\'s orders and was against crucifixion of the slave children. Daenerys agrees.\\n\\n===At the Dreadfort===\\nYara and Ironborn soldiers attack the Dreadfort. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'fb353e098294f5f7985609eefabcc98b'}>,\n",
-       "  <Document: {'content': \"Hizdahr zo Loraq requests to bury his father, who was one of the Meereenese masters crucified on Daenerys's orders and was against crucifixion of the slave children. Daenerys agrees.\\n\\n===At the Dreadfort===\\nYara and Ironborn soldiers attack the Dreadfort. Yara finds Theon, but he refuses her help and says that his name is Reek, not Theon. Ramsay and his men corner the Ironborn, who return to their boats.\\n\\n===In King's Landing===\\nVarys tells Tywin of Daenerys' conquest of Meereen and Tywin decides to take measures to prevent her from launching an invasion. Tyrion's trial begins with Tywin, Mace and Oberyn as judges. Several witnesses are called including Meryn, Pycelle, Cersei and Varys, who all testify against Tyrion. At recess, Jaime tells Tywin that he will go to Casterly Rock and be his heir if Tywin spares Tyrion. He agrees and tells that Tyrion will be sent to Night's Watch after being found guilty of Joffrey's murder. Returning to trial, Shae is called as next witness. She falsely testifies that Tyrion and Sansa planned Joffrey's murder. Tyrion has an outburst against the people of King's Landing and his father and demands a trial by combat.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'e52ab4d524b08aff889d08f55403333c'}>,\n",
-       "  <Document: {'content': 'She falsely testifies that Tyrion and Sansa planned Joffrey\\'s murder. Tyrion has an outburst against the people of King\\'s Landing and his father and demands a trial by combat.\\n\\n==Production==\\n\\n===Writing===\\nSeries veteran Bryan Cogman wrote this episode.\\n\"The Laws of Gods and Men\" was written by Bryan Cogman, based upon the source material, Martin\\'s \\'\\'A Storm of Swords\\'\\'. Chapters adapted from \\'\\'A Storm of Swords\\'\\' to the episode were chapters 66 and 70 (Tyrion IX and Tyrion X).\\n\\n==Reception==\\n\\n===Ratings===\\n\"The Laws of Gods and Men\" was watched by an estimated 6.4 million people during its original broadcast. In the United Kingdom, the episode was viewed by 1.657 million viewers, making it the highest-rated broadcast that week. It also received 0.1 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode received universal acclaim reviews from critics, with most reviewers singling out Tyrion\\'s trial as the highlight of the episode, particularly praising Peter Dinklage\\'s performance. It has received a 95% rating on the review aggregator website Rotten Tomatoes from 38 reviews with an average score of 9 out of 10. Matt Fowler of IGN called the final scene \"one of the best things I’ve seen Tyrion do in a long while. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '2f5a24320d1fee905cb3233ecacbec24'}>,\n",
-       "  <Document: {'content': 'Matt Fowler of IGN called the final scene \"one of the best things I’ve seen Tyrion do in a long while. He let a lifetime of hate ooze out of him like never before.\"===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2014\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Art Direction for a Single-Camera Fantasy Series\\n\\nDeborah Riley, Paul Ghirardani, and Rob Cameron\\n\\n2015\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '92110f669802c45f7141ea8f506dc22b'}>,\n",
-       "  <Document: {'content': '\\n\\'\\'\\'Rickon Stark\\'\\'\\' is a fictional character in the \\'\\'A Song of Ice and Fire\\'\\' series of fantasy novels by American author George R. R. Martin, and its television adaptation \\'\\'Game of Thrones\\'\\'.\\n\\nIntroduced in 1996\\'s \\'\\'A Game of Thrones\\'\\', Rickon is the youngest child of Eddard Stark, the honorable lord of Winterfell, an ancient fortress in the North of the fictional kingdom of Westeros. He subsequently appeared in Martin\\'s \\'\\'A Clash of Kings\\'\\' (1998). The \\'\\'Publishers Weekly\\'\\' review of \\'\\'A Game of Thrones\\'\\' noted, \"It is fascinating to watch Martin\\'s characters mature and grow, particularly Stark\\'s children, who stand at the center of the book.\"Rickon is portrayed by Irish actor Art Parkinson in the HBO television adaptation.\\n\\n== Character description ==\\nRickon is the fifth and youngest child of Eddard \"Ned\" Stark and his wife Catelyn, and has five siblings—Robb, Sansa, Arya, Bran, and his illegitimate half-brother Jon Snow. Like his brothers and sisters, Rickon is constantly accompanied by his direwolf, Shaggydog, with whom he shares a strong connection. Martin describes Rickon as favoring his mother in appearance. He is naturally aggressive, strong-willed, and violent: traits reflected in his direwolf companion \\'Shaggydog\\'.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'ebf640f2bff919a26b723cf4eaaaed31'}>,\n",
-       "  <Document: {'content': \"Martin describes Rickon as favoring his mother in appearance. He is naturally aggressive, strong-willed, and violent: traits reflected in his direwolf companion 'Shaggydog'.\\n\\n== Overview ==\\nRickon Stark is not a point of view character in the novels and is mostly a background character.  His actions are witnessed and interpreted through the eyes of his older brother Bran.\\n\\n== Storylines ==\\nCoat of arms of House Stark\\n\\n=== ''A Game of Thrones'' ===\\nIn ''A Game of Thrones'' (1996), Ned Stark departs Winterfell with his daughters to serve King Robert Baratheon in King's Landing, while his wife Catelyn remains behind with their sons, grief-stricken over their comatose son Bran. An overwhelmed young Rickon can only follow around his oldest brother Robb, crying. Upset when Robb prepares to leave himself, Rickon hides in the crypts of Winterfell. Shortly after Ned's death in King's Landing, both Bran and Rickon share a vision of their father's spirit in the crypts.\\n\\n=== ''A Clash of Kings'' ===\\nFollowing Ned's death, Rickon is largely unsupervised as Robb and Catelyn leave for war and his sisters are held captive at King's Landing in 1998's ''A Clash of Kings''. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '3e357fe290de771429f9d5717a908f13'}>,\n",
-       "  <Document: {'content': \"=== ''A Clash of Kings'' ===\\nFollowing Ned's death, Rickon is largely unsupervised as Robb and Catelyn leave for war and his sisters are held captive at King's Landing in 1998's ''A Clash of Kings''. Alone with his crippled brother Bran, Rickon develops an unruly, often violent temper. His fear and rage are reflected in Shaggydog, who turns largely feral and attacks several people before being restrained. Several times, Bran's direwolf Summer is forced to fight Shaggydog into submission. Bran and Rickon become Theon Greyjoy's hostages when Theon takes Winterfell by force. He later manages to escape and hide in the crypts below the castle with Bran, Hodor, the Reed siblings Meera and Jojen, and the wildling woman Osha. After Winterfell is sacked and burned by Ramsay Bolton, the group emerges to find Winterfell in ruins, and a mortally wounded Maester Luwin tells them that Bran and Rickon need to be separated and taken into hiding. Osha flees with Rickon in her care, and the others accompany Bran.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '7d713f4b60288d1ff6d2687d1f5fa38f'}>,\n",
-       "  <Document: {'content': \"After Winterfell is sacked and burned by Ramsay Bolton, the group emerges to find Winterfell in ruins, and a mortally wounded Maester Luwin tells them that Bran and Rickon need to be separated and taken into hiding. Osha flees with Rickon in her care, and the others accompany Bran.\\n\\n=== ''A Dance with Dragons'' ===\\nIn ''A Dance with Dragons'' (2011), most of Westeros believes Rickon is dead, but Lord Wyman Manderly hears from a survivor of the sack of Winterfell that Rickon is allegedly alive and has fled with a woman to the island of Skagos. Skagos is a large, northern island in the mouth of the Bay of Seals and is subject to the rule of House Stark. In return for pledging his loyalty to Stannis Baratheon instead of the Lannisters, Manderly tasks Ser Davos Seaworth with retrieving Rickon from Skagos so that they may reveal him to the Northern lords and inspire them to rally against Roose Bolton, Ramsay Bolton, and Walder Frey for betraying the Starks.\\n\\n=== Family tree of House Stark ===\\n\\n== TV adaptation ==\\nRickon Stark is played by Art Parkinson in the television adaption of the book series. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '9b3b3e615d615454bd8cccaac310296c'}>,\n",
-       "  <Document: {'content': '=== Family tree of House Stark ===\\n\\n== TV adaptation ==\\nRickon Stark is played by Art Parkinson in the television adaption of the book series. During an interview with Flicks and the City in 2014, Parkinson mentioned that he was not allowed by his parents to watch \\'\\'Game of Thrones\\'\\' due to its adult content, apart from a few scenes that were related to him. In a later interview published in the \\'\\'New Zealand Herald\\'\\' on 7 April 2015, Parkinson admitted that he was put off watching \\'\\'Game of Thrones\\'\\' after seeing a 3-year-old child getting his throat slit in one episode. In a later interview with IGN, he admitted that he had begun watching the series.\\n\\nIn an interview with IGN, Parkinson spoke about his return and death, saying: \"Whenever I was told that I was coming back for Season 6, before they sent me through the scripts and stuff, they sent me through a ring just to say, \\'Listen, so that you don\\'t get a shock whenever you read the scripts, just know that you die this season.\\'\"He continued, \"Whenever I came back, I was excited to come back, and the scenes all seemed pretty amazing. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '6417ac6be068cce83d9746d3c00cff57'}>,\n",
-       "  <Document: {'content': 'In an interview with IGN, Parkinson spoke about his return and death, saying: \"Whenever I was told that I was coming back for Season 6, before they sent me through the scripts and stuff, they sent me through a ring just to say, \\'Listen, so that you don\\'t get a shock whenever you read the scripts, just know that you die this season.\\'\"He continued, \"Whenever I came back, I was excited to come back, and the scenes all seemed pretty amazing. I was so happy to re-embrace the character.\"Parkinson has said that his fellow castmember Natalia Tena, who played Rickon\\'s travelling companion Osha, helped him with his acting. He also shared what he believes Rickon went through after the death of his father Ned Stark, saying \"After the death of his father, I think he became very independent and very angry, I think Rickon is a little bit more mature now. He\\'s been away for quite a long time at this point. I think he\\'s a bit more independent and stronger-willed.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '39635bb434c8c5b9e1edc64d76cf0718'}>,\n",
-       "  <Document: {'content': \"He's been away for quite a long time at this point. I think he's a bit more independent and stronger-willed.\\n\\nRemarking on the character's exit and death:\\n\\nIt was sad at first, but once I realized how I was going to die in it, then it was cool. It was a cool death, and it was always going to be a good death, so at the same time, I was pretty happy.\\n\\n=== Storylines ===\\nRickon Stark is Lord Eddard and Lady Catelyn's youngest child, naturally aggressive, and strong-willed. His black direwolf Shaggydog shares these qualities. When Theon Greyjoy captures Winterfell in Season 2, Rickon hides in the crypts with Bran, Hodor, and the Wildling woman Osha. After Theon can't seem to find where Rickon and Bran are, he has two farm boys killed and burned, to pass their charred bodies off as the Stark boys'. After Winterfell is sacked and burned, Rickon, Bran, Hodor, Osha, and the direwolves travel through the North. In Season 3 before they reach the Wall, Rickon, Osha, and Shaggydog split up from the rest of the group and head to Last Hearth, the seat house of the Umbers.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '63e818f17059fa51ef9681d607c63c6b'}>,\n",
-       "  <Document: {'content': 'In Season 3 before they reach the Wall, Rickon, Osha, and Shaggydog split up from the rest of the group and head to Last Hearth, the seat house of the Umbers.\\n\\n====Season 6====\\nFollowing Lord Greatjon Umber\\'s death, Rickon and Osha are betrayed by Greatjon\\'s heir Smalljon and handed over to the new Warden of the North, Ramsay Bolton, in order to secure an alliance with the Boltons against the wildlings Jon Snow has let through the Wall. To prove Rickon\\'s identity, Smalljon kills Shaggydog and presents his head to Ramsay. Ramsay kills Osha, locks Rickon in the dungeons, and sends a letter to Jon at Castle Black demanding the return of Sansa Stark (his wife) and threatening to kill Rickon if Jon does not comply. In retaliation, Jon gathers an army of Stark loyalists and marches on Winterfell, now occupied by House Bolton.\\n\\nAs the armies prepare for battle outside Winterfell, Ramsay brings out Rickon and orders him to run to Jon in one of his sadistic \"games\", shooting arrows at Rickon in order to lure Jon into the open. Jon charges out to save Rickon, but Rickon is shot through the heart and dies almost instantly. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'bb37903e542bdda6d6060751ea7e0918'}>,\n",
-       "  <Document: {'content': 'As the armies prepare for battle outside Winterfell, Ramsay brings out Rickon and orders him to run to Jon in one of his sadistic \"games\", shooting arrows at Rickon in order to lure Jon into the open. Jon charges out to save Rickon, but Rickon is shot through the heart and dies almost instantly. In the aftermath of the ensuing battle, Rickon\\'s body is recovered, and Jon buries him in the crypt, alongside Ned\\'s remains.\\n\\nBecause many Northern noble houses were plotting against the Boltons in the books, the show\\'s decision to have them betray Rickon led to fan theories that the direwolf head Smalljon Umber showed to Ramsay was fake and the Umbers would turn on the Boltons. Parkinson dispelled the rumours, confirming the direwolf head was intended to be real. Rickon\\'s death scene referenced his first appearance in the series pilot, where he laughs at Bran\\'s poor archery shot before handing off arrows to the archer and running to stand by the target.\\n\\n== References ==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '833e5c57a5237c075d7b8513f729436d'}>,\n",
-       "  <Document: {'content': '\\n\\nThe soundtrack album of the fourth season of HBO series \\'\\'Game of Thrones\\'\\', titled \\'\\'\\'\\'\\'Game of Thrones: Season 4\\'\\'\\'\\'\\' was released digitally on June 10, 2014, and on CD on July 1, 2014. Season 4 of \\'\\'Game of Thrones\\'\\' saw the Icelandic band Sigur Rós perform their rendition of \"The Rains of Castamere\" in a cameo appearance at King Joffrey\\'s wedding in the second episode, \"The Lion and the Rose\".\\n\\n==Reception==\\nThe soundtrack received mostly positive reviews from critics. The soundtrack was awarded a score of 4/5 by Heather Phares of AllMusic.\\n\\n==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* Sigur Rós – primary artist\\n* George R.R. Martin – lyricist\\n* D.B. Weiss – liner notes\\n\\n==Charts==\\n\\nChart (2014)\\n\\nPeak position\\n\\n==Awards and nominations==\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2014\\n\\n66th Primetime Creative Arts Emmy Awards\\n\\nOutstanding Music Composition For A Series (Original Dramatic Score)\\n\\nEpisode: \"The Mountain and the Viper\"\\n\\nHollywood Music in Media Awards\\n\\nBest Original Score - TV Show/Digital Streaming Series\\n\\n==References==', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '1e766074e8b99365ca04ae681660c66f'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''Souad Adel Faress''' (born 25 March 1948) is a Ghanaian stage, radio, television and film actress. She is best known for her portrayal of the solicitor Usha Gupta, in the long-running BBC Radio 4 serial ''The Archers'' and for her role as the High Priestess of the Dosh Khaleen in the sixth season of the HBO series ''Game of Thrones''.\\n\\n==Biography==\\nFaress was born in 1948 in Accra, Ghana, to Irish and Syrian parents. She studied drama at the Guildhall School of Music and Drama and lives in London. She has been exploring playwriting as a medium since before 2004. Faress completed a writing course at City Literary Institute adult education college. Her other skills include dancing, horse riding, cricket and yoga.\\n\\n===Career===\\nFaress appeared in two episodes of the BBC serial ''I, Claudius'' (1976) as a slave girl (shouting fire) and as a dancer who at Messalina's (second and bigamous) marriage party realises troops are coming to arrest them. In another early BBC appearance, she played the character Selma in the ''Blake's 7'' episode 'Horizon'. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '27ac53133aa6a56df7d08bb690f4f390'}>,\n",
-       "  <Document: {'content': '===Career===\\nFaress appeared in two episodes of the BBC serial \\'\\'I, Claudius\\'\\' (1976) as a slave girl (shouting fire) and as a dancer who at Messalina\\'s (second and bigamous) marriage party realises troops are coming to arrest them. In another early BBC appearance, she played the character Selma in the \\'\\'Blake\\'s 7\\'\\' episode \\'Horizon\\'. Since then, Faress has had substantial roles in films such as \\'\\'My Beautiful Laundrette\\'\\' (1985) and \\'\\'Sixth Happiness\\'\\' (1997), as well as much stage work around the UK.\\n\\nIn October 2001 she appeared briefly in \\'\\'Coronation Street\\'\\' as Dev Alahan\\'s mother Umila.\\n\\nSouad Faress also appeared as the Old Rani in \\'\\'The Sarah Jane Adventures\\'\\' serial \\'\\'The Mad Woman in the Attic\\'\\' in October 2009.\\n\\nIn October 2014, it was announced that Faress would perform in \\'\\'Hurried Steps\\'\\', Sharon Wood\\'s translation of \"Passi Affrettati by Dacia Maraini. The play\\'s narratives were mainly sourced from Amnesty International, the subject matter including rape, honour killings, sex trafficking and other issues of violence against women.\\n\\nBetween 2010 and 2011 she played Jay Faldren\\'s grandmother,  Maryam Shakiba, in \\'\\'Casualty\\'\\'.\\n\\nFaress appeared in an episode of popular nursing drama \\'\\'No Angels\\'\\' as lead character, Anji Mittel\\'s aunt Di. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'cbb88065bead8bfe14a6f2754b496c1e'}>,\n",
-       "  <Document: {'content': 'Between 2010 and 2011 she played Jay Faldren\\'s grandmother,  Maryam Shakiba, in \\'\\'Casualty\\'\\'.\\n\\nFaress appeared in an episode of popular nursing drama \\'\\'No Angels\\'\\' as lead character, Anji Mittel\\'s aunt Di. Faress appeared briefly as a hospital doctor in \"The Dead of Jericho\", the first episode of \\'\\'Inspector Morse\\'\\'.\\n\\nIn 2016 she joined the HBO series \\'\\'Game of Thrones\\'\\' in Season 6 as the High Priestess of the Dosh Khaleen. Faress will also be appearing in ITV drama \\'\\'Brief Encounters\\'\\', \"a loose adaptation of Ann Summers CEO Jacqueline Gold\\'s memoirs, \\'\\'Good Vibrations\\'\\'.\"==See also==\\n* List of Ghanaian actors and actresses\\n\\n==References==\\n\\n== External links ==\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'e03b86e6b2c36b851a4569ec18862d24'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Hardhome\\'\\'\\'\" is the eighth episode of the fifth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 48th overall. The episode was written by the series\\' creators David Benioff and D. B. Weiss, and directed by Miguel Sapochnik.\\n\\nThe episode features a climactic battle sequence at the episode\\'s eponymous Wildling village, a battle mentioned but not seen in the original source material. It has since been hailed by many reviewers and fans as one of the series\\' best episodes. Filming of the episode\\'s eponymous battle required nearly a month of shooting. In the United States, the episode achieved a viewership of 7.01 million in its initial broadcast. The episode earned \\'\\'Game of Thrones\\'\\' several nominations at the 67th Primetime Emmy Awards and was also Dinklage\\'s pick to support his nomination for Outstanding Supporting Actor.\\n\\nThis episode marks the only appearance for Birgitte Hjort Sørensen.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nSepta Unella visits Cersei in her cell and offers her water if she confesses her crimes, but Cersei refuses to confess. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '4d51426234fb91f256ed7b9f69481af9'}>,\n",
-       "  <Document: {'content': \"==Plot==\\n\\n===In King's Landing===\\nSepta Unella visits Cersei in her cell and offers her water if she confesses her crimes, but Cersei refuses to confess. Qyburn visits her and informs her that she is being charged for incest and Robert's murder and that Kevan returned from Casterly Rock and serves as Hand, at Pycelle's order.\\n\\n===In Braavos===\\nArya takes the identity of Lanna. Jaqen tells her that the family of one of the insurance salesman's clients hired the Faceless Men to kill him after he refused to pay when the client drowned at sea. He tells her to learn everything about the insurer and poison him.\\n\\n===At the Wall===\\nSam tells Olly that they have no chance of defeating the White Walkers without wildlings.\\n\\n===In Winterfell===\\nReek tells Sansa that there is no escape from Ramsay and admits that he didn't kill Bran and Rickon. Roose and Ramsay\\xa0plan for battle with Stannis' army. Ramsay asks Roose for 20 men.\\n\\n===In Meereen===\\nTyrion convinces Daenerys to allow him to advise her. Tyrion convinces her to spare Jorah's life and exile him. Tyrion tells her that she will not succeed in taking the Iron Throne without a powerful Westerosi house. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '3b8c966669c812b2cc6518098ade56cc'}>,\n",
-       "  <Document: {'content': \"Tyrion convinces her to spare Jorah's life and exile him. Tyrion tells her that she will not succeed in taking the Iron Throne without a powerful Westerosi house. Jorah returns to Yezzan and asks to fight in the fighting pits.\\n\\n===At Hardhome===\\nJon and Tormund arrive at Hardhome and meet the Lord of Bones, who is killed by Tormund after a tense standoff. Jon shares dragonglass with the wildlings and offers them to settle south of the Wall if they help the Night's Watch against the White Walkers. Jon and Tormund convince 5,000 wildlings to join them, but Thenns, led by Loboda, refuse. They are attacked by wights. A White Walker kills Loboda and forces Jon to flee without the dragonglass. Jon kills the White Walker with his Valyrian steel sword. Hardhome walls fall to the wights and Jon and his remaining allies are forced to flee. The Night King revives the dead as wights.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode was written by David Benioff and D. B. Weiss, the series' creators. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'fe75f2b47dbc61c2c6858865a789e20'}>,\n",
-       "  <Document: {'content': '==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode was written by David Benioff and D. B. Weiss, the series\\' creators. It contains content from George Martin\\'s novels \\'\\'A Feast for Crows\\'\\', chapters Cersei X and Cat of the Canals and \\'\\'A Dance with Dragons\\'\\', chapter The Ugly Little Girl.\\n\\nLike other episodes this season, \"Hardhome\" contained a large amount of original material that does not appear in Martin\\'s novels. This includes the battle scene in which the Wildlings and Night\\'s Watch are ambushed by the White Walkers and army of the undead, as well as the meeting of Daenerys Targaryen and Tyrion Lannister. According to Erik Kain of \\'\\'Forbes,\\'\\' \"We have now fully parted ways with the books. If the rest of Season 5 hadn\\'t convinced you that the show was forging its own path, this episode is the final nail in the coffin.\"===Casting===\\nBirgitte Hjort Sørensen was cast as Karsi, a wildling chieftain originally written as a male character. Zachary Baharov appeared as Loboda, a Thenn leader, and Ross O\\'Hennessy replaced Edward Dogliani as the Lord Of Bones in this episode. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '5ca2ea090b4d6172d332ed99267b66dd'}>,\n",
-       "  <Document: {'content': 'Zachary Baharov appeared as Loboda, a Thenn leader, and Ross O\\'Hennessy replaced Edward Dogliani as the Lord Of Bones in this episode. Ian Whyte, who had previously been cast as a White Walker, played the giant. Members of the metal band Mastodon acted as wildlings at Hardhome in a cameo appearance.\\n\\n===Filming===\\nMiguel Sapochnik served as director for the episode, his second episode for the series.\\n\"Hardhome\" was directed by Miguel Sapochnik. He joined the series as a director in the fifth season. He also directed the previous episode, \"The Gift\".\\nThe sequence at Hardhome was filmed at the Magheramorne quarry and took nearly a month to film.\\n\\n==Reception==\\n\\n===Ratings===\\n\"Hardhome\" was watched by an estimated 7.01 million American viewers during its first airing.\\n\\nWith Live+7 DVR viewing factored in, the episode had an overall rating of 9.94 million viewers, and a 5.1 in the 18–49 demographic. In the United Kingdom, the episode was viewed by 2.383 million viewers, making it the highest-rated broadcast that week. It also received 0.211 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode received universal acclaim from both critics and viewers, with critics calling it the best episode of the series. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'ce60ba055085051dd612a9fb26d3f61f'}>,\n",
-       "  <Document: {'content': '===Critical reception===\\nThe episode received universal acclaim from both critics and viewers, with critics calling it the best episode of the series. It received a 100% rating on the review aggregator Rotten Tomatoes from 35 reviews with an overall rating of 9.7 out of 10 and the critics\\' consensus reading \"Following several episodes of setup, \\'Hardhome\\' blends a sharp script with spectacular blockbuster action to offer viewers a powerful, game-changing payoff.\"\\'\\'The Atlantic\\'\\' named \"Hardhome\" one of the best television episodes of 2015. Erik Kain of \\'\\'Forbes\\'\\' called this \"one of the best, most exciting episodes I\\'ve seen in the entire show\\'s run, let alone this season,\" citing \"High action and a series of pretty crazy revelations.\"Matt Fowler of IGN gave the episode 10/10, labeling it a \"masterpiece\". He praised the Tyrion/Daenerys scenes but predominantly the final sequence, which he described as \"edge-of-your-seat exciting\" and also \"quite terrifying\". This made \"Hardhome\" the first episode in season 5 to receive a 10/10 rating from IGN. Matt Fowler also named it the best episode of the entire series. Both Myles McNutt and Erik Adams of \\'\\'The A.V. Club\\'\\' gave the episode the website\\'s highest grade, \"A\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '24cee4d10cd732fbaf40739eecb8b086'}>,\n",
-       "  <Document: {'content': 'Matt Fowler also named it the best episode of the entire series. Both Myles McNutt and Erik Adams of \\'\\'The A.V. Club\\'\\' gave the episode the website\\'s highest grade, \"A\". They called it \"a welcome reminder that the show is still unpredictable,\" with McNutt citing it as his favorite episode of the series thus far. Bridle Roman of SFX gave the episode a perfect five stars, and highlighted the character of Karsi, played by Birgitte Hjort Sørensen, as a great addition and \"her death hits hard\" even if \"we have only seen her for a few scenes\". The episode received praise even from some of its usual critics: Madeline Davies of \\'\\'Jezebel\\'\\' wrote, \"I feel like I haven\\'t said this in a long time, but last night\\'s \\'\\'Game of Thrones\\'\\' was ...cool?\"Davies cited the quality of the battle scene and unifying theme of hope in desperate situations as the episode\\'s key strengths, specifically that the abused and tormented Sansa learns that her brothers may still be alive and that though Jon faces \"the seemingly impossible task of defeating a constantly growing army of White Walkers, he at least possesses a sword that can kill them.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '3cbe777f9a780623dba6e75bd070c6a6'}>,\n",
-       "  <Document: {'content': 'Davies cited the quality of the battle scene and unifying theme of hope in desperate situations as the episode\\'s key strengths, specifically that the abused and tormented Sansa learns that her brothers may still be alive and that though Jon faces \"the seemingly impossible task of defeating a constantly growing army of White Walkers, he at least possesses a sword that can kill them.\"Kirsten Acuna of \\'\\'Business Insider\\'\\' reports that this is the single most popular episode to date as rated by fans, noting that the \"tremendous reaction is in complete juxtaposition with reactions to an episode that aired two weeks ago,\" which received one of the series\\' lowest fan ratings. Acuna credits this response to the quality of the battle scene, which featured \"a fighting army of the walking dead which would easily give the AMC series of the same name a run for its money.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '1c80d431610a34a4e95df8ec76671350'}>,\n",
-       "  <Document: {'content': 'Acuna credits this response to the quality of the battle scene, which featured \"a fighting army of the walking dead which would easily give the AMC series of the same name a run for its money.\"===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Episode\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters\\n\\nOutstanding Color Grading\\n\\nJoe Finley\\n\\nOutstanding Editing\\n\\nTim Porter\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\nIGN People\\'s Choice Awards\\n\\nBest TV Episode\\n\\n2016\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley\\n\\nShorty Awards\\n\\nGIF of The Year\\n\\nCome At Me Bro\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner\\n\\nAmerican Cinema Editors Awards\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D.B. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '4dea0e403b251f50e5e4d9c9550f28da'}>,\n",
-       "  <Document: {'content': \"===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actor in a Drama Series\\n\\nPeter Dinklage as Tyrion Lannister\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nFabian Wagner\\n\\nOutstanding Production Design for a Fantasy Program\\n\\nDeborah Riley, Paul Ghirardani, Rob Cameron\\n\\nOutstanding Prosthetic Makeup for a Series\\n\\nJane Walker, Barrie Gower, and Sarah Gower\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nTim Porter\\n\\nOutstanding Sound Editing for a Series\\n\\nTim Kimmel, Paula Fairfield, Bradley C. Katona, Peter Bercovitch, David Klotz, Jeffrey Wilhoit, Dylan T. Wilhoit\\n\\nOutstanding Sound Mixing for a Series\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters\\n\\nBritish Society of Cinematographers\\n\\nBest Cinematography in a Television Drama\\n\\nFabian Wagner\\n\\nACO/BSC/GBCT Operators TV Drama Award\\n\\nDavid Morgan, Sean Savage, Ben Wilson, David Worley\\n\\nGold Derby TV Awards 2015\\n\\nBest Drama Episode\\n\\nHollywood Professional Alliance\\n\\nOutstanding Sound\\n\\nTim Kimmel, Paula Fairfield, Bradley Katona, Paul Bercovitch, Onnalee Blank, Mathew Waters\\n\\nOutstanding Color Grading\\n\\nJoe Finley\\n\\nOutstanding Editing\\n\\nTim Porter\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\nIGN People's Choice Awards\\n\\nBest TV Episode\\n\\n2016\\n\\nADG Excellence in Production Design Award\\n\\nOne-Hour Single Camera Fantasy Television Series\\n\\nDeborah Riley\\n\\nShorty Awards\\n\\nGIF of The Year\\n\\nCome At Me Bro\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Richard Dyer, Onnalee Blank, Mathew Waters, Brett Voss\\n\\nAmerican Society of Cinematographers\\n\\nOutstanding Achievement in Cinematography in Regular Series\\n\\nFabian Wagner\\n\\nAmerican Cinema Editors Awards\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nTim Porter\\n\\nGolden Reel Awards\\n\\nBest Sound Editing in Television, Short Form: FX/Foley\\n\\nTim Kimmel\\n\\nBest Sound Editing in Television, Short Form: Dialogue / ADR\\n\\nTim Kimmel\\n\\nBest Sound Editing in Television, Short Form: Music\\n\\nDavid Klotz\\n\\nUSC Scripter Award\\n\\nBest Adapted Screenplay\\n\\nDavid Benioff and D.B. Weiss\\n\\nVisual Effects Society Awards\\n\\nOutstanding Effects Simulations in an Episode, Commercial, or Real-Time Project\\n\\nDavid Ramos, Antonio Lado, Piotr Weiss, Félix Bergés\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nEduardo Díaz, Guillermo Orbe, Oscar Perea, Inmaculada Nadela\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '70c83a920eb0d48271be8288bcfb2813'}>,\n",
-       "  <Document: {'content': '\\n\\n\\'\\'\\'Robert Baratheon\\'\\'\\' is a fictional character in the \\'\\'A Song of Ice and Fire\\'\\' series of epic fantasy novels by American author George R. R. Martin, and its television adaptation \\'\\'Game of Thrones\\'\\'.\\n\\nIntroduced in 1996\\'s \\'\\'A Game of Thrones\\'\\', Robert Baratheon is the eldest son and heir of Lord Steffon Baratheon and Lady Cassana Estermont of Storm\\'s End, the lord paramount of the Stormlands, one of the Seven Kingdoms of Westeros, and brother to Stannis and Renly. After his betrothed Lyanna Stark was kidnapped by Rhaegar Targaryen, Robert, along with the aid of her brother and his close friend, Eddard Stark, and their foster guardian Jon Arryn, initiated a bloody rebellion against the \"Mad King\" Aerys II Targaryen.  After crushing the Targaryen dynasty and winning the war, Robert took the Iron Throne and established the Baratheon dynasty as its first king.  However, due to Lyanna\\'s untimely death during the war, Robert instead married Tywin Lannister\\'s daughter Cersei to ensure political stability. Although Robert\\'s rule has been relatively peaceful, he proves to be an ineffective ruler and is unhappy in both his marriage to Cersei and his responsibilities as king, instead living a life of infidelity and wanton excess. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '2cbf41381f70f1d7efdc0dfbd28d0f29'}>,\n",
-       "  <Document: {'content': \"Although Robert's rule has been relatively peaceful, he proves to be an ineffective ruler and is unhappy in both his marriage to Cersei and his responsibilities as king, instead living a life of infidelity and wanton excess. He has fathered many bastards, unaware that his three children by Cersei were in fact fathered by her twin brother Jaime Lannister as products of incest.\\n\\nAlthough Robert is killed in the first novel, the legacy of his rebellion and rule continues to have great impact on the contemporary events of Westeros. His death creates a power vacuum in which both of his brothers, Cersei's eldest son Joffrey, and several other claimants fight for control of the Seven Kingdoms, known as the War of Five Kings.\\n\\nRobert is portrayed by English actor Mark Addy in the HBO television adaptation.\\n\\n== Character ==\\nRobert Baratheon is not a point of view character in the novels, so his actions are mainly witnessed and interpreted through the eyes of Ned Stark.  He only appears in the first book of the series ''A Game of Thrones'', though is mentioned numerous times in the later books by characters such as Cersei Lannister.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'b1eb34a53afeed839b99465096738b14'}>,\n",
-       "  <Document: {'content': 'He only appears in the first book of the series \\'\\'A Game of Thrones\\'\\', though is mentioned numerous times in the later books by characters such as Cersei Lannister.\\n\\n===Background===\\nBefore he became king, Robert Baratheon was the ward of Jon Arryn and was raised at the Eyrie alongside Eddard Stark.  Fifteen years before the novels, Robert was betrothed to Ned\\'s younger sister Lyanna, with whom he was madly (and unrequitedly) in love.  After Lyanna disappeared with Rhaegar Targaryen, Robert led a rebellion to overthrow the Targaryen dynasty and later killed Rhaegar in single combat.  He then married Cersei Lannister to ensure House Lannister\\'s support for his rule, but remained attached to the memory of Lyanna, resulting in an estranged marriage with Cersei.\\n\\n===Personality and description===\\nRobert is in his mid-thirties when the events of the books begin.  He has the classical Baratheon look: black hair and bright blue eyes, and is a very tall man, with Eddard Stark estimating him to be six and a half feet.  As a young adult, Robert was handsome, clean-shaven and muscled \"like a maiden\\'s fantasy\".  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'ac0023bae046927508cda64dcc0763b3'}>,\n",
-       "  <Document: {'content': 'He has the classical Baratheon look: black hair and bright blue eyes, and is a very tall man, with Eddard Stark estimating him to be six and a half feet.  As a young adult, Robert was handsome, clean-shaven and muscled \"like a maiden\\'s fantasy\".  However, after he won the Iron Throne, Robert becomes very obese due to excessive feasting and drinking, gaining weight, and turns into an often red-faced man with dark circles underneath his eyes and appears half-drunk and sweating when walking, with a wild, thick, fierce beard that hides his double chin.\\n\\nIn his youth, Robert is as strong as a bull and fearless in battle with a powerful voice, wielding a large spiked iron war hammer too heavy for Eddard Stark to lift, and was a formidable warrior well loved by soldiers.  Though headstrong, rash and impatient, Robert can be merciful towards his enemies as long as they are honest and brave, and can inspire loyalty and friendship in even enemies via charisma alone.  Although deconditioned after becoming king due to weight gain and frequent drinking, Jaime Lannister still believes that Robert is stronger than him.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '8b5c20002ea65b8ecd8bb9c55f47c4e6'}>,\n",
-       "  <Document: {'content': \"Although deconditioned after becoming king due to weight gain and frequent drinking, Jaime Lannister still believes that Robert is stronger than him.\\n\\nRobert is a jovial man of huge appetites and knows to indulge in pleasures.  He is quite promiscuous, having fathered multiple bastard children (17 according to the prophecy by Lannisport fortuneteller Maggy the Frog) with whores or any women he encounters, and his lusts are the subject of ribald drinking songs throughout the realms.  As king, Robert is known to impose upon the hospitality (voluntarily or not) of his subjects, but at the same time also possesses a rather careless generosity.  A proud man, Robert rarely back down on words spoken in a drunken bravado.  As king, Robert is no longer used to someone disagreeing with him, which makes him vulnerable to manipulation by others. Robert loathes his responsibilities as king and frequently expresses his preference to winning the throne in battle rather than sitting on it. He has never truly loved his wife and is unaware that none of his three children with her are his, but instead Jaime Lannister's. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '89b34d7eaa2578dc0c832c7451b2142b'}>,\n",
-       "  <Document: {'content': 'He has never truly loved his wife and is unaware that none of his three children with her are his, but instead Jaime Lannister\\'s. Under his reign, the realm has been bankrupted, and Robert is deeply in debt to his wife\\'s family. Tyrion Lannister considers Robert to be \"a great blustering oaf\", while Varys describes him as a fool.  His queen wife Cersei Lannister considers him to be an ignorant, dumb, slow-witted, drunken brute who does not have the ruthless streak she believes a king requires.  According to Petyr Baelish, Robert is practised at closing his eyes to things he would rather not see.\\n\\n== Storylines ==\\nCoat of arms of House Baratheon\\n\\n====\\'\\'A Game of Thrones\\'\\'====\\n\\nKing Robert has come to Winterfell to appoint his close friend Eddard Stark the Hand of The King, after the untimely death of Jon Arryn. He was unaware that Cersei\\'s three children were in fact fathered by her twin brother Jaime.  More interested in food, drink, and tourneys than in governance, Robert has squandered the royal treasury, leaving the crown heavily in debt.  ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'b79edfdc15086a1830792ef0025d6dec'}>,\n",
-       "  <Document: {'content': \"More interested in food, drink, and tourneys than in governance, Robert has squandered the royal treasury, leaving the crown heavily in debt.  After a hunting accident orchestrated by Cersei, Robert is mortally wounded and appoints Eddard as the regent for his son Joffrey.  After Robert's death, Eddard loses the political struggle against the Lannisters and is later publicly executed by beheading, and the kingdoms plunge into civil war known as the War of the Five Kings (which encompasses the entirety of the following two books) in which Robert's two brothers Renly and Stannis Baratheon both declare themselves the rightful kings. Eddard's vengeful son Robb and later Balon Greyjoy also secede and declare kingships.\\n\\n====''A Clash of Kings''====\\n\\nRobert briefly appears in ''A Clash of Kings'' in a flashback sequence.\\n\\n=== Family tree of House Baratheon ===\\n\\n==TV adaptation==\\nRobert Baratheon is played by Mark Addy in the television adaption of the series of books. Addy's audition for the role was according to showrunners David Benioff and D. B. Weiss the best they saw, him being the easiest actor to cast for the show.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '49b3f9add838d9b1b53dc5bc3056a719'}>,\n",
-       "  <Document: {'content': \"Addy's audition for the role was according to showrunners David Benioff and D. B. Weiss the best they saw, him being the easiest actor to cast for the show.\\n\\n==== Season 1 ====\\nRobert's storyline is the same in the show as it is in the novels, however, his wife Cersei reveals that she had one stillborn son by him, which is not the case in the novels, where Cersei uses guile and trickery to prevent him from ever actually engaging in sexual intercourse with her while drunk.\\n\\n==== Season 2 ====\\nAfter his death, Robert's bastards are ordered to be killed by Joffrey Baratheon, Robert's supposed legal heir and the new king. Gendry is subsequently forced to flee the capital.\\n\\n==== Seasons 6 & 7 ====\\nIn seasons six and seven, Bran Stark's gift of vision as the Three-Eyed Raven revealed to him the truth of Rhaegar and Lyanna's relationship and states that 'Robert's Rebellion was built on a lie'.\\n\\n==== Season 8 ====\\nDaenerys Targaryen legitimizes Gendry, naming him Robert's lawful son and Lord of Storm's End.\\n\\n== References ==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'e3d5907057730199e4ff332e47f5890d'}>,\n",
-       "  <Document: {'content': \"\\nThe soundtrack album of the third season of HBO series ''Game of Thrones'', titled '''''Game of Thrones: Season 3''''', was released digitally on June 4, 2013, and on CD on July 2, 2013. The album was composed by Ramin Djawadi.\\n\\n==Reception==\\nThe soundtrack received positive reviews from critics.\\n\\n==Track listing==\\n\\n==Credits and personnel==\\nPersonnel adapted from the album liner notes.\\n\\n* David Benioff – liner notes\\n* Ramin Djawadi – composer, primary artist, producer\\n* The Hold Steady – band, primary artist\\n\\n* George R.R. Martin – lyricist\\n* Kerry Ingram – primary artist\\n* D.B. Weiss – liner notes\\n\\n==Charts==\\n\\nChart (2013)\\n\\nPeak position\\n\\n==Awards and nominations==\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2013\\n\\nASCAP Awards\\n\\nTop Television Series\\n\\nInternational Film Music Critics Association\\n\\nBest Original Score for a Television Series\\n\\n==References==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '6ade481abd50ec0ee6d19db25479ae30'}>,\n",
-       "  <Document: {'content': '\\n\"\\'\\'\\'Breaker of Chains\\'\\'\\'\" is the third episode of the fourth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 33rd overall. The episode was written by series co-creators David Benioff and D. B. Weiss, and directed by Alex Graves. It aired on April 20, 2014.\\n\\nThe episode received critical praise overall, but triggered a public controversy as many interpreted the sexual encounter between Jaime and Cersei Lannister as rape. It marks the final appearance of Jack Gleeson (Joffrey Baratheon).\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nSansa flees with Dontos while Cersei and Tywin order a search for Sansa. Dontos leads Sansa to a ship, led by Petyr, who kills Dontos and tells Sansa that necklace Dontos gave her was fake and part of the plan. In the Sept, Tywin speaks to Tommen about becoming the king. Cersei grieves for Joffrey. Jaime arrives and Cersei asks him to kill Tyrion, but he refuses. Tywin suspects that Oberyn had a hand in Joffrey\\'s murder and Oberyn accuses Tywin of ordering the Mountain to murder\\xa0Elia. Tywin offers to have the Mountain speak with Oberyn in exchange for Oberyn serving as one of the three judges in Tyrion\\'s trial. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '9354a7c969a062927d53c4357dda45d6'}>,\n",
-       "  <Document: {'content': \"Tywin offers to have the Mountain speak with Oberyn in exchange for Oberyn serving as one of the three judges in Tyrion's trial. Podrick visits Tyrion in his cell. Tyrion asks Podrick to bring Jaime.\\n\\n===In the North===\\nSam takes Gilly to Mole's Town for her safety. Tormund's wildling group raids a village and Styr forces a young boy, Olly, to run to Castle Black and tell the Night's Watch what they have done. Edd Tollett and Grenn return to Castle Black and inform them that Karl's group is living at Craster's Keep. Jon remembers his lie to Tormund about having 1,000 men guarding the Wall and says they must kill Karl's group before Tormund finds out the truth about the Night's Watch forces.\\n\\n===At Dragonstone===\\nStannis informs Davos that Joffrey is dead and chastises him for\\xa0releasing Gendry. Davos has Shireen write a message to the Iron Bank of Braavos in Stannis' name.\\n\\n===In the Riverlands===\\nSandor and Arya continue their journey to the Vale.\\n\\n===Outside Meereen===\\nDaenerys’ army arrive at Meereen. Jorah informs Daenerys that she must send a champion to fight the Champion of Meereen. Daario is selected and he kills the Champion. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'be4feee0d7b384cb006759ad1c1e23d9'}>,\n",
-       "  <Document: {'content': 'Jorah informs Daenerys that she must send a champion to fight the Champion of Meereen. Daario is selected and he kills the Champion. Daenerys tells slaves from Meereen that she intends to bring them freedom.\\n\\n==Production==\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\"Breaker of Chains\" was written by executive producers David Benioff and D. B. Weiss, based upon the source material, Martin\\'s \\'\\'A Storm of Swords\\'\\'. The controversial Jaime-Cersei scene was adapted from chapter 62 of \\'\\'A Storm of Swords\\'\\' (Jaime VII). Other content came from chapters Sansa V, Tyrion IX and Daenerys V.\\n\\n==Reception==\\n===Ratings===\\n\"Breaker of Chains\" was watched by an estimated 6.6 million people during the first hour. Another 1.6 million watched it on rerun. In the United Kingdom, the episode was viewed by 1.665 million viewers, making it the highest-rated broadcast that week. It also received 0.099 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode was well received by critics, with a 95% rating from 37 reviews on Rotten Tomatoes. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '2c4bc34fa1af0c75779b43c4bc07479'}>,\n",
-       "  <Document: {'content': 'It also received 0.099 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode was well received by critics, with a 95% rating from 37 reviews on Rotten Tomatoes. Matt Fowler of IGN wrote that the episode deals \"with the direct aftermath of King Joffrey\\'s violent demise, though it also feels like the most bereft of the first three episodes\" and that it \"ended with a big, grandiose Daenerys moment – though if one were to compare her launching canisters filled with broken shackles over the walls of Meereen to last year\\'s flambéing of Astapor and stealing off with an entire army (which came at the end of episode 4), this moment lacked \\'oomph.\\'\"====Rape scene====\\nThe episode attracted controversy for a scene where Jaime Lannister forces himself upon Cersei Lannister in the Great Sept of Baelor. In the episode, Cersei repeatedly tells Jaime \"no\", \"stop\", and \"it\\'s not right\" while attempting to push him away. In the source novel, however, Cersei initially rejects Jaime\\'s advances but changes her mind, saying \"yes\", though some readers interpret it as implying rape.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'f65de6f205501f58ed1f50fa68fc24f7'}>,\n",
-       "  <Document: {'content': 'In the source novel, however, Cersei initially rejects Jaime\\'s advances but changes her mind, saying \"yes\", though some readers interpret it as implying rape.\\n\\nAlan Sepinwall of HitFix stated that viewers interpreted the scene as a rape: \"Though there aren\\'t comments on these reviews at the moment, the reaction I\\'ve seen on Twitter, in emails and on other blogs suggests nobody is agreeing with director Alex Graves\\' interpretation of the scene and are viewing it as rape, plain and simple.\"Sandy Hingston of \\'\\'Philadelphia\\'\\' wrote that the scene had outraged many viewers, but had led others to make \"tentative attempts in comments sections to explain why maybe actually this wasn\\'t rape.\"Critics, including Erik Kain of \\'\\'Forbes\\'\\', Sonia Saraiya of \\'\\'The A.V. Club\\'\\', Megan Gibson of \\'\\'Time\\'\\', Amanda Marcotte of \\'\\'Slate\\'\\', and Alyssa Rosenberg of the \\'\\'Washington Post\\'\\', also viewed the scene as rape.\\n\\nThe episode\\'s director, Alex Graves, described the scene in an interview with \\'\\'The Hollywood Reporter\\'\\' as \"and then Jaime comes in and he rapes her\". In another interview with HitFix, Graves said \"it becomes consensual by the end, because anything for Jaime and Cersei ultimately results in a turn-on, especially a power struggle.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '486c66a1703efb7b6deb03b531d911b7'}>,\n",
-       "  <Document: {'content': 'In another interview with HitFix, Graves said \"it becomes consensual by the end, because anything for Jaime and Cersei ultimately results in a turn-on, especially a power struggle.\"In an interview with Vulture, Graves further elaborated: \"The consensual part of it was that she wraps her legs around him, and she\\'s holding on to the table, clearly not to escape but to get some grounding in what\\'s going on. And also, the other thing that I think is clear before they hit the ground is she starts to make out with him. ... before he rips her undergarment, she\\'s way into kissing him back.\"Graves stated that it was important for him and others involved with filming the scene to indicate to viewers that the sexual encounter was not completely one-sided, and that he hoped this aspect would not be overlooked. Noting that during filming, \"nobody really wanted to talk about what was going on between the two characters\", he described the scene as \"one of my favorite scenes I\\'ve ever done\".\\n\\nOthers involved with the series also commented on the scene. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '9a3245bd261de2b603c1ea407dfd02cf'}>,\n",
-       "  <Document: {'content': 'Noting that during filming, \"nobody really wanted to talk about what was going on between the two characters\", he described the scene as \"one of my favorite scenes I\\'ve ever done\".\\n\\nOthers involved with the series also commented on the scene. David Benioff, who co-wrote the episode and serves as showrunner with D. B. Weiss, described the interaction of the two siblings as \"a really kind of horrifying scene, because you see, obviously, Joffrey\\'s body right there, and you see that Cersei is resisting this. She\\'s saying no, and he\\'s forcing himself on her. So it was a really uncomfortable scene, and a tricky scene to shoot.\"George R.R. Martin, author of the novels from which the series is adapted, stated that the dynamic is different between Jaime and Cersei in the show because, as opposed to the books, \"Jaime has been back for weeks at the least, maybe longer.\"He stated that while the setting is the same, \"neither character is in the same place as in the books\" and that he surmises this \"may be why Dan Weiss & David Benioff played the sept out differently.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'bc0b2b5c16b3e2d432a5717087e24af2'}>,\n",
-       "  <Document: {'content': 'He stated that while the setting is the same, \"neither character is in the same place as in the books\" and that he surmises this \"may be why Dan Weiss & David Benioff played the sept out differently.\"Martin added that he never discussed the scene with them, and that the scene \"was always intended to be disturbing... but I regret if it has disturbed people for the wrong reasons.\"Nikolaj Coster-Waldau, who portrays Jaime, said in an interview that while many saw the scene as a brutal rape, \"that was obviously never intended. I understand that one can see it as that, but for us it was much more complex.\"Lena Headey, who portrays Cersei, declined to comment about whether she interpreted the sex as consensual, but said that \"it\\'s a very complicated moment for many reasons ... There was this need and it wasn\\'t right and yet it felt great and yet it wasn\\'t right and it played out the way it did. And I was really happy with the scene.\"To Saraiya, the series\\' choice to portray this scene as rape appeared to be an act of \"exploitation for shock value\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '2cde0197266754eacc01c3ad98aeaa75'}>,\n",
-       "  <Document: {'content': 'To Saraiya, the series\\' choice to portray this scene as rape appeared to be an act of \"exploitation for shock value\". Marcotte and Josh Wigler of MTV commented that the rape scene may have damaged Jaime\\'s character\\'s arc of redemption from his earlier crimes, and Marcotte wrote that it might never recover from the rape. Dustin Rowles of \\'\\'Salon\\'\\' noted that viewers who were previously able to sympathize with Jaime despite his earlier crimes of murder and incest now became angry at the series\\' creators \"for allowing a terrible person to do something more terrible than our minds will allow us to forgive.\"Andrew Romano of \\'\\'The Daily Beast\\'\\' opined that the scene \"wasn\\'t supposed to be a rape. It was supposed to look consensual. The filmmakers messed up.\"He cited the fact that \"neither the director of the scene nor the two actors who played it seem to think that Jaime raped Cersei—and the story itself is continuing to chug along as if the rape never happened and Jaime is still a character we\\'re supposed to root for.\"He suggested that viewers \"ignore the rape—at least from a narrative perspective\" because Benioff and Weiss had \"botched\" it. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '693b8260ec70f5f09187c18e099bc9fb'}>,\n",
-       "  <Document: {'content': 'He cited the fact that \"neither the director of the scene nor the two actors who played it seem to think that Jaime raped Cersei—and the story itself is continuing to chug along as if the rape never happened and Jaime is still a character we\\'re supposed to root for.\"He suggested that viewers \"ignore the rape—at least from a narrative perspective\" because Benioff and Weiss had \"botched\" it. In a broader context, Hingston credited the episode with furthering \"the furious debate over consent going on in our culture\". Laura Hudson of \\'\\'Wired\\'\\' described the scene and its appraisal by its director as \"one that encourages the most dangerous thinking about rape imaginable: that when a woman is held down on the ground, screaming for the man to stop, that deep down inside her she might still really want it.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '8ceed197475bf28e604c2e69110c63e8'}>,\n",
-       "  <Document: {'content': 'Laura Hudson of \\'\\'Wired\\'\\' described the scene and its appraisal by its director as \"one that encourages the most dangerous thinking about rape imaginable: that when a woman is held down on the ground, screaming for the man to stop, that deep down inside her she might still really want it.\"She considered Graves\\' lack of realization that he was filming a rape scene disturbing because, according to Hudson, his comments encouraged the thinking that a man\\'s persistence might \"\\'turn\\' a rape into something consensual\", and that it is a \"dysfunctional, dangerous way of looking at sex and consent, one that is based on the idea of forcing women to give it\".\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'eecca780997ea9dee149f3a1f458d167'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'A Man Without Honor\\'\\'\\'\" is the seventh episode of the second season of HBO\\'s medieval fantasy television series \\'\\'Game of Thrones\\'\\'.\\n\\nThe episode is written by series co-creators David Benioff and D. B. Weiss and directed, for the second time in this season, by David Nutter. It premiered on May 13, 2012.\\n\\nThe name of the episode comes from Catelyn Stark\\'s assessment of Ser Jaime Lannister: \"You are a man without honor,\" after he kills a member of his own family to attempt escape.\\n\\n==Plot==\\n===In King\\'s Landing===\\nSansa awakens from a nightmare to find she has had her first period, meaning she can bear Joffrey’s children. Shae tries to help conceal the blood, but discovers the Hound has seen the sheet. Cersei tells Sansa it will be impossible to love Joffrey.\\n\\nTyrion tells Cersei his doubts about Joffrey\\'s plan to repel Stannis’ fleet. Cersei confesses her belief that Joffrey is punishment for her incest with Jaime.\\n\\n===Beyond the Wall===\\nJon searches for his companions, and captive Ygritte tells him life would be better with the wildlings. She teases his sexual inexperience, but he rebuffs her advances. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '652e1b70d3e023bdb5db38b2fc7a7955'}>,\n",
-       "  <Document: {'content': '===Beyond the Wall===\\nJon searches for his companions, and captive Ygritte tells him life would be better with the wildlings. She teases his sexual inexperience, but he rebuffs her advances. Ygritte escapes, and Jon is unable to find her until she reveals he is surrounded by wildlings.\\n\\n===At Harrenhal===\\nTywin orders Ser Gregor \"The Mountain\" Clegane to find Lorch\\'s killer, believing the murder was an attempt on his own life. Dining with Arya, Tywin deduces she is highborn and advises her to hide her status more carefully.\\n\\n===In the Westerlands===\\nRobb hears Alton Lannister\\'s unfavorable reply to the peace terms brought to Cersei and, with his prisons full, orders Alton placed with Jaime. Talisa agrees to accompany Robb to the Crag for medical supplies.\\n\\nJaime kills Alton to draw the jailer, Torrhen Karstark, into the pen, strangling him before fleeing. Jaime is recaptured, and Lord Rickard Karstark demands his head for murdering his son, but Catelyn delays the execution until Robb’s return. Confronting Jaime, Catelyn calls him \"a man without honor\", and Jaime bitterly explains the contradictions he faced in his vows as a knight. He provokes Catelyn, who asks for Brienne\\'s sword.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '389adde09d94902f1826f078270568a0'}>,\n",
-       "  <Document: {'content': 'Confronting Jaime, Catelyn calls him \"a man without honor\", and Jaime bitterly explains the contradictions he faced in his vows as a knight. He provokes Catelyn, who asks for Brienne\\'s sword.\\n\\n===In Qarth===\\nDaenerys has difficulty trusting Xaro, who claims he did not take her dragons. Vowing to find them, Jorah confronts the shadowbinder Quaithe. At a meeting of the Thirteen, Pyat Pree tells Daenerys that he has her dragons in the “House of the Undying”. Xaro proclaims himself King of Qarth as Pree, having multiplied himself around the room, assassinates the remaining Thirteen, and Daenerys flees with Jorah and Kovarro.\\n\\n===At Winterfell===\\nRealizing Osha has fled with Bran, Rickon, and Hodor, Theon beats Lorren for challenging him and hunts for the fugitives against Maester Luwin’s wishes, following the trail of Bran and Rickon.\\n\\nReturning from the hunt, Theon reveals the charred remains of two children as a public warning. Luwin cries out in horror for Bran and Rickon, while Theon struggles to hide his guilt.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '20b4f7c6fe7ea26adcbc8e8e719897b3'}>,\n",
-       "  <Document: {'content': 'Luwin cries out in horror for Bran and Rickon, while Theon struggles to hide his guilt.\\n\\n== Production ==\\n\\n=== Writing ===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThe episode was written by producers David Benioff and D. B. Weiss, their fourth of six scripts for the second season. This episode adapts content from the second novel, \\'\\'A Clash of Kings\\'\\', chapters Theon IV, Sansa IV and parts of Catelyn V and Catelyn VII.\\n\\n== Reception ==\\n=== Ratings ===\\n\"A Man Without Honor\" was watched by 3.69 million American households in its initial telecast on HBO. In the United Kingdom, the episode was seen by 0.935 million viewers on Sky Atlantic, being the channel\\'s highest-rated broadcast that week.\\n\\n=== Critical reception ===\\nThe episode received positive reviews from critics. Review aggregator Rotten Tomatoes surveyed 12 reviews of the episode and judged 92% of them to be positive, with an average score of 8.85 out of 10. The website\\'s critical consensus reads, \"Quieter and more meditative than previous episodes, \\'A Man Without Honor\\' is bolstered by incredible performances and a welcome return to an unlikely but surprisingly satisfying onscreen duo.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '2442704f1f60cdc3404d499dcb062e44'}>,\n",
-       "  <Document: {'content': 'The website\\'s critical consensus reads, \"Quieter and more meditative than previous episodes, \\'A Man Without Honor\\' is bolstered by incredible performances and a welcome return to an unlikely but surprisingly satisfying onscreen duo.\"IGN\\'s Matt Fowler gave the episode a 9 out of 10, noting that \"While \\'\\'A Man Without Honor\\'\\' didn\\'t have as many big, striking scenes as some of the past few episodes, it made up for its lack of face-melting moments with some absolutely fantastic one-on-one scenes.\"The A.V. Club\\'s Emily VanDerWerff gave it an A-.\\n\\n== References ==\\n\\n== External links ==\\n\\n*  \"A Man Without Honor\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'e455fcecc23f8d83fc6ca2e23af5eb7b'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''''Thronecast''''' is a British television series on Sky Atlantic which is presented by Sue Perkins and Jamie East. It is an aftershow to ''Game of Thrones'', which also airs on the channel. Each episode is an hour in length and features interviews with members of the cast and crew, interaction with the audience and social media alongside analysis of the previous episode and a preview of the next episode of ''Game of Thrones''. It was the only official aftershow for the American series until HBO aired ''After the Thrones'' in 2016 – a show which was cancelled after only one series.\\n\\n''Thronecast'' was originally devised by Koink Productions Ltd for Sky Digital Entertainment. The series began airing online in April 2011 as a series of 10-minute podcasts presented by Geoff Lloyd to correspond with the first series of ''Game of Thrones''. Annabel Port joined Geoff for the second series, which corresponded with the broadcast of the second series of ''Game of Thrones''. For the third series, ''Thronecast'' became a 15-minute on-air show that was broadcast on Sky Atlantic following each episode of the third series of ''Game of Thrones'', and featured new guest presenter Grace Dent. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '795fd6d6964d9325b6ad7fc981cf98b0'}>,\n",
-       "  <Document: {'content': \"For the third series, ''Thronecast'' became a 15-minute on-air show that was broadcast on Sky Atlantic following each episode of the third series of ''Game of Thrones'', and featured new guest presenter Grace Dent. The fourth series was presented by Jamie East and Rachel Parris, and was 30 minutes long. The fifth series was presented by Sue Perkins and Jamie East and was 30 minutes long, except for the series finale which was an extended hour long special. Perkins and East continued to present the seventh and eighth series of the show which were all 60 minutes long with the exception of the final episode of the show, which was 70 minutes. Series 7 and 8 included pre-series episodes titled ''War Room'', and the eighth series also included a 90 minutes long gameshow titled ''Gameshow of Thrones'' and a 30-minute pre-finale episode titled ''Thronecast: The End is Coming''.\\n\\nEach series was also made available on Sky Go, On Demand (previously Sky Anytime and Sky Anytime+) and NOW TV. Series 1 to 4 of Thronecast were produced by Koink Productions Ltd for Sky Atlantic. Series 5 of Thronecast was produced by CPL Productions for Sky Atlantic.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '84eed040cfb7a35294d7e8f4dc0e7244'}>,\n",
-       "  <Document: {'content': \"Series 1 to 4 of Thronecast were produced by Koink Productions Ltd for Sky Atlantic. Series 5 of Thronecast was produced by CPL Productions for Sky Atlantic.  Series 6 and 7 of the show were produced by Sky's in-house production company, Sky Vision Productions. Series 8 of the show was produced by independent production company Znak & Co Ltd.\\n\\n==Episodes==\\n\\n===Series overview===\\n\\n===Series 1 (2011)===\\n\\n===Series 2 (2012)===\\nA special episode of Thronecast was filmed before the start of the second series, which featured interviews with Emilia Clarke (Daenerys Targaryen), Alfie Allen (Theon Greyjoy), Michelle Fairley (Catelyn Stark), Kit Harington (Jon Snow), Charles Dance (Tywin Lannister), Liam Cunningham (Davos Seaworth), and Nikolaj Coster-Waldau (Jaime Lannister).\\n\\n===Series 3 (2013)===\\n\\n===Series 4 (2014)===\\n\\n===Series 5 (2015)===\\nThe series finale was an extended one-hour episode and was viewed by 560,000 viewers, making it the second highest-rated broadcast that week, behind the actual episode. It also received 29,000 timeshifted viewers.\\n\\n===Series 6 (2016)===\\nThe series finale was an extended one-hour episode and was viewed by 587,000 viewers, making it the second highest-rated broadcast that week, behind the actual episode. It also received 45,000 timeshifted viewers.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'e96098c95c6c5136e35c8a03fda6a03a'}>,\n",
-       "  <Document: {'content': '===Series 6 (2016)===\\nThe series finale was an extended one-hour episode and was viewed by 587,000 viewers, making it the second highest-rated broadcast that week, behind the actual episode. It also received 45,000 timeshifted viewers.\\n\\n===Specials (2017)===\\n\\nA pre-series episode titled \"War Room\", filmed on location, was broadcast the week before the start of Series 7.\\n\\n===Series 7 (2017)===\\n\\n===Specials (2019)===\\n\\nTwo specials aired before Series 8: \"Gameshow of Thrones\" featuring a number of stars of the show with superfans, plus \"War Room\" featuring superfans Al Murray, Lauren Laverne and Jonathan Ross.\\n\\n===Series 8 (2019)===\\n\\nThe series finale includes a special 30-minute pre-show episode titled \"The End is Coming\", and a 70-minute post-show episode. These episodes incorporated an additional studio segment titled \"Littlefinger\\'s Establishment\", where Jamie East talked to stars of the show and superfans about their memories of working on the show.\\n\\nEpisode 3 of Series 8 was the most viewed episode of \\'\\'Thronecast\\'\\' according to BARB figures.\\n\\n==See also==\\n*\\'\\'After the Thrones\\'\\' – a similar talk show hosted by Andy Greenwald and Chris Ryan which also discusses episodes of \\'\\'Game of Thrones\\'\\'.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '6b7655e1da49844a355f46abd1fe9b23'}>,\n",
-       "  <Document: {'content': \"==See also==\\n*''After the Thrones'' – a similar talk show hosted by Andy Greenwald and Chris Ryan which also discusses episodes of ''Game of Thrones''.\\n*''Talking Dead'' – a similar talk show hosted by Chris Hardwick which discusses episodes of ''The Walking Dead'' and ''Fear the Walking Dead''.\\n*''Talking Bad'' – a similar talk show hosted by Hardwick which discussed episodes of ''Breaking Bad''s fifth series.\\n*''Talking Saul'' – a similar talk show hosted by Hardwick which discusses episodes of ''Better Call Saul''.\\n*''Talking Preacher'' – a similar talk show hosted by Hardwick which discusses episodes of ''Preacher''.\\n\\n== References ==\\n\\n==External links==\\n*\\n* Official Thronecast website at Sky Atlantic\\n* Official Game of Thrones website at Sky Atlantic\\n* Sue Perkins Twitter\\n* Jamie East\\n* CPL Productions\\n* Koink Productions Ltd\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'ce07875ca5bfe5b21ddf13d84045d57f'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Rains of Castamere\\'\\'\\'\" is the ninth and penultimate episode of the third season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 29th episode of the series. The episode was written by executive producers David Benioff and D. B. Weiss, and directed by David Nutter. It aired on .\\n\\nThe episode is centered on the wedding of Edmure Tully and Roslin Frey, one of the most memorable events of the book series, commonly called \"The Red Wedding\", during which Robb Stark and his banner-men are massacred. Other storylines include Bran Stark\\'s group\\'s having to separate, Jon Snow\\'s loyalties being tested, and Daenerys Targaryen\\'s plotting her invasion of the city of Yunkai. The title is a song belonging to the Lannister family, whose lyrics foreshadow the Red Wedding and which is played by the band at the wedding right before the slaughter begins.\\n\\nThis episode earned Benioff and Weiss a nomination for Primetime Emmy Award for Outstanding Writing for a Drama Series. This episode marks the final appearance of Richard Madden (Robb Stark), Oona Chaplin (Talisa Stark), and Michelle Fairley (Catelyn Stark).\\n\\n==Plot==\\n\\n===Beyond the Wall===\\nSam and Gilly continue their march south. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'fbba1d232a2731a95779b6bdc5b07e4b'}>,\n",
-       "  <Document: {'content': \"This episode marks the final appearance of Richard Madden (Robb Stark), Oona Chaplin (Talisa Stark), and Michelle Fairley (Catelyn Stark).\\n\\n==Plot==\\n\\n===Beyond the Wall===\\nSam and Gilly continue their march south. Sam tells Gilly he plans for them to cross the Wall using the entrance at the Nightfort, an abandoned castle along the Wall.\\n\\n===In the North===\\nBran and his group take shelter in an abandoned mill. They see a horse breeder captured by Jon's wildling group. Hodor, scared, begins yelling, which threatens to give away their location to the wildlings. Bran uses his warg abilities to enter Hodor's mind and knocks him out. Orell tells Jon to kill the old man to prove his loyalty, but Jon is unable to kill him and Ygritte kills him. Tormund realizes that Jon is not loyal to them and orders his men to kill Jon. Bran enters Summer's mind and helps Jon to defeat Tormund's men and Orell. Jon steals a horse and escapes. Bran asks Rickon to leave for Last Hearth, the home of the Umber family.\\n\\n===In Yunkai===\\nDaario, Jorah and Grey Worm enter the city and kill Yunkai's slave soldiers. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '5fabb59e866b5b5a964a510ad38a60e8'}>,\n",
-       "  <Document: {'content': \"Bran asks Rickon to leave for Last Hearth, the home of the Umber family.\\n\\n===In Yunkai===\\nDaario, Jorah and Grey Worm enter the city and kill Yunkai's slave soldiers. They tell Daenerys that she is now in control of the city.\\n\\n===At the Twins===\\nRobb's army arrive at the Twins. Robb apologizes to Walder and his daughters. Sandor, travels to the Twins with Arya, steals the cart of food. Edmure marries Roslin, Walder calls for the bedding ceremony and the couple are taken to their chamber. Talisa tells Robb that she wants to name their son Eddard. Freys begin playing a Lannister song The Rains of Castamere. Arya and Sandor arrive, but are turned away by the guards, but Arya sneaks in. Catelyn realizes that they have been betrayed by Roose Bolton. Frey orders his men to attack Stark men. Lothar kills Talisa. Arya witnesses Frey men kill Stark soldiers and Robb's direwolf, Grey Wind. She is saved by Sandor. Bolton kills Robb, Catelyn kills Walder's wife Joyeuse and Black Walder kills Catelyn.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'c84c1c0f6b1dd6c512753c3db007f32c'}>,\n",
-       "  <Document: {'content': 'Arya witnesses Frey men kill Stark soldiers and Robb\\'s direwolf, Grey Wind. She is saved by Sandor. Bolton kills Robb, Catelyn kills Walder\\'s wife Joyeuse and Black Walder kills Catelyn.\\n\\n==Production==\\n\\n===Writing===\\n\"The Rains of Castamere\" was written by executive producers David Benioff and D. B. Weiss, based on George R. R. Martin\\'s original work from his novel \\'\\'A Storm of Swords\\'\\'. The episode adapts content from chapters 41 to 43 and 50 to 53 (Bran III, Jon V, Daenerys IV, Catelyn VI, Arya X, Catelyn VII, and Arya XI).\\n\\nThe episode includes one of the most important plot turns of the series: the betrayal and assassination of the Stark forces during a marriage ceremony in what came to be known as the \"Red Wedding\". The event culminates in Roose Bolton delivering Jaime Lannister\\'s message from \"The Bear and the Maiden Fair\", before killing Robb. This tragic turn of events had a profound impact on Benioff and Weiss in their first read of the novels and it was the scene that convinced them to attempt to obtain the rights for a television series.\\nThe Red Wedding was inspired by the Black Dinner that took place in 1440 at Edinburgh Castle.\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '89301da14a1ac3bd973e3eef7ec1d133'}>,\n",
-       "  <Document: {'content': 'This tragic turn of events had a profound impact on Benioff and Weiss in their first read of the novels and it was the scene that convinced them to attempt to obtain the rights for a television series.\\nThe Red Wedding was inspired by the Black Dinner that took place in 1440 at Edinburgh Castle.\\nGeorge R. R. Martin conceived The Red Wedding during the earliest stages of the planning of his saga, when he was envisioning a trilogy with The Red Wedding as one of the climactic events at the end of the first of the three books. Martin was inspired by a couple of events in Scottish history. One of them was the 15th century historical event known as the \"Black Dinner\", where the Scottish king invited the chieftains of the powerful Clan Douglas to a feast at Edinburgh Castle. A black bull\\'s head, the symbol of death, was served as the last course of the dinner while a single drum was playing in the background, and the Douglases were murdered. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'e84878e7f82fcf249a6ce4e5e070e626'}>,\n",
-       "  <Document: {'content': 'A black bull\\'s head, the symbol of death, was served as the last course of the dinner while a single drum was playing in the background, and the Douglases were murdered. Another event from which the author drew inspiration was the 1692 Massacre of Glencoe, where Clan MacDonald hosted the Campbell Clan who killed thirty-eight of their hosts overnight.\\n\\nMartin has said The Red Wedding was the hardest thing he has ever written. He explained that he always tries to put himself in the skin of his characters when writing from their perspective, and develops bonds with them. He even felt attached to the minor characters killed during the massacre. It was so painful for him that he skipped the chapter and continued writing, and only when the rest of the book was finished, he \"forced himself\" to come back to the dreaded scene. In 2012, at ComicCon he even joked that \"he will visit a country with no television when the episode goes on air\".\\n\\nMartin also said he killed off Robb because he believed the audience would assume that the story was about Ned Stark\\'s heir avenging his death and wished to keep them guessing. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'fe788ce98d10d3027b5bf2364630115d'}>,\n",
-       "  <Document: {'content': 'Martin also said he killed off Robb because he believed the audience would assume that the story was about Ned Stark\\'s heir avenging his death and wished to keep them guessing. Richard Madden suggested Talisa — whose counterpart Jeyne Westerling was not killed in the books — died so Robb\\'s heir could not avenge his death.\\n\\n===Casting===\\nWill Champion, the drummer and backing vocalist of the band Coldplay, has a cameo appearance as one of the musicians who play at the wedding.\\n\\n==Reception==\\n\\n===Ratings===\\n\"The Rains of Castamere\" premiered to 5.22 million viewers and received a 2.8 ratings in adults 18–49. The second airing was viewed by 1.08 million people, bringing total viewership for the night to 6.30 million. In the United Kingdom, the episode was viewed by 1.013 million viewers, making it the highest-rated broadcast that week. It also received 0.112 million timeshift viewers.\\n\\n===Critical reception===\\nThe episode was widely praised by critics and cited as one of the best of the series. Rotten Tomatoes, a prominent review aggregator, surveyed 21 reviews of the installment and judged 100% of them to be positive with an average score of 9.9 out of 10. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '70b7759fa6448380e4886737d20e0af7'}>,\n",
-       "  <Document: {'content': 'Rotten Tomatoes, a prominent review aggregator, surveyed 21 reviews of the installment and judged 100% of them to be positive with an average score of 9.9 out of 10. The website\\'s critical consensus reads, \"The most unforgettable episode of Game of Thrones thus far, \\'The Rains of Castamere\\' (or as it shall forever be known, \\'The Red Wedding\\') packs a dramatic wallop that feels as exquisitely shocking as it does ultimately inevitable.\"The majority of the comments were directed at the massacre at the end of the episode, where praise was especially given to Michelle Fairley\\'s performance, leading to the disappointment of many critics when she was not nominated for the Primetime Emmy Award for Outstanding Supporting Actress in a Drama Series for the 65th Primetime Emmy Awards. IGN\\'s Matt Fowler gave the episode a perfect 10/10, calling it \"an exquisitely awful event that managed to out-do the unpredictable and horrifying death of Ned Stark back in Season 1\". Fowler also said he believed that the episode\\'s depiction of the Red Wedding was more powerful than its depiction in \\'\\'A Song of Ice and Fire\\'\\'.\\n\\nWriting for \\'\\'The A.V. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '53fde72fdbdb67914710af337636e2ab'}>,\n",
-       "  <Document: {'content': 'Fowler also said he believed that the episode\\'s depiction of the Red Wedding was more powerful than its depiction in \\'\\'A Song of Ice and Fire\\'\\'.\\n\\nWriting for \\'\\'The A.V. Club\\'\\', both David Sims and Emily VanDerWerff gave the episode an \"A\" grade. Sims (writing for people who have not read the novels) expressed shock at the deaths of several main characters, writing, \"I don’t think I’ve really processed what I just watched\". VanDerWerff, who reviews the episodes for people who have read the novels, wrote \"If the reader doesn’t terribly want to deal with the thought of the deaths of Catelyn and Robb, well, he or she can read that much more quickly. Or he or she can read that much more slowly if there’s a need to process the emotions more fully. On TV, you can\\'t really do that.\"Reviewing for \\'\\'Forbes\\'\\', Erik Kain called the episode \"one of the best episodes of HBO\\'s dark drama yet\", and noted \"there was a deeper sense of tragedy knowing Robb also lost his unborn child\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '4ed6e508e6ed59039413398caec3bac4'}>,\n",
-       "  <Document: {'content': 'Reviewing for \\'\\'Forbes\\'\\', Erik Kain called the episode \"one of the best episodes of HBO\\'s dark drama yet\", and noted \"there was a deeper sense of tragedy knowing Robb also lost his unborn child\". Sean Collins of the \\'\\'Rolling Stone\\'\\' also praised the episode, and commented on the unusual step the show took in ending one of its central conflicts. Sarah Hughes of \\'\\'The Guardian\\'\\' highlighted the decision to kill Talisa, writing that her \"heartbreaking end was unbearable\".\\n\\nFor its 65th anniversary, \\'\\'TV Guide\\'\\' picked \"The Rains of Castamere\" as the third best episode of the 21st century.\\n\\n===Viewer reception===\\nThe episode was also notable for the intense and emotional response it pulled from viewers, many of whom were unaware of what was about to transpire and had their reactions filmed by people who had read the book on which it was based. This led to George R. R. Martin giving his personal analysis of the reactions, which he stated were on par with the responses he received from readers of \\'\\'A Storm of Swords\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'c76bf9b76d7f84de2f9d679a9cdbea18'}>,\n",
-       "  <Document: {'content': 'This led to George R. R. Martin giving his personal analysis of the reactions, which he stated were on par with the responses he received from readers of \\'\\'A Storm of Swords\\'\\'. Madden said that he sobbed \"very loudly\" on an airplane flight right after filming the scene, and that he and Fairley were \"both in tears\" when they watched the episode.\\n\\n===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2013\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D.B. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '79777c23748161db647f515238696edc'}>,\n",
-       "  <Document: {'content': '===Awards and nominations===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2013\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Writing for a Drama Series\\n\\nDavid Benioff and D.B. Weiss\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Single-Camera Picture Editing for a Drama Series\\n\\nOral Ottey\\n\\nGold Derby TV Awards 2013\\n\\nBest Drama Episode\\n\\nIGN Awards\\n\\nBest TV Episode\\n\\nIGN People\\'s Choice Awards\\n\\n2014\\n\\nAmerican Cinema Editors\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nOral Norrie Ottey\\n\\nCinema Audio Society Awards\\n\\nOutstanding Achievement in Sound Mixing - Television Series – One Hour\\n\\nRonan Hill, Onnalee Blank, Mathew Waters, and Brett Voss\\n\\nDirectors Guild of America Award\\n\\nDramatic Series\\n\\nDavid Nutter\\n\\nGolden Reel Awards\\n\\nBest Sound Editing — Short Form Dialogue and ADR in Television\\n\\nJed Dodge\\n\\nBest Sound Editing — Short Form Music\\n\\nDavid Klotz\\n\\nBest Sound Editing — Short Form Sound Effects and Foley\\n\\nTim Kimmel\\n\\nHugo Awards\\n\\nBest Dramatic Presentation, Short Form\\n\\nDavid Benioff, David Nutter, and D. B. Weiss\\n\\n==References==\\n\\n==External links==\\n\\n*  \"The Rains of Castamere\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'e926d15ae3d4fe118317b735ca79e3de'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Red Woman\\'\\'\\'\" is the sixth season premiere episode of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 51st overall. The episode was written by series creators David Benioff and D. B. Weiss, and directed by Jeremy Podeswa.\\n\\nThe body of Jon Snow is discovered by Ser Davos Seaworth and loyal members of the Night\\'s Watch protect his lifeless body; Melisandre has begun to lose her faith in the Lord of Light; Sansa Stark and Theon Greyjoy flee Winterfell, Ellaria Sand and the Sand Snakes seize control of Dorne; Jaime Lannister returns to King\\'s Landing with the body of his dead daughter; and Daenerys Targaryen is taken prisoner by the khalasar of Khal Moro.\\n\\n\"The Red Woman\" was positively received by critics who found the episode to be a satisfactory launching point for the season, and praising the scenes involving Sansa and Brienne, as well as the closing reveal with Melisandre, although the Dorne storyline was criticized once again for feeling too abrupt and deviating greatly from the books. For filming of the episode\\'s closing reveal, the director used similar technique to the body double of Cersei Lannister in the previous episode. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '1bf93c7b6f62b21fae850873738b9165'}>,\n",
-       "  <Document: {'content': \"For filming of the episode's closing reveal, the director used similar technique to the body double of Cersei Lannister in the previous episode. The episode title is an allusion to the epithet used to describe the Red Priestess Melisandre. In the United States, the episode premiere achieved a viewership of 7.94 million in its initial broadcast, and a same-day total including the streaming services HBO Go and HBO Now of 10.7 million viewers, a record for the series.\\n\\n==Plot==\\n\\n===In the North===\\nAt Winterfell, Ramsay Bolton mourns the death of Myranda, but commands that her corpse be fed to the hounds. Roose Bolton warns him that, despite their victory, they could be facing the wrath of the Lannisters in the future, and as such, they must secure the loyalty of the North's other nobles. Roose blames Ramsay for losing Sansa Stark, who, as a child of Eddard Stark, could have been used as a figurehead for the North. Roose implies that he will disinherit Ramsay if he cannot retrieve her and if Roose's unborn child with Walda is a son.\\n\\nMeanwhile, Sansa and Theon Greyjoy flee through the nearby forests. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'f0afe745436da8557c3dc3f8e5eaad4d'}>,\n",
-       "  <Document: {'content': 'Roose implies that he will disinherit Ramsay if he cannot retrieve her and if Roose\\'s unborn child with Walda is a son.\\n\\nMeanwhile, Sansa and Theon Greyjoy flee through the nearby forests. Upon being cornered by a squad of Bolton soldiers, they are rescued by Brienne of Tarth and Podrick Payne, who kill the soldiers. Brienne again offers to pledge her loyalty to Sansa, who accepts.\\n\\n===In Dorne===\\nDoran Martell receives news of Myrcella Baratheon\\'s death and is aghast, but he and Areo Hotah are immediately murdered by Ellaria Sand and Tyene Sand, while the palace guards look on without intervening. Ellaria states that his people have become dissatisfied with Doran\\'s inaction against the Lannisters. As Doran dies, he pleads for his son Trystane\\'s life, but Ellaria vows that \"weak men\" will never rule Dorne again.\\n\\n===In King\\'s Landing===\\nJaime Lannister arrives in King\\'s Landing bearing Myrcella\\'s body, and returns to Cersei Lannister. Cersei recounts Maggy the Frog\\'s prophecy that all her children would die before her and she would be left with nothing. Jaime promises that they will have revenge against all who have wronged the Lannisters, heightening the importance of his and Cersei\\'s staying close.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'ee279e5240daf734ffe3e1cfed357479'}>,\n",
-       "  <Document: {'content': \"Jaime promises that they will have revenge against all who have wronged the Lannisters, heightening the importance of his and Cersei's staying close.\\n\\nAcross the city, Margaery Tyrell, who is still held captive by the Faith Militant, converses with the High Sparrow, but he refuses to give her information about her brother Loras.\\n\\nIn his ship, still docked in the harbor, Trystane is painting stones for Myrcella's funeral. Obara and Nymeria Sand board his ship and enter his cabin, stating their intent to kill him. Despite proclaiming that he does not want to fight his family, he attempts to defend himself from Nymeria but is stabbed through the back of the head by Obara.\\n\\n===In Meereen===\\nTyrion Lannister and Varys walk through the streets, which are largely empty because of Daenerys Targaryen' absence and fear of the Sons of the Harpy. Tyrion observes that Daenerys' enemies are growing, including former slaves and former masters. Varys promises that his spies will find the leader of the Sons of the Harpy. The two discover that someone has set fire to all of the ships in Meereen's harbor, and Tyrion realizes that Daenerys' forces will be unable to sail to Westeros.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '4563c705212d754d5e15932f5456f84d'}>,\n",
-       "  <Document: {'content': \"The two discover that someone has set fire to all of the ships in Meereen's harbor, and Tyrion realizes that Daenerys' forces will be unable to sail to Westeros.\\n\\n===In the Dothraki Sea===\\nDaario Naharis and Jorah Mormont continue to trace Daenerys' location. Jorah takes a moment to observe his worsening greyscale. They discover her ring in the middle of thousands of hoof-prints, deducing that she has been taken by the Dothraki.\\n\\nDaenerys is presented to Khal Moro, who has taken her prisoner. Moro initially wishes to rape her, but treats her with respect after Daenerys explains that she was Khal Drogo's wife. Daenerys asks Moro to escort her back to Meereen, but he refuses to let her leave, as widows of Khals must live out their lives in Vaes Dothrak, the Dothraki's sacred city.\\n\\n===In Braavos===\\nArya Stark, now blinded, is sent to beg on the streets. The Waif suddenly appears and forces Arya to duel her using a wooden staff. Arya fails because of her blindness, and the Waif promises that she will return the following day.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '35ededa05ad4782ad219f51965c41e16'}>,\n",
-       "  <Document: {'content': \"The Waif suddenly appears and forces Arya to duel her using a wooden staff. Arya fails because of her blindness, and the Waif promises that she will return the following day.\\n\\n===At the Wall===\\nFollowing the murder of Jon Snow, his direwolf Ghost begins to howl as the sun rises above Castle Black, attracting the attention of Davos Seaworth, Eddison Tollett, and a few other loyalists. They release Ghost from his nearby pen and take Jon's body into a store-room. Melisandre briefly visits and is troubled, as she had experienced a vision in the flames of Jon fighting at Winterfell. The loyalists become fearful and also lock themselves in from the mutineers, with the exception of Edd, who sneaks out of Castle Black to get assistance from the Wildlings against the mutineers.\\n\\nSer Alliser Thorne convenes the other black brothers and takes responsibility for Jon's murder. Several black brothers accuse Thorne and the other officers of treason, but Thorne wins them over by citing Jon's actions and stating that Jon would have destroyed the Night's Watch by letting in the Wildlings. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '54a70562b07691abd4865bc67c96e6b4'}>,\n",
-       "  <Document: {'content': 'Several black brothers accuse Thorne and the other officers of treason, but Thorne wins them over by citing Jon\\'s actions and stating that Jon would have destroyed the Night\\'s Watch by letting in the Wildlings. Thorne and the other mutineers then surround the store-room and promise Jon\\'s friends amnesty if they surrender, but threaten to attack if they don\\'t open the door by nightfall.\\n\\nElsewhere, Melisandre, who has been sullen following Stannis\\'s defeat and Jon\\'s death, goes to sleep in her bedroom. As she undresses, she removes her ruby necklace, which proves to be enchanted, as she is revealed to have a physical body many decades older than she normally appears.\\n\\n==Production==\\n\\n===Writing===\\nSeries co-creators David Benioff and D. B. Weiss wrote the episode.\\n\"The Red Woman\" was written by the series\\' creators David Benioff and D. B. Weiss. Some elements in the episode are based on the sixth novel in the \\'\\'A Song of Ice and Fire\\'\\' series, \\'\\'The Winds of Winter\\'\\', which author George R. R. Martin had hoped to have completed before the sixth season began airing. It also contains elements from the chapters \"The Sacrifice\" and \"The Blind Girl\" from \\'\\'A Dance with Dragons\\'\\'. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '54536e8ea2f527248d86d324d813bc09'}>,\n",
-       "  <Document: {'content': 'Some elements in the episode are based on the sixth novel in the \\'\\'A Song of Ice and Fire\\'\\' series, \\'\\'The Winds of Winter\\'\\', which author George R. R. Martin had hoped to have completed before the sixth season began airing. It also contains elements from the chapters \"The Sacrifice\" and \"The Blind Girl\" from \\'\\'A Dance with Dragons\\'\\'. With this episode, Jonathan Pryce (High Sparrow) is promoted to series regular. The episode has the introduction of new recurring cast member Joe Naufahu, who plays Khal Moro. It was the first episode followed by \\'\\'After the Thrones\\'\\', HBO\\'s after-show hosted by Andy Greenwald and Chris Ryan. The episode had a premiere at the TCL Chinese Theatre two weeks before the premier of the episode on HBO. There was a lot of speculation prior to the episode regarding whether the character Jon Snow would remain dead or return to life. Fans speculated that Melisandre would be the one to bring back Snow to life, after and before the title of the first episode was revealed to be \"The Red Woman\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'ffd82d20b15a41aaefc7ec2a2df0396e'}>,\n",
-       "  <Document: {'content': 'Fans speculated that Melisandre would be the one to bring back Snow to life, after and before the title of the first episode was revealed to be \"The Red Woman\".\\n\\nLiam Cunningham, who portrays Davos Seaworth, spoke about the writing of the episode following its airing, and revealed how he reacted to the scene, saying \"It was initially shocking. You know what I thought was my favorite bit, and I said it to David and Dan, it was at that moment when the reveal comes and you kind of go, \\'Oh my God,\\' it puts things into context with Melisandre. It doesn\\'t explain or blah, blah, blah. … It\\'s confirmed like, she\\'s a witch, but there was such a touch of humanity. When she went to the bed and got into the bed and covered herself up… it was like a really striking moment in this weird madness of humanity.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '91d8c93014637c650d33cebd1b8efad'}>,\n",
-       "  <Document: {'content': 'When she went to the bed and got into the bed and covered herself up… it was like a really striking moment in this weird madness of humanity.\"John Bradley, who portrays Samwell Tarly, also spoke about the scene, saying \"And what I like about that, and seeing her like that is you know then that her sexuality over the course of the last few seasons, we\\'ve seen her use as such a tool -- that\\'s all very deliberate. She presents herself in this way as this beautiful woman because she knows the effect that that can have on people. She knows the effect that that has on Stannis, and that can make men do unadvisable things. So the fact that she did that and she uses that power in that way to have this influence on people. That puts her into context as somebody who really does know what she\\'s doing.\"===Filming===\\nCarice van Houten portrays the titular character of the episode, the Red Woman Melisandre.\\n\"The Red Woman\" was directed by Jeremy Podeswa. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'fa0dfabb6976f49bb8cb77dc562fcc27'}>,\n",
-       "  <Document: {'content': '===Filming===\\nCarice van Houten portrays the titular character of the episode, the Red Woman Melisandre.\\n\"The Red Woman\" was directed by Jeremy Podeswa. Podeswa previously directed the fifth season episodes \"Kill the Boy\" and \"Unbowed, Unbent, Unbroken\", the latter of which received a Primetime Emmy Award nomination for Outstanding Directing for a Drama Series. The budget for the sixth season increased compared to the previous seasons as each episode had an average cost of over $10 million, totaling approximately $100 million for the full season, setting a new high for the series.\\n\\nFor the closing reveal involving Melisandre, the director of the episode stated that a similar technique to the body double of Cersei Lannister in \"Mother\\'s Mercy\" was used, with Carice van Houten wearing prosthetic makeup for the face that was then transposed onto the real body of an old woman. Podeswa stated, \"The idea is there\\'s an indefinite indeterminate quality that she could be ancient. We were limited by choosing to use a real person rather than a complete  creation. Because what does a 400-year-old person look like? We don\\'t know. So if you try to create that, then you\\'re creating something that looks beyond our known reality. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '84c02b376e133b1484570aae185a39d0'}>,\n",
-       "  <Document: {'content': 'We don\\'t know. So if you try to create that, then you\\'re creating something that looks beyond our known reality. Here you feel like she\\'s very old without putting a number on it.\"==Reception==\\n\\n===Ratings===\\n\"The Red Woman\" was viewed by 7.94 million American households on its first viewing, which is slightly lower than number of viewers for the fifth-season premiere, 8.00 million, marking the first time in the show\\'s history a season premiere received less ratings than the premiere and finale of the season that came before it. HBO notes that two replays later in the night and early figures from HBO Go and HBO Now push the total to 10.7 million viewers, a same-day record for the show, meaning that the episode received an increase in viewerships. The episode also acquired a 4.0 rating in the 18–49 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was watched by 2.19 million viewers according to overnight ratings (2.289 million viewers over 7 days and 2.554 over 28 days), a record for the pay channel Sky Atlantic. The UK viewing figures reached an all-time high. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '599dc47db957734ee5c73e0841341057'}>,\n",
-       "  <Document: {'content': 'In the United Kingdom, the episode was watched by 2.19 million viewers according to overnight ratings (2.289 million viewers over 7 days and 2.554 over 28 days), a record for the pay channel Sky Atlantic. The UK viewing figures reached an all-time high. The 2am simulcast attracted 60,000 viewers. Similarly it broke the Australian record as the most watched show on pay TV, with 721,000 viewers in the overnight figures and 1.1 million in the final tally. Over a million had downloaded the episode within 12 hours of airing, with Australia having the most illegal downloads of the episode by a single nation.\\n\\n===Critical reception===\\nReviews for \"The Red Woman\" were very positive. The episode was praised for its humor, Brienne\\'s reunion with Sansa and Theon, and the revelation regarding Melisandre\\'s true age. Rotten Tomatoes assembled 38 reviews and calculated an approval percentage of 86% with an average rating of 7.6/10, and the consensus reading: \"A solid season opener, The Red Woman balanced its ongoing mysteries with a bit of humor and featured Sansa\\'s touching reunion with Brienne of Tarth.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '23ccfad374485e23f520622b4d6fce67'}>,\n",
-       "  <Document: {'content': 'Rotten Tomatoes assembled 38 reviews and calculated an approval percentage of 86% with an average rating of 7.6/10, and the consensus reading: \"A solid season opener, The Red Woman balanced its ongoing mysteries with a bit of humor and featured Sansa\\'s touching reunion with Brienne of Tarth.\"James Hunt of \\'\\'What Culture\\'\\' wrote in his review of the episode; \"It was a good return to the Seven Kingdoms (and beyond), one that was filled with tension throughout, and set about re-establishing the pieces on the board.\"Ellen Gray of \\'\\'Philadelphia Daily News\\'\\' noted in her review of the episode; \"First episodes are always difficult. So many people to check in on. Or kill. Or swear vengeance upon. Still, things are off to several promising starts.\"Mark W. Pleiss of \\'\\'PopMatters\\'\\' wrote in his review of the episode; \"The most recent episode of HBO\\'s Game of Thrones largely evaded the two major questions from the previous chapter, and instead lined up its sixth season to gravitate around the heroics and cunning of its female protagonists.\"Tim Surette of \\'\\'TV.com\\'\\' wrote that Melisandre is one of his favorite characters.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '3dfb1c6e514b11baacb8847997a174a6'}>,\n",
-       "  <Document: {'content': 'Mark W. Pleiss of \\'\\'PopMatters\\'\\' wrote in his review of the episode; \"The most recent episode of HBO\\'s Game of Thrones largely evaded the two major questions from the previous chapter, and instead lined up its sixth season to gravitate around the heroics and cunning of its female protagonists.\"Tim Surette of \\'\\'TV.com\\'\\' wrote that Melisandre is one of his favorite characters.\\n\\nIn isolation from the rest of the episode, many critics were baffled by the drastic changes to the Dorne storyline. For io9, Charlie Jane Anders called it \"the absolute worst\" part of the episode, and criticized the Dorne storyline in general for giving Doran Martell \"a grand total of ten minutes\\' screentime\" before suddenly killing him off, given that he is alive in the novels and his major subplot from the books - that he was simply \\'\\'feigning\\'\\' a desire for peace while planning to betray the Lannisters - was not introduced at all before his departure. Alan Sepinwall from HitFix also disliked the Dorne scenes and summarized, \"I\\'ll settle for not needing to audibly groan every time the story returns to Dorne, frankly.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '5bd205926a58a1becb38489f93f0bc8b'}>,\n",
-       "  <Document: {'content': 'Alan Sepinwall from HitFix also disliked the Dorne scenes and summarized, \"I\\'ll settle for not needing to audibly groan every time the story returns to Dorne, frankly.\"For Vulture, Nate Jones wrote an op-ed piece specifically criticizing the Dorne storyline in seasons five and six, culminating in the changes in the season six premiere, which it felt to be bizarre and illogical - purely relying on shock value when the characters\\' actions don\\'t make sense upon closer analysis. Jones was also critical of how it was altering the female characters in Dorne, saying that they were changed from the novels to be \"the kind of violent, scantily clad women that emerge when creators want to pay lip service to feminism, but don\\'t have the time or inclination to create actual three-dimensional female characters.\"==References==\\n\\n==External links==\\n\\n*  \"The Red Woman\" at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': 'c12e287a4510acb0bdc91bd7a78b4471'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Two Swords\\'\\'\\'\" is the fourth season premiere episode of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 31st overall. The episode was written by series co-creators and showrunners David Benioff and D. B. Weiss, and directed by Weiss. It premiered on April 6, 2014.\\n\\nThe title of the episode refers to the two swords Tywin Lannister forges from Ice, Ned Stark\\'s large Valyrian steel sword, in the opening sequence.\\n\\n==Plot==\\n\\n===In King\\'s Landing===\\nTywin melts down Stark\\'s greatsword Ice and forges it into two new swords. One is given to Jaime as the new Lord Commander of the Kingsguard. Tywin wants to send Jaime to rule Casterly Rock, but he refuses. Tyrion waits for the arrival of Prince Doran Martell, but his brother Oberyn comes in his stead. Oberyn tells Tyrion that he seeks vengeance for his sister Elia, who was married to Rhaeger and killed by Gregor. Dontos gives Sansa his mother\\'s necklace for saving his life. Shae tries to rebuff her romance with Tyrion, but is overheard by another Sansa\\'s handmaiden. Qyburn gives Jaime a gilded steel hand. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'b9996fe677a3249612d831a85fb23f64'}>,\n",
-       "  <Document: {'content': \"Shae tries to rebuff her romance with Tyrion, but is overheard by another Sansa's handmaiden. Qyburn gives Jaime a gilded steel hand. Brienne tries to convince Jaime to follow through on his vow to return the Stark girls to safety, but he argues the matter is now complicated following Catelyn's death. Brienne tells Margaery that she didn't kill Renly and vows to kill his killer, Stannis.\\n\\n===In the North===\\nTormund and Ygritte meet with a group of Thenns, led by Styr.\\n\\n===At Castle Black===\\nThorne, now Lord Commander and Janos Slynt, call for Jon to be executed for killing Qhorin, but Aemon releases him.\\n\\n===In Slaver's Bay===\\nDaenerys leads her army on the march to Meereen. They discover a dead slave girl pointing the route to Meereen. Jorah tells there is one dead slave child on every mile to Meereen and Daenerys declares that she will see every dead child's face, before burying them.\\n\\n===In the Riverlands===\\nSandor tells Arya that he will ransom her to Lysa. They stop at an inn, where they come across Gregor's men, including Polliver. Sandor kills others, while Arya reclaims Needle from Polliver and kills him.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'ca6e18cfdbe3452cedab8a37ce186501'}>,\n",
-       "  <Document: {'content': 'They stop at an inn, where they come across Gregor\\'s men, including Polliver. Sandor kills others, while Arya reclaims Needle from Polliver and kills him.\\n\\n==Production==\\n\"Two Swords\" was written and directed by producers D.B. Weiss and David Benioff.\\nThe episode was directed by the writing team itself, although to comply with the rules of the Directors Guild of America only Weiss is credited for directing. They previously co-directed the season three episode \"Walk of Punishment\", in which Benioff received sole directing credit.\\n\\n===Writing===\\n\"Two Swords\" was written by executive producers David Benioff and D. B. Weiss, based on George R. R. Martin\\'s original work from his novel \\'\\'A Clash of Kings\\'\\', chapters Sansa II and Sansa VIII, and \\'\\'A Storm of Swords\\'\\', chapters Jaime VII, Tyrion V, Tyrion IV, Jon VI, Jon IX, Daenerys V, and Arya XIII. Arya\\'s revenge against Lommy\\'s murderer using the same method of killing was depicted in \\'\\'The Winds of Winter\\'\\' chapter Mercy.\\n\\n===Casting===\\nWith this episode, Gwendoline Christie (Brienne of Tarth) and Kristofer Hivju (Tormund Giantsbane) are promoted to series regulars. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '2a5eb7ff0e3b5ed8a9c2ce7a66682e3b'}>,\n",
-       "  <Document: {'content': 'Arya\\'s revenge against Lommy\\'s murderer using the same method of killing was depicted in \\'\\'The Winds of Winter\\'\\' chapter Mercy.\\n\\n===Casting===\\nWith this episode, Gwendoline Christie (Brienne of Tarth) and Kristofer Hivju (Tormund Giantsbane) are promoted to series regulars. The episode has the introduction of new recurring cast members Pedro Pascal (Oberyn Martell) and Indira Varma (Ellaria Sand), while Michiel Huisman replaces Ed Skrein as the recurring character Daario Naharis. Owen Teale (Alliser Thorne), Dominic Carter (Janos Slynt) and Tony Way (Ser Dontos Hollard) make return appearances after an absence of several years (Teale in the first season, Carter and Way in the second).\\n\\n==Reception==\\n===Television ratings===\\n\"Two Swords\" broke the viewership record for \\'\\'Game of Thrones\\'\\', which was set by episode six the previous season. 6.64 million people watched the premiere airing, and when coupled with encore airings, that number rose to 8.2 million total viewers. This was at the time HBO\\'s highest ratings for any show since the finale episode of \\'\\'The Sopranos\\'\\', though it was shortly exceeded. In the UK on Sky Atlantic, \"Two Swords\" was top in the week with a viewership of 1.21 million.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '2a3ac16a894436824d22e6f27aedcfbf'}>,\n",
-       "  <Document: {'content': 'This was at the time HBO\\'s highest ratings for any show since the finale episode of \\'\\'The Sopranos\\'\\', though it was shortly exceeded. In the UK on Sky Atlantic, \"Two Swords\" was top in the week with a viewership of 1.21 million.\\n\\n===Critical reception===\\nThe season premiere obtained a 97% rating from 25 reviews with an average score of 9 out of 10 according to Rotten Tomatoes.\\n\\nReviewing it for IGN, Matt Fowler gave the episode a mark of 8.5 out of 10, and wrote that \"\\'Two Swords\\' was a solid GoT premiere, though it certainly didn\\'t hide the fact that the show, post-Red Wedding, is going to possibly meander more than people might enjoy or expect.\"Two reviews from \\'\\'The A.V. Club\\'\\' were written, with one intended for those who have read the novels and one for those who have not. Reviewing for the novel readers, Emily VanDerWerff gave the episode an \"A-\", while Erik Adams, who reviewed for the non-readers, rated the episode a \"B+\". Myles McNutt at \\'\\'Cultural Learnings\\'\\' also gave the episode a positive review, singling out the final scene with Arya and Sandor as the episode\\'s best sequence.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'c790a7eddfc25a921fdca7df135ebc49'}>,\n",
-       "  <Document: {'content': \"Myles McNutt at ''Cultural Learnings'' also gave the episode a positive review, singling out the final scene with Arya and Sandor as the episode's best sequence.\\n\\n===Accolades===\\nThe episode was nominated for Outstanding Cinematography for a Single-Camera Series at the 66th Primetime Creative Arts Emmy Awards.\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'a597fdcc43daf590f2ad5d33d4e75582'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''The High Sparrow''' is a fictional character in the ''A Song of Ice and Fire'' series of high fantasy novels by American author George R. R. Martin and its television adaptation ''Game of Thrones''.\\n\\nThe High Sparrow first appears in ''A Feast for Crows'' (2005) and subsequently appeared in Martin's ''A Dance with Dragons'' (2011). He is the de facto leader of the protest 'sparrow' movement that arises from the Faith of the Seven as a result of the carnage inflicted by the War of Five Kings. Although he appears to be humble and compassionate, his demeanor belies his shrewd and unrelenting fanaticism, which often borders on dangerous zealotry. He becomes of central importance to the shadow conflicts at court between Houses Lannister and Tyrell, and is open about his disgust with the corruption and impiety in the capital. His true name is unknown; his title was given mockingly by political opponents as a comparison to the formal leader of the Faith, the High Septon.\\n\\nThe character is portrayed by Welsh actor Jonathan Pryce in the HBO television adaptation.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'f12bbaf1e8a852af1a94dc85eb984ceb'}>,\n",
-       "  <Document: {'content': 'His true name is unknown; his title was given mockingly by political opponents as a comparison to the formal leader of the Faith, the High Septon.\\n\\nThe character is portrayed by Welsh actor Jonathan Pryce in the HBO television adaptation.\\n\\n==Character profile==\\nThe High Sparrow is a prominent member of the \"sparrows\", a religious movement formed during the War of the Five Kings, and a septon (priest) in the Faith of the Seven. He is a small, thin, hard-eyed, grey-haired old man with a heavily lined face.  He does not wear rich robes or any gold; instead, he wears a simple white wool tunic.\\n\\nThe High Sparrow is not a point of view character in the novels, so his actions are witnessed and interpreted through the eyes of other people, such as Cersei Lannister. The High Sparrow is mostly a background character in the novels.\\n\\n==Storylines==\\n\\n==== \\'\\'A Feast for Crows\\'\\' ====\\nIn the aftermath of the War of Five Kings, \"Sparrows\", religious folk seeking the protection of King Tommen Baratheon following a spate of atrocities committed against members of the Faith of the Seven, converge on King\\'s Landing. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '76e1abdb429353c0f0a0a414caad5d16'}>,\n",
-       "  <Document: {'content': '==Storylines==\\n\\n==== \\'\\'A Feast for Crows\\'\\' ====\\nIn the aftermath of the War of Five Kings, \"Sparrows\", religious folk seeking the protection of King Tommen Baratheon following a spate of atrocities committed against members of the Faith of the Seven, converge on King\\'s Landing. When the Most Devout convene to elect the new High Septon, the sparrows hijack the selection process and force the selection of their leader, who is dubbed the \"High Sparrow\" by the royal fool Moon Boy. Cersei Lannister permits the High Sparrow to re-establish the Faith Militant, the military arm of the Faith, in return for the Faith forgiving the Crown\\'s debt to it and blessing Tommen. The Faith Militant subsequently arrest Margaery Tyrell and several of her cousins when Cersei\\'s confidante Osney Kettleblack claims to having slept with the women. The High Sparrow is suspicious of Osney\\'s testimony (not unfairly, as Osney had been persuaded to perjure Margaery by Cersei to shore up her own power) and has him tortured until he confesses not only that Cersei arranged for his false testimony, but also that she ordered him to kill the previous High Septon. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'f6bb19dbc4be08093499769f2a526fc8'}>,\n",
-       "  <Document: {'content': \"The High Sparrow is suspicious of Osney's testimony (not unfairly, as Osney had been persuaded to perjure Margaery by Cersei to shore up her own power) and has him tortured until he confesses not only that Cersei arranged for his false testimony, but also that she ordered him to kill the previous High Septon. When Cersei visits the Great Sept of Baelor, the High Sparrow has her arrested and imprisoned.\\n\\n==== ''A Dance with Dragons'' ====\\nThe High Sparrow releases Margaery and her cousins into Randyll Tarly's custody due to the weakness of the evidence against them, but continues to hold Cersei prisoner. In order to obtain visitors, Cersei confesses to bedding the Kettleblack brothers as well as her cousin Lancel. The High Sparrow agrees to release her on condition of performing a walk of penance, naked, through the streets of King's Landing.\\n\\n==TV adaptation==\\nJonathan Pryce plays the role of The High Sparrow in the television series.\\nThe High Sparrow is played by the Welsh actor Jonathan Pryce in the television adaption of the series of books. Pryce admitted that one of the main reasons he took the role was because of how influential the character is plot-wise. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'c2fe8d0774a127789b54bcf1623b1bce'}>,\n",
-       "  <Document: {'content': 'Pryce admitted that one of the main reasons he took the role was because of how influential the character is plot-wise. While at first being quite sceptical about \"sword and sorcery\" shows, he later had a change of heart after his positive experiences on the \\'\\'Game of Thrones\\'\\' sets.\\n\\nRemarking on the character:\\n\\nI quite like the fact that people are going, \"Oh, he\\'s a horrible character!\"And I\\'m going, \"No! He\\'s one of the good people in Game of Thrones! He\\'s clearing out all the bad people!\\n\\n====Season 5====\\nThe High Sparrow arrives in King\\'s Landing after Tywin Lannister\\'s death to serve the poor, downtrodden, and infirm. He quickly amasses a large following, including Cersei Lannister\\'s cousin and former lover Lancel, who are dubbed the \"sparrows\". He comes to the attention of Cersei after Lancel and the other sparrows force the High Septon to walk naked through the streets as punishment for soliciting prostitutes. The High Septon demands the High Sparrow\\'s execution, but Cersei instead has the High Septon imprisoned and the High Sparrow appointed as his successor. To further gain his support, Cersei also reinstates the Faith Militant. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '2a83fa690ad001a2aa38c5c3ddd8d8bb'}>,\n",
-       "  <Document: {'content': \"The High Septon demands the High Sparrow's execution, but Cersei instead has the High Septon imprisoned and the High Sparrow appointed as his successor. To further gain his support, Cersei also reinstates the Faith Militant. Hoping to destabilise House Tyrell, Cersei arranges for the Faith Militant to arrest Loras Tyrell for being homosexual, with Margaery also arrested when she lies in an attempt to exonerate him. However, Lancel confesses to his affair with Cersei, as well as their role in Robert Baratheon's death. Cersei is subsequently arrested when she visits the Great Sept of Baelor. She eventually confesses to her adultery with Lancel to secure her release, and is ordered to walk naked through the streets of King's Landing as punishment.\\n\\n====Season 6====\\nThe High Sparrow is confronted by Jaime Lannister, who is furious at Cersei's treatment at his hands, though Jaime is forced to stand down when the Faith Militant surround him. When Tommen begins visiting the High Sparrow to negotiate visits with Margaery, he begins to fall under the High Sparrow's influence. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'd2f554cc0e499bd9c979cd10d8fac64a'}>,\n",
-       "  <Document: {'content': \"When Tommen begins visiting the High Sparrow to negotiate visits with Margaery, he begins to fall under the High Sparrow's influence. Jaime marches on the Great Sept with the Tyrell army to secure Margaery and Loras' release, but is thwarted when Tommen reveals that he has formed an alliance between the Crown and the Faith. The High Sparrow's thrall over Tommen leads him to abolish trial by combat, thwarting Cersei's hopes of being absolved by that means.\\n\\nTommen announces that Loras and Cersei's trials will be held on the same day, but on the day of the trial both Cersei and Tommen fail to appear. Margaery realises that Cersei has set a trap and tries to persuade the High Sparrow to evacuate the Great Sept, but he refuses and orders the Faith Militant to restrain the crowd. Moments later, wildfire primed by Cersei's subordinate Qyburn detonates beneath the Sept, with the High Sparrow instantly vaporised in the resulting explosion.\\n\\n==References==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '13d9ab540b962d1a4c1a0bbf7097bcd7'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''Oberyn Nymeros Martell''', nicknamed '''the Red Viper''', is a fictional character in the ''A Song of Ice and Fire'' series of epic fantasy novels by American author George R. R. Martin, and its television adaptation ''Game of Thrones''.\\n\\nIntroduced in 2000's ''A Storm of Swords'', Oberyn is the younger brother of Doran Martell, from the desert kingdom of Dorne. Unlike his sickly and pensive brother, Oberyn is notorious both for his dangerous and unpredictable nature as well as his affinity for poisons, for which he earned his nickname. He is subsequently sporadically mentioned in ''A Feast for Crows'' and ''A Dance with Dragons''. As his brother is sickly, he travels to King's Landing in order to claim the Dornish seat on the small council, as well as to seek vengeance for his sister Elia Martell's death at the hands of Ser Gregor Clegane, whom he suspects acted directly on orders from Tywin Lannister.\\n\\nOberyn is portrayed by Chilean-American actor Pedro Pascal in the HBO television adaptation.\\n\\n== Character description ==\\nOberyn Martell is the youngest brother of Prince Doran Martell, the ruler of the southern principality of Dorne.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '27336e1604b266d0b6f220231f3b2bfc'}>,\n",
-       "  <Document: {'content': '== Character description ==\\nOberyn Martell is the youngest brother of Prince Doran Martell, the ruler of the southern principality of Dorne.  He is a hot-headed, forceful, and lustful man with a quick wit and a barbed tongue.  He is a formidable fighter, and is nicknamed \"the Red Viper\" due to his preference for red clothing, as well as the rumor of him using poisoned weapons in duels.  He has travelled the world, served in the famous mercenary company Second Sons before founding a company of his own, and briefly studied poisons and dark arts as a novice at the Citadel, managing to obtain six links of the maester\\'s chain before quitting due to boredom.  He is bisexual and has eight illegitimate daughters, collectively known as the \"Sand Snakes\".  He was very close to his older sister Elia, and seeks revenge for her death.\\n\\nOberyn Martell is not a point of view character in the novels.  His actions are directly witnessed and interpreted through the eyes of Tyrion Lannister, and is later mentioned via the chapters of his niece Arianne Martell and Areo Hotah, the bodyguard of his brother Doran.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '119e7faee91ca9a4d42ca05047b3df7e'}>,\n",
-       "  <Document: {'content': \"His actions are directly witnessed and interpreted through the eyes of Tyrion Lannister, and is later mentioned via the chapters of his niece Arianne Martell and Areo Hotah, the bodyguard of his brother Doran.\\n\\n== Storylines ==\\nCoat of arms of House Martell\\n\\nIn ''A Storm of Swords'', Oberyn leads a Dornish envoy to King's Landing to claim the seat on the small council on Prince Doran Martell's behalf and obtain justice for his sister Elia Martell's murderer, as was agreed with the acting Hand of the King, Tyrion Lannister.  When Tyrion is accused of murdering King Joffrey Baratheon, Oberyn volunteers to champion for Tyrion in the trial by combat, so he can fight and seek revenge from the rival champion, Ser Gregor Clegane, who is Elia's murderer.  Though Oberyn manages to gain the upper hand in the duel and impale Clegane to the ground with his spear, Clegane manages to surprise him by foot sweeping him to the ground and bear-hugging him, then gouging his eyes, and finally killing him by repeatedly smashing his face with a mailed fist.  \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'd077e79078caa7479a4e6829bf6e1803'}>,\n",
-       "  <Document: {'content': \"Though Oberyn manages to gain the upper hand in the duel and impale Clegane to the ground with his spear, Clegane manages to surprise him by foot sweeping him to the ground and bear-hugging him, then gouging his eyes, and finally killing him by repeatedly smashing his face with a mailed fist.  However, it is later revealed that Oberyn has coated his spear in manticore venom to ensure that Clegane will die a slow and agonizing death.\\n\\nIn ''A Feast for Crows'', Doran Martell reveals to his daughter Arianne that Oberyn has actually been acting as Doran's enforcer to carry out his secret plan for House Lannister's downfall.  Three of Oberyn's bastard daughters, known as the Sand Snakes, are assigned missions to infiltrate the Iron Throne as sleeper agents.\\n\\n== TV adaptation ==\\nPedro Pascal plays the role of Oberyn Martell in the television series.\\n\\nOberyn Martell is played by Pedro Pascal in the television adaption of the series of books.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '76e15d51a05222b783add2cb9c36990a'}>,\n",
-       "  <Document: {'content': \"== TV adaptation ==\\nPedro Pascal plays the role of Oberyn Martell in the television series.\\n\\nOberyn Martell is played by Pedro Pascal in the television adaption of the series of books.\\n\\n=== Season 4 ===\\nOberyn arrives in King's Landing with his paramour, Ellaria Sand, to attend Joffrey's wedding in his brother's stead, and his meeting with Tyrion makes it clear that he has actually come to take revenge against the Lannisters for their role in the deaths of his sister, nephew and niece. At the wedding, Joffrey dies after being poisoned, and Tywin initially suspects Oberyn of having a hand in the murder since Oberyn has a past with poison chemistry, while Oberyn denies involvement and accuses Tywin of ordering Gregor Clegane to rape and murder Elia. The two reach a settlement when Tywin promises Oberyn a meeting with Clegane in exchange for Oberyn serving as one of the three judges at Tyrion's trial. At the trial, Oberyn implies that he is not convinced of Tyrion's guilt, and openly questions Cersei's testimony and asks Shae why Tyrion would tell her about all of his plans to murder Joffrey if he was the perpetrator.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'fa8bc255e3521f50335cc42e42612b2d'}>,\n",
-       "  <Document: {'content': \"At the trial, Oberyn implies that he is not convinced of Tyrion's guilt, and openly questions Cersei's testimony and asks Shae why Tyrion would tell her about all of his plans to murder Joffrey if he was the perpetrator.\\n\\nWhen Tyrion demands a trial by combat and Gregor Clegane is chosen as Cersei's champion, Oberyn volunteers to fight for Tyrion, proclaiming that he will exact his vengeance, starting with Ser Gregor. Martell valiantly fights Clegane, his superior speed making up for Clegane's size, and manages to wound him in the shoulder and the leg, flooring him. Refusing to kill him immediately, Oberyn furiously demands that Clegane admit to raping and killing Elia and her children, and that the order came from Tywin himself. Distracted for a moment, Oberyn is floored by Clegane, who – in a manner self-admittedly paraphrasing the murder of Elia – knocks out his teeth, straddles him and slowly gouges out his eyes, admitting to the rape and murder of Elia before crushing his skull. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'd842faacf49761e78b0b701c07c32d58'}>,\n",
-       "  <Document: {'content': \"Distracted for a moment, Oberyn is floored by Clegane, who – in a manner self-admittedly paraphrasing the murder of Elia – knocks out his teeth, straddles him and slowly gouges out his eyes, admitting to the rape and murder of Elia before crushing his skull. Tyrion is subsequently sentenced to death, but Oberyn's objective of vengeance was not in vain, since his spear is revealed to have been laced with the deadly venom of the manticore, which is slowly killing Clegane.\\n\\n=== Reception ===\\nThe Chilean-American actor Pedro Pascal has garnered positive reviews for his role as Oberyn Martell in the television show. For his performance, Pascal was awarded the Ewwy Award for Best Guest Actor – Drama, and was nominated for the NewNowNext Award for Best New Television Actor, and, as part of the ensemble cast, the Screen Actors Guild Award for Outstanding Performance by an Ensemble in a Drama Series.\\n\\n== Family tree of House Martell ==\\n\\n== References ==\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '2203592c9e3b7d957bcfa3cfa9f8dc6f'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'The Dance of Dragons\\'\\'\\'\" is the ninth and penultimate episode of the fifth season of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 49th overall. The episode was written by the series\\' creators David Benioff and D. B. Weiss based on material primarily found on George R.R. Martin\\'s novel \\'\\'A Dance with Dragons\\'\\', from which the title of the episode is derived. It was directed by David Nutter, who also directed the season finale.\\n\\nIn the episode, Jon Snow retreats from Hardhome to the Wall defeated, accompanied by the surviving wildlings, much to the chagrin of some of the Night\\'s Watch. In the North, Stannis Baratheon reluctantly allows Melisandre to sacrifice his daughter Shireen after Ramsay Bolton sabotages his resources, damaging his army\\'s morale in the process. In Braavos, Arya Stark detours from her mission given by Jaqen H\\'ghar to reconnoiter Meryn Trant instead. In Dorne, Jaime Lannister secures Myrcella Baratheon\\'s release from Doran Martell\\'s court against an indignant Ellaria Sand. In Meereen, the Sons of the Harpy attack the stadium of Daznak\\'s Pit in an attempt to assassinate Daenerys Targaryen, who is rescued by Jorah Mormont and her firstborn dragon, Drogon. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '7d00c5a896e7249aeca4816896a30216'}>,\n",
-       "  <Document: {'content': 'In Meereen, the Sons of the Harpy attack the stadium of Daznak\\'s Pit in an attempt to assassinate Daenerys Targaryen, who is rescued by Jorah Mormont and her firstborn dragon, Drogon. Leaving Tyrion Lannister and her retainers behind with awe, Daenerys rides the dragon for the first time and flees the city.\\n\\n\"The Dance of Dragons\" received a positive response from critics, who lauded its conclusion on Daznak\\'s Pit but was polarized by the characterization of Stannis Baratheon. In the United States, it received a viewership of 7.14 million in its initial broadcast. It won two Primetime Creative Arts Emmy Awards for Outstanding Single-Camera Picture Editing for a Drama Series and Outstanding Special Visual Effects, and was submitted by actress Emilia Clarke (who portrayed Daenerys Targaryen) for her nomination for a Primetime Emmy Award for Outstanding Supporting Actress in a Drama Series.\\n\\nThis episode marks the final appearance of Joel Fry (Hizdahr Zo Loraq).\\n\\n==Plot==\\n\\n===In Braavos===\\nArya, still in the disguise of an oyster vendor, continues her mission to poison and kill the Thin Man. However, as she is on the way to her target, she sees Mace Tyrell and Meryn Trant arrive to negotiate with the Iron Bank. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '62886a6205bf3bc12a697bd326a3653'}>,\n",
-       "  <Document: {'content': \"However, as she is on the way to her target, she sees Mace Tyrell and Meryn Trant arrive to negotiate with the Iron Bank. Arya, recognising Trant, ignores her mission and follows Mace and Trant into the city. She eventually tracks Trant to a brothel, where she learns that he is sexually attracted to pubescent girls. Upon returning to Jaqen, Arya lies that the Thin Man was not hungry and did not buy her oysters, and asks to try again the next day. Jaqen allows this, though it is unclear whether he believes Arya or not.\\n\\n===At the Wall===\\nJon and Tormund return to Castle Black, accompanied by the surviving Night's Watch and wildlings from Hardhome. Thorne, despite his disagreements with Jon, ultimately orders the gate to be opened to let them pass. Jon believes that his mission was a failure, though Sam points out that he still saved thousands of wildlings that would have been killed by the White Walkers. Jon is also aware that many of his Night's Watch brothers are unhappy with wildlings being let past the Wall, and Thorne warns him that his compassion will get them all killed.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': 'aebd78054bf3aafb9846ac8c84fd4aa4'}>,\n",
-       "  <Document: {'content': \"Jon is also aware that many of his Night's Watch brothers are unhappy with wildlings being let past the Wall, and Thorne warns him that his compassion will get them all killed.\\n\\n===In Dorne===\\nJaime is brought before Doran to explain his actions. Jaime reveals that he was sent to rescue Myrcella due to a threatening message sent to them from Dorne. Doran quickly realizes that Ellaria was behind the threat, and apologizes. In order to avoid an escalation of hostilities, Doran grants Jaime's freedom and Myrcella's return to King's Landing in exchange for Trystane assuming Oberyn's position on the Small Council. Trystane also allows Bronn to go free under the condition that he allow Hotah to strike him in the face in revenge for being struck by Bronn earlier.\\n\\nAfter the meeting, Doran privately confronts Ellaria and presents her with an ultimatum: pledge loyalty to him or be executed. Ellaria tearfully pledges her loyalty in front of the Sand Snakes, and Doran warns her that this is the last chance he will ever give her. Ellaria then meets with Jaime and makes amends with him, conceding that it is neither his nor Myrcella's fault that Oberyn was killed.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '8af45a0b6d2ffb8feae0915a2de35d87'}>,\n",
-       "  <Document: {'content': 'Ellaria then meets with Jaime and makes amends with him, conceding that it is neither his nor Myrcella\\'s fault that Oberyn was killed.\\n\\n===In the North===\\nRamsay and his group of 20 men infiltrate Stannis\\'s camp and burn his army\\'s supplies. With his supplies gone, Stannis realizes that his army will not survive the winter. Stannis refuses to retreat to Castle Black as Davos suggests, and points out that they lack the supplies to make it back regardless. He orders Davos to return to Castle Black to request more supplies and reinforcements in return for Stannis fully manning the Wall with troops when he takes the Iron Throne. When Davos leaves, Stannis reluctantly allows Melisandre to burn Shireen at the stake as a sacrifice, believing that the \"king\\'s blood\" in her veins will grant them the Lord of Light\\'s favor. As Shireen screams for her parents, Selyse, initially supportive of Stannis\\'s decision, has a change of heart and attempts to stop the ceremony, but is restrained while her child is burned alive in front of Stannis\\'s horrified troops.\\n\\n===In Meereen===\\nDaenerys, accompanied by Tyrion, Missandei, Daario and Hizdahr, oversees the beginning of the fighting season in Daznak\\'s Pit. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '43b2960f24952b707b2904e26877be0b'}>,\n",
-       "  <Document: {'content': \"===In Meereen===\\nDaenerys, accompanied by Tyrion, Missandei, Daario and Hizdahr, oversees the beginning of the fighting season in Daznak's Pit. One of the fighters reveals himself to be Jorah. After successfully defeating the other combatants, Jorah suddenly picks up a spear and throws it towards the royal stage, killing an assassin sneaking up on Daenerys. Immediately, the Sons of the Harpy emerge from the crowd and begin killing guards and spectators, including Hizdahr. Daenerys and her retinue attempt to escape, but the Sons of the Harpy seal the exits, leaving the survivors surrounded in the center of the fighting ring. Daenerys seemingly accepts her fate, when Drogon suddenly appears, setting ablaze and killing many Sons of the Harpy and causing many others to flee. While a group of the Sons attack Drogon by throwing spears at it, Daenerys climbs on to Drogon's back, ordering the dragon to fly. The Unsullied drive the Sons of the Harpy away, while Drogon takes to the sky with Daenerys, with her allies left awe-struck as they watch her fly away.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '7e5b37dd8ffba6cca2d7956ad9cd7764'}>,\n",
-       "  <Document: {'content': 'The Unsullied drive the Sons of the Harpy away, while Drogon takes to the sky with Daenerys, with her allies left awe-struck as they watch her fly away.\\n\\n==Production==\\n\\n===Writing===\\nThe episode was written by series co-creators David Benioff and D. B. Weiss.\\nThis episode was written for television by David Benioff and D. B. Weiss, the series\\' creators. It contains content from George Martin\\'s novel \\'\\'A Dance with Dragons\\'\\', chapter \"The Sacrifice\", \"The Watcher\", and \"Daenerys IX\". It also contains material from the preview chapter \"Mercy\" from the forthcoming \\'\\'The Winds of Winter\\'\\'.\\nLike other episodes this season, \"The Dance of Dragons\" contains original content not found in Martin\\'s novels, but it is in this case notable for containing content that had not \\'\\'yet\\'\\' appeared in the novels: Executive Producers David Benioff and D.B. Weiss confirmed that George R.R. Martin told them that Shireen would be burned to death as a sacrifice in an unpublished future novel. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '81c10c63e6c19bdbc6b00e59e46c5394'}>,\n",
-       "  <Document: {'content': 'Weiss confirmed that George R.R. Martin told them that Shireen would be burned to death as a sacrifice in an unpublished future novel. Benioff said: \"When George first told us about this, it was one of those moments where I remember looking at Dan, it was just, like, god it\\'s so, so horrible, and it\\'s so good in a story sense, because it all comes together.\"Weiss says he believes the decision to kill Shireen in this way is, \"entirely narratively justified,\" questioning why \"we\\'re all highly selective about which characters deserve our empathy. Stannis has been burning people alive for seemingly trivial reasons since season 2.\"===Filming===\\n\"The Dance of Dragons\" was directed by David Nutter, he also directed the subsequent episode, \"Mother\\'s Mercy\".\\n\\n==Reception==\\n\\n===Ratings===\\n\"The Dance of Dragons\" was watched by an estimated 7.14 million American viewers during its first airing. With Live+7 DVR viewing factored in, the episode had an overall rating of 9.92 million viewers, and a 5.2 in the 18–49 demographic. In the United Kingdom, the episode was viewed by 2.473 million viewers, making it the highest-rated broadcast that week. It also received 0.141 million timeshift viewers.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'cce8ad1b2d3263ac0ea725295db0e4a7'}>,\n",
-       "  <Document: {'content': 'In the United Kingdom, the episode was viewed by 2.473 million viewers, making it the highest-rated broadcast that week. It also received 0.141 million timeshift viewers.\\n\\n===Critical reception===\\n\"The Dance of Dragons\" received highly positive reviews, with many critics praising the episode\\'s conclusion in Daznak\\'s Pit. The showrunners\\'s decision to have Stannis sacrifice Shireen, however, polarized critics, with some praising it as strong character development and others feeling it betrayed Stannis\\'s character. The episode received a rating of 88% on the review aggregator Rotten Tomatoes from 33 reviews with an average rating of 8.9 out of 10 and the critics consensus reading \"Underscored by an especially heartwrenching moment and a spectacular display of power, \\'\\'The Dance of Dragons\\'\\' successfully delivers the shock and awe audiences have come to expect from the series\\' penultimate episodes.\"Matt Fowler of IGN heavily praised the episode, awarding it 9.3/10, an \"amazing\" score. He especially praised the episode\\'s final act, stating \"And just the spectacle of Dany climbing on top of Drogon, with care, and then soaring up and off into the sky was remarkable. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'ef23017bbc392789288c9cfa4f544015'}>,\n",
-       "  <Document: {'content': 'He especially praised the episode\\'s final act, stating \"And just the spectacle of Dany climbing on top of Drogon, with care, and then soaring up and off into the sky was remarkable. It\\'s funny that a huge moment for her as a Targaryen and the possible future leader of Westeros came right during one of her most bloody and spectacular failures as a Queen.\"He summarized his review by saying \"GoT delivered another exciting (and unsettling) episode as Stannis made a big decision & Daenerys attended a tournament\" and labelled Stannis\\'s decision a strong point of the episode. Charlotte Runcie of \\'\\'The Daily Telegraph\\'\\' also reviewed the episode favorably: \"Hurling Hollywood-level budgets behind a multi-season TV show paid rich rewards this week, from the enormous \\'\\'Gladiator\\'\\'-style sweep of the crowded coliseum arena to thrillingly choreographed fights to the death before a huge, baying audience.\"Runcie was also impressed with the Shireen sacrifice scene, calling it \"one of the most upsetting scenes\" in season 5.\\n\\nWriting for \\'\\'The A.V. Club\\'\\', both writers responded positively to the episode. Writing for people who have not read the novels, Brandon Nowalk awarded the episode an A-, praising Stannis\\'s sacrifice of Shireen. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '54b62276a62ffa006a108a806e4e98ae'}>,\n",
-       "  <Document: {'content': 'Writing for people who have not read the novels, Brandon Nowalk awarded the episode an A-, praising Stannis\\'s sacrifice of Shireen. He said \"Stannis burning his daughter Shireen at the stake is the hardest \\'\\'Game of Thrones\\'\\' has hit since the Red Wedding, only this violence is completely drained of excitement. It’s not a shock moment, and there’s no gross-out gore as in Oberyn’s death. Instead it’s a long, cold death march. What’s most impressive is the pacing, which is not the season’s strongest suit.\"He also praised the episode\\'s conclusion in Meereen by saying \"The close-up of Dany taking Missandei’s hand is so vivid I’m practically getting misty now\", and further stated \"The CGI could use some polish, but nothing short of Shireen can turn my smile upside down.\"Writing for people who have read the novels, Myles McNutt awarded the episode a B+, praising the Meereen story by saying \"It’s a powerful image, rendered as effectively as it could be given the budgetary limitations of the series. Watching Dany soar about the stadium on Drogon is an iconic image, but it’s not one that any other characters could really experience, which is meaningful in and of itself.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': '91fbbe7673222cb76f6bfc3b1944873e'}>,\n",
-       "  <Document: {'content': 'Watching Dany soar about the stadium on Drogon is an iconic image, but it’s not one that any other characters could really experience, which is meaningful in and of itself.\"Erik Kain of \\'\\'Forbes\\'\\' was more critical of the sacrifice scene. While he praised the episode overall as \"thrilling and tragic and intense,\" he called the sacrifice scene \"a horrible, no-good, very bad, infuriating way to ruin Stannis as a character\" and \"a monstrosity of a writing decision.\"He also said the scene was \"one of the most disturbing, baffling, and unnecessary departures from the books\" that the show has so far presented, though he noted that the sacrifice scene had actually been George R. R. Martin\\'s idea and that (with two installments of \\'\\'The Song of Ice and Fire\\'\\' still unpublished) it was unclear exactly how much Shireen\\'s death in the show would deviate from her story arc in the books.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'a010758b96d11a9067bcc9ba1d9827d6'}>,\n",
-       "  <Document: {'content': 'He also said the scene was \"one of the most disturbing, baffling, and unnecessary departures from the books\" that the show has so far presented, though he noted that the sacrifice scene had actually been George R. R. Martin\\'s idea and that (with two installments of \\'\\'The Song of Ice and Fire\\'\\' still unpublished) it was unclear exactly how much Shireen\\'s death in the show would deviate from her story arc in the books.\\n\\nFinding a middle ground, Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' agreed that the sacrifice scene was disturbing, but she found that David Nutter \"did a beautiful job\" directing it and believed that the scene wondrously developed Stannis as a character who is so fixated on \"his own chosenness\" that he will do anything to fulfill his claim to the throne. She also noted that the scene departed from the books so far, but would possibly be reconciled in one of the future installments.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'aece7a849275e9fd1c6ed4cec6932dc1'}>,\n",
-       "  <Document: {'content': 'She also noted that the scene departed from the books so far, but would possibly be reconciled in one of the future installments.\\n\\n===Accolades===\\n\\nYear\\n\\nAward\\n\\nCategory\\n\\nNominee(s)\\n\\nResult\\n\\n2015\\n\\nPrimetime Emmy Awards\\n\\nOutstanding Supporting Actress in a Drama Series\\n\\nEmilia Clarke as Daenerys Targaryen\\n\\nPrimetime Creative Arts Emmy Awards\\n\\nOutstanding Cinematography for a Single-Camera Series\\n\\nRob McLachlan\\n\\nOutstanding Costumes for a Fantasy Series\\n\\nMichele Clapton, Sheena Wichary, Nina Ayres, Alex Fordham\\n\\nOutstanding Single-Camera Picture Editing for a Drama series\\n\\nKatie Weiland\\n\\nOutstanding Special Visual Effects\\n\\nSteve Kullback, Joe Bauer, Adam Chazen, Jabbar Raisani, Eric Carney, Stuart Brisdon, Derek Spears, James Kinnings, Matthew Rouleau\\n\\nHollywood Professional Alliance\\n\\nOutstanding Visual Effects\\n\\nJoe Bauer, Steve Kullback, Derek Spears, Eric Carney, Jabbar Raisani\\n\\n2016\\n\\nAmerican Cinema Editors Awards 2016\\n\\nBest Edited One-Hour Series For Non-Commercial Television\\n\\nKatie Weiland\\n\\nVisual Effects Society Awards 2015\\n\\nOutstanding Visual Effects in a Photoreal Episode\\n\\nJoe Bauer, Steve Kullback, Eric Carney, Derek Spears, Stuart Brisdon for \"The Dance of Dragons\"\\n\\nOutstanding Animated Performance in an Episode, Commercial, or Real-Time Project\\n\\nJames Kinnings, Michael Holzl, Joseph Hoback, Matt Derksen for \"Dance of Dragons\" - Drogon Arena Rescue\\n\\nOutstanding Created Environment in an Episode, Commercial, or Real-Time Project\\n\\nRajeev B R., Loganathan Perumal, Ramesh Shankers, Anders Ericson for \"Drogon Arena\"\\n\\nOutstanding Compositing in a Photoreal Episode\\n\\nDan Breckwoldt, Martin Furman, Sophie Marfleet, Eric Andrusyszyn for \"Drogon Arena\"\\n\\nCanadian Society of Cinematographers\\n\\nTV Series Cinematography\\n\\nRobert McLachlan\\n\\n==References==\\n\\n==External links==\\n\\n*  at HBO.com\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '9d4549eb79823ee570a740a8ba8c39c7'}>,\n",
-       "  <Document: {'content': \"\\n\\n'''Sansa Stark''' is a fictional character created by American author George R. R. Martin. She is a prominent character in Martin's award-winning ''A Song of Ice and Fire'' series.\\n\\nIntroduced in ''A Game of Thrones'' (1996), Sansa is the elder daughter and second child of Lord Eddard Stark and his wife Lady Catelyn Stark. She subsequently appeared in the following three novels: ''A Clash of Kings'' (1998), ''A Storm of Swords'' (2000), and ''A Feast for Crows'' (2005). While absent from the fifth novel ''A Dance with Dragons'', as the books are separated geographically, Sansa is confirmed to return in the forthcoming next book in the series, ''The Winds of Winter''.\\n\\nIn HBO's adaptation of the series, ''Game of Thrones'', Sansa is portrayed by English actress Sophie Turner. The character has received critical acclaim, including praise as the 4th greatest character in the series by ''Rolling Stone''. She and the rest of the cast were nominated for Screen Actors Guild Awards for Outstanding Performance by an Ensemble in a Drama Series in 2012, 2014, 2015 and 2016. Furthermore, Turner received a nomination for the Primetime Emmy Award for Outstanding Supporting Actress in a Drama Series in 2019.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': '2f5bcc5a944e6d329f81a40ee7d4509d'}>,\n",
-       "  <Document: {'content': \"Furthermore, Turner received a nomination for the Primetime Emmy Award for Outstanding Supporting Actress in a Drama Series in 2019.\\n\\n==Character and appearances==\\nSansa Stark is the second child and elder daughter of Eddard Stark and Catelyn Stark. She was born and raised in Winterfell, until leaving with her father and sister at the beginning of the series. She was raised with a younger sister Arya Stark, two younger brothers Rickon Stark and Bran Stark, as well as an older brother Robb Stark, and an older illegitimate half-brother, Jon Snow.\\n\\nRaised as a lady, Sansa is traditionally feminine. Sansa's interests are music, poetry, and singing. She strives to become like the heroines of romantic tales by attempting to find a prince, knight, or gentleman to fall in love with. For a companion animal, she owned a direwolf named Lady. However, Lady was killed in place of Arya's direwolf, Nymeria, after Nymeria attacked the Crown Prince, Joffrey Baratheon, and later fled.\\n\\nSansa has been described as tall, slim, womanly, and beautiful, destined to be a lady or a queen. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': '4120a29e784c7b5d795f113d155c83bc'}>,\n",
-       "  <Document: {'content': 'However, Lady was killed in place of Arya\\'s direwolf, Nymeria, after Nymeria attacked the Crown Prince, Joffrey Baratheon, and later fled.\\n\\nSansa has been described as tall, slim, womanly, and beautiful, destined to be a lady or a queen. She has blue eyes and thick auburn hair that she inherits from her mother, who came from House Tully in the Riverlands region prior to her marriage to Eddard Stark. She has her hair dyed dark brown later on while in the Vale, disguised as Alayne Stone, the bastard daughter of Petyr Baelish.  Sansa is 11 years old in \\'\\'A Game of Thrones\\'\\' and nearly 14 in \\'\\'A Feast for Crows\\'\\'. Arguably the most naive of the Stark children at the start of the series, Sansa often finds herself used as a pawn in the machinations of the other characters. However, as the story progresses, she matures and becomes more of a player of the game rather than a pawn for other characters. She is the most beautiful woman in Westeros at the time of the events of \"A Song of Ice and Fire\".\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '244e2a5926e7f89b5f267a9d99493c97'}>,\n",
-       "  <Document: {'content': 'She is the most beautiful woman in Westeros at the time of the events of \"A Song of Ice and Fire\".\\n\\n==Storylines==\\nCoat of arms of House Stark\\n\\n===\\'\\'A Game of Thrones\\'\\'===\\n\\nSansa Stark begins the novel by being betrothed to Crown Prince Joffrey Baratheon, believing Joffrey to be a gallant prince. While Joffrey and Sansa are walking through the woods, Joffrey notices Arya sparring with the butcher\\'s boy, Mycah. A fight breaks out and Joffrey is attacked by Nymeria (Arya\\'s direwolf) after Joffrey threatens to hurt Arya. Sansa lies to King Robert about the circumstances of the fight in order to protect both Joffrey and her sister Arya.  Since Arya ran off with her wolf to save it, Sansa\\'s wolf is killed instead, estranging the Stark daughters.\\n\\nDuring the Tourney of the Hand to honour her father Lord Eddard Stark, Sansa Stark is enchanted by the knights performing in the event.  At the request of his mother, Queen Cersei Lannister, Joffrey spends a portion of the tourney with Sansa, but near the end he commands his guard Sandor Clegane, better known as The Hound, to take her back to her quarters. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'a80ae4f1d1187bde6a34a29aeeb0837d'}>,\n",
-       "  <Document: {'content': 'At the request of his mother, Queen Cersei Lannister, Joffrey spends a portion of the tourney with Sansa, but near the end he commands his guard Sandor Clegane, better known as The Hound, to take her back to her quarters. Sandor explains how his older brother, Gregor, aka \"Mountain that Rides\" pushed his face into a brazier of hot coals, for playing with one of his wooden toys.\\n\\nAfter Eddard discovers the truth of Joffrey\\'s paternity, he tells Sansa that they will be heading back to Winterfell. Sansa is devastated and wishes to stay in King\\'s Landing, so she runs off to inform Queen Cersei of her father\\'s plans, unwittingly providing Cersei with the information needed to arrest her father. After Robert dies, Sansa begs Joffrey to show mercy on her father and he agrees, if Ned will swear an oath of loyalty, but executes him anyway, in front of Sansa.  Sansa is now effectively a hostage in King\\'s Landing and finally sees Joffrey\\'s true nature, after he forces her to look at the tarred head of her now-deceased father.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': 'c4ea66d17668dd910673351c384d29d5'}>,\n",
-       "  <Document: {'content': \"Sansa is now effectively a hostage in King's Landing and finally sees Joffrey's true nature, after he forces her to look at the tarred head of her now-deceased father.\\n\\n===''A Clash of Kings''===\\nSansa is a hostage in King's Landing, and has learned to be outwardly loyal to King Joffrey to avoid severe physical abuse.\\n\\nAt the celebration for Joffrey's name day, she ostensibly saves the life of Ser Dontos Hollard, when he shows up late and inebriated, begging Joffrey to spare him, in turn winning Dontos' loyalty. Sansa is routinely beaten by Joffrey's guards. After her brother has won a battle against Joffrey's maternal family, she is publicly beaten and has her clothes torn.  Tyrion Lannister intervenes on her behalf, and Sandor Clegane gives her his cloak to cover herself.  Later, Sandor Clegane saves Sansa from a riot in King's Landing.\\n\\nDuring the Battle of Blackwater Bay, all of the highborn ladies in King's Landing seek refuge within a doubly secure, moated chamber within the Red Keep. There, Queen Cersei drunkenly mocks and berates Sansa, then flees when the tide of the battle falls, while Sansa stays to give comfort to the ladies before retreating to her own quarters. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'a5cf07b8a1b8a3472a257d09c5daaf75'}>,\n",
-       "  <Document: {'content': 'There, Queen Cersei drunkenly mocks and berates Sansa, then flees when the tide of the battle falls, while Sansa stays to give comfort to the ladies before retreating to her own quarters. There she finds a drunken Sandor Clegane, who offers to take her away from any imminent danger and the Lannisters themselves. She declines, but he insists she sing for him, at dagger point, then rips his cloak off and gives it to her a second time before fleeing the city. When Sansa awakens, the battle is over. House Lannister has won, but her betrothal to Joffrey is soon ended, so as he may marry Margaery Tyrell whose family just helped turn the tide of battle. However, Joffrey informs Sansa that he still has use for her when married, and to \"expect a nightly visitor for a long while\".\\n\\n===\\'\\'A Storm of Swords\\'\\'===\\n\\nIn the third book, Sansa is invited to dine with Margaery and her grandmother, Olenna Redwyne.  The two women seek to learn the true nature of Joffrey Baratheon now that he and Margaery are betrothed. In turn, Olenna suggests that Sansa should marry her grandson Willas Tyrell. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': '6f2d35910be97cbad64e649738ddefd7'}>,\n",
-       "  <Document: {'content': \"The two women seek to learn the true nature of Joffrey Baratheon now that he and Margaery are betrothed. In turn, Olenna suggests that Sansa should marry her grandson Willas Tyrell. Sansa reveals the marriage plan to Ser Dontos, who warns her of the Tyrells; however, Sansa develops a close friendship with Margaery and is excited about becoming a part of her family. However, when Tywin learns of the marriage plot he schemes to have Sansa's brother Robb Stark killed, knowing that would leave Sansa to inherit Winterfell and the North.  He then commands his son Tyrion Lannister to marry Sansa. Tyrion is initially opposed to the marriage, but is eventually enticed by the prospect of becoming Lord of Winterfell, and so agrees to marry her.\\n\\nSansa is shocked one morning to learn that she is being fitted for a gown to marry Tyrion that day. Joffrey taunts Sansa and acts on behalf of her father to give her away during the ceremony to add further insult.  Sansa ignores Tyrion and refuses to bend as he attempts to put his cloak around her, a marriage ceremony custom in Westeros. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': 'd80bb11bb158e59de63310bb6a179ff3'}>,\n",
-       "  <Document: {'content': \"Sansa ignores Tyrion and refuses to bend as he attempts to put his cloak around her, a marriage ceremony custom in Westeros. In turn, Joffrey commands Ser Dontos to act as a stool so that Tyrion can cloak his bride in spite of his short stature. At the banquet, Sansa dances with many lords, who offer words of comfort; however, Joffrey merely threatens to rape her. Tyrion intervenes and states a desire to castrate Joffrey. After the wedding ceremony, Tyrion chooses not to consummate the marriage due to Sansa's lack of desire in him. It is not long before many in King's Landing come to know that the marriage was never consummated.\\n\\nNot long after Sansa's marriage, Joffrey and Margaery are wed and afterward a grand feast. At Joffrey's wedding, Joffrey is poisoned, and Cersei orders both Tyrion and Sansa arrested. As Joffrey begins to choke to death, Sansa manages to flee during the chaos.  Once in her room, she gathers her belongings and notices that one of the amethysts from her hairnet, a gift from Ser Dontos, is missing. Sansa immediately realizes that the prince had been poisoned and starts to doubt Ser Dontos' rescue plan. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': 'd64d31fdbda2973ffbd76628071090dc'}>,\n",
-       "  <Document: {'content': \"Once in her room, she gathers her belongings and notices that one of the amethysts from her hairnet, a gift from Ser Dontos, is missing. Sansa immediately realizes that the prince had been poisoned and starts to doubt Ser Dontos' rescue plan. Understanding that she will be implicated in the murder of Joffrey, she chooses to flee King's Landing with the knight anyway. Ser Dontos is later killed by Petyr Baelish, who reveals that he is the mastermind behind nearly all of the capital intrigue. He reveals that he was the one who sent Dontos to her and that Olenna took the amethyst from Sansa's hairnet.\\n\\nBaelish smuggles Sansa to safety in the Vale of Arryn, where she poses as his bastard daughter Alayne Stone. She is taken to her aunt Lysa Arryn, now married to Baelish. Lysa declares that Sansa must marry her sickly boy Robert, heir to the Vale.  Petyr Baelish and Lysa are wed; however, Lysa becomes jealous when she witnesses Littlefinger kissing her niece. Lysa later attempts to murder Sansa, but she is saved once again by Baelish, who kills Lysa.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '9f65498b4695fd77f50f8009024ea390'}>,\n",
-       "  <Document: {'content': \"Petyr Baelish and Lysa are wed; however, Lysa becomes jealous when she witnesses Littlefinger kissing her niece. Lysa later attempts to murder Sansa, but she is saved once again by Baelish, who kills Lysa.\\n\\n===''A Feast for Crows''===\\nAfter Lysa's death, Sansa becomes mistress of the Eyrie and still pretends to be Baelish's illegitimate daughter, Alayne Stone. Baelish successfully pacifies the lords of the Vale, who suspected Baelish's hand in Lysa's death. Afterwards, Baelish reveals to Sansa his plans to eventually marry her to the heir to the Vale, Harrold Hardyng, and his long-range plans to reveal her true identity and reclaim the North. Sansa acts as a mother figure to Robert Arryn, caring for him after the death of Lysa. By now she has lost much of her naivety, as well as trust for Baelish.\\n\\n===Family tree of House Stark===\\n\\n==TV adaptation==\\n===Storylines===\\n====Season 1====\\nSansa is first seen with Arya at Winterfell, during their embroidery lesson with Septa Mordane. Following the arrival of Robert Baratheon and his escort at Winterfell, he insists to Ned that Joffrey and Sansa should be married in order to join their houses. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'dd379c0d358d1faa82aebdeb1c454a74'}>,\n",
-       "  <Document: {'content': 'Following the arrival of Robert Baratheon and his escort at Winterfell, he insists to Ned that Joffrey and Sansa should be married in order to join their houses. Sansa, who is desperate to leave Winterfell, begs Catelyn to make Ned agree to the engagement.\\n\\nJoffrey is bitten by Arya\\'s direwolf Nymeria while bullying Mycah the butcher\\'s boy and Arya. Sansa, an eyewitness, claims to be ignorant of the event at the inquest. As retribution for Joffrey\\'s injury, Cersei convinces Robert to have Sansa\\'s direwolf, Lady, killed in place of the now-missing Nymeria.\\n\\nAfter arriving at King\\'s Landing, Sansa attends the Hand\\'s Tourney where Petyr \"Littlefinger\" Baelish tells Sansa and Arya the story behind Sandor \"The Hound\" Clegane\\'s gruesome facial burns. As time passes, Sansa wears her hair like a southerner and is more flippant with Mordane, expressing fears she will fail to give Joffrey a male heir.\\n\\nFollowing Ned\\'s initial resignation as Hand of the King, Sansa is devastated to hear she must return to Winterfell. She likens Joffrey to a lion and says he is nothing like Robert Baratheon. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': '3f34ba351fd2baf088e9122eba805693'}>,\n",
-       "  <Document: {'content': \"Following Ned's initial resignation as Hand of the King, Sansa is devastated to hear she must return to Winterfell. She likens Joffrey to a lion and says he is nothing like Robert Baratheon. This statement inspires Ned to investigate the Baratheon family line, prompting him to realise that Cersei's children are bastards fathered by her twin brother Jaime Lannister, not Robert Baratheon.\\n\\nFollowing Robert's death and Ned's arrest for treason, all Stark servants in King's Landing are executed. Cersei exhorts Sansa to write Robb and Catelyn, imploring them to swear fealty to Joffrey. At court, Sansa pleads for her father's life; all agree on the condition Ned confesses his treason and swears fealty. Sansa is present at the Great Sept of Baelor and is horrified when Joffrey orders Ned's execution, fainting as Ned is beheaded.\\n\\nGrieving the death of her father, Sansa is forced by Joffrey to look upon the spiked heads of both Ned and Septa Mordane. She begs to return home, but he informs Sansa that they are still to be married, and she will stay and obey. Joffrey promises to present Robb's head also, to which she retorts that Robb may give her Joffrey's head, instead. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': '936f9740585bb6ed0390ae031034523a'}>,\n",
-       "  <Document: {'content': \"She begs to return home, but he informs Sansa that they are still to be married, and she will stay and obey. Joffrey promises to present Robb's head also, to which she retorts that Robb may give her Joffrey's head, instead. While on the catwalk, Sansa moves to push Joffrey to his death but is stopped by Sandor Clegane, who offers practical consoling advice.\\n\\n====Season 2====\\nAs the War of the Five Kings progresses, Sansa's position in King's Landing becomes increasingly perilous. On Joffrey's name day celebration, Sansa saves an inebriated Ser Dontos Hollard from execution, by convincing Joffrey to instead make Dontos his fool. While at the celebration, Tyrion offers his condolences for Ned's death, in response to which Sansa insists her family are all traitors, and she is loyal to Joffrey. Later on, when her eldest brother Robb wins a battle against the Lannisters, Sansa is publicly beaten and humiliated in front of the court by Joffrey and Ser Meryn (on Joffrey's orders), as payment for her brother's crimes.\\n\\nTyrion Lannister enters the court and rescues Sansa. Despite being a dwarf, Tyrion takes pity on Sansa's situation and offers to have the engagement called off. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '7fd562d9ed01e31618a75d66901c8e83'}>,\n",
-       "  <Document: {'content': \"Tyrion Lannister enters the court and rescues Sansa. Despite being a dwarf, Tyrion takes pity on Sansa's situation and offers to have the engagement called off. Sansa maintains her facade that she is loyal to Joffrey, which impresses Tyrion to the point he believes Sansa might just survive King's Landing.\\n\\nTyrion has his lover, Shae, positioned as Sansa's handmaiden. Sansa and Shae form a friendship in which Sansa is able to vent about her hatred of the Lannisters without fear of being betrayed. Sansa is present when the royal family bids farewell to Joffrey's sister, Myrcella, on her departure to Dorne to form an alliance between the Lannisters and the Martells. While returning to the Red Keep, a riot breaks out in the streets of King's Landing, amidst which Sansa finds herself caught in the fray. Three peasant men chase Sansa and attempt to rape her before she is rescued and returned to the castle by Sandor Clegane.\\n\\nThe following morning, Sansa has a nightmare of the incident and wakes up in a bloodstained bed. Sansa has her first period, thus meaning she can now bear Joffrey's children. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 14}, 'embedding': None, 'id': '85306cb112a6524d6d0c2ae8150afa1b'}>,\n",
-       "  <Document: {'content': \"The following morning, Sansa has a nightmare of the incident and wakes up in a bloodstained bed. Sansa has her first period, thus meaning she can now bear Joffrey's children. Sansa and Shae attempt to conceal this, which involves Shae's threatening to kill a witness handmaiden if she tells anyone. However, Sandor Clegane sees the blood, and both Cersei and Joffrey are informed.\\n\\nCersei invites Sansa to her chambers to share some of her wisdom and experience as a wife and a mother. Cersei reminisces that her husband Robert was not interested in her childbirth. Cersei explains to Sansa that while Sansa may never love Joffrey (and vice versa), she will love his children. Cersei warns Sansa that the more people she loves, the weaker she will be. Therefore, Sansa should only love her children, as it is the only love she has no choice in.\\n\\nBefore the Battle of the Blackwater, Joffrey forces Sansa to kiss the blade of his sword, while bragging he will kill Stannis himself. Sansa taunts Joffrey by remarking he must be battling in the vanguard, which he would not be. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 15}, 'embedding': None, 'id': '6fbfee99ca10835030ab399a15792ce'}>,\n",
-       "  <Document: {'content': \"Before the Battle of the Blackwater, Joffrey forces Sansa to kiss the blade of his sword, while bragging he will kill Stannis himself. Sansa taunts Joffrey by remarking he must be battling in the vanguard, which he would not be. Joffrey promises that she will lick Stannis's blood, and later her brother Robb's blood off his sword. During the battle, Sansa takes refuge with Cersei, Shae, and the other women and children of King's Landing. A drunk Cersei declares to Sansa and the women that if the city falls, they will all be raped while the city is sacked. Cersei continues her taunting by also telling Sansa that tears are not the only weapons women have; the greatest weapon they own is between their legs. Following Cersei's departure from the refuge with Tommen, Sansa leaves to hide in her bedchamber, where she finds Sandor Clegane. Sandor, about to leave King's Landing, offers to take Sansa home. Sansa declines, insisting Stannis would not harm her. After the battle is won by the Lannister-Tyrell forces, Loras Tyrell asks Joffrey to take his sister, Margaery, as his bride. Joffrey accepts the proposal, which annuls his marriage with Sansa. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 16}, 'embedding': None, 'id': '205810d38b0da175211929d37314e470'}>,\n",
-       "  <Document: {'content': \"After the battle is won by the Lannister-Tyrell forces, Loras Tyrell asks Joffrey to take his sister, Margaery, as his bride. Joffrey accepts the proposal, which annuls his marriage with Sansa. Sansa, while pretending to be devastated, is secretly delighted she no longer has to marry Joffrey. However, Petyr Baelish warns that while Sansa is no longer engaged, Joffrey would have greater rein to abuse Sansa, especially now she is a woman. Petyr assures Sansa he will help get her home, in which Sansa once again displays a facade, asserting King's Landing is her home. Petyr advises Sansa that everyone in King's Landing is a liar and that they are much more cunning than her.\\n\\n====Season 3====\\nWith her engagement to Joffrey annulled, Sansa does not have to worry about spending the rest of her life with him, but is with lesser protection from tormentors. Petyr Baelish, an old friend of her mother's with a reputation for being sadistic and cunning, tells her that he can smuggle her out of the city, but she is reluctant. Sansa finds a friend in Loras Tyrell, who is kind to her and whom she hopes will ask for her hand. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 17}, 'embedding': None, 'id': 'a9ebd352935a8d1fe957c3a39d090d71'}>,\n",
-       "  <Document: {'content': \"Sansa finds a friend in Loras Tyrell, who is kind to her and whom she hopes will ask for her hand. His sister, Joffrey's new fiancé, Margaery, is also kind to her and takes her to dine with her grandmother, Olenna Tyrell, who asks her for her opinion of Joffrey. Sansa reveals Joffrey's true, cruel personality, but Margaery and Lady Olenna merely pass it by as a trivial matter, saying there is nothing to be done to change a man's character, especially a king's. Sansa's affection for Loras grows; she is unaware that Loras is gay and, while he likes her and enjoys spending time with her, he can never love her. Margaery proposes the idea that Loras will marry Sansa, meaning Sansa can leave King's Landing, which delights Sansa. However, when this plan is reported to the Lannisters, they fear that the Tyrells will pose an even greater threat with a member of House Stark as one of their allies and quickly end the idea of the engagement, by betrothing Loras to Cersei and engaging Sansa to Tyrion, which both Sansa and Shae are against. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 18}, 'embedding': None, 'id': 'db60fa32adbed6d082e4315e191554dd'}>,\n",
-       "  <Document: {'content': 'However, when this plan is reported to the Lannisters, they fear that the Tyrells will pose an even greater threat with a member of House Stark as one of their allies and quickly end the idea of the engagement, by betrothing Loras to Cersei and engaging Sansa to Tyrion, which both Sansa and Shae are against. However, the day of the wedding, Tyrion promises not to harm her and, as she prepares to consummate the marriage, Tyrion realizes how unhappy Sansa is and tells her that she doesn\\'t have to consummate it unless she wants to. When Sansa asks Tyrion what would happen if she never wants him in her bed, he quips, \"And so my watch begins.\"Sansa and Tyrion do form a somewhat friendly relationship, as he is kind to her and treats her well, and she soon realizes there are worse Lannisters to be wed to. However, their cordial relationship suffers a crushing blow when Sansa receives news of Robb and Catelyn\\'s deaths at the Red Wedding, an event orchestrated by Tywin Lannister, Tyrion\\'s father.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 19}, 'embedding': None, 'id': 'b8b3477904ca4209fd8b0d63ad837bda'}>,\n",
-       "  <Document: {'content': \"However, their cordial relationship suffers a crushing blow when Sansa receives news of Robb and Catelyn's deaths at the Red Wedding, an event orchestrated by Tywin Lannister, Tyrion's father.\\n\\n====Season 4====\\nSansa, still distraught over the death of Robb and Catelyn, is approached by Dontos Hollard, a former knight whom Sansa had convinced Joffrey to make his fool instead of executing him. Dontos gives her a necklace, claiming it was his mother's. However, the necklace turns out to be a fake; one of the gems contains the poison which Lady Olenna Tyrell uses to poison Joffrey at his wedding to Margaery Tyrell. In the commotion, Sansa is taken by Dontos, to Baelish's boat in Blackwater Bay. Baelish smuggles Sansa from King's Landing after revealing the nature of the necklace and has Dontos killed with a crossbow.\\n\\nLord Baelish, with Sansa posing as his niece Alayne Stone, pass through the Blood Gates to the Eyrie and to the keep of Lysa Arryn. Lysa initially invites them with open arms, revealing she knows exactly who her niece is, and they are welcome to be housed. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 20}, 'embedding': None, 'id': '8f155df13762cc2834114d9d14d27b2'}>,\n",
-       "  <Document: {'content': \"Lysa initially invites them with open arms, revealing she knows exactly who her niece is, and they are welcome to be housed. It is soon revealed however, Lysa mistrusts the relationship between Sansa and Baelish accusing Baelish of violating Sansa and accusing him of never loving her. Later in the keep, Sansa strikes Lysa's son Robin and Baelish appears. Baelish then proclaims his undying love for her deceased mother, Catelyn, and he shares a kiss with a stunned Sansa, with Lysa watching from above. Sansa is immediately summoned to Lysa's throne room, where she believes she had been summoned for striking her son. Lysa reveals she had observed the kiss, and though defending herself and Baelish's actions against her, Lysa holds her to the Moon Door, a trap door that leads hundreds of feet down into the mountains below. Baelish intervenes before she gets the chance to execute Sansa and pushes Lysa to her death instead as he proclaimed his love for her sister. Then Baelish later claimed to the lords of the Vale that she committed suicide. Sansa is called to give testimony, and although she reveals her true identity, she supports Baelish's story. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 21}, 'embedding': None, 'id': '7c62ef850448e4d10104525d1ca72006'}>,\n",
-       "  <Document: {'content': \"Then Baelish later claimed to the lords of the Vale that she committed suicide. Sansa is called to give testimony, and although she reveals her true identity, she supports Baelish's story. She then joins Baelish and her cousin Robin Arryn on a tour of the Vale.\\n\\n====Season 5====\\nBaelish brokers a marriage between Sansa and Ramsay Bolton, now the heir to the North after the death of Robb Stark. Though Sansa is reluctant to marry Ramsay, as his father Roose had personally murdered Robb, Baelish persuades her by claiming that the marriage will give her the opportunity to avenge her family. On the way to Winterfell, they encounter Brienne of Tarth, who had sworn to Catelyn Stark to take Sansa to safety and tries to convince Sansa to come with her; Baelish has her chased off by his men, but Brienne follows Sansa to Winterfell regardless. Though initially charming, Ramsay's sadistic nature becomes apparent when Sansa discovers that he has captured and enslaved Ned's former ward Theon Greyjoy, and forced him to assume the identity of his serving man, Reek. Sansa and Ramsay wed in front of the Godswood. That night, Ramsay rapes Sansa, and forces Reek to watch. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 22}, 'embedding': None, 'id': 'd2b8de9143e2a49fbd7dc726c53e2e96'}>,\n",
-       "  <Document: {'content': \"Though initially charming, Ramsay's sadistic nature becomes apparent when Sansa discovers that he has captured and enslaved Ned's former ward Theon Greyjoy, and forced him to assume the identity of his serving man, Reek. Sansa and Ramsay wed in front of the Godswood. That night, Ramsay rapes Sansa, and forces Reek to watch. Over the next few days, Ramsay continues to rape and beat Sansa every night, and keeps her locked in her bedchamber. Sansa begs Reek to help her signal her northern allies by lighting a candle in the broken tower. Reek, wishing to spare Sansa from Bolton's wrath, instead tells Ramsay. He proceeds to flay the maid who had told Sansa of the signal, and forces Sansa to look at her corpse. Furious, Sansa confronts Reek, who admits that he had failed to capture Sansa's brothers Bran and Rickon, and killed two farm boys in their place. While the Boltons prepare to battle Stannis Baratheon's advancing forces, Sansa signals to Brienne, unaware that she has left to kill Stannis. When help does not come, Sansa attempts to return to her room but is caught by Ramsay's paramour Myranda, who threatens to mutilate Sansa. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 23}, 'embedding': None, 'id': 'aef5de17b5a5e23fb8e8eddcea29097d'}>,\n",
-       "  <Document: {'content': \"When help does not come, Sansa attempts to return to her room but is caught by Ramsay's paramour Myranda, who threatens to mutilate Sansa. Finally snapping, Theon throws Myranda to her death, just as the Bolton forces return. Fearful of Ramsay's reaction, Theon and Sansa jump from Winterfell's battlements into the snow.\\n\\n====Season 6====\\nSansa and Theon are captured by Bolton soldiers in the forest outside Winterfell, but Brienne and her squire Podrick Payne arrive in time to rescue them and kill the Bolton soldiers. This time, Sansa accepts Brienne's loyalty. While Theon returns to the Iron Islands, Sansa, Brienne, and Podrick journey on to Castle Black, where she reunites with her half-brother Jon Snow, who has just resigned as Lord Commander of the Night's Watch. Sansa tries to persuade Jon to help her drive the Boltons out of Winterfell; although Jon initially refuses, he changes his mind after Ramsay sends a letter to Jon in which he gloats that he holds Rickon Stark captive and threatens to kill the Starks and the Wildlings Jon has let through the Wall if Sansa is not returned. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 24}, 'embedding': None, 'id': 'daa882aa7e1fa27aea4564521f589c11'}>,\n",
-       "  <Document: {'content': 'Sansa tries to persuade Jon to help her drive the Boltons out of Winterfell; although Jon initially refuses, he changes his mind after Ramsay sends a letter to Jon in which he gloats that he holds Rickon Stark captive and threatens to kill the Starks and the Wildlings Jon has let through the Wall if Sansa is not returned. Before Jon and Sansa leave Castle Black, Baelish arranges a meeting with Sansa in Mole\\'s Town. He insists that he was unaware of Ramsay\\'s nature and offers the support of the Knights of the Vale, also mentioning that her great-uncle Brynden \"Blackfish\" Tully has captured Riverrun from House Frey. Sansa sends Baelish away, declaring that she never wants to see him again, but sends Brienne to the Riverlands to convince the Blackfish to aid the Starks.\\n\\nAlthough Jon and Sansa are only able to win over a handful of Northern lords, Jon insists that they must march on Winterfell, despite Sansa\\'s objections. Sansa sends a letter to Baelish asking for his aid, and the Vale forces arrive at Winterfell in time to defeat the Boltons. Ramsay is captured and Sansa has Ramsay fed to his hounds. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 25}, 'embedding': None, 'id': '8a95bf1a490a7aa2a28e92edd3a60414'}>,\n",
-       "  <Document: {'content': \"Sansa sends a letter to Baelish asking for his aid, and the Vale forces arrive at Winterfell in time to defeat the Boltons. Ramsay is captured and Sansa has Ramsay fed to his hounds. In the aftermath of the battle, Sansa apologizes for not telling Jon about Baelish and the Knights of the Vale. Jon forgives her and asks that they trust each other completely from now on. They remember their father when Sansa says she received a white raven informing them that Winter is here. While in the godswood, Baelish tells Sansa the North will rally behind her and confesses his ambition to rule Westeros with Sansa at his side, but Sansa rebuffs his advances. Later, the Northmen and Valemen declare Jon the new King in the North. Sansa's smile dies when she notices Littlefinger watching her.\\n\\n====Season 7====\\nJon travels to Dragonstone to negotiate with Daenerys Targaryen for her support against the White Walkers, leaving Sansa as regent in his absence. Soon after, Bran and Arya return to Winterfell. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 26}, 'embedding': None, 'id': 'd8390ef5cfc53957d2d8093534080ac7'}>,\n",
-       "  <Document: {'content': '====Season 7====\\nJon travels to Dragonstone to negotiate with Daenerys Targaryen for her support against the White Walkers, leaving Sansa as regent in his absence. Soon after, Bran and Arya return to Winterfell. Littlefinger seeks to drive a wedge between Sansa and Arya by letting Arya find Sansa\\'s letter to Robb asking him to bend the knee to Joffrey, causing Arya to confront Sansa. Sansa sneaks into Arya\\'s quarters and comes across the \"faces\" Arya has taken from the various people she has killed on her travels; Arya catches Sansa and tells Sansa of her ability to assume people\\'s identities with the faces before she threatens her.\\n\\nSansa later receives an invitation to King\\'s Landing, where Jon intends to present Cersei, who is now Queen of the Seven Kingdoms, with proof of the White Walkers\\' existence. Refusing to return to King\\'s Landing, she sends Brienne as her representative.\\n\\nSansa shows Littlefinger the letter she received from Jon following the meeting in King\\'s Landing, in which Jon states that he had pledged his support to Daenerys Targaryen. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 27}, 'embedding': None, 'id': '1f598e072485a95ce65163cb569b286d'}>,\n",
-       "  <Document: {'content': 'Sansa shows Littlefinger the letter she received from Jon following the meeting in King\\'s Landing, in which Jon states that he had pledged his support to Daenerys Targaryen. Littlefinger continues his manipulation of Sansa by claiming that Jon has betrayed the North, Sansa should seize power from Jon, and suggests Arya intends to murder her to become Lady of Winterfell. Sansa summons Arya to the great hall and begins an accusation of treason and murder, before directing the accusation towards Littlefinger. With help from their brother Bran (now known as Three-Eyed Raven), Sansa and Arya reveal that they are aware of Baelish\\'s numerous crimes, including the murder of Lysa Arryn, orchestrating the murders of both Jon Arryn and Eddard Stark, and manipulating the Starks and Lannisters to war. Baelish tries to plead for his life, but Sansa refuses and sentences Littlefinger to death. Arya executes him. The Stark sisters later resolve their differences and acknowledge that the Starks must stand together to survive the winter. They remember their father telling them, \"The lone wolf dies, but the pack survives.\"====Season 8====\\nSansa welcomes Daenerys and her court, including Tyrion, to Winterfell. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 28}, 'embedding': None, 'id': '285133b6c632c99c80e035b8f587e624'}>,\n",
-       "  <Document: {'content': 'They remember their father telling them, \"The lone wolf dies, but the pack survives.\"====Season 8====\\nSansa welcomes Daenerys and her court, including Tyrion, to Winterfell. Tyrion declares that the Lannister troops will be marching north as well to defend against the dead, but Sansa is skeptical; her fears are confirmed when Jaime Lannister arrives in Winterfell and he reveals Cersei\\'s treachery. Sansa wishes Jaime dead for attacking Ned in King\\'s Landing, but spares him when Brienne vouches for him. Sansa suspects that Jon\\'s decision to pledge to Daenerys as queen is due to his love for Daenerys. Sansa is wary of Daenerys but when speaking with her in private, Daenerys assures Sansa that her love for Jon is also genuine and they reach common ground. However, the situation becomes tense when Sansa asks of the North\\'s independence after Daenerys has conquered Westeros. They are interrupted by Theon\\'s arrival, who has come to help defend Winterfell.\\n\\nSansa watches the battle against the dead from Winterfell\\'s battlements, but when the Dothraki are easily overrun, Arya orders Sansa to seek refuge in the crypts with the other non-combatants. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 29}, 'embedding': None, 'id': 'af36a5a185883769093daf9d45cd5e90'}>,\n",
-       "  <Document: {'content': \"Sansa watches the battle against the dead from Winterfell's battlements, but when the Dothraki are easily overrun, Arya orders Sansa to seek refuge in the crypts with the other non-combatants. The Night King reanimates the dead around Winterfell, including the Starks buried in the crypts, but the undead fall when Arya kills the Night King, allowing Sansa, Tyrion, and the others in the crypts to survive. Sansa is present for the funeral for those fallen in battle, including Theon, who was killed defending Bran. She places a Stark pin in Theon's armor before he is cremated to honor him as an ally of the Starks.\\n\\nSansa is reunited with the Hound at the victory feast. The Hound declares that Sansa would not have experienced the horrors she faced had she fled King's Landing with him, but Sansa acknowledges that her experiences made her wiser. At the war council, Sansa and Daenerys disagree over giving the Northern army time to recover before marching on King's Landing. After the council, Sansa and Arya tell Jon they don't trust Daenerys but Jon defends Daenerys. Jon confides he is the son of Rhaegar Targaryen and Lyanna Stark after swearing Sansa and Arya to secrecy. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 30}, 'embedding': None, 'id': '254c2c7fdcbab7b9bae4b26c16d68c5b'}>,\n",
-       "  <Document: {'content': \"After the council, Sansa and Arya tell Jon they don't trust Daenerys but Jon defends Daenerys. Jon confides he is the son of Rhaegar Targaryen and Lyanna Stark after swearing Sansa and Arya to secrecy. Sansa reveals this to Tyrion.\\n\\nDaenerys takes King's Landing, but lays waste to the surrendered populace during which Cersei is also killed. Jon tries but fails to dissuade Daenerys from further destruction and ultimately assassinates her. When Jon is arrested by the Unsullied, Sansa camps the Northern army outside the city and travels to King's Landing to convene a council to determine the fates of Jon, Tyrion, and Westeros. Tyrion suggests Bran be crowned king, to near-unanimous approval. Arya and Sansa abstain, and Sansa asserts to Bran that the North has sacrificed too much to remain part of the Seven Kingdoms, declaring the North's independence. Sansa, Arya, and Bran bid farewell to Jon, who is exiled. Sansa returns to Winterfell and is crowned Queen in the North.\\n\\n\", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 31}, 'embedding': None, 'id': '5b8e10a0ddb019eeb64026e0e2c4ac4c'}>,\n",
-       "  <Document: {'content': 'Sansa, Arya, and Bran bid farewell to Jon, who is exiled. Sansa returns to Winterfell and is crowned Queen in the North.\\n\\n===Reception===\\nSophie Turner plays the role of Sansa Stark in the television series\\n\\nAs her storyline has progressed, Sansa has received critical acclaim for the development of her character and her emergence from a naive young girl to a strong young woman. \\'\\'Rolling Stone\\'\\' ranked Sansa as No. 4 on a list of the \"Top 40 \\'\\'Game of Thrones\\'\\' Characters\", saying that Sansa is \"often overlooked in favor of her killer kid sister\", but that her \"quiet, innate political shrewedness and emotional strength have enabled her to survive\", and calling her \"the show\\'s best-kept secret\". In a ranking of the 48 best \\'\\'Game of Thrones\\'\\' characters listed in the main credits in the first five seasons on the website \\'\\'The Wrap\\'\\', Sansa was ranked at No. 4, ahead of the more popular Daenerys Targaryen, Jon Snow, and Tyrion Lannister, saying that \"Sansa has been kind of great in Season 6, turning into the sort of badass we always hoped but never thought she actually could become\".\\n\\nIn an article published on \\'\\'Mic.', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 32}, 'embedding': None, 'id': '870bd1eb81c0d7a99653d7c5b0197f01'}>,\n",
-       "  <Document: {'content': '4, ahead of the more popular Daenerys Targaryen, Jon Snow, and Tyrion Lannister, saying that \"Sansa has been kind of great in Season 6, turning into the sort of badass we always hoped but never thought she actually could become\".\\n\\nIn an article published on \\'\\'Mic.\\'\\'com, Julianne Ross says that \"the elder Stark daughter is often cited as one of the most reviled characters on \\'\\'Game of Thrones\\'\\'\", while also saying that \"not coincidentally, Sansa Stark is also one of the most classically feminine characters on the show.\"Ross criticized the heavy hatred for Sansa, particularly in contrast to \"her universally (and rightly) adored tomboy little sister Arya\", stating that Sansa \"arguably gets a disproportionate amount of fan hate because she doesn\\'t fit the narrow \\'strong female character\\' mold we\\'re used to rooting for.\"Blogger Rhiannon Thomas of \\'\\'Feminist Fiction\\'\\' wrote in 2012 that \"the focus on this sort of female character - the oft-cited \\'strong female character\\' - seems to suggest that femininity is still \\'\\'bad,\\'\\' and that women can only be strong by adopting stereotypically male roles and attitudes\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 33}, 'embedding': None, 'id': '673fd50ffc7840442e6eb3cb12e9fe47'}>,\n",
-       "  <Document: {'content': 'Blogger Rhiannon Thomas of \\'\\'Feminist Fiction\\'\\' wrote in 2012 that \"the focus on this sort of female character - the oft-cited \\'strong female character\\' - seems to suggest that femininity is still \\'\\'bad,\\'\\' and that women can only be strong by adopting stereotypically male roles and attitudes\". Thomas went on to say that \"in an abusive situation that would break so many people, Sansa survives\" and that she has a \"woman\\'s courage\" that \"keeps her alive and in the game where characters like Arya would not last five minutes\".\\n\\nIn an article published on MTV.com by Crystal Bell titled \"Sansa Stark is the Only \\'\\'Game of Thrones\\'\\' Hero Worth Rooting For\", Bell writes: \"Sansa is the most relatable character in George R.R. Martin\\'s canon. She\\'s often despised for having no agency, but the way I see it, Sansa is hated for being a woman. Unlike Brienne, Arya, Cersei, and Margaery -- models of the \"strong female character\" archetype—Sansa\\'s passivity denotes weakness. She doesn\\'t have cool swordplay skills like her sister Arya; she isn\\'t a smart seductress like Margaery Tyrell or a fierce queen like Cersei. She is the epitome of femininity on \\'\\'Game of Thrones\\'\\', and therefore, she is dismissed.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 34}, 'embedding': None, 'id': 'd7fc4d209e94f5fd6e7f01b4dd9e732e'}>,\n",
-       "  <Document: {'content': 'She doesn\\'t have cool swordplay skills like her sister Arya; she isn\\'t a smart seductress like Margaery Tyrell or a fierce queen like Cersei. She is the epitome of femininity on \\'\\'Game of Thrones\\'\\', and therefore, she is dismissed.\"Bell went on to say: \"However, Sansa\\'s greatest strength as a character has been her unwavering resilience. She was tortured and humiliated for seasons by the unhinged man-boys around her. She\\'s been the subject of everyday sexism and misogyny since day one. And yet, she survives, even as armor-clad heroes fall before her. She is the show\\'s survivor. She continuously \\'\\'endures\\'\\' the pain and humiliation of being a woman in Westeros. Just because Sansa doesn\\'t wield a sword as fiercely as Arya and Brienne, or command a horde of dragons like Daenerys Targaryen, doesn\\'t make her any less of a hero.\"Sansa received particular acclaim in Season 6 of the show, during which she began her quest to retake her family home and exact revenge on those who wronged her. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 35}, 'embedding': None, 'id': '8d1fa41215fd69d25630886e871375c1'}>,\n",
-       "  <Document: {'content': 'Sansa received particular acclaim in Season 6 of the show, during which she began her quest to retake her family home and exact revenge on those who wronged her. In an interview with \\'\\'The New York Times\\'\\', actress Sophie Turner said that \"she\\'s Sansa no longer a pawn in anyone\\'s game; she\\'s no longer a prisoner...she\\'s the one taking charge and doing her own thing, which is very exciting\". Megan Garber of \\'\\'The Atlantic\\'\\' praised the show\\'s decision to have Sansa be the one orchestrating Ramsay\\'s death in Battle of the Bastards, saying that \"In the end, it was Sansa and her abuser, alone again in a darkened chamber; in the end though, it was Sansa making the decisions about who would be the victim.\"On the scene, Turner said: \"It\\'s amazing. It\\'s Sansa\\'s first kill and it\\'s such a strong moment for her because all her life she\\'s been affected by these men who have just done such terrible things to her....\"\\n\\nFollowing the penultimate Season 6 episode, Bennett Madison of \\'\\'Vanity Fair\\'\\' wrote \"When Sansa icily reminds her dopey brother that \\'No one can protect you\\', it\\'s because she\\'s always been on her own. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 36}, 'embedding': None, 'id': '20efbb043768c1d919702547478f2056'}>,\n",
-       "  <Document: {'content': 'It\\'s Sansa\\'s first kill and it\\'s such a strong moment for her because all her life she\\'s been affected by these men who have just done such terrible things to her....\"\\n\\nFollowing the penultimate Season 6 episode, Bennett Madison of \\'\\'Vanity Fair\\'\\' wrote \"When Sansa icily reminds her dopey brother that \\'No one can protect you\\', it\\'s because she\\'s always been on her own. As far back as King\\'s Landing, Sansa\\'s between quietly protecting herself, working on her stitchery while taking cool measure of everything going on around her, learning how to game the system, and slithering through situations that would have gotten the best of the show\\'s more flashy or impulsive characters. In \\'Battle of the Bastards\\', she got to show a little flash of her own; by being defiantly, gloriously correct in her convictions, by saving the day with her foresight and savvy, and by feeding Ramsay to the dogs.\"Turner later told \\'\\'Time\\'\\' magazine about how gratifying it was to watch Sansa\\'s development during Season 6 and defended the show on its cruel treatment of women: \"In my opinion, \\'\\'Game of Thrones\\'\\' is not sexist, and it\\'s accurate to medieval time. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 37}, 'embedding': None, 'id': 'db30e0e58750158e9b28f8c09d045cb8'}>,\n",
-       "  <Document: {'content': 'Turner later told \\'\\'Time\\'\\' magazine about how gratifying it was to watch Sansa\\'s development during Season 6 and defended the show on its cruel treatment of women: \"In my opinion, \\'\\'Game of Thrones\\'\\' is not sexist, and it\\'s accurate to medieval time. The show puts social boundaries on the women, and they break out of these boundaries.\"====Controversy====\\nIn the episode \"Unbowed, Unbent, Unbroken\", Sansa\\'s rape was the main subject of controversy for the season\\'s deviations from the books. The majority of professional criticism concerned the decision to have Ramsay rape Sansa on their wedding night, with most critics describing the scene as gratuitous and artistically unnecessary. \"This grim scene was difficult for the show to justify,\" said Charlotte Runcie of \\'\\'The Daily Telegraph\\'\\'. Joanna Robinson of \\'\\'Vanity Fair\\'\\' added, \"this rape scene undercuts all the agency that’s been growing in Sansa since the end of last season. ... I’d never advocate that \\'\\'Game of Thrones\\'\\' (or any work of fiction) shy away from edgy plots out of fear of pushback or controversy. But edgy plots should always accomplish something above pure titillation or shock value and what, \\'\\'exactly\\'\\', was accomplished here?\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 38}, 'embedding': None, 'id': '22c5206819014916c0cf2bf42385c540'}>,\n",
-       "  <Document: {'content': 'Joanna Robinson of \\'\\'Vanity Fair\\'\\' added, \"this rape scene undercuts all the agency that’s been growing in Sansa since the end of last season. ... I’d never advocate that \\'\\'Game of Thrones\\'\\' (or any work of fiction) shy away from edgy plots out of fear of pushback or controversy. But edgy plots should always accomplish something above pure titillation or shock value and what, \\'\\'exactly\\'\\', was accomplished here?\"Christopher Orr wrote in \\'\\'The Atlantic\\'\\', \"I continue to be astonished that showrunners Benioff and Weiss still apparently believe that their tendency to ramp up the sex, violence, and—especially—sexual violence of George R.R. Martin’s source material is a strength rather than the defining weakness of their adaptation.\"Myles McNutt of \\'\\'The A.V. Club\\'\\' wrote, \"The issue with the show returning to rape as a trope is not simply because there have been thinkpieces speaking out against it, and is not solely driven by the rational concerns lying at the heart of those thinkpieces. It’s also that the show has lost my faith as a viewer.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 39}, 'embedding': None, 'id': '9b6363c4a07a8e17dac9e2689bcfade9'}>,\n",
-       "  <Document: {'content': 'Club\\'\\' wrote, \"The issue with the show returning to rape as a trope is not simply because there have been thinkpieces speaking out against it, and is not solely driven by the rational concerns lying at the heart of those thinkpieces. It’s also that the show has lost my faith as a viewer.\"Writers from \\'\\'Vanity Fair\\'\\', \\'\\'The Mary Sue\\'\\' and \\'\\'The Daily Beast\\'\\' all disapproved of the decision to use Sansa\\'s victimization as a motivating agent for Theon, saying that the scene undermined Sansa\\'s character development: \"Was it really important to make that scene about Theon\\'s pain?\"wrote Joanna Robinson of \\'\\'Vanity Fair\\'\\'.\\n\\nOther critics responded positively to the scene. Sean T. Collins of \\'\\'Rolling Stone\\'\\' wrote: \"By involving a multidimensional main character instead of one introduced primarily to suffer, the series has a chance to grant this story the gravity and seriousness it deserves. Sarah Hughes of \\'\\'The Guardian\\'\\' wrote: \"I have repeatedly made clear that I’m not a fan of rape as a plot device – but the story of Ramsay and Sansa’s wedding was more than that. ... The writers are walking a very fine line here. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 40}, 'embedding': None, 'id': 'e13805acc400132db056176ea6bef537'}>,\n",
-       "  <Document: {'content': 'Sarah Hughes of \\'\\'The Guardian\\'\\' wrote: \"I have repeatedly made clear that I’m not a fan of rape as a plot device – but the story of Ramsay and Sansa’s wedding was more than that. ... The writers are walking a very fine line here. They handled it well tonight, telling a gothic tale of innocence sacrificed\". Alyssa Rosenberg of \\'\\'The Washington Post\\'\\' wrote that the scene \"managed to maintain a fine balance, employing a dignity and care for the experiences of victims that \\'\\'Game of Thrones\\'\\' has not always demonstrated.\"Some critics questioned why this scene in particular should generate outrage when similar scenes have not. Sara Stewart of the \\'\\'New York Post\\'\\' pointed out that the rape and sexual abuse of both female and male characters is typical for \\'\\'Game of Thrones\\'\\': \"Why are we suddenly so outraged about the rape of Sansa Stark, when this show has served up a steady diet of sexual assault and violence against women since its first season began?\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 41}, 'embedding': None, 'id': '9663511852c9cd49ae9261c1811d74d9'}>,\n",
-       "  <Document: {'content': 'Sara Stewart of the \\'\\'New York Post\\'\\' pointed out that the rape and sexual abuse of both female and male characters is typical for \\'\\'Game of Thrones\\'\\': \"Why are we suddenly so outraged about the rape of Sansa Stark, when this show has served up a steady diet of sexual assault and violence against women since its first season began?\"Cathy Young of \\'\\'Reason\\'\\' magazine, writing in \\'\\'Time\\'\\' noted what she calls a lack of complaint in response to the sexual mistreatment of male characters in earlier seasons, specifically the literal emasculation of Theon Greyjoy and the sexual assault of Gendry.\\n\\nCriticism of the scene has not extended to the quality of the acting. Joanna Robinson of \\'\\'Vanity Fair\\'\\' wrote, \"And if we can say one positive thing about that scene it\\'s that Allen nailed his performance. Theon\\'s horror mirrored our own and the camera—focusing on his reaction—let our minds fill in the blanks.\"Sophie Turner defended the scene as an artistic challenge for herself as an actor, saying, \"When I read that scene, I kinda loved it. I love the way Ramsay had Theon watching. It was all so messed up. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 42}, 'embedding': None, 'id': '2e8b973c15b469f7aca568e0e696e80c'}>,\n",
-       "  <Document: {'content': 'Sophie Turner defended the scene as an artistic challenge for herself as an actor, saying, \"When I read that scene, I kinda loved it. I love the way Ramsay had Theon watching. It was all so messed up. It\\'s also so daunting for me to do it. ... I think it\\'s going to be the most challenging season for me so far, just because it\\'s so emotional for her. It\\'s not just crying all the time, like seasons 2 or 3, it\\'s super messed up.\"Iwan Rheon (Ramsay Bolton) agreed, referring to Turner\\'s performances this season as \"absolutely amazing.\"Some viewers, including U.S. Senator Claire McCaskill, announced that they would stop watching the show because of this scene. According to \\'\\'Business Insider\\'\\', this scene and increased use of streaming services are likely reasons why ratings dropped from 6.2 million viewers for this episode to 5.4 million for the next episode, \"The Gift.\"===Recognition and awards===\\nSophie Turner has received several award nominations for her portrayal of Sansa including an Emmy nomination for Outstanding Supporting Actress in a Drama Series in 2019. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 43}, 'embedding': None, 'id': '5cd937c122eaf2594689bb4564d64942'}>,\n",
-       "  <Document: {'content': '===Recognition and awards===\\nSophie Turner has received several award nominations for her portrayal of Sansa including an Emmy nomination for Outstanding Supporting Actress in a Drama Series in 2019. For her performance in the series she earned the Glamour Award for Best UK TV Actress in 2016 and 2017, and an EWwy Award for Best Supporting Actress in a Drama Series in 2016. Other nominations include the Young Artist Award for Best Performance in a TV Series by a Supporting Young Actress in 2012, and the EWwy Award for Best Supporting Actress in a Drama Series in 2015.\\n\\n==References==\\n===Inline citations===\\n\\n===General references===\\n*\\n*\\n*\\n*', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 44}, 'embedding': None, 'id': 'bf0e06e8448aa75ce075c488dc4f3e35'}>,\n",
-       "  <Document: {'content': '\\n\\n\"\\'\\'\\'Mother\\'s Mercy\\'\\'\\'\" is the fifth season finale episode of HBO\\'s fantasy television series \\'\\'Game of Thrones\\'\\', and the 50th overall. The tenth and final episode of the fifth season, the episode was written by the series creators David Benioff and D. B. Weiss and directed by David Nutter.\\n\\n\"Mother\\'s Mercy\" garnered critical acclaim, with critics lauding Lena Headey\\'s performance, David Nutter\\'s direction and the writing of Benioff and Weiss. Particular praise was directed to Cersei Lannister\\'s walk of atonement, which involved a body double and the use of CGI. In the United States, the episode premiere achieved a viewership of 8.11 million, making it the show\\'s most watched episode, until it was surpassed by the season six finale, \"The Winds of Winter\". The episode won Emmy Awards for Writing in a Drama Series and Directing in a Drama Series for Benioff, Weiss, and Nutter.\\n\\nThis episode marks the final appearance for Stephen Dillane (Stannis Baratheon), Tara Fitzgerald (Selyse Baratheon), and Ian Beattie (Ser Meryn Trant).\\n\\n==Plot==\\n\\n===In the North===\\nThe weather conditions at Stannis\\' camp drastically improve, clearing the route towards Winterfell. Melisandre takes credit, claiming that the Lord of Light is rewarding Stannis for sacrificing Shireen. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 0}, 'embedding': None, 'id': 'a4725872397d98c52ac8385fe4b1cfea'}>,\n",
-       "  <Document: {'content': '==Plot==\\n\\n===In the North===\\nThe weather conditions at Stannis\\' camp drastically improve, clearing the route towards Winterfell. Melisandre takes credit, claiming that the Lord of Light is rewarding Stannis for sacrificing Shireen. However, Stannis\\' officers inform him that Shireen\\'s sacrifice has also prompted a mass desertion of his troops, leaving him with only half his army and no cavalry, and driven Selyse to hang herself. Melisandre immediately flees to Castle Black. Despite all of these setbacks, Stannis orders his remaining troops to march forward towards Winterfell. As Stannis\\' army marches through the Wolfswood, Podrick sees them and warns Brienne, who leaves to take revenge on Stannis.\\n\\nAs Stannis prepares to besiege the castle, the Bolton cavalry, led by Ramsay, suddenly emerges. It becomes clear they greatly outnumber Stannis\\'s army; nonetheless, Stannis leads his men into battle. After a quick yet fierce battle, the Boltons emerge as the victors. Stannis survives the destruction of his army, but is severely wounded after fighting several Bolton soldiers. Brienne then arrives and announces her intentions to execute Stannis for the murder of Renly (\"The Ghost of Harrenhal\"). Stannis accepts his fate, and Brienne executes him.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 1}, 'embedding': None, 'id': 'fcc519116e0df9a1b87056ba70f90f94'}>,\n",
-       "  <Document: {'content': 'Brienne then arrives and announces her intentions to execute Stannis for the murder of Renly (\"The Ghost of Harrenhal\"). Stannis accepts his fate, and Brienne executes him.\\n\\nMeanwhile, in Winterfell, Sansa escapes her room using the corkscrew she stole and lights a candle in the ruined tower to signal for help, but Brienne has already left. After witnessing the defeat of the Baratheon army, she tries to return to her room but is confronted by Myranda, who threatens to mutilate Sansa. Theon finally finds his inner strength and throws Myranda from the castle ramparts to her death, just as the victorious Bolton army returns to Winterfell. Theon and Sansa realize they have to escape at once, and jump from the castle walls into the deep snow below.\\n\\n===In Dorne===\\nJaime, Myrcella, Bronn and Trystane leave for King\\'s Landing. On the ship, Myrcella reveals that she knows Jaime is her real father and that she is glad that he is her father. However, Myrcella suddenly collapses, and it is revealed that Ellaria had secretly poisoned Myrcella by kissing her with poisoned lipstick.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '5708cc764e7470ddc59e82070e3b6f0c'}>,\n",
-       "  <Document: {'content': 'However, Myrcella suddenly collapses, and it is revealed that Ellaria had secretly poisoned Myrcella by kissing her with poisoned lipstick.\\n\\n===In Braavos===\\nArya infiltrates the brothel patronized by Trant by taking one of the faces from the House of Black and White. She stabs Trant multiple times, revealing her true identity and confronting him for the murder of Syrio Forel before cutting his throat. When she returns to the House of Black and White, she is confronted by Jaqen and the Waif. Jaqen berates Arya for taking a life not meant for her to take, and that another life must be taken in order to appease the Many Faced God. Jaqen then proceeds to drink from a vial and collapses, much to Arya\\'s shock. However, the Waif changes her face to that of Jaqen and points out to Arya that \"Jaqen\" never existed, and that she had been talking to \"No One\" the entire time. The Waif also warns Arya that putting on a new face if one is not \"No One\" is like poison. Because of this, Arya is struck blind as punishment.\\n\\n===In Meereen===\\nDaario and Jorah decide to leave Meereen to search for Daenerys. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 3}, 'embedding': None, 'id': '727e4445d1ae38eb1b462ccb6ca83869'}>,\n",
-       "  <Document: {'content': \"Because of this, Arya is struck blind as punishment.\\n\\n===In Meereen===\\nDaario and Jorah decide to leave Meereen to search for Daenerys. Tyrion wishes to join them, but Daario points out that he is better suited to govern Meereen in Daenerys' absence, assisted by Missandei and the still-recovering Grey Worm. As Tyrion watches Jorah and Daario leave and ponders his next move, Varys suddenly appears. Varys compares Meereen's current state to that of King's Landing when it was threatened by Stannis, and offers use of his spy network to aid Tyrion.\\n\\n===In the Dothraki Sea===\\nDrogon brings Daenerys back to his lair. Daenerys orders Drogon to take her back to Meereen, but Drogon, still exhausted and wounded, ignores her and goes to sleep. Daenerys wanders off on her own in search of food, eventually spotting a Dothraki horde advancing in the distance. Daenerys drops a ring to the ground before being surrounded.\\n\\n===In King's Landing===\\nCersei confesses to committing adultery with Lancel to the High Sparrow, but denies having slept with Jaime or conspiring to have Robert Baratheon killed. \", 'content_type': 'text', 'score': None, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '86b74581e114fea88507444b9436dcf'}>,\n",
-       "  <Document: {'content': '===In King\\'s Landing===\\nCersei confesses to committing adultery with Lancel to the High Sparrow, but denies having slept with Jaime or conspiring to have Robert Baratheon killed. The High Sparrow decides to grant the \"Mother\\'s Mercy\" and let Cersei return to the Red Keep, but says she must return to face trial for the other sins she has not confessed to, and must undergo an \"atonement\". Cersei is stripped, her hair cut short, and she is forced to walk naked the entire way from the Great Sept of Baelor to the Red Keep. As she walks, she is relentlessly harassed, jeered and pelted with garbage by the angry populace, ultimately breaking down in tears when she finally returns to the Red Keep. She is covered up by Qyburn, who reveals to her the newest member of the Kingsguard, a re-animated Gregor \"the Mountain\" Clegane.\\n\\n===At the Wall===\\nJon sends Samwell with Gilly and her baby to Oldtown to become a maester, acknowledging (and even somewhat endorsing) the romantic relationship blossoming between Samwell and Gilly. Davos arrives shortly after to ask for supplies and Wildling reinforcements, but Jon refuses, reminding Davos that the Wildlings will never fight for Stannis. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 5}, 'embedding': None, 'id': 'bed59e431632e3ba9668af2b13ba60f8'}>,\n",
-       "  <Document: {'content': 'Davos arrives shortly after to ask for supplies and Wildling reinforcements, but Jon refuses, reminding Davos that the Wildlings will never fight for Stannis. Melisandre arrives soon after; her demeanour leads Jon and Davos to realize that Stannis has been defeated and that Shireen and Selyse are dead.\\n\\nAt night, Olly tells Jon that a wildling had recently seen his uncle Benjen Stark, and takes him to speak to him. Led outside to the courtyard, he only finds Thorne, Bowen Marsh, Othell Yarwyck and several other men, along with a grave-marker that reads \"Traitor\". As Jon realizes that he has been betrayed, Thorne, Marsh, Yarwyck, Olly, and the others take turns stabbing Jon, each uttering \"For the Watch\". The men leave Jon as he dies alone in the snow.\\n\\n==Production==\\n\\n===Writing===\\n\"Mother\\'s Mercy\" was written by the series\\' creators David Benioff and D. B. Weiss. It primarily covers elements from George Martin\\'s novels \\'\\'A Feast for Crows\\'\\', chapters \"Samwell I\" and \"Cat of the Canals\" and \\'\\'A Dance with Dragons\\'\\', chapters \"Jon II\", \"Theon I\", \"Daenerys X\", \"Cersei I\", \"Cersei II\" and \"Jon XIII\". ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 6}, 'embedding': None, 'id': 'bf7fc088ba953929b61ade782a1f72d3'}>,\n",
-       "  <Document: {'content': 'It primarily covers elements from George Martin\\'s novels \\'\\'A Feast for Crows\\'\\', chapters \"Samwell I\" and \"Cat of the Canals\" and \\'\\'A Dance with Dragons\\'\\', chapters \"Jon II\", \"Theon I\", \"Daenerys X\", \"Cersei I\", \"Cersei II\" and \"Jon XIII\". It also contains material from Martin\\'s upcoming sixth novel in the series, \\'\\'The Winds of Winter\\'\\', chapter \"Mercy\".\\n\\n===Filming===\\n\"Mother\\'s Mercy\" was directed by David Nutter, He also directed the previous episode, \"The Dance of Dragons\". It was photographed by Robert McLachlan and was edited by Tim Porter, one of the show\\'s main editors, under the supervision of Nutter.\\n\\n===Cersei\\'s walk of atonement===\\nActress Lena Headey portrays Cersei Lannister in the series.\\nFor Cersei Lannister\\'s nude \"walk of atonement\" through King\\'s Landing, HBO employed actress Rebecca Van Cleave as a body double for Lena Headey. While filming the scene in Dubrovnik, Croatia in October 2014, Van Cleave performed fully nude while Headey wore a simple beige shift. Both performances were then merged by the show\\'s editing team, combining close-ups of Headey\\'s facial expressions and long shots of Van Cleave\\'s nude walk through the city. In some instances CGI was used to put Headey\\'s face on Van Cleave\\'s body.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 7}, 'embedding': None, 'id': '813c60620dd13cf09aa1de16f3b11615'}>,\n",
-       "  <Document: {'content': 'Both performances were then merged by the show\\'s editing team, combining close-ups of Headey\\'s facial expressions and long shots of Van Cleave\\'s nude walk through the city. In some instances CGI was used to put Headey\\'s face on Van Cleave\\'s body.\\n\\nHeadey says that the showrunners decided to cast a body double for \"several reasons.\"Headey says she was glad to use a body double for the scene because she wanted to focus on the character\\'s emotions. Another actor noted that Headey\\'s \"extensive tattoos\" may have been another reason. Months later, Headey announced that she was pregnant with her second child, though it is unclear if she was pregnant when this scene had to be filmed. Sarah Buchanan of \\'\\'Express\\'\\' notes the strangeness of casting a much younger actress to play the middle-aged Cersei when the key outcome of the walk of atonement in \\'\\'A Dance with Dragons\\'\\' is that when the people see that Cersei\\'s body is not beautiful, she loses a large part of her mystique. The 27-year-old Van Cleave did not show any of the specific signs of aging or past pregnancy for which the Cersei of the books was mocked by the crowd.\\n\\n', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 8}, 'embedding': None, 'id': '4a550f5e2a1203d8d22db3212ccc68d8'}>,\n",
-       "  <Document: {'content': 'The 27-year-old Van Cleave did not show any of the specific signs of aging or past pregnancy for which the Cersei of the books was mocked by the crowd.\\n\\nWhen casting for the role, HBO received applications from more than  actresses to act as Headey\\'s body double for the scene. A selection of seven finalists, including Van Cleave, was then flown to Belfast, Northern Ireland to audition before the show\\'s producers. Headey also opted out of the casting process, but director David Nutter said finding the right double was one of the most important parts of making the finale. \"We needed somebody who could do The Walk of Shame physically, somebody who could match Lena\\'s integrity, intensity, and sensibility. We found a tremendous actress in Rebecca; she was a godsend. The courage of this girl, who\\'s never done anything like this in her life, who understood what was important about this...once I had her I felt like I could accomplish anything.\"Van Cleave herself described the scene as \"one of the scariest, most wonderful, most gratifying experiences I could have imagined ... ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 9}, 'embedding': None, 'id': 'a137a6c5c80a57fcec082cd6d7b779f3'}>,\n",
-       "  <Document: {'content': 'Van Cleave herself described the scene as \"one of the scariest, most wonderful, most gratifying experiences I could have imagined ... I never in a million years would have thought I would be in Dubrovnik surrounded by hundreds of extras and crew members throwing food at me, but it was amazing\" and \"I hope the next thing I do will have my head in it.\"She also praised Headey\\'s willingness to help her, describing the two of them as a \"tag-team\"; Headey would walk behind Van Cleave during the shoot, coaching her on the character\\'s responses.\\n\\nWhen the showrunners first announced the plans of shooting a nude scene in Dubrovnik, the city\\'s Catholic Church of St. Nicholas strongly opposed the idea, due to it being immoral to walk their sacred grounds in the nude. Eventually, both parties came to the agreement that the shooting could commence, provided no nude scenes would be filmed in a place of worship. During the three-day shoot, Headey walked Van Cleave through each shot, giving insight into all the emotions Cersei was feeling. The six-minute scene was filmed using 500 extras.\\n\\n===Fate of Jon Snow===\\n\\nD.B. ', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 10}, 'embedding': None, 'id': 'efd8bd56faee8305a0b915e230d51ac8'}>,\n",
-       "  <Document: {'content': 'During the three-day shoot, Headey walked Van Cleave through each shot, giving insight into all the emotions Cersei was feeling. The six-minute scene was filmed using 500 extras.\\n\\n===Fate of Jon Snow===\\n\\nD.B. Weiss discussed the decision to kill Jon Snow by saying that he felt the visual medium did not give him the freedom to leave Jon\\'s fate open: \"In a book, you can present that kind of ambiguity. In a show, everybody sees it for what it is. It’s that rule: \\'If we don\\'t see the body then they’re not really dead.\\'Like when we cut Ned’s head off, we didn’t want a gory Monty Python geyser of blood, but we needed to see the blade enter his neck and cut out on the frame where the blade was mid-neck. ... we needed Ned\\'s death to be totally unambiguous.\"Actor Kit Harington said of the scene, \"I loved it. I loved how they brought Olly in to be the person who kills me. I love how the storyline with Thorne was wrapped up.\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 11}, 'embedding': None, 'id': 'adbc8679d21fad07b830618ee5894073'}>,\n",
-       "  <Document: {'content': 'I loved how they brought Olly in to be the person who kills me. I love how the storyline with Thorne was wrapped up.\"Although Weiss meant the scene to be unambiguous, the episode left many viewers uncertain as to whether Jon Snow had been killed or merely injured (his fate in the novels is left unconfirmed as of \\'\\'A Dance with Dragons\\'\\', with much debate about Martin\\'s future intentions). Some of the confusion may be because, as reviewers have noted, Jon was killed in what can be seen as the middle of his story arc and his death does not have an obvious narrative purpose. For example, Nate Jones of \\'\\'Vulture\\'\\' notes, \"It\\'s easy to see what other characters\\' deaths meant for the series\\' sprawling narrative: Ned\\'s execution sent the Stark kids adrift in a universe where there was nobody looking out for them, while Robb\\'s murder was the final death knell for the hopes that the saga would ever have a traditional \\'happy\\' ending. What would be accomplished, narratively, by getting rid of Jon permanently right now?\"', 'content_type': 'text', 'score': None, 'meta': {'_split_id': 12}, 'embedding': None, 'id': 'c6fcb975e9ecc5aac9e316adfcbe4a8e'}>,\n",
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-       "  'data/build_a_scalable_question_answering_system/116.txt',\n",
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-       "  'data/build_a_scalable_question_answering_system/175.txt',\n",
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-       "  'data/build_a_scalable_question_answering_system/74.txt',\n",
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-       "  'data/build_a_scalable_question_answering_system/73.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/67.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/9.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/172.txt',\n",
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-       "  'data/build_a_scalable_question_answering_system/181.txt',\n",
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-       "  'data/build_a_scalable_question_answering_system/5.txt',\n",
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-       "  'data/build_a_scalable_question_answering_system/156.txt',\n",
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-       "  'data/build_a_scalable_question_answering_system/94.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/80.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/81.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/95.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/143.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/157.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/42.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/56.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/4.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/180.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/182.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/68.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/40.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/6.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/54.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/141.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/155.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/83.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/169.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/97.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/168.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/96.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/82.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/154.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/140.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/7.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/55.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/41.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/69.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/45.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/51.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/3.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/79.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/178.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/86.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/92.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/144.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/150.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/151.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/145.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/93.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/179.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/87.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/78.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/50.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/2.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/44.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/0.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/52.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/46.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/91.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/85.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/153.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/147.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/146.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/152.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/84.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/90.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/47.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/1.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/53.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/34.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/20.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/135.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/121.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/109.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/108.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/120.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/134.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/21.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/35.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/23.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/37.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/122.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/136.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/137.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/123.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/36.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/22.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/26.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/32.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/127.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/133.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/132.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/126.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/33.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/27.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/31.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/25.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/19.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/118.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/130.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/124.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/125.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/131.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/119.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/18.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/24.txt',\n",
-       "  'data/build_a_scalable_question_answering_system/30.txt'],\n",
-       " 'node_id': 'DocumentStore'}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "files_to_index = [doc_dir + \"/\" + f for f in os.listdir(doc_dir)]\n",
     "indexing_pipeline.run_batch(file_paths=files_to_index)"
@@ -1679,36 +473,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'answers': [<Answer {'answer': 'Eddard', 'type': 'extractive', 'score': 0.9933727979660034, 'context': \"s Nymeria after a legendary warrior queen. She travels with her father, Eddard, to King's Landing when he is made Hand of the King. Before she leaves,\", 'offsets_in_document': [{'start': 207, 'end': 213}], 'offsets_in_context': [{'start': 72, 'end': 78}], 'document_id': '9e3c863097d66aeed9992e0b6bf1f2f4', 'meta': {'_split_id': 4}}>,\n",
-      "             <Answer {'answer': 'Ned', 'type': 'extractive', 'score': 0.975361168384552, 'context': \"k in the television series.\\n\\n====Season 1====\\nArya accompanies her father Ned and her sister Sansa to King's Landing. Before their departure, Arya's h\", 'offsets_in_document': [{'start': 630, 'end': 633}], 'offsets_in_context': [{'start': 74, 'end': 77}], 'document_id': '7d3360fa29130e69ea6b2ba5c5a8f9c8', 'meta': {'_split_id': 13}}>,\n",
-      "             <Answer {'answer': 'Lord Eddard Stark', 'type': 'extractive', 'score': 0.9566042423248291, 'context': 'rk daughters.\\n\\nDuring the Tourney of the Hand to honour her father Lord Eddard Stark, Sansa Stark is enchanted by the knights performing in the event.', 'offsets_in_document': [{'start': 804, 'end': 821}], 'offsets_in_context': [{'start': 67, 'end': 84}], 'document_id': 'a80ae4f1d1187bde6a34a29aeeb0837d', 'meta': {'_split_id': 3}}>,\n",
-      "             <Answer {'answer': 'Joffrey', 'type': 'extractive', 'score': 0.6413302421569824, 'context': \"Mycah, sparring in the woods with broomsticks.  Arya defends Mycah from Joffrey's torments and her direwolf Nymeria helps Arya fight off Joffrey, woun\", 'offsets_in_document': [{'start': 634, 'end': 641}], 'offsets_in_context': [{'start': 72, 'end': 79}], 'document_id': '9e3c863097d66aeed9992e0b6bf1f2f4', 'meta': {'_split_id': 4}}>,\n",
-      "             <Answer {'answer': 'King Robert', 'type': 'extractive', 'score': 0.5891151428222656, 'context': \"en refuses to yield Gendry, who is actually a bastard son of the late King Robert, to the Lannisters.  The Night's Watch convoy is overrun and massacr\", 'offsets_in_document': [{'start': 820, 'end': 831}], 'offsets_in_context': [{'start': 70, 'end': 81}], 'document_id': '87e8469dcf7354fd2a25fbd2ba07c543', 'meta': {'_split_id': 5}}>],\n",
-      " 'documents': [<Document: {'content': '== Storylines ==\\n=== Novels ===\\n==== \\'\\'A Game of Thrones\\'\\' ====\\nCoat of arms of House Stark\\n\\nArya adopts a direwolf cub, which she names Nymeria after a legendary warrior queen. She travels with her father, Eddard, to King\\'s Landing when he is made Hand of the King. Before she leaves, her half-brother Jon Snow has a smallsword made for her as a parting gift, which she names \"Needle\" after her least favorite ladylike activity.\\n\\nWhile taking a walk together, Prince Joffrey and her sister Sansa happen upon Arya and her friend, the low-born butcher apprentice Mycah, sparring in the woods with broomsticks.  Arya defends Mycah from Joffrey\\'s torments and her direwolf Nymeria helps Arya fight off Joffrey, wounding his arm in the process.  Knowing that Nymeria will likely be killed in retribution, Arya chases her wolf away; but Sansa\\'s direwolf Lady is killed in Nymeria\\'s stead and Mycah is hunted down and killed by Sandor Clegane, Joffrey\\'s bodyguard.\\n\\nIn King\\'s Landing, her father discovers Arya\\'s possession of Needle, but instead of confiscating it he arranges for fencing lessons under the Braavosi swordmaster Syrio Forel, who teaches her the style of fighting known as \"water dancing\".  ', 'content_type': 'text', 'score': 0.7704076817912666, 'meta': {'_split_id': 4}, 'embedding': None, 'id': '9e3c863097d66aeed9992e0b6bf1f2f4'}>,\n",
-      "               <Document: {'content': \"She was also the future bride of Prince Joffrey, and thus the future Queen of the Seven Kingdoms as well. She names her direwolf Lady; she is the smallest of the pack and the first to die, sentenced to death by Cersei after Arya's direwolf, Nymeria, bit a violent Joffrey.\\n\\n===Arya Stark===\\nMaisie Williams\\n\\n'''Arya Stark''' portrayed by Maisie Williams. Arya Stark of House Stark is the younger daughter and third child of Lord Eddard and Catelyn Stark of Winterfell. Ever the tomboy, Arya would rather be training to use weapons than sewing with a needle. She names her direwolf Nymeria, after a legendary warrior queen.\\n\\n===Robb Stark===\\nRichard Madden\\n\\n'''Robb Stark''' (seasons 1–3) portrayed by Richard Madden. Robb Stark of House Stark is the eldest son of Eddard and Catelyn Stark and the heir to Winterfell. His dire wolf is called Grey Wind. Robb becomes involved in the war against the Lannisters after his father, Ned Stark, is arrested for treason. Robb summons his bannermen for war against House Lannister and marches to the Riverlands. Eventually, crossing the river at the Twins becomes strategically necessary. \", 'content_type': 'text', 'score': 0.7546451298652186, 'meta': {'_split_id': 22}, 'embedding': None, 'id': '726b0655306246943c29202b25bd4733'}>,\n",
-      "               <Document: {'content': 'When Rafford begs for a healer, Arya cuts his throat in the same fashion as he had killed Lommy and throws his corpse into a canal.  She heads back to perform the play, knowing this murder will most likely ruin her Mercedene identity.\\n\\n=== Family tree of House Stark ===\\n\\n=== Television series ===\\n\\nArya Stark is portrayed by English actress Maisie Williams in the television adaption of the book series, this being Williams\\' first role as an actress. Williams was chosen from among 300 actresses across England.\\nMaisie Williams plays the role of Arya Stark in the television series.\\n\\n====Season 1====\\nArya accompanies her father Ned and her sister Sansa to King\\'s Landing. Before their departure, Arya\\'s half-brother Jon Snow gifts Arya a sword which she dubs \"Needle\". On the Kingsroad, Arya is sparring with a butcher\\'s boy, Mycah, when Sansa\\'s betrothed Prince Joffrey Baratheon attacks Mycah, prompting Arya\\'s direwolf Nymeria to bite Joffrey. Arya shoos Nymeria away so she is not killed, but is furious when Sansa later refuses to support her version of events. Mycah is later killed by Joffrey\\'s bodyguard Sandor \"The Hound\" Clegane, earning him Arya\\'s hatred. ', 'content_type': 'text', 'score': 0.7499777030244019, 'meta': {'_split_id': 13}, 'embedding': None, 'id': '7d3360fa29130e69ea6b2ba5c5a8f9c8'}>,\n",
-      "               <Document: {'content': '* \\'\\'\\'Lothar Frey\\'\\'\\' (seasons 3, 6) portrayed by Tom Brooke in season 3, and by Daniel Tuite in season 6. One of Lord Walder Frey\\'s many sons, nicknamed “Lame Lothar” because of his twisted leg. He and his half-brother Black Walder are sent by their father to Riverrun to propose a marriage between Lord Edmure Tully and Roslin Frey as terms for House Frey rejoining Robb Stark\\'s campaign against the Lannisters. He is one of the first to commence the \"Red Wedding\", stabbing Talisa Stark in the womb several times and killing her and her unborn child. In the sixth season, he is ordered by Walder to retake Riverrun from Brynden Tully. Though they succeed with Lannister help, he is killed by Arya Stark, who subsequently bakes him into a pie.\\n* \\'\\'\\'Black Walder Rivers\\'\\'\\' (seasons 3, 6) portrayed by Tim Plester. One of Lord Walder Frey\\'s many bastard sons, nicknamed “Black Walder” for his dark demeanor. He and his half-brother Lame Lothar are sent by their father to Riverrun to propose a marriage between Lord Edmure Tully and Roslin Frey as terms for House Frey rejoining Robb Stark\\'s campaign against the Lannister. ', 'content_type': 'text', 'score': 0.7489998506563029, 'meta': {'_split_id': 76}, 'embedding': None, 'id': 'ef7ad089df4cc85d00e671a3f32153a4'}>,\n",
-      "               <Document: {'content': 'She is the most beautiful woman in Westeros at the time of the events of \"A Song of Ice and Fire\".\\n\\n==Storylines==\\nCoat of arms of House Stark\\n\\n===\\'\\'A Game of Thrones\\'\\'===\\n\\nSansa Stark begins the novel by being betrothed to Crown Prince Joffrey Baratheon, believing Joffrey to be a gallant prince. While Joffrey and Sansa are walking through the woods, Joffrey notices Arya sparring with the butcher\\'s boy, Mycah. A fight breaks out and Joffrey is attacked by Nymeria (Arya\\'s direwolf) after Joffrey threatens to hurt Arya. Sansa lies to King Robert about the circumstances of the fight in order to protect both Joffrey and her sister Arya.  Since Arya ran off with her wolf to save it, Sansa\\'s wolf is killed instead, estranging the Stark daughters.\\n\\nDuring the Tourney of the Hand to honour her father Lord Eddard Stark, Sansa Stark is enchanted by the knights performing in the event.  At the request of his mother, Queen Cersei Lannister, Joffrey spends a portion of the tourney with Sansa, but near the end he commands his guard Sandor Clegane, better known as The Hound, to take her back to her quarters. ', 'content_type': 'text', 'score': 0.7477537651604484, 'meta': {'_split_id': 3}, 'embedding': None, 'id': 'a80ae4f1d1187bde6a34a29aeeb0837d'}>,\n",
-      "               <Document: {'content': 'In King\\'s Landing, her father discovers Arya\\'s possession of Needle, but instead of confiscating it he arranges for fencing lessons under the Braavosi swordmaster Syrio Forel, who teaches her the style of fighting known as \"water dancing\".  After her father\\'s arrest, Syrio is killed protecting her and Arya narrowly escapes capture.  She later witnesses the public execution of her father before falling under the protection of the Night\\'s Watch recruiter Yoren.\\n\\n==== \\'\\'A Clash of Kings\\'\\' ====\\nArya escapes King\\'s Landing with Yoren and his party of recruits; and on the road, she clashes with the other Night\\'s Watch child recruits Lommy, Gendry, and Hot Pie but eventually befriends them. On the way, the party is attacked by Amory Lorch when Yoren refuses to yield Gendry, who is actually a bastard son of the late King Robert, to the Lannisters.  The Night\\'s Watch convoy is overrun and massacred, but Arya and the other children escape through a tunnel.  Before escaping, she rescues three prisoners locked in a wagon cage, among them a mysterious man named Jaqen H\\'ghar.\\n\\nArya and her friends are later captured by Ser Gregor Clegane and taken to Harrenhal as slave laborers.  ', 'content_type': 'text', 'score': 0.7445102113414457, 'meta': {'_split_id': 5}, 'embedding': None, 'id': '87e8469dcf7354fd2a25fbd2ba07c543'}>,\n",
-      "               <Document: {'content': 'Tyrion is consigned to the Eyrie\\'s \"sky cells\" while Lysa prepares to pass judgment on him as an accomplice in her husband\\'s murder.\\n\\n===In the North===\\nAt Winterfell, Theon Greyjoy grows jealous of Tyrion after his favorite prostitute Ros taunts him. To take Bran\\'s mind off his paralysis and his mother\\'s departure, Maester Luwin teaches him the Dothraki art of horseback archery.\\n\\n===In King\\'s Landing===\\nAfter Ned Stark convinces King Robert not to join the tourney, the crowd watches the fearsome Ser Gregor \"The Mountain\" Clegane joust with Ser Loras Tyrell, the \"Knight of Flowers\", who wins by riding a mare in heat, distracting Clegane\\'s stallion. Clegane beheads his horse and attempts to kill Loras, but Sandor \"The Hound\" Clegane intervenes.\\n\\nVarys reveals to Ned that Jon Arryn was killed by a poison called the \"Tears of Lys\", and suggests that Arryn\\'s slain squire Ser Hugh of the Vale was the poisoner.\\n\\nIn training, Arya chases a cat through the Red Keep and overhears a conversation between Varys and Illyrio, who appear to be plotting against the throne. Arya tries to warn her father but is unable to identify the plotters. ', 'content_type': 'text', 'score': 0.7395308845606815, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '1d850d17893da5959cd677e4d82c144'}>,\n",
-      "               <Document: {'content': '\"Asha\" is portrayed as a fairly provocative and independent woman, a captain of thirty ships, as opposed to her television counterpart Yara, who did not retain \"Asha\\'s\" traits, although her rivalry with Theon remained intact. Their father Balon Greyjoy was played by Patrick Malahide. Many of the characters involved in the Greyjoys\\' storyline weren\\'t introduced, most notably Balon\\'s brother Aeron Greyjoy. Nonetheless, the storyline received enormous praise, with the alteration of Yara\\'s name and persona being the only criticism.\\n\\nTom Wlaschiha is cast as Jaqen H\\'ghar, a mysterious prisoner who develops a murderous relationship with young Arya Stark (Maisie Williams). Wlaschiha\\'s pronunciation of his character\\'s name, \\'\\'Jack-in\\'\\', was adopted for use in the show. Natalie Dormer, best known for her portrayal as seductive Anne Boleyn in Showtime\\'s \\'\\'The Tudors\\'\\', was cast in a similar role as Margaery Tyrell, a noblewoman and the wife of the third claimant to the throne, Renly Baratheon. Gwendoline Christie played, to much praise, Brienne of Tarth, a female warrior who joins Renly Baratheon\\'s guard, but later becomes a follower of Catelyn Stark. To prepare for the role, Christie took up an intense training regimen, adding over a stone (6.4\\xa0kg) of muscle mass. ', 'content_type': 'text', 'score': 0.7392356266647196, 'meta': {'_split_id': 9}, 'embedding': None, 'id': '937a3c260025a30112a7a29da8d1d242'}>,\n",
-      "               <Document: {'content': \"===On the Iron Islands===\\nReturning to his homeland, Theon Greyjoy tries to seduce a young woman, Yara. At Pyke, Theon presents his father Balon with Robb's offer that will make Balon King of the Iron Islands. Balon refuses, wishing to take his crown with Yara, revealed to be Theon's sister, at the helm of his fleet. Theon realises Balon's intention is to take the North for himself.\\n\\n===In the Red Waste===\\nRakharo's horse returns to Daenerys Targaryen's camp carrying his severed head, which Ser Jorah Mormont explains is a message from another khal, and Daenerys vows revenge.\\n\\n===On the Kingsroad===\\nCity Watchmen search the caravan for Gendry but are turned away by Yoren. Gendry tells Arya Stark that he knows she is a girl, and she reveals she is actually Arya Stark after learning that her father met Gendry before he was executed.\\n\\n===At Craster's Keep===\\nSamwell Tarly asks Jon Snow about taking Gilly, one of Craster's daughter-wives, with them but Jon refuses. Gilly is pregnant, and Jon wonders what happens to Craster's sons. That night, Jon follows Craster taking a newborn child into the woods, and sees a White Walker retrieve the baby, but Craster knocks Jon unconscious.\\n\\n\", 'content_type': 'text', 'score': 0.7388862068321547, 'meta': {'_split_id': 2}, 'embedding': None, 'id': '8a495456ded3ab7e15cd66c6d3ca82ad'}>,\n",
-      "               <Document: {'content': \"Decades before the events of ''A Game of Thrones'', he was sent to the Citadel in Oldtown on the orders of his grandfather Daeron II, who felt there were too many Targaryens, and became a Maester (one of an order of scholars and healers). He was later offered the throne of the Seven Kingdoms after the death of his father Maekar, but ceded the rule to Aegon and joined the Night's Watch. By ''A Game of Thrones'', he is elderly and blind, but provides guidance to the men of the Watch. In ''A Feast for Crows'', Jon Snow sends him to the Citadel by sea, but Maester Aemon dies on the voyage between Braavos and Oldtown.\\n\\nIn the HBO television adaptation, he is portrayed by Peter Vaughan.\\n\\n=== Yoren ===\\nYoren is a recruiter of the Night's Watch. In ''A Game of Thrones'' Yoren travels with Tyrion Lannister from the Wall to King's Landing, and is present when Tyrion is arrested by Catelyn Stark. He then races to King's Landing to inform Eddard Stark. During Lord Eddard's execution, he finds Arya Stark and shields her from seeing her father's death. \", 'content_type': 'text', 'score': 0.7351640317180352, 'meta': {'_split_id': 91}, 'embedding': None, 'id': '1a134f1ce82ea118cb873dfd9ba090e2'}>],\n",
-      " 'no_ans_gap': 11.405375480651855,\n",
-      " 'node_id': 'Reader',\n",
-      " 'params': {'Reader': {'top_k': 5}, 'Retriever': {'top_k': 10}},\n",
-      " 'query': 'Who is the father of Arya Stark?',\n",
-      " 'root_node': 'Query'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pprint import pprint\n",
     "\n",
@@ -1751,7 +518,7 @@
    "source": [
     "# Next Steps\n",
     "\n",
-    "To learn how to improve the performance of the Reader, see [Fine-Tune a Reader](https://haystack.deepset.ai/tutorials/03_fine_tune_a_reader)."
+    "To learn how to improve the performance of the Reader, see [Fine-Tune a Reader](https://haystack.deepset.ai/tutorials/02_finetune_a_model_on_your_data)."
    ]
   },
   {
@@ -1778,7 +545,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.8.12 ('haystack_py38')",
    "language": "python",
    "name": "python3"
   },
@@ -1792,11 +559,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]"
+   "version": "3.8.12"
   },
   "vscode": {
    "interpreter": {
-    "hash": "3fd1520721e0740ccf655b87fc1a15949508d880e32fa0665a9483d3c4c5735c"
+    "hash": "85ea2c107d7945555de8e73270cf8a4d668bafec7aac344fa62e3415dc7bf5ec"
    }
   }
  },

From ee96e7e396e7dc4f1337126fddaee13d22b30290 Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Mon, 16 Jan 2023 19:03:18 +0100
Subject: [PATCH 029/206] Update nightly.yml (#102)

---
 .github/workflows/nightly.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index fdd9584a..126e2865 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -24,7 +24,7 @@ jobs:
         notebook:
           # Note: use the name of the file without the extension
           - 01_Basic_QA_Pipeline
-          - 03_Basic_QA_Pipeline_without_Elasticsearch
+          - 03_Scalable_QA_System
           - 04_FAQ_style_QA
           - 05_Evaluation
           - 06_Better_Retrieval_via_Embedding_Retrieval

From 28f832f570f47d2957adec0e47a3a1993ad8829c Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Mon, 23 Jan 2023 17:19:44 +0100
Subject: [PATCH 030/206] fixing wrong url in tutorial (#103)

---
 markdowns/01_Basic_QA_Pipeline.md    | 4 ++--
 tutorials/01_Basic_QA_Pipeline.ipynb | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/markdowns/01_Basic_QA_Pipeline.md b/markdowns/01_Basic_QA_Pipeline.md
index dc359eb0..f4be231b 100644
--- a/markdowns/01_Basic_QA_Pipeline.md
+++ b/markdowns/01_Basic_QA_Pipeline.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb
 toc: True
 title: "Build Your First Question Answering System"
-last_updated: 2023-01-11
+last_updated: 2023-01-23
 level: "beginner"
 weight: 10
 description: Get Started by creating a Retriever Reader pipeline.
@@ -196,7 +196,7 @@ And there you have it! Congratulations on building your first machine learning b
 
 # Next Steps
 
-Check out [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_Scalable_QA_System) to learn how to make a more advanced question answering system that uses an Elasticsearch backed DocumentStore and makes more use of the flexibility that pipelines offer.
+Check out [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_scalable_qa_system) to learn how to make a more advanced question answering system that uses an Elasticsearch backed DocumentStore and makes more use of the flexibility that pipelines offer.
 
 ## About us
 
diff --git a/tutorials/01_Basic_QA_Pipeline.ipynb b/tutorials/01_Basic_QA_Pipeline.ipynb
index 396910dd..7a192a12 100644
--- a/tutorials/01_Basic_QA_Pipeline.ipynb
+++ b/tutorials/01_Basic_QA_Pipeline.ipynb
@@ -354,7 +354,7 @@
    "source": [
     "# Next Steps\n",
     "\n",
-    "Check out [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_Scalable_QA_System) to learn how to make a more advanced question answering system that uses an Elasticsearch backed DocumentStore and makes more use of the flexibility that pipelines offer."
+    "Check out [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_scalable_qa_system) to learn how to make a more advanced question answering system that uses an Elasticsearch backed DocumentStore and makes more use of the flexibility that pipelines offer."
    ]
   },
   {

From 953cfc7151aa0ed37a11cbc19345530330fa5b76 Mon Sep 17 00:00:00 2001
From: ZanSara <sara.zanzottera@deepset.ai>
Date: Tue, 24 Jan 2023 12:03:35 +0100
Subject: [PATCH 031/206] Remove imports from `datasets` (#104)

* Remove imports from `datasets`

* add markdown
---
 markdowns/03_Scalable_QA_System.md    | 4 +---
 tutorials/03_Scalable_QA_System.ipynb | 2 --
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/markdowns/03_Scalable_QA_System.md b/markdowns/03_Scalable_QA_System.md
index 93a37435..78544bd1 100644
--- a/markdowns/03_Scalable_QA_System.md
+++ b/markdowns/03_Scalable_QA_System.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb
 toc: True
 title: "Build a Scalable Question Answering System"
-last_updated: 2023-01-12
+last_updated: 2023-01-24
 level: "beginner"
 weight: 15
 description: Create a scalable Retriever Reader pipeline that uses an ElasticsearchDocumentStore.
@@ -128,8 +128,6 @@ Once we combine these nodes into a pipeline, the pipeline will ingest `.txt` fil
 
 
 ```python
-from datasets import load_dataset
-
 from haystack.utils import fetch_archive_from_http
 
 doc_dir = "data/build_a_scalable_question_answering_system"
diff --git a/tutorials/03_Scalable_QA_System.ipynb b/tutorials/03_Scalable_QA_System.ipynb
index fa85d74a..2da1327b 100644
--- a/tutorials/03_Scalable_QA_System.ipynb
+++ b/tutorials/03_Scalable_QA_System.ipynb
@@ -225,8 +225,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from datasets import load_dataset\n",
-    "\n",
     "from haystack.utils import fetch_archive_from_http\n",
     "\n",
     "doc_dir = \"data/build_a_scalable_question_answering_system\"\n",

From 1c4a99a2d939fea419278c5547683470ae4ec911 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Fri, 27 Jan 2023 12:01:10 +0100
Subject: [PATCH 032/206] remove bullet point about text field (#106)

* remove bullet point about text field

* generate markdown
---
 markdowns/04_FAQ_style_QA.md    | 3 +--
 tutorials/04_FAQ_style_QA.ipynb | 1 -
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/markdowns/04_FAQ_style_QA.md b/markdowns/04_FAQ_style_QA.md
index 012ef3d5..b6295359 100644
--- a/markdowns/04_FAQ_style_QA.md
+++ b/markdowns/04_FAQ_style_QA.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb
 toc: True
 title: "Utilizing Existing FAQs for Question Answering"
-last_updated: 2022-12-08
+last_updated: 2023-01-27
 level: "beginner"
 weight: 20
 description: Create a smarter way to answer new questions using your existing FAQ documents.
@@ -105,7 +105,6 @@ sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
 ### Init the DocumentStore
 In contrast to Tutorial 1 (Build your first QA system), we:
 
-* specify the name of our `text_field` in Elasticsearch that we want to return as an answer
 * specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
 * set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
 
diff --git a/tutorials/04_FAQ_style_QA.ipynb b/tutorials/04_FAQ_style_QA.ipynb
index a92af09a..48ecda6f 100644
--- a/tutorials/04_FAQ_style_QA.ipynb
+++ b/tutorials/04_FAQ_style_QA.ipynb
@@ -184,7 +184,6 @@
     "### Init the DocumentStore\n",
     "In contrast to Tutorial 1 (Build your first QA system), we:\n",
     "\n",
-    "* specify the name of our `text_field` in Elasticsearch that we want to return as an answer\n",
     "* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question\n",
     "* set `excluded_meta_data=[\"question_emb\"]` so that we don't return the huge embedding vectors in our search results"
    ]

From a1d2de69fd2d13d7c629e80625acfcbeaaad29f1 Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Tue, 7 Feb 2023 21:43:03 +0100
Subject: [PATCH 033/206] chore: use images from this repo (#110)

* use images from this repo

* bump action version

* try

* update md
---
 .github/workflows/run_tutorials.yml                      | 6 +++++-
 markdowns/02_Finetune_a_model_on_your_data.md            | 6 +++---
 markdowns/04_FAQ_style_QA.md                             | 4 ++--
 markdowns/05_Evaluation.md                               | 4 ++--
 markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md | 4 ++--
 markdowns/07_RAG_Generator.md                            | 4 ++--
 markdowns/12_LFQA.md                                     | 4 ++--
 markdowns/13_Question_generation.md                      | 4 ++--
 markdowns/14_Query_Classifier.md                         | 4 ++--
 markdowns/15_TableQA.md                                  | 4 ++--
 markdowns/17_Audio.md                                    | 4 ++--
 markdowns/18_GPL.md                                      | 4 ++--
 tutorials/02_Finetune_a_model_on_your_data.ipynb         | 8 +++++---
 tutorials/04_FAQ_style_QA.ipynb                          | 9 +++++----
 tutorials/05_Evaluation.ipynb                            | 3 ++-
 .../06_Better_Retrieval_via_Embedding_Retrieval.ipynb    | 5 +++--
 tutorials/07_RAG_Generator.ipynb                         | 9 +++++----
 tutorials/12_LFQA.ipynb                                  | 3 ++-
 tutorials/13_Question_generation.ipynb                   | 3 ++-
 tutorials/14_Query_Classifier.ipynb                      | 3 ++-
 tutorials/15_TableQA.ipynb                               | 5 +++--
 tutorials/17_Audio.ipynb                                 | 3 ++-
 tutorials/18_GPL.ipynb                                   | 2 +-
 23 files changed, 60 insertions(+), 45 deletions(-)

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 33fa9a4e..5d6e05df 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -34,9 +34,13 @@ jobs:
         with:
           fetch-depth: 2
 
+      # See https://github.com/actions/runner-images/issues/6775
+      - name: Change Owner of Container Working Directory
+        run: chown root:root .
+
       - name: Get changed files
         id: files
-        uses: tj-actions/changed-files@v34
+        uses: tj-actions/changed-files@v35
 
       - name: Convert notebooks to Python
         shell: bash
diff --git a/markdowns/02_Finetune_a_model_on_your_data.md b/markdowns/02_Finetune_a_model_on_your_data.md
index dc0d1bc5..741221a5 100644
--- a/markdowns/02_Finetune_a_model_on_your_data.md
+++ b/markdowns/02_Finetune_a_model_on_your_data.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb
 toc: True
 title: "Fine-Tuning a Model on Your Own Data"
-last_updated: 2022-12-06
+last_updated: 2023-02-03
 level: "intermediate"
 weight: 50
 description: Improve the performance of your Reader by performing fine-tuning.
@@ -26,7 +26,7 @@ This tutorial shows you how to fine-tune a pretrained model on your own dataset.
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 
 ```python
@@ -72,7 +72,7 @@ There are two ways to generate training data
 
 1. **Annotation**: You can use the [annotation tool](https://haystack.deepset.ai/guides/annotation) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
 
-![Snapshot of the annotation tool](https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/annotation_tool.png)
+![Snapshot of the annotation tool](https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/annotation_tool.png)
 
 2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
 
diff --git a/markdowns/04_FAQ_style_QA.md b/markdowns/04_FAQ_style_QA.md
index b6295359..af087927 100644
--- a/markdowns/04_FAQ_style_QA.md
+++ b/markdowns/04_FAQ_style_QA.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb
 toc: True
 title: "Utilizing Existing FAQs for Question Answering"
-last_updated: 2023-01-27
+last_updated: 2023-02-03
 level: "beginner"
 weight: 20
 description: Create a smarter way to answer new questions using your existing FAQ documents.
@@ -34,7 +34,7 @@ In some use cases, a combination of extractive QA and FAQ-style can also be an i
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 You can double check whether the GPU runtime is enabled with the following command:
 
diff --git a/markdowns/05_Evaluation.md b/markdowns/05_Evaluation.md
index e533b465..75ee2b1c 100644
--- a/markdowns/05_Evaluation.md
+++ b/markdowns/05_Evaluation.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb
 toc: True
 title: "Evaluation of a QA System"
-last_updated: 2022-11-24
+last_updated: 2023-02-03
 level: "advanced"
 weight: 100
 description: Learn how to evaluate the performance of individual nodes as well as entire pipelines.
@@ -23,7 +23,7 @@ The results of the evaluation can be saved as CSV files, which contain all the i
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 You can double check whether the GPU runtime is enabled with the following command:
 
diff --git a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
index 6a8a732e..091f1f08 100644
--- a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
+++ b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
 toc: True
 title: "Better Retrieval with Embedding Retrieval"
-last_updated: 2022-11-24
+last_updated: 2023-02-03
 level: "intermediate"
 weight: 55
 description: Use Transformer based dense Retrievers to improve your system’s performance.
@@ -60,7 +60,7 @@ Some models have been fine-tuned on massive Information Retrieval data and can b
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 You can double check whether the GPU runtime is enabled with the following command:
 
diff --git a/markdowns/07_RAG_Generator.md b/markdowns/07_RAG_Generator.md
index d6f55ed5..581b5b38 100644
--- a/markdowns/07_RAG_Generator.md
+++ b/markdowns/07_RAG_Generator.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb
 toc: True
 title: "Generative QA with Retrieval-Augmented Generation"
-last_updated: 2022-11-24
+last_updated: 2023-02-03
 level: "intermediate"
 weight: 60
 description: Try out a generative model in place of the extractive Reader.
@@ -27,7 +27,7 @@ answer generator on a set of retrieved documents.
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 You can double check whether the GPU runtime is enabled with the following command:
 
diff --git a/markdowns/12_LFQA.md b/markdowns/12_LFQA.md
index f04ff30e..2a3b0061 100644
--- a/markdowns/12_LFQA.md
+++ b/markdowns/12_LFQA.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb
 toc: True
 title: "Generative QA with LFQA"
-last_updated: 2022-11-24
+last_updated: 2023-02-03
 level: "intermediate"
 weight: 70
 description: Try out a generative model in place of the extractive Reader.
@@ -22,7 +22,7 @@ Follow this tutorial to learn how to build and use a pipeline for Long-Form Ques
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.  
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 You can double check whether the GPU runtime is enabled with the following command:
 
diff --git a/markdowns/13_Question_generation.md b/markdowns/13_Question_generation.md
index 3135b73f..af4dace1 100644
--- a/markdowns/13_Question_generation.md
+++ b/markdowns/13_Question_generation.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb
 toc: True
 title: "Question Generation"
-last_updated: 2022-12-07
+last_updated: 2023-02-03
 level: "intermediate"
 weight: 75
 description: Generate a set of questions that can be answered by a given Document.
@@ -23,7 +23,7 @@ generate questions which the question generation model thinks can be answered by
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.  
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 
 ```python
diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
index 2b98d534..d7d5f1fb 100644
--- a/markdowns/14_Query_Classifier.md
+++ b/markdowns/14_Query_Classifier.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb
 toc: True
 title: "Query Classifier"
-last_updated: 2023-01-11
+last_updated: 2023-02-03
 level: "intermediate"
 weight: 80
 description: Classify incoming queries so that they can be routed to the nodes that are best at handling them.
@@ -42,7 +42,7 @@ With all of that explanation out of the way, let's dive in!
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.  
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 You can double check whether the GPU runtime is enabled with the following command:
 
diff --git a/markdowns/15_TableQA.md b/markdowns/15_TableQA.md
index 7778e232..b3104a4f 100644
--- a/markdowns/15_TableQA.md
+++ b/markdowns/15_TableQA.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb
 toc: True
 title: "Open-Domain QA on Tables"
-last_updated: 2022-12-15
+last_updated: 2023-02-03
 level: "advanced"
 weight: 130
 description: Perform question answering on tabular data.
@@ -22,7 +22,7 @@ This tutorial shows you how to perform question-answering on tables using the `E
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 You can double check whether the GPU runtime is enabled with the following command:
 
diff --git a/markdowns/17_Audio.md b/markdowns/17_Audio.md
index ff57c7b1..6a02c797 100644
--- a/markdowns/17_Audio.md
+++ b/markdowns/17_Audio.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb
 toc: True
 title: "Make Your QA Pipelines Talk!"
-last_updated: 2022-12-08
+last_updated: 2023-02-03
 level: "intermediate"
 weight: 90
 description: Convert text Answers into speech.
@@ -26,7 +26,7 @@ In this tutorial, we're going to see how to use `AnswerToSpeech` to convert answ
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 You can double check whether the GPU runtime is enabled with the following command:
 
diff --git a/markdowns/18_GPL.md b/markdowns/18_GPL.md
index 86cbb265..96583f03 100644
--- a/markdowns/18_GPL.md
+++ b/markdowns/18_GPL.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb
 toc: True
 title: "Generative Pseudo Labeling for Domain Adaptation"
-last_updated: 2022-11-24
+last_updated: 2023-02-03
 level: "advanced"
 weight: 140
 description: Use a Retriever and a query generator to perform unsupervised domain adaptation.
@@ -49,7 +49,7 @@ If we search again with the updated model, we get the search results we would ex
 Make sure you enable the GPU runtime to experience decent speed in this tutorial.
 **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
 
-<img src="https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg">
+<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
 
 
 
diff --git a/tutorials/02_Finetune_a_model_on_your_data.ipynb b/tutorials/02_Finetune_a_model_on_your_data.ipynb
index 343d55b2..63aa9ebe 100644
--- a/tutorials/02_Finetune_a_model_on_your_data.ipynb
+++ b/tutorials/02_Finetune_a_model_on_your_data.ipynb
@@ -14,6 +14,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -25,7 +26,7 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">"
+    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">"
    ]
   },
   {
@@ -107,6 +108,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -117,7 +119,7 @@
     "\n",
     "1. **Annotation**: You can use the [annotation tool](https://haystack.deepset.ai/guides/annotation) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.\n",
     "\n",
-    "![Snapshot of the annotation tool](https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/annotation_tool.png)\n",
+    "![Snapshot of the annotation tool](https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/annotation_tool.png)\n",
     "\n",
     "2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.\n",
     "\n",
@@ -309,7 +311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.9.6 (default, Aug  5 2022, 15:21:02) \n[Clang 14.0.0 (clang-1400.0.29.102)]"
   },
   "vscode": {
    "interpreter": {
diff --git a/tutorials/04_FAQ_style_QA.ipynb b/tutorials/04_FAQ_style_QA.ipynb
index 48ecda6f..e8f67a9a 100644
--- a/tutorials/04_FAQ_style_QA.ipynb
+++ b/tutorials/04_FAQ_style_QA.ipynb
@@ -22,6 +22,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -33,7 +34,7 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
     "\n",
     "You can double check whether the GPU runtime is enabled with the following command:"
    ]
@@ -372,7 +373,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4 ('haystack-tutorials')",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -386,11 +387,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.6 (main, Aug 11 2022, 13:36:31) [Clang 13.1.6 (clang-1316.0.21.2.5)]"
   },
   "vscode": {
    "interpreter": {
-    "hash": "1e4fa2e1c496b8379da88afac82c60055e1be33cd79040f849449f398c153e43"
+    "hash": "bda33b16be7e844498c7c2d368d72665b4f1d165582b9547ed22a0249a29ca2e"
    }
   }
  },
diff --git a/tutorials/05_Evaluation.ipynb b/tutorials/05_Evaluation.ipynb
index 09374f65..df25381f 100644
--- a/tutorials/05_Evaluation.ipynb
+++ b/tutorials/05_Evaluation.ipynb
@@ -13,6 +13,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "lEKOjCS5U7so",
@@ -27,7 +28,7 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
     "\n",
     "You can double check whether the GPU runtime is enabled with the following command:"
    ]
diff --git a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
index 370ac602..5c3a332b 100644
--- a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+++ b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
@@ -49,6 +49,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "3K27Y5FbA6NV"
@@ -60,7 +61,7 @@
         "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
         "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
         "\n",
-        "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+        "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
         "\n",
         "You can double check whether the GPU runtime is enabled with the following command:"
       ]
@@ -450,7 +451,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.9"
+      "version": "3.9.6 (default, Aug  5 2022, 15:21:02) \n[Clang 14.0.0 (clang-1400.0.29.102)]"
     },
     "vscode": {
       "interpreter": {
diff --git a/tutorials/07_RAG_Generator.ipynb b/tutorials/07_RAG_Generator.ipynb
index b8114475..597a4141 100644
--- a/tutorials/07_RAG_Generator.ipynb
+++ b/tutorials/07_RAG_Generator.ipynb
@@ -17,6 +17,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -28,7 +29,7 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
     "\n",
     "You can double check whether the GPU runtime is enabled with the following command:"
    ]
@@ -365,7 +366,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.9 64-bit",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -379,11 +380,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "3.8.9"
+   "version": "3.10.6 (main, Aug 11 2022, 13:36:31) [Clang 13.1.6 (clang-1316.0.21.2.5)]"
   },
   "vscode": {
    "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+    "hash": "bda33b16be7e844498c7c2d368d72665b4f1d165582b9547ed22a0249a29ca2e"
    }
   }
  },
diff --git a/tutorials/12_LFQA.ipynb b/tutorials/12_LFQA.ipynb
index 7d928939..146dd125 100644
--- a/tutorials/12_LFQA.ipynb
+++ b/tutorials/12_LFQA.ipynb
@@ -12,6 +12,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "3K27Y5FbA6NV"
@@ -23,7 +24,7 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.  \n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
     "\n",
     "You can double check whether the GPU runtime is enabled with the following command:"
    ]
diff --git a/tutorials/13_Question_generation.ipynb b/tutorials/13_Question_generation.ipynb
index b2e37110..67c62ac2 100644
--- a/tutorials/13_Question_generation.ipynb
+++ b/tutorials/13_Question_generation.ipynb
@@ -16,6 +16,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "yaaKv3_ZN-gb",
@@ -30,7 +31,7 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.  \n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">"
+    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">"
    ]
   },
   {
diff --git a/tutorials/14_Query_Classifier.ipynb b/tutorials/14_Query_Classifier.ipynb
index cb95f0c3..9e2732e7 100644
--- a/tutorials/14_Query_Classifier.ipynb
+++ b/tutorials/14_Query_Classifier.ipynb
@@ -35,6 +35,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "yaaKv3_ZN-gb",
@@ -49,7 +50,7 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.  \n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
     "\n",
     "You can double check whether the GPU runtime is enabled with the following command:"
    ]
diff --git a/tutorials/15_TableQA.ipynb b/tutorials/15_TableQA.ipynb
index 0c564ea1..f15934d4 100644
--- a/tutorials/15_TableQA.ipynb
+++ b/tutorials/15_TableQA.ipynb
@@ -12,6 +12,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "vbR3bETlvi-3"
@@ -23,7 +24,7 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
     "\n",
     "You can double check whether the GPU runtime is enabled with the following command:"
    ]
@@ -802,4 +803,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
\ No newline at end of file
+}
diff --git a/tutorials/17_Audio.ipynb b/tutorials/17_Audio.ipynb
index d15bfe94..c6c7398d 100644
--- a/tutorials/17_Audio.ipynb
+++ b/tutorials/17_Audio.ipynb
@@ -16,6 +16,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -28,7 +29,7 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n",
+    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
     "\n",
     "You can double check whether the GPU runtime is enabled with the following command:"
    ]
diff --git a/tutorials/18_GPL.ipynb b/tutorials/18_GPL.ipynb
index 36f09a65..eeb12b42 100644
--- a/tutorials/18_GPL.ipynb
+++ b/tutorials/18_GPL.ipynb
@@ -46,7 +46,7 @@
     "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
     "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/colab_gpu_runtime.jpg\">\n"
+    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n"
    ]
   },
   {

From 023e08804a0cb2fc8df5bd2b8544afc65aeeb4c4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 8 Feb 2023 15:56:02 +0300
Subject: [PATCH 034/206] fix links for preparing colab environment (#115)

---
 ...t_to_Image_search_pipeline_with_MultiModal_Retriever.md | 7 +++----
 ...o_Image_search_pipeline_with_MultiModal_Retriever.ipynb | 5 ++---
 2 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
index 37ec4d37..cc9d139b 100644
--- a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
+++ b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
 toc: True
 title: "Text-To-Image Search Pipeline with Multimodal Retriever"
-last_updated: 2022-11-25
+last_updated: 2023-02-08
 level: "intermediate"
 weight: 95
 description: Use a MultiModalRetriever to build a cross-modal search pipeline.
@@ -32,9 +32,8 @@ Let's build a text-to-image search pipeline using a small animal dataset!
 
 ## Preparing the Colab Environment
 
-- [Enable GPU Runtime in GPU](https://docs.haystack.deepset.ai/v5.2-unstable/docs/enable-gpu-runtime-in-colab)
-- [Check if GPU is Enabled](https://docs.haystack.deepset.ai/v5.2-unstable/docs/check-if-gpu-is-enabled)
-- [Set logging level to INFO](https://docs.haystack.deepset.ai/v5.2-unstable/docs/set-the-logging-level)
+- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)
+- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)
 
 ## Installing Haystack
 
diff --git a/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb b/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
index b0fe17c6..82a25132 100644
--- a/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
+++ b/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
@@ -33,9 +33,8 @@
    "source": [
     "## Preparing the Colab Environment\n",
     "\n",
-    "- [Enable GPU Runtime in GPU](https://docs.haystack.deepset.ai/v5.2-unstable/docs/enable-gpu-runtime-in-colab)\n",
-    "- [Check if GPU is Enabled](https://docs.haystack.deepset.ai/v5.2-unstable/docs/check-if-gpu-is-enabled)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v5.2-unstable/docs/set-the-logging-level)"
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
    ]
   },
   {

From 9b848736628353f7842fd0ca347ba6c0acebccf5 Mon Sep 17 00:00:00 2001
From: Julian Risch <julian.risch@deepset.ai>
Date: Mon, 13 Feb 2023 09:57:19 +0100
Subject: [PATCH 035/206] fix: Typo in tutorial 12 (#120)

* fix typo

* run pre-commit hook
---
 markdowns/12_LFQA.md    | 4 ++--
 tutorials/12_LFQA.ipynb | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/markdowns/12_LFQA.md b/markdowns/12_LFQA.md
index 2a3b0061..b15a1d91 100644
--- a/markdowns/12_LFQA.md
+++ b/markdowns/12_LFQA.md
@@ -3,7 +3,7 @@ layout: tutorial
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb
 toc: True
 title: "Generative QA with LFQA"
-last_updated: 2023-02-03
+last_updated: 2023-02-13
 level: "intermediate"
 weight: 70
 description: Try out a generative model in place of the extractive Reader.
@@ -14,7 +14,7 @@ download: "/downloads/12_LFQA.ipynb"
     
 
 
-Follow this tutorial to learn how to build and use a pipeline for Long-Form Question Answering (LFQA). LFQA is a variety of the generative question answering task. LFQA systems query large document stores for relevant information and then use this information to generate accurate, multi-sentence answers. In a regular question answering system, the retrieved documents related to the query (context passages) act as source tokens for extracted answers. In an LFQS system, context passages provide the context the system uses to generate original, abstractive, long-form answers.
+Follow this tutorial to learn how to build and use a pipeline for Long-Form Question Answering (LFQA). LFQA is a variety of the generative question answering task. LFQA systems query large document stores for relevant information and then use this information to generate accurate, multi-sentence answers. In a regular question answering system, the retrieved documents related to the query (context passages) act as source tokens for extracted answers. In an LFQA system, context passages provide the context the system uses to generate original, abstractive, long-form answers.
 
 ### Prepare environment
 
diff --git a/tutorials/12_LFQA.ipynb b/tutorials/12_LFQA.ipynb
index 146dd125..47c1c768 100644
--- a/tutorials/12_LFQA.ipynb
+++ b/tutorials/12_LFQA.ipynb
@@ -8,7 +8,7 @@
    "source": [
     "# Long-Form Question Answering\n",
     "\n",
-    "Follow this tutorial to learn how to build and use a pipeline for Long-Form Question Answering (LFQA). LFQA is a variety of the generative question answering task. LFQA systems query large document stores for relevant information and then use this information to generate accurate, multi-sentence answers. In a regular question answering system, the retrieved documents related to the query (context passages) act as source tokens for extracted answers. In an LFQS system, context passages provide the context the system uses to generate original, abstractive, long-form answers."
+    "Follow this tutorial to learn how to build and use a pipeline for Long-Form Question Answering (LFQA). LFQA is a variety of the generative question answering task. LFQA systems query large document stores for relevant information and then use this information to generate accurate, multi-sentence answers. In a regular question answering system, the retrieved documents related to the query (context passages) act as source tokens for extracted answers. In an LFQA system, context passages provide the context the system uses to generate original, abstractive, long-form answers."
    ]
   },
   {

From e18c445023eb612022d13945c10fb60227cb9c51 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 15 Feb 2023 00:42:14 +0300
Subject: [PATCH 036/206] New information to tutorials (#122)

* Add "new" to frontmatter

* New tutorials should be stated as `new = true` in index.toml

* creating the new optional frontmatter labels and an extra 'all option'

* changing the keyword to 'featured'

* removing telemetry change

---------

Co-authored-by: Tuana Celik <tuana.celik@deepset.ai>
---
 index.toml                                    |  5 +++++
 markdowns/01_Basic_QA_Pipeline.md             |  2 ++
 markdowns/02_Finetune_a_model_on_your_data.md |  2 ++
 markdowns/03_Scalable_QA_System.md            |  2 ++
 markdowns/04_FAQ_style_QA.md                  |  2 ++
 markdowns/05_Evaluation.md                    |  2 ++
 ...etter_Retrieval_via_Embedding_Retrieval.md |  2 ++
 markdowns/07_RAG_Generator.md                 |  2 ++
 markdowns/08_Preprocessing.md                 |  2 ++
 markdowns/09_DPR_training.md                  |  2 ++
 markdowns/10_Knowledge_Graph.md               |  2 ++
 markdowns/11_Pipelines.md                     |  2 ++
 markdowns/12_LFQA.md                          |  2 ++
 markdowns/13_Question_generation.md           |  2 ++
 markdowns/14_Query_Classifier.md              |  2 ++
 markdowns/15_TableQA.md                       |  2 ++
 .../16_Document_Classifier_at_Index_Time.md   |  2 ++
 markdowns/17_Audio.md                         |  2 ++
 markdowns/18_GPL.md                           |  2 ++
 ...arch_pipeline_with_MultiModal_Retriever.md |  2 ++
 markdowns/20_Using_Haystack_with_REST_API.md  |  2 ++
 scripts/generate_markdowns.py                 | 22 ++++++++++++++-----
 22 files changed, 61 insertions(+), 6 deletions(-)

diff --git a/index.toml b/index.toml
index ba9ed8ee..92c2bd7e 100644
--- a/index.toml
+++ b/index.toml
@@ -10,6 +10,7 @@ level = "beginner"
 weight = 10
 notebook = "01_Basic_QA_Pipeline.ipynb"
 aliases = ["first-qa-system", "without-elasticsearch", "03_basic_qa_pipeline_without_elasticsearch"]
+completion_time = "15 min"
 
 [[tutorial]]
 title = "Fine-Tuning a Model on Your Own Data"
@@ -26,6 +27,7 @@ level = "beginner"
 weight = 15
 notebook = "03_Scalable_QA_System.ipynb"
 aliases = []
+completion_time = "20 min"
 
 [[tutorial]]
 title = "Utilizing Existing FAQs for Question Answering"
@@ -154,6 +156,7 @@ level = "intermediate"
 weight = 95
 notebook = "19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
 aliases = ["multimodal"]
+completion_time = "20 min"
 
 [[tutorial]]
 title = "Using Haystack with REST API"
@@ -163,3 +166,5 @@ weight = 115
 notebook = "20_Using_Haystack_with_REST_API.ipynb"
 aliases = ["using-haystack-with-rest-api"]
 colab = false
+completion_time = "30 min"
+featured = true
diff --git a/markdowns/01_Basic_QA_Pipeline.md b/markdowns/01_Basic_QA_Pipeline.md
index f4be231b..88b97618 100644
--- a/markdowns/01_Basic_QA_Pipeline.md
+++ b/markdowns/01_Basic_QA_Pipeline.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb
 toc: True
 title: "Build Your First Question Answering System"
@@ -10,6 +11,7 @@ description: Get Started by creating a Retriever Reader pipeline.
 category: "QA"
 aliases: ['/tutorials/first-qa-system', '/tutorials/without-elasticsearch', '/tutorials/03_basic_qa_pipeline_without_elasticsearch']
 download: "/downloads/01_Basic_QA_Pipeline.ipynb"
+completion_time: 15 min
 ---
     
 
diff --git a/markdowns/02_Finetune_a_model_on_your_data.md b/markdowns/02_Finetune_a_model_on_your_data.md
index 741221a5..34c169fd 100644
--- a/markdowns/02_Finetune_a_model_on_your_data.md
+++ b/markdowns/02_Finetune_a_model_on_your_data.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb
 toc: True
 title: "Fine-Tuning a Model on Your Own Data"
@@ -10,6 +11,7 @@ description: Improve the performance of your Reader by performing fine-tuning.
 category: "QA"
 aliases: ['/tutorials/fine-tuning-a-model']
 download: "/downloads/02_Finetune_a_model_on_your_data.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/03_Scalable_QA_System.md b/markdowns/03_Scalable_QA_System.md
index 78544bd1..f952fe5a 100644
--- a/markdowns/03_Scalable_QA_System.md
+++ b/markdowns/03_Scalable_QA_System.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb
 toc: True
 title: "Build a Scalable Question Answering System"
@@ -10,6 +11,7 @@ description: Create a scalable Retriever Reader pipeline that uses an Elasticsea
 category: "QA"
 aliases: []
 download: "/downloads/03_Scalable_QA_System.ipynb"
+completion_time: 20 min
 ---
     
 
diff --git a/markdowns/04_FAQ_style_QA.md b/markdowns/04_FAQ_style_QA.md
index af087927..1d27f30e 100644
--- a/markdowns/04_FAQ_style_QA.md
+++ b/markdowns/04_FAQ_style_QA.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb
 toc: True
 title: "Utilizing Existing FAQs for Question Answering"
@@ -10,6 +11,7 @@ description: Create a smarter way to answer new questions using your existing FA
 category: "QA"
 aliases: ['/tutorials/existing-faqs']
 download: "/downloads/04_FAQ_style_QA.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/05_Evaluation.md b/markdowns/05_Evaluation.md
index 75ee2b1c..0368ee52 100644
--- a/markdowns/05_Evaluation.md
+++ b/markdowns/05_Evaluation.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb
 toc: True
 title: "Evaluation of a QA System"
@@ -10,6 +11,7 @@ description: Learn how to evaluate the performance of individual nodes as well a
 category: "QA"
 aliases: ['/tutorials/evaluation']
 download: "/downloads/05_Evaluation.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
index 091f1f08..19a77464 100644
--- a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
+++ b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
 toc: True
 title: "Better Retrieval with Embedding Retrieval"
@@ -10,6 +11,7 @@ description: Use Transformer based dense Retrievers to improve your system’s p
 category: "QA"
 aliases: ['/tutorials/embedding-retrieval']
 download: "/downloads/06_Better_Retrieval_via_Embedding_Retrieval.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/07_RAG_Generator.md b/markdowns/07_RAG_Generator.md
index 581b5b38..781da822 100644
--- a/markdowns/07_RAG_Generator.md
+++ b/markdowns/07_RAG_Generator.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb
 toc: True
 title: "Generative QA with Retrieval-Augmented Generation"
@@ -10,6 +11,7 @@ description: Try out a generative model in place of the extractive Reader.
 category: "QA"
 aliases: ['/tutorials/retrieval-augmented-generation']
 download: "/downloads/07_RAG_Generator.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/08_Preprocessing.md b/markdowns/08_Preprocessing.md
index 8e08793c..fc44b195 100644
--- a/markdowns/08_Preprocessing.md
+++ b/markdowns/08_Preprocessing.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb
 toc: True
 title: "Preprocessing Your Documents"
@@ -10,6 +11,7 @@ description: Start converting, cleaning, and splitting Documents using Haystack
 category: "QA"
 aliases: ['/tutorials/preprocessing']
 download: "/downloads/08_Preprocessing.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/09_DPR_training.md b/markdowns/09_DPR_training.md
index 0e440d5c..f70a5246 100644
--- a/markdowns/09_DPR_training.md
+++ b/markdowns/09_DPR_training.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb
 toc: True
 title: "Training Your Own Dense Passage Retrieval Model"
@@ -10,6 +11,7 @@ description: Learn about training a Dense Passage Retrieval model and the data n
 category: "QA"
 aliases: ['/tutorials/train-dpr']
 download: "/downloads/09_DPR_training.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/10_Knowledge_Graph.md b/markdowns/10_Knowledge_Graph.md
index 3916ce66..7da1ce2c 100644
--- a/markdowns/10_Knowledge_Graph.md
+++ b/markdowns/10_Knowledge_Graph.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb
 toc: True
 title: "Question Answering on a Knowledge Graph"
@@ -10,6 +11,7 @@ description: Experiment with a question answering system that draws upon knowled
 category: "QA"
 aliases: ['/tutorials/knowledge-graph']
 download: "/downloads/10_Knowledge_Graph.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/11_Pipelines.md b/markdowns/11_Pipelines.md
index 37a0ae92..76e974b4 100644
--- a/markdowns/11_Pipelines.md
+++ b/markdowns/11_Pipelines.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb
 toc: True
 title: "How to Use Pipelines"
@@ -10,6 +11,7 @@ description: Learn about the many ways which you can route queries through the n
 category: "QA"
 aliases: ['/tutorials/pipelines']
 download: "/downloads/11_Pipelines.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/12_LFQA.md b/markdowns/12_LFQA.md
index b15a1d91..b86adf5b 100644
--- a/markdowns/12_LFQA.md
+++ b/markdowns/12_LFQA.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb
 toc: True
 title: "Generative QA with LFQA"
@@ -10,6 +11,7 @@ description: Try out a generative model in place of the extractive Reader.
 category: "QA"
 aliases: ['/tutorials/lfqa']
 download: "/downloads/12_LFQA.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/13_Question_generation.md b/markdowns/13_Question_generation.md
index af4dace1..85d55ada 100644
--- a/markdowns/13_Question_generation.md
+++ b/markdowns/13_Question_generation.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb
 toc: True
 title: "Question Generation"
@@ -10,6 +11,7 @@ description: Generate a set of questions that can be answered by a given Documen
 category: "QA"
 aliases: ['/tutorials/question-generation']
 download: "/downloads/13_Question_generation.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
index d7d5f1fb..195656a1 100644
--- a/markdowns/14_Query_Classifier.md
+++ b/markdowns/14_Query_Classifier.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb
 toc: True
 title: "Query Classifier"
@@ -10,6 +11,7 @@ description: Classify incoming queries so that they can be routed to the nodes t
 category: "QA"
 aliases: ['/tutorials/query-classifier']
 download: "/downloads/14_Query_Classifier.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/15_TableQA.md b/markdowns/15_TableQA.md
index b3104a4f..68922292 100644
--- a/markdowns/15_TableQA.md
+++ b/markdowns/15_TableQA.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb
 toc: True
 title: "Open-Domain QA on Tables"
@@ -10,6 +11,7 @@ description: Perform question answering on tabular data.
 category: "QA"
 aliases: ['/tutorials/table-qa']
 download: "/downloads/15_TableQA.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/16_Document_Classifier_at_Index_Time.md b/markdowns/16_Document_Classifier_at_Index_Time.md
index 9812573b..dcb67921 100644
--- a/markdowns/16_Document_Classifier_at_Index_Time.md
+++ b/markdowns/16_Document_Classifier_at_Index_Time.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb
 toc: True
 title: "Document Classification at Index Time"
@@ -10,6 +11,7 @@ description: Generate and attach classification labels to your Documents when in
 category: "QA"
 aliases: ['/tutorials/doc-class-index']
 download: "/downloads/16_Document_Classifier_at_Index_Time.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/17_Audio.md b/markdowns/17_Audio.md
index 6a02c797..f701b2b9 100644
--- a/markdowns/17_Audio.md
+++ b/markdowns/17_Audio.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb
 toc: True
 title: "Make Your QA Pipelines Talk!"
@@ -10,6 +11,7 @@ description: Convert text Answers into speech.
 category: "QA"
 aliases: ['/tutorials/audio']
 download: "/downloads/17_Audio.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/18_GPL.md b/markdowns/18_GPL.md
index 96583f03..525790f3 100644
--- a/markdowns/18_GPL.md
+++ b/markdowns/18_GPL.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb
 toc: True
 title: "Generative Pseudo Labeling for Domain Adaptation"
@@ -10,6 +11,7 @@ description: Use a Retriever and a query generator to perform unsupervised domai
 category: "QA"
 aliases: ['/tutorials/gpl']
 download: "/downloads/18_GPL.ipynb"
+completion_time: False
 ---
     
 
diff --git a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
index cc9d139b..e9cbf9e3 100644
--- a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
+++ b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
 toc: True
 title: "Text-To-Image Search Pipeline with Multimodal Retriever"
@@ -10,6 +11,7 @@ description: Use a MultiModalRetriever to build a cross-modal search pipeline.
 category: "QA"
 aliases: ['/tutorials/multimodal']
 download: "/downloads/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
+completion_time: 20 min
 ---
     
 
diff --git a/markdowns/20_Using_Haystack_with_REST_API.md b/markdowns/20_Using_Haystack_with_REST_API.md
index df1ceee5..60680803 100644
--- a/markdowns/20_Using_Haystack_with_REST_API.md
+++ b/markdowns/20_Using_Haystack_with_REST_API.md
@@ -1,5 +1,6 @@
 ---
 layout: tutorial
+featured: True
 colab: False
 toc: True
 title: "Using Haystack with REST API"
@@ -10,6 +11,7 @@ description: Create a production-ready pipeline and interact with Haystack REST
 category: "QA"
 aliases: ['/tutorials/using-haystack-with-rest-api']
 download: "/downloads/20_Using_Haystack_with_REST_API.ipynb"
+completion_time: 30 min
 ---
     
 
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index 2f75b97b..4b422da9 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -18,6 +18,7 @@ def generate_frontmatter(config, tutorial):
 
     frontmatter = f"""---
 layout: {config["layout"]}
+featured: {tutorial.get("featured", False)}
 colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')}
 toc: {config["toc"]}
 title: "{tutorial["title"]}"
@@ -28,6 +29,7 @@ def generate_frontmatter(config, tutorial):
 category: "QA"
 aliases: {aliases}
 download: "/downloads/{tutorial["notebook"]}"
+completion_time: {tutorial.get("completion_time", False)}
 ---
     """
     return frontmatter
@@ -58,10 +60,18 @@ def generate_markdown_from_notebook(config, tutorial, output_path, tutorials_pat
     args = parser.parse_args()
     index = read_index(args.index)
 
-    nb_to_config = {cfg["notebook"]: cfg for cfg in index["tutorial"]}
+    if args.notebooks == ["all"]:
+        print("got here!")
+        for config in index["tutorial"]:
+            notebook = "tutorials/" + config["notebook"]
+            print(notebook)
+            generate_markdown_from_notebook(index["config"], config, args.output, notebook)
 
-    for notebook in args.notebooks:
-        nb_name = notebook.split("/")[-1]
-        tutorial_cfg = nb_to_config.get(nb_name)
-        if tutorial_cfg:
-            generate_markdown_from_notebook(index["config"], tutorial_cfg, args.output, notebook)
+    else:
+        nb_to_config = {cfg["notebook"]: cfg for cfg in index["tutorial"]}
+            
+        for notebook in args.notebooks:
+            nb_name = notebook.split("/")[-1]
+            tutorial_cfg = nb_to_config.get(nb_name)
+            if tutorial_cfg:
+                generate_markdown_from_notebook(index["config"], tutorial_cfg, args.output, notebook)

From ffb5b9c1a8c73359c049dbf256738c4c45fc241c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 16 Feb 2023 10:55:39 +0100
Subject: [PATCH 037/206] Update contributing information (#119)

* Add "Preparing the Colab Environment" section to the template

* Change the discord and Website links at the footer of template.ipynb

* update contributing markdown

* Explain the steps for editing and creating

* Change the section about pre-commit hooks
---
 Contributing.md          | 14 ++++++++------
 tutorials/template.ipynb | 18 ++++++++++++++----
 2 files changed, 22 insertions(+), 10 deletions(-)

diff --git a/Contributing.md b/Contributing.md
index 613aabfe..9983fb9d 100644
--- a/Contributing.md
+++ b/Contributing.md
@@ -18,12 +18,14 @@ Here's what you need to do to add or edit tutorials 👇:
    - Install the Python requirements with `pip install -r requirements.txt`
    - Install the pre-commit hooks with `pre-commit install`. This utility will run some formatting/checking
    tasks right before all git commit operations.
-2. If you're creating a new tutorial:
-   - Follow the [naming convention](#naming-convention-for-file-names) for file names.
-   - Add your new tutorial to [index.toml](/index.toml). Here, `weight` is the order in which your tutorial appears. For example, a tutorial with `weight = 15` comes after a tutorial with `weight = 10` and before `20`. Each tutorial comes with a Google Colab link and `Open in Colab` button on the top of the tutorial by default. If your new tutorial cannot be run on Google Colab, set `colab = false` not to display `Open in Colab` button on top the tutorial.
-3. Edit an existing tutorial or copy the [tutorial template](/tutorials/template.ipynb) to create a new tutorial. 
-4. Pre-commit hooks will ensure the `markdowns` folder reflects your changes but you can update the docs at any time:
-    - Run `python scripts/generate_markdowns.py --index index.toml --notebooks tutorials/your-tutorial.ipynb --output markdowns/`. This generates or updates the relevant markdown file in `/markdowns`.
+2. If you're **creating** a new tutorial:
+   - Create a copy of [tutorial template](/tutorials/template.ipynb) in `/tutorials` folder.
+   - Rename the new `.ipynb` file by following the [naming convention](#naming-convention-for-file-names).
+   - Follow the outline in the template as you create the tutorial.
+   - After the tutorial is complete, add necessary information to [index.toml](/index.toml). Here, `weight` is the order in which your tutorial appears. For example, a tutorial with `weight = 15` comes after a tutorial with `weight = 10` and before `20`. Each tutorial comes with a Google Colab link and `Open in Colab` button on the top of the tutorial by default. If your new tutorial cannot be run on Google Colab, set `colab = false` not to display `Open in Colab` button on top the tutorial.
+3. If you're **editing** an existing tutorial:
+   - Make necessary changes in the `.ipynb` file of the tutorial and save them. You don't need to make changes in the markdown file. 
+4. Run `python scripts/generate_markdowns.py --index index.toml --notebooks tutorials/your-tutorial.ipynb --output markdowns/`. This generates or updates the relevant markdown file. Pre-commit hooks will ensure the `markdowns` folder reflects your changes. Don't forget to add changed files to your commit if pre-commit hooks updated `.ipynb` or `.md` files.
 5. Create a pull request.
 6. Wait for the [CI](#ci-continuous-integration) checks to pass.
    These checks pass if the relevant markdown files are created.
diff --git a/tutorials/template.ipynb b/tutorials/template.ipynb
index aeb34afd..9970a3a4 100644
--- a/tutorials/template.ipynb
+++ b/tutorials/template.ipynb
@@ -21,6 +21,16 @@
     "*Here provide a short description of the tutorial. What does it teach? What's its expected outcome?*"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -85,7 +95,7 @@
     "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
     "\n",
     "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
+    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://discord.com/invite/VBpFzsgRVF) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Haystack Website](https://deepset.ai)\n",
     "\n",
     "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
    ]
@@ -93,18 +103,18 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.13 64-bit (microsoft store)",
+   "display_name": "Python 3.9.6 64-bit",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "name": "python",
-   "version": "3.9.13"
+   "version": "3.9.6"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "9075e6086e4e65b56cd3eb170a15e0fca54180da9a114ef73f891ab1378b8e41"
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
   }
  },

From 8fe67cfdfef82b7c385b0269488307869b177cc6 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Thu, 16 Feb 2023 11:13:40 +0100
Subject: [PATCH 038/206] adding created at dates (#123)

---
 index.toml                                    | 20 +++++++++++++++++++
 markdowns/01_Basic_QA_Pipeline.md             |  1 +
 markdowns/02_Finetune_a_model_on_your_data.md |  1 +
 markdowns/03_Scalable_QA_System.md            |  1 +
 markdowns/04_FAQ_style_QA.md                  |  1 +
 markdowns/05_Evaluation.md                    |  1 +
 ...etter_Retrieval_via_Embedding_Retrieval.md |  1 +
 markdowns/07_RAG_Generator.md                 |  1 +
 markdowns/08_Preprocessing.md                 |  1 +
 markdowns/09_DPR_training.md                  |  1 +
 markdowns/10_Knowledge_Graph.md               |  1 +
 markdowns/11_Pipelines.md                     |  1 +
 markdowns/12_LFQA.md                          |  1 +
 markdowns/13_Question_generation.md           |  1 +
 markdowns/14_Query_Classifier.md              |  1 +
 markdowns/15_TableQA.md                       |  1 +
 .../16_Document_Classifier_at_Index_Time.md   |  1 +
 markdowns/17_Audio.md                         |  1 +
 markdowns/18_GPL.md                           |  1 +
 ...arch_pipeline_with_MultiModal_Retriever.md |  1 +
 markdowns/20_Using_Haystack_with_REST_API.md  |  1 +
 scripts/generate_markdowns.py                 |  2 +-
 22 files changed, 41 insertions(+), 1 deletion(-)

diff --git a/index.toml b/index.toml
index 92c2bd7e..f33546ca 100644
--- a/index.toml
+++ b/index.toml
@@ -11,6 +11,7 @@ weight = 10
 notebook = "01_Basic_QA_Pipeline.ipynb"
 aliases = ["first-qa-system", "without-elasticsearch", "03_basic_qa_pipeline_without_elasticsearch"]
 completion_time = "15 min"
+created_at = 2023-11-01
 
 [[tutorial]]
 title = "Fine-Tuning a Model on Your Own Data"
@@ -19,6 +20,7 @@ level = "intermediate"
 weight = 50
 notebook = "02_Finetune_a_model_on_your_data.ipynb"
 aliases = ["fine-tuning-a-model"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Build a Scalable Question Answering System"
@@ -28,6 +30,7 @@ weight = 15
 notebook = "03_Scalable_QA_System.ipynb"
 aliases = []
 completion_time = "20 min"
+created_at = 2023-11-01
 
 [[tutorial]]
 title = "Utilizing Existing FAQs for Question Answering"
@@ -36,6 +39,7 @@ level = "beginner"
 weight = 20
 notebook = "04_FAQ_style_QA.ipynb"
 aliases = ["existing-faqs"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Evaluation of a QA System"
@@ -44,6 +48,7 @@ level = "advanced"
 weight = 100
 notebook = "05_Evaluation.ipynb"
 aliases = ["evaluation"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Better Retrieval with Embedding Retrieval"
@@ -52,6 +57,7 @@ level = "intermediate"
 weight = 55
 notebook = "06_Better_Retrieval_via_Embedding_Retrieval.ipynb"
 aliases = ["embedding-retrieval"]
+created_at = 2022-03-08
 
 [[tutorial]]
 title = "Generative QA with Retrieval-Augmented Generation"
@@ -60,6 +66,7 @@ level = "intermediate"
 weight = 60
 notebook = "07_RAG_Generator.ipynb"
 aliases = ["retrieval-augmented-generation"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Preprocessing Your Documents"
@@ -68,6 +75,7 @@ level = "beginner"
 weight = 25
 notebook = "08_Preprocessing.ipynb"
 aliases = ["preprocessing"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Training Your Own Dense Passage Retrieval Model"
@@ -76,6 +84,7 @@ level = "advanced"
 weight = 110
 notebook = "09_DPR_training.ipynb"
 aliases = ["train-dpr"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Question Answering on a Knowledge Graph"
@@ -84,6 +93,7 @@ level = "advanced"
 weight = 120
 notebook = "10_Knowledge_Graph.ipynb"
 aliases = ["knowledge-graph"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "How to Use Pipelines"
@@ -92,6 +102,7 @@ level = "intermediate"
 weight = 65
 notebook = "11_Pipelines.ipynb"
 aliases = ["pipelines"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Generative QA with LFQA"
@@ -100,6 +111,7 @@ level = "intermediate"
 weight = 70
 notebook = "12_LFQA.ipynb"
 aliases = ["lfqa"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Question Generation"
@@ -108,6 +120,7 @@ level = "intermediate"
 weight = 75
 notebook = "13_Question_generation.ipynb"
 aliases = ["question-generation"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Query Classifier"
@@ -116,6 +129,7 @@ level = "intermediate"
 weight = 80
 notebook = "14_Query_Classifier.ipynb"
 aliases = ["query-classifier"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Open-Domain QA on Tables"
@@ -124,6 +138,7 @@ level = "advanced"
 weight = 130
 notebook = "15_TableQA.ipynb"
 aliases = ["table-qa"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Document Classification at Index Time"
@@ -132,6 +147,7 @@ level = "intermediate"
 weight = 85
 notebook = "16_Document_Classifier_at_Index_Time.ipynb"
 aliases = ["doc-class-index"]
+created_at = 2021-08-12
 
 [[tutorial]]
 title = "Make Your QA Pipelines Talk!"
@@ -140,6 +156,7 @@ level = "intermediate"
 weight = 90
 notebook = "17_Audio.ipynb"
 aliases = ["audio"]
+created_at = 2022-06-07
 
 [[tutorial]]
 title = "Generative Pseudo Labeling for Domain Adaptation"
@@ -148,6 +165,7 @@ level = "advanced"
 weight = 140
 notebook = "18_GPL.ipynb"
 aliases = ["gpl"]
+created_at = 2022-06-07
 
 [[tutorial]]
 title = "Text-To-Image Search Pipeline with Multimodal Retriever"
@@ -157,6 +175,7 @@ weight = 95
 notebook = "19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
 aliases = ["multimodal"]
 completion_time = "20 min"
+created_at = 2022-07-11
 
 [[tutorial]]
 title = "Using Haystack with REST API"
@@ -168,3 +187,4 @@ aliases = ["using-haystack-with-rest-api"]
 colab = false
 completion_time = "30 min"
 featured = true
+created_at = 2023-11-01
\ No newline at end of file
diff --git a/markdowns/01_Basic_QA_Pipeline.md b/markdowns/01_Basic_QA_Pipeline.md
index 88b97618..35dc66f8 100644
--- a/markdowns/01_Basic_QA_Pipeline.md
+++ b/markdowns/01_Basic_QA_Pipeline.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/first-qa-system', '/tutorials/without-elasticsearch', '/tutorials/03_basic_qa_pipeline_without_elasticsearch']
 download: "/downloads/01_Basic_QA_Pipeline.ipynb"
 completion_time: 15 min
+created_at: 2023-11-01
 ---
     
 
diff --git a/markdowns/02_Finetune_a_model_on_your_data.md b/markdowns/02_Finetune_a_model_on_your_data.md
index 34c169fd..8e606290 100644
--- a/markdowns/02_Finetune_a_model_on_your_data.md
+++ b/markdowns/02_Finetune_a_model_on_your_data.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/fine-tuning-a-model']
 download: "/downloads/02_Finetune_a_model_on_your_data.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/03_Scalable_QA_System.md b/markdowns/03_Scalable_QA_System.md
index f952fe5a..7467dad5 100644
--- a/markdowns/03_Scalable_QA_System.md
+++ b/markdowns/03_Scalable_QA_System.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: []
 download: "/downloads/03_Scalable_QA_System.ipynb"
 completion_time: 20 min
+created_at: 2023-11-01
 ---
     
 
diff --git a/markdowns/04_FAQ_style_QA.md b/markdowns/04_FAQ_style_QA.md
index 1d27f30e..50653c42 100644
--- a/markdowns/04_FAQ_style_QA.md
+++ b/markdowns/04_FAQ_style_QA.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/existing-faqs']
 download: "/downloads/04_FAQ_style_QA.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/05_Evaluation.md b/markdowns/05_Evaluation.md
index 0368ee52..4bca4cba 100644
--- a/markdowns/05_Evaluation.md
+++ b/markdowns/05_Evaluation.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/evaluation']
 download: "/downloads/05_Evaluation.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
index 19a77464..cf4a8cf4 100644
--- a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
+++ b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/embedding-retrieval']
 download: "/downloads/06_Better_Retrieval_via_Embedding_Retrieval.ipynb"
 completion_time: False
+created_at: 2022-03-08
 ---
     
 
diff --git a/markdowns/07_RAG_Generator.md b/markdowns/07_RAG_Generator.md
index 781da822..29043683 100644
--- a/markdowns/07_RAG_Generator.md
+++ b/markdowns/07_RAG_Generator.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/retrieval-augmented-generation']
 download: "/downloads/07_RAG_Generator.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/08_Preprocessing.md b/markdowns/08_Preprocessing.md
index fc44b195..cd599217 100644
--- a/markdowns/08_Preprocessing.md
+++ b/markdowns/08_Preprocessing.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/preprocessing']
 download: "/downloads/08_Preprocessing.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/09_DPR_training.md b/markdowns/09_DPR_training.md
index f70a5246..1178bda7 100644
--- a/markdowns/09_DPR_training.md
+++ b/markdowns/09_DPR_training.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/train-dpr']
 download: "/downloads/09_DPR_training.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/10_Knowledge_Graph.md b/markdowns/10_Knowledge_Graph.md
index 7da1ce2c..0aecd716 100644
--- a/markdowns/10_Knowledge_Graph.md
+++ b/markdowns/10_Knowledge_Graph.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/knowledge-graph']
 download: "/downloads/10_Knowledge_Graph.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/11_Pipelines.md b/markdowns/11_Pipelines.md
index 76e974b4..309c0d15 100644
--- a/markdowns/11_Pipelines.md
+++ b/markdowns/11_Pipelines.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/pipelines']
 download: "/downloads/11_Pipelines.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/12_LFQA.md b/markdowns/12_LFQA.md
index b86adf5b..201cf302 100644
--- a/markdowns/12_LFQA.md
+++ b/markdowns/12_LFQA.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/lfqa']
 download: "/downloads/12_LFQA.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/13_Question_generation.md b/markdowns/13_Question_generation.md
index 85d55ada..a8e52412 100644
--- a/markdowns/13_Question_generation.md
+++ b/markdowns/13_Question_generation.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/question-generation']
 download: "/downloads/13_Question_generation.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
index 195656a1..e5aad46f 100644
--- a/markdowns/14_Query_Classifier.md
+++ b/markdowns/14_Query_Classifier.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/query-classifier']
 download: "/downloads/14_Query_Classifier.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/15_TableQA.md b/markdowns/15_TableQA.md
index 68922292..1d97e8f2 100644
--- a/markdowns/15_TableQA.md
+++ b/markdowns/15_TableQA.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/table-qa']
 download: "/downloads/15_TableQA.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/16_Document_Classifier_at_Index_Time.md b/markdowns/16_Document_Classifier_at_Index_Time.md
index dcb67921..c90e60e9 100644
--- a/markdowns/16_Document_Classifier_at_Index_Time.md
+++ b/markdowns/16_Document_Classifier_at_Index_Time.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/doc-class-index']
 download: "/downloads/16_Document_Classifier_at_Index_Time.ipynb"
 completion_time: False
+created_at: 2021-08-12
 ---
     
 
diff --git a/markdowns/17_Audio.md b/markdowns/17_Audio.md
index f701b2b9..1e88f256 100644
--- a/markdowns/17_Audio.md
+++ b/markdowns/17_Audio.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/audio']
 download: "/downloads/17_Audio.ipynb"
 completion_time: False
+created_at: 2022-06-07
 ---
     
 
diff --git a/markdowns/18_GPL.md b/markdowns/18_GPL.md
index 525790f3..613da212 100644
--- a/markdowns/18_GPL.md
+++ b/markdowns/18_GPL.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/gpl']
 download: "/downloads/18_GPL.ipynb"
 completion_time: False
+created_at: 2022-06-07
 ---
     
 
diff --git a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
index e9cbf9e3..8fa559ca 100644
--- a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
+++ b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/multimodal']
 download: "/downloads/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
 completion_time: 20 min
+created_at: 2022-07-11
 ---
     
 
diff --git a/markdowns/20_Using_Haystack_with_REST_API.md b/markdowns/20_Using_Haystack_with_REST_API.md
index 60680803..ab17bbb8 100644
--- a/markdowns/20_Using_Haystack_with_REST_API.md
+++ b/markdowns/20_Using_Haystack_with_REST_API.md
@@ -12,6 +12,7 @@ category: "QA"
 aliases: ['/tutorials/using-haystack-with-rest-api']
 download: "/downloads/20_Using_Haystack_with_REST_API.ipynb"
 completion_time: 30 min
+created_at: 2023-11-01
 ---
     
 
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index 4b422da9..a4b2b0c8 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -30,6 +30,7 @@ def generate_frontmatter(config, tutorial):
 aliases: {aliases}
 download: "/downloads/{tutorial["notebook"]}"
 completion_time: {tutorial.get("completion_time", False)}
+created_at: {tutorial["created_at"]}
 ---
     """
     return frontmatter
@@ -61,7 +62,6 @@ def generate_markdown_from_notebook(config, tutorial, output_path, tutorials_pat
     index = read_index(args.index)
 
     if args.notebooks == ["all"]:
-        print("got here!")
         for config in index["tutorial"]:
             notebook = "tutorials/" + config["notebook"]
             print(notebook)

From d973a188b690a03708fdd76f3e8687e8e803b147 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 16 Feb 2023 14:56:13 +0100
Subject: [PATCH 039/206] Create Tutorial 21 for PromptNode (#118)

* create draft for tutorial 21 prompt node

* Update tutorial 21

* Remove remaining md file from the prev version

* Update code blocks in the quote blocks

* Update lg

* Update md file of PromptNode tutorial

---------

Co-authored-by: agnieszka-m <amarzec13@gmail.com>
---
 README.md                                 |   3 +-
 index.toml                                |  17 +-
 markdowns/21_Customizing_PromptNode.md    | 231 +++++++++
 tutorials/21_Customizing_PromptNode.ipynb | 560 ++++++++++++++++++++++
 4 files changed, 806 insertions(+), 5 deletions(-)
 create mode 100644 markdowns/21_Customizing_PromptNode.md
 create mode 100644 tutorials/21_Customizing_PromptNode.ipynb

diff --git a/README.md b/README.md
index 2ae22b08..17859257 100644
--- a/README.md
+++ b/README.md
@@ -37,4 +37,5 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 | Audio                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb)                                                   | [17_Audio.ipynb](./tutorials/17_Audio.ipynb)                                                                                                     |
 | Generative Pseudo Labeling               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)                                                     | [18_GPL.ipynb](./tutorials/18_GPL.ipynb)                                                                                                         |
 | Text-to-Image search                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) | [19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) |
-| Using Haystack with REST API             | <a href="https://haystack.deepset.ai/downloads/20_Using_Haystack_with_REST_API.ipynb" target="_blank"><img alt="Download" src="https://img.shields.io/badge/DOWNLOAD-blue?&style=for-the-badge"/> <a>                                                                                                                                                                                                    | [20_Using_Haystack_with_REST_API.ipynb](./tutorials/20_Using_Haystack_with_REST_API.ipynb)                                                       |
\ No newline at end of file
+| Using Haystack with REST API             | <a href="https://haystack.deepset.ai/downloads/20_Using_Haystack_with_REST_API.ipynb" target="_blank"><img alt="Download" src="https://img.shields.io/badge/DOWNLOAD-blue?&style=for-the-badge"/> <a>                                                                                                                                                                                                    | [20_Using_Haystack_with_REST_API.ipynb](./tutorials/20_Using_Haystack_with_REST_API.ipynb)                                                       |
+| Customizing PromptNode                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb) | [21_Customizing_PromptNode.ipynb](./tutorials/21_Customizing_PromptNode.ipynb) |
\ No newline at end of file
diff --git a/index.toml b/index.toml
index f33546ca..e6ae4913 100644
--- a/index.toml
+++ b/index.toml
@@ -11,7 +11,7 @@ weight = 10
 notebook = "01_Basic_QA_Pipeline.ipynb"
 aliases = ["first-qa-system", "without-elasticsearch", "03_basic_qa_pipeline_without_elasticsearch"]
 completion_time = "15 min"
-created_at = 2023-11-01
+created_at = 2023-01-11
 
 [[tutorial]]
 title = "Fine-Tuning a Model on Your Own Data"
@@ -30,7 +30,7 @@ weight = 15
 notebook = "03_Scalable_QA_System.ipynb"
 aliases = []
 completion_time = "20 min"
-created_at = 2023-11-01
+created_at = 2023-01-11
 
 [[tutorial]]
 title = "Utilizing Existing FAQs for Question Answering"
@@ -186,5 +186,14 @@ notebook = "20_Using_Haystack_with_REST_API.ipynb"
 aliases = ["using-haystack-with-rest-api"]
 colab = false
 completion_time = "30 min"
-featured = true
-created_at = 2023-11-01
\ No newline at end of file
+created_at = 2023-01-11
+
+[[tutorial]]
+title = "Customizing PromptNode for NLP Tasks"
+description = "Use PromptNode and PromptTemplate for your custom NLP tasks"
+level = "intermediate"
+weight = 57
+notebook = "21_Customizing_PromptNode.ipynb"
+aliases = ["customizing-promptnode"]
+created_at = 2023-02-16
+featured = true
\ No newline at end of file
diff --git a/markdowns/21_Customizing_PromptNode.md b/markdowns/21_Customizing_PromptNode.md
new file mode 100644
index 00000000..2245ed1f
--- /dev/null
+++ b/markdowns/21_Customizing_PromptNode.md
@@ -0,0 +1,231 @@
+---
+layout: tutorial
+featured: True
+colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb
+toc: True
+title: "Customizing PromptNode for NLP Tasks"
+last_updated: 2023-02-16
+level: "intermediate"
+weight: 57
+description: Use PromptNode and PromptTemplate for your custom NLP tasks
+category: "QA"
+aliases: ['/tutorials/customizing-promptnode']
+download: "/downloads/21_Customizing_PromptNode.ipynb"
+completion_time: False
+created_at: 2023-02-16
+---
+    
+
+
+- **Level**: Intermediate
+- **Time to complete**: 20 minutes
+- **Nodes Used**: `PromptNode`, `PromptTemplate`
+- **Goal**: After completing this tutorial, you will have learned the basics of using PromptNode and PromptTemplates and you'll have added titles to articles from The Guardian and categorized them. 
+
+## Overview
+
+Use large language models (LLMs) through PromptNode and PromptTemplate to summarize and categorize your documents, and find a suitable title for them. In this tutorial, we'll use news from [The Guardian](https://www.theguardian.com/international) as documents, but you can replace them with any text you want.  
+
+This tutorial introduces you to the basics of LLMs and PromptNode, showcases the pre-defined "summarization" template, and explains how to use PromptTemplate to generate titles for documents and categorize them with custom prompts.
+
+## Preparing the Colab Environment
+
+- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)
+- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)
+
+## Installing Haystack
+
+To start, let's install the latest release of Haystack with `pip`:
+
+
+```bash
+%%bash
+
+pip install --upgrade pip
+pip install farm-haystack[colab]
+```
+
+## Trying Out PromptNode
+
+The PromptNode is the central abstraction in Haystack's large language model (LLM) support. It uses [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) model by default, but you can replace the default model with a flan-t5 model of a different size such as `google/flan-t5-large` or a model by OpenAI such as `text-davinci-003`.
+
+[Large language models](https://docs.haystack.deepset.ai/docs/language_models#large-language-models-llms) are huge models trained on enormous amounts of data. That’s why these models have general knowledge of the world, so you can ask them anything and they will be able to answer.
+
+As a warm-up, let's initialize PromptNode and see what it can do when run stand-alone: 
+
+1. Initialize a PromptNode instance with [`google/flan-t5-large`](https://huggingface.co/google/flan-t5-large):
+
+
+```python
+from haystack.nodes import PromptNode
+
+prompt_node = PromptNode(model_name_or_path="google/flan-t5-large")
+```
+
+> Note: To use PromptNode with an OpenAI model, change the model name and provide an `api_key`: 
+> ```python
+> prompt_node = PromptNode(model_name_or_path="text-davinci-003", api_key=<YOUR_API_KEY>)
+> ```
+
+2. Ask any general question that comes to your mind, for example:
+
+
+```python
+prompt_node("What is the capital of Germany?")
+```
+
+
+```python
+prompt_node("What is the highest mountain?")
+```
+
+As `google/flan-t5-large` was trained on school math problems dataset named [GSM8K](https://huggingface.co/datasets/gsm8k) you can also ask some basic math questions:
+
+
+```python
+prompt_node("If Bob is 20 and Sara is 11, who is older?")
+```
+
+Now that you've initialized PromptNode and saw how it works, let's see how we can use it for more advanced tasks.
+
+## Summarizing Documents with PromptNode
+
+PromptNode comes with out-of-the-box prompt templates that can perform multiple tasks, such as summarization, question answering, question generation, and more. To use a templates, just provide its name to the PromptNode. 
+
+For this task, we'll use the summarization template and news from The Guardian. Let's see how to do it.
+
+
+1. Define news to use as `documents` for the PromptNode. We'll use these documents throughout the whole tutorial.
+
+
+```python
+from haystack.schema import Document
+
+# https://www.theguardian.com/business/2023/feb/12/inflation-may-have-peaked-but-the-cost-of-living-pain-is-far-from-over
+news_economics = Document(
+    """At long last, Britain’s annual inflation rate is on the way down. After hitting the highest level since the 1980s, heaping pressure on millions of households as living costs soared, official figures this week could bring some rare good news.
+City economists expect UK inflation to have cooled for a third month running in January – the exact number is announced on Wednesday – helped by falling petrol prices and a broader decline in the global price of oil and gas in recent months. The hope now is for a sustained decline in the months ahead, continuing a steady drop from the peak of 11.1% seen in October.
+The message from the Bank of England has been clear. Inflation is on track for a “rapid” decline over the coming months, raising hopes that the worst of Britain’s cost of living crisis is now in the rearview mirror.
+There are two good reasons for this. Energy costs are moving in the right direction, while the initial rise in wholesale oil and gas prices that followed Russia’s invasion of Ukraine in February last year will soon drop from the calculation of the annual inflation rate."""
+)
+
+# https://www.theguardian.com/science/2023/feb/13/starwatch-orions-belt-and-sirius-lead-way-to-hydras-head
+news_science = Document(
+    """On northern winter nights, it is so easy to be beguiled by the gloriously bright constellations of Orion, the hunter, and Taurus, the bull, that one can overlook the fainter constellations.
+So this week, find the three stars of Orion’s belt, follow them down to Sirius, the brightest star in the night sky, and then look eastward until you find the faint ring of stars that makes up the head of Hydra, the water snake. The chart shows the view looking south-east from London at 8pm GMT on Monday, but the view will be similar every night this week.
+Hydra is the largest of the 88 modern constellations covering an area of 1,303 square degrees. To compare, nearby Orion only covers 594 square degrees. Hydra accounts for most of its area by its length, crossing more than 100 degrees of the sky (the full moon spans half a degree).
+As evening becomes night and into the early hours, the rotation of Earth causes Hydra to slither its way across the southern meridian until dawn washes it from the sky. From the southern hemisphere, the constellation is easily visible in the eastern sky by mid-evening."""
+)
+
+# https://www.theguardian.com/music/2023/jan/30/salisbury-cathedral-pipe-organ-new-life-holst-the-planets
+news_culture = Document(
+    """A unique performance of Gustav Holst’s masterwork The Planets – played on a magnificent pipe organ rather than by an orchestra and punctuated by poems inspired by children’s responses to the music – is to be staged in the suitably vast Salisbury Cathedral.
+The idea of the community music project is to introduce more people, young and old, to the 140-year-old “Father” Willis organ, one of the treasures of the cathedral.
+It is also intended to get the children who took part and the adults who will watch and listen thinking afresh about the themes Holst’s suite tackles – war, peace, joy and mysticism – which seem as relevant now as when he wrote the work a century ago.
+John Challenger, the cathedral’s principal organist, said: “We have a fantastic pipe organ largely as it was when built. It’s a thrilling thing. I view it as my purpose in life to share it with as many people as possible.”
+The Planets is written for a large orchestra. “Holst calls for huge instrumental forces and an unseen distant choir of sopranos and altos,” said Challenger. But he has transposed the suite for the organ, not copying the effect of the orchestral instruments but finding a new version of the suite."""
+)
+
+# https://www.theguardian.com/sport/blog/2023/feb/14/multi-million-dollar-wpl-auction-signals-huge-step-forward-for-womens-sport
+news_sport = Document(
+    """It was only a few days ago that members of the Australian women’s cricket team were contemplating how best to navigate the impending “distraction” of the inaugural Women’s Premier League auction, scheduled during the first week of the T20 World Cup. “It’s a little bit awkward,” captain Meg Lanning said in South Africa last week. “But it’s just trying to embrace that and understanding it’s actually a really exciting time and you actually don’t have a lot of control over most of it, so you’ve just got to wait and see.”
+What a pleasant distraction it turned out to be. Lanning herself will be $192,000 richer for three weeks’ work with the Delhi Capitals. Her teammate, Ash Gardner, will earn three times that playing for the Gujarat Giants. The allrounder’s figure of $558,000 is more than Sam Kerr pockets in a season with Chelsea and more than the WNBA’s top earner, Jackie Young.
+If that sounds like a watershed moment, it’s perhaps because it is. And it is not the only one this past week. The NRLW made its own wage-related headlines on Tuesday, to the effect that the next (agreed in principle) collective bargaining agreement will bring with it a $1.5m salary cap in 2027, at an average salary of $62,500. Women’s rugby, too, is making moves, with news on the weekend that Rugby Australia will begin contracting the Wallaroos."""
+)
+
+news = [news_economics, news_science, news_culture, news_sport]
+```
+
+> The token limit for `google/flan-t5-large` is 512. So, all news pieces should be shorter than the limit.
+
+2. List pre-defined templates using the `get_prompt_template_names()` method. All templates come with the prompts needed to perform these tasks. 
+
+
+```python
+prompt_node.get_prompt_template_names()
+```
+
+3. Use the `summarization` template to generate a summary for each piece of news:
+
+
+```python
+prompt_node.prompt(prompt_template="summarization", documents=news)
+```
+
+Here you go! You have generated summaries of your news articles. But we're missing titles for them. Let's see how PromptNode can help us there.
+
+## Generating Titles for News Articles with a Custom Template
+
+The biggest benefit of PromptNode is its versatility. You can use it to perform practically any NLP task if you define your own prompt templates for them. By creating your prompt templates, you can extend the model's capabilities and use it for a broader range of NLP tasks in Haystack. 
+
+You can define custom templates for each NLP task and register them with PromptNode. Let's create a custom template to generate descriptive titles for news:
+
+1. Initialize a `PromptTemplate` instance. Give your template a `name` and define the prompt in `prompt_text`. To define any parameters for the prompt, add them to the `prompt_text` preceded by the `$` sign. We need a template to generate titles for our news articles. We'll call it `give-a-title`. The only parameter we need is `$news`, so let's add it to the `prompt_text`:
+
+
+```python
+from haystack.nodes import PromptTemplate
+
+title_generator = PromptTemplate(
+    name="give-a-title",
+    prompt_text="Provide a short, descriptive title for the given piece of news. News: $news; Title:",
+)
+```
+
+2. To use the new template, pass `title_generator` as the `prompt_template` to the `prompt()` method:
+
+
+
+
+```python
+prompt_node.prompt(prompt_template=title_generator, news=news)
+```
+
+> Note: To add a custom template to the template list, call `add_prompt_template()` with the `PromptTemplate` object pass the template contents to it. Once you do this, the next time you want to use this template, just call its name: 
+> ```python
+> prompt_node.add_prompt_template(PromptTemplate(name="give-a-title", prompt_text="Provide a short, descriptive title for the given piece of news. News: $news; Title:"))
+> prompt_node.prompt(prompt_template="give-a-title", news=news)
+> ```
+
+There you go! You should have the titles for your news articles ready. Let's now categorize them.
+
+## Categorizing Documents with PromptNode
+
+You can customize PromptTemplates as much as you need. Let's try to create a template to categorize the news articles. 
+
+1. Create another PromptTemplate called `categorize-news`. In the `prompt_text`, define the `$news` parameter, specify the categories you want to use, and ask the model not to categorize the news if it doesn't fit in the provided category list: 
+
+
+```python
+news_categorizer = PromptTemplate(
+    name="categorize-news",
+    prompt_text="Given the categories: sport, economics, culture; classify the news: $news. Only pick a category from the list, otherwise say: no suitable category",
+)
+```
+
+2. Run the `prompt()` method with the `news_categorizer` template:
+
+
+```python
+prompt_node.prompt(prompt_template=news_categorizer, news=news)
+```
+
+Congratulations! You've summarized your documents, generated titles for them, and put them into categories, all using custom prompt templates. 
+
+## About us
+
+
+This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
+
+We bring NLP to the industry via open source!  
+Our focus: Industry specific language models & large scale QA systems.  
+  
+Some of our other work: 
+- [German BERT](https://deepset.ai/german-bert)
+- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
+
+Get in touch:
+[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://discord.com/invite/VBpFzsgRVF) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Haystack Website](https://deepset.ai)
+
+By the way: [we're hiring!](https://www.deepset.ai/jobs)
+
diff --git a/tutorials/21_Customizing_PromptNode.ipynb b/tutorials/21_Customizing_PromptNode.ipynb
new file mode 100644
index 00000000..a074b37a
--- /dev/null
+++ b/tutorials/21_Customizing_PromptNode.ipynb
@@ -0,0 +1,560 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BlTn1yekNONP"
+   },
+   "source": [
+    "# Tutorial: Customizing PromptNode for NLP Tasks\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 20 minutes\n",
+    "- **Nodes Used**: `PromptNode`, `PromptTemplate`\n",
+    "- **Goal**: After completing this tutorial, you will have learned the basics of using PromptNode and PromptTemplates and you'll have added titles to articles from The Guardian and categorized them. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ffYFUAjUNONS"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "Use large language models (LLMs) through PromptNode and PromptTemplate to summarize and categorize your documents, and find a suitable title for them. In this tutorial, we'll use news from [The Guardian](https://www.theguardian.com/international) as documents, but you can replace them with any text you want.  \n",
+    "\n",
+    "This tutorial introduces you to the basics of LLMs and PromptNode, showcases the pre-defined \"summarization\" template, and explains how to use PromptTemplate to generate titles for documents and categorize them with custom prompts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ugkQ42iJNONS"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "t0rQfgOVNONS"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "S-00smnxNONT",
+    "outputId": "5488121a-81f1-4591-acd7-89a1c9b2daf1"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RYtbl6qBNONU"
+   },
+   "source": [
+    "## Trying Out PromptNode\n",
+    "\n",
+    "The PromptNode is the central abstraction in Haystack's large language model (LLM) support. It uses [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) model by default, but you can replace the default model with a flan-t5 model of a different size such as `google/flan-t5-large` or a model by OpenAI such as `text-davinci-003`.\n",
+    "\n",
+    "[Large language models](https://docs.haystack.deepset.ai/docs/language_models#large-language-models-llms) are huge models trained on enormous amounts of data. That’s why these models have general knowledge of the world, so you can ask them anything and they will be able to answer.\n",
+    "\n",
+    "As a warm-up, let's initialize PromptNode and see what it can do when run stand-alone: "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2wYbU8WhNX08"
+   },
+   "source": [
+    "1. Initialize a PromptNode instance with [`google/flan-t5-large`](https://huggingface.co/google/flan-t5-large):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "wa-I31YZNONU"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PromptNode\n",
+    "\n",
+    "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZE7s-cMRNh5f"
+   },
+   "source": [
+    "> Note: To use PromptNode with an OpenAI model, change the model name and provide an `api_key`: \n",
+    "> ```python\n",
+    "> prompt_node = PromptNode(model_name_or_path=\"text-davinci-003\", api_key=<YOUR_API_KEY>)\n",
+    "> ```"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NO5z2H_mO8cg"
+   },
+   "source": [
+    "2. Ask any general question that comes to your mind, for example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dBxL4odqNg6K",
+    "outputId": "2e12c341-b24d-45e7-f758-a93e81cdc570"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node(\"What is the capital of Germany?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cg-8tKebDCa0",
+    "outputId": "e108ad6d-5e44-4108-e308-4c85da2b1951"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node(\"What is the highest mountain?\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0MVeOCWSNONV"
+   },
+   "source": [
+    "As `google/flan-t5-large` was trained on school math problems dataset named [GSM8K](https://huggingface.co/datasets/gsm8k) you can also ask some basic math questions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-ZpTIQEUNONW",
+    "outputId": "fe97895b-c475-4de2-ad70-858201f008ad"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node(\"If Bob is 20 and Sara is 11, who is older?\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aybEtTGIQQSX"
+   },
+   "source": [
+    "Now that you've initialized PromptNode and saw how it works, let's see how we can use it for more advanced tasks."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "W6hK9AcsNONW"
+   },
+   "source": [
+    "## Summarizing Documents with PromptNode\n",
+    "\n",
+    "PromptNode comes with out-of-the-box prompt templates that can perform multiple tasks, such as summarization, question answering, question generation, and more. To use a templates, just provide its name to the PromptNode. \n",
+    "\n",
+    "For this task, we'll use the summarization template and news from The Guardian. Let's see how to do it.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zECQSKkWDYns"
+   },
+   "source": [
+    "1. Define news to use as `documents` for the PromptNode. We'll use these documents throughout the whole tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "DjYmEk9_dyHZ"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.schema import Document\n",
+    "\n",
+    "# https://www.theguardian.com/business/2023/feb/12/inflation-may-have-peaked-but-the-cost-of-living-pain-is-far-from-over\n",
+    "news_economics = Document(\n",
+    "    \"\"\"At long last, Britain’s annual inflation rate is on the way down. After hitting the highest level since the 1980s, heaping pressure on millions of households as living costs soared, official figures this week could bring some rare good news.\n",
+    "City economists expect UK inflation to have cooled for a third month running in January – the exact number is announced on Wednesday – helped by falling petrol prices and a broader decline in the global price of oil and gas in recent months. The hope now is for a sustained decline in the months ahead, continuing a steady drop from the peak of 11.1% seen in October.\n",
+    "The message from the Bank of England has been clear. Inflation is on track for a “rapid” decline over the coming months, raising hopes that the worst of Britain’s cost of living crisis is now in the rearview mirror.\n",
+    "There are two good reasons for this. Energy costs are moving in the right direction, while the initial rise in wholesale oil and gas prices that followed Russia’s invasion of Ukraine in February last year will soon drop from the calculation of the annual inflation rate.\"\"\"\n",
+    ")\n",
+    "\n",
+    "# https://www.theguardian.com/science/2023/feb/13/starwatch-orions-belt-and-sirius-lead-way-to-hydras-head\n",
+    "news_science = Document(\n",
+    "    \"\"\"On northern winter nights, it is so easy to be beguiled by the gloriously bright constellations of Orion, the hunter, and Taurus, the bull, that one can overlook the fainter constellations.\n",
+    "So this week, find the three stars of Orion’s belt, follow them down to Sirius, the brightest star in the night sky, and then look eastward until you find the faint ring of stars that makes up the head of Hydra, the water snake. The chart shows the view looking south-east from London at 8pm GMT on Monday, but the view will be similar every night this week.\n",
+    "Hydra is the largest of the 88 modern constellations covering an area of 1,303 square degrees. To compare, nearby Orion only covers 594 square degrees. Hydra accounts for most of its area by its length, crossing more than 100 degrees of the sky (the full moon spans half a degree).\n",
+    "As evening becomes night and into the early hours, the rotation of Earth causes Hydra to slither its way across the southern meridian until dawn washes it from the sky. From the southern hemisphere, the constellation is easily visible in the eastern sky by mid-evening.\"\"\"\n",
+    ")\n",
+    "\n",
+    "# https://www.theguardian.com/music/2023/jan/30/salisbury-cathedral-pipe-organ-new-life-holst-the-planets\n",
+    "news_culture = Document(\n",
+    "    \"\"\"A unique performance of Gustav Holst’s masterwork The Planets – played on a magnificent pipe organ rather than by an orchestra and punctuated by poems inspired by children’s responses to the music – is to be staged in the suitably vast Salisbury Cathedral.\n",
+    "The idea of the community music project is to introduce more people, young and old, to the 140-year-old “Father” Willis organ, one of the treasures of the cathedral.\n",
+    "It is also intended to get the children who took part and the adults who will watch and listen thinking afresh about the themes Holst’s suite tackles – war, peace, joy and mysticism – which seem as relevant now as when he wrote the work a century ago.\n",
+    "John Challenger, the cathedral’s principal organist, said: “We have a fantastic pipe organ largely as it was when built. It’s a thrilling thing. I view it as my purpose in life to share it with as many people as possible.”\n",
+    "The Planets is written for a large orchestra. “Holst calls for huge instrumental forces and an unseen distant choir of sopranos and altos,” said Challenger. But he has transposed the suite for the organ, not copying the effect of the orchestral instruments but finding a new version of the suite.\"\"\"\n",
+    ")\n",
+    "\n",
+    "# https://www.theguardian.com/sport/blog/2023/feb/14/multi-million-dollar-wpl-auction-signals-huge-step-forward-for-womens-sport\n",
+    "news_sport = Document(\n",
+    "    \"\"\"It was only a few days ago that members of the Australian women’s cricket team were contemplating how best to navigate the impending “distraction” of the inaugural Women’s Premier League auction, scheduled during the first week of the T20 World Cup. “It’s a little bit awkward,” captain Meg Lanning said in South Africa last week. “But it’s just trying to embrace that and understanding it’s actually a really exciting time and you actually don’t have a lot of control over most of it, so you’ve just got to wait and see.”\n",
+    "What a pleasant distraction it turned out to be. Lanning herself will be $192,000 richer for three weeks’ work with the Delhi Capitals. Her teammate, Ash Gardner, will earn three times that playing for the Gujarat Giants. The allrounder’s figure of $558,000 is more than Sam Kerr pockets in a season with Chelsea and more than the WNBA’s top earner, Jackie Young.\n",
+    "If that sounds like a watershed moment, it’s perhaps because it is. And it is not the only one this past week. The NRLW made its own wage-related headlines on Tuesday, to the effect that the next (agreed in principle) collective bargaining agreement will bring with it a $1.5m salary cap in 2027, at an average salary of $62,500. Women’s rugby, too, is making moves, with news on the weekend that Rugby Australia will begin contracting the Wallaroos.\"\"\"\n",
+    ")\n",
+    "\n",
+    "news = [news_economics, news_science, news_culture, news_sport]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ezq9NVmFdzz1"
+   },
+   "source": [
+    "> The token limit for `google/flan-t5-large` is 512. So, all news pieces should be shorter than the limit."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "okw4m5aeL7hy"
+   },
+   "source": [
+    "2. List pre-defined templates using the `get_prompt_template_names()` method. All templates come with the prompts needed to perform these tasks. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JqwLmdePNONW",
+    "outputId": "b33d9123-9cf3-4444-fc04-1bc8311e7338"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node.get_prompt_template_names()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "syB5rl3xIgHr"
+   },
+   "source": [
+    "3. Use the `summarization` template to generate a summary for each piece of news:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ygxFMwb4NONX",
+    "outputId": "e56ed89a-b020-4f00-9310-b1a643cb87ea"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node.prompt(prompt_template=\"summarization\", documents=news)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lUTckmA1PzLc"
+   },
+   "source": [
+    "Here you go! You have generated summaries of your news articles. But we're missing titles for them. Let's see how PromptNode can help us there."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f0vQ45tHNONX"
+   },
+   "source": [
+    "## Generating Titles for News Articles with a Custom Template"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "992kb_WKNONX"
+   },
+   "source": [
+    "The biggest benefit of PromptNode is its versatility. You can use it to perform practically any NLP task if you define your own prompt templates for them. By creating your prompt templates, you can extend the model's capabilities and use it for a broader range of NLP tasks in Haystack. \n",
+    "\n",
+    "You can define custom templates for each NLP task and register them with PromptNode. Let's create a custom template to generate descriptive titles for news:"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "en6IbPRsTOTz"
+   },
+   "source": [
+    "1. Initialize a `PromptTemplate` instance. Give your template a `name` and define the prompt in `prompt_text`. To define any parameters for the prompt, add them to the `prompt_text` preceded by the `$` sign. We need a template to generate titles for our news articles. We'll call it `give-a-title`. The only parameter we need is `$news`, so let's add it to the `prompt_text`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "OiOiya2UV4WW"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PromptTemplate\n",
+    "\n",
+    "title_generator = PromptTemplate(\n",
+    "    name=\"give-a-title\",\n",
+    "    prompt_text=\"Provide a short, descriptive title for the given piece of news. News: $news; Title:\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jttb_ROnWYwS"
+   },
+   "source": [
+    "2. To use the new template, pass `title_generator` as the `prompt_template` to the `prompt()` method:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zX_nALriWXxx",
+    "outputId": "b027b36c-ef1f-4c5b-c392-7bc9d6364838"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node.prompt(prompt_template=title_generator, news=news)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "p0Mx3lHyXK_I"
+   },
+   "source": [
+    "> Note: To add a custom template to the template list, call `add_prompt_template()` with the `PromptTemplate` object pass the template contents to it. Once you do this, the next time you want to use this template, just call its name: \n",
+    "> ```python\n",
+    "> prompt_node.add_prompt_template(PromptTemplate(name=\"give-a-title\", prompt_text=\"Provide a short, descriptive title for the given piece of news. News: $news; Title:\"))\n",
+    "> prompt_node.prompt(prompt_template=\"give-a-title\", news=news)\n",
+    "> ```"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There you go! You should have the titles for your news articles ready. Let's now categorize them."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eH4QbygONONX"
+   },
+   "source": [
+    "## Categorizing Documents with PromptNode\n",
+    "\n",
+    "You can customize PromptTemplates as much as you need. Let's try to create a template to categorize the news articles. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FAELXO0MhbrV"
+   },
+   "source": [
+    "1. Create another PromptTemplate called `categorize-news`. In the `prompt_text`, define the `$news` parameter, specify the categories you want to use, and ask the model not to categorize the news if it doesn't fit in the provided category list: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xHgOjTwkNONY"
+   },
+   "outputs": [],
+   "source": [
+    "news_categorizer = PromptTemplate(\n",
+    "    name=\"categorize-news\",\n",
+    "    prompt_text=\"Given the categories: sport, economics, culture; classify the news: $news. Only pick a category from the list, otherwise say: no suitable category\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nC4gVHCyuXHl"
+   },
+   "source": [
+    "2. Run the `prompt()` method with the `news_categorizer` template:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "taktlLIfNONY",
+    "outputId": "1e9a5947-1074-47a6-849b-3b55992983b2"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node.prompt(prompt_template=news_categorizer, news=news)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Congratulations! You've summarized your documents, generated titles for them, and put them into categories, all using custom prompt templates. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QVKsx2KbNONY"
+   },
+   "source": [
+    "## About us\n",
+    "\n",
+    "\n",
+    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
+    "\n",
+    "We bring NLP to the industry via open source!  \n",
+    "Our focus: Industry specific language models & large scale QA systems.  \n",
+    "  \n",
+    "Some of our other work: \n",
+    "- [German BERT](https://deepset.ai/german-bert)\n",
+    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
+    "\n",
+    "Get in touch:\n",
+    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://discord.com/invite/VBpFzsgRVF) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Haystack Website](https://deepset.ai)\n",
+    "\n",
+    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "76c0ba09435b0d7ab7f3e4f9fd6b1554cdb12adf349a7242a1470606c432d777"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From 9cd175da99fb4dc4e976c81bb33511cf1a208727 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 16 Feb 2023 15:09:52 +0100
Subject: [PATCH 040/206] Tut 20 remove featured (#125)

* Remove featured tag of tut 20

* Change the created at dates of tutorial 1&3

* Revert last updated date
---
 markdowns/01_Basic_QA_Pipeline.md            | 2 +-
 markdowns/03_Scalable_QA_System.md           | 2 +-
 markdowns/20_Using_Haystack_with_REST_API.md | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/markdowns/01_Basic_QA_Pipeline.md b/markdowns/01_Basic_QA_Pipeline.md
index 35dc66f8..04acbdad 100644
--- a/markdowns/01_Basic_QA_Pipeline.md
+++ b/markdowns/01_Basic_QA_Pipeline.md
@@ -12,7 +12,7 @@ category: "QA"
 aliases: ['/tutorials/first-qa-system', '/tutorials/without-elasticsearch', '/tutorials/03_basic_qa_pipeline_without_elasticsearch']
 download: "/downloads/01_Basic_QA_Pipeline.ipynb"
 completion_time: 15 min
-created_at: 2023-11-01
+created_at: 2023-01-11
 ---
     
 
diff --git a/markdowns/03_Scalable_QA_System.md b/markdowns/03_Scalable_QA_System.md
index 7467dad5..cb94ce13 100644
--- a/markdowns/03_Scalable_QA_System.md
+++ b/markdowns/03_Scalable_QA_System.md
@@ -12,7 +12,7 @@ category: "QA"
 aliases: []
 download: "/downloads/03_Scalable_QA_System.ipynb"
 completion_time: 20 min
-created_at: 2023-11-01
+created_at: 2023-01-11
 ---
     
 
diff --git a/markdowns/20_Using_Haystack_with_REST_API.md b/markdowns/20_Using_Haystack_with_REST_API.md
index ab17bbb8..b8d2ebcf 100644
--- a/markdowns/20_Using_Haystack_with_REST_API.md
+++ b/markdowns/20_Using_Haystack_with_REST_API.md
@@ -1,6 +1,6 @@
 ---
 layout: tutorial
-featured: True
+featured: False
 colab: False
 toc: True
 title: "Using Haystack with REST API"
@@ -12,7 +12,7 @@ category: "QA"
 aliases: ['/tutorials/using-haystack-with-rest-api']
 download: "/downloads/20_Using_Haystack_with_REST_API.ipynb"
 completion_time: 30 min
-created_at: 2023-11-01
+created_at: 2023-01-11
 ---
     
 

From 78ff7450aab3a0c9add4e747ad3d3f3bb2aaa2b9 Mon Sep 17 00:00:00 2001
From: Malte Pietsch <malte.pietsch@deepset.ai>
Date: Fri, 17 Feb 2023 11:23:05 +0100
Subject: [PATCH 041/206] Improve FAQ Tutorial  (#126)

* Change to InMemoryDocumentstore and change descriptions

* add updated markdown
---
 markdowns/04_FAQ_style_QA.md    |   80 +--
 tutorials/04_FAQ_style_QA.ipynb | 1149 ++++++++++++++++++++-----------
 2 files changed, 774 insertions(+), 455 deletions(-)

diff --git a/markdowns/04_FAQ_style_QA.md b/markdowns/04_FAQ_style_QA.md
index 50653c42..51ed8ce2 100644
--- a/markdowns/04_FAQ_style_QA.md
+++ b/markdowns/04_FAQ_style_QA.md
@@ -4,7 +4,7 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb
 toc: True
 title: "Utilizing Existing FAQs for Question Answering"
-last_updated: 2023-02-03
+last_updated: 2023-02-17
 level: "beginner"
 weight: 20
 description: Create a smarter way to answer new questions using your existing FAQ documents.
@@ -16,7 +16,12 @@ created_at: 2021-08-12
 ---
     
 
+- **Level**: Beginner
+- **Time to complete**: 15 minutes
+- **Nodes Used**: `InMemoryDocumentStore`, `EmbeddingRetriever`
+- **Goal**: Learn how to use the `EmbeddingRetriever` in a `FAQPipeline` to answer incoming questions by matching them to the most similar questions in your existing FAQ.
 
+# Overview
 While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
 
 **Pros**:
@@ -73,67 +78,14 @@ logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logg
 logging.getLogger("haystack").setLevel(logging.INFO)
 ```
 
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
+### Create a simple DocumentStore
+The InMemoryDocumentStore is good for quick development and prototyping. For more scalable options, check-out the [docs](https://docs.haystack.deepset.ai/docs/document_store).
 
 
 ```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
+from haystack.document_stores import InMemoryDocumentStore
 
-launch_es()
-```
-
-### Start an Elasticsearch server in Colab
-
-If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```bash
-%%bash
-
-wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-chown -R daemon:daemon elasticsearch-7.9.2
-
-```
-
-
-```bash
-%%bash --bg
-
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
-```
-
-### Init the DocumentStore
-In contrast to Tutorial 1 (Build your first QA system), we:
-
-* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
-* set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results
-
-
-```python
-import os
-import time
-
-from haystack.document_stores import ElasticsearchDocumentStore
-
-# Wait 30 seconds only to be sure Elasticsearch is ready before continuing
-time.sleep(30)
-
-# Get the host where Elasticsearch is running, default to localhost
-host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
-
-document_store = ElasticsearchDocumentStore(
-    host=host,
-    username="",
-    password="",
-    index="document",
-    embedding_field="question_emb",
-    embedding_dim=384,
-    excluded_meta_data=["question_emb"],
-    similarity="cosine",
-)
+document_store = InMemoryDocumentStore()
 ```
 
 ### Create a Retriever using embeddings
@@ -153,7 +105,7 @@ retriever = EmbeddingRetriever(
 ```
 
 ### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.
+We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in our documentstore.
 Here: We download some question-answer pairs related to COVID-19
 
 
@@ -175,9 +127,11 @@ df.fillna(value="", inplace=True)
 df["question"] = df["question"].apply(lambda x: x.strip())
 print(df.head())
 
-# Get embeddings for our questions from the FAQs
+# Create embeddings for our questions from the FAQs
+# In contrast to most other search use cases, we don't create the embeddings here from the content of our documents,
+# but rather from the additional text field "question" as we want to match "incoming question" <-> "stored question".
 questions = list(df["question"].values)
-df["question_emb"] = retriever.embed_queries(queries=questions).tolist()
+df["embedding"] = retriever.embed_queries(queries=questions).tolist()
 df = df.rename(columns={"question": "content"})
 
 # Convert Dataframe to list of dicts and index them in our DocumentStore
@@ -199,7 +153,9 @@ pipe = FAQPipeline(retriever=retriever)
 ```python
 from haystack.utils import print_answers
 
-prediction = pipe.run(query="How is the virus spreading?", params={"Retriever": {"top_k": 10}})
+# Run any question and change top_k to see more or less answers
+prediction = pipe.run(query="How is the virus spreading?", params={"Retriever": {"top_k": 1}})
+
 print_answers(prediction, details="medium")
 ```
 
diff --git a/tutorials/04_FAQ_style_QA.ipynb b/tutorials/04_FAQ_style_QA.ipynb
index e8f67a9a..32f77f18 100644
--- a/tutorials/04_FAQ_style_QA.ipynb
+++ b/tutorials/04_FAQ_style_QA.ipynb
@@ -1,400 +1,763 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Utilizing existing FAQs for Question Answering\n",
-    "\n",
-    "While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.\n",
-    "\n",
-    "**Pros**:\n",
-    "\n",
-    "- Very fast at inference time\n",
-    "- Utilize existing FAQ data\n",
-    "- Quite good control over answers\n",
-    "\n",
-    "**Cons**:\n",
-    "\n",
-    "- Generalizability: We can only answer questions that are similar to existing ones in FAQ\n",
-    "\n",
-    "In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### Prepare environment\n",
-    "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
-    "\n",
-    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
-    "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jUbPUmtaozIP"
+      },
+      "source": [
+        "# Utilizing existing FAQs for Question Answering\n",
+        "- **Level**: Beginner\n",
+        "- **Time to complete**: 15 minutes\n",
+        "- **Nodes Used**: `InMemoryDocumentStore`, `EmbeddingRetriever`\n",
+        "- **Goal**: Learn how to use the `EmbeddingRetriever` in a `FAQPipeline` to answer incoming questions by matching them to the most similar questions in your existing FAQ.\n",
+        "\n",
+        "# Overview\n",
+        "While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.\n",
+        "\n",
+        "**Pros**:\n",
+        "\n",
+        "- Very fast at inference time\n",
+        "- Utilize existing FAQ data\n",
+        "- Quite good control over answers\n",
+        "\n",
+        "**Cons**:\n",
+        "\n",
+        "- Generalizability: We can only answer questions that are similar to existing ones in FAQ\n",
+        "\n",
+        "In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option."
+      ]
     },
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "nvidia-smi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To start, install the latest release of Haystack with `pip`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Logging\n",
-    "\n",
-    "We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
-    "Example log message:\n",
-    "INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
-    "Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "\n",
-    "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
-    "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Start an Elasticsearch server\n",
-    "You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Recommended: Start Elasticsearch using Docker via the Haystack utility function\n",
-    "from haystack.utils import launch_es\n",
-    "\n",
-    "launch_es()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Start an Elasticsearch server in Colab\n",
-    "\n",
-    "If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": false,
+        "id": "zBOtphIMozIT"
+      },
+      "source": [
+        "### Prepare environment\n",
+        "\n",
+        "#### Colab: Enable the GPU runtime\n",
+        "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
+        "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+        "\n",
+        "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
+        "\n",
+        "You can double check whether the GPU runtime is enabled with the following command:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "pycharm": {
+          "name": "#%%\n"
+        },
+        "vscode": {
+          "languageId": "shellscript"
+        },
+        "id": "ZIS-RfISozIU"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "nvidia-smi"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ENpLjBejozIW"
+      },
+      "source": [
+        "To start, install the latest release of Haystack with `pip`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "vscode": {
+          "languageId": "shellscript"
+        },
+        "id": "q_y78_4LozIW"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install --upgrade pip\n",
+        "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": false,
+        "pycharm": {
+          "name": "#%% md\n"
+        },
+        "id": "Wl9Q6E3hozIW"
+      },
+      "source": [
+        "## Logging\n",
+        "\n",
+        "We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
+        "Example log message:\n",
+        "INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
+        "Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "pycharm": {
+          "name": "#%%\n"
+        },
+        "id": "Edvocv1ZozIX"
+      },
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "\n",
+        "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
+        "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "noVtM20ZozIX"
+      },
+      "source": [
+        "### Create a simple DocumentStore\n",
+        "The InMemoryDocumentStore is good for quick development and prototyping. For more scalable options, check-out the [docs](https://docs.haystack.deepset.ai/docs/document_store)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zeVfvRLZozIY"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.document_stores import InMemoryDocumentStore\n",
+        "\n",
+        "document_store = InMemoryDocumentStore()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": false,
+        "id": "zHevRxxaozIa"
+      },
+      "source": [
+        "### Create a Retriever using embeddings\n",
+        "Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).\n",
+        "We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "pycharm": {
+          "name": "#%%\n"
+        },
+        "id": "oFNXb3kIozIb"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.nodes import EmbeddingRetriever\n",
+        "\n",
+        "retriever = EmbeddingRetriever(\n",
+        "    document_store=document_store,\n",
+        "    embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
+        "    use_gpu=True,\n",
+        "    scale_score=False,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": false,
+        "id": "uLv8ysluozIb"
+      },
+      "source": [
+        "### Prepare & Index FAQ data\n",
+        "We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in our documentstore.\n",
+        "Here: We download some question-answer pairs related to COVID-19"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "pycharm": {
+          "name": "#%%\n"
+        },
+        "id": "AHiSltp4ozIb"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "from haystack.utils import fetch_archive_from_http\n",
+        "\n",
+        "\n",
+        "# Download\n",
+        "doc_dir = \"data/tutorial4\"\n",
+        "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/small_faq_covid.csv.zip\"\n",
+        "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
+        "\n",
+        "# Get dataframe with columns \"question\", \"answer\" and some custom metadata\n",
+        "df = pd.read_csv(f\"{doc_dir}/small_faq_covid.csv\")\n",
+        "# Minimal cleaning\n",
+        "df.fillna(value=\"\", inplace=True)\n",
+        "df[\"question\"] = df[\"question\"].apply(lambda x: x.strip())\n",
+        "print(df.head())\n",
+        "\n",
+        "# Create embeddings for our questions from the FAQs\n",
+        "# In contrast to most other search use cases, we don't create the embeddings here from the content of our documents,\n",
+        "# but rather from the additional text field \"question\" as we want to match \"incoming question\" <-> \"stored question\".\n",
+        "questions = list(df[\"question\"].values)\n",
+        "df[\"embedding\"] = retriever.embed_queries(queries=questions).tolist()\n",
+        "df = df.rename(columns={\"question\": \"content\"})\n",
+        "\n",
+        "# Convert Dataframe to list of dicts and index them in our DocumentStore\n",
+        "docs_to_index = df.to_dict(orient=\"records\")\n",
+        "document_store.write_documents(docs_to_index)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": false,
+        "id": "MXteNgYRozIb"
+      },
+      "source": [
+        "### Ask questions\n",
+        "Initialize a Pipeline (this time without a reader) and ask questions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "pycharm": {
+          "name": "#%%\n"
+        },
+        "id": "F5O7r3poozIb"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.pipelines import FAQPipeline\n",
+        "\n",
+        "pipe = FAQPipeline(retriever=retriever)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "pycharm": {
+          "name": "#%%\n"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 709,
+          "referenced_widgets": [
+            "070f7d6a12804647b2c4f5ec98241ced",
+            "8678507de5e748219ba28bb7970c0e63",
+            "35855d91133f474092381950bdbfce58",
+            "0656e34a277141d184aef005e4d39f88",
+            "612af309a6a94477b56dcea22c7a0940",
+            "dc9c54def7bf47d39819a97b7ceed839",
+            "09f4ba018a514f1ca2929ece4d0335e2",
+            "58f3458cddc747d7b1a1c05f8f0664ed",
+            "cd2614a0933c48a391966cb572044710",
+            "52abbb2d8eb043a0924d705a99577303",
+            "04495cdbd0e04e02a91ae3b026ef4c46"
+          ]
+        },
+        "id": "QX6qbic2ozIc",
+        "outputId": "af0a8eda-f7f6-4c97-cda7-13566ff888b1"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Query: How is the virus spreading?\n",
+            "Answers:\n",
+            "[   {   'answer': 'This virus was first detected in Wuhan City, Hubei '\n",
+            "                  'Province, China. The first infections were linked to a live '\n",
+            "                  'animal market, but the virus is now spreading from '\n",
+            "                  'person-to-person. It’s important to note that '\n",
+            "                  'person-to-person spread can happen on a continuum. Some '\n",
+            "                  'viruses are highly contagious (like measles), while other '\n",
+            "                  'viruses are less so.\\n'\n",
+            "                  '\\n'\n",
+            "                  'The virus that causes COVID-19 seems to be spreading easily '\n",
+            "                  'and sustainably in the community (“community spread”) in '\n",
+            "                  'some affected geographic areas. Community spread means '\n",
+            "                  'people have been infected with the virus in an area, '\n",
+            "                  'including some who are not sure how or where they became '\n",
+            "                  'infected.\\n'\n",
+            "                  '\\n'\n",
+            "                  'Learn what is known about the spread of newly emerged '\n",
+            "                  'coronaviruses.',\n",
+            "        'context': 'This virus was first detected in Wuhan City, Hubei '\n",
+            "                   'Province, China. The first infections were linked to a '\n",
+            "                   'live animal market, but the virus is now spreading from '\n",
+            "                   'person-to-person. It’s important to note that '\n",
+            "                   'person-to-person spread can happen on a continuum. Some '\n",
+            "                   'viruses are highly contagious (like measles), while other '\n",
+            "                   'viruses are less so.\\n'\n",
+            "                   '\\n'\n",
+            "                   'The virus that causes COVID-19 seems to be spreading '\n",
+            "                   'easily and sustainably in the community (“community '\n",
+            "                   'spread”) in some affected geographic areas. Community '\n",
+            "                   'spread means people have been infected with the virus in '\n",
+            "                   'an area, including some who are not sure how or where they '\n",
+            "                   'became infected.\\n'\n",
+            "                   '\\n'\n",
+            "                   'Learn what is known about the spread of newly emerged '\n",
+            "                   'coronaviruses.',\n",
+            "        'score': 0.9358832836151123}]\n"
+          ]
+        }
+      ],
+      "source": [
+        "from haystack.utils import print_answers\n",
+        "\n",
+        "# Run any question and change top_k to see more or less answers\n",
+        "prediction = pipe.run(query=\"How is the virus spreading?\", params={\"Retriever\": {\"top_k\": 1}})\n",
+        "\n",
+        "print_answers(prediction, details=\"medium\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": false,
+        "id": "UpOTgy-fozIc"
+      },
+      "source": [
+        "## About us\n",
+        "\n",
+        "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
+        "\n",
+        "We bring NLP to the industry via open source!  \n",
+        "Our focus: Industry specific language models & large scale QA systems.  \n",
+        "  \n",
+        "Some of our other work: \n",
+        "- [German BERT](https://deepset.ai/german-bert)\n",
+        "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
+        "- [FARM](https://github.com/deepset-ai/FARM)\n",
+        "\n",
+        "Get in touch:\n",
+        "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
+        "\n",
+        "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
+      ]
     }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
-    "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.6 (main, Aug 11 2022, 13:36:31) [Clang 13.1.6 (clang-1316.0.21.2.5)]"
+    },
     "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash --bg\n",
-    "\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### Init the DocumentStore\n",
-    "In contrast to Tutorial 1 (Build your first QA system), we:\n",
-    "\n",
-    "* specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question\n",
-    "* set `excluded_meta_data=[\"question_emb\"]` so that we don't return the huge embedding vectors in our search results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import time\n",
-    "\n",
-    "from haystack.document_stores import ElasticsearchDocumentStore\n",
-    "\n",
-    "# Wait 30 seconds only to be sure Elasticsearch is ready before continuing\n",
-    "time.sleep(30)\n",
-    "\n",
-    "# Get the host where Elasticsearch is running, default to localhost\n",
-    "host = os.environ.get(\"ELASTICSEARCH_HOST\", \"localhost\")\n",
-    "\n",
-    "document_store = ElasticsearchDocumentStore(\n",
-    "    host=host,\n",
-    "    username=\"\",\n",
-    "    password=\"\",\n",
-    "    index=\"document\",\n",
-    "    embedding_field=\"question_emb\",\n",
-    "    embedding_dim=384,\n",
-    "    excluded_meta_data=[\"question_emb\"],\n",
-    "    similarity=\"cosine\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### Create a Retriever using embeddings\n",
-    "Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).\n",
-    "We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.nodes import EmbeddingRetriever\n",
-    "\n",
-    "retriever = EmbeddingRetriever(\n",
-    "    document_store=document_store,\n",
-    "    embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
-    "    use_gpu=True,\n",
-    "    scale_score=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### Prepare & Index FAQ data\n",
-    "We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in elasticsearch.\n",
-    "Here: We download some question-answer pairs related to COVID-19"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "from haystack.utils import fetch_archive_from_http\n",
-    "\n",
-    "\n",
-    "# Download\n",
-    "doc_dir = \"data/tutorial4\"\n",
-    "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/small_faq_covid.csv.zip\"\n",
-    "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
-    "\n",
-    "# Get dataframe with columns \"question\", \"answer\" and some custom metadata\n",
-    "df = pd.read_csv(f\"{doc_dir}/small_faq_covid.csv\")\n",
-    "# Minimal cleaning\n",
-    "df.fillna(value=\"\", inplace=True)\n",
-    "df[\"question\"] = df[\"question\"].apply(lambda x: x.strip())\n",
-    "print(df.head())\n",
-    "\n",
-    "# Get embeddings for our questions from the FAQs\n",
-    "questions = list(df[\"question\"].values)\n",
-    "df[\"question_emb\"] = retriever.embed_queries(queries=questions).tolist()\n",
-    "df = df.rename(columns={\"question\": \"content\"})\n",
-    "\n",
-    "# Convert Dataframe to list of dicts and index them in our DocumentStore\n",
-    "docs_to_index = df.to_dict(orient=\"records\")\n",
-    "document_store.write_documents(docs_to_index)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### Ask questions\n",
-    "Initialize a Pipeline (this time without a reader) and ask questions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.pipelines import FAQPipeline\n",
-    "\n",
-    "pipe = FAQPipeline(retriever=retriever)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
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-     "name": "#%%\n"
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+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "04495cdbd0e04e02a91ae3b026ef4c46": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
     }
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.utils import print_answers\n",
-    "\n",
-    "prediction = pipe.run(query=\"How is the virus spreading?\", params={\"Retriever\": {\"top_k\": 10}})\n",
-    "print_answers(prediction, details=\"medium\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6 (main, Aug 11 2022, 13:36:31) [Clang 13.1.6 (clang-1316.0.21.2.5)]"
   },
-  "vscode": {
-   "interpreter": {
-    "hash": "bda33b16be7e844498c7c2d368d72665b4f1d165582b9547ed22a0249a29ca2e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

From 3e5e46294cbdd17a2a837eeb466055ed85c7f52d Mon Sep 17 00:00:00 2001
From: simekent <simon@waywise.io>
Date: Mon, 20 Feb 2023 07:05:09 -0800
Subject: [PATCH 042/206] Fix typo in 14_Query_Classifier (#117)

* Fix typo in 14_Query_Classifier

* Reran 'python scripts/generate_markdowns.py --index index.toml --output markdowns --notebooks ...'

* Update md file for tests to pass

---------

Co-authored-by: bilgeyucel <bilgeyucel96@gmail.com>
---
 markdowns/14_Query_Classifier.md    | 4 ++--
 tutorials/14_Query_Classifier.ipynb | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
index e5aad46f..650fcee2 100644
--- a/markdowns/14_Query_Classifier.md
+++ b/markdowns/14_Query_Classifier.md
@@ -4,7 +4,7 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb
 toc: True
 title: "Query Classifier"
-last_updated: 2023-02-03
+last_updated: 2023-02-20
 level: "intermediate"
 weight: 80
 description: Classify incoming queries so that they can be routed to the nodes that are best at handling them.
@@ -392,7 +392,7 @@ print_documents(res_2)
 For example, we may be interested in detecting the sentiment or classifying the topics.  We can do this by loading a custom classification model from the Hugging Face Hub or by using zero-shot classification.
 
 #### Custom classification model vs zero-shot classification
-- Rraditional text classification models are trained to predict one of a few "hard-coded" classes and require a dedicated training dataset. In the Hugging Face Hub, you can find many pre-trained models, maybe even related to your domain of interest.
+- Traditional text classification models are trained to predict one of a few "hard-coded" classes and require a dedicated training dataset. In the Hugging Face Hub, you can find many pre-trained models, maybe even related to your domain of interest.
 - Zero-shot classification is very versatile: by choosing a suitable base transformer, you can classify the text without any training dataset. You just have to provide the candidate categories.
 
 #### Using custom classification models
diff --git a/tutorials/14_Query_Classifier.ipynb b/tutorials/14_Query_Classifier.ipynb
index 9e2732e7..c8bd10b3 100644
--- a/tutorials/14_Query_Classifier.ipynb
+++ b/tutorials/14_Query_Classifier.ipynb
@@ -671,7 +671,7 @@
     "For example, we may be interested in detecting the sentiment or classifying the topics.  We can do this by loading a custom classification model from the Hugging Face Hub or by using zero-shot classification.\n",
     "\n",
     "#### Custom classification model vs zero-shot classification\n",
-    "- Rraditional text classification models are trained to predict one of a few \"hard-coded\" classes and require a dedicated training dataset. In the Hugging Face Hub, you can find many pre-trained models, maybe even related to your domain of interest.\n",
+    "- Traditional text classification models are trained to predict one of a few \"hard-coded\" classes and require a dedicated training dataset. In the Hugging Face Hub, you can find many pre-trained models, maybe even related to your domain of interest.\n",
     "- Zero-shot classification is very versatile: by choosing a suitable base transformer, you can classify the text without any training dataset. You just have to provide the candidate categories."
    ]
   },

From 204d9e6b000db904455cb7cfecbe475265071676 Mon Sep 17 00:00:00 2001
From: Slavina S <3056639+blancadesal@users.noreply.github.com>
Date: Mon, 20 Feb 2023 16:14:12 +0100
Subject: [PATCH 043/206] Preprocessing.md: fix typo (#111)

* 08_Preprocessing.ipynb: fix typo

* Update md file for tests to pass

---------

Co-authored-by: bilgeyucel <bilgeyucel96@gmail.com>
---
 markdowns/08_Preprocessing.md    | 4 ++--
 tutorials/08_Preprocessing.ipynb | 3 ++-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/markdowns/08_Preprocessing.md b/markdowns/08_Preprocessing.md
index cd599217..975b92bd 100644
--- a/markdowns/08_Preprocessing.md
+++ b/markdowns/08_Preprocessing.md
@@ -4,7 +4,7 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb
 toc: True
 title: "Preprocessing Your Documents"
-last_updated: 2022-11-25
+last_updated: 2023-02-20
 level: "beginner"
 weight: 25
 description: Start converting, cleaning, and splitting Documents using Haystack’s preprocessing capabilities.
@@ -21,7 +21,7 @@ Haystack includes a suite of tools to extract text from different file types, no
 and split text into smaller pieces to optimize retrieval.
 These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
 
-Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:
+Ultimately, Haystack expects data to be provided as a list of documents in the following dictionary format:
 ``` python
 docs = [
     {
diff --git a/tutorials/08_Preprocessing.ipynb b/tutorials/08_Preprocessing.ipynb
index 3764599b..ab7bab82 100644
--- a/tutorials/08_Preprocessing.ipynb
+++ b/tutorials/08_Preprocessing.ipynb
@@ -14,6 +14,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -22,7 +23,7 @@
     }
    },
    "source": [
-    "Ultimately, Haystack expects data to be provided as a list documents in the following dictionary format:\n",
+    "Ultimately, Haystack expects data to be provided as a list of documents in the following dictionary format:\n",
     "``` python\n",
     "docs = [\n",
     "    {\n",

From 86e201be78f84263292f765f7fab5c1240ae9d94 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 21 Feb 2023 13:00:24 +0100
Subject: [PATCH 044/206] Remove `last_updated` date from frontmatter (#128)

* This field will be handled by Haystack Website
---
 markdowns/01_Basic_QA_Pipeline.md                             | 1 -
 markdowns/02_Finetune_a_model_on_your_data.md                 | 1 -
 markdowns/03_Scalable_QA_System.md                            | 1 -
 markdowns/04_FAQ_style_QA.md                                  | 1 -
 markdowns/05_Evaluation.md                                    | 1 -
 markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md      | 1 -
 markdowns/07_RAG_Generator.md                                 | 1 -
 markdowns/08_Preprocessing.md                                 | 1 -
 markdowns/09_DPR_training.md                                  | 1 -
 markdowns/10_Knowledge_Graph.md                               | 1 -
 markdowns/11_Pipelines.md                                     | 1 -
 markdowns/12_LFQA.md                                          | 1 -
 markdowns/13_Question_generation.md                           | 1 -
 markdowns/14_Query_Classifier.md                              | 1 -
 markdowns/15_TableQA.md                                       | 1 -
 markdowns/16_Document_Classifier_at_Index_Time.md             | 1 -
 markdowns/17_Audio.md                                         | 1 -
 markdowns/18_GPL.md                                           | 1 -
 ...Text_to_Image_search_pipeline_with_MultiModal_Retriever.md | 1 -
 markdowns/20_Using_Haystack_with_REST_API.md                  | 1 -
 markdowns/21_Customizing_PromptNode.md                        | 1 -
 scripts/generate_markdowns.py                                 | 4 +---
 22 files changed, 1 insertion(+), 24 deletions(-)

diff --git a/markdowns/01_Basic_QA_Pipeline.md b/markdowns/01_Basic_QA_Pipeline.md
index 04acbdad..8ac18745 100644
--- a/markdowns/01_Basic_QA_Pipeline.md
+++ b/markdowns/01_Basic_QA_Pipeline.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb
 toc: True
 title: "Build Your First Question Answering System"
-last_updated: 2023-01-23
 level: "beginner"
 weight: 10
 description: Get Started by creating a Retriever Reader pipeline.
diff --git a/markdowns/02_Finetune_a_model_on_your_data.md b/markdowns/02_Finetune_a_model_on_your_data.md
index 8e606290..b6aaad2f 100644
--- a/markdowns/02_Finetune_a_model_on_your_data.md
+++ b/markdowns/02_Finetune_a_model_on_your_data.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb
 toc: True
 title: "Fine-Tuning a Model on Your Own Data"
-last_updated: 2023-02-03
 level: "intermediate"
 weight: 50
 description: Improve the performance of your Reader by performing fine-tuning.
diff --git a/markdowns/03_Scalable_QA_System.md b/markdowns/03_Scalable_QA_System.md
index cb94ce13..f5afff7a 100644
--- a/markdowns/03_Scalable_QA_System.md
+++ b/markdowns/03_Scalable_QA_System.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb
 toc: True
 title: "Build a Scalable Question Answering System"
-last_updated: 2023-01-24
 level: "beginner"
 weight: 15
 description: Create a scalable Retriever Reader pipeline that uses an ElasticsearchDocumentStore.
diff --git a/markdowns/04_FAQ_style_QA.md b/markdowns/04_FAQ_style_QA.md
index 51ed8ce2..5fb0bc3c 100644
--- a/markdowns/04_FAQ_style_QA.md
+++ b/markdowns/04_FAQ_style_QA.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb
 toc: True
 title: "Utilizing Existing FAQs for Question Answering"
-last_updated: 2023-02-17
 level: "beginner"
 weight: 20
 description: Create a smarter way to answer new questions using your existing FAQ documents.
diff --git a/markdowns/05_Evaluation.md b/markdowns/05_Evaluation.md
index 4bca4cba..5da0a678 100644
--- a/markdowns/05_Evaluation.md
+++ b/markdowns/05_Evaluation.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb
 toc: True
 title: "Evaluation of a QA System"
-last_updated: 2023-02-03
 level: "advanced"
 weight: 100
 description: Learn how to evaluate the performance of individual nodes as well as entire pipelines.
diff --git a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
index cf4a8cf4..3664b223 100644
--- a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
+++ b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
 toc: True
 title: "Better Retrieval with Embedding Retrieval"
-last_updated: 2023-02-03
 level: "intermediate"
 weight: 55
 description: Use Transformer based dense Retrievers to improve your system’s performance.
diff --git a/markdowns/07_RAG_Generator.md b/markdowns/07_RAG_Generator.md
index 29043683..2b95625d 100644
--- a/markdowns/07_RAG_Generator.md
+++ b/markdowns/07_RAG_Generator.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb
 toc: True
 title: "Generative QA with Retrieval-Augmented Generation"
-last_updated: 2023-02-03
 level: "intermediate"
 weight: 60
 description: Try out a generative model in place of the extractive Reader.
diff --git a/markdowns/08_Preprocessing.md b/markdowns/08_Preprocessing.md
index 975b92bd..2f759073 100644
--- a/markdowns/08_Preprocessing.md
+++ b/markdowns/08_Preprocessing.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb
 toc: True
 title: "Preprocessing Your Documents"
-last_updated: 2023-02-20
 level: "beginner"
 weight: 25
 description: Start converting, cleaning, and splitting Documents using Haystack’s preprocessing capabilities.
diff --git a/markdowns/09_DPR_training.md b/markdowns/09_DPR_training.md
index 1178bda7..df767354 100644
--- a/markdowns/09_DPR_training.md
+++ b/markdowns/09_DPR_training.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb
 toc: True
 title: "Training Your Own Dense Passage Retrieval Model"
-last_updated: 2022-11-24
 level: "advanced"
 weight: 110
 description: Learn about training a Dense Passage Retrieval model and the data needed to do so.
diff --git a/markdowns/10_Knowledge_Graph.md b/markdowns/10_Knowledge_Graph.md
index 0aecd716..12acc980 100644
--- a/markdowns/10_Knowledge_Graph.md
+++ b/markdowns/10_Knowledge_Graph.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb
 toc: True
 title: "Question Answering on a Knowledge Graph"
-last_updated: 2022-11-24
 level: "advanced"
 weight: 120
 description: Experiment with a question answering system that draws upon knowledge graph.h
diff --git a/markdowns/11_Pipelines.md b/markdowns/11_Pipelines.md
index 309c0d15..fbf81f2b 100644
--- a/markdowns/11_Pipelines.md
+++ b/markdowns/11_Pipelines.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb
 toc: True
 title: "How to Use Pipelines"
-last_updated: 2022-12-09
 level: "intermediate"
 weight: 65
 description: Learn about the many ways which you can route queries through the nodes in a pipeline.
diff --git a/markdowns/12_LFQA.md b/markdowns/12_LFQA.md
index 201cf302..bbd4cef8 100644
--- a/markdowns/12_LFQA.md
+++ b/markdowns/12_LFQA.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb
 toc: True
 title: "Generative QA with LFQA"
-last_updated: 2023-02-13
 level: "intermediate"
 weight: 70
 description: Try out a generative model in place of the extractive Reader.
diff --git a/markdowns/13_Question_generation.md b/markdowns/13_Question_generation.md
index a8e52412..b655582c 100644
--- a/markdowns/13_Question_generation.md
+++ b/markdowns/13_Question_generation.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb
 toc: True
 title: "Question Generation"
-last_updated: 2023-02-03
 level: "intermediate"
 weight: 75
 description: Generate a set of questions that can be answered by a given Document.
diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
index 650fcee2..df069f54 100644
--- a/markdowns/14_Query_Classifier.md
+++ b/markdowns/14_Query_Classifier.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb
 toc: True
 title: "Query Classifier"
-last_updated: 2023-02-20
 level: "intermediate"
 weight: 80
 description: Classify incoming queries so that they can be routed to the nodes that are best at handling them.
diff --git a/markdowns/15_TableQA.md b/markdowns/15_TableQA.md
index 1d97e8f2..d2ae12bd 100644
--- a/markdowns/15_TableQA.md
+++ b/markdowns/15_TableQA.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb
 toc: True
 title: "Open-Domain QA on Tables"
-last_updated: 2023-02-03
 level: "advanced"
 weight: 130
 description: Perform question answering on tabular data.
diff --git a/markdowns/16_Document_Classifier_at_Index_Time.md b/markdowns/16_Document_Classifier_at_Index_Time.md
index c90e60e9..38a53b6c 100644
--- a/markdowns/16_Document_Classifier_at_Index_Time.md
+++ b/markdowns/16_Document_Classifier_at_Index_Time.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb
 toc: True
 title: "Document Classification at Index Time"
-last_updated: 2022-12-22
 level: "intermediate"
 weight: 85
 description: Generate and attach classification labels to your Documents when indexing.
diff --git a/markdowns/17_Audio.md b/markdowns/17_Audio.md
index 1e88f256..fda1c07e 100644
--- a/markdowns/17_Audio.md
+++ b/markdowns/17_Audio.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb
 toc: True
 title: "Make Your QA Pipelines Talk!"
-last_updated: 2023-02-03
 level: "intermediate"
 weight: 90
 description: Convert text Answers into speech.
diff --git a/markdowns/18_GPL.md b/markdowns/18_GPL.md
index 613da212..66a97a65 100644
--- a/markdowns/18_GPL.md
+++ b/markdowns/18_GPL.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb
 toc: True
 title: "Generative Pseudo Labeling for Domain Adaptation"
-last_updated: 2023-02-03
 level: "advanced"
 weight: 140
 description: Use a Retriever and a query generator to perform unsupervised domain adaptation.
diff --git a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
index 8fa559ca..1466c1cf 100644
--- a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
+++ b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
@@ -4,7 +4,6 @@ featured: False
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
 toc: True
 title: "Text-To-Image Search Pipeline with Multimodal Retriever"
-last_updated: 2023-02-08
 level: "intermediate"
 weight: 95
 description: Use a MultiModalRetriever to build a cross-modal search pipeline.
diff --git a/markdowns/20_Using_Haystack_with_REST_API.md b/markdowns/20_Using_Haystack_with_REST_API.md
index b8d2ebcf..7d53f980 100644
--- a/markdowns/20_Using_Haystack_with_REST_API.md
+++ b/markdowns/20_Using_Haystack_with_REST_API.md
@@ -4,7 +4,6 @@ featured: False
 colab: False
 toc: True
 title: "Using Haystack with REST API"
-last_updated: 2023-01-11
 level: "advanced"
 weight: 115
 description: Create a production-ready pipeline and interact with Haystack REST API.
diff --git a/markdowns/21_Customizing_PromptNode.md b/markdowns/21_Customizing_PromptNode.md
index 2245ed1f..2cbdd15e 100644
--- a/markdowns/21_Customizing_PromptNode.md
+++ b/markdowns/21_Customizing_PromptNode.md
@@ -4,7 +4,6 @@ featured: True
 colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb
 toc: True
 title: "Customizing PromptNode for NLP Tasks"
-last_updated: 2023-02-16
 level: "intermediate"
 weight: 57
 description: Use PromptNode and PromptTemplate for your custom NLP tasks
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index a4b2b0c8..d19357a8 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -1,5 +1,4 @@
 import argparse
-from datetime import date
 import tomli
 from nbconvert import MarkdownExporter
 from nbconvert.filters.strings import get_lines
@@ -22,7 +21,6 @@ def generate_frontmatter(config, tutorial):
 colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')}
 toc: {config["toc"]}
 title: "{tutorial["title"]}"
-last_updated: {date.today()}
 level: "{tutorial["level"]}"
 weight: {tutorial["weight"]}
 description: {tutorial["description"]}
@@ -69,7 +67,7 @@ def generate_markdown_from_notebook(config, tutorial, output_path, tutorials_pat
 
     else:
         nb_to_config = {cfg["notebook"]: cfg for cfg in index["tutorial"]}
-            
+
         for notebook in args.notebooks:
             nb_name = notebook.split("/")[-1]
             tutorial_cfg = nb_to_config.get(nb_name)

From c018ace219142483fc76ee5e2b8b3dcdff32e88c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 21 Feb 2023 14:06:08 +0100
Subject: [PATCH 045/206] Add `.DS_Store` to .gitignore  (#127)

* Add .DS_Store to gitignore file

* Add capital letter
---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index b6e47617..4343f073 100644
--- a/.gitignore
+++ b/.gitignore
@@ -127,3 +127,6 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+# Mac
+.DS_Store

From 6007c0c8caf01b6e4b2498b7d0ff7eac5665c1e5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 2 Mar 2023 18:07:12 +0300
Subject: [PATCH 046/206] Remove markdowns (#131)

* Bring back last_updated date as lastmod to the frontmatter
* Markdowns will be generated on the fly by haystack-home
---
 markdowns/01_Basic_QA_Pipeline.md             | 218 ------
 markdowns/02_Finetune_a_model_on_your_data.md | 178 -----
 markdowns/03_Scalable_QA_System.md            | 297 --------
 markdowns/04_FAQ_style_QA.md                  | 176 -----
 markdowns/05_Evaluation.md                    | 662 ------------------
 ...etter_Retrieval_via_Embedding_Retrieval.md | 261 -------
 markdowns/07_RAG_Generator.md                 | 212 ------
 markdowns/08_Preprocessing.md                 | 229 ------
 markdowns/09_DPR_training.md                  | 268 -------
 markdowns/10_Knowledge_Graph.md               | 217 ------
 markdowns/11_Pipelines.md                     | 473 -------------
 markdowns/12_LFQA.md                          | 193 -----
 markdowns/13_Question_generation.md           | 207 ------
 markdowns/14_Query_Classifier.md              | 504 -------------
 markdowns/15_TableQA.md                       | 444 ------------
 .../16_Document_Classifier_at_Index_Time.md   | 306 --------
 markdowns/17_Audio.md                         | 400 -----------
 markdowns/18_GPL.md                           | 303 --------
 ...arch_pipeline_with_MultiModal_Retriever.md | 210 ------
 markdowns/20_Using_Haystack_with_REST_API.md  | 315 ---------
 markdowns/21_Customizing_PromptNode.md        | 230 ------
 scripts/generate_markdowns.py                 |   6 +
 22 files changed, 6 insertions(+), 6303 deletions(-)
 delete mode 100644 markdowns/01_Basic_QA_Pipeline.md
 delete mode 100644 markdowns/02_Finetune_a_model_on_your_data.md
 delete mode 100644 markdowns/03_Scalable_QA_System.md
 delete mode 100644 markdowns/04_FAQ_style_QA.md
 delete mode 100644 markdowns/05_Evaluation.md
 delete mode 100644 markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
 delete mode 100644 markdowns/07_RAG_Generator.md
 delete mode 100644 markdowns/08_Preprocessing.md
 delete mode 100644 markdowns/09_DPR_training.md
 delete mode 100644 markdowns/10_Knowledge_Graph.md
 delete mode 100644 markdowns/11_Pipelines.md
 delete mode 100644 markdowns/12_LFQA.md
 delete mode 100644 markdowns/13_Question_generation.md
 delete mode 100644 markdowns/14_Query_Classifier.md
 delete mode 100644 markdowns/15_TableQA.md
 delete mode 100644 markdowns/16_Document_Classifier_at_Index_Time.md
 delete mode 100644 markdowns/17_Audio.md
 delete mode 100644 markdowns/18_GPL.md
 delete mode 100644 markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
 delete mode 100644 markdowns/20_Using_Haystack_with_REST_API.md
 delete mode 100644 markdowns/21_Customizing_PromptNode.md

diff --git a/markdowns/01_Basic_QA_Pipeline.md b/markdowns/01_Basic_QA_Pipeline.md
deleted file mode 100644
index 8ac18745..00000000
--- a/markdowns/01_Basic_QA_Pipeline.md
+++ /dev/null
@@ -1,218 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb
-toc: True
-title: "Build Your First Question Answering System"
-level: "beginner"
-weight: 10
-description: Get Started by creating a Retriever Reader pipeline.
-category: "QA"
-aliases: ['/tutorials/first-qa-system', '/tutorials/without-elasticsearch', '/tutorials/03_basic_qa_pipeline_without_elasticsearch']
-download: "/downloads/01_Basic_QA_Pipeline.ipynb"
-completion_time: 15 min
-created_at: 2023-01-11
----
-    
-
-
-> We've modified this first tutorial to make it simpler to start with. If you're looking for a Question Answering tutorial that uses a DocumentStore such as Elasticsearch, go to our new [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_scalable_qa_system) tutorial.
-
-- **Level**: Beginner
-- **Time to complete**: 15 minutes
-- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `FARMReader`
-- **Goal**: After completing this tutorial, you will have learned about the Reader and Retriever, and built a question answering pipeline that can answer questions about the Game of Thrones series.
-
-
-## Overview
-
-Learn how to build a question answering system using Haystack's DocumentStore, Retriever, and Reader. Your system will use Game of Thrones files and will be able to answer questions like "Who is the father of Arya Stark?". But you can use it to run on any other set of documents, such as your company's internal wikis or a collection of financial reports. 
-
-To help you get started quicker, we simplified certain steps in this tutorial. For example, Document preparation and pipeline initialization are handled by ready-made classes that replace lines of initialization code. But don't worry! This doesn't affect how well the question answering system performs.
-
-
-## Preparing the Colab Environment
-
-- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)
-- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)
-
-
-## Installing Haystack
-
-To start, let's install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install farm-haystack[colab]
-```
-
-Set the logging level to INFO:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-## Initializing the DocumentStore
-
-We'll start creating our question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, we're using the `InMemoryDocumentStore`, which is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store).
-
-Let's initialize the the DocumentStore:
-
-
-```python
-from haystack.document_stores import InMemoryDocumentStore
-
-document_store = InMemoryDocumentStore(use_bm25=True)
-```
-
-The DocumentStore is now ready. Now it's time to fill it with some Documents.
-
-## Preparing Documents
-
-1. Download 517 articles from the Game of Thrones Wikipedia. You can find them in *data/build_your_first_question_answering_system* as a set of *.txt* files.
-
-
-```python
-from haystack.utils import fetch_archive_from_http
-
-doc_dir = "data/build_your_first_question_answering_system"
-
-fetch_archive_from_http(
-    url="https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt1.zip",
-    output_dir=doc_dir
-)
-```
-
-2. Use `TextIndexingPipeline` to convert the files you just downloaded into Haystack [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore:
-
-
-```python
-import os
-from haystack.pipelines.standard_pipelines import TextIndexingPipeline
-
-files_to_index = [doc_dir + "/" + f for f in os.listdir(doc_dir)]
-indexing_pipeline = TextIndexingPipeline(document_store)
-indexing_pipeline.run_batch(file_paths=files_to_index)
-
-
-```
-
-The code in this tutorial uses the Game of Thrones data, but you can also supply your own *.txt* files and index them in the same way.
-
-As an alternative, you can cast you text data into [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore using `DocumentStore.write_documents()`.
-
-## Initializing the Retriever
-
-Our search system will use a Retriever, so we need to initialize it. A Retriever sifts through all the Documents and returns only the ones relevant to the question. This tutorial uses the BM25 algorithm. For more Retriever options, see [Retriever](https://docs.haystack.deepset.ai/docs/retriever).
-
-Let's initialize a BM25Retriever and make it use the InMemoryDocumentStore we initialized earlier in this tutorial:
-
-
-```python
-from haystack.nodes import BM25Retriever
-
-retriever = BM25Retriever(document_store=document_store)
-```
-
-The Retriever is ready but we still need to initialize the Reader. 
-
-## Initializing the Reader
-
-A Reader scans the texts it received from the Retriever and extracts the top answer candidates. Readers are based on powerful deep learning models but are much slower than Retrievers at processing the same amount of text. In this tutorial, we're using a FARMReader with a base-sized RoBERTa question answering model called [`deepset/roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2). It's a strong all-round model that's good as a starting point. To find the best model for your use case, see [Models](https://haystack.deepset.ai/pipeline_nodes/reader#models).
-
-Let's initialize the Reader:
-
-
-```python
-from haystack.nodes import FARMReader
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-We've initalized all the components for our pipeline. We're now ready to create the pipeline.
-
-## Creating the Retriever-Reader Pipeline
-
-In this tutorial, we're using a ready-made pipeline called `ExtractiveQAPipeline`. It connects the Reader and the Retriever. The combination of the two speeds up processing because the Reader only processes the Documents that the Retriever has passed on. To learn more about pipelines, see [Pipelines](https://docs.haystack.deepset.ai/docs/pipelines).
-
-To create the pipeline, run:
-
-
-```python
-from haystack.pipelines import ExtractiveQAPipeline
-
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-The pipeline's ready, you can now go ahead and ask a question!
-
-## Asking a Question
-
-1. Use the pipeline `run()` method to ask a question. The query argument is where you type your question. Additionally, you can set the number of documents you want the Reader and Retriever to return using the `top-k` parameter. To learn more about setting arguments, see [Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments). To understand the importance of the `top-k` parameter, see [Choosing the Right top-k Values](https://docs.haystack.deepset.ai/docs/optimization#choosing-the-right-top-k-values).
-
-
-```python
-prediction = pipe.run(
-    query="Who is the father of Arya Stark?",
-    params={
-        "Retriever": {"top_k": 10},
-        "Reader": {"top_k": 5}
-    }
-)
-```
-
-Here are some questions you could try out:
-- Who is the father of Arya Stark?
-- Who created the Dothraki vocabulary?
-- Who is the sister of Sansa?
-
-2. Print out the answers the pipeline returned:
-
-
-```python
-from pprint import pprint
-
-pprint(prediction)
-```
-
-3. Simplify the printed answers:
-
-
-```python
-from haystack.utils import print_answers
-
-print_answers(
-    prediction,
-    details="minimum" ## Choose from `minimum`, `medium`, and `all`
-)
-```
-
-And there you have it! Congratulations on building your first machine learning based question answering system!
-
-# Next Steps
-
-Check out [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_scalable_qa_system) to learn how to make a more advanced question answering system that uses an Elasticsearch backed DocumentStore and makes more use of the flexibility that pipelines offer.
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
-
diff --git a/markdowns/02_Finetune_a_model_on_your_data.md b/markdowns/02_Finetune_a_model_on_your_data.md
deleted file mode 100644
index b6aaad2f..00000000
--- a/markdowns/02_Finetune_a_model_on_your_data.md
+++ /dev/null
@@ -1,178 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb
-toc: True
-title: "Fine-Tuning a Model on Your Own Data"
-level: "intermediate"
-weight: 50
-description: Improve the performance of your Reader by performing fine-tuning.
-category: "QA"
-aliases: ['/tutorials/fine-tuning-a-model']
-download: "/downloads/02_Finetune_a_model_on_your_data.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).
-However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.
-While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.
-
-This tutorial shows you how to fine-tune a pretrained model on your own dataset.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-
-```python
-# Make sure you have a GPU running
-!nvidia-smi
-```
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest main of Haystack
-!pip install --upgrade pip
-!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-
-```python
-from haystack.nodes import FARMReader
-from haystack.utils import fetch_archive_from_http
-```
-
-
-## Create Training Data
-
-There are two ways to generate training data
-
-1. **Annotation**: You can use the [annotation tool](https://haystack.deepset.ai/guides/annotation) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.
-
-![Snapshot of the annotation tool](https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/annotation_tool.png)
-
-2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.
-
-
-## Fine-tune your model
-
-Once you have collected training data, you can fine-tune your base models.
-We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).
-We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.
-
-**Recommendation**: Run training on a GPU.
-If you are using Colab: Enable this in the menu "Runtime" > "Change Runtime type" > Select "GPU" in dropdown.
-Then change the `use_gpu` arguments below to `True`
-
-
-```python
-reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
-data_dir = "data/squad20"
-# data_dir = "PATH/TO_YOUR/TRAIN_DATA"
-reader.train(data_dir=data_dir, train_filename="dev-v2.0.json", use_gpu=True, n_epochs=1, save_dir="my_model")
-```
-
-
-```python
-# Saving the model happens automatically at the end of training into the `save_dir` you specified
-# However, you could also save a reader manually again via:
-reader.save(directory="my_model")
-```
-
-
-```python
-# If you want to load it at a later point, just do:
-new_reader = FARMReader(model_name_or_path="my_model")
-```
-
-## Distill your model
-In this case, we have used "distilbert-base-uncased" as our base model. This model was trained using a process called distillation. In this process, a bigger model is trained first and is used to train a smaller model which increases its accuracy. This is why "distilbert-base-uncased" can achieve quite competitive performance while being very small.
-
-Sometimes, however, you can't use an already distilled model and have to distil it yourself. For this case, haystack has implemented [distillation features](https://haystack.deepset.ai/guides/model-distillation).
-
-### Augmenting your training data
-To get the most out of model distillation, we recommend increasing the size of your training data by using data augmentation. You can do this by running the [`augment_squad.py` script](https://github.com/deepset-ai/haystack/blob/main/haystack/utils/augment_squad.py):
-
-
-```python
-# Downloading script
-!wget https://raw.githubusercontent.com/deepset-ai/haystack/main/haystack/utils/augment_squad.py
-
-glove_dir = "data/tutorial2/gloves"
-squad_dir = "data/tutorial2/squad_small"
-
-# Downloading smaller glove vector file (only for demonstration purposes)
-glove_url = "https://nlp.stanford.edu/data/glove.6B.zip"
-fetch_archive_from_http(url=glove_url, output_dir=glove_dir)
-
-# Downloading very small dataset to make tutorial faster (please use a bigger dataset for real use cases)
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/squad_small.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=squad_dir)
-
-# Just replace the path with your dataset and adjust the output (also please remove glove path to use bigger glove vector file)
-!python augment_squad.py --squad_path data/tutorial2/squad_small/squad_small.json --output_path augmented_dataset.json --multiplication_factor 2 --glove_path data/tutorial2/gloves/glove.6B.300d.txt
-```
-
-In this case, we use a multiplication factor of 2 to keep this example lightweight. Usually you would use a factor like 20 depending on the size of your training data. Augmenting this small dataset with a multiplication factor of 2, should take about 5 to 10 minutes to run on one V100 GPU.
-
-### Running distillation
-Distillation in haystack is done in two steps: First, you run intermediate layer distillation on the augmented dataset to ensure the two models behave similarly. After that, you run the prediction layer distillation on the non-augmented dataset to optimize the model for your specific task.
-
-If you want, you can leave out the intermediate layer distillation step and only run the prediction layer distillation. This way you also do not need to perform data augmentation. However, this will make the model significantly less accurate.
-
-
-```python
-# Loading a fine-tuned model as teacher e.g. "deepset/​bert-​base-​uncased-​squad2"
-teacher = FARMReader(model_name_or_path="my_model", use_gpu=True)
-
-# You can use any pre-trained language model as teacher that uses the same tokenizer as the teacher model.
-# The number of the layers in the teacher model also needs to be a multiple of the number of the layers in the student.
-student = FARMReader(model_name_or_path="huawei-noah/TinyBERT_General_6L_768D", use_gpu=True)
-
-student.distil_intermediate_layers_from(teacher, data_dir=".", train_filename="augmented_dataset.json", use_gpu=True)
-student.distil_prediction_layer_from(teacher, data_dir="data/squad20", train_filename="dev-v2.0.json", use_gpu=True)
-
-student.save(directory="my_distilled_model")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/03_Scalable_QA_System.md b/markdowns/03_Scalable_QA_System.md
deleted file mode 100644
index f5afff7a..00000000
--- a/markdowns/03_Scalable_QA_System.md
+++ /dev/null
@@ -1,297 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb
-toc: True
-title: "Build a Scalable Question Answering System"
-level: "beginner"
-weight: 15
-description: Create a scalable Retriever Reader pipeline that uses an ElasticsearchDocumentStore.
-category: "QA"
-aliases: []
-download: "/downloads/03_Scalable_QA_System.ipynb"
-completion_time: 20 min
-created_at: 2023-01-11
----
-    
-
-
-- **Level**: Beginner
-- **Time to complete**: 20 minutes
-- **Nodes Used**: `ElasticsearchDocumentStore`, `BM25Retriever`, `FARMReader`
-- **Goal**: After completing this tutorial, you'll have built a scalable search system that runs on text files and can answer questions about Game of Thrones. You'll then be able to expand this system for your needs.
-
-
-## Overview
-
-Learn how to set up a question answering system that can search through complex knowledge bases and highlight answers to questions such as "Who is the father of Arya Stark?". In this tutorial, we'll work on a set of Wikipedia pages about Game of Thrones, but you can adapt it to search through internal wikis or a collection of financial reports, for example.
-
-This tutorial introduces you to all the concepts needed to build such a question answering system. It also uses Haystack components, such as indexing pipelines, querying pipelines, and DocumentStores backed by external database services.
-
-Let's learn how to build a question answering system and discover more about the marvelous seven kingdoms!
-
-
-## Preparing the Colab Environment
-
-- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)
-
-
-## Installing Haystack
-
-To start, let's install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install farm-haystack[colab]
-```
-
-Set the logging level to INFO:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-## Initializing the ElasticsearchDocumentStore
-
-A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. Here, we're using the [`ElasticsearchDocumentStore`](https://docs.haystack.deepset.ai/reference/document-store-api#module-elasticsearch) which connects to a running Elasticsearch service. It's a fast and scalable text-focused storage option. This service runs independently from Haystack and persists even after the Haystack program has finished running. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store).
-
-1. Download, extract, and set the permissions for the Elasticsearch installation image:
-
-
-```bash
-%%bash
-
-wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-chown -R daemon:daemon elasticsearch-7.9.2
-```
-
-2. Start the server:
-
-
-```bash
-%%bash --bg
-
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
-```
-
-If you are working in an environment where Docker is available, you can also start Elasticsearch using Docker. You can do this manually, or using our [`launch_es()`](https://docs.haystack.deepset.ai/reference/utils-api#module-doc_store) utility function.
-
-3. Wait 30 seconds for the server to fully start up:
-
-
-```python
-import time
-time.sleep(30)
-```
-
-4. Initialize the ElasticsearchDocumentStore:
-
-
-
-```python
-from haystack.utils import launch_es
-launch_es()
-```
-
-
-```python
-import os
-from haystack.document_stores import ElasticsearchDocumentStore
-
-# Get the host where Elasticsearch is running, default to localhost
-host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
-
-document_store = ElasticsearchDocumentStore(
-    host=host,
-    username="",
-    password="",
-    index="document"
-)
-```
-
-ElasticsearchDocumentStore is up and running and ready to store the Documents.
-
-## Indexing Documents with a Pipeline
-
-The next step is adding the files to the DocumentStore. The indexing pipeline turns your files into Document objects and writes them to the DocumentStore. Our indexing pipeline will have two nodes: `TextConverter`, which turns `.txt` files into Haystack `Document` objects, and `PreProcessor`, which cleans and splits the text within a `Document`.
-
-Once we combine these nodes into a pipeline, the pipeline will ingest `.txt` file paths, preprocess them, and write them into the DocumentStore.
-
-
-1. Download 517 articles from the Game of Thrones Wikipedia. You can find them in *data/build_a_scalable_question_answering_system* as a set of *.txt* files.
-
-
-```python
-from haystack.utils import fetch_archive_from_http
-
-doc_dir = "data/build_a_scalable_question_answering_system"
-
-fetch_archive_from_http(
-    url="https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt3.zip", 
-    output_dir=doc_dir
-)
-```
-
-2. Initialize the pipeline, TextConverter, and PreProcessor:
-
-
-```python
-from haystack import Pipeline
-from haystack.nodes import TextConverter, PreProcessor
-
-indexing_pipeline = Pipeline()
-text_converter = TextConverter()
-preprocessor = PreProcessor(
-    clean_whitespace=True,
-    clean_header_footer=True,
-    clean_empty_lines=True,
-    split_by="word",
-    split_length=200,
-    split_overlap=20,
-    split_respect_sentence_boundary=True,
-)
-
-```
-
-To learn more about the parameters of the `PreProcessor`, see [Usage](https://docs.haystack.deepset.ai/docs/preprocessor#usage). To understand why document splitting is important for your question answering system's performance, see [Document Length](https://docs.haystack.deepset.ai/docs/optimization#document-length).
-
-2. Add the nodes into an indexing pipeline. You should provide the `name` or `name`s of preceding nodes as the `input` argument. Note that in an indexing pipeline, the input to the first node is `File`.
-
-
-```python
-import os
-
-indexing_pipeline.add_node(component=text_converter, name="TextConverter", inputs=["File"])
-indexing_pipeline.add_node(component=preprocessor, name="PreProcessor", inputs=["TextConverter"])
-indexing_pipeline.add_node(component=document_store, name="DocumentStore", inputs=["PreProcessor"])
-
-```
-
-3. Run the indexing pipeline to write the text data into the DocumentStore:
-
-
-```python
-files_to_index = [doc_dir + "/" + f for f in os.listdir(doc_dir)]
-indexing_pipeline.run_batch(file_paths=files_to_index)
-```
-
-The code in this tutorial uses Game of Thrones data, but you can also supply your own `.txt` files and index them in the same way.
-
-As an alternative, you can cast you text data into [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore using [`DocumentStore.write_documents()`](https://docs.haystack.deepset.ai/reference/document-store-api#basedocumentstorewrite_documents).
-
-Now that the Documents are in the DocumentStore, let's initialize the nodes we want to use in our query pipeline.
-
-## Initializing the Retriever
-
-Our query pipeline is going to use a Retriever, so we need to initialize it. A Retriever sifts through all the Documents and returns only those that are relevant to the question. This tutorial uses the BM25Retriever. This is the recommended Retriever for a question answering system like the one we're creating. For more Retriever options, see [Retriever](https://docs.haystack.deepset.ai/docs/retriever).
-
-
-```python
-from haystack.nodes import BM25Retriever
-
-retriever = BM25Retriever(document_store=document_store)
-```
-
-The BM25Retriever is initialized and ready for the pipeline.
-
-## Initializing the Reader
-
-Our query pipeline also needs a Reader, so we'll initialize it next. A Reader scans the texts it received from the Retriever and extracts the top answer candidates. Readers are based on powerful deep learning models but are much slower than Retrievers at processing the same amount of text. This tutorials uses a FARMReader with a base-sized RoBERTa question answering model called [`deepset/roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2). It's a good all-round model to start with. To find a model that's best for your use case, see [Models](https://docs.haystack.deepset.ai/docs/reader#models).
-
-
-```python
-from haystack.nodes import FARMReader
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-## Creating the Retriever-Reader Pipeline
-
-You can combine the Reader and Retriever in a querying pipeline using the `Pipeline` class. The combination of the two speeds up processing because the Reader only processes the Documents that it received from the Retriever. 
-
-Initialize the `Pipeline` object and add the Retriever and Reader as nodes. You should provide the `name` or `name`s of preceding nodes as the input argument. Note that in a querying pipeline, the input to the first node is `Query`.
-
-
-```python
-from haystack import Pipeline
-
-querying_pipeline = Pipeline()
-querying_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
-querying_pipeline.add_node(component=reader, name="Reader", inputs=["Retriever"])
-
-```
-
-That's it! Your pipeline's ready to answer your questions!
-
-## Asking a Question
-
-1. Use the pipeline's `run()` method to ask a question. The query argument is where you type your question. Additionally, you can set the number of documents you want the Reader and Retriever to return using the `top-k` parameter. To learn more about setting arguments, see [Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments). To understand the importance of the `top-k` parameter, see [Choosing the Right top-k Values](https://docs.haystack.deepset.ai/docs/optimization#choosing-the-right-top-k-values).
-
-
-
-```python
-prediction = querying_pipeline.run(
-    query="Who is the father of Arya Stark?",
-    params={
-        "Retriever": {"top_k": 10},
-        "Reader": {"top_k": 5}
-    }
-)
-```
-
-Here are some questions you could try out:
-- Who is the father of Arya Stark?
-- Who created the Dothraki vocabulary?
-- Who is the sister of Sansa?
-
-2. Print out the answers the pipeline returns:
-
-
-```python
-from pprint import pprint
-
-pprint(prediction)
-```
-
-3. Simplify the printed answers:
-
-
-```python
-from haystack.utils import print_answers
-
-print_answers(
-    prediction,
-    details="minimum" ## Choose from `minimum`, `medium` and `all`
-)
-```
-
-And there you have it! Congratulations on building a scalable machine learning based question answering system!
-
-# Next Steps
-
-To learn how to improve the performance of the Reader, see [Fine-Tune a Reader](https://haystack.deepset.ai/tutorials/02_finetune_a_model_on_your_data).
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
-
diff --git a/markdowns/04_FAQ_style_QA.md b/markdowns/04_FAQ_style_QA.md
deleted file mode 100644
index 5fb0bc3c..00000000
--- a/markdowns/04_FAQ_style_QA.md
+++ /dev/null
@@ -1,176 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb
-toc: True
-title: "Utilizing Existing FAQs for Question Answering"
-level: "beginner"
-weight: 20
-description: Create a smarter way to answer new questions using your existing FAQ documents.
-category: "QA"
-aliases: ['/tutorials/existing-faqs']
-download: "/downloads/04_FAQ_style_QA.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-- **Level**: Beginner
-- **Time to complete**: 15 minutes
-- **Nodes Used**: `InMemoryDocumentStore`, `EmbeddingRetriever`
-- **Goal**: Learn how to use the `EmbeddingRetriever` in a `FAQPipeline` to answer incoming questions by matching them to the most similar questions in your existing FAQ.
-
-# Overview
-While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
-
-**Pros**:
-
-- Very fast at inference time
-- Utilize existing FAQ data
-- Quite good control over answers
-
-**Cons**:
-
-- Generalizability: We can only answer questions that are similar to existing ones in FAQ
-
-In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-You can double check whether the GPU runtime is enabled with the following command:
-
-
-```bash
-%%bash
-
-nvidia-smi
-```
-
-To start, install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-### Create a simple DocumentStore
-The InMemoryDocumentStore is good for quick development and prototyping. For more scalable options, check-out the [docs](https://docs.haystack.deepset.ai/docs/document_store).
-
-
-```python
-from haystack.document_stores import InMemoryDocumentStore
-
-document_store = InMemoryDocumentStore()
-```
-
-### Create a Retriever using embeddings
-Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
-We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
-
-
-```python
-from haystack.nodes import EmbeddingRetriever
-
-retriever = EmbeddingRetriever(
-    document_store=document_store,
-    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
-    use_gpu=True,
-    scale_score=False,
-)
-```
-
-### Prepare & Index FAQ data
-We create a pandas dataframe containing some FAQ data (i.e curated pairs of question + answer) and index those in our documentstore.
-Here: We download some question-answer pairs related to COVID-19
-
-
-```python
-import pandas as pd
-
-from haystack.utils import fetch_archive_from_http
-
-
-# Download
-doc_dir = "data/tutorial4"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/small_faq_covid.csv.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Get dataframe with columns "question", "answer" and some custom metadata
-df = pd.read_csv(f"{doc_dir}/small_faq_covid.csv")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-df["question"] = df["question"].apply(lambda x: x.strip())
-print(df.head())
-
-# Create embeddings for our questions from the FAQs
-# In contrast to most other search use cases, we don't create the embeddings here from the content of our documents,
-# but rather from the additional text field "question" as we want to match "incoming question" <-> "stored question".
-questions = list(df["question"].values)
-df["embedding"] = retriever.embed_queries(queries=questions).tolist()
-df = df.rename(columns={"question": "content"})
-
-# Convert Dataframe to list of dicts and index them in our DocumentStore
-docs_to_index = df.to_dict(orient="records")
-document_store.write_documents(docs_to_index)
-```
-
-### Ask questions
-Initialize a Pipeline (this time without a reader) and ask questions
-
-
-```python
-from haystack.pipelines import FAQPipeline
-
-pipe = FAQPipeline(retriever=retriever)
-```
-
-
-```python
-from haystack.utils import print_answers
-
-# Run any question and change top_k to see more or less answers
-prediction = pipe.run(query="How is the virus spreading?", params={"Retriever": {"top_k": 1}})
-
-print_answers(prediction, details="medium")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/05_Evaluation.md b/markdowns/05_Evaluation.md
deleted file mode 100644
index 5da0a678..00000000
--- a/markdowns/05_Evaluation.md
+++ /dev/null
@@ -1,662 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb
-toc: True
-title: "Evaluation of a QA System"
-level: "advanced"
-weight: 100
-description: Learn how to evaluate the performance of individual nodes as well as entire pipelines.
-category: "QA"
-aliases: ['/tutorials/evaluation']
-download: "/downloads/05_Evaluation.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-To be able to make a statement about the quality of results a question-answering pipeline or any other pipeline in haystack produces, it is important to evaluate it. Furthermore, evaluation allows determining which components of the pipeline can be improved.
-The results of the evaluation can be saved as CSV files, which contain all the information to calculate additional metrics later on or inspect individual predictions.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-You can double check whether the GPU runtime is enabled with the following command:
-
-
-```bash
-%%bash
-
-nvidia-smi
-```
-
-To start, install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-## Start an Elasticsearch server
-
-You can start Elasticsearch on your local machine instance using Docker:
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source:
-
-
-```bash
-%%bash
-
-wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-chown -R daemon:daemon elasticsearch-7.9.2
-```
-
-
-```bash
-%%bash --bg
-
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
-```
-
-Wait 30 seconds only to be sure Elasticsearch is ready before continuing:
-
-
-```python
-import time
-
-time.sleep(30)
-```
-
-## Fetch, Store And Preprocess the Evaluation Dataset
-
-
-```python
-from haystack.utils import fetch_archive_from_http
-
-
-# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents with one question per document and multiple annotated answers
-doc_dir = "data/tutorial5"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-import os
-
-from haystack.document_stores import ElasticsearchDocumentStore
-
-
-# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
-doc_index = "tutorial5_docs"
-label_index = "tutorial5_labels"
-
-# Get the host where Elasticsearch is running, default to localhost
-host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
-
-# Connect to Elasticsearch
-document_store = ElasticsearchDocumentStore(
-    host=host,
-    username="",
-    password="",
-    index=doc_index,
-    label_index=label_index,
-    embedding_field="emb",
-    embedding_dim=768,
-    excluded_meta_data=["emb"],
-)
-```
-
-
-```python
-from haystack.nodes import PreProcessor
-
-# Add evaluation data to Elasticsearch Document Store
-# We first delete the custom tutorial indices to not have duplicate elements
-# and also split our documents into shorter passages using the PreProcessor
-preprocessor = PreProcessor(
-    split_by="word",
-    split_length=200,
-    split_overlap=0,
-    split_respect_sentence_boundary=False,
-    clean_empty_lines=False,
-    clean_whitespace=False,
-)
-document_store.delete_documents(index=doc_index)
-document_store.delete_documents(index=label_index)
-
-# The add_eval_data() method converts the given dataset in json format into Haystack document and label objects. Those objects are then indexed in their respective document and label index in the document store. The method can be used with any dataset in SQuAD format.
-document_store.add_eval_data(
-    filename="data/tutorial5/nq_dev_subset_v2.json",
-    doc_index=doc_index,
-    label_index=label_index,
-    preprocessor=preprocessor,
-)
-```
-
-## Initialize the Two Components of an ExtractiveQAPipeline: Retriever and Reader
-
-
-```python
-# Initialize Retriever
-from haystack.nodes import BM25Retriever
-
-retriever = BM25Retriever(document_store=document_store)
-
-# Alternative: Evaluate dense retrievers (EmbeddingRetriever or DensePassageRetriever)
-# The EmbeddingRetriever uses a single transformer based encoder model for query and document.
-# In contrast, DensePassageRetriever uses two separate encoders for both.
-
-# Please make sure the "embedding_dim" parameter in the DocumentStore above matches the output dimension of your models!
-# Please also take care that the PreProcessor splits your files into chunks that can be completely converted with
-#        the max_seq_len limitations of Transformers
-# The SentenceTransformer model "sentence-transformers/multi-qa-mpnet-base-dot-v1" generally works well with the EmbeddingRetriever on any kind of English text.
-# For more information and suggestions on different models check out the documentation at: https://www.sbert.net/docs/pretrained_models.html
-
-# from haystack.retriever import EmbeddingRetriever, DensePassageRetriever
-# retriever = EmbeddingRetriever(document_store=document_store,
-#                                embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1")
-# retriever = DensePassageRetriever(document_store=document_store,
-#                                   query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-#                                   passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-#                                   use_gpu=True,
-#                                   max_seq_len_passage=256,
-#                                   embed_title=True)
-# document_store.update_embeddings(retriever, index=doc_index)
-```
-
-
-```python
-# Initialize Reader
-from haystack.nodes import FARMReader
-
-reader = FARMReader("deepset/roberta-base-squad2", top_k=4, return_no_answer=True)
-
-# Define a pipeline consisting of the initialized retriever and reader
-from haystack.pipelines import ExtractiveQAPipeline
-
-pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever)
-
-# The evaluation also works with any other pipeline.
-# For example you could use a DocumentSearchPipeline as an alternative:
-
-# from haystack.pipelines import DocumentSearchPipeline
-# pipeline = DocumentSearchPipeline(retriever=retriever)
-```
-
-## Evaluation of an ExtractiveQAPipeline
-Here we evaluate retriever and reader in open domain fashion on the full corpus of documents i.e. a document is considered
-correctly retrieved if it contains the gold answer string within it. The reader is evaluated based purely on the
-predicted answer string, regardless of which document this came from and the position of the extracted span.
-
-The generation of predictions is separated from the calculation of metrics. This allows you to run the computation-heavy model predictions only once and then iterate flexibly on the metrics or reports you want to generate.
-
-
-
-```python
-from haystack.schema import EvaluationResult, MultiLabel
-
-# We can load evaluation labels from the document store
-# We are also opting to filter out no_answer samples
-eval_labels = document_store.get_all_labels_aggregated(drop_negative_labels=True, drop_no_answers=True)
-
-## Alternative: Define queries and labels directly
-
-# eval_labels = [
-#    MultiLabel(
-#        labels=[
-#            Label(
-#                query="who is written in the book of life",
-#                answer=Answer(
-#                    answer="every person who is destined for Heaven or the World to Come",
-#                    offsets_in_context=[Span(374, 434)]
-#                ),
-#                document=Document(
-#                    id='1b090aec7dbd1af6739c4c80f8995877-0',
-#                    content_type="text",
-#                    content='Book of Life - wikipedia Book of Life Jump to: navigation, search This article is
-#                       about the book mentioned in Christian and Jewish religious teachings...'
-#                ),
-#                is_correct_answer=True,
-#                is_correct_document=True,
-#                origin="gold-label"
-#            )
-#        ]
-#    )
-# ]
-
-# Similar to pipeline.run() we can execute pipeline.eval()
-eval_result = pipeline.eval(labels=eval_labels, params={"Retriever": {"top_k": 5}})
-```
-
-
-```python
-# The EvaluationResult contains a pandas dataframe for each pipeline node.
-# That's why there are two dataframes in the EvaluationResult of an ExtractiveQAPipeline.
-
-retriever_result = eval_result["Retriever"]
-retriever_result.head()
-```
-
-
-```python
-reader_result = eval_result["Reader"]
-reader_result.head()
-```
-
-
-```python
-# We can filter for all documents retrieved for a given query
-query = "who is written in the book of life"
-retriever_book_of_life = retriever_result[retriever_result["query"] == query]
-```
-
-
-```python
-# We can also filter for all answers predicted for a given query
-reader_book_of_life = reader_result[reader_result["query"] == query]
-```
-
-
-```python
-# Save the evaluation result so that we can reload it later and calculate evaluation metrics without running the pipeline again.
-eval_result.save("../")
-```
-
-## Calculating Evaluation Metrics
-Load an EvaluationResult to quickly calculate standard evaluation metrics for all predictions,
-such as F1-score of each individual prediction of the Reader node or recall of the retriever.
-To learn more about the metrics, see [Evaluation Metrics](https://haystack.deepset.ai/guides/evaluation#metrics-retrieval)
-
-
-```python
-saved_eval_result = EvaluationResult.load("../")
-metrics = saved_eval_result.calculate_metrics()
-print(f'Retriever - Recall (single relevant document): {metrics["Retriever"]["recall_single_hit"]}')
-print(f'Retriever - Recall (multiple relevant documents): {metrics["Retriever"]["recall_multi_hit"]}')
-print(f'Retriever - Mean Reciprocal Rank: {metrics["Retriever"]["mrr"]}')
-print(f'Retriever - Precision: {metrics["Retriever"]["precision"]}')
-print(f'Retriever - Mean Average Precision: {metrics["Retriever"]["map"]}')
-
-print(f'Reader - F1-Score: {metrics["Reader"]["f1"]}')
-print(f'Reader - Exact Match: {metrics["Reader"]["exact_match"]}')
-```
-
-## Generating an Evaluation Report
-A summary of the evaluation results can be printed to get a quick overview. It includes some aggregated metrics and also shows a few wrongly predicted examples.
-
-
-```python
-pipeline.print_eval_report(saved_eval_result)
-```
-
-## Advanced Evaluation Metrics
-As an advanced evaluation metric, semantic answer similarity (SAS) can be calculated. This metric takes into account whether the meaning of a predicted answer is similar to the annotated gold answer rather than just doing string comparison.
-To this end SAS relies on pre-trained models. For English, we recommend "cross-encoder/stsb-roberta-large", whereas for German we recommend "deepset/gbert-large-sts". A good multilingual model is "sentence-transformers/paraphrase-multilingual-mpnet-base-v2".
-More info on this metric can be found in our [paper](https://arxiv.org/abs/2108.06130) or in our [blog post](https://www.deepset.ai/blog/semantic-answer-similarity-to-evaluate-qa).
-
-
-```python
-advanced_eval_result = pipeline.eval(
-    labels=eval_labels, params={"Retriever": {"top_k": 5}}, sas_model_name_or_path="cross-encoder/stsb-roberta-large"
-)
-
-metrics = advanced_eval_result.calculate_metrics()
-print(metrics["Reader"]["sas"])
-```
-
-## Isolated Evaluation Mode
-The isolated node evaluation uses labels as input to the Reader node instead of the output of the preceeding Retriever node.
-Thereby, we can additionally calculate the upper bounds of the evaluation metrics of the Reader. Note that even with isolated evaluation enabled, integrated evaluation will still be running.
-
-
-
-```python
-eval_result_with_upper_bounds = pipeline.eval(
-    labels=eval_labels, params={"Retriever": {"top_k": 5}, "Reader": {"top_k": 5}}, add_isolated_node_eval=True
-)
-```
-
-
-```python
-pipeline.print_eval_report(eval_result_with_upper_bounds)
-```
-
-## Advanced Label Scopes
-Answers are considered correct if the predicted answer matches the gold answer in the labels. Documents are considered correct if the predicted document ID matches the gold document ID in the labels. Sometimes, these simple definitions of "correctness" are not sufficient. There are cases where you want to further specify the "scope" within which an answer or a document is considered correct. For this reason, `EvaluationResult.calculate_metrics()` offers the parameters `answer_scope` and `document_scope`.
-
-Say you want to ensure that an answer is only considered correct if it stems from a specific context of surrounding words. This is especially useful if your answer is very short, like a date (for example, "2011") or a place ("Berlin"). Such short answer might easily appear in multiple completely different contexts. Some of those contexts might perfectly fit the actual question and answer it. Some others might not: they don't relate to the question at all but still contain the answer string. In that case, you might want to ensure that only answers that stem from the correct context are considered correct. To do that, specify `answer_scope="context"` in `calculate_metrics()`. 
-
-`answer_scope` takes the following values:
-- `any` (default): Any matching answer is considered correct.
-- `context`: The answer is only considered correct if its context matches as well. It uses fuzzy matching (see `context_matching` parameters of `pipeline.eval()`).
-- `document_id`: The answer is only considered correct if its document ID matches as well. You can specify a custom document ID through the `custom_document_id_field` parameter of `pipeline.eval()`.
-- `document_id_and_context`: The answer is only considered correct if its document ID and its context match as well.
-
-In Question Answering, to enforce that the retrieved document is considered correct whenever the answer is correct, set `document_scope` to `answer` or `document_id_or_answer`.
-
-`document_scope` takes the following values:
-- `document_id`: Specifies that the document ID must match. You can specify a custom document ID through the `custom_document_id_field` parameter of `pipeline.eval()`.
-- `context`: Specifies that the content of the document must match. It uses fuzzy matching (see the `context_matching` parameters of `pipeline.eval()`).
-- `document_id_and_context`: A Boolean operation specifying that both `'document_id' AND 'context'` must match.
-- `document_id_or_context`: A Boolean operation specifying that either `'document_id' OR 'context'` must match.
-- `answer`: Specifies that the document contents must include the answer. The selected `answer_scope` is enforced.
-- `document_id_or_answer` (default): A Boolean operation specifying that either `'document_id' OR 'answer'` must match.
-
-
-```python
-metrics = saved_eval_result.calculate_metrics(answer_scope="context")
-print(f'Retriever - Recall (single relevant document): {metrics["Retriever"]["recall_single_hit"]}')
-print(f'Retriever - Recall (multiple relevant documents): {metrics["Retriever"]["recall_multi_hit"]}')
-print(f'Retriever - Mean Reciprocal Rank: {metrics["Retriever"]["mrr"]}')
-print(f'Retriever - Precision: {metrics["Retriever"]["precision"]}')
-print(f'Retriever - Mean Average Precision: {metrics["Retriever"]["map"]}')
-
-print(f'Reader - F1-Score: {metrics["Reader"]["f1"]}')
-print(f'Reader - Exact Match: {metrics["Reader"]["exact_match"]}')
-```
-
-
-```python
-document_store.get_all_documents()[0]
-```
-
-
-```python
-# Let's try Document Retrieval on a file level (it's sufficient if the correct file identified by its name (for example, 'Book of Life') was retrieved).
-eval_result_custom_doc_id = pipeline.eval(
-    labels=eval_labels, params={"Retriever": {"top_k": 5}}, custom_document_id_field="name"
-)
-metrics = eval_result_custom_doc_id.calculate_metrics(document_scope="document_id")
-print(f'Retriever - Recall (single relevant document): {metrics["Retriever"]["recall_single_hit"]}')
-print(f'Retriever - Recall (multiple relevant documents): {metrics["Retriever"]["recall_multi_hit"]}')
-print(f'Retriever - Mean Reciprocal Rank: {metrics["Retriever"]["mrr"]}')
-print(f'Retriever - Precision: {metrics["Retriever"]["precision"]}')
-print(f'Retriever - Mean Average Precision: {metrics["Retriever"]["map"]}')
-```
-
-
-```python
-# Let's enforce the context again:
-metrics = eval_result_custom_doc_id.calculate_metrics(document_scope="document_id_and_context")
-print(f'Retriever - Recall (single relevant document): {metrics["Retriever"]["recall_single_hit"]}')
-print(f'Retriever - Recall (multiple relevant documents): {metrics["Retriever"]["recall_multi_hit"]}')
-print(f'Retriever - Mean Reciprocal Rank: {metrics["Retriever"]["mrr"]}')
-print(f'Retriever - Precision: {metrics["Retriever"]["precision"]}')
-print(f'Retriever - Mean Average Precision: {metrics["Retriever"]["map"]}')
-```
-
-## Storing results in MLflow
-Storing evaluation results in CSVs is fine but not enough if you want to compare and track multiple evaluation runs. MLflow is a handy tool when it comes to tracking experiments. So we decided to use it to track all of `Pipeline.eval()` with reproducability of your experiments in mind.
-
-### Host your own MLflow or use deepset's public MLflow
-
-If you don't want to use deepset's public MLflow instance under https://public-mlflow.deepset.ai, you can easily host it yourself.
-
-
-```python
-# !pip install mlflow
-# !mlflow server --serve-artifacts
-```
-
-### Preprocessing the dataset
-Preprocessing the dataset works a bit differently than before. Instead of directly generating documents (and labels) out of a SQuAD file, we first save them to disk. This is necessary to experiment with different indexing pipelines. 
-
-
-```python
-import tempfile
-from pathlib import Path
-from haystack.nodes import PreProcessor
-from haystack.document_stores import InMemoryDocumentStore
-
-document_store = InMemoryDocumentStore()
-
-label_preprocessor = PreProcessor(
-    split_length=200,
-    split_overlap=0,
-    split_respect_sentence_boundary=False,
-    clean_empty_lines=False,
-    clean_whitespace=False,
-)
-
-# The add_eval_data() method converts the given dataset in json format into Haystack document and label objects.
-# Those objects are then indexed in their respective document and label index in the document store.
-# The method can be used with any dataset in SQuAD format.
-# We only use it to get the evaluation set labels and the corpus files.
-document_store.add_eval_data(
-    filename="data/tutorial5/nq_dev_subset_v2.json",
-    doc_index=document_store.index,
-    label_index=document_store.label_index,
-    preprocessor=label_preprocessor,
-)
-
-# the evaluation set to evaluate the pipelines on
-evaluation_set_labels = document_store.get_all_labels_aggregated(drop_negative_labels=True, drop_no_answers=True)
-
-# Pipelines need files as input to be able to test different preprocessors.
-# Even though this looks a bit cumbersome to write the documents back to files we gain a lot of evaluation potential and reproducibility.
-docs = document_store.get_all_documents()
-temp_dir = tempfile.TemporaryDirectory()
-file_paths = []
-for doc in docs:
-    file_name = doc.id + ".txt"
-    file_path = Path(temp_dir.name) / file_name
-    file_paths.append(file_path)
-    with open(file_path, "w") as f:
-        f.write(doc.content)
-file_metas = [d.meta for d in docs]
-```
-
-### Run experiments
-In this experiment we evaluate extractive QA pipelines with two different retrievers on the evaluation set given the corpus:
-**ElasticsearchRetriever vs. EmbeddingRetriever**
-
-
-```python
-from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader, TextConverter
-from haystack.pipelines import Pipeline
-from haystack.document_stores import ElasticsearchDocumentStore
-```
-
-
-```python
-# helper function to create query and index pipeline
-def create_pipelines(document_store, preprocessor, retriever, reader):
-    query_pipeline = Pipeline()
-    query_pipeline.add_node(component=retriever, inputs=["Query"], name="Retriever")
-    query_pipeline.add_node(component=reader, inputs=["Retriever"], name="Reader")
-    index_pipeline = Pipeline()
-    index_pipeline.add_node(component=TextConverter(), inputs=["File"], name="TextConverter")
-    index_pipeline.add_node(component=preprocessor, inputs=["TextConverter"], name="Preprocessor")
-    index_pipeline.add_node(component=retriever, inputs=["Preprocessor"], name="Retriever")
-    index_pipeline.add_node(component=document_store, inputs=["Retriever"], name="DocumentStore")
-    return query_pipeline, index_pipeline
-```
-
-
-```python
-# Name of the experiment in MLflow
-EXPERIMENT_NAME = "haystack-tutorial-5"
-```
-
-#### Run using BM25Retriever
-
-
-```python
-document_store = ElasticsearchDocumentStore(host=host, index="sparse_index", recreate_index=True)
-preprocessor = PreProcessor(
-    split_length=200,
-    split_overlap=0,
-    split_respect_sentence_boundary=False,
-    clean_empty_lines=False,
-    clean_whitespace=False,
-)
-es_retriever = BM25Retriever(document_store=document_store)
-reader = FARMReader("deepset/roberta-base-squad2", top_k=3, return_no_answer=True, batch_size=8)
-query_pipeline, index_pipeline = create_pipelines(document_store, preprocessor, es_retriever, reader)
-
-sparse_eval_result = Pipeline.execute_eval_run(
-    index_pipeline=index_pipeline,
-    query_pipeline=query_pipeline,
-    evaluation_set_labels=evaluation_set_labels,
-    corpus_file_paths=file_paths,
-    corpus_file_metas=file_metas,
-    experiment_name=EXPERIMENT_NAME,
-    experiment_run_name="sparse",
-    corpus_meta={"name": "nq_dev_subset_v2.json"},
-    evaluation_set_meta={"name": "nq_dev_subset_v2.json"},
-    pipeline_meta={"name": "sparse-pipeline"},
-    add_isolated_node_eval=True,
-    experiment_tracking_tool="mlflow",
-    experiment_tracking_uri="https://public-mlflow.deepset.ai",
-    reuse_index=True,
-)
-```
-
-#### Run using EmbeddingRetriever
-
-
-```python
-document_store = ElasticsearchDocumentStore(host=host, index="dense_index", recreate_index=True)
-emb_retriever = EmbeddingRetriever(
-    document_store=document_store,
-    model_format="sentence_transformers",
-    embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
-    batch_size=8,
-)
-query_pipeline, index_pipeline = create_pipelines(document_store, preprocessor, emb_retriever, reader)
-
-dense_eval_result = Pipeline.execute_eval_run(
-    index_pipeline=index_pipeline,
-    query_pipeline=query_pipeline,
-    evaluation_set_labels=evaluation_set_labels,
-    corpus_file_paths=file_paths,
-    corpus_file_metas=file_metas,
-    experiment_name=EXPERIMENT_NAME,
-    experiment_run_name="embedding",
-    corpus_meta={"name": "nq_dev_subset_v2.json"},
-    evaluation_set_meta={"name": "nq_dev_subset_v2.json"},
-    pipeline_meta={"name": "embedding-pipeline"},
-    add_isolated_node_eval=True,
-    experiment_tracking_tool="mlflow",
-    experiment_tracking_uri="https://public-mlflow.deepset.ai",
-    reuse_index=True,
-    answer_scope="context",
-)
-```
-
-You can now open MLflow (e.g. https://public-mlflow.deepset.ai/ if you used the public one hosted by deepset) and look for the haystack-eval-experiment experiment. Try out mlflow's compare function and have fun...
-
-Note that on our public mlflow instance we are not able to log artifacts like the evaluation results or the piplines.yaml file.
-
-## Evaluation of Individual Components: Retriever
-Sometimes you might want to evaluate individual components, for example, if you don't have a pipeline but only a retriever or a reader with a model that you trained yourself.
-Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
-
-
-```python
-## Evaluate Retriever on its own
-# Note that no_answer samples are omitted when evaluation is performed with this method
-retriever_eval_results = retriever.eval(top_k=5, label_index=label_index, doc_index=doc_index)
-# Retriever Recall is the proportion of questions for which the correct document containing the answer is
-# among the correct documents
-print("Retriever Recall:", retriever_eval_results["recall"])
-# Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
-print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
-```
-
-Just as a sanity check, we can compare the recall from `retriever.eval()` with the multi hit recall from `pipeline.eval(add_isolated_node_eval=True)`.
-These two recall metrics are only comparable since we chose to filter out no_answer samples when generating eval_labels and setting document_scope to `"document_id"`. Per default `calculate_metrics()` has document_scope set to `"document_id_or_answer"` which interprets documents as relevant if they either match the gold document ID or contain the answer.
-
-
-```python
-metrics = eval_result_with_upper_bounds.calculate_metrics(document_scope="document_id")
-print(metrics["Retriever"]["recall_multi_hit"])
-```
-
-## Evaluation of Individual Components: Reader
-Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
-and its corresponding relevant document and metrics are calculated on whether the right position in this text is selected by
-the model as the answer span (i.e. SQuAD style)
-
-
-```python
-# Evaluate Reader on its own
-reader_eval_results = reader.eval(document_store=document_store, label_index=label_index, doc_index=doc_index)
-top_n = reader_eval_results["top_n"]
-# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
-# reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
-
-# Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer including no_answers
-print(f"Reader Top-{top_n}-Accuracy:", reader_eval_results["top_n_accuracy"])
-# Reader Top-1-Exact Match is the proportion of questions where the first predicted answer is exactly the same as the correct answer including no_answers
-print("Reader Top-1-Exact Match:", reader_eval_results["EM"])
-# Reader Top-1-F1-Score is the average overlap between the first predicted answers and the correct answers including no_answers
-print("Reader Top-1-F1-Score:", reader_eval_results["f1"])
-# Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer excluding no_answers
-print(f"Reader Top-{top_n}-Accuracy (without no_answers):", reader_eval_results["top_n_accuracy_text_answer"])
-# Reader Top-N-Exact Match is the proportion of questions where the predicted answer within the first n results is exactly the same as the correct answer excluding no_answers (no_answers are always present within top n).
-print(f"Reader Top-{top_n}-Exact Match (without no_answers):", reader_eval_results["top_n_EM_text_answer"])
-# Reader Top-N-F1-Score is the average overlap between the top n predicted answers and the correct answers excluding no_answers (no_answers are always present within top n).
-print(f"Reader Top-{top_n}-F1-Score (without no_answers):", reader_eval_results["top_n_f1_text_answer"])
-```
-
-Just as a sanity check, we can compare the top-n exact_match and f1 metrics from `reader.eval()` with the exact_match and f1 from `pipeline.eval(add_isolated_node_eval=True)`.
-These two approaches return the same values because pipeline.eval() calculates top-n metrics per default. Small discrepancies might occur due to string normalization in pipeline.eval()'s answer-to-label comparison. reader.eval() does not use string normalization.
-
-
-```python
-metrics = eval_result_with_upper_bounds.calculate_metrics(eval_mode="isolated")
-print(metrics["Reader"]["exact_match"])
-print(metrics["Reader"]["f1"])
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md b/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
deleted file mode 100644
index 3664b223..00000000
--- a/markdowns/06_Better_Retrieval_via_Embedding_Retrieval.md
+++ /dev/null
@@ -1,261 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
-toc: True
-title: "Better Retrieval with Embedding Retrieval"
-level: "intermediate"
-weight: 55
-description: Use Transformer based dense Retrievers to improve your system’s performance.
-category: "QA"
-aliases: ['/tutorials/embedding-retrieval']
-download: "/downloads/06_Better_Retrieval_via_Embedding_Retrieval.ipynb"
-completion_time: False
-created_at: 2022-03-08
----
-    
-
-
-### Importance of Retrievers
-
-The Retriever has a huge impact on the performance of our overall search pipeline.
-
-
-### Different types of Retrievers
-#### Sparse
-Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.
-
-**Examples**: BM25, TF-IDF
-
-**Pros**: Simple, fast, well explainable
-
-**Cons**: Relies on exact keyword matches between query and text
- 
-
-#### Dense
-These retrievers use neural network models to create "dense" embedding vectors. Within this family, there are two different approaches:
-
-a) Single encoder: Use a **single model** to embed both the query and the passage.
-b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage.
-
-**Examples**: REALM, DPR, Sentence-Transformers
-
-**Pros**: Captures semantic similarity instead of "word matches" (for example, synonyms, related topics).
-
-**Cons**: Computationally more heavy to use, initial training of the model (though this is less of an issue nowadays as many pre-trained models are available and most of the time, it's not needed to train the model).
-
-
-### Embedding Retrieval
-
-In this Tutorial, we use an `EmbeddingRetriever` with [Sentence Transformers](https://www.sbert.net/index.html) models.
-
-These models are trained to embed similar sentences close to each other in a shared embedding space.
-
-Some models have been fine-tuned on massive Information Retrieval data and can be used to retrieve documents based on a short query (for example, `multi-qa-mpnet-base-dot-v1`). There are others that are more suited to semantic similarity tasks where you are trying to find the most similar documents to a given document (for example, `all-mpnet-base-v2`). There are even models that are multilingual (for example, `paraphrase-multilingual-mpnet-base-v2`). For a good overview of different models with their evaluation metrics, see the [Pretrained Models](https://www.sbert.net/docs/pretrained_models.html#) in the Sentence Transformers documentation.
-
-*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb) *to open the notebook in Google Colab.*
-
-
-### Prepare the Environment
-
-#### Colab: Enable the GPU Runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-You can double check whether the GPU runtime is enabled with the following command:
-
-
-```bash
-%%bash
-
-nvidia-smi
-```
-
-To start, install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,faiss]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-### Document Store
-
-#### Option 1: FAISS
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_stores import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
-```
-
-#### Option 2: Milvus
-
-Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.
-Like FAISS it has both a "Flat" and "HNSW" mode but it outperforms FAISS when it comes to dynamic data management.
-It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.
-See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details.
-
-
-```python
-# Milvus cannot be run on COlab, so this cell is commented out.
-# To run Milvus you need Docker (versions below 2.0.0) or a docker-compose (versions >= 2.0.0), neither of which is available on Colab.
-# See Milvus' documentation for more details: https://milvus.io/docs/install_standalone-docker.md
-
-# !pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[milvus]
-
-# from haystack.utils import launch_milvus
-# from haystack.document_stores import MilvusDocumentStore
-
-# launch_milvus()
-# document_store = MilvusDocumentStore()
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http
-
-
-# Let's first get some files that we want to use
-doc_dir = "data/tutorial6"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt6.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(docs)
-```
-
-### Initialize Retriever, Reader & Pipeline
-
-#### Retriever
-
-**Here:** We use an `EmbeddingRetriever`.
-
-**Alternatives:**
-
-- `BM25Retriever` with custom queries (for example, boosting) and filters
-- `DensePassageRetriever` which uses two encoder models, one to embed the query and one to embed the passage, and then compares the embedding for retrieval
-- `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
-
-
-```python
-from haystack.nodes import EmbeddingRetriever
-
-retriever = EmbeddingRetriever(
-    document_store=document_store,
-    embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
-    model_format="sentence_transformers",
-)
-# Important:
-# Now that we initialized the Retriever, we need to call update_embeddings() to iterate over all
-# previously indexed documents and update their embedding representation.
-# While this can be a time consuming operation (depending on the corpus size), it only needs to be done once.
-# At query time, we only need to embed the query and compare it to the existing document embeddings, which is very fast.
-document_store.update_embeddings(retriever)
-```
-
-#### Reader
-
-Similar to previous Tutorials we now initalize our reader.
-
-Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)
-
-
-
-##### FARMReader
-
-
-```python
-from haystack.nodes import FARMReader
-
-
-# Load a  local model or any of the QA models on
-# Hugging Face's model hub (https://huggingface.co/models)
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipelines import ExtractiveQAPipeline
-
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-# You can configure how many candidates the reader and retriever shall return
-# The higher top_k for retriever, the better (but also the slower) your answers.
-prediction = pipe.run(
-    query="Who created the Dothraki vocabulary?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
-)
-```
-
-
-```python
-from haystack.utils import print_answers
-
-
-print_answers(prediction, details="minimum")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/07_RAG_Generator.md b/markdowns/07_RAG_Generator.md
deleted file mode 100644
index 2b95625d..00000000
--- a/markdowns/07_RAG_Generator.md
+++ /dev/null
@@ -1,212 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb
-toc: True
-title: "Generative QA with Retrieval-Augmented Generation"
-level: "intermediate"
-weight: 60
-description: Try out a generative model in place of the extractive Reader.
-category: "QA"
-aliases: ['/tutorials/retrieval-augmented-generation']
-download: "/downloads/07_RAG_Generator.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-
-While extractive QA highlights the span of text that answers a query,
-generative QA can return a novel text answer that it has composed.
-In this tutorial, you will learn how to set up a generative system using the
-[RAG model](https://arxiv.org/abs/2005.11401) which conditions the
-answer generator on a set of retrieved documents.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-You can double check whether the GPU runtime is enabled with the following command:
-
-
-```bash
-%%bash
-
-nvidia-smi
-```
-
-To start, install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,faiss]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-Let's download a csv containing some sample text and preprocess the data.
-
-
-
-```python
-import pandas as pd
-
-from haystack.utils import fetch_archive_from_http
-
-
-# Download sample
-doc_dir = "data/tutorial7/"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/small_generator_dataset.csv.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Create dataframe with columns "title" and "text"
-df = pd.read_csv(f"{doc_dir}/small_generator_dataset.csv", sep=",")
-# Minimal cleaning
-df.fillna(value="", inplace=True)
-
-print(df.head())
-```
-
-We can cast our data into Haystack Document objects.
-Alternatively, we can also just use dictionaries with "text" and "meta" fields
-
-
-```python
-from haystack import Document
-
-
-# Use data to initialize Document objects
-titles = list(df["title"].values)
-texts = list(df["text"].values)
-documents = []
-for title, text in zip(titles, texts):
-    documents.append(Document(content=text, meta={"name": title or ""}))
-```
-
-Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.
-FAISS is chosen here since it is optimized vector storage.
-
-
-```python
-from haystack.document_stores import FAISSDocumentStore
-from haystack.nodes import RAGenerator, DensePassageRetriever
-
-
-# Initialize FAISS document store.
-# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat", return_embedding=True)
-
-# Initialize DPR Retriever to encode documents, encode question and query documents
-retriever = DensePassageRetriever(
-    document_store=document_store,
-    query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-    passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-    use_gpu=True,
-    embed_title=True,
-)
-
-# Initialize RAG Generator
-generator = RAGenerator(
-    model_name_or_path="facebook/rag-token-nq",
-    use_gpu=True,
-    top_k=1,
-    max_length=200,
-    min_length=2,
-    embed_title=True,
-    num_beams=2,
-)
-```
-
-We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.
-The `update_embeddings()` method uses the retriever to create an embedding for each document.
-
-
-
-```python
-# Delete existing documents in documents store
-document_store.delete_documents()
-
-# Write documents to document store
-document_store.write_documents(documents)
-
-# Add documents embeddings to index
-document_store.update_embeddings(retriever=retriever)
-```
-
-Here are our questions:
-
-
-```python
-QUESTIONS = [
-    "who got the first nobel prize in physics",
-    "when is the next deadpool movie being released",
-    "which mode is used for short wave broadcast service",
-    "who is the owner of reading football club",
-    "when is the next scandal episode coming out",
-    "when is the last time the philadelphia won the superbowl",
-    "what is the most current adobe flash player version",
-    "how many episodes are there in dragon ball z",
-    "what is the first step in the evolution of the eye",
-    "where is gall bladder situated in human body",
-    "what is the main mineral in lithium batteries",
-    "who is the president of usa right now",
-    "where do the greasers live in the outsiders",
-    "panda is a national animal of which country",
-    "what is the name of manchester united stadium",
-]
-```
-
-Now let's run our system!
-The retriever will pick out a small subset of documents that it finds relevant.
-These are used to condition the generator as it generates the answer.
-What it should return then are novel text spans that form and answer to your question!
-
-
-```python
-# Or alternatively use the Pipeline class
-from haystack.pipelines import GenerativeQAPipeline
-from haystack.utils import print_answers
-
-pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)
-for question in QUESTIONS:
-    res = pipe.run(query=question, params={"Generator": {"top_k": 1}, "Retriever": {"top_k": 5}})
-    print_answers(res, details="minimum")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/08_Preprocessing.md b/markdowns/08_Preprocessing.md
deleted file mode 100644
index 2f759073..00000000
--- a/markdowns/08_Preprocessing.md
+++ /dev/null
@@ -1,229 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb
-toc: True
-title: "Preprocessing Your Documents"
-level: "beginner"
-weight: 25
-description: Start converting, cleaning, and splitting Documents using Haystack’s preprocessing capabilities.
-category: "QA"
-aliases: ['/tutorials/preprocessing']
-download: "/downloads/08_Preprocessing.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-Haystack includes a suite of tools to extract text from different file types, normalize white space
-and split text into smaller pieces to optimize retrieval.
-These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack.
-
-Ultimately, Haystack expects data to be provided as a list of documents in the following dictionary format:
-``` python
-docs = [
-    {
-        'content': DOCUMENT_TEXT_HERE,
-        'meta': {'name': DOCUMENT_NAME, ...}
-    }, ...
-]
-```
-
-This tutorial will show you all the tools that Haystack provides to help you cast your data into this format.
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr]
-
-# For Colab/linux based machines:
-wget https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz
-tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin
-
-# For macOS machines:
-# wget https://dl.xpdfreader.com/xpdf-tools-mac-4.03.tar.gz
-# tar -xvf xpdf-tools-mac-4.03.tar.gz && sudo cp xpdf-tools-mac-4.03/bin64/pdftotext /usr/local/bin
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-
-```python
-from haystack.utils import fetch_archive_from_http
-
-
-# This fetches some sample files to work with
-doc_dir = "data/tutorial8"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial8.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-## Converters
-
-Haystack's converter classes are designed to help you turn files on your computer into the documents
-that can be processed by the Haystack pipeline.
-There are file converters for txt, pdf, docx files as well as a converter that is powered by Apache Tika.
-The parameter `valid_languages` does not convert files to the target language, but checks if the conversion worked as expected. Here are some examples of how you would use file converters:
-
-
-```python
-from haystack.nodes import TextConverter, PDFToTextConverter, DocxToTextConverter, PreProcessor
-
-
-converter = TextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_txt = converter.convert(file_path="data/tutorial8/classics.txt", meta=None)[0]
-
-converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
-doc_pdf = converter.convert(file_path="data/tutorial8/bert.pdf", meta=None)[0]
-
-converter = DocxToTextConverter(remove_numeric_tables=False, valid_languages=["en"])
-doc_docx = converter.convert(file_path="data/tutorial8/heavy_metal.docx", meta=None)[0]
-```
-
-Haystack also has a convenience function that will automatically apply the right converter to each file in a directory:
-
-
-```python
-from haystack.utils import convert_files_to_docs
-
-
-all_docs = convert_files_to_docs(dir_path=doc_dir)
-```
-
-## PreProcessor
-
-The PreProcessor class is designed to help you clean text and split text into sensible units.
-File splitting can have a very significant impact on the system's performance and is absolutely mandatory for Dense Passage Retrieval models.
-In general, we recommend you split the text from your files into small documents of around 100 words for dense retrieval methods
-and no more than 10,000 words for sparse methods.
-Have a look at the [Preprocessing](https://docs.haystack.deepset.ai/docs/preprocessor)
-and [Optimization](https://docs.haystack.deepset.ai/docs/optimization) pages on our website for more details.
-
-
-```python
-from haystack.nodes import PreProcessor
-
-
-# This is a default usage of the PreProcessor.
-# Here, it performs cleaning of consecutive whitespaces
-# and splits a single large document into smaller documents.
-# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
-# Note how the single document passed into the document gets split into 5 smaller documents
-
-preprocessor = PreProcessor(
-    clean_empty_lines=True,
-    clean_whitespace=True,
-    clean_header_footer=False,
-    split_by="word",
-    split_length=100,
-    split_respect_sentence_boundary=True,
-)
-docs_default = preprocessor.process([doc_txt])
-print(f"n_docs_input: 1\nn_docs_output: {len(docs_default)}")
-```
-
-## Cleaning
-
-- `clean_empty_lines` will normalize 3 or more consecutive empty lines to be just a two empty lines
-- `clean_whitespace` will remove any whitespace at the beginning or end of each line in the text
-- `clean_header_footer` will remove any long header or footer texts that are repeated on each page
-
-## Splitting
-By default, the PreProcessor will respect sentence boundaries, meaning that documents will not start or end
-midway through a sentence.
-This will help reduce the possibility of answer phrases being split between two documents.
-This feature can be turned off by setting `split_respect_sentence_boundary=False`.
-
-
-```python
-# Not respecting sentence boundary vs respecting sentence boundary
-
-preprocessor_nrsb = PreProcessor(split_respect_sentence_boundary=False)
-docs_nrsb = preprocessor_nrsb.process([doc_txt])
-
-print("RESPECTING SENTENCE BOUNDARY")
-end_text = docs_default[0].content[-50:]
-print('End of document: "...' + end_text + '"')
-print()
-print("NOT RESPECTING SENTENCE BOUNDARY")
-end_text_nrsb = docs_nrsb[0].content[-50:]
-print('End of document: "...' + end_text_nrsb + '"')
-```
-
-A commonly used strategy to split long documents, especially in the field of Question Answering,
-is the sliding window approach. If `split_length=10` and `split_overlap=3`, your documents will look like this:
-
-- doc1 = words[0:10]
-- doc2 = words[7:17]
-- doc3 = words[14:24]
-- ...
-
-You can use this strategy by following the code below.
-
-
-```python
-# Sliding window approach
-
-preprocessor_sliding_window = PreProcessor(split_overlap=3, split_length=10, split_respect_sentence_boundary=False)
-docs_sliding_window = preprocessor_sliding_window.process([doc_txt])
-
-doc1 = docs_sliding_window[0].content[:200]
-doc2 = docs_sliding_window[1].content[:100]
-doc3 = docs_sliding_window[2].content[:100]
-
-print('Document 1: "' + doc1 + '..."')
-print('Document 2: "' + doc2 + '..."')
-print('Document 3: "' + doc3 + '..."')
-```
-
-## Bringing it all together
-
-
-```python
-all_docs = convert_files_to_docs(dir_path=doc_dir)
-preprocessor = PreProcessor(
-    clean_empty_lines=True,
-    clean_whitespace=True,
-    clean_header_footer=False,
-    split_by="word",
-    split_length=100,
-    split_respect_sentence_boundary=True,
-)
-docs = preprocessor.process(all_docs)
-
-print(f"n_files_input: {len(all_docs)}\nn_docs_output: {len(docs)}")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
-
diff --git a/markdowns/09_DPR_training.md b/markdowns/09_DPR_training.md
deleted file mode 100644
index df767354..00000000
--- a/markdowns/09_DPR_training.md
+++ /dev/null
@@ -1,268 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb
-toc: True
-title: "Training Your Own Dense Passage Retrieval Model"
-level: "advanced"
-weight: 110
-description: Learn about training a Dense Passage Retrieval model and the data needed to do so.
-category: "QA"
-aliases: ['/tutorials/train-dpr']
-download: "/downloads/09_DPR_training.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-Haystack contains all the tools needed to train your own Dense Passage Retrieval model.
-This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain.
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest main of Haystack
-!pip install --upgrade pip
-!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-
-```python
-# Here are some imports that we'll need
-
-from haystack.nodes import DensePassageRetriever
-from haystack.utils import fetch_archive_from_http
-from haystack.document_stores import InMemoryDocumentStore
-```
-
-## Training Data
-
-DPR training performed using Information Retrieval data.
-More specifically, you want to feed in pairs of queries and relevant documents.
-
-To train a model, we will need a dataset that has the same format as the original DPR training data.
-Each data point in the dataset should have the following dictionary structure.
-
-``` python
-    {
-        "dataset": str,
-        "question": str,
-        "answers": list of str
-        "positive_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
-        "negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
-        "hard_negative_ctxs": list of dictionaries of format {'title': str, 'text': str, 'score': int, 'title_score': int, 'passage_id': str}
-    }
-```
-
-`positive_ctxs` are context passages which are relevant to the query.
-In some datasets, queries might have more than one positive context
-in which case you can set the `num_positives` parameter to be higher than the default 1.
-Note that `num_positives` needs to be lower or equal to the minimum number of `positive_ctxs` for queries in your data.
-If you have an unequal number of positive contexts per example,
-you might want to generate some soft labels by retrieving similar contexts which contain the answer.
-
-DPR is standardly trained using a method known as in-batch negatives.
-This means that positive contexts for a given query are treated as negative contexts for the other queries in the batch.
-Doing so allows for a high degree of computational efficiency, thus allowing the model to be trained on large amounts of data.
-
-`negative_ctxs` is not actually used in Haystack's DPR training so we recommend you set it to an empty list.
-They were used by the original DPR authors in an experiment to compare it against the in-batch negatives method.
-
-`hard_negative_ctxs` are passages that are not relevant to the query.
-In the original DPR paper, these are fetched using a retriever to find the most relevant passages to the query.
-Passages which contain the answer text are filtered out.
-
-If you'd like to convert your SQuAD format data into something that can train a DPR model,
-check out the utility script at [`haystack/utils/squad_to_dpr.py`](https://github.com/deepset-ai/haystack/blob/main/haystack/utils/squad_to_dpr.py)
-
-## Using Question Answering Data
-
-Question Answering datasets can sometimes be used as training data.
-Google's Natural Questions dataset, is sufficiently large
-and contains enough unique passages, that it can be converted into a DPR training set.
-This is done simply by considering answer containing passages as relevant documents to the query.
-
-The SQuAD dataset, however, is not as suited to this use case since its question and answer pairs
-are created on only a very small slice of wikipedia documents.
-
-## Download Original DPR Training Data
-
-WARNING: These files are large! The train set is 7.4GB and the dev set is 800MB
-
-We can download the original DPR training data with the following cell.
-Note that this data is probably only useful if you are trying to train from scratch.
-
-
-```python
-# Download original DPR data
-# WARNING: the train set is 7.4GB and the dev set is 800MB
-
-doc_dir = "data/tutorial9"
-
-s3_url_train = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz"
-s3_url_dev = "https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz"
-
-fetch_archive_from_http(s3_url_train, output_dir=doc_dir + "/train")
-fetch_archive_from_http(s3_url_dev, output_dir=doc_dir + "/dev")
-```
-
-## Option 1: Training DPR from Scratch
-
-The default variables that we provide below are chosen to train a DPR model from scratch.
-Here, both passage and query embedding models are initialized using BERT base
-and the model is trained using Google's Natural Questions dataset (in a format specialised for DPR).
-
-If you are working in a language other than English,
-you will want to initialize the passage and query embedding models with a language model that supports your language
-and also provide a dataset in your language.
-
-
-```python
-# Here are the variables to specify our training data, the models that we use to initialize DPR
-# and the directory where we'll be saving the model
-
-train_filename = "train/biencoder-nq-train.json"
-dev_filename = "dev/biencoder-nq-dev.json"
-
-query_model = "bert-base-uncased"
-passage_model = "bert-base-uncased"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Option 2: Finetuning DPR
-
-If you have your own domain specific question answering or information retrieval dataset,
-you might instead be interested in finetuning a pretrained DPR model.
-In this case, you would initialize both query and passage models using the original pretrained model.
-You will want to load something like this set of variables instead of the ones above
-
-
-```python
-# Here are the variables you might want to use instead of the set above
-# in order to perform pretraining
-
-doc_dir = "PATH_TO_YOUR_DATA_DIR"
-train_filename = "TRAIN_FILENAME"
-dev_filename = "DEV_FILENAME"
-
-query_model = "facebook/dpr-question_encoder-single-nq-base"
-passage_model = "facebook/dpr-ctx_encoder-single-nq-base"
-
-save_dir = "../saved_models/dpr"
-```
-
-## Initialization
-
-Here we want to initialize our model either with plain language model weights for training from scratch
-or else with pretrained DPR weights for finetuning.
-We follow the [original DPR parameters](https://github.com/facebookresearch/DPR#best-hyperparameter-settings)
-for their max passage length but set max query length to 64 since queries are very rarely longer.
-
-
-```python
-## Initialize DPR model
-
-retriever = DensePassageRetriever(
-    document_store=InMemoryDocumentStore(),
-    query_embedding_model=query_model,
-    passage_embedding_model=passage_model,
-    max_seq_len_query=64,
-    max_seq_len_passage=256,
-)
-```
-
-## Training
-
-Let's start training and save our trained model!
-
-On a V100 GPU, you can fit up to batch size 16 so we set gradient accumulation steps to 8 in order
-to simulate the batch size 128 of the original DPR experiment.
-
-When `embed_title=True`, the document title is prepended to the input text sequence with a `[SEP]` token
-between it and document text.
-
-When training from scratch with the above variables, 1 epoch takes around an hour and we reached the following performance:
-
-```
-loss: 0.046580662854042276
-task_name: text_similarity
-acc: 0.992524064068483
-f1: 0.8804297774366846
-acc_and_f1: 0.9364769207525838
-average_rank: 0.19631619339984652
-report:
-                precision    recall  f1-score   support
-
-hard_negative     0.9961    0.9961    0.9961    201887
-     positive     0.8804    0.8804    0.8804      6515
-
-     accuracy                         0.9925    208402
-    macro avg     0.9383    0.9383    0.9383    208402
- weighted avg     0.9925    0.9925    0.9925    208402
-
-```
-
-
-```python
-# Start training our model and save it when it is finished
-
-retriever.train(
-    data_dir=doc_dir,
-    train_filename=train_filename,
-    dev_filename=dev_filename,
-    test_filename=dev_filename,
-    n_epochs=1,
-    batch_size=16,
-    grad_acc_steps=8,
-    save_dir=save_dir,
-    evaluate_every=3000,
-    embed_title=True,
-    num_positives=1,
-    num_hard_negatives=1,
-)
-```
-
-## Loading
-
-Loading our newly trained model is simple!
-
-
-```python
-reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/10_Knowledge_Graph.md b/markdowns/10_Knowledge_Graph.md
deleted file mode 100644
index 12acc980..00000000
--- a/markdowns/10_Knowledge_Graph.md
+++ /dev/null
@@ -1,217 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb
-toc: True
-title: "Question Answering on a Knowledge Graph"
-level: "advanced"
-weight: 120
-description: Experiment with a question answering system that draws upon knowledge graph.h
-category: "QA"
-aliases: ['/tutorials/knowledge-graph']
-download: "/downloads/10_Knowledge_Graph.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-Haystack allows storing and querying knowledge graphs with the help of pre-trained models that translate text queries to SPARQL queries.
-This tutorial demonstrates how to load an existing knowledge graph into haystack, load a pre-trained retriever, and execute text queries on the knowledge graph.
-The training of models that translate text queries into SPARQL queries is currently not supported.
-
-To start, install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,inmemorygraph]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-## Downloading Knowledge Graph and Model
-
-
-```python
-from haystack.utils import fetch_archive_from_http
-
-
-# Let's first fetch some triples that we want to store in our knowledge graph
-# Here: exemplary triples from the wizarding world
-graph_dir = "data/tutorial10"
-s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/triples_and_config.zip"
-fetch_archive_from_http(url=s3_url, output_dir=graph_dir)
-
-# Fetch a pre-trained BART model that translates text queries to SPARQL queries
-model_dir = "../saved_models/tutorial10_knowledge_graph/"
-s3_url = "https://fandom-qa.s3-eu-west-1.amazonaws.com/saved_models/hp_v3.4.zip"
-fetch_archive_from_http(url=s3_url, output_dir=model_dir)
-```
-
-## Initialize a knowledge graph and load data
-
-Currently, Haystack supports two alternative implementations for knowledge graphs:
-* simple InMemoryKnowledgeGraph (based on RDFLib in-memory store)
-* GraphDBKnowledgeGraph, which runs on GraphDB.
-
-### InMemoryKnowledgeGraph 
-
-
-```python
-from pathlib import Path
-
-from haystack.document_stores import InMemoryKnowledgeGraph
-
-
-# Initialize a in memory knowledge graph and use "tutorial_10_index" as the name of the index
-kg = InMemoryKnowledgeGraph(index="tutorial_10_index")
-
-# Delete the index as it might have been already created in previous runs
-kg.delete_index()
-
-# Create the index
-kg.create_index()
-
-# Import triples of subject, predicate, and object statements from a ttl file
-kg.import_from_ttl_file(index="tutorial_10_index", path=Path(graph_dir) / "triples.ttl")
-print(f"The last triple stored in the knowledge graph is: {kg.get_all_triples()[-1]}")
-print(f"There are {len(kg.get_all_triples())} triples stored in the knowledge graph.")
-```
-
-### GraphDBKnowledgeGraph (alternative)
-
-#### Launching a GraphDB instance
-
-Unfortunately, there seems to be no good way to run GraphDB in colab environments.
-In your local environment, you could start a GraphDB server with docker, feel free to check GraphDB's website for the free version https://www.ontotext.com/products/graphdb/graphdb-free/
-
-
-```python
-# import os
-# import subprocess
-# import time
-
-# LAUNCH_GRAPHDB = os.environ.get("LAUNCH_GRAPHDB", False)
-
-# if LAUNCH_GRAPHDB:
-#     print("Starting GraphDB ...")
-#     status = subprocess.run(
-#         [
-#             "docker run -d -p 7200:7200 --name graphdb-instance-tutorial docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11"
-#         ],
-#         shell=True,
-#     )
-#     if status.returncode:
-#         raise Exception(
-#             "Failed to launch GraphDB. Maybe it is already running or you already have a container with that name that you could start?"
-#         )
-#     time.sleep(5)
-```
-
-#### Creating a new GraphDB repository (also known as index in haystack's document stores)
-
-
-```python
-# from haystack.document_stores import GraphDBKnowledgeGraph
-
-# # Initialize a knowledge graph connected to GraphDB and use "tutorial_10_index" as the name of the index
-# kg = GraphDBKnowledgeGraph(index="tutorial_10_index")
-
-# # Delete the index as it might have been already created in previous runs
-# kg.delete_index()
-
-# # Create the index based on a configuration file
-# kg.create_index(config_path=Path(graph_dir) / "repo-config.ttl")
-
-# # Import triples of subject, predicate, and object statements from a ttl file
-# kg.import_from_ttl_file(index="tutorial_10_index", path=Path(graph_dir) / "triples.ttl")
-# print(f"The last triple stored in the knowledge graph is: {kg.get_all_triples()[-1]}")
-# print(f"There are {len(kg.get_all_triples())} triples stored in the knowledge graph.")
-```
-
-
-```python
-# # Define prefixes for names of resources so that we can use shorter resource names in queries
-# prefixes = """PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
-# PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
-# PREFIX hp: <https://deepset.ai/harry_potter/>
-# """
-# kg.prefixes = prefixes
-```
-
-## Load the pre-trained retriever
-
-
-```python
-from haystack.nodes import Text2SparqlRetriever
-
-
-# Load a pre-trained model that translates text queries to SPARQL queries
-kgqa_retriever = Text2SparqlRetriever(knowledge_graph=kg, model_name_or_path=Path(model_dir) / "hp_v3.4")
-```
-
-## Query Execution
-
-We can now ask questions that will be answered by our knowledge graph!
-One limitation though: our pre-trained model can only generate questions about resources it has seen during training.
-Otherwise, it cannot translate the name of the resource to the identifier used in the knowledge graph.
-E.g. "Harry" -> "hp:Harry_potter"
-
-
-```python
-query = "In which house is Harry Potter?"
-print(f'Translating the text query "{query}" to a SPARQL query and executing it on the knowledge graph...')
-result = kgqa_retriever.retrieve(query=query)
-print(result)
-# Correct SPARQL query: select ?a { hp:Harry_potter hp:house ?a . }
-# Correct answer: Gryffindor
-
-print("Executing a SPARQL query with prefixed names of resources...")
-result = kgqa_retriever._query_kg(
-    sparql_query="select distinct ?sbj where { ?sbj hp:job hp:Keeper_of_keys_and_grounds . }"
-)
-print(result)
-# Paraphrased question: Who is the keeper of keys and grounds?
-# Correct answer: Rubeus Hagrid
-
-print("Executing a SPARQL query with full names of resources...")
-result = kgqa_retriever._query_kg(
-    sparql_query="select distinct ?obj where { <https://deepset.ai/harry_potter/Hermione_granger> <https://deepset.ai/harry_potter/patronus> ?obj . }"
-)
-print(result)
-# Paraphrased question: What is the patronus of Hermione?
-# Correct answer: Otter
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/11_Pipelines.md b/markdowns/11_Pipelines.md
deleted file mode 100644
index fbf81f2b..00000000
--- a/markdowns/11_Pipelines.md
+++ /dev/null
@@ -1,473 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb
-toc: True
-title: "How to Use Pipelines"
-level: "intermediate"
-weight: 65
-description: Learn about the many ways which you can route queries through the nodes in a pipeline.
-category: "QA"
-aliases: ['/tutorials/pipelines']
-download: "/downloads/11_Pipelines.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-In this tutorial, you will learn how the `Pipeline` class acts as a connector between all the different
-building blocks that are found in FARM. Whether you are using a Reader, Generator, Summarizer
-or Retriever (or 2), the `Pipeline` class will help you build a Directed Acyclic Graph (DAG) that
-determines how to route the output of one component into the input of another.
-
-
-## Setting Up the Environment
-
-Let's start by ensuring we have a GPU running to ensure decent speed in this tutorial.
-In Google colab, you can change to a GPU runtime in the menu:
-- **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-You can double check whether the GPU runtime is enabled with the following command:
-
-
-```bash
-%%bash
-
-nvidia-smi
-```
-
-To start, install the latest release of Haystack with `pip` along with `pygraphviz`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
-
-apt install libgraphviz-dev
-pip install pygraphviz
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-### Start an Elasticsearch server in Colab
-
-If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```bash
-%%bash
-
-wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-chown -R daemon:daemon elasticsearch-7.9.2
-
-```
-
-
-```bash
-%%bash --bg
-
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
-```
-
-## Initialization
-
-Then let's fetch some data (in this case, pages from the Game of Thrones wiki) and prepare it so that it can
-be used indexed into our `DocumentStore`
-
-
-```python
-from haystack.utils import fetch_archive_from_http, convert_files_to_docs, clean_wiki_text
-
-# Download and prepare data - 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/tutorial11"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt11.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-```
-
-Here we initialize the core components that we will be gluing together using the `Pipeline` class.
-We have a `DocumentStore`, an `BM25Retriever` and a `FARMReader`.
-These can be combined to create a classic Retriever-Reader pipeline that is designed
-to perform Open Domain Question Answering.
-
-
-```python
-import os
-import time
-
-from haystack.document_stores import ElasticsearchDocumentStore
-from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader
-
-# Wait 30 seconds only to be sure Elasticsearch is ready before continuing
-time.sleep(30)
-
-# Get the host where Elasticsearch is running, default to localhost
-host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
-
-# Initialize DocumentStore and index documents
-document_store = ElasticsearchDocumentStore(host=host)
-document_store.delete_documents()
-document_store.write_documents(got_docs)
-
-# Initialize Sparse retriever
-bm25_retriever = BM25Retriever(document_store=document_store)
-
-# Initialize dense retriever
-embedding_retriever = EmbeddingRetriever(
-    document_store=document_store, embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1"
-)
-document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False)
-
-# Initialize reader
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
-```
-
-## Prebuilt Pipelines
-
-Haystack features many prebuilt pipelines that cover common tasks.
-Here we have an `ExtractiveQAPipeline` (the successor to the now deprecated `Finder` class).
-
-
-```python
-from haystack.pipelines import ExtractiveQAPipeline
-from haystack.utils import print_answers
-
-# Prebuilt pipeline
-p_extractive_premade = ExtractiveQAPipeline(reader=reader, retriever=bm25_retriever)
-res = p_extractive_premade.run(
-    query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
-)
-print_answers(res, details="minimum")
-```
-
-If you want to just do the retrieval step, you can use a `DocumentSearchPipeline`
-
-
-```python
-from haystack.pipelines import DocumentSearchPipeline
-from haystack.utils import print_documents
-
-p_retrieval = DocumentSearchPipeline(bm25_retriever)
-res = p_retrieval.run(query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}})
-print_documents(res, max_text_len=200)
-```
-
-Or if you want to use a `Generator` instead of a `Reader`,
-you can initialize a `GenerativeQAPipeline` like this:
-
-
-```python
-from haystack.pipelines import GenerativeQAPipeline
-from haystack.nodes import RAGenerator
-
-# We set this to True so that the document store returns document embeddings with each document
-# This is needed by the Generator
-document_store.return_embedding = True
-
-# Initialize generator
-rag_generator = RAGenerator()
-
-# Generative QA
-p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=embedding_retriever)
-res = p_generator.run(query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}})
-print_answers(res, details="minimum")
-
-# We are setting this to False so that in later pipelines,
-# we get a cleaner printout
-document_store.return_embedding = False
-```
-
-Haystack features prebuilt pipelines to do:
-- just document search (DocumentSearchPipeline),
-- document search with summarization (SearchSummarizationPipeline)
-- generative QA (GenerativeQAPipeline)
-- FAQ style QA (FAQPipeline)
-- translated search (TranslationWrapperPipeline)
-To find out more about these pipelines, have a look at our [documentation](https://docs.haystack.deepset.ai/docs/pipelines)
-
-
-With any Pipeline, whether prebuilt or custom constructed,
-you can save a diagram showing how all the components are connected.
-
-![image](https://github.com/deepset-ai/haystack/blob/main/docs/img/retriever-reader-pipeline.png?raw=true)
-
-
-```python
-# Uncomment the following to generate the images
-
-# p_extractive_premade.draw("pipeline_extractive_premade.png")
-# p_retrieval.draw("pipeline_retrieval.png")
-# p_generator.draw("pipeline_generator.png")
-```
-
-## Custom Pipelines
-
-Now we are going to rebuild the `ExtractiveQAPipelines` using the generic Pipeline class.
-We do this by adding the building blocks that we initialized as nodes in the graph.
-
-
-```python
-from haystack.pipelines import Pipeline
-
-
-# Custom built extractive QA pipeline
-p_extractive = Pipeline()
-p_extractive.add_node(component=bm25_retriever, name="Retriever", inputs=["Query"])
-p_extractive.add_node(component=reader, name="Reader", inputs=["Retriever"])
-
-# Now we can run it
-res = p_extractive.run(
-    query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
-)
-print_answers(res, details="minimum")
-
-# Uncomment the following to generate the pipeline image
-# p_extractive.draw("pipeline_extractive.png")
-```
-
-Pipelines offer a very simple way to ensemble together different components.
-In this example, we are going to combine the power of an `EmbeddingRetriever`
-with the keyword based `BM25Retriever`.
-See our [documentation](https://docs.haystack.deepset.ai/docs/retriever) to understand why
-we might want to combine a dense and sparse retriever.
-
-![image](https://github.com/deepset-ai/haystack/blob/main/docs/img/tutorial11_custompipelines_pipeline_ensemble.png?raw=true)
-
-Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together.
-
-
-```python
-from haystack.nodes import JoinDocuments
-
-# Create ensembled pipeline
-p_ensemble = Pipeline()
-p_ensemble.add_node(component=bm25_retriever, name="ESRetriever", inputs=["Query"])
-p_ensemble.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["Query"])
-p_ensemble.add_node(
-    component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "EmbeddingRetriever"]
-)
-p_ensemble.add_node(component=reader, name="Reader", inputs=["JoinResults"])
-
-# Uncomment the following to generate the pipeline image
-# p_ensemble.draw("pipeline_ensemble.png")
-
-# Run pipeline
-res = p_ensemble.run(
-    query="Who is the father of Arya Stark?", params={"EmbeddingRetriever": {"top_k": 5}, "ESRetriever": {"top_k": 5}}
-)
-print_answers(res, details="minimum")
-```
-
-## Custom Nodes
-
-Nodes are relatively simple objects
-and we encourage our users to design their own if they don't see on that fits their use case
-
-The only requirements are:
-- Create a class that inherits `BaseComponent`.
-- Add a method run() to your class. Add the mandatory and optional arguments it needs to process. These arguments must be passed as input to the pipeline, inside `params`, or output by preceding nodes.
-- Add processing logic inside the run() (e.g. reformatting the query).
-- Return a tuple that contains your output data (for the next node)
-and the name of the outgoing edge (by default "output_1" for nodes that have one output)
-- Add a class attribute outgoing_edges = 1 that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).
-
-Here we have a template for a Node:
-
-
-```python
-from haystack import BaseComponent
-from typing import Optional, List
-
-
-class CustomNode(BaseComponent):
-    outgoing_edges = 1
-
-    def run(self, query: str, my_optional_param: Optional[int]):
-        # process the inputs
-        output = {"my_output": ...}
-        return output, "output_1"
-
-    def run_batch(self, queries: List[str], my_optional_param: Optional[int]):
-        # process the inputs
-        output = {"my_output": ...}
-        return output, "output_1"
-```
-
-## Decision Nodes
-
-Decision Nodes help you route your data so that only certain branches of your `Pipeline` are run.
-One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to EmbeddingRetriever + Reader.
-With this approach you keep optimal speed and simplicity for keywords while going deep with transformers when it's most helpful.
-
-![image](https://github.com/deepset-ai/haystack/blob/main/docs/img/tutorial11_decision_nodes_pipeline_classifier.png?raw=true)
-
-Though this looks very similar to the ensembled pipeline shown above,
-the key difference is that only one of the retrievers is run for each request.
-By contrast both retrievers are always run in the ensembled approach.
-
-Below, we define a very naive `QueryClassifier` and show how to use it:
-
-
-```python
-class CustomQueryClassifier(BaseComponent):
-    outgoing_edges = 2
-
-    def run(self, query: str):
-        if "?" in query:
-            return {}, "output_2"
-        else:
-            return {}, "output_1"
-
-    def run_batch(self, queries: List[str]):
-        split = {"output_1": {"queries": []}, "output_2": {"queries": []}}
-        for query in queries:
-            if "?" in query:
-                split["output_2"]["queries"].append(query)
-            else:
-                split["output_1"]["queries"].append(query)
-
-        return split, "split"
-
-
-# Here we build the pipeline
-p_classifier = Pipeline()
-p_classifier.add_node(component=CustomQueryClassifier(), name="QueryClassifier", inputs=["Query"])
-p_classifier.add_node(component=bm25_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
-p_classifier.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["QueryClassifier.output_2"])
-p_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "EmbeddingRetriever"])
-# Uncomment the following to generate the pipeline image
-# p_classifier.draw("pipeline_classifier.png")
-
-# Run only the dense retriever on the full sentence query
-res_1 = p_classifier.run(query="Who is the father of Arya Stark?")
-print("Embedding Retriever Results" + "\n" + "=" * 15)
-print_answers(res_1)
-
-# Run only the sparse retriever on a keyword based query
-res_2 = p_classifier.run(query="Arya Stark father")
-print("ES Results" + "\n" + "=" * 15)
-print_answers(res_2)
-```
-
-## Evaluation Nodes
-
-We have also designed a set of nodes that can be used to evaluate the performance of a system.
-Have a look at our [tutorial](https://haystack.deepset.ai/tutorials/evaluation) to get hands on with the code and learn more about Evaluation Nodes!
-
-## Debugging Pipelines
-
-You can print out debug information from nodes in your pipelines in a few different ways.
-
-
-```python
-# 1) You can set the `debug` attribute of a given node.
-bm25_retriever.debug = True
-
-# 2) You can provide `debug` as a parameter when running your pipeline
-result = p_classifier.run(query="Who is the father of Arya Stark?", params={"ESRetriever": {"debug": True}})
-
-# 3) You can provide the `debug` paramter to all nodes in your pipeline
-result = p_classifier.run(query="Who is the father of Arya Stark?", params={"debug": True})
-
-result["_debug"]
-```
-
-## YAML Configs
-
-A full `Pipeline` can be defined in a YAML file and simply loaded.
-Having your pipeline available in a YAML is particularly useful
-when you move between experimentation and production environments.
-Just export the YAML from your notebook / IDE and import it into your production environment.
-It also helps with version control of pipelines,
-allows you to share your pipeline easily with colleagues,
-and simplifies the configuration of pipeline parameters in production.
-
-It consists of two main sections: you define all objects (e.g. a reader) in components
-and then stick them together to a pipeline in pipelines.
-You can also set one component to be multiple nodes of a pipeline or to be a node across multiple pipelines.
-It will be loaded just once in memory and therefore doesn't hurt your resources more than actually needed.
-
-The contents of a YAML file should look something like this:
-
-```yaml
-version: '0.7'
-components:    # define all the building-blocks for Pipeline
-- name: MyReader       # custom-name for the component; helpful for visualization & debugging
-  type: FARMReader    # Haystack Class name for the component
-  params:
-    no_ans_boost: -10
-    model_name_or_path: deepset/roberta-base-squad2
-- name: MyESRetriever
-  type: BM25Retriever
-  params:
-    document_store: MyDocumentStore    # params can reference other components defined in the YAML
-    custom_query: null
-- name: MyDocumentStore
-  type: ElasticsearchDocumentStore
-  params:
-    index: haystack_test
-pipelines:    # multiple Pipelines can be defined using the components from above
-- name: my_query_pipeline    # a simple extractive-qa Pipeline
-  nodes:
-  - name: MyESRetriever
-    inputs: [Query]
-  - name: MyReader
-    inputs: [MyESRetriever]
-```
-
-To load, simply call:
-``` python
-yaml_pipeline = Pipeline.load_from_yaml(Path("sample.yaml"))
-```
-
-## Conclusion
-
-The possibilities are endless with the `Pipeline` class and we hope that this tutorial will inspire you
-to build custom pipeplines that really work for your use case!
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/12_LFQA.md b/markdowns/12_LFQA.md
deleted file mode 100644
index bbd4cef8..00000000
--- a/markdowns/12_LFQA.md
+++ /dev/null
@@ -1,193 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb
-toc: True
-title: "Generative QA with LFQA"
-level: "intermediate"
-weight: 70
-description: Try out a generative model in place of the extractive Reader.
-category: "QA"
-aliases: ['/tutorials/lfqa']
-download: "/downloads/12_LFQA.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-Follow this tutorial to learn how to build and use a pipeline for Long-Form Question Answering (LFQA). LFQA is a variety of the generative question answering task. LFQA systems query large document stores for relevant information and then use this information to generate accurate, multi-sentence answers. In a regular question answering system, the retrieved documents related to the query (context passages) act as source tokens for extracted answers. In an LFQA system, context passages provide the context the system uses to generate original, abstractive, long-form answers.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.  
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-You can double check whether the GPU runtime is enabled with the following command:
-
-
-```bash
-%%bash
-
-nvidia-smi
-```
-
-To start, install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,faiss]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-### Document Store
-
-FAISS is a library for efficient similarity search on a cluster of dense vectors.
-The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood
-to store the document text and other meta data. The vector embeddings of the text are
-indexed on a FAISS Index that later is queried for searching answers.
-The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
-faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
-For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-
-```python
-from haystack.document_stores import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(embedding_dim=128, faiss_index_factory_str="Flat")
-```
-
-### Cleaning & indexing documents
-
-Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore
-
-
-```python
-from haystack.utils import convert_files_to_docs, fetch_archive_from_http, clean_wiki_text
-
-
-# Let's first get some files that we want to use
-doc_dir = "data/tutorial12"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt12.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# Convert files to dicts
-docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-
-# Now, let's write the dicts containing documents to our DB.
-document_store.write_documents(docs)
-```
-
-### Initialize Retriever and Reader/Generator
-
-#### Retriever
-
-We use a `DensePassageRetriever` and we invoke `update_embeddings` to index the embeddings of documents in the `FAISSDocumentStore`
-
-
-
-
-```python
-from haystack.nodes import DensePassageRetriever
-
-retriever = DensePassageRetriever(
-    document_store=document_store,
-    query_embedding_model="vblagoje/dpr-question_encoder-single-lfqa-wiki",
-    passage_embedding_model="vblagoje/dpr-ctx_encoder-single-lfqa-wiki",
-)
-
-document_store.update_embeddings(retriever)
-```
-
-Before we blindly use the `DensePassageRetriever` let's empirically test it to make sure a simple search indeed finds the relevant documents.
-
-
-```python
-from haystack.utils import print_documents
-from haystack.pipelines import DocumentSearchPipeline
-
-p_retrieval = DocumentSearchPipeline(retriever)
-res = p_retrieval.run(query="Tell me something about Arya Stark?", params={"Retriever": {"top_k": 10}})
-print_documents(res, max_text_len=512)
-```
-
-#### Reader/Generator
-
-Similar to previous Tutorials we now initalize our reader/generator.
-
-Here we use a `Seq2SeqGenerator` with the *vblagoje/bart_lfqa* model (see: https://huggingface.co/vblagoje/bart_lfqa)
-
-
-
-
-```python
-from haystack.nodes import Seq2SeqGenerator
-
-
-generator = Seq2SeqGenerator(model_name_or_path="vblagoje/bart_lfqa")
-```
-
-### Pipeline
-
-With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
-Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
-To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `GenerativeQAPipeline` that combines a retriever and a reader/generator to answer our questions.
-You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
-
-
-```python
-from haystack.pipelines import GenerativeQAPipeline
-
-pipe = GenerativeQAPipeline(generator, retriever)
-```
-
-## Voilà! Ask a question!
-
-
-```python
-pipe.run(
-    query="How did Arya Stark's character get portrayed in a television adaptation?", params={"Retriever": {"top_k": 3}}
-)
-```
-
-
-```python
-pipe.run(query="Why is Arya Stark an unusual character?", params={"Retriever": {"top_k": 3}})
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/13_Question_generation.md b/markdowns/13_Question_generation.md
deleted file mode 100644
index b655582c..00000000
--- a/markdowns/13_Question_generation.md
+++ /dev/null
@@ -1,207 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb
-toc: True
-title: "Question Generation"
-level: "intermediate"
-weight: 75
-description: Generate a set of questions that can be answered by a given Document.
-category: "QA"
-aliases: ['/tutorials/question-generation']
-download: "/downloads/13_Question_generation.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-This is a bare bones tutorial showing what is possible with the QuestionGenerator Nodes and Pipelines which automatically
-generate questions which the question generation model thinks can be answered by a given document.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.  
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-
-```python
-# Install the latest release of Haystack in your own environment
-#! pip install farm-haystack
-
-# Install the latest main of Haystack
-!pip install --upgrade pip
-!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-
-```python
-# Imports needed to run this notebook
-
-from pprint import pprint
-from tqdm.auto import tqdm
-from haystack.nodes import QuestionGenerator, BM25Retriever, FARMReader
-from haystack.document_stores import ElasticsearchDocumentStore
-from haystack.pipelines import (
-    QuestionGenerationPipeline,
-    RetrieverQuestionGenerationPipeline,
-    QuestionAnswerGenerationPipeline,
-)
-from haystack.utils import launch_es, print_questions
-```
-
-Let's start an Elasticsearch instance with one of the options below:
-
-
-```python
-# Option 1: Start Elasticsearch service via Docker
-launch_es()
-```
-
-
-```python
-# Option 2: In Colab / No Docker environments: Start Elasticsearch from source
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-
-es_server = Popen(
-    ["elasticsearch-7.9.2/bin/elasticsearch"], stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1)  # as daemon
-)
-# wait until ES has started
-! sleep 30
-```
-
-Let's initialize some core components
-
-
-```python
-text1 = "Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace."
-text2 = "Princess Arya Stark is the third child and second daughter of Lord Eddard Stark and his wife, Lady Catelyn Stark. She is the sister of the incumbent Westerosi monarchs, Sansa, Queen in the North, and Brandon, King of the Andals and the First Men. After narrowly escaping the persecution of House Stark by House Lannister, Arya is trained as a Faceless Man at the House of Black and White in Braavos, using her abilities to avenge her family. Upon her return to Westeros, she exacts retribution for the Red Wedding by exterminating the Frey male line."
-text3 = "Dry Cleaning are an English post-punk band who formed in South London in 2018.[3] The band is composed of vocalist Florence Shaw, guitarist Tom Dowse, bassist Lewis Maynard and drummer Nick Buxton. They are noted for their use of spoken word primarily in lieu of sung vocals, as well as their unconventional lyrics. Their musical stylings have been compared to Wire, Magazine and Joy Division.[4] The band released their debut single, 'Magic of Meghan' in 2019. Shaw wrote the song after going through a break-up and moving out of her former partner's apartment the same day that Meghan Markle and Prince Harry announced they were engaged.[5] This was followed by the release of two EPs that year: Sweet Princess in August and Boundary Road Snacks and Drinks in October. The band were included as part of the NME 100 of 2020,[6] as well as DIY magazine's Class of 2020.[7] The band signed to 4AD in late 2020 and shared a new single, 'Scratchcard Lanyard'.[8] In February 2021, the band shared details of their debut studio album, New Long Leg. They also shared the single 'Strong Feelings'.[9] The album, which was produced by John Parish, was released on 2 April 2021.[10]"
-
-docs = [{"content": text1}, {"content": text2}, {"content": text3}]
-
-# Initialize document store and write in the documents
-document_store = ElasticsearchDocumentStore()
-document_store.write_documents(docs)
-
-# Initialize Question Generator
-question_generator = QuestionGenerator()
-```
-
-## Question Generation Pipeline
-
-The most basic version of a question generator pipeline takes a document as input and outputs generated questions
-which the the document can answer.
-
-
-```python
-question_generation_pipeline = QuestionGenerationPipeline(question_generator)
-for idx, document in enumerate(document_store):
-
-    print(f"\n * Generating questions for document {idx}: {document.content[:100]}...\n")
-    result = question_generation_pipeline.run(documents=[document])
-    print_questions(result)
-```
-
-## Retriever Question Generation Pipeline
-
-This pipeline takes a query as input. It retrieves relevant documents and then generates questions based on these.
-
-
-```python
-retriever = BM25Retriever(document_store=document_store)
-rqg_pipeline = RetrieverQuestionGenerationPipeline(retriever, question_generator)
-
-print(f"\n * Generating questions for documents matching the query 'Arya Stark'\n")
-result = rqg_pipeline.run(query="Arya Stark")
-print_questions(result)
-```
-
-## Question Answer Generation Pipeline
-
-This pipeline takes a document as input, generates questions on it, and attempts to answer these questions using
-a Reader model
-
-
-```python
-reader = FARMReader("deepset/roberta-base-squad2")
-qag_pipeline = QuestionAnswerGenerationPipeline(question_generator, reader)
-for idx, document in enumerate(tqdm(document_store)):
-
-    print(f"\n * Generating questions and answers for document {idx}: {document.content[:100]}...\n")
-    result = qag_pipeline.run(documents=[document])
-    print_questions(result)
-```
-
-## Translated Question Answer Generation Pipeline
-Trained models for Question Answer Generation are not available in many languages other than English. Haystack
-provides a workaround for that issue by machine-translating a pipeline's inputs and outputs with the
-TranslationWrapperPipeline. The following example generates German questions and answers on a German text
-document - by using an English model for Question Answer Generation.
-
-
-```python
-# Fill the document store with a German document.
-text1 = "Python ist eine interpretierte Hochsprachenprogrammiersprache für allgemeine Zwecke. Sie wurde von Guido van Rossum entwickelt und 1991 erstmals veröffentlicht. Die Design-Philosophie von Python legt den Schwerpunkt auf die Lesbarkeit des Codes und die Verwendung von viel Leerraum (Whitespace)."
-docs = [{"content": text1}]
-document_store.delete_documents()
-document_store.write_documents(docs)
-
-# Load machine translation models
-from haystack.nodes import TransformersTranslator
-
-in_translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-de-en")
-out_translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-de")
-
-# Wrap the previously defined QuestionAnswerGenerationPipeline
-from haystack.pipelines import TranslationWrapperPipeline
-
-pipeline_with_translation = TranslationWrapperPipeline(
-    input_translator=in_translator, output_translator=out_translator, pipeline=qag_pipeline
-)
-
-for idx, document in enumerate(tqdm(document_store)):
-    print(f"\n * Generating questions and answers for document {idx}: {document.content[:100]}...\n")
-    result = pipeline_with_translation.run(documents=[document])
-    print_questions(result)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!
-Our focus: Industry specific language models & large scale QA systems.
-
-Some of our other work:
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/14_Query_Classifier.md b/markdowns/14_Query_Classifier.md
deleted file mode 100644
index df069f54..00000000
--- a/markdowns/14_Query_Classifier.md
+++ /dev/null
@@ -1,504 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb
-toc: True
-title: "Query Classifier"
-level: "intermediate"
-weight: 80
-description: Classify incoming queries so that they can be routed to the nodes that are best at handling them.
-category: "QA"
-aliases: ['/tutorials/query-classifier']
-download: "/downloads/14_Query_Classifier.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-One of the great benefits of using state-of-the-art NLP models like those available in Haystack is that it allows users to state their queries as *plain natural language questions*: rather than trying to come up with just the right set of keywords to find the answer to their question, users can simply ask their question in much the same way that they would ask it of a (very knowledgeable!) person.
-
-But just because users *can* ask their questions in "plain English" (or "plain German", etc.), that doesn't mean they always *will*. For instance, a user might input a few keywords rather than a complete question because they don't understand the pipeline's full capabilities, or because they are so accustomed to keyword search. While a standard Haystack pipeline might handle such queries with reasonable accuracy, for a variety of reasons we still might prefer that our pipeline be sensitive to the type of query it is receiving, so that it behaves differently when a user inputs, say, a collection of keywords instead of a question.
-
-For this reason, Haystack comes with built-in capabilities to distinguish between three types of queries: **keyword queries**, **interrogative queries**, and **statement queries**, described below.
-
-1. **Keyword queries** can be thought of more or less as lists of words, such as "Alaska cruises summer". While the meanings of individual words may matter in a keyword query, the linguistic connections *between* words do not. Hence, in a keyword query the order of words is largely irrelevant: "Alaska cruises summer", "summer Alaska cruises", and "summer cruises Alaska" are functionally the same.
-
-2. **Interrogative queries** (or **question queries**) are queries phrased as natural language questions, such as "Who was the father of Eddard Stark?". Unlike with keyword queries, word order very much matters here: "Who was the father of Eddard Stark?" and "Who was Eddard Stark the father of?" are very different questions, despite having exactly the same words. (Note that while we often write questions with question marks, Haystack can find interrogative queries without such a dead giveaway!)
-
-3. **Statement queries** are just declarative sentences, such as "Daenerys loved Jon". These are like interrogative queries in that word order matters&mdash;again, "Daenerys loved Jon" and "Jon loved Daenerys" mean very different things&mdash;but they are statements instead of questions.
-
-In this tutorial you will learn how to use **query classifiers** to branch your Haystack pipeline based on the type of query it receives. Haystack comes with two out-of-the-box query classification schemas, each of which routes a given query into one of two branches:
-
-1. **Keyword vs. Question/Statement** &mdash; routes a query into one of two branches depending on whether it is a full question/statement or a collection of keywords.
-
-2. **Question vs. Statement** &mdash; routes a natural language query into one of two branches depending on whether it is a question or a statement.
-
-Furthermore, for each classification schema there are two types of nodes capable of performing this classification: a **`TransformersQueryClassifier`** that uses a transformer model, and an **`SklearnQueryClassifier`** that uses a more lightweight model built in `sklearn`.
-
-With all of that explanation out of the way, let's dive in!
-
-### Prepare the Environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.  
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-You can double check whether the GPU runtime is enabled with the following command:
-
-
-```bash
-%%bash
-
-nvidia-smi
-```
-
-Next we make sure the latest version of Haystack is installed:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
-
-# Install these to allow pipeline visualization
-apt install libgraphviz-dev
-pip install pygraphviz
-```
-
-### Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-### Trying Some Query Classifiers on their Own
-
-Before integrating query classifiers into our pipelines, let's test them out on their own and see what they actually do. First we initiate a simple, out-of-the-box **keyword vs. question/statement** `SklearnQueryClassifier`:
-
-
-```python
-# Here we create the keyword vs question/statement query classifier
-from haystack.nodes import SklearnQueryClassifier
-
-keyword_classifier = SklearnQueryClassifier()
-```
-
-Now let's feed some queries into this query classifier. We'll test with one keyword query, one interrogative query, and one statement query. Note that we don't need to use any punctuation, such as question marks, for the query classifier to make the right decision.
-
-
-```python
-queries = [
-    "Arya Stark father",  # Keyword Query
-    "Who was the father of Arya Stark",  # Interrogative Query
-    "Lord Eddard was the father of Arya Stark",  # Statement Query
-]
-```
-
-Below, you can see what the classifier does with these queries: it correctly determines that  "Arya Stark father" is a keyword query and sends it to branch 2. It also correctly classifies both the interrogative query "Who was the father of Arya Stark" and the statement query "Lord Eddard was the father of Arya Stark" as non-keyword queries, and sends them to branch 1.
-
-
-```python
-import pandas as pd
-
-k_vs_qs_results = {"Query": [], "Output Branch": [], "Class": []}
-
-for query in queries:
-    result = keyword_classifier.run(query=query)
-    k_vs_qs_results["Query"].append(query)
-    k_vs_qs_results["Output Branch"].append(result[1])
-    k_vs_qs_results["Class"].append("Question/Statement" if result[1] == "output_1" else "Keyword")
-
-pd.DataFrame.from_dict(k_vs_qs_results)
-```
-
-Next, we will illustrate a **question vs. statement** `SklearnQueryClassifier`. We define our classifier below. Note that this time we have to explicitly specify the model and vectorizer since the default for a `SklearnQueryClassifier` (and a `TransformersQueryClassifier`) is keyword vs. question/statement classification.
-
-
-```python
-# Here we create the question vs statement query classifier
-model_url = (
-    "https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/model.pickle"
-)
-vectorizer_url = (
-    "https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/vectorizer.pickle"
-)
-
-question_classifier = SklearnQueryClassifier(model_name_or_path=model_url, vectorizer_name_or_path=vectorizer_url)
-```
-
-We will test this classifier on the two question/statement queries from the last go-round:
-
-
-```python
-queries = [
-    "Who was the father of Arya Stark",  # Interrogative Query
-    "Lord Eddard was the father of Arya Stark",  # Statement Query
-]
-
-q_vs_s_results = {"Query": [], "Output Branch": [], "Class": []}
-
-for query in queries:
-    result = question_classifier.run(query=query)
-    q_vs_s_results["Query"].append(query)
-    q_vs_s_results["Output Branch"].append(result[1])
-    q_vs_s_results["Class"].append("Question" if result[1] == "output_1" else "Statement")
-
-pd.DataFrame.from_dict(q_vs_s_results)
-```
-
-And as we see, the question "Who was the father of Arya Stark" is sent to branch 1, while the statement "Lord Eddard was the father of Arya Stark" is sent to branch 2. This means we can have our pipeline treat statements and questions differently.
-
-### Using Query Classifiers in a Pipeline
-
-Now let's see how we can use query classifiers in a question-answering (QA) pipeline. We start by initiating Elasticsearch:
-
-#### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-#### Start an Elasticsearch server in Colab
-
-If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```bash
-%%bash
-
-wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-chown -R daemon:daemon elasticsearch-7.9.2
-
-```
-
-
-```bash
-%%bash --bg
-
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
-```
-
-Next we fetch some data&mdash;for our example we'll use pages from the Game of Thrones wiki&mdash;and index it in our `DocumentStore`:
-
-
-```python
-from haystack.utils import fetch_archive_from_http, convert_files_to_docs, clean_wiki_text
-
-# Download and prepare data - 517 Wikipedia articles for Game of Thrones
-doc_dir = "data/tutorial14"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt14.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-
-# convert files to dicts containing documents that can be indexed to our datastore
-got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
-```
-
-
-```python
-import os
-import time
-
-from haystack.document_stores import ElasticsearchDocumentStore
-
-
-# Wait 30 seconds only to be sure Elasticsearch is ready before continuing
-time.sleep(30)
-
-# Get the host where Elasticsearch is running, default to localhost
-host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
-
-
-document_store = ElasticsearchDocumentStore(host=host)
-document_store.delete_documents()
-document_store.write_documents(got_docs)
-```
-
-#### Pipelines with Keyword vs. Question/Statement Classification
-
-Our first illustration will be a simple retriever-reader QA pipeline, but the choice of which retriever we use will depend on the type of query received: **keyword** queries will use a sparse **`BM25Retriever`**, while **question/statement** queries will use the more accurate but also more computationally expensive **`EmbeddingRetriever`**.
-
-We start by initializing our retrievers and reader:
-
-
-```python
-from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader
-
-
-# Initialize sparse retriever for keyword queries
-bm25_retriever = BM25Retriever(document_store=document_store)
-
-# Initialize dense retriever for question/statement queries
-embedding_retriever = EmbeddingRetriever(
-    document_store=document_store, embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1"
-)
-document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False)
-
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
-```
-
-Now we define our pipeline. As promised, the question/statement branch `output_1` from the query classifier is fed into an `EmbeddingRetriever`, while the keyword branch `output_2` from the same classifier is fed into a `BM25Retriever`. Both of these retrievers are then fed into our reader. Our pipeline can thus be thought of as having something of a diamond shape: all queries are sent into the classifier, which splits those queries into two different retrievers, and those retrievers feed their outputs to the same reader.
-
-
-```python
-from haystack.pipelines import Pipeline
-
-
-# Here we build the pipeline
-sklearn_keyword_classifier = Pipeline()
-sklearn_keyword_classifier.add_node(component=SklearnQueryClassifier(), name="QueryClassifier", inputs=["Query"])
-sklearn_keyword_classifier.add_node(
-    component=embedding_retriever, name="EmbeddingRetriever", inputs=["QueryClassifier.output_1"]
-)
-sklearn_keyword_classifier.add_node(component=bm25_retriever, name="BM25Retriever", inputs=["QueryClassifier.output_2"])
-sklearn_keyword_classifier.add_node(component=reader, name="QAReader", inputs=["BM25Retriever", "EmbeddingRetriever"])
-
-# To generate a visualization of the pipeline, uncomment the following:
-# sklearn_keyword_classifier.draw("sklearn_keyword_classifier.png")
-```
-
-Below, we can see how this choice affects the branching structure: the keyword query "arya stark father" and the question query "Who is the father of Arya Stark?" generate noticeably different results, a distinction that is likely due to the use of different retrievers for keyword vs. question/statement queries.
-
-
-```python
-from haystack.utils import print_answers
-
-
-# Useful for framing headers
-equal_line = "=" * 30
-
-# Run only the dense retriever on the full sentence query
-res_1 = sklearn_keyword_classifier.run(query="Who is the father of Arya Stark?")
-print(f"\n\n{equal_line}\nQUESTION QUERY RESULTS\n{equal_line}")
-print_answers(res_1, details="minimum")
-print("\n\n")
-
-# Run only the sparse retriever on a keyword based query
-res_2 = sklearn_keyword_classifier.run(query="arya stark father")
-print(f"\n\n{equal_line}\nKEYWORD QUERY RESULTS\n{equal_line}")
-print_answers(res_2, details="minimum")
-```
-
-The above example uses an `SklearnQueryClassifier`, but of course we can do precisely the same thing with a `TransformersQueryClassifier`. This is illustrated below, where we have constructed the same diamond-shaped pipeline.
-
-
-```python
-from haystack.nodes import TransformersQueryClassifier
-
-# Here we build the pipeline
-transformer_keyword_classifier = Pipeline()
-transformer_keyword_classifier.add_node(
-    component=TransformersQueryClassifier(), name="QueryClassifier", inputs=["Query"]
-)
-transformer_keyword_classifier.add_node(
-    component=embedding_retriever, name="EmbeddingRetriever", inputs=["QueryClassifier.output_1"]
-)
-transformer_keyword_classifier.add_node(
-    component=bm25_retriever, name="BM25Retriever", inputs=["QueryClassifier.output_2"]
-)
-transformer_keyword_classifier.add_node(
-    component=reader, name="QAReader", inputs=["BM25Retriever", "EmbeddingRetriever"]
-)
-
-
-# Useful for framing headers
-equal_line = "=" * 30
-
-# Run only the dense retriever on the full sentence query
-res_1 = transformer_keyword_classifier.run(query="Who is the father of Arya Stark?")
-print(f"\n\n{equal_line}\nQUESTION QUERY RESULTS\n{equal_line}")
-print_answers(res_1, details="minimum")
-print("\n\n")
-
-# Run only the sparse retriever on a keyword based query
-res_2 = transformer_keyword_classifier.run(query="arya stark father")
-print(f"\n\n{equal_line}\nKEYWORD QUERY RESULTS\n{equal_line}")
-print_answers(res_2, details="minimum")
-```
-
-#### Pipeline with Question vs. Statement Classification
-
-Above we saw a potential use for keyword vs. question/statement classification: we might choose to use a less resource-intensive retriever for keyword queries than for question/statement queries. But what about question vs. statement classification?
-
-To illustrate one potential use for question vs. statement classification, we will build a pipeline that looks as follows:
-
-1. The pipeline will start with a retriever that **every query** will go through.
-2. The pipeline will end with a reader that **only question queries** will go through.
-
-In other words, our pipeline will be a **retriever-only pipeline for statement queries**&mdash;given the statement "Arya Stark was the daughter of a Lord", all we will get back are the most relevant documents&mdash;but it will be a **retriever-reader pipeline for question queries**.
-
-To make things more concrete, our pipeline will start with a retriever, which is then fed into a `TransformersQueryClassifier` that is set to do question vs. statement classification. Note that this means we need to explicitly choose the model, since as mentioned previously a default `TransformersQueryClassifier` performs keyword vs. question/statement classification. The classifier's first branch, which handles question queries, will then be sent to the reader, while the second branch will not be connected to any other nodes. As a result, the last node of the pipeline depends on the type of query: questions go all the way through the reader, while statements only go through the retriever. This pipeline is illustrated below:
-
-
-```python
-# Here we build the pipeline
-transformer_question_classifier = Pipeline()
-transformer_question_classifier.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["Query"])
-transformer_question_classifier.add_node(
-    component=TransformersQueryClassifier(model_name_or_path="shahrukhx01/question-vs-statement-classifier"),
-    name="QueryClassifier",
-    inputs=["EmbeddingRetriever"],
-)
-transformer_question_classifier.add_node(component=reader, name="QAReader", inputs=["QueryClassifier.output_1"])
-
-# To generate a visualization of the pipeline, uncomment the following:
-# transformer_question_classifier.draw("transformer_question_classifier.png")
-```
-
-And here are the results of this pipeline: with a question query like "Who is the father of Arya Stark?", we obtain answers from a reader, and with a statement query like "Arya Stark was the daughter of a Lord", we just obtain documents from a retriever.
-
-
-```python
-from haystack.utils import print_documents
-
-
-# Useful for framing headers
-equal_line = "=" * 30
-
-# Run the retriever + reader on the question query
-res_1 = transformer_question_classifier.run(query="Who is the father of Arya Stark?")
-print(f"\n\n{equal_line}\nQUESTION QUERY RESULTS\n{equal_line}")
-print_answers(res_1, details="minimum")
-print("\n\n")
-
-# Run only the retriever on the statement query
-res_2 = transformer_question_classifier.run(query="Arya Stark was the daughter of a Lord.")
-print(f"\n\n{equal_line}\nSTATEMENT QUERY RESULTS\n{equal_line}")
-print_documents(res_2)
-```
-
-### Other use cases for Query Classifiers: custom classification models and zero-shot classification.
-
-`TransformersQueryClassifier` is very flexible and also supports other options for classifying queries.
-For example, we may be interested in detecting the sentiment or classifying the topics.  We can do this by loading a custom classification model from the Hugging Face Hub or by using zero-shot classification.
-
-#### Custom classification model vs zero-shot classification
-- Traditional text classification models are trained to predict one of a few "hard-coded" classes and require a dedicated training dataset. In the Hugging Face Hub, you can find many pre-trained models, maybe even related to your domain of interest.
-- Zero-shot classification is very versatile: by choosing a suitable base transformer, you can classify the text without any training dataset. You just have to provide the candidate categories.
-
-#### Using custom classification models
-We can use a public model, available in the Hugging Face Hub. For example, if we want to classify the sentiment of the queries, we can choose an appropriate model, such as https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment.
-
-*In this case, the `labels` parameter must contain a list with the exact model labels.
-The first label we provide corresponds to output_1, the second label to output_2, and so on.*
-
-
-```python
-# Remember to compile a list with the exact model labels
-# The first label you provide corresponds to output_1, the second label to output_2, and so on.
-labels = ["LABEL_0", "LABEL_1", "LABEL_2"]
-
-sentiment_query_classifier = TransformersQueryClassifier(
-    model_name_or_path="cardiffnlp/twitter-roberta-base-sentiment",
-    use_gpu=True,
-    task="text-classification",
-    labels=labels,
-)
-```
-
-
-```python
-queries = [
-    "What's the answer?",  # neutral query
-    "Would you be so lovely to tell me the answer?",  # positive query
-    "Can you give me the damn right answer for once??",  # negative query
-]
-```
-
-
-```python
-import pandas as pd
-
-sent_results = {"Query": [], "Output Branch": [], "Class": []}
-
-for query in queries:
-    result = sentiment_query_classifier.run(query=query)
-    sent_results["Query"].append(query)
-    sent_results["Output Branch"].append(result[1])
-    if result[1] == "output_1":
-        sent_results["Class"].append("negative")
-    elif result[1] == "output_2":
-        sent_results["Class"].append("neutral")
-    elif result[1] == "output_3":
-        sent_results["Class"].append("positive")
-
-pd.DataFrame.from_dict(sent_results)
-```
-
-#### Using zero-shot classification
-You can also perform zero-shot classification by providing a suitable base transformer model and **choosing** the classes the model should predict.
-For example, we may be interested in whether the user query is related to music or cinema.
-
-In this case, the `labels` parameter is a list containing the candidate classes.
-
-
-```python
-# In zero-shot-classification, you can choose the labels
-labels = ["music", "cinema"]
-
-query_classifier = TransformersQueryClassifier(
-    model_name_or_path="typeform/distilbert-base-uncased-mnli",
-    use_gpu=True,
-    task="zero-shot-classification",
-    labels=labels,
-)
-```
-
-
-```python
-queries = [
-    "In which films does John Travolta appear?",  # query about cinema
-    "What is the Rolling Stones first album?",  # query about music
-    "Who was Sergio Leone?",  # query about cinema
-]
-```
-
-
-```python
-import pandas as pd
-
-query_classification_results = {"Query": [], "Output Branch": [], "Class": []}
-
-for query in queries:
-    result = query_classifier.run(query=query)
-    query_classification_results["Query"].append(query)
-    query_classification_results["Output Branch"].append(result[1])
-    query_classification_results["Class"].append("music" if result[1] == "output_1" else "cinema")
-
-pd.DataFrame.from_dict(query_classification_results)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs) 
diff --git a/markdowns/15_TableQA.md b/markdowns/15_TableQA.md
deleted file mode 100644
index d2ae12bd..00000000
--- a/markdowns/15_TableQA.md
+++ /dev/null
@@ -1,444 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb
-toc: True
-title: "Open-Domain QA on Tables"
-level: "advanced"
-weight: 130
-description: Perform question answering on tabular data.
-category: "QA"
-aliases: ['/tutorials/table-qa']
-download: "/downloads/15_TableQA.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-This tutorial shows you how to perform question-answering on tables using the `EmbeddingRetriever` or `BM25Retriever` as retriever node and the `TableReader` as reader node.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-You can double check whether the GPU runtime is enabled with the following command:
-
-
-```bash
-%%bash
-
-nvidia-smi
-```
-
-To start, install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
-
-# Install pygraphviz for visualization of Pipelines
-apt install libgraphviz-dev
-pip install pygraphviz
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-### Start an Elasticsearch server in Colab
-
-If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```bash
-%%bash
-
-wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-chown -R daemon:daemon elasticsearch-7.9.2
-
-```
-
-
-```bash
-%%bash --bg
-
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
-```
-
-
-```python
-# Connect to Elasticsearch
-import os
-import time
-from haystack.document_stores import ElasticsearchDocumentStore
-
-
-# Wait 30 seconds only to be sure Elasticsearch is ready before continuing
-time.sleep(30)
-
-# Get the host where Elasticsearch is running, default to localhost
-host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
-
-document_index = "document"
-document_store = ElasticsearchDocumentStore(host=host, username="", password="", index=document_index)
-```
-
-## Add Tables to DocumentStore
-To quickly demonstrate the capabilities of the `EmbeddingRetriever` and the `TableReader` we use a subset of 1000 tables and text documents from a dataset we have published in [this paper](https://arxiv.org/abs/2108.04049).
-
-Just as text passages, tables are represented as `Document` objects in Haystack. The content field, though, is a pandas DataFrame instead of a string.
-
-
-```python
-# Let's first fetch some tables that we want to query
-# Here: 1000 tables from OTT-QA
-from haystack.utils import fetch_archive_from_http
-
-doc_dir = "data/tutorial15"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/table_text_dataset.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-# Add the tables to the DocumentStore
-import json
-from haystack import Document
-import pandas as pd
-
-
-def read_tables(filename):
-    processed_tables = []
-    with open(filename) as tables:
-        tables = json.load(tables)
-        for key, table in tables.items():
-            current_columns = table["header"]
-            current_rows = table["data"]
-            current_df = pd.DataFrame(columns=current_columns, data=current_rows)
-            document = Document(content=current_df, content_type="table", id=key)
-            processed_tables.append(document)
-
-    return processed_tables
-
-
-tables = read_tables(f"{doc_dir}/tables.json")
-document_store.write_documents(tables, index=document_index)
-
-# Showing content field and meta field of one of the Documents of content_type 'table'
-print(tables[0].content)
-print(tables[0].meta)
-```
-
-## Initialize Retriever, Reader & Pipeline
-
-### Retriever
-
-Retrievers help narrowing down the scope for the Reader to a subset of tables where a given question could be answered.
-They use some simple but fast algorithm.
-
-**Here:** We specify an embedding model that is finetuned so it can also generate embeddings for tables (instead of just text).
-
-**Alternatives:**
-
-- `BM25Retriever` that uses BM25 algorithm
-
-
-
-```python
-from haystack.nodes.retriever import EmbeddingRetriever
-
-retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/all-mpnet-base-v2-table")
-```
-
-
-```python
-# Add table embeddings to the tables in DocumentStore
-document_store.update_embeddings(retriever=retriever)
-```
-
-
-```python
-## Alternative: BM25Retriever
-# from haystack.nodes.retriever import BM25Retriever
-# retriever = BM25Retriever(document_store=document_store)
-```
-
-
-```python
-# Try the Retriever
-retrieved_tables = retriever.retrieve("Who won the Super Bowl?", top_k=5)
-
-# Get highest scored table
-print(retrieved_tables[0].content)
-```
-
-### Reader
-The `TableReader` is based on TaPas, a transformer-based language model capable of grasping the two-dimensional structure of a table. It scans the tables returned by the retriever and extracts the anser. The available TableReader models can be found [here](https://huggingface.co/models?pipeline_tag=table-question-answering&sort=downloads).
-
-**Notice**: The `TableReader` will return an answer for each table, even if the query cannot be answered by the table. Furthermore, the confidence scores are not useful as of now, given that they will *always* be very high (i.e. 1 or close to 1).
-
-
-```python
-from haystack.nodes import TableReader
-
-reader = TableReader(model_name_or_path="google/tapas-base-finetuned-wtq", max_seq_len=512)
-```
-
-
-```python
-# Try the TableReader on one Table
-
-table_doc = document_store.get_document_by_id("36964e90-3735-4ba1-8e6a-bec236e88bb2")
-print(table_doc.content)
-```
-
-
-```python
-from haystack.utils import print_answers
-
-prediction = reader.predict(query="Who played Gregory House in the series House?", documents=[table_doc])
-print_answers(prediction, details="all")
-```
-
-The offsets in the `offsets_in_document` and `offsets_in_context` field indicate the table cells that the model predicts to be part of the answer. They need to be interpreted on the linearized table, i.e., a flat list containing all of the table cells.
-
-
-```python
-print(f"Predicted answer: {prediction['answers'][0].answer}")
-print(f"Meta field: {prediction['answers'][0].meta}")
-```
-
-### Pipeline
-The Retriever and the Reader can be sticked together to a pipeline in order to first retrieve relevant tables and then extract the answer.
-
-**Notice**: Given that the `TableReader` does not provide useful confidence scores and returns an answer for each of the tables, the sorting of the answers might be not helpful.
-
-
-```python
-# Initialize pipeline
-from haystack import Pipeline
-
-table_qa_pipeline = Pipeline()
-table_qa_pipeline.add_node(component=retriever, name="EmbeddingRetriever", inputs=["Query"])
-table_qa_pipeline.add_node(component=reader, name="TableReader", inputs=["EmbeddingRetriever"])
-```
-
-
-```python
-prediction = table_qa_pipeline.run("When was Guilty Gear Xrd : Sign released?", params={"top_k": 30})
-print_answers(prediction, details="minimum")
-```
-
-
-```python
-# Add 500 text passages to our document store.
-
-
-def read_texts(filename):
-    processed_passages = []
-    with open(filename) as passages:
-        passages = json.load(passages)
-        for key, content in passages.items():
-            document = Document(content=content, content_type="text", id=key)
-            processed_passages.append(document)
-
-    return processed_passages
-
-
-passages = read_texts(f"{doc_dir}/texts.json")
-document_store.write_documents(passages, index=document_index)
-```
-
-
-```python
-document_store.update_embeddings(retriever=retriever, update_existing_embeddings=False)
-```
-
-## Pipeline for QA on Combination of Text and Tables
-We are using one node for retrieving both texts and tables, the `EmbeddingRetriever`. In order to do question-answering on the Documents coming from the `EmbeddingRetriever`, we need to route Documents of type `"text"` to a `FARMReader` (or alternatively `TransformersReader`) and Documents of type `"table"` to a `TableReader`.
-
-To achieve this, we make use of two additional nodes:
-- `RouteDocuments`: Splits the List of Documents retrieved by the `EmbeddingRetriever` into two lists containing only Documents of type `"text"` or `"table"`, respectively.
-- `JoinAnswers`: Takes Answers coming from two different Readers (in this case `FARMReader` and `TableReader`) and joins them to a single list of Answers.
-
-
-```python
-from haystack.nodes import FARMReader, RouteDocuments, JoinAnswers
-
-text_reader = FARMReader("deepset/roberta-base-squad2")
-# In order to get meaningful scores from the TableReader, use "deepset/tapas-large-nq-hn-reader" or
-# "deepset/tapas-large-nq-reader" as TableReader models. The disadvantage of these models is, however,
-# that they are not capable of doing aggregations over multiple table cells.
-table_reader = TableReader("deepset/tapas-large-nq-hn-reader")
-route_documents = RouteDocuments()
-join_answers = JoinAnswers()
-```
-
-
-```python
-text_table_qa_pipeline = Pipeline()
-text_table_qa_pipeline.add_node(component=retriever, name="EmbeddingRetriever", inputs=["Query"])
-text_table_qa_pipeline.add_node(component=route_documents, name="RouteDocuments", inputs=["EmbeddingRetriever"])
-text_table_qa_pipeline.add_node(component=text_reader, name="TextReader", inputs=["RouteDocuments.output_1"])
-text_table_qa_pipeline.add_node(component=table_reader, name="TableReader", inputs=["RouteDocuments.output_2"])
-text_table_qa_pipeline.add_node(component=join_answers, name="JoinAnswers", inputs=["TextReader", "TableReader"])
-```
-
-
-```python
-# Remove the following comment to generate the structure of the combined Table an Text QA pipeline.
-# text_table_qa_pipeline.draw("pipeline.png")
-```
-
-![image](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/img/table-qa-pipeline.png?raw=true)
-
-
-```python
-# Example query whose answer resides in a text passage
-predictions = text_table_qa_pipeline.run(query="Who was Thomas Alva Edison?")
-```
-
-
-```python
-# We can see both text passages and tables as contexts of the predicted answers.
-print_answers(predictions, details="minimum")
-```
-
-
-```python
-# Example query whose answer resides in a table
-predictions = text_table_qa_pipeline.run(query="Which country does the film Macaroni come from?")
-```
-
-
-```python
-# We can see both text passages and tables as contexts of the predicted answers.
-print_answers(predictions, details="minimum")
-```
-
-## Evaluation
-To evaluate our pipeline, we can use haystack's evaluation feature. We just need to convert our labels into `MultiLabel` objects and the `eval` method will do the rest.
-
-
-```python
-from haystack import Label, MultiLabel, Answer
-
-
-def read_labels(filename, tables):
-    processed_labels = []
-    with open(filename) as labels:
-        labels = json.load(labels)
-        for table in tables:
-            if table.id not in labels:
-                continue
-            label = labels[table.id]
-            label = Label(
-                query=label["query"],
-                document=table,
-                is_correct_answer=True,
-                is_correct_document=True,
-                answer=Answer(answer=label["answer"]),
-                origin="gold-label",
-            )
-            processed_labels.append(MultiLabel(labels=[label]))
-    return processed_labels
-
-
-table_labels = read_labels(f"{doc_dir}/labels.json", tables)
-passage_labels = read_labels(f"{doc_dir}/labels.json", passages)
-```
-
-
-```python
-eval_results = text_table_qa_pipeline.eval(table_labels + passage_labels, params={"top_k": 10})
-```
-
-
-```python
-# Calculating and printing the evaluation metrics
-print(eval_results.calculate_metrics())
-```
-
-## Adding tables from PDFs
-It can sometimes be hard to provide your data in form of a pandas DataFrame. For this case, we provide the `ParsrConverter` wrapper that can help you to convert, for example, a PDF file into a document that you can index.
-
-**Attention: `parsr` needs a docker environment for execution, but Colab doesn't support docker.**
-**If you have a local docker environment, you can uncomment and run the following cells.**
-
-
-```python
-# import time
-
-# !docker run -d -p 3001:3001 axarev/parsr
-# time.sleep(30)
-```
-
-
-```python
-# !wget https://www.w3.org/WAI/WCAG21/working-examples/pdf-table/table.pdf
-```
-
-
-```python
-# from haystack.nodes import ParsrConverter
-
-# converter = ParsrConverter()
-
-# docs = converter.convert("table.pdf")
-
-# tables = [doc for doc in docs if doc.content_type == "table"]
-```
-
-
-```python
-# print(tables)
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
-
diff --git a/markdowns/16_Document_Classifier_at_Index_Time.md b/markdowns/16_Document_Classifier_at_Index_Time.md
deleted file mode 100644
index 38a53b6c..00000000
--- a/markdowns/16_Document_Classifier_at_Index_Time.md
+++ /dev/null
@@ -1,306 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb
-toc: True
-title: "Document Classification at Index Time"
-level: "intermediate"
-weight: 85
-description: Generate and attach classification labels to your Documents when indexing.
-category: "QA"
-aliases: ['/tutorials/doc-class-index']
-download: "/downloads/16_Document_Classifier_at_Index_Time.ipynb"
-completion_time: False
-created_at: 2021-08-12
----
-    
-
-
-With DocumentClassifier it's possible to automatically enrich your documents with categories, sentiments, topics or whatever metadata you like. This metadata could be used for efficient filtering or further processing. Say you have some categories your users typically filter on. If the documents are tagged manually with these categories, you could automate this process by training a model. Or you can leverage the full power and flexibility of zero shot classification. All you need to do is pass your categories to the classifier, no labels required. This tutorial shows how to integrate it in your indexing pipeline.
-
-DocumentClassifier adds the classification result (label and score) to Document's meta property.
-Hence, we can use it to classify documents at index time. \
-The result can be accessed at query time: for example by applying a filter for "classification.label".
-
-This tutorial will show you how to integrate a classification model into your preprocessing steps and how you can filter for this additional metadata at query time. In the last section we show how to put it all together and create an indexing pipeline.
-
-
-```bash
-%%bash
-
-# Install the latest main of Haystack
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr]
-
-wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz
-tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin
-
-apt install libgraphviz-dev
-pip install pygraphviz
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-## Read and preprocess documents
-
-
-
-```python
-from haystack.utils import fetch_archive_from_http
-
-
-# This fetches some sample files to work with
-doc_dir = "data/tutorial16"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/preprocessing_tutorial16.zip"
-fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
-```
-
-
-```python
-from haystack.nodes import PreProcessor
-from haystack.utils import convert_files_to_docs
-
-# note that you can also use the document classifier before applying the PreProcessor, e.g. before splitting your documents
-all_docs = convert_files_to_docs(dir_path=doc_dir)
-preprocessor_sliding_window = PreProcessor(split_overlap=3, split_length=10, split_respect_sentence_boundary=False)
-docs_sliding_window = preprocessor_sliding_window.process(all_docs)
-```
-
-## Apply DocumentClassifier
-
-We can enrich the document metadata at index time using any transformers document classifier model. While traditional classification models are trained to predict one of a few "hard-coded" classes and required a dedicated training dataset, zero-shot classification is super flexible and you can easily switch the classes the model should predict on the fly. Just supply them via the labels param.
-Here we use a zero shot model that is supposed to classify our documents in 'music', 'natural language processing' and 'history'. Feel free to change them for whatever you like to classify. \
-These classes can later on be accessed at query time.
-
-
-```python
-from haystack.nodes import TransformersDocumentClassifier
-
-
-doc_classifier = TransformersDocumentClassifier(
-    model_name_or_path="cross-encoder/nli-distilroberta-base",
-    task="zero-shot-classification",
-    labels=["music", "natural language processing", "history"],
-    batch_size=16,
-)
-```
-
-
-```python
-# we can also use any other transformers model besides zero shot classification
-
-# doc_classifier_model = 'bhadresh-savani/distilbert-base-uncased-emotion'
-# doc_classifier = TransformersDocumentClassifier(model_name_or_path=doc_classifier_model, batch_size=16, use_gpu=-1)
-```
-
-
-```python
-# we could also specifiy a different field we want to run the classification on
-
-# doc_classifier = TransformersDocumentClassifier(model_name_or_path="cross-encoder/nli-distilroberta-base",
-#    task="zero-shot-classification",
-#    labels=["music", "natural language processing", "history"],
-#    batch_size=16, use_gpu=-1,
-#    classification_field="description")
-```
-
-
-```python
-# classify using gpu, batch_size makes sure we do not run out of memory
-classified_docs = doc_classifier.predict(docs_sliding_window)
-```
-
-
-```python
-# let's see how it looks: there should be a classification result in the meta entry containing labels and scores.
-print(classified_docs[0].to_dict())
-```
-
-## Indexing
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-### Start an Elasticsearch server in Colab
-
-If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```bash
-%%bash
-
-wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-chown -R daemon:daemon elasticsearch-7.9.2
-
-```
-
-
-```bash
-%%bash --bg
-
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
-```
-
-
-```python
-# Connect to Elasticsearch
-import os
-import time
-
-from haystack.document_stores.elasticsearch import ElasticsearchDocumentStore
-
-# Wait 30 seconds only to be sure Elasticsearch is ready before continuing
-time.sleep(30)
-
-# Get the host where Elasticsearch is running, default to localhost
-host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
-
-document_store = ElasticsearchDocumentStore(host=host, username="", password="", index="document")
-```
-
-
-```python
-# Now, let's write the docs to our DB.
-document_store.delete_all_documents()
-document_store.write_documents(classified_docs)
-```
-
-
-```python
-# check if indexed docs contain classification results
-test_doc = document_store.get_all_documents()[0]
-print(
-    f'document {test_doc.id} with content \n\n{test_doc.content}\n\nhas label {test_doc.meta["classification"]["label"]}'
-)
-```
-
-## Querying the data
-
-All we have to do to filter for one of our classes is to set a filter on "classification.label".
-
-
-```python
-# Initialize QA-Pipeline
-from haystack.pipelines import ExtractiveQAPipeline
-from haystack.nodes import FARMReader, BM25Retriever
-
-
-retriever = BM25Retriever(document_store=document_store)
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
-pipe = ExtractiveQAPipeline(reader, retriever)
-```
-
-
-```python
-## Voilà! Ask a question while filtering for "music"-only documents
-prediction = pipe.run(
-    query="What is heavy metal?",
-    params={"Retriever": {"top_k": 10, "filters": {"classification.label": ["music"]}}, "Reader": {"top_k": 5}},
-)
-```
-
-
-```python
-from haystack.utils import print_answers
-
-
-print_answers(prediction, details="high")
-```
-
-## Wrapping it up in an indexing pipeline
-
-
-```python
-from pathlib import Path
-from haystack.pipelines import Pipeline
-from haystack.nodes import TextConverter, PreProcessor, FileTypeClassifier, PDFToTextConverter, DocxToTextConverter
-
-
-file_type_classifier = FileTypeClassifier()
-text_converter = TextConverter()
-pdf_converter = PDFToTextConverter()
-docx_converter = DocxToTextConverter()
-
-indexing_pipeline_with_classification = Pipeline()
-indexing_pipeline_with_classification.add_node(
-    component=file_type_classifier, name="FileTypeClassifier", inputs=["File"]
-)
-indexing_pipeline_with_classification.add_node(
-    component=text_converter, name="TextConverter", inputs=["FileTypeClassifier.output_1"]
-)
-indexing_pipeline_with_classification.add_node(
-    component=pdf_converter, name="PdfConverter", inputs=["FileTypeClassifier.output_2"]
-)
-indexing_pipeline_with_classification.add_node(
-    component=docx_converter, name="DocxConverter", inputs=["FileTypeClassifier.output_4"]
-)
-indexing_pipeline_with_classification.add_node(
-    component=preprocessor_sliding_window,
-    name="Preprocessor",
-    inputs=["TextConverter", "PdfConverter", "DocxConverter"],
-)
-indexing_pipeline_with_classification.add_node(
-    component=doc_classifier, name="DocumentClassifier", inputs=["Preprocessor"]
-)
-indexing_pipeline_with_classification.add_node(
-    component=document_store, name="DocumentStore", inputs=["DocumentClassifier"]
-)
-# Uncomment the following to generate the pipeline image
-# indexing_pipeline_with_classification.draw("index_time_document_classifier.png")
-
-document_store.delete_documents()
-txt_files = [f for f in Path(doc_dir).iterdir() if f.suffix == ".txt"]
-pdf_files = [f for f in Path(doc_dir).iterdir() if f.suffix == ".pdf"]
-docx_files = [f for f in Path(doc_dir).iterdir() if f.suffix == ".docx"]
-indexing_pipeline_with_classification.run(file_paths=txt_files)
-indexing_pipeline_with_classification.run(file_paths=pdf_files)
-indexing_pipeline_with_classification.run(file_paths=docx_files)
-
-document_store.get_all_documents()[0]
-```
-
-
-```python
-# we can store this pipeline and use it from the REST-API
-indexing_pipeline_with_classification.save_to_yaml("indexing_pipeline_with_classification.yaml")
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
-
diff --git a/markdowns/17_Audio.md b/markdowns/17_Audio.md
deleted file mode 100644
index fda1c07e..00000000
--- a/markdowns/17_Audio.md
+++ /dev/null
@@ -1,400 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb
-toc: True
-title: "Make Your QA Pipelines Talk!"
-level: "intermediate"
-weight: 90
-description: Convert text Answers into speech.
-category: "QA"
-aliases: ['/tutorials/audio']
-download: "/downloads/17_Audio.ipynb"
-completion_time: False
-created_at: 2022-06-07
----
-    
-
-
-<img style="float: right;" src="https://upload.wikimedia.org/wikipedia/en/d/d8/Game_of_Thrones_title_card.jpg">
-
-Question answering works primarily on text, but Haystack provides some features for audio files that contain speech as well.
-
-In this tutorial, we're going to see how to use `AnswerToSpeech` to convert answers into audio files.
-
-### Prepare environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-You can double check whether the GPU runtime is enabled with the following command:
-
-
-```bash
-%%bash
-
-nvidia-smi
-```
-
-To start, install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,audio]
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-### Start an Elasticsearch server
-You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```python
-# Recommended: Start Elasticsearch using Docker via the Haystack utility function
-from haystack.utils import launch_es
-
-launch_es()
-```
-
-### Start an Elasticsearch server in Colab
-
-If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
-
-
-```bash
-%%bash
-
-wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-chown -R daemon:daemon elasticsearch-7.9.2
-
-```
-
-
-```bash
-%%bash --bg
-
-sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch
-```
-
-### Populate the document store with `SpeechDocuments`
-
-First of all, we will populate the document store with a simple indexing pipeline. See [Tutorial 1](https://colab.research.google.com/github/deepset-ai/haystack/blob/main/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb) for more details about these steps.
-
-To the basic version, we can add here a DocumentToSpeech node that also generates an audio file for each of the indexed documents. This will make possible, during querying, to access the audio version of the documents the answers were extracted from without having to generate it on the fly.
-
-**Note**: this additional step can slow down your indexing quite a lot if you are not running on GPU. Experiment with very small corpora to start.
-
-
-```python
-import os
-import time
-
-from haystack.document_stores import ElasticsearchDocumentStore
-from haystack.utils import fetch_archive_from_http, launch_es
-from pathlib import Path
-from haystack import Pipeline
-from haystack.nodes import FileTypeClassifier, TextConverter, PreProcessor, DocumentToSpeech
-
-
-# Wait 30 seconds only to be sure Elasticsearch is ready before continuing
-time.sleep(30)
-
-# Get the host where Elasticsearch is running, default to localhost
-host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
-
-document_store = ElasticsearchDocumentStore(host=host, username="", password="", index="document")
-
-# Get the documents
-documents_path = "data/tutorial17"
-s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt17.zip"
-fetch_archive_from_http(url=s3_url, output_dir=documents_path)
-
-# List all the paths
-file_paths = [p for p in Path(documents_path).glob("**/*")]
-
-# NOTE: In this example we're going to use only one text file from the wiki, as the DocumentToSpeech node is quite slow
-# on CPU machines. Comment out this line to use all documents from the dataset if you machine is powerful enough.
-file_paths = [p for p in file_paths if "Stormborn" in p.name]
-
-# Prepare some basic metadata for the files
-files_metadata = [{"name": path.name} for path in file_paths]
-
-# Here we create a basic indexing pipeline
-indexing_pipeline = Pipeline()
-
-# - Makes sure the file is a TXT file (FileTypeClassifier node)
-classifier = FileTypeClassifier()
-indexing_pipeline.add_node(classifier, name="classifier", inputs=["File"])
-
-# - Converts a file into text and performs basic cleaning (TextConverter node)
-text_converter = TextConverter(remove_numeric_tables=True)
-indexing_pipeline.add_node(text_converter, name="text_converter", inputs=["classifier.output_1"])
-
-# - Pre-processes the text by performing splits and adding metadata to the text (Preprocessor node)
-preprocessor = PreProcessor(
-    clean_whitespace=True,
-    clean_empty_lines=True,
-    split_length=100,
-    split_overlap=50,
-    split_respect_sentence_boundary=True,
-)
-indexing_pipeline.add_node(preprocessor, name="preprocessor", inputs=["text_converter"])
-
-#
-# DocumentToSpeech
-#
-# Here is where we convert all documents to be indexed into SpeechDocuments, that will hold not only
-# the text content, but also their audio version.
-#
-# Note that DocumentToSpeech implements a light caching, so if a document's audio have already
-# been generated in a previous pass in the same folder, it will reuse the existing file instead
-# of generating it again.
-doc2speech = DocumentToSpeech(
-    model_name_or_path="espnet/kan-bayashi_ljspeech_vits", generated_audio_dir=Path("./generated_audio_documents")
-)
-indexing_pipeline.add_node(doc2speech, name="doc2speech", inputs=["preprocessor"])
-
-# - Writes the resulting documents into the document store (ElasticsearchDocumentStore node from the previous cell)
-indexing_pipeline.add_node(document_store, name="document_store", inputs=["doc2speech"])
-
-# Then we run it with the documents and their metadata as input
-output = indexing_pipeline.run(file_paths=file_paths, meta=files_metadata)
-```
-
-
-```python
-from pprint import pprint
-
-# You can now check the document store and verify that documents have been enriched with a path
-# to the generated audio file
-document = next(document_store.get_all_documents_generator())
-pprint(document)
-
-# Sample output:
-#
-# <Document: {
-# 'content': "'Stormborn' received praise from critics, who considered Euron Greyjoy's raid on Yara's Iron Fleet,
-#             the assembly of Daenerys' allies at Dragonstone, and Arya's reunion with her direwolf Nymeria as
-#             highlights of the episode. In the United States, it achieved a viewership of 9.27 million in its
-#             initial broadcast.",
-# 'content_type': 'audio',
-# 'score': None,
-# 'meta': {
-#       'content_audio': './generated_audio_documents/f218707624d9c4f9487f508e4603bf5b.wav',
-#       '__initialised__': True,
-#       'type': 'generative',
-#       '_split_id': 0,
-#       'audio_format': 'wav',
-#       'sample_rate': 22050,
-#       'name': '2_Stormborn.txt'},
-#       'embedding': None,
-#       'id': '2733e698301f8f94eb70430b874177fd'
-# }>
-```
-
-### Querying
-   
-Now we will create a pipeline very similar to the basic `ExtractiveQAPipeline` of Tutorial 1,
-with the addition of a node that converts our answers into audio files! Once the answer is retrieved, we can also listen to the audio version of the document where the answer came from.
-
-
-```python
-from pathlib import Path
-from haystack import Pipeline
-from haystack.nodes import BM25Retriever, FARMReader, AnswerToSpeech
-
-retriever = BM25Retriever(document_store=document_store)
-reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2-distilled", use_gpu=True)
-answer2speech = AnswerToSpeech(
-    model_name_or_path="espnet/kan-bayashi_ljspeech_vits", generated_audio_dir=Path("./audio_answers")
-)
-
-audio_pipeline = Pipeline()
-audio_pipeline.add_node(retriever, name="Retriever", inputs=["Query"])
-audio_pipeline.add_node(reader, name="Reader", inputs=["Retriever"])
-audio_pipeline.add_node(answer2speech, name="AnswerToSpeech", inputs=["Reader"])
-```
-
-## Ask a question!
-
-
-```python
-# You can configure how many candidates the Reader and Retriever shall return
-# The higher top_k_retriever, the better (but also the slower) your answers.
-prediction = audio_pipeline.run(
-    query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
-)
-```
-
-
-```python
-# Now you can either print the object directly...
-from pprint import pprint
-
-pprint(prediction)
-
-# Sample output:
-# {
-#     'answers': [ <SpeechAnswer:
-#                       answer_audio=PosixPath('generated_audio_answers/fc704210136643b833515ba628eb4b2a.wav'),
-#                       answer="Daenerys Targaryen",
-#                       context_audio=PosixPath('generated_audio_answers/8c562ebd7e7f41e1f9208384957df173.wav'),
-#                       context='...'
-#                       type='extractive', score=0.9919578731060028,
-#                       offsets_in_document=[{'start': 608, 'end': 615}], offsets_in_context=[{'start': 72, 'end': 79}],
-#                       document_id='cc75f739897ecbf8c14657b13dda890e', meta={'name': '43_Arya_Stark.txt'}}  >,
-#                  <SpeechAnswer:
-#                       answer_audio=PosixPath('generated_audio_answers/07d6265486b22356362387c5a098ba7d.wav'),
-#                       answer="Daenerys",
-#                       context_audio=PosixPath('generated_audio_answers/3f1ca228d6c4cfb633e55f89e97de7ac.wav'),
-#                       context='...'
-#                       type='extractive', score=0.9767240881919861,
-#                       offsets_in_document=[{'start': 3687, 'end': 3801}], offsets_in_context=[{'start': 18, 'end': 132}],
-#                       document_id='9acf17ec9083c4022f69eb4a37187080', meta={'name': '43_Arya_Stark.txt'}}>,
-#                  ...
-#                ]
-#     'documents': [ <SpeechDocument:
-#                        content_type='text', score=0.8034909798951382, meta={'name': '43_Arya_Stark.txt'}, embedding=None, id=d1f36ec7170e4c46cde65787fe125dfe',
-#                        content_audio=PosixPath('generated_audio_documents/07d6265486b22356362387c5a098ba7d.wav'),
-#                        content='The title of the episode refers to both Daenerys Targaryen, who was born during a  ...'>,
-#                    <SpeechDocument:
-#                        content_type='text', score=0.8002150354529785, meta={'name': '191_Gendry.txt'}, embedding=None, id='dd4e070a22896afa81748d6510006d2',
-#                        content_audio=PosixPath('generated_audio_documents/07d6265486b22356362387c5a098ba7d.wav'),
-#                        content='"Stormborn" received praise from critics, who considered Euron Greyjoy's raid on ...'>,
-#                    ...
-#                  ],
-#     'no_ans_gap':  11.688868522644043,
-#     'node_id': 'Reader',
-#     'params': {'Reader': {'top_k': 5}, 'Retriever': {'top_k': 5}},
-#     'query': 'Who was born during a storm?',
-#     'root_node': 'Query'
-# }
-```
-
-
-```python
-from haystack.utils import print_answers
-
-# ...or use a util to simplify the output
-# Change `minimum` to `medium` or `all` to raise the level of detail
-print_answers(prediction, details="minimum")
-
-# Sample output:
-#
-# Query: Who was born during a storm
-# Answers:
-# [   {   'answer_audio': PosixPath('generated_audio_answers/07d6265486b22356362387c5a098ba7d.wav'),
-#         'answer': 'Daenerys Targaryen',
-#         'context_transcript': PosixPath('generated_audio_answers/3f1ca228d6c4cfb633e55f89e97de7ac.wav'),
-#         'context': ' refers to both Daenerys Targaryen, who was born during a terrible storm, and '},
-#    {   'answer_audio': PosixPath('generated_audio_answers/83c3a02141cac4caffe0718cfd6c405c.wav'),
-#        'answer': 'Daenerys',
-#        'context_audio': PosixPath('generated_audio_answers/8c562ebd7e7f41e1f9208384957df173.wav'),
-#        'context': 'The title of the episode refers to both Daenerys Targaryen, who was born during a terrible storm'},
-#    ...
-```
-
-
-```python
-# The document the first answer was extracted from
-original_document = [doc for doc in prediction["documents"] if doc.id == prediction["answers"][0].document_id][0]
-pprint(original_document)
-
-# Sample output
-#
-# <Document: {
-#   'content': '"'''Stormborn'''" is the second episode of the seventh season of HBO's fantasy television
-#               series ''Game of Thrones'', and the 62nd overall. The episode was written by Bryan Cogman,
-#               and directed by Mark Mylod. The title of the episode refers to both Daenerys Targaryen,
-#               who was born during a terrible storm, and Euron Greyjoy, who declares himself to be "the storm".',
-#   'content_type': 'audio',
-#   'score': 0.6269117688771539,
-#   'embedding': None,
-#   'id': '9352f650b36f93ab99684fd4746af5c1'
-#   'meta': {
-#       'content_audio': '/home/sara/work/haystack/generated_audio_documents/2c9223d47801b0918f2db2ad778c3d5a.wav',
-#       'type': 'generative',
-#       '_split_id': 19,
-#       'audio_format': 'wav',
-#       'sample_rate': 22050,
-#       'name': '2_Stormborn.txt'}
-# }>
-```
-
-### Hear them out!
-
-
-```python
-from IPython.display import display, Audio
-import soundfile as sf
-```
-
-
-```python
-# The first answer in isolation
-
-print("Answer: ", prediction["answers"][0].answer)
-
-speech, _ = sf.read(prediction["answers"][0].answer_audio)
-display(Audio(speech, rate=24000))
-```
-
-
-```python
-# The context of the first answer
-
-print("Context: ", prediction["answers"][0].context)
-
-speech, _ = sf.read(prediction["answers"][0].context_audio)
-display(Audio(speech, rate=24000))
-```
-
-
-```python
-# The document the first answer was extracted from
-
-document = [doc for doc in prediction["documents"] if doc.id == prediction["answers"][0].document_id][0]
-
-print("Document: ", document.content)
-
-speech, _ = sf.read(document.meta["content_audio"])
-display(Audio(speech, rate=24000))
-```
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
-
diff --git a/markdowns/18_GPL.md b/markdowns/18_GPL.md
deleted file mode 100644
index 66a97a65..00000000
--- a/markdowns/18_GPL.md
+++ /dev/null
@@ -1,303 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb
-toc: True
-title: "Generative Pseudo Labeling for Domain Adaptation"
-level: "advanced"
-weight: 140
-description: Use a Retriever and a query generator to perform unsupervised domain adaptation.
-category: "QA"
-aliases: ['/tutorials/gpl']
-download: "/downloads/18_GPL.ipynb"
-completion_time: False
-created_at: 2022-06-07
----
-    
-
-
-*Note: Adapted to Haystack from Nils Reimers' original [notebook](https://colab.research.google.com/gist/jamescalam/d2c888775c87f9882bb7c379a96adbc8/gpl-domain-adaptation.ipynb#scrollTo=183ff7ab)
-
-The NLP models we use every day were trained on a corpus of data that reflects the world from the past. In the meantime, we've experienced world-changing events, like the COVID pandemics, and we'd like our models to know about them. Training a model from scratch is tedious work but what if we could just update the models with new data? Generative Pseudo Labeling comes to the rescue.
-
-The example below shows you how to use GPL to fine-tune a model so that it can answer the query: "How is COVID-19 transmitted?".
-
-We're using TAS-B: A DistilBERT model that achieves state-of-the-art performance on MS MARCO (500k queries from Bing Search Engine). Both DistilBERT and MS MARCO were created with data from 2018 and before, hence, it lacks the knowledge of any COVID-related information.
-
-For this example, we're using just four documents. When you ask the model ""How is COVID-19 transmitted?", here are the answers that you get (dot-score and document):
-- 94.84	Ebola is transmitted via direct contact with blood
-- 92.87	HIV is transmitted via sex or sharing needles
-- 92.31	Corona is transmitted via the air
-- 91.54	Polio is transmitted via contaminated water or food
-
-
-You can see that the correct document is only third, outranked by Ebola and HIV information. Let's see how we can make this better.
-
-## Efficient Domain Adaptation with GPL
-This notebook demonstrates [Generative Pseudo Labeling (GPL)](https://arxiv.org/abs/2112.07577), an efficient approach to adapt existing dense retrieval models to new domains and data.
-
-We get a collection of 10k scientific papers on COVID-19 and then fine-tune the model within 15-60 minutes (depending on your GPU) so that it includes the COVID knowledge.
-
-If we search again with the updated model, we get the search results we would expect:
-- Query: How is COVID-19 transmitted
-- 97.70	Corona is transmitted via the air
-- 96.71	Ebola is transmitted via direct contact with blood
-- 95.14	Polio is transmitted via contaminated water or food
-- 94.13	HIV is transmitted via sex or sharing needles
-
-### Prepare the Environment
-
-#### Colab: Enable the GPU runtime
-Make sure you enable the GPU runtime to experience decent speed in this tutorial.
-**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**
-
-<img src="https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg">
-
-
-
-```python
-!nvidia-smi
-```
-
-
-```python
-!pip install -q datasets
-!pip install "faiss-gpu>=1.6.3,<2"
-!pip install -q git+https://github.com/deepset-ai/haystack.git
-```
-
-## Logging
-
-We configure how logging messages should be displayed and which log level should be used before importing Haystack.
-Example log message:
-INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt
-Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
-
-
-```python
-import logging
-
-logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
-logging.getLogger("haystack").setLevel(logging.INFO)
-```
-
-
-```python
-from sentence_transformers import SentenceTransformer, util
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-from datasets import load_dataset
-```
-
-
-```python
-# We load the TAS-B model, a state-of-the-art model trained on MS MARCO
-max_seq_length = 200
-model_name = "msmarco-distilbert-base-tas-b"
-
-org_model = SentenceTransformer(model_name)
-org_model.max_seq_length = max_seq_length
-```
-
-
-```python
-# We define a simple query and some documents how diseases are transmitted
-# As TAS-B was trained on rather out-dated data (2018 and older), it has now idea about COVID-19
-# So in the below example, it fails to recognize the relationship between COVID-19 and Corona
-
-
-def show_examples(model):
-    query = "How is COVID-19 transmitted"
-    docs = [
-        "Corona is transmitted via the air",
-        "Ebola is transmitted via direct contact with blood",
-        "HIV is transmitted via sex or sharing needles",
-        "Polio is transmitted via contaminated water or food",
-    ]
-
-    query_emb = model.encode(query)
-    docs_emb = model.encode(docs)
-    scores = util.dot_score(query_emb, docs_emb)[0]
-    doc_scores = sorted(zip(docs, scores), key=lambda x: x[1], reverse=True)
-
-    print("Query:", query)
-    for doc, score in doc_scores:
-        # print(doc, score)
-        print(f"{score:0.02f}\t{doc}")
-
-
-print("Original Model")
-show_examples(org_model)
-```
-
-# Get Some Data on COVID-19
-We select 10k scientific publications (title + abstract) that are connected to COVID-19. As a dataset, we use [TREC-COVID-19](https://huggingface.co/datasets/nreimers/trec-covid).
-
-
-```python
-dataset = load_dataset("nreimers/trec-covid", split="train")
-num_documents = 10000
-corpus = []
-for row in dataset:
-    if len(row["title"]) > 20 and len(row["text"]) > 100:
-        text = row["title"] + " " + row["text"]
-
-        text_lower = text.lower()
-
-        # The dataset also contains many papers on other diseases. To make the training in this demo
-        # more efficient, we focus on papers that talk about COVID.
-        if "covid" in text_lower or "corona" in text_lower or "sars-cov-2" in text_lower:
-            corpus.append(text)
-
-        if len(corpus) >= num_documents:
-            break
-
-print("Len Corpus:", len(corpus))
-```
-
-# Initialize Haystack Retriever and DocumentStore
-
-Let's add corpus documents to `FAISSDocumentStore` and update corpus embeddings via `EmbeddingRetriever`
-
-
-```python
-from haystack.nodes.retriever import EmbeddingRetriever
-from haystack.document_stores import FAISSDocumentStore
-
-document_store = FAISSDocumentStore(faiss_index_factory_str="Flat", similarity="cosine")
-document_store.write_documents([{"content": t} for t in corpus])
-
-
-retriever = EmbeddingRetriever(
-    document_store=document_store,
-    embedding_model="sentence-transformers/msmarco-distilbert-base-tas-b",
-    model_format="sentence_transformers",
-    max_seq_len=max_seq_length,
-    progress_bar=False,
-)
-document_store.update_embeddings(retriever)
-```
-
-## (Optional) Download Pre-Generated Questions or Generate Them Outside of Haystack
-
-The first step of the GPL algorithm requires us to generate questions for a given text passage. Even though our pre-COVID trained model hasn't seen any COVID-related content, it can still produce sensible queries by copying words from the input text. As generating questions from 10k documents is a bit slow (depending on the GPU used), we'll download question/document pairs directly from the Hugging Face hub.
-
-
-
-```python
-from tqdm.auto import tqdm
-
-query_doc_pairs = []
-
-load_queries_from_hub = True
-
-# Generation of the queries is quite slow in Colab due to the old GPU and the limited CPU
-# I pre-computed the queries and uploaded these to the HF dataset hub. Here we just download them
-if load_queries_from_hub:
-    generated_queries = load_dataset("nreimers/trec-covid-generated-queries", split="train")
-    for row in generated_queries:
-        query_doc_pairs.append({"question": row["query"], "document": row["doc"]})
-else:
-    # Load doc2query model
-    t5_name = "doc2query/msmarco-t5-base-v1"
-    t5_tokenizer = AutoTokenizer.from_pretrained(t5_name)
-    t5_model = AutoModelForSeq2SeqLM.from_pretrained(t5_name).cuda()
-
-    batch_size = 32
-    queries_per_doc = 3
-
-    for start_idx in tqdm(range(0, len(corpus), batch_size)):
-        corpus_batch = corpus[start_idx : start_idx + batch_size]
-        enc_inp = t5_tokenizer(
-            corpus_batch, max_length=max_seq_length, truncation=True, padding=True, return_tensors="pt"
-        )
-
-        outputs = t5_model.generate(
-            input_ids=enc_inp["input_ids"].cuda(),
-            attention_mask=enc_inp["attention_mask"].cuda(),
-            max_length=64,
-            do_sample=True,
-            top_p=0.95,
-            num_return_sequences=queries_per_doc,
-        )
-
-        decoded_output = t5_tokenizer.batch_decode(outputs, skip_special_tokens=True)
-
-        for idx, query in enumerate(decoded_output):
-            corpus_id = int(idx / queries_per_doc)
-            query_doc_pairs.append({"question": query, "document": corpus_batch[corpus_id]})
-
-
-print("Generated queries:", len(query_doc_pairs))
-```
-
-# Use PseudoLabelGenerator to Genenerate Retriever Adaptation Training Data
-
-PseudoLabelGenerator run will execute all three steps of the GPL [algorithm](https://github.com/UKPLab/gpl#how-does-gpl-work):
- 1. Question generation - optional step
- 2. Negative mining
- 3. Pseudo labeling (margin scoring)
-
-The output of the `PseudoLabelGenerator` is the training data we'll use to adapt our `EmbeddingRetriever`.
-
-
-
-```python
-from haystack.nodes.question_generator import QuestionGenerator
-from haystack.nodes.label_generator import PseudoLabelGenerator
-
-use_question_generator = False
-
-
-if use_question_generator:
-    questions_producer = QuestionGenerator(
-        model_name_or_path="doc2query/msmarco-t5-base-v1",
-        max_length=64,
-        split_length=128,
-        batch_size=32,
-        num_queries_per_doc=3,
-    )
-
-else:
-    questions_producer = query_doc_pairs
-
-# We can use either QuestionGenerator or already generated questions in PseudoLabelGenerator
-psg = PseudoLabelGenerator(questions_producer, retriever, max_questions_per_document=10, batch_size=32, top_k=10)
-output, pipe_id = psg.run(documents=document_store.get_all_documents())
-```
-
-# Update the Retriever
-
-Now that we have the generated training data produced by `PseudoLabelGenerator`, we'll update the `EmbeddingRetriever`. Let's take a peek at the training data.
-
-
-```python
-output["gpl_labels"][0]
-```
-
-
-```python
-len(output["gpl_labels"])
-```
-
-
-```python
-retriever.train(output["gpl_labels"])
-```
-
-## Verify that EmbeddingRetriever Is Adapted and Save It For Future Use
-
-Let's repeat our query to see if the Retriever learned about COVID and can now rank it as #1 among the answers.
-
-
-```python
-print("Original Model")
-show_examples(org_model)
-
-print("\n\nAdapted Model")
-show_examples(retriever.embedding_encoder.embedding_model)
-```
-
-
-```python
-retriever.save("adapted_retriever")
-```
diff --git a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md b/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
deleted file mode 100644
index 1466c1cf..00000000
--- a/markdowns/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.md
+++ /dev/null
@@ -1,210 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
-toc: True
-title: "Text-To-Image Search Pipeline with Multimodal Retriever"
-level: "intermediate"
-weight: 95
-description: Use a MultiModalRetriever to build a cross-modal search pipeline.
-category: "QA"
-aliases: ['/tutorials/multimodal']
-download: "/downloads/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
-completion_time: 20 min
-created_at: 2022-07-11
----
-    
-
-
-**Level**: Intermediate
-
-**Time to complete**: 20 minutes
-
-**Prerequisites**: This tutorial assumes basic knowledge of Haystack Retrievers and Pipelines. If you want to learn about them, have a look at our tutorials on [Build Your First QA System](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb) and [Fine-Tuning a Model on Your Own Data](https://github.com/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb).
-
-Prepare the Colab environment (see links below).
-
-**Nodes Used**: InMemoryDocumentStore, MultiModalRetriever
-
-**Goal**: After completing this tutorial, you will have built a search system that retrieves images as answers to a text query.
-
-**Description**: In this tutorial, you'll download a set of images that you'll then turn into embeddings using a transformers model, OpenAI CLIP. You'll then use the same model to embed the text query. Finally, you'll perform a nearest neighbor search to retrieve the images relevant to the text query.
-
-Let's build a text-to-image search pipeline using a small animal dataset!
-
-## Preparing the Colab Environment
-
-- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)
-- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)
-
-## Installing Haystack
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install farm-haystack[colab]
-```
-
-## Initializing the DocumentStore
-
-A DocumentStore stores references to the images that Haystack will compare with your query. But before it can do that, you need to initialize it. In this tutorial, you'll use the InMemoryDocumentStore.
-
-If you want to learn more, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store).
-
-
-```python
-from haystack.document_stores import InMemoryDocumentStore
-
-# Here Here we initialize the DocumentStore to store 512 dim image embeddings
-# obtained using OpenAI CLIP model
-document_store = InMemoryDocumentStore(embedding_dim=512)
-```
-
-## Downloading Data
-
-Download 18 sample images of different animals and store it. You can find them in data/tutorial19/spirit-animals/ as a set of .jpg files.
-
-
-```python
-from haystack.utils import fetch_archive_from_http
-
-doc_dir = "data/tutorial19"
-
-fetch_archive_from_http(
-    url="https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/spirit-animals.zip",
-    output_dir=doc_dir,
-)
-```
-
-Add the images you just downloaded into Haystack Document objects and write them into the DocumentStore.
-
-
-```python
-import os
-
-from haystack import Document
-
-images = [
-    Document(content=f"./{doc_dir}/spirit-animals/{filename}", content_type="image")
-    for filename in os.listdir(f"./{doc_dir}/spirit-animals/")
-]
-
-document_store.write_documents(images)
-```
-
-You have successfully stored your images in the DocumentStore.
-
-
-
-## Initializing the Retriever
-
-Retrievers sift through all the images and return only those that are relevant to the query. To run a search on images, you'll use the MultiModalRetriever with the [OpenAI CLIP model](https://github.com/openai/CLIP/blob/main/model-card.md). 
-
-For more details on supported modalities, see [MultiModalRetriever](https://docs.haystack.deepset.ai/docs/retriever#multimodal-retrieval).
-
-Before adding the Retriever to your pipeline, let's configure its parameters
-
-
-```python
-from haystack.nodes.retriever.multimodal import MultiModalRetriever
-
-retriever_text_to_image = MultiModalRetriever(
-    document_store=document_store,
-    query_embedding_model="sentence-transformers/clip-ViT-B-32",
-    query_type="text",
-    document_embedding_models={"image": "sentence-transformers/clip-ViT-B-32"},
-)
-
-# Now let's turn our images into embeddings and store them in the DocumentStore.
-document_store.update_embeddings(retriever=retriever_text_to_image)
-```
-
- Your retriever is now ready for search!
-
-## Creating the MultiModal Search Pipeline
-
-We are populating a pipeline with a MultiModalRetriever node. This search pipeline queries the image database with text and returns the most relevant images.
-
-
-```python
-from haystack import Pipeline
-
-pipeline = Pipeline()
-pipeline.add_node(component=retriever_text_to_image, name="retriever_text_to_image", inputs=["Query"])
-```
-
-Now, you have a pipeline that uses the MultiModalRetriever and takes a text query as input. Let's try it out.
-
-## Searching Through the Images
-
-Use the pipeline `run()` method to query the images in the DocumentStore. The query argument is where you type your text query. Additionally, you can set the number of images you want the MultiModalRetriever to return using the `top-k` parameter. To learn more about setting arguments, see [Pipeline Arguments](https://docs.haystack.deepset.ai/docs/pipelines#arguments).
-
-
-```python
-results = pipeline.run(query="Animal that lives in the water", params={"retriever_text_to_image": {"top_k": 3}})
-
-# Sort the results based on the scores
-results = sorted(results["documents"], key=lambda d: d.score, reverse=True)
-
-for doc in results:
-    print(doc.score, doc.content)
-```
-
-Here are some more query strings you could try out:
-
-1.   King of the Jungle
-2.   Fastest animal
-3.   Bird that can see clearly even in the dark
-
-
-
-You can also easily vizualize these images together with their score using this code:
-
-
-
-
-
-```python
-from io import BytesIO
-from PIL import Image, ImageDraw, ImageOps
-from IPython.display import display, Image as IPImage
-
-
-def display_img_array(ima, score):
-    im = Image.open(ima)
-    img_with_border = ImageOps.expand(im, border=20, fill="white")
-
-    # Add Text to an image
-    img = ImageDraw.Draw(img_with_border)
-    img.text((20, 0), f"Score: {score},    Path: {ima}", fill=(0, 0, 0))
-
-    bio = BytesIO()
-    img_with_border.save(bio, format="png")
-    display(IPImage(bio.getvalue(), format="png"))
-
-
-images_array = [doc.content for doc in results]
-scores = [doc.score for doc in results]
-for ima, score in zip(images_array, scores):
-    display_img_array(ima, score)
-```
-
-Congratulations! You've created a search system that returns images of animals in answer to a text query.
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
diff --git a/markdowns/20_Using_Haystack_with_REST_API.md b/markdowns/20_Using_Haystack_with_REST_API.md
deleted file mode 100644
index 7d53f980..00000000
--- a/markdowns/20_Using_Haystack_with_REST_API.md
+++ /dev/null
@@ -1,315 +0,0 @@
----
-layout: tutorial
-featured: False
-colab: False
-toc: True
-title: "Using Haystack with REST API"
-level: "advanced"
-weight: 115
-description: Create a production-ready pipeline and interact with Haystack REST API.
-category: "QA"
-aliases: ['/tutorials/using-haystack-with-rest-api']
-download: "/downloads/20_Using_Haystack_with_REST_API.ipynb"
-completion_time: 30 min
-created_at: 2023-01-11
----
-    
-
-
-- **Level**: Advanced
-- **Time to complete**: 30 minutes
-- **Prerequisites**: Basic understanding of Docker and basic knowledge of Haystack pipelines. 
-- **Nodes Used**: `ElasticsearchDocumentStore`, `EmbeddingRetriever`
-- **Goal**: After you complete this tutorial, you will have learned how to interact with Haystack through REST API.
-
-## Overview
-
-Learn how you can interact with Haystack through REST API. This tutorial introduces you to all the concepts needed to build an end-to-end document search application.  
-
-With Haystack, you can apply the latest NLP technology to your own data and create production-ready applications. Building an end-to-end NLP application requires the combination of multiple concepts:
-* **DocumentStore** is the component in Haystack responsible for loading and storing text data in the form of [Documents](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document). In this tutorial, the DocumentStore uses Elasticsearch behind the scene.
-* **Haystack pipelines** convert files into Documents, index them to the DocumentStore, and run NLP tasks such as question answering and document search.
-* **REST API**, as a concept, makes it possible for applications to interact with each other by handling their queries and returning responses. There is `rest_api` application within Haystack that exposes Haystack's functionalities through a RESTful API.
-* **Docker** simplifies the environment setup needed to run Elasticsearch and Haystack API.
-
-
-
-## Preparing the Environment
-
-1. Install [Docker Compose](https://docs.docker.com/compose/) and launch Docker.
-If you installed Docker Desktop, just start the application. Run `docker info` to see if Docker is up and running:
-
-   ```bash
-   docker info
-   ```
-
-2. Download the *docker-compose.yml* file. Haystack provides a *docker-compose.yml* file that defines services for Haystack API and Elasticsearch. 
-    1. Create a new folder called *doc-search* in a directory where you want to keep all tutorial related files.
-    2. Save the latest [*docker-compose.yml*](https://github.com/deepset-ai/haystack/blob/main/docker-compose.yml) file from GitHub into the folder. To save the *docker-compose.yml* file into the directory directly, run:
-
-         ```bash
-         curl --output docker-compose.yml https://raw.githubusercontent.com/deepset-ai/haystack/main/docker-compose.yml
-         ```
-
-    Here's what the */doc-search* folder should look like:
-    ```
-    /doc-search
-    └── docker-compose.yml
-    ```
-
-Now that your environment's ready, you can start creating your indexing and query pipelines.
-
-## Creating the Pipeline YAML File
-
-You can define components and pipelines using YAML code that Haystack translates into Python objects. In a pipeline YAML file, the `components` section lists all pipeline nodes and the `pipelines` section defines how these nodes are connected to each other. Let's start with defining two different pipelines, one to index your documents and another one to query them. We'll use one YAML file to define both pipelines.
-
-1. Create a document search pipeline. This will be your query pipeline:
-   1. In the newly created *doc-search* folder, create a file named *document-search.haystack-pipeline.yml*. The *docker-compose.yml* file and the new pipeline YAML file should be on the same level in the directory:
-
-      ```
-      /doc-search
-      ├── docker-compose.yml
-      └── document-search.haystack-pipeline.yml
-      ```
-
-   2. Provide the path to *document-search.haystack-pipeline.yml* as the `volume` source value in the *docker-compose.yml* file. The path must be relative to *docker-compose.yml*. As both files are in the same directory, the source value will be `./`. 
-
-      ```yaml
-      haystack-api:
-        ...
-        volumes:
-          - ./:/opt/pipelines
-      ```
-
-   3. Update the `PIPELINE_YAML_PATH` variable in *docker-compose.yml* with the name of the pipeline YAML file. The `PIPELINE_YAML_PATH` variable tells `rest_api` which YAML file to run. 
-
-      ```yaml
-      environment:
-        ...
-        - PIPELINE_YAML_PATH=/opt/pipelines/document-search.haystack-pipeline.yml
-        ...
-      ```
-   4. Define the pipeline nodes in the `components` section of the file. A document search pipeline requires a DocumentStore and a Retriever. Our pipeline will use `ElasticsearchDocumentStore` and `EmbeddingRetriever`:
-
-      ```yaml
-      components:
-        - name: DocumentStore # How you want to call this node here
-          type: ElasticsearchDocumentStore # This is the Haystack node class
-          params: # The node parameters
-            embedding_dim: 384 # This parameter is required for the embedding_model
-        - name: Retriever
-          type: EmbeddingRetriever
-          params:
-            document_store: DocumentStore
-            top_k: 10
-            embedding_model: sentence-transformers/all-MiniLM-L6-v2
-      ```
-
-   5. Create a query pipeline in the `pipelines` section. Here, `name` refers to the name of the pipeline, and `nodes` defines the order of the nodes in the pipeline: 
-
-      ```yaml
-      pipelines:
-        - name: query 
-          nodes:
-            - name: Retriever
-              inputs: [Query]
-      ```
-
-2. In the same YAML file, create an indexing pipeline. This pipeline will index your documents to Elasticsearch through `rest_api`. 
-   1. Define `FileTypeClassifier`, `TextConverter`, and `PreProcessor` nodes for the pipeline:
-
-      ```yaml
-      components:
-        ...
-        - name: FileTypeClassifier
-          type: FileTypeClassifier
-        - name: TextFileConverter
-          type: TextConverter
-        - name: Preprocessor
-          type: PreProcessor
-          params: # These parameters define how you want to split your documents
-            split_by: word
-            split_length: 250
-            split_overlap: 30 
-            split_respect_sentence_boundary: True 
-      ```
-
-   2. In the `pipelines` section of the YAML file, create a new pipeline called `indexing`. In this pipeline, indicate how the nodes you just defined are connected to each other, Retriever, and DocumentStore. This indexing pipeline supports *.TXT* files and preprocesses them before loading to Elasticsearch.
-
-      ```yaml
-      pipelines:
-        ...
-        - name: indexing
-          nodes:
-            - name: FileTypeClassifier
-              inputs: [File]
-            - name: TextFileConverter
-              inputs: [FileTypeClassifier.output_1]
-            - name: Preprocessor
-              inputs: [TextFileConverter]
-            - name: Retriever
-              inputs: [Preprocessor]
-            - name: DocumentStore
-              inputs: [Retriever]
-      ```
-
-3. After creating query and indexing pipelines, add `version: 1.12.1` to the top of the file. This is the Haystack version that comes with the Docker image in the *docker-compose.yml*. Now, the pipeline YAML is ready.
-
-```yaml
-version: 1.12.1
-
-components:
-  - name: DocumentStore
-    type: ElasticsearchDocumentStore
-    params:
-      embedding_dim: 384
-  - name: Retriever
-    type: EmbeddingRetriever
-    params:
-      document_store: DocumentStore
-      top_k: 10 
-      embedding_model: sentence-transformers/all-MiniLM-L6-v2
-  - name: FileTypeClassifier
-    type: FileTypeClassifier
-  - name: TextFileConverter
-    type: TextConverter
-  - name: Preprocessor
-    type: PreProcessor
-    params:
-      split_by: word
-      split_length: 250
-      split_overlap: 30 
-      split_respect_sentence_boundary: True
-
-pipelines:
-  - name: query 
-    nodes:
-      - name: Retriever
-        inputs: [Query]
-  - name: indexing
-    nodes:
-      - name: FileTypeClassifier
-        inputs: [File]
-      - name: TextFileConverter
-        inputs: [FileTypeClassifier.output_1]
-      - name: Preprocessor
-        inputs: [TextFileConverter]
-      - name: Retriever
-        inputs: [Preprocessor]
-      - name: DocumentStore
-        inputs: [Retriever]
-```
-
-Feel free to play with the pipeline setup later on. Add or remove some nodes, change the parameters, or add new ones. For more options for nodes and parameters, check out [Haystack API Reference](https://docs.haystack.deepset.ai/reference/answer-generator-api).
-
-## Launching Haystack API and Elasticsearch
-
-Pipelines are ready. Now it's time to start Elasticsearch and Haystack API.
-1. Run `docker-compose up` to start the `elasticsearch` and `haystack-api` containers. This command installs all the necessary packages, sets up the environment, and launches both Elasticsearch and Haystack API. Launching may take 2-3 minutes. 
-
-   ```bash
-   docker-compose up
-   ```
-
-2. Test if everything is OK with the Haystack API by sending a cURL request to the `/initialized` endpoint. If everything works fine, you will get `true` as a response.
-
-   ```bash
-   curl --request GET http://127.0.0.1:8000/initialized
-   ```
-
-
-Both containers are initialized. Time to fill your DocumentStore with files.  
-
-## Indexing Files to Elasticsearch
-
-Right now, your Elasticsearch instance is empty. Haystack API provides a `/file-upload` endpoint to upload files to Elasticsearch. This endpoint uses the indexing pipeline you defined in the pipeline YAML. After indexing files to Elasticsearch, you can perform document search.
-
-1. Download the [example files](https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/article_txt_countries_and_capitals.zip) to the *doc-search* folder. The .zip file contains text files about countries and capitals crawled from [Wikipedia](https://en.wikipedia.org/wiki/Category:Lists_of_countries_by_continent).
-
-   ```
-   /doc-search
-   ├── docker-compose.yml
-   ├── document-search.haystack-pipeline.yml
-   └── /article_txt_countries_and_capitals
-       ├── 0_Minsk.txt
-       └── ...
-   ```
-
-2. Index files to Elasticsearch. You can send cURL requests to the `/file-upload` endpoint to upload files to the Elasticsearch instance. If the file is successfully uploaded, you will get `null` as a response.
-
-   ```bash
-   curl --request POST \
-        --url http://127.0.0.1:8000/file-upload \
-        --header 'accept: application/json' \
-        --header 'content-type: multipart/form-data' \
-        --form files=@article_txt_countries_and_capitals/0_Minsk.txt \
-        --form meta=null
-   ```
-
-   This method is not the best one if you have multiple files to upload. That's because you need to replace file names in the request by hand. Instead, you can run a command that takes all *.TXT* files in the *article_txt_countries_and_capitals* folder and sends a POST request to index each file:   
-
-   ```bash
-   find ./article_txt_countries_and_capitals -name '*.txt' -exec \
-        curl --request POST \
-             --url http://127.0.0.1:8000/file-upload \
-             --header 'accept: application/json' \
-             --header 'content-type: multipart/form-data' \
-             --form files="@{}" \
-             --form meta=null \;
-   ```
-
-## Querying Your Pipeline
-
-That's it, the application is ready! Send another POST request to retrieve documents about _"climate in Scandinavia"_: 
-
-```bash
-curl --request POST \
-     --url http://127.0.0.1:8000/query \
-     --header 'accept: application/json' \
-     --header 'content-type: application/json' \
-     --data '{
-     "query": "climate in Scandinavia"
-     }'
-```
-
-As a response, you will get a `QueryResponse` object consisting of `query`, `answers`, and `documents`. Documents related to your query will be under the `documents` attribute of the object.
-
-```python
-{
-  "query": "climate in Scandinavia",
-  "answers": [],
-  "documents": [
-    {
-      "id": "24904f783ea4b90a47c33434a3e9df7a",
-      "content": "Because of Sweden's high latitude, the length of daylight varies greatly. North of the Arctic Circle, the sun never sets for part of each summer, and it never rises for part of each winter. In the capital, Stockholm, daylight lasts for more than 18 hours in late June but only around 6 hours in late December. Sweden receives between 1,100 and 1,900 hours of sunshine annually...",
-      "content_type": "text",
-      "meta": {
-        "_split_id": 33,
-        "name": "43_Sweden.txt"
-      },
-      "score": 0.5017639926813274
-    },
-    ...
-  ]
-}
-```  
-
-Congratulations! You have created a proper search system that runs using Haystack REST API.
-
-## About us
-
-This [Haystack](https://github.com/deepset-ai/haystack/) tutorial was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-- [FARM](https://github.com/deepset-ai/FARM)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
-
diff --git a/markdowns/21_Customizing_PromptNode.md b/markdowns/21_Customizing_PromptNode.md
deleted file mode 100644
index 2cbdd15e..00000000
--- a/markdowns/21_Customizing_PromptNode.md
+++ /dev/null
@@ -1,230 +0,0 @@
----
-layout: tutorial
-featured: True
-colab: https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb
-toc: True
-title: "Customizing PromptNode for NLP Tasks"
-level: "intermediate"
-weight: 57
-description: Use PromptNode and PromptTemplate for your custom NLP tasks
-category: "QA"
-aliases: ['/tutorials/customizing-promptnode']
-download: "/downloads/21_Customizing_PromptNode.ipynb"
-completion_time: False
-created_at: 2023-02-16
----
-    
-
-
-- **Level**: Intermediate
-- **Time to complete**: 20 minutes
-- **Nodes Used**: `PromptNode`, `PromptTemplate`
-- **Goal**: After completing this tutorial, you will have learned the basics of using PromptNode and PromptTemplates and you'll have added titles to articles from The Guardian and categorized them. 
-
-## Overview
-
-Use large language models (LLMs) through PromptNode and PromptTemplate to summarize and categorize your documents, and find a suitable title for them. In this tutorial, we'll use news from [The Guardian](https://www.theguardian.com/international) as documents, but you can replace them with any text you want.  
-
-This tutorial introduces you to the basics of LLMs and PromptNode, showcases the pre-defined "summarization" template, and explains how to use PromptTemplate to generate titles for documents and categorize them with custom prompts.
-
-## Preparing the Colab Environment
-
-- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)
-- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)
-
-## Installing Haystack
-
-To start, let's install the latest release of Haystack with `pip`:
-
-
-```bash
-%%bash
-
-pip install --upgrade pip
-pip install farm-haystack[colab]
-```
-
-## Trying Out PromptNode
-
-The PromptNode is the central abstraction in Haystack's large language model (LLM) support. It uses [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) model by default, but you can replace the default model with a flan-t5 model of a different size such as `google/flan-t5-large` or a model by OpenAI such as `text-davinci-003`.
-
-[Large language models](https://docs.haystack.deepset.ai/docs/language_models#large-language-models-llms) are huge models trained on enormous amounts of data. That’s why these models have general knowledge of the world, so you can ask them anything and they will be able to answer.
-
-As a warm-up, let's initialize PromptNode and see what it can do when run stand-alone: 
-
-1. Initialize a PromptNode instance with [`google/flan-t5-large`](https://huggingface.co/google/flan-t5-large):
-
-
-```python
-from haystack.nodes import PromptNode
-
-prompt_node = PromptNode(model_name_or_path="google/flan-t5-large")
-```
-
-> Note: To use PromptNode with an OpenAI model, change the model name and provide an `api_key`: 
-> ```python
-> prompt_node = PromptNode(model_name_or_path="text-davinci-003", api_key=<YOUR_API_KEY>)
-> ```
-
-2. Ask any general question that comes to your mind, for example:
-
-
-```python
-prompt_node("What is the capital of Germany?")
-```
-
-
-```python
-prompt_node("What is the highest mountain?")
-```
-
-As `google/flan-t5-large` was trained on school math problems dataset named [GSM8K](https://huggingface.co/datasets/gsm8k) you can also ask some basic math questions:
-
-
-```python
-prompt_node("If Bob is 20 and Sara is 11, who is older?")
-```
-
-Now that you've initialized PromptNode and saw how it works, let's see how we can use it for more advanced tasks.
-
-## Summarizing Documents with PromptNode
-
-PromptNode comes with out-of-the-box prompt templates that can perform multiple tasks, such as summarization, question answering, question generation, and more. To use a templates, just provide its name to the PromptNode. 
-
-For this task, we'll use the summarization template and news from The Guardian. Let's see how to do it.
-
-
-1. Define news to use as `documents` for the PromptNode. We'll use these documents throughout the whole tutorial.
-
-
-```python
-from haystack.schema import Document
-
-# https://www.theguardian.com/business/2023/feb/12/inflation-may-have-peaked-but-the-cost-of-living-pain-is-far-from-over
-news_economics = Document(
-    """At long last, Britain’s annual inflation rate is on the way down. After hitting the highest level since the 1980s, heaping pressure on millions of households as living costs soared, official figures this week could bring some rare good news.
-City economists expect UK inflation to have cooled for a third month running in January – the exact number is announced on Wednesday – helped by falling petrol prices and a broader decline in the global price of oil and gas in recent months. The hope now is for a sustained decline in the months ahead, continuing a steady drop from the peak of 11.1% seen in October.
-The message from the Bank of England has been clear. Inflation is on track for a “rapid” decline over the coming months, raising hopes that the worst of Britain’s cost of living crisis is now in the rearview mirror.
-There are two good reasons for this. Energy costs are moving in the right direction, while the initial rise in wholesale oil and gas prices that followed Russia’s invasion of Ukraine in February last year will soon drop from the calculation of the annual inflation rate."""
-)
-
-# https://www.theguardian.com/science/2023/feb/13/starwatch-orions-belt-and-sirius-lead-way-to-hydras-head
-news_science = Document(
-    """On northern winter nights, it is so easy to be beguiled by the gloriously bright constellations of Orion, the hunter, and Taurus, the bull, that one can overlook the fainter constellations.
-So this week, find the three stars of Orion’s belt, follow them down to Sirius, the brightest star in the night sky, and then look eastward until you find the faint ring of stars that makes up the head of Hydra, the water snake. The chart shows the view looking south-east from London at 8pm GMT on Monday, but the view will be similar every night this week.
-Hydra is the largest of the 88 modern constellations covering an area of 1,303 square degrees. To compare, nearby Orion only covers 594 square degrees. Hydra accounts for most of its area by its length, crossing more than 100 degrees of the sky (the full moon spans half a degree).
-As evening becomes night and into the early hours, the rotation of Earth causes Hydra to slither its way across the southern meridian until dawn washes it from the sky. From the southern hemisphere, the constellation is easily visible in the eastern sky by mid-evening."""
-)
-
-# https://www.theguardian.com/music/2023/jan/30/salisbury-cathedral-pipe-organ-new-life-holst-the-planets
-news_culture = Document(
-    """A unique performance of Gustav Holst’s masterwork The Planets – played on a magnificent pipe organ rather than by an orchestra and punctuated by poems inspired by children’s responses to the music – is to be staged in the suitably vast Salisbury Cathedral.
-The idea of the community music project is to introduce more people, young and old, to the 140-year-old “Father” Willis organ, one of the treasures of the cathedral.
-It is also intended to get the children who took part and the adults who will watch and listen thinking afresh about the themes Holst’s suite tackles – war, peace, joy and mysticism – which seem as relevant now as when he wrote the work a century ago.
-John Challenger, the cathedral’s principal organist, said: “We have a fantastic pipe organ largely as it was when built. It’s a thrilling thing. I view it as my purpose in life to share it with as many people as possible.”
-The Planets is written for a large orchestra. “Holst calls for huge instrumental forces and an unseen distant choir of sopranos and altos,” said Challenger. But he has transposed the suite for the organ, not copying the effect of the orchestral instruments but finding a new version of the suite."""
-)
-
-# https://www.theguardian.com/sport/blog/2023/feb/14/multi-million-dollar-wpl-auction-signals-huge-step-forward-for-womens-sport
-news_sport = Document(
-    """It was only a few days ago that members of the Australian women’s cricket team were contemplating how best to navigate the impending “distraction” of the inaugural Women’s Premier League auction, scheduled during the first week of the T20 World Cup. “It’s a little bit awkward,” captain Meg Lanning said in South Africa last week. “But it’s just trying to embrace that and understanding it’s actually a really exciting time and you actually don’t have a lot of control over most of it, so you’ve just got to wait and see.”
-What a pleasant distraction it turned out to be. Lanning herself will be $192,000 richer for three weeks’ work with the Delhi Capitals. Her teammate, Ash Gardner, will earn three times that playing for the Gujarat Giants. The allrounder’s figure of $558,000 is more than Sam Kerr pockets in a season with Chelsea and more than the WNBA’s top earner, Jackie Young.
-If that sounds like a watershed moment, it’s perhaps because it is. And it is not the only one this past week. The NRLW made its own wage-related headlines on Tuesday, to the effect that the next (agreed in principle) collective bargaining agreement will bring with it a $1.5m salary cap in 2027, at an average salary of $62,500. Women’s rugby, too, is making moves, with news on the weekend that Rugby Australia will begin contracting the Wallaroos."""
-)
-
-news = [news_economics, news_science, news_culture, news_sport]
-```
-
-> The token limit for `google/flan-t5-large` is 512. So, all news pieces should be shorter than the limit.
-
-2. List pre-defined templates using the `get_prompt_template_names()` method. All templates come with the prompts needed to perform these tasks. 
-
-
-```python
-prompt_node.get_prompt_template_names()
-```
-
-3. Use the `summarization` template to generate a summary for each piece of news:
-
-
-```python
-prompt_node.prompt(prompt_template="summarization", documents=news)
-```
-
-Here you go! You have generated summaries of your news articles. But we're missing titles for them. Let's see how PromptNode can help us there.
-
-## Generating Titles for News Articles with a Custom Template
-
-The biggest benefit of PromptNode is its versatility. You can use it to perform practically any NLP task if you define your own prompt templates for them. By creating your prompt templates, you can extend the model's capabilities and use it for a broader range of NLP tasks in Haystack. 
-
-You can define custom templates for each NLP task and register them with PromptNode. Let's create a custom template to generate descriptive titles for news:
-
-1. Initialize a `PromptTemplate` instance. Give your template a `name` and define the prompt in `prompt_text`. To define any parameters for the prompt, add them to the `prompt_text` preceded by the `$` sign. We need a template to generate titles for our news articles. We'll call it `give-a-title`. The only parameter we need is `$news`, so let's add it to the `prompt_text`:
-
-
-```python
-from haystack.nodes import PromptTemplate
-
-title_generator = PromptTemplate(
-    name="give-a-title",
-    prompt_text="Provide a short, descriptive title for the given piece of news. News: $news; Title:",
-)
-```
-
-2. To use the new template, pass `title_generator` as the `prompt_template` to the `prompt()` method:
-
-
-
-
-```python
-prompt_node.prompt(prompt_template=title_generator, news=news)
-```
-
-> Note: To add a custom template to the template list, call `add_prompt_template()` with the `PromptTemplate` object pass the template contents to it. Once you do this, the next time you want to use this template, just call its name: 
-> ```python
-> prompt_node.add_prompt_template(PromptTemplate(name="give-a-title", prompt_text="Provide a short, descriptive title for the given piece of news. News: $news; Title:"))
-> prompt_node.prompt(prompt_template="give-a-title", news=news)
-> ```
-
-There you go! You should have the titles for your news articles ready. Let's now categorize them.
-
-## Categorizing Documents with PromptNode
-
-You can customize PromptTemplates as much as you need. Let's try to create a template to categorize the news articles. 
-
-1. Create another PromptTemplate called `categorize-news`. In the `prompt_text`, define the `$news` parameter, specify the categories you want to use, and ask the model not to categorize the news if it doesn't fit in the provided category list: 
-
-
-```python
-news_categorizer = PromptTemplate(
-    name="categorize-news",
-    prompt_text="Given the categories: sport, economics, culture; classify the news: $news. Only pick a category from the list, otherwise say: no suitable category",
-)
-```
-
-2. Run the `prompt()` method with the `news_categorizer` template:
-
-
-```python
-prompt_node.prompt(prompt_template=news_categorizer, news=news)
-```
-
-Congratulations! You've summarized your documents, generated titles for them, and put them into categories, all using custom prompt templates. 
-
-## About us
-
-
-This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany
-
-We bring NLP to the industry via open source!  
-Our focus: Industry specific language models & large scale QA systems.  
-  
-Some of our other work: 
-- [German BERT](https://deepset.ai/german-bert)
-- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)
-
-Get in touch:
-[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://discord.com/invite/VBpFzsgRVF) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Haystack Website](https://deepset.ai)
-
-By the way: [we're hiring!](https://www.deepset.ai/jobs)
-
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index d19357a8..b76b69fe 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -2,6 +2,7 @@
 import tomli
 from nbconvert import MarkdownExporter
 from nbconvert.filters.strings import get_lines
+from subprocess import check_output
 
 
 def read_index(path):
@@ -15,12 +16,17 @@ def generate_frontmatter(config, tutorial):
         for alias in tutorial["aliases"]:
             aliases.append(f"/tutorials/{alias}")
 
+    last_commit_date = (
+        check_output(f'git log -1 --pretty=format:"%cs" tutorials/{tutorial["notebook"]}'.split()).decode().strip()
+    )
+
     frontmatter = f"""---
 layout: {config["layout"]}
 featured: {tutorial.get("featured", False)}
 colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')}
 toc: {config["toc"]}
 title: "{tutorial["title"]}"
+lastmod: {last_commit_date}
 level: "{tutorial["level"]}"
 weight: {tutorial["weight"]}
 description: {tutorial["description"]}

From 6ce40e4858f2e7fb2e9eaafe6f8bf22d046879e4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 3 Mar 2023 16:57:32 +0300
Subject: [PATCH 047/206] remove workflows and pre-commit hooks to generate md
 files (#134)

---
 .github/workflows/markdowns.yml | 53 ---------------------------------
 .pre-commit-config.yaml         |  8 -----
 2 files changed, 61 deletions(-)
 delete mode 100644 .github/workflows/markdowns.yml

diff --git a/.github/workflows/markdowns.yml b/.github/workflows/markdowns.yml
deleted file mode 100644
index e14db335..00000000
--- a/.github/workflows/markdowns.yml
+++ /dev/null
@@ -1,53 +0,0 @@
-name: Create Markdowns
-
-on:
-  workflow_dispatch:  # Activate this workflow manually
-  pull_request:
-    paths:
-      - 'tutorials/*.*'
-
-
-jobs:
-
-  docs-check:
-    runs-on: ubuntu-latest
-    steps:
-
-      - uses: actions/checkout@v3
-
-      - uses: actions/setup-python@v4
-        with:
-          python-version: '3.9'
-          cache: 'pip' # caching pip dependencies
-
-      - name: Get changed notebooks
-        id: changed-files
-        uses: tj-actions/changed-files@v35
-        with:
-          files: |
-            tutorials/*.ipynb
-
-      - name: Install Dependencies and Generate Markdown
-        run: |
-          pip install -r requirements.txt
-          python scripts/generate_markdowns.py --index index.toml --output markdowns --notebooks ${{ steps.changed-files.outputs.all_changed_files }}
-
-      - name: Status
-        run: |
-          if [[ `git status --porcelain` ]]; then
-            git status
-            echo "##################################################################################################"
-            echo "#"
-            echo "# CHECK FAILED! You need to update the static version of the tutorials."
-            echo "#"
-            echo "# Please run the tutorials markdown update script:"
-            echo "#"
-            echo "#    python scripts/generate_markdowns.py --index index.toml --output markdowns --notebooks ..."
-            echo "#"
-            echo "# or see https://github.com/deepset-ai/haystack-tutorials/blob/main/Contributing.md for help."
-            echo "#"
-            echo "# If you have further problems, please open an issue: https://github.com/deepset-ai/haystack-tutorials/issues"
-            echo "#"
-            echo "##################################################################################################"
-            exit 1
-          fi
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 7ac5303c..71d44d07 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -13,12 +13,4 @@ repos:
   rev: 22.6.0
   hooks:
   - id: black-jupyter
-
-- repo: local
-  hooks:
-    - id: tutorials-markdown
-      name: Markdown version of the tutorials
-      entry: python scripts/generate_markdowns.py --notebooks
-      language: system
-      types: [jupyter]  
       
\ No newline at end of file

From 8c24e98dc1af6b18a8f9c8fb27bdbbf1d06c4c39 Mon Sep 17 00:00:00 2001
From: Julian Risch <julian.risch@deepset.ai>
Date: Mon, 6 Mar 2023 12:25:23 +0100
Subject: [PATCH 048/206] remove automatically inferred model_format param
 (#135)

---
 tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
index 5c3a332b..3bf808b1 100644
--- a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+++ b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
@@ -293,7 +293,6 @@
         "retriever = EmbeddingRetriever(\n",
         "    document_store=document_store,\n",
         "    embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\",\n",
-        "    model_format=\"sentence_transformers\",\n",
         ")\n",
         "# Important:\n",
         "# Now that we initialized the Retriever, we need to call update_embeddings() to iterate over all\n",

From c6ef3fd4948ea7e67f8b00a1425bfd83018dfa73 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Mon, 6 Mar 2023 12:58:18 +0100
Subject: [PATCH 049/206] fixing broken link in tutorial 6 (#136)

---
 tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
index 3bf808b1..ba0ddf94 100644
--- a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+++ b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
@@ -336,6 +336,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "unhLD18yA6OF"
@@ -346,7 +347,7 @@
         "With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.\n",
         "Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
         "To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.\n",
-        "You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd)."
+        "You can learn more about `Pipelines` in the [docs](https://docs.haystack.deepset.ai/docs/pipelines)."
       ]
     },
     {

From 7020ecc5e39590ba248a1f41d7c7859222a575b1 Mon Sep 17 00:00:00 2001
From: David G <davidgerva@gmail.com>
Date: Tue, 7 Mar 2023 14:48:39 +0100
Subject: [PATCH 050/206] [Tutorial 8 and 16] Remove `xpdf` installation (#137)

* Remove xpdf dependency from tutorial 8 and 16
- no more needed as per https://github.com/deepset-ai/haystack/pull/4314

* Also add pdf in haystack installation
needed for PDFToTextConverter
---
 tutorials/08_Preprocessing.ipynb                     | 10 +---------
 tutorials/16_Document_Classifier_at_Index_Time.ipynb |  5 +----
 2 files changed, 2 insertions(+), 13 deletions(-)

diff --git a/tutorials/08_Preprocessing.ipynb b/tutorials/08_Preprocessing.ipynb
index ab7bab82..26cccaab 100644
--- a/tutorials/08_Preprocessing.ipynb
+++ b/tutorials/08_Preprocessing.ipynb
@@ -63,15 +63,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr]\n",
-    "\n",
-    "# For Colab/linux based machines:\n",
-    "wget https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz\n",
-    "tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin\n",
-    "\n",
-    "# For macOS machines:\n",
-    "# wget https://dl.xpdfreader.com/xpdf-tools-mac-4.03.tar.gz\n",
-    "# tar -xvf xpdf-tools-mac-4.03.tar.gz && sudo cp xpdf-tools-mac-4.03/bin64/pdftotext /usr/local/bin"
+    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr,pdf]"
    ]
   },
   {
diff --git a/tutorials/16_Document_Classifier_at_Index_Time.ipynb b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
index 7faae66d..225beda0 100644
--- a/tutorials/16_Document_Classifier_at_Index_Time.ipynb
+++ b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
@@ -46,10 +46,7 @@
     "\n",
     "# Install the latest main of Haystack\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr]\n",
-    "\n",
-    "wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz\n",
-    "tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin\n",
+    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr,pdf]\n",
     "\n",
     "apt install libgraphviz-dev\n",
     "pip install pygraphviz"

From ad3205d05205c794050e63c60d6247c80b3ea98e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 13 Mar 2023 14:10:24 +0300
Subject: [PATCH 051/206] Update Contributing.md after removing markdowns from
 this repo (#139)

* Update Contributing.md after removing markdowns from this repo

* update contributing.md
---
 Contributing.md | 22 +++++++++-------------
 1 file changed, 9 insertions(+), 13 deletions(-)

diff --git a/Contributing.md b/Contributing.md
index 9983fb9d..dd667f96 100644
--- a/Contributing.md
+++ b/Contributing.md
@@ -24,34 +24,30 @@ Here's what you need to do to add or edit tutorials 👇:
    - Follow the outline in the template as you create the tutorial.
    - After the tutorial is complete, add necessary information to [index.toml](/index.toml). Here, `weight` is the order in which your tutorial appears. For example, a tutorial with `weight = 15` comes after a tutorial with `weight = 10` and before `20`. Each tutorial comes with a Google Colab link and `Open in Colab` button on the top of the tutorial by default. If your new tutorial cannot be run on Google Colab, set `colab = false` not to display `Open in Colab` button on top the tutorial.
 3. If you're **editing** an existing tutorial:
-   - Make necessary changes in the `.ipynb` file of the tutorial and save them. You don't need to make changes in the markdown file. 
-4. Run `python scripts/generate_markdowns.py --index index.toml --notebooks tutorials/your-tutorial.ipynb --output markdowns/`. This generates or updates the relevant markdown file. Pre-commit hooks will ensure the `markdowns` folder reflects your changes. Don't forget to add changed files to your commit if pre-commit hooks updated `.ipynb` or `.md` files.
-5. Create a pull request.
-6. Wait for the [CI](#ci-continuous-integration) checks to pass.
-   These checks pass if the relevant markdown files are created.
-7. Update the [README](./README.md), if necessary.
-8. Wait for a review and merge 🎉. Thank you for contributing 💙.
+   - Make necessary changes in the `.ipynb` file of the tutorial and save them.
+4. Create a pull request.
+5. Wait for the [CI](#ci-continuous-integration) checks to pass.
+6. Update the [README](./README.md), if necessary.
+7. Wait for a review and merge 🎉. Thank you for contributing 💙.
 
 ## Slugs
 
-The default behaviour for markdown files is that it gets the same name as the corresponfing `.ipynb` notebook of that tutorial. The name of the markdown file is also the location at which the tutorial will appear on the website. 
-For example, "01_Basic_QA_Pipeline" will be on https://haystack.deepset.ai/tutorials/01_basic_qa_pipeline
+By default, the name of each tutorial becomes the slug of its page on the website. For example, "01_Basic_QA_Pipeline" will be on https://haystack.deepset.ai/tutorials/01_basic_qa_pipeline.
 
-In `index.toml` you have the option of adding an optional `slug` entry for a tutorial which will generate the correspoinding markdown under the name you give for the `slug`. This is useful for scenarios where you are updating a tutorial to the point where it makes sense that the name of the `.ipynb` file changes, but you would still like people to access the tutorial on the same URL.
+In `index.toml`,  you can optionally add a `slug` entry for a tutorial to use a custom slug. Adding a slug is also useful if you're updating a tutorial to the point where it makes sense for the name of the `.ipynb` file to change, but you still want people to access the tutorial under the same URL.
 
 # Continuous Integration (CI)
 
-We use a GitHub action for our Continuous Integration tasks. This means that as soon as you open a PR, GitHub starts executing some workflows on your code. Here, the workflow checks that you've generated the required `.md` files for the tutorials you've edited or created.
+We use a GitHub action for our Continuous Integration tasks. This means that as soon as you open a PR, GitHub starts executing some workflows on your code. Here, the workflow checks whether the tutorial you created or edited runs without an error.
 
 If all goes well, at the bottom of your PR page, you should see something like this, where all checks are green.
 
 ![](https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/ci-success.png)
 
-If you see some red checks, then something didn't work and you need to take some action. The most likely reason is that the `.md` file accompanying the `.ipynb` tutorial hasn't been updated or created. You can try to run repeat step 4 in [Contributing to Haystack Tutorials](#contributing-to-haystack-tutorials).
+If you see some red checks, then something didn't work and you need to take some action. Please check the tutorial again and fix the errors.
 
 # Naming Convention for File Names
 
 - Each tutorial name starts with a number. If you create a new tutorial, its name should start with the number following the last tutorial. 
 - Separate words in the title with an `_` underscore.
 - Use a short descriptive name for the filename, for example: *22_creating_a_summarizer_pipeline*.
-- Generated markdown files only have the number of the tutorial (use the `scripts/generate_markdowns.py` script for this).

From c653ee414859d19773d3404a662e3c19197e1539 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Mon, 13 Mar 2023 16:37:15 +0100
Subject: [PATCH 052/206] install from pypi (#143)

---
 tutorials/15_TableQA.ipynb | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/tutorials/15_TableQA.ipynb b/tutorials/15_TableQA.ipynb
index f15934d4..13535604 100644
--- a/tutorials/15_TableQA.ipynb
+++ b/tutorials/15_TableQA.ipynb
@@ -65,7 +65,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]\n",
+    "pip install farm-haystack[colab]\n",
     "\n",
     "# Install pygraphviz for visualization of Pipelines\n",
     "apt install libgraphviz-dev\n",
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "id": "RmxepXZtwQ0E"
    },
@@ -435,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "id": "G-aZZvyv4-Mf"
    },
@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "id": "4CBcIjIq_uFx"
    },
@@ -537,7 +537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "id": "Zdq6JnF5m3aP"
    },
@@ -641,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -725,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -793,7 +793,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

From 49e90f15b8a1fd351879325c1b41eea7103f9b89 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 14 Mar 2023 13:22:06 +0300
Subject: [PATCH 053/206] Add telemetry line to tutorial 21 (#142)

---
 tutorials/21_Customizing_PromptNode.ipynb | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/tutorials/21_Customizing_PromptNode.ipynb b/tutorials/21_Customizing_PromptNode.ipynb
index a074b37a..8f86f0e4 100644
--- a/tutorials/21_Customizing_PromptNode.ipynb
+++ b/tutorials/21_Customizing_PromptNode.ipynb
@@ -70,6 +70,26 @@
     "pip install farm-haystack[colab]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(21)"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",

From f45014591354f675a9522f02022eeb5b7fb7aa43 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 14 Mar 2023 13:22:29 +0300
Subject: [PATCH 054/206] Add PromptNode in a pipeline tutorial (#141)

* Add PromptNode in a pipeline tutorial

* Update tutorial 22
---
 index.toml                                  |  11 +
 tutorials/22_Pipeline_with_PromptNode.ipynb | 323 ++++++++++++++++++++
 2 files changed, 334 insertions(+)
 create mode 100644 tutorials/22_Pipeline_with_PromptNode.ipynb

diff --git a/index.toml b/index.toml
index e6ae4913..4a8015b0 100644
--- a/index.toml
+++ b/index.toml
@@ -196,4 +196,15 @@ weight = 57
 notebook = "21_Customizing_PromptNode.ipynb"
 aliases = ["customizing-promptnode"]
 created_at = 2023-02-16
+featured = true
+
+[[tutorial]]
+title = "Creating a Generative QA Pipeline with PromptNode"
+description = "Use a large language model in your search system through PromptNode and Shaper."
+level = "advanced"
+weight = 105
+notebook = "22_Pipeline_with_PromptNode.ipynb"
+aliases = ["pipeline-with-promptnode"]
+completion_time = "15 min"
+created_at = 2023-03-13
 featured = true
\ No newline at end of file
diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
new file mode 100644
index 00000000..4d97947d
--- /dev/null
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: Creating a Generative QA Pipeline with PromptNode\n",
+    "\n",
+    "- **Level**: Advanced\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `PromptNode`, `PromptTemplate` `Shaper`\n",
+    "- **Goal**: After completing this tutorial, you'll have created a generative question answering search system that uses a large language model through PromptNode with the help of Shaper. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "Learn how to build a generative question answering pipeline using the power of LLMs with PromptNode. In this tutorial, we'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want. \n",
+    "\n",
+    "This tutorial introduces you to the new Shaper node and explains how to use Shaper to integrate PromptNode in the pipeline. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]\n",
+    "pip install datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(22)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the DocumentStore\n",
+    "\n",
+    "We'll start creating our question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, we're using the `InMemoryDocumentStore`.\n",
+    "\n",
+    "Let's initialize our DocumentStore. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.document_stores import InMemoryDocumentStore\n",
+    "\n",
+    "document_store = InMemoryDocumentStore(use_bm25=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The DocumentStore is now ready. Now it's time to fill it with some Documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fetching and Writing Documents\n",
+    "\n",
+    "We'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, we don't need to perform any additional cleaning or splitting. \n",
+    "\n",
+    "Let's fetch the data and write it to the DocumentStore: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
+    "\n",
+    "document_store.write_documents(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the Retriever\n",
+    "\n",
+    "Let's initialize a BM25Retriever and make it use the InMemoryDocumentStore we initialized earlier in this tutorial:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import BM25Retriever\n",
+    "\n",
+    "retriever = BM25Retriever(document_store=document_store, top_k=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the PromptNode\n",
+    "\n",
+    "Let's define a custom prompt to use with our PromptNode. This prompt will accept `$texts` and `$query` as parameters. `$text` will match the output of the Shaper and `$query` will match the `query` we pass at runtime. \n",
+    "\n",
+    "We'll initialize PromptNode with the new PromptTemplate and `google/flan-t5-large` model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PromptNode, PromptTemplate\n",
+    "\n",
+    "lfqa_prompt = PromptTemplate(\n",
+    "    name=\"lfqa\",\n",
+    "    prompt_text=\"\"\"Synthesize a comprehensive answer from the following text for the given question. \n",
+    "                             Provide a clear and concise response that summarizes the key points and information presented in the text. \n",
+    "                             Your answer should be in your own words and be no longer than 50 words. \n",
+    "                             \\n\\n Related text: $texts \\n\\n Question: $query \\n\\n Answer:\"\"\",\n",
+    ")\n",
+    "\n",
+    "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\", default_prompt_template=lfqa_prompt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ">To learn about how to use custom templates with PromptNode, check out [Customizing PromptNode for NLP Tasks](https://haystack.deepset.ai/tutorials/21_customizing_promptnode) tutorial. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the Shaper\n",
+    "\n",
+    "[Shaper](https://docs.haystack.deepset.ai/docs/shaper) is necessary when the output of one node does not match what the next node expects as input. In our pipeline, we need to join retrieved Documents so that we can inject these Documents into the prompt. We can solve this problem by defining a Shaper that uses [`join_documents`](https://docs.haystack.deepset.ai/reference/shaper-api#join_documents) as its function (`func`). Retriever refers Documents as `documents` and `join_documents` expects `documents` parameter, so, we can pass `{\"documents\": \"documents\"}` to the Shaper as `inputs`. To output joined Documents as `texts`, we need to define `outputs=[\"texts\"]`.    \n",
+    "\n",
+    "Let's initialize the Shaper:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import Shaper\n",
+    "\n",
+    "shaper = Shaper(func=\"join_documents\", inputs={\"documents\": \"documents\"}, outputs=[\"texts\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining the Pipeline\n",
+    "\n",
+    "We'll use a custom pipeline with the Retriever, Shaper, and PromptNode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.pipelines import Pipeline\n",
+    "\n",
+    "pipe = Pipeline()\n",
+    "pipe.add_node(component=retriever, name=\"retriever\", inputs=[\"Query\"])\n",
+    "pipe.add_node(component=shaper, name=\"shaper\", inputs=[\"retriever\"])\n",
+    "pipe.add_node(component=prompt_node, name=\"prompt_node\", inputs=[\"shaper\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's it! The pipeline's ready to generate answers to questions!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Asking a Question\n",
+    "\n",
+    "We use the pipeline `run()` method to ask a question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = pipe.run(query=\"How does Rhodes Statue look like?\")\n",
+    "\n",
+    "print(output[\"results\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are some other example queries to test:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "examples = [\n",
+    "    \"Where is Gardens of Babylon?\",\n",
+    "    \"Why did people build Great Pyramid of Giza?\",\n",
+    "    \"How does Rhodes Statue look like?\",\n",
+    "    \"Why did people visit the Temple of Artemis?\",\n",
+    "    \"What is the importance of Colossus of Rhodes?\",\n",
+    "    \"What happened to the Tomb of Mausolus?\",\n",
+    "    \"How did Colossus of Rhodes collapse?\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🎉 Congratulations! You've learned how to create a generative QA system for your documents with PromptNode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.6 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 1648c1279c8767821e18efb7f3e3a5932597cc4c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 14 Mar 2023 13:34:59 +0300
Subject: [PATCH 055/206] Add tutorial 22 to README (#145)

* Add completion time and remove featured flag from tutorial 21
---
 README.md  | 3 ++-
 index.toml | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 17859257..12d54d74 100644
--- a/README.md
+++ b/README.md
@@ -38,4 +38,5 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 | Generative Pseudo Labeling               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)                                                     | [18_GPL.ipynb](./tutorials/18_GPL.ipynb)                                                                                                         |
 | Text-to-Image search                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) | [19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) |
 | Using Haystack with REST API             | <a href="https://haystack.deepset.ai/downloads/20_Using_Haystack_with_REST_API.ipynb" target="_blank"><img alt="Download" src="https://img.shields.io/badge/DOWNLOAD-blue?&style=for-the-badge"/> <a>                                                                                                                                                                                                    | [20_Using_Haystack_with_REST_API.ipynb](./tutorials/20_Using_Haystack_with_REST_API.ipynb)                                                       |
-| Customizing PromptNode                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb) | [21_Customizing_PromptNode.ipynb](./tutorials/21_Customizing_PromptNode.ipynb) |
\ No newline at end of file
+| Customizing PromptNode                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb) | [21_Customizing_PromptNode.ipynb](./tutorials/21_Customizing_PromptNode.ipynb) |
+| Creating a Generative QA Pipeline with PromptNode                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/22_Pipeline_with_PromptNode.ipynb) | [22_Pipeline_with_PromptNode.ipynb](./tutorials/22_Pipeline_with_PromptNode.ipynb) |
\ No newline at end of file
diff --git a/index.toml b/index.toml
index 4a8015b0..baea60ea 100644
--- a/index.toml
+++ b/index.toml
@@ -195,8 +195,8 @@ level = "intermediate"
 weight = 57
 notebook = "21_Customizing_PromptNode.ipynb"
 aliases = ["customizing-promptnode"]
+completion_time = "20 min"
 created_at = 2023-02-16
-featured = true
 
 [[tutorial]]
 title = "Creating a Generative QA Pipeline with PromptNode"

From 2d1d5f7a315b2cb473dd3d7ae44e0f952d2b78de Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 20 Mar 2023 19:43:55 +0300
Subject: [PATCH 056/206] Temporary version fix for the tutorial 5 (#146)

* Temporary version fix for the tutorial 5

* Add tut 5 to skip list
---
 .github/workflows/run_tutorials.yml | 1 +
 tutorials/05_Evaluation.ipynb       | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 5d6e05df..e7661703 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -47,6 +47,7 @@ jobs:
         run: |
           skiplist=(
               "tutorials/02_Finetune_a_model_on_your_data.ipynb"
+              "tutorials/05_Evaluation.ipynb"
               "tutorials/09_DPR_training.ipynb"
               "tutorials/13_Question_generation.ipynb"
               "tutorials/14_Query_Classifier.ipynb"
diff --git a/tutorials/05_Evaluation.ipynb b/tutorials/05_Evaluation.ipynb
index df25381f..a17f8734 100644
--- a/tutorials/05_Evaluation.ipynb
+++ b/tutorials/05_Evaluation.ipynb
@@ -85,7 +85,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]"
+    "pip install farm-haystack==1.13.0"
    ]
   },
   {

From 48ed2c79d9be294b88f802f4194466cbc46c51a4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 24 Mar 2023 10:59:19 +0100
Subject: [PATCH 057/206] Fix tutorial 22 (#148)

---
 tutorials/22_Pipeline_with_PromptNode.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index 4d97947d..4343eb8a 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -179,7 +179,7 @@
     "    prompt_text=\"\"\"Synthesize a comprehensive answer from the following text for the given question. \n",
     "                             Provide a clear and concise response that summarizes the key points and information presented in the text. \n",
     "                             Your answer should be in your own words and be no longer than 50 words. \n",
-    "                             \\n\\n Related text: $texts \\n\\n Question: $query \\n\\n Answer:\"\"\",\n",
+    "                             \\n\\n Related text: $documents \\n\\n Question: $query \\n\\n Answer:\"\"\",\n",
     ")\n",
     "\n",
     "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\", default_prompt_template=lfqa_prompt)"
@@ -198,7 +198,7 @@
    "source": [
     "## Initializing the Shaper\n",
     "\n",
-    "[Shaper](https://docs.haystack.deepset.ai/docs/shaper) is necessary when the output of one node does not match what the next node expects as input. In our pipeline, we need to join retrieved Documents so that we can inject these Documents into the prompt. We can solve this problem by defining a Shaper that uses [`join_documents`](https://docs.haystack.deepset.ai/reference/shaper-api#join_documents) as its function (`func`). Retriever refers Documents as `documents` and `join_documents` expects `documents` parameter, so, we can pass `{\"documents\": \"documents\"}` to the Shaper as `inputs`. To output joined Documents as `texts`, we need to define `outputs=[\"texts\"]`.    \n",
+    "[Shaper](https://docs.haystack.deepset.ai/docs/shaper) is necessary when the output of one node does not match what the next node expects as input. In our pipeline, we need to join retrieved Documents so that we can inject these Documents into the prompt. We can solve this problem by defining a Shaper that uses [`join_documents`](https://docs.haystack.deepset.ai/reference/shaper-api#join_documents) as its function (`func`). Retriever refers Documents as `documents` and `join_documents` expects `documents` parameter, so, we can pass `{\"documents\": \"documents\"}` to the Shaper as `inputs`. To output joined Documents as `documents`, we need to define `outputs=[\"documents\"]`.    \n",
     "\n",
     "Let's initialize the Shaper:"
    ]
@@ -211,7 +211,7 @@
    "source": [
     "from haystack.nodes import Shaper\n",
     "\n",
-    "shaper = Shaper(func=\"join_documents\", inputs={\"documents\": \"documents\"}, outputs=[\"texts\"])"
+    "shaper = Shaper(func=\"join_documents\", inputs={\"documents\": \"documents\"}, outputs=[\"documents\"])"
    ]
   },
   {

From 8b7d2380904a30f5ba2d4b3e5d5aaa662be99e4c Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Tue, 28 Mar 2023 08:56:05 +0200
Subject: [PATCH 058/206] Add workflow to sync tutorials with deepset Cloud
 when they change (#150)

* Add script to generate txt files

* Add workflow to upload tutorial files to deepset Cloud
---
 .github/workflows/dc_sync.yml | 90 +++++++++++++++++++++++++++++++++++
 scripts/generate_txt.py       | 75 +++++++++++++++++++++++++++++
 2 files changed, 165 insertions(+)
 create mode 100644 .github/workflows/dc_sync.yml
 create mode 100644 scripts/generate_txt.py

diff --git a/.github/workflows/dc_sync.yml b/.github/workflows/dc_sync.yml
new file mode 100644
index 00000000..e2a5cb6c
--- /dev/null
+++ b/.github/workflows/dc_sync.yml
@@ -0,0 +1,90 @@
+name: Sync tutorial with dC workspace
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - "./tutorials/[0-9]*.ipynb"
+
+jobs:
+  get-tutorials:
+    runs-on: ubuntu-latest
+    outputs:
+      modified-matrix: ${{ steps.create_matrix.outputs.modified-matrix }}
+      deleted-matrix: ${{ steps.create_matrix.outputs.deleted-matrix }}
+      any-changed: ${{ steps.files.outputs.any-changed }}
+      any-deleted: ${{ steps.files.outputs.any-deleted }}
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+
+      - name: Get changed files
+        id: files
+        uses: tj-actions/changed-files@v34
+        with:
+          json: true
+          files: |
+            ./tutorials/[0-9]*.ipynb
+
+      - name: Create matrixes
+        id: create_matrix
+        run: |
+          echo "modified-matrix={\"file\": ${{ steps.files.outputs.all_changed_files }}}" >> "$GITHUB_OUTPUT"
+          echo "deleted-matrix={\"file\": ${{ steps.files.outputs.deleted_files }}}" >> "$GITHUB_OUTPUT"
+
+  modified:
+    needs: get-tutorials
+    if: needs.get-tutorials.outputs.any-changed
+    runs-on: ubuntu-latest
+    strategy:
+      matrix: ${{ fromJSON(needs.get-tutorials.outputs.modified-matrix) }}
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Generate file to upload
+        id: file-generator
+        run: |
+          FILE="$(basename ${{ matrix.file }} .ipynb).txt"
+          echo "file=text/$FILE" >> "$GITHUB_OUTPUT"
+          METADATA="$(python scripts/generate_txt.py --print-metadata --notebooks ${{ matrix.file }})"
+          echo "metadata=$METADATA" >> "$GITHUB_OUTPUT"
+
+      - name: Upload tutorial to deepset Cloud
+        uses: silvanocerza/deepset-cloud-file-uploader@v1
+        with:
+          api-key: ${{ secrets.DEEPSET_CLOUD_API_KEY }}
+          workspace-name: ${{ secrets.DEEPSET_CLOUD_WORKSPACE }}
+          file: ${{ steps.file-generator.outputs.file }}
+          write-mode: OVERWRITE
+          metadata: ${{ steps.file-generator.outputs.metadata }}
+
+  deleted:
+    needs: get-tutorials
+    if: needs.get-tutorials.outputs.any-deleted
+    runs-on: ubuntu-latest
+    strategy:
+      matrix: ${{ fromJSON(needs.get-tutorials.outputs.deleted-matrix) }}
+
+    steps:
+      # This step is really important as when we remove a tutorial
+      # notebook we also want to remove the relative file from
+      # deepset Cloud, but since the remote file will have a .txt
+      # extension we must first the full file name with this step.
+      - name: Get file with correct extension
+        id: extension-changer
+        run: |
+          FILE="$(basename ${{ matrix.file }} .ipynb).txt"
+          echo "file=$FILE" >> "$GITHUB_OUTPUT"
+
+      - name: Delete file from deepset Cloud
+        uses: silvanocerza/deepset-cloud-file-deleter@v1
+        with:
+          api-key: ${{ secrets.DEEPSET_CLOUD_API_KEY }}
+          workspace-name: ${{ secrets.DEEPSET_CLOUD_WORKSPACE }}
+          file: ${{ steps.extension-changer.outputs.file}}
diff --git a/scripts/generate_txt.py b/scripts/generate_txt.py
new file mode 100644
index 00000000..29f081f2
--- /dev/null
+++ b/scripts/generate_txt.py
@@ -0,0 +1,75 @@
+import argparse
+import tomli
+from nbconvert import MarkdownExporter
+from nbconvert.filters.strings import get_lines
+from subprocess import check_output
+
+from pathlib import Path
+
+
+def read_index(path):
+    with open(path, "rb") as f:
+        return tomli.load(f)
+
+
+def generate_metadata(config, tutorial):
+    aliases = []
+    if "aliases" in tutorial:
+        for alias in tutorial["aliases"]:
+            aliases.append(f"/tutorials/{alias}")
+
+    last_commit_date = (
+        check_output(f'git log -1 --pretty=format:"%cs" tutorials/{tutorial["notebook"]}'.split()).decode().strip()
+    )
+
+    return f"""layout: {config["layout"]}
+featured: {tutorial.get("featured", False)}
+colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')}
+toc: {config["toc"]}
+title: "{tutorial["title"]}"
+lastmod: {last_commit_date}
+level: "{tutorial["level"]}"
+weight: {tutorial["weight"]}
+description: {tutorial["description"]}
+category: "QA"
+aliases: {aliases}
+download: "/downloads/{tutorial["notebook"]}"
+completion_time: {tutorial.get("completion_time", False)}
+created_at: {tutorial["created_at"]}"""
+
+
+def generate_markdown_from_notebook(tutorial, output_path, tutorials_path):
+    md_exporter = MarkdownExporter(exclude_output=True)
+    body, _ = md_exporter.from_filename(f"{tutorials_path}")
+    body = get_lines(body, start=1)
+    filename = tutorial.get("slug", tutorial["notebook"][:-6])
+    Path(output_path).mkdir(exist_ok=True)
+    with open(f"{output_path}/{filename}.txt", "w", encoding="utf-8") as f:
+        f.write(body)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--index", dest="index", default="index.toml")
+    parser.add_argument("--notebooks", dest="notebooks", nargs="+", default=[])
+    parser.add_argument("--output", dest="output", default="text")
+    parser.add_argument("--print-metadata", dest="metadata", action="store_true")
+    args = parser.parse_args()
+    index = read_index(args.index)
+
+    notebooks = args.notebooks
+    if args.notebooks == ["all"]:
+        tutorials_path = Path(".", "tutorials")
+        notebooks = tutorials_path.glob("[0-9]*.ipynb")
+
+    notebooks_configs = {cfg["notebook"]: cfg for cfg in index["tutorial"]}
+
+    for notebook in notebooks:
+        notebook_name = notebook.split("/")[-1]
+        tutorial_config = notebooks_configs.get(notebook_name)
+        if tutorial_config:
+            generate_markdown_from_notebook(tutorial_config, args.output, notebook)
+
+            if args.metadata:
+                meta = generate_metadata(index["config"], tutorial_config)
+                print(meta)

From 2d16056f0e7e3a88dbf196a12c6fbad53e84cff7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Thu, 30 Mar 2023 11:18:06 +0200
Subject: [PATCH 059/206] First Agents Tutorial (#140)

* First Agents Tutorial

* making some readability changes

* Update tutorials/23_MultiHop_QA_With_Agents.ipynb

Co-authored-by: Agnieszka Marzec <97166305+agnieszka-m@users.noreply.github.com>

* Update tutorials/23_MultiHop_QA_With_Agents.ipynb

Co-authored-by: Agnieszka Marzec <97166305+agnieszka-m@users.noreply.github.com>

* Update tutorials/23_MultiHop_QA_With_Agents.ipynb

Co-authored-by: Agnieszka Marzec <97166305+agnieszka-m@users.noreply.github.com>

* Update tutorials/23_MultiHop_QA_With_Agents.ipynb

Co-authored-by: Agnieszka Marzec <97166305+agnieszka-m@users.noreply.github.com>

* Update tutorials/23_MultiHop_QA_With_Agents.ipynb

Co-authored-by: Agnieszka Marzec <97166305+agnieszka-m@users.noreply.github.com>

* Update tutorials/23_MultiHop_QA_With_Agents.ipynb

Co-authored-by: Agnieszka Marzec <97166305+agnieszka-m@users.noreply.github.com>

* Update tutorials/23_MultiHop_QA_With_Agents.ipynb

Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>

* Update tutorials/23_MultiHop_QA_With_Agents.ipynb

Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>

* Update tutorials/23_MultiHop_QA_With_Agents.ipynb

Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>

* skipping tests and fixing comments

* Delete settings.json

* skip test for this tutorial

* fix typos

* removing the about us section

* fixing error caused by latest agent update

* minor updates and ignoring markdowns folder

* changing some questions

* adding docs

* changing installation

---------

Co-authored-by: Agnieszka Marzec <97166305+agnieszka-m@users.noreply.github.com>
Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai>
---
 .github/workflows/run_tutorials.yml           |    1 +
 .gitignore                                    |    2 +
 index.toml                                    |   11 +
 ...ering_Multihop_Questions_with_Agents.ipynb | 5691 +++++++++++++++++
 4 files changed, 5705 insertions(+)
 create mode 100644 tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index e7661703..205c959c 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -52,6 +52,7 @@ jobs:
               "tutorials/13_Question_generation.ipynb"
               "tutorials/14_Query_Classifier.ipynb"
               "tutorials/18_GPL.ipynb"
+              "tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/.gitignore b/.gitignore
index 4343f073..fac98588 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,5 @@
+markdowns/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/index.toml b/index.toml
index baea60ea..d2676d6c 100644
--- a/index.toml
+++ b/index.toml
@@ -198,6 +198,17 @@ aliases = ["customizing-promptnode"]
 completion_time = "20 min"
 created_at = 2023-02-16
 
+[[tutorial]]
+title = "Answering Multihop Questions with Agents"
+description = "Use Agent to answer multihop questions with extractive models"
+level = "intermediate"
+weight = 63
+notebook = "23_Answering_Multihop_Questions_with_Agents.ipynb"
+aliases = [multihop-qa-with-agents]
+completion_time = "10 min"
+created_at = 2023-03-27
+featured = true
+
 [[tutorial]]
 title = "Creating a Generative QA Pipeline with PromptNode"
 description = "Use a large language model in your search system through PromptNode and Shaper."
diff --git a/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb b/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
new file mode 100644
index 00000000..88e919cd
--- /dev/null
+++ b/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
@@ -0,0 +1,5691 @@
+{
+  "cells": [
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h8gcxwg4sAXd"
+      },
+      "source": [
+        "# Tutorial: Answering Multihop Questions with Agents\n",
+        "\n",
+        "- **Level**: Intermediate\n",
+        "- **Time to complete**: 10 minutes\n",
+        "- **Nodes Used**: `Agent`, `PromptNode`, `InMemoryDocumentStore`, `FARMReader` and `ExtractiveQAPipeline`\n",
+        "- **Goal**: After completing this tutorial, you will have learned how to use Agents to build a multi-hop question answering system with an `ExtractiveQAPipeline` as a tool\n",
+        "- **Prerequisites**: An [OpenAI API Key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key)\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": false,
+        "id": "ifCja5PasAXf"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "The [Agent](https://docs.haystack.deepset.ai/docs/agent) class uses a large language model (LLM) to make decisions and come up with the best next course of action. You can provide the `Agent` with a set of [`Tools`](https://docs.haystack.deepset.ai/docs/agent#tools) that it can choose to use to reach a result. At each iteration, the agent will pick a tool from the ones available to it. Based on the result, the Agent has two options: It will either decide to select a tool again and do another iteration, or it will decide that it has reached a conclusion and return the final answer.\n",
+        "\n",
+        "In this tutorial, we will provide the Agent with just one tool to answer questions: a commonly used Haystack component, the `ExtractiveQAPipeline`.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jsUTxX7y5XS_"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kn47_SD-sAXg"
+      },
+      "source": [
+        "## Installing Haystack\n",
+        "\n",
+        "To start, let's install the latest release of Haystack with `pip`. In this tutorial, we'll use a [Hugging Face dataset](https://huggingface.co/datasets/Tuana/presidents) that has already been prepared as Haystack `Documents`, so we will install `datasets` too:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "A5_XfePisAXg",
+        "outputId": "584fbb90-714a-4016-d21f-ca9b16d2a307"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install --upgrade pip\n",
+        "pip install farm-haystack[colab]\n",
+        "pip install datasets"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n",
+        "### Enabling Telemetry \n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(23)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s5RGKEgGsAXh"
+      },
+      "source": [
+        "Set the logging level to INFO:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dCLBdwjlsAXh"
+      },
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "\n",
+        "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
+        "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4jngOMq_6FVT"
+      },
+      "source": [
+        "## Create an Extractive QA Pipeline\n",
+        "\n",
+        "Now, we will introduce an `ExtractiveQAPipeline` as a `Tool` to our `Agent`. To do so, we'll first write our documents about the presidents of the USA into a `DocumentStore` and then create our pipeline.\n",
+        "\n",
+        "### 1) Write documents to the DocumentStore"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "cea9f7f44bd642708bae169102424ac4",
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+            "1f696487a6964f6194cbcf45dc3a180a",
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+            "90416ae4368245cfae35b72ae12eca94",
+            "b631fb303ef849febcbcdbc3d209e4c0",
+            "c092076ba1d2409c866375d920a70274",
+            "f7d31179764847cfa587f7657174c27e",
+            "6e45aba3c3ea41bcbf8140a484faa933",
+            "a59842dba57742a7a7a4e09b1dc8725b",
+            "55c9a4b58ff8487fb94671c56f83c12f",
+            "98e65b4d35214fcc8a483e618393b374",
+            "7a3530ab6d794fce8bd2a5f11d57256d",
+            "e931ad893c2c46ea97fcc1e17c227304",
+            "2678baf41af84b10a17d8765f2ab8bdf",
+            "2d71fb2cd0cc4e62b7d9c02170a1b53f",
+            "dad9654ec0834e4b83f792010d84daec",
+            "4d40fba94263477bbc74eb66d02adec9",
+            "e36adfcf52fc4c46b5eba86bfab3dc49",
+            "10ac00d0c9094e47832282d6deb1272c",
+            "14ef2250ccf24fd48e0992a550687ff7"
+          ]
+        },
+        "id": "eFJg6a4PaDR_",
+        "outputId": "8692dfa0-c2c5-4c69-dece-1c0214e2070c"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.document_stores import InMemoryDocumentStore\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "remote_dataset = load_dataset('Tuana/presidents', split=\"train\")\n",
+        "\n",
+        "document_store = InMemoryDocumentStore(use_bm25=True)\n",
+        "document_store.write_documents(remote_dataset)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s-6-JT8O6prc"
+      },
+      "source": [
+        "### 2) Create an ExtractiveQAPipeline:\n",
+        "Let's define our retriever and reader to to use in an `ExtractiveQAPipeline`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 317,
+          "referenced_widgets": [
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+            "de12fe6708a4455ca942a45965194d8a",
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+            "fa0bf4ca5f9b406082a7d1a6dccb32b5",
+            "e22644ca58294d4dbcddd208583886b9",
+            "272225718b18418f9d9832662abdca00",
+            "3d85d0886e1f4e2fb9b812e89fa34bd8",
+            "c594db0163e64685929954ce4b4c0a6e",
+            "896b831869b24f3bb06a611375e0feea",
+            "110e84e5a4344d27b750e272e24ca523",
+            "d6e19afe43c640e894ca47f9a38fbc60"
+          ]
+        },
+        "id": "n9bmrIUCatMk",
+        "outputId": "9f110081-72ce-4f80-fea0-10b3f8a61654"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.nodes import EmbeddingRetriever, FARMReader\n",
+        "from haystack.pipelines import ExtractiveQAPipeline\n",
+        "\n",
+        "retriever = EmbeddingRetriever(document_store=document_store, embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\", use_gpu=True)\n",
+        "document_store.update_embeddings(retriever=retriever)\n",
+        "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)\n",
+        "presidents_qa = ExtractiveQAPipeline(reader=reader, retriever=retriever)\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mgQAP-4T7Fh6"
+      },
+      "source": [
+        "### 3) Let's test the pipeline!\n",
+        "\n",
+        "Now that you have an `ExtractiveQAPipeline`, go ahead and ask it a question about the presidents of the USA. \n",
+        "\n",
+        "Below, we're asking the question: \"What year was the 1st president of the USA born?\"\n",
+        "\n",
+        "Notice how this is 2 questions in one. An extractive model will struggle to find the answer to this question unless the answer is phrased clearly in our documents. For example: \"The first president of the USA was born in 1732\".\n",
+        "\n",
+        "On the other hand, it does well with a question such as \"Who was the 1st president of the USA?\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 930,
+          "referenced_widgets": [
+            "d31a483cf6e443a5891ca14672ffed3b",
+            "a0dcb1d860704bbdb54447dfd0cc92bd",
+            "5e85990cb7374ddc9f7107a72f812e5e",
+            "acf1cdc5c6484d8ab5f78ac217656b17",
+            "083cb8b3aa1240129a75e5c2a7eacbb6",
+            "7f4d17a4998f44b58f813b815d16f7f8",
+            "6ffba94cc23846209c4629cb10bce042",
+            "de52fc90bd3f4db0b540535246b33c4f",
+            "acd5844ab2d64071a1d59a1df7baafbd",
+            "744746530a4e401bb6e9e28d89e54eed",
+            "3bb3e922fc8c471a8c1aaaf5bbca2d92"
+          ]
+        },
+        "id": "HdS-satXbSsw",
+        "outputId": "751e30c8-50ae-48d3-8e34-df7023d1252a"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.utils import print_answers\n",
+        "\n",
+        "#result = presidents_qa.run(\"Who was the 1st president of the USA?\")\n",
+        "result = presidents_qa.run(\"What year was the 1st president of the USA born?\")\n",
+        "\n",
+        "print_answers(result, \"minimum\")"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bn83I_Dg8Q1f"
+      },
+      "source": [
+        "## Create an Agent with the `ExtractiveQAPipeline` as a `Tool`\n",
+        "### 1) To create the Agent, we'll make use of an Open AI model. So first, provide your Open AI key:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kBkMr86vvl9h",
+        "outputId": "8366a06d-9064-485e-aa88-c82859c68265"
+      },
+      "outputs": [],
+      "source": [
+        "from getpass import getpass\n",
+        "\n",
+        "api_key_prompt = \"Enter OpenAI API key:\" \n",
+        "api_key = getpass(api_key_prompt)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qajsCzjx9C6j"
+      },
+      "source": [
+        "### 2) Initialize the Agent \n",
+        "\n",
+        "The `Agent` needs to determine the next best course of action at each iteration. It does this by using an LLM, and a prompt designed specially for this use case. Our `Agent` uses a `PromptNode` with the default [\"zero-shot-react\" `PromptTemplate` ](https://github.com/deepset-ai/haystack/blob/444a3116c42d2c8852d27aa8093ac92c8e85ab88/haystack/nodes/prompt/prompt_node.py#L337). \n",
+        "\n",
+        "Here, let's define an `Agent` that uses the `text-davinci-003` model by OpenAI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uU20HAGXcf-q"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.agents import Agent\n",
+        "from haystack.nodes import PromptNode\n",
+        "\n",
+        "prompt_node = PromptNode(model_name_or_path=\"text-davinci-003\", api_key=api_key, stop_words=[\"Observation:\"])\n",
+        "agent = Agent(prompt_node=prompt_node)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ndeax5iGA1IQ"
+      },
+      "source": [
+        "### 3) Provide the Agent with a Tool\n",
+        "Next, let's add our `ExtractiveQAPipeline` into the Agent's arsenal. The Agent will then be able to use this pipeline when it decides it could be useful.\n",
+        "\n",
+        "To do so, let's define a tool and make sure to give it a description. The exact wording of your description matters a lot here. The agent uses it to understand in which cases it should pick this tool. If the agent fails to pick the right tool, adjusting the description might help."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qWuCvmKCcu1R"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.agents import Tool\n",
+        "\n",
+        "search_tool = Tool(name=\"USA_Presidents_QA\",pipeline_or_node=presidents_qa,\n",
+        "                  description=\"useful for when you need to answer questions related to the presidents of the USA.\",\n",
+        "                  output_variable=\"answers\"\n",
+        "              )\n",
+        "agent.add_tool(search_tool)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yjizLaApBg2Y"
+      },
+      "source": [
+        "### 4) Ask a question!\n"
+      ]
+    },
+    {
+      "cell_type": "code",
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+        "id": "7FeTZtN8dC-m",
+        "outputId": "003dfde4-82d4-46e8-864a-9b5a83dbd9f5"
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+      "outputs": [],
+      "source": [
+        "result = agent.run(\"What year was the 1st president of the USA born?\")\n",
+        "\n",
+        "print(result[\"transcript\"].split('---')[1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7Y_Ku4eWByqf"
+      },
+      "source": [
+        "Congratulations! 🎉 You've used an Agent that can use an extractive model iteratively, to arrive at a final answer to a multi-hop question!!!"
+      ]
+    }
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+            "value": "Downloading data: 100%"
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+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From 692c6bfbbd243444653d1f28d87bc6ae9af16d74 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Thu, 30 Mar 2023 11:31:27 +0200
Subject: [PATCH 060/206] fix toml (#155)

---
 index.toml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/index.toml b/index.toml
index d2676d6c..d9415166 100644
--- a/index.toml
+++ b/index.toml
@@ -204,7 +204,7 @@ description = "Use Agent to answer multihop questions with extractive models"
 level = "intermediate"
 weight = 63
 notebook = "23_Answering_Multihop_Questions_with_Agents.ipynb"
-aliases = [multihop-qa-with-agents]
+aliases = ["multihop-qa-with-agents"]
 completion_time = "10 min"
 created_at = 2023-03-27
 featured = true
@@ -217,5 +217,4 @@ weight = 105
 notebook = "22_Pipeline_with_PromptNode.ipynb"
 aliases = ["pipeline-with-promptnode"]
 completion_time = "15 min"
-created_at = 2023-03-13
-featured = true
\ No newline at end of file
+created_at = 2023-03-13
\ No newline at end of file

From 539ba8b6c3b657014e6c767bc1a0da11c56a0b9e Mon Sep 17 00:00:00 2001
From: Mayank Jobanputra <mayankjobanputra@gmail.com>
Date: Thu, 30 Mar 2023 12:43:57 +0200
Subject: [PATCH 061/206] Add agents tutorial to README (#156)

* Add agents tutorial to README

* fixed colab link
---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 12d54d74..6b5d3409 100644
--- a/README.md
+++ b/README.md
@@ -39,4 +39,5 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 | Text-to-Image search                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) | [19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) |
 | Using Haystack with REST API             | <a href="https://haystack.deepset.ai/downloads/20_Using_Haystack_with_REST_API.ipynb" target="_blank"><img alt="Download" src="https://img.shields.io/badge/DOWNLOAD-blue?&style=for-the-badge"/> <a>                                                                                                                                                                                                    | [20_Using_Haystack_with_REST_API.ipynb](./tutorials/20_Using_Haystack_with_REST_API.ipynb)                                                       |
 | Customizing PromptNode                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb) | [21_Customizing_PromptNode.ipynb](./tutorials/21_Customizing_PromptNode.ipynb) |
-| Creating a Generative QA Pipeline with PromptNode                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/22_Pipeline_with_PromptNode.ipynb) | [22_Pipeline_with_PromptNode.ipynb](./tutorials/22_Pipeline_with_PromptNode.ipynb) |
\ No newline at end of file
+| Creating a Generative QA Pipeline with PromptNode                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/22_Pipeline_with_PromptNode.ipynb) | [22_Pipeline_with_PromptNode.ipynb](./tutorials/22_Pipeline_with_PromptNode.ipynb) |
+| Answering Complex Questions with Agents                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) | [23_Answering_Multihop_Questions_with_Agents.ipynb](./tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) |
\ No newline at end of file

From 3784c5181f00a178f7e5ed6d2c33ae1c9fe11314 Mon Sep 17 00:00:00 2001
From: Jet <qinjet@gmail.com>
Date: Fri, 31 Mar 2023 01:36:03 +0800
Subject: [PATCH 062/206] Update 22_Pipeline_with_PromptNode.ipynb (#149)

* Update 22_Pipeline_with_PromptNode.ipynb

* Update 22_Pipeline_with_PromptNode.ipynb

* Update 22_Pipeline_with_PromptNode.ipynb

not pinning the version, added '>'

* Update 22_Pipeline_with_PromptNode.ipynb

removed extra whitespace

* Update 22_Pipeline_with_PromptNode.ipynb

* Update 22_Pipeline_with_PromptNode.ipynb

version greater than 2.6.0 is recommended

---------

Co-authored-by: Mayank Jobanputra <mayankjobanputra@gmail.com>
---
 tutorials/22_Pipeline_with_PromptNode.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index 4343eb8a..20c01f6b 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -52,7 +52,7 @@
     "\n",
     "pip install --upgrade pip\n",
     "pip install farm-haystack[colab]\n",
-    "pip install datasets"
+    "pip install datasets>=2.6.1"
    ]
   },
   {

From 621a91dbe44b843fca416d237169936f6e2c2b27 Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Fri, 31 Mar 2023 15:48:10 +0200
Subject: [PATCH 063/206] Fix dc_sync.yml never getting triggered (#158)

---
 .github/workflows/dc_sync.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/dc_sync.yml b/.github/workflows/dc_sync.yml
index e2a5cb6c..5657ac8d 100644
--- a/.github/workflows/dc_sync.yml
+++ b/.github/workflows/dc_sync.yml
@@ -5,7 +5,7 @@ on:
     branches:
       - main
     paths:
-      - "./tutorials/[0-9]*.ipynb"
+      - "tutorials/[0-9]*.ipynb"
 
 jobs:
   get-tutorials:

From 88e9480fb1ac8bd33ce0942bfbbd610eb6d17e4d Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Fri, 31 Mar 2023 16:21:10 +0200
Subject: [PATCH 064/206] Add workflow to verify tutorials generation works
 correctly (#157)

---
 .github/workflows/verify_generation.yml | 26 +++++++++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 .github/workflows/verify_generation.yml

diff --git a/.github/workflows/verify_generation.yml b/.github/workflows/verify_generation.yml
new file mode 100644
index 00000000..c893c7ff
--- /dev/null
+++ b/.github/workflows/verify_generation.yml
@@ -0,0 +1,26 @@
+name: Verify tutorials generation
+
+on:
+  pull_request:
+    paths:
+      - "tutorials/*.ipynb"
+      - "index.toml"
+
+jobs:
+  run-tutorials:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: pip install -r requirements.txt
+
+      - name: Generate all tutorials
+        run: |
+          mkdir output
+          python scripts/generate_markdowns.py --index index.toml --notebooks all --output ./output

From 74b0e7d480df221aaf01302464f580c49f8f95eb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 3 Apr 2023 21:29:18 +0300
Subject: [PATCH 065/206] Fix broken tutorials (#154)

* Fix broken tutorials

* Add haystack-extras text2speech dependencies

* Fix tutorial 17

* Remove parts about DocumentToSpeech
---
 .github/workflows/run_tutorials.yml |   6 +-
 tutorials/05_Evaluation.ipynb       |   2 +-
 tutorials/17_Audio.ipynb            | 332 +++++-----------------------
 3 files changed, 65 insertions(+), 275 deletions(-)

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 205c959c..4fad1c0c 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -29,6 +29,11 @@ jobs:
           pip install nbconvert ipython
           pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
 
+      
+      - name: Install Haystack Extras text2speech dependencies
+        run: |
+          pip install farm-haystack-text2speech
+
       - name: Checkout
         uses: actions/checkout@v3
         with:
@@ -47,7 +52,6 @@ jobs:
         run: |
           skiplist=(
               "tutorials/02_Finetune_a_model_on_your_data.ipynb"
-              "tutorials/05_Evaluation.ipynb"
               "tutorials/09_DPR_training.ipynb"
               "tutorials/13_Question_generation.ipynb"
               "tutorials/14_Query_Classifier.ipynb"
diff --git a/tutorials/05_Evaluation.ipynb b/tutorials/05_Evaluation.ipynb
index a17f8734..86e8f29c 100644
--- a/tutorials/05_Evaluation.ipynb
+++ b/tutorials/05_Evaluation.ipynb
@@ -85,7 +85,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack==1.13.0"
+    "pip install farm-haystack[colab]"
    ]
   },
   {
diff --git a/tutorials/17_Audio.ipynb b/tutorials/17_Audio.ipynb
index c6c7398d..ed855e54 100644
--- a/tutorials/17_Audio.ipynb
+++ b/tutorials/17_Audio.ipynb
@@ -16,7 +16,15 @@
    ]
   },
   {
-   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "n7sF5gZnPgnG"
+   },
+   "source": [
+    ">*Update:* AnswerToSpeech lives in the [text2speech](https://github.com/deepset-ai/haystack-extras/tree/main/nodes/text2speech) package. Main [Haystack](https://github.com/deepset-ai/haystack) reposity doesn't include it anymore."
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -55,9 +63,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "nBvGUPVKN2oJ"
+   },
    "source": [
-    "To start, install the latest release of Haystack with `pip`:"
+    "To start, let's install the latest release of Haystack with `pip`. In this tutorial, we'll use components from [text2speech](https://github.com/deepset-ai/haystack-extras/tree/main/nodes/text2speech) which contains some extra Haystack components, so we'll install `farm-haystack-text2speech`"
    ]
   },
   {
@@ -74,13 +84,15 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,audio]"
+    "pip install farm-haystack[colab]\n",
+    "pip install farm-haystack-text2speech"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "r3kPhIxKN2oK",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -96,9 +108,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
-    "collapsed": false,
+    "id": "DyCNTb-zN2oK",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -111,80 +123,15 @@
     "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Start an Elasticsearch server\n",
-    "You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Recommended: Start Elasticsearch using Docker via the Haystack utility function\n",
-    "from haystack.utils import launch_es\n",
-    "\n",
-    "launch_es()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Start an Elasticsearch server in Colab\n",
-    "\n",
-    "If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
-    "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash --bg\n",
-    "\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "pbGu92rAB3Sl"
    },
    "source": [
-    "### Populate the document store with `SpeechDocuments`\n",
-    "\n",
-    "First of all, we will populate the document store with a simple indexing pipeline. See [Tutorial 1](https://colab.research.google.com/github/deepset-ai/haystack/blob/main/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb) for more details about these steps.\n",
-    "\n",
-    "To the basic version, we can add here a DocumentToSpeech node that also generates an audio file for each of the indexed documents. This will make possible, during querying, to access the audio version of the documents the answers were extracted from without having to generate it on the fly.\n",
+    "## Indexing Documents\n",
     "\n",
-    "**Note**: this additional step can slow down your indexing quite a lot if you are not running on GPU. Experiment with very small corpora to start."
+    "First of all, we will populate the document store with a simple indexing pipeline. See [Tutorial 1](https://colab.research.google.com/github/deepset-ai/haystack/blob/main/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb) for more details about these steps."
    ]
   },
   {
@@ -198,23 +145,14 @@
    },
    "outputs": [],
    "source": [
-    "import os\n",
-    "import time\n",
-    "\n",
-    "from haystack.document_stores import ElasticsearchDocumentStore\n",
-    "from haystack.utils import fetch_archive_from_http, launch_es\n",
+    "from haystack.document_stores import InMemoryDocumentStore\n",
+    "from haystack.utils import fetch_archive_from_http\n",
     "from pathlib import Path\n",
     "from haystack import Pipeline\n",
-    "from haystack.nodes import FileTypeClassifier, TextConverter, PreProcessor, DocumentToSpeech\n",
-    "\n",
-    "\n",
-    "# Wait 30 seconds only to be sure Elasticsearch is ready before continuing\n",
-    "time.sleep(30)\n",
+    "from haystack.nodes import FileTypeClassifier, TextConverter, PreProcessor\n",
     "\n",
-    "# Get the host where Elasticsearch is running, default to localhost\n",
-    "host = os.environ.get(\"ELASTICSEARCH_HOST\", \"localhost\")\n",
-    "\n",
-    "document_store = ElasticsearchDocumentStore(host=host, username=\"\", password=\"\", index=\"document\")\n",
+    "# Initialize the DocumentStore\n",
+    "document_store = InMemoryDocumentStore(use_bm25=True)\n",
     "\n",
     "# Get the documents\n",
     "documents_path = \"data/tutorial17\"\n",
@@ -224,90 +162,32 @@
     "# List all the paths\n",
     "file_paths = [p for p in Path(documents_path).glob(\"**/*\")]\n",
     "\n",
-    "# NOTE: In this example we're going to use only one text file from the wiki, as the DocumentToSpeech node is quite slow\n",
-    "# on CPU machines. Comment out this line to use all documents from the dataset if you machine is powerful enough.\n",
+    "# NOTE: In this example we're going to use only one text file from the wiki\n",
     "file_paths = [p for p in file_paths if \"Stormborn\" in p.name]\n",
     "\n",
     "# Prepare some basic metadata for the files\n",
     "files_metadata = [{\"name\": path.name} for path in file_paths]\n",
     "\n",
-    "# Here we create a basic indexing pipeline\n",
-    "indexing_pipeline = Pipeline()\n",
-    "\n",
-    "# - Makes sure the file is a TXT file (FileTypeClassifier node)\n",
+    "# Makes sure the file is a TXT file (FileTypeClassifier node)\n",
     "classifier = FileTypeClassifier()\n",
-    "indexing_pipeline.add_node(classifier, name=\"classifier\", inputs=[\"File\"])\n",
     "\n",
-    "# - Converts a file into text and performs basic cleaning (TextConverter node)\n",
+    "# Converts a file into text and performs basic cleaning (TextConverter node)\n",
     "text_converter = TextConverter(remove_numeric_tables=True)\n",
-    "indexing_pipeline.add_node(text_converter, name=\"text_converter\", inputs=[\"classifier.output_1\"])\n",
     "\n",
     "# - Pre-processes the text by performing splits and adding metadata to the text (Preprocessor node)\n",
-    "preprocessor = PreProcessor(\n",
-    "    clean_whitespace=True,\n",
-    "    clean_empty_lines=True,\n",
-    "    split_length=100,\n",
-    "    split_overlap=50,\n",
-    "    split_respect_sentence_boundary=True,\n",
-    ")\n",
-    "indexing_pipeline.add_node(preprocessor, name=\"preprocessor\", inputs=[\"text_converter\"])\n",
-    "\n",
-    "#\n",
-    "# DocumentToSpeech\n",
-    "#\n",
-    "# Here is where we convert all documents to be indexed into SpeechDocuments, that will hold not only\n",
-    "# the text content, but also their audio version.\n",
-    "#\n",
-    "# Note that DocumentToSpeech implements a light caching, so if a document's audio have already\n",
-    "# been generated in a previous pass in the same folder, it will reuse the existing file instead\n",
-    "# of generating it again.\n",
-    "doc2speech = DocumentToSpeech(\n",
-    "    model_name_or_path=\"espnet/kan-bayashi_ljspeech_vits\", generated_audio_dir=Path(\"./generated_audio_documents\")\n",
-    ")\n",
-    "indexing_pipeline.add_node(doc2speech, name=\"doc2speech\", inputs=[\"preprocessor\"])\n",
+    "preprocessor = PreProcessor(clean_header_footer=True, split_length=200, split_overlap=20)\n",
     "\n",
-    "# - Writes the resulting documents into the document store (ElasticsearchDocumentStore node from the previous cell)\n",
-    "indexing_pipeline.add_node(document_store, name=\"document_store\", inputs=[\"doc2speech\"])\n",
+    "# Here we create a basic indexing pipeline\n",
+    "indexing_pipeline = Pipeline()\n",
+    "indexing_pipeline.add_node(classifier, name=\"classifier\", inputs=[\"File\"])\n",
+    "indexing_pipeline.add_node(text_converter, name=\"text_converter\", inputs=[\"classifier.output_1\"])\n",
+    "indexing_pipeline.add_node(preprocessor, name=\"preprocessor\", inputs=[\"text_converter\"])\n",
+    "indexing_pipeline.add_node(document_store, name=\"document_store\", inputs=[\"preprocessor\"])\n",
     "\n",
     "# Then we run it with the documents and their metadata as input\n",
     "output = indexing_pipeline.run(file_paths=file_paths, meta=files_metadata)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pprint import pprint\n",
-    "\n",
-    "# You can now check the document store and verify that documents have been enriched with a path\n",
-    "# to the generated audio file\n",
-    "document = next(document_store.get_all_documents_generator())\n",
-    "pprint(document)\n",
-    "\n",
-    "# Sample output:\n",
-    "#\n",
-    "# <Document: {\n",
-    "# 'content': \"'Stormborn' received praise from critics, who considered Euron Greyjoy's raid on Yara's Iron Fleet,\n",
-    "#             the assembly of Daenerys' allies at Dragonstone, and Arya's reunion with her direwolf Nymeria as\n",
-    "#             highlights of the episode. In the United States, it achieved a viewership of 9.27 million in its\n",
-    "#             initial broadcast.\",\n",
-    "# 'content_type': 'audio',\n",
-    "# 'score': None,\n",
-    "# 'meta': {\n",
-    "#       'content_audio': './generated_audio_documents/f218707624d9c4f9487f508e4603bf5b.wav',\n",
-    "#       '__initialised__': True,\n",
-    "#       'type': 'generative',\n",
-    "#       '_split_id': 0,\n",
-    "#       'audio_format': 'wav',\n",
-    "#       'sample_rate': 22050,\n",
-    "#       'name': '2_Stormborn.txt'},\n",
-    "#       'embedding': None,\n",
-    "#       'id': '2733e698301f8f94eb70430b874177fd'\n",
-    "# }>"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -330,10 +210,11 @@
    "source": [
     "from pathlib import Path\n",
     "from haystack import Pipeline\n",
-    "from haystack.nodes import BM25Retriever, FARMReader, AnswerToSpeech\n",
+    "from haystack.nodes import BM25Retriever, FARMReader\n",
+    "from text2speech import AnswerToSpeech\n",
     "\n",
     "retriever = BM25Retriever(document_store=document_store)\n",
-    "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2-distilled\", use_gpu=True)\n",
+    "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)\n",
     "answer2speech = AnswerToSpeech(\n",
     "    model_name_or_path=\"espnet/kan-bayashi_ljspeech_vits\", generated_audio_dir=Path(\"./audio_answers\")\n",
     ")\n",
@@ -379,111 +260,23 @@
    },
    "outputs": [],
    "source": [
-    "# Now you can either print the object directly...\n",
+    "# Now you can print prediction\n",
     "from pprint import pprint\n",
     "\n",
-    "pprint(prediction)\n",
-    "\n",
-    "# Sample output:\n",
-    "# {\n",
-    "#     'answers': [ <SpeechAnswer:\n",
-    "#                       answer_audio=PosixPath('generated_audio_answers/fc704210136643b833515ba628eb4b2a.wav'),\n",
-    "#                       answer=\"Daenerys Targaryen\",\n",
-    "#                       context_audio=PosixPath('generated_audio_answers/8c562ebd7e7f41e1f9208384957df173.wav'),\n",
-    "#                       context='...'\n",
-    "#                       type='extractive', score=0.9919578731060028,\n",
-    "#                       offsets_in_document=[{'start': 608, 'end': 615}], offsets_in_context=[{'start': 72, 'end': 79}],\n",
-    "#                       document_id='cc75f739897ecbf8c14657b13dda890e', meta={'name': '43_Arya_Stark.txt'}}  >,\n",
-    "#                  <SpeechAnswer:\n",
-    "#                       answer_audio=PosixPath('generated_audio_answers/07d6265486b22356362387c5a098ba7d.wav'),\n",
-    "#                       answer=\"Daenerys\",\n",
-    "#                       context_audio=PosixPath('generated_audio_answers/3f1ca228d6c4cfb633e55f89e97de7ac.wav'),\n",
-    "#                       context='...'\n",
-    "#                       type='extractive', score=0.9767240881919861,\n",
-    "#                       offsets_in_document=[{'start': 3687, 'end': 3801}], offsets_in_context=[{'start': 18, 'end': 132}],\n",
-    "#                       document_id='9acf17ec9083c4022f69eb4a37187080', meta={'name': '43_Arya_Stark.txt'}}>,\n",
-    "#                  ...\n",
-    "#                ]\n",
-    "#     'documents': [ <SpeechDocument:\n",
-    "#                        content_type='text', score=0.8034909798951382, meta={'name': '43_Arya_Stark.txt'}, embedding=None, id=d1f36ec7170e4c46cde65787fe125dfe',\n",
-    "#                        content_audio=PosixPath('generated_audio_documents/07d6265486b22356362387c5a098ba7d.wav'),\n",
-    "#                        content='The title of the episode refers to both Daenerys Targaryen, who was born during a  ...'>,\n",
-    "#                    <SpeechDocument:\n",
-    "#                        content_type='text', score=0.8002150354529785, meta={'name': '191_Gendry.txt'}, embedding=None, id='dd4e070a22896afa81748d6510006d2',\n",
-    "#                        content_audio=PosixPath('generated_audio_documents/07d6265486b22356362387c5a098ba7d.wav'),\n",
-    "#                        content='\"Stormborn\" received praise from critics, who considered Euron Greyjoy's raid on ...'>,\n",
-    "#                    ...\n",
-    "#                  ],\n",
-    "#     'no_ans_gap':  11.688868522644043,\n",
-    "#     'node_id': 'Reader',\n",
-    "#     'params': {'Reader': {'top_k': 5}, 'Retriever': {'top_k': 5}},\n",
-    "#     'query': 'Who was born during a storm?',\n",
-    "#     'root_node': 'Query'\n",
-    "# }"
+    "pprint(prediction)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "YFCfiP97B3S3",
-    "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
-    }
+    "id": "Xg6BN4v8N2oM"
    },
    "outputs": [],
-   "source": [
-    "from haystack.utils import print_answers\n",
-    "\n",
-    "# ...or use a util to simplify the output\n",
-    "# Change `minimum` to `medium` or `all` to raise the level of detail\n",
-    "print_answers(prediction, details=\"minimum\")\n",
-    "\n",
-    "# Sample output:\n",
-    "#\n",
-    "# Query: Who was born during a storm\n",
-    "# Answers:\n",
-    "# [   {   'answer_audio': PosixPath('generated_audio_answers/07d6265486b22356362387c5a098ba7d.wav'),\n",
-    "#         'answer': 'Daenerys Targaryen',\n",
-    "#         'context_transcript': PosixPath('generated_audio_answers/3f1ca228d6c4cfb633e55f89e97de7ac.wav'),\n",
-    "#         'context': ' refers to both Daenerys Targaryen, who was born during a terrible storm, and '},\n",
-    "#    {   'answer_audio': PosixPath('generated_audio_answers/83c3a02141cac4caffe0718cfd6c405c.wav'),\n",
-    "#        'answer': 'Daenerys',\n",
-    "#        'context_audio': PosixPath('generated_audio_answers/8c562ebd7e7f41e1f9208384957df173.wav'),\n",
-    "#        'context': 'The title of the episode refers to both Daenerys Targaryen, who was born during a terrible storm'},\n",
-    "#    ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "# The document the first answer was extracted from\n",
-    "original_document = [doc for doc in prediction[\"documents\"] if doc.id == prediction[\"answers\"][0].document_id][0]\n",
-    "pprint(original_document)\n",
-    "\n",
-    "# Sample output\n",
-    "#\n",
-    "# <Document: {\n",
-    "#   'content': '\"'''Stormborn'''\" is the second episode of the seventh season of HBO's fantasy television\n",
-    "#               series ''Game of Thrones'', and the 62nd overall. The episode was written by Bryan Cogman,\n",
-    "#               and directed by Mark Mylod. The title of the episode refers to both Daenerys Targaryen,\n",
-    "#               who was born during a terrible storm, and Euron Greyjoy, who declares himself to be \"the storm\".',\n",
-    "#   'content_type': 'audio',\n",
-    "#   'score': 0.6269117688771539,\n",
-    "#   'embedding': None,\n",
-    "#   'id': '9352f650b36f93ab99684fd4746af5c1'\n",
-    "#   'meta': {\n",
-    "#       'content_audio': '/home/sara/work/haystack/generated_audio_documents/2c9223d47801b0918f2db2ad778c3d5a.wav',\n",
-    "#       'type': 'generative',\n",
-    "#       '_split_id': 19,\n",
-    "#       'audio_format': 'wav',\n",
-    "#       'sample_rate': 22050,\n",
-    "#       'name': '2_Stormborn.txt'}\n",
-    "# }>"
+    "original_document = [doc for doc in prediction[\"documents\"] if doc.id == prediction[\"answers\"][0].document_ids[0]][0]\n",
+    "pprint(original_document)"
    ]
   },
   {
@@ -492,12 +285,12 @@
     "id": "FXf-kTn4B3S6"
    },
    "source": [
-    "### Hear them out!"
+    "## Hear them out!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "id": "cJJVpT7dB3S7"
    },
@@ -511,31 +304,26 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "usGVf1N6B3S8"
+    "id": "iRIotUuz-mpF"
    },
    "outputs": [],
    "source": [
-    "# The first answer in isolation\n",
-    "\n",
-    "print(\"Answer: \", prediction[\"answers\"][0].answer)\n",
-    "\n",
-    "speech, _ = sf.read(prediction[\"answers\"][0].answer_audio)\n",
-    "display(Audio(speech, rate=24000))"
+    "prediction[\"answers\"][0]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "yTFwNJqtB3S9"
+    "id": "usGVf1N6B3S8"
    },
    "outputs": [],
    "source": [
-    "# The context of the first answer\n",
+    "# The first answer in isolation\n",
     "\n",
-    "print(\"Context: \", prediction[\"answers\"][0].context)\n",
+    "print(\"Answer: \", prediction[\"answers\"][0].meta[\"answer_text\"])\n",
     "\n",
-    "speech, _ = sf.read(prediction[\"answers\"][0].context_audio)\n",
+    "speech, _ = sf.read(prediction[\"answers\"][0].answer)\n",
     "display(Audio(speech, rate=24000))"
    ]
   },
@@ -543,17 +331,15 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "xAj7Xm0EB3S-"
+    "id": "yTFwNJqtB3S9"
    },
    "outputs": [],
    "source": [
-    "# The document the first answer was extracted from\n",
-    "\n",
-    "document = [doc for doc in prediction[\"documents\"] if doc.id == prediction[\"answers\"][0].document_id][0]\n",
+    "# The context of the first answer\n",
     "\n",
-    "print(\"Document: \", document.content)\n",
+    "print(\"Context: \", prediction[\"answers\"][0].meta[\"context_text\"])\n",
     "\n",
-    "speech, _ = sf.read(document.meta[\"content_audio\"])\n",
+    "speech, _ = sf.read(prediction[\"answers\"][0].context)\n",
     "display(Audio(speech, rate=24000))"
    ]
   },
@@ -589,11 +375,11 @@
  "metadata": {
   "accelerator": "GPU",
   "colab": {
-   "name": "Tutorial17_Audio.ipynb",
    "provenance": []
   },
+  "gpuClass": "standard",
   "kernelspec": {
-   "display_name": "Python 3.10.6 64-bit",
+   "display_name": "Python 3.9.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -607,11 +393,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.6"
   },
   "vscode": {
    "interpreter": {
-    "hash": "bda33b16be7e844498c7c2d368d72665b4f1d165582b9547ed22a0249a29ca2e"
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
   }
  },

From c1a4d7b45e0d9e15e789d4222b221deeb3b02470 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 4 Apr 2023 12:50:57 +0300
Subject: [PATCH 066/206] Update the format of tutorial 17 (#160)

---
 tutorials/17_Audio.ipynb | 134 ++++++++++++++-------------------------
 1 file changed, 49 insertions(+), 85 deletions(-)

diff --git a/tutorials/17_Audio.ipynb b/tutorials/17_Audio.ipynb
index ed855e54..a58aa2b6 100644
--- a/tutorials/17_Audio.ipynb
+++ b/tutorials/17_Audio.ipynb
@@ -6,22 +6,25 @@
     "id": "Dne2XSNzB3SK"
    },
    "source": [
-    "# Make Your QA Pipelines Talk!\n",
+    "# Tutorial: Make Your QA Pipelines Talk!\n",
     "\n",
-    "<img style=\"float: right;\" src=\"https://upload.wikimedia.org/wikipedia/en/d/d8/Game_of_Thrones_title_card.jpg\">\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `FARMReader`, `AnswerToSpeech`\n",
+    "- **Goal**: After completing this tutorial, you'll have created a extractive question answering system that can read out the answer.\n",
     "\n",
-    "Question answering works primarily on text, but Haystack provides some features for audio files that contain speech as well.\n",
-    "\n",
-    "In this tutorial, we're going to see how to use `AnswerToSpeech` to convert answers into audio files."
+    ">**Update:** AnswerToSpeech lives in the [text2speech](https://github.com/deepset-ai/haystack-extras/tree/main/nodes/text2speech) package. Main [Haystack](https://github.com/deepset-ai/haystack) repository doesn't include it anymore."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "id": "n7sF5gZnPgnG"
-   },
+   "metadata": {},
    "source": [
-    ">*Update:* AnswerToSpeech lives in the [text2speech](https://github.com/deepset-ai/haystack-extras/tree/main/nodes/text2speech) package. Main [Haystack](https://github.com/deepset-ai/haystack) reposity doesn't include it anymore."
+    "## Overview\n",
+    "\n",
+    "Question answering works primarily on text, but Haystack provides some features for audio files that contain speech as well.\n",
+    "\n",
+    "In this tutorial, we're going to see how to use `AnswerToSpeech` to convert answers into audio files."
    ]
   },
   {
@@ -31,34 +34,34 @@
     "id": "4UBjfz4LB3SS"
    },
    "source": [
-    "### Prepare environment\n",
-    "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "## Preparing the Colab Environment\n",
     "\n",
-    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry\n",
     "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "uDHmaD2gB3SX",
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "vscode": {
      "languageId": "shellscript"
     }
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
+    "from haystack.telemetry import tutorial_running\n",
     "\n",
-    "nvidia-smi"
+    "tutorial_running(17)"
    ]
   },
   {
@@ -67,7 +70,9 @@
     "id": "nBvGUPVKN2oJ"
    },
    "source": [
-    "To start, let's install the latest release of Haystack with `pip`. In this tutorial, we'll use components from [text2speech](https://github.com/deepset-ai/haystack-extras/tree/main/nodes/text2speech) which contains some extra Haystack components, so we'll install `farm-haystack-text2speech`"
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`. In this tutorial, we'll use components from [text2speech](https://github.com/deepset-ai/haystack-extras/tree/main/nodes/text2speech) which contains some extra Haystack components, so we'll install `farm-haystack-text2speech`."
    ]
   },
   {
@@ -88,41 +93,6 @@
     "pip install farm-haystack-text2speech"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "id": "r3kPhIxKN2oK",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Logging\n",
-    "\n",
-    "We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
-    "Example log message:\n",
-    "INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
-    "Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "id": "DyCNTb-zN2oK",
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "\n",
-    "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
-    "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -131,7 +101,7 @@
    "source": [
     "## Indexing Documents\n",
     "\n",
-    "First of all, we will populate the document store with a simple indexing pipeline. See [Tutorial 1](https://colab.research.google.com/github/deepset-ai/haystack/blob/main/tutorials/Tutorial1_Basic_QA_Pipeline.ipynb) for more details about these steps."
+    "We will populate the document store with a simple indexing pipeline. See [Tutorial: Build Your First Question Answering System](https://haystack.deepset.ai/tutorials/01_basic_qa_pipeline) for more details about these steps."
    ]
   },
   {
@@ -145,10 +115,10 @@
    },
    "outputs": [],
    "source": [
+    "from pathlib import Path\n",
     "from haystack.document_stores import InMemoryDocumentStore\n",
     "from haystack.utils import fetch_archive_from_http\n",
-    "from pathlib import Path\n",
-    "from haystack import Pipeline\n",
+    "from haystack.pipelines import Pipeline\n",
     "from haystack.nodes import FileTypeClassifier, TextConverter, PreProcessor\n",
     "\n",
     "# Initialize the DocumentStore\n",
@@ -185,7 +155,7 @@
     "indexing_pipeline.add_node(document_store, name=\"document_store\", inputs=[\"preprocessor\"])\n",
     "\n",
     "# Then we run it with the documents and their metadata as input\n",
-    "output = indexing_pipeline.run(file_paths=file_paths, meta=files_metadata)"
+    "indexing_pipeline.run(file_paths=file_paths, meta=files_metadata)"
    ]
   },
   {
@@ -194,10 +164,9 @@
     "id": "zW5qaqn1B3St"
    },
    "source": [
-    "### Querying\n",
+    "## Creating a QA Pipeline with AnswerToSpeech\n",
     "   \n",
-    "Now we will create a pipeline very similar to the basic `ExtractiveQAPipeline` of Tutorial 1,\n",
-    "with the addition of a node that converts our answers into audio files! Once the answer is retrieved, we can also listen to the audio version of the document where the answer came from."
+    "Now we will create a pipeline very similar to the basic `ExtractiveQAPipeline` of [Tutorial: Build Your First Question Answering System](https://haystack.deepset.ai/tutorials/01_basic_qa_pipeline), with the addition of a node that converts our answers into audio files: AnswerToSpeech. Once the answer is retrieved, we can also listen to the audio version of the document where the answer came from."
    ]
   },
   {
@@ -208,8 +177,6 @@
    },
    "outputs": [],
    "source": [
-    "from pathlib import Path\n",
-    "from haystack import Pipeline\n",
     "from haystack.nodes import BM25Retriever, FARMReader\n",
     "from text2speech import AnswerToSpeech\n",
     "\n",
@@ -231,7 +198,9 @@
     "id": "oV1KHzXGB3Sy"
    },
    "source": [
-    "## Ask a question!"
+    "## Asking a question!\n",
+    "\n",
+    "Use the pipeline `run()` method to ask a question. The query argument is where you type your question. Additionally, you can set the number of documents you want the Reader and Retriever to return using the `top-k` parameter."
    ]
   },
   {
@@ -245,8 +214,6 @@
    },
    "outputs": [],
    "source": [
-    "# You can configure how many candidates the Reader and Retriever shall return\n",
-    "# The higher top_k_retriever, the better (but also the slower) your answers.\n",
     "prediction = audio_pipeline.run(\n",
     "    query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
     ")"
@@ -285,7 +252,9 @@
     "id": "FXf-kTn4B3S6"
    },
    "source": [
-    "## Hear them out!"
+    "## Hear Answers out!\n",
+    "\n",
+    "Let's hear the answers and the context they are extracted from."
    ]
   },
   {
@@ -300,17 +269,6 @@
     "import soundfile as sf"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "iRIotUuz-mpF"
-   },
-   "outputs": [],
-   "source": [
-    "prediction[\"answers\"][0]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -343,6 +301,13 @@
     "display(Audio(speech, rate=24000))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🎉 Congratulations! You've learned how to create a extactive QA system that can read out the answer."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -363,12 +328,11 @@
     "Some of our other work: \n",
     "- [German BERT](https://deepset.ai/german-bert)\n",
     "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
     "\n",
     "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
+    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://discord.com/invite/VBpFzsgRVF) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Haystack Website](https://deepset.ai)\n",
     "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
+    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
    ]
   }
  ],

From d42dd1e1195b847b9290b1787139d865617c9241 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 4 Apr 2023 17:14:45 +0300
Subject: [PATCH 067/206] Update template structure (#161)

* Update the PromptTemplate Structure and remove Shaper

* Update the Template structure on tutorial 21

* remove output cells
---
 tutorials/21_Customizing_PromptNode.ipynb   | 3459 ++++++++++++++++---
 tutorials/22_Pipeline_with_PromptNode.ipynb | 1052 ++++--
 2 files changed, 3615 insertions(+), 896 deletions(-)

diff --git a/tutorials/21_Customizing_PromptNode.ipynb b/tutorials/21_Customizing_PromptNode.ipynb
index 8f86f0e4..a7c20100 100644
--- a/tutorials/21_Customizing_PromptNode.ipynb
+++ b/tutorials/21_Customizing_PromptNode.ipynb
@@ -1,580 +1,2887 @@
 {
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "BlTn1yekNONP"
-   },
-   "source": [
-    "# Tutorial: Customizing PromptNode for NLP Tasks\n",
-    "\n",
-    "- **Level**: Intermediate\n",
-    "- **Time to complete**: 20 minutes\n",
-    "- **Nodes Used**: `PromptNode`, `PromptTemplate`\n",
-    "- **Goal**: After completing this tutorial, you will have learned the basics of using PromptNode and PromptTemplates and you'll have added titles to articles from The Guardian and categorized them. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ffYFUAjUNONS"
-   },
-   "source": [
-    "## Overview\n",
-    "\n",
-    "Use large language models (LLMs) through PromptNode and PromptTemplate to summarize and categorize your documents, and find a suitable title for them. In this tutorial, we'll use news from [The Guardian](https://www.theguardian.com/international) as documents, but you can replace them with any text you want.  \n",
-    "\n",
-    "This tutorial introduces you to the basics of LLMs and PromptNode, showcases the pre-defined \"summarization\" template, and explains how to use PromptTemplate to generate titles for documents and categorize them with custom prompts."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ugkQ42iJNONS"
-   },
-   "source": [
-    "## Preparing the Colab Environment\n",
-    "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "t0rQfgOVNONS"
-   },
-   "source": [
-    "## Installing Haystack\n",
-    "\n",
-    "To start, let's install the latest release of Haystack with `pip`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "S-00smnxNONT",
-    "outputId": "5488121a-81f1-4591-acd7-89a1c9b2daf1"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Enabling Telemetry\n",
-    "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack.telemetry import tutorial_running\n",
-    "\n",
-    "tutorial_running(21)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "RYtbl6qBNONU"
-   },
-   "source": [
-    "## Trying Out PromptNode\n",
-    "\n",
-    "The PromptNode is the central abstraction in Haystack's large language model (LLM) support. It uses [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) model by default, but you can replace the default model with a flan-t5 model of a different size such as `google/flan-t5-large` or a model by OpenAI such as `text-davinci-003`.\n",
-    "\n",
-    "[Large language models](https://docs.haystack.deepset.ai/docs/language_models#large-language-models-llms) are huge models trained on enormous amounts of data. That’s why these models have general knowledge of the world, so you can ask them anything and they will be able to answer.\n",
-    "\n",
-    "As a warm-up, let's initialize PromptNode and see what it can do when run stand-alone: "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2wYbU8WhNX08"
-   },
-   "source": [
-    "1. Initialize a PromptNode instance with [`google/flan-t5-large`](https://huggingface.co/google/flan-t5-large):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "wa-I31YZNONU"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.nodes import PromptNode\n",
-    "\n",
-    "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZE7s-cMRNh5f"
-   },
-   "source": [
-    "> Note: To use PromptNode with an OpenAI model, change the model name and provide an `api_key`: \n",
-    "> ```python\n",
-    "> prompt_node = PromptNode(model_name_or_path=\"text-davinci-003\", api_key=<YOUR_API_KEY>)\n",
-    "> ```"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "NO5z2H_mO8cg"
-   },
-   "source": [
-    "2. Ask any general question that comes to your mind, for example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "dBxL4odqNg6K",
-    "outputId": "2e12c341-b24d-45e7-f758-a93e81cdc570"
-   },
-   "outputs": [],
-   "source": [
-    "prompt_node(\"What is the capital of Germany?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "cg-8tKebDCa0",
-    "outputId": "e108ad6d-5e44-4108-e308-4c85da2b1951"
-   },
-   "outputs": [],
-   "source": [
-    "prompt_node(\"What is the highest mountain?\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0MVeOCWSNONV"
-   },
-   "source": [
-    "As `google/flan-t5-large` was trained on school math problems dataset named [GSM8K](https://huggingface.co/datasets/gsm8k) you can also ask some basic math questions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "-ZpTIQEUNONW",
-    "outputId": "fe97895b-c475-4de2-ad70-858201f008ad"
-   },
-   "outputs": [],
-   "source": [
-    "prompt_node(\"If Bob is 20 and Sara is 11, who is older?\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "aybEtTGIQQSX"
-   },
-   "source": [
-    "Now that you've initialized PromptNode and saw how it works, let's see how we can use it for more advanced tasks."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "W6hK9AcsNONW"
-   },
-   "source": [
-    "## Summarizing Documents with PromptNode\n",
-    "\n",
-    "PromptNode comes with out-of-the-box prompt templates that can perform multiple tasks, such as summarization, question answering, question generation, and more. To use a templates, just provide its name to the PromptNode. \n",
-    "\n",
-    "For this task, we'll use the summarization template and news from The Guardian. Let's see how to do it.\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zECQSKkWDYns"
-   },
-   "source": [
-    "1. Define news to use as `documents` for the PromptNode. We'll use these documents throughout the whole tutorial."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "DjYmEk9_dyHZ"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.schema import Document\n",
-    "\n",
-    "# https://www.theguardian.com/business/2023/feb/12/inflation-may-have-peaked-but-the-cost-of-living-pain-is-far-from-over\n",
-    "news_economics = Document(\n",
-    "    \"\"\"At long last, Britain’s annual inflation rate is on the way down. After hitting the highest level since the 1980s, heaping pressure on millions of households as living costs soared, official figures this week could bring some rare good news.\n",
-    "City economists expect UK inflation to have cooled for a third month running in January – the exact number is announced on Wednesday – helped by falling petrol prices and a broader decline in the global price of oil and gas in recent months. The hope now is for a sustained decline in the months ahead, continuing a steady drop from the peak of 11.1% seen in October.\n",
-    "The message from the Bank of England has been clear. Inflation is on track for a “rapid” decline over the coming months, raising hopes that the worst of Britain’s cost of living crisis is now in the rearview mirror.\n",
-    "There are two good reasons for this. Energy costs are moving in the right direction, while the initial rise in wholesale oil and gas prices that followed Russia’s invasion of Ukraine in February last year will soon drop from the calculation of the annual inflation rate.\"\"\"\n",
-    ")\n",
-    "\n",
-    "# https://www.theguardian.com/science/2023/feb/13/starwatch-orions-belt-and-sirius-lead-way-to-hydras-head\n",
-    "news_science = Document(\n",
-    "    \"\"\"On northern winter nights, it is so easy to be beguiled by the gloriously bright constellations of Orion, the hunter, and Taurus, the bull, that one can overlook the fainter constellations.\n",
-    "So this week, find the three stars of Orion’s belt, follow them down to Sirius, the brightest star in the night sky, and then look eastward until you find the faint ring of stars that makes up the head of Hydra, the water snake. The chart shows the view looking south-east from London at 8pm GMT on Monday, but the view will be similar every night this week.\n",
-    "Hydra is the largest of the 88 modern constellations covering an area of 1,303 square degrees. To compare, nearby Orion only covers 594 square degrees. Hydra accounts for most of its area by its length, crossing more than 100 degrees of the sky (the full moon spans half a degree).\n",
-    "As evening becomes night and into the early hours, the rotation of Earth causes Hydra to slither its way across the southern meridian until dawn washes it from the sky. From the southern hemisphere, the constellation is easily visible in the eastern sky by mid-evening.\"\"\"\n",
-    ")\n",
-    "\n",
-    "# https://www.theguardian.com/music/2023/jan/30/salisbury-cathedral-pipe-organ-new-life-holst-the-planets\n",
-    "news_culture = Document(\n",
-    "    \"\"\"A unique performance of Gustav Holst’s masterwork The Planets – played on a magnificent pipe organ rather than by an orchestra and punctuated by poems inspired by children’s responses to the music – is to be staged in the suitably vast Salisbury Cathedral.\n",
-    "The idea of the community music project is to introduce more people, young and old, to the 140-year-old “Father” Willis organ, one of the treasures of the cathedral.\n",
-    "It is also intended to get the children who took part and the adults who will watch and listen thinking afresh about the themes Holst’s suite tackles – war, peace, joy and mysticism – which seem as relevant now as when he wrote the work a century ago.\n",
-    "John Challenger, the cathedral’s principal organist, said: “We have a fantastic pipe organ largely as it was when built. It’s a thrilling thing. I view it as my purpose in life to share it with as many people as possible.”\n",
-    "The Planets is written for a large orchestra. “Holst calls for huge instrumental forces and an unseen distant choir of sopranos and altos,” said Challenger. But he has transposed the suite for the organ, not copying the effect of the orchestral instruments but finding a new version of the suite.\"\"\"\n",
-    ")\n",
-    "\n",
-    "# https://www.theguardian.com/sport/blog/2023/feb/14/multi-million-dollar-wpl-auction-signals-huge-step-forward-for-womens-sport\n",
-    "news_sport = Document(\n",
-    "    \"\"\"It was only a few days ago that members of the Australian women’s cricket team were contemplating how best to navigate the impending “distraction” of the inaugural Women’s Premier League auction, scheduled during the first week of the T20 World Cup. “It’s a little bit awkward,” captain Meg Lanning said in South Africa last week. “But it’s just trying to embrace that and understanding it’s actually a really exciting time and you actually don’t have a lot of control over most of it, so you’ve just got to wait and see.”\n",
-    "What a pleasant distraction it turned out to be. Lanning herself will be $192,000 richer for three weeks’ work with the Delhi Capitals. Her teammate, Ash Gardner, will earn three times that playing for the Gujarat Giants. The allrounder’s figure of $558,000 is more than Sam Kerr pockets in a season with Chelsea and more than the WNBA’s top earner, Jackie Young.\n",
-    "If that sounds like a watershed moment, it’s perhaps because it is. And it is not the only one this past week. The NRLW made its own wage-related headlines on Tuesday, to the effect that the next (agreed in principle) collective bargaining agreement will bring with it a $1.5m salary cap in 2027, at an average salary of $62,500. Women’s rugby, too, is making moves, with news on the weekend that Rugby Australia will begin contracting the Wallaroos.\"\"\"\n",
-    ")\n",
-    "\n",
-    "news = [news_economics, news_science, news_culture, news_sport]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Ezq9NVmFdzz1"
-   },
-   "source": [
-    "> The token limit for `google/flan-t5-large` is 512. So, all news pieces should be shorter than the limit."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "okw4m5aeL7hy"
-   },
-   "source": [
-    "2. List pre-defined templates using the `get_prompt_template_names()` method. All templates come with the prompts needed to perform these tasks. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "JqwLmdePNONW",
-    "outputId": "b33d9123-9cf3-4444-fc04-1bc8311e7338"
-   },
-   "outputs": [],
-   "source": [
-    "prompt_node.get_prompt_template_names()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "syB5rl3xIgHr"
-   },
-   "source": [
-    "3. Use the `summarization` template to generate a summary for each piece of news:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "ygxFMwb4NONX",
-    "outputId": "e56ed89a-b020-4f00-9310-b1a643cb87ea"
-   },
-   "outputs": [],
-   "source": [
-    "prompt_node.prompt(prompt_template=\"summarization\", documents=news)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "lUTckmA1PzLc"
-   },
-   "source": [
-    "Here you go! You have generated summaries of your news articles. But we're missing titles for them. Let's see how PromptNode can help us there."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "f0vQ45tHNONX"
-   },
-   "source": [
-    "## Generating Titles for News Articles with a Custom Template"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "992kb_WKNONX"
-   },
-   "source": [
-    "The biggest benefit of PromptNode is its versatility. You can use it to perform practically any NLP task if you define your own prompt templates for them. By creating your prompt templates, you can extend the model's capabilities and use it for a broader range of NLP tasks in Haystack. \n",
-    "\n",
-    "You can define custom templates for each NLP task and register them with PromptNode. Let's create a custom template to generate descriptive titles for news:"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "en6IbPRsTOTz"
-   },
-   "source": [
-    "1. Initialize a `PromptTemplate` instance. Give your template a `name` and define the prompt in `prompt_text`. To define any parameters for the prompt, add them to the `prompt_text` preceded by the `$` sign. We need a template to generate titles for our news articles. We'll call it `give-a-title`. The only parameter we need is `$news`, so let's add it to the `prompt_text`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "OiOiya2UV4WW"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.nodes import PromptTemplate\n",
-    "\n",
-    "title_generator = PromptTemplate(\n",
-    "    name=\"give-a-title\",\n",
-    "    prompt_text=\"Provide a short, descriptive title for the given piece of news. News: $news; Title:\",\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "jttb_ROnWYwS"
-   },
-   "source": [
-    "2. To use the new template, pass `title_generator` as the `prompt_template` to the `prompt()` method:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "zX_nALriWXxx",
-    "outputId": "b027b36c-ef1f-4c5b-c392-7bc9d6364838"
-   },
-   "outputs": [],
-   "source": [
-    "prompt_node.prompt(prompt_template=title_generator, news=news)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "p0Mx3lHyXK_I"
-   },
-   "source": [
-    "> Note: To add a custom template to the template list, call `add_prompt_template()` with the `PromptTemplate` object pass the template contents to it. Once you do this, the next time you want to use this template, just call its name: \n",
-    "> ```python\n",
-    "> prompt_node.add_prompt_template(PromptTemplate(name=\"give-a-title\", prompt_text=\"Provide a short, descriptive title for the given piece of news. News: $news; Title:\"))\n",
-    "> prompt_node.prompt(prompt_template=\"give-a-title\", news=news)\n",
-    "> ```"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There you go! You should have the titles for your news articles ready. Let's now categorize them."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "eH4QbygONONX"
-   },
-   "source": [
-    "## Categorizing Documents with PromptNode\n",
-    "\n",
-    "You can customize PromptTemplates as much as you need. Let's try to create a template to categorize the news articles. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FAELXO0MhbrV"
-   },
-   "source": [
-    "1. Create another PromptTemplate called `categorize-news`. In the `prompt_text`, define the `$news` parameter, specify the categories you want to use, and ask the model not to categorize the news if it doesn't fit in the provided category list: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "xHgOjTwkNONY"
-   },
-   "outputs": [],
-   "source": [
-    "news_categorizer = PromptTemplate(\n",
-    "    name=\"categorize-news\",\n",
-    "    prompt_text=\"Given the categories: sport, economics, culture; classify the news: $news. Only pick a category from the list, otherwise say: no suitable category\",\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nC4gVHCyuXHl"
-   },
-   "source": [
-    "2. Run the `prompt()` method with the `news_categorizer` template:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BlTn1yekNONP"
+      },
+      "source": [
+        "# Tutorial: Customizing PromptNode for NLP Tasks\n",
+        "\n",
+        "- **Level**: Intermediate\n",
+        "- **Time to complete**: 20 minutes\n",
+        "- **Nodes Used**: `PromptNode`, `PromptTemplate`\n",
+        "- **Goal**: After completing this tutorial, you will have learned the basics of using PromptNode and PromptTemplates and you'll have added titles to articles from The Guardian and categorized them. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ffYFUAjUNONS"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "Use large language models (LLMs) through PromptNode and PromptTemplate to summarize and categorize your documents, and find a suitable title for them. In this tutorial, we'll use news from [The Guardian](https://www.theguardian.com/international) as documents, but you can replace them with any text you want.  \n",
+        "\n",
+        "This tutorial introduces you to the basics of LLMs and PromptNode, showcases the pre-defined \"summarization\" template, and explains how to use PromptTemplate to generate titles for documents and categorize them with custom prompts."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ugkQ42iJNONS"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t0rQfgOVNONS"
+      },
+      "source": [
+        "## Installing Haystack\n",
+        "\n",
+        "To start, let's install the latest release of Haystack with `pip`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "S-00smnxNONT"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install --upgrade pip\n",
+        "pip install farm-haystack[colab]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VTfmApFpZKn6"
+      },
+      "source": [
+        "### Enabling Telemetry\n",
+        "\n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "HtImiSaHZKn6"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(21)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RYtbl6qBNONU"
+      },
+      "source": [
+        "## Trying Out PromptNode\n",
+        "\n",
+        "The PromptNode is the central abstraction in Haystack's large language model (LLM) support. It uses [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) model by default, but you can replace the default model with a flan-t5 model of a different size such as `google/flan-t5-large` or a model by OpenAI such as `text-davinci-003`.\n",
+        "\n",
+        "[Large language models](https://docs.haystack.deepset.ai/docs/language_models#large-language-models-llms) are huge models trained on enormous amounts of data. That’s why these models have general knowledge of the world, so you can ask them anything and they will be able to answer.\n",
+        "\n",
+        "As a warm-up, let's initialize PromptNode and see what it can do when run stand-alone: "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2wYbU8WhNX08"
+      },
+      "source": [
+        "1. Initialize a PromptNode instance with [`google/flan-t5-large`](https://huggingface.co/google/flan-t5-large):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 264,
+          "referenced_widgets": [
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+            "b5b0b7507559446f993abaceaca64f31",
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+            "c7f836da067b44b6b4de6424f8dac477",
+            "0a49948f4ed94d80b7ccf5ab4af88c75",
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+            "7c6ec120be1542c3b6e7decac877b504",
+            "7682a05ceec5480088fad3e95cff8c8d",
+            "d2d0a93c3cf64644aef19987e2a29f57"
+          ]
+        },
+        "id": "wa-I31YZNONU",
+        "outputId": "23de7946-35bd-4b91-c664-47ce7088dc45"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "06e6d57302514c30961c7159cd505489",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)lve/main/config.json:   0%|          | 0.00/662 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c4ddc552487a46d49cf016ea43828555",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading pytorch_model.bin:   0%|          | 0.00/3.13G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.9/dist-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
+            "  return self.fget.__get__(instance, owner)()\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9f1667dc558f47758b2a3d5845ef2a41",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c47adf0770814fcba9a8b0be938832a1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "493c3b3dca05469d87cfbae22b9f8669",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "970aafb2dd7443baa2080602c929636c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from haystack.nodes import PromptNode\n",
+        "\n",
+        "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZE7s-cMRNh5f"
+      },
+      "source": [
+        "> Note: To use PromptNode with an OpenAI model, change the model name and provide an `api_key`: \n",
+        "> ```python\n",
+        "> prompt_node = PromptNode(model_name_or_path=\"text-davinci-003\", api_key=<YOUR_API_KEY>)\n",
+        "> ```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NO5z2H_mO8cg"
+      },
+      "source": [
+        "2. Ask any general question that comes to your mind, for example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dBxL4odqNg6K",
+        "outputId": "a0c0fadd-06a2-44fb-e2a3-a101ed0d753c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['berlin']"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "prompt_node(\"What is the capital of Germany?\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cg-8tKebDCa0",
+        "outputId": "99873954-6d29-4d24-c717-628b1633cd82"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['mount everest']"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "prompt_node(\"What is the highest mountain?\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0MVeOCWSNONV"
+      },
+      "source": [
+        "As `google/flan-t5-large` was trained on school math problems dataset named [GSM8K](https://huggingface.co/datasets/gsm8k) you can also ask some basic math questions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-ZpTIQEUNONW",
+        "outputId": "d8e5a6e0-7b99-4095-f48e-296ff3eec641"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['Bob']"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "prompt_node(\"If Bob is 20 and Sara is 11, who is older?\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aybEtTGIQQSX"
+      },
+      "source": [
+        "Now that you've initialized PromptNode and saw how it works, let's see how we can use it for more advanced tasks."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W6hK9AcsNONW"
+      },
+      "source": [
+        "## Summarizing Documents with PromptNode\n",
+        "\n",
+        "PromptNode comes with out-of-the-box prompt templates that can perform multiple tasks, such as summarization, question answering, question generation, and more. To use a templates, just provide its name to the PromptNode. \n",
+        "\n",
+        "For this task, we'll use the summarization template and news from The Guardian. Let's see how to do it.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zECQSKkWDYns"
+      },
+      "source": [
+        "1. Define news to use as `documents` for the PromptNode. We'll use these documents throughout the whole tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "DjYmEk9_dyHZ"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.schema import Document\n",
+        "\n",
+        "# https://www.theguardian.com/business/2023/feb/12/inflation-may-have-peaked-but-the-cost-of-living-pain-is-far-from-over\n",
+        "news_economics = Document(\n",
+        "    \"\"\"At long last, Britain’s annual inflation rate is on the way down. After hitting the highest level since the 1980s, heaping pressure on millions of households as living costs soared, official figures this week could bring some rare good news.\n",
+        "City economists expect UK inflation to have cooled for a third month running in January – the exact number is announced on Wednesday – helped by falling petrol prices and a broader decline in the global price of oil and gas in recent months. The hope now is for a sustained decline in the months ahead, continuing a steady drop from the peak of 11.1% seen in October.\n",
+        "The message from the Bank of England has been clear. Inflation is on track for a “rapid” decline over the coming months, raising hopes that the worst of Britain’s cost of living crisis is now in the rearview mirror.\n",
+        "There are two good reasons for this. Energy costs are moving in the right direction, while the initial rise in wholesale oil and gas prices that followed Russia’s invasion of Ukraine in February last year will soon drop from the calculation of the annual inflation rate.\"\"\"\n",
+        ")\n",
+        "\n",
+        "# https://www.theguardian.com/science/2023/feb/13/starwatch-orions-belt-and-sirius-lead-way-to-hydras-head\n",
+        "news_science = Document(\n",
+        "    \"\"\"On northern winter nights, it is so easy to be beguiled by the gloriously bright constellations of Orion, the hunter, and Taurus, the bull, that one can overlook the fainter constellations.\n",
+        "So this week, find the three stars of Orion’s belt, follow them down to Sirius, the brightest star in the night sky, and then look eastward until you find the faint ring of stars that makes up the head of Hydra, the water snake. The chart shows the view looking south-east from London at 8pm GMT on Monday, but the view will be similar every night this week.\n",
+        "Hydra is the largest of the 88 modern constellations covering an area of 1,303 square degrees. To compare, nearby Orion only covers 594 square degrees. Hydra accounts for most of its area by its length, crossing more than 100 degrees of the sky (the full moon spans half a degree).\n",
+        "As evening becomes night and into the early hours, the rotation of Earth causes Hydra to slither its way across the southern meridian until dawn washes it from the sky. From the southern hemisphere, the constellation is easily visible in the eastern sky by mid-evening.\"\"\"\n",
+        ")\n",
+        "\n",
+        "# https://www.theguardian.com/music/2023/jan/30/salisbury-cathedral-pipe-organ-new-life-holst-the-planets\n",
+        "news_culture = Document(\n",
+        "    \"\"\"A unique performance of Gustav Holst’s masterwork The Planets – played on a magnificent pipe organ rather than by an orchestra and punctuated by poems inspired by children’s responses to the music – is to be staged in the suitably vast Salisbury Cathedral.\n",
+        "The idea of the community music project is to introduce more people, young and old, to the 140-year-old “Father” Willis organ, one of the treasures of the cathedral.\n",
+        "It is also intended to get the children who took part and the adults who will watch and listen thinking afresh about the themes Holst’s suite tackles – war, peace, joy and mysticism – which seem as relevant now as when he wrote the work a century ago.\n",
+        "John Challenger, the cathedral’s principal organist, said: “We have a fantastic pipe organ largely as it was when built. It’s a thrilling thing. I view it as my purpose in life to share it with as many people as possible.”\n",
+        "The Planets is written for a large orchestra. “Holst calls for huge instrumental forces and an unseen distant choir of sopranos and altos,” said Challenger. But he has transposed the suite for the organ, not copying the effect of the orchestral instruments but finding a new version of the suite.\"\"\"\n",
+        ")\n",
+        "\n",
+        "# https://www.theguardian.com/sport/blog/2023/feb/14/multi-million-dollar-wpl-auction-signals-huge-step-forward-for-womens-sport\n",
+        "news_sport = Document(\n",
+        "    \"\"\"It was only a few days ago that members of the Australian women’s cricket team were contemplating how best to navigate the impending “distraction” of the inaugural Women’s Premier League auction, scheduled during the first week of the T20 World Cup. “It’s a little bit awkward,” captain Meg Lanning said in South Africa last week. “But it’s just trying to embrace that and understanding it’s actually a really exciting time and you actually don’t have a lot of control over most of it, so you’ve just got to wait and see.”\n",
+        "What a pleasant distraction it turned out to be. Lanning herself will be $192,000 richer for three weeks’ work with the Delhi Capitals. Her teammate, Ash Gardner, will earn three times that playing for the Gujarat Giants. The allrounder’s figure of $558,000 is more than Sam Kerr pockets in a season with Chelsea and more than the WNBA’s top earner, Jackie Young.\n",
+        "If that sounds like a watershed moment, it’s perhaps because it is. And it is not the only one this past week. The NRLW made its own wage-related headlines on Tuesday, to the effect that the next (agreed in principle) collective bargaining agreement will bring with it a $1.5m salary cap in 2027, at an average salary of $62,500. Women’s rugby, too, is making moves, with news on the weekend that Rugby Australia will begin contracting the Wallaroos.\"\"\"\n",
+        ")\n",
+        "\n",
+        "news = [news_economics, news_science, news_culture, news_sport]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ezq9NVmFdzz1"
+      },
+      "source": [
+        "> The token limit for `google/flan-t5-large` is 512. So, all news pieces should be shorter than the limit."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "okw4m5aeL7hy"
+      },
+      "source": [
+        "2. List pre-defined templates using the `get_prompt_template_names()` method. All templates come with the prompts needed to perform these tasks. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JqwLmdePNONW",
+        "outputId": "b9cc94d9-b804-4223-871f-97f2c74f7090"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['question-answering',\n",
+              " 'question-answering-per-document',\n",
+              " 'question-answering-with-references',\n",
+              " 'question-answering-with-document-scores',\n",
+              " 'question-generation',\n",
+              " 'conditioned-question-generation',\n",
+              " 'summarization',\n",
+              " 'question-answering-check',\n",
+              " 'sentiment-analysis',\n",
+              " 'multiple-choice-question-answering',\n",
+              " 'topic-classification',\n",
+              " 'language-detection',\n",
+              " 'translation',\n",
+              " 'zero-shot-react']"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "prompt_node.get_prompt_template_names()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "syB5rl3xIgHr"
+      },
+      "source": [
+        "3. Use the `summarization` template to generate a summary for each piece of news:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ygxFMwb4NONX",
+        "outputId": "1106be62-4df2-4291-82cf-55e3ab966ed0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['The Bank of England has said that inflation is on track for a “rapid” decline over the coming months, raising hopes that the worst of Britain’s cost of living crisis is now in the rearview mirror.',\n",
+              " 'Look for the head of Hydra, the water snake, this week.',\n",
+              " 'A community music project is to be staged in Salisbury Cathedral to celebrate the centenary of the composer’s death.',\n",
+              " 'The Women’s Premier League auction has been a welcome distraction for the Australian women’s cricket team.']"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "prompt_node.prompt(prompt_template=\"summarization\", documents=news)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lUTckmA1PzLc"
+      },
+      "source": [
+        "Here you go! You have generated summaries of your news articles. But we're missing titles for them. Let's see how PromptNode can help us there."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0vQ45tHNONX"
+      },
+      "source": [
+        "## Generating Titles for News Articles with a Custom Template"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "992kb_WKNONX"
+      },
+      "source": [
+        "The biggest benefit of PromptNode is its versatility. You can use it to perform practically any NLP task if you define your own prompt templates for them. By creating your prompt templates, you can extend the model's capabilities and use it for a broader range of NLP tasks in Haystack. \n",
+        "\n",
+        "You can define custom templates for each NLP task and register them with PromptNode. Let's create a custom template to generate descriptive titles for news:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "en6IbPRsTOTz"
+      },
+      "source": [
+        "1. Initialize a `PromptTemplate` instance. Give your template a `name` and define the prompt in `prompt_text`. To define any parameters for the prompt, add them to the `prompt_text` wrapped with curly brackets. We need a template to generate titles for our news articles. We'll call it `give-a-title`. The only parameter we need is `{news}`, so let's add it to the `prompt_text`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "OiOiya2UV4WW"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.nodes import PromptTemplate\n",
+        "\n",
+        "title_generator = PromptTemplate(\n",
+        "    name=\"give-a-title\",\n",
+        "    prompt_text=\"Provide a short, descriptive title for the given piece of news. News: {news}; Title:\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jttb_ROnWYwS"
+      },
+      "source": [
+        "2. To use the new template, pass `title_generator` as the `prompt_template` to the `prompt()` method:\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zX_nALriWXxx",
+        "outputId": "7052c817-fb6b-4e5e-ed4d-676d7507e91e"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.9/dist-packages/transformers/pipelines/base.py:1043: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "['Britain’s inflation rate on the way down',\n",
+              " 'The Orion constellation',\n",
+              " 'The Planets: A unique performance of Gustav Holst’s masterwork',\n",
+              " \"Australia women's cricket team to play Pakistan in ODI\"]"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "prompt_node.prompt(prompt_template=title_generator, news=news)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p0Mx3lHyXK_I"
+      },
+      "source": [
+        "> Note: To add a custom template to the template list, call `add_prompt_template()` with the `PromptTemplate` object pass the template contents to it. Once you do this, the next time you want to use this template, just call its name: \n",
+        "> ```python\n",
+        "> prompt_node.add_prompt_template(PromptTemplate(name=\"give-a-title\", prompt_text=\"Provide a short, descriptive title for the given piece of news. News: {news}; Title:\"))\n",
+        "> prompt_node.prompt(prompt_template=\"give-a-title\", news=news)\n",
+        "> ```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8t9pdZo9ZKn9"
+      },
+      "source": [
+        "There you go! You should have the titles for your news articles ready. Let's now categorize them."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eH4QbygONONX"
+      },
+      "source": [
+        "## Categorizing Documents with PromptNode\n",
+        "\n",
+        "You can customize PromptTemplates as much as you need. Let's try to create a template to categorize the news articles. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FAELXO0MhbrV"
+      },
+      "source": [
+        "1. Create another PromptTemplate called `categorize-news`. In the `prompt_text`, define the `{news}` parameter, specify the categories you want to use, and ask the model not to categorize the news if it doesn't fit in the provided category list: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "xHgOjTwkNONY"
+      },
+      "outputs": [],
+      "source": [
+        "news_categorizer = PromptTemplate(\n",
+        "    name=\"categorize-news\",\n",
+        "    prompt_text=\"Given the categories: sport, economics, culture; classify the news: {news}. Only pick a category from the list, otherwise say: no suitable category\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nC4gVHCyuXHl"
+      },
+      "source": [
+        "2. Run the `prompt()` method with the `news_categorizer` template:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "taktlLIfNONY",
+        "outputId": "36b9ae31-0165-4f7e-e809-4c7fa8d6985a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['economics', 'no suitable category', 'culture', 'sport']"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "prompt_node.prompt(prompt_template=news_categorizer, news=news)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aU1e7ak4ZKn9"
+      },
+      "source": [
+        "Congratulations! You've summarized your documents, generated titles for them, and put them into categories, all using custom prompt templates. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QVKsx2KbNONY"
+      },
+      "source": [
+        "## About us\n",
+        "\n",
+        "\n",
+        "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
+        "\n",
+        "We bring NLP to the industry via open source!  \n",
+        "Our focus: Industry specific language models & large scale QA systems.  \n",
+        "  \n",
+        "Some of our other work: \n",
+        "- [German BERT](https://deepset.ai/german-bert)\n",
+        "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
+        "\n",
+        "Get in touch:\n",
+        "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://discord.com/invite/VBpFzsgRVF) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Haystack Website](https://deepset.ai)\n",
+        "\n",
+        "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
     "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "taktlLIfNONY",
-    "outputId": "1e9a5947-1074-47a6-849b-3b55992983b2"
-   },
-   "outputs": [],
-   "source": [
-    "prompt_node.prompt(prompt_template=news_categorizer, news=news)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Congratulations! You've summarized your documents, generated titles for them, and put them into categories, all using custom prompt templates. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "QVKsx2KbNONY"
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://discord.com/invite/VBpFzsgRVF) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Haystack Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "provenance": []
-  },
-  "gpuClass": "standard",
-  "kernelspec": {
-   "display_name": "Python 3.9.12 ('base')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3.9.12 ('base')",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.12"
+    },
+    "orig_nbformat": 4,
+    "vscode": {
+      "interpreter": {
+        "hash": "76c0ba09435b0d7ab7f3e4f9fd6b1554cdb12adf349a7242a1470606c432d777"
+      }
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "0673436ebd4142c39242973b93aeec8e": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HTMLModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
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+            "description_tooltip": null,
+            "layout": "IPY_MODEL_b5b0b7507559446f993abaceaca64f31",
+            "placeholder": "​",
+            "style": "IPY_MODEL_7b974567f3bc4f6986e125060bc43a21",
+            "value": " 792k/792k [00:00&lt;00:00, 18.1MB/s]"
+          }
+        },
+        "06e6d57302514c30961c7159cd505489": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
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+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_6877c499c906463291bbebc3e34b2339",
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diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index 20c01f6b..e4574f58 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -1,323 +1,735 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial: Creating a Generative QA Pipeline with PromptNode\n",
-    "\n",
-    "- **Level**: Advanced\n",
-    "- **Time to complete**: 15 minutes\n",
-    "- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `PromptNode`, `PromptTemplate` `Shaper`\n",
-    "- **Goal**: After completing this tutorial, you'll have created a generative question answering search system that uses a large language model through PromptNode with the help of Shaper. "
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2OvkPji9O-qX"
+      },
+      "source": [
+        "# Tutorial: Creating a Generative QA Pipeline with PromptNode\n",
+        "\n",
+        "- **Level**: Advanced\n",
+        "- **Time to complete**: 15 minutes\n",
+        "- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `PromptNode`, `PromptTemplate`\n",
+        "- **Goal**: After completing this tutorial, you'll have created a generative question answering search system that uses a large language model through PromptNode with the new PromptTemplate structure. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LFqHcXYPO-qZ"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "Learn how to build a generative question answering pipeline using the power of LLMs with PromptNode. In this tutorial, we'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want. \n",
+        "\n",
+        "This tutorial introduces you to the new PrompTemplate structure and explains how to use the new PrompTemplate to integrate PromptNode into a pipeline. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QXjVlbPiO-qZ"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kww5B_vXO-qZ"
+      },
+      "source": [
+        "## Installing Haystack\n",
+        "\n",
+        "To start, let's install the latest release of Haystack with `pip`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UQbU8GUfO-qZ",
+        "outputId": "47bc8b21-0907-49be-d4c8-9b70f6f002a4"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install --upgrade pip\n",
+        "pip install farm-haystack[colab]\n",
+        "pip install datasets>=2.6.1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Wl_jYERtO-qa"
+      },
+      "source": [
+        "### Enabling Telemetry\n",
+        "\n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "A76B4S49O-qa"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(22)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_lvfew16O-qa"
+      },
+      "source": [
+        "## Initializing the DocumentStore\n",
+        "\n",
+        "We'll start creating our question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, we're using the `InMemoryDocumentStore`.\n",
+        "\n",
+        "Let's initialize our DocumentStore. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CbVN-s5LO-qa"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.document_stores import InMemoryDocumentStore\n",
+        "\n",
+        "document_store = InMemoryDocumentStore(use_bm25=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yL8nuJdWO-qa"
+      },
+      "source": [
+        "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XvLVaFHTO-qb"
+      },
+      "source": [
+        "The DocumentStore is now ready. Now it's time to fill it with some Documents."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HryYZP9ZO-qb"
+      },
+      "source": [
+        "## Fetching and Writing Documents\n",
+        "\n",
+        "We'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, we don't need to perform any additional cleaning or splitting. \n",
+        "\n",
+        "Let's fetch the data and write it to the DocumentStore: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 86,
+          "referenced_widgets": [
+            "483577d477414459bedb984fc98c3991",
+            "afb27557357348f2b8175cbed70c6e1f",
+            "bebb7f98a2b94886a126ded8c937ae12",
+            "58a0354b9c1d4edcbc65f410f8ccd7db",
+            "b7b78a92b7b44e10826e771112fc58c6",
+            "19b74a865c1f47e5a84f03b4ca45c10e",
+            "adde2fd48f5045419d70dc214eb46008",
+            "722edaae172042f284235265b7f55264",
+            "2f039c7861524e49a2e3e4382e46af67",
+            "30b1764ddcc34928b503b3c38c2583a6",
+            "40144b3240624af4907c329501c50ac7"
+          ]
+        },
+        "id": "INdC3WvLO-qb",
+        "outputId": "e7d20db8-a079-4eb6-b27e-50ba41ce8131"
+      },
+      "outputs": [],
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
+        "\n",
+        "document_store.write_documents(dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0_cj-5m-O-qb"
+      },
+      "source": [
+        "## Initializing the Retriever\n",
+        "\n",
+        "Let's initialize a BM25Retriever and make it use the InMemoryDocumentStore we initialized earlier in this tutorial:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-uo-6fjiO-qb"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.nodes import BM25Retriever\n",
+        "\n",
+        "retriever = BM25Retriever(document_store=document_store, top_k=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6CEuQpB7O-qb"
+      },
+      "source": [
+        "## Initializing the PromptNode\n",
+        "\n",
+        "Let's define a custom prompt for PromptTemplate to use with PromptNode. As parameters, this prompt will accept Documents that our Retriever fetched from our DocumentStore and `query` we pass at runtime. To join the content of the Documents, we'll use `join()` function. To learn about using functions in PromptTemplate, check out [PromptTemplate Structure](https://docs.haystack.deepset.ai/docs/prompt_node#prompttemplate-structure). \n",
+        "\n",
+        "We'll initialize PromptNode with the PromptTemplate and `google/flan-t5-large` model. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f6NFmpjEO-qb",
+        "outputId": "9213a8a2-2d05-4a71-cfa0-b3e5dc3f404f"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.nodes import PromptNode, PromptTemplate\n",
+        "\n",
+        "lfqa_prompt = PromptTemplate(\n",
+        "    name=\"lfqa\",\n",
+        "    prompt_text=\"\"\"Synthesize a comprehensive answer from the following text for the given question. \n",
+        "                             Provide a clear and concise response that summarizes the key points and information presented in the text. \n",
+        "                             Your answer should be in your own words and be no longer than 50 words. \n",
+        "                             \\n\\n Related text: {join(documents)} \\n\\n Question: {query} \\n\\n Answer:\"\"\",\n",
+        ")\n",
+        "\n",
+        "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\", default_prompt_template=lfqa_prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sIrajz1EO-qb"
+      },
+      "source": [
+        ">To learn about how to use custom templates with PromptNode, check out [Customizing PromptNode for NLP Tasks](https://haystack.deepset.ai/tutorials/21_customizing_promptnode) tutorial. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yfMCHStjO-qc"
+      },
+      "source": [
+        "## Defining the Pipeline\n",
+        "\n",
+        "We'll use a custom pipeline with the Retriever, and PromptNode."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DZC9Dr-xO-qc"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.pipelines import Pipeline\n",
+        "\n",
+        "pipe = Pipeline()\n",
+        "pipe.add_node(component=retriever, name=\"retriever\", inputs=[\"Query\"])\n",
+        "pipe.add_node(component=prompt_node, name=\"prompt_node\", inputs=[\"retriever\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6NqyLhx7O-qc"
+      },
+      "source": [
+        "That's it! The pipeline's ready to generate answers to questions!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DBAyF5tVO-qc"
+      },
+      "source": [
+        "## Asking a Question\n",
+        "\n",
+        "We use the pipeline `run()` method to ask a question."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Vnt283M5O-qc",
+        "outputId": "785ed13c-465c-496b-a170-3c627716a740"
+      },
+      "outputs": [],
+      "source": [
+        "output = pipe.run(query=\"How does Rhodes Statue look like?\")\n",
+        "\n",
+        "print(output[\"results\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IWQN-aoGO-qc"
+      },
+      "source": [
+        "Here are some other example queries to test:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_OHUQ5xxO-qc"
+      },
+      "outputs": [],
+      "source": [
+        "examples = [\n",
+        "    \"Where is Gardens of Babylon?\",\n",
+        "    \"Why did people build Great Pyramid of Giza?\",\n",
+        "    \"How does Rhodes Statue look like?\",\n",
+        "    \"Why did people visit the Temple of Artemis?\",\n",
+        "    \"What is the importance of Colossus of Rhodes?\",\n",
+        "    \"What happened to the Tomb of Mausolus?\",\n",
+        "    \"How did Colossus of Rhodes collapse?\",\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XueCK3y4O-qc"
+      },
+      "source": [
+        "🎉 Congratulations! You've learned how to create a generative QA system for your documents with PromptNode."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o3jq25OFO-qc"
+      },
+      "source": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3.9.6 64-bit",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.9.6"
+    },
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   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Overview\n",
-    "\n",
-    "Learn how to build a generative question answering pipeline using the power of LLMs with PromptNode. In this tutorial, we'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want. \n",
-    "\n",
-    "This tutorial introduces you to the new Shaper node and explains how to use Shaper to integrate PromptNode in the pipeline. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Preparing the Colab Environment\n",
-    "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Installing Haystack\n",
-    "\n",
-    "To start, let's install the latest release of Haystack with `pip`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]\n",
-    "pip install datasets>=2.6.1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Enabling Telemetry\n",
-    "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack.telemetry import tutorial_running\n",
-    "\n",
-    "tutorial_running(22)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initializing the DocumentStore\n",
-    "\n",
-    "We'll start creating our question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, we're using the `InMemoryDocumentStore`.\n",
-    "\n",
-    "Let's initialize our DocumentStore. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack.document_stores import InMemoryDocumentStore\n",
-    "\n",
-    "document_store = InMemoryDocumentStore(use_bm25=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The DocumentStore is now ready. Now it's time to fill it with some Documents."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fetching and Writing Documents\n",
-    "\n",
-    "We'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, we don't need to perform any additional cleaning or splitting. \n",
-    "\n",
-    "Let's fetch the data and write it to the DocumentStore: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
-    "\n",
-    "document_store.write_documents(dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initializing the Retriever\n",
-    "\n",
-    "Let's initialize a BM25Retriever and make it use the InMemoryDocumentStore we initialized earlier in this tutorial:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack.nodes import BM25Retriever\n",
-    "\n",
-    "retriever = BM25Retriever(document_store=document_store, top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initializing the PromptNode\n",
-    "\n",
-    "Let's define a custom prompt to use with our PromptNode. This prompt will accept `$texts` and `$query` as parameters. `$text` will match the output of the Shaper and `$query` will match the `query` we pass at runtime. \n",
-    "\n",
-    "We'll initialize PromptNode with the new PromptTemplate and `google/flan-t5-large` model. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack.nodes import PromptNode, PromptTemplate\n",
-    "\n",
-    "lfqa_prompt = PromptTemplate(\n",
-    "    name=\"lfqa\",\n",
-    "    prompt_text=\"\"\"Synthesize a comprehensive answer from the following text for the given question. \n",
-    "                             Provide a clear and concise response that summarizes the key points and information presented in the text. \n",
-    "                             Your answer should be in your own words and be no longer than 50 words. \n",
-    "                             \\n\\n Related text: $documents \\n\\n Question: $query \\n\\n Answer:\"\"\",\n",
-    ")\n",
-    "\n",
-    "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\", default_prompt_template=lfqa_prompt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ">To learn about how to use custom templates with PromptNode, check out [Customizing PromptNode for NLP Tasks](https://haystack.deepset.ai/tutorials/21_customizing_promptnode) tutorial. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initializing the Shaper\n",
-    "\n",
-    "[Shaper](https://docs.haystack.deepset.ai/docs/shaper) is necessary when the output of one node does not match what the next node expects as input. In our pipeline, we need to join retrieved Documents so that we can inject these Documents into the prompt. We can solve this problem by defining a Shaper that uses [`join_documents`](https://docs.haystack.deepset.ai/reference/shaper-api#join_documents) as its function (`func`). Retriever refers Documents as `documents` and `join_documents` expects `documents` parameter, so, we can pass `{\"documents\": \"documents\"}` to the Shaper as `inputs`. To output joined Documents as `documents`, we need to define `outputs=[\"documents\"]`.    \n",
-    "\n",
-    "Let's initialize the Shaper:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack.nodes import Shaper\n",
-    "\n",
-    "shaper = Shaper(func=\"join_documents\", inputs={\"documents\": \"documents\"}, outputs=[\"documents\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Defining the Pipeline\n",
-    "\n",
-    "We'll use a custom pipeline with the Retriever, Shaper, and PromptNode."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack.pipelines import Pipeline\n",
-    "\n",
-    "pipe = Pipeline()\n",
-    "pipe.add_node(component=retriever, name=\"retriever\", inputs=[\"Query\"])\n",
-    "pipe.add_node(component=shaper, name=\"shaper\", inputs=[\"retriever\"])\n",
-    "pipe.add_node(component=prompt_node, name=\"prompt_node\", inputs=[\"shaper\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's it! The pipeline's ready to generate answers to questions!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Asking a Question\n",
-    "\n",
-    "We use the pipeline `run()` method to ask a question."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = pipe.run(query=\"How does Rhodes Statue look like?\")\n",
-    "\n",
-    "print(output[\"results\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are some other example queries to test:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "examples = [\n",
-    "    \"Where is Gardens of Babylon?\",\n",
-    "    \"Why did people build Great Pyramid of Giza?\",\n",
-    "    \"How does Rhodes Statue look like?\",\n",
-    "    \"Why did people visit the Temple of Artemis?\",\n",
-    "    \"What is the importance of Colossus of Rhodes?\",\n",
-    "    \"What happened to the Tomb of Mausolus?\",\n",
-    "    \"How did Colossus of Rhodes collapse?\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "🎉 Congratulations! You've learned how to create a generative QA system for your documents with PromptNode."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.6 64-bit",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.9.6"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

From dd2c6048df8b5600dc26bb4d58f09300629219e7 Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Tue, 11 Apr 2023 09:59:21 +0200
Subject: [PATCH 068/206] Fix output variable names in dc_sync.yml (#163)

---
 .github/workflows/dc_sync.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/dc_sync.yml b/.github/workflows/dc_sync.yml
index 5657ac8d..6eea5b5d 100644
--- a/.github/workflows/dc_sync.yml
+++ b/.github/workflows/dc_sync.yml
@@ -13,8 +13,8 @@ jobs:
     outputs:
       modified-matrix: ${{ steps.create_matrix.outputs.modified-matrix }}
       deleted-matrix: ${{ steps.create_matrix.outputs.deleted-matrix }}
-      any-changed: ${{ steps.files.outputs.any-changed }}
-      any-deleted: ${{ steps.files.outputs.any-deleted }}
+      any-changed: ${{ steps.files.outputs.any_changed }}
+      any-deleted: ${{ steps.files.outputs.any_deleted }}
 
     steps:
       - name: Checkout

From 1a40e338b0d0a79bba45724c7eabd4ba6a0d0334 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 11 Apr 2023 11:12:57 +0300
Subject: [PATCH 069/206] Install farm-haystack-text2speech in nightly (#164)

* Add missing tutorials to the list
---
 .github/workflows/nightly.yml       | 7 +++++++
 .github/workflows/run_tutorials.yml | 1 -
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index 126e2865..e9fcaeab 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -37,6 +37,9 @@ jobs:
           - 15_TableQA
           - 16_Document_Classifier_at_Index_Time
           - 17_Audio
+          - 19_Text_to_Image_search_pipeline_with_MultiModal_Retriever
+          - 21_Customizing_PromptNode
+          - 22_Pipeline_with_PromptNode
 
     env:
       HAYSTACK_TELEMETRY_ENABLED: "False"
@@ -53,6 +56,10 @@ jobs:
           pip install pyzmq==23.2.1
           pip install nbconvert ipython
           pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
+      
+      - name: Install Haystack Extras text2speech dependencies
+        run: |
+          pip install farm-haystack-text2speech
 
       - name: Convert notebook to Python
         run: |
diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 4fad1c0c..472df77a 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -29,7 +29,6 @@ jobs:
           pip install nbconvert ipython
           pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
 
-      
       - name: Install Haystack Extras text2speech dependencies
         run: |
           pip install farm-haystack-text2speech

From f4ee31f7f1ee075d38ece2df97beda3ce95a0b3b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Fri, 14 Apr 2023 17:41:08 +0200
Subject: [PATCH 070/206] Updating the tutorials telemetry and some cleanups
 (#167)

---
 tutorials/01_Basic_QA_Pipeline.ipynb          |  43 ++-
 .../02_Finetune_a_model_on_your_data.ipynb    |  86 ++---
 tutorials/03_Scalable_QA_System.ipynb         |  41 ++-
 tutorials/04_FAQ_style_QA.ipynb               | 294 ++++++++----------
 tutorials/05_Evaluation.ipynb                 |  89 ++----
 ...er_Retrieval_via_Embedding_Retrieval.ipynb |  69 ++--
 tutorials/07_RAG_Generator.ipynb              |  72 ++---
 tutorials/08_Preprocessing.ipynb              |  61 ++--
 tutorials/09_DPR_training.ipynb               |  62 ++--
 tutorials/10_Knowledge_Graph.ipynb            |  72 +++--
 tutorials/11_Pipelines.ipynb                  |  80 ++---
 tutorials/12_LFQA.ipynb                       |  63 ++--
 tutorials/13_Question_generation.ipynb        |  69 ++--
 tutorials/14_Query_Classifier.ipynb           |  77 ++---
 tutorials/15_TableQA.ipynb                    |  74 ++---
 ...16_Document_Classifier_at_Index_Time.ipynb |  69 ++--
 tutorials/17_Audio.ipynb                      |  68 +---
 tutorials/18_GPL.ipynb                        |  81 ++---
 ...h_pipeline_with_MultiModal_Retriever.ipynb |  42 ++-
 .../20_Using_Haystack_with_REST_API.ipynb     |  22 --
 tutorials/21_Customizing_PromptNode.ipynb     |  26 +-
 21 files changed, 628 insertions(+), 932 deletions(-)

diff --git a/tutorials/01_Basic_QA_Pipeline.ipynb b/tutorials/01_Basic_QA_Pipeline.ipynb
index 7a192a12..83401d17 100644
--- a/tutorials/01_Basic_QA_Pipeline.ipynb
+++ b/tutorials/01_Basic_QA_Pipeline.ipynb
@@ -62,6 +62,26 @@
     "pip install farm-haystack[colab]"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(1)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -356,27 +376,6 @@
     "\n",
     "Check out [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_scalable_qa_system) to learn how to make a more advanced question answering system that uses an Elasticsearch backed DocumentStore and makes more use of the flexibility that pipelines offer."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
-   ]
   }
  ],
  "metadata": {
@@ -395,7 +394,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {
diff --git a/tutorials/02_Finetune_a_model_on_your_data.ipynb b/tutorials/02_Finetune_a_model_on_your_data.ipynb
index 63aa9ebe..cd7fd6a6 100644
--- a/tutorials/02_Finetune_a_model_on_your_data.ipynb
+++ b/tutorials/02_Finetune_a_model_on_your_data.ipynb
@@ -20,28 +20,42 @@
     "collapsed": false
    },
    "source": [
-    "### Prepare environment\n",
     "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "## Preparing the Colab Environment\n",
     "\n",
-    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">"
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Make sure you have a GPU running\n",
-    "!nvidia-smi"
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
@@ -50,15 +64,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install the latest release of Haystack in your own environment\n",
-    "#! pip install farm-haystack\n",
+    "from haystack.telemetry import tutorial_running\n",
     "\n",
-    "# Install the latest main of Haystack\n",
-    "!pip install --upgrade pip\n",
-    "!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]"
+    "tutorial_running(2)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -67,12 +79,7 @@
     }
    },
    "source": [
-    "## Logging\n",
-    "\n",
-    "We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
-    "Example log message:\n",
-    "INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
-    "Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
+    "Set the logging level to INFO:"
    ]
   },
   {
@@ -266,33 +273,6 @@
     "\n",
     "student.save(directory=\"my_distilled_model\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
   }
  ],
  "metadata": {
@@ -311,7 +291,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6 (default, Aug  5 2022, 15:21:02) \n[Clang 14.0.0 (clang-1400.0.29.102)]"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {
diff --git a/tutorials/03_Scalable_QA_System.ipynb b/tutorials/03_Scalable_QA_System.ipynb
index 2da1327b..db5901f5 100644
--- a/tutorials/03_Scalable_QA_System.ipynb
+++ b/tutorials/03_Scalable_QA_System.ipynb
@@ -59,6 +59,26 @@
     "pip install farm-haystack[colab]"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(3)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -518,27 +538,6 @@
     "\n",
     "To learn how to improve the performance of the Reader, see [Fine-Tune a Reader](https://haystack.deepset.ai/tutorials/02_finetune_a_model_on_your_data)."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/04_FAQ_style_QA.ipynb b/tutorials/04_FAQ_style_QA.ipynb
index 32f77f18..42013168 100644
--- a/tutorials/04_FAQ_style_QA.ipynb
+++ b/tutorials/04_FAQ_style_QA.ipynb
@@ -29,94 +29,87 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "collapsed": false,
         "id": "zBOtphIMozIT"
       },
       "source": [
-        "### Prepare environment\n",
         "\n",
-        "#### Colab: Enable the GPU runtime\n",
-        "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-        "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+        "## Preparing the Colab Environment\n",
         "\n",
-        "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
+        "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ENpLjBejozIW"
+      },
+      "source": [
+        "## Installing Haystack\n",
         "\n",
-        "You can double check whether the GPU runtime is enabled with the following command:"
+        "To start, let's install the latest release of Haystack with `pip`:"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "pycharm": {
-          "name": "#%%\n"
-        },
-        "vscode": {
-          "languageId": "shellscript"
-        },
-        "id": "ZIS-RfISozIU"
+        "id": "q_y78_4LozIW"
       },
       "outputs": [],
       "source": [
         "%%bash\n",
         "\n",
-        "nvidia-smi"
+        "pip install --upgrade pip\n",
+        "pip install farm-haystack[colab]"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
-      "metadata": {
-        "id": "ENpLjBejozIW"
-      },
+      "metadata": {},
       "source": [
-        "To start, install the latest release of Haystack with `pip`:"
+        "### Enabling Telemetry \n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "vscode": {
-          "languageId": "shellscript"
-        },
-        "id": "q_y78_4LozIW"
-      },
+      "execution_count": 1,
+      "metadata": {},
       "outputs": [],
       "source": [
-        "%%bash\n",
+        "from haystack.telemetry import tutorial_running\n",
         "\n",
-        "pip install --upgrade pip\n",
-        "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]"
+        "tutorial_running(4)"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "collapsed": false,
+        "id": "Wl9Q6E3hozIW",
         "pycharm": {
           "name": "#%% md\n"
-        },
-        "id": "Wl9Q6E3hozIW"
+        }
       },
       "source": [
-        "## Logging\n",
-        "\n",
-        "We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
-        "Example log message:\n",
-        "INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
-        "Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
+        "Set the logging level to INFO:"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": 1,
       "metadata": {
+        "id": "Edvocv1ZozIX",
         "pycharm": {
           "name": "#%%\n"
-        },
-        "id": "Edvocv1ZozIX"
+        }
       },
       "outputs": [],
       "source": [
@@ -165,10 +158,10 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
+        "id": "oFNXb3kIozIb",
         "pycharm": {
           "name": "#%%\n"
-        },
-        "id": "oFNXb3kIozIb"
+        }
       },
       "outputs": [],
       "source": [
@@ -198,10 +191,10 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
+        "id": "AHiSltp4ozIb",
         "pycharm": {
           "name": "#%%\n"
-        },
-        "id": "AHiSltp4ozIb"
+        }
       },
       "outputs": [],
       "source": [
@@ -249,10 +242,10 @@
       "cell_type": "code",
       "execution_count": 29,
       "metadata": {
+        "id": "F5O7r3poozIb",
         "pycharm": {
           "name": "#%%\n"
-        },
-        "id": "F5O7r3poozIb"
+        }
       },
       "outputs": [],
       "source": [
@@ -265,9 +258,6 @@
       "cell_type": "code",
       "execution_count": 34,
       "metadata": {
-        "pycharm": {
-          "name": "#%%\n"
-        },
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 709,
@@ -286,26 +276,29 @@
           ]
         },
         "id": "QX6qbic2ozIc",
-        "outputId": "af0a8eda-f7f6-4c97-cda7-13566ff888b1"
+        "outputId": "af0a8eda-f7f6-4c97-cda7-13566ff888b1",
+        "pycharm": {
+          "name": "#%%\n"
+        }
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "070f7d6a12804647b2c4f5ec98241ced",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "070f7d6a12804647b2c4f5ec98241ced"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "Query: How is the virus spreading?\n",
@@ -356,34 +349,14 @@
         "\n",
         "print_answers(prediction, details=\"medium\")"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "collapsed": false,
-        "id": "UpOTgy-fozIc"
-      },
-      "source": [
-        "## About us\n",
-        "\n",
-        "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-        "\n",
-        "We bring NLP to the industry via open source!  \n",
-        "Our focus: Industry specific language models & large scale QA systems.  \n",
-        "  \n",
-        "Some of our other work: \n",
-        "- [German BERT](https://deepset.ai/german-bert)\n",
-        "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-        "- [FARM](https://github.com/deepset-ai/FARM)\n",
-        "\n",
-        "Get in touch:\n",
-        "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-        "\n",
-        "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-      ]
     }
   ],
   "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "gpuClass": "standard",
     "kernelspec": {
       "display_name": "Python 3",
       "language": "python",
@@ -399,24 +372,55 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.6 (main, Aug 11 2022, 13:36:31) [Clang 13.1.6 (clang-1316.0.21.2.5)]"
+      "version": "3.10.9"
     },
     "vscode": {
       "interpreter": {
         "hash": "bda33b16be7e844498c7c2d368d72665b4f1d165582b9547ed22a0249a29ca2e"
       }
     },
-    "colab": {
-      "provenance": []
-    },
-    "accelerator": "GPU",
-    "gpuClass": "standard",
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {
+        "04495cdbd0e04e02a91ae3b026ef4c46": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "0656e34a277141d184aef005e4d39f88": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HTMLModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_52abbb2d8eb043a0924d705a99577303",
+            "placeholder": "​",
+            "style": "IPY_MODEL_04495cdbd0e04e02a91ae3b026ef4c46",
+            "value": " 1/1 [00:00&lt;00:00, 12.19it/s]"
+          }
+        },
         "070f7d6a12804647b2c4f5ec98241ced": {
           "model_module": "@jupyter-widgets/controls",
-          "model_name": "HBoxModel",
           "model_module_version": "1.5.0",
+          "model_name": "HBoxModel",
           "state": {
             "_dom_classes": [],
             "_model_module": "@jupyter-widgets/controls",
@@ -435,31 +439,25 @@
             "layout": "IPY_MODEL_612af309a6a94477b56dcea22c7a0940"
           }
         },
-        "8678507de5e748219ba28bb7970c0e63": {
+        "09f4ba018a514f1ca2929ece4d0335e2": {
           "model_module": "@jupyter-widgets/controls",
-          "model_name": "HTMLModel",
           "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
           "state": {
-            "_dom_classes": [],
             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "_model_name": "HTMLModel",
+            "_model_name": "DescriptionStyleModel",
             "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "HTMLView",
-            "description": "",
-            "description_tooltip": null,
-            "layout": "IPY_MODEL_dc9c54def7bf47d39819a97b7ceed839",
-            "placeholder": "​",
-            "style": "IPY_MODEL_09f4ba018a514f1ca2929ece4d0335e2",
-            "value": "Batches: 100%"
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
           }
         },
         "35855d91133f474092381950bdbfce58": {
           "model_module": "@jupyter-widgets/controls",
-          "model_name": "FloatProgressModel",
           "model_module_version": "1.5.0",
+          "model_name": "FloatProgressModel",
           "state": {
             "_dom_classes": [],
             "_model_module": "@jupyter-widgets/controls",
@@ -480,31 +478,10 @@
             "value": 1
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         },
-        "0656e34a277141d184aef005e4d39f88": {
-          "model_module": "@jupyter-widgets/controls",
-          "model_name": "HTMLModel",
-          "model_module_version": "1.5.0",
-          "state": {
-            "_dom_classes": [],
-            "_model_module": "@jupyter-widgets/controls",
-            "_model_module_version": "1.5.0",
-            "_model_name": "HTMLModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "HTMLView",
-            "description": "",
-            "description_tooltip": null,
-            "layout": "IPY_MODEL_52abbb2d8eb043a0924d705a99577303",
-            "placeholder": "​",
-            "style": "IPY_MODEL_04495cdbd0e04e02a91ae3b026ef4c46",
-            "value": " 1/1 [00:00&lt;00:00, 12.19it/s]"
-          }
-        },
-        "612af309a6a94477b56dcea22c7a0940": {
+        "52abbb2d8eb043a0924d705a99577303": {
           "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -553,10 +530,10 @@
             "width": null
           }
         },
-        "dc9c54def7bf47d39819a97b7ceed839": {
+        "58f3458cddc747d7b1a1c05f8f0664ed": {
           "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -605,25 +582,10 @@
             "width": null
           }
         },
-        "09f4ba018a514f1ca2929ece4d0335e2": {
-          "model_module": "@jupyter-widgets/controls",
-          "model_name": "DescriptionStyleModel",
-          "model_module_version": "1.5.0",
-          "state": {
-            "_model_module": "@jupyter-widgets/controls",
-            "_model_module_version": "1.5.0",
-            "_model_name": "DescriptionStyleModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
-            "_view_name": "StyleView",
-            "description_width": ""
-          }
-        },
-        "58f3458cddc747d7b1a1c05f8f0664ed": {
+        "612af309a6a94477b56dcea22c7a0940": {
           "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -672,10 +634,31 @@
             "width": null
           }
         },
+        "8678507de5e748219ba28bb7970c0e63": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HTMLModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_dc9c54def7bf47d39819a97b7ceed839",
+            "placeholder": "​",
+            "style": "IPY_MODEL_09f4ba018a514f1ca2929ece4d0335e2",
+            "value": "Batches: 100%"
+          }
+        },
         "cd2614a0933c48a391966cb572044710": {
           "model_module": "@jupyter-widgets/controls",
-          "model_name": "ProgressStyleModel",
           "model_module_version": "1.5.0",
+          "model_name": "ProgressStyleModel",
           "state": {
             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
@@ -688,10 +671,10 @@
             "description_width": ""
           }
         },
-        "52abbb2d8eb043a0924d705a99577303": {
+        "dc9c54def7bf47d39819a97b7ceed839": {
           "model_module": "@jupyter-widgets/base",
-          "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
+          "model_name": "LayoutModel",
           "state": {
             "_model_module": "@jupyter-widgets/base",
             "_model_module_version": "1.2.0",
@@ -739,21 +722,6 @@
             "visibility": null,
             "width": null
           }
-        },
-        "04495cdbd0e04e02a91ae3b026ef4c46": {
-          "model_module": "@jupyter-widgets/controls",
-          "model_name": "DescriptionStyleModel",
-          "model_module_version": "1.5.0",
-          "state": {
-            "_model_module": "@jupyter-widgets/controls",
-            "_model_module_version": "1.5.0",
-            "_model_name": "DescriptionStyleModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
-            "_view_name": "StyleView",
-            "description_width": ""
-          }
         }
       }
     }
diff --git a/tutorials/05_Evaluation.ipynb b/tutorials/05_Evaluation.ipynb
index 86e8f29c..bc5902c6 100644
--- a/tutorials/05_Evaluation.ipynb
+++ b/tutorials/05_Evaluation.ipynb
@@ -22,15 +22,20 @@
     }
    },
    "source": [
-    "### Prepare environment\n",
     "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "## Preparing the Colab Environment\n",
     "\n",
-    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
     "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
+    "To start, let's install the latest release of Haystack with `pip`:"
    ]
   },
   {
@@ -38,57 +43,45 @@
    "execution_count": null,
    "metadata": {
     "colab": {
-     "base_uri": "https://localhost:8080/"
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
-    "id": "xhFIMX_7U7ss",
-    "outputId": "285b2491-01e5-4bfd-cba9-c2279d4417c4",
+    "id": "vgmFOp82Oht_",
+    "outputId": "5bbcbb42-3a90-43a9-ebfd-598a98fa7143",
     "pycharm": {
      "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
     }
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
-    "nvidia-smi"
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To start, install the latest release of Haystack with `pip`:"
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "vgmFOp82Oht_",
-    "outputId": "5bbcbb42-3a90-43a9-ebfd-598a98fa7143",
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "%%bash\n",
+    "from haystack.telemetry import tutorial_running\n",
     "\n",
-    "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]"
+    "tutorial_running(5)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -97,12 +90,7 @@
     }
    },
    "source": [
-    "## Logging\n",
-    "\n",
-    "We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
-    "Example log message:\n",
-    "INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
-    "Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
+    "Set the logging level to INFO:"
    ]
   },
   {
@@ -1142,33 +1130,6 @@
     "print(metrics[\"Reader\"][\"exact_match\"])\n",
     "print(metrics[\"Reader\"][\"f1\"])"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "8QJ68G12U7tb",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
index ba0ddf94..6dd93a65 100644
--- a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+++ b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
@@ -55,55 +55,54 @@
         "id": "3K27Y5FbA6NV"
       },
       "source": [
-        "### Prepare the Environment\n",
         "\n",
-        "#### Colab: Enable the GPU Runtime\n",
-        "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-        "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+        "## Preparing the Colab Environment\n",
         "\n",
-        "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
+        "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Installing Haystack\n",
         "\n",
-        "You can double check whether the GPU runtime is enabled with the following command:"
+        "To start, let's install the latest release of Haystack with `pip`:"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "JlZgP8q1A6NW",
-        "vscode": {
-          "languageId": "shellscript"
-        }
+        "id": "NM36kbRFA6Nc"
       },
       "outputs": [],
       "source": [
         "%%bash\n",
         "\n",
-        "nvidia-smi"
+        "pip install --upgrade pip\n",
+        "pip install farm-haystack[colab,faiss]"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "To start, install the latest release of Haystack with `pip`:"
+        "### Enabling Telemetry \n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {
-        "id": "NM36kbRFA6Nc",
-        "vscode": {
-          "languageId": "shellscript"
-        }
-      },
+      "metadata": {},
       "outputs": [],
       "source": [
-        "%%bash\n",
+        "from haystack.telemetry import tutorial_running\n",
         "\n",
-        "pip install --upgrade pip\n",
-        "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,faiss]"
+        "tutorial_running(6)"
       ]
     },
     {
@@ -400,32 +399,6 @@
         "\n",
         "print_answers(prediction, details=\"minimum\")"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "collapsed": false,
-        "id": "kXE84-2_zqLa"
-      },
-      "source": [
-        "## About us\n",
-        "\n",
-        "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-        "\n",
-        "We bring NLP to the industry via open source!\n",
-        "  \n",
-        "Our focus: Industry specific language models & large scale QA systems.  \n",
-        "  \n",
-        "Some of our other work: \n",
-        "- [German BERT](https://deepset.ai/german-bert)\n",
-        "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-        "- [FARM](https://github.com/deepset-ai/FARM)\n",
-        "\n",
-        "Get in touch:\n",
-        "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-        "\n",
-        "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-      ]
     }
   ],
   "metadata": {
@@ -451,7 +424,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.6 (default, Aug  5 2022, 15:21:02) \n[Clang 14.0.0 (clang-1400.0.29.102)]"
+      "version": "3.10.9"
     },
     "vscode": {
       "interpreter": {
diff --git a/tutorials/07_RAG_Generator.ipynb b/tutorials/07_RAG_Generator.ipynb
index 597a4141..25133437 100644
--- a/tutorials/07_RAG_Generator.ipynb
+++ b/tutorials/07_RAG_Generator.ipynb
@@ -23,15 +23,22 @@
     "collapsed": false
    },
    "source": [
-    "### Prepare environment\n",
     "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "## Preparing the Colab Environment\n",
     "\n",
-    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Installing Haystack\n",
     "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
+    "To start, let's install the latest release of Haystack with `pip`:"
    ]
   },
   {
@@ -41,45 +48,34 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
     }
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
-    "nvidia-smi"
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab,faiss]"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
-    "To start, install the latest release of Haystack with `pip`:"
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "%%bash\n",
+    "from haystack.telemetry import tutorial_running\n",
     "\n",
-    "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,faiss]"
+    "tutorial_running(7)"
    ]
   },
   {
@@ -338,30 +334,6 @@
     "    res = pipe.run(query=question, params={\"Generator\": {\"top_k\": 1}, \"Retriever\": {\"top_k\": 5}})\n",
     "    print_answers(res, details=\"minimum\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/08_Preprocessing.ipynb b/tutorials/08_Preprocessing.ipynb
index 26cccaab..0e9ae9ae 100644
--- a/tutorials/08_Preprocessing.ipynb
+++ b/tutorials/08_Preprocessing.ipynb
@@ -46,6 +46,15 @@
     "This tutorial will show you all the tools that Haystack provides to help you cast your data into this format."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 26,
@@ -53,9 +62,6 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
     }
    },
    "outputs": [],
@@ -63,7 +69,27 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr,pdf]"
+    "pip install farm-haystack[colab,ocr,pdf]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(8)"
    ]
   },
   {
@@ -374,33 +400,6 @@
     "\n",
     "print(f\"n_files_input: {len(all_docs)}\\nn_docs_output: {len(docs)}\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/09_DPR_training.ipynb b/tutorials/09_DPR_training.ipynb
index 3bbab970..7a4a8509 100644
--- a/tutorials/09_DPR_training.ipynb
+++ b/tutorials/09_DPR_training.ipynb
@@ -15,6 +15,16 @@
     "This tutorial will guide you through the steps required to create a retriever that is specifically tailored to your domain."
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -26,12 +36,30 @@
    },
    "outputs": [],
    "source": [
-    "# Install the latest release of Haystack in your own environment\n",
-    "#! pip install farm-haystack\n",
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
     "\n",
-    "# Install the latest main of Haystack\n",
-    "!pip install --upgrade pip\n",
-    "!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]"
+    "tutorial_running(9)"
    ]
   },
   {
@@ -426,30 +454,6 @@
    "source": [
     "reloaded_retriever = DensePassageRetriever.load(load_dir=save_dir, document_store=None)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/10_Knowledge_Graph.ipynb b/tutorials/10_Knowledge_Graph.ipynb
index 92a82864..3dfcfea2 100644
--- a/tutorials/10_Knowledge_Graph.ipynb
+++ b/tutorials/10_Knowledge_Graph.ipynb
@@ -17,6 +17,27 @@
     "To start, install the latest release of Haystack with `pip`:"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -27,9 +48,6 @@
     },
     "pycharm": {
      "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
     }
    },
    "outputs": [],
@@ -37,7 +55,27 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,inmemorygraph]"
+    "pip install farm-haystack[colab,inmemorygraph]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(10)"
    ]
   },
   {
@@ -295,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,28 +397,6 @@
     "# Paraphrased question: What is the patronus of Hermione?\n",
     "# Correct answer: Otter"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
   }
  ],
  "metadata": {
@@ -399,7 +415,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {
diff --git a/tutorials/11_Pipelines.ipynb b/tutorials/11_Pipelines.ipynb
index 3085177d..cbccf1b1 100644
--- a/tutorials/11_Pipelines.ipynb
+++ b/tutorials/11_Pipelines.ipynb
@@ -18,6 +18,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -26,13 +27,20 @@
     }
    },
    "source": [
-    "## Setting Up the Environment\n",
     "\n",
-    "Let's start by ensuring we have a GPU running to ensure decent speed in this tutorial.\n",
-    "In Google colab, you can change to a GPU runtime in the menu:\n",
-    "- **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "## Preparing the Colab Environment\n",
     "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip` along with `pygraphviz`:"
    ]
   },
   {
@@ -42,51 +50,37 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
     }
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
-    "nvidia-smi"
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]\n",
+    "\n",
+    "apt install libgraphviz-dev\n",
+    "pip install pygraphviz"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
-    "To start, install the latest release of Haystack with `pip` along with `pygraphviz`:"
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]\n",
+    "from haystack.telemetry import tutorial_running\n",
     "\n",
-    "apt install libgraphviz-dev\n",
-    "pip install pygraphviz"
+    "tutorial_running(11)"
    ]
   },
   {
@@ -810,30 +804,6 @@
     "The possibilities are endless with the `Pipeline` class and we hope that this tutorial will inspire you\n",
     "to build custom pipeplines that really work for your use case!"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/12_LFQA.ipynb b/tutorials/12_LFQA.ipynb
index 47c1c768..f9153a09 100644
--- a/tutorials/12_LFQA.ipynb
+++ b/tutorials/12_LFQA.ipynb
@@ -18,52 +18,54 @@
     "id": "3K27Y5FbA6NV"
    },
    "source": [
-    "### Prepare environment\n",
     "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.  \n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "## Preparing the Colab Environment\n",
     "\n",
-    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
     "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
+    "To start, let's install the latest release of Haystack with `pip`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "JlZgP8q1A6NW"
+    "id": "NM36kbRFA6Nc"
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
-    "nvidia-smi"
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab,faiss]"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To start, install the latest release of Haystack with `pip`:"
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "id": "NM36kbRFA6Nc",
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "%%bash\n",
+    "from haystack.telemetry import tutorial_running\n",
     "\n",
-    "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,faiss]"
+    "tutorial_running(12)"
    ]
   },
   {
@@ -321,31 +323,6 @@
    "source": [
     "pipe.run(query=\"Why is Arya Stark an unusual character?\", params={\"Retriever\": {\"top_k\": 3}})"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "id": "i88KdOc2wUXQ"
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!\n",
-    "Our focus: Industry specific language models & large scale QA systems.\n",
-    "\n",
-    "Some of our other work:\n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/13_Question_generation.ipynb b/tutorials/13_Question_generation.ipynb
index 67c62ac2..1e681455 100644
--- a/tutorials/13_Question_generation.ipynb
+++ b/tutorials/13_Question_generation.ipynb
@@ -25,13 +25,20 @@
     }
    },
    "source": [
-    "### Prepare environment\n",
     "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.  \n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "## Preparing the Colab Environment\n",
     "\n",
-    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">"
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
    ]
   },
   {
@@ -45,12 +52,30 @@
    },
    "outputs": [],
    "source": [
-    "# Install the latest release of Haystack in your own environment\n",
-    "#! pip install farm-haystack\n",
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
     "\n",
-    "# Install the latest main of Haystack\n",
-    "!pip install --upgrade pip\n",
-    "!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]"
+    "tutorial_running(13)"
    ]
   },
   {
@@ -353,30 +378,6 @@
     "    result = pipeline_with_translation.run(documents=[document])\n",
     "    print_questions(result)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!\n",
-    "Our focus: Industry specific language models & large scale QA systems.\n",
-    "\n",
-    "Some of our other work:\n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/14_Query_Classifier.ipynb b/tutorials/14_Query_Classifier.ipynb
index c8bd10b3..245348bf 100644
--- a/tutorials/14_Query_Classifier.ipynb
+++ b/tutorials/14_Query_Classifier.ipynb
@@ -44,33 +44,14 @@
     }
    },
    "source": [
-    "### Prepare the Environment\n",
     "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.  \n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "## Preparing the Colab Environment\n",
     "\n",
-    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
-    "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "nvidia-smi"
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "TNlqD5HeN-gc",
@@ -79,7 +60,9 @@
     }
    },
    "source": [
-    "Next we make sure the latest version of Haystack is installed:"
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
    ]
   },
   {
@@ -90,9 +73,6 @@
     "id": "CjA5n5lMN-gd",
     "pycharm": {
      "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
     }
    },
    "outputs": [],
@@ -100,13 +80,32 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]\n",
+    "pip install farm-haystack[colab]\n",
     "\n",
     "# Install these to allow pipeline visualization\n",
     "apt install libgraphviz-dev\n",
     "pip install pygraphviz"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(14)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -800,30 +799,6 @@
     "\n",
     "pd.DataFrame.from_dict(query_classification_results)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "_wS8NzRoRh_G"
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.\n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs) "
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/15_TableQA.ipynb b/tutorials/15_TableQA.ipynb
index 13535604..54495e78 100644
--- a/tutorials/15_TableQA.ipynb
+++ b/tutorials/15_TableQA.ipynb
@@ -18,58 +18,56 @@
     "id": "vbR3bETlvi-3"
    },
    "source": [
-    "### Prepare environment\n",
     "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "## Preparing the Colab Environment\n",
     "\n",
-    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n",
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
     "\n",
-    "You can double check whether the GPU runtime is enabled with the following command:"
+    "To start, let's install the latest release of Haystack with `pip`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "id": "HW66x0rfujyO",
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
-    "nvidia-smi"
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]\n",
+    "\n",
+    "# Install pygraphviz for visualization of Pipelines\n",
+    "apt install libgraphviz-dev\n",
+    "pip install pygraphviz"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To start, install the latest release of Haystack with `pip`:"
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "%%bash\n",
+    "from haystack.telemetry import tutorial_running\n",
     "\n",
-    "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]\n",
-    "\n",
-    "# Install pygraphviz for visualization of Pipelines\n",
-    "apt install libgraphviz-dev\n",
-    "pip install pygraphviz"
+    "tutorial_running(15)"
    ]
   },
   {
@@ -746,30 +744,6 @@
    "source": [
     "# print(tables)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "RyeK3s28_X1C"
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/16_Document_Classifier_at_Index_Time.ipynb b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
index 225beda0..51641eff 100644
--- a/tutorials/16_Document_Classifier_at_Index_Time.ipynb
+++ b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
@@ -29,15 +29,31 @@
     "This tutorial will show you how to integrate a classification model into your preprocessing steps and how you can filter for this additional metadata at query time. In the last section we show how to put it all together and create an indexing pipeline."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "vscode": {
-     "languageId": "shellscript"
     }
    },
    "outputs": [],
@@ -46,12 +62,31 @@
     "\n",
     "# Install the latest main of Haystack\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,ocr,pdf]\n",
+    "pip install farm-haystack[colab,ocr,pdf]\n",
     "\n",
     "apt install libgraphviz-dev\n",
     "pip install pygraphviz"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(16)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -453,32 +488,6 @@
     "# we can store this pipeline and use it from the REST-API\n",
     "indexing_pipeline_with_classification.save_to_yaml(\"indexing_pipeline_with_classification.yaml\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/17_Audio.ipynb b/tutorials/17_Audio.ipynb
index a58aa2b6..fa79b49c 100644
--- a/tutorials/17_Audio.ipynb
+++ b/tutorials/17_Audio.ipynb
@@ -42,55 +42,48 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "nBvGUPVKN2oJ"
+   },
    "source": [
-    "### Enabling Telemetry\n",
+    "## Installing Haystack\n",
     "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+    "To start, let's install the latest release of Haystack with `pip`. In this tutorial, we'll use components from [text2speech](https://github.com/deepset-ai/haystack-extras/tree/main/nodes/text2speech) which contains some extra Haystack components, so we'll install `farm-haystack-text2speech`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
+    "id": "QsY0HC8JB3Sc"
    },
    "outputs": [],
    "source": [
-    "from haystack.telemetry import tutorial_running\n",
+    "%%bash\n",
     "\n",
-    "tutorial_running(17)"
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]\n",
+    "pip install farm-haystack-text2speech"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "id": "nBvGUPVKN2oJ"
-   },
+   "metadata": {},
    "source": [
-    "## Installing Haystack\n",
+    "### Enabling Telemetry\n",
     "\n",
-    "To start, let's install the latest release of Haystack with `pip`. In this tutorial, we'll use components from [text2speech](https://github.com/deepset-ai/haystack-extras/tree/main/nodes/text2speech) which contains some extra Haystack components, so we'll install `farm-haystack-text2speech`."
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "id": "QsY0HC8JB3Sc",
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "%%bash\n",
+    "from haystack.telemetry import tutorial_running\n",
     "\n",
-    "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]\n",
-    "pip install farm-haystack-text2speech"
+    "tutorial_running(17)"
    ]
   },
   {
@@ -307,33 +300,6 @@
    "source": [
     "🎉 Congratulations! You've learned how to create a extactive QA system that can read out the answer."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "id": "wJpoQQNdB3S-",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://discord.com/invite/VBpFzsgRVF) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Haystack Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
   }
  ],
  "metadata": {
@@ -357,7 +323,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {
diff --git a/tutorials/18_GPL.ipynb b/tutorials/18_GPL.ipynb
index eeb12b42..d5159e42 100644
--- a/tutorials/18_GPL.ipynb
+++ b/tutorials/18_GPL.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -38,20 +39,34 @@
     "- 97.70\tCorona is transmitted via the air\n",
     "- 96.71\tEbola is transmitted via direct contact with blood\n",
     "- 95.14\tPolio is transmitted via contaminated water or food\n",
-    "- 94.13\tHIV is transmitted via sex or sharing needles\n",
+    "- 94.13\tHIV is transmitted via sex or sharing needles\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bafc58b",
+   "metadata": {},
+   "source": [
     "\n",
-    "### Prepare the Environment\n",
+    "## Preparing the Colab Environment\n",
     "\n",
-    "#### Colab: Enable the GPU runtime\n",
-    "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
-    "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecba2dd7",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
     "\n",
-    "<img src=\"https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/colab_gpu_runtime.jpg\">\n"
+    "To start, let's install the latest release of Haystack with `pip`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "0dbc252e",
    "metadata": {
     "collapsed": false,
     "pycharm": {
@@ -60,23 +75,32 @@
    },
    "outputs": [],
    "source": [
-    "!nvidia-smi"
+    "%%bash\n",
+    "\n",
+    "pip install -q datasets\n",
+    "pip install \"faiss-gpu>=1.6.3,<2\"\n",
+    "pip install farm-haystack[colab,faiss-gpu]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50f1b9a2",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "id": "affaa08d",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q datasets\n",
-    "!pip install \"faiss-gpu>=1.6.3,<2\"\n",
-    "!pip install -q git+https://github.com/deepset-ai/haystack.git"
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(18)"
    ]
   },
   {
@@ -482,28 +506,7 @@
      "name": "#%%\n"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original Model\n",
-      "Query: How is COVID-19 transmitted\n",
-      "94.84\tEbola is transmitted via direct contact with blood\n",
-      "92.87\tHIV is transmitted via sex or sharing needles\n",
-      "92.31\tCorona is transmitted via the air\n",
-      "91.54\tPolio is transmitted via contaminated water or food\n",
-      "\n",
-      "\n",
-      "Adapted Model\n",
-      "Query: How is COVID-19 transmitted\n",
-      "100.73\tCorona is transmitted via the air\n",
-      "100.27\tEbola is transmitted via direct contact with blood\n",
-      "98.41\tHIV is transmitted via sex or sharing needles\n",
-      "98.21\tPolio is transmitted via contaminated water or food\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Original Model\")\n",
     "show_examples(org_model)\n",
@@ -557,7 +560,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {
diff --git a/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb b/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
index 82a25132..5dc677f3 100644
--- a/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
+++ b/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
@@ -60,6 +60,25 @@
     "pip install farm-haystack[colab]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(19)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -364,29 +383,6 @@
    "source": [
     "Congratulations! You've created a search system that returns images of animals in answer to a text query."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "8RyMcCI2_yHf"
-   },
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/20_Using_Haystack_with_REST_API.ipynb b/tutorials/20_Using_Haystack_with_REST_API.ipynb
index 987d796e..9c53bc0d 100644
--- a/tutorials/20_Using_Haystack_with_REST_API.ipynb
+++ b/tutorials/20_Using_Haystack_with_REST_API.ipynb
@@ -349,28 +349,6 @@
    "source": [
     "Congratulations! You have created a proper search system that runs using Haystack REST API."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## About us\n",
-    "\n",
-    "This [Haystack](https://github.com/deepset-ai/haystack/) tutorial was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-    "\n",
-    "We bring NLP to the industry via open source!  \n",
-    "Our focus: Industry specific language models & large scale QA systems.  \n",
-    "  \n",
-    "Some of our other work: \n",
-    "- [German BERT](https://deepset.ai/german-bert)\n",
-    "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-    "- [FARM](https://github.com/deepset-ai/FARM)\n",
-    "\n",
-    "Get in touch:\n",
-    "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
-    "\n",
-    "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
-   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/21_Customizing_PromptNode.ipynb b/tutorials/21_Customizing_PromptNode.ipynb
index a7c20100..ed9cb7fe 100644
--- a/tutorials/21_Customizing_PromptNode.ipynb
+++ b/tutorials/21_Customizing_PromptNode.ipynb
@@ -770,30 +770,6 @@
       "source": [
         "Congratulations! You've summarized your documents, generated titles for them, and put them into categories, all using custom prompt templates. "
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QVKsx2KbNONY"
-      },
-      "source": [
-        "## About us\n",
-        "\n",
-        "\n",
-        "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
-        "\n",
-        "We bring NLP to the industry via open source!  \n",
-        "Our focus: Industry specific language models & large scale QA systems.  \n",
-        "  \n",
-        "Some of our other work: \n",
-        "- [German BERT](https://deepset.ai/german-bert)\n",
-        "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
-        "\n",
-        "Get in touch:\n",
-        "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://discord.com/invite/VBpFzsgRVF) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Haystack Website](https://deepset.ai)\n",
-        "\n",
-        "By the way: [we're hiring!](https://www.deepset.ai/jobs)\n"
-      ]
     }
   ],
   "metadata": {
@@ -817,7 +793,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.12"
+      "version": "3.10.9"
     },
     "orig_nbformat": 4,
     "vscode": {

From 394af9254bf70efa69f11a166b03a57117eedf7e Mon Sep 17 00:00:00 2001
From: ZanSara <sara.zanzottera@deepset.ai>
Date: Wed, 26 Apr 2023 10:20:05 +0200
Subject: [PATCH 071/206] `preprocessing`, `file-conversion`, and
 `elasticsearch` extras (#165)

* new extra preprocessing and file-conversion

* Add elasticsearch extra where needed

---------

Co-authored-by: bogdankostic <bogdankostic@web.de>
---
 tutorials/03_Scalable_QA_System.ipynb         |   2 +-
 tutorials/05_Evaluation.ipynb                 | 140 ++++--------------
 tutorials/08_Preprocessing.ipynb              |   2 +-
 tutorials/11_Pipelines.ipynb                  |   7 +-
 tutorials/13_Question_generation.ipynb        |  85 +++--------
 tutorials/14_Query_Classifier.ipynb           |  47 ++----
 tutorials/15_TableQA.ipynb                    |  14 +-
 ...16_Document_Classifier_at_Index_Time.ipynb |  42 +-----
 tutorials/17_Audio.ipynb                      |   2 +-
 9 files changed, 70 insertions(+), 271 deletions(-)

diff --git a/tutorials/03_Scalable_QA_System.ipynb b/tutorials/03_Scalable_QA_System.ipynb
index db5901f5..59b7cb24 100644
--- a/tutorials/03_Scalable_QA_System.ipynb
+++ b/tutorials/03_Scalable_QA_System.ipynb
@@ -56,7 +56,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]"
+    "pip install farm-haystack[colab,preprocessing,elasticsearch]"
    ]
   },
   {
diff --git a/tutorials/05_Evaluation.ipynb b/tutorials/05_Evaluation.ipynb
index bc5902c6..a9ff83e1 100644
--- a/tutorials/05_Evaluation.ipynb
+++ b/tutorials/05_Evaluation.ipynb
@@ -47,17 +47,14 @@
      "height": 1000
     },
     "id": "vgmFOp82Oht_",
-    "outputId": "5bbcbb42-3a90-43a9-ebfd-598a98fa7143",
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "outputId": "5bbcbb42-3a90-43a9-ebfd-598a98fa7143"
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]"
+    "pip install farm-haystack[colab,preprocessing,elasticsearch]"
    ]
   },
   {
@@ -84,10 +81,7 @@
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    },
    "source": [
     "Set the logging level to INFO:"
@@ -97,10 +91,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -205,8 +196,7 @@
     "id": "tTXxr6TAOhuz",
     "outputId": "586d4775-4354-4ed9-a72c-c30bedcdfbee",
     "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -224,10 +214,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "id": "B_NEtezLOhu5",
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "id": "B_NEtezLOhu5"
    },
    "outputs": [],
    "source": [
@@ -264,10 +251,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "bRFsQUAJOhu_",
-    "outputId": "477031b9-5c2c-4128-ef5f-54db86259734",
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "outputId": "477031b9-5c2c-4128-ef5f-54db86259734"
    },
    "outputs": [],
    "source": [
@@ -299,10 +283,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "gy8YwmSYOhvE",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "id": "gy8YwmSYOhvE"
    },
    "source": [
     "## Initialize the Two Components of an ExtractiveQAPipeline: Retriever and Reader"
@@ -312,10 +293,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "id": "JkhaPMIJOhvF",
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "id": "JkhaPMIJOhvF"
    },
    "outputs": [],
    "source": [
@@ -423,10 +401,7 @@
      ]
     },
     "id": "cW3Ypn_gOhvK",
-    "outputId": "4b5feff7-ae9f-4cd8-de1e-944f0eb66f66",
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "outputId": "4b5feff7-ae9f-4cd8-de1e-944f0eb66f66"
    },
    "outputs": [],
    "source": [
@@ -450,10 +425,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "7i84KXONOhvc",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "id": "7i84KXONOhvc"
    },
    "source": [
     "## Evaluation of an ExtractiveQAPipeline\n",
@@ -467,11 +439,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from haystack.schema import EvaluationResult, MultiLabel\n",
@@ -512,11 +480,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# The EvaluationResult contains a pandas dataframe for each pipeline node.\n",
@@ -529,11 +493,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "reader_result = eval_result[\"Reader\"]\n",
@@ -543,11 +503,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We can filter for all documents retrieved for a given query\n",
@@ -558,11 +514,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We can also filter for all answers predicted for a given query\n",
@@ -572,11 +524,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Save the evaluation result so that we can reload it later and calculate evaluation metrics without running the pipeline again.\n",
@@ -585,11 +533,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Calculating Evaluation Metrics\n",
     "Load an EvaluationResult to quickly calculate standard evaluation metrics for all predictions,\n",
@@ -600,11 +544,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "saved_eval_result = EvaluationResult.load(\"../\")\n",
@@ -621,11 +561,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Generating an Evaluation Report\n",
     "A summary of the evaluation results can be printed to get a quick overview. It includes some aggregated metrics and also shows a few wrongly predicted examples."
@@ -634,11 +570,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pipeline.print_eval_report(saved_eval_result)"
@@ -657,11 +589,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "advanced_eval_result = pipeline.eval(\n",
@@ -684,11 +612,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "eval_result_with_upper_bounds = pipeline.eval(\n",
@@ -699,11 +623,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pipeline.print_eval_report(eval_result_with_upper_bounds)"
@@ -1037,11 +957,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "## Evaluate Retriever on its own\n",
@@ -1085,11 +1001,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Evaluate Reader on its own\n",
diff --git a/tutorials/08_Preprocessing.ipynb b/tutorials/08_Preprocessing.ipynb
index 0e9ae9ae..1b1c03a0 100644
--- a/tutorials/08_Preprocessing.ipynb
+++ b/tutorials/08_Preprocessing.ipynb
@@ -69,7 +69,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,ocr,pdf]"
+    "pip install farm-haystack[colab,ocr,preprocessing,file-conversion,pdf]"
    ]
   },
   {
diff --git a/tutorials/11_Pipelines.ipynb b/tutorials/11_Pipelines.ipynb
index cbccf1b1..09a3eaf7 100644
--- a/tutorials/11_Pipelines.ipynb
+++ b/tutorials/11_Pipelines.ipynb
@@ -47,17 +47,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]\n",
+    "pip install farm-haystack[colab,elasticsearch]\n",
     "\n",
     "apt install libgraphviz-dev\n",
     "pip install pygraphviz"
diff --git a/tutorials/13_Question_generation.ipynb b/tutorials/13_Question_generation.ipynb
index 1e681455..e9650891 100644
--- a/tutorials/13_Question_generation.ipynb
+++ b/tutorials/13_Question_generation.ipynb
@@ -3,10 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": true
    },
    "source": [
     "# Question Generation\n",
@@ -19,10 +16,7 @@
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {
-    "id": "yaaKv3_ZN-gb",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "id": "yaaKv3_ZN-gb"
    },
    "source": [
     "\n",
@@ -45,17 +39,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]"
+    "pip install farm-haystack[colab,elasticsearch]"
    ]
   },
   {
@@ -81,10 +72,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    },
    "source": [
     "## Logging\n",
@@ -99,10 +87,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -116,10 +101,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -140,10 +122,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    },
    "source": [
     "Let's start an Elasticsearch instance with one of the options below:"
@@ -153,10 +132,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -193,10 +169,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    },
    "source": [
     "Let's initialize some core components"
@@ -206,10 +179,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -230,10 +200,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    },
    "source": [
     "## Question Generation Pipeline\n",
@@ -246,10 +213,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -264,10 +228,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    },
    "source": [
     "## Retriever Question Generation Pipeline\n",
@@ -279,10 +240,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -297,10 +255,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    },
    "source": [
     "## Question Answer Generation Pipeline\n",
@@ -315,8 +270,7 @@
    "metadata": {
     "collapsed": false,
     "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
+     "is_executing": true
     }
    },
    "outputs": [],
@@ -347,10 +301,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
diff --git a/tutorials/14_Query_Classifier.ipynb b/tutorials/14_Query_Classifier.ipynb
index 245348bf..d31282c4 100644
--- a/tutorials/14_Query_Classifier.ipynb
+++ b/tutorials/14_Query_Classifier.ipynb
@@ -3,10 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "O-W2ZQ6CN-gZ",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "id": "O-W2ZQ6CN-gZ"
    },
    "source": [
     "# Query Classifier Tutorial\n",
@@ -38,10 +35,7 @@
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {
-    "id": "yaaKv3_ZN-gb",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "id": "yaaKv3_ZN-gb"
    },
    "source": [
     "\n",
@@ -54,10 +48,7 @@
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {
-    "id": "TNlqD5HeN-gc",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "id": "TNlqD5HeN-gc"
    },
    "source": [
     "## Installing Haystack\n",
@@ -70,17 +61,14 @@
    "execution_count": null,
    "metadata": {
     "collapsed": true,
-    "id": "CjA5n5lMN-gd",
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "id": "CjA5n5lMN-gd"
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]\n",
+    "pip install farm-haystack[colab,elasticsearch]\n",
     "\n",
     "# Install these to allow pipeline visualization\n",
     "apt install libgraphviz-dev\n",
@@ -109,10 +97,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    },
    "source": [
     "### Logging\n",
@@ -127,10 +112,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -366,10 +348,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "Vm9gqTioN-gf",
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "id": "Vm9gqTioN-gf"
    },
    "source": [
     "Next we fetch some data&mdash;for our example we'll use pages from the Game of Thrones wiki&mdash;and index it in our `DocumentStore`:"
@@ -380,10 +359,7 @@
    "execution_count": null,
    "metadata": {
     "collapsed": true,
-    "id": "Ig7dgfdHN-gg",
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "id": "Ig7dgfdHN-gg"
    },
    "outputs": [],
    "source": [
@@ -473,10 +449,7 @@
    "execution_count": null,
    "metadata": {
     "collapsed": true,
-    "id": "Sz-oZ5eJN-gl",
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "id": "Sz-oZ5eJN-gl"
    },
    "outputs": [],
    "source": [
diff --git a/tutorials/15_TableQA.ipynb b/tutorials/15_TableQA.ipynb
index 54495e78..a24e4b4d 100644
--- a/tutorials/15_TableQA.ipynb
+++ b/tutorials/15_TableQA.ipynb
@@ -43,7 +43,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]\n",
+    "pip install farm-haystack[colab,elasticsearch]\n",
     "\n",
     "# Install pygraphviz for visualization of Pipelines\n",
     "apt install libgraphviz-dev\n",
@@ -72,11 +72,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Logging\n",
     "\n",
@@ -89,11 +85,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import logging\n",
diff --git a/tutorials/16_Document_Classifier_at_Index_Time.ipynb b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
index 51641eff..a2ba13f6 100644
--- a/tutorials/16_Document_Classifier_at_Index_Time.ipynb
+++ b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
@@ -20,11 +20,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "This tutorial will show you how to integrate a classification model into your preprocessing steps and how you can filter for this additional metadata at query time. In the last section we show how to put it all together and create an indexing pipeline."
    ]
@@ -51,18 +47,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
     "# Install the latest main of Haystack\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,ocr,pdf]\n",
+    "pip install farm-haystack[colab,ocr,preprocessing,file-conversion,pdf,elasticsearch]\n",
     "\n",
     "apt install libgraphviz-dev\n",
     "pip install pygraphviz"
@@ -90,10 +82,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    },
    "source": [
     "## Logging\n",
@@ -108,10 +97,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -123,11 +109,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Read and preprocess documents\n"
    ]
@@ -135,11 +117,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from haystack.utils import fetch_archive_from_http\n",
@@ -154,11 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from haystack.nodes import PreProcessor\n",
diff --git a/tutorials/17_Audio.ipynb b/tutorials/17_Audio.ipynb
index fa79b49c..6dde333a 100644
--- a/tutorials/17_Audio.ipynb
+++ b/tutorials/17_Audio.ipynb
@@ -62,7 +62,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]\n",
+    "pip install farm-haystack[colab,preprocessing]\n",
     "pip install farm-haystack-text2speech"
    ]
   },

From ee3ec99a7d7456fc458399da0c8c8183b4894aa3 Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Wed, 26 Apr 2023 18:12:37 +0200
Subject: [PATCH 072/206] Add requirements installation step in dc_sync.yml
 (#172)

---
 .github/workflows/dc_sync.yml | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/dc_sync.yml b/.github/workflows/dc_sync.yml
index 6eea5b5d..5c878cae 100644
--- a/.github/workflows/dc_sync.yml
+++ b/.github/workflows/dc_sync.yml
@@ -47,6 +47,15 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v3
 
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+
+      - name: Install requirements
+        run: |
+          pip install --upgrade pip
+          pip install -r requirements.txt
+
       - name: Generate file to upload
         id: file-generator
         run: |
@@ -75,7 +84,7 @@ jobs:
       # This step is really important as when we remove a tutorial
       # notebook we also want to remove the relative file from
       # deepset Cloud, but since the remote file will have a .txt
-      # extension we must first the full file name with this step.
+      # extension we must first generate the full file name with this step.
       - name: Get file with correct extension
         id: extension-changer
         run: |

From 3a9d79ef4cd4a80c515ddb0f90eccf406dbd1b6f Mon Sep 17 00:00:00 2001
From: bsenst <benjamin.senst@gmail.com>
Date: Wed, 26 Apr 2023 18:13:11 +0200
Subject: [PATCH 073/206] change name of the hyperlink (#170)

I suppose the link name was mistaken
Change link name from 01_Basic_QA_Pipeline_without_Elasticsearch.ipynb to 03_Scalable_QA_System.ipynb
---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 6b5d3409..ab41d9bd 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,7 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 | ---------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
 | Build Your First Question Answering System | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)                                       | [01_Basic_QA_Pipeline.ipynb](./tutorials/01_Basic_QA_Pipeline.ipynb)                                                                             |
 | Fine Tune a Model on Your Data           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb)                           | [02_Finetune_a_model_on_your_data.ipynb](./tutorials/02_Finetune_a_model_on_your_data.ipynb)                                                     |
-| Build a Scalable Question Answering System  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb)                 | [03_Basic_QA_Pipeline_without_Elasticsearch.ipynb](./tutorials/03_Scalable_QA_System.ipynb)                                 |
+| Build a Scalable Question Answering System  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb)                 | [03_Scalable_QA_System.ipynb](./tutorials/03_Scalable_QA_System.ipynb)                                 |
 | FAQ Style QA                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            | [04_FAQ_style_QA.ipynb](./tutorials/04_FAQ_style_QA.ipynb)                                                                                       |
 | Evaluation                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              | [05_Evaluation.ipynb](./tutorials/05_Evaluation.ipynb)                                                                                           |
 | Better Retrieval via Embedding Retrieval | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                | [06_Better_Retrieval_via_Embedding_Retrieval.ipynb](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                               |
@@ -40,4 +40,4 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 | Using Haystack with REST API             | <a href="https://haystack.deepset.ai/downloads/20_Using_Haystack_with_REST_API.ipynb" target="_blank"><img alt="Download" src="https://img.shields.io/badge/DOWNLOAD-blue?&style=for-the-badge"/> <a>                                                                                                                                                                                                    | [20_Using_Haystack_with_REST_API.ipynb](./tutorials/20_Using_Haystack_with_REST_API.ipynb)                                                       |
 | Customizing PromptNode                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb) | [21_Customizing_PromptNode.ipynb](./tutorials/21_Customizing_PromptNode.ipynb) |
 | Creating a Generative QA Pipeline with PromptNode                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/22_Pipeline_with_PromptNode.ipynb) | [22_Pipeline_with_PromptNode.ipynb](./tutorials/22_Pipeline_with_PromptNode.ipynb) |
-| Answering Complex Questions with Agents                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) | [23_Answering_Multihop_Questions_with_Agents.ipynb](./tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) |
\ No newline at end of file
+| Answering Complex Questions with Agents                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) | [23_Answering_Multihop_Questions_with_Agents.ipynb](./tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) |

From 7657be49d21b3baa1041e1e57a5bfc86b6e7fe8b Mon Sep 17 00:00:00 2001
From: bogdankostic <bogdankostic@web.de>
Date: Fri, 28 Apr 2023 17:00:21 +0200
Subject: [PATCH 074/206] Add `metrics` extra to Tutorial 5 (#173)

---
 tutorials/05_Evaluation.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/05_Evaluation.ipynb b/tutorials/05_Evaluation.ipynb
index a9ff83e1..e774a32e 100644
--- a/tutorials/05_Evaluation.ipynb
+++ b/tutorials/05_Evaluation.ipynb
@@ -54,7 +54,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,preprocessing,elasticsearch]"
+    "pip install farm-haystack[colab,preprocessing,elasticsearch,metrics]"
    ]
   },
   {

From fb12b1972294b48ffc00d1fff05ae421af290048 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 2 May 2023 10:52:59 +0300
Subject: [PATCH 075/206] Add dataset import to tests (#175)

---
 .github/workflows/nightly.yml       | 4 ++++
 .github/workflows/run_tutorials.yml | 4 ++++
 2 files changed, 8 insertions(+)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index e9fcaeab..528575dd 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -60,6 +60,10 @@ jobs:
       - name: Install Haystack Extras text2speech dependencies
         run: |
           pip install farm-haystack-text2speech
+      
+      - name: Install Hugging Face datasets 
+        run: |
+          pip install datasets>=2.6.1
 
       - name: Convert notebook to Python
         run: |
diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 472df77a..92d82e83 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -32,6 +32,10 @@ jobs:
       - name: Install Haystack Extras text2speech dependencies
         run: |
           pip install farm-haystack-text2speech
+      
+      - name: Install Hugging Face datasets 
+        run: |
+          pip install datasets>=2.6.1
 
       - name: Checkout
         uses: actions/checkout@v3

From bfb9066ced093f8f4ad029540405e4014a446dbc Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Tue, 2 May 2023 11:58:31 +0200
Subject: [PATCH 076/206] Change how metadata is handled in dc_sync.yml (#176)

---
 .github/workflows/dc_sync.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/dc_sync.yml b/.github/workflows/dc_sync.yml
index 5c878cae..ff5da011 100644
--- a/.github/workflows/dc_sync.yml
+++ b/.github/workflows/dc_sync.yml
@@ -62,7 +62,7 @@ jobs:
           FILE="$(basename ${{ matrix.file }} .ipynb).txt"
           echo "file=text/$FILE" >> "$GITHUB_OUTPUT"
           METADATA="$(python scripts/generate_txt.py --print-metadata --notebooks ${{ matrix.file }})"
-          echo "metadata=$METADATA" >> "$GITHUB_OUTPUT"
+          echo "$METADATA" >> "text/metadata.yml"
 
       - name: Upload tutorial to deepset Cloud
         uses: silvanocerza/deepset-cloud-file-uploader@v1
@@ -71,7 +71,7 @@ jobs:
           workspace-name: ${{ secrets.DEEPSET_CLOUD_WORKSPACE }}
           file: ${{ steps.file-generator.outputs.file }}
           write-mode: OVERWRITE
-          metadata: ${{ steps.file-generator.outputs.metadata }}
+          meta-file: text/metadata.yml
 
   deleted:
     needs: get-tutorials

From 5810e9b1e31e1c1cbcc9894238657ee1842e46ab Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Tue, 2 May 2023 11:58:39 +0200
Subject: [PATCH 077/206] Add missing tutorial dependency (#177)

---
 tutorials/15_TableQA.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/15_TableQA.ipynb b/tutorials/15_TableQA.ipynb
index a24e4b4d..c7786860 100644
--- a/tutorials/15_TableQA.ipynb
+++ b/tutorials/15_TableQA.ipynb
@@ -43,7 +43,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,elasticsearch]\n",
+    "pip install farm-haystack[colab,elasticsearch,metrics]\n",
     "\n",
     "# Install pygraphviz for visualization of Pipelines\n",
     "apt install libgraphviz-dev\n",

From 6f764df0e5109fcf60578808ffd6c98789ced78e Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Wed, 3 May 2023 12:24:18 +0200
Subject: [PATCH 078/206] Change generate_txt.py to save metadata to file
 (#183)

---
 .github/workflows/dc_sync.yml | 7 ++++---
 scripts/generate_txt.py       | 7 ++++---
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/dc_sync.yml b/.github/workflows/dc_sync.yml
index ff5da011..996fb08e 100644
--- a/.github/workflows/dc_sync.yml
+++ b/.github/workflows/dc_sync.yml
@@ -60,9 +60,10 @@ jobs:
         id: file-generator
         run: |
           FILE="$(basename ${{ matrix.file }} .ipynb).txt"
+          METADATA_FILE="$(basename ${{ matrix.file }} .ipynb).yml"
           echo "file=text/$FILE" >> "$GITHUB_OUTPUT"
-          METADATA="$(python scripts/generate_txt.py --print-metadata --notebooks ${{ matrix.file }})"
-          echo "$METADATA" >> "text/metadata.yml"
+          echo "metadata_file=text/$METADATA_FILE" >> "$GITHUB_OUTPUT"
+          python scripts/generate_txt.py --metadata --notebooks ${{ matrix.file }}
 
       - name: Upload tutorial to deepset Cloud
         uses: silvanocerza/deepset-cloud-file-uploader@v1
@@ -71,7 +72,7 @@ jobs:
           workspace-name: ${{ secrets.DEEPSET_CLOUD_WORKSPACE }}
           file: ${{ steps.file-generator.outputs.file }}
           write-mode: OVERWRITE
-          meta-file: text/metadata.yml
+          meta-file: ${{ steps.file-generator.outputs.metadata_file }}
 
   deleted:
     needs: get-tutorials
diff --git a/scripts/generate_txt.py b/scripts/generate_txt.py
index 29f081f2..4cbe1f00 100644
--- a/scripts/generate_txt.py
+++ b/scripts/generate_txt.py
@@ -53,7 +53,7 @@ def generate_markdown_from_notebook(tutorial, output_path, tutorials_path):
     parser.add_argument("--index", dest="index", default="index.toml")
     parser.add_argument("--notebooks", dest="notebooks", nargs="+", default=[])
     parser.add_argument("--output", dest="output", default="text")
-    parser.add_argument("--print-metadata", dest="metadata", action="store_true")
+    parser.add_argument("--metadata", dest="metadata", action="store_true")
     args = parser.parse_args()
     index = read_index(args.index)
 
@@ -65,11 +65,12 @@ def generate_markdown_from_notebook(tutorial, output_path, tutorials_path):
     notebooks_configs = {cfg["notebook"]: cfg for cfg in index["tutorial"]}
 
     for notebook in notebooks:
-        notebook_name = notebook.split("/")[-1]
+        notebook_name = str(notebook).split("/")[-1]
         tutorial_config = notebooks_configs.get(notebook_name)
         if tutorial_config:
             generate_markdown_from_notebook(tutorial_config, args.output, notebook)
 
             if args.metadata:
                 meta = generate_metadata(index["config"], tutorial_config)
-                print(meta)
+                meta_file_name = f"{notebook_name.split('.')[0]}.yml"
+                Path(args.output, meta_file_name).write_text(meta)

From a3ae2f44dba8d45ff07d547ac247c97cf2a3f696 Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Wed, 3 May 2023 12:25:55 +0200
Subject: [PATCH 079/206] Add script and related workflow to fully sync
 tutorials with dC (#184)

* git ignore generated txt files folder

* Add script and related workflow to fully sync tutorials with dC
---
 .github/workflows/full_dc_sync.yml | 25 ++++++++++
 .gitignore                         |  3 ++
 requirements.txt                   |  4 +-
 scripts/full_dc_sync.py            | 79 ++++++++++++++++++++++++++++++
 4 files changed, 110 insertions(+), 1 deletion(-)
 create mode 100644 .github/workflows/full_dc_sync.yml
 create mode 100644 scripts/full_dc_sync.py

diff --git a/.github/workflows/full_dc_sync.yml b/.github/workflows/full_dc_sync.yml
new file mode 100644
index 00000000..801ad989
--- /dev/null
+++ b/.github/workflows/full_dc_sync.yml
@@ -0,0 +1,25 @@
+name: Sync tutorial with dC workspace
+
+on:
+  workflow_dispatch:
+
+jobs:
+  full_sync:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Install requirements
+        run: |
+          pip install --upgrade pip
+          pip install -r requirements.txt
+
+      - name: Generate all tutorials and their metadata
+        run: python scripts/generate_txt.py --notebooks all --metadata
+
+      - name: Full sync
+        env:
+          DEEPSET_CLOUD_WORKSPACE: ${{ secrets.DEEPSET_CLOUD_WORKSPACE }}
+          DEEPSET_CLOUD_API_KEY: ${{ secrets.DEEPSET_CLOUD_API_KEY }}
+        run: python scripts/full_dc_sync.py
diff --git a/.gitignore b/.gitignore
index fac98588..69f508ce 100644
--- a/.gitignore
+++ b/.gitignore
@@ -132,3 +132,6 @@ dmypy.json
 
 # Mac
 .DS_Store
+
+# Generate tutorial files for dc_sync.yml and full_dc_sync.yml
+text/**
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 194a07f5..a81599ab 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,5 @@
 nbconvert
 pre-commit
-tomli
\ No newline at end of file
+tomli
+requests
+PyYAML
\ No newline at end of file
diff --git a/scripts/full_dc_sync.py b/scripts/full_dc_sync.py
new file mode 100644
index 00000000..8ef41d83
--- /dev/null
+++ b/scripts/full_dc_sync.py
@@ -0,0 +1,79 @@
+from typing import List, Dict
+
+import os
+import sys
+import yaml
+import requests
+import asyncio
+import json
+from pathlib import Path
+
+
+class dCAuth(requests.auth.AuthBase):
+    def __call__(self, r):
+        r.headers["authorization"] = f"Bearer {dc_token()}"
+        return r
+
+
+def dc_token():
+    api_key = os.getenv("DEEPSET_CLOUD_API_KEY", None)
+    if not api_key:
+        raise Exception("DEEPSET_CLOUD_API_KEY env var is not set")
+    return api_key
+
+
+async def upload_to_dc(workspace: str, file: Path, meta: Dict):
+    url = f"https://api.cloud.deepset.ai/api/v1/workspaces/{workspace}/files?write_mode=OVERWRITE"
+    files = {"file": (file.name, file.open("rb"), "text/plain")}
+    data = {"meta": json.dumps(meta, default=str)}
+    print(f"Uploading {file.name}")
+    res = requests.post(url, data=data, files=files, auth=dCAuth())
+    try:
+        res.raise_for_status()
+    except requests.HTTPError as e:
+        raise requests.HTTPError(f"{file.name} upload failed.\n{e}: {res.text}")
+    print(res.json())
+
+
+async def upload_file(file: Path, workspace: str):
+    metadata_file = Path("text", f"{file.stem}.yml")
+    meta = {}
+    with metadata_file.open(encoding="utf-8") as f:
+        meta = yaml.unsafe_load(f)
+
+    await upload_to_dc(workspace, file, meta)
+
+
+async def upload_files(files: List[Path], workspace: str):
+    async with asyncio.TaskGroup() as tg:
+        for f in files:
+            tg.create_task(upload_file(f, workspace))
+
+
+def delete_all_cloud_files(workspace: str):
+    url = f"https://api.cloud.deepset.ai/api/v1/workspaces/{workspace}/files"
+    res = requests.delete(url, auth=dCAuth())
+    try:
+        res.raise_for_status()
+    except requests.HTTPError as e:
+        raise requests.HTTPError(f"{e}: {res.text}")
+
+
+if __name__ == "__main__":
+    workspace = os.getenv("DEEPSET_CLOUD_WORKSPACE", None)
+    if not workspace:
+        print("DEEPSET_CLOUD_WORKSPACE env var not set")
+        sys.exit(1)
+
+    if "DEEPSET_CLOUD_API_KEY" not in os.environ:
+        print("DEEPSET_CLOUD_API_KEY env var not set")
+        sys.exit(1)
+
+    delete_all_cloud_files(workspace)
+    print(f"Deleted all files from workspace {workspace}")
+
+    loop = asyncio.get_event_loop()
+
+    tutorials = Path(".", "text").glob("*.txt")
+    loop = asyncio.get_event_loop()
+    loop.run_until_complete(upload_files(tutorials, workspace))

From c37a9cc4cd81ea0d8202422bb68821ebda37f684 Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Wed, 3 May 2023 12:29:21 +0200
Subject: [PATCH 080/206] Change dc_sync.yml matrix skip condition to prevent
 failures (#185)

---
 .github/workflows/dc_sync.yml | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/dc_sync.yml b/.github/workflows/dc_sync.yml
index 996fb08e..43c5a2ab 100644
--- a/.github/workflows/dc_sync.yml
+++ b/.github/workflows/dc_sync.yml
@@ -13,8 +13,6 @@ jobs:
     outputs:
       modified-matrix: ${{ steps.create_matrix.outputs.modified-matrix }}
       deleted-matrix: ${{ steps.create_matrix.outputs.deleted-matrix }}
-      any-changed: ${{ steps.files.outputs.any_changed }}
-      any-deleted: ${{ steps.files.outputs.any_deleted }}
 
     steps:
       - name: Checkout
@@ -33,15 +31,16 @@ jobs:
       - name: Create matrixes
         id: create_matrix
         run: |
-          echo "modified-matrix={\"file\": ${{ steps.files.outputs.all_changed_files }}}" >> "$GITHUB_OUTPUT"
-          echo "deleted-matrix={\"file\": ${{ steps.files.outputs.deleted_files }}}" >> "$GITHUB_OUTPUT"
+          echo "modified-matrix=${{ steps.files.outputs.all_changed_files }}" >> "$GITHUB_OUTPUT"
+          echo "deleted-matrix=${{ steps.files.outputs.deleted_files }}" >> "$GITHUB_OUTPUT"
 
   modified:
     needs: get-tutorials
-    if: needs.get-tutorials.outputs.any-changed
+    if: ${{ needs.get-tutorials.outputs.modified-matrix != '[]' && needs.get-tutorials.outputs.modified-matrix != '' }}
     runs-on: ubuntu-latest
     strategy:
-      matrix: ${{ fromJSON(needs.get-tutorials.outputs.modified-matrix) }}
+      matrix:
+        file: ${{ fromJSON(needs.get-tutorials.outputs.modified-matrix) }}
 
     steps:
       - name: Checkout
@@ -76,10 +75,11 @@ jobs:
 
   deleted:
     needs: get-tutorials
-    if: needs.get-tutorials.outputs.any-deleted
+    if: ${{ needs.get-tutorials.outputs.deleted-matrix != '[]' && needs.get-tutorials.outputs.deleted-matrix != '' }}
     runs-on: ubuntu-latest
     strategy:
-      matrix: ${{ fromJSON(needs.get-tutorials.outputs.deleted-matrix) }}
+      matrix:
+        file: ${{ fromJSON(needs.get-tutorials.outputs.deleted-matrix) }}
 
     steps:
       # This step is really important as when we remove a tutorial

From 904aee451cba30ba86a1093d30242bf1c674b7dd Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Wed, 3 May 2023 12:37:58 +0200
Subject: [PATCH 081/206] Change full_dc_sync.yml workflow name (#186)

---
 .github/workflows/full_dc_sync.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/full_dc_sync.yml b/.github/workflows/full_dc_sync.yml
index 801ad989..d2be384e 100644
--- a/.github/workflows/full_dc_sync.yml
+++ b/.github/workflows/full_dc_sync.yml
@@ -1,4 +1,4 @@
-name: Sync tutorial with dC workspace
+name: Sync all tutorials with dC workspace
 
 on:
   workflow_dispatch:

From 9f9e680065d91ed5f175e50d26397448f715551f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 3 May 2023 13:39:06 +0300
Subject: [PATCH 082/206] Keep only necessary information as metadata (#182)

* Keep only necessary information as metadata

* Improve code quality

* make the file_name lowercase to use as link
---
 scripts/generate_txt.py | 29 ++++++++---------------------
 1 file changed, 8 insertions(+), 21 deletions(-)

diff --git a/scripts/generate_txt.py b/scripts/generate_txt.py
index 4cbe1f00..b8a8ec82 100644
--- a/scripts/generate_txt.py
+++ b/scripts/generate_txt.py
@@ -12,30 +12,17 @@ def read_index(path):
         return tomli.load(f)
 
 
-def generate_metadata(config, tutorial):
-    aliases = []
-    if "aliases" in tutorial:
-        for alias in tutorial["aliases"]:
-            aliases.append(f"/tutorials/{alias}")
+def generate_metadata(tutorial):
+    file_name = tutorial["notebook"].split(".")[0].lower()
+    slug = tutorial.get("slug", f"tutorials/{file_name}")
 
-    last_commit_date = (
-        check_output(f'git log -1 --pretty=format:"%cs" tutorials/{tutorial["notebook"]}'.split()).decode().strip()
-    )
-
-    return f"""layout: {config["layout"]}
-featured: {tutorial.get("featured", False)}
-colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')}
-toc: {config["toc"]}
+    return f"""featured: {tutorial.get("featured", False)}
 title: "{tutorial["title"]}"
-lastmod: {last_commit_date}
 level: "{tutorial["level"]}"
-weight: {tutorial["weight"]}
 description: {tutorial["description"]}
-category: "QA"
-aliases: {aliases}
-download: "/downloads/{tutorial["notebook"]}"
-completion_time: {tutorial.get("completion_time", False)}
-created_at: {tutorial["created_at"]}"""
+completion_time: {tutorial.get("completion_time", "")}
+link: {slug}
+"""
 
 
 def generate_markdown_from_notebook(tutorial, output_path, tutorials_path):
@@ -71,6 +58,6 @@ def generate_markdown_from_notebook(tutorial, output_path, tutorials_path):
             generate_markdown_from_notebook(tutorial_config, args.output, notebook)
 
             if args.metadata:
-                meta = generate_metadata(index["config"], tutorial_config)
+                meta = generate_metadata(tutorial_config)
                 meta_file_name = f"{notebook_name.split('.')[0]}.yml"
                 Path(args.output, meta_file_name).write_text(meta)

From 2055a8f54aa3f5de26e73ce92b7574b5f71f9bd6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 3 May 2023 13:51:29 +0300
Subject: [PATCH 083/206] Add deprecation note to tutorial 12 (#180)

* Add deprecation note to tutorial 12

* Fix typo
---
 tutorials/12_LFQA.ipynb | 47 ++++++++++++++++++++++++-----------------
 1 file changed, 28 insertions(+), 19 deletions(-)

diff --git a/tutorials/12_LFQA.ipynb b/tutorials/12_LFQA.ipynb
index f9153a09..a9f612c5 100644
--- a/tutorials/12_LFQA.ipynb
+++ b/tutorials/12_LFQA.ipynb
@@ -6,8 +6,20 @@
     "id": "bEH-CRbeA6NU"
    },
    "source": [
-    "# Long-Form Question Answering\n",
-    "\n",
+    "# Long-Form Question Answering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> As of version 1.16, Seq2SeqGenerator has been deprecated in Haystack and will be completely removed from Haystack as of v1.18. We recommend following the tutorial on [Creating a Generative QA Pipeline with PromptNode](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) instead. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Follow this tutorial to learn how to build and use a pipeline for Long-Form Question Answering (LFQA). LFQA is a variety of the generative question answering task. LFQA systems query large document stores for relevant information and then use this information to generate accurate, multi-sentence answers. In a regular question answering system, the retrieved documents related to the query (context passages) act as source tokens for extracted answers. In an LFQA system, context passages provide the context the system uses to generate original, abstractive, long-form answers."
    ]
   },
@@ -108,7 +120,7 @@
     "id": "q3dSo7ZtA6Nl"
    },
    "source": [
-    "### Document Store\n",
+    "## Initializing the DocumentStore\n",
     "\n",
     "FAISS is a library for efficient similarity search on a cluster of dense vectors.\n",
     "The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood\n",
@@ -144,9 +156,9 @@
     }
    },
    "source": [
-    "### Cleaning & indexing documents\n",
+    "## Cleaning and Writing Documents\n",
     "\n",
-    "Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore"
+    "Similarly to the previous tutorials, we download, convert and write some Game of Thrones articles to our DocumentStore."
    ]
   },
   {
@@ -181,12 +193,9 @@
     "id": "wgjedxx_A6N6"
    },
    "source": [
-    "### Initialize Retriever and Reader/Generator\n",
-    "\n",
-    "#### Retriever\n",
+    "## Initializing the Retriever\n",
     "\n",
-    "We use a `DensePassageRetriever` and we invoke `update_embeddings` to index the embeddings of documents in the `FAISSDocumentStore`\n",
-    "\n"
+    "We use a `DensePassageRetriever` and we invoke `update_embeddings` to index the embeddings of documents in the `FAISSDocumentStore`."
    ]
   },
   {
@@ -242,12 +251,11 @@
     "id": "rnVR28OXA6OA"
    },
    "source": [
-    "#### Reader/Generator\n",
+    "## Initializing the Generator\n",
     "\n",
-    "Similar to previous Tutorials we now initalize our reader/generator.\n",
+    "Similar to previous Tutorials we now initalize our Generator.\n",
     "\n",
-    "Here we use a `Seq2SeqGenerator` with the *vblagoje/bart_lfqa* model (see: https://huggingface.co/vblagoje/bart_lfqa)\n",
-    "\n"
+    "Here we use a `Seq2SeqGenerator` with the [*vblagoje/bart_lfqa*](https://huggingface.co/vblagoje/bart_lfqa) model."
    ]
   },
   {
@@ -270,12 +278,12 @@
     "id": "unhLD18yA6OF"
    },
    "source": [
-    "### Pipeline\n",
+    "## Initializing the Pipeline\n",
     "\n",
     "With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.\n",
     "Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
-    "To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `GenerativeQAPipeline` that combines a retriever and a reader/generator to answer our questions.\n",
-    "You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd)."
+    "To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `GenerativeQAPipeline` that combines a Retriever and a Generator to answer our questions.\n",
+    "You can learn more about `Pipelines` in the [docs](https://docs.haystack.deepset.ai/docs/pipelines)."
    ]
   },
   {
@@ -297,7 +305,8 @@
     "id": "bXlBBxKXA6OL"
    },
    "source": [
-    "## Voilà! Ask a question!"
+    "## Asking a Question\n",
+    "We use the pipeline `run()` method to ask a question."
    ]
   },
   {
@@ -347,7 +356,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.9.6"
   },
   "vscode": {
    "interpreter": {

From 69d96d3f7f44284d2e2f7bac83871b76b420aa9e Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Wed, 3 May 2023 12:58:07 +0200
Subject: [PATCH 084/206] Add setup python step in full_dc_sync.yml (#187)

---
 .github/workflows/full_dc_sync.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.github/workflows/full_dc_sync.yml b/.github/workflows/full_dc_sync.yml
index d2be384e..8b680059 100644
--- a/.github/workflows/full_dc_sync.yml
+++ b/.github/workflows/full_dc_sync.yml
@@ -10,6 +10,10 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v3
 
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+
       - name: Install requirements
         run: |
           pip install --upgrade pip

From 98f83689d79e79900535574cb0e38f36757fca4c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 3 May 2023 19:02:11 +0300
Subject: [PATCH 085/206] Fix completion_time type (#188)

* Make it string
---
 scripts/generate_txt.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/generate_txt.py b/scripts/generate_txt.py
index b8a8ec82..9bb77443 100644
--- a/scripts/generate_txt.py
+++ b/scripts/generate_txt.py
@@ -20,7 +20,7 @@ def generate_metadata(tutorial):
 title: "{tutorial["title"]}"
 level: "{tutorial["level"]}"
 description: {tutorial["description"]}
-completion_time: {tutorial.get("completion_time", "")}
+completion_time: "{tutorial.get("completion_time", "")}"
 link: {slug}
 """
 

From 6843b60a3add5ec81fd135cdeaeeb4b4ad13b7e3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 3 May 2023 19:04:56 +0300
Subject: [PATCH 086/206] Downgrade tut 22 level to intermediate (#178)

* Downgrade tut 22 level to intermediate

* Change level on the tutorial notebook
---
 index.toml                                  | 4 ++--
 tutorials/22_Pipeline_with_PromptNode.ipynb | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/index.toml b/index.toml
index d9415166..aa003867 100644
--- a/index.toml
+++ b/index.toml
@@ -212,8 +212,8 @@ featured = true
 [[tutorial]]
 title = "Creating a Generative QA Pipeline with PromptNode"
 description = "Use a large language model in your search system through PromptNode and Shaper."
-level = "advanced"
-weight = 105
+level = "intermediate"
+weight = 61
 notebook = "22_Pipeline_with_PromptNode.ipynb"
 aliases = ["pipeline-with-promptnode"]
 completion_time = "15 min"
diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index e4574f58..654d2733 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -8,7 +8,7 @@
       "source": [
         "# Tutorial: Creating a Generative QA Pipeline with PromptNode\n",
         "\n",
-        "- **Level**: Advanced\n",
+        "- **Level**: Intermediate\n",
         "- **Time to complete**: 15 minutes\n",
         "- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `PromptNode`, `PromptTemplate`\n",
         "- **Goal**: After completing this tutorial, you'll have created a generative question answering search system that uses a large language model through PromptNode with the new PromptTemplate structure. "

From f17d0ec3a77beddf1154b9cb5aedd7b3725cecab Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 3 May 2023 19:06:42 +0300
Subject: [PATCH 087/206] Add deprecation note to tutorial 7 (#179)

* Add deprecation note to tutorial 7

* Fix typo
---
 tutorials/07_RAG_Generator.ipynb | 149 ++++++++++++++++++++-----------
 1 file changed, 98 insertions(+), 51 deletions(-)

diff --git a/tutorials/07_RAG_Generator.ipynb b/tutorials/07_RAG_Generator.ipynb
index 25133437..7adbf9b6 100644
--- a/tutorials/07_RAG_Generator.ipynb
+++ b/tutorials/07_RAG_Generator.ipynb
@@ -6,9 +6,20 @@
     "collapsed": false
    },
    "source": [
-    "# Generative QA with \"Retrieval-Augmented Generation\"\n",
-    "\n",
-    "\n",
+    "# Generative QA with \"Retrieval-Augmented Generation\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> As of version 1.16, RAGenerator has been deprecated in Haystack and will be completely removed from Haystack as of v1.18. We recommend following the tutorial on [Creating a Generative QA Pipeline with PromptNode](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) instead."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "While extractive QA highlights the span of text that answers a query,\n",
     "generative QA can return a novel text answer that it has composed.\n",
     "In this tutorial, you will learn how to set up a generative system using the\n",
@@ -112,6 +123,13 @@
     "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fetching and Cleaning Documents"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -133,10 +151,8 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "\n",
     "from haystack.utils import fetch_archive_from_http\n",
     "\n",
-    "\n",
     "# Download sample\n",
     "doc_dir = \"data/tutorial7/\"\n",
     "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/small_generator_dataset.csv.zip\"\n",
@@ -173,7 +189,6 @@
    "source": [
     "from haystack import Document\n",
     "\n",
-    "\n",
     "# Use data to initialize Document objects\n",
     "titles = list(df[\"title\"].values)\n",
     "texts = list(df[\"text\"].values)\n",
@@ -188,8 +203,29 @@
     "collapsed": false
    },
    "source": [
-    "Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.\n",
-    "FAISS is chosen here since it is optimized vector storage."
+    "## Initializing the DocumentStore\n",
+    "\n",
+    "Here we initialize the FAISSDocumentStore. Set `return_embedding` to `True`, so Generator doesn't have to perform re-embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.document_stores import FAISSDocumentStore\n",
+    "\n",
+    "document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\", return_embedding=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the Retriever\n",
+    "\n",
+    "We initialize DensePassageRetriever to encode documents, encode question and query documents."
    ]
   },
   {
@@ -203,24 +239,32 @@
    },
    "outputs": [],
    "source": [
-    "from haystack.document_stores import FAISSDocumentStore\n",
     "from haystack.nodes import RAGenerator, DensePassageRetriever\n",
     "\n",
-    "\n",
-    "# Initialize FAISS document store.\n",
-    "# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding\n",
-    "document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\", return_embedding=True)\n",
-    "\n",
-    "# Initialize DPR Retriever to encode documents, encode question and query documents\n",
     "retriever = DensePassageRetriever(\n",
     "    document_store=document_store,\n",
     "    query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
     "    passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
     "    use_gpu=True,\n",
     "    embed_title=True,\n",
-    ")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the Generator\n",
     "\n",
-    "# Initialize RAG Generator\n",
+    "We initialize RAGenerator to generate answers from retrieved Documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "generator = RAGenerator(\n",
     "    model_name_or_path=\"facebook/rag-token-nq\",\n",
     "    use_gpu=True,\n",
@@ -238,6 +282,8 @@
     "collapsed": false
    },
    "source": [
+    "## Writing Documents\n",
+    "\n",
     "We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.\n",
     "The `update_embeddings()` method uses the retriever to create an embedding for each document.\n"
    ]
@@ -263,13 +309,41 @@
     "document_store.update_embeddings(retriever=retriever)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the Pipeline\n",
+    "\n",
+    "With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.\n",
+    "Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
+    "To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.\n",
+    "You can learn more about `Pipelines` in the [docs](https://docs.haystack.deepset.ai/docs/pipelines)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.pipelines import GenerativeQAPipeline\n",
+    "\n",
+    "pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
    },
    "source": [
-    "Here are our questions:"
+    "## Asking a Question\n",
+    "\n",
+    "Now let's ask questions to our system!\n",
+    "The Retriever will pick out a small subset of documents that it finds relevant.\n",
+    "These are used to condition the Generator as it generates the answer.\n",
+    "What it should return then are novel text spans that form and answer to your question!"
    ]
   },
   {
@@ -283,6 +357,8 @@
    },
    "outputs": [],
    "source": [
+    "from haystack.utils import print_answers\n",
+    "\n",
     "QUESTIONS = [\n",
     "    \"who got the first nobel prize in physics\",\n",
     "    \"when is the next deadpool movie being released\",\n",
@@ -299,37 +375,8 @@
     "    \"where do the greasers live in the outsiders\",\n",
     "    \"panda is a national animal of which country\",\n",
     "    \"what is the name of manchester united stadium\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "Now let's run our system!\n",
-    "The retriever will pick out a small subset of documents that it finds relevant.\n",
-    "These are used to condition the generator as it generates the answer.\n",
-    "What it should return then are novel text spans that form and answer to your question!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Or alternatively use the Pipeline class\n",
-    "from haystack.pipelines import GenerativeQAPipeline\n",
-    "from haystack.utils import print_answers\n",
+    "]\n",
     "\n",
-    "pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)\n",
     "for question in QUESTIONS:\n",
     "    res = pipe.run(query=question, params={\"Generator\": {\"top_k\": 1}, \"Retriever\": {\"top_k\": 5}})\n",
     "    print_answers(res, details=\"minimum\")"
@@ -338,7 +385,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -352,11 +399,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "3.10.6 (main, Aug 11 2022, 13:36:31) [Clang 13.1.6 (clang-1316.0.21.2.5)]"
+   "version": "3.9.6"
   },
   "vscode": {
    "interpreter": {
-    "hash": "bda33b16be7e844498c7c2d368d72665b4f1d165582b9547ed22a0249a29ca2e"
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
   }
  },

From 6bb0b9f427259595f6c96836c02babf055b919cf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 3 May 2023 19:06:55 +0300
Subject: [PATCH 088/206] Add deprecation note for Milvus (#181)

---
 ...er_Retrieval_via_Embedding_Retrieval.ipynb | 841 +++++++++---------
 1 file changed, 409 insertions(+), 432 deletions(-)

diff --git a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
index 6dd93a65..f08f4d8c 100644
--- a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+++ b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
@@ -1,437 +1,414 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bEH-CRbeA6NU"
-      },
-      "source": [
-        "# Better Retrieval via \"Embedding Retrieval\"\n",
-        "\n",
-        "### Importance of Retrievers\n",
-        "\n",
-        "The Retriever has a huge impact on the performance of our overall search pipeline.\n",
-        "\n",
-        "\n",
-        "### Different types of Retrievers\n",
-        "#### Sparse\n",
-        "Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.\n",
-        "\n",
-        "**Examples**: BM25, TF-IDF\n",
-        "\n",
-        "**Pros**: Simple, fast, well explainable\n",
-        "\n",
-        "**Cons**: Relies on exact keyword matches between query and text\n",
-        " \n",
-        "\n",
-        "#### Dense\n",
-        "These retrievers use neural network models to create \"dense\" embedding vectors. Within this family, there are two different approaches:\n",
-        "\n",
-        "a) Single encoder: Use a **single model** to embed both the query and the passage.\n",
-        "b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage.\n",
-        "\n",
-        "**Examples**: REALM, DPR, Sentence-Transformers\n",
-        "\n",
-        "**Pros**: Captures semantic similarity instead of \"word matches\" (for example, synonyms, related topics).\n",
-        "\n",
-        "**Cons**: Computationally more heavy to use, initial training of the model (though this is less of an issue nowadays as many pre-trained models are available and most of the time, it's not needed to train the model).\n",
-        "\n",
-        "\n",
-        "### Embedding Retrieval\n",
-        "\n",
-        "In this Tutorial, we use an `EmbeddingRetriever` with [Sentence Transformers](https://www.sbert.net/index.html) models.\n",
-        "\n",
-        "These models are trained to embed similar sentences close to each other in a shared embedding space.\n",
-        "\n",
-        "Some models have been fine-tuned on massive Information Retrieval data and can be used to retrieve documents based on a short query (for example, `multi-qa-mpnet-base-dot-v1`). There are others that are more suited to semantic similarity tasks where you are trying to find the most similar documents to a given document (for example, `all-mpnet-base-v2`). There are even models that are multilingual (for example, `paraphrase-multilingual-mpnet-base-v2`). For a good overview of different models with their evaluation metrics, see the [Pretrained Models](https://www.sbert.net/docs/pretrained_models.html#) in the Sentence Transformers documentation.\n",
-        "\n",
-        "*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb) *to open the notebook in Google Colab.*\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3K27Y5FbA6NV"
-      },
-      "source": [
-        "\n",
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Installing Haystack\n",
-        "\n",
-        "To start, let's install the latest release of Haystack with `pip`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "NM36kbRFA6Nc"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install --upgrade pip\n",
-        "pip install farm-haystack[colab,faiss]"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Enabling Telemetry \n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(6)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "collapsed": false,
-        "id": "GbM2ml-ozqLX",
-        "pycharm": {
-          "name": "#%% md\n"
-        }
-      },
-      "source": [
-        "## Logging\n",
-        "\n",
-        "We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
-        "Example log message:\n",
-        "INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
-        "Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "kQWEUUMnzqLX",
-        "pycharm": {
-          "name": "#%%\n"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "\n",
-        "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
-        "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "q3dSo7ZtA6Nl"
-      },
-      "source": [
-        "### Document Store\n",
-        "\n",
-        "#### Option 1: FAISS\n",
-        "\n",
-        "FAISS is a library for efficient similarity search on a cluster of dense vectors.\n",
-        "The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood\n",
-        "to store the document text and other meta data. The vector embeddings of the text are\n",
-        "indexed on a FAISS Index that later is queried for searching answers.\n",
-        "The default flavour of FAISSDocumentStore is \"Flat\" but can also be set to \"HNSW\" for\n",
-        "faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.\n",
-        "For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "1cYgDJmrA6Nv",
-        "pycharm": {
-          "name": "#%%\n"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.document_stores import FAISSDocumentStore\n",
-        "\n",
-        "document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "collapsed": false,
-        "id": "s4HK5l0qzqLZ",
-        "pycharm": {
-          "name": "#%% md\n"
-        }
-      },
-      "source": [
-        "#### Option 2: Milvus\n",
-        "\n",
-        "Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.\n",
-        "Like FAISS it has both a \"Flat\" and \"HNSW\" mode but it outperforms FAISS when it comes to dynamic data management.\n",
-        "It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.\n",
-        "See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2Ur4h-E3zqLZ",
-        "pycharm": {
-          "name": "#%%\n"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# Milvus cannot be run on COlab, so this cell is commented out.\n",
-        "# To run Milvus you need Docker (versions below 2.0.0) or a docker-compose (versions >= 2.0.0), neither of which is available on Colab.\n",
-        "# See Milvus' documentation for more details: https://milvus.io/docs/install_standalone-docker.md\n",
-        "\n",
-        "# !pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[milvus]\n",
-        "\n",
-        "# from haystack.utils import launch_milvus\n",
-        "# from haystack.document_stores import MilvusDocumentStore\n",
-        "\n",
-        "# launch_milvus()\n",
-        "# document_store = MilvusDocumentStore()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "06LatTJBA6N0",
-        "pycharm": {
-          "name": "#%% md\n"
-        }
-      },
-      "source": [
-        "### Cleaning & indexing documents\n",
-        "\n",
-        "Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "iqKnu6wxA6N1",
-        "pycharm": {
-          "name": "#%%\n"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http\n",
-        "\n",
-        "\n",
-        "# Let's first get some files that we want to use\n",
-        "doc_dir = \"data/tutorial6\"\n",
-        "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt6.zip\"\n",
-        "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
-        "\n",
-        "# Convert files to dicts\n",
-        "docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n",
-        "\n",
-        "# Now, let's write the dicts containing documents to our DB.\n",
-        "document_store.write_documents(docs)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wgjedxx_A6N6"
-      },
-      "source": [
-        "### Initialize Retriever, Reader & Pipeline\n",
-        "\n",
-        "#### Retriever\n",
-        "\n",
-        "**Here:** We use an `EmbeddingRetriever`.\n",
-        "\n",
-        "**Alternatives:**\n",
-        "\n",
-        "- `BM25Retriever` with custom queries (for example, boosting) and filters\n",
-        "- `DensePassageRetriever` which uses two encoder models, one to embed the query and one to embed the passage, and then compares the embedding for retrieval\n",
-        "- `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "kFwiPP60A6N7",
-        "pycharm": {
-          "is_executing": true
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.nodes import EmbeddingRetriever\n",
-        "\n",
-        "retriever = EmbeddingRetriever(\n",
-        "    document_store=document_store,\n",
-        "    embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\",\n",
-        ")\n",
-        "# Important:\n",
-        "# Now that we initialized the Retriever, we need to call update_embeddings() to iterate over all\n",
-        "# previously indexed documents and update their embedding representation.\n",
-        "# While this can be a time consuming operation (depending on the corpus size), it only needs to be done once.\n",
-        "# At query time, we only need to embed the query and compare it to the existing document embeddings, which is very fast.\n",
-        "document_store.update_embeddings(retriever)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rnVR28OXA6OA"
-      },
-      "source": [
-        "#### Reader\n",
-        "\n",
-        "Similar to previous Tutorials we now initalize our reader.\n",
-        "\n",
-        "Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)\n",
-        "\n",
-        "\n",
-        "\n",
-        "##### FARMReader"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "fyIuWVwhA6OB"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.nodes import FARMReader\n",
-        "\n",
-        "\n",
-        "# Load a  local model or any of the QA models on\n",
-        "# Hugging Face's model hub (https://huggingface.co/models)\n",
-        "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "unhLD18yA6OF"
-      },
-      "source": [
-        "### Pipeline\n",
-        "\n",
-        "With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.\n",
-        "Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
-        "To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.\n",
-        "You can learn more about `Pipelines` in the [docs](https://docs.haystack.deepset.ai/docs/pipelines)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "TssPQyzWA6OG"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.pipelines import ExtractiveQAPipeline\n",
-        "\n",
-        "pipe = ExtractiveQAPipeline(reader, retriever)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bXlBBxKXA6OL"
-      },
-      "source": [
-        "## Voilà! Ask a question!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Zi97Hif2A6OM"
-      },
-      "outputs": [],
-      "source": [
-        "# You can configure how many candidates the reader and retriever shall return\n",
-        "# The higher top_k for retriever, the better (but also the slower) your answers.\n",
-        "prediction = pipe.run(\n",
-        "    query=\"Who created the Dothraki vocabulary?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pI0wrHylzqLa"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.utils import print_answers\n",
-        "\n",
-        "\n",
-        "print_answers(prediction, details=\"minimum\")"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bEH-CRbeA6NU"
+   },
+   "source": [
+    "# Better Retrieval via \"Embedding Retrieval\"\n",
+    "\n",
+    "### Importance of Retrievers\n",
+    "\n",
+    "The Retriever has a huge impact on the performance of our overall search pipeline.\n",
+    "\n",
+    "\n",
+    "### Different types of Retrievers\n",
+    "#### Sparse\n",
+    "Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.\n",
+    "\n",
+    "**Examples**: BM25, TF-IDF\n",
+    "\n",
+    "**Pros**: Simple, fast, well explainable\n",
+    "\n",
+    "**Cons**: Relies on exact keyword matches between query and text\n",
+    " \n",
+    "\n",
+    "#### Dense\n",
+    "These retrievers use neural network models to create \"dense\" embedding vectors. Within this family, there are two different approaches:\n",
+    "\n",
+    "a) Single encoder: Use a **single model** to embed both the query and the passage.\n",
+    "b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage.\n",
+    "\n",
+    "**Examples**: REALM, DPR, Sentence-Transformers\n",
+    "\n",
+    "**Pros**: Captures semantic similarity instead of \"word matches\" (for example, synonyms, related topics).\n",
+    "\n",
+    "**Cons**: Computationally more heavy to use, initial training of the model (though this is less of an issue nowadays as many pre-trained models are available and most of the time, it's not needed to train the model).\n",
+    "\n",
+    "\n",
+    "### Embedding Retrieval\n",
+    "\n",
+    "In this Tutorial, we use an `EmbeddingRetriever` with [Sentence Transformers](https://www.sbert.net/index.html) models.\n",
+    "\n",
+    "These models are trained to embed similar sentences close to each other in a shared embedding space.\n",
+    "\n",
+    "Some models have been fine-tuned on massive Information Retrieval data and can be used to retrieve documents based on a short query (for example, `multi-qa-mpnet-base-dot-v1`). There are others that are more suited to semantic similarity tasks where you are trying to find the most similar documents to a given document (for example, `all-mpnet-base-v2`). There are even models that are multilingual (for example, `paraphrase-multilingual-mpnet-base-v2`). For a good overview of different models with their evaluation metrics, see the [Pretrained Models](https://www.sbert.net/docs/pretrained_models.html#) in the Sentence Transformers documentation.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3K27Y5FbA6NV"
+   },
+   "source": [
+    "\n",
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NM36kbRFA6Nc"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab,faiss]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "id": "GbM2ml-ozqLX",
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Logging\n",
+    "\n",
+    "We configure how logging messages should be displayed and which log level should be used before importing Haystack.\n",
+    "Example log message:\n",
+    "INFO - haystack.utils.preprocessing -  Converting data/tutorial1/218_Olenna_Tyrell.txt\n",
+    "Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "kQWEUUMnzqLX",
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
+    "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "q3dSo7ZtA6Nl"
+   },
+   "source": [
+    "## Initializing the DocumentStore\n",
+    "\n",
+    "FAISS is a library for efficient similarity search on a cluster of dense vectors.\n",
+    "The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood\n",
+    "to store the document text and other meta data. The vector embeddings of the text are\n",
+    "indexed on a FAISS Index that later is queried for searching answers.\n",
+    "The default flavour of FAISSDocumentStore is \"Flat\" but can also be set to \"HNSW\" for\n",
+    "faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.\n",
+    "For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "1cYgDJmrA6Nv",
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.document_stores import FAISSDocumentStore\n",
+    "\n",
+    "document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Option 2: Milvus\n",
+    "\n",
+    "> As of version 1.15, MilvusDocumentStore has been deprecated in Haystack. It will be deleted from the haystack repository as of version 1.17 and moved to [haystack-extras](https://github.com/deepset-ai/haystack-extras/tree/main). For more details, check out [Deprecation of MilvusDocumentStore](https://github.com/deepset-ai/haystack/discussions/4785).\n",
+    "\n",
+    "Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.\n",
+    "Like FAISS it has both a \"Flat\" and \"HNSW\" mode but it outperforms FAISS when it comes to dynamic data management.\n",
+    "It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.\n",
+    "See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Milvus cannot be run on Colab, so this cell is commented out.\n",
+    "# To run Milvus you need Docker (versions below 2.0.0) or a docker-compose (versions >= 2.0.0), neither of which is available on Colab.\n",
+    "# See Milvus' documentation for more details: https://milvus.io/docs/install_standalone-docker.md\n",
+    "\n",
+    "# !pip install farm-haystack[milvus]\n",
+    "\n",
+    "# from haystack.utils import launch_milvus\n",
+    "# from haystack.document_stores import MilvusDocumentStore\n",
+    "\n",
+    "# launch_milvus()\n",
+    "# document_store = MilvusDocumentStore()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "06LatTJBA6N0",
+    "pycharm": {
+     "name": "#%% md\n"
     }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "collapsed_sections": [],
-      "name": "Tutorial6_Better_Retrieval_via_Embedding_Retrieval.ipynb",
-      "provenance": []
-    },
-    "gpuClass": "standard",
-    "kernelspec": {
-      "display_name": "Python 3.8.9 64-bit",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.9"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-      }
+   },
+   "source": [
+    "## Cleaning and Writing Documents\n",
+    "\n",
+    "Similarly to the previous tutorials, we download, convert and write some Game of Thrones articles to our DocumentStore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "iqKnu6wxA6N1",
+    "pycharm": {
+     "name": "#%%\n"
     }
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http\n",
+    "\n",
+    "\n",
+    "# Let's first get some files that we want to use\n",
+    "doc_dir = \"data/tutorial6\"\n",
+    "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt6.zip\"\n",
+    "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
+    "\n",
+    "# Convert files to dicts\n",
+    "docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n",
+    "\n",
+    "# Now, let's write the dicts containing documents to our DB.\n",
+    "document_store.write_documents(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wgjedxx_A6N6"
+   },
+   "source": [
+    "## Initializing the Retriever\n",
+    "\n",
+    "**Here:** We use an `EmbeddingRetriever`.\n",
+    "\n",
+    "**Alternatives:**\n",
+    "\n",
+    "- `BM25Retriever` with custom queries (for example, boosting) and filters\n",
+    "- `DensePassageRetriever` which uses two encoder models, one to embed the query and one to embed the passage, and then compares the embedding for retrieval\n",
+    "- `TfidfRetriever` in combination with a SQL or InMemory DocumentStore for simple prototyping and debugging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "kFwiPP60A6N7",
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import EmbeddingRetriever\n",
+    "\n",
+    "retriever = EmbeddingRetriever(\n",
+    "    document_store=document_store, embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
+    ")\n",
+    "# Important:\n",
+    "# Now that we initialized the Retriever, we need to call update_embeddings() to iterate over all\n",
+    "# previously indexed documents and update their embedding representation.\n",
+    "# While this can be a time consuming operation (depending on the corpus size), it only needs to be done once.\n",
+    "# At query time, we only need to embed the query and compare it to the existing document embeddings, which is very fast.\n",
+    "document_store.update_embeddings(retriever)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rnVR28OXA6OA"
+   },
+   "source": [
+    "## Initializing the Reader\n",
+    "\n",
+    "Similar to previous tutorials we now initalize our Reader.\n",
+    "\n",
+    "Here we use a FARMReader with the [*deepset/roberta-base-squad2*](https://huggingface.co/deepset/roberta-base-squad2) model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "fyIuWVwhA6OB"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import FARMReader\n",
+    "\n",
+    "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "unhLD18yA6OF"
+   },
+   "source": [
+    "## Initializing the Pipeline\n",
+    "\n",
+    "With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.\n",
+    "Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
+    "To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.\n",
+    "You can learn more about `Pipelines` in the [docs](https://docs.haystack.deepset.ai/docs/pipelines)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "TssPQyzWA6OG"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.pipelines import ExtractiveQAPipeline\n",
+    "\n",
+    "pipe = ExtractiveQAPipeline(reader, retriever)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bXlBBxKXA6OL"
+   },
+   "source": [
+    "## Asking a Question\n",
+    "\n",
+    "We use the pipeline `run()` method to ask a question. With the `run()` method, you can configure how many candidates the Reader and Retriever shall return. The higher top_k for Retriever, the better (but also the slower) your answers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Zi97Hif2A6OM"
+   },
+   "outputs": [],
+   "source": [
+    "prediction = pipe.run(\n",
+    "    query=\"Who created the Dothraki vocabulary?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "pI0wrHylzqLa"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.utils import print_answers\n",
+    "\n",
+    "\n",
+    "print_answers(prediction, details=\"minimum\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Tutorial6_Better_Retrieval_via_Embedding_Retrieval.ipynb",
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "Python 3.8.9 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }

From b272129d10a57af8bf0923c06429cfcadf763240 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 17 May 2023 10:42:56 +0300
Subject: [PATCH 089/206] Add deprecatio note for knowledge graphs (#192)

---
 .github/workflows/nightly.yml       |  1 -
 .github/workflows/run_tutorials.yml |  1 +
 tutorials/10_Knowledge_Graph.ipynb  | 10 ++++++----
 3 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index 528575dd..fe98face 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -30,7 +30,6 @@ jobs:
           - 06_Better_Retrieval_via_Embedding_Retrieval
           - 07_RAG_Generator
           - 08_Preprocessing
-          - 10_Knowledge_Graph
           - 11_Pipelines
           - 12_LFQA
           - 14_Query_Classifier
diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 92d82e83..741ba991 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -56,6 +56,7 @@ jobs:
           skiplist=(
               "tutorials/02_Finetune_a_model_on_your_data.ipynb"
               "tutorials/09_DPR_training.ipynb"
+              "tutorials/10_Knowledge_Graph.ipynb"
               "tutorials/13_Question_generation.ipynb"
               "tutorials/14_Query_Classifier.ipynb"
               "tutorials/18_GPL.ipynb"
diff --git a/tutorials/10_Knowledge_Graph.ipynb b/tutorials/10_Knowledge_Graph.ipynb
index 3dfcfea2..3dd30643 100644
--- a/tutorials/10_Knowledge_Graph.ipynb
+++ b/tutorials/10_Knowledge_Graph.ipynb
@@ -10,6 +10,8 @@
    "source": [
     "# Question Answering on a Knowledge Graph\n",
     "\n",
+    "> Starting from version 1.15, `BaseKnowledgeGraph`, `GraphDBKnowledgeGraph`, `InMemoryKnowledgeGraph`, and `Text2SparqlRetriever` are being deprecated and will be removed from Haystack as of version 1.17. For more details about this deprecation, check out [our announcement](https://github.com/deepset-ai/haystack/discussions/4882) on Github. \n",
+    "\n",
     "Haystack allows storing and querying knowledge graphs with the help of pre-trained models that translate text queries to SPARQL queries.\n",
     "This tutorial demonstrates how to load an existing knowledge graph into haystack, load a pre-trained retriever, and execute text queries on the knowledge graph.\n",
     "The training of models that translate text queries into SPARQL queries is currently not supported.\n",
@@ -55,7 +57,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,inmemorygraph]"
+    "pip install farm-haystack[colab,inmemorygraph]==1.16.0"
    ]
   },
   {
@@ -401,7 +403,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.6 64-bit",
+   "display_name": "Python 3.9.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -415,11 +417,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.6"
   },
   "vscode": {
    "interpreter": {
-    "hash": "bda33b16be7e844498c7c2d368d72665b4f1d165582b9547ed22a0249a29ca2e"
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
   }
  },

From d2be0aedb01e4d323f78fa8e3f38870512d4c77f Mon Sep 17 00:00:00 2001
From: smcady <smcady@gmail.com>
Date: Wed, 17 May 2023 04:24:18 -0400
Subject: [PATCH 090/206] Update 11_Pipelines.ipynb (#190)

Made a couple of small grammar corrections to the documentation
---
 tutorials/11_Pipelines.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/11_Pipelines.ipynb b/tutorials/11_Pipelines.ipynb
index 09a3eaf7..a1664a9d 100644
--- a/tutorials/11_Pipelines.ipynb
+++ b/tutorials/11_Pipelines.ipynb
@@ -232,7 +232,7 @@
    },
    "source": [
     "Here we initialize the core components that we will be gluing together using the `Pipeline` class.\n",
-    "We have a `DocumentStore`, an `BM25Retriever` and a `FARMReader`.\n",
+    "We have a `DocumentStore`, a `BM25Retriever` and a `FARMReader`.\n",
     "These can be combined to create a classic Retriever-Reader pipeline that is designed\n",
     "to perform Open Domain Question Answering."
    ]
@@ -540,7 +540,7 @@
     "## Custom Nodes\n",
     "\n",
     "Nodes are relatively simple objects\n",
-    "and we encourage our users to design their own if they don't see on that fits their use case\n",
+    "and we encourage our users to design their own if they don't see one that fits their use case\n",
     "\n",
     "The only requirements are:\n",
     "- Create a class that inherits `BaseComponent`.\n",

From cb182a7a7faf33c55fbbbabb74ca48ae156ebd30 Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Wed, 17 May 2023 11:20:08 +0200
Subject: [PATCH 091/206] Remove unused variable from scripts/full_dc_sync.py
 (#189)

---
 scripts/full_dc_sync.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/scripts/full_dc_sync.py b/scripts/full_dc_sync.py
index 8ef41d83..dfcefe9f 100644
--- a/scripts/full_dc_sync.py
+++ b/scripts/full_dc_sync.py
@@ -72,8 +72,6 @@ def delete_all_cloud_files(workspace: str):
     delete_all_cloud_files(workspace)
     print(f"Deleted all files from workspace {workspace}")
 
-    loop = asyncio.get_event_loop()
-
     tutorials = Path(".", "text").glob("*.txt")
     loop = asyncio.get_event_loop()
     loop.run_until_complete(upload_files(tutorials, workspace))

From 5e8dec784e145550d418a94eed8c98eaa6dcc635 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Fri, 19 May 2023 11:34:10 +0200
Subject: [PATCH 092/206] Datadog events (#193)

* datadog action

* fake fail step

* Fix api-url and event text

* Add source in Datadog event payload

* Remove source type

* Try again with different source

* Try another source

* add workflow url

* add name

* remove fake test

---------

Co-authored-by: Silvano Cerza <silvanocerza@gmail.com>
---
 .github/workflows/nightly.yml | 27 ++++++++++++++++++++++-----
 1 file changed, 22 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index fe98face..1fce5162 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -1,9 +1,9 @@
 name: Run Tutorials Nightly
 
 on:
-  workflow_dispatch:  # Activate this workflow manually
+  workflow_dispatch: # Activate this workflow manually
   schedule:
-    - cron: '0 0 * * *'
+    - cron: "0 0 * * *"
 
 jobs:
   run-tutorials:
@@ -55,12 +55,12 @@ jobs:
           pip install pyzmq==23.2.1
           pip install nbconvert ipython
           pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
-      
+
       - name: Install Haystack Extras text2speech dependencies
         run: |
           pip install farm-haystack-text2speech
-      
-      - name: Install Hugging Face datasets 
+
+      - name: Install Hugging Face datasets
         run: |
           pip install datasets>=2.6.1
 
@@ -71,3 +71,20 @@ jobs:
       - name: Run the converted notebook
         run: |
           python ./tutorials/${{ matrix.notebook }}.py
+
+      - name: Send Failure to Datadog
+        if: failure()
+        uses: masci/datadog@v1
+        with:
+          api-key: ${{ secrets.CORE_DATADOG_API_KEY }}
+          api-url: https://api.datadoghq.eu
+          events: |
+            - title: "Tutorial ${{ matrix.notebook }} failed"
+              text: "Branch ${{ github.ref_name }} tests failed"
+              alert_type: "error"
+              source_type_name: "Github"
+              host: ${{ github.repository_owner }}
+              tags:
+                - "project:${{ github.repository }}"
+                - "name:${{ matrix.notebook }}"
+                - "url:https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}"

From 800e483a950c562d1eabe7282f53306fca277cf7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Mon, 22 May 2023 13:57:34 +0200
Subject: [PATCH 093/206] install pdf separately (#194)

---
 .github/workflows/nightly.yml       | 1 +
 .github/workflows/run_tutorials.yml | 1 +
 2 files changed, 2 insertions(+)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index 1fce5162..0b4d5cfd 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -55,6 +55,7 @@ jobs:
           pip install pyzmq==23.2.1
           pip install nbconvert ipython
           pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
+          pip install farm-haystack[pdf]
 
       - name: Install Haystack Extras text2speech dependencies
         run: |
diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 741ba991..8d11a30d 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -28,6 +28,7 @@ jobs:
           apt-get update && apt-get install -y git build-essential gcc libsndfile1 ffmpeg && rm -rf /var/lib/apt/lists/*
           pip install nbconvert ipython
           pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
+          pip install farm-haystack[pdf]
 
       - name: Install Haystack Extras text2speech dependencies
         run: |

From 3d7f5143892e63d9b1a7f510908f7c6a657f0e26 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 30 May 2023 15:13:17 +0300
Subject: [PATCH 094/206] Add Tutorial 24: Building a Conversational Chat
 Application (#196)

* Add first draft of the tutorial 24

* Resolve comments

* Add links to predefined PromptTemplates

* Add emoji to the last sections title

* Add new title

* Change the ConversationSummaryMemory description

* Add documentation links

* Remove commented out code blocks

* Update the name of the tutorial

* Update the tutorial test
---
 .github/workflows/run_tutorials.yml  |    1 +
 README.md                            |    1 +
 index.toml                           |   14 +-
 tutorials/24_Building_Chat_App.ipynb | 1967 ++++++++++++++++++++++++++
 4 files changed, 1981 insertions(+), 2 deletions(-)
 create mode 100644 tutorials/24_Building_Chat_App.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 8d11a30d..f9e6336b 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -62,6 +62,7 @@ jobs:
               "tutorials/14_Query_Classifier.ipynb"
               "tutorials/18_GPL.ipynb"
               "tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb"
+              "tutorials/24_Building_Chat_App.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index ab41d9bd..ec9769d7 100644
--- a/README.md
+++ b/README.md
@@ -41,3 +41,4 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 | Customizing PromptNode                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb) | [21_Customizing_PromptNode.ipynb](./tutorials/21_Customizing_PromptNode.ipynb) |
 | Creating a Generative QA Pipeline with PromptNode                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/22_Pipeline_with_PromptNode.ipynb) | [22_Pipeline_with_PromptNode.ipynb](./tutorials/22_Pipeline_with_PromptNode.ipynb) |
 | Answering Complex Questions with Agents                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) | [23_Answering_Multihop_Questions_with_Agents.ipynb](./tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) |
+| Building a Conversational Chat App                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/24_Building_Chat_App.ipynb) | [24_Building_Chat_App.ipynb](./tutorials/24_Building_Chat_App.ipynb) |
diff --git a/index.toml b/index.toml
index aa003867..e936aff7 100644
--- a/index.toml
+++ b/index.toml
@@ -207,7 +207,6 @@ notebook = "23_Answering_Multihop_Questions_with_Agents.ipynb"
 aliases = ["multihop-qa-with-agents"]
 completion_time = "10 min"
 created_at = 2023-03-27
-featured = true
 
 [[tutorial]]
 title = "Creating a Generative QA Pipeline with PromptNode"
@@ -217,4 +216,15 @@ weight = 61
 notebook = "22_Pipeline_with_PromptNode.ipynb"
 aliases = ["pipeline-with-promptnode"]
 completion_time = "15 min"
-created_at = 2023-03-13
\ No newline at end of file
+created_at = 2023-03-13
+
+[[tutorial]]
+title = "Building a Conversational Chat App"
+description = "Use ConversationalAgent to build a human-like chat application"
+level = "intermediate"
+weight = 64
+notebook = "24_Building_Chat_App.ipynb"
+aliases = ["building-chat-app"]
+completion_time = "10 min"
+created_at = 2023-05-30
+featured = true
\ No newline at end of file
diff --git a/tutorials/24_Building_Chat_App.ipynb b/tutorials/24_Building_Chat_App.ipynb
new file mode 100644
index 00000000..a818cfc2
--- /dev/null
+++ b/tutorials/24_Building_Chat_App.ipynb
@@ -0,0 +1,1967 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TEebQqubxa4G"
+   },
+   "source": [
+    "# Tutorial: Building a Conversational Chat App\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 10 minutes\n",
+    "- **Nodes Used**: `PromptNode`, `ConversationalAgent` and `ConversationSummaryMemory`\n",
+    "- **Goal**: After completing this tutorial, you will have learned how to use ConversationalAgent to build a conversational chat application\n",
+    "- **Prerequisites**: A [Hugging Face API Key](https://huggingface.co/settings/tokens)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DQLWWW1Yy_Ta"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "A [ConversationalAgent](https://docs.haystack.deepset.ai/docs/agent#conversational-agent) is a type of agent that is specifically implemented to create chat applications. With its memory integration, the new ConversationalAgent enables human-like conversation with large language models (LLMs).  \n",
+    "\n",
+    "This tutorial introduces you to the ConversationalAgent, ConversationSummaryMemory and explains how you can create your conversational chat application.  \n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "obaSuZBHy8PF"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2_nC2XoPzDVh"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "JiZktTKoaHp5"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RrPgO_1vzWb6"
+   },
+   "source": [
+    "\n",
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6YZjKAvOzZRq"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(24)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "n0pnO7S6tbyW"
+   },
+   "source": [
+    "## Initializing the ConversationalAgent\n",
+    "\n",
+    "To initialize a ConversationalAgent, we'll first need to create a PromptNode to define the LLM that our chat application will use. Then, we'll add a memory to enable the application to store previous conversation and use this memory to make the interaction more human-like.\n",
+    "\n",
+    "Let's create necessary components for a ConversationalAgent:"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1Omji8PK_675"
+   },
+   "source": [
+    "### 1) Provide a Hugging Face API Key\n",
+    "\n",
+    "Hugging Face offers [hosted Inference API](https://huggingface.co/docs/api-inference/index) which we can use to access machine learning models using simple HTTP requests. This way, we don't need to download models from the hub. To use service, we need to provide an API key from Hugging Face:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "qWuRxFWGcAL4",
+    "outputId": "316ab19c-9a80-4d58-8c6a-80a51c2b8360"
+   },
+   "outputs": [],
+   "source": [
+    "from getpass import getpass\n",
+    "\n",
+    "model_api_key = getpass(\"Enter model provider API key:\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "V4LI3vzH7Xvo"
+   },
+   "source": [
+    "### 2) Create a PromptNode\n",
+    "\n",
+    "We'll initialize a PromptNode with the `model_name`, `api_key`, and `max_length` to control the output length of the model. In this tutorial, we'll use [OpenAssistant/oasst-sft-1-pythia-12b](https://huggingface.co/OpenAssistant/oasst-sft-1-pythia-12b), an open source Transformer-based text generation model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 113,
+     "referenced_widgets": [
+      "f936d65746724060bc0525752fe18e59",
+      "7b5929365227442b95d8890125abf10d",
+      "53d551dd051d4aa4b7554e4beeccdf2c",
+      "8d37bd9866054d58953d769bdb6c8077",
+      "5f40dae689634170a399f2ae096b55c7",
+      "34ab953b2864485eb9c156987fb4bc80",
+      "e4bf06cef8304775ab0ae703217c70f9",
+      "9f6d98341f584427a15cfe51992bdac4",
+      "a8f1701c0592474787a700429f6e1bcd",
+      "b9b307bc661548cf920d3f9f3f78750c",
+      "072d33b006924831ad1fa60996883304",
+      "10bfa975fbba460989195bb8c4f49f37",
+      "a462115762144f08b3b40787b9c5e5e3",
+      "78ce4d0354794e8b9b9d8c20a310a974",
+      "5741cac36a274cfea4b56025ac5ca4be",
+      "1b636844c76d4968b1df7114e3d6fc73",
+      "dc14c6e6689c4456ac10c74ec772e1a5",
+      "8d6f66ff445f4231bc39221d4fb50d59",
+      "4ed95ec26f7d4229b0de7fd97e54ef82",
+      "a81dd24a7ad44786ae3fe4f5ffe190f8",
+      "75005b5e98d741ce9bd44a59c38185be",
+      "b825023aa5bd4a8ea20c2048bdc9c0ef",
+      "474e1f34434941baa436be2b6d82af34",
+      "981a4797c70346d989028fc933c5d91b",
+      "12111fdedeb14372a58cb61a50eb9d4b",
+      "fcd3fd68a99d4baebdd833b436c5e626",
+      "f4737f68e99349feb331bb114abd90cc",
+      "f10905112827478ba0cc76d103512c3f",
+      "50f9a8f3d3064ef08fa3d85e4df2aa6c",
+      "08494d1dcf3e4d16b73ab5e21eed5172",
+      "ee096d7b6ab643b5b6f86426502ff5a3",
+      "f04ce3fdbd9c4167b2b3a7f85ba1f392",
+      "25f2468a83254faaa0d36387e2b90f02"
+     ]
+    },
+    "id": "RckAPBT3bSoh",
+    "outputId": "6d076cd4-a677-4010-f8b8-5f3ec2b24efc"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PromptNode\n",
+    "\n",
+    "model_name = \"OpenAssistant/oasst-sft-1-pythia-12b\"\n",
+    "prompt_node = PromptNode(model_name, api_key=model_api_key, max_length=256)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E1e15LLV8ULF"
+   },
+   "source": [
+    "### 3) Create a ConversationSummaryMemory\n",
+    "\n",
+    "To have a chat application closer to a human interaction, we need to provide [memory](https://docs.haystack.deepset.ai/docs/agent#conversational-agent-memory) to our ConversationalAgent. There are two types of memory options in Haystack: \n",
+    "\n",
+    "1.   **ConversationMemory**: stores the conversation history (default).\n",
+    "2.   **ConversationSummaryMemory**: stores the conversation history and periodically generates summaries.\n",
+    "\n",
+    "These memory nodes inject the conversation history into the prompt for the large language model with every run. Instead of using the full conversation history, we'll use ConversationSummaryMemory that sums up the conversation without losing important information, thus saving the token limit. \n",
+    "\n",
+    "We can use the same PromptNode in ConversationSummaryMemory, so the same `OpenAssistant/oasst-sft-1-pythia-12b` model generates chat summaries. By default, ConversationSummaryMemory summarizes the chat with every `3` runs using the predefined [`conversational-summary`](https://github.com/deepset-ai/haystack/blob/b8911dfae01643dccf0803648ef3fb705d43dfbd/haystack/nodes/prompt/prompt_template.py#L441) PromptTemplate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "iBisS_dI8kan"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.agents.memory import ConversationSummaryMemory\n",
+    "\n",
+    "summary_memory = ConversationSummaryMemory(prompt_node)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RpeKtIsSSNSh"
+   },
+   "source": [
+    "> Optionally, you can define a separate PromptNode with another LLM and PromptTemplate for generating conversation summary and use it in the ConversationSummaryMemory."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MiGGpDW98XjM"
+   },
+   "source": [
+    "### 4) Create a ConversationalAgent\n",
+    "\n",
+    "Now that we have all the necessary components, we can initialize the ConversationalAgent. As PromptTemplate, ConversationalAgent uses [`conversational-agent`](https://github.com/deepset-ai/haystack/blob/b8911dfae01643dccf0803648ef3fb705d43dfbd/haystack/nodes/prompt/prompt_template.py#L437) by default."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_gt2DqNzllPQ"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.agents.conversational import ConversationalAgent\n",
+    "\n",
+    "conversational_agent = ConversationalAgent(prompt_node=prompt_node, memory=summary_memory)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Mgeb6nlsGRTy"
+   },
+   "source": [
+    "Now, our conversational agent is ready to chat!"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Uq_yBlEXGe18"
+   },
+   "source": [
+    "## Trying Out a Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "gAi6DN-LySIH",
+    "outputId": "833a911c-3842-4132-d1e3-dd641d0a4556"
+   },
+   "outputs": [],
+   "source": [
+    "conversational_agent.run(\"Tell me three most interesting things about Istanbul, Turkey\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "bBwcAcbgMTQN",
+    "outputId": "c2f50686-3971-464f-d0d6-5bc9981c51c5"
+   },
+   "outputs": [],
+   "source": [
+    "conversational_agent.run(\"Can you elaborate on the second item?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-wl8dcWpMmNv",
+    "outputId": "18f92ea0-c096-4b71-8308-76ced457e393"
+   },
+   "outputs": [],
+   "source": [
+    "conversational_agent.run(\"Can you turn this info into a twitter thread?\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mhrNe3xKIVsx"
+   },
+   "source": [
+    "* At any point during the chat, you can use `load()` function to check the chat summary: \n",
+    "\n",
+    "```python\n",
+    "print(conversational_agent.memory.load())\n",
+    "```\n",
+    "\n",
+    "* To delete the whole chat history, call `clear()` method:\n",
+    "\n",
+    "```python\n",
+    "conversational_agent.memory.clear()\n",
+    "```\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KGu3QLQXJo-z"
+   },
+   "source": [
+    "Congratulations! 🎉 You've learned how to use ConversationalAgent to create a chat application with a summarized memory. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wQyYPjmAG2gJ"
+   },
+   "source": [
+    "## 💬 Example Application\n",
+    "\n",
+    "To take the chat experience to another level, check out this example application. Run the code cell below and use the textarea to interact with the conversational agent. Use the buttons on the right to load or delete the chat summary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 217,
+     "referenced_widgets": [
+      "4e50a66f4bf345268d9806ba8913ff65",
+      "7b1fbb65dca145c5a17256e2a908397b",
+      "33d0bb13874240628b7aff7c1d5aedcb",
+      "8d46db726306413ea3ed83eec82da929",
+      "0c63fb7fca4a43adb20ad50f24e8e700",
+      "8ba109e7c030448684d8c5cc610e56d2",
+      "798daf459ea14c6e97ce878e4f57dace",
+      "ab89b87e958a454db835bf747c801e2d",
+      "41a4de40b3e4482abc15a17d89466d8f",
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+    "outputId": "095ff9a0-c6d8-44dc-a7fb-daf6969146ad"
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+   "outputs": [],
+   "source": [
+    "import ipywidgets as widgets\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "## Text Input\n",
+    "user_input = widgets.Textarea(\n",
+    "    value=\"\",\n",
+    "    placeholder=\"Type your prompt here\",\n",
+    "    disabled=False,\n",
+    "    style={\"description_width\": \"initial\"},\n",
+    "    layout=widgets.Layout(width=\"100%\", height=\"100%\"),\n",
+    ")\n",
+    "\n",
+    "## Submit Button\n",
+    "submit_button = widgets.Button(\n",
+    "    description=\"Submit\", button_style=\"success\", layout=widgets.Layout(width=\"100%\", height=\"80%\")\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def on_button_clicked(b):\n",
+    "    user_prompt = user_input.value\n",
+    "    user_input.value = \"\"\n",
+    "    print(\"\\nUser:\\n\", user_prompt)\n",
+    "    conversational_agent.run(user_prompt)\n",
+    "\n",
+    "\n",
+    "submit_button.on_click(on_button_clicked)\n",
+    "\n",
+    "## Show Memory Button\n",
+    "memory_button = widgets.Button(\n",
+    "    description=\"Show Memory\", button_style=\"info\", layout=widgets.Layout(width=\"100%\", height=\"100%\")\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def on_memory_button_clicked(b):\n",
+    "    memory = conversational_agent.memory.load()\n",
+    "    if len(memory):\n",
+    "        print(\"\\nMemory:\\n\", memory)\n",
+    "    else:\n",
+    "        print(\"Memory is empty\")\n",
+    "\n",
+    "\n",
+    "memory_button.on_click(on_memory_button_clicked)\n",
+    "\n",
+    "## Clear Memory Button\n",
+    "clear_button = widgets.Button(\n",
+    "    description=\"Clear Memory\", button_style=\"warning\", layout=widgets.Layout(width=\"100%\", height=\"100%\")\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def on_clear_button_button_clicked(b):\n",
+    "    conversational_agent.memory.clear()\n",
+    "    print(\"\\nMemory is cleared\\n\")\n",
+    "\n",
+    "\n",
+    "clear_button.on_click(on_clear_button_button_clicked)\n",
+    "\n",
+    "## Layout\n",
+    "grid = widgets.GridspecLayout(3, 3, height=\"200px\", width=\"800px\", grid_gap=\"10px\")\n",
+    "grid[0, 2] = clear_button\n",
+    "grid[0:2, 0:2] = user_input\n",
+    "grid[2, 0:] = submit_button\n",
+    "grid[1, 2] = memory_button\n",
+    "display(grid)"
+   ]
+  }
+ ],
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From efb1d6b1889f1920fc55d6dcd2945a714838b82e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Mon, 5 Jun 2023 15:12:37 +0200
Subject: [PATCH 095/206] fixing url to use canonical (#198)

---
 tutorials/11_Pipelines.ipynb | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tutorials/11_Pipelines.ipynb b/tutorials/11_Pipelines.ipynb
index a1664a9d..b2a99d29 100644
--- a/tutorials/11_Pipelines.ipynb
+++ b/tutorials/11_Pipelines.ipynb
@@ -669,10 +669,11 @@
     "## Evaluation Nodes\n",
     "\n",
     "We have also designed a set of nodes that can be used to evaluate the performance of a system.\n",
-    "Have a look at our [tutorial](https://haystack.deepset.ai/tutorials/evaluation) to get hands on with the code and learn more about Evaluation Nodes!"
+    "Have a look at our [tutorial](https://haystack.deepset.ai/tutorials/05_evaluation) to get hands on with the code and learn more about Evaluation Nodes!"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,

From 81c5cbde9ad45fd5a5cfa0b2a3efd9e7ae1f05dd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 12 Jun 2023 16:32:32 +0300
Subject: [PATCH 096/206] Temporary fix for tutorial 2 (#201)

* Pin the version
---
 .../02_Finetune_a_model_on_your_data.ipynb    | 30 +++++--------------
 1 file changed, 8 insertions(+), 22 deletions(-)

diff --git a/tutorials/02_Finetune_a_model_on_your_data.ipynb b/tutorials/02_Finetune_a_model_on_your_data.ipynb
index cd7fd6a6..251588ba 100644
--- a/tutorials/02_Finetune_a_model_on_your_data.ipynb
+++ b/tutorials/02_Finetune_a_model_on_your_data.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -39,14 +40,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]"
+    "pip install farm-haystack[colab]==1.16.1"
    ]
   },
   {
@@ -60,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,30 +145,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "04/28/2020 14:39:27 - INFO - farm.utils -   device: cpu n_gpu: 0, distributed training: False, automatic mixed precision training: None\n",
-      "04/28/2020 14:39:27 - INFO - farm.infer -   Could not find `distilbert-base-uncased-distilled-squad` locally. Try to download from model hub ...\n",
-      "04/28/2020 14:39:29 - WARNING - farm.modeling.language_model -   Could not automatically detect from language model name what language it is. \n",
-      "\t We guess it's an *ENGLISH* model ... \n",
-      "\t If not: Init the language model by supplying the 'language' param.\n",
-      "04/28/2020 14:39:31 - WARNING - farm.modeling.prediction_head -   Some unused parameters are passed to the QuestionAnsweringHead. Might not be a problem. Params: {\"loss_ignore_index\": -1}\n",
-      "04/28/2020 14:39:33 - INFO - farm.utils -   device: cpu n_gpu: 0, distributed training: False, automatic mixed precision training: None\n",
-      "04/28/2020 14:39:33 - INFO - farm.utils -   device: cpu n_gpu: 0, distributed training: False, automatic mixed precision training: None\n",
-      "Preprocessing Dataset data/squad20/dev-v2.0.json: 100%|██████████| 1204/1204 [00:02<00:00, 402.13 Dicts/s]\n",
-      "Train epoch 0/1 (Cur. train loss: 0.0000):   0%|          | 0/1213 [00:00<?, ?it/s]"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "reader = FARMReader(model_name_or_path=\"distilbert-base-uncased-distilled-squad\", use_gpu=True)\n",
     "data_dir = \"data/squad20\"\n",
@@ -207,6 +191,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -244,6 +229,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [

From 47496c172f027369e2858a2fd721e78fdc0c8f26 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 29 Jun 2023 12:48:10 +0300
Subject: [PATCH 097/206] Add "inference" dependency if necessary (#211)

* Add "inference" dependency if necessary

* Fix tutorial 23
---
 tutorials/01_Basic_QA_Pipeline.ipynb          |    24 +-
 tutorials/03_Scalable_QA_System.ipynb         |    42 +-
 tutorials/04_FAQ_style_QA.ipynb               |     7 +-
 tutorials/05_Evaluation.ipynb                 |    28 +-
 ...er_Retrieval_via_Embedding_Retrieval.ipynb |    10 +-
 tutorials/09_DPR_training.ipynb               |    13 +-
 tutorials/13_Question_generation.ipynb        |    10 +-
 tutorials/14_Query_Classifier.ipynb           |    24 +-
 tutorials/15_TableQA.ipynb                    |    15 +-
 ...16_Document_Classifier_at_Index_Time.ipynb |    18 +-
 tutorials/17_Audio.ipynb                      |    14 +-
 tutorials/18_GPL.ipynb                        |    12 +-
 ...h_pipeline_with_MultiModal_Retriever.ipynb |    18 +-
 ...ering_Multihop_Questions_with_Agents.ipynb | 11383 ++++++++--------
 14 files changed, 5884 insertions(+), 5734 deletions(-)

diff --git a/tutorials/01_Basic_QA_Pipeline.ipynb b/tutorials/01_Basic_QA_Pipeline.ipynb
index 83401d17..b06c4099 100644
--- a/tutorials/01_Basic_QA_Pipeline.ipynb
+++ b/tutorials/01_Basic_QA_Pipeline.ipynb
@@ -42,6 +42,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -59,7 +60,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]"
+    "pip install farm-haystack[colab,inference]"
    ]
   },
   {
@@ -83,6 +84,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -154,7 +156,7 @@
     "\n",
     "fetch_archive_from_http(\n",
     "    url=\"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt1.zip\",\n",
-    "    output_dir=doc_dir\n",
+    "    output_dir=doc_dir,\n",
     ")"
    ]
   },
@@ -177,8 +179,7 @@
     "\n",
     "files_to_index = [doc_dir + \"/\" + f for f in os.listdir(doc_dir)]\n",
     "indexing_pipeline = TextIndexingPipeline(document_store)\n",
-    "indexing_pipeline.run_batch(file_paths=files_to_index)\n",
-    "\n"
+    "indexing_pipeline.run_batch(file_paths=files_to_index)"
    ]
   },
   {
@@ -301,15 +302,12 @@
    "outputs": [],
    "source": [
     "prediction = pipe.run(\n",
-    "    query=\"Who is the father of Arya Stark?\",\n",
-    "    params={\n",
-    "        \"Retriever\": {\"top_k\": 10},\n",
-    "        \"Reader\": {\"top_k\": 5}\n",
-    "    }\n",
+    "    query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
     ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -339,6 +337,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -353,13 +352,11 @@
    "source": [
     "from haystack.utils import print_answers\n",
     "\n",
-    "print_answers(\n",
-    "    prediction,\n",
-    "    details=\"minimum\" ## Choose from `minimum`, `medium`, and `all`\n",
-    ")"
+    "print_answers(prediction, details=\"minimum\")  ## Choose from `minimum`, `medium`, and `all`"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -367,6 +364,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
diff --git a/tutorials/03_Scalable_QA_System.ipynb b/tutorials/03_Scalable_QA_System.ipynb
index 59b7cb24..dc32f91c 100644
--- a/tutorials/03_Scalable_QA_System.ipynb
+++ b/tutorials/03_Scalable_QA_System.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -13,6 +14,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -39,6 +41,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -56,7 +59,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,preprocessing,elasticsearch]"
+    "pip install farm-haystack[colab,preprocessing,elasticsearch,inference]"
    ]
   },
   {
@@ -80,6 +83,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -149,6 +153,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -170,6 +175,7 @@
    "outputs": [],
    "source": [
     "import time\n",
+    "\n",
     "time.sleep(30)"
    ]
   },
@@ -188,6 +194,7 @@
    "outputs": [],
    "source": [
     "from haystack.utils import launch_es\n",
+    "\n",
     "launch_es()"
    ]
   },
@@ -203,12 +210,7 @@
     "# Get the host where Elasticsearch is running, default to localhost\n",
     "host = os.environ.get(\"ELASTICSEARCH_HOST\", \"localhost\")\n",
     "\n",
-    "document_store = ElasticsearchDocumentStore(\n",
-    "    host=host,\n",
-    "    username=\"\",\n",
-    "    password=\"\",\n",
-    "    index=\"document\"\n",
-    ")"
+    "document_store = ElasticsearchDocumentStore(host=host, username=\"\", password=\"\", index=\"document\")"
    ]
   },
   {
@@ -250,8 +252,8 @@
     "doc_dir = \"data/build_a_scalable_question_answering_system\"\n",
     "\n",
     "fetch_archive_from_http(\n",
-    "    url=\"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt3.zip\", \n",
-    "    output_dir=doc_dir\n",
+    "    url=\"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt3.zip\",\n",
+    "    output_dir=doc_dir,\n",
     ")"
    ]
   },
@@ -282,10 +284,11 @@
     "    split_length=200,\n",
     "    split_overlap=20,\n",
     "    split_respect_sentence_boundary=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -310,7 +313,7 @@
     "\n",
     "indexing_pipeline.add_node(component=text_converter, name=\"TextConverter\", inputs=[\"File\"])\n",
     "indexing_pipeline.add_node(component=preprocessor, name=\"PreProcessor\", inputs=[\"TextConverter\"])\n",
-    "indexing_pipeline.add_node(component=document_store, name=\"DocumentStore\", inputs=[\"PreProcessor\"])\n"
+    "indexing_pipeline.add_node(component=document_store, name=\"DocumentStore\", inputs=[\"PreProcessor\"])"
    ]
   },
   {
@@ -427,7 +430,7 @@
     "\n",
     "querying_pipeline = Pipeline()\n",
     "querying_pipeline.add_node(component=retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
-    "querying_pipeline.add_node(component=reader, name=\"Reader\", inputs=[\"Retriever\"])\n"
+    "querying_pipeline.add_node(component=reader, name=\"Reader\", inputs=[\"Retriever\"])"
    ]
   },
   {
@@ -463,15 +466,12 @@
    ],
    "source": [
     "prediction = querying_pipeline.run(\n",
-    "    query=\"Who is the father of Arya Stark?\",\n",
-    "    params={\n",
-    "        \"Retriever\": {\"top_k\": 10},\n",
-    "        \"Reader\": {\"top_k\": 5}\n",
-    "    }\n",
+    "    query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
     ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -501,6 +501,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -515,13 +516,11 @@
    "source": [
     "from haystack.utils import print_answers\n",
     "\n",
-    "print_answers(\n",
-    "    prediction,\n",
-    "    details=\"minimum\" ## Choose from `minimum`, `medium` and `all`\n",
-    ")"
+    "print_answers(prediction, details=\"minimum\")  ## Choose from `minimum`, `medium` and `all`"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -529,6 +528,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
diff --git a/tutorials/04_FAQ_style_QA.ipynb b/tutorials/04_FAQ_style_QA.ipynb
index 42013168..4bb2cf38 100644
--- a/tutorials/04_FAQ_style_QA.ipynb
+++ b/tutorials/04_FAQ_style_QA.ipynb
@@ -1,6 +1,7 @@
 {
   "cells": [
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "jUbPUmtaozIP"
@@ -65,7 +66,7 @@
         "%%bash\n",
         "\n",
         "pip install --upgrade pip\n",
-        "pip install farm-haystack[colab]"
+        "pip install farm-haystack[colab,inference]"
       ]
     },
     {
@@ -120,6 +121,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "noVtM20ZozIX"
@@ -143,6 +145,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "collapsed": false,
@@ -176,6 +179,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "collapsed": false,
@@ -228,6 +232,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "collapsed": false,
diff --git a/tutorials/05_Evaluation.ipynb b/tutorials/05_Evaluation.ipynb
index e774a32e..b6aad683 100644
--- a/tutorials/05_Evaluation.ipynb
+++ b/tutorials/05_Evaluation.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -54,7 +55,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,preprocessing,elasticsearch,metrics]"
+    "pip install farm-haystack[colab,preprocessing,elasticsearch,metrics,inference]"
    ]
   },
   {
@@ -102,6 +103,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -123,6 +125,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -162,6 +165,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -180,6 +184,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -281,6 +286,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "gy8YwmSYOhvE"
@@ -423,6 +429,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "7i84KXONOhvc"
@@ -532,6 +539,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -560,6 +568,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -577,6 +586,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -601,6 +611,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -630,6 +641,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -715,6 +727,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -723,6 +736,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -730,6 +744,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -747,6 +762,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -804,6 +820,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -853,6 +870,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -896,6 +914,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -937,6 +956,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -946,6 +966,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -971,6 +992,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -989,6 +1011,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1025,6 +1048,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1065,7 +1089,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.12"
   },
   "vscode": {
    "interpreter": {
diff --git a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
index f08f4d8c..d069f824 100644
--- a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+++ b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "bEH-CRbeA6NU"
@@ -80,7 +81,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,faiss]"
+    "pip install farm-haystack[colab,faiss,inference]"
    ]
   },
   {
@@ -104,6 +105,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -139,6 +141,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "q3dSo7ZtA6Nl"
@@ -172,6 +175,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -205,6 +209,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "06LatTJBA6N0",
@@ -245,6 +250,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "wgjedxx_A6N6"
@@ -286,6 +292,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "rnVR28OXA6OA"
@@ -340,6 +347,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "bXlBBxKXA6OL"
diff --git a/tutorials/09_DPR_training.ipynb b/tutorials/09_DPR_training.ipynb
index 7a4a8509..9ffae5e7 100644
--- a/tutorials/09_DPR_training.ipynb
+++ b/tutorials/09_DPR_training.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -39,7 +40,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]"
+    "pip install farm-haystack[colab,inference]"
    ]
   },
   {
@@ -63,6 +64,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -115,6 +117,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -165,6 +168,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -185,6 +189,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -225,6 +230,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -268,6 +274,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -309,6 +316,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -348,6 +356,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -368,6 +377,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -428,6 +438,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
diff --git a/tutorials/13_Question_generation.ipynb b/tutorials/13_Question_generation.ipynb
index e9650891..1f50b5d6 100644
--- a/tutorials/13_Question_generation.ipynb
+++ b/tutorials/13_Question_generation.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true
@@ -46,7 +47,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,elasticsearch]"
+    "pip install farm-haystack[colab,elasticsearch,inference]"
    ]
   },
   {
@@ -70,6 +71,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -120,6 +122,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -167,6 +170,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -198,6 +202,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -226,6 +231,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -253,6 +259,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -285,6 +292,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
diff --git a/tutorials/14_Query_Classifier.ipynb b/tutorials/14_Query_Classifier.ipynb
index d31282c4..f222f8d7 100644
--- a/tutorials/14_Query_Classifier.ipynb
+++ b/tutorials/14_Query_Classifier.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "O-W2ZQ6CN-gZ"
@@ -68,7 +69,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,elasticsearch]\n",
+    "pip install farm-haystack[colab,elasticsearch,inference]\n",
     "\n",
     "# Install these to allow pipeline visualization\n",
     "apt install libgraphviz-dev\n",
@@ -76,6 +77,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -95,6 +97,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -123,6 +126,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "sJcWRK4Hwyx2"
@@ -149,6 +153,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "1NHjy9aa9FKx"
@@ -173,6 +178,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "UbKlyXcNj-nx"
@@ -203,6 +209,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "VyMZzRVHlG5O"
@@ -232,6 +239,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "zdAY1CUYnTFa"
@@ -265,6 +273,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "Fk2kpvQR6Fa0"
@@ -274,6 +283,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "eEwDIq9KXXke"
@@ -285,6 +295,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -305,6 +316,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -346,6 +358,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "Vm9gqTioN-gf"
@@ -399,6 +412,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "CbAgZ2MZn2qm"
@@ -436,6 +450,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "K4wZ3xkQCHjY"
@@ -470,6 +485,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "imqRRCGTwQav"
@@ -506,6 +522,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "dQ5YMyd4CQPC"
@@ -556,6 +573,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "zLwdVwMXDcoS"
@@ -599,6 +617,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "QU1B6JQEDrol"
@@ -634,6 +653,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -648,6 +668,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -714,6 +735,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
diff --git a/tutorials/15_TableQA.ipynb b/tutorials/15_TableQA.ipynb
index c7786860..ed8fea8a 100644
--- a/tutorials/15_TableQA.ipynb
+++ b/tutorials/15_TableQA.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "DeAkZwDhufYA"
@@ -43,7 +44,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,elasticsearch,metrics]\n",
+    "pip install farm-haystack[colab,elasticsearch,metrics,inference]\n",
     "\n",
     "# Install pygraphviz for visualization of Pipelines\n",
     "apt install libgraphviz-dev\n",
@@ -71,6 +72,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -95,6 +97,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -115,6 +118,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -180,6 +184,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "fFh26LIlxldw"
@@ -249,6 +254,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "hmQC1sDmw3d7"
@@ -326,6 +332,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "zbwkXScm2-gy"
@@ -387,6 +394,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "jkAYNMb7R9qu"
@@ -412,6 +420,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "pgmG7pzL5ceh"
@@ -493,6 +502,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "c2sk_uNHj0DY"
@@ -559,6 +569,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -622,6 +633,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -682,6 +694,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
diff --git a/tutorials/16_Document_Classifier_at_Index_Time.ipynb b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
index a2ba13f6..57b23caf 100644
--- a/tutorials/16_Document_Classifier_at_Index_Time.ipynb
+++ b/tutorials/16_Document_Classifier_at_Index_Time.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -10,6 +11,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -19,6 +21,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -26,6 +29,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -36,6 +40,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -54,13 +59,14 @@
     "\n",
     "# Install the latest main of Haystack\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,ocr,preprocessing,file-conversion,pdf,elasticsearch]\n",
+    "pip install farm-haystack[colab,ocr,preprocessing,file-conversion,pdf,elasticsearch,inference]\n",
     "\n",
     "apt install libgraphviz-dev\n",
     "pip install pygraphviz"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -80,6 +86,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -108,6 +115,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -145,6 +153,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -152,6 +161,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -225,6 +235,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -232,6 +243,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -252,6 +264,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -338,6 +351,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -345,6 +359,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -393,6 +408,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
diff --git a/tutorials/17_Audio.ipynb b/tutorials/17_Audio.ipynb
index 6dde333a..4e1e11ca 100644
--- a/tutorials/17_Audio.ipynb
+++ b/tutorials/17_Audio.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "Dne2XSNzB3SK"
@@ -17,6 +18,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -28,6 +30,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -41,6 +44,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "nBvGUPVKN2oJ"
@@ -62,11 +66,12 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,preprocessing]\n",
+    "pip install farm-haystack[colab,preprocessing,inference]\n",
     "pip install farm-haystack-text2speech"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -87,6 +92,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "pbGu92rAB3Sl"
@@ -152,6 +158,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "zW5qaqn1B3St"
@@ -186,6 +193,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "oV1KHzXGB3Sy"
@@ -240,6 +248,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "FXf-kTn4B3S6"
@@ -295,6 +304,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -323,7 +333,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.6"
   },
   "vscode": {
    "interpreter": {
diff --git a/tutorials/18_GPL.ipynb b/tutorials/18_GPL.ipynb
index d5159e42..9ca92467 100644
--- a/tutorials/18_GPL.ipynb
+++ b/tutorials/18_GPL.ipynb
@@ -43,6 +43,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "4bafc58b",
    "metadata": {},
@@ -54,6 +55,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "ecba2dd7",
    "metadata": {},
@@ -79,10 +81,11 @@
     "\n",
     "pip install -q datasets\n",
     "pip install \"faiss-gpu>=1.6.3,<2\"\n",
-    "pip install farm-haystack[colab,faiss-gpu]"
+    "pip install farm-haystack[colab,faiss-gpu,inference]"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "50f1b9a2",
    "metadata": {},
@@ -104,6 +107,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -213,6 +217,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -257,6 +262,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -299,6 +305,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -370,6 +377,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -423,6 +431,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -479,6 +488,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
diff --git a/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb b/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
index 5dc677f3..24386806 100644
--- a/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
+++ b/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "CQyfa3akfIEZ"
@@ -26,6 +27,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "fzn2uA1Be1Km"
@@ -38,6 +40,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "tJU29jj0fX5m"
@@ -57,10 +60,11 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]"
+    "pip install farm-haystack[colab,inference]"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -80,6 +84,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "KkVAG7FdXsEU"
@@ -108,6 +113,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "oGNwBu0yYcDq"
@@ -137,6 +143,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "oOJC6m8cqzCl"
@@ -166,6 +173,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "_U-RlPJWHMjO"
@@ -176,6 +184,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "nfmeRIE9wz9o"
@@ -212,6 +221,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "mcopKii2MBCd"
@@ -221,6 +231,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "vMNYvDjd9sqY"
@@ -246,6 +257,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "wbEkh5oSMJbq"
@@ -255,6 +267,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "TSjizWzAF6T9"
@@ -283,6 +296,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "FoEt2cHHTdIZ"
@@ -297,6 +311,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "KBukVUVVU0if"
@@ -376,6 +391,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "rcteNc-Rn5R7"
diff --git a/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb b/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
index 88e919cd..41af9477 100644
--- a/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
+++ b/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
@@ -1,5691 +1,5700 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "h8gcxwg4sAXd"
-      },
-      "source": [
-        "# Tutorial: Answering Multihop Questions with Agents\n",
-        "\n",
-        "- **Level**: Intermediate\n",
-        "- **Time to complete**: 10 minutes\n",
-        "- **Nodes Used**: `Agent`, `PromptNode`, `InMemoryDocumentStore`, `FARMReader` and `ExtractiveQAPipeline`\n",
-        "- **Goal**: After completing this tutorial, you will have learned how to use Agents to build a multi-hop question answering system with an `ExtractiveQAPipeline` as a tool\n",
-        "- **Prerequisites**: An [OpenAI API Key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key)\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "collapsed": false,
-        "id": "ifCja5PasAXf"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "The [Agent](https://docs.haystack.deepset.ai/docs/agent) class uses a large language model (LLM) to make decisions and come up with the best next course of action. You can provide the `Agent` with a set of [`Tools`](https://docs.haystack.deepset.ai/docs/agent#tools) that it can choose to use to reach a result. At each iteration, the agent will pick a tool from the ones available to it. Based on the result, the Agent has two options: It will either decide to select a tool again and do another iteration, or it will decide that it has reached a conclusion and return the final answer.\n",
-        "\n",
-        "In this tutorial, we will provide the Agent with just one tool to answer questions: a commonly used Haystack component, the `ExtractiveQAPipeline`.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jsUTxX7y5XS_"
-      },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kn47_SD-sAXg"
-      },
-      "source": [
-        "## Installing Haystack\n",
-        "\n",
-        "To start, let's install the latest release of Haystack with `pip`. In this tutorial, we'll use a [Hugging Face dataset](https://huggingface.co/datasets/Tuana/presidents) that has already been prepared as Haystack `Documents`, so we will install `datasets` too:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "A5_XfePisAXg",
-        "outputId": "584fbb90-714a-4016-d21f-ca9b16d2a307"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install --upgrade pip\n",
-        "pip install farm-haystack[colab]\n",
-        "pip install datasets"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "### Enabling Telemetry \n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(23)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "s5RGKEgGsAXh"
-      },
-      "source": [
-        "Set the logging level to INFO:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "dCLBdwjlsAXh"
-      },
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "\n",
-        "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
-        "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "4jngOMq_6FVT"
-      },
-      "source": [
-        "## Create an Extractive QA Pipeline\n",
-        "\n",
-        "Now, we will introduce an `ExtractiveQAPipeline` as a `Tool` to our `Agent`. To do so, we'll first write our documents about the presidents of the USA into a `DocumentStore` and then create our pipeline.\n",
-        "\n",
-        "### 1) Write documents to the DocumentStore"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000,
-          "referenced_widgets": [
-            "cea9f7f44bd642708bae169102424ac4",
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-            "e931ad893c2c46ea97fcc1e17c227304",
-            "2678baf41af84b10a17d8765f2ab8bdf",
-            "2d71fb2cd0cc4e62b7d9c02170a1b53f",
-            "dad9654ec0834e4b83f792010d84daec",
-            "4d40fba94263477bbc74eb66d02adec9",
-            "e36adfcf52fc4c46b5eba86bfab3dc49",
-            "10ac00d0c9094e47832282d6deb1272c",
-            "14ef2250ccf24fd48e0992a550687ff7"
-          ]
-        },
-        "id": "eFJg6a4PaDR_",
-        "outputId": "8692dfa0-c2c5-4c69-dece-1c0214e2070c"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.document_stores import InMemoryDocumentStore\n",
-        "from datasets import load_dataset\n",
-        "\n",
-        "remote_dataset = load_dataset('Tuana/presidents', split=\"train\")\n",
-        "\n",
-        "document_store = InMemoryDocumentStore(use_bm25=True)\n",
-        "document_store.write_documents(remote_dataset)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "s-6-JT8O6prc"
-      },
-      "source": [
-        "### 2) Create an ExtractiveQAPipeline:\n",
-        "Let's define our retriever and reader to to use in an `ExtractiveQAPipeline`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
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-          ]
-        },
-        "id": "n9bmrIUCatMk",
-        "outputId": "9f110081-72ce-4f80-fea0-10b3f8a61654"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.nodes import EmbeddingRetriever, FARMReader\n",
-        "from haystack.pipelines import ExtractiveQAPipeline\n",
-        "\n",
-        "retriever = EmbeddingRetriever(document_store=document_store, embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\", use_gpu=True)\n",
-        "document_store.update_embeddings(retriever=retriever)\n",
-        "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)\n",
-        "presidents_qa = ExtractiveQAPipeline(reader=reader, retriever=retriever)\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mgQAP-4T7Fh6"
-      },
-      "source": [
-        "### 3) Let's test the pipeline!\n",
-        "\n",
-        "Now that you have an `ExtractiveQAPipeline`, go ahead and ask it a question about the presidents of the USA. \n",
-        "\n",
-        "Below, we're asking the question: \"What year was the 1st president of the USA born?\"\n",
-        "\n",
-        "Notice how this is 2 questions in one. An extractive model will struggle to find the answer to this question unless the answer is phrased clearly in our documents. For example: \"The first president of the USA was born in 1732\".\n",
-        "\n",
-        "On the other hand, it does well with a question such as \"Who was the 1st president of the USA?\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 930,
-          "referenced_widgets": [
-            "d31a483cf6e443a5891ca14672ffed3b",
-            "a0dcb1d860704bbdb54447dfd0cc92bd",
-            "5e85990cb7374ddc9f7107a72f812e5e",
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-            "083cb8b3aa1240129a75e5c2a7eacbb6",
-            "7f4d17a4998f44b58f813b815d16f7f8",
-            "6ffba94cc23846209c4629cb10bce042",
-            "de52fc90bd3f4db0b540535246b33c4f",
-            "acd5844ab2d64071a1d59a1df7baafbd",
-            "744746530a4e401bb6e9e28d89e54eed",
-            "3bb3e922fc8c471a8c1aaaf5bbca2d92"
-          ]
-        },
-        "id": "HdS-satXbSsw",
-        "outputId": "751e30c8-50ae-48d3-8e34-df7023d1252a"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.utils import print_answers\n",
-        "\n",
-        "#result = presidents_qa.run(\"Who was the 1st president of the USA?\")\n",
-        "result = presidents_qa.run(\"What year was the 1st president of the USA born?\")\n",
-        "\n",
-        "print_answers(result, \"minimum\")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bn83I_Dg8Q1f"
-      },
-      "source": [
-        "## Create an Agent with the `ExtractiveQAPipeline` as a `Tool`\n",
-        "### 1) To create the Agent, we'll make use of an Open AI model. So first, provide your Open AI key:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "kBkMr86vvl9h",
-        "outputId": "8366a06d-9064-485e-aa88-c82859c68265"
-      },
-      "outputs": [],
-      "source": [
-        "from getpass import getpass\n",
-        "\n",
-        "api_key_prompt = \"Enter OpenAI API key:\" \n",
-        "api_key = getpass(api_key_prompt)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qajsCzjx9C6j"
-      },
-      "source": [
-        "### 2) Initialize the Agent \n",
-        "\n",
-        "The `Agent` needs to determine the next best course of action at each iteration. It does this by using an LLM, and a prompt designed specially for this use case. Our `Agent` uses a `PromptNode` with the default [\"zero-shot-react\" `PromptTemplate` ](https://github.com/deepset-ai/haystack/blob/444a3116c42d2c8852d27aa8093ac92c8e85ab88/haystack/nodes/prompt/prompt_node.py#L337). \n",
-        "\n",
-        "Here, let's define an `Agent` that uses the `text-davinci-003` model by OpenAI."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "uU20HAGXcf-q"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.agents import Agent\n",
-        "from haystack.nodes import PromptNode\n",
-        "\n",
-        "prompt_node = PromptNode(model_name_or_path=\"text-davinci-003\", api_key=api_key, stop_words=[\"Observation:\"])\n",
-        "agent = Agent(prompt_node=prompt_node)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Ndeax5iGA1IQ"
-      },
-      "source": [
-        "### 3) Provide the Agent with a Tool\n",
-        "Next, let's add our `ExtractiveQAPipeline` into the Agent's arsenal. The Agent will then be able to use this pipeline when it decides it could be useful.\n",
-        "\n",
-        "To do so, let's define a tool and make sure to give it a description. The exact wording of your description matters a lot here. The agent uses it to understand in which cases it should pick this tool. If the agent fails to pick the right tool, adjusting the description might help."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qWuCvmKCcu1R"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.agents import Tool\n",
-        "\n",
-        "search_tool = Tool(name=\"USA_Presidents_QA\",pipeline_or_node=presidents_qa,\n",
-        "                  description=\"useful for when you need to answer questions related to the presidents of the USA.\",\n",
-        "                  output_variable=\"answers\"\n",
-        "              )\n",
-        "agent.add_tool(search_tool)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yjizLaApBg2Y"
-      },
-      "source": [
-        "### 4) Ask a question!\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 315,
-          "referenced_widgets": [
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-            "c8aa20f439fe417897c47ff5e1f22758",
-            "ee80af1ef66d491ab58fd8bc85d2ae8c",
-            "d1da6edc05c8452f9037b239075b50a8",
-            "94354a2e44d24b97ae3c3e9eaa2e7fa5",
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-          ]
-        },
-        "id": "7FeTZtN8dC-m",
-        "outputId": "003dfde4-82d4-46e8-864a-9b5a83dbd9f5"
-      },
-      "outputs": [],
-      "source": [
-        "result = agent.run(\"What year was the 1st president of the USA born?\")\n",
-        "\n",
-        "print(result[\"transcript\"].split('---')[1])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7Y_Ku4eWByqf"
-      },
-      "source": [
-        "Congratulations! 🎉 You've used an Agent that can use an extractive model iteratively, to arrive at a final answer to a multi-hop question!!!"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h8gcxwg4sAXd"
+   },
+   "source": [
+    "# Tutorial: Answering Multihop Questions with Agents\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 10 minutes\n",
+    "- **Nodes Used**: `Agent`, `PromptNode`, `InMemoryDocumentStore`, `FARMReader` and `ExtractiveQAPipeline`\n",
+    "- **Goal**: After completing this tutorial, you will have learned how to use Agents to build a multi-hop question answering system with an `ExtractiveQAPipeline` as a tool\n",
+    "- **Prerequisites**: An [OpenAI API Key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "id": "ifCja5PasAXf"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "The [Agent](https://docs.haystack.deepset.ai/docs/agent) class uses a large language model (LLM) to make decisions and come up with the best next course of action. You can provide the `Agent` with a set of [`Tools`](https://docs.haystack.deepset.ai/docs/agent#tools) that it can choose to use to reach a result. At each iteration, the agent will pick a tool from the ones available to it. Based on the result, the Agent has two options: It will either decide to select a tool again and do another iteration, or it will decide that it has reached a conclusion and return the final answer.\n",
+    "\n",
+    "In this tutorial, we will provide the Agent with just one tool to answer questions: a commonly used Haystack component, the `ExtractiveQAPipeline`.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jsUTxX7y5XS_"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kn47_SD-sAXg"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`. In this tutorial, we'll use a [Hugging Face dataset](https://huggingface.co/datasets/Tuana/presidents) that has already been prepared as Haystack `Documents`, so we will install `datasets` too:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
     "colab": {
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+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "A5_XfePisAXg",
+    "outputId": "584fbb90-714a-4016-d21f-ca9b16d2a307"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab,inference]\n",
+    "pip install datasets"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Enabling Telemetry \n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(23)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "s5RGKEgGsAXh"
+   },
+   "source": [
+    "Set the logging level to INFO:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "dCLBdwjlsAXh"
+   },
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
+    "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4jngOMq_6FVT"
+   },
+   "source": [
+    "## Create an Extractive QA Pipeline\n",
+    "\n",
+    "Now, we will introduce an `ExtractiveQAPipeline` as a `Tool` to our `Agent`. To do so, we'll first write our documents about the presidents of the USA into a `DocumentStore` and then create our pipeline.\n",
+    "\n",
+    "### 1) Write documents to the DocumentStore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
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+     ]
+    },
+    "id": "eFJg6a4PaDR_",
+    "outputId": "8692dfa0-c2c5-4c69-dece-1c0214e2070c"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.document_stores import InMemoryDocumentStore\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "remote_dataset = load_dataset(\"Tuana/presidents\", split=\"train\")\n",
+    "\n",
+    "document_store = InMemoryDocumentStore(use_bm25=True)\n",
+    "document_store.write_documents(remote_dataset)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "s-6-JT8O6prc"
+   },
+   "source": [
+    "### 2) Create an ExtractiveQAPipeline:\n",
+    "Let's define our retriever and reader to to use in an `ExtractiveQAPipeline`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 317,
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+      "e22644ca58294d4dbcddd208583886b9",
+      "272225718b18418f9d9832662abdca00",
+      "3d85d0886e1f4e2fb9b812e89fa34bd8",
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+      "d6e19afe43c640e894ca47f9a38fbc60"
+     ]
+    },
+    "id": "n9bmrIUCatMk",
+    "outputId": "9f110081-72ce-4f80-fea0-10b3f8a61654"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import EmbeddingRetriever, FARMReader\n",
+    "from haystack.pipelines import ExtractiveQAPipeline\n",
+    "\n",
+    "retriever = EmbeddingRetriever(\n",
+    "    document_store=document_store, embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\", use_gpu=True\n",
+    ")\n",
+    "document_store.update_embeddings(retriever=retriever)\n",
+    "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)\n",
+    "presidents_qa = ExtractiveQAPipeline(reader=reader, retriever=retriever)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mgQAP-4T7Fh6"
+   },
+   "source": [
+    "### 3) Let's test the pipeline!\n",
+    "\n",
+    "Now that you have an `ExtractiveQAPipeline`, go ahead and ask it a question about the presidents of the USA. \n",
+    "\n",
+    "Below, we're asking the question: \"What year was the 1st president of the USA born?\"\n",
+    "\n",
+    "Notice how this is 2 questions in one. An extractive model will struggle to find the answer to this question unless the answer is phrased clearly in our documents. For example: \"The first president of the USA was born in 1732\".\n",
+    "\n",
+    "On the other hand, it does well with a question such as \"Who was the 1st president of the USA?\""
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 930,
+     "referenced_widgets": [
+      "d31a483cf6e443a5891ca14672ffed3b",
+      "a0dcb1d860704bbdb54447dfd0cc92bd",
+      "5e85990cb7374ddc9f7107a72f812e5e",
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+      "083cb8b3aa1240129a75e5c2a7eacbb6",
+      "7f4d17a4998f44b58f813b815d16f7f8",
+      "6ffba94cc23846209c4629cb10bce042",
+      "de52fc90bd3f4db0b540535246b33c4f",
+      "acd5844ab2d64071a1d59a1df7baafbd",
+      "744746530a4e401bb6e9e28d89e54eed",
+      "3bb3e922fc8c471a8c1aaaf5bbca2d92"
+     ]
+    },
+    "id": "HdS-satXbSsw",
+    "outputId": "751e30c8-50ae-48d3-8e34-df7023d1252a"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.utils import print_answers\n",
+    "\n",
+    "# result = presidents_qa.run(\"Who was the 1st president of the USA?\")\n",
+    "result = presidents_qa.run(\"What year was the 1st president of the USA born?\")\n",
+    "\n",
+    "print_answers(result, \"minimum\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bn83I_Dg8Q1f"
+   },
+   "source": [
+    "## Create an Agent with the `ExtractiveQAPipeline` as a `Tool`\n",
+    "### 1) To create the Agent, we'll make use of an Open AI model. So first, provide your Open AI key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "kBkMr86vvl9h",
+    "outputId": "8366a06d-9064-485e-aa88-c82859c68265"
+   },
+   "outputs": [],
+   "source": [
+    "from getpass import getpass\n",
+    "\n",
+    "api_key_prompt = \"Enter OpenAI API key:\"\n",
+    "api_key = getpass(api_key_prompt)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qajsCzjx9C6j"
+   },
+   "source": [
+    "### 2) Initialize the Agent \n",
+    "\n",
+    "The `Agent` needs to determine the next best course of action at each iteration. It does this by using an LLM, and a prompt designed specially for this use case. Our `Agent` uses a `PromptNode` with the default [\"zero-shot-react\" `PromptTemplate` ](https://github.com/deepset-ai/haystack/blob/444a3116c42d2c8852d27aa8093ac92c8e85ab88/haystack/nodes/prompt/prompt_node.py#L337). \n",
+    "\n",
+    "Here, let's define an `Agent` that uses the `text-davinci-003` model by OpenAI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "uU20HAGXcf-q"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.agents import Agent\n",
+    "from haystack.nodes import PromptNode\n",
+    "\n",
+    "prompt_node = PromptNode(model_name_or_path=\"text-davinci-003\", api_key=api_key, stop_words=[\"Observation:\"])\n",
+    "agent = Agent(prompt_node=prompt_node)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ndeax5iGA1IQ"
+   },
+   "source": [
+    "### 3) Provide the Agent with a Tool\n",
+    "Next, let's add our `ExtractiveQAPipeline` into the Agent's arsenal. The Agent will then be able to use this pipeline when it decides it could be useful.\n",
+    "\n",
+    "To do so, let's define a tool and make sure to give it a description. The exact wording of your description matters a lot here. The agent uses it to understand in which cases it should pick this tool. If the agent fails to pick the right tool, adjusting the description might help."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "qWuCvmKCcu1R"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.agents import Tool\n",
+    "\n",
+    "search_tool = Tool(\n",
+    "    name=\"USA_Presidents_QA\",\n",
+    "    pipeline_or_node=presidents_qa,\n",
+    "    description=\"useful for when you need to answer questions related to the presidents of the USA.\",\n",
+    "    output_variable=\"answers\",\n",
+    ")\n",
+    "agent.add_tool(search_tool)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yjizLaApBg2Y"
+   },
+   "source": [
+    "### 4) Ask a question!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
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+    "result = agent.run(\"What year was the 1st president of the USA born?\")\n",
+    "\n",
+    "print(result[\"transcript\"].split(\"---\")[0])"
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+ "nbformat_minor": 0
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From 86a464bcabacee62e7e5cc32902966736585e90d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 29 Jun 2023 12:48:22 +0300
Subject: [PATCH 098/206] Pin tutorial versions (#209)

---
 ...tter_Retrieval_via_Embedding_Retrieval.ipynb |  4 ++--
 tutorials/07_RAG_Generator.ipynb                | 17 +++++++++++++++--
 tutorials/12_LFQA.ipynb                         | 15 +++++++++++++--
 3 files changed, 30 insertions(+), 6 deletions(-)

diff --git a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
index d069f824..922fd8b5 100644
--- a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+++ b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
@@ -181,7 +181,7 @@
    "source": [
     "### Option 2: Milvus\n",
     "\n",
-    "> As of version 1.15, MilvusDocumentStore has been deprecated in Haystack. It will be deleted from the haystack repository as of version 1.17 and moved to [haystack-extras](https://github.com/deepset-ai/haystack-extras/tree/main). For more details, check out [Deprecation of MilvusDocumentStore](https://github.com/deepset-ai/haystack/discussions/4785).\n",
+    "> As of version 1.15, MilvusDocumentStore has been deprecated in Haystack. It is deleted from the haystack repository as of version 1.17 and moved to [haystack-extras](https://github.com/deepset-ai/haystack-extras/tree/main). For more details, check out [Deprecation of MilvusDocumentStore](https://github.com/deepset-ai/haystack/discussions/4785).\n",
     "\n",
     "Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.\n",
     "Like FAISS it has both a \"Flat\" and \"HNSW\" mode but it outperforms FAISS when it comes to dynamic data management.\n",
@@ -199,7 +199,7 @@
     "# To run Milvus you need Docker (versions below 2.0.0) or a docker-compose (versions >= 2.0.0), neither of which is available on Colab.\n",
     "# See Milvus' documentation for more details: https://milvus.io/docs/install_standalone-docker.md\n",
     "\n",
-    "# !pip install farm-haystack[milvus]\n",
+    "# !pip install farm-haystack[milvus]==1.16.1\n",
     "\n",
     "# from haystack.utils import launch_milvus\n",
     "# from haystack.document_stores import MilvusDocumentStore\n",
diff --git a/tutorials/07_RAG_Generator.ipynb b/tutorials/07_RAG_Generator.ipynb
index 7adbf9b6..3bc74f8d 100644
--- a/tutorials/07_RAG_Generator.ipynb
+++ b/tutorials/07_RAG_Generator.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -10,13 +11,15 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> As of version 1.16, RAGenerator has been deprecated in Haystack and will be completely removed from Haystack as of v1.18. We recommend following the tutorial on [Creating a Generative QA Pipeline with PromptNode](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) instead."
+    "> As of version 1.16, `RAGenerator` has been deprecated in Haystack and completely removed from Haystack as of v1.18. We recommend following the tutorial on [Creating a Generative QA Pipeline with PromptNode](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) instead. For more details about this deprecation, check out [our announcement](https://github.com/deepset-ai/haystack/discussions/4816) on Github."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -66,7 +69,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,faiss]"
+    "pip install farm-haystack[colab,faiss]==1.17.2"
    ]
   },
   {
@@ -90,6 +93,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -124,6 +128,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -131,6 +136,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -167,6 +173,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -198,6 +205,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -220,6 +228,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -251,6 +260,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -277,6 +287,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
@@ -310,6 +321,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -333,6 +345,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false
diff --git a/tutorials/12_LFQA.ipynb b/tutorials/12_LFQA.ipynb
index a9f612c5..fe93380e 100644
--- a/tutorials/12_LFQA.ipynb
+++ b/tutorials/12_LFQA.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "bEH-CRbeA6NU"
@@ -10,13 +11,15 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> As of version 1.16, Seq2SeqGenerator has been deprecated in Haystack and will be completely removed from Haystack as of v1.18. We recommend following the tutorial on [Creating a Generative QA Pipeline with PromptNode](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) instead. "
+    "> As of version 1.16, `Seq2SeqGenerator` has been deprecated in Haystack and completely removed from Haystack as of v1.18. We recommend following the tutorial on [Creating a Generative QA Pipeline with PromptNode](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) instead. For more details about this deprecation, check out [our announcement](https://github.com/deepset-ai/haystack/discussions/4816) on Github."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -57,7 +60,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,faiss]"
+    "pip install farm-haystack[colab,faiss]==1.17.2"
    ]
   },
   {
@@ -81,6 +84,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -115,6 +119,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "q3dSo7ZtA6Nl"
@@ -148,6 +153,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "06LatTJBA6N0",
@@ -188,6 +194,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "wgjedxx_A6N6"
@@ -221,6 +228,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "sMlVEnJ2NkZZ"
@@ -246,6 +254,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "rnVR28OXA6OA"
@@ -273,6 +282,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "unhLD18yA6OF"
@@ -300,6 +310,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "bXlBBxKXA6OL"

From 66cbfbb356e63a490150ad117467748c1276db82 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 29 Jun 2023 12:48:33 +0300
Subject: [PATCH 099/206] Update PromptTemplate parameters  (#203)

* Update tutorial 21 for the upcoming release

* Update tutorial 22 to work with Haystack 1.18
---
 tutorials/21_Customizing_PromptNode.ipynb   | 3381 +++----------------
 tutorials/22_Pipeline_with_PromptNode.ipynb | 1451 ++++----
 2 files changed, 1254 insertions(+), 3578 deletions(-)

diff --git a/tutorials/21_Customizing_PromptNode.ipynb b/tutorials/21_Customizing_PromptNode.ipynb
index ed9cb7fe..4cbf7b9c 100644
--- a/tutorials/21_Customizing_PromptNode.ipynb
+++ b/tutorials/21_Customizing_PromptNode.ipynb
@@ -1,2863 +1,526 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BlTn1yekNONP"
-      },
-      "source": [
-        "# Tutorial: Customizing PromptNode for NLP Tasks\n",
-        "\n",
-        "- **Level**: Intermediate\n",
-        "- **Time to complete**: 20 minutes\n",
-        "- **Nodes Used**: `PromptNode`, `PromptTemplate`\n",
-        "- **Goal**: After completing this tutorial, you will have learned the basics of using PromptNode and PromptTemplates and you'll have added titles to articles from The Guardian and categorized them. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ffYFUAjUNONS"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "Use large language models (LLMs) through PromptNode and PromptTemplate to summarize and categorize your documents, and find a suitable title for them. In this tutorial, we'll use news from [The Guardian](https://www.theguardian.com/international) as documents, but you can replace them with any text you want.  \n",
-        "\n",
-        "This tutorial introduces you to the basics of LLMs and PromptNode, showcases the pre-defined \"summarization\" template, and explains how to use PromptTemplate to generate titles for documents and categorize them with custom prompts."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ugkQ42iJNONS"
-      },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "t0rQfgOVNONS"
-      },
-      "source": [
-        "## Installing Haystack\n",
-        "\n",
-        "To start, let's install the latest release of Haystack with `pip`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "S-00smnxNONT"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install --upgrade pip\n",
-        "pip install farm-haystack[colab]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VTfmApFpZKn6"
-      },
-      "source": [
-        "### Enabling Telemetry\n",
-        "\n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "HtImiSaHZKn6"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(21)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RYtbl6qBNONU"
-      },
-      "source": [
-        "## Trying Out PromptNode\n",
-        "\n",
-        "The PromptNode is the central abstraction in Haystack's large language model (LLM) support. It uses [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) model by default, but you can replace the default model with a flan-t5 model of a different size such as `google/flan-t5-large` or a model by OpenAI such as `text-davinci-003`.\n",
-        "\n",
-        "[Large language models](https://docs.haystack.deepset.ai/docs/language_models#large-language-models-llms) are huge models trained on enormous amounts of data. That’s why these models have general knowledge of the world, so you can ask them anything and they will be able to answer.\n",
-        "\n",
-        "As a warm-up, let's initialize PromptNode and see what it can do when run stand-alone: "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2wYbU8WhNX08"
-      },
-      "source": [
-        "1. Initialize a PromptNode instance with [`google/flan-t5-large`](https://huggingface.co/google/flan-t5-large):"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 264,
-          "referenced_widgets": [
-            "06e6d57302514c30961c7159cd505489",
-            "6877c499c906463291bbebc3e34b2339",
-            "a980ee904a634ba8b324f8f3d0abe90d",
-            "09d5499534da4f849afb4b225b5df002",
-            "0706c3a7c40c46f7b9bd15c091997b74",
-            "1abc99aaff014d798ceaf73366dfff8f",
-            "a1e24858d088402ca603a2f52b7a76ef",
-            "68da29638e8d4406b6c1a49dbfa27dd0",
-            "6ae9252b438640e5b324e1e78e1950e9",
-            "db3aa48277094c85a6ff68900d112523",
-            "3d0639348e2641deb9bfbf2b67fa3ce2",
-            "c4ddc552487a46d49cf016ea43828555",
-            "561d497e76d342a48468e27dbb20043c",
-            "b2ed1132e09342998f45e2087dbf3241",
-            "084abecf90314e84876ee6ca20f4900b",
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-            "cb456fb464544121bb12b108decf3f80",
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-            "b5b0b7507559446f993abaceaca64f31",
-            "7b974567f3bc4f6986e125060bc43a21",
-            "493c3b3dca05469d87cfbae22b9f8669",
-            "2ec6be2826c7409ca11c99bb5578cd51",
-            "400a0ac0e6e94e56a3a99b84095ba89a",
-            "399000dbe7594854947ae913360b7812",
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-            "44297bbee6ba4ebf9797b5d191a570c6",
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-            "2466751373f4419cac19dfe44ca6314d",
-            "4e626e19247740f5907331c2ec9bbab3",
-            "401c3de1a8064d3e8e067175b2ba5458",
-            "970aafb2dd7443baa2080602c929636c",
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-            "0a49948f4ed94d80b7ccf5ab4af88c75",
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-            "435ce9da919d4ea7997284401d94c6cb",
-            "461d5d15645242aba5dd11775d5aba2f",
-            "6239fd2e0c2447cfb146f8207fe1aa7b",
-            "7c6ec120be1542c3b6e7decac877b504",
-            "7682a05ceec5480088fad3e95cff8c8d",
-            "d2d0a93c3cf64644aef19987e2a29f57"
-          ]
-        },
-        "id": "wa-I31YZNONU",
-        "outputId": "23de7946-35bd-4b91-c664-47ce7088dc45"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "06e6d57302514c30961c7159cd505489",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)lve/main/config.json:   0%|          | 0.00/662 [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "c4ddc552487a46d49cf016ea43828555",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading pytorch_model.bin:   0%|          | 0.00/3.13G [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.9/dist-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-            "  return self.fget.__get__(instance, owner)()\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "9f1667dc558f47758b2a3d5845ef2a41",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "c47adf0770814fcba9a8b0be938832a1",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "493c3b3dca05469d87cfbae22b9f8669",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "970aafb2dd7443baa2080602c929636c",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "from haystack.nodes import PromptNode\n",
-        "\n",
-        "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZE7s-cMRNh5f"
-      },
-      "source": [
-        "> Note: To use PromptNode with an OpenAI model, change the model name and provide an `api_key`: \n",
-        "> ```python\n",
-        "> prompt_node = PromptNode(model_name_or_path=\"text-davinci-003\", api_key=<YOUR_API_KEY>)\n",
-        "> ```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "NO5z2H_mO8cg"
-      },
-      "source": [
-        "2. Ask any general question that comes to your mind, for example:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "dBxL4odqNg6K",
-        "outputId": "a0c0fadd-06a2-44fb-e2a3-a101ed0d753c"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['berlin']"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "prompt_node(\"What is the capital of Germany?\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cg-8tKebDCa0",
-        "outputId": "99873954-6d29-4d24-c717-628b1633cd82"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['mount everest']"
-            ]
-          },
-          "execution_count": 5,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "prompt_node(\"What is the highest mountain?\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0MVeOCWSNONV"
-      },
-      "source": [
-        "As `google/flan-t5-large` was trained on school math problems dataset named [GSM8K](https://huggingface.co/datasets/gsm8k) you can also ask some basic math questions:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "-ZpTIQEUNONW",
-        "outputId": "d8e5a6e0-7b99-4095-f48e-296ff3eec641"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['Bob']"
-            ]
-          },
-          "execution_count": 6,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "prompt_node(\"If Bob is 20 and Sara is 11, who is older?\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aybEtTGIQQSX"
-      },
-      "source": [
-        "Now that you've initialized PromptNode and saw how it works, let's see how we can use it for more advanced tasks."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "W6hK9AcsNONW"
-      },
-      "source": [
-        "## Summarizing Documents with PromptNode\n",
-        "\n",
-        "PromptNode comes with out-of-the-box prompt templates that can perform multiple tasks, such as summarization, question answering, question generation, and more. To use a templates, just provide its name to the PromptNode. \n",
-        "\n",
-        "For this task, we'll use the summarization template and news from The Guardian. Let's see how to do it.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zECQSKkWDYns"
-      },
-      "source": [
-        "1. Define news to use as `documents` for the PromptNode. We'll use these documents throughout the whole tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "id": "DjYmEk9_dyHZ"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.schema import Document\n",
-        "\n",
-        "# https://www.theguardian.com/business/2023/feb/12/inflation-may-have-peaked-but-the-cost-of-living-pain-is-far-from-over\n",
-        "news_economics = Document(\n",
-        "    \"\"\"At long last, Britain’s annual inflation rate is on the way down. After hitting the highest level since the 1980s, heaping pressure on millions of households as living costs soared, official figures this week could bring some rare good news.\n",
-        "City economists expect UK inflation to have cooled for a third month running in January – the exact number is announced on Wednesday – helped by falling petrol prices and a broader decline in the global price of oil and gas in recent months. The hope now is for a sustained decline in the months ahead, continuing a steady drop from the peak of 11.1% seen in October.\n",
-        "The message from the Bank of England has been clear. Inflation is on track for a “rapid” decline over the coming months, raising hopes that the worst of Britain’s cost of living crisis is now in the rearview mirror.\n",
-        "There are two good reasons for this. Energy costs are moving in the right direction, while the initial rise in wholesale oil and gas prices that followed Russia’s invasion of Ukraine in February last year will soon drop from the calculation of the annual inflation rate.\"\"\"\n",
-        ")\n",
-        "\n",
-        "# https://www.theguardian.com/science/2023/feb/13/starwatch-orions-belt-and-sirius-lead-way-to-hydras-head\n",
-        "news_science = Document(\n",
-        "    \"\"\"On northern winter nights, it is so easy to be beguiled by the gloriously bright constellations of Orion, the hunter, and Taurus, the bull, that one can overlook the fainter constellations.\n",
-        "So this week, find the three stars of Orion’s belt, follow them down to Sirius, the brightest star in the night sky, and then look eastward until you find the faint ring of stars that makes up the head of Hydra, the water snake. The chart shows the view looking south-east from London at 8pm GMT on Monday, but the view will be similar every night this week.\n",
-        "Hydra is the largest of the 88 modern constellations covering an area of 1,303 square degrees. To compare, nearby Orion only covers 594 square degrees. Hydra accounts for most of its area by its length, crossing more than 100 degrees of the sky (the full moon spans half a degree).\n",
-        "As evening becomes night and into the early hours, the rotation of Earth causes Hydra to slither its way across the southern meridian until dawn washes it from the sky. From the southern hemisphere, the constellation is easily visible in the eastern sky by mid-evening.\"\"\"\n",
-        ")\n",
-        "\n",
-        "# https://www.theguardian.com/music/2023/jan/30/salisbury-cathedral-pipe-organ-new-life-holst-the-planets\n",
-        "news_culture = Document(\n",
-        "    \"\"\"A unique performance of Gustav Holst’s masterwork The Planets – played on a magnificent pipe organ rather than by an orchestra and punctuated by poems inspired by children’s responses to the music – is to be staged in the suitably vast Salisbury Cathedral.\n",
-        "The idea of the community music project is to introduce more people, young and old, to the 140-year-old “Father” Willis organ, one of the treasures of the cathedral.\n",
-        "It is also intended to get the children who took part and the adults who will watch and listen thinking afresh about the themes Holst’s suite tackles – war, peace, joy and mysticism – which seem as relevant now as when he wrote the work a century ago.\n",
-        "John Challenger, the cathedral’s principal organist, said: “We have a fantastic pipe organ largely as it was when built. It’s a thrilling thing. I view it as my purpose in life to share it with as many people as possible.”\n",
-        "The Planets is written for a large orchestra. “Holst calls for huge instrumental forces and an unseen distant choir of sopranos and altos,” said Challenger. But he has transposed the suite for the organ, not copying the effect of the orchestral instruments but finding a new version of the suite.\"\"\"\n",
-        ")\n",
-        "\n",
-        "# https://www.theguardian.com/sport/blog/2023/feb/14/multi-million-dollar-wpl-auction-signals-huge-step-forward-for-womens-sport\n",
-        "news_sport = Document(\n",
-        "    \"\"\"It was only a few days ago that members of the Australian women’s cricket team were contemplating how best to navigate the impending “distraction” of the inaugural Women’s Premier League auction, scheduled during the first week of the T20 World Cup. “It’s a little bit awkward,” captain Meg Lanning said in South Africa last week. “But it’s just trying to embrace that and understanding it’s actually a really exciting time and you actually don’t have a lot of control over most of it, so you’ve just got to wait and see.”\n",
-        "What a pleasant distraction it turned out to be. Lanning herself will be $192,000 richer for three weeks’ work with the Delhi Capitals. Her teammate, Ash Gardner, will earn three times that playing for the Gujarat Giants. The allrounder’s figure of $558,000 is more than Sam Kerr pockets in a season with Chelsea and more than the WNBA’s top earner, Jackie Young.\n",
-        "If that sounds like a watershed moment, it’s perhaps because it is. And it is not the only one this past week. The NRLW made its own wage-related headlines on Tuesday, to the effect that the next (agreed in principle) collective bargaining agreement will bring with it a $1.5m salary cap in 2027, at an average salary of $62,500. Women’s rugby, too, is making moves, with news on the weekend that Rugby Australia will begin contracting the Wallaroos.\"\"\"\n",
-        ")\n",
-        "\n",
-        "news = [news_economics, news_science, news_culture, news_sport]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Ezq9NVmFdzz1"
-      },
-      "source": [
-        "> The token limit for `google/flan-t5-large` is 512. So, all news pieces should be shorter than the limit."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "okw4m5aeL7hy"
-      },
-      "source": [
-        "2. List pre-defined templates using the `get_prompt_template_names()` method. All templates come with the prompts needed to perform these tasks. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "JqwLmdePNONW",
-        "outputId": "b9cc94d9-b804-4223-871f-97f2c74f7090"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['question-answering',\n",
-              " 'question-answering-per-document',\n",
-              " 'question-answering-with-references',\n",
-              " 'question-answering-with-document-scores',\n",
-              " 'question-generation',\n",
-              " 'conditioned-question-generation',\n",
-              " 'summarization',\n",
-              " 'question-answering-check',\n",
-              " 'sentiment-analysis',\n",
-              " 'multiple-choice-question-answering',\n",
-              " 'topic-classification',\n",
-              " 'language-detection',\n",
-              " 'translation',\n",
-              " 'zero-shot-react']"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "prompt_node.get_prompt_template_names()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "syB5rl3xIgHr"
-      },
-      "source": [
-        "3. Use the `summarization` template to generate a summary for each piece of news:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ygxFMwb4NONX",
-        "outputId": "1106be62-4df2-4291-82cf-55e3ab966ed0"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['The Bank of England has said that inflation is on track for a “rapid” decline over the coming months, raising hopes that the worst of Britain’s cost of living crisis is now in the rearview mirror.',\n",
-              " 'Look for the head of Hydra, the water snake, this week.',\n",
-              " 'A community music project is to be staged in Salisbury Cathedral to celebrate the centenary of the composer’s death.',\n",
-              " 'The Women’s Premier League auction has been a welcome distraction for the Australian women’s cricket team.']"
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "prompt_node.prompt(prompt_template=\"summarization\", documents=news)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "lUTckmA1PzLc"
-      },
-      "source": [
-        "Here you go! You have generated summaries of your news articles. But we're missing titles for them. Let's see how PromptNode can help us there."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f0vQ45tHNONX"
-      },
-      "source": [
-        "## Generating Titles for News Articles with a Custom Template"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "992kb_WKNONX"
-      },
-      "source": [
-        "The biggest benefit of PromptNode is its versatility. You can use it to perform practically any NLP task if you define your own prompt templates for them. By creating your prompt templates, you can extend the model's capabilities and use it for a broader range of NLP tasks in Haystack. \n",
-        "\n",
-        "You can define custom templates for each NLP task and register them with PromptNode. Let's create a custom template to generate descriptive titles for news:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "en6IbPRsTOTz"
-      },
-      "source": [
-        "1. Initialize a `PromptTemplate` instance. Give your template a `name` and define the prompt in `prompt_text`. To define any parameters for the prompt, add them to the `prompt_text` wrapped with curly brackets. We need a template to generate titles for our news articles. We'll call it `give-a-title`. The only parameter we need is `{news}`, so let's add it to the `prompt_text`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "id": "OiOiya2UV4WW"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.nodes import PromptTemplate\n",
-        "\n",
-        "title_generator = PromptTemplate(\n",
-        "    name=\"give-a-title\",\n",
-        "    prompt_text=\"Provide a short, descriptive title for the given piece of news. News: {news}; Title:\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jttb_ROnWYwS"
-      },
-      "source": [
-        "2. To use the new template, pass `title_generator` as the `prompt_template` to the `prompt()` method:\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "zX_nALriWXxx",
-        "outputId": "7052c817-fb6b-4e5e-ed4d-676d7507e91e"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.9/dist-packages/transformers/pipelines/base.py:1043: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
-            "  warnings.warn(\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "['Britain’s inflation rate on the way down',\n",
-              " 'The Orion constellation',\n",
-              " 'The Planets: A unique performance of Gustav Holst’s masterwork',\n",
-              " \"Australia women's cricket team to play Pakistan in ODI\"]"
-            ]
-          },
-          "execution_count": 15,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "prompt_node.prompt(prompt_template=title_generator, news=news)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "p0Mx3lHyXK_I"
-      },
-      "source": [
-        "> Note: To add a custom template to the template list, call `add_prompt_template()` with the `PromptTemplate` object pass the template contents to it. Once you do this, the next time you want to use this template, just call its name: \n",
-        "> ```python\n",
-        "> prompt_node.add_prompt_template(PromptTemplate(name=\"give-a-title\", prompt_text=\"Provide a short, descriptive title for the given piece of news. News: {news}; Title:\"))\n",
-        "> prompt_node.prompt(prompt_template=\"give-a-title\", news=news)\n",
-        "> ```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8t9pdZo9ZKn9"
-      },
-      "source": [
-        "There you go! You should have the titles for your news articles ready. Let's now categorize them."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eH4QbygONONX"
-      },
-      "source": [
-        "## Categorizing Documents with PromptNode\n",
-        "\n",
-        "You can customize PromptTemplates as much as you need. Let's try to create a template to categorize the news articles. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FAELXO0MhbrV"
-      },
-      "source": [
-        "1. Create another PromptTemplate called `categorize-news`. In the `prompt_text`, define the `{news}` parameter, specify the categories you want to use, and ask the model not to categorize the news if it doesn't fit in the provided category list: "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {
-        "id": "xHgOjTwkNONY"
-      },
-      "outputs": [],
-      "source": [
-        "news_categorizer = PromptTemplate(\n",
-        "    name=\"categorize-news\",\n",
-        "    prompt_text=\"Given the categories: sport, economics, culture; classify the news: {news}. Only pick a category from the list, otherwise say: no suitable category\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nC4gVHCyuXHl"
-      },
-      "source": [
-        "2. Run the `prompt()` method with the `news_categorizer` template:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "taktlLIfNONY",
-        "outputId": "36b9ae31-0165-4f7e-e809-4c7fa8d6985a"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['economics', 'no suitable category', 'culture', 'sport']"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "prompt_node.prompt(prompt_template=news_categorizer, news=news)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aU1e7ak4ZKn9"
-      },
-      "source": [
-        "Congratulations! You've summarized your documents, generated titles for them, and put them into categories, all using custom prompt templates. "
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BlTn1yekNONP"
+   },
+   "source": [
+    "# Tutorial: Customizing PromptNode for NLP Tasks\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 20 minutes\n",
+    "- **Nodes Used**: `PromptNode`, `PromptTemplate`\n",
+    "- **Goal**: After completing this tutorial, you will have learned the basics of using PromptNode and PromptTemplates and you'll have added titles to articles from The Guardian and categorized them. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ffYFUAjUNONS"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "Use large language models (LLMs) through PromptNode and PromptTemplate to summarize and categorize your documents, and find a suitable title for them. In this tutorial, we'll use news from [The Guardian](https://www.theguardian.com/international) as documents, but you can replace them with any text you want.  \n",
+    "\n",
+    "This tutorial introduces you to the basics of LLMs and PromptNode, showcases the pre-defined \"deepset/summarization\" template, and explains how to use PromptTemplate to generate titles for documents and categorize them with custom prompts."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ugkQ42iJNONS"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "t0rQfgOVNONS"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "S-00smnxNONT"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install git+https://github.com/deepset-ai/haystack.git"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VTfmApFpZKn6"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "HtImiSaHZKn6"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(21)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RYtbl6qBNONU"
+   },
+   "source": [
+    "## Trying Out PromptNode\n",
+    "\n",
+    "The PromptNode is the central abstraction in Haystack's large language model (LLM) support. It uses [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) model by default, but you can replace the default model with a flan-t5 model of a different size such as `google/flan-t5-large` or a model by OpenAI such as `text-davinci-003`.\n",
+    "\n",
+    "[Large language models](https://docs.haystack.deepset.ai/docs/language_models#large-language-models-llms) are huge models trained on enormous amounts of data. That’s why these models have general knowledge of the world, so you can ask them anything and they will be able to answer.\n",
+    "\n",
+    "As a warm-up, let's initialize PromptNode and see what it can do when run stand-alone: "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2wYbU8WhNX08"
+   },
+   "source": [
+    "1. Initialize a PromptNode instance with [`google/flan-t5-large`](https://huggingface.co/google/flan-t5-large):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
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+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wa-I31YZNONU",
+    "outputId": "7b101632-4300-43f9-d9d4-78ea78581529"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PromptNode\n",
+    "\n",
+    "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZE7s-cMRNh5f"
+   },
+   "source": [
+    "> Note: To use PromptNode with an OpenAI model, change the model name and provide an `api_key`: \n",
+    "> ```python\n",
+    "> prompt_node = PromptNode(model_name_or_path=\"text-davinci-003\", api_key=<YOUR_API_KEY>)\n",
+    "> ```"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NO5z2H_mO8cg"
+   },
+   "source": [
+    "2. Ask any general question that comes to your mind, for example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dBxL4odqNg6K",
+    "outputId": "c835d67a-f22f-4062-c1ad-7ace72006995"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node(\"What is the capital of Germany?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cg-8tKebDCa0",
+    "outputId": "051810e1-3c82-43cc-aef3-2ce9c125a891"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node(\"What is the highest mountain?\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0MVeOCWSNONV"
+   },
+   "source": [
+    "As `google/flan-t5-large` was trained on school math problems dataset named [GSM8K](https://huggingface.co/datasets/gsm8k) you can also ask some basic math questions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-ZpTIQEUNONW",
+    "outputId": "8daa23ba-881e-4ccd-e14d-0b4bb2d107e4"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node(\"If Bob is 20 and Sara is 11, who is older?\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aybEtTGIQQSX"
+   },
+   "source": [
+    "Now that you've initialized PromptNode and saw how it works, let's see how we can use it for more advanced tasks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "W6hK9AcsNONW"
+   },
+   "source": [
+    "## Summarizing Documents with PromptNode\n",
+    "\n",
+    "PromptNode is integrated to [PromptHub](https://prompthub.deepset.ai/) that includes ready-made prompts for the most common NLP tasks such as summarization, question answering, question generation, and more. To use a prompt template from the PromptHub, just provide its name to the PromptNode. \n",
+    "\n",
+    "For this task, we'll use the `deepset/summarization` template from the PromptHub and news from The Guardian. Let's see how to do it."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zECQSKkWDYns"
+   },
+   "source": [
+    "1. Define news to use as `documents` for the PromptNode. We'll use these documents throughout the whole tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "DjYmEk9_dyHZ"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.schema import Document\n",
+    "\n",
+    "# https://www.theguardian.com/business/2023/feb/12/inflation-may-have-peaked-but-the-cost-of-living-pain-is-far-from-over\n",
+    "news_economics = Document(\n",
+    "    \"\"\"At long last, Britain’s annual inflation rate is on the way down. After hitting the highest level since the 1980s, heaping pressure on millions of households as living costs soared, official figures this week could bring some rare good news.\n",
+    "City economists expect UK inflation to have cooled for a third month running in January – the exact number is announced on Wednesday – helped by falling petrol prices and a broader decline in the global price of oil and gas in recent months. The hope now is for a sustained decline in the months ahead, continuing a steady drop from the peak of 11.1% seen in October.\n",
+    "The message from the Bank of England has been clear. Inflation is on track for a “rapid” decline over the coming months, raising hopes that the worst of Britain’s cost of living crisis is now in the rearview mirror.\n",
+    "There are two good reasons for this. Energy costs are moving in the right direction, while the initial rise in wholesale oil and gas prices that followed Russia’s invasion of Ukraine in February last year will soon drop from the calculation of the annual inflation rate.\"\"\"\n",
+    ")\n",
+    "\n",
+    "# https://www.theguardian.com/science/2023/feb/13/starwatch-orions-belt-and-sirius-lead-way-to-hydras-head\n",
+    "news_science = Document(\n",
+    "    \"\"\"On northern winter nights, it is so easy to be beguiled by the gloriously bright constellations of Orion, the hunter, and Taurus, the bull, that one can overlook the fainter constellations.\n",
+    "So this week, find the three stars of Orion’s belt, follow them down to Sirius, the brightest star in the night sky, and then look eastward until you find the faint ring of stars that makes up the head of Hydra, the water snake. The chart shows the view looking south-east from London at 8pm GMT on Monday, but the view will be similar every night this week.\n",
+    "Hydra is the largest of the 88 modern constellations covering an area of 1,303 square degrees. To compare, nearby Orion only covers 594 square degrees. Hydra accounts for most of its area by its length, crossing more than 100 degrees of the sky (the full moon spans half a degree).\n",
+    "As evening becomes night and into the early hours, the rotation of Earth causes Hydra to slither its way across the southern meridian until dawn washes it from the sky. From the southern hemisphere, the constellation is easily visible in the eastern sky by mid-evening.\"\"\"\n",
+    ")\n",
+    "\n",
+    "# https://www.theguardian.com/music/2023/jan/30/salisbury-cathedral-pipe-organ-new-life-holst-the-planets\n",
+    "news_culture = Document(\n",
+    "    \"\"\"A unique performance of Gustav Holst’s masterwork The Planets – played on a magnificent pipe organ rather than by an orchestra and punctuated by poems inspired by children’s responses to the music – is to be staged in the suitably vast Salisbury Cathedral.\n",
+    "The idea of the community music project is to introduce more people, young and old, to the 140-year-old “Father” Willis organ, one of the treasures of the cathedral.\n",
+    "It is also intended to get the children who took part and the adults who will watch and listen thinking afresh about the themes Holst’s suite tackles – war, peace, joy and mysticism – which seem as relevant now as when he wrote the work a century ago.\n",
+    "John Challenger, the cathedral’s principal organist, said: “We have a fantastic pipe organ largely as it was when built. It’s a thrilling thing. I view it as my purpose in life to share it with as many people as possible.”\n",
+    "The Planets is written for a large orchestra. “Holst calls for huge instrumental forces and an unseen distant choir of sopranos and altos,” said Challenger. But he has transposed the suite for the organ, not copying the effect of the orchestral instruments but finding a new version of the suite.\"\"\"\n",
+    ")\n",
+    "\n",
+    "# https://www.theguardian.com/sport/blog/2023/feb/14/multi-million-dollar-wpl-auction-signals-huge-step-forward-for-womens-sport\n",
+    "news_sport = Document(\n",
+    "    \"\"\"It was only a few days ago that members of the Australian women’s cricket team were contemplating how best to navigate the impending “distraction” of the inaugural Women’s Premier League auction, scheduled during the first week of the T20 World Cup. “It’s a little bit awkward,” captain Meg Lanning said in South Africa last week. “But it’s just trying to embrace that and understanding it’s actually a really exciting time and you actually don’t have a lot of control over most of it, so you’ve just got to wait and see.”\n",
+    "What a pleasant distraction it turned out to be. Lanning herself will be $192,000 richer for three weeks’ work with the Delhi Capitals. Her teammate, Ash Gardner, will earn three times that playing for the Gujarat Giants. The allrounder’s figure of $558,000 is more than Sam Kerr pockets in a season with Chelsea and more than the WNBA’s top earner, Jackie Young.\n",
+    "If that sounds like a watershed moment, it’s perhaps because it is. And it is not the only one this past week. The NRLW made its own wage-related headlines on Tuesday, to the effect that the next (agreed in principle) collective bargaining agreement will bring with it a $1.5m salary cap in 2027, at an average salary of $62,500. Women’s rugby, too, is making moves, with news on the weekend that Rugby Australia will begin contracting the Wallaroos.\"\"\"\n",
+    ")\n",
+    "\n",
+    "news = [news_economics, news_science, news_culture, news_sport]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ezq9NVmFdzz1"
+   },
+   "source": [
+    "> The token limit for `google/flan-t5-large` is 512. So, all news pieces should be shorter than the limit."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "syB5rl3xIgHr"
+   },
+   "source": [
+    "2. Use the `deepset/summarization` template to generate a summary for each piece of news:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ygxFMwb4NONX",
+    "outputId": "cef66f16-f9bc-4286-de68-dac0692a64c1"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node.prompt(prompt_template=\"deepset/summarization\", documents=news)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lUTckmA1PzLc"
+   },
+   "source": [
+    "Here you go! You have generated summaries of your news articles. But we're missing titles for them. Let's see how PromptNode can help us there."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f0vQ45tHNONX"
+   },
+   "source": [
+    "## Generating Titles for News Articles with a Custom Template"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "992kb_WKNONX"
+   },
+   "source": [
+    "The biggest benefit of PromptNode is its versatility. You can use it to perform practically any NLP task if you define your own prompt templates for them. By creating your prompt templates, you can extend the model's capabilities and use it for a broader range of NLP tasks in Haystack. \n",
+    "\n",
+    "You can define custom templates for each NLP task and register them with PromptNode. Let's create a custom template to generate descriptive titles for news:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "en6IbPRsTOTz"
+   },
+   "source": [
+    "1. Initialize a `PromptTemplate` instance by defining the prompt text in `prompt`. To define any parameters for the prompt, add them to the `prompt` wrapped with curly brackets. We need a template to generate titles for our news articles. The only parameter we need is `{news}`, so let's create a PromptTemplate for it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "OiOiya2UV4WW"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PromptTemplate\n",
+    "\n",
+    "title_generator = PromptTemplate(\n",
+    "    prompt=\"Provide a short, descriptive title for the given piece of news. News: {documents}; Title:\"\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jttb_ROnWYwS"
+   },
+   "source": [
+    "2. To use the new template, pass `title_generator` as the `prompt_template` to the `prompt()` method:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zX_nALriWXxx",
+    "outputId": "8f8fb802-5116-4e85-b0b3-8ddb1138ae5a"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node.prompt(prompt_template=title_generator, documents=news)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8t9pdZo9ZKn9"
+   },
+   "source": [
+    "There you go! You should have the titles for your news articles ready. Let's now categorize them."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eH4QbygONONX"
+   },
+   "source": [
+    "## Categorizing Documents with PromptNode\n",
+    "\n",
+    "You can customize PromptTemplates as much as you need. Let's try to create a template to categorize the news articles. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FAELXO0MhbrV"
+   },
+   "source": [
+    "1. Define the `{news}` and `{categories}` parameters. As we will accept an list of strings as `categories`, we need to join the list before injecting categories to the prompt with `\", \".join(categories)` function. See how you can [further customize prompt variables](https://docs.haystack.deepset.ai/docs/prompt_node#prompttemplate-structure) in the documentation.\n",
+    "Finally, in the prompt, ask the model not to categorize the news if it doesn't fit in the provided category list: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xHgOjTwkNONY"
+   },
+   "outputs": [],
+   "source": [
+    "news_categorizer = PromptTemplate(\n",
+    "    prompt=\"Given the categories: {', '.join(categories)}; classify the news: {documents}. Only pick a category from the list, otherwise say: no suitable category\"\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nC4gVHCyuXHl"
+   },
+   "source": [
+    "2. Run the `prompt()` method with the `news_categorizer` template:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "taktlLIfNONY",
+    "outputId": "1aceac5a-403d-4bca-cfc6-0a28e4d11691"
+   },
+   "outputs": [],
+   "source": [
+    "prompt_node.prompt(\n",
+    "    prompt_template=news_categorizer, documents=news, categories=[\"sport\", \"economics\", \"culture\"]\n",
+    ")  # Answer: ['economics', 'science', 'culture', 'sport']"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aU1e7ak4ZKn9"
+   },
+   "source": [
+    "Congratulations! You've summarized your documents, generated titles for them, and put them into categories, all using custom prompt templates. "
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "76c0ba09435b0d7ab7f3e4f9fd6b1554cdb12adf349a7242a1470606c432d777"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index 654d2733..54c6a76b 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -1,735 +1,748 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2OvkPji9O-qX"
-      },
-      "source": [
-        "# Tutorial: Creating a Generative QA Pipeline with PromptNode\n",
-        "\n",
-        "- **Level**: Intermediate\n",
-        "- **Time to complete**: 15 minutes\n",
-        "- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `PromptNode`, `PromptTemplate`\n",
-        "- **Goal**: After completing this tutorial, you'll have created a generative question answering search system that uses a large language model through PromptNode with the new PromptTemplate structure. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LFqHcXYPO-qZ"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "Learn how to build a generative question answering pipeline using the power of LLMs with PromptNode. In this tutorial, we'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want. \n",
-        "\n",
-        "This tutorial introduces you to the new PrompTemplate structure and explains how to use the new PrompTemplate to integrate PromptNode into a pipeline. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QXjVlbPiO-qZ"
-      },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Kww5B_vXO-qZ"
-      },
-      "source": [
-        "## Installing Haystack\n",
-        "\n",
-        "To start, let's install the latest release of Haystack with `pip`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "UQbU8GUfO-qZ",
-        "outputId": "47bc8b21-0907-49be-d4c8-9b70f6f002a4"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install --upgrade pip\n",
-        "pip install farm-haystack[colab]\n",
-        "pip install datasets>=2.6.1"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Wl_jYERtO-qa"
-      },
-      "source": [
-        "### Enabling Telemetry\n",
-        "\n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "A76B4S49O-qa"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(22)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_lvfew16O-qa"
-      },
-      "source": [
-        "## Initializing the DocumentStore\n",
-        "\n",
-        "We'll start creating our question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, we're using the `InMemoryDocumentStore`.\n",
-        "\n",
-        "Let's initialize our DocumentStore. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "CbVN-s5LO-qa"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.document_stores import InMemoryDocumentStore\n",
-        "\n",
-        "document_store = InMemoryDocumentStore(use_bm25=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yL8nuJdWO-qa"
-      },
-      "source": [
-        "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XvLVaFHTO-qb"
-      },
-      "source": [
-        "The DocumentStore is now ready. Now it's time to fill it with some Documents."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HryYZP9ZO-qb"
-      },
-      "source": [
-        "## Fetching and Writing Documents\n",
-        "\n",
-        "We'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, we don't need to perform any additional cleaning or splitting. \n",
-        "\n",
-        "Let's fetch the data and write it to the DocumentStore: "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 86,
-          "referenced_widgets": [
-            "483577d477414459bedb984fc98c3991",
-            "afb27557357348f2b8175cbed70c6e1f",
-            "bebb7f98a2b94886a126ded8c937ae12",
-            "58a0354b9c1d4edcbc65f410f8ccd7db",
-            "b7b78a92b7b44e10826e771112fc58c6",
-            "19b74a865c1f47e5a84f03b4ca45c10e",
-            "adde2fd48f5045419d70dc214eb46008",
-            "722edaae172042f284235265b7f55264",
-            "2f039c7861524e49a2e3e4382e46af67",
-            "30b1764ddcc34928b503b3c38c2583a6",
-            "40144b3240624af4907c329501c50ac7"
-          ]
-        },
-        "id": "INdC3WvLO-qb",
-        "outputId": "e7d20db8-a079-4eb6-b27e-50ba41ce8131"
-      },
-      "outputs": [],
-      "source": [
-        "from datasets import load_dataset\n",
-        "\n",
-        "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
-        "\n",
-        "document_store.write_documents(dataset)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0_cj-5m-O-qb"
-      },
-      "source": [
-        "## Initializing the Retriever\n",
-        "\n",
-        "Let's initialize a BM25Retriever and make it use the InMemoryDocumentStore we initialized earlier in this tutorial:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "-uo-6fjiO-qb"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.nodes import BM25Retriever\n",
-        "\n",
-        "retriever = BM25Retriever(document_store=document_store, top_k=2)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6CEuQpB7O-qb"
-      },
-      "source": [
-        "## Initializing the PromptNode\n",
-        "\n",
-        "Let's define a custom prompt for PromptTemplate to use with PromptNode. As parameters, this prompt will accept Documents that our Retriever fetched from our DocumentStore and `query` we pass at runtime. To join the content of the Documents, we'll use `join()` function. To learn about using functions in PromptTemplate, check out [PromptTemplate Structure](https://docs.haystack.deepset.ai/docs/prompt_node#prompttemplate-structure). \n",
-        "\n",
-        "We'll initialize PromptNode with the PromptTemplate and `google/flan-t5-large` model. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "f6NFmpjEO-qb",
-        "outputId": "9213a8a2-2d05-4a71-cfa0-b3e5dc3f404f"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.nodes import PromptNode, PromptTemplate\n",
-        "\n",
-        "lfqa_prompt = PromptTemplate(\n",
-        "    name=\"lfqa\",\n",
-        "    prompt_text=\"\"\"Synthesize a comprehensive answer from the following text for the given question. \n",
-        "                             Provide a clear and concise response that summarizes the key points and information presented in the text. \n",
-        "                             Your answer should be in your own words and be no longer than 50 words. \n",
-        "                             \\n\\n Related text: {join(documents)} \\n\\n Question: {query} \\n\\n Answer:\"\"\",\n",
-        ")\n",
-        "\n",
-        "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\", default_prompt_template=lfqa_prompt)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sIrajz1EO-qb"
-      },
-      "source": [
-        ">To learn about how to use custom templates with PromptNode, check out [Customizing PromptNode for NLP Tasks](https://haystack.deepset.ai/tutorials/21_customizing_promptnode) tutorial. "
-      ]
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2OvkPji9O-qX"
+   },
+   "source": [
+    "# Tutorial: Creating a Generative QA Pipeline with PromptNode\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `PromptNode`, `PromptTemplate`\n",
+    "- **Goal**: After completing this tutorial, you'll have created a generative question answering search system that uses a large language model through PromptNode with the new PromptTemplate structure."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LFqHcXYPO-qZ"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "Learn how to build a generative question answering pipeline using the power of LLMs with PromptNode. In this tutorial, we'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n",
+    "\n",
+    "This tutorial introduces you to the new PrompTemplate structure and explains how to use the new PrompTemplate to integrate PromptNode into a pipeline."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QXjVlbPiO-qZ"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Kww5B_vXO-qZ"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yfMCHStjO-qc"
-      },
-      "source": [
-        "## Defining the Pipeline\n",
-        "\n",
-        "We'll use a custom pipeline with the Retriever, and PromptNode."
-      ]
+    "id": "UQbU8GUfO-qZ",
+    "outputId": "1bcd012f-8a71-4921-b9f6-f4baa1088de5"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]\n",
+    "pip install datasets>=2.6.1"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Wl_jYERtO-qa"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "A76B4S49O-qa"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(22)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_lvfew16O-qa"
+   },
+   "source": [
+    "## Initializing the DocumentStore\n",
+    "\n",
+    "We'll start creating our question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, we're using the `InMemoryDocumentStore`.\n",
+    "\n",
+    "Let's initialize our DocumentStore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "CbVN-s5LO-qa"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.document_stores import InMemoryDocumentStore\n",
+    "\n",
+    "document_store = InMemoryDocumentStore(use_bm25=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yL8nuJdWO-qa"
+   },
+   "source": [
+    "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store)."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XvLVaFHTO-qb"
+   },
+   "source": [
+    "The DocumentStore is now ready. Now it's time to fill it with some Documents."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HryYZP9ZO-qb"
+   },
+   "source": [
+    "## Fetching and Writing Documents\n",
+    "\n",
+    "We'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, we don't need to perform any additional cleaning or splitting.\n",
+    "\n",
+    "Let's fetch the data and write it to the DocumentStore:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 86,
+     "referenced_widgets": [
+      "e7af7bc04b27477289213c9275acfa0a",
+      "a73a2936ac784b6c890a0186105c7c71",
+      "0feb0341c8a44b13a36c349017c31ce0",
+      "9575340c5e2f44e1a8c6501991bbfbc5",
+      "c417bf683d2a46e68664d9d761ff7051",
+      "4113a9b237f84ba1805090115e723aec",
+      "aa934494ecc64d76b52fb6b1ed6c7d99",
+      "c9fcdad4525d4538a7ca507a24aea72d",
+      "5cfb85b586ff460a89844a06ac8e9a12",
+      "af7f9fd6fb01447f8296f59fb71e5b31",
+      "5cc7b9d993ba4f69a188cc4de776e29a"
+     ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "DZC9Dr-xO-qc"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.pipelines import Pipeline\n",
-        "\n",
-        "pipe = Pipeline()\n",
-        "pipe.add_node(component=retriever, name=\"retriever\", inputs=[\"Query\"])\n",
-        "pipe.add_node(component=prompt_node, name=\"prompt_node\", inputs=[\"retriever\"])"
-      ]
+    "id": "INdC3WvLO-qb",
+    "outputId": "0c5af785-4d48-4f22-f863-1a6185c5a89d"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
+    "\n",
+    "document_store.write_documents(dataset)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0_cj-5m-O-qb"
+   },
+   "source": [
+    "## Initializing the Retriever\n",
+    "\n",
+    "Let's initialize a BM25Retriever and make it use the InMemoryDocumentStore we initialized earlier in this tutorial:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-uo-6fjiO-qb"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import BM25Retriever\n",
+    "\n",
+    "retriever = BM25Retriever(document_store=document_store, top_k=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6CEuQpB7O-qb"
+   },
+   "source": [
+    "## Initializing the PromptNode\n",
+    "\n",
+    "Let's define a custom prompt for PromptTemplate to use with PromptNode. As parameters, this prompt will accept Documents that our Retriever fetched from our DocumentStore and `query` we pass at runtime. To join the content of the Documents, we'll use `join()` function. To learn about using functions in PromptTemplate, check out [PromptTemplate Structure](https://docs.haystack.deepset.ai/docs/prompt_node#prompttemplate-structure). Finally, we'll use [AnswerParser](https://docs.haystack.deepset.ai/reference/prompt-node-api#answerparser) to parse the output of the LLM into a Haystack Answer object.\n",
+    "\n",
+    "We'll initialize PromptNode with the PromptTemplate and `google/flan-t5-large` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "f6NFmpjEO-qb"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PromptNode, PromptTemplate, AnswerParser\n",
+    "\n",
+    "lfqa_prompt = PromptTemplate(\n",
+    "    prompt=\"\"\"Synthesize a comprehensive answer from the following text for the given question.\n",
+    "                             Provide a clear and concise response that summarizes the key points and information presented in the text.\n",
+    "                             Your answer should be in your own words and be no longer than 50 words.\n",
+    "                             \\n\\n Related text: {join(documents)} \\n\\n Question: {query} \\n\\n Answer:\"\"\",\n",
+    "    output_parser=AnswerParser(),\n",
+    ")\n",
+    "\n",
+    "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\", default_prompt_template=lfqa_prompt)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sIrajz1EO-qb"
+   },
+   "source": [
+    ">To learn about how to use custom templates with PromptNode, check out [Customizing PromptNode for NLP Tasks](https://haystack.deepset.ai/tutorials/21_customizing_promptnode) tutorial."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yfMCHStjO-qc"
+   },
+   "source": [
+    "## Defining the Pipeline\n",
+    "\n",
+    "We'll use a custom pipeline with the Retriever, and PromptNode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "DZC9Dr-xO-qc"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.pipelines import Pipeline\n",
+    "\n",
+    "pipe = Pipeline()\n",
+    "pipe.add_node(component=retriever, name=\"retriever\", inputs=[\"Query\"])\n",
+    "pipe.add_node(component=prompt_node, name=\"prompt_node\", inputs=[\"retriever\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6NqyLhx7O-qc"
+   },
+   "source": [
+    "That's it! The pipeline's ready to generate answers to questions!"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DBAyF5tVO-qc"
+   },
+   "source": [
+    "## Asking a Question\n",
+    "\n",
+    "We use the pipeline `run()` method to ask a question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6NqyLhx7O-qc"
-      },
-      "source": [
-        "That's it! The pipeline's ready to generate answers to questions!"
-      ]
+    "id": "Vnt283M5O-qc",
+    "outputId": "b7dc413e-2ee1-4173-deca-41e0d989a821"
+   },
+   "outputs": [],
+   "source": [
+    "output = pipe.run(query=\"How does Rhodes Statue look like?\")\n",
+    "\n",
+    "print(output[\"answers\"][0].answer)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IWQN-aoGO-qc"
+   },
+   "source": [
+    "Here are some other example queries to test:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_OHUQ5xxO-qc"
+   },
+   "outputs": [],
+   "source": [
+    "examples = [\n",
+    "    \"Where is Gardens of Babylon?\",\n",
+    "    \"Why did people build Great Pyramid of Giza?\",\n",
+    "    \"How does Rhodes Statue look like?\",\n",
+    "    \"Why did people visit the Temple of Artemis?\",\n",
+    "    \"What is the importance of Colossus of Rhodes?\",\n",
+    "    \"What happened to the Tomb of Mausolus?\",\n",
+    "    \"How did Colossus of Rhodes collapse?\",\n",
+    "]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XueCK3y4O-qc"
+   },
+   "source": [
+    "🎉 Congratulations! You've learned how to create a generative QA system for your documents with PromptNode."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o3jq25OFO-qc"
+   },
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "Python 3.9.6 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  },
+  "widgets": {
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+    "0feb0341c8a44b13a36c349017c31ce0": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_c9fcdad4525d4538a7ca507a24aea72d",
+      "max": 151,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_5cfb85b586ff460a89844a06ac8e9a12",
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     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DBAyF5tVO-qc"
-      },
-      "source": [
-        "## Asking a Question\n",
-        "\n",
-        "We use the pipeline `run()` method to ask a question."
-      ]
+    "4113a9b237f84ba1805090115e723aec": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
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+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
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     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Vnt283M5O-qc",
-        "outputId": "785ed13c-465c-496b-a170-3c627716a740"
-      },
-      "outputs": [],
-      "source": [
-        "output = pipe.run(query=\"How does Rhodes Statue look like?\")\n",
-        "\n",
-        "print(output[\"results\"])"
-      ]
+    "5cc7b9d993ba4f69a188cc4de776e29a": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "IWQN-aoGO-qc"
-      },
-      "source": [
-        "Here are some other example queries to test:"
-      ]
+    "5cfb85b586ff460a89844a06ac8e9a12": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_OHUQ5xxO-qc"
-      },
-      "outputs": [],
-      "source": [
-        "examples = [\n",
-        "    \"Where is Gardens of Babylon?\",\n",
-        "    \"Why did people build Great Pyramid of Giza?\",\n",
-        "    \"How does Rhodes Statue look like?\",\n",
-        "    \"Why did people visit the Temple of Artemis?\",\n",
-        "    \"What is the importance of Colossus of Rhodes?\",\n",
-        "    \"What happened to the Tomb of Mausolus?\",\n",
-        "    \"How did Colossus of Rhodes collapse?\",\n",
-        "]"
-      ]
+    "9575340c5e2f44e1a8c6501991bbfbc5": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_af7f9fd6fb01447f8296f59fb71e5b31",
+      "placeholder": "​",
+      "style": "IPY_MODEL_5cc7b9d993ba4f69a188cc4de776e29a",
+      "value": " 151/151 [00:00&lt;00:00, 3143.38 docs/s]"
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XueCK3y4O-qc"
-      },
-      "source": [
-        "🎉 Congratulations! You've learned how to create a generative QA system for your documents with PromptNode."
-      ]
+    "a73a2936ac784b6c890a0186105c7c71": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_4113a9b237f84ba1805090115e723aec",
+      "placeholder": "​",
+      "style": "IPY_MODEL_aa934494ecc64d76b52fb6b1ed6c7d99",
+      "value": "Updating BM25 representation...: 100%"
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "o3jq25OFO-qc"
-      },
-      "source": []
-    }
-  ],
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From 3780c7c6b29669dd47e0e795cf3c83d054868c2c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 29 Jun 2023 12:48:44 +0300
Subject: [PATCH 100/206] Add a note about adding Tools to ConversationalAgent
 (#207)

* Add a note about adding Tools to ConversationalAgent

* Change the voice in the tutorial

* we -> you

* Remove "let's" phrase

* Add comma
---
 tutorials/24_Building_Chat_App.ipynb | 2401 +++++---------------------
 1 file changed, 438 insertions(+), 1963 deletions(-)

diff --git a/tutorials/24_Building_Chat_App.ipynb b/tutorials/24_Building_Chat_App.ipynb
index a818cfc2..c33d15a5 100644
--- a/tutorials/24_Building_Chat_App.ipynb
+++ b/tutorials/24_Building_Chat_App.ipynb
@@ -1,1967 +1,442 @@
 {
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "TEebQqubxa4G"
-   },
-   "source": [
-    "# Tutorial: Building a Conversational Chat App\n",
-    "\n",
-    "- **Level**: Intermediate\n",
-    "- **Time to complete**: 10 minutes\n",
-    "- **Nodes Used**: `PromptNode`, `ConversationalAgent` and `ConversationSummaryMemory`\n",
-    "- **Goal**: After completing this tutorial, you will have learned how to use ConversationalAgent to build a conversational chat application\n",
-    "- **Prerequisites**: A [Hugging Face API Key](https://huggingface.co/settings/tokens)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DQLWWW1Yy_Ta"
-   },
-   "source": [
-    "## Overview\n",
-    "\n",
-    "A [ConversationalAgent](https://docs.haystack.deepset.ai/docs/agent#conversational-agent) is a type of agent that is specifically implemented to create chat applications. With its memory integration, the new ConversationalAgent enables human-like conversation with large language models (LLMs).  \n",
-    "\n",
-    "This tutorial introduces you to the ConversationalAgent, ConversationSummaryMemory and explains how you can create your conversational chat application.  \n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "obaSuZBHy8PF"
-   },
-   "source": [
-    "## Preparing the Colab Environment\n",
-    "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2_nC2XoPzDVh"
-   },
-   "source": [
-    "## Installing Haystack\n",
-    "\n",
-    "To start, let's install the latest release of Haystack with `pip`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JiZktTKoaHp5"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "RrPgO_1vzWb6"
-   },
-   "source": [
-    "\n",
-    "### Enabling Telemetry \n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6YZjKAvOzZRq"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.telemetry import tutorial_running\n",
-    "\n",
-    "tutorial_running(24)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "n0pnO7S6tbyW"
-   },
-   "source": [
-    "## Initializing the ConversationalAgent\n",
-    "\n",
-    "To initialize a ConversationalAgent, we'll first need to create a PromptNode to define the LLM that our chat application will use. Then, we'll add a memory to enable the application to store previous conversation and use this memory to make the interaction more human-like.\n",
-    "\n",
-    "Let's create necessary components for a ConversationalAgent:"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1Omji8PK_675"
-   },
-   "source": [
-    "### 1) Provide a Hugging Face API Key\n",
-    "\n",
-    "Hugging Face offers [hosted Inference API](https://huggingface.co/docs/api-inference/index) which we can use to access machine learning models using simple HTTP requests. This way, we don't need to download models from the hub. To use service, we need to provide an API key from Hugging Face:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "qWuRxFWGcAL4",
-    "outputId": "316ab19c-9a80-4d58-8c6a-80a51c2b8360"
-   },
-   "outputs": [],
-   "source": [
-    "from getpass import getpass\n",
-    "\n",
-    "model_api_key = getpass(\"Enter model provider API key:\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "V4LI3vzH7Xvo"
-   },
-   "source": [
-    "### 2) Create a PromptNode\n",
-    "\n",
-    "We'll initialize a PromptNode with the `model_name`, `api_key`, and `max_length` to control the output length of the model. In this tutorial, we'll use [OpenAssistant/oasst-sft-1-pythia-12b](https://huggingface.co/OpenAssistant/oasst-sft-1-pythia-12b), an open source Transformer-based text generation model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 113,
-     "referenced_widgets": [
-      "f936d65746724060bc0525752fe18e59",
-      "7b5929365227442b95d8890125abf10d",
-      "53d551dd051d4aa4b7554e4beeccdf2c",
-      "8d37bd9866054d58953d769bdb6c8077",
-      "5f40dae689634170a399f2ae096b55c7",
-      "34ab953b2864485eb9c156987fb4bc80",
-      "e4bf06cef8304775ab0ae703217c70f9",
-      "9f6d98341f584427a15cfe51992bdac4",
-      "a8f1701c0592474787a700429f6e1bcd",
-      "b9b307bc661548cf920d3f9f3f78750c",
-      "072d33b006924831ad1fa60996883304",
-      "10bfa975fbba460989195bb8c4f49f37",
-      "a462115762144f08b3b40787b9c5e5e3",
-      "78ce4d0354794e8b9b9d8c20a310a974",
-      "5741cac36a274cfea4b56025ac5ca4be",
-      "1b636844c76d4968b1df7114e3d6fc73",
-      "dc14c6e6689c4456ac10c74ec772e1a5",
-      "8d6f66ff445f4231bc39221d4fb50d59",
-      "4ed95ec26f7d4229b0de7fd97e54ef82",
-      "a81dd24a7ad44786ae3fe4f5ffe190f8",
-      "75005b5e98d741ce9bd44a59c38185be",
-      "b825023aa5bd4a8ea20c2048bdc9c0ef",
-      "474e1f34434941baa436be2b6d82af34",
-      "981a4797c70346d989028fc933c5d91b",
-      "12111fdedeb14372a58cb61a50eb9d4b",
-      "fcd3fd68a99d4baebdd833b436c5e626",
-      "f4737f68e99349feb331bb114abd90cc",
-      "f10905112827478ba0cc76d103512c3f",
-      "50f9a8f3d3064ef08fa3d85e4df2aa6c",
-      "08494d1dcf3e4d16b73ab5e21eed5172",
-      "ee096d7b6ab643b5b6f86426502ff5a3",
-      "f04ce3fdbd9c4167b2b3a7f85ba1f392",
-      "25f2468a83254faaa0d36387e2b90f02"
-     ]
-    },
-    "id": "RckAPBT3bSoh",
-    "outputId": "6d076cd4-a677-4010-f8b8-5f3ec2b24efc"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.nodes import PromptNode\n",
-    "\n",
-    "model_name = \"OpenAssistant/oasst-sft-1-pythia-12b\"\n",
-    "prompt_node = PromptNode(model_name, api_key=model_api_key, max_length=256)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "E1e15LLV8ULF"
-   },
-   "source": [
-    "### 3) Create a ConversationSummaryMemory\n",
-    "\n",
-    "To have a chat application closer to a human interaction, we need to provide [memory](https://docs.haystack.deepset.ai/docs/agent#conversational-agent-memory) to our ConversationalAgent. There are two types of memory options in Haystack: \n",
-    "\n",
-    "1.   **ConversationMemory**: stores the conversation history (default).\n",
-    "2.   **ConversationSummaryMemory**: stores the conversation history and periodically generates summaries.\n",
-    "\n",
-    "These memory nodes inject the conversation history into the prompt for the large language model with every run. Instead of using the full conversation history, we'll use ConversationSummaryMemory that sums up the conversation without losing important information, thus saving the token limit. \n",
-    "\n",
-    "We can use the same PromptNode in ConversationSummaryMemory, so the same `OpenAssistant/oasst-sft-1-pythia-12b` model generates chat summaries. By default, ConversationSummaryMemory summarizes the chat with every `3` runs using the predefined [`conversational-summary`](https://github.com/deepset-ai/haystack/blob/b8911dfae01643dccf0803648ef3fb705d43dfbd/haystack/nodes/prompt/prompt_template.py#L441) PromptTemplate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "iBisS_dI8kan"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.agents.memory import ConversationSummaryMemory\n",
-    "\n",
-    "summary_memory = ConversationSummaryMemory(prompt_node)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "RpeKtIsSSNSh"
-   },
-   "source": [
-    "> Optionally, you can define a separate PromptNode with another LLM and PromptTemplate for generating conversation summary and use it in the ConversationSummaryMemory."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "MiGGpDW98XjM"
-   },
-   "source": [
-    "### 4) Create a ConversationalAgent\n",
-    "\n",
-    "Now that we have all the necessary components, we can initialize the ConversationalAgent. As PromptTemplate, ConversationalAgent uses [`conversational-agent`](https://github.com/deepset-ai/haystack/blob/b8911dfae01643dccf0803648ef3fb705d43dfbd/haystack/nodes/prompt/prompt_template.py#L437) by default."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_gt2DqNzllPQ"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.agents.conversational import ConversationalAgent\n",
-    "\n",
-    "conversational_agent = ConversationalAgent(prompt_node=prompt_node, memory=summary_memory)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Mgeb6nlsGRTy"
-   },
-   "source": [
-    "Now, our conversational agent is ready to chat!"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Uq_yBlEXGe18"
-   },
-   "source": [
-    "## Trying Out a Prompt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "gAi6DN-LySIH",
-    "outputId": "833a911c-3842-4132-d1e3-dd641d0a4556"
-   },
-   "outputs": [],
-   "source": [
-    "conversational_agent.run(\"Tell me three most interesting things about Istanbul, Turkey\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "bBwcAcbgMTQN",
-    "outputId": "c2f50686-3971-464f-d0d6-5bc9981c51c5"
-   },
-   "outputs": [],
-   "source": [
-    "conversational_agent.run(\"Can you elaborate on the second item?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "-wl8dcWpMmNv",
-    "outputId": "18f92ea0-c096-4b71-8308-76ced457e393"
-   },
-   "outputs": [],
-   "source": [
-    "conversational_agent.run(\"Can you turn this info into a twitter thread?\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mhrNe3xKIVsx"
-   },
-   "source": [
-    "* At any point during the chat, you can use `load()` function to check the chat summary: \n",
-    "\n",
-    "```python\n",
-    "print(conversational_agent.memory.load())\n",
-    "```\n",
-    "\n",
-    "* To delete the whole chat history, call `clear()` method:\n",
-    "\n",
-    "```python\n",
-    "conversational_agent.memory.clear()\n",
-    "```\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KGu3QLQXJo-z"
-   },
-   "source": [
-    "Congratulations! 🎉 You've learned how to use ConversationalAgent to create a chat application with a summarized memory. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "wQyYPjmAG2gJ"
-   },
-   "source": [
-    "## 💬 Example Application\n",
-    "\n",
-    "To take the chat experience to another level, check out this example application. Run the code cell below and use the textarea to interact with the conversational agent. Use the buttons on the right to load or delete the chat summary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
+  "cells": [
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TEebQqubxa4G"
+      },
+      "source": [
+        "# Tutorial: Building a Conversational Chat App\n",
+        "\n",
+        "- **Level**: Intermediate\n",
+        "- **Time to complete**: 10 minutes\n",
+        "- **Nodes Used**: `PromptNode`, `ConversationalAgent` and `ConversationSummaryMemory`\n",
+        "- **Goal**: After completing this tutorial, you will have learned how to use ConversationalAgent to build a conversational chat application\n",
+        "- **Prerequisites**: A [Hugging Face API Key](https://huggingface.co/settings/tokens)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DQLWWW1Yy_Ta"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "A [ConversationalAgent](https://docs.haystack.deepset.ai/docs/agent#conversational-agent) is a type of Agent that is specifically implemented to create chat applications easily. With its memory integration, the ConversationalAgent enables human-like conversation with large language models (LLMs).\n",
+        "\n",
+        "This tutorial introduces you to the ConversationalAgent, ConversationSummaryMemory and explains how you can create your conversational chat application.\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "obaSuZBHy8PF"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2_nC2XoPzDVh"
+      },
+      "source": [
+        "## Installing Haystack\n",
+        "\n",
+        "To start, install the latest release of Haystack with `pip`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JiZktTKoaHp5"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install --upgrade pip\n",
+        "pip install farm-haystack[colab]"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RrPgO_1vzWb6"
+      },
+      "source": [
+        "\n",
+        "### Enabling Telemetry\n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product, but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6YZjKAvOzZRq"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(24)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n0pnO7S6tbyW"
+      },
+      "source": [
+        "## Initializing the ConversationalAgent\n",
+        "\n",
+        "To initialize a ConversationalAgent, you'll first need to create a PromptNode to define the LLM that your chat application will use. Then, you'll add a memory to enable the application to store previous conversation and use this memory to make the interaction more human-like.\n",
+        "\n",
+        "Now, create necessary components for a ConversationalAgent:"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1Omji8PK_675"
+      },
+      "source": [
+        "### 1) Provide a Hugging Face API Key\n",
+        "\n",
+        "Hugging Face offers [hosted Inference API](https://huggingface.co/docs/api-inference/index) which you can use to access machine learning models using simple HTTP requests. This way, you don't need to download models from the hub. To use service, you need to provide an API key from Hugging Face:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qWuRxFWGcAL4"
+      },
+      "outputs": [],
+      "source": [
+        "from getpass import getpass\n",
+        "\n",
+        "model_api_key = getpass(\"Enter model provider API key:\")"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V4LI3vzH7Xvo"
+      },
+      "source": [
+        "### 2) Create a PromptNode\n",
+        "\n",
+        "You'll initialize a PromptNode with the `model_name`, `api_key`, and `max_length` to control the output length of the model. In this tutorial, you'll use [OpenAssistant/oasst-sft-1-pythia-12b](https://huggingface.co/OpenAssistant/oasst-sft-1-pythia-12b), an open source Transformer-based text generation model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RckAPBT3bSoh"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.nodes import PromptNode\n",
+        "\n",
+        "model_name = \"OpenAssistant/oasst-sft-1-pythia-12b\"\n",
+        "prompt_node = PromptNode(model_name, api_key=model_api_key, max_length=256)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E1e15LLV8ULF"
+      },
+      "source": [
+        "### 3) Create a ConversationSummaryMemory\n",
+        "\n",
+        "To have a chat application closer to a human interaction, you need to provide [memory](https://docs.haystack.deepset.ai/docs/agent#conversational-agent-memory) to the ConversationalAgent. There are two types of memory options in Haystack:\n",
+        "\n",
+        "1.   **ConversationMemory**: stores the conversation history (default).\n",
+        "2.   **ConversationSummaryMemory**: stores the conversation history and periodically generates summaries.\n",
+        "\n",
+        "These memory nodes inject the conversation history into the prompt for the large language model with every run. Instead of using the full conversation history, you'll use ConversationSummaryMemory that sums up the conversation without losing important information, thus saving the token limit.\n",
+        "\n",
+        "You can use the same PromptNode in ConversationSummaryMemory, so the same `OpenAssistant/oasst-sft-1-pythia-12b` model generates chat summaries. By default, ConversationSummaryMemory summarizes the chat with every `3` runs using the predefined [`conversational-summary`](https://github.com/deepset-ai/haystack/blob/b8911dfae01643dccf0803648ef3fb705d43dfbd/haystack/nodes/prompt/prompt_template.py#L441) PromptTemplate."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iBisS_dI8kan"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.agents.memory import ConversationSummaryMemory\n",
+        "\n",
+        "summary_memory = ConversationSummaryMemory(prompt_node)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RpeKtIsSSNSh"
+      },
+      "source": [
+        "> Optionally, you can define a separate PromptNode with another LLM and PromptTemplate for generating conversation summary and use it in the ConversationSummaryMemory."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MiGGpDW98XjM"
+      },
+      "source": [
+        "### 4) Create a ConversationalAgent\n",
+        "\n",
+        "Now that you have all the necessary components, you can initialize the ConversationalAgent. If you don't provide any tools, the ConversationalAgent uses the `conversational-agent-without-tools` prompt by default."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_gt2DqNzllPQ"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.agents.conversational import ConversationalAgent\n",
+        "\n",
+        "conversational_agent = ConversationalAgent(prompt_node=prompt_node, memory=summary_memory)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "brdbkCGryKe0"
+      },
+      "source": [
+        "> You can add tools to your chat application using `tools` params of the ConversationalAgent:\n",
+        "> ```python\n",
+        "> conversational_agent = ConversationalAgent(\n",
+        ">    prompt_node=prompt_node,\n",
+        ">    memory=summary_memory,\n",
+        ">    tools=[search_tool]\n",
+        ">)\n",
+        ">```\n",
+        ">To learn how to create tools, check out [Haystack documentation](https://docs.haystack.deepset.ai/docs/agent#tools)."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mgeb6nlsGRTy"
+      },
+      "source": [
+        "Now, your conversational agent is ready to chat!"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Uq_yBlEXGe18"
+      },
+      "source": [
+        "## Trying Out a Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gAi6DN-LySIH"
+      },
+      "outputs": [],
+      "source": [
+        "conversational_agent.run(\"Tell me three most interesting things about Istanbul, Turkey\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bBwcAcbgMTQN"
+      },
+      "outputs": [],
+      "source": [
+        "conversational_agent.run(\"Can you elaborate on the second item?\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-wl8dcWpMmNv"
+      },
+      "outputs": [],
+      "source": [
+        "conversational_agent.run(\"Can you turn this info into a twitter thread?\")"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mhrNe3xKIVsx"
+      },
+      "source": [
+        "* At any point during the chat, you can use `load()` function to check the chat summary:\n",
+        "\n",
+        "```python\n",
+        "print(conversational_agent.memory.load())\n",
+        "```\n",
+        "\n",
+        "* To delete the whole chat history, call `clear()` method:\n",
+        "\n",
+        "```python\n",
+        "conversational_agent.memory.clear()\n",
+        "```\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KGu3QLQXJo-z"
+      },
+      "source": [
+        "Congratulations! 🎉 You've learned how to use ConversationalAgent to create a chat application with a summarized memory."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wQyYPjmAG2gJ"
+      },
+      "source": [
+        "## 💬 Example Application\n",
+        "\n",
+        "To take the chat experience to another level, check out this example application. Run the code cell below and use the textarea to interact with the conversational agent. Use the buttons on the right to load or delete the chat summary."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IEcpPCLKKasg"
+      },
+      "outputs": [],
+      "source": [
+        "import ipywidgets as widgets\n",
+        "from IPython.display import clear_output\n",
+        "\n",
+        "## Text Input\n",
+        "user_input = widgets.Textarea(\n",
+        "    value=\"\",\n",
+        "    placeholder=\"Type your prompt here\",\n",
+        "    disabled=False,\n",
+        "    style={\"description_width\": \"initial\"},\n",
+        "    layout=widgets.Layout(width=\"100%\", height=\"100%\"),\n",
+        ")\n",
+        "\n",
+        "## Submit Button\n",
+        "submit_button = widgets.Button(\n",
+        "    description=\"Submit\", button_style=\"success\", layout=widgets.Layout(width=\"100%\", height=\"80%\")\n",
+        ")\n",
+        "\n",
+        "\n",
+        "def on_button_clicked(b):\n",
+        "    user_prompt = user_input.value\n",
+        "    user_input.value = \"\"\n",
+        "    print(\"\\nUser:\\n\", user_prompt)\n",
+        "    conversational_agent.run(user_prompt)\n",
+        "\n",
+        "\n",
+        "submit_button.on_click(on_button_clicked)\n",
+        "\n",
+        "## Show Memory Button\n",
+        "memory_button = widgets.Button(\n",
+        "    description=\"Show Memory\", button_style=\"info\", layout=widgets.Layout(width=\"100%\", height=\"100%\")\n",
+        ")\n",
+        "\n",
+        "\n",
+        "def on_memory_button_clicked(b):\n",
+        "    memory = conversational_agent.memory.load()\n",
+        "    if len(memory):\n",
+        "        print(\"\\nMemory:\\n\", memory)\n",
+        "    else:\n",
+        "        print(\"Memory is empty\")\n",
+        "\n",
+        "\n",
+        "memory_button.on_click(on_memory_button_clicked)\n",
+        "\n",
+        "## Clear Memory Button\n",
+        "clear_button = widgets.Button(\n",
+        "    description=\"Clear Memory\", button_style=\"warning\", layout=widgets.Layout(width=\"100%\", height=\"100%\")\n",
+        ")\n",
+        "\n",
+        "\n",
+        "def on_clear_button_button_clicked(b):\n",
+        "    conversational_agent.memory.clear()\n",
+        "    print(\"\\nMemory is cleared\\n\")\n",
+        "\n",
+        "\n",
+        "clear_button.on_click(on_clear_button_button_clicked)\n",
+        "\n",
+        "## Layout\n",
+        "grid = widgets.GridspecLayout(3, 3, height=\"200px\", width=\"800px\", grid_gap=\"10px\")\n",
+        "grid[0, 2] = clear_button\n",
+        "grid[0:2, 0:2] = user_input\n",
+        "grid[2, 0:] = submit_button\n",
+        "grid[1, 2] = memory_button\n",
+        "display(grid)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
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-   },
-   "outputs": [],
-   "source": [
-    "import ipywidgets as widgets\n",
-    "from IPython.display import clear_output\n",
-    "\n",
-    "## Text Input\n",
-    "user_input = widgets.Textarea(\n",
-    "    value=\"\",\n",
-    "    placeholder=\"Type your prompt here\",\n",
-    "    disabled=False,\n",
-    "    style={\"description_width\": \"initial\"},\n",
-    "    layout=widgets.Layout(width=\"100%\", height=\"100%\"),\n",
-    ")\n",
-    "\n",
-    "## Submit Button\n",
-    "submit_button = widgets.Button(\n",
-    "    description=\"Submit\", button_style=\"success\", layout=widgets.Layout(width=\"100%\", height=\"80%\")\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def on_button_clicked(b):\n",
-    "    user_prompt = user_input.value\n",
-    "    user_input.value = \"\"\n",
-    "    print(\"\\nUser:\\n\", user_prompt)\n",
-    "    conversational_agent.run(user_prompt)\n",
-    "\n",
-    "\n",
-    "submit_button.on_click(on_button_clicked)\n",
-    "\n",
-    "## Show Memory Button\n",
-    "memory_button = widgets.Button(\n",
-    "    description=\"Show Memory\", button_style=\"info\", layout=widgets.Layout(width=\"100%\", height=\"100%\")\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def on_memory_button_clicked(b):\n",
-    "    memory = conversational_agent.memory.load()\n",
-    "    if len(memory):\n",
-    "        print(\"\\nMemory:\\n\", memory)\n",
-    "    else:\n",
-    "        print(\"Memory is empty\")\n",
-    "\n",
-    "\n",
-    "memory_button.on_click(on_memory_button_clicked)\n",
-    "\n",
-    "## Clear Memory Button\n",
-    "clear_button = widgets.Button(\n",
-    "    description=\"Clear Memory\", button_style=\"warning\", layout=widgets.Layout(width=\"100%\", height=\"100%\")\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def on_clear_button_button_clicked(b):\n",
-    "    conversational_agent.memory.clear()\n",
-    "    print(\"\\nMemory is cleared\\n\")\n",
-    "\n",
-    "\n",
-    "clear_button.on_click(on_clear_button_button_clicked)\n",
-    "\n",
-    "## Layout\n",
-    "grid = widgets.GridspecLayout(3, 3, height=\"200px\", width=\"800px\", grid_gap=\"10px\")\n",
-    "grid[0, 2] = clear_button\n",
-    "grid[0:2, 0:2] = user_input\n",
-    "grid[2, 0:] = submit_button\n",
-    "grid[1, 2] = memory_button\n",
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 }

From 60a18c7a5bd68e4dfbe689d6d63b1e3f96fd5ea1 Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <44616784+anakin87@users.noreply.github.com>
Date: Thu, 29 Jun 2023 16:45:38 +0200
Subject: [PATCH 101/206] change version and deps (#213)

---
 tutorials/02_Finetune_a_model_on_your_data.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/02_Finetune_a_model_on_your_data.ipynb b/tutorials/02_Finetune_a_model_on_your_data.ipynb
index 251588ba..0d4c6405 100644
--- a/tutorials/02_Finetune_a_model_on_your_data.ipynb
+++ b/tutorials/02_Finetune_a_model_on_your_data.ipynb
@@ -47,7 +47,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab]==1.16.1"
+    "pip install farm-haystack[colab, inference]"
    ]
   },
   {

From 62fa169f10ff166f00d243be7a2c3bb907b194b5 Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <44616784+anakin87@users.noreply.github.com>
Date: Mon, 3 Jul 2023 10:49:14 +0200
Subject: [PATCH 102/206] Fix installation command in tutorial 21 (#214)

* fix installation command

* rm inference dep
---
 tutorials/21_Customizing_PromptNode.ipynb | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tutorials/21_Customizing_PromptNode.ipynb b/tutorials/21_Customizing_PromptNode.ipynb
index 4cbf7b9c..43f8744d 100644
--- a/tutorials/21_Customizing_PromptNode.ipynb
+++ b/tutorials/21_Customizing_PromptNode.ipynb
@@ -65,7 +65,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install git+https://github.com/deepset-ai/haystack.git"
+    "pip install farm-haystack[colab]"
    ]
   },
   {
@@ -225,6 +225,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "W6hK9AcsNONW"
@@ -360,6 +361,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "en6IbPRsTOTz"
@@ -432,6 +434,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "FAELXO0MhbrV"

From a5a39d873c4b9207d5a4dba1565761c6dc81623c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 3 Jul 2023 13:02:20 +0300
Subject: [PATCH 103/206] Update tutorial 11 (#210)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Update tutorial 11

* Remove deprecated RAG node
* Make it to beginner level

* Update 11_Pipelines.ipynb

---------

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>
---
 index.toml                   |    4 +-
 tutorials/11_Pipelines.ipynb | 4694 ++++++++++++++++++++++++++++++++--
 2 files changed, 4440 insertions(+), 258 deletions(-)

diff --git a/index.toml b/index.toml
index e936aff7..d8e444b6 100644
--- a/index.toml
+++ b/index.toml
@@ -98,8 +98,8 @@ created_at = 2021-08-12
 [[tutorial]]
 title = "How to Use Pipelines"
 description = "Learn about the many ways which you can route queries through the nodes in a pipeline."
-level = "intermediate"
-weight = 65
+level = "beginner"
+weight = 40
 notebook = "11_Pipelines.ipynb"
 aliases = ["pipelines"]
 created_at = 2021-08-12
diff --git a/tutorials/11_Pipelines.ipynb b/tutorials/11_Pipelines.ipynb
index b2a99d29..86a9f7cb 100644
--- a/tutorials/11_Pipelines.ipynb
+++ b/tutorials/11_Pipelines.ipynb
@@ -1,18 +1,19 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "Oo2_F2jwsA8J",
     "pycharm": {
      "name": "#%% md\n"
     }
    },
    "source": [
-    "# Pipelines Tutorial\n",
+    "# Tutorial: How to Use Pipelines\n",
     "\n",
-    "In this tutorial, you will learn how the `Pipeline` class acts as a connector between all the different\n",
-    "building blocks that are found in FARM. Whether you are using a Reader, Generator, Summarizer\n",
+    "In this tutorial, you will learn how the `Pipeline` connects the different components in Haystack. Whether you are using a Reader, Summarizer\n",
     "or Retriever (or 2), the `Pipeline` class will help you build a Directed Acyclic Graph (DAG) that\n",
     "determines how to route the output of one component into the input of another.\n"
    ]
@@ -22,6 +23,7 @@
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "DwGR2m4MsA8K",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -36,43 +38,50 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "Wd2rYyussA8K"
+   },
    "source": [
     "## Installing Haystack\n",
     "\n",
-    "To start, let's install the latest release of Haystack with `pip` along with `pygraphviz`:"
+    "To start, let's install the latest release of Haystack with `pip`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5W4mAfAasA8L",
+    "outputId": "5fd29b21-811d-499c-bbbc-9599f96e40d9"
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,elasticsearch]\n",
-    "\n",
-    "apt install libgraphviz-dev\n",
-    "pip install pygraphviz"
+    "pip install farm-haystack[colab,inference]"
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "6q-xTi6ksA8L"
+   },
    "source": [
-    "### Enabling Telemetry \n",
+    "### Enabling Telemetry\n",
     "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "QadbeTutsA8L"
+   },
    "outputs": [],
    "source": [
     "from haystack.telemetry import tutorial_running\n",
@@ -81,9 +90,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "CwZDy9f-sA8M",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -101,7 +112,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
+    "id": "1YXK3IqDsA8M",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -115,96 +126,64 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Start an Elasticsearch server\n",
-    "You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Recommended: Start Elasticsearch using Docker via the Haystack utility function\n",
-    "from haystack.utils import launch_es\n",
-    "\n",
-    "launch_es()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Start an Elasticsearch server in Colab\n",
-    "\n",
-    "If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
+    "id": "zceM4-j_sA8M"
    },
-   "outputs": [],
    "source": [
-    "%%bash\n",
+    "## Initializing the DocumentStore\n",
+    "\n",
+    "A DocumentStore stores the Documents that a system uses to retrieve or find answers to your questions. In this tutorial, you'll use the `InMemoryDocumentStore`.\n",
     "\n",
-    "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
-    "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2\n"
+    "Let's initialize the DocumentStore."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ktcvOryoCudy",
+    "outputId": "09747c54-aae4-4e60-b309-380b676657e9"
    },
    "outputs": [],
    "source": [
-    "%%bash --bg\n",
+    "from haystack.document_stores import InMemoryDocumentStore\n",
     "\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch"
+    "document_store = InMemoryDocumentStore(use_bm25=True)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "lDs0kKPmsA8N",
     "pycharm": {
      "name": "#%% md\n"
     }
    },
    "source": [
-    "## Initialization"
+    "## Fetching and Writing Documents\n",
+    "\n",
+    "Let's fetch the txt files (in this case, pages from the Game of Thrones wiki) and prepare them so that they can be indexed into the DocumentStore:"
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Then let's fetch some data (in this case, pages from the Game of Thrones wiki) and prepare it so that it can\n",
-    "be used indexed into our `DocumentStore`"
-   ]
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
+    "id": "V80mCVopsA8N",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -219,21 +198,45 @@
     "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
     "\n",
     "# convert files to dicts containing documents that can be indexed to our datastore\n",
-    "got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)"
+    "got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n",
+    "document_store.delete_documents()\n",
+    "document_store.write_documents(got_docs)"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader\n",
+    "\n",
+    "bm25_retriever = BM25Retriever()\n",
+    "\n",
+    "from haystack.pipelines import DocumentSearchPipeline\n",
+    "from haystack.utils import print_documents\n",
+    "\n",
+    "p_retrieval = DocumentSearchPipeline(bm25_retriever)\n",
+    "# res = p_retrieval.run(query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}})\n",
+    "# print_documents(res, max_text_len=200)"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "X79w6kHrsA8N",
     "pycharm": {
      "name": "#%% md\n"
     }
    },
    "source": [
+    "## Initializing Core Components\n",
+    "\n",
     "Here we initialize the core components that we will be gluing together using the `Pipeline` class.\n",
-    "We have a `DocumentStore`, a `BM25Retriever` and a `FARMReader`.\n",
-    "These can be combined to create a classic Retriever-Reader pipeline that is designed\n",
+    "Initialize a `BM25Retriever`, an `EmbeddingRetriever`, and a `FARMReader`.\n",
+    "You can combine these components to create a classic Retriever-Reader pipeline that is designed\n",
     "to perform Open Domain Question Answering."
    ]
   },
@@ -241,47 +244,34 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
+    "id": "jJFkt1LVsA8N",
     "pycharm": {
      "name": "#%%\n"
     }
    },
    "outputs": [],
    "source": [
-    "import os\n",
-    "import time\n",
-    "\n",
-    "from haystack.document_stores import ElasticsearchDocumentStore\n",
     "from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader\n",
     "\n",
-    "# Wait 30 seconds only to be sure Elasticsearch is ready before continuing\n",
-    "time.sleep(30)\n",
-    "\n",
-    "# Get the host where Elasticsearch is running, default to localhost\n",
-    "host = os.environ.get(\"ELASTICSEARCH_HOST\", \"localhost\")\n",
-    "\n",
-    "# Initialize DocumentStore and index documents\n",
-    "document_store = ElasticsearchDocumentStore(host=host)\n",
-    "document_store.delete_documents()\n",
-    "document_store.write_documents(got_docs)\n",
-    "\n",
-    "# Initialize Sparse retriever\n",
+    "# Initialize Sparse Retriever\n",
     "bm25_retriever = BM25Retriever(document_store=document_store)\n",
     "\n",
-    "# Initialize dense retriever\n",
+    "# Initialize embedding Retriever\n",
     "embedding_retriever = EmbeddingRetriever(\n",
     "    document_store=document_store, embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
     ")\n",
     "document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False)\n",
     "\n",
-    "# Initialize reader\n",
+    "# Initialize Reader\n",
     "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "RwqtQRoAsA8N",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -289,15 +279,33 @@
    "source": [
     "## Prebuilt Pipelines\n",
     "\n",
-    "Haystack features many prebuilt pipelines that cover common tasks.\n",
-    "Here we have an `ExtractiveQAPipeline` (the successor to the now deprecated `Finder` class)."
+    "Haystack features many prebuilt pipelines that cover common tasks. The most used one is `ExtractiveQAPipeline`.\n",
+    "Now, initialize the ExtractiveQAPipeline and run it with a query:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 483,
+     "referenced_widgets": [
+      "3ff3b8f606f44adfa413314526d0f51b",
+      "4b436abd81db41ad98f24f3148bb1fcb",
+      "a20d8628bdb5486a8ccab95660a986c3",
+      "42c2a9dbc747478e9f4691d01f1246da",
+      "616af910d6ba45039b3b455631fe545c",
+      "422ce6e179ca44439e0e1bb574daecc8",
+      "5f05f9f76d5149e0a3c42e08dd85a82b",
+      "4c514834225147eab4ba48b7ece93ecb",
+      "c70f352bec2f441990a5e538cf644d6a",
+      "4afe18f598764e90807ac2f32fca4853",
+      "63850c8393aa4c1aa8a5e195857ceca3"
+     ]
+    },
+    "id": "fZZ_Tt08sA8N",
+    "outputId": "7aa1446f-6de7-45df-cd4e-f3b333610294",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -307,7 +315,6 @@
     "from haystack.pipelines import ExtractiveQAPipeline\n",
     "from haystack.utils import print_answers\n",
     "\n",
-    "# Prebuilt pipeline\n",
     "p_extractive_premade = ExtractiveQAPipeline(reader=reader, retriever=bm25_retriever)\n",
     "res = p_extractive_premade.run(\n",
     "    query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
@@ -316,19 +323,39 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "id": "PqKe8Nw9sA8N"
    },
    "source": [
-    "If you want to just do the retrieval step, you can use a `DocumentSearchPipeline`"
+    "If you want to just do the retrieval step, you can use a DocumentSearchPipeline:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "7f57abc22d3a4231a6b02eb025b35f49",
+      "a9e34231df9548e584c81ea89b814ee6",
+      "9989d5d79a3a41fa9bc0431465997611",
+      "f73977385dce49bead6c2012f2e42f71",
+      "cc2586d0b77349b2b68a0b05e410f95f",
+      "c78aa680ada54f29bc8bbe8ce94dfe31",
+      "0e3b35c207e34b738c179ed6de3fb906",
+      "333a2e1b38f14049bcd8633fb614dfd1",
+      "05e19ba65d5b4df09c6a93551affbf67",
+      "bd2fb1c05642482bbed1e4f4e2824f80",
+      "5b64bdd8ce404f8290511d9bf9c7f29f"
+     ]
+    },
+    "id": "lA1DyIhLsA8N",
+    "outputId": "018fa621-f08e-424c-ef4a-8d7f50231ec0",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -338,76 +365,40 @@
     "from haystack.pipelines import DocumentSearchPipeline\n",
     "from haystack.utils import print_documents\n",
     "\n",
-    "p_retrieval = DocumentSearchPipeline(bm25_retriever)\n",
+    "p_retrieval = DocumentSearchPipeline(embedding_retriever)\n",
     "res = p_retrieval.run(query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}})\n",
     "print_documents(res, max_text_len=200)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "Or if you want to use a `Generator` instead of a `Reader`,\n",
-    "you can initialize a `GenerativeQAPipeline` like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.pipelines import GenerativeQAPipeline\n",
-    "from haystack.nodes import RAGenerator\n",
-    "\n",
-    "# We set this to True so that the document store returns document embeddings with each document\n",
-    "# This is needed by the Generator\n",
-    "document_store.return_embedding = True\n",
-    "\n",
-    "# Initialize generator\n",
-    "rag_generator = RAGenerator()\n",
-    "\n",
-    "# Generative QA\n",
-    "p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=embedding_retriever)\n",
-    "res = p_generator.run(query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}})\n",
-    "print_answers(res, details=\"minimum\")\n",
-    "\n",
-    "# We are setting this to False so that in later pipelines,\n",
-    "# we get a cleaner printout\n",
-    "document_store.return_embedding = False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
+    "id": "_ISxlB1zsA8N"
    },
    "source": [
     "Haystack features prebuilt pipelines to do:\n",
-    "- just document search (DocumentSearchPipeline),\n",
+    "- document search (DocumentSearchPipeline),\n",
     "- document search with summarization (SearchSummarizationPipeline)\n",
-    "- generative QA (GenerativeQAPipeline)\n",
     "- FAQ style QA (FAQPipeline)\n",
     "- translated search (TranslationWrapperPipeline)\n",
-    "To find out more about these pipelines, have a look at our [documentation](https://docs.haystack.deepset.ai/docs/pipelines)\n"
+    "To find out more about these pipelines, have a look at our [documentation](https://docs.haystack.deepset.ai/docs/pipelines).\n"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "YYLCqoYTsA8O",
     "pycharm": {
      "name": "#%% md\n"
     }
    },
    "source": [
+    "## Generating a Pipeline Diagram\n",
+    "\n",
     "With any Pipeline, whether prebuilt or custom constructed,\n",
     "you can save a diagram showing how all the components are connected.\n",
     "\n",
@@ -418,7 +409,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "krDbpxY3sA8O",
+    "outputId": "fcd13061-591b-451d-eb05-f93f3546b119",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -426,32 +421,55 @@
    "outputs": [],
    "source": [
     "# Uncomment the following to generate the images\n",
+    "# !apt install libgraphviz-dev\n",
+    "# !pip install pygraphviz\n",
     "\n",
     "# p_extractive_premade.draw(\"pipeline_extractive_premade.png\")\n",
-    "# p_retrieval.draw(\"pipeline_retrieval.png\")\n",
-    "# p_generator.draw(\"pipeline_generator.png\")"
+    "# p_retrieval.draw(\"pipeline_retrieval.png\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "RB9PEOKssA8O",
     "pycharm": {
      "name": "#%% md\n"
     }
    },
    "source": [
-    "## Custom Pipelines\n",
+    "## Custom Extractive QA Pipeline\n",
     "\n",
-    "Now we are going to rebuild the `ExtractiveQAPipelines` using the generic Pipeline class.\n",
-    "We do this by adding the building blocks that we initialized as nodes in the graph."
+    "If you need a custom pipeline for your task or add extra nodes to a pipeline, you can create a pipeline from scratch instead of using a prebuilt one.\n",
+    "\n",
+    "Now, try to rebuild the ExtractiveQAPipeline using the generic Pipeline class.\n",
+    "You can do this by adding the building blocks as shown in the graph."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 864,
+     "referenced_widgets": [
+      "579ff5067d6b46c893612bd233906b34",
+      "9a4a31fda4e64a27ae2c884ad1e9d9c9",
+      "3ee68111e3844c70a6ea72f295ef0392",
+      "d5c66b0a453f4a03954fabfd85187e83",
+      "650915e12d2644c38cf2f6ec72faba92",
+      "ac6c13dab196486d87d249b3f5382e86",
+      "d5e111a594d3475dbdff20071822e139",
+      "93a607e6574f4a0fb3b9b1463a4aad4c",
+      "8c4d1d61155e4064bdf76599be6c8bbe",
+      "99945c16563d40a5aa0825a1134a6300",
+      "f39c9f27960e4ff99bd7aef02f14ffd2"
+     ]
+    },
+    "id": "x5WMGbCgsA8O",
+    "outputId": "a22deeba-b303-43cf-b51f-4b1514c5d85a",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -460,16 +478,13 @@
    "source": [
     "from haystack.pipelines import Pipeline\n",
     "\n",
-    "\n",
     "# Custom built extractive QA pipeline\n",
     "p_extractive = Pipeline()\n",
     "p_extractive.add_node(component=bm25_retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
     "p_extractive.add_node(component=reader, name=\"Reader\", inputs=[\"Retriever\"])\n",
     "\n",
     "# Now we can run it\n",
-    "res = p_extractive.run(\n",
-    "    query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
-    ")\n",
+    "res = p_extractive.run(query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}})\n",
     "print_answers(res, details=\"minimum\")\n",
     "\n",
     "# Uncomment the following to generate the pipeline image\n",
@@ -477,30 +492,63 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "Z_R1488-sA8O",
     "pycharm": {
      "name": "#%% md\n"
     }
    },
    "source": [
+    "## Combining Retrievers Using a Custom Pipeline\n",
+    "\n",
     "Pipelines offer a very simple way to ensemble together different components.\n",
-    "In this example, we are going to combine the power of an `EmbeddingRetriever`\n",
+    "In this example, you'll combine the power of an `EmbeddingRetriever`\n",
     "with the keyword based `BM25Retriever`.\n",
-    "See our [documentation](https://docs.haystack.deepset.ai/docs/retriever) to understand why\n",
-    "we might want to combine a dense and sparse retriever.\n",
+    "See our [documentation](https://docs.haystack.deepset.ai/docs/retriever#deeper-dive-vector-based-vs-keyword-based) to understand why\n",
+    "you might want to combine a dense and sparse retriever.\n",
     "\n",
     "![image](https://github.com/deepset-ai/haystack/blob/main/docs/img/tutorial11_custompipelines_pipeline_ensemble.png?raw=true)\n",
     "\n",
-    "Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together."
+    "Here you'll use a `JoinDocuments` node to merge the predictions from each retriever:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 862,
+     "referenced_widgets": [
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+      "d861455dc8344b1fbb03c966e8a803ad",
+      "156b4ec959084fbdbcdfb9f7a12ece5f"
+     ]
+    },
+    "id": "jHY1iwlysA8O",
+    "outputId": "2c278d74-0de9-452d-e7d8-539930a278e9",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -511,10 +559,10 @@
     "\n",
     "# Create ensembled pipeline\n",
     "p_ensemble = Pipeline()\n",
-    "p_ensemble.add_node(component=bm25_retriever, name=\"ESRetriever\", inputs=[\"Query\"])\n",
+    "p_ensemble.add_node(component=bm25_retriever, name=\"BM25Retriever\", inputs=[\"Query\"])\n",
     "p_ensemble.add_node(component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"Query\"])\n",
     "p_ensemble.add_node(\n",
-    "    component=JoinDocuments(join_mode=\"concatenate\"), name=\"JoinResults\", inputs=[\"ESRetriever\", \"EmbeddingRetriever\"]\n",
+    "    component=JoinDocuments(join_mode=\"concatenate\"), name=\"JoinResults\", inputs=[\"BM25Retriever\", \"EmbeddingRetriever\"]\n",
     ")\n",
     "p_ensemble.add_node(component=reader, name=\"Reader\", inputs=[\"JoinResults\"])\n",
     "\n",
@@ -523,15 +571,17 @@
     "\n",
     "# Run pipeline\n",
     "res = p_ensemble.run(\n",
-    "    query=\"Who is the father of Arya Stark?\", params={\"EmbeddingRetriever\": {\"top_k\": 5}, \"ESRetriever\": {\"top_k\": 5}}\n",
+    "    query=\"Who is the father of Arya Stark?\", params={\"EmbeddingRetriever\": {\"top_k\": 5}, \"BM25Retriever\": {\"top_k\": 5}}\n",
     ")\n",
     "print_answers(res, details=\"minimum\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "Z2_Sonx6sA8O",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -550,14 +600,14 @@
     "and the name of the outgoing edge (by default \"output_1\" for nodes that have one output)\n",
     "- Add a class attribute outgoing_edges = 1 that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).\n",
     "\n",
-    "Here we have a template for a Node:"
+    "Here is a template for a custom node:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
+    "id": "1cZYUg4hsA8O",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -583,9 +633,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "810qrYXwsA8O",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -610,7 +662,47 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 619,
+     "referenced_widgets": [
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+      "b096be01767543418953eae9bc148cb1"
+     ]
+    },
+    "id": "tzHCRC0ksA8O",
+    "outputId": "b0fffbf8-f7dd-4cba-f3f3-fa91b6a24ed0",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -640,9 +732,9 @@
     "# Here we build the pipeline\n",
     "p_classifier = Pipeline()\n",
     "p_classifier.add_node(component=CustomQueryClassifier(), name=\"QueryClassifier\", inputs=[\"Query\"])\n",
-    "p_classifier.add_node(component=bm25_retriever, name=\"ESRetriever\", inputs=[\"QueryClassifier.output_1\"])\n",
+    "p_classifier.add_node(component=bm25_retriever, name=\"BM25Retriever\", inputs=[\"QueryClassifier.output_1\"])\n",
     "p_classifier.add_node(component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"QueryClassifier.output_2\"])\n",
-    "p_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"ESRetriever\", \"EmbeddingRetriever\"])\n",
+    "p_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"BM25Retriever\", \"EmbeddingRetriever\"])\n",
     "# Uncomment the following to generate the pipeline image\n",
     "# p_classifier.draw(\"pipeline_classifier.png\")\n",
     "\n",
@@ -653,14 +745,16 @@
     "\n",
     "# Run only the sparse retriever on a keyword based query\n",
     "res_2 = p_classifier.run(query=\"Arya Stark father\")\n",
-    "print(\"ES Results\" + \"\\n\" + \"=\" * 15)\n",
+    "print(\"BM25Retriever Results\" + \"\\n\" + \"=\" * 15)\n",
     "print_answers(res_2)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "KhsZ_vZqsA8O",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -677,6 +771,7 @@
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
+    "id": "D7d-L6A3sA8O",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -691,7 +786,58 @@
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-    "collapsed": false,
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+     ]
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+    "id": "ZakI7jn9sA8O",
+    "outputId": "93a8faf2-89ea-4190-f738-7648dfbd88cd",
     "pycharm": {
      "name": "#%%\n"
     }
@@ -702,7 +848,7 @@
     "bm25_retriever.debug = True\n",
     "\n",
     "# 2) You can provide `debug` as a parameter when running your pipeline\n",
-    "result = p_classifier.run(query=\"Who is the father of Arya Stark?\", params={\"ESRetriever\": {\"debug\": True}})\n",
+    "result = p_classifier.run(query=\"Who is the father of Arya Stark?\", params={\"BM25Retriever\": {\"debug\": True}})\n",
     "\n",
     "# 3) You can provide the `debug` paramter to all nodes in your pipeline\n",
     "result = p_classifier.run(query=\"Who is the father of Arya Stark?\", params={\"debug\": True})\n",
@@ -711,87 +857,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## YAML Configs\n",
-    "\n",
-    "A full `Pipeline` can be defined in a YAML file and simply loaded.\n",
-    "Having your pipeline available in a YAML is particularly useful\n",
-    "when you move between experimentation and production environments.\n",
-    "Just export the YAML from your notebook / IDE and import it into your production environment.\n",
-    "It also helps with version control of pipelines,\n",
-    "allows you to share your pipeline easily with colleagues,\n",
-    "and simplifies the configuration of pipeline parameters in production.\n",
-    "\n",
-    "It consists of two main sections: you define all objects (e.g. a reader) in components\n",
-    "and then stick them together to a pipeline in pipelines.\n",
-    "You can also set one component to be multiple nodes of a pipeline or to be a node across multiple pipelines.\n",
-    "It will be loaded just once in memory and therefore doesn't hurt your resources more than actually needed.\n",
-    "\n",
-    "The contents of a YAML file should look something like this:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "```yaml\n",
-    "version: '0.7'\n",
-    "components:    # define all the building-blocks for Pipeline\n",
-    "- name: MyReader       # custom-name for the component; helpful for visualization & debugging\n",
-    "  type: FARMReader    # Haystack Class name for the component\n",
-    "  params:\n",
-    "    no_ans_boost: -10\n",
-    "    model_name_or_path: deepset/roberta-base-squad2\n",
-    "- name: MyESRetriever\n",
-    "  type: BM25Retriever\n",
-    "  params:\n",
-    "    document_store: MyDocumentStore    # params can reference other components defined in the YAML\n",
-    "    custom_query: null\n",
-    "- name: MyDocumentStore\n",
-    "  type: ElasticsearchDocumentStore\n",
-    "  params:\n",
-    "    index: haystack_test\n",
-    "pipelines:    # multiple Pipelines can be defined using the components from above\n",
-    "- name: my_query_pipeline    # a simple extractive-qa Pipeline\n",
-    "  nodes:\n",
-    "  - name: MyESRetriever\n",
-    "    inputs: [Query]\n",
-    "  - name: MyReader\n",
-    "    inputs: [MyESRetriever]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "To load, simply call:\n",
-    "``` python\n",
-    "yaml_pipeline = Pipeline.load_from_yaml(Path(\"sample.yaml\"))\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
+    "id": "VSTHlO55sA8S",
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -800,34 +870,4146 @@
     "## Conclusion\n",
     "\n",
     "The possibilities are endless with the `Pipeline` class and we hope that this tutorial will inspire you\n",
-    "to build custom pipeplines that really work for your use case!"
+    "to build custom pipelines that really work for your use case!"
    ]
   }
  ],
  "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
   "kernelspec": {
-   "display_name": "Python 3.8.9 64-bit",
-   "language": "python",
+   "display_name": "Python 3",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "3.8.9"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
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+      "layout": "IPY_MODEL_7165f68ae5fd4b57a3f30942519f2d29",
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+      "style": "IPY_MODEL_4c41e83814e44a578a6e62585cafd34d",
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+      "_view_module": "@jupyter-widgets/base",
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+    },
+    "f73977385dce49bead6c2012f2e42f71": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_bd2fb1c05642482bbed1e4f4e2824f80",
+      "placeholder": "​",
+      "style": "IPY_MODEL_5b64bdd8ce404f8290511d9bf9c7f29f",
+      "value": " 1/1 [00:00&lt;00:00, 17.25it/s]"
+     }
+    },
+    "f76772d14e4a4f0487044dcbd253f82e": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
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+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_c321ff97706d465193a2b3dfcb107c1c",
+       "IPY_MODEL_842019f6aed44d41976b11a0352d4596",
+       "IPY_MODEL_729a5070b09f4b2aa511d58e2b5833d5"
+      ],
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+     }
+    },
+    "fd2f080261c74992bd7fb9a0b6f6010b": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_7fd49ced837540d2b3e560a6dd507ac3",
+       "IPY_MODEL_7f69e47ac44c401f9b26f8c88de4c481",
+       "IPY_MODEL_0466c5b6e97b467b844ff2b72ab9de8e"
+      ],
+      "layout": "IPY_MODEL_6baf2644608242a6a431cd3939201d51"
+     }
+    }
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 0
 }

From 974aee41fc464cf3d7ccc76eb16f52e40462b242 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 5 Jul 2023 15:15:00 +0300
Subject: [PATCH 104/206] Add latest possible haystack_version to outdated
 tutorials (#215)

* These tutorials will have the 'outdated' tag
---
 index.toml                         |  3 +++
 scripts/generate_markdowns.py      |  1 +
 scripts/generate_txt.py            |  1 +
 tutorials/10_Knowledge_Graph.ipynb | 13 ++++++++++++-
 4 files changed, 17 insertions(+), 1 deletion(-)

diff --git a/index.toml b/index.toml
index d8e444b6..6e59f815 100644
--- a/index.toml
+++ b/index.toml
@@ -67,6 +67,7 @@ weight = 60
 notebook = "07_RAG_Generator.ipynb"
 aliases = ["retrieval-augmented-generation"]
 created_at = 2021-08-12
+haystack_version = "1.17.2"
 
 [[tutorial]]
 title = "Preprocessing Your Documents"
@@ -94,6 +95,7 @@ weight = 120
 notebook = "10_Knowledge_Graph.ipynb"
 aliases = ["knowledge-graph"]
 created_at = 2021-08-12
+haystack_version = "1.16.1"
 
 [[tutorial]]
 title = "How to Use Pipelines"
@@ -112,6 +114,7 @@ weight = 70
 notebook = "12_LFQA.ipynb"
 aliases = ["lfqa"]
 created_at = 2021-08-12
+haystack_version = "1.17.2"
 
 [[tutorial]]
 title = "Question Generation"
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index b76b69fe..40ef0411 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -23,6 +23,7 @@ def generate_frontmatter(config, tutorial):
     frontmatter = f"""---
 layout: {config["layout"]}
 featured: {tutorial.get("featured", False)}
+haystack_version: "{tutorial.get("haystack_version", "latest")}"
 colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')}
 toc: {config["toc"]}
 title: "{tutorial["title"]}"
diff --git a/scripts/generate_txt.py b/scripts/generate_txt.py
index 9bb77443..a95a4fa9 100644
--- a/scripts/generate_txt.py
+++ b/scripts/generate_txt.py
@@ -18,6 +18,7 @@ def generate_metadata(tutorial):
 
     return f"""featured: {tutorial.get("featured", False)}
 title: "{tutorial["title"]}"
+haystack_version: "{tutorial.get("haystack_version", "latest")}"
 level: "{tutorial["level"]}"
 description: {tutorial["description"]}
 completion_time: "{tutorial.get("completion_time", "")}"
diff --git a/tutorials/10_Knowledge_Graph.ipynb b/tutorials/10_Knowledge_Graph.ipynb
index 3dd30643..5f728a64 100644
--- a/tutorials/10_Knowledge_Graph.ipynb
+++ b/tutorials/10_Knowledge_Graph.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
@@ -57,7 +58,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,inmemorygraph]==1.16.0"
+    "pip install farm-haystack[colab,inmemorygraph]==1.16.1"
    ]
   },
   {
@@ -81,6 +82,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
@@ -115,6 +117,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
@@ -155,6 +158,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -162,6 +166,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -171,6 +176,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -204,6 +210,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "jp-MarkdownHeadingCollapsed": true,
@@ -214,6 +221,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
@@ -263,6 +271,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
@@ -327,6 +336,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -347,6 +357,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {

From bcd6a9ed086a0d5d5bb0566274e1e3c8dc8041e8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 18 Jul 2023 12:17:12 +0300
Subject: [PATCH 105/206] Add link to the HF api key page (#217)

* Add link to the HF api key page

* Grammar fixes

* Add links to the prompts from the hub
---
 tutorials/24_Building_Chat_App.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tutorials/24_Building_Chat_App.ipynb b/tutorials/24_Building_Chat_App.ipynb
index c33d15a5..33ab0b0e 100644
--- a/tutorials/24_Building_Chat_App.ipynb
+++ b/tutorials/24_Building_Chat_App.ipynb
@@ -117,7 +117,7 @@
       "source": [
         "### 1) Provide a Hugging Face API Key\n",
         "\n",
-        "Hugging Face offers [hosted Inference API](https://huggingface.co/docs/api-inference/index) which you can use to access machine learning models using simple HTTP requests. This way, you don't need to download models from the hub. To use service, you need to provide an API key from Hugging Face:"
+        "Hugging Face offers [a hosted Inference API](https://huggingface.co/docs/api-inference/index) which you can use to access machine learning models using simple HTTP requests. This way, you don't need to download models from the hub. To use the service, you need to provide an [API key](https://huggingface.co/settings/tokens) from Hugging Face:"
       ]
     },
     {
@@ -175,7 +175,7 @@
         "\n",
         "These memory nodes inject the conversation history into the prompt for the large language model with every run. Instead of using the full conversation history, you'll use ConversationSummaryMemory that sums up the conversation without losing important information, thus saving the token limit.\n",
         "\n",
-        "You can use the same PromptNode in ConversationSummaryMemory, so the same `OpenAssistant/oasst-sft-1-pythia-12b` model generates chat summaries. By default, ConversationSummaryMemory summarizes the chat with every `3` runs using the predefined [`conversational-summary`](https://github.com/deepset-ai/haystack/blob/b8911dfae01643dccf0803648ef3fb705d43dfbd/haystack/nodes/prompt/prompt_template.py#L441) PromptTemplate."
+        "You can use the same PromptNode in ConversationSummaryMemory, so the same `OpenAssistant/oasst-sft-1-pythia-12b` model generates chat summaries. By default, ConversationSummaryMemory summarizes the chat with every `3` runs using the predefined [`conversational-summary`](https://prompthub.deepset.ai/?prompt=deepset%2Fconversational-summary) PromptTemplate on PromptHub."
       ]
     },
     {
@@ -210,7 +210,7 @@
       "source": [
         "### 4) Create a ConversationalAgent\n",
         "\n",
-        "Now that you have all the necessary components, you can initialize the ConversationalAgent. If you don't provide any tools, the ConversationalAgent uses the `conversational-agent-without-tools` prompt by default."
+        "Now that you have all the necessary components, you can initialize the ConversationalAgent. If you don't provide any tools, the ConversationalAgent uses the [`conversational-agent-without-tools`](https://prompthub.deepset.ai/?prompt=deepset%2Fconversational-agent-without-tools) prompt by default."
       ]
     },
     {

From f67dcad05a677363cc68e477f949b7e722155665 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 18 Jul 2023 12:17:44 +0300
Subject: [PATCH 106/206] Remove tutorial 7 and 12 from the tests (#219)

---
 .github/workflows/nightly.yml       | 2 --
 .github/workflows/run_tutorials.yml | 2 ++
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index 0b4d5cfd..d48b3399 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -28,10 +28,8 @@ jobs:
           - 04_FAQ_style_QA
           - 05_Evaluation
           - 06_Better_Retrieval_via_Embedding_Retrieval
-          - 07_RAG_Generator
           - 08_Preprocessing
           - 11_Pipelines
-          - 12_LFQA
           - 14_Query_Classifier
           - 15_TableQA
           - 16_Document_Classifier_at_Index_Time
diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index f9e6336b..dc1f012f 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -56,8 +56,10 @@ jobs:
         run: |
           skiplist=(
               "tutorials/02_Finetune_a_model_on_your_data.ipynb"
+              "tutorials/07_RAG_Generator.ipynb"
               "tutorials/09_DPR_training.ipynb"
               "tutorials/10_Knowledge_Graph.ipynb"
+              "tutorials/12_LFQA.ipynb"
               "tutorials/13_Question_generation.ipynb"
               "tutorials/14_Query_Classifier.ipynb"
               "tutorials/18_GPL.ipynb"

From 4136efe514dd435e2284761b73a593098b37030f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 18 Jul 2023 14:13:38 +0300
Subject: [PATCH 107/206] Get env variable first for api keys (#222)

* Required for tests
---
 ...Answering_Multihop_Questions_with_Agents.ipynb |  6 +++---
 tutorials/24_Building_Chat_App.ipynb              | 15 ++++++++++++---
 2 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb b/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
index 41af9477..03de149d 100644
--- a/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
+++ b/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
@@ -402,10 +402,10 @@
    },
    "outputs": [],
    "source": [
+    "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "api_key_prompt = \"Enter OpenAI API key:\"\n",
-    "api_key = getpass(api_key_prompt)"
+    "api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")"
    ]
   },
   {
@@ -553,7 +553,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.12"
   },
   "vscode": {
    "interpreter": {
diff --git a/tutorials/24_Building_Chat_App.ipynb b/tutorials/24_Building_Chat_App.ipynb
index 33ab0b0e..5844ff67 100644
--- a/tutorials/24_Building_Chat_App.ipynb
+++ b/tutorials/24_Building_Chat_App.ipynb
@@ -122,15 +122,16 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "id": "qWuRxFWGcAL4"
       },
       "outputs": [],
       "source": [
+        "import os\n",
         "from getpass import getpass\n",
         "\n",
-        "model_api_key = getpass(\"Enter model provider API key:\")"
+        "model_api_key = os.getenv(\"HF_API_KEY\", None) or getpass(\"Enter HF API key:\")"
       ]
     },
     {
@@ -428,8 +429,16 @@
       "name": "python3"
     },
     "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
       "name": "python",
-      "version": "3.9.6"
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.12"
     },
     "vscode": {
       "interpreter": {

From 194d77a05d5461197eaf4c907a3d8184d6f564cf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 21 Jul 2023 14:20:08 +0100
Subject: [PATCH 108/206] Add tutorial 25 (#223)

* Add tutorial 25

* Fix language

* Add more explanation about prompt parameter resolver

* Skip the tutorial tests for now

* Improve language

* Improve resolver section

* Last improvements
---
 .github/workflows/run_tutorials.yml  |    1 +
 README.md                            |    1 +
 index.toml                           |   10 +
 tutorials/25_Customizing_Agent.ipynb | 4863 ++++++++++++++++++++++++++
 4 files changed, 4875 insertions(+)
 create mode 100644 tutorials/25_Customizing_Agent.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index dc1f012f..91b35048 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -65,6 +65,7 @@ jobs:
               "tutorials/18_GPL.ipynb"
               "tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb"
               "tutorials/24_Building_Chat_App.ipynb"
+              "tutorials/25_Customizing_Agent.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index ec9769d7..3859019b 100644
--- a/README.md
+++ b/README.md
@@ -42,3 +42,4 @@ To contribute to the tutorials please check out our [Contributing Guidelines](./
 | Creating a Generative QA Pipeline with PromptNode                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/22_Pipeline_with_PromptNode.ipynb) | [22_Pipeline_with_PromptNode.ipynb](./tutorials/22_Pipeline_with_PromptNode.ipynb) |
 | Answering Complex Questions with Agents                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) | [23_Answering_Multihop_Questions_with_Agents.ipynb](./tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) |
 | Building a Conversational Chat App                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/24_Building_Chat_App.ipynb) | [24_Building_Chat_App.ipynb](./tutorials/24_Building_Chat_App.ipynb) |
+| Customizing Agent to Chat with Your Documents                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/25_Customizing_Agent.ipynb) | [25_Customizing_Agent.ipynb](./tutorials/25_Customizing_Agent.ipynb) |
diff --git a/index.toml b/index.toml
index 6e59f815..18f910f2 100644
--- a/index.toml
+++ b/index.toml
@@ -230,4 +230,14 @@ notebook = "24_Building_Chat_App.ipynb"
 aliases = ["building-chat-app"]
 completion_time = "10 min"
 created_at = 2023-05-30
+
+[[tutorial]]
+title = "Customizing Agent to Chat with Your Documents"
+description = "Advanced Customizations for Agents with Memory"
+level = "advanced"
+weight = 117
+notebook = "25_Customizing_Agent.ipynb"
+aliases = ["customizing-agent"]
+completion_time = "15 min"
+created_at = 2023-07-19
 featured = true
\ No newline at end of file
diff --git a/tutorials/25_Customizing_Agent.ipynb b/tutorials/25_Customizing_Agent.ipynb
new file mode 100644
index 00000000..1d33dd88
--- /dev/null
+++ b/tutorials/25_Customizing_Agent.ipynb
@@ -0,0 +1,4863 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9jBXFRNCc39j"
+   },
+   "source": [
+    "# Tutorial: Customizing Agent to Chat with Your Documents\n",
+    "\n",
+    "- **Level**: Advanced\n",
+    "- **Time to complete**: 20 minutes\n",
+    "- **Nodes Used**: `BM25Retriever`, `PromptNode`, `Agent`, and `Memory`\n",
+    "- **Goal**: After completing this tutorial, you will have learned about how to customize an Agent to create a chat system for your documents.\n",
+    "- **Prerequisites**: An [OpenAI API Key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WkbVwuhwdXFf"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "The [Agent](https://docs.haystack.deepset.ai/docs/agent) class is designed to use a large language model (LLM) to make decisions and determine the best course of action to find the most suitable answers. It offers great flexibility with the ability to provide multiple [`Tools`](https://docs.haystack.deepset.ai/docs/agent#tools), [`Memory`](https://docs.haystack.deepset.ai/docs/agent#conversational-agent-memory), and a custom prompt with a parameter resolver. This means you have full control over the agent's prompt, allowing you to customize it according to your specific use case, whether it's a personal assistant, a chatbot, or a multihop search system.\n",
+    "\n",
+    "In this tutorial, you will learn how to set up the Agent with one tool and a summarized memory, as well as how to customize the prompt and resolve parameters within it. By the end of the tutorial, you will have a system ready to engage in conversations using your own documents."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VHQwS2I3dkR5"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NDpJAnZWdqM2"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k63wWsotCwJZ",
+    "outputId": "1b8e8a84-a397-4323-d1e7-31314c02747c"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]\n",
+    "pip install datasets>=2.6.1"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8E8ImsGtdwwv"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "d2QAuGrVdy7Z"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(25)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lxkzUgxnfiOt"
+   },
+   "source": [
+    "## Creating a Generative QA Pipeline Tool"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TilLQBskd3lQ"
+   },
+   "source": [
+    "### 1) Initialize the DocumentStore and Write Documents\n",
+    "\n",
+    "You'll start creating a generative pipeline by initializing a DocumentStore, which will store the Documents to be chatted with.\n",
+    "\n",
+    "As Documents, you will use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World). These pages are crawled, preprocessed and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders).\n",
+    "\n",
+    "> To learn about the preprocessing step, check out [Tutorial: Preprocessing Your Documents](https://haystack.deepset.ai/tutorials/08_preprocessing).\n",
+    "\n",
+    "Initialize `InMemoryDocumentStore` and write Documents to the DocumentStore:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 233,
+     "referenced_widgets": [
+      "c0961ac887464865badae59103904085",
+      "7512070b139f44368a8a77bd2a01b55b",
+      "a3fd711ad7684472a771fb89b774b87c",
+      "9a5896b46a5f4ca6833f06ae953cbd75",
+      "dd42ed8df3824fe6b68d86f6dec9c1fc",
+      "33f5f52e776f4a86b9f9796827e705a6",
+      "eb70271a8f764275b65f59f8ddc05ebf",
+      "d48b62f985fb4ae396115b1c77c7970b",
+      "c989e9832b704930858f6b04a6797a99",
+      "aa66d1f4f342431e89c6641db1450a4b",
+      "767acdcf885a4e3e84785144365ccc3d",
+      "eb10d391684f49aea6aadf6f471209fb",
+      "ba6ebe0b129a4bd0b31f2c77b8252a2e",
+      "84a657d6296f4660a516ad8381395909",
+      "a46510dbb1724bfdb09547697b7a12ee",
+      "54724d03f90248438e93aac56fc3d637",
+      "4ae025c03d91420a912e81957f953e37",
+      "0d44d164598a4575859544e62c0ddb0c",
+      "05c38e9d25aa415c9ab9541ddb30c21e",
+      "2dd08a52a9f3454da858b5983359da0b",
+      "0850b08e96c14ce68df21929f48c501e",
+      "bdfdc0a9c3a9478db95bd0d55f56488e",
+      "938c2457f3ea4d58838f730ff11cc232",
+      "778e979b0a0a481982d55b04079be354",
+      "b91f031b55594fbcb3a466331bd6c031",
+      "10778171b629495eb2e102a0f0cfa45d",
+      "64ccdaf5a91b412599ac0d3d7c0f1228",
+      "1ccb2708cfe24a63803c4f0c78e4850a",
+      "b8db7a03164e4d4ab62ebaf49a951888",
+      "b7bdf9677c654234958e24f85e5c7490",
+      "66d630578ecb474186af9720b4576a10",
+      "1f7a7a36dfa046f5af19588459682727",
+      "80e9fab28b9546be86048faa77227a78",
+      "fab111fd715c4ecbb63f1cb59ca4ed1b",
+      "b974c33cecc34215b76e2c5b0449e2f0",
+      "ba89eb2697df43b8a518c1c4a7464712",
+      "977fe5b785f74f0d956ec2e518dd02e0",
+      "651c708c548b495cb83d775f15813de5",
+      "a6ab693099164b5b9bf5a6727d3e5fe8",
+      "86ce390c988348b4ac4b962db8c5faa8",
+      "b00ba3a4135f4deeaa23d2060af47cbd",
+      "3d64673b5f5b45a0a18f0b53b143f64f",
+      "7880864fa3334f5e913a2ebf14e7d0fe",
+      "62255d8941b84339a85f31191bdbfd47",
+      "35e83557bd05439aaf99200dbfa921f1",
+      "93ea07119a8e4639b84d47d98aea919a",
+      "4295edb53b2d42778cf60ff955e623cb",
+      "d3799ec512b44ccd9ce7d1a61d35faf3",
+      "88785668c2a74119b614f35ee0ea24ee",
+      "eb39b602b82f4d0b90af6ce89e29c348",
+      "7d6a89785144422cb2da78e047a6007c",
+      "585c4dc5105143ac95c5ad4d0a1024d4",
+      "578d3839629945269c4e6dfe0620ca48",
+      "2a290efa524a443aa4fde0705244c9ea",
+      "c65dd8262de44126af80b363f45c878e",
+      "e798c577fafb4d589fbd84b56972de71",
+      "a594a5821c614ba39725afc195a79f7e",
+      "a0f7ed55418a4a22b21c77a580ea96d5",
+      "c8f7bb38e0e54a088cf68b307a292bf5",
+      "f81ed45a0c364ae1a980820430ab86db",
+      "31f224ee266743efb4eac9a02d569e43",
+      "1cabeb84c08c4f27bd3d4af2585af3d6",
+      "e40338eb81b9401289ceeb230509a11f",
+      "a021c0c6de88477593d4799495e61c2a",
+      "66b35a867dc84ef09b5fc3f3d96e0795",
+      "cc644bccd44a46f8ba0ba6615dad8e13"
+     ]
+    },
+    "id": "ZPOL5m8sTUqV",
+    "outputId": "60fa44af-b5a6-41d0-8180-f45404bde599"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from haystack.document_stores import InMemoryDocumentStore\n",
+    "\n",
+    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
+    "\n",
+    "document_store = InMemoryDocumentStore(use_bm25=True)\n",
+    "document_store.write_documents(dataset)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "w6Q-0zjoeyxw"
+   },
+   "source": [
+    "### 2) Provide an OpenAI API Key\n",
+    "\n",
+    "You need an the API key to use OpenAI models for generative pipeline and the agent.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rihKmFLEjbpq",
+    "outputId": "a9e9c161-a9af-49c7-eb1a-2c8227fe8327"
+   },
+   "outputs": [],
+   "source": [
+    "from getpass import getpass\n",
+    "\n",
+    "api_key_prompt = \"Enter OpenAI API key:\"\n",
+    "openai_api_key = getpass(api_key_prompt)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-BySV5jvfG_T"
+   },
+   "source": [
+    "### 3) Create a Generative QA Pipeline\n",
+    "\n",
+    "A generative QA pipeline consists of a PromptNode and a Retriever. In this pipeline, Retriever gets the related Documents, and PromptNode generates the answer using the retrieval augmented generation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach.\n",
+    "\n",
+    "> To learn about the details of a generative pipeline with RAG, check out [Tutorial: Creating a Generative QA Pipeline with PromptNode](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "L16CxpDB5Rix"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PromptNode, PromptTemplate, AnswerParser, BM25Retriever\n",
+    "from haystack.pipelines import Pipeline\n",
+    "\n",
+    "retriever = BM25Retriever(document_store=document_store, top_k=3)\n",
+    "\n",
+    "prompt_template = PromptTemplate(\n",
+    "    prompt=\"\"\"\n",
+    "    Answer the question truthfully based solely on the given documents. If the documents do not contain the answer to the question, say that answering is not possible given the available information. Your answer should be no longer than 50 words.\n",
+    "    Documents:{join(documents)}\n",
+    "    Question:{query}\n",
+    "    Answer:\n",
+    "    \"\"\",\n",
+    "    output_parser=AnswerParser(),\n",
+    ")\n",
+    "\n",
+    "prompt_node = PromptNode(\n",
+    "    model_name_or_path=\"text-davinci-003\", api_key=openai_api_key, default_prompt_template=prompt_template\n",
+    ")\n",
+    "\n",
+    "generative_pipeline = Pipeline()\n",
+    "generative_pipeline.add_node(component=retriever, name=\"retriever\", inputs=[\"Query\"])\n",
+    "generative_pipeline.add_node(component=prompt_node, name=\"prompt_node\", inputs=[\"retriever\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "16RQNPD4LhA5"
+   },
+   "source": [
+    "Test out your pipeline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tENNJoon6LT7",
+    "outputId": "5582adb1-0beb-42d5-9ddf-e2d53c5911e4"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.utils import print_answers\n",
+    "\n",
+    "response = generative_pipeline.run(\"How does Rhodes Statue look like?\")\n",
+    "print_answers(response, details=\"minimum\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Eq7YLVQjNoUY"
+   },
+   "source": [
+    "To make sure that the generative pipeline uses only the provided Documents, test it out with an out-of-context query:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UtaIYUKPtyxS",
+    "outputId": "2e5bc971-6595-4c51-f48b-27c2d3edf7db"
+   },
+   "outputs": [],
+   "source": [
+    "response = generative_pipeline.run(\"How does Taylor Swift look like?\")\n",
+    "print_answers(response, details=\"minimum\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "a5aqoO2wUFk-"
+   },
+   "source": [
+    "### 4) Define a Tool with the Generative QA Pipeline\n",
+    "\n",
+    "Later, you will provide this Tool to your Agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "e8Ncz-JKvvLZ"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.agents import Tool\n",
+    "\n",
+    "search_tool = Tool(\n",
+    "    name=\"seven_wonders_search\",\n",
+    "    pipeline_or_node=generative_pipeline,\n",
+    "    description=\"useful for when you need to answer questions about the seven wonders of the world\",\n",
+    "    output_variable=\"answers\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GBYQTrJNMECA"
+   },
+   "source": [
+    "Now that you have a working pipeline as a Tool, time to initialize an Agent!"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O2JD-ffHVT29"
+   },
+   "source": [
+    "## Creating an Agent\n",
+    "\n",
+    "> If you are not familiar with the Agent class, you can go through [Tutorial: Answering Multihop Questions with Agents](https://haystack.deepset.ai/tutorials/23_answering_multihop_questions_with_agents) before you continue."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BxJiF423VnZH"
+   },
+   "source": [
+    "### 1) Initialize PromptNode for the Agent\n",
+    "\n",
+    "For your Agent to be most efficient, you need an LLM that can perform dynamic reasoning with [ReAct](https://arxiv.org/pdf/2210.03629.pdf) prompts, which can be achieved by configuring the `stop_words` and `temperature`. Once set, the PromptNode will be ready to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "a6AdZUdHmZkT"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PromptNode\n",
+    "\n",
+    "agent_prompt_node = PromptNode(\n",
+    "    \"gpt-3.5-turbo\",\n",
+    "    api_key=openai_api_key,\n",
+    "    max_length=256,\n",
+    "    stop_words=[\"Observation:\"],\n",
+    "    model_kwargs={\"temperature\": 0.5},\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iHKX_ooAV1L2"
+   },
+   "source": [
+    "### 2) Initialize Memory\n",
+    "\n",
+    "Memory makes the interaction with the Agent more human-like. Haystack offers various memory options, one of which, `ConversationSummaryMemory`, will create the summary of every three exchanges. You will be using the [`philschmid/bart-large-cnn-samsum`](https://huggingface.co/philschmid/bart-large-cnn-samsum) model which is especially good at generating chat summaries.\n",
+    "\n",
+    "> To discover other memory options in Haystack, check out [Good Listener: How Memory Enables Conversational Agents](https://haystack.deepset.ai/blog/memory-conversational-agents#summarizing-memory) blog post."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 265,
+     "referenced_widgets": [
+      "80e83188b519406199db2ddecb45119c",
+      "b61a5ce1ae8a4f998b9682e32027b0be",
+      "05c1a4ac8ff94d07aafefea44b3b2f27",
+      "b9acb8f4637f4974b2c2571f4248d39c",
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+   "source": [
+    "from haystack.agents.memory import ConversationSummaryMemory\n",
+    "from haystack.nodes import PromptNode\n",
+    "\n",
+    "memory_prompt_node = PromptNode(\n",
+    "    \"philschmid/bart-large-cnn-samsum\", max_length=256, model_kwargs={\"task_name\": \"text2text-generation\"}\n",
+    ")\n",
+    "memory = ConversationSummaryMemory(memory_prompt_node, prompt_template=\"{chat_transcript}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1zaD0q3HeFpq"
+   },
+   "source": [
+    "### 3) Define the Prompt\n",
+    "\n",
+    "To have an Agent with chat ability, you need to use a prompt similar to [`conversational-agent`](https://prompthub.deepset.ai/?prompt=deepset%2Fconversational-agent). This prompt is useful especially when you want to inject the memory into the prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "STL7vTNFM9pH"
+   },
+   "outputs": [],
+   "source": [
+    "agent_prompt = \"\"\"\n",
+    "In the following conversation, a human user interacts with an AI Agent. The human user poses questions, and the AI Agent goes through several steps to provide well-informed answers.\n",
+    "The AI Agent must use the available tools to find the up-to-date information. The final answer to the question should be truthfully based solely on the output of the tools. The AI Agent should ignore its knowledge when answering the questions.\n",
+    "The AI Agent has access to these tools:\n",
+    "{tool_names_with_descriptions}\n",
+    "\n",
+    "The following is the previous conversation between a human and The AI Agent:\n",
+    "{memory}\n",
+    "\n",
+    "AI Agent responses must start with one of the following:\n",
+    "\n",
+    "Thought: [the AI Agent's reasoning process]\n",
+    "Tool: [tool names] (on a new line) Tool Input: [input as a question for the selected tool WITHOUT quotation marks and on a new line] (These must always be provided together and on separate lines.)\n",
+    "Observation: [tool's result]\n",
+    "Final Answer: [final answer to the human user's question]\n",
+    "When selecting a tool, the AI Agent must provide both the \"Tool:\" and \"Tool Input:\" pair in the same response, but on separate lines.\n",
+    "\n",
+    "The AI Agent should not ask the human user for additional information, clarification, or context.\n",
+    "If the AI Agent cannot find a specific answer after exhausting available tools and approaches, it answers with Final Answer: inconclusive\n",
+    "\n",
+    "Question: {query}\n",
+    "Thought:\n",
+    "{transcript}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YePIZcJThkB7"
+   },
+   "source": [
+    "### 4) Initialize Prompt Parameter Resolver\n",
+    "\n",
+    "> Keep in mind that for common use cases of Agent such as simple chatbots or ReAct-based Agents, there are pre-defined resolver functions in Haystack that you can import and start using without defining one from scratch. [Here](https://github.com/deepset-ai/haystack/blob/main/haystack/agents/utils.py) is the full list of all resolver functions. Below is an example of how you may customize it yourself.\n",
+    "\n",
+    "`prompt_parameter_resolver` is a callback function that returns a dictionary of parameters which will resolve the variables in the `prompt_template`. This function customizes Agent's behaviour by taking inputs from internal Agent classes and enabling preprocessing these before returning prompt parameters. \n",
+    "\n",
+    "Currently, resolver functions can access to the query from the user, `AgentStep`, and `Agent`. `Agent` class is required to get all memory related attributes or when you need some tool information in the prompt which is essential for the ReAct-based Agents to determine their next action. `AgentStep` class is useful when you need the transcript of an Agent's internal monologue for each iteration or the information of at which iteration the Agent is at that given time.\n",
+    "\n",
+    "These are the parameters you need to define in the resolver function for the prompt above:\n",
+    "\n",
+    "- `query`: User input\n",
+    "- `tool_names_with_descriptions`: Names of all tools and their descriptions\n",
+    "- `transcript`: Transcript of the ReAct-based Agent's iterative process that generates thought, action, and observation\n",
+    "- `memory`: History of the previous conversation\n",
+    "\n",
+    "Now, initialize the resolver function with these parameters using the `Agent`, `AgentStep` and query:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "sywghBytmrmQ"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.agents import AgentStep, Agent\n",
+    "\n",
+    "\n",
+    "def resolver_function(query, agent, agent_step):\n",
+    "    return {\n",
+    "        \"query\": query,\n",
+    "        \"tool_names_with_descriptions\": agent.tm.get_tool_names_with_descriptions(),\n",
+    "        \"transcript\": agent_step.transcript,\n",
+    "        \"memory\": agent.memory.load(),\n",
+    "    }"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "c9SsWSV17cdU"
+   },
+   "source": [
+    "### 5) Create the Agent\n",
+    "\n",
+    "Bring together all the components and create the Agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "cigud_--LA9F"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.agents.base import Agent, ToolsManager\n",
+    "\n",
+    "conversational_agent = Agent(\n",
+    "    agent_prompt_node,\n",
+    "    prompt_template=agent_prompt,\n",
+    "    prompt_parameters_resolver=resolver_function,\n",
+    "    memory=memory,\n",
+    "    tools_manager=ToolsManager([search_tool]),\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6) Start Chatting!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1RPZ-ccXXUSN",
+    "outputId": "dae063d4-1e04-4656-d384-cc34f3c93830"
+   },
+   "outputs": [],
+   "source": [
+    "conversational_agent.run(\"What did Rhodes Statue look like?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cl3cuQUFQWtN",
+    "outputId": "d2e03fee-9cee-4b68-9878-a21ea313d46a"
+   },
+   "outputs": [],
+   "source": [
+    "conversational_agent.run(\"When did it collapse?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vggG9Vaopowo",
+    "outputId": "2b8ed320-b5da-4b6d-cc06-de7857e8d1a1"
+   },
+   "outputs": [],
+   "source": [
+    "conversational_agent.run(\"How tall was it?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "D_g0Civ7TwlR",
+    "outputId": "fed16ade-858c-45ad-d342-abbbfa6dc453"
+   },
+   "outputs": [],
+   "source": [
+    "conversational_agent.run(\"How long did it stand?\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "05USwl0oHJbh"
+   },
+   "source": [
+    "Congratulations! 🎉 You’ve customized an Agent to chat with your documents!"
+   ]
+  }
+ ],
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+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
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+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "f81ed45a0c364ae1a980820430ab86db": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
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+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
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+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "fab111fd715c4ecbb63f1cb59ca4ed1b": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_b974c33cecc34215b76e2c5b0449e2f0",
+       "IPY_MODEL_ba89eb2697df43b8a518c1c4a7464712",
+       "IPY_MODEL_977fe5b785f74f0d956ec2e518dd02e0"
+      ],
+      "layout": "IPY_MODEL_651c708c548b495cb83d775f15813de5"
+     }
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From d4b59fdef8359742c545e40a9447ec9da0e83a18 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 24 Jul 2023 17:37:44 +0100
Subject: [PATCH 109/206] Run tutorials on latest Haystack version + on main
 (#224)

* Add script to generate matrix of testable notebooks

* Add generate_matrix.py to the nightly workflow

* add gh token

* Update API Key handling for tut 25

* Marks some tutorials as "needs gpu" not to run them on nightly tests

* Run tutorials on main branch in nightly tests

* Fix handle for tutorial that has no colab

---------

Co-authored-by: Silvano Cerza <silvanocerza@gmail.com>
---
 .github/workflows/nightly.yml        | 38 +++++++++++----------
 index.toml                           |  4 +++
 scripts/generate_matrix.py           | 50 ++++++++++++++++++++++++++++
 tutorials/25_Customizing_Agent.ipynb |  4 +--
 4 files changed, 76 insertions(+), 20 deletions(-)
 create mode 100644 scripts/generate_matrix.py

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index d48b3399..4f7e8f13 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -6,9 +6,27 @@ on:
     - cron: "0 0 * * *"
 
 jobs:
+  generate-matrix:
+    runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.generator.outputs.matrix }}
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: '3.11'
+      - id: generator
+        env:
+          GH_TOKEN: ${{ github.token }}  
+        run: |
+          VERSION=$(gh api /repos/deepset-ai/haystack/releases/latest | jq -r .tag_name)
+          NOTEBOOKS=$(python ./scripts/generate_matrix.py --haystack-version "$VERSION" --include-main)
+          echo "matrix={\"include\":$NOTEBOOKS}" >> "$GITHUB_OUTPUT"
+
   run-tutorials:
+    needs: generate-matrix
     runs-on: ubuntu-latest
-    container: deepset/haystack:base-cpu-main
+    container: deepset/haystack:base-cpu-${{ matrix.haystack_version }}
 
     services:
       elasticsearch:
@@ -20,23 +38,7 @@ jobs:
     strategy:
       max-parallel: 2
       fail-fast: false
-      matrix:
-        notebook:
-          # Note: use the name of the file without the extension
-          - 01_Basic_QA_Pipeline
-          - 03_Scalable_QA_System
-          - 04_FAQ_style_QA
-          - 05_Evaluation
-          - 06_Better_Retrieval_via_Embedding_Retrieval
-          - 08_Preprocessing
-          - 11_Pipelines
-          - 14_Query_Classifier
-          - 15_TableQA
-          - 16_Document_Classifier_at_Index_Time
-          - 17_Audio
-          - 19_Text_to_Image_search_pipeline_with_MultiModal_Retriever
-          - 21_Customizing_PromptNode
-          - 22_Pipeline_with_PromptNode
+      matrix: ${{ fromJSON(needs.generate-matrix.outputs.matrix) }}
 
     env:
       HAYSTACK_TELEMETRY_ENABLED: "False"
diff --git a/index.toml b/index.toml
index 18f910f2..fcbd4af0 100644
--- a/index.toml
+++ b/index.toml
@@ -21,6 +21,7 @@ weight = 50
 notebook = "02_Finetune_a_model_on_your_data.ipynb"
 aliases = ["fine-tuning-a-model"]
 created_at = 2021-08-12
+needs_gpu = true
 
 [[tutorial]]
 title = "Build a Scalable Question Answering System"
@@ -86,6 +87,7 @@ weight = 110
 notebook = "09_DPR_training.ipynb"
 aliases = ["train-dpr"]
 created_at = 2021-08-12
+needs_gpu = true
 
 [[tutorial]]
 title = "Question Answering on a Knowledge Graph"
@@ -124,6 +126,7 @@ weight = 75
 notebook = "13_Question_generation.ipynb"
 aliases = ["question-generation"]
 created_at = 2021-08-12
+needs_gpu = true
 
 [[tutorial]]
 title = "Query Classifier"
@@ -169,6 +172,7 @@ weight = 140
 notebook = "18_GPL.ipynb"
 aliases = ["gpl"]
 created_at = 2022-06-07
+needs_gpu = true
 
 [[tutorial]]
 title = "Text-To-Image Search Pipeline with Multimodal Retriever"
diff --git a/scripts/generate_matrix.py b/scripts/generate_matrix.py
new file mode 100644
index 00000000..fb8098f8
--- /dev/null
+++ b/scripts/generate_matrix.py
@@ -0,0 +1,50 @@
+import tomllib
+import argparse
+import json
+
+
+def read_index(path):
+    with open(path, "rb") as f:
+        return tomllib.load(f)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(usage="""python generate_matrix.py --haystack-version v1.18.1""")
+    parser.add_argument("--index", dest="index", default="index.toml")
+    parser.add_argument("--notebooks", dest="notebooks", nargs="+", default=[])
+    parser.add_argument("--haystack-version", dest="version", required=True)
+    parser.add_argument("--include-main", dest="main", action="store_true")
+
+    args = parser.parse_args()
+    index = read_index(args.index)
+
+    matrix = []
+    for tutorial in index["tutorial"]:
+        notebook = tutorial["notebook"]
+
+        if args.notebooks and notebook not in args.notebooks:
+            # If the user specified a list of notebooks to run, only run those
+            # otherwise run all of them
+            continue
+
+        if tutorial.get("needs_gpu", False):
+            # We're not running GPU tutorials on GitHub Actions
+            # since we don't have a GPUs there
+            continue
+
+        if not tutorial.get("colab", True):
+            # This tutorial doesn't have any runnable Python code
+            # so there's nothing to test
+            continue
+
+        version = tutorial.get("haystack_version", args.version)
+        if version[0] != "v":
+            version = f"v{version}"
+
+        matrix.append({"notebook": notebook[:-6], "haystack_version": version})
+
+        if args.main and "haystack_version" not in tutorial:
+            # If a tutorial doesn't specify a version, we also test it on main
+            matrix.append({"notebook": notebook[:-6], "haystack_version": "main"})
+
+    print(json.dumps(matrix))
diff --git a/tutorials/25_Customizing_Agent.ipynb b/tutorials/25_Customizing_Agent.ipynb
index 1d33dd88..d6261877 100644
--- a/tutorials/25_Customizing_Agent.ipynb
+++ b/tutorials/25_Customizing_Agent.ipynb
@@ -241,10 +241,10 @@
    },
    "outputs": [],
    "source": [
+    "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "api_key_prompt = \"Enter OpenAI API key:\"\n",
-    "openai_api_key = getpass(api_key_prompt)"
+    "api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")"
    ]
   },
   {

From a6d2e6e12cdabe34c35b1de9cc00c26f424593f3 Mon Sep 17 00:00:00 2001
From: Julian Risch <julian.risch@deepset.ai>
Date: Mon, 31 Jul 2023 16:15:36 +0200
Subject: [PATCH 110/206] fix: Remove whitespace from pip install extras in
 tutorial 2 (#225)

---
 tutorials/02_Finetune_a_model_on_your_data.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/02_Finetune_a_model_on_your_data.ipynb b/tutorials/02_Finetune_a_model_on_your_data.ipynb
index 0d4c6405..d281e171 100644
--- a/tutorials/02_Finetune_a_model_on_your_data.ipynb
+++ b/tutorials/02_Finetune_a_model_on_your_data.ipynb
@@ -47,7 +47,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab, inference]"
+    "pip install farm-haystack[colab,inference]"
    ]
   },
   {

From 271e551d1015d7d6db8a5ceffc3565edf9be8454 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 2 Aug 2023 19:23:51 +0300
Subject: [PATCH 111/206] Fix nightly failing tests (#227)

* Add api keys to env

* Fix tutorial 25

* Add extra dependency ipywidgets for tutorial 24
---
 .github/workflows/nightly.yml        | 6 ++++++
 tutorials/25_Customizing_Agent.ipynb | 2 +-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index 4f7e8f13..8cc0ee80 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -43,6 +43,8 @@ jobs:
     env:
       HAYSTACK_TELEMETRY_ENABLED: "False"
       ELASTICSEARCH_HOST: "elasticsearch"
+      HF_API_KEY: ${{ secrets.HF_API_KEY }}
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
 
     steps:
       - name: Checkout
@@ -64,6 +66,10 @@ jobs:
       - name: Install Hugging Face datasets
         run: |
           pip install datasets>=2.6.1
+      
+      - name: Install ipywidgets for tutorial 24
+        run: |
+          pip install ipywidgets 
 
       - name: Convert notebook to Python
         run: |
diff --git a/tutorials/25_Customizing_Agent.ipynb b/tutorials/25_Customizing_Agent.ipynb
index d6261877..58ef9c42 100644
--- a/tutorials/25_Customizing_Agent.ipynb
+++ b/tutorials/25_Customizing_Agent.ipynb
@@ -244,7 +244,7 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")"
+    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")"
    ]
   },
   {

From 6effe94938e96fc3d1dbcdd05fff8165ac8c46a1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 3 Aug 2023 13:26:12 +0300
Subject: [PATCH 112/206] Import 'display' that is missing for .py files (#230)

---
 tutorials/24_Building_Chat_App.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/24_Building_Chat_App.ipynb b/tutorials/24_Building_Chat_App.ipynb
index 5844ff67..ebac94cf 100644
--- a/tutorials/24_Building_Chat_App.ipynb
+++ b/tutorials/24_Building_Chat_App.ipynb
@@ -349,7 +349,7 @@
       "outputs": [],
       "source": [
         "import ipywidgets as widgets\n",
-        "from IPython.display import clear_output\n",
+        "from IPython.display import clear_output, display\n",
         "\n",
         "## Text Input\n",
         "user_input = widgets.Textarea(\n",

From 28544a3e020b0acc644ed933e55dcd509145d338 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 3 Aug 2023 16:45:38 +0300
Subject: [PATCH 113/206] Update tutorials and emphasize RAG in tutorial 22
 (#229)

---
 index.toml                                  | 12 ++++++------
 tutorials/07_RAG_Generator.ipynb            |  4 ++--
 tutorials/12_LFQA.ipynb                     |  4 ++--
 tutorials/22_Pipeline_with_PromptNode.ipynb | 17 +++++++++--------
 tutorials/25_Customizing_Agent.ipynb        |  6 +++---
 5 files changed, 22 insertions(+), 21 deletions(-)

diff --git a/index.toml b/index.toml
index fcbd4af0..6f6a420d 100644
--- a/index.toml
+++ b/index.toml
@@ -61,12 +61,12 @@ aliases = ["embedding-retrieval"]
 created_at = 2022-03-08
 
 [[tutorial]]
-title = "Generative QA with Retrieval-Augmented Generation"
+title = "Generative QA with RAGenerator"
 description = "Try out a generative model in place of the extractive Reader."
 level = "intermediate"
 weight = 60
 notebook = "07_RAG_Generator.ipynb"
-aliases = ["retrieval-augmented-generation"]
+aliases = []
 created_at = 2021-08-12
 haystack_version = "1.17.2"
 
@@ -109,7 +109,7 @@ aliases = ["pipelines"]
 created_at = 2021-08-12
 
 [[tutorial]]
-title = "Generative QA with LFQA"
+title = "Generative QA with Seq2SeqGenerator"
 description = "Try out a generative model in place of the extractive Reader."
 level = "intermediate"
 weight = 70
@@ -216,12 +216,12 @@ completion_time = "10 min"
 created_at = 2023-03-27
 
 [[tutorial]]
-title = "Creating a Generative QA Pipeline with PromptNode"
-description = "Use a large language model in your search system through PromptNode and Shaper."
+title = "Creating a Generative QA Pipeline with Retrieval-Augmentation"
+description = "Use a large language model in your search system through PromptNode"
 level = "intermediate"
 weight = 61
 notebook = "22_Pipeline_with_PromptNode.ipynb"
-aliases = ["pipeline-with-promptnode"]
+aliases = ["pipeline-with-promptnode", "retrieval-augmented-generation"]
 completion_time = "15 min"
 created_at = 2023-03-13
 
diff --git a/tutorials/07_RAG_Generator.ipynb b/tutorials/07_RAG_Generator.ipynb
index 3bc74f8d..2b239d31 100644
--- a/tutorials/07_RAG_Generator.ipynb
+++ b/tutorials/07_RAG_Generator.ipynb
@@ -7,7 +7,7 @@
     "collapsed": false
    },
    "source": [
-    "# Generative QA with \"Retrieval-Augmented Generation\""
+    "# Generative QA with RAGenerator"
    ]
   },
   {
@@ -15,7 +15,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> As of version 1.16, `RAGenerator` has been deprecated in Haystack and completely removed from Haystack as of v1.18. We recommend following the tutorial on [Creating a Generative QA Pipeline with PromptNode](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) instead. For more details about this deprecation, check out [our announcement](https://github.com/deepset-ai/haystack/discussions/4816) on Github."
+    "> As of version 1.16, `RAGenerator` has been deprecated in Haystack and completely removed from Haystack as of v1.18. We recommend following the tutorial on [Creating a Generative QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) instead. For more details about this deprecation, check out [our announcement](https://github.com/deepset-ai/haystack/discussions/4816) on Github."
    ]
   },
   {
diff --git a/tutorials/12_LFQA.ipynb b/tutorials/12_LFQA.ipynb
index fe93380e..c70f96c9 100644
--- a/tutorials/12_LFQA.ipynb
+++ b/tutorials/12_LFQA.ipynb
@@ -7,7 +7,7 @@
     "id": "bEH-CRbeA6NU"
    },
    "source": [
-    "# Long-Form Question Answering"
+    "# Generative QA with Seq2SeqGenerator"
    ]
   },
   {
@@ -15,7 +15,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> As of version 1.16, `Seq2SeqGenerator` has been deprecated in Haystack and completely removed from Haystack as of v1.18. We recommend following the tutorial on [Creating a Generative QA Pipeline with PromptNode](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) instead. For more details about this deprecation, check out [our announcement](https://github.com/deepset-ai/haystack/discussions/4816) on Github."
+    "> As of version 1.16, `Seq2SeqGenerator` has been deprecated in Haystack and completely removed from Haystack as of v1.18. We recommend following the tutorial on [Creating a Generative QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) instead. For more details about this deprecation, check out [our announcement](https://github.com/deepset-ai/haystack/discussions/4816) on Github."
    ]
   },
   {
diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index 54c6a76b..7326bcfa 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -7,12 +7,12 @@
     "id": "2OvkPji9O-qX"
    },
    "source": [
-    "# Tutorial: Creating a Generative QA Pipeline with PromptNode\n",
+    "# Tutorial: Creating a Generative QA Pipeline with Retrieval-Augmentation\n",
     "\n",
     "- **Level**: Intermediate\n",
     "- **Time to complete**: 15 minutes\n",
     "- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `PromptNode`, `PromptTemplate`\n",
-    "- **Goal**: After completing this tutorial, you'll have created a generative question answering search system that uses a large language model through PromptNode with the new PromptTemplate structure."
+    "- **Goal**: After completing this tutorial, you'll have created a generative question answering search system that uses a large language model through PromptNode with PromptTemplate."
    ]
   },
   {
@@ -24,9 +24,9 @@
    "source": [
     "## Overview\n",
     "\n",
-    "Learn how to build a generative question answering pipeline using the power of LLMs with PromptNode. In this tutorial, we'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n",
+    "Learn how to build a generative question answering pipeline using the power of LLMs with PromptNode. In this generative pipeline, BM25Retriever gets the related Documents, and PromptNode generates the answer using the retrieval augmented generation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach. In this tutorial, we'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want. \n",
     "\n",
-    "This tutorial introduces you to the new PrompTemplate structure and explains how to use the new PrompTemplate to integrate PromptNode into a pipeline."
+    "This tutorial introduces you to the PrompTemplate structure and explains how to use the new PrompTemplate to integrate PromptNode into a pipeline."
    ]
   },
   {
@@ -218,6 +218,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "6CEuQpB7O-qb"
@@ -240,7 +241,7 @@
    "source": [
     "from haystack.nodes import PromptNode, PromptTemplate, AnswerParser\n",
     "\n",
-    "lfqa_prompt = PromptTemplate(\n",
+    "rag_prompt = PromptTemplate(\n",
     "    prompt=\"\"\"Synthesize a comprehensive answer from the following text for the given question.\n",
     "                             Provide a clear and concise response that summarizes the key points and information presented in the text.\n",
     "                             Your answer should be in your own words and be no longer than 50 words.\n",
@@ -248,7 +249,7 @@
     "    output_parser=AnswerParser(),\n",
     ")\n",
     "\n",
-    "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\", default_prompt_template=lfqa_prompt)"
+    "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\", default_prompt_template=rag_prompt)"
    ]
   },
   {
@@ -322,7 +323,7 @@
    },
    "outputs": [],
    "source": [
-    "output = pipe.run(query=\"How does Rhodes Statue look like?\")\n",
+    "output = pipe.run(query=\"What does Rhodes Statue look like?\")\n",
     "\n",
     "print(output[\"answers\"][0].answer)"
    ]
@@ -348,7 +349,7 @@
     "examples = [\n",
     "    \"Where is Gardens of Babylon?\",\n",
     "    \"Why did people build Great Pyramid of Giza?\",\n",
-    "    \"How does Rhodes Statue look like?\",\n",
+    "    \"What does Rhodes Statue look like?\",\n",
     "    \"Why did people visit the Temple of Artemis?\",\n",
     "    \"What is the importance of Colossus of Rhodes?\",\n",
     "    \"What happened to the Tomb of Mausolus?\",\n",
diff --git a/tutorials/25_Customizing_Agent.ipynb b/tutorials/25_Customizing_Agent.ipynb
index 58ef9c42..e9e058f8 100644
--- a/tutorials/25_Customizing_Agent.ipynb
+++ b/tutorials/25_Customizing_Agent.ipynb
@@ -258,7 +258,7 @@
     "\n",
     "A generative QA pipeline consists of a PromptNode and a Retriever. In this pipeline, Retriever gets the related Documents, and PromptNode generates the answer using the retrieval augmented generation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach.\n",
     "\n",
-    "> To learn about the details of a generative pipeline with RAG, check out [Tutorial: Creating a Generative QA Pipeline with PromptNode](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode)."
+    "> To learn about the details of a generative pipeline with RAG, check out [Tutorial: Creating a Generative QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode)."
    ]
   },
   {
@@ -317,7 +317,7 @@
    "source": [
     "from haystack.utils import print_answers\n",
     "\n",
-    "response = generative_pipeline.run(\"How does Rhodes Statue look like?\")\n",
+    "response = generative_pipeline.run(\"What does Rhodes Statue look like?\")\n",
     "print_answers(response, details=\"minimum\")"
    ]
   },
@@ -343,7 +343,7 @@
    },
    "outputs": [],
    "source": [
-    "response = generative_pipeline.run(\"How does Taylor Swift look like?\")\n",
+    "response = generative_pipeline.run(\"What does Taylor Swift look like?\")\n",
     "print_answers(response, details=\"minimum\")"
    ]
   },

From 16eeea465ea4118c6b619373e98133b924a8cef4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 4 Aug 2023 13:31:12 +0300
Subject: [PATCH 114/206] Update Tutorial 14 (#231)

* Update Tutorial 14

* Remove parts about SklearnQueryClassifier
* split into sections
* simplify the content

* Update the text
---
 tutorials/14_Query_Classifier.ipynb | 13886 +++++++++++++++++++++++++-
 1 file changed, 13629 insertions(+), 257 deletions(-)

diff --git a/tutorials/14_Query_Classifier.ipynb b/tutorials/14_Query_Classifier.ipynb
index f222f8d7..a2a4da9d 100644
--- a/tutorials/14_Query_Classifier.ipynb
+++ b/tutorials/14_Query_Classifier.ipynb
@@ -7,27 +7,26 @@
     "id": "O-W2ZQ6CN-gZ"
    },
    "source": [
-    "# Query Classifier Tutorial\n",
+    "# Tutorial: Query Classifier\n",
     "\n",
-    "One of the great benefits of using state-of-the-art NLP models like those available in Haystack is that it allows users to state their queries as *plain natural language questions*: rather than trying to come up with just the right set of keywords to find the answer to their question, users can simply ask their question in much the same way that they would ask it of a (very knowledgeable!) person.\n",
-    "\n",
-    "But just because users *can* ask their questions in \"plain English\" (or \"plain German\", etc.), that doesn't mean they always *will*. For instance, a user might input a few keywords rather than a complete question because they don't understand the pipeline's full capabilities, or because they are so accustomed to keyword search. While a standard Haystack pipeline might handle such queries with reasonable accuracy, for a variety of reasons we still might prefer that our pipeline be sensitive to the type of query it is receiving, so that it behaves differently when a user inputs, say, a collection of keywords instead of a question.\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Nodes Used**: `TransformersQueryClassifier`, `InMemoryDocumentStore`, `BM25Retriever`, `EmbeddingRetriever`, `FARMReader`\n",
+    "- **Goal**: After completing this tutorial, you will have learned about TransformersQueryClassifier and how to use it in a pipeline. \n",
     "\n",
-    "For this reason, Haystack comes with built-in capabilities to distinguish between three types of queries: **keyword queries**, **interrogative queries**, and **statement queries**, described below.\n",
+    "## Overview\n",
     "\n",
-    "1. **Keyword queries** can be thought of more or less as lists of words, such as \"Alaska cruises summer\". While the meanings of individual words may matter in a keyword query, the linguistic connections *between* words do not. Hence, in a keyword query the order of words is largely irrelevant: \"Alaska cruises summer\", \"summer Alaska cruises\", and \"summer cruises Alaska\" are functionally the same.\n",
-    "\n",
-    "2. **Interrogative queries** (or **question queries**) are queries phrased as natural language questions, such as \"Who was the father of Eddard Stark?\". Unlike with keyword queries, word order very much matters here: \"Who was the father of Eddard Stark?\" and \"Who was Eddard Stark the father of?\" are very different questions, despite having exactly the same words. (Note that while we often write questions with question marks, Haystack can find interrogative queries without such a dead giveaway!)\n",
+    "One of the great benefits of using state-of-the-art NLP models like those available in Haystack is that it allows users to state their queries as *plain natural language questions*: rather than trying to come up with just the right set of keywords to find the answer to their question, users can simply ask their question in much the same way that they would ask it of a (very knowledgeable!) person.\n",
     "\n",
-    "3. **Statement queries** are just declarative sentences, such as \"Daenerys loved Jon\". These are like interrogative queries in that word order matters&mdash;again, \"Daenerys loved Jon\" and \"Jon loved Daenerys\" mean very different things&mdash;but they are statements instead of questions.\n",
+    "But just because users *can* ask their questions in \"plain English\" (or \"plain German\", etc.), that doesn't mean they always *will*. For instance, users might input a few keywords rather than a complete question because they don't understand the pipeline's full capabilities or are so accustomed to keyword search. While a standard Haystack pipeline might handle such queries with reasonable accuracy, for a variety of reasons we still might prefer that our pipeline is sensitive to the type of query it is receiving, so that it behaves differently when a user inputs, say, a collection of keywords instead of a question. For this reason, Haystack comes with built-in capabilities to distinguish between three types of queries: **keyword queries**, **interrogative queries(questions)**, and **statement queries**.\n",
     "\n",
-    "In this tutorial you will learn how to use **query classifiers** to branch your Haystack pipeline based on the type of query it receives. Haystack comes with two out-of-the-box query classification schemas, each of which routes a given query into one of two branches:\n",
+    "In this tutorial you will learn how to use TransformersQueryClassifier to branch your Haystack pipeline based on the type of query it receives. Haystack comes with two out-of-the-box query classification schemas:\n",
     "\n",
     "1. **Keyword vs. Question/Statement** &mdash; routes a query into one of two branches depending on whether it is a full question/statement or a collection of keywords.\n",
     "\n",
     "2. **Question vs. Statement** &mdash; routes a natural language query into one of two branches depending on whether it is a question or a statement.\n",
     "\n",
-    "Furthermore, for each classification schema there are two types of nodes capable of performing this classification: a **`TransformersQueryClassifier`** that uses a transformer model, and an **`SklearnQueryClassifier`** that uses a more lightweight model built in `sklearn`.\n",
+    "With TransformersQueryClassifier, it's also possible to route query based on custom cases with custom classification models or zero-shot classification.\n",
     "\n",
     "With all of that explanation out of the way, let's dive in!"
    ]
@@ -54,15 +53,19 @@
    "source": [
     "## Installing Haystack\n",
     "\n",
-    "To start, let's install the latest release of Haystack with `pip`:"
+    "To start, install the latest release of Haystack with `pip`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
     "collapsed": true,
-    "id": "CjA5n5lMN-gd"
+    "id": "CjA5n5lMN-gd",
+    "outputId": "c64a9125-c801-4d68-ae05-154275f3d4c9"
    },
    "outputs": [],
    "source": [
@@ -72,23 +75,27 @@
     "pip install farm-haystack[colab,elasticsearch,inference]\n",
     "\n",
     "# Install these to allow pipeline visualization\n",
-    "apt install libgraphviz-dev\n",
-    "pip install pygraphviz"
+    "# apt install libgraphviz-dev\n",
+    "# pip install pygraphviz"
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "2NaWOkfTvLgn"
+   },
    "source": [
-    "### Enabling Telemetry \n",
+    "### Enabling Telemetry\n",
     "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "szGOCDF7vLgn"
+   },
    "outputs": [],
    "source": [
     "from haystack.telemetry import tutorial_running\n",
@@ -100,7 +107,8 @@
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "id": "gEEHCPauvLgn"
    },
    "source": [
     "### Logging\n",
@@ -115,7 +123,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "id": "J8N1zZKavLgn"
    },
    "outputs": [],
    "source": [
@@ -132,24 +140,85 @@
     "id": "sJcWRK4Hwyx2"
    },
    "source": [
-    "### Trying Some Query Classifiers on their Own\n",
+    "## Trying out TransformersQueryClassifier\n",
     "\n",
-    "Before integrating query classifiers into our pipelines, let's test them out on their own and see what they actually do. First we initiate a simple, out-of-the-box **keyword vs. question/statement** `SklearnQueryClassifier`:"
+    "Before integrating query classifier into the pipeline, test it out on its own and see what it actually does. First, initiate a simple, out-of-the-box **keyword vs. question/statement** TransformersQueryClassifier. By default, it uses [shahrukhx01/bert-mini-finetune-question-detection](https://huggingface.co/shahrukhx01/bert-mini-finetune-question-detection) model."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 233,
+     "referenced_widgets": [
+      "1082b0eb77224487975ef0bac70cab1b",
+      "cf74aacfe8154a10bf7d80314adad4b1",
+      "4f53beab2531426192050c00aeac64d4",
+      "c4d25cc02eab42769ff32a7a4b93a768",
+      "ab51741d88dc4be0886fb253901ebf72",
+      "bc4ff3b4eaa44edeaa0e94a7d255ce92",
+      "ff630124bf0d4927abec886186305922",
+      "6744ce652df44049b5f27631981d41ee",
+      "7384c8b306f74d91af6efd0ce7b56e22",
+      "e980882d4e0046daada8c9c2d479253d",
+      "c50a838bea2d4477a83b2f39ee96b109",
+      "592b40abdab34aed85e58123ca75aba6",
+      "a71826b3e89644c68f1cb11b62fce362",
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+      "268625c034c84a9a827b63ee7a14f257",
+      "4d0faf99b9e64545986a4d6545684e19",
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+      "61cee21b2cb04a6ba3e6c450b5dc9d3a",
+      "f3624e1205534d969670d41508a13453",
+      "175750723640441c9369bc9642267906",
+      "9fc1006b1d7849e3b619e632a3aa63f8",
+      "9a7c1eebfad649708dfed88f75911d69",
+      "9b5c1880cc3e47268749c5ab2e4e6ff2",
+      "f4751710a8454b308ec231ea85e61909",
+      "9275e7b1dd5346f696a406bfe117cbe9",
+      "9251f11b82584d9d9922b418ee0d7ceb"
+     ]
+    },
     "collapsed": true,
-    "id": "XhPMEqBzxA8V"
+    "id": "XhPMEqBzxA8V",
+    "outputId": "52c61dcd-cedb-4850-967c-e374f094369f"
    },
    "outputs": [],
    "source": [
-    "# Here we create the keyword vs question/statement query classifier\n",
-    "from haystack.nodes import SklearnQueryClassifier\n",
+    "from haystack.nodes import TransformersQueryClassifier\n",
     "\n",
-    "keyword_classifier = SklearnQueryClassifier()"
+    "keyword_classifier = TransformersQueryClassifier()"
    ]
   },
   {
@@ -159,7 +228,7 @@
     "id": "1NHjy9aa9FKx"
    },
    "source": [
-    "Now let's feed some queries into this query classifier. We'll test with one keyword query, one interrogative query, and one statement query. Note that we don't need to use any punctuation, such as question marks, for the query classifier to make the right decision."
+    "Now feed some queries into this query classifier. Test with one keyword query, one interrogative query, and one statement query. Note that you don't need to use any punctuation, such as question marks, for the query classifier to make the right decision."
    ]
   },
   {
@@ -191,7 +260,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "NYROmSHnE4zp"
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 161
+    },
+    "id": "NYROmSHnE4zp",
+    "outputId": "f7d64de8-7329-4613-ac6b-c1ecb24be48e"
    },
    "outputs": [],
    "source": [
@@ -215,47 +289,81 @@
     "id": "VyMZzRVHlG5O"
    },
    "source": [
-    "Next, we will illustrate a **question vs. statement** `SklearnQueryClassifier`. We define our classifier below. Note that this time we have to explicitly specify the model and vectorizer since the default for a `SklearnQueryClassifier` (and a `TransformersQueryClassifier`) is keyword vs. question/statement classification."
+    "Next, you will illustrate a **question vs. statement** TransformersQueryClassifier. For this task, you need to define a new query classifier. Note that this time you have to explicitly specify the model as the default model of TransformersQueryClassifier is for  keyword vs. question/statement classification."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true,
-    "id": "l4eH3SSaxZ0O"
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 290,
+     "referenced_widgets": [
+      "6e4627d383d2481aafeb89af5d97fc4c",
+      "77172ba953aa44a69afcd28484285476",
+      "765a706d1a894b46938a6723709348c4",
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+      "f648b454aa544432949021f8dd0525e7",
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+      "123530e4906f4f049671b750c587a5c7",
+      "139db627a2e64aa9ba9e31a5d8d451f7",
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+      "fcd0f2a8a0e14a4193aa5032721a9214",
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+      "cf27edc4570c4e8bbc590b328351b12c",
+      "81ef1d2ca017435baf209491b93c63c8"
+     ]
+    },
+    "id": "1ZULHEBVmqq2",
+    "outputId": "57bedece-644c-431e-8477-fdf0a2f1fe13"
    },
    "outputs": [],
    "source": [
-    "# Here we create the question vs statement query classifier\n",
-    "model_url = (\n",
-    "    \"https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/model.pickle\"\n",
-    ")\n",
-    "vectorizer_url = (\n",
-    "    \"https://ext-models-haystack.s3.eu-central-1.amazonaws.com/gradboost_query_classifier_statements/vectorizer.pickle\"\n",
-    ")\n",
+    "question_classifier = TransformersQueryClassifier(model_name_or_path=\"shahrukhx01/question-vs-statement-classifier\")\n",
     "\n",
-    "question_classifier = SklearnQueryClassifier(model_name_or_path=model_url, vectorizer_name_or_path=vectorizer_url)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zdAY1CUYnTFa"
-   },
-   "source": [
-    "We will test this classifier on the two question/statement queries from the last go-round:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "1ZULHEBVmqq2"
-   },
-   "outputs": [],
-   "source": [
     "queries = [\n",
     "    \"Who was the father of Arya Stark\",  # Interrogative Query\n",
     "    \"Lord Eddard was the father of Arya Stark\",  # Statement Query\n",
@@ -279,7 +387,7 @@
     "id": "Fk2kpvQR6Fa0"
    },
    "source": [
-    "And as we see, the question \"Who was the father of Arya Stark\" is sent to branch 1, while the statement \"Lord Eddard was the father of Arya Stark\" is sent to branch 2. This means we can have our pipeline treat statements and questions differently."
+    "And as you see, the question \"Who was the father of Arya Stark\" is sent to branch 1, while the statement \"Lord Eddard was the father of Arya Stark\" is sent to branch 2. This means you can have your pipeline treat statements and questions differently."
    ]
   },
   {
@@ -289,125 +397,47 @@
     "id": "eEwDIq9KXXke"
    },
    "source": [
-    "### Using Query Classifiers in a Pipeline\n",
+    "## Pipeline with Keyword vs. Question/Statement Query Classifiers\n",
     "\n",
-    "Now let's see how we can use query classifiers in a question-answering (QA) pipeline. We start by initiating Elasticsearch:"
+    "Now you will create question-answering (QA) pipeline with keyword vs. question/statement query classifier."
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Start an Elasticsearch server\n",
-    "You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Recommended: Start Elasticsearch using Docker via the Haystack utility function\n",
-    "from haystack.utils import launch_es\n",
-    "\n",
-    "launch_es()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Start an Elasticsearch server in Colab\n",
-    "\n",
-    "If Docker is not readily available in your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
-    "tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
-    "chown -R daemon:daemon elasticsearch-7.9.2\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
+    "id": "H2VqDAVJvLgo"
    },
-   "outputs": [],
    "source": [
-    "%%bash --bg\n",
+    "### 1) Initialize the DocumentStore and Write Documents\n",
+    "You'll start creating a pipeline by initializing a DocumentStore, which will store the Documents. As Documents, you'll use pages from the Game of Thrones wiki.\n",
     "\n",
-    "sudo -u daemon -- elasticsearch-7.9.2/bin/elasticsearch"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Vm9gqTioN-gf"
-   },
-   "source": [
-    "Next we fetch some data&mdash;for our example we'll use pages from the Game of Thrones wiki&mdash;and index it in our `DocumentStore`:"
+    "Initialize InMemoryDocumentStore, fetch Documents and index them to the DocumentStore:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
     "collapsed": true,
-    "id": "Ig7dgfdHN-gg"
+    "id": "Ig7dgfdHN-gg",
+    "outputId": "fe048f8e-403a-4e72-9b1e-1b2bcb550d89"
    },
    "outputs": [],
    "source": [
+    "from haystack.document_stores import InMemoryDocumentStore\n",
     "from haystack.utils import fetch_archive_from_http, convert_files_to_docs, clean_wiki_text\n",
     "\n",
-    "# Download and prepare data - 517 Wikipedia articles for Game of Thrones\n",
+    "document_store = InMemoryDocumentStore(use_bm25=True)\n",
+    "\n",
     "doc_dir = \"data/tutorial14\"\n",
     "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt14.zip\"\n",
     "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
     "\n",
-    "# convert files to dicts containing documents that can be indexed to our datastore\n",
-    "got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import time\n",
-    "\n",
-    "from haystack.document_stores import ElasticsearchDocumentStore\n",
-    "\n",
-    "\n",
-    "# Wait 30 seconds only to be sure Elasticsearch is ready before continuing\n",
-    "time.sleep(30)\n",
-    "\n",
-    "# Get the host where Elasticsearch is running, default to localhost\n",
-    "host = os.environ.get(\"ELASTICSEARCH_HOST\", \"localhost\")\n",
-    "\n",
-    "\n",
-    "document_store = ElasticsearchDocumentStore(host=host)\n",
-    "document_store.delete_documents()\n",
+    "got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n",
     "document_store.write_documents(got_docs)"
    ]
   },
@@ -418,35 +448,282 @@
     "id": "CbAgZ2MZn2qm"
    },
    "source": [
-    "#### Pipelines with Keyword vs. Question/Statement Classification\n",
+    "### 2) Initialize Retrievers, Reader and QueryClassifier\n",
     "\n",
-    "Our first illustration will be a simple retriever-reader QA pipeline, but the choice of which retriever we use will depend on the type of query received: **keyword** queries will use a sparse **`BM25Retriever`**, while **question/statement** queries will use the more accurate but also more computationally expensive **`EmbeddingRetriever`**.\n",
+    "Your pipeline will be a simple Retriever-Reader QA pipeline, but the Retriever choice will depend on the type of query received: **keyword** queries will use a sparse BM25Retriever, while **question/statement** queries will use the more accurate but also more computationally expensive EmbeddingRetriever.\n",
     "\n",
-    "We start by initializing our retrievers and reader:"
+    "Now, initialize both Retrievers, Reader and QueryClassifier:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
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     "collapsed": true,
-    "id": "m7zOPYQ-Ylep"
+    "id": "m7zOPYQ-Ylep",
+    "outputId": "d5489c01-d0a0-4cf5-f9ff-675f33989728"
    },
    "outputs": [],
    "source": [
-    "from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader\n",
-    "\n",
+    "from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader, TransformersQueryClassifier\n",
     "\n",
-    "# Initialize sparse retriever for keyword queries\n",
     "bm25_retriever = BM25Retriever(document_store=document_store)\n",
     "\n",
-    "# Initialize dense retriever for question/statement queries\n",
     "embedding_retriever = EmbeddingRetriever(\n",
     "    document_store=document_store, embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
     ")\n",
     "document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False)\n",
     "\n",
-    "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\")"
+    "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\")\n",
+    "keyword_classifier = TransformersQueryClassifier()"
    ]
   },
   {
@@ -456,29 +733,32 @@
     "id": "K4wZ3xkQCHjY"
    },
    "source": [
-    "Now we define our pipeline. As promised, the question/statement branch `output_1` from the query classifier is fed into an `EmbeddingRetriever`, while the keyword branch `output_2` from the same classifier is fed into a `BM25Retriever`. Both of these retrievers are then fed into our reader. Our pipeline can thus be thought of as having something of a diamond shape: all queries are sent into the classifier, which splits those queries into two different retrievers, and those retrievers feed their outputs to the same reader."
+    "### 3) Define the Pipeline\n",
+    "\n",
+    "As promised, the question/statement branch `output_1` from the query classifier is fed into the EmbeddingRetriever, while the keyword branch `output_2` from the same classifier is fed into the BM25Retriever. Both of these retrievers are then fed into our reader. Our pipeline can thus be thought of as having something of a diamond shape: all queries are sent into the classifier, which splits those queries into two different retrievers, and those retrievers feed their outputs to the same reader."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true,
-    "id": "Sz-oZ5eJN-gl"
+    "id": "D0nzuQQGna2n"
    },
    "outputs": [],
    "source": [
     "from haystack.pipelines import Pipeline\n",
     "\n",
-    "\n",
-    "# Here we build the pipeline\n",
-    "sklearn_keyword_classifier = Pipeline()\n",
-    "sklearn_keyword_classifier.add_node(component=SklearnQueryClassifier(), name=\"QueryClassifier\", inputs=[\"Query\"])\n",
-    "sklearn_keyword_classifier.add_node(\n",
+    "transformer_keyword_classifier = Pipeline()\n",
+    "transformer_keyword_classifier.add_node(component=keyword_classifier, name=\"QueryClassifier\", inputs=[\"Query\"])\n",
+    "transformer_keyword_classifier.add_node(\n",
     "    component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"QueryClassifier.output_1\"]\n",
     ")\n",
-    "sklearn_keyword_classifier.add_node(component=bm25_retriever, name=\"BM25Retriever\", inputs=[\"QueryClassifier.output_2\"])\n",
-    "sklearn_keyword_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"BM25Retriever\", \"EmbeddingRetriever\"])\n",
+    "transformer_keyword_classifier.add_node(\n",
+    "    component=bm25_retriever, name=\"BM25Retriever\", inputs=[\"QueryClassifier.output_2\"]\n",
+    ")\n",
+    "transformer_keyword_classifier.add_node(\n",
+    "    component=reader, name=\"QAReader\", inputs=[\"BM25Retriever\", \"EmbeddingRetriever\"]\n",
+    ")\n",
     "\n",
     "# To generate a visualization of the pipeline, uncomment the following:\n",
     "# sklearn_keyword_classifier.draw(\"sklearn_keyword_classifier.png\")"
@@ -491,32 +771,51 @@
     "id": "imqRRCGTwQav"
    },
    "source": [
-    "Below, we can see how this choice affects the branching structure: the keyword query \"arya stark father\" and the question query \"Who is the father of Arya Stark?\" generate noticeably different results, a distinction that is likely due to the use of different retrievers for keyword vs. question/statement queries."
+    "### 4) Run the Pipeline\n",
+    "\n",
+    "Below, you can see how this choice affects the branching structure: the keyword query \"arya stark father\" and the question query \"Who is the father of Arya Stark?\" generate noticeably different results, a distinction that is likely due to the use of different retrievers for keyword vs. question/statement queries."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "a7ccc66a6f154ae38d1c7a349b6a8858",
+      "2b49ba55d3214f03aca37001f95567b6",
+      "9265a41106ff4c2892ded08bbdcf564d",
+      "5ac29b9749e34cf582fbb3994f5ec73b",
+      "f8c164be79094ed4a6f06913a1006a81",
+      "74fe70592ced4ac7bbab04634d61c31e",
+      "7b48b9c4246a437793720d5cd629468c",
+      "8eb61361eacb4a73bc2fbe8ba5da7d92",
+      "3424ba5d300f4e2a9ede53eee8c79e37",
+      "431ecc3860d54bb4b255c066f47c7096",
+      "03335f3d388c404e83203156bc8d3ae3"
+     ]
+    },
     "collapsed": true,
-    "id": "fP6Cpcb-o0HK"
+    "id": "fP6Cpcb-o0HK",
+    "outputId": "c948cc35-0803-4440-8c27-f8a1c8550d4c"
    },
    "outputs": [],
    "source": [
     "from haystack.utils import print_answers\n",
     "\n",
-    "\n",
     "# Useful for framing headers\n",
     "equal_line = \"=\" * 30\n",
     "\n",
     "# Run only the dense retriever on the full sentence query\n",
-    "res_1 = sklearn_keyword_classifier.run(query=\"Who is the father of Arya Stark?\")\n",
+    "res_1 = transformer_keyword_classifier.run(query=\"Who is the father of Arya Stark?\")\n",
     "print(f\"\\n\\n{equal_line}\\nQUESTION QUERY RESULTS\\n{equal_line}\")\n",
     "print_answers(res_1, details=\"minimum\")\n",
     "print(\"\\n\\n\")\n",
     "\n",
     "# Run only the sparse retriever on a keyword based query\n",
-    "res_2 = sklearn_keyword_classifier.run(query=\"arya stark father\")\n",
+    "res_2 = transformer_keyword_classifier.run(query=\"arya stark father\")\n",
     "print(f\"\\n\\n{equal_line}\\nKEYWORD QUERY RESULTS\\n{equal_line}\")\n",
     "print_answers(res_2, details=\"minimum\")"
    ]
@@ -525,72 +824,62 @@
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {
-    "id": "dQ5YMyd4CQPC"
+    "id": "JPq9mWlI-bsK"
    },
    "source": [
-    "The above example uses an `SklearnQueryClassifier`, but of course we can do precisely the same thing with a `TransformersQueryClassifier`. This is illustrated below, where we have constructed the same diamond-shaped pipeline."
+    "Above you saw a potential use for keyword vs. question/statement classification: you might choose to use a less resource-intensive retriever for keyword queries than for question/statement queries. But what about question vs. statement classification?"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "attachments": {},
+   "cell_type": "markdown",
    "metadata": {
-    "id": "yuddZL3FCPeq"
+    "id": "zLwdVwMXDcoS"
    },
-   "outputs": [],
    "source": [
-    "from haystack.nodes import TransformersQueryClassifier\n",
+    "## Pipeline with Question vs. Statement Query Classifier\n",
     "\n",
-    "# Here we build the pipeline\n",
-    "transformer_keyword_classifier = Pipeline()\n",
-    "transformer_keyword_classifier.add_node(\n",
-    "    component=TransformersQueryClassifier(), name=\"QueryClassifier\", inputs=[\"Query\"]\n",
-    ")\n",
-    "transformer_keyword_classifier.add_node(\n",
-    "    component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"QueryClassifier.output_1\"]\n",
-    ")\n",
-    "transformer_keyword_classifier.add_node(\n",
-    "    component=bm25_retriever, name=\"BM25Retriever\", inputs=[\"QueryClassifier.output_2\"]\n",
-    ")\n",
-    "transformer_keyword_classifier.add_node(\n",
-    "    component=reader, name=\"QAReader\", inputs=[\"BM25Retriever\", \"EmbeddingRetriever\"]\n",
-    ")\n",
+    "To illustrate one potential use for question vs. statement classification, you will build a pipeline that looks as follows:\n",
     "\n",
+    "1. The pipeline will start with a retriever that **every query** will go through.\n",
+    "2. The pipeline will end with a reader that **only question queries** will go through.\n",
     "\n",
-    "# Useful for framing headers\n",
-    "equal_line = \"=\" * 30\n",
+    "In other words, your pipeline will be a **retriever-only pipeline for statement queries**&mdash;given the statement \"Arya Stark was the daughter of a Lord\", all you will get back are the most relevant documents&mdash;but it will be a **retriever-reader pipeline for question queries**.\n",
     "\n",
-    "# Run only the dense retriever on the full sentence query\n",
-    "res_1 = transformer_keyword_classifier.run(query=\"Who is the father of Arya Stark?\")\n",
-    "print(f\"\\n\\n{equal_line}\\nQUESTION QUERY RESULTS\\n{equal_line}\")\n",
-    "print_answers(res_1, details=\"minimum\")\n",
-    "print(\"\\n\\n\")\n",
+    "To make things more concrete, your pipeline will start with a Retriever, which is then fed into a QueryClassifier that is set to do question vs. statement classification. The QueryClassifier's first branch, which handles question queries, will then be sent to the Reader, while the second branch will not be connected to any other nodes. As a result, the last node of the pipeline depends on the type of query: **questions** go all the way through the Reader, while **statements** only go through the Retriever. \n",
     "\n",
-    "# Run only the sparse retriever on a keyword based query\n",
-    "res_2 = transformer_keyword_classifier.run(query=\"arya stark father\")\n",
-    "print(f\"\\n\\n{equal_line}\\nKEYWORD QUERY RESULTS\\n{equal_line}\")\n",
-    "print_answers(res_2, details=\"minimum\")"
+    "Now, define the pipeline. Keep in mind that you don't need to write Documents to the DocumentStore again as they are already indexed."
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {
-    "id": "zLwdVwMXDcoS"
+    "id": "tuS8EBOuABVq"
    },
    "source": [
-    "#### Pipeline with Question vs. Statement Classification\n",
-    "\n",
-    "Above we saw a potential use for keyword vs. question/statement classification: we might choose to use a less resource-intensive retriever for keyword queries than for question/statement queries. But what about question vs. statement classification?\n",
-    "\n",
-    "To illustrate one potential use for question vs. statement classification, we will build a pipeline that looks as follows:\n",
-    "\n",
-    "1. The pipeline will start with a retriever that **every query** will go through.\n",
-    "2. The pipeline will end with a reader that **only question queries** will go through.\n",
-    "\n",
-    "In other words, our pipeline will be a **retriever-only pipeline for statement queries**&mdash;given the statement \"Arya Stark was the daughter of a Lord\", all we will get back are the most relevant documents&mdash;but it will be a **retriever-reader pipeline for question queries**.\n",
-    "\n",
-    "To make things more concrete, our pipeline will start with a retriever, which is then fed into a `TransformersQueryClassifier` that is set to do question vs. statement classification. Note that this means we need to explicitly choose the model, since as mentioned previously a default `TransformersQueryClassifier` performs keyword vs. question/statement classification. The classifier's first branch, which handles question queries, will then be sent to the reader, while the second branch will not be connected to any other nodes. As a result, the last node of the pipeline depends on the type of query: questions go all the way through the reader, while statements only go through the retriever. This pipeline is illustrated below:"
+    "### 1) Define a new TransformersQueryClassifier\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "qbO5h19iAE1n"
+   },
+   "outputs": [],
+   "source": [
+    "question_classifier = TransformersQueryClassifier(model_name_or_path=\"shahrukhx01/question-vs-statement-classifier\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ldJpK8aVAUZO"
+   },
+   "source": [
+    "### 2) Define the Pipeline"
    ]
   },
   {
@@ -602,13 +891,10 @@
    },
    "outputs": [],
    "source": [
-    "# Here we build the pipeline\n",
     "transformer_question_classifier = Pipeline()\n",
     "transformer_question_classifier.add_node(component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"Query\"])\n",
     "transformer_question_classifier.add_node(\n",
-    "    component=TransformersQueryClassifier(model_name_or_path=\"shahrukhx01/question-vs-statement-classifier\"),\n",
-    "    name=\"QueryClassifier\",\n",
-    "    inputs=[\"EmbeddingRetriever\"],\n",
+    "    component=question_classifier, name=\"QueryClassifier\", inputs=[\"EmbeddingRetriever\"]\n",
     ")\n",
     "transformer_question_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"QueryClassifier.output_1\"])\n",
     "\n",
@@ -623,7 +909,8 @@
     "id": "QU1B6JQEDrol"
    },
    "source": [
-    "And here are the results of this pipeline: with a question query like \"Who is the father of Arya Stark?\", we obtain answers from a reader, and with a statement query like \"Arya Stark was the daughter of a Lord\", we just obtain documents from a retriever."
+    "### 2) Run the Pipeline\n",
+    "And here are the results of this pipeline: with a question query like \"Who is the father of Arya Stark?\", you obtain answers from a Reader, and with a statement query like \"Arya Stark was the daughter of a Lord\", you just obtain documents from a Retriever."
    ]
   },
   {
@@ -636,7 +923,6 @@
    "source": [
     "from haystack.utils import print_documents\n",
     "\n",
-    "\n",
     "# Useful for framing headers\n",
     "equal_line = \"=\" * 30\n",
     "\n",
@@ -655,14 +941,16 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "fzxOEXENvLgt"
+   },
    "source": [
-    "### Other use cases for Query Classifiers: custom classification models and zero-shot classification.\n",
+    "## Custom Use Cases for Query Classifiers\n",
     "\n",
-    "`TransformersQueryClassifier` is very flexible and also supports other options for classifying queries.\n",
-    "For example, we may be interested in detecting the sentiment or classifying the topics.  We can do this by loading a custom classification model from the Hugging Face Hub or by using zero-shot classification.\n",
+    "TransformersQueryClassifier is very flexible and also supports other options for classifying queries.\n",
+    "For example, you may be interested in detecting the sentiment of the query or classifying the topics. You can do this by loading a custom classification model from the Hugging Face Hub or by using zero-shot classification.\n",
     "\n",
-    "#### Custom classification model vs zero-shot classification\n",
+    "### Custom classification model vs zero-shot classification\n",
     "- Traditional text classification models are trained to predict one of a few \"hard-coded\" classes and require a dedicated training dataset. In the Hugging Face Hub, you can find many pre-trained models, maybe even related to your domain of interest.\n",
     "- Zero-shot classification is very versatile: by choosing a suitable base transformer, you can classify the text without any training dataset. You just have to provide the candidate categories."
    ]
@@ -670,23 +958,86 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "SHCSX05NvLgt"
+   },
    "source": [
-    "#### Using custom classification models\n",
-    "We can use a public model, available in the Hugging Face Hub. For example, if we want to classify the sentiment of the queries, we can choose an appropriate model, such as https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment.\n",
+    "### Using custom classification models\n",
+    "For this use case, you can use a public model available in the Hugging Face Hub. For example, if you want to classify the sentiment of the queries, you can choose an appropriate model, such as [`cardiffnlp/twitter-roberta-base-sentiment`](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment).\n",
     "\n",
-    "*In this case, the `labels` parameter must contain a list with the exact model labels.\n",
-    "The first label we provide corresponds to output_1, the second label to output_2, and so on.*"
+    "> In this case, the `labels` parameter must contain a list with the exact model labels. The first label corresponds to output_1, the second label to output_2, and so on. For `cardiffnlp/twitter-roberta-base-sentiment`, labels are `0 -> Negative; 1 -> Neutral; 2 -> Positive`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177,
+     "referenced_widgets": [
+      "5c55e5e7936b4b4fb7b0010ea2f9e5f3",
+      "851056276cfc43c7b231e2bd6149b627",
+      "ad4ae055a8dd4a8db1c3908910091a6b",
+      "32b0203e2e9c462aa61a25350b79bbe7",
+      "18db6c8a9dde4b308a31d5aa94799778",
+      "28651356455d4188b9e852483b3988a6",
+      "54220d8ecfc846d8bdb324052218fb5b",
+      "50266548923d4217ba0715d1eb475fac",
+      "cf6d36860b754883b2a14ad0ff9e97b5",
+      "7e33b39472254937ba734ec8bcf2f760",
+      "7176daa6887847af9ccc1a70ebcdc081",
+      "467673482ddd457d80257ede8980a2c9",
+      "db01ac60957f480ba19d04dc9671d0d9",
+      "b7047679eb0748bea1d2a73cbb19b694",
+      "746378041de5488792870f39f38ab9ba",
+      "984af7c1e1fb4bde92577e4db200a6ec",
+      "1d50d7ad02a24d7787c42cb333786c27",
+      "94f47d8639214bb08ba1cd2c0ab810df",
+      "ebe54395ce8641a38b2fdb70b68e6a07",
+      "33495554189e4a8a8f275bedd92bdee4",
+      "1ce2204a882d4e86919ee0f461a19f62",
+      "40d64e27f7a846b3a36c42662f886525",
+      "c11a53f71bf34863992c0c4bcd642217",
+      "237115164ca74a2a82e7d63f61bc78ca",
+      "5515aff461c64aa7ab51d22a75bbbccb",
+      "784466851b364dddad311a1556486d88",
+      "7016d245758c43b79fd34165e6b96a11",
+      "34dec7d815104f8a8099d3130d455ab9",
+      "7f3aa0c6afd34a918e1e6d949b90ee07",
+      "e14be537fea14172a6f2d9bdab754ba1",
+      "fe9a12bc82384276a1b546fd3f12d16b",
+      "415dc5a8c8944c329dd026bf1ee6b052",
+      "9b9018192be1475e91af74db724769ab",
+      "c9ee42905adf493d8b2d8c3c48dab0ea",
+      "79c59be9c09e4e169512a2317b51cf19",
+      "a5f3be1b73614f158eacf2c6d55f83c3",
+      "20d5c8d0117e4298bcc10831d93e9a7b",
+      "7ff3f6163c014513b2ee2e4b48ace9f1",
+      "ad63d3e3f4f9434da6eda9279c953e27",
+      "33cb72b2c2504c029ad4f32b8dcd0bec",
+      "500cfe3688af4f0aa53c1d30049f772e",
+      "ecf4d99014b14e299d9e10dd09704e8c",
+      "57cc747f502442b49ffc440ed3fd3214",
+      "c388f4dde7704a87a875866ef1e6bf98",
+      "f6e5ae999683443e92d42157432a0f4b",
+      "7a606731713743649dd37266679204cc",
+      "25a1b7a1edf545beba4dfd42d4795546",
+      "c6c2ab7aee4b4a2981b8ed04e4b06331",
+      "8b0541e794364081a27e4ba0e53bf492",
+      "a2ac39c9c72449a080e0fc6f85526770",
+      "8c054a4055a24ba2894d70e45dc1d629",
+      "8eb0eb01a5574c4eb6421dcdd97d779b",
+      "0bdc90350cce49338dbfed02ef5cf0bc",
+      "fd70c5001bd64d1eba85883e772619b6",
+      "9427c67fc66f4085a4ffe2f0e8f0a78a"
+     ]
+    },
+    "id": "qUOfTzi5vLgt",
+    "outputId": "016402b4-25e8-4e1f-dbbc-af29864692e9"
+   },
    "outputs": [],
    "source": [
-    "# Remember to compile a list with the exact model labels\n",
-    "# The first label you provide corresponds to output_1, the second label to output_2, and so on.\n",
     "labels = [\"LABEL_0\", \"LABEL_1\", \"LABEL_2\"]\n",
     "\n",
     "sentiment_query_classifier = TransformersQueryClassifier(\n",
@@ -700,7 +1051,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "8hQUejENvLgt"
+   },
    "outputs": [],
    "source": [
     "queries = [\n",
@@ -713,7 +1066,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 161
+    },
+    "id": "fX6nUR8OvLgu",
+    "outputId": "f04d0817-5c89-45d4-84df-8b1961d859bf"
+   },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -737,22 +1097,24 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "-6R8jfJVvLgu"
+   },
    "source": [
-    "#### Using zero-shot classification\n",
-    "You can also perform zero-shot classification by providing a suitable base transformer model and **choosing** the classes the model should predict.\n",
-    "For example, we may be interested in whether the user query is related to music or cinema.\n",
+    "### Using zero-shot classification\n",
+    "You can also perform zero-shot classification by providing a suitable base transformer model and **defining** the classes the model should predict.\n",
     "\n",
-    "In this case, the `labels` parameter is a list containing the candidate classes."
+    "For example, you may be interested in whether the user query is related to music or cinema. In this case, the `labels` parameter is a list containing the candidate classes."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "xO7sr516vLgu"
+   },
    "outputs": [],
    "source": [
-    "# In zero-shot-classification, you can choose the labels\n",
     "labels = [\"music\", \"cinema\"]\n",
     "\n",
     "query_classifier = TransformersQueryClassifier(\n",
@@ -766,20 +1128,24 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "lsgh0_YIvLgu"
+   },
    "outputs": [],
    "source": [
     "queries = [\n",
-    "    \"In which films does John Travolta appear?\",  # query about cinema\n",
-    "    \"What is the Rolling Stones first album?\",  # query about music\n",
-    "    \"Who was Sergio Leone?\",  # query about cinema\n",
+    "    \"In which films does John Travolta appear?\",  # cinema\n",
+    "    \"What is the Rolling Stones first album?\",  # music\n",
+    "    \"Who was Sergio Leone?\",  # cinema\n",
     "]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "GrGR1xzmvLgu"
+   },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -794,13 +1160,19 @@
     "\n",
     "pd.DataFrame.from_dict(query_classification_results)"
    ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Congratulations! 🎉 You’ve learned how TransformersQueryClassifier works and how you can use it in a pipeline."
+   ]
   }
  ],
  "metadata": {
   "accelerator": "GPU",
   "colab": {
-   "collapsed_sections": [],
-   "name": "Tutorial14_Query_Classifier.ipynb",
    "provenance": []
   },
   "kernelspec": {
@@ -824,6 +1196,13006 @@
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+       "IPY_MODEL_45ffd4c778554622abf4a146e3ff20b9",
+       "IPY_MODEL_8ce310c400b745c1bc4dd06e3157c17e",
+       "IPY_MODEL_c1631a0cc8d941f9b33e1c5596269a11"
+      ],
+      "layout": "IPY_MODEL_39717879c9cb4fcb950c21ca174646ff"
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From e7984db0c33f45fb1f426e2139e9839551baf46c Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <44616784+anakin87@users.noreply.github.com>
Date: Tue, 8 Aug 2023 09:22:06 +0200
Subject: [PATCH 115/206] improve elasticsearch start part (#232)

---
 tutorials/03_Scalable_QA_System.ipynb | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/tutorials/03_Scalable_QA_System.ipynb b/tutorials/03_Scalable_QA_System.ipynb
index dc32f91c..810889f7 100644
--- a/tutorials/03_Scalable_QA_System.ipynb
+++ b/tutorials/03_Scalable_QA_System.ipynb
@@ -157,7 +157,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you are working in an environment where Docker is available, you can also start Elasticsearch using Docker. You can do this manually, or using our [`launch_es()`](https://docs.haystack.deepset.ai/reference/utils-api#module-doc_store) utility function."
+    "If Docker is available in your environment (Colab notebooks do not support Docker), you can also start Elasticsearch using Docker. You can do this manually, or using our [`launch_es()`](https://docs.haystack.deepset.ai/reference/utils-api#module-doc_store) utility function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from haystack.utils import launch_es\n",
+    "\n",
+    "# launch_es()"
    ]
   },
   {
@@ -187,17 +198,6 @@
     "4. Initialize the ElasticsearchDocumentStore:\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack.utils import launch_es\n",
-    "\n",
-    "launch_es()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 12,

From 0c8a7cebec57281865794d0df1ad1b2a5cf2a21c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 28 Aug 2023 12:39:08 +0300
Subject: [PATCH 116/206] Update README.md (#235)

* Update README.md

Change Haystack definition
Improve tutorial table

* Update README.md

Replace NLP with LLM
---
 README.md | 60 +++++++++++++++++++++++++++----------------------------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/README.md b/README.md
index 3859019b..517e8829 100644
--- a/README.md
+++ b/README.md
@@ -6,40 +6,40 @@
   <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png#gh-light-mode-only" width="30%"/>
 </p>
 
-<strong><a href="https://github.com/deepset-ai/haystack">Haystack</a></strong> is an open source NLP framework by <strong><a href="https://deepset.ai">deepset</a></strong> to help you build production ready search systems or applications powered by various NLP tasks such as Question Answering. Haystack is designed to help you build systems that work intelligently over large document collections. It achieves this with the concept of <strong>Pipelines</strong> consisting of various <strong>Nodes</strong> such as a <strong>DocumentStore</strong>, a <strong>Retriever</strong> and a <strong>Reader</strong>.
+<strong><a href="https://github.com/deepset-ai/haystack">Haystack</a></strong> is an open source LLM framework by <strong><a href="https://deepset.ai">deepset</a></strong> to help you build production ready applications. Haystack is designed to help you build systems that work intelligently over large document collections to perform tasks like document retrieval, text generation, question answering, or summarization. It achieves this with the concept of <strong>Pipelines</strong> consisting of various <strong>Components</strong> such as a <strong>DocumentStore</strong>, a <strong>Retriever</strong>, and a <strong>PromptNode</strong>.
 
-This is the repository where we keep all the Haystack tutorials 📓 👇 These tutorials are also published to the [Haystack Website](https://haystack.deepset.ai/tutorials/first-qa-system)
+This is the repository where we keep all the Haystack tutorials 📓 👇 These tutorials are also published to the [Haystack Website](https://haystack.deepset.ai/tutorials/).
 
-To contribute to the tutorials please check out our [Contributing Guidelines](./Contributing.md)
+To contribute to the tutorials, please check out our [Contributing Guidelines](./Contributing.md).
 
 [![Run Tutorials Nightly](https://github.com/deepset-ai/haystack-tutorials/actions/workflows/nightly.yml/badge.svg)](https://github.com/deepset-ai/haystack-tutorials/actions/workflows/nightly.yml)
 [![Publish tutorials on Haystack Home](https://github.com/deepset-ai/haystack-tutorials/actions/workflows/publish_tutorials.yml/badge.svg)](https://github.com/deepset-ai/haystack-tutorials/actions/workflows/publish_tutorials.yml)
 
 ## Tutorials
-| Name                                     | Colab                                                                                                                                                                                                                                     | Source Code                                                                                                                                      |
-| ---------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
-| Build Your First Question Answering System | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)                                       | [01_Basic_QA_Pipeline.ipynb](./tutorials/01_Basic_QA_Pipeline.ipynb)                                                                             |
-| Fine Tune a Model on Your Data           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb)                           | [02_Finetune_a_model_on_your_data.ipynb](./tutorials/02_Finetune_a_model_on_your_data.ipynb)                                                     |
-| Build a Scalable Question Answering System  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb)                 | [03_Scalable_QA_System.ipynb](./tutorials/03_Scalable_QA_System.ipynb)                                 |
-| FAQ Style QA                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            | [04_FAQ_style_QA.ipynb](./tutorials/04_FAQ_style_QA.ipynb)                                                                                       |
-| Evaluation                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              | [05_Evaluation.ipynb](./tutorials/05_Evaluation.ipynb)                                                                                           |
-| Better Retrieval via Embedding Retrieval | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                | [06_Better_Retrieval_via_Embedding_Retrieval.ipynb](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                               |
-| RAG Generator                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           | [07_RAG_Generator.ipynb](./tutorials/07_RAG_Generator.ipynb)                                                                                     |
-| Preprocessing                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           | [08_Preprocessing.ipynb](./tutorials/08_Preprocessing.ipynb)                                                                                     |
-| DPR Training                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            | [09_DPR_training.ipynb](./tutorials/09_DPR_training.ipynb)                                                                                       |
-| Knowledge Graph                          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         | [10_Knowledge_Graph.ipynb](./tutorials/10_Knowledge_Graph.ipynb)                                                                                 |
-| Pipelines                                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               | [11_Pipelines.ipynb](./tutorials/11_Pipelines.ipynb)                                                                                             |
-| Long-Form Question Answering             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    | [12_LFQA.ipynb](./tutorials/12_LFQA.ipynb)                                                                                                       |
-| Question Generation                      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     | [13_Question_generation.ipynb](./tutorials/13_Question_generation.ipynb)                                                                         |
-| Query Classifier                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)                                        | [14_Query_Classifier.ipynb](./tutorials/14_Query_Classifier.ipynb)                                                                               |
-| Table QA                                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)                                                 | [15_TableQA.ipynb](./tutorials/15_TableQA.ipynb)                                                                                                 |
-| Document Classifier at Index Time        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb)                       | [16_Document_Classifier_at_Index_Time.ipynb](./tutorials/16_Document_Classifier_at_Index_Time.ipynb)                                             |
-| Audio                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb)                                                   | [17_Audio.ipynb](./tutorials/17_Audio.ipynb)                                                                                                     |
-| Generative Pseudo Labeling               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)                                                     | [18_GPL.ipynb](./tutorials/18_GPL.ipynb)                                                                                                         |
-| Text-to-Image search                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) | [19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) |
-| Using Haystack with REST API             | <a href="https://haystack.deepset.ai/downloads/20_Using_Haystack_with_REST_API.ipynb" target="_blank"><img alt="Download" src="https://img.shields.io/badge/DOWNLOAD-blue?&style=for-the-badge"/> <a>                                                                                                                                                                                                    | [20_Using_Haystack_with_REST_API.ipynb](./tutorials/20_Using_Haystack_with_REST_API.ipynb)                                                       |
-| Customizing PromptNode                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb) | [21_Customizing_PromptNode.ipynb](./tutorials/21_Customizing_PromptNode.ipynb) |
-| Creating a Generative QA Pipeline with PromptNode                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/22_Pipeline_with_PromptNode.ipynb) | [22_Pipeline_with_PromptNode.ipynb](./tutorials/22_Pipeline_with_PromptNode.ipynb) |
-| Answering Complex Questions with Agents                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) | [23_Answering_Multihop_Questions_with_Agents.ipynb](./tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) |
-| Building a Conversational Chat App                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/24_Building_Chat_App.ipynb) | [24_Building_Chat_App.ipynb](./tutorials/24_Building_Chat_App.ipynb) |
-| Customizing Agent to Chat with Your Documents                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/25_Customizing_Agent.ipynb) | [25_Customizing_Agent.ipynb](./tutorials/25_Customizing_Agent.ipynb) |
+| Source Code                                     | Colab                                     | 
+| ---------------------------------------- | -------------------------------------------------|
+| [Build Your First Question Answering System]((./tutorials/01_Basic_QA_Pipeline.ipynb)) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)                                       |
+| [Fine Tune a Model on Your Data](./tutorials/02_Finetune_a_model_on_your_data.ipynb)            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb)|
+| [Build a Scalable Question Answering System](./tutorials/03_Scalable_QA_System.ipynb)   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb)                 |
+| [FAQ Style QA](./tutorials/04_FAQ_style_QA.ipynb)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            |
+| [Evaluation](./tutorials/05_Evaluation.ipynb)                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              |
+| [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                |
+| [RAG Generator](./tutorials/07_RAG_Generator.ipynb)                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           |
+| [Preprocessing](./tutorials/08_Preprocessing.ipynb)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           |
+| [DPR Training](./tutorials/09_DPR_training.ipynb)                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            |
+| [Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         |
+| [Pipelines](./tutorials/11_Pipelines.ipynb)                                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               |
+| [Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    |
+| [Question Generation](./tutorials/13_Question_generation.ipynb)                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     |
+| [Query Classifier](./tutorials/14_Query_Classifier.ipynb)                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)                                        |
+| [Table QA](./tutorials/15_TableQA.ipynb)                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)                                                 |
+| [Document Classifier at Index Time](./tutorials/16_Document_Classifier_at_Index_Time.ipynb)        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb)                       |
+| [Make Your QA Pipelines Talk!](./tutorials/17_Audio.ipynb)                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb)                                                   |
+| [Generative Pseudo Labeling](./tutorials/18_GPL.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)                                                     |
+| [Text-to-Image search](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb)                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) |
+| [Using Haystack with REST API](./tutorials/20_Using_Haystack_with_REST_API.ipynb)             | <a href="https://haystack.deepset.ai/downloads/20_Using_Haystack_with_REST_API.ipynb" target="_blank"><img alt="Download" src="https://img.shields.io/badge/DOWNLOAD-blue?&style=for-the-badge"/> <a>     |
+| [Customizing PromptNode](./tutorials/21_Customizing_PromptNode.ipynb)                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb) |
+| [Generative QA Pipeline with Retrieval-Augmentation](./tutorials/22_Pipeline_with_PromptNode.ipynb)                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/22_Pipeline_with_PromptNode.ipynb) |
+| [Answering Complex Questions with Agents](./tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb)                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) |
+| [Building a Conversational Chat App](./tutorials/24_Building_Chat_App.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/24_Building_Chat_App.ipynb) |
+| [Customizing Agent to Chat with Your Documents](./tutorials/25_Customizing_Agent.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/25_Customizing_Agent.ipynb) |

From 8800aeb65ce7e31481c15fd62e2a87dcbc0d12cb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 31 Aug 2023 12:15:35 +0300
Subject: [PATCH 117/206] Add repository link to the tutorial (#236)

* Add repository link to the tutorial

* Change version in the text
---
 tutorials/20_Using_Haystack_with_REST_API.ipynb | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/tutorials/20_Using_Haystack_with_REST_API.ipynb b/tutorials/20_Using_Haystack_with_REST_API.ipynb
index 9c53bc0d..55d66296 100644
--- a/tutorials/20_Using_Haystack_with_REST_API.ipynb
+++ b/tutorials/20_Using_Haystack_with_REST_API.ipynb
@@ -28,7 +28,8 @@
     "* **Haystack pipelines** convert files into Documents, index them to the DocumentStore, and run NLP tasks such as question answering and document search.\n",
     "* **REST API**, as a concept, makes it possible for applications to interact with each other by handling their queries and returning responses. There is `rest_api` application within Haystack that exposes Haystack's functionalities through a RESTful API.\n",
     "* **Docker** simplifies the environment setup needed to run Elasticsearch and Haystack API.\n",
-    "\n"
+    "\n",
+    "> You can find ready-made base files (*document-search.haystack-pipeline.yml* and *docker-compose.yml*) for this tutorial in the [document-search-demo](https://github.com/bilgeyucel/document-search-demo) repository. After following the instructions in the repository, you can proceed directly to the ['Indexing Files to Elasticsearch'](#indexing-files-to-elasticsearch) section of this tutorial and begin indexing your documents."
    ]
   },
   {
@@ -173,10 +174,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "3. After creating query and indexing pipelines, add `version: 1.12.1` to the top of the file. This is the Haystack version that comes with the Docker image in the *docker-compose.yml*. Now, the pipeline YAML is ready.\n",
+    "3. After creating query and indexing pipelines, add `version: 1.19.0` to the top of the file. This is the Haystack version that comes with the Docker image in the *docker-compose.yml*. Now, the pipeline YAML is ready.\n",
     "\n",
     "```yaml\n",
-    "version: 1.12.1\n",
+    "version: 1.19.0\n",
     "\n",
     "components:\n",
     "  - name: DocumentStore\n",
@@ -224,6 +225,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -244,6 +246,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -316,6 +319,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -367,7 +371,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.9.12"
   },
   "orig_nbformat": 4,
   "vscode": {

From e95564fa83da5b83d276926f236139c0f4cad421 Mon Sep 17 00:00:00 2001
From: HaveF <iamaplayer@gmail.com>
Date: Mon, 2 Oct 2023 22:44:22 +0800
Subject: [PATCH 118/206] fix typo in README.md (#237)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 517e8829..79003df7 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ To contribute to the tutorials, please check out our [Contributing Guidelines](.
 ## Tutorials
 | Source Code                                     | Colab                                     | 
 | ---------------------------------------- | -------------------------------------------------|
-| [Build Your First Question Answering System]((./tutorials/01_Basic_QA_Pipeline.ipynb)) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)                                       |
+| [Build Your First Question Answering System](./tutorials/01_Basic_QA_Pipeline.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)                                       |
 | [Fine Tune a Model on Your Data](./tutorials/02_Finetune_a_model_on_your_data.ipynb)            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb)|
 | [Build a Scalable Question Answering System](./tutorials/03_Scalable_QA_System.ipynb)   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb)                 |
 | [FAQ Style QA](./tutorials/04_FAQ_style_QA.ipynb)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            |

From 3c172e663be52700e7a1e575327d87a9710840f6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 4 Oct 2023 19:12:33 +0300
Subject: [PATCH 119/206] Change the model used in RAG tutorial (#239)

* Change the model used in tutorial 22

* Formatting

* Change the name of the openai api key

* Skip this tutorial for PR tests

* Make RAG tutorial one of the featured ones
---
 .github/workflows/run_tutorials.yml         |    1 +
 index.toml                                  |    1 +
 tutorials/22_Pipeline_with_PromptNode.ipynb | 1649 +++++++++++++++++--
 3 files changed, 1544 insertions(+), 107 deletions(-)

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 91b35048..44a06b31 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -63,6 +63,7 @@ jobs:
               "tutorials/13_Question_generation.ipynb"
               "tutorials/14_Query_Classifier.ipynb"
               "tutorials/18_GPL.ipynb"
+              "tutorials/22_Pipeline_with_PromptNode.ipynb"
               "tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb"
               "tutorials/24_Building_Chat_App.ipynb"
               "tutorials/25_Customizing_Agent.ipynb"
diff --git a/index.toml b/index.toml
index 6f6a420d..5d128ae6 100644
--- a/index.toml
+++ b/index.toml
@@ -224,6 +224,7 @@ notebook = "22_Pipeline_with_PromptNode.ipynb"
 aliases = ["pipeline-with-promptnode", "retrieval-augmented-generation"]
 completion_time = "15 min"
 created_at = 2023-03-13
+featured = true
 
 [[tutorial]]
 title = "Building a Conversational Chat App"
diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index 7326bcfa..8ab387e0 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -24,7 +24,7 @@
    "source": [
     "## Overview\n",
     "\n",
-    "Learn how to build a generative question answering pipeline using the power of LLMs with PromptNode. In this generative pipeline, BM25Retriever gets the related Documents, and PromptNode generates the answer using the retrieval augmented generation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach. In this tutorial, we'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want. \n",
+    "Learn how to build a generative question answering pipeline using the power of LLMs with PromptNode. In this generative pipeline, BM25Retriever gets the related Documents, and PromptNode generates the answer using the retrieval augmented generation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach. In this tutorial, we'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n",
     "\n",
     "This tutorial introduces you to the PrompTemplate structure and explains how to use the new PrompTemplate to integrate PromptNode into a pipeline."
    ]
@@ -62,7 +62,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "UQbU8GUfO-qZ",
-    "outputId": "1bcd012f-8a71-4921-b9f6-f4baa1088de5"
+    "outputId": "977ba296-dac5-46c5-b99e-167c90ed1519"
    },
    "outputs": [],
    "source": [
@@ -165,23 +165,67 @@
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 86,
+     "height": 194,
      "referenced_widgets": [
-      "e7af7bc04b27477289213c9275acfa0a",
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-      "af7f9fd6fb01447f8296f59fb71e5b31",
-      "5cc7b9d993ba4f69a188cc4de776e29a"
+      "d21be889d88742c881c34024a6110e94",
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+      "bdc213b8572a4d059020e5606af16f33",
+      "9b479e56f3814b21ae0b2257cf7a9243",
+      "ba3091edbf504781957b3e50a3f5e1c4",
+      "1af0938965c14b4cb91c20b86c43395f",
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      ]
     },
     "id": "INdC3WvLO-qb",
-    "outputId": "0c5af785-4d48-4f22-f863-1a6185c5a89d"
+    "outputId": "99243738-1fd4-42b5-efcb-d46744a1a732"
    },
    "outputs": [],
    "source": [
@@ -226,9 +270,39 @@
    "source": [
     "## Initializing the PromptNode\n",
     "\n",
-    "Let's define a custom prompt for PromptTemplate to use with PromptNode. As parameters, this prompt will accept Documents that our Retriever fetched from our DocumentStore and `query` we pass at runtime. To join the content of the Documents, we'll use `join()` function. To learn about using functions in PromptTemplate, check out [PromptTemplate Structure](https://docs.haystack.deepset.ai/docs/prompt_node#prompttemplate-structure). Finally, we'll use [AnswerParser](https://docs.haystack.deepset.ai/reference/prompt-node-api#answerparser) to parse the output of the LLM into a Haystack Answer object.\n",
+    "[PromptNode](https://docs.haystack.deepset.ai/docs/prompt_node) is the central abstraction in Haystack's large language model (LLM) support. It's possible to interact with LLMs through PromptNode by providing `model_name_or_path` and if necessary, `api_key`.\n",
     "\n",
-    "We'll initialize PromptNode with the PromptTemplate and `google/flan-t5-large` model."
+    "For this tutorial, we'll use OpenAI's `text-davinci-003`, so, we need to enter a `OPENAI_API_KEY`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "SavE_FAqfApo",
+    "outputId": "a689aca5-e95c-45df-e069-4b4b4a9f2c1c"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WCzGckP5gqem"
+   },
+   "source": [
+    "Let's define a custom prompt for PromptTemplate to use with PromptNode. As parameters, this prompt will accept `documents` that our Retriever fetched from our DocumentStore and `query` we pass at runtime. To join the content of the Documents, we'll use `join()` function. To learn about using functions in PromptTemplate, check out [PromptTemplate Structure](https://docs.haystack.deepset.ai/docs/prompt_node#prompttemplate-structure). Finally, we'll use [AnswerParser](https://docs.haystack.deepset.ai/reference/prompt-node-api#answerparser) to parse the output of the LLM into a Haystack Answer object.\n",
+    "\n",
+    "We'll initialize PromptNode with the PromptTemplate, the `text-davinci-003` model and the `api_key`."
    ]
   },
   {
@@ -249,7 +323,9 @@
     "    output_parser=AnswerParser(),\n",
     ")\n",
     "\n",
-    "prompt_node = PromptNode(model_name_or_path=\"google/flan-t5-large\", default_prompt_template=rag_prompt)"
+    "prompt_node = PromptNode(\n",
+    "    model_name_or_path=\"text-davinci-003\", api_key=openai_api_key, default_prompt_template=rag_prompt\n",
+    ")"
    ]
   },
   {
@@ -319,7 +395,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "Vnt283M5O-qc",
-    "outputId": "b7dc413e-2ee1-4173-deca-41e0d989a821"
+    "outputId": "14700d1b-7cd9-4dba-9750-4d665d1aa561"
    },
    "outputs": [],
    "source": [
@@ -366,25 +442,16 @@
    "source": [
     "🎉 Congratulations! You've learned how to create a generative QA system for your documents with PromptNode."
    ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "o3jq25OFO-qc"
-   },
-   "source": []
   }
  ],
  "metadata": {
   "accelerator": "GPU",
   "colab": {
+   "gpuType": "T4",
    "provenance": []
   },
-  "gpuClass": "standard",
   "kernelspec": {
-   "display_name": "Python 3.9.6 64-bit",
-   "language": "python",
+   "display_name": "Python 3",
    "name": "python3"
   },
   "language_info": {
@@ -399,31 +466,65 @@
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
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      "model_module_version": "1.5.0",
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+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
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+    "12c45cd4f42941e4b693e753fd5b2059": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
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       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
-      "_model_name": "FloatProgressModel",
+      "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
-      "_view_name": "ProgressView",
-      "bar_style": "success",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_1a8b7da9931340e085f92d67a97d3a1d",
+       "IPY_MODEL_696cbf80932045ed97c95cc63071ec21",
+       "IPY_MODEL_160cca251a2342af97888ee5942b18cf"
+      ],
+      "layout": "IPY_MODEL_84a63bd9fdea4a69b87c91e19f8c940e"
+     }
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+     "model_module_version": "1.5.0",
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+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
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-      "max": 151,
-      "min": 0,
-      "orientation": "horizontal",
-      "style": "IPY_MODEL_5cfb85b586ff460a89844a06ac8e9a12",
-      "value": 151
+      "layout": "IPY_MODEL_1fcb825071d2417e810e7bf470ab7f38",
+      "placeholder": "​",
+      "style": "IPY_MODEL_019445545b2b4528adf23537b53d44da",
+      "value": " 1/1 [00:00&lt;00:00,  1.52it/s]"
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@@ -521,49 +591,81 @@
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-      "layout": "IPY_MODEL_af7f9fd6fb01447f8296f59fb71e5b31",
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       "placeholder": "​",
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From 42e9c887c2ea8cfd070b7fd9205661e4dfbdec8a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 12 Oct 2023 11:15:05 +0300
Subject: [PATCH 120/206] Update Fine-Tuning Tutorial (#243)

* Update tut 2

* Language fixes
---
 index.toml                                    |   1 +
 .../02_Finetune_a_model_on_your_data.ipynb    | 183 +++++++-----------
 2 files changed, 73 insertions(+), 111 deletions(-)

diff --git a/index.toml b/index.toml
index 5d128ae6..cacbc49f 100644
--- a/index.toml
+++ b/index.toml
@@ -21,6 +21,7 @@ weight = 50
 notebook = "02_Finetune_a_model_on_your_data.ipynb"
 aliases = ["fine-tuning-a-model"]
 created_at = 2021-08-12
+completion_time = "15 min"
 needs_gpu = true
 
 [[tutorial]]
diff --git a/tutorials/02_Finetune_a_model_on_your_data.ipynb b/tutorials/02_Finetune_a_model_on_your_data.ipynb
index d281e171..2d4ce507 100644
--- a/tutorials/02_Finetune_a_model_on_your_data.ipynb
+++ b/tutorials/02_Finetune_a_model_on_your_data.ipynb
@@ -5,13 +5,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Fine-tuning a Model on Your Own Data\n",
+    "# Tutorial: Fine-Tuning a Model on Your Own Data\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Nodes Used**: `FARMReader`\n",
+    "- **Goal**: After completing this tutorial, you will have learned how to fine-tune a pretrained Reader model on your own data."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
     "\n",
     "For many use cases it is sufficient to just use one of the existing public models that were trained on SQuAD or other public QA datasets (e.g. Natural Questions).\n",
     "However, if you have domain-specific questions, fine-tuning your model on custom examples will very likely boost your performance.\n",
-    "While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.\n",
-    "\n",
-    "This tutorial shows you how to fine-tune a pretrained model on your own dataset."
+    "While this varies by domain, we saw that ~ 2000 examples can easily increase performance by +5-20%.\n"
    ]
   },
   {
@@ -25,7 +36,7 @@
     "## Preparing the Colab Environment\n",
     "\n",
     "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)\n"
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
    ]
   },
   {
@@ -73,46 +84,68 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
-    "Set the logging level to INFO:"
+    "\n",
+    "## Create Training Data\n",
+    "\n",
+    "There are two ways to generate training data:\n",
+    "\n",
+    "1. **Annotation**: You can use the [annotation tool](https://haystack.deepset.ai/guides/annotation) to label your data, which means highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.\n",
+    "\n",
+    "![Snapshot of the annotation tool](https://raw.githubusercontent.com/deepset-ai/haystack/main/docs/img/annotation_tool.png)\n",
+    "\n",
+    "2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.\n",
+    "\n",
+    "\n",
+    "## Fine-Tune Your Model\n",
+    "\n",
+    "Once you have collected training data, you can fine-tune your base model. To do that, you need to initialize a reader as a base model and fine-tune it on your custom dataset (should be in SQuAD-like format). We recommend using a base model that was trained on SQuAD or a similar QA dataset beforehand to benefit from Transfer Learning effects.\n",
+    "\n",
+    "**Recommendation**: Run training on a GPU.\n",
+    "If you are using Colab: Enable this in the menu \"Runtime\" > \"Change Runtime type\" > Select \"GPU\" in dropdown.\n",
+    "Then change the `use_gpu` arguments below to `True`\n",
+    "\n",
+    "1. Initialize a `Reader` with the model to fine-tune:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
    "outputs": [],
    "source": [
-    "import logging\n",
+    "from haystack.nodes import FARMReader\n",
     "\n",
-    "logging.basicConfig(format=\"%(levelname)s - %(name)s -  %(message)s\", level=logging.WARNING)\n",
-    "logging.getLogger(\"haystack\").setLevel(logging.INFO)"
+    "reader = FARMReader(model_name_or_path=\"distilbert-base-uncased-distilled-squad\", use_gpu=True, devices=[\"mps\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Get SQUAD-style data for training. You can use this dataset we prepared:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from haystack.nodes import FARMReader\n",
-    "from haystack.utils import fetch_archive_from_http"
+    "from haystack.utils import fetch_archive_from_http\n",
+    "\n",
+    "data_dir = \"data/fine-tuning\"\n",
+    "\n",
+    "\n",
+    "fetch_archive_from_http(\n",
+    "    url=\"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-downstream/squad20.tar.gz\", output_dir=data_dir\n",
+    ")"
    ]
   },
   {
@@ -120,59 +153,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
-    "## Create Training Data\n",
-    "\n",
-    "There are two ways to generate training data\n",
-    "\n",
-    "1. **Annotation**: You can use the [annotation tool](https://haystack.deepset.ai/guides/annotation) to label your data, i.e. highlighting answers to your questions in a document. The tool supports structuring your workflow with organizations, projects, and users. The labels can be exported in SQuAD format that is compatible for training with Haystack.\n",
-    "\n",
-    "![Snapshot of the annotation tool](https://github.com/deepset-ai/haystack-tutorials/raw/main/tutorials/img/annotation_tool.png)\n",
-    "\n",
-    "2. **Feedback**: For production systems, you can collect training data from direct user feedback via Haystack's [REST API interface](https://github.com/deepset-ai/haystack#rest-api). This includes a customizable user feedback API for providing feedback on the answer returned by the API. The API provides a feedback export endpoint to obtain the feedback data for fine-tuning your model further.\n",
-    "\n",
-    "\n",
-    "## Fine-tune your model\n",
-    "\n",
-    "Once you have collected training data, you can fine-tune your base models.\n",
-    "We initialize a reader as a base model and fine-tune it on our own custom dataset (should be in SQuAD-like format).\n",
-    "We recommend using a base model that was trained on SQuAD or a similar QA dataset before to benefit from Transfer Learning effects.\n",
-    "\n",
-    "**Recommendation**: Run training on a GPU.\n",
-    "If you are using Colab: Enable this in the menu \"Runtime\" > \"Change Runtime type\" > Select \"GPU\" in dropdown.\n",
-    "Then change the `use_gpu` arguments below to `True`"
+    "3. Train the model on your own data and save it to \"my_model\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "reader = FARMReader(model_name_or_path=\"distilbert-base-uncased-distilled-squad\", use_gpu=True)\n",
-    "data_dir = \"data/squad20\"\n",
-    "# data_dir = \"PATH/TO_YOUR/TRAIN_DATA\"\n",
-    "reader.train(data_dir=data_dir, train_filename=\"dev-v2.0.json\", use_gpu=True, n_epochs=1, save_dir=\"my_model\")"
+    "reader.train(data_dir=data_dir, train_filename=\"squad20/dev-v2.0.json\", use_gpu=True, n_epochs=1, save_dir=\"my_model\")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "attachments": {},
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [],
    "source": [
-    "# Saving the model happens automatically at the end of training into the `save_dir` you specified\n",
-    "# However, you could also save a reader manually again via:\n",
-    "reader.save(directory=\"my_model\")"
+    "4. Initialize a new reader with your fine-tuned model:"
    ]
   },
   {
@@ -186,7 +189,6 @@
    },
    "outputs": [],
    "source": [
-    "# If you want to load it at a later point, just do:\n",
     "new_reader = FARMReader(model_name_or_path=\"my_model\")"
    ]
   },
@@ -195,13 +197,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Distill your model\n",
-    "In this case, we have used \"distilbert-base-uncased\" as our base model. This model was trained using a process called distillation. In this process, a bigger model is trained first and is used to train a smaller model which increases its accuracy. This is why \"distilbert-base-uncased\" can achieve quite competitive performance while being very small.\n",
-    "\n",
-    "Sometimes, however, you can't use an already distilled model and have to distil it yourself. For this case, haystack has implemented [distillation features](https://haystack.deepset.ai/guides/model-distillation).\n",
-    "\n",
-    "### Augmenting your training data\n",
-    "To get the most out of model distillation, we recommend increasing the size of your training data by using data augmentation. You can do this by running the [`augment_squad.py` script](https://github.com/deepset-ai/haystack/blob/main/haystack/utils/augment_squad.py):"
+    "5. Finally, use the `new_reader` that was initialized with your fine-tuned model."
    ]
   },
   {
@@ -210,22 +206,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Downloading script\n",
-    "!wget https://raw.githubusercontent.com/deepset-ai/haystack/main/haystack/utils/augment_squad.py\n",
+    "from haystack.schema import Document\n",
     "\n",
-    "glove_dir = \"data/tutorial2/gloves\"\n",
-    "squad_dir = \"data/tutorial2/squad_small\"\n",
-    "\n",
-    "# Downloading smaller glove vector file (only for demonstration purposes)\n",
-    "glove_url = \"https://nlp.stanford.edu/data/glove.6B.zip\"\n",
-    "fetch_archive_from_http(url=glove_url, output_dir=glove_dir)\n",
-    "\n",
-    "# Downloading very small dataset to make tutorial faster (please use a bigger dataset for real use cases)\n",
-    "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/squad_small.json.zip\"\n",
-    "fetch_archive_from_http(url=s3_url, output_dir=squad_dir)\n",
-    "\n",
-    "# Just replace the path with your dataset and adjust the output (also please remove glove path to use bigger glove vector file)\n",
-    "!python augment_squad.py --squad_path data/tutorial2/squad_small/squad_small.json --output_path augmented_dataset.json --multiplication_factor 2 --glove_path data/tutorial2/gloves/glove.6B.300d.txt"
+    "new_reader.predict(\n",
+    "    query=\"What is the capital of Germany?\", documents=[Document(content=\"The capital of Germany is Berlin\")]\n",
+    ")"
    ]
   },
   {
@@ -233,37 +218,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this case, we use a multiplication factor of 2 to keep this example lightweight. Usually you would use a factor like 20 depending on the size of your training data. Augmenting this small dataset with a multiplication factor of 2, should take about 5 to 10 minutes to run on one V100 GPU.\n",
-    "\n",
-    "### Running distillation\n",
-    "Distillation in haystack is done in two steps: First, you run intermediate layer distillation on the augmented dataset to ensure the two models behave similarly. After that, you run the prediction layer distillation on the non-augmented dataset to optimize the model for your specific task.\n",
-    "\n",
-    "If you want, you can leave out the intermediate layer distillation step and only run the prediction layer distillation. This way you also do not need to perform data augmentation. However, this will make the model significantly less accurate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Loading a fine-tuned model as teacher e.g. \"deepset/​bert-​base-​uncased-​squad2\"\n",
-    "teacher = FARMReader(model_name_or_path=\"my_model\", use_gpu=True)\n",
-    "\n",
-    "# You can use any pre-trained language model as teacher that uses the same tokenizer as the teacher model.\n",
-    "# The number of the layers in the teacher model also needs to be a multiple of the number of the layers in the student.\n",
-    "student = FARMReader(model_name_or_path=\"huawei-noah/TinyBERT_General_6L_768D\", use_gpu=True)\n",
-    "\n",
-    "student.distil_intermediate_layers_from(teacher, data_dir=\".\", train_filename=\"augmented_dataset.json\", use_gpu=True)\n",
-    "student.distil_prediction_layer_from(teacher, data_dir=\"data/squad20\", train_filename=\"dev-v2.0.json\", use_gpu=True)\n",
-    "\n",
-    "student.save(directory=\"my_distilled_model\")"
+    "Congratulations! 🎉 You’ve fine-tuned a base model on your own data!"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.9 64-bit",
+   "display_name": "Python 3.10.4 ('haystack-tutorials')",
    "language": "python",
    "name": "python3"
   },
@@ -277,11 +238,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.4"
   },
   "vscode": {
    "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+    "hash": "1e4fa2e1c496b8379da88afac82c60055e1be33cd79040f849449f398c153e43"
    }
   }
  },

From 6881e2490e9e2df91e53d5166443ddf48125efaf Mon Sep 17 00:00:00 2001
From: Nicola Procopio <nico.pro412@gmail.com>
Date: Fri, 13 Oct 2023 13:29:31 +0200
Subject: [PATCH 121/206] Hybrid retrieval tutorial (#241)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* added Hybrid Retrieval tutorial

* Add files via upload

* updated index

* updated notebook

* Update tutorials/26_Hybrid_Retrieval.ipynb

Co-authored-by: Daria Fokina <daria.f93@gmail.com>

* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* requested changes

* Update index.toml

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* Update index.toml

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* Update index.toml

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* Update index.toml

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* added new section

* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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* Update tutorials/26_Hybrid_Retrieval.ipynb

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---------

Co-authored-by: Daria Fokina <daria.f93@gmail.com>
Co-authored-by: Bilge Yücel <bilge.yucel@deepset.ai>
---
 index.toml                                    |  11 +
 tutorials/26_Hybrid_Retrieval.ipynb           | 453 ++++++++++++++++++
 tutorials/img/tutorial_26_pipeline_hybrid.png | Bin 0 -> 30797 bytes
 3 files changed, 464 insertions(+)
 create mode 100644 tutorials/26_Hybrid_Retrieval.ipynb
 create mode 100644 tutorials/img/tutorial_26_pipeline_hybrid.png

diff --git a/index.toml b/index.toml
index cacbc49f..4f20aa4a 100644
--- a/index.toml
+++ b/index.toml
@@ -246,4 +246,15 @@ notebook = "25_Customizing_Agent.ipynb"
 aliases = ["customizing-agent"]
 completion_time = "15 min"
 created_at = 2023-07-19
+featured = true
+
+[[tutorial]]
+title = "Creating a Hybrid Retrieval Pipeline"
+description = "Learn how to combine Retrievers to enhance retrieval"
+level = "intermediate"
+weight = 63
+notebook = "26_Hybrid_Retrieval.ipynb"
+aliases = ["hybrid-retrieval"]
+completion_time = "15 min"
+created_at = 2023-10-10
 featured = true
\ No newline at end of file
diff --git a/tutorials/26_Hybrid_Retrieval.ipynb b/tutorials/26_Hybrid_Retrieval.ipynb
new file mode 100644
index 00000000..d03bcacc
--- /dev/null
+++ b/tutorials/26_Hybrid_Retrieval.ipynb
@@ -0,0 +1,453 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kTas9ZQ7lXP7"
+   },
+   "source": [
+    "# Tutorial: Creating a Hybrid Retrieval Pipeline\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Nodes Used**: `EmbeddingRetriever`, `BM25Retriever`, `JoinDocuments`, `SentenceTransformersRanker` and `InMemoryDocumentStore`\n",
+    "- **Goal**: After completing this tutorial, you will have learned about creating your first hybrid retrieval and when it's useful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0hw_zoKolXQL"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "\n",
+    "**Hybrid Retrieval** merges dense and sparse vectors together to deliver the best of both search methods. Generally speaking, dense vectors excel at understanding the context of the query, whereas sparse vectors excel at keyword matches.\n",
+    "\n",
+    "There are many cases when a simple sparse retrieval like BM25 performs better than a dense retrieval (for example in a specific domain like healthcare) because a dense encoder model needs to be trained on data. For more details about Hybrid Retrieval, check out [Blog Post: Hybrid Document Retrieval](https://haystack.deepset.ai/blog/hybrid-retrieval)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ITs3WTT5lXQT"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2g9fhjxDlXQb"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, let's install the latest release of Haystack with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "L40ZxZW8lXQh"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install datasets>=2.6.1\n",
+    "pip install farm-haystack[inference]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CJBcPNbBlXQq"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "lUbTGVo4lXQv"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(26)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5HLBUYOplXQ1"
+   },
+   "source": [
+    "## Creating a Hybrid Retrieval Pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "usdANiAGlXQ9"
+   },
+   "source": [
+    "### 1) Initialize the DocumentStore and Clean Documents\n",
+    "\n",
+    "\n",
+    "You'll start creating a hybrid pipeline by initializing a DocumentStore and preprocessing documents before storing them in the DocumentStore.\n",
+    "\n",
+    "You will use the PubMed Abstracts as Documents. There are a lot of datasets from PubMed on Hugging Face Hub; you will use [ywchoi/pubmed_abstract_3](https://huggingface.co/datasets/ywchoi/pubmed_abstract_3/viewer/default/test) in this tutorial.\n",
+    "\n",
+    "Initialize `InMemoryDocumentStore` and don't forget to set `use_bm25=True` and the dimension of your embeddings in `embedding_dim`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "cLbh-UtelXRL"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from haystack.document_stores import InMemoryDocumentStore\n",
+    "\n",
+    "dataset = load_dataset(\"ywchoi/pubmed_abstract_3\", split=\"test\")\n",
+    "\n",
+    "document_store = InMemoryDocumentStore(use_bm25=True, embedding_dim=384)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WgxFjbGgdQla"
+   },
+   "source": [
+    "You can create your document list with a simple for loop.\n",
+    "The data has 3 features:\n",
+    "* *pmid*\n",
+    "* *title*\n",
+    "* *text*\n",
+    "\n",
+    "Concatenate *title* and *text* to embed and search both. The single features will be stored as metadata, and you will use them to have a **pretty print** of the search results.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RvrG_QzirSsq"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.schema import Document\n",
+    "\n",
+    "documents = []\n",
+    "for doc in dataset:\n",
+    "    documents.append(\n",
+    "        Document(\n",
+    "            content=doc[\"title\"] + \" \" + doc[\"text\"],\n",
+    "            meta={\"title\": doc[\"title\"], \"abstract\": doc[\"text\"], \"pmid\": doc[\"pmid\"]},\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tNJkztzWaWzZ"
+   },
+   "source": [
+    "The PreProcessor class is designed to help you clean and split text into sensible units.\n",
+    "\n",
+    "> To learn about the preprocessing step, check out [Tutorial: Preprocessing Your Documents](https://haystack.deepset.ai/tutorials/08_preprocessing).\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RrCCmLvGqhYw"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PreProcessor\n",
+    "\n",
+    "preprocessor = PreProcessor(\n",
+    "    clean_empty_lines=True,\n",
+    "    clean_whitespace=True,\n",
+    "    clean_header_footer=True,\n",
+    "    split_by=\"word\",\n",
+    "    split_length=512,\n",
+    "    split_overlap=32,\n",
+    "    split_respect_sentence_boundary=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "8PzBU_jnsBTZ"
+   },
+   "outputs": [],
+   "source": [
+    "docs_to_index = preprocessor.process(documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ii9x0gr9lXRT"
+   },
+   "source": [
+    "### 2) Initialize the Retrievers\n",
+    "\n",
+    "Initialize a sparse retriever using [BM25](https://docs.haystack.deepset.ai/docs/retriever#bm25-recommended) and a dense retriever using a [sentence-transformers model](https://docs.haystack.deepset.ai/docs/retriever#embedding-retrieval-recommended)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "rXHbHru0lXRY"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import EmbeddingRetriever, BM25Retriever\n",
+    "\n",
+    "sparse_retriever = BM25Retriever(document_store=document_store)\n",
+    "dense_retriever = EmbeddingRetriever(\n",
+    "    document_store=document_store,\n",
+    "    embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
+    "    use_gpu=True,\n",
+    "    scale_score=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cx8307ZglXRd"
+   },
+   "source": [
+    "### 3) Write Documents ad Update Embeddings\n",
+    "\n",
+    "Write documents to the DocumentStore, first by deleting any remaining documents and then calling `write_documents()`. The `update_embeddings()` method uses the given retriever to create an embedding for each document."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7S-QdaDYlXRg"
+   },
+   "outputs": [],
+   "source": [
+    "document_store.delete_documents()\n",
+    "document_store.write_documents(docs_to_index)\n",
+    "document_store.update_embeddings(retriever=dense_retriever)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_gugk_k2lXRi"
+   },
+   "source": [
+    "### 4) Initialize JoinDocuments and Ranker\n",
+    "\n",
+    "While exploring hybrid search, we needed a way to combine the results of BM25 and dense vector search into a single ranked list. It may not be obvious how to combine them:\n",
+    "\n",
+    "* Different retrievers use incompatible score types, like BM25 and cosine similarity.\n",
+    "* Documents may come from single or multiple sources at the same time. There should be a way to deal with duplicates in the final ranking.\n",
+    "\n",
+    "The merging and ranking of the documents from different retrievers is an open problem, however, Haystack offers several methods in [`JoinDocuments`](https://docs.haystack.deepset.ai/docs/join_documents). Here, you will use the simplest, `concatenate`, and pass the task to the ranker.\n",
+    "\n",
+    "Use a [re-ranker based on a cross-encoder](https://docs.haystack.deepset.ai/docs/ranker#sentencetransformersranker) that scores the relevancy of all candidates for the given search query.\n",
+    "For more information about the `Ranker`, check the Haystack [docs](https://docs.haystack.deepset.ai/docs/ranker)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "d_RiKspTlXRl"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import JoinDocuments, SentenceTransformersRanker\n",
+    "\n",
+    "join_documents = JoinDocuments(join_mode=\"concatenate\")\n",
+    "rerank = SentenceTransformersRanker(model_name_or_path=\"cross-encoder/ms-marco-MiniLM-L-6-v2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PexSrsBLlXRp"
+   },
+   "source": [
+    "### 5) Create the Hybrid Retrieval Pipeline\n",
+    "\n",
+    "With a Haystack `Pipeline`, you can connect your building blocks into a search pipeline. Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
+    "You can learn more about Pipelines in the [docs](https://docs.haystack.deepset.ai/docs/pipelines)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "i0XLbnAXlXRt"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.pipelines import Pipeline\n",
+    "\n",
+    "pipeline = Pipeline()\n",
+    "pipeline.add_node(component=sparse_retriever, name=\"SparseRetriever\", inputs=[\"Query\"])\n",
+    "pipeline.add_node(component=dense_retriever, name=\"DenseRetriever\", inputs=[\"Query\"])\n",
+    "pipeline.add_node(component=join_documents, name=\"JoinDocuments\", inputs=[\"SparseRetriever\", \"DenseRetriever\"])\n",
+    "pipeline.add_node(component=rerank, name=\"ReRanker\", inputs=[\"JoinDocuments\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "V3bsFkHuhHn4"
+   },
+   "source": [
+    "### Generating a Pipeline Diagram\n",
+    "\n",
+    "With any Pipeline, whether prebuilt or custom constructed, you can save a diagram showing how all the components are connected. For example, the hybrid pipeline should look like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "oCIMtwmThQG4"
+   },
+   "outputs": [],
+   "source": [
+    "# Uncomment the following to generate the images\n",
+    "# !apt install libgraphviz-dev\n",
+    "# !pip install pygraphviz\n",
+    "\n",
+    "# pipeline.draw(\"pipeline_hybrid.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sTTVLUJylXRx"
+   },
+   "source": [
+    "## Trying Out the Hybrid Pipeline\n",
+    "\n",
+    "Search an article with Hybrid Retrieval. If you want to see all the steps, enable `debug=True` in `JoinDocuments`'s `params`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "p-5WbeBulXR0"
+   },
+   "outputs": [],
+   "source": [
+    "prediction = pipeline.run(\n",
+    "    query=\"treatment for HIV\",\n",
+    "    params={\n",
+    "        \"SparseRetriever\": {\"top_k\": 10},\n",
+    "        \"DenseRetriever\": {\"top_k\": 10},\n",
+    "        \"JoinDocuments\": {\"top_k_join\": 15},  # comment for debug\n",
+    "        # \"JoinDocuments\": {\"top_k_join\": 15, \"debug\":True}, #uncomment for debug\n",
+    "        \"ReRanker\": {\"top_k\": 5},\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WvPv1cJ6gbBJ"
+   },
+   "source": [
+    "Create a function to print a kind of *search page*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "raL_z_sByDoQ"
+   },
+   "outputs": [],
+   "source": [
+    "def pretty_print_results(prediction):\n",
+    "    for doc in prediction[\"documents\"]:\n",
+    "        print(doc.meta[\"title\"], \"\\t\", doc.score)\n",
+    "        print(doc.meta[\"abstract\"])\n",
+    "        print(\"\\n\", \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "mSUiizGNytwX"
+   },
+   "outputs": [],
+   "source": [
+    "pretty_print_results(prediction)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
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literal 0
HcmV?d00001


From 1b61e223fdd79e76694b85d5c1899fae6371703b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 27 Oct 2023 12:06:35 +0300
Subject: [PATCH 122/206] Update README.md (#245)

Add tutorial 26
---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 79003df7..5df6e7d0 100644
--- a/README.md
+++ b/README.md
@@ -43,3 +43,4 @@ To contribute to the tutorials, please check out our [Contributing Guidelines](.
 | [Answering Complex Questions with Agents](./tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb)                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) |
 | [Building a Conversational Chat App](./tutorials/24_Building_Chat_App.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/24_Building_Chat_App.ipynb) |
 | [Customizing Agent to Chat with Your Documents](./tutorials/25_Customizing_Agent.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/25_Customizing_Agent.ipynb) |
+| [Creating a Hybrid Retrieval Pipeline](./tutorials/26_Hybrid_Retrieval.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/26_Hybrid_Retrieval.ipynb) |

From 8649533ca46e3b2a6d0252fc1e1b850bfb8ea8be Mon Sep 17 00:00:00 2001
From: Sean Keegan <seanryankeegan@gmail.com>
Date: Fri, 27 Oct 2023 12:32:41 +0300
Subject: [PATCH 123/206] Fix minor typo (#244)

---
 tutorials/01_Basic_QA_Pipeline.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/01_Basic_QA_Pipeline.ipynb b/tutorials/01_Basic_QA_Pipeline.ipynb
index b06c4099..3c0f3174 100644
--- a/tutorials/01_Basic_QA_Pipeline.ipynb
+++ b/tutorials/01_Basic_QA_Pipeline.ipynb
@@ -189,7 +189,7 @@
    "source": [
     "The code in this tutorial uses the Game of Thrones data, but you can also supply your own *.txt* files and index them in the same way.\n",
     "\n",
-    "As an alternative, you can cast you text data into [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore using `DocumentStore.write_documents()`."
+    "As an alternative, you can cast your text data into [Document objects](https://docs.haystack.deepset.ai/docs/documents_answers_labels#document) and write them into the DocumentStore using `DocumentStore.write_documents()`."
    ]
   },
   {

From d2d7a436bac1ef0a3df7a0e085ec2dbd07967bab Mon Sep 17 00:00:00 2001
From: sahusiddharth <112792547+sahusiddharth@users.noreply.github.com>
Date: Mon, 6 Nov 2023 16:37:36 +0530
Subject: [PATCH 124/206] corrected spelling mistake (#247)

ad to and
---
 tutorials/26_Hybrid_Retrieval.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/26_Hybrid_Retrieval.ipynb b/tutorials/26_Hybrid_Retrieval.ipynb
index d03bcacc..261b0c2b 100644
--- a/tutorials/26_Hybrid_Retrieval.ipynb
+++ b/tutorials/26_Hybrid_Retrieval.ipynb
@@ -246,7 +246,7 @@
     "id": "cx8307ZglXRd"
    },
    "source": [
-    "### 3) Write Documents ad Update Embeddings\n",
+    "### 3) Write Documents and Update Embeddings\n",
     "\n",
     "Write documents to the DocumentStore, first by deleting any remaining documents and then calling `write_documents()`. The `update_embeddings()` method uses the given retriever to create an embedding for each document."
    ]

From 4775aa98936b81ca0e4602dd0cb6393e35585996 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 6 Nov 2023 14:15:38 +0300
Subject: [PATCH 125/206] Update tut 6 FaissDocumentStore in memory sqlite to
 allow re-running the tutorial (#248)

---
 tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
index 922fd8b5..76ce64d7 100644
--- a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+++ b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
@@ -171,7 +171,7 @@
    "source": [
     "from haystack.document_stores import FAISSDocumentStore\n",
     "\n",
-    "document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\")"
+    "document_store = FAISSDocumentStore(sql_url=\"sqlite:///\", faiss_index_factory_str=\"Flat\")"
    ]
   },
   {

From 3fbe59baf035e8b5d2c81fa77d9e1267d8b7fe0e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 14 Nov 2023 16:14:34 +0300
Subject: [PATCH 126/206] Hide deprecated tutorials (#249)

* Hide deprecated tutorials

* Hide these tutorials from the overview page
* Don't index them for search

* Remove prints
---
 index.toml                    | 3 +++
 scripts/generate_markdowns.py | 1 +
 scripts/generate_txt.py       | 3 ++-
 3 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/index.toml b/index.toml
index 4f20aa4a..c09b0be7 100644
--- a/index.toml
+++ b/index.toml
@@ -70,6 +70,7 @@ notebook = "07_RAG_Generator.ipynb"
 aliases = []
 created_at = 2021-08-12
 haystack_version = "1.17.2"
+hidden = true
 
 [[tutorial]]
 title = "Preprocessing Your Documents"
@@ -99,6 +100,7 @@ notebook = "10_Knowledge_Graph.ipynb"
 aliases = ["knowledge-graph"]
 created_at = 2021-08-12
 haystack_version = "1.16.1"
+hidden = true
 
 [[tutorial]]
 title = "How to Use Pipelines"
@@ -118,6 +120,7 @@ notebook = "12_LFQA.ipynb"
 aliases = ["lfqa"]
 created_at = 2021-08-12
 haystack_version = "1.17.2"
+hidden = true
 
 [[tutorial]]
 title = "Question Generation"
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index 40ef0411..ad6ef7ed 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -24,6 +24,7 @@ def generate_frontmatter(config, tutorial):
 layout: {config["layout"]}
 featured: {tutorial.get("featured", False)}
 haystack_version: "{tutorial.get("haystack_version", "latest")}"
+hidden: {tutorial.get("hidden", False)}
 colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')}
 toc: {config["toc"]}
 title: "{tutorial["title"]}"
diff --git a/scripts/generate_txt.py b/scripts/generate_txt.py
index a95a4fa9..6c789d92 100644
--- a/scripts/generate_txt.py
+++ b/scripts/generate_txt.py
@@ -55,7 +55,8 @@ def generate_markdown_from_notebook(tutorial, output_path, tutorials_path):
     for notebook in notebooks:
         notebook_name = str(notebook).split("/")[-1]
         tutorial_config = notebooks_configs.get(notebook_name)
-        if tutorial_config:
+        if tutorial_config and not tutorial_config.get("hidden", False):
+            # Skip tutorials that needs to be hidden
             generate_markdown_from_notebook(tutorial_config, args.output, notebook)
 
             if args.metadata:

From 31f406254327fb388856bf6883cb55d55e11ace6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 15 Nov 2023 12:57:05 +0300
Subject: [PATCH 127/206] Update full_dc_sync.py

---
 scripts/full_dc_sync.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/full_dc_sync.py b/scripts/full_dc_sync.py
index dfcefe9f..78faef14 100644
--- a/scripts/full_dc_sync.py
+++ b/scripts/full_dc_sync.py
@@ -23,7 +23,7 @@ def dc_token():
 
 
 async def upload_to_dc(workspace: str, file: Path, meta: Dict):
-    url = f"https://api.cloud.deepset.ai/api/v1/workspaces/{workspace}/files?write_mode=OVERWRITE"
+    url = f"https://api.cloud.deepset.ai/api/v1/workspaces/{workspace}/files"
     files = {"file": (file.name, file.open("rb"), "text/plain")}
     data = {"meta": json.dumps(meta, default=str)}
     print(f"Uploading {file.name}")

From 948b7acd943649435f620433619ff1de530597c3 Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <44616784+anakin87@users.noreply.github.com>
Date: Mon, 20 Nov 2023 12:25:19 +0100
Subject: [PATCH 128/206] add missing metrics installation group (#253)

---
 tutorials/09_DPR_training.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/09_DPR_training.ipynb b/tutorials/09_DPR_training.ipynb
index 9ffae5e7..ffe0dd7b 100644
--- a/tutorials/09_DPR_training.ipynb
+++ b/tutorials/09_DPR_training.ipynb
@@ -40,7 +40,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install farm-haystack[colab,inference]"
+    "pip install farm-haystack[colab,inference,metrics]"
    ]
   },
   {

From ed8d7c05a3a790a94be2662e142e492af790f910 Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <44616784+anakin87@users.noreply.github.com>
Date: Mon, 20 Nov 2023 12:28:12 +0100
Subject: [PATCH 129/206] fix tutorial 2 (#252)

---
 tutorials/02_Finetune_a_model_on_your_data.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/02_Finetune_a_model_on_your_data.ipynb b/tutorials/02_Finetune_a_model_on_your_data.ipynb
index 2d4ce507..3620e168 100644
--- a/tutorials/02_Finetune_a_model_on_your_data.ipynb
+++ b/tutorials/02_Finetune_a_model_on_your_data.ipynb
@@ -121,7 +121,7 @@
    "source": [
     "from haystack.nodes import FARMReader\n",
     "\n",
-    "reader = FARMReader(model_name_or_path=\"distilbert-base-uncased-distilled-squad\", use_gpu=True, devices=[\"mps\"])"
+    "reader = FARMReader(model_name_or_path=\"distilbert-base-uncased-distilled-squad\", use_gpu=True)"
    ]
   },
   {

From a9207588539dc90a18b8d87a32fb2f8942c999ce Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 27 Nov 2023 17:17:18 +0300
Subject: [PATCH 130/206] Mark outdated tutorials on README (#250)

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 5df6e7d0..7b991710 100644
--- a/README.md
+++ b/README.md
@@ -24,12 +24,12 @@ To contribute to the tutorials, please check out our [Contributing Guidelines](.
 | [FAQ Style QA](./tutorials/04_FAQ_style_QA.ipynb)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            |
 | [Evaluation](./tutorials/05_Evaluation.ipynb)                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              |
 | [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                |
-| [RAG Generator](./tutorials/07_RAG_Generator.ipynb)                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           |
+| [[OUTDATED] RAG Generator](./tutorials/07_RAG_Generator.ipynb)                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           |
 | [Preprocessing](./tutorials/08_Preprocessing.ipynb)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           |
 | [DPR Training](./tutorials/09_DPR_training.ipynb)                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            |
-| [Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         |
+| [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         |
 | [Pipelines](./tutorials/11_Pipelines.ipynb)                                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               |
-| [Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    |
+| [[OUTDATED] Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    |
 | [Question Generation](./tutorials/13_Question_generation.ipynb)                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     |
 | [Query Classifier](./tutorials/14_Query_Classifier.ipynb)                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)                                        |
 | [Table QA](./tutorials/15_TableQA.ipynb)                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)                                                 |

From e1548d46903826dee07c997061a9a2da1858f627 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 27 Nov 2023 17:17:31 +0300
Subject: [PATCH 131/206] Remove hidden tutorials from the sitemap (#251)

---
 index.toml                    | 3 +++
 scripts/generate_markdowns.py | 1 +
 2 files changed, 4 insertions(+)

diff --git a/index.toml b/index.toml
index c09b0be7..ba016a14 100644
--- a/index.toml
+++ b/index.toml
@@ -71,6 +71,7 @@ aliases = []
 created_at = 2021-08-12
 haystack_version = "1.17.2"
 hidden = true
+sitemap_exclude = true
 
 [[tutorial]]
 title = "Preprocessing Your Documents"
@@ -101,6 +102,7 @@ aliases = ["knowledge-graph"]
 created_at = 2021-08-12
 haystack_version = "1.16.1"
 hidden = true
+sitemap_exclude = true
 
 [[tutorial]]
 title = "How to Use Pipelines"
@@ -121,6 +123,7 @@ aliases = ["lfqa"]
 created_at = 2021-08-12
 haystack_version = "1.17.2"
 hidden = true
+sitemap_exclude = true
 
 [[tutorial]]
 title = "Question Generation"
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index ad6ef7ed..3332b916 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -25,6 +25,7 @@ def generate_frontmatter(config, tutorial):
 featured: {tutorial.get("featured", False)}
 haystack_version: "{tutorial.get("haystack_version", "latest")}"
 hidden: {tutorial.get("hidden", False)}
+sitemap_exclude: {tutorial.get("sitemap_exclude", False)}
 colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')}
 toc: {config["toc"]}
 title: "{tutorial["title"]}"

From 1002829a7ebf1e7bcd794e928ff5eca66618888d Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <44616784+anakin87@users.noreply.github.com>
Date: Wed, 6 Dec 2023 09:30:00 +0100
Subject: [PATCH 132/206] improve tutorial (#258)

---
 tutorials/24_Building_Chat_App.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tutorials/24_Building_Chat_App.ipynb b/tutorials/24_Building_Chat_App.ipynb
index ebac94cf..e9652b8c 100644
--- a/tutorials/24_Building_Chat_App.ipynb
+++ b/tutorials/24_Building_Chat_App.ipynb
@@ -143,7 +143,7 @@
       "source": [
         "### 2) Create a PromptNode\n",
         "\n",
-        "You'll initialize a PromptNode with the `model_name`, `api_key`, and `max_length` to control the output length of the model. In this tutorial, you'll use [OpenAssistant/oasst-sft-1-pythia-12b](https://huggingface.co/OpenAssistant/oasst-sft-1-pythia-12b), an open source Transformer-based text generation model."
+        "You'll initialize a PromptNode with the `model_name`, `api_key`, and `max_length` to control the output length of the model. In this tutorial, you'll use [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), an open source chat Language Model."
       ]
     },
     {
@@ -156,8 +156,8 @@
       "source": [
         "from haystack.nodes import PromptNode\n",
         "\n",
-        "model_name = \"OpenAssistant/oasst-sft-1-pythia-12b\"\n",
-        "prompt_node = PromptNode(model_name, api_key=model_api_key, max_length=256)"
+        "model_name = \"https://huggingface.co/HuggingFaceH4/zephyr-7b-beta\"\n",
+        "prompt_node = PromptNode(model_name, api_key=model_api_key, max_length=256, stop_words=[\"Human\"])"
       ]
     },
     {

From 9e9d6faa8363b6b74bfb79c6af4836f8bb166307 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 6 Dec 2023 19:31:09 +0300
Subject: [PATCH 133/206] Add RAG Pipeline tutorial for Haystack 2.0 (#257)

* Add rag pipeline tut with Haystack 2.0

* Remove featured tag

* Skip Haystack 2.0 tutorials

* Add links to the overview
---
 .github/workflows/run_tutorials.yml   |    1 +
 README.md                             |    1 +
 index.toml                            |   13 +-
 scripts/generate_markdowns.py         |    1 +
 scripts/generate_matrix.py            |    4 +
 tutorials/27_First_RAG_Pipeline.ipynb | 2555 +++++++++++++++++++++++++
 6 files changed, 2574 insertions(+), 1 deletion(-)
 create mode 100644 tutorials/27_First_RAG_Pipeline.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 44a06b31..c7e71e10 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -67,6 +67,7 @@ jobs:
               "tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb"
               "tutorials/24_Building_Chat_App.ipynb"
               "tutorials/25_Customizing_Agent.ipynb"
+              "tutorials/27_First_RAG_Pipeline.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index 7b991710..6b99c002 100644
--- a/README.md
+++ b/README.md
@@ -44,3 +44,4 @@ To contribute to the tutorials, please check out our [Contributing Guidelines](.
 | [Building a Conversational Chat App](./tutorials/24_Building_Chat_App.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/24_Building_Chat_App.ipynb) |
 | [Customizing Agent to Chat with Your Documents](./tutorials/25_Customizing_Agent.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/25_Customizing_Agent.ipynb) |
 | [Creating a Hybrid Retrieval Pipeline](./tutorials/26_Hybrid_Retrieval.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/26_Hybrid_Retrieval.ipynb) |
+| [Your First QA Pipeline with Retrieval-Augmentation](./tutorials/27_First_RAG_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/27_First_RAG_Pipeline.ipynb) |
diff --git a/index.toml b/index.toml
index ba016a14..3749eb3e 100644
--- a/index.toml
+++ b/index.toml
@@ -263,4 +263,15 @@ notebook = "26_Hybrid_Retrieval.ipynb"
 aliases = ["hybrid-retrieval"]
 completion_time = "15 min"
 created_at = 2023-10-10
-featured = true
\ No newline at end of file
+featured = true
+
+[[tutorial]]
+title = "Creating Your First QA Pipeline with Retrieval-Augmentation"
+description = "Build your first generative QA pipeline with OpenAI GPT models"
+level = "beginner"
+weight = 5
+notebook = "27_First_RAG_Pipeline.ipynb"
+aliases = []
+completion_time = "10 min"
+created_at = 2023-12-05
+haystack_2 = true
\ No newline at end of file
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index 3332b916..89f156c6 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -24,6 +24,7 @@ def generate_frontmatter(config, tutorial):
 layout: {config["layout"]}
 featured: {tutorial.get("featured", False)}
 haystack_version: "{tutorial.get("haystack_version", "latest")}"
+haystack_2: {tutorial.get("haystack_2", False)}
 hidden: {tutorial.get("hidden", False)}
 sitemap_exclude: {tutorial.get("sitemap_exclude", False)}
 colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')}
diff --git a/scripts/generate_matrix.py b/scripts/generate_matrix.py
index fb8098f8..4f9ca07d 100644
--- a/scripts/generate_matrix.py
+++ b/scripts/generate_matrix.py
@@ -37,6 +37,10 @@ def read_index(path):
             # so there's nothing to test
             continue
 
+        if tutorial.get("haystack_2", False):
+            # Haystack 2.0 tutorials should be skipped for now
+            continue
+
         version = tutorial.get("haystack_version", args.version)
         if version[0] != "v":
             version = f"v{version}"
diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
new file mode 100644
index 00000000..49734e55
--- /dev/null
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -0,0 +1,2555 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2OvkPji9O-qX"
+   },
+   "source": [
+    "# Tutorial: Creating Your First QA Pipeline with Retrieval-Augmentation\n",
+    "\n",
+    "- **Level**: Beginner\n",
+    "- **Time to complete**: 10 minutes\n",
+    "- **Nodes Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `GPTGenerator`\n",
+    "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI.\n",
+    "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and GPTGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LFqHcXYPO-qZ"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves three main components: [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever] for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) for creating a template prompt, and [GPTGenerator](https://docs.haystack.deepset.ai/v2.0/docs/gptgenerator) for generating responses.\n",
+    "\n",
+    "For this tutorial, you'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QXjVlbPiO-qZ"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Kww5B_vXO-qZ"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "Install Haystack 2.0 Beta and [datasets](https://pypi.org/project/datasets/) with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "UQbU8GUfO-qZ"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai\n",
+    "pip install datasets>=2.6.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Wl_jYERtO-qa"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "A76B4S49O-qa"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(27)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_lvfew16O-qa"
+   },
+   "source": [
+    "## Initializing the DocumentStore\n",
+    "\n",
+    "You'll start creating your question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, you'll be using the `InMemoryDocumentStore`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "CbVN-s5LO-qa"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.document_stores import InMemoryDocumentStore\n",
+    "\n",
+    "document_store = InMemoryDocumentStore()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yL8nuJdWO-qa"
+   },
+   "source": [
+    "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XvLVaFHTO-qb"
+   },
+   "source": [
+    "The DocumentStore is now ready. Now it's time to fill it with some Documents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HryYZP9ZO-qb"
+   },
+   "source": [
+    "## Fetching and Writing Documents\n",
+    "\n",
+    "You'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, you don't need to perform any additional cleaning or splitting.\n",
+    "\n",
+    "Fetch the data and write it to the DocumentStore:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 194,
+     "referenced_widgets": [
+      "2b80b4402f1c4245835b0a6ff9724f17",
+      "46333addbfc5402b909f8671494b324f",
+      "ea2aad5049ba46339c12eb1d8f6e5069",
+      "de37e5d8932d4c6fab471c8e6aa69d38",
+      "42eb5725b3454b41924e9cbbe1526bda",
+      "d20a813e1de54cce85837e3f89d9ac72",
+      "f723dc909e054ff0a2dec3ce425793b4",
+      "3f9c5812fc6d480185467e804082ae2b",
+      "147c9c74cba740608709d4f20577ef01",
+      "5ee3bc5478824ab097a5f4205018bd7f",
+      "3b84d92477174d319edceab41aade78a",
+      "35cdea243a1e4bd29ea077d5a453b86a",
+      "fbfab62f10e14d909b5e64a8c982fa71",
+      "674f8660be5d4ee6ba5fe657934065f2",
+      "1bcea8e4134449eea12d43ffbbfe0c7d",
+      "9fde7b64a0964e1e929ba9a10ba9dce3",
+      "368afc1b3aa143e085dfbc053e34b1aa",
+      "4c26080f262b4688be7d67eccd871a5c",
+      "44388b7afeaa4d059bd93debb114b931",
+      "cbaba46a13ac45afbb7e37482608fbe1",
+      "9733632c4d2843a09551ba51ab29dff0",
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+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from haystack import Document\n",
+    "\n",
+    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
+    "docs = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]\n",
+    "document_store.write_documents(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0_cj-5m-O-qb"
+   },
+   "source": [
+    "## Initializing the Retriever\n",
+    "\n",
+    "Initialize a [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) and make it use the InMemoryDocumentStore we initialized earlier in this tutorial. This Retriever will get the relevant documents to the query:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-uo-6fjiO-qb"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.retrievers import InMemoryBM25Retriever\n",
+    "\n",
+    "retriever = InMemoryBM25Retriever(document_store)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6CEuQpB7O-qb"
+   },
+   "source": [
+    "## Defining a Template Prompt\n",
+    "\n",
+    "Create a custom prompt for a generative question answering task using the RAG approach. The prompt should take in two parameters: `documents`, which are retrieved from a document store, and a `question` from the user. Use the Jinja2 looping syntax to combine the content of the retrieved documents in the prompt.\n",
+    "\n",
+    "Next, initialize a [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) instance with your prompt template. The PromptBuilder, when given the necessary values, will automatically fill in the variable values and generate a complete prompt. This approach allows for a more tailored and effective question-answering experience."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ObahTh45FqOT"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.builders import PromptBuilder\n",
+    "\n",
+    "template = \"\"\"\n",
+    "Given the following information, answer the question.\n",
+    "\n",
+    "Context:\n",
+    "{% for document in documents %}\n",
+    "    {{ document.content }}\n",
+    "{% endfor %}\n",
+    "\n",
+    "Question: {{question}}\n",
+    "Answer:\n",
+    "\"\"\"\n",
+    "\n",
+    "prompt_builder = PromptBuilder(template=template)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HR14lbfcFtXj"
+   },
+   "source": [
+    "## Initializing a Generator\n",
+    "\n",
+    "\n",
+    "Generators are the components that interacts with large language models (LLMs). Now, initialize a [GPTGenerator](https://docs.haystack.deepset.ai/v2.0/docs/gptgenerator) that can communicate with OpenAI GPT models with an OpenAI `api_key` and the model name you'd like to use. If you don't provide any `model_name`, the GPTGenerator defaults to `gpt-3.5-turbo`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "SavE_FAqfApo",
+    "outputId": "4c1fa657-8ca0-42ca-8e49-161edf3be505"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "from haystack.components.generators import GPTGenerator\n",
+    "\n",
+    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", getpass(\"Enter OpenAI API key:\"))\n",
+    "generator = GPTGenerator(api_key=openai_api_key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nenbo2SvycHd"
+   },
+   "source": [
+    "> You can replace `GPTGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1bfHwOQwycHe"
+   },
+   "source": [
+    "## Building the Pipeline\n",
+    "\n",
+    "To build a pipeline, add all components to your pipeline and connect them. Create connections from `retriever` to the `prompt_builder` and from `prompt_builder` to `llm`. Explicitly connect the output of `retriever` with \"documents\" input of the `prompt_builder` to make the connection obvious as `prompt_builder` has two inputs (\"documents\" and \"question\"). For more information on pipelines and creating connections, refer to [Creating Pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines) documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "f6NFmpjEO-qb"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.pipeline import Pipeline\n",
+    "\n",
+    "basic_rag_pipeline = Pipeline()\n",
+    "# Add components to your pipeline\n",
+    "basic_rag_pipeline.add_component(\"retriever\", retriever)\n",
+    "basic_rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n",
+    "basic_rag_pipeline.add_component(\"llm\", generator)\n",
+    "\n",
+    "# Now, connect the components to each other\n",
+    "basic_rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
+    "basic_rag_pipeline.connect(\"prompt_builder\", \"llm\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mtcrMpjdzBYo"
+   },
+   "source": [
+    "### Visualize the Pipeline\n",
+    "\n",
+    "Draw the pipeline with the `draw()` method to confirm the connections are correct. You can find the diagram in the Files section of this Colab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "DAvgZnaczEVd"
+   },
+   "outputs": [],
+   "source": [
+    "basic_rag_pipeline.draw(\"basic-rag-pipeline.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6NqyLhx7O-qc"
+   },
+   "source": [
+    "That's it! The pipeline's ready to generate answers to questions!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DBAyF5tVO-qc"
+   },
+   "source": [
+    "## Asking a Question\n",
+    "\n",
+    "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to both the `retriever` and the `prompt_builder`. This ensures that the `{{question}}` variable in the template prompt gets replaced with your specific question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 86,
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+    },
+    "id": "Vnt283M5O-qc",
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+   "source": [
+    "question = \"What does Rhodes Statue look like?\"\n",
+    "\n",
+    "response = basic_rag_pipeline.run({\"retriever\": {\"query\": question}, \"prompt_builder\": {\"question\": question}})\n",
+    "\n",
+    "print(response[\"llm\"][\"replies\"][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IWQN-aoGO-qc"
+   },
+   "source": [
+    "Here are some other example questions to test:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_OHUQ5xxO-qc"
+   },
+   "outputs": [],
+   "source": [
+    "examples = [\n",
+    "    \"Where is Gardens of Babylon?\",\n",
+    "    \"Why did people build Great Pyramid of Giza?\",\n",
+    "    \"What does Rhodes Statue look like?\",\n",
+    "    \"Why did people visit the Temple of Artemis?\",\n",
+    "    \"What is the importance of Colossus of Rhodes?\",\n",
+    "    \"What happened to the Tomb of Mausolus?\",\n",
+    "    \"How did Colossus of Rhodes collapse?\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XueCK3y4O-qc"
+   },
+   "source": [
+    "🎉 Congratulations! You've learned how to create a generative QA system for your documents with RAG approach."
+   ]
+  }
+ ],
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From 532b62951ce4830c4e91e797bcd1e4971edc116e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 6 Dec 2023 19:40:49 +0300
Subject: [PATCH 134/206] Update 27_First_RAG_Pipeline.ipynb

---
 tutorials/27_First_RAG_Pipeline.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 49734e55..4c34f0fa 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -25,7 +25,7 @@
    "source": [
     "## Overview\n",
     "\n",
-    "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves three main components: [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever] for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) for creating a template prompt, and [GPTGenerator](https://docs.haystack.deepset.ai/v2.0/docs/gptgenerator) for generating responses.\n",
+    "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves three main components: [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) for creating a template prompt, and [GPTGenerator](https://docs.haystack.deepset.ai/v2.0/docs/gptgenerator) for generating responses.\n",
     "\n",
     "For this tutorial, you'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"
    ]

From c1fe5f2e4d46a90580eca1db80dd4de02ecc4572 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Christoph=20D=C3=B6berl?= <christoph@doeberl.at>
Date: Thu, 7 Dec 2023 14:23:43 +0100
Subject: [PATCH 135/206] Update 27_First_RAG_Pipeline.ipynb (#259)

haystack 2.0 changes naming convention from nodes to components
---
 tutorials/27_First_RAG_Pipeline.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 4c34f0fa..191e481d 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "- **Level**: Beginner\n",
     "- **Time to complete**: 10 minutes\n",
-    "- **Nodes Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `GPTGenerator`\n",
+    "- **Components Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `GPTGenerator`\n",
     "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI.\n",
     "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and GPTGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n",
     "\n",

From fc06c8a93993e43d1d3591cebe7f7f0001ca3ab4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 8 Dec 2023 20:15:39 +0300
Subject: [PATCH 136/206] Fix the model name error (#262)

---
 tutorials/24_Building_Chat_App.ipynb | 878 +++++++++++++--------------
 1 file changed, 432 insertions(+), 446 deletions(-)

diff --git a/tutorials/24_Building_Chat_App.ipynb b/tutorials/24_Building_Chat_App.ipynb
index e9652b8c..e45a37b6 100644
--- a/tutorials/24_Building_Chat_App.ipynb
+++ b/tutorials/24_Building_Chat_App.ipynb
@@ -1,451 +1,437 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TEebQqubxa4G"
-      },
-      "source": [
-        "# Tutorial: Building a Conversational Chat App\n",
-        "\n",
-        "- **Level**: Intermediate\n",
-        "- **Time to complete**: 10 minutes\n",
-        "- **Nodes Used**: `PromptNode`, `ConversationalAgent` and `ConversationSummaryMemory`\n",
-        "- **Goal**: After completing this tutorial, you will have learned how to use ConversationalAgent to build a conversational chat application\n",
-        "- **Prerequisites**: A [Hugging Face API Key](https://huggingface.co/settings/tokens)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DQLWWW1Yy_Ta"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "A [ConversationalAgent](https://docs.haystack.deepset.ai/docs/agent#conversational-agent) is a type of Agent that is specifically implemented to create chat applications easily. With its memory integration, the ConversationalAgent enables human-like conversation with large language models (LLMs).\n",
-        "\n",
-        "This tutorial introduces you to the ConversationalAgent, ConversationSummaryMemory and explains how you can create your conversational chat application.\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "obaSuZBHy8PF"
-      },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2_nC2XoPzDVh"
-      },
-      "source": [
-        "## Installing Haystack\n",
-        "\n",
-        "To start, install the latest release of Haystack with `pip`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "JiZktTKoaHp5"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install --upgrade pip\n",
-        "pip install farm-haystack[colab]"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RrPgO_1vzWb6"
-      },
-      "source": [
-        "\n",
-        "### Enabling Telemetry\n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product, but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "6YZjKAvOzZRq"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(24)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "n0pnO7S6tbyW"
-      },
-      "source": [
-        "## Initializing the ConversationalAgent\n",
-        "\n",
-        "To initialize a ConversationalAgent, you'll first need to create a PromptNode to define the LLM that your chat application will use. Then, you'll add a memory to enable the application to store previous conversation and use this memory to make the interaction more human-like.\n",
-        "\n",
-        "Now, create necessary components for a ConversationalAgent:"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1Omji8PK_675"
-      },
-      "source": [
-        "### 1) Provide a Hugging Face API Key\n",
-        "\n",
-        "Hugging Face offers [a hosted Inference API](https://huggingface.co/docs/api-inference/index) which you can use to access machine learning models using simple HTTP requests. This way, you don't need to download models from the hub. To use the service, you need to provide an [API key](https://huggingface.co/settings/tokens) from Hugging Face:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "qWuRxFWGcAL4"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from getpass import getpass\n",
-        "\n",
-        "model_api_key = os.getenv(\"HF_API_KEY\", None) or getpass(\"Enter HF API key:\")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "V4LI3vzH7Xvo"
-      },
-      "source": [
-        "### 2) Create a PromptNode\n",
-        "\n",
-        "You'll initialize a PromptNode with the `model_name`, `api_key`, and `max_length` to control the output length of the model. In this tutorial, you'll use [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), an open source chat Language Model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "RckAPBT3bSoh"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.nodes import PromptNode\n",
-        "\n",
-        "model_name = \"https://huggingface.co/HuggingFaceH4/zephyr-7b-beta\"\n",
-        "prompt_node = PromptNode(model_name, api_key=model_api_key, max_length=256, stop_words=[\"Human\"])"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "E1e15LLV8ULF"
-      },
-      "source": [
-        "### 3) Create a ConversationSummaryMemory\n",
-        "\n",
-        "To have a chat application closer to a human interaction, you need to provide [memory](https://docs.haystack.deepset.ai/docs/agent#conversational-agent-memory) to the ConversationalAgent. There are two types of memory options in Haystack:\n",
-        "\n",
-        "1.   **ConversationMemory**: stores the conversation history (default).\n",
-        "2.   **ConversationSummaryMemory**: stores the conversation history and periodically generates summaries.\n",
-        "\n",
-        "These memory nodes inject the conversation history into the prompt for the large language model with every run. Instead of using the full conversation history, you'll use ConversationSummaryMemory that sums up the conversation without losing important information, thus saving the token limit.\n",
-        "\n",
-        "You can use the same PromptNode in ConversationSummaryMemory, so the same `OpenAssistant/oasst-sft-1-pythia-12b` model generates chat summaries. By default, ConversationSummaryMemory summarizes the chat with every `3` runs using the predefined [`conversational-summary`](https://prompthub.deepset.ai/?prompt=deepset%2Fconversational-summary) PromptTemplate on PromptHub."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "iBisS_dI8kan"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.agents.memory import ConversationSummaryMemory\n",
-        "\n",
-        "summary_memory = ConversationSummaryMemory(prompt_node)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RpeKtIsSSNSh"
-      },
-      "source": [
-        "> Optionally, you can define a separate PromptNode with another LLM and PromptTemplate for generating conversation summary and use it in the ConversationSummaryMemory."
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MiGGpDW98XjM"
-      },
-      "source": [
-        "### 4) Create a ConversationalAgent\n",
-        "\n",
-        "Now that you have all the necessary components, you can initialize the ConversationalAgent. If you don't provide any tools, the ConversationalAgent uses the [`conversational-agent-without-tools`](https://prompthub.deepset.ai/?prompt=deepset%2Fconversational-agent-without-tools) prompt by default."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_gt2DqNzllPQ"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.agents.conversational import ConversationalAgent\n",
-        "\n",
-        "conversational_agent = ConversationalAgent(prompt_node=prompt_node, memory=summary_memory)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "brdbkCGryKe0"
-      },
-      "source": [
-        "> You can add tools to your chat application using `tools` params of the ConversationalAgent:\n",
-        "> ```python\n",
-        "> conversational_agent = ConversationalAgent(\n",
-        ">    prompt_node=prompt_node,\n",
-        ">    memory=summary_memory,\n",
-        ">    tools=[search_tool]\n",
-        ">)\n",
-        ">```\n",
-        ">To learn how to create tools, check out [Haystack documentation](https://docs.haystack.deepset.ai/docs/agent#tools)."
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Mgeb6nlsGRTy"
-      },
-      "source": [
-        "Now, your conversational agent is ready to chat!"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Uq_yBlEXGe18"
-      },
-      "source": [
-        "## Trying Out a Prompt"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "gAi6DN-LySIH"
-      },
-      "outputs": [],
-      "source": [
-        "conversational_agent.run(\"Tell me three most interesting things about Istanbul, Turkey\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bBwcAcbgMTQN"
-      },
-      "outputs": [],
-      "source": [
-        "conversational_agent.run(\"Can you elaborate on the second item?\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "-wl8dcWpMmNv"
-      },
-      "outputs": [],
-      "source": [
-        "conversational_agent.run(\"Can you turn this info into a twitter thread?\")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mhrNe3xKIVsx"
-      },
-      "source": [
-        "* At any point during the chat, you can use `load()` function to check the chat summary:\n",
-        "\n",
-        "```python\n",
-        "print(conversational_agent.memory.load())\n",
-        "```\n",
-        "\n",
-        "* To delete the whole chat history, call `clear()` method:\n",
-        "\n",
-        "```python\n",
-        "conversational_agent.memory.clear()\n",
-        "```\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KGu3QLQXJo-z"
-      },
-      "source": [
-        "Congratulations! 🎉 You've learned how to use ConversationalAgent to create a chat application with a summarized memory."
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wQyYPjmAG2gJ"
-      },
-      "source": [
-        "## 💬 Example Application\n",
-        "\n",
-        "To take the chat experience to another level, check out this example application. Run the code cell below and use the textarea to interact with the conversational agent. Use the buttons on the right to load or delete the chat summary."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "IEcpPCLKKasg"
-      },
-      "outputs": [],
-      "source": [
-        "import ipywidgets as widgets\n",
-        "from IPython.display import clear_output, display\n",
-        "\n",
-        "## Text Input\n",
-        "user_input = widgets.Textarea(\n",
-        "    value=\"\",\n",
-        "    placeholder=\"Type your prompt here\",\n",
-        "    disabled=False,\n",
-        "    style={\"description_width\": \"initial\"},\n",
-        "    layout=widgets.Layout(width=\"100%\", height=\"100%\"),\n",
-        ")\n",
-        "\n",
-        "## Submit Button\n",
-        "submit_button = widgets.Button(\n",
-        "    description=\"Submit\", button_style=\"success\", layout=widgets.Layout(width=\"100%\", height=\"80%\")\n",
-        ")\n",
-        "\n",
-        "\n",
-        "def on_button_clicked(b):\n",
-        "    user_prompt = user_input.value\n",
-        "    user_input.value = \"\"\n",
-        "    print(\"\\nUser:\\n\", user_prompt)\n",
-        "    conversational_agent.run(user_prompt)\n",
-        "\n",
-        "\n",
-        "submit_button.on_click(on_button_clicked)\n",
-        "\n",
-        "## Show Memory Button\n",
-        "memory_button = widgets.Button(\n",
-        "    description=\"Show Memory\", button_style=\"info\", layout=widgets.Layout(width=\"100%\", height=\"100%\")\n",
-        ")\n",
-        "\n",
-        "\n",
-        "def on_memory_button_clicked(b):\n",
-        "    memory = conversational_agent.memory.load()\n",
-        "    if len(memory):\n",
-        "        print(\"\\nMemory:\\n\", memory)\n",
-        "    else:\n",
-        "        print(\"Memory is empty\")\n",
-        "\n",
-        "\n",
-        "memory_button.on_click(on_memory_button_clicked)\n",
-        "\n",
-        "## Clear Memory Button\n",
-        "clear_button = widgets.Button(\n",
-        "    description=\"Clear Memory\", button_style=\"warning\", layout=widgets.Layout(width=\"100%\", height=\"100%\")\n",
-        ")\n",
-        "\n",
-        "\n",
-        "def on_clear_button_button_clicked(b):\n",
-        "    conversational_agent.memory.clear()\n",
-        "    print(\"\\nMemory is cleared\\n\")\n",
-        "\n",
-        "\n",
-        "clear_button.on_click(on_clear_button_button_clicked)\n",
-        "\n",
-        "## Layout\n",
-        "grid = widgets.GridspecLayout(3, 3, height=\"200px\", width=\"800px\", grid_gap=\"10px\")\n",
-        "grid[0, 2] = clear_button\n",
-        "grid[0:2, 0:2] = user_input\n",
-        "grid[2, 0:] = submit_button\n",
-        "grid[1, 2] = memory_button\n",
-        "display(grid)"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TEebQqubxa4G"
+   },
+   "source": [
+    "# Tutorial: Building a Conversational Chat App\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 10 minutes\n",
+    "- **Nodes Used**: `PromptNode`, `ConversationalAgent` and `ConversationSummaryMemory`\n",
+    "- **Goal**: After completing this tutorial, you will have learned how to use ConversationalAgent to build a conversational chat application\n",
+    "- **Prerequisites**: A [Hugging Face API Key](https://huggingface.co/settings/tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DQLWWW1Yy_Ta"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "A [ConversationalAgent](https://docs.haystack.deepset.ai/docs/agent#conversational-agent) is a type of Agent that is specifically implemented to create chat applications easily. With its memory integration, the ConversationalAgent enables human-like conversation with large language models (LLMs).\n",
+    "\n",
+    "This tutorial introduces you to the ConversationalAgent, ConversationSummaryMemory and explains how you can create your conversational chat application.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "obaSuZBHy8PF"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2_nC2XoPzDVh"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "To start, install the latest release of Haystack with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "JiZktTKoaHp5"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install --upgrade pip\n",
+    "pip install farm-haystack[colab]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RrPgO_1vzWb6"
+   },
+   "source": [
+    "\n",
+    "### Enabling Telemetry\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product, but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6YZjKAvOzZRq"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(24)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "n0pnO7S6tbyW"
+   },
+   "source": [
+    "## Initializing the ConversationalAgent\n",
+    "\n",
+    "To initialize a ConversationalAgent, you'll first need to create a PromptNode to define the LLM that your chat application will use. Then, you'll add a memory to enable the application to store previous conversation and use this memory to make the interaction more human-like.\n",
+    "\n",
+    "Now, create necessary components for a ConversationalAgent:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1Omji8PK_675"
+   },
+   "source": [
+    "### 1) Provide a Hugging Face API Key\n",
+    "\n",
+    "Hugging Face offers [a hosted Inference API](https://huggingface.co/docs/api-inference/index) which you can use to access machine learning models using simple HTTP requests. This way, you don't need to download models from the hub. To use the service, you need to provide an [API key](https://huggingface.co/settings/tokens) from Hugging Face:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "qWuRxFWGcAL4"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "model_api_key = os.getenv(\"HF_API_KEY\", getpass(\"Enter HF API key:\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "V4LI3vzH7Xvo"
+   },
+   "source": [
+    "### 2) Create a PromptNode\n",
+    "\n",
+    "You'll initialize a PromptNode with the `model_name_or_path`, `api_key`, and `max_length` to control the output length of the model. In this tutorial, you'll use [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), an open source chat Language Model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "RckAPBT3bSoh"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.nodes import PromptNode\n",
+    "\n",
+    "prompt_node = PromptNode(\n",
+    "    model_name_or_path=\"HuggingFaceH4/zephyr-7b-beta\", api_key=model_api_key, max_length=256, stop_words=[\"Human\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E1e15LLV8ULF"
+   },
+   "source": [
+    "### 3) Create a ConversationSummaryMemory\n",
+    "\n",
+    "To have a chat application closer to a human interaction, you need to provide [memory](https://docs.haystack.deepset.ai/docs/agent#conversational-agent-memory) to the ConversationalAgent. There are two types of memory options in Haystack:\n",
+    "\n",
+    "1.   **ConversationMemory**: stores the conversation history (default).\n",
+    "2.   **ConversationSummaryMemory**: stores the conversation history and periodically generates summaries.\n",
+    "\n",
+    "These memory nodes inject the conversation history into the prompt for the large language model with every run. Instead of using the full conversation history, you'll use ConversationSummaryMemory that sums up the conversation without losing important information, thus saving the token limit.\n",
+    "\n",
+    "You can use the same PromptNode in ConversationSummaryMemory, so the same `HuggingFaceH4/zephyr-7b-beta` model generates chat summaries. By default, ConversationSummaryMemory summarizes the chat with every `3` runs using the predefined [`conversational-summary`](https://prompthub.deepset.ai/?prompt=deepset%2Fconversational-summary) PromptTemplate on PromptHub."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "iBisS_dI8kan"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.agents.memory import ConversationSummaryMemory\n",
+    "\n",
+    "summary_memory = ConversationSummaryMemory(prompt_node)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RpeKtIsSSNSh"
+   },
+   "source": [
+    "> Optionally, you can define a separate PromptNode with another LLM and PromptTemplate for generating conversation summary and use it in the ConversationSummaryMemory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MiGGpDW98XjM"
+   },
+   "source": [
+    "### 4) Create a ConversationalAgent\n",
+    "\n",
+    "Now that you have all the necessary components, you can initialize the ConversationalAgent. If you don't provide any tools, the ConversationalAgent uses the [`conversational-agent-without-tools`](https://prompthub.deepset.ai/?prompt=deepset%2Fconversational-agent-without-tools) prompt by default."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
     "colab": {
-      "gpuType": "T4",
-      "machine_shape": "hm",
-      "provenance": [],
-      "toc_visible": true
+     "base_uri": "https://localhost:8080/"
     },
-    "gpuClass": "standard",
-    "kernelspec": {
-      "display_name": "Python 3.9.6 64-bit",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.12"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-      }
-    }
+    "id": "_gt2DqNzllPQ",
+    "outputId": "bbbb2aba-d524-4d32-d081-432e43c9b26a"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.agents.conversational import ConversationalAgent\n",
+    "\n",
+    "conversational_agent = ConversationalAgent(prompt_node=prompt_node, memory=summary_memory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "brdbkCGryKe0"
+   },
+   "source": [
+    "> You can add tools to your chat application using `tools` params of the ConversationalAgent:\n",
+    "> ```python\n",
+    "> conversational_agent = ConversationalAgent(\n",
+    ">    prompt_node=prompt_node,\n",
+    ">    memory=summary_memory,\n",
+    ">    tools=[search_tool]\n",
+    ">)\n",
+    ">```\n",
+    ">To learn how to create tools, check out [Haystack documentation](https://docs.haystack.deepset.ai/docs/agent#tools)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Mgeb6nlsGRTy"
+   },
+   "source": [
+    "Now, your conversational agent is ready to chat!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Uq_yBlEXGe18"
+   },
+   "source": [
+    "## Trying Out a Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "gAi6DN-LySIH"
+   },
+   "outputs": [],
+   "source": [
+    "conversational_agent.run(\"Tell me three most interesting things about Istanbul, Turkey\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "bBwcAcbgMTQN"
+   },
+   "outputs": [],
+   "source": [
+    "conversational_agent.run(\"Can you elaborate on the second item?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-wl8dcWpMmNv"
+   },
+   "outputs": [],
+   "source": [
+    "conversational_agent.run(\"Can you turn this info into a twitter thread?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mhrNe3xKIVsx"
+   },
+   "source": [
+    "* At any point during the chat, you can use `load()` function to check the chat summary:\n",
+    "\n",
+    "```python\n",
+    "print(conversational_agent.memory.load())\n",
+    "```\n",
+    "\n",
+    "* To delete the whole chat history, call `clear()` method:\n",
+    "\n",
+    "```python\n",
+    "conversational_agent.memory.clear()\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KGu3QLQXJo-z"
+   },
+   "source": [
+    "Congratulations! 🎉 You've learned how to use ConversationalAgent to create a chat application with a summarized memory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wQyYPjmAG2gJ"
+   },
+   "source": [
+    "## 💬 Example Application\n",
+    "\n",
+    "To take the chat experience to another level, check out this example application. Run the code cell below and use the textarea to interact with the conversational agent. Use the buttons on the right to load or delete the chat summary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "IEcpPCLKKasg"
+   },
+   "outputs": [],
+   "source": [
+    "import ipywidgets as widgets\n",
+    "from IPython.display import clear_output, display\n",
+    "\n",
+    "## Text Input\n",
+    "user_input = widgets.Textarea(\n",
+    "    value=\"\",\n",
+    "    placeholder=\"Type your prompt here\",\n",
+    "    disabled=False,\n",
+    "    style={\"description_width\": \"initial\"},\n",
+    "    layout=widgets.Layout(width=\"100%\", height=\"100%\"),\n",
+    ")\n",
+    "\n",
+    "## Submit Button\n",
+    "submit_button = widgets.Button(\n",
+    "    description=\"Submit\", button_style=\"success\", layout=widgets.Layout(width=\"100%\", height=\"80%\")\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def on_button_clicked(b):\n",
+    "    user_prompt = user_input.value\n",
+    "    user_input.value = \"\"\n",
+    "    print(\"\\nUser:\\n\", user_prompt)\n",
+    "    conversational_agent.run(user_prompt)\n",
+    "\n",
+    "\n",
+    "submit_button.on_click(on_button_clicked)\n",
+    "\n",
+    "## Show Memory Button\n",
+    "memory_button = widgets.Button(\n",
+    "    description=\"Show Memory\", button_style=\"info\", layout=widgets.Layout(width=\"100%\", height=\"100%\")\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def on_memory_button_clicked(b):\n",
+    "    memory = conversational_agent.memory.load()\n",
+    "    if len(memory):\n",
+    "        print(\"\\nMemory:\\n\", memory)\n",
+    "    else:\n",
+    "        print(\"Memory is empty\")\n",
+    "\n",
+    "\n",
+    "memory_button.on_click(on_memory_button_clicked)\n",
+    "\n",
+    "## Clear Memory Button\n",
+    "clear_button = widgets.Button(\n",
+    "    description=\"Clear Memory\", button_style=\"warning\", layout=widgets.Layout(width=\"100%\", height=\"100%\")\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def on_clear_button_button_clicked(b):\n",
+    "    conversational_agent.memory.clear()\n",
+    "    print(\"\\nMemory is cleared\\n\")\n",
+    "\n",
+    "\n",
+    "clear_button.on_click(on_clear_button_button_clicked)\n",
+    "\n",
+    "## Layout\n",
+    "grid = widgets.GridspecLayout(3, 3, height=\"200px\", width=\"800px\", grid_gap=\"10px\")\n",
+    "grid[0, 2] = clear_button\n",
+    "grid[0:2, 0:2] = user_input\n",
+    "grid[2, 0:] = submit_button\n",
+    "grid[1, 2] = memory_button\n",
+    "display(grid)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "machine_shape": "hm",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }

From a6445382a00eabed73aa5ab7f4b4afe22eadcf9f Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <44616784+anakin87@users.noreply.github.com>
Date: Mon, 11 Dec 2023 09:57:10 +0100
Subject: [PATCH 137/206] fix getpass error (#263)

---
 tutorials/24_Building_Chat_App.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/24_Building_Chat_App.ipynb b/tutorials/24_Building_Chat_App.ipynb
index e45a37b6..ee15a6a9 100644
--- a/tutorials/24_Building_Chat_App.ipynb
+++ b/tutorials/24_Building_Chat_App.ipynb
@@ -124,7 +124,7 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "model_api_key = os.getenv(\"HF_API_KEY\", getpass(\"Enter HF API key:\"))"
+    "model_api_key = os.getenv(\"HF_API_KEY\", None) or getpass(\"Enter HF API key:\")"
    ]
   },
   {

From f921634eeeec91837306a1e751a18bed5c8d6f7c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 13 Dec 2023 13:51:14 +0300
Subject: [PATCH 138/206] Add tutorial 28 about loops (#255)

* Add tutorial 27 about loops

* Add tutorial to readme

* change tutorial number

* Update tut 28

* Skip the tests for Haystack 2.0 tutorials

* Fix tutorial number for telemetry

* Make the tutorial more clear

* Make the example passage more complex and remove random corruption happening in the OutputValidator

* Change how to get openai api key
---
 .github/workflows/run_tutorials.yml           |   1 +
 README.md                                     |   1 +
 index.toml                                    |  13 +-
 tutorials/27_First_RAG_Pipeline.ipynb         |   2 +-
 .../28_Structured_Output_With_Loop.ipynb      | 485 ++++++++++++++++++
 5 files changed, 500 insertions(+), 2 deletions(-)
 create mode 100644 tutorials/28_Structured_Output_With_Loop.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index c7e71e10..8e7fdfe0 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -68,6 +68,7 @@ jobs:
               "tutorials/24_Building_Chat_App.ipynb"
               "tutorials/25_Customizing_Agent.ipynb"
               "tutorials/27_First_RAG_Pipeline.ipynb"
+              "tutorials/28_Structured_Output_With_Loop.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index 6b99c002..4b3ef0de 100644
--- a/README.md
+++ b/README.md
@@ -45,3 +45,4 @@ To contribute to the tutorials, please check out our [Contributing Guidelines](.
 | [Customizing Agent to Chat with Your Documents](./tutorials/25_Customizing_Agent.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/25_Customizing_Agent.ipynb) |
 | [Creating a Hybrid Retrieval Pipeline](./tutorials/26_Hybrid_Retrieval.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/26_Hybrid_Retrieval.ipynb) |
 | [Your First QA Pipeline with Retrieval-Augmentation](./tutorials/27_First_RAG_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/27_First_RAG_Pipeline.ipynb) |
+| [Generating Structured Output with Loop-Based Auto-Correction](./tutorials/28_Structured_Output_With_Loop.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/28_Structured_Output_With_Loop.ipynb) |
diff --git a/index.toml b/index.toml
index 3749eb3e..3d12288f 100644
--- a/index.toml
+++ b/index.toml
@@ -274,4 +274,15 @@ notebook = "27_First_RAG_Pipeline.ipynb"
 aliases = []
 completion_time = "10 min"
 created_at = 2023-12-05
-haystack_2 = true
\ No newline at end of file
+haystack_2 = true
+
+[[tutorial]]
+title = "Generating Structured Output with Loop-Based Auto-Correction"
+description = "Learn how to extract structured data using an LLM, and to validate the generated output against a predefined schema."
+level = "intermediate"
+weight = 71
+notebook = "28_Structured_Output_With_Loop.ipynb"
+aliases = []
+completion_time = "15 min"
+created_at = 2023-11-30
+haystack_2 = true
diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 191e481d..80c97b1c 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -315,7 +315,7 @@
     "\n",
     "from haystack.components.generators import GPTGenerator\n",
     "\n",
-    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", getpass(\"Enter OpenAI API key:\"))\n",
+    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")\n",
     "generator = GPTGenerator(api_key=openai_api_key)"
    ]
   },
diff --git a/tutorials/28_Structured_Output_With_Loop.ipynb b/tutorials/28_Structured_Output_With_Loop.ipynb
new file mode 100644
index 00000000..d8adf841
--- /dev/null
+++ b/tutorials/28_Structured_Output_With_Loop.ipynb
@@ -0,0 +1,485 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AVBtOVlNJ51C"
+      },
+      "source": [
+        "# Tutorial: Generating Structured Output with Loop-Based Auto-Correction\n",
+        "\n",
+        "- **Level**: Intermediate\n",
+        "- **Time to complete**: 15 minutes\n",
+        "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI.\n",
+        "- **Components Used**: `PromptBuilder`, `GPTGenerator`, `OutputValidator` (Custom component)\n",
+        "- **Goal**: After completing this tutorial, you will have built a system that extracts unstructured data, puts it in a JSON schema, and automatically corrects errors in the JSON output from a large language model (LLM) to make sure it follows the specified structure.\n",
+        "\n",
+        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Beta Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
+        "\n",
+        "## Overview\n",
+        "This tutorial demonstrates how to use Haystack 2.0-Beta's advanced [looping pipelines](https://docs.haystack.deepset.ai/v2.0/docs/pipelines#loops) with LLMs for more dynamic and flexible data processing. You'll learn how to extract structured data from unstructured data using an LLM, and to validate the generated output against a predefined schema.\n",
+        "\n",
+        "This tutorial uses `gpt-3.5-turbo` to change unstructured passages into JSON outputs that follow the [Pydantic](https://github.com/pydantic/pydantic) schema. It uses a custom OutputValidator component to validate the JSON and loop back to make corrections, if necessary."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jmiAHh1oGsKI"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "Enable the debug mode of logging:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Vor9IHuNRvEh"
+      },
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "\n",
+        "logging.basicConfig()\n",
+        "logging.getLogger(\"canals.pipeline.pipeline\").setLevel(logging.DEBUG)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ljbWiyJkKiPw"
+      },
+      "source": [
+        "## Installing Dependencies\n",
+        "Install Haystack 2.0 Beta and [colorama](https://pypi.org/project/colorama/) with pip:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kcc1AlLQd_jI",
+        "outputId": "efc4bbab-a9fe-46ee-d8af-9d86edacaf04"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install haystack-ai\n",
+        "pip install colorama"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nTA5fdvCLMKD"
+      },
+      "source": [
+        "### Enabling Telemetry\n",
+        "\n",
+        "Enable telemetry to let us know you're using this tutorial. (You can always opt out by commenting out this line). For details, see [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Apay3QSQLKdM"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(28)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Cmjfa8CiCeFl"
+      },
+      "source": [
+        "## Defining a Schema to Parse the JSON Object\n",
+        "\n",
+        "Define a simple JSON schema for the data you want to extract from a text passsage using the LLM. As the first step, define two [Pydantic models](https://docs.pydantic.dev/1.10/usage/models/), `City` and `CitiesData`, with suitable fields and types."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xwKrDOOGdaAz"
+      },
+      "outputs": [],
+      "source": [
+        "from typing import List\n",
+        "from pydantic import BaseModel\n",
+        "\n",
+        "\n",
+        "class City(BaseModel):\n",
+        "    name: str\n",
+        "    country: str\n",
+        "    population: int\n",
+        "\n",
+        "\n",
+        "class CitiesData(BaseModel):\n",
+        "    cities: List[City]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zv-6-l_PCeFl"
+      },
+      "source": [
+        "> You can change these models according to the format you wish to extract from the text."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ouk1mAOUCeFl"
+      },
+      "source": [
+        "Then, generate a JSON schema from Pydantic models using `schema_json()`. You will later on use this schema in the prompt to instruct the LLM.\n",
+        "\n",
+        "To learn more about the JSON schemas, visit [Pydantic Schema](https://docs.pydantic.dev/1.10/usage/schema/).  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8Lg9_72jCeFl"
+      },
+      "outputs": [],
+      "source": [
+        "json_schema = CitiesData.schema_json(indent=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KvNhg0bP7kfg"
+      },
+      "source": [
+        "## Creating a Custom Component: OutputValidator\n",
+        "\n",
+        "`OutputValidator` is a custom component that validates if the JSON object the LLM generates complies with the provided [Pydantic model](https://docs.pydantic.dev/1.10/usage/models/). If it doesn't, OutputValidator returns an error message along with the incorrect JSON object to get it fixed in the next loop.\n",
+        "\n",
+        "For more details about custom components, see [Creating Custom Components](https://docs.haystack.deepset.ai/v2.0/docs/custom-components)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yr6D8RN2d7Vy"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "import random\n",
+        "import pydantic\n",
+        "from pydantic import ValidationError\n",
+        "from typing import Optional, List\n",
+        "from colorama import Fore\n",
+        "from haystack import component\n",
+        "\n",
+        "# Define the component input parameters\n",
+        "@component\n",
+        "class OutputValidator:\n",
+        "    def __init__(self, pydantic_model: pydantic.BaseModel):\n",
+        "        self.pydantic_model = pydantic_model\n",
+        "        self.iteration_counter = 0\n",
+        "\n",
+        "    # Define the component output\n",
+        "    @component.output_types(valid_replies=List[str], invalid_replies=Optional[List[str]], error_message=Optional[str])\n",
+        "    def run(self, replies: List[str]):\n",
+        "\n",
+        "        self.iteration_counter += 1\n",
+        "\n",
+        "        ## Try to parse the LLM's reply ##\n",
+        "        # If the LLM's reply is a valid object, return `\"valid_replies\"`\n",
+        "        try:\n",
+        "            output_dict = json.loads(replies[0])\n",
+        "            self.pydantic_model.parse_obj(output_dict)\n",
+        "            print(\n",
+        "                Fore.GREEN\n",
+        "                + f\"OutputValidator at Iteration {self.iteration_counter}: Valid JSON from LLM - No need for looping: {replies[0]}\"\n",
+        "            )\n",
+        "            return {\"valid_replies\": replies}\n",
+        "\n",
+        "        # If the LLM's reply is corrupted or not valid, return \"invalid_replies\" and the \"error_message\" for LLM to try again\n",
+        "        except (ValueError, ValidationError) as e:\n",
+        "            print(\n",
+        "                Fore.RED\n",
+        "                + f\"OutputValidator at Iteration {self.iteration_counter}: Invalid JSON from LLM - Let's try again.\\n\"\n",
+        "                f\"Output from LLM:\\n {replies[0]} \\n\"\n",
+        "                f\"Error from OutputValidator: {e}\"\n",
+        "            )\n",
+        "            return {\"invalid_replies\": replies, \"error_message\": str(e)}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vQ_TfSBkCeFm"
+      },
+      "source": [
+        "Then, create an OutputValidator instance with `CitiesData` that you have created before."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bhPCLCBCCeFm"
+      },
+      "outputs": [],
+      "source": [
+        "output_validator = OutputValidator(pydantic_model=CitiesData)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xcIWKjW4k42r"
+      },
+      "source": [
+        "## Creating the Prompt\n",
+        "\n",
+        "Write instructions for the LLM for converting a passage into a JSON format. Ensure the instructions explain how to identify and correct errors if the JSON doesn't match the required schema. Once you create the prompt, initialize PromptBuilder to use it.  \n",
+        "\n",
+        "For information about Jinja2 template and PromptBuilder, see [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ohPpNALjdVKt"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.builders import PromptBuilder\n",
+        "\n",
+        "prompt_template = \"\"\"\n",
+        "Create a JSON object from the information present in this passage: {{passage}}.\n",
+        "Only use information that is present in the passage. Follow this JSON schema, but only return the actual instances without any additional schema definition:\n",
+        "{{schema}}\n",
+        "Make sure your response is a dict and not a list.\n",
+        "{% if invalid_replies and error_message %}\n",
+        "  You already created the following output in a previous attempt: {{invalid_replies}}\n",
+        "  However, this doesn't comply with the format requirements from above and triggered this Python exception: {{error_message}}\n",
+        "  Correct the output and try again. Just return the corrected output without any extra explanations.\n",
+        "{% endif %}\n",
+        "\"\"\"\n",
+        "prompt_builder = PromptBuilder(template=prompt_template)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KM9-Zq2FL7Nn"
+      },
+      "source": [
+        "## Initalizing the Generator\n",
+        "\n",
+        "[GPTGenerator](https://docs.haystack.deepset.ai/v2.0/docs/gptgenerator) generates\n",
+        "text using OpenAI's `gpt-3.5-turbo` model by default. Provide an API key and a model name to the Generator."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Z4cQteIgunUR"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "\n",
+        "from haystack.components.generators import GPTGenerator\n",
+        "\n",
+        "llm_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")\n",
+        "generator = GPTGenerator(api_key=llm_api_key)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zbotIOgXHkC5"
+      },
+      "source": [
+        "## Building the Pipeline\n",
+        "\n",
+        "Add all components to your pipeline and connect them. Add connections from `output_validator` back to the `prompt_builder` for cases where the produced JSON doesn't comply with the JSON schema. Set `max_loops_allowed` to avoid infinite looping."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eFglN9YEv-1W"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack import Pipeline\n",
+        "\n",
+        "pipeline = Pipeline(max_loops_allowed=5)\n",
+        "\n",
+        "# Add components to your pipeline\n",
+        "pipeline.add_component(instance=prompt_builder, name=\"prompt_builder\")\n",
+        "pipeline.add_component(instance=generator, name=\"llm\")\n",
+        "pipeline.add_component(instance=output_validator, name=\"output_validator\")\n",
+        "\n",
+        "# Now, connect the components to each other\n",
+        "pipeline.connect(\"prompt_builder\", \"llm\")\n",
+        "pipeline.connect(\"llm\", \"output_validator\")\n",
+        "# If a component has more than one output or input, explicitly specify the connections:\n",
+        "pipeline.connect(\"output_validator.invalid_replies\", \"prompt_builder.invalid_replies\")\n",
+        "pipeline.connect(\"output_validator.error_message\", \"prompt_builder.error_message\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-UKW5wtIIT7w"
+      },
+      "source": [
+        "### Visualize the Pipeline\n",
+        "\n",
+        "Draw the pipeline with the [`draw()`](https://docs.haystack.deepset.ai/v2.0/docs/drawing-pipeline-graphs) method to confirm the connections are correct. You can find the diagram in the Files section of this Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RZJg6YHId300"
+      },
+      "outputs": [],
+      "source": [
+        "pipeline.draw(\"auto-correct-pipeline.png\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kV_kexTjImpo"
+      },
+      "source": [
+        "## Testing the Pipeline\n",
+        "\n",
+        "Run the pipeline with an example passage that you want to convert into a JSON format and the `json_schema` you have created for `CitiesData`. For the given example passage, the generated JSON object should be like:\n",
+        "```json\n",
+        "{\n",
+        "  \"cities\": [\n",
+        "    {\n",
+        "      \"name\": \"Berlin\",\n",
+        "      \"country\": \"Germany\",\n",
+        "      \"population\": 3850809\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"Paris\",\n",
+        "      \"country\": \"France\",\n",
+        "      \"population\": 2161000\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"Lisbon\",\n",
+        "      \"country\": \"Portugal\",\n",
+        "      \"population\": 504718\n",
+        "    }\n",
+        "  ]\n",
+        "}\n",
+        "```\n",
+        "The output of the LLM should be compliant with the `json_schema`. If the LLM doesn't generate the correct JSON object, it will loop back and try again."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yIoMedb6eKia",
+        "outputId": "4a9ef924-cf26-4908-d83f-b0bc0dc03b54"
+      },
+      "outputs": [],
+      "source": [
+        "passage = \"Berlin is the capital of Germany. It has a population of 3,850,809. Paris, France's capital, has 2.161 million residents. Lisbon is the capital and the largest city of Portugal with the population of 504,718.\"\n",
+        "result = pipeline.run({\"prompt_builder\": {\"passage\": passage, \"schema\": json_schema}})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WWxmPgADS_Fa"
+      },
+      "source": [
+        "> If you encounter `PipelineMaxLoops: Maximum loops count (5) exceeded for component 'prompt_builder'.` error, consider increasing the maximum loop count or simply rerun the pipeline."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eWPawSjgSJAM"
+      },
+      "source": [
+        "### Print the Correct JSON\n",
+        "If you didn't get any error, you can now print the corrected JSON."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BVO47gXQQnDC",
+        "outputId": "460a10d4-a69a-49cd-bbb2-fc4980907299"
+      },
+      "outputs": [],
+      "source": [
+        "valid_reply = result[\"output_validator\"][\"valid_replies\"][0]\n",
+        "valid_json = json.loads(valid_reply)\n",
+        "print(valid_json)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Egz_4h2vI_QL"
+      },
+      "source": [
+        "🎉 Congratulations! You've built a system that generates structured JSON out of unstructured text passages, and auto-corrects it by using the looping functionality of Haystack pipelines."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From 4ff8e46e9c05b4e607f0115e2439da824efeaa47 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Thu, 14 Dec 2023 14:45:02 +0300
Subject: [PATCH 139/206] Add info box for the Haystack 2.0 version of the same
 tutorial (#261)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add info box for the Haystack 2.0 tutorial

* Update tutorials/22_Pipeline_with_PromptNode.ipynb

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>

* Update tutorials/22_Pipeline_with_PromptNode.ipynb

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>

---------

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>
---
 tutorials/22_Pipeline_with_PromptNode.ipynb | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index 8ab387e0..36360df7 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -15,6 +15,15 @@
     "- **Goal**: After completing this tutorial, you'll have created a generative question answering search system that uses a large language model through PromptNode with PromptTemplate."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0-Beta and would like to follow the updated version of this tutorial, check out [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline). \n",
+    ">\n",
+    "> For more information on Haystack 2.0-Beta, you can also read the [announcement post](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent)."
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -272,7 +281,7 @@
     "\n",
     "[PromptNode](https://docs.haystack.deepset.ai/docs/prompt_node) is the central abstraction in Haystack's large language model (LLM) support. It's possible to interact with LLMs through PromptNode by providing `model_name_or_path` and if necessary, `api_key`.\n",
     "\n",
-    "For this tutorial, we'll use OpenAI's `text-davinci-003`, so, we need to enter a `OPENAI_API_KEY`:"
+    "For this tutorial, we'll use OpenAI's `gpt-3.5-turbo`, so, we need to enter a `OPENAI_API_KEY`:"
    ]
   },
   {
@@ -290,7 +299,7 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")"
+    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", getpass(\"Enter OpenAI API key:\"))"
    ]
   },
   {
@@ -302,7 +311,7 @@
    "source": [
     "Let's define a custom prompt for PromptTemplate to use with PromptNode. As parameters, this prompt will accept `documents` that our Retriever fetched from our DocumentStore and `query` we pass at runtime. To join the content of the Documents, we'll use `join()` function. To learn about using functions in PromptTemplate, check out [PromptTemplate Structure](https://docs.haystack.deepset.ai/docs/prompt_node#prompttemplate-structure). Finally, we'll use [AnswerParser](https://docs.haystack.deepset.ai/reference/prompt-node-api#answerparser) to parse the output of the LLM into a Haystack Answer object.\n",
     "\n",
-    "We'll initialize PromptNode with the PromptTemplate, the `text-davinci-003` model and the `api_key`."
+    "We'll initialize PromptNode with the PromptTemplate, the `gpt-3.5-turbo` model and the `api_key`."
    ]
   },
   {
@@ -323,9 +332,7 @@
     "    output_parser=AnswerParser(),\n",
     ")\n",
     "\n",
-    "prompt_node = PromptNode(\n",
-    "    model_name_or_path=\"text-davinci-003\", api_key=openai_api_key, default_prompt_template=rag_prompt\n",
-    ")"
+    "prompt_node = PromptNode(model_name_or_path=\"gpt-3.5-turbo\", api_key=openai_api_key, default_prompt_template=rag_prompt)"
    ]
   },
   {
@@ -456,7 +463,7 @@
   },
   "language_info": {
    "name": "python",
-   "version": "3.9.6"
+   "version": "3.9.12"
   },
   "orig_nbformat": 4,
   "vscode": {

From c53407f626b44b0895b172fceb2e8256ad327b8e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 22 Dec 2023 15:54:19 +0300
Subject: [PATCH 140/206] Update 22_Pipeline_with_PromptNode.ipynb (#264)

---
 tutorials/22_Pipeline_with_PromptNode.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index 36360df7..a5b0895e 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -299,7 +299,7 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", getpass(\"Enter OpenAI API key:\"))"
+    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")"
    ]
   },
   {

From 9d24b04eb74e31aa466f5a20432a70d1aaae3ace Mon Sep 17 00:00:00 2001
From: Julian Risch <julian.risch@deepset.ai>
Date: Fri, 29 Dec 2023 09:51:19 +0100
Subject: [PATCH 141/206] fix: Replace deprecated text-davinci-003 model with
 gpt-3.5-turbo-instruct model (#265)

---
 tutorials/21_Customizing_PromptNode.ipynb                   | 4 ++--
 tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb | 4 ++--
 tutorials/25_Customizing_Agent.ipynb                        | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/tutorials/21_Customizing_PromptNode.ipynb b/tutorials/21_Customizing_PromptNode.ipynb
index 43f8744d..f682b006 100644
--- a/tutorials/21_Customizing_PromptNode.ipynb
+++ b/tutorials/21_Customizing_PromptNode.ipynb
@@ -102,7 +102,7 @@
    "source": [
     "## Trying Out PromptNode\n",
     "\n",
-    "The PromptNode is the central abstraction in Haystack's large language model (LLM) support. It uses [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) model by default, but you can replace the default model with a flan-t5 model of a different size such as `google/flan-t5-large` or a model by OpenAI such as `text-davinci-003`.\n",
+    "The PromptNode is the central abstraction in Haystack's large language model (LLM) support. It uses [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) model by default, but you can replace the default model with a flan-t5 model of a different size such as `google/flan-t5-large` or a model by OpenAI such as `gpt-3.5-turbo-instruct`.\n",
     "\n",
     "[Large language models](https://docs.haystack.deepset.ai/docs/language_models#large-language-models-llms) are huge models trained on enormous amounts of data. That’s why these models have general knowledge of the world, so you can ask them anything and they will be able to answer.\n",
     "\n",
@@ -145,7 +145,7 @@
    "source": [
     "> Note: To use PromptNode with an OpenAI model, change the model name and provide an `api_key`: \n",
     "> ```python\n",
-    "> prompt_node = PromptNode(model_name_or_path=\"text-davinci-003\", api_key=<YOUR_API_KEY>)\n",
+    "> prompt_node = PromptNode(model_name_or_path=\"gpt-3.5-turbo-instruct\", api_key=<YOUR_API_KEY>)\n",
     "> ```"
    ]
   },
diff --git a/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb b/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
index 03de149d..12f7b5f8 100644
--- a/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
+++ b/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
@@ -419,7 +419,7 @@
     "\n",
     "The `Agent` needs to determine the next best course of action at each iteration. It does this by using an LLM, and a prompt designed specially for this use case. Our `Agent` uses a `PromptNode` with the default [\"zero-shot-react\" `PromptTemplate` ](https://github.com/deepset-ai/haystack/blob/444a3116c42d2c8852d27aa8093ac92c8e85ab88/haystack/nodes/prompt/prompt_node.py#L337). \n",
     "\n",
-    "Here, let's define an `Agent` that uses the `text-davinci-003` model by OpenAI."
+    "Here, let's define an `Agent` that uses the `gpt-3.5-turbo-instruct` model by OpenAI."
    ]
   },
   {
@@ -433,7 +433,7 @@
     "from haystack.agents import Agent\n",
     "from haystack.nodes import PromptNode\n",
     "\n",
-    "prompt_node = PromptNode(model_name_or_path=\"text-davinci-003\", api_key=api_key, stop_words=[\"Observation:\"])\n",
+    "prompt_node = PromptNode(model_name_or_path=\"gpt-3.5-turbo-instruct\", api_key=api_key, stop_words=[\"Observation:\"])\n",
     "agent = Agent(prompt_node=prompt_node)"
    ]
   },
diff --git a/tutorials/25_Customizing_Agent.ipynb b/tutorials/25_Customizing_Agent.ipynb
index e9e058f8..9d10fb12 100644
--- a/tutorials/25_Customizing_Agent.ipynb
+++ b/tutorials/25_Customizing_Agent.ipynb
@@ -285,7 +285,7 @@
     ")\n",
     "\n",
     "prompt_node = PromptNode(\n",
-    "    model_name_or_path=\"text-davinci-003\", api_key=openai_api_key, default_prompt_template=prompt_template\n",
+    "    model_name_or_path=\"gpt-3.5-turbo-instruct\", api_key=openai_api_key, default_prompt_template=prompt_template\n",
     ")\n",
     "\n",
     "generative_pipeline = Pipeline()\n",

From b870aec9f78cc8dff7249ab557e4f3ed75f399f8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Thu, 11 Jan 2024 12:21:54 +0300
Subject: [PATCH 142/206] updating component names (#266)

---
 tutorials/27_First_RAG_Pipeline.ipynb          | 14 +++++++-------
 tutorials/28_Structured_Output_With_Loop.ipynb |  8 ++++----
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 80c97b1c..6a0bcc1b 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -10,9 +10,9 @@
     "\n",
     "- **Level**: Beginner\n",
     "- **Time to complete**: 10 minutes\n",
-    "- **Components Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `GPTGenerator`\n",
+    "- **Components Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `OpenAIGenerator`\n",
     "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI.\n",
-    "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and GPTGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n",
+    "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and OpenAIGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n",
     "\n",
     "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
    ]
@@ -25,7 +25,7 @@
    "source": [
     "## Overview\n",
     "\n",
-    "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves three main components: [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) for creating a template prompt, and [GPTGenerator](https://docs.haystack.deepset.ai/v2.0/docs/gptgenerator) for generating responses.\n",
+    "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves three main components: [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) for creating a template prompt, and [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) for generating responses.\n",
     "\n",
     "For this tutorial, you'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"
    ]
@@ -295,7 +295,7 @@
     "## Initializing a Generator\n",
     "\n",
     "\n",
-    "Generators are the components that interacts with large language models (LLMs). Now, initialize a [GPTGenerator](https://docs.haystack.deepset.ai/v2.0/docs/gptgenerator) that can communicate with OpenAI GPT models with an OpenAI `api_key` and the model name you'd like to use. If you don't provide any `model_name`, the GPTGenerator defaults to `gpt-3.5-turbo`:"
+    "Generators are the components that interacts with large language models (LLMs). Now, initialize a [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/OpenAIGenerator) that can communicate with OpenAI GPT models with an OpenAI `api_key` and the model name you'd like to use. If you don't provide any `model_name`, the OpenAIGenerator defaults to `gpt-3.5-turbo`:"
    ]
   },
   {
@@ -313,10 +313,10 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "from haystack.components.generators import GPTGenerator\n",
+    "from haystack.components.generators import OpenAIGenerator\n",
     "\n",
     "openai_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")\n",
-    "generator = GPTGenerator(api_key=openai_api_key)"
+    "generator = OpenAIGenerator(api_key=openai_api_key)"
    ]
   },
   {
@@ -325,7 +325,7 @@
     "id": "nenbo2SvycHd"
    },
    "source": [
-    "> You can replace `GPTGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
+    "> You can replace `OpenAIGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
    ]
   },
   {
diff --git a/tutorials/28_Structured_Output_With_Loop.ipynb b/tutorials/28_Structured_Output_With_Loop.ipynb
index d8adf841..a335825e 100644
--- a/tutorials/28_Structured_Output_With_Loop.ipynb
+++ b/tutorials/28_Structured_Output_With_Loop.ipynb
@@ -11,7 +11,7 @@
         "- **Level**: Intermediate\n",
         "- **Time to complete**: 15 minutes\n",
         "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI.\n",
-        "- **Components Used**: `PromptBuilder`, `GPTGenerator`, `OutputValidator` (Custom component)\n",
+        "- **Components Used**: `PromptBuilder`, `OpenAIGenerator`, `OutputValidator` (Custom component)\n",
         "- **Goal**: After completing this tutorial, you will have built a system that extracts unstructured data, puts it in a JSON schema, and automatically corrects errors in the JSON output from a large language model (LLM) to make sure it follows the specified structure.\n",
         "\n",
         "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Beta Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
@@ -292,7 +292,7 @@
       "source": [
         "## Initalizing the Generator\n",
         "\n",
-        "[GPTGenerator](https://docs.haystack.deepset.ai/v2.0/docs/gptgenerator) generates\n",
+        "[OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) generates\n",
         "text using OpenAI's `gpt-3.5-turbo` model by default. Provide an API key and a model name to the Generator."
       ]
     },
@@ -307,10 +307,10 @@
         "import os\n",
         "from getpass import getpass\n",
         "\n",
-        "from haystack.components.generators import GPTGenerator\n",
+        "from haystack.components.generators import OpenAIGenerator\n",
         "\n",
         "llm_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")\n",
-        "generator = GPTGenerator(api_key=llm_api_key)"
+        "generator = OpenAIGenerator(api_key=llm_api_key)"
       ]
     },
     {

From 2c0887e4e0ce14d1e7ecb9a01836ddf3cade97b0 Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <stefanofiorucci@gmail.com>
Date: Fri, 12 Jan 2024 12:24:05 +0100
Subject: [PATCH 143/206] Update tj-actions/changed-files (#269)

* update tj-actions/changed-files

* other update
---
 .github/workflows/dc_sync.yml       | 2 +-
 .github/workflows/run_tutorials.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/dc_sync.yml b/.github/workflows/dc_sync.yml
index 43c5a2ab..66987fdb 100644
--- a/.github/workflows/dc_sync.yml
+++ b/.github/workflows/dc_sync.yml
@@ -22,7 +22,7 @@ jobs:
 
       - name: Get changed files
         id: files
-        uses: tj-actions/changed-files@v34
+        uses: tj-actions/changed-files@v41
         with:
           json: true
           files: |
diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 8e7fdfe0..4692fe9a 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -49,7 +49,7 @@ jobs:
 
       - name: Get changed files
         id: files
-        uses: tj-actions/changed-files@v35
+        uses: tj-actions/changed-files@v41
 
       - name: Convert notebooks to Python
         shell: bash

From a57e1fe513bc46b6bc6ea1e62a4921af96c11e7c Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <stefanofiorucci@gmail.com>
Date: Fri, 12 Jan 2024 13:23:38 +0100
Subject: [PATCH 144/206] fix datasets installation command (#268)

---
 .github/workflows/nightly.yml               | 2 +-
 .github/workflows/run_tutorials.yml         | 2 +-
 tutorials/22_Pipeline_with_PromptNode.ipynb | 2 +-
 tutorials/25_Customizing_Agent.ipynb        | 2 +-
 tutorials/26_Hybrid_Retrieval.ipynb         | 2 +-
 tutorials/27_First_RAG_Pipeline.ipynb       | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index 8cc0ee80..c8571fba 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -65,7 +65,7 @@ jobs:
 
       - name: Install Hugging Face datasets
         run: |
-          pip install datasets>=2.6.1
+          pip install "datasets>=2.6.1"
       
       - name: Install ipywidgets for tutorial 24
         run: |
diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 4692fe9a..c5c33540 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -36,7 +36,7 @@ jobs:
       
       - name: Install Hugging Face datasets 
         run: |
-          pip install datasets>=2.6.1
+          pip install "datasets>=2.6.1"
 
       - name: Checkout
         uses: actions/checkout@v3
diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index a5b0895e..c4f65d84 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -79,7 +79,7 @@
     "\n",
     "pip install --upgrade pip\n",
     "pip install farm-haystack[colab]\n",
-    "pip install datasets>=2.6.1"
+    "pip install \"datasets>=2.6.1\""
    ]
   },
   {
diff --git a/tutorials/25_Customizing_Agent.ipynb b/tutorials/25_Customizing_Agent.ipynb
index 9d10fb12..cd076d69 100644
--- a/tutorials/25_Customizing_Agent.ipynb
+++ b/tutorials/25_Customizing_Agent.ipynb
@@ -71,7 +71,7 @@
     "\n",
     "pip install --upgrade pip\n",
     "pip install farm-haystack[colab]\n",
-    "pip install datasets>=2.6.1"
+    "pip install \"datasets>=2.6.1\""
    ]
   },
   {
diff --git a/tutorials/26_Hybrid_Retrieval.ipynb b/tutorials/26_Hybrid_Retrieval.ipynb
index 261b0c2b..0801c6d3 100644
--- a/tutorials/26_Hybrid_Retrieval.ipynb
+++ b/tutorials/26_Hybrid_Retrieval.ipynb
@@ -62,7 +62,7 @@
     "%%bash\n",
     "\n",
     "pip install --upgrade pip\n",
-    "pip install datasets>=2.6.1\n",
+    "pip install \"datasets>=2.6.1\"\n",
     "pip install farm-haystack[inference]"
    ]
   },
diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 6a0bcc1b..d2127331 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -64,7 +64,7 @@
     "%%bash\n",
     "\n",
     "pip install haystack-ai\n",
-    "pip install datasets>=2.6.1"
+    "pip install \"datasets>=2.6.1\""
    ]
   },
   {

From 43d07274396ec5fb07d5a0274a210b6d0fbb2d60 Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Wed, 17 Jan 2024 18:13:44 +0100
Subject: [PATCH 145/206] update import path for in-memory DS and retrievers
 (#267)

---
 tutorials/27_First_RAG_Pipeline.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index d2127331..7e0f02a6 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -110,7 +110,7 @@
    },
    "outputs": [],
    "source": [
-    "from haystack.document_stores import InMemoryDocumentStore\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
     "\n",
     "document_store = InMemoryDocumentStore()"
    ]
@@ -243,7 +243,7 @@
    },
    "outputs": [],
    "source": [
-    "from haystack.components.retrievers import InMemoryBM25Retriever\n",
+    "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
     "\n",
     "retriever = InMemoryBM25Retriever(document_store)"
    ]

From aa71c708fda974b492e74f79af28504ee2abcac4 Mon Sep 17 00:00:00 2001
From: ZanSara <sara.zanzottera@deepset.ai>
Date: Mon, 22 Jan 2024 18:18:14 +0100
Subject: [PATCH 146/206] update model_name to simply model (#270)

---
 tutorials/27_First_RAG_Pipeline.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 7e0f02a6..724bc4a9 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -295,7 +295,7 @@
     "## Initializing a Generator\n",
     "\n",
     "\n",
-    "Generators are the components that interacts with large language models (LLMs). Now, initialize a [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/OpenAIGenerator) that can communicate with OpenAI GPT models with an OpenAI `api_key` and the model name you'd like to use. If you don't provide any `model_name`, the OpenAIGenerator defaults to `gpt-3.5-turbo`:"
+    "Generators are the components that interacts with large language models (LLMs). Now, initialize a [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/OpenAIGenerator) that can communicate with OpenAI GPT models with an OpenAI `api_key` and the model name you'd like to use. If you don't provide any `model`, the OpenAIGenerator defaults to `gpt-3.5-turbo`:"
    ]
   },
   {

From 53e18aea30fc1e4d5dde44bed4a3e3275dda3dcb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Tue, 30 Jan 2024 16:43:04 +0100
Subject: [PATCH 147/206] adding the serialization tutorial (#272)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* adding the serialization tutorial

* skip test (until we add 2.0 tests)

* Update tutorials/29_Serializing_Pipelines.ipynb

Co-authored-by: Massimiliano Pippi <mpippi@gmail.com>

* Update index.toml

Co-authored-by: Tilde Thurium <tilde.thurium@deepset.ai>

* Fixing comments

* update sentence about serialization

* Update index.toml

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>

* resolving comments

---------

Co-authored-by: Massimiliano Pippi <mpippi@gmail.com>
Co-authored-by: Tilde Thurium <tilde.thurium@deepset.ai>
Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>
---
 .github/workflows/run_tutorials.yml      |   1 +
 .gitignore                               |   5 +-
 README.md                                |   2 +
 index.toml                               |  11 +
 tutorials/29_Serializing_Pipelines.ipynb | 415 +++++++++++++++++++++++
 5 files changed, 433 insertions(+), 1 deletion(-)
 create mode 100644 tutorials/29_Serializing_Pipelines.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index c5c33540..2fbaec98 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -69,6 +69,7 @@ jobs:
               "tutorials/25_Customizing_Agent.ipynb"
               "tutorials/27_First_RAG_Pipeline.ipynb"
               "tutorials/28_Structured_Output_With_Loop.ipynb"
+              "tutorials/29_Serializing_Pipelines.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/.gitignore b/.gitignore
index 69f508ce..965f62ac 100644
--- a/.gitignore
+++ b/.gitignore
@@ -134,4 +134,7 @@ dmypy.json
 .DS_Store
 
 # Generate tutorial files for dc_sync.yml and full_dc_sync.yml
-text/**
\ No newline at end of file
+text/**
+
+tutorials/data
+saved_models
diff --git a/README.md b/README.md
index 4b3ef0de..b44db12f 100644
--- a/README.md
+++ b/README.md
@@ -46,3 +46,5 @@ To contribute to the tutorials, please check out our [Contributing Guidelines](.
 | [Creating a Hybrid Retrieval Pipeline](./tutorials/26_Hybrid_Retrieval.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/26_Hybrid_Retrieval.ipynb) |
 | [Your First QA Pipeline with Retrieval-Augmentation](./tutorials/27_First_RAG_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/27_First_RAG_Pipeline.ipynb) |
 | [Generating Structured Output with Loop-Based Auto-Correction](./tutorials/28_Structured_Output_With_Loop.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/28_Structured_Output_With_Loop.ipynb) |
+| [Serializing Pipelines](./tutorials/29_Serializing_Pipelines.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/29_Serializing_Pipelines.ipynb) |
+
diff --git a/index.toml b/index.toml
index 3d12288f..c4bfff2b 100644
--- a/index.toml
+++ b/index.toml
@@ -286,3 +286,14 @@ aliases = []
 completion_time = "15 min"
 created_at = 2023-11-30
 haystack_2 = true
+
+[[tutorial]]
+title = "Serializing LLM Pipelines"
+description = "Learn how to serialize and deserialize your pipelines between YAML and Python"
+level = "beginner"
+weight = 6
+notebook = "29_Serializing_Pipelines.ipynb"
+aliases = []
+completion_time = "10 min"
+created_at = 2024-01-29
+haystack_2 = true
\ No newline at end of file
diff --git a/tutorials/29_Serializing_Pipelines.ipynb b/tutorials/29_Serializing_Pipelines.ipynb
new file mode 100644
index 00000000..233ac402
--- /dev/null
+++ b/tutorials/29_Serializing_Pipelines.ipynb
@@ -0,0 +1,415 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cFFW8D-weE2S"
+      },
+      "source": [
+        "# Tutorial: Serializing LLM Pipelines\n",
+        "\n",
+        "- **Level**: Beginner\n",
+        "- **Time to complete**: 10 minutes\n",
+        "- **Components Used**: [`HuggingFaceLocalGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacelocalgenerator), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder)\n",
+        "- **Prerequisites**: None\n",
+        "- **Goal**: After completing this tutorial, you'll understand how to serialize and deserialize between YAML and Python code."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DxhqjpHfenQl"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "**📚 Useful Documentation:** [Serialization](https://docs.haystack.deepset.ai/v2.0/docs/serialization)\n",
+        "\n",
+        "Serialization means converting a pipeline to a format that you can save on your disk and load later. It's especially useful because a serialized pipeline can be saved on disk or a database, get sent over a network and more. \n",
+        "\n",
+        "Although it's possible to serialize into other formats too, Haystack supports YAML our of the box to make it easy for humans to make changes without the need to go back and forth with Python code. In this tutorial, we will create a very simple pipeline in Python code, serialize it into YAML, make changes to it, and deserialize it back into a Haystack `Pipeline`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9smrsiIqfS7J"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TLaHxdJcfWtI"
+      },
+      "source": [
+        "## Installing Haystack\n",
+        "\n",
+        "Install Haystack 2.0 Beta with `pip`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CagzMFdkeBBp",
+        "outputId": "e304450a-24e3-4ef8-e642-1fbb573e7d55"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: haystack-ai in /usr/local/lib/python3.10/dist-packages (2.0.0b5)\n",
+            "Requirement already satisfied: boilerpy3 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.0.7)\n",
+            "Requirement already satisfied: haystack-bm25 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.0.2)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (3.1.3)\n",
+            "Requirement already satisfied: lazy-imports in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (0.3.1)\n",
+            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (10.1.0)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (3.2.1)\n",
+            "Requirement already satisfied: openai>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.10.0)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.5.3)\n",
+            "Requirement already satisfied: posthog in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (3.3.3)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (6.0.1)\n",
+            "Requirement already satisfied: tenacity in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (8.2.3)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (4.66.1)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (4.9.0)\n",
+            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai) (3.7.1)\n",
+            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai>=1.1.0->haystack-ai) (1.7.0)\n",
+            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai) (0.26.0)\n",
+            "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai) (1.10.14)\n",
+            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai) (1.3.0)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from haystack-bm25->haystack-ai) (1.23.5)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->haystack-ai) (2.1.4)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->haystack-ai) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->haystack-ai) (2023.3.post1)\n",
+            "Requirement already satisfied: requests<3.0,>=2.7 in /usr/local/lib/python3.10/dist-packages (from posthog->haystack-ai) (2.31.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog->haystack-ai) (1.16.0)\n",
+            "Requirement already satisfied: monotonic>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog->haystack-ai) (1.6)\n",
+            "Requirement already satisfied: backoff>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from posthog->haystack-ai) (2.2.1)\n",
+            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai>=1.1.0->haystack-ai) (3.6)\n",
+            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai>=1.1.0->haystack-ai) (1.2.0)\n",
+            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (2023.11.17)\n",
+            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (1.0.2)\n",
+            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (0.14.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.7->posthog->haystack-ai) (3.3.2)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.7->posthog->haystack-ai) (2.0.7)\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install haystack-ai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MhnSGxXWHNsD"
+      },
+      "source": [
+        "### Enabling Telemetry\n",
+        "\n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ikIM1o9cHNcS"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(29)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kS8rz9gGgMBb"
+      },
+      "source": [
+        "## Creating a Simple Pipeline\n",
+        "\n",
+        "First, let's create a very simple pipeline that expects a `topic` from the user, and generates a summary about the topic with `google/flan-t5-large`. Feel free to modify the pipeline as you wish. Note that in this pipeline we are using a local model that we're getting from Hugging Face. We're using a relatively small, open-source LLM."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "odZJjD7KgO1g"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack import Pipeline\n",
+        "from haystack.components.builders import PromptBuilder\n",
+        "from haystack.components.generators import HuggingFaceLocalGenerator\n",
+        "\n",
+        "template = \"\"\"\n",
+        "Please create a summary about the following topic:\n",
+        "{{ topic }}\n",
+        "\"\"\"\n",
+        "builder = PromptBuilder(template=template)\n",
+        "llm = HuggingFaceLocalGenerator(model=\"google/flan-t5-large\",\n",
+        "                                      task=\"text2text-generation\",\n",
+        "                                      generation_kwargs={\n",
+        "                                        \"max_new_tokens\": 150,\n",
+        "                                        })\n",
+        "\n",
+        "pipeline = Pipeline()\n",
+        "pipeline.add_component(name=\"builder\", instance=builder)\n",
+        "pipeline.add_component(name=\"llm\", instance=llm)\n",
+        "\n",
+        "pipeline.connect(\"builder\", \"llm\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W-onTCXfqFjG",
+        "outputId": "e81cd5ea-db66-4f0e-f787-5aed7a7b4692"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Climate change is a major threat to the planet.\n"
+          ]
+        }
+      ],
+      "source": [
+        "topic = \"Climate change\"\n",
+        "result = pipeline.run(data={\"builder\": {\"topic\": topic}})\n",
+        "print(result['llm']['replies'][0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "61r7hc1vuUMH"
+      },
+      "source": [
+        "## Serialize the Pipeline to YAML\n",
+        "\n",
+        "Out of the box, Haystack supports YAML. Use `dumps()` to convert the pipeline to YAML:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vYOEAesbrn4w",
+        "outputId": "ef037904-79f4-46a4-c8e7-d03ea8dcb6c2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "components:\n",
+            "  builder:\n",
+            "    init_parameters:\n",
+            "      template: \"\\nPlease create a summary about the following topic: \\n{{ topic }}\\n\"\n",
+            "    type: haystack.components.builders.prompt_builder.PromptBuilder\n",
+            "  llm:\n",
+            "    init_parameters:\n",
+            "      generation_kwargs:\n",
+            "        max_new_tokens: 150\n",
+            "      huggingface_pipeline_kwargs:\n",
+            "        device: cpu\n",
+            "        model: google/flan-t5-large\n",
+            "        task: text2text-generation\n",
+            "        token: null\n",
+            "      stop_words: null\n",
+            "    type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator\n",
+            "connections:\n",
+            "- receiver: llm.prompt\n",
+            "  sender: builder.prompt\n",
+            "max_loops_allowed: 100\n",
+            "metadata: {}\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "yaml_pipeline = pipeline.dumps()\n",
+        "\n",
+        "print(yaml_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0C7zGsUCGszq"
+      },
+      "source": [
+        "You should get a pipeline YAML that looks like the following:\n",
+        "\n",
+        "```yaml\n",
+        "components:\n",
+        "  builder:\n",
+        "    init_parameters:\n",
+        "      template: \"\\nPlease create a summary about the following topic: \\n{{ topic }}\\n\"\n",
+        "    type: haystack.components.builders.prompt_builder.PromptBuilder\n",
+        "  llm:\n",
+        "    init_parameters:\n",
+        "      generation_kwargs:\n",
+        "        max_new_tokens: 150\n",
+        "      huggingface_pipeline_kwargs:\n",
+        "        device: cpu\n",
+        "        model: google/flan-t5-large\n",
+        "        task: text2text-generation\n",
+        "        token: null\n",
+        "      stop_words: null\n",
+        "    type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator\n",
+        "connections:\n",
+        "- receiver: llm.prompt\n",
+        "  sender: builder.prompt\n",
+        "max_loops_allowed: 100\n",
+        "metadata: {}\n",
+        "\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f9MknQ-1vQ8r"
+      },
+      "source": [
+        "## Editing a Pipeline in YAML\n",
+        "\n",
+        "Let's see how we can make changes to serialized pipelines. For example, below, let's modify the promptbuilder's template to translate provided `sentence` to French:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "U332-VjovFfn"
+      },
+      "outputs": [],
+      "source": [
+        "yaml_pipeline = \"\"\"\n",
+        "components:\n",
+        "  builder:\n",
+        "    init_parameters:\n",
+        "      template: \"\\nPlease translate the following to French: \\n{{ sentence }}\\n\"\n",
+        "    type: haystack.components.builders.prompt_builder.PromptBuilder\n",
+        "  llm:\n",
+        "    init_parameters:\n",
+        "      generation_kwargs:\n",
+        "        max_new_tokens: 150\n",
+        "      huggingface_pipeline_kwargs:\n",
+        "        device: cpu\n",
+        "        model: google/flan-t5-large\n",
+        "        task: text2text-generation\n",
+        "        token: null\n",
+        "      stop_words: null\n",
+        "    type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator\n",
+        "connections:\n",
+        "- receiver: llm.prompt\n",
+        "  sender: builder.prompt\n",
+        "max_loops_allowed: 100\n",
+        "metadata: {}\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xLBtgY0Ov8nX"
+      },
+      "source": [
+        "## Deseriazling a YAML Pipeline back to Python\n",
+        "\n",
+        "You can deserialize a pipeline by calling `loads()`. Below, we're deserializing our edited `yaml_pipeline`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OdlLnw-9wVN-"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack import Pipeline\n",
+        "from haystack.components.builders import PromptBuilder\n",
+        "from haystack.components.generators import HuggingFaceLocalGenerator\n",
+        "\n",
+        "new_pipeline = Pipeline.loads(yaml_pipeline)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eVPh2cV6wcu9"
+      },
+      "source": [
+        "Now we can run the new pipeline we defined in YAML. We had changed it so that the `PromptBuilder` expects a `sentence` and translates the sentence to French:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oGLi3EB_wbu6",
+        "outputId": "ec6eae9f-a7ea-401d-c0ab-792748f6db6f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'llm': {'replies': ['Je me félicite des capybaras !']}}"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "new_pipeline.run(data={\"builder\": {\"sentence\": \"I love capybaras\"}})"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From 8cf1de91dabd1c6802c9e2fb09d82a98a94dec16 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Wed, 31 Jan 2024 11:10:43 +0100
Subject: [PATCH 148/206] Metadata filtering tutorial (#275)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* metadata filtering tutorial

* add tutorial to skiplist

* formatting fixes

* spellcheck

* Update index.toml

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>

---------

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>
---
 .github/workflows/run_tutorials.yml   |   1 +
 README.md                             |   1 +
 index.toml                            |  11 +
 tutorials/31_Metadata_Filtering.ipynb | 966 ++++++++++++++++++++++++++
 4 files changed, 979 insertions(+)
 create mode 100644 tutorials/31_Metadata_Filtering.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 2fbaec98..b572e025 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -70,6 +70,7 @@ jobs:
               "tutorials/27_First_RAG_Pipeline.ipynb"
               "tutorials/28_Structured_Output_With_Loop.ipynb"
               "tutorials/29_Serializing_Pipelines.ipynb"
+              "tutorials/31_Metadata_Filtering.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index b44db12f..b5898781 100644
--- a/README.md
+++ b/README.md
@@ -47,4 +47,5 @@ To contribute to the tutorials, please check out our [Contributing Guidelines](.
 | [Your First QA Pipeline with Retrieval-Augmentation](./tutorials/27_First_RAG_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/27_First_RAG_Pipeline.ipynb) |
 | [Generating Structured Output with Loop-Based Auto-Correction](./tutorials/28_Structured_Output_With_Loop.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/28_Structured_Output_With_Loop.ipynb) |
 | [Serializing Pipelines](./tutorials/29_Serializing_Pipelines.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/29_Serializing_Pipelines.ipynb) |
+| [Metadata Filtering](./tutorials/31_Metadata_Filtering.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/31_Metadata_Filtering.ipynb) |
 
diff --git a/index.toml b/index.toml
index c4bfff2b..66f45f61 100644
--- a/index.toml
+++ b/index.toml
@@ -296,4 +296,15 @@ notebook = "29_Serializing_Pipelines.ipynb"
 aliases = []
 completion_time = "10 min"
 created_at = 2024-01-29
+haystack_2 = true
+
+[[tutorial]]
+title = "Filtering Documents with Metadata"
+description = "Learn how to filter down to specific documents at retrieval time using metadata"
+level = "beginner"
+weight = 4
+notebook = "31_Metadata_Filtering.ipynb"
+aliases = []
+completion_time = "10 min"
+created_at = 2024-01-30
 haystack_2 = true
\ No newline at end of file
diff --git a/tutorials/31_Metadata_Filtering.ipynb b/tutorials/31_Metadata_Filtering.ipynb
new file mode 100644
index 00000000..34f48d3a
--- /dev/null
+++ b/tutorials/31_Metadata_Filtering.ipynb
@@ -0,0 +1,966 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c1WR4wDbHj7i"
+      },
+      "source": [
+        "# Tutorial: Filter Documents Based on Metadata\n",
+        "\n",
+        "- **Level**: Beginner\n",
+        "- **Time to complete**: 5 minutes\n",
+        "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever)\n",
+        "- **Prerequisites**: None\n",
+        "- **Goal**: Filter documents in a document store based on given metadata"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WUNZTr1FH0Iw"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "**📚 Useful Documentation: [Metadata Filtering](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering)**\n",
+        "\n",
+        "Although new retrieval techniques are great, sometimes you just know that you want to perform search on a specific group of documents in your document store. This can be anything from all the documents that are related to a specific _user_, or that were published after a certain _date_ and so on. Metadata filtering is very useful in these situations. In this tutorial, we will create a few simple documents containing information about Haystack, where the metadata includes information on what version of Haystack the information relates to. We will then do metadata filtering to make sure we are answering the question based only on information about Haystack 2.0.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tM3U5KyegTAE"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "utuZlm-AgWIN"
+      },
+      "source": [
+        "## Installing Haystack\n",
+        "\n",
+        "Install Haystack 2.0 Beta with `pip`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Y27k0kO_Hz3D",
+        "outputId": "715de352-470c-454d-da4b-501141b7630c"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install haystack-ai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "07gGBcxBgi8Y"
+      },
+      "source": [
+        "### Enabling Telemetry\n",
+        "\n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PtJZlNtkHZr-"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(31)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HKBltMXLgzvq"
+      },
+      "source": [
+        "## Preparing Documents\n",
+        "\n",
+        "First, let's prepare some documents. Below, we're manually creating 3 simple documents with `meta` attached. We're then writing these documents to an `InMemoryDocumentStore`, but you can [use any of the available document stores](https://docs.haystack.deepset.ai/v2.0/docs/choosing-a-document-store) instead such as OpenSearch, Chroma, Pinecone and more.. (Note that not all of them have options to store in memory and may require extra setup).\n",
+        "\n",
+        "> ⭐️ For more information on how to write documents into different document stores, you can follow our tutorial on indexing different file types."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mvEyXZV9hKio",
+        "outputId": "a7425c3c-585f-440d-d6f3-dbf7b9ccbca3"
+      },
+      "outputs": [],
+      "source": [
+        "from datetime import datetime\n",
+        "\n",
+        "from haystack import  Document\n",
+        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+        "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
+        "\n",
+        "documents = [Document(content=\"Use pip to install a basic version of Haystack's latest release: pip install farm-haystack. All the core Haystack components live in the haystack repo. But there's also the haystack-extras repo which contains components that are not as widely used, and you need to install them separately.\",\n",
+        "                      meta={\"version\": 1.15, \"date\": datetime(2023, 3, 30)}),\n",
+        "             Document(content=\"Use pip to install a basic version of Haystack's latest release: pip install farm-haystack[inference]. All the core Haystack components live in the haystack repo. But there's also the haystack-extras repo which contains components that are not as widely used, and you need to install them separately.\",\n",
+        "                      meta={\"version\": 1.22, \"date\": datetime(2023, 11, 7)}),\n",
+        "             Document(content=\"Use pip to install only the Haystack 2.0 code: pip install haystack-ai. The haystack-ai package is built on the main branch which is an unstable beta version, but it's useful if you want to try the new features as soon as they are merged.\",\n",
+        "                      meta={\"version\": 2.0, \"date\": datetime(2023, 12, 4)}),\n",
+        "]\n",
+        "document_store = InMemoryDocumentStore(bm25_algorithm=\"BM25Plus\")\n",
+        "document_store.write_documents(documents=documents)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o9RaTQ_ZhdyY"
+      },
+      "source": [
+        "## Building a Document Search Pipeline\n",
+        "\n",
+        "As an example, below we are building a simple document search pipeline that simply has a retriever. However, you can also change this pipeline to do more, such as generating answers to questions or more."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lo-CGv8mhyyL"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack import Pipeline\n",
+        "\n",
+        "pipeline = Pipeline()\n",
+        "pipeline.add_component(instance=InMemoryBM25Retriever(document_store=document_store), name=\"retriever\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-3Ggh01Wh5Z5"
+      },
+      "source": [
+        "## Do Metadata Filtering\n",
+        "\n",
+        "Finally, ask a question by filtering the documents to `\"version\" > 1.21`.\n",
+        "\n",
+        "To see what kind of comparison operators you can use for your metadata, including logical comparistons such as `NOT`, `AND` and so on, check out the [Metadata Filtering documentation](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering#comparison)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
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+        "id": "sO52YJ9Xi5YZ",
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+      },
+      "outputs": [],
+      "source": [
+        "query = \"Haystack installation\"\n",
+        "pipeline.run(data={\"retriever\": {\"query\": query, \"filters\": { \"field\": \"meta.version\", \"operator\": \">\", \"value\": 1.21}}})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7_d1QNmSYECu"
+      },
+      "source": [
+        "As a final step, let's see how we can add logical operators to our filters. This time, we are asking for retrieved documents to be filtered to `version > 1.21` _AND_ we're also asking their `date` to be later than November 7th 2023."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
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+        "colab": {
+          "base_uri": "https://localhost:8080/",
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+        "id": "PmWrtr7SWnRy",
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+      },
+      "outputs": [],
+      "source": [
+        "query = \"Haystack installation\"\n",
+        "pipeline.run(data={\"retriever\": {\"query\": query, \"filters\": { \"operator\": \"AND\",\n",
+        "      \t\t\t                                              \"conditions\": [{ \"field\": \"meta.version\", \"operator\": \">\", \"value\": 1.21},\n",
+        "                                                                             { \"field\": \"meta.date\", \"operator\": \">\", \"value\": datetime(2023, 11, 7)}]}}})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rejLvPEIj4FS"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "🎉 Congratulations! You've filtered retrieved documents with metadata!\n",
+        "\n",
+        "If you liked this tutorial, you may also enjoy:\n",
+        "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+        "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=metadata_filtering). Thanks for reading!"
+      ]
+    }
+  ],
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+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From a118ab9408f9d7db96ea371194706f2017b6ff33 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Wed, 31 Jan 2024 11:15:16 +0100
Subject: [PATCH 149/206] Update index.toml

---
 index.toml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/index.toml b/index.toml
index 66f45f61..b995fdcc 100644
--- a/index.toml
+++ b/index.toml
@@ -291,7 +291,7 @@ haystack_2 = true
 title = "Serializing LLM Pipelines"
 description = "Learn how to serialize and deserialize your pipelines between YAML and Python"
 level = "beginner"
-weight = 6
+weight = 7
 notebook = "29_Serializing_Pipelines.ipynb"
 aliases = []
 completion_time = "10 min"
@@ -302,9 +302,9 @@ haystack_2 = true
 title = "Filtering Documents with Metadata"
 description = "Learn how to filter down to specific documents at retrieval time using metadata"
 level = "beginner"
-weight = 4
+weight = 6
 notebook = "31_Metadata_Filtering.ipynb"
 aliases = []
 completion_time = "10 min"
 created_at = 2024-01-30
-haystack_2 = true
\ No newline at end of file
+haystack_2 = true

From 6f4c8dce6684da8cf31c97810ee46dbe96c249f6 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Wed, 31 Jan 2024 13:24:48 +0100
Subject: [PATCH 150/206] trying thumbnails

---
 scripts/generate_markdowns.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index 89f156c6..591bd4d3 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -23,6 +23,8 @@ def generate_frontmatter(config, tutorial):
     frontmatter = f"""---
 layout: {config["layout"]}
 featured: {tutorial.get("featured", False)}
+featured_image: /images/tutorials.png
+images: ["/images/tutorials.png"]
 haystack_version: "{tutorial.get("haystack_version", "latest")}"
 haystack_2: {tutorial.get("haystack_2", False)}
 hidden: {tutorial.get("hidden", False)}

From ce7178c56d6cc4dbde233e3339debf402e9af782 Mon Sep 17 00:00:00 2001
From: Tilde Thurium <tilde.thurium@deepset.ai>
Date: Thu, 1 Feb 2024 04:31:20 -0800
Subject: [PATCH 151/206] Add tutorial for file preprocessing index pipeline
 (#274)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* add file type preprocessing tutorial

* update index.toml

* update workflows and README

* Update tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>

* Update index.toml

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>

* Update index.toml

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>

* Update README.md

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>

* address feedback

* remove commented out code and add telemetry text

* adding a mini explanation on DocumentJoiner

* moving pipeline drawing up

---------

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>
Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>
---
 .github/workflows/run_tutorials.yml           |    1 +
 README.md                                     |    1 +
 index.toml                                    |   15 +-
 tutorials/29_Serializing_Pipelines.ipynb      |   14 +
 ...le_Type_Preprocessing_Index_Pipeline.ipynb | 5110 +++++++++++++++++
 5 files changed, 5139 insertions(+), 2 deletions(-)
 create mode 100644 tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index b572e025..02ee944f 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -70,6 +70,7 @@ jobs:
               "tutorials/27_First_RAG_Pipeline.ipynb"
               "tutorials/28_Structured_Output_With_Loop.ipynb"
               "tutorials/29_Serializing_Pipelines.ipynb"
+              "tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb"
               "tutorials/31_Metadata_Filtering.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
diff --git a/README.md b/README.md
index b5898781..b4de73dd 100644
--- a/README.md
+++ b/README.md
@@ -47,5 +47,6 @@ To contribute to the tutorials, please check out our [Contributing Guidelines](.
 | [Your First QA Pipeline with Retrieval-Augmentation](./tutorials/27_First_RAG_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/27_First_RAG_Pipeline.ipynb) |
 | [Generating Structured Output with Loop-Based Auto-Correction](./tutorials/28_Structured_Output_With_Loop.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/28_Structured_Output_With_Loop.ipynb) |
 | [Serializing Pipelines](./tutorials/29_Serializing_Pipelines.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/29_Serializing_Pipelines.ipynb) |
+| [Preprocessing Different File Types](./tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb) |
 | [Metadata Filtering](./tutorials/31_Metadata_Filtering.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/31_Metadata_Filtering.ipynb) |
 
diff --git a/index.toml b/index.toml
index b995fdcc..e0081367 100644
--- a/index.toml
+++ b/index.toml
@@ -298,6 +298,17 @@ completion_time = "10 min"
 created_at = 2024-01-29
 haystack_2 = true
 
+[[tutorial]]
+title = "Preprocessing Different File Types"
+description = "Learn how to build an indexing pipeline that will preprocess files based on their file type"
+level = "beginner"
+weight = 72
+notebook = "30_File_Type_Preprocessing_Index_Pipeline.ipynb"
+aliases = []
+completion_time = "10 min"
+created_at = 2024-01-30
+haystack_2 = true
+
 [[tutorial]]
 title = "Filtering Documents with Metadata"
 description = "Learn how to filter down to specific documents at retrieval time using metadata"
@@ -305,6 +316,6 @@ level = "beginner"
 weight = 6
 notebook = "31_Metadata_Filtering.ipynb"
 aliases = []
-completion_time = "10 min"
+completion_time = "15 min"
 created_at = 2024-01-30
-haystack_2 = true
+haystack_2 = true
\ No newline at end of file
diff --git a/tutorials/29_Serializing_Pipelines.ipynb b/tutorials/29_Serializing_Pipelines.ipynb
index 233ac402..cf0841fd 100644
--- a/tutorials/29_Serializing_Pipelines.ipynb
+++ b/tutorials/29_Serializing_Pipelines.ipynb
@@ -396,6 +396,20 @@
       "source": [
         "new_pipeline.run(data={\"builder\": {\"sentence\": \"I love capybaras\"}})"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## What's next\n",
+        "\n",
+        "🎉 Congratulations! You've serialzed a pipeline into YAML, edited it and ran it again!\n",
+        "\n",
+        "If you liked this tutorial, you may also enjoy:\n",
+        "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=serialization). Thanks for reading!"
+      ]
     }
   ],
   "metadata": {
diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
new file mode 100644
index 00000000..1cbdd28c
--- /dev/null
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -0,0 +1,5110 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zAKy_OPy81EZ"
+      },
+      "source": [
+        "# Tutorial: Preprocessing Different File Types\n",
+        "\n",
+        "- **Level**: Beginner\n",
+        "- **Time to complete**: 15 minutes\n",
+        "- **Goal**: After completing this tutorial, you'll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the `FileTypeRouter`.\n",
+        "\n",
+        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
+        "\n",
+        "> 💡 (Optional): After creating the indexing pipeline in this tutorial, there is an optional section that shows you how to create a RAG pipeline on top of the document store you just created. You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section\n",
+        "\n",
+        "## Components Used\n",
+        "\n",
+        "- [`FileTypeRouter`](https://docs.haystack.deepset.ai/v2.0/docs/filetyperouter): This component will help you route files based on their corresponding MIME type to different components\n",
+        "- [`MarkdownToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/markdowntodocument): This component will help you convert markdown files into Haystack Documents\n",
+        "- [`PyPDFToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/pypdftodocument): This component will help you convert pdf files into Haystack Documents\n",
+        "- [`TextFileToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/textfiletodocument): This component will help you convert text files into Haystack Documents\n",
+        "- [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner): This component will help you to join Documents coming from different branches of a pipeline\n",
+        "- [`DocumentCleaner`](https://docs.haystack.deepset.ai/v2.0/docs/documentcleaner) (optional): This component will help you to make Documents more readable by removing extra whitespaces etc.\n",
+        "- [`DocumentSplitter`](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter): This component will help you to split your Document into chunks\n",
+        "- [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder): This component will help you create embeddings for Documents.\n",
+        "- [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter): This component will help you write Documents into the DocumentStore"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "96w6PrcPk4Fc"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "In this tutorial, you'll build an indexing pipeline that preprocesses different types of files (markdown, txt and pdf). Each file will have its own `FileConverter`. The rest of the indexing pipeline is fairly standard - split the documents into chunks, trim whitespace, create embeddings and write them to a Document Store.\n",
+        "\n",
+        "Optionally, you can keep going to see how to use these documents in a query pipeline as well."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rns_B_NGN0Ze"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_pG2jycZLYYr"
+      },
+      "source": [
+        "## Installing dependencies\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2mP4empwf_x4",
+        "outputId": "fbe648c0-716f-4f02-b764-f42bd8c68077"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "pip install haystack-ai\n",
+        "pip install sentence-transformers>=2.2.0\n",
+        "pip install markdown-it-py mdit_plain\n",
+        "pip install pypdf"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HnXumz7EarJx"
+      },
+      "source": [
+        "### Enabling Telemetry\n",
+        "\n",
+        "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CkvJIU7FmDf9"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(30)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7GWbC28fX0Yp"
+      },
+      "source": [
+        "## Download All Files\n",
+        "\n",
+        "All required files will be downloaded into this Colab notebook. You can see these files in \"files\" tab on the left. Just like most real life data, these files are a mishmash of different types."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8wIRuFcTIKF1",
+        "outputId": "8728b32b-08a7-4a23-fbb8-fa873d592b84"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "gdown https://drive.google.com/drive/folders/1n9yqq5Gl_HWfND5bTlrCwAOycMDt5EMj -O /content --folder"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DH8HEymp6XFZ"
+      },
+      "source": [
+        "## Create a Pipeline to Index Documents\n",
+        "\n",
+        "Next, you'll create a pipeline to index documents. To keep things uncomplicated, you'll use an `InMemoryDocumentStore` but this approach would also work with any other flavor of `DocumentStore`.\n",
+        "\n",
+        "You'll need a different file converter class for each file type in our data sources: `.pdf`, `.txt`, and `.md` in this case. Our `FileTypeRouter` connects each file type to the proper converter.\n",
+        "\n",
+        "Once all our files have been converted to Haystack Documents, we can use the `DocumentJoiner` component to make these a single list of documents that can be fed through the rest of the indexing pipeline all together."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c_eM4C1cA4j6"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.writers import DocumentWriter\n",
+        "from haystack.components.converters import MarkdownToDocument, PyPDFToDocument, TextFileToDocument\n",
+        "from haystack.components.preprocessors import DocumentSplitter, DocumentCleaner\n",
+        "from haystack.components.routers import FileTypeRouter\n",
+        "from haystack.components.joiners import DocumentJoiner\n",
+        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+        "from haystack.pipeline import Pipeline\n",
+        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+        "\n",
+        "document_store = InMemoryDocumentStore()\n",
+        "file_type_router = FileTypeRouter(mime_types=[\"text/plain\", \"application/pdf\", \"text/markdown\"])\n",
+        "text_file_converter = TextFileToDocument()\n",
+        "markdown_converter = MarkdownToDocument()\n",
+        "pdf_converter = PyPDFToDocument()\n",
+        "document_joiner = DocumentJoiner()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ef8okackBSmk"
+      },
+      "source": [
+        "From there, the steps to this indexing pipeline are a bit more standard. The `DocumentCleaner` removes whitespace. Then this `DocumentSplitter` breaks them into chunks of 150 words, with a bit of overlap to avoid missing context."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hCWlpiQCBYOg"
+      },
+      "outputs": [],
+      "source": [
+        "document_cleaner = DocumentCleaner()\n",
+        "document_splitter = DocumentSplitter(\n",
+        "    split_by = \"word\",\n",
+        "    split_length = 150,\n",
+        "    split_overlap = 50\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Do4nhM4tBaZL"
+      },
+      "source": [
+        "Now you'll add a `SentenceTransformersDocumentEmbedder` to create embeddings from the documents. As the last step in this pipeline, the `DocumentWriter` will write them to the `InMemoryDocumentStore`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TVXSX0GHBtdj"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "document_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+        "document_writer = DocumentWriter(document_store)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hJaJmGanBv1b"
+      },
+      "source": [
+        "After creating all the components, add them to the indexing pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4yGXKHEXIZxi"
+      },
+      "outputs": [],
+      "source": [
+        "preprocessing_pipeline = Pipeline()\n",
+        "preprocessing_pipeline.add_component(instance=file_type_router, name=\"file_type_router\")\n",
+        "preprocessing_pipeline.add_component(instance=text_file_converter, name=\"text_file_converter\")\n",
+        "preprocessing_pipeline.add_component(instance=markdown_converter, name=\"markdown_converter\")\n",
+        "preprocessing_pipeline.add_component(instance=pdf_converter, name=\"pypdf_converter\")\n",
+        "preprocessing_pipeline.add_component(instance=document_joiner, name=\"document_joiner\")\n",
+        "preprocessing_pipeline.add_component(instance=document_cleaner, name=\"document_cleaner\")\n",
+        "preprocessing_pipeline.add_component(instance=document_splitter, name=\"document_splitter\")\n",
+        "preprocessing_pipeline.add_component(instance=document_embedder,name=\"document_embedder\")\n",
+        "preprocessing_pipeline.add_component(instance=document_writer, name=\"document_writer\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, connect them 👇"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "preprocessing_pipeline.connect(\"file_type_router.text/plain\", \"text_file_converter.sources\")\n",
+        "preprocessing_pipeline.connect(\"file_type_router.application/pdf\", \"pypdf_converter.sources\")\n",
+        "preprocessing_pipeline.connect(\"file_type_router.text/markdown\", \"markdown_converter.sources\")\n",
+        "preprocessing_pipeline.connect(\"text_file_converter\", \"document_joiner\")\n",
+        "preprocessing_pipeline.connect(\"pypdf_converter\", \"document_joiner\")\n",
+        "preprocessing_pipeline.connect(\"markdown_converter\", \"document_joiner\")\n",
+        "preprocessing_pipeline.connect(\"document_joiner\", \"document_cleaner\")\n",
+        "preprocessing_pipeline.connect(\"document_cleaner\", \"document_splitter\")\n",
+        "preprocessing_pipeline.connect(\"document_splitter\", \"document_embedder\")\n",
+        "preprocessing_pipeline.connect(\"document_embedder\", \"document_writer\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qcr2hXtlVhJt"
+      },
+      "source": [
+        "If you want to see a diagram of the pipeline, you can draw it.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "epx9Fv-3VgEI"
+      },
+      "outputs": [],
+      "source": [
+        "preprocessing_pipeline.draw(\"pipeline.png\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3NGinkHPB9C2"
+      },
+      "source": [
+        "Let's test this pipeline with a few recipes I've written. Are you getting hungry yet?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 560,
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+          ]
+        },
+        "id": "9Mw5kwZiqehc",
+        "outputId": "c2781237-b0e0-4e7c-e009-7402353e1917"
+      },
+      "outputs": [],
+      "source": [
+        "preprocessing_pipeline.run({\n",
+        "    \"file_type_router\": {\"sources\":[\"/content/vegan_sunflower_hemp_cheese_recipe.txt\",\n",
+        "                                    \"/content/vegan_keto_eggplant_recipe.pdf\",\n",
+        "                                    \"/content/vegan_flan_recipe.md\"]}\n",
+        "})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TVGb-rteg7E5"
+      },
+      "source": [
+        "🎉 If you only wanted to learn how to preprocess documents, you can stop here! If you want to see an example of using those documents in a RAG pipeline, read on.  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "16PnegDR2EmY"
+      },
+      "source": [
+        "## (Optional) Build a pipeline to query documents"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I06qdWsZibSz"
+      },
+      "source": [
+        "Now, let's build a RAG pipeline that answers queries based ont he documents you just created in the section above. For this step, we will be using the [`HuggingFaceTGIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `mistralai/Mixtral-8x7B-Instruct-v0.1` model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bB344ADZr-eG",
+        "outputId": "5adbdf4f-439d-4fe4-b63e-d72077ac0c18"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "import os\n",
+        "from getpass import getpass\n",
+        "hf_token = os.getenv(\"HF_TOKEN\", None) or getpass(\"Enter Hugging Face token:\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QASEGJhnIMQL"
+      },
+      "source": [
+        "In this step you'll build a query pipeline to answer questions about the documents.\n",
+        "\n",
+        "This pipeline takes the prompt, searches the document store for relevant documents, and passes those documents along to the LLM to formulate an answer.\n",
+        "\n",
+        "> ⚠️ Notice how we used `sentence-transformers/all-MiniLM-L6-v2` to create embeddings for our documents before. This is why we will be using the same model to embed incoming questions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_s--8xEWq8Y9"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+        "from haystack.components.builders import PromptBuilder\n",
+        "from haystack.components.generators import HuggingFaceTGIGenerator\n",
+        "\n",
+        "template = \"\"\"\n",
+        "Answer the questions based on the given context.\n",
+        "\n",
+        "Context:\n",
+        "{% for document in documents %}\n",
+        "    {{ document.content }}\n",
+        "{% endfor %}\n",
+        "\n",
+        "Question: {{ question }}\n",
+        "Answer:\n",
+        "\"\"\"\n",
+        "pipe = Pipeline()\n",
+        "pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+        "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+        "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mixtral-8x7B-Instruct-v0.1\", token=hf_token))\n",
+        "\n",
+        "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+        "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+        "pipe.connect(\"prompt_builder\", \"llm\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ba5puJxIg3V"
+      },
+      "source": [
+        "Try it out yourself by running the code below. If all has gone well, you should have a complete shopping list from all the recipe sources. 🧂🥥🧄"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 327,
+          "referenced_widgets": [
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+          ]
+        },
+        "id": "qDqrU5emtBWQ",
+        "outputId": "762b9b1e-ac90-4bc6-d746-f1940a380fa3"
+      },
+      "outputs": [],
+      "source": [
+        "question = \"What ingredients would I need to make vegan keto eggplant lasagna, vegan persimmon flan, and vegan hemp cheese?\"\n",
+        "\n",
+        "pipe.run({\n",
+        "    \"embedder\": {\"text\": question},\n",
+        "    \"prompt_builder\": {\n",
+        "        \"question\": question\n",
+        "    },\n",
+        "    \"llm\": {\"generation_kwargs\": {\"max_new_tokens\": 350}}\n",
+        "})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZJueu_V4KP6w"
+      },
+      "source": [
+        "```python\n",
+        "{'llm': {'replies': [' To make vegan keto eggplant lasagna, you would need 2 large eggplants, a lot of salt, 1/2 cup store-bought vegan mozzarella for topping, 4 oz basil, 1/4 cup almonds, 1/4 cup nutritional yeast, 1/4 cup olive oil, 1 recipe vegan pesto, 1 recipe spinach tofu ricotta, 1 tsp garlic powder, juice of half a lemon, and salt to taste. To make vegan persimmon flan, you would need 1/2 cup persimmon pulp, strained, 1 tbsp cornstarch, 1/2 tsp agar agar, 1 tbsp agave nectar or to taste, 2 tbsp granulated sugar, 1/4 cup coconut creme, 1/2 cup almond milk, and 1/2 tsp vanilla. To make vegan hemp cheese, you would need 1/2 cup sunflower seeds, 1/2 cup hemp hearts, 1.5 teaspoons miso paste, 1 tsp nutritional yeast, 1/4 cup rejuvelac, and 1/4th teaspoon salt or to taste.'],\n",
+        "  'meta': [{'model': 'mistralai/Mixtral-8x7B-Instruct-v0.1',\n",
+        "    'index': 0,\n",
+        "    'finish_reason': 'eos_token',\n",
+        "    'usage': {'completion_tokens': 287,\n",
+        "     'prompt_tokens': 3452,\n",
+        "     'total_tokens': 3739}}]}}\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zA7xHckYJHsG"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "Congratulations on building an indexing pipeline that can preprocess different file types. Go forth and ingest all the messy real-world data into your workflows. 💥\n",
+        "\n",
+        "If you liked this tutorial, you may also enjoy:\n",
+        "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+        "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=filte_type_routing). Thanks for reading!"
+      ]
+    }
+  ],
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+      "gpuType": "T4",
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From 3cafeaf9e86bd95d5dc913be91fc653ed44799d8 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Thu, 1 Feb 2024 13:35:04 +0100
Subject: [PATCH 152/206] fixing times to complete

---
 index.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/index.toml b/index.toml
index e0081367..03e6ad1e 100644
--- a/index.toml
+++ b/index.toml
@@ -305,7 +305,7 @@ level = "beginner"
 weight = 72
 notebook = "30_File_Type_Preprocessing_Index_Pipeline.ipynb"
 aliases = []
-completion_time = "10 min"
+completion_time = "15 min"
 created_at = 2024-01-30
 haystack_2 = true
 
@@ -316,6 +316,6 @@ level = "beginner"
 weight = 6
 notebook = "31_Metadata_Filtering.ipynb"
 aliases = []
-completion_time = "15 min"
+completion_time = "5 min"
 created_at = 2024-01-30
 haystack_2 = true
\ No newline at end of file

From d3b97e187d06a71ed06e6586627532c09d103e28 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 5 Feb 2024 21:33:22 +0300
Subject: [PATCH 153/206] Fix breaking changes (#276)

---
 tutorials/27_First_RAG_Pipeline.ipynb         |     6 +-
 .../28_Structured_Output_With_Loop.ipynb      |     6 +-
 ...le_Type_Preprocessing_Index_Pipeline.ipynb | 10215 ++++++++--------
 3 files changed, 5115 insertions(+), 5112 deletions(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 724bc4a9..b47fb0fb 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -295,7 +295,7 @@
     "## Initializing a Generator\n",
     "\n",
     "\n",
-    "Generators are the components that interacts with large language models (LLMs). Now, initialize a [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/OpenAIGenerator) that can communicate with OpenAI GPT models with an OpenAI `api_key` and the model name you'd like to use. If you don't provide any `model`, the OpenAIGenerator defaults to `gpt-3.5-turbo`:"
+    "Generators are the components that interacts with large language models (LLMs). Now, set `OPENAI_API_KEY` environment variable and initialize a [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/OpenAIGenerator) that can communicate with OpenAI GPT models. If you don't provide any `model`, the OpenAIGenerator defaults to `gpt-3.5-turbo`:"
    ]
   },
   {
@@ -315,8 +315,8 @@
     "\n",
     "from haystack.components.generators import OpenAIGenerator\n",
     "\n",
-    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")\n",
-    "generator = OpenAIGenerator(api_key=openai_api_key)"
+    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key: \")\n",
+    "generator = OpenAIGenerator()"
    ]
   },
   {
diff --git a/tutorials/28_Structured_Output_With_Loop.ipynb b/tutorials/28_Structured_Output_With_Loop.ipynb
index a335825e..010c36b1 100644
--- a/tutorials/28_Structured_Output_With_Loop.ipynb
+++ b/tutorials/28_Structured_Output_With_Loop.ipynb
@@ -293,7 +293,7 @@
         "## Initalizing the Generator\n",
         "\n",
         "[OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) generates\n",
-        "text using OpenAI's `gpt-3.5-turbo` model by default. Provide an API key and a model name to the Generator."
+        "text using OpenAI's `gpt-3.5-turbo` model by default. Set the `OPENAI_API_KEY` variable and provide a model name to the Generator."
       ]
     },
     {
@@ -309,8 +309,8 @@
         "\n",
         "from haystack.components.generators import OpenAIGenerator\n",
         "\n",
-        "llm_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")\n",
-        "generator = OpenAIGenerator(api_key=llm_api_key)"
+        "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key: \")\n",
+        "generator = OpenAIGenerator()"
       ]
     },
     {
diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index 1cbdd28c..5a2470ab 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -1,5110 +1,5113 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zAKy_OPy81EZ"
-      },
-      "source": [
-        "# Tutorial: Preprocessing Different File Types\n",
-        "\n",
-        "- **Level**: Beginner\n",
-        "- **Time to complete**: 15 minutes\n",
-        "- **Goal**: After completing this tutorial, you'll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the `FileTypeRouter`.\n",
-        "\n",
-        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
-        "\n",
-        "> 💡 (Optional): After creating the indexing pipeline in this tutorial, there is an optional section that shows you how to create a RAG pipeline on top of the document store you just created. You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section\n",
-        "\n",
-        "## Components Used\n",
-        "\n",
-        "- [`FileTypeRouter`](https://docs.haystack.deepset.ai/v2.0/docs/filetyperouter): This component will help you route files based on their corresponding MIME type to different components\n",
-        "- [`MarkdownToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/markdowntodocument): This component will help you convert markdown files into Haystack Documents\n",
-        "- [`PyPDFToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/pypdftodocument): This component will help you convert pdf files into Haystack Documents\n",
-        "- [`TextFileToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/textfiletodocument): This component will help you convert text files into Haystack Documents\n",
-        "- [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner): This component will help you to join Documents coming from different branches of a pipeline\n",
-        "- [`DocumentCleaner`](https://docs.haystack.deepset.ai/v2.0/docs/documentcleaner) (optional): This component will help you to make Documents more readable by removing extra whitespaces etc.\n",
-        "- [`DocumentSplitter`](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter): This component will help you to split your Document into chunks\n",
-        "- [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder): This component will help you create embeddings for Documents.\n",
-        "- [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter): This component will help you write Documents into the DocumentStore"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "96w6PrcPk4Fc"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "In this tutorial, you'll build an indexing pipeline that preprocesses different types of files (markdown, txt and pdf). Each file will have its own `FileConverter`. The rest of the indexing pipeline is fairly standard - split the documents into chunks, trim whitespace, create embeddings and write them to a Document Store.\n",
-        "\n",
-        "Optionally, you can keep going to see how to use these documents in a query pipeline as well."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rns_B_NGN0Ze"
-      },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_pG2jycZLYYr"
-      },
-      "source": [
-        "## Installing dependencies\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "2mP4empwf_x4",
-        "outputId": "fbe648c0-716f-4f02-b764-f42bd8c68077"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "pip install haystack-ai\n",
-        "pip install sentence-transformers>=2.2.0\n",
-        "pip install markdown-it-py mdit_plain\n",
-        "pip install pypdf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HnXumz7EarJx"
-      },
-      "source": [
-        "### Enabling Telemetry\n",
-        "\n",
-        "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "CkvJIU7FmDf9"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(30)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7GWbC28fX0Yp"
-      },
-      "source": [
-        "## Download All Files\n",
-        "\n",
-        "All required files will be downloaded into this Colab notebook. You can see these files in \"files\" tab on the left. Just like most real life data, these files are a mishmash of different types."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8wIRuFcTIKF1",
-        "outputId": "8728b32b-08a7-4a23-fbb8-fa873d592b84"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "gdown https://drive.google.com/drive/folders/1n9yqq5Gl_HWfND5bTlrCwAOycMDt5EMj -O /content --folder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DH8HEymp6XFZ"
-      },
-      "source": [
-        "## Create a Pipeline to Index Documents\n",
-        "\n",
-        "Next, you'll create a pipeline to index documents. To keep things uncomplicated, you'll use an `InMemoryDocumentStore` but this approach would also work with any other flavor of `DocumentStore`.\n",
-        "\n",
-        "You'll need a different file converter class for each file type in our data sources: `.pdf`, `.txt`, and `.md` in this case. Our `FileTypeRouter` connects each file type to the proper converter.\n",
-        "\n",
-        "Once all our files have been converted to Haystack Documents, we can use the `DocumentJoiner` component to make these a single list of documents that can be fed through the rest of the indexing pipeline all together."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "c_eM4C1cA4j6"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.components.writers import DocumentWriter\n",
-        "from haystack.components.converters import MarkdownToDocument, PyPDFToDocument, TextFileToDocument\n",
-        "from haystack.components.preprocessors import DocumentSplitter, DocumentCleaner\n",
-        "from haystack.components.routers import FileTypeRouter\n",
-        "from haystack.components.joiners import DocumentJoiner\n",
-        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
-        "from haystack.pipeline import Pipeline\n",
-        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-        "\n",
-        "document_store = InMemoryDocumentStore()\n",
-        "file_type_router = FileTypeRouter(mime_types=[\"text/plain\", \"application/pdf\", \"text/markdown\"])\n",
-        "text_file_converter = TextFileToDocument()\n",
-        "markdown_converter = MarkdownToDocument()\n",
-        "pdf_converter = PyPDFToDocument()\n",
-        "document_joiner = DocumentJoiner()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef8okackBSmk"
-      },
-      "source": [
-        "From there, the steps to this indexing pipeline are a bit more standard. The `DocumentCleaner` removes whitespace. Then this `DocumentSplitter` breaks them into chunks of 150 words, with a bit of overlap to avoid missing context."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "hCWlpiQCBYOg"
-      },
-      "outputs": [],
-      "source": [
-        "document_cleaner = DocumentCleaner()\n",
-        "document_splitter = DocumentSplitter(\n",
-        "    split_by = \"word\",\n",
-        "    split_length = 150,\n",
-        "    split_overlap = 50\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Do4nhM4tBaZL"
-      },
-      "source": [
-        "Now you'll add a `SentenceTransformersDocumentEmbedder` to create embeddings from the documents. As the last step in this pipeline, the `DocumentWriter` will write them to the `InMemoryDocumentStore`.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "TVXSX0GHBtdj"
-      },
-      "outputs": [],
-      "source": [
-        "\n",
-        "document_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
-        "document_writer = DocumentWriter(document_store)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hJaJmGanBv1b"
-      },
-      "source": [
-        "After creating all the components, add them to the indexing pipeline."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "4yGXKHEXIZxi"
-      },
-      "outputs": [],
-      "source": [
-        "preprocessing_pipeline = Pipeline()\n",
-        "preprocessing_pipeline.add_component(instance=file_type_router, name=\"file_type_router\")\n",
-        "preprocessing_pipeline.add_component(instance=text_file_converter, name=\"text_file_converter\")\n",
-        "preprocessing_pipeline.add_component(instance=markdown_converter, name=\"markdown_converter\")\n",
-        "preprocessing_pipeline.add_component(instance=pdf_converter, name=\"pypdf_converter\")\n",
-        "preprocessing_pipeline.add_component(instance=document_joiner, name=\"document_joiner\")\n",
-        "preprocessing_pipeline.add_component(instance=document_cleaner, name=\"document_cleaner\")\n",
-        "preprocessing_pipeline.add_component(instance=document_splitter, name=\"document_splitter\")\n",
-        "preprocessing_pipeline.add_component(instance=document_embedder,name=\"document_embedder\")\n",
-        "preprocessing_pipeline.add_component(instance=document_writer, name=\"document_writer\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Next, connect them 👇"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "preprocessing_pipeline.connect(\"file_type_router.text/plain\", \"text_file_converter.sources\")\n",
-        "preprocessing_pipeline.connect(\"file_type_router.application/pdf\", \"pypdf_converter.sources\")\n",
-        "preprocessing_pipeline.connect(\"file_type_router.text/markdown\", \"markdown_converter.sources\")\n",
-        "preprocessing_pipeline.connect(\"text_file_converter\", \"document_joiner\")\n",
-        "preprocessing_pipeline.connect(\"pypdf_converter\", \"document_joiner\")\n",
-        "preprocessing_pipeline.connect(\"markdown_converter\", \"document_joiner\")\n",
-        "preprocessing_pipeline.connect(\"document_joiner\", \"document_cleaner\")\n",
-        "preprocessing_pipeline.connect(\"document_cleaner\", \"document_splitter\")\n",
-        "preprocessing_pipeline.connect(\"document_splitter\", \"document_embedder\")\n",
-        "preprocessing_pipeline.connect(\"document_embedder\", \"document_writer\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qcr2hXtlVhJt"
-      },
-      "source": [
-        "If you want to see a diagram of the pipeline, you can draw it.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "epx9Fv-3VgEI"
-      },
-      "outputs": [],
-      "source": [
-        "preprocessing_pipeline.draw(\"pipeline.png\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3NGinkHPB9C2"
-      },
-      "source": [
-        "Let's test this pipeline with a few recipes I've written. Are you getting hungry yet?"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
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-          ]
-        },
-        "id": "9Mw5kwZiqehc",
-        "outputId": "c2781237-b0e0-4e7c-e009-7402353e1917"
-      },
-      "outputs": [],
-      "source": [
-        "preprocessing_pipeline.run({\n",
-        "    \"file_type_router\": {\"sources\":[\"/content/vegan_sunflower_hemp_cheese_recipe.txt\",\n",
-        "                                    \"/content/vegan_keto_eggplant_recipe.pdf\",\n",
-        "                                    \"/content/vegan_flan_recipe.md\"]}\n",
-        "})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TVGb-rteg7E5"
-      },
-      "source": [
-        "🎉 If you only wanted to learn how to preprocess documents, you can stop here! If you want to see an example of using those documents in a RAG pipeline, read on.  "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "16PnegDR2EmY"
-      },
-      "source": [
-        "## (Optional) Build a pipeline to query documents"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "I06qdWsZibSz"
-      },
-      "source": [
-        "Now, let's build a RAG pipeline that answers queries based ont he documents you just created in the section above. For this step, we will be using the [`HuggingFaceTGIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `mistralai/Mixtral-8x7B-Instruct-v0.1` model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bB344ADZr-eG",
-        "outputId": "5adbdf4f-439d-4fe4-b63e-d72077ac0c18"
-      },
-      "outputs": [],
-      "source": [
-        "\n",
-        "import os\n",
-        "from getpass import getpass\n",
-        "hf_token = os.getenv(\"HF_TOKEN\", None) or getpass(\"Enter Hugging Face token:\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QASEGJhnIMQL"
-      },
-      "source": [
-        "In this step you'll build a query pipeline to answer questions about the documents.\n",
-        "\n",
-        "This pipeline takes the prompt, searches the document store for relevant documents, and passes those documents along to the LLM to formulate an answer.\n",
-        "\n",
-        "> ⚠️ Notice how we used `sentence-transformers/all-MiniLM-L6-v2` to create embeddings for our documents before. This is why we will be using the same model to embed incoming questions."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_s--8xEWq8Y9"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
-        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-        "from haystack.components.builders import PromptBuilder\n",
-        "from haystack.components.generators import HuggingFaceTGIGenerator\n",
-        "\n",
-        "template = \"\"\"\n",
-        "Answer the questions based on the given context.\n",
-        "\n",
-        "Context:\n",
-        "{% for document in documents %}\n",
-        "    {{ document.content }}\n",
-        "{% endfor %}\n",
-        "\n",
-        "Question: {{ question }}\n",
-        "Answer:\n",
-        "\"\"\"\n",
-        "pipe = Pipeline()\n",
-        "pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
-        "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
-        "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
-        "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mixtral-8x7B-Instruct-v0.1\", token=hf_token))\n",
-        "\n",
-        "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
-        "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
-        "pipe.connect(\"prompt_builder\", \"llm\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1ba5puJxIg3V"
-      },
-      "source": [
-        "Try it out yourself by running the code below. If all has gone well, you should have a complete shopping list from all the recipe sources. 🧂🥥🧄"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 327,
-          "referenced_widgets": [
-            "55186726e0fa45139703b9cf9032b855",
-            "147622f4e4d94992ab535dece72c4c3d",
-            "609bbb1ffa194cd59a9c44acf5327402",
-            "6f2a25c748634b5db0f75b60ce2da334",
-            "4b3e9c0135cd4c3983202560faba2076",
-            "09de7d0c59ec4e7c98b4556e5e995e38",
-            "c4e895304dd64893800df5969e0d256f",
-            "54fb47c4b0fe49b3b08a770d92696555",
-            "9131299f801941adb8f0aece6b0d7575",
-            "62f5b250db8943d88515c3554da3bf92",
-            "6c1a19f079054ce284712b8a1e918d42"
-          ]
-        },
-        "id": "qDqrU5emtBWQ",
-        "outputId": "762b9b1e-ac90-4bc6-d746-f1940a380fa3"
-      },
-      "outputs": [],
-      "source": [
-        "question = \"What ingredients would I need to make vegan keto eggplant lasagna, vegan persimmon flan, and vegan hemp cheese?\"\n",
-        "\n",
-        "pipe.run({\n",
-        "    \"embedder\": {\"text\": question},\n",
-        "    \"prompt_builder\": {\n",
-        "        \"question\": question\n",
-        "    },\n",
-        "    \"llm\": {\"generation_kwargs\": {\"max_new_tokens\": 350}}\n",
-        "})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZJueu_V4KP6w"
-      },
-      "source": [
-        "```python\n",
-        "{'llm': {'replies': [' To make vegan keto eggplant lasagna, you would need 2 large eggplants, a lot of salt, 1/2 cup store-bought vegan mozzarella for topping, 4 oz basil, 1/4 cup almonds, 1/4 cup nutritional yeast, 1/4 cup olive oil, 1 recipe vegan pesto, 1 recipe spinach tofu ricotta, 1 tsp garlic powder, juice of half a lemon, and salt to taste. To make vegan persimmon flan, you would need 1/2 cup persimmon pulp, strained, 1 tbsp cornstarch, 1/2 tsp agar agar, 1 tbsp agave nectar or to taste, 2 tbsp granulated sugar, 1/4 cup coconut creme, 1/2 cup almond milk, and 1/2 tsp vanilla. To make vegan hemp cheese, you would need 1/2 cup sunflower seeds, 1/2 cup hemp hearts, 1.5 teaspoons miso paste, 1 tsp nutritional yeast, 1/4 cup rejuvelac, and 1/4th teaspoon salt or to taste.'],\n",
-        "  'meta': [{'model': 'mistralai/Mixtral-8x7B-Instruct-v0.1',\n",
-        "    'index': 0,\n",
-        "    'finish_reason': 'eos_token',\n",
-        "    'usage': {'completion_tokens': 287,\n",
-        "     'prompt_tokens': 3452,\n",
-        "     'total_tokens': 3739}}]}}\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zA7xHckYJHsG"
-      },
-      "source": [
-        "## What's next\n",
-        "\n",
-        "Congratulations on building an indexing pipeline that can preprocess different file types. Go forth and ingest all the messy real-world data into your workflows. 💥\n",
-        "\n",
-        "If you liked this tutorial, you may also enjoy:\n",
-        "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
-        "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
-        "\n",
-        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=filte_type_routing). Thanks for reading!"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zAKy_OPy81EZ"
+   },
+   "source": [
+    "# Tutorial: Preprocessing Different File Types\n",
+    "\n",
+    "- **Level**: Beginner\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Goal**: After completing this tutorial, you'll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the `FileTypeRouter`.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
+    "\n",
+    "> 💡 (Optional): After creating the indexing pipeline in this tutorial, there is an optional section that shows you how to create a RAG pipeline on top of the document store you just created. You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section\n",
+    "\n",
+    "## Components Used\n",
+    "\n",
+    "- [`FileTypeRouter`](https://docs.haystack.deepset.ai/v2.0/docs/filetyperouter): This component will help you route files based on their corresponding MIME type to different components\n",
+    "- [`MarkdownToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/markdowntodocument): This component will help you convert markdown files into Haystack Documents\n",
+    "- [`PyPDFToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/pypdftodocument): This component will help you convert pdf files into Haystack Documents\n",
+    "- [`TextFileToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/textfiletodocument): This component will help you convert text files into Haystack Documents\n",
+    "- [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner): This component will help you to join Documents coming from different branches of a pipeline\n",
+    "- [`DocumentCleaner`](https://docs.haystack.deepset.ai/v2.0/docs/documentcleaner) (optional): This component will help you to make Documents more readable by removing extra whitespaces etc.\n",
+    "- [`DocumentSplitter`](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter): This component will help you to split your Document into chunks\n",
+    "- [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder): This component will help you create embeddings for Documents.\n",
+    "- [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter): This component will help you write Documents into the DocumentStore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "96w6PrcPk4Fc"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In this tutorial, you'll build an indexing pipeline that preprocesses different types of files (markdown, txt and pdf). Each file will have its own `FileConverter`. The rest of the indexing pipeline is fairly standard - split the documents into chunks, trim whitespace, create embeddings and write them to a Document Store.\n",
+    "\n",
+    "Optionally, you can keep going to see how to use these documents in a query pipeline as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rns_B_NGN0Ze"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_pG2jycZLYYr"
+   },
+   "source": [
+    "## Installing dependencies\n"
+   ]
+  },
+  {
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+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "2mP4empwf_x4",
+    "outputId": "fbe648c0-716f-4f02-b764-f42bd8c68077"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "pip install haystack-ai\n",
+    "pip install sentence-transformers>=2.2.0\n",
+    "pip install markdown-it-py mdit_plain\n",
+    "pip install pypdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HnXumz7EarJx"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "CkvJIU7FmDf9"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7GWbC28fX0Yp"
+   },
+   "source": [
+    "## Download All Files\n",
+    "\n",
+    "All required files will be downloaded into this Colab notebook. You can see these files in \"files\" tab on the left. Just like most real life data, these files are a mishmash of different types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8wIRuFcTIKF1",
+    "outputId": "8728b32b-08a7-4a23-fbb8-fa873d592b84"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "gdown https://drive.google.com/drive/folders/1n9yqq5Gl_HWfND5bTlrCwAOycMDt5EMj -O /content --folder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DH8HEymp6XFZ"
+   },
+   "source": [
+    "## Create a Pipeline to Index Documents\n",
+    "\n",
+    "Next, you'll create a pipeline to index documents. To keep things uncomplicated, you'll use an `InMemoryDocumentStore` but this approach would also work with any other flavor of `DocumentStore`.\n",
+    "\n",
+    "You'll need a different file converter class for each file type in our data sources: `.pdf`, `.txt`, and `.md` in this case. Our `FileTypeRouter` connects each file type to the proper converter.\n",
+    "\n",
+    "Once all our files have been converted to Haystack Documents, we can use the `DocumentJoiner` component to make these a single list of documents that can be fed through the rest of the indexing pipeline all together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "c_eM4C1cA4j6"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.writers import DocumentWriter\n",
+    "from haystack.components.converters import MarkdownToDocument, PyPDFToDocument, TextFileToDocument\n",
+    "from haystack.components.preprocessors import DocumentSplitter, DocumentCleaner\n",
+    "from haystack.components.routers import FileTypeRouter\n",
+    "from haystack.components.joiners import DocumentJoiner\n",
+    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+    "from haystack.pipeline import Pipeline\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "\n",
+    "document_store = InMemoryDocumentStore()\n",
+    "file_type_router = FileTypeRouter(mime_types=[\"text/plain\", \"application/pdf\", \"text/markdown\"])\n",
+    "text_file_converter = TextFileToDocument()\n",
+    "markdown_converter = MarkdownToDocument()\n",
+    "pdf_converter = PyPDFToDocument()\n",
+    "document_joiner = DocumentJoiner()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ef8okackBSmk"
+   },
+   "source": [
+    "From there, the steps to this indexing pipeline are a bit more standard. The `DocumentCleaner` removes whitespace. Then this `DocumentSplitter` breaks them into chunks of 150 words, with a bit of overlap to avoid missing context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "hCWlpiQCBYOg"
+   },
+   "outputs": [],
+   "source": [
+    "document_cleaner = DocumentCleaner()\n",
+    "document_splitter = DocumentSplitter(split_by=\"word\", split_length=150, split_overlap=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Do4nhM4tBaZL"
+   },
+   "source": [
+    "Now you'll add a `SentenceTransformersDocumentEmbedder` to create embeddings from the documents. As the last step in this pipeline, the `DocumentWriter` will write them to the `InMemoryDocumentStore`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "TVXSX0GHBtdj"
+   },
+   "outputs": [],
+   "source": [
+    "document_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+    "document_writer = DocumentWriter(document_store)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hJaJmGanBv1b"
+   },
+   "source": [
+    "After creating all the components, add them to the indexing pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "4yGXKHEXIZxi"
+   },
+   "outputs": [],
+   "source": [
+    "preprocessing_pipeline = Pipeline()\n",
+    "preprocessing_pipeline.add_component(instance=file_type_router, name=\"file_type_router\")\n",
+    "preprocessing_pipeline.add_component(instance=text_file_converter, name=\"text_file_converter\")\n",
+    "preprocessing_pipeline.add_component(instance=markdown_converter, name=\"markdown_converter\")\n",
+    "preprocessing_pipeline.add_component(instance=pdf_converter, name=\"pypdf_converter\")\n",
+    "preprocessing_pipeline.add_component(instance=document_joiner, name=\"document_joiner\")\n",
+    "preprocessing_pipeline.add_component(instance=document_cleaner, name=\"document_cleaner\")\n",
+    "preprocessing_pipeline.add_component(instance=document_splitter, name=\"document_splitter\")\n",
+    "preprocessing_pipeline.add_component(instance=document_embedder, name=\"document_embedder\")\n",
+    "preprocessing_pipeline.add_component(instance=document_writer, name=\"document_writer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, connect them 👇"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preprocessing_pipeline.connect(\"file_type_router.text/plain\", \"text_file_converter.sources\")\n",
+    "preprocessing_pipeline.connect(\"file_type_router.application/pdf\", \"pypdf_converter.sources\")\n",
+    "preprocessing_pipeline.connect(\"file_type_router.text/markdown\", \"markdown_converter.sources\")\n",
+    "preprocessing_pipeline.connect(\"text_file_converter\", \"document_joiner\")\n",
+    "preprocessing_pipeline.connect(\"pypdf_converter\", \"document_joiner\")\n",
+    "preprocessing_pipeline.connect(\"markdown_converter\", \"document_joiner\")\n",
+    "preprocessing_pipeline.connect(\"document_joiner\", \"document_cleaner\")\n",
+    "preprocessing_pipeline.connect(\"document_cleaner\", \"document_splitter\")\n",
+    "preprocessing_pipeline.connect(\"document_splitter\", \"document_embedder\")\n",
+    "preprocessing_pipeline.connect(\"document_embedder\", \"document_writer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qcr2hXtlVhJt"
+   },
+   "source": [
+    "If you want to see a diagram of the pipeline, you can draw it.\n"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "epx9Fv-3VgEI"
+   },
+   "outputs": [],
+   "source": [
+    "preprocessing_pipeline.draw(\"pipeline.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3NGinkHPB9C2"
+   },
+   "source": [
+    "Let's test this pipeline with a few recipes I've written. Are you getting hungry yet?"
+   ]
+  },
+  {
+   "cell_type": "code",
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+     ]
+    },
+    "id": "9Mw5kwZiqehc",
+    "outputId": "c2781237-b0e0-4e7c-e009-7402353e1917"
+   },
+   "outputs": [],
+   "source": [
+    "preprocessing_pipeline.run(\n",
+    "    {\n",
+    "        \"file_type_router\": {\n",
+    "            \"sources\": [\n",
+    "                \"/content/vegan_sunflower_hemp_cheese_recipe.txt\",\n",
+    "                \"/content/vegan_keto_eggplant_recipe.pdf\",\n",
+    "                \"/content/vegan_flan_recipe.md\",\n",
+    "            ]\n",
+    "        }\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TVGb-rteg7E5"
+   },
+   "source": [
+    "🎉 If you only wanted to learn how to preprocess documents, you can stop here! If you want to see an example of using those documents in a RAG pipeline, read on.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "16PnegDR2EmY"
+   },
+   "source": [
+    "## (Optional) Build a pipeline to query documents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "I06qdWsZibSz"
+   },
+   "source": [
+    "Now, let's build a RAG pipeline that answers queries based ont he documents you just created in the section above. For this step, we will be using the [`HuggingFaceTGIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `mistralai/Mixtral-8x7B-Instruct-v0.1` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "bB344ADZr-eG",
+    "outputId": "5adbdf4f-439d-4fe4-b63e-d72077ac0c18"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter Hugging Face token: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QASEGJhnIMQL"
+   },
+   "source": [
+    "In this step you'll build a query pipeline to answer questions about the documents.\n",
+    "\n",
+    "This pipeline takes the prompt, searches the document store for relevant documents, and passes those documents along to the LLM to formulate an answer.\n",
+    "\n",
+    "> ⚠️ Notice how we used `sentence-transformers/all-MiniLM-L6-v2` to create embeddings for our documents before. This is why we will be using the same model to embed incoming questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_s--8xEWq8Y9"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "from haystack.components.builders import PromptBuilder\n",
+    "from haystack.components.generators import HuggingFaceTGIGenerator\n",
+    "\n",
+    "template = \"\"\"\n",
+    "Answer the questions based on the given context.\n",
+    "\n",
+    "Context:\n",
+    "{% for document in documents %}\n",
+    "    {{ document.content }}\n",
+    "{% endfor %}\n",
+    "\n",
+    "Question: {{ question }}\n",
+    "Answer:\n",
+    "\"\"\"\n",
+    "pipe = Pipeline()\n",
+    "pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+    "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+    "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+    "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mixtral-8x7B-Instruct-v0.1\"))\n",
+    "\n",
+    "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+    "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+    "pipe.connect(\"prompt_builder\", \"llm\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1ba5puJxIg3V"
+   },
+   "source": [
+    "Try it out yourself by running the code below. If all has gone well, you should have a complete shopping list from all the recipe sources. 🧂🥥🧄"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 327,
+     "referenced_widgets": [
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+    "id": "qDqrU5emtBWQ",
+    "outputId": "762b9b1e-ac90-4bc6-d746-f1940a380fa3"
+   },
+   "outputs": [],
+   "source": [
+    "question = (\n",
+    "    \"What ingredients would I need to make vegan keto eggplant lasagna, vegan persimmon flan, and vegan hemp cheese?\"\n",
+    ")\n",
+    "\n",
+    "pipe.run(\n",
+    "    {\n",
+    "        \"embedder\": {\"text\": question},\n",
+    "        \"prompt_builder\": {\"question\": question},\n",
+    "        \"llm\": {\"generation_kwargs\": {\"max_new_tokens\": 350}},\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZJueu_V4KP6w"
+   },
+   "source": [
+    "```python\n",
+    "{'llm': {'replies': [' To make vegan keto eggplant lasagna, you would need 2 large eggplants, a lot of salt, 1/2 cup store-bought vegan mozzarella for topping, 4 oz basil, 1/4 cup almonds, 1/4 cup nutritional yeast, 1/4 cup olive oil, 1 recipe vegan pesto, 1 recipe spinach tofu ricotta, 1 tsp garlic powder, juice of half a lemon, and salt to taste. To make vegan persimmon flan, you would need 1/2 cup persimmon pulp, strained, 1 tbsp cornstarch, 1/2 tsp agar agar, 1 tbsp agave nectar or to taste, 2 tbsp granulated sugar, 1/4 cup coconut creme, 1/2 cup almond milk, and 1/2 tsp vanilla. To make vegan hemp cheese, you would need 1/2 cup sunflower seeds, 1/2 cup hemp hearts, 1.5 teaspoons miso paste, 1 tsp nutritional yeast, 1/4 cup rejuvelac, and 1/4th teaspoon salt or to taste.'],\n",
+    "  'meta': [{'model': 'mistralai/Mixtral-8x7B-Instruct-v0.1',\n",
+    "    'index': 0,\n",
+    "    'finish_reason': 'eos_token',\n",
+    "    'usage': {'completion_tokens': 287,\n",
+    "     'prompt_tokens': 3452,\n",
+    "     'total_tokens': 3739}}]}}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zA7xHckYJHsG"
+   },
+   "source": [
+    "## What's next\n",
+    "\n",
+    "Congratulations on building an indexing pipeline that can preprocess different file types. Go forth and ingest all the messy real-world data into your workflows. 💥\n",
+    "\n",
+    "If you liked this tutorial, you may also enjoy:\n",
+    "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+    "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=filte_type_routing). Thanks for reading!"
+   ]
+  }
+ ],
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From 1ffde06e8fba3f5023742f84478b732d8e0e1d83 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Tue, 6 Feb 2024 17:18:08 +0100
Subject: [PATCH 154/206] Update README.md (#278)

---
 README.md | 79 ++++++++++++++++++++++++++++++++-----------------------
 1 file changed, 46 insertions(+), 33 deletions(-)

diff --git a/README.md b/README.md
index b4de73dd..d5db737f 100644
--- a/README.md
+++ b/README.md
@@ -16,37 +16,50 @@ To contribute to the tutorials, please check out our [Contributing Guidelines](.
 [![Publish tutorials on Haystack Home](https://github.com/deepset-ai/haystack-tutorials/actions/workflows/publish_tutorials.yml/badge.svg)](https://github.com/deepset-ai/haystack-tutorials/actions/workflows/publish_tutorials.yml)
 
 ## Tutorials
-| Source Code                                     | Colab                                     | 
-| ---------------------------------------- | -------------------------------------------------|
-| [Build Your First Question Answering System](./tutorials/01_Basic_QA_Pipeline.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)                                       |
-| [Fine Tune a Model on Your Data](./tutorials/02_Finetune_a_model_on_your_data.ipynb)            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb)|
-| [Build a Scalable Question Answering System](./tutorials/03_Scalable_QA_System.ipynb)   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb)                 |
-| [FAQ Style QA](./tutorials/04_FAQ_style_QA.ipynb)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            |
-| [Evaluation](./tutorials/05_Evaluation.ipynb)                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              |
-| [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                |
-| [[OUTDATED] RAG Generator](./tutorials/07_RAG_Generator.ipynb)                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           |
-| [Preprocessing](./tutorials/08_Preprocessing.ipynb)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           |
-| [DPR Training](./tutorials/09_DPR_training.ipynb)                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            |
-| [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         |
-| [Pipelines](./tutorials/11_Pipelines.ipynb)                                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               |
-| [[OUTDATED] Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    |
-| [Question Generation](./tutorials/13_Question_generation.ipynb)                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     |
-| [Query Classifier](./tutorials/14_Query_Classifier.ipynb)                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)                                        |
-| [Table QA](./tutorials/15_TableQA.ipynb)                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)                                                 |
-| [Document Classifier at Index Time](./tutorials/16_Document_Classifier_at_Index_Time.ipynb)        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb)                       |
-| [Make Your QA Pipelines Talk!](./tutorials/17_Audio.ipynb)                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb)                                                   |
-| [Generative Pseudo Labeling](./tutorials/18_GPL.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)                                                     |
-| [Text-to-Image search](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb)                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) |
-| [Using Haystack with REST API](./tutorials/20_Using_Haystack_with_REST_API.ipynb)             | <a href="https://haystack.deepset.ai/downloads/20_Using_Haystack_with_REST_API.ipynb" target="_blank"><img alt="Download" src="https://img.shields.io/badge/DOWNLOAD-blue?&style=for-the-badge"/> <a>     |
-| [Customizing PromptNode](./tutorials/21_Customizing_PromptNode.ipynb)                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb) |
-| [Generative QA Pipeline with Retrieval-Augmentation](./tutorials/22_Pipeline_with_PromptNode.ipynb)                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/22_Pipeline_with_PromptNode.ipynb) |
-| [Answering Complex Questions with Agents](./tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb)                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb) |
-| [Building a Conversational Chat App](./tutorials/24_Building_Chat_App.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/24_Building_Chat_App.ipynb) |
-| [Customizing Agent to Chat with Your Documents](./tutorials/25_Customizing_Agent.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/25_Customizing_Agent.ipynb) |
-| [Creating a Hybrid Retrieval Pipeline](./tutorials/26_Hybrid_Retrieval.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/26_Hybrid_Retrieval.ipynb) |
-| [Your First QA Pipeline with Retrieval-Augmentation](./tutorials/27_First_RAG_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/27_First_RAG_Pipeline.ipynb) |
-| [Generating Structured Output with Loop-Based Auto-Correction](./tutorials/28_Structured_Output_With_Loop.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/28_Structured_Output_With_Loop.ipynb) |
-| [Serializing Pipelines](./tutorials/29_Serializing_Pipelines.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/29_Serializing_Pipelines.ipynb) |
-| [Preprocessing Different File Types](./tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb) |
-| [Metadata Filtering](./tutorials/31_Metadata_Filtering.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/31_Metadata_Filtering.ipynb) |
+
+<table>
+<tr>
+<th align="center">
+<img width="465px">
+<p> 
+Haystack 1.x
+</p>
+</th>
+<th align="center">
+<img width="535px">
+<p> 
+Haystack 2.0
+</p>
+</th>
+</tr>
+</table>
+
+| Code                                                                                                      | Colab                                                                                                                                                                                                                                     | Code                                                                                                             | Colab                                                                                                                                                                                                                    |
+| :-------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| [Build Your First Question Answering System](./tutorials/01_Basic_QA_Pipeline.ipynb)                      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/01_Basic_QA_Pipeline.ipynb)                                       | [Your First QA Pipeline with Retrieval-Augmentation](./tutorials/27_First_RAG_Pipeline.ipynb)                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/27_First_RAG_Pipeline.ipynb)                     |
+| [Fine Tune a Model on Your Data](./tutorials/02_Finetune_a_model_on_your_data.ipynb)                      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/02_Finetune_a_model_on_your_data.ipynb)                           | [Generating Structured Output with Loop-Based Auto-Correction](./tutorials/28_Structured_Output_With_Loop.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/28_Structured_Output_With_Loop.ipynb)            |
+| [Build a Scalable Question Answering System](./tutorials/03_Scalable_QA_System.ipynb)                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb)                                      | [Serializing Pipelines](./tutorials/29_Serializing_Pipelines.ipynb)                                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/29_Serializing_Pipelines.ipynb)                  |
+| [FAQ Style QA](./tutorials/04_FAQ_style_QA.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            | [Preprocessing Different File Types](./tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb) |
+| [Evaluation](./tutorials/05_Evaluation.ipynb)                                                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              | [Metadata Filtering](./tutorials/31_Metadata_Filtering.ipynb)                                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/31_Metadata_Filtering.ipynb)                     |
+| [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                |                                                                                                                  |                                                                                                                                                                                                                          |
+| [[OUTDATED] RAG Generator](./tutorials/07_RAG_Generator.ipynb)                                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Preprocessing](./tutorials/08_Preprocessing.ipynb)                                                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           |                                                                                                                  |                                                                                                                                                                                                                          |
+| [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            |                                                                                                                  |                                                                                                                                                                                                                          |
+| [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Pipelines](./tutorials/11_Pipelines.ipynb)                                                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               |                                                                                                                  |                                                                                                                                                                                                                          |
+| [[OUTDATED] Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Question Generation](./tutorials/13_Question_generation.ipynb)                                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Query Classifier](./tutorials/14_Query_Classifier.ipynb)                                                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)                                        |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Table QA](./tutorials/15_TableQA.ipynb)                                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)                                                 |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Document Classifier at Index Time](./tutorials/16_Document_Classifier_at_Index_Time.ipynb)               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb)                       |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Make Your QA Pipelines Talk!](./tutorials/17_Audio.ipynb)                                                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/17_Audio.ipynb)                                                   |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Generative Pseudo Labeling](./tutorials/18_GPL.ipynb)                                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)                                                     |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Text-to-Image search](./tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb) |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Using Haystack with REST API](./tutorials/20_Using_Haystack_with_REST_API.ipynb)                         | <a href="https://haystack.deepset.ai/downloads/20_Using_Haystack_with_REST_API.ipynb" target="_blank"><img alt="Download" src="https://img.shields.io/badge/DOWNLOAD-blue?&style=for-the-badge"/> <a>                                     |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Customizing PromptNode](./tutorials/21_Customizing_PromptNode.ipynb)                                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/21_Customizing_PromptNode.ipynb)                                  |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Generative QA Pipeline with Retrieval-Augmentation](./tutorials/22_Pipeline_with_PromptNode.ipynb)       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/22_Pipeline_with_PromptNode.ipynb)                                |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Answering Complex Questions with Agents](./tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb)  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb)                |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Building a Conversational Chat App](./tutorials/24_Building_Chat_App.ipynb)                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/24_Building_Chat_App.ipynb)                                       |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Customizing Agent to Chat with Your Documents](./tutorials/25_Customizing_Agent.ipynb)                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/25_Customizing_Agent.ipynb)                                       |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Creating a Hybrid Retrieval Pipeline](./tutorials/26_Hybrid_Retrieval.ipynb)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/26_Hybrid_Retrieval.ipynb)                                        |                                                                                                                  |                                                                                                                                                                                                                          |
 

From adec33bac7acbaa3287554202a828b01abdf1b6a Mon Sep 17 00:00:00 2001
From: Tilde Thurium <tilde.thurium@deepset.ai>
Date: Tue, 6 Feb 2024 10:15:04 -0800
Subject: [PATCH 155/206] add language classification tutorial (#281)

* add language classification tutorial

* fixing review comments

* Update README.md

* Update run_tutorials.yml

* make intermediate

---------

Co-authored-by: Tuana Celik <tuana.celik@deepset.ai>
---
 .github/workflows/run_tutorials.yml           |    1 +
 README.md                                     |    3 +-
 index.toml                                    |   11 +
 ...ng_Documents_and_Queries_by_Language.ipynb | 1424 +++++++++++++++++
 4 files changed, 1437 insertions(+), 2 deletions(-)
 create mode 100644 tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 02ee944f..a28f63de 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -72,6 +72,7 @@ jobs:
               "tutorials/29_Serializing_Pipelines.ipynb"
               "tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb"
               "tutorials/31_Metadata_Filtering.ipynb"
+              "tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index d5db737f..77b94f46 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ Haystack 2.0
 | [Build a Scalable Question Answering System](./tutorials/03_Scalable_QA_System.ipynb)                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/03_Scalable_QA_System.ipynb)                                      | [Serializing Pipelines](./tutorials/29_Serializing_Pipelines.ipynb)                                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/29_Serializing_Pipelines.ipynb)                  |
 | [FAQ Style QA](./tutorials/04_FAQ_style_QA.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            | [Preprocessing Different File Types](./tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb) |
 | [Evaluation](./tutorials/05_Evaluation.ipynb)                                                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              | [Metadata Filtering](./tutorials/31_Metadata_Filtering.ipynb)                                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/31_Metadata_Filtering.ipynb)                     |
-| [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                | [Classifying Documents & Queries by Language](./tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb)|
 | [[OUTDATED] RAG Generator](./tutorials/07_RAG_Generator.ipynb)                                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Preprocessing](./tutorials/08_Preprocessing.ipynb)                                                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           |                                                                                                                  |                                                                                                                                                                                                                          |
 | [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            |                                                                                                                  |                                                                                                                                                                                                                          |
@@ -62,4 +62,3 @@ Haystack 2.0
 | [Building a Conversational Chat App](./tutorials/24_Building_Chat_App.ipynb)                              | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/24_Building_Chat_App.ipynb)                                       |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Customizing Agent to Chat with Your Documents](./tutorials/25_Customizing_Agent.ipynb)                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/25_Customizing_Agent.ipynb)                                       |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Creating a Hybrid Retrieval Pipeline](./tutorials/26_Hybrid_Retrieval.ipynb)                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/26_Hybrid_Retrieval.ipynb)                                        |                                                                                                                  |                                                                                                                                                                                                                          |
-
diff --git a/index.toml b/index.toml
index 03e6ad1e..1081b53a 100644
--- a/index.toml
+++ b/index.toml
@@ -318,4 +318,15 @@ notebook = "31_Metadata_Filtering.ipynb"
 aliases = []
 completion_time = "5 min"
 created_at = 2024-01-30
+haystack_2 = true
+
+[[tutorial]]
+title = "Classifying Documents & Queries by Language"
+description = "Learn how to classify documents and route queries by language, for both indexing and RAG pipelines"
+level = "intermediate"
+weight = 75
+notebook = "32_Classifying_Documents_and_Queries_by_Language.ipynb"
+aliases = []
+completion_time = "15 min"
+created_at = 2024-02-06
 haystack_2 = true
\ No newline at end of file
diff --git a/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb b/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
new file mode 100644
index 00000000..44d48867
--- /dev/null
+++ b/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
@@ -0,0 +1,1424 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sZVlYSmRjzuk"
+      },
+      "source": [
+        "# Tutorial: Classifying Documents & Queries by Language\n",
+        "\n",
+        "- **Level**: Beginner\n",
+        "- **Time to complete**: 15 minutes\n",
+        "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/v2.0/docs/documentlanguageclassifier), [`MetadataRouter`](https://docs.haystack.deepset.ai/v2.0/docs/metadatarouter), [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter), [`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter), [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator)\n",
+        "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline to classify documents based on the (human) language they were written in.\n",
+        "- Optionally, at the end you'll also incorporate language clasification and query routing into a RAG pipeline, so you can query documents based on the language a question was written in.\n",
+        "\n",
+        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G8qw1k7nf7yH"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "In a gobalized society with over 7,000 human languages spoken worldwide today, handling multilingual input is a common use case for NLP applications.\n",
+        "\n",
+        "Good news: Haystack has a [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/v2.0/docs/documentlanguageclassifier) built in. This component detects the language a document was written in. This functionality lets you create *branches* in your Haystack pipelines, granting the flexibility to add different processing steps for each language. For example, you could use a LLM that performs better in German to answer German queries. Or, you could fetch only French restaurant reviews for your French users.\n",
+        "\n",
+        "In this tutorial, you'll take a text samples from hotel reviews, written in different languages. The text samples will be made into Haystack documents and classified by language. Then each document will be written to a language-specific `DocumentStore`. To validate that the language detection is working correctly, you'll filter the document stores to display their contents.\n",
+        "\n",
+        "In the last section, you'll build a multi-lingual RAG pipeline. The language of a question is detected, and only documents in that language are used to generate the answer. For this section, the [`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter) will come in handy.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oBa4Q25cGTr6"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oC7ff5x0XTfN"
+      },
+      "source": [
+        "# Installing Haystack\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lxgAfuxcdftS",
+        "outputId": "36339d6b-f7a8-4686-911a-60642a8adbe6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: haystack-ai in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (2.0.0b6)\n",
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+            "Requirement already satisfied: distro<2,>=1.7.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (1.9.0)\n",
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+            "Requirement already satisfied: pydantic<3,>=1.9.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (2.6.0)\n",
+            "Requirement already satisfied: sniffio in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (1.3.0)\n",
+            "Requirement already satisfied: numpy in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-bm25->haystack-ai) (1.26.3)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from jinja2->haystack-ai) (2.1.4)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from pandas->haystack-ai) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from pandas->haystack-ai) (2023.4)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from pandas->haystack-ai) (2023.4)\n",
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+            "Requirement already satisfied: six>=1.5 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from posthog->haystack-ai) (1.16.0)\n",
+            "Requirement already satisfied: monotonic>=1.5 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from posthog->haystack-ai) (1.6)\n",
+            "Requirement already satisfied: backoff>=1.10.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from posthog->haystack-ai) (2.2.1)\n",
+            "Requirement already satisfied: idna>=2.8 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from anyio<5,>=3.5.0->openai>=1.1.0->haystack-ai) (3.6)\n",
+            "Requirement already satisfied: certifi in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (2023.11.17)\n",
+            "Requirement already satisfied: httpcore==1.* in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (1.0.2)\n",
+            "Requirement already satisfied: h11<0.15,>=0.13 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (0.14.0)\n",
+            "Requirement already satisfied: annotated-types>=0.4.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->haystack-ai) (0.6.0)\n",
+            "Requirement already satisfied: pydantic-core==2.16.1 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->haystack-ai) (2.16.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from requests<3.0,>=2.7->posthog->haystack-ai) (3.3.2)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from requests<3.0,>=2.7->posthog->haystack-ai) (2.2.0)\n",
+            "Collecting langdetect\n",
+            "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py): started\n",
+            "  Preparing metadata (setup.py): finished with status 'done'\n",
+            "Requirement already satisfied: six in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from langdetect) (1.16.0)\n",
+            "Building wheels for collected packages: langdetect\n",
+            "  Building wheel for langdetect (setup.py): started\n",
+            "  Building wheel for langdetect (setup.py): finished with status 'done'\n",
+            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993224 sha256=8ec8e722a5a05de5f2df58f42eeb655dad2d4dce611f88b04c375fd435f3f36a\n",
+            "  Stored in directory: /Users/tuanacelik/Library/Caches/pip/wheels/c1/67/88/e844b5b022812e15a52e4eaa38a1e709e99f06f6639d7e3ba7\n",
+            "Successfully built langdetect\n",
+            "Installing collected packages: langdetect\n",
+            "Successfully installed langdetect-1.0.9\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install haystack-ai\n",
+        "pip install langdetect"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "32oB-HJlGXmY"
+      },
+      "source": [
+        "### Enabling Telemetry\n",
+        "\n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "Ubr7yVt6Gbnj"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(32)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K0wRwkyvkV3Z"
+      },
+      "source": [
+        "## Write Documents Into `InMemoryDocumentStore`\n",
+        "\n",
+        "The following indexing pipeline writes French and English documents into their own `InMemoryDocumentStores` based on language.\n",
+        "\n",
+        "Import the modules you'll need. Then instantiate a list of Haystack `Documents` that are snippets of hotel reviews in various languages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "mN2fFuWWP_8D"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+            "  from .autonotebook import tqdm as notebook_tqdm\n"
+          ]
+        }
+      ],
+      "source": [
+        "from haystack import Document, Pipeline\n",
+        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+        "from haystack.components.classifiers import DocumentLanguageClassifier\n",
+        "from haystack.components.routers import MetadataRouter\n",
+        "from haystack.components.writers import DocumentWriter\n",
+        "\n",
+        "\n",
+        "documents = [\n",
+        "             Document(content = \"Super appartement. Juste au dessus de plusieurs bars qui ferment très tard. A savoir à l'avance. (Bouchons d'oreilles fournis !)\"),\n",
+        "             Document(content = \"El apartamento estaba genial y muy céntrico, todo a mano. Al lado de la librería Lello y De la Torre de los clérigos. Está situado en una zona de marcha, así que si vais en fin de semana , habrá ruido, aunque a nosotros no nos molestaba para dormir\"),\n",
+        "             Document(content = \"The keypad with a code is convenient and the location is convenient. Basically everything else, very noisy, wi-fi didn't work, check-in person didn't explain anything about facilities, shower head was broken, there's no cleaning and everything else one may need is charged.\"),\n",
+        "             Document(content = \"It is very central and appartement has a nice appearance (even though a lot IKEA stuff), *W A R N I N G** the appartement presents itself as a elegant and as a place to relax, very wrong place to relax - you cannot sleep in this appartement, even the beds are vibrating from the bass of the clubs in the same building - you get ear plugs from the hotel -> now I understand why -> I missed a trip as it was so loud and I could not hear the alarm next day due to the ear plugs.- there is a green light indicating 'emergency exit' just above the bed, which shines very bright at night - during the arrival process, you felt the urge of the agent to leave as soon as possible. - try to go to 'RVA clerigos appartements' -> same price, super quiet, beautiful, city center and very nice staff (not an agency)- you are basically sleeping next to the fridge, which makes a lot of noise, when the compressor is running -> had to switch it off - but then had no cool food and drinks. - the bed was somehow broken down - the wooden part behind the bed was almost falling appart and some hooks were broken before- when the neighbour room is cooking you hear the fan very loud. I initially thought that I somehow activated the kitchen fan\"),\n",
+        "             Document(content = \"Un peu salé surtout le sol. Manque de service et de souplesse\"),\n",
+        "             Document(content = \"Nous avons passé un séjour formidable. Merci aux personnes , le bonjours à Ricardo notre taxi man, très sympathique. Je pense refaire un séjour parmi vous, après le confinement, tout était parfait, surtout leur gentillesse, aucune chaude négative. Je n'ai rien à redire de négative, Ils étaient a notre écoute, un gentil message tout les matins, pour nous demander si nous avions besoins de renseignement et savoir si tout allait bien pendant notre séjour.\"),\n",
+        "             Document(content = \"Céntrico. Muy cómodo para moverse y ver Oporto. Edificio con terraza propia en la última planta. Todo reformado y nuevo. Te traen un estupendo desayuno todas las mañanas al apartamento. Solo que se puede escuchar algo de ruido de la calle a primeras horas de la noche. Es un zona de ocio nocturno. Pero respetan los horarios.\")]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TcZbAvjbRJLA"
+      },
+      "source": [
+        "Each language gets its own `DocumentStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "rfC1ZCigQJgI"
+      },
+      "outputs": [],
+      "source": [
+        "en_document_store = InMemoryDocumentStore()\n",
+        "fr_document_store = InMemoryDocumentStore()\n",
+        "es_document_store = InMemoryDocumentStore()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d9fyP-ThRTue"
+      },
+      "source": [
+        "The `DocumentLanguageClassifier` takes a list of languages. The `MetadataRouter` needs a dictionary of rules.  These rules specify which node to route a document to (in this case, which language-specific `DocumentWriter`), based on the document's metadata.\n",
+        "\n",
+        "The keys of the dictionary are the names of the output connections, and the values are dictionaries that follow the format of [filtering expressions in Haystack.](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "FlqGdbuxQNKk"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "language_classifier = DocumentLanguageClassifier(languages = [\"en\", \"fr\", \"es\"])\n",
+        "router_rules = {\"en\": {\"language\": {\"$eq\": \"en\"}},\n",
+        "                \"fr\": {\"language\": {\"$eq\": \"fr\"}},\n",
+        "                \"es\": {\"language\": {\"$eq\": \"es\"}}}\n",
+        "router = MetadataRouter(rules=router_rules)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "FEw5pfmBQRBB"
+      },
+      "outputs": [],
+      "source": [
+        "en_writer = DocumentWriter(document_store = en_document_store)\n",
+        "fr_writer = DocumentWriter(document_store = fr_document_store)\n",
+        "es_writer = DocumentWriter(document_store = es_document_store)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kAQvRdtESq_J"
+      },
+      "source": [
+        "Now that all the components have been created, instantiate the `Pipeline`. Add the components to the pipeline. Connect the outputs of one component to the input of the following component."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "BdvO_fEfcVAY"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "indexing_pipeline = Pipeline()\n",
+        "indexing_pipeline.add_component(instance=language_classifier, name=\"language_classifier\")\n",
+        "indexing_pipeline.add_component(instance=router, name=\"router\")\n",
+        "indexing_pipeline.add_component(instance=en_writer, name=\"en_writer\")\n",
+        "indexing_pipeline.add_component(instance=fr_writer, name=\"fr_writer\")\n",
+        "indexing_pipeline.add_component(instance=es_writer, name=\"es_writer\")\n",
+        "\n",
+        "\n",
+        "indexing_pipeline.connect(\"language_classifier\", \"router\")\n",
+        "indexing_pipeline.connect(\"router.en\", \"en_writer\")\n",
+        "indexing_pipeline.connect(\"router.fr\", \"fr_writer\")\n",
+        "indexing_pipeline.connect(\"router.es\", \"es_writer\")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ulAiCB1vTIbr"
+      },
+      "source": [
+        "Draw a diagram of the pipeline to see what the graph looks like."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "598ZTa7RzNeR"
+      },
+      "outputs": [],
+      "source": [
+        "indexing_pipeline.draw('indexing_pipeline.png')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UzQX7zFLS_Bk"
+      },
+      "source": [
+        "Run the pipeline and it will tell you how many documents were written in each language. Voila!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lE5XE8cPXN5-",
+        "outputId": "43017d9b-65f8-48ad-dadb-66ad0de3af43"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'router': {'unmatched': []},\n",
+              " 'en_writer': {'documents_written': 2},\n",
+              " 'fr_writer': {'documents_written': 3},\n",
+              " 'es_writer': {'documents_written': 2}}"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "indexing_pipeline.run(data={\"language_classifier\": {\"documents\": documents}})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R-Q2SxDnu3v-"
+      },
+      "source": [
+        "### Check the Contents of Your Document Stores\n",
+        "\n",
+        "You can check the contents of your document stores. Each one should only contain documents in the correct language."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LNHzxz52uxZV",
+        "outputId": "d0459677-73c0-4bb6-f5d3-87c0c00b1552"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "English documents:  [Document(id=8f64ab234c6a5d5652d02bed144d069ec6e988903b071d16fffbf400abfc1047, content: 'The keypad with a code is convenient and the location is convenient. Basically everything else, very...', meta: {'language': 'en'}), Document(id=d4d878288efba5e28a43ae0195e43dadd0298fe36d3d9b3075c5c5120d27763e, content: 'It is very central and appartement has a nice appearance (even though a lot IKEA stuff), *W A R N I ...', meta: {'language': 'en'})]\n",
+            "French documents:  [Document(id=ea7ea338874232de2d8105a258813f50345db82772e21ad2c4549dbb7adce8a3, content: 'Super appartement. Juste au dessus de plusieurs bars qui ferment très tard. A savoir à l'avance. (Bo...', meta: {'language': 'fr'}), Document(id=6b64c8a60543ee32b81cd39bc8d6e09fae4bff1b22c6ccdcf414db26fa354e7a, content: 'Un peu salé surtout le sol. Manque de service et de souplesse', meta: {'language': 'fr'}), Document(id=b1be23526f19a8af80a190e775bfd05e65878e585529037cb45b47267a4eaa98, content: 'Nous avons passé un séjour formidable. Merci aux personnes , le bonjours à Ricardo notre taxi man, t...', meta: {'language': 'fr'})]\n",
+            "Spanish documents:  [Document(id=72b094c163b22a660528bc5adbdf0fecf96b4b4d753c1b117f15dba482d2f948, content: 'El apartamento estaba genial y muy céntrico, todo a mano. Al lado de la librería Lello y De la Torre...', meta: {'language': 'es'}), Document(id=4b37b8bdfffccfb3211ea167b4fdc5121ca51fc5f869b4f834e8da473f0d3353, content: 'Céntrico. Muy cómodo para moverse y ver Oporto. Edificio con terraza propia en la última planta. Tod...', meta: {'language': 'es'})]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"English documents: \", en_document_store.filter_documents())\n",
+        "print(\"French documents: \", fr_document_store.filter_documents())\n",
+        "print(\"Spanish documents: \", es_document_store.filter_documents())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A6J0ac9UWdrT"
+      },
+      "source": [
+        "## (Optional) Create a Multi-Lingual RAG pipeline\n",
+        "\n",
+        "To build a multi-lingual RAG pipeline, you can use the[`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter) to detect the language of the query. Then, fetch documents in that same language from the correct `DocumentStore`.\n",
+        "\n",
+        "In order to do this you'll need an [OpenAI access token](https://help.openai.com/en/articles/4936850-where-do-i-find-my-api-key), although this approach would also work with any other [generator Haystack supports](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hVJaARodWezy",
+        "outputId": "d9bdcb42-bd50-4fd9-f4d8-a69e8b4b64f8"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "\n",
+        "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ei8up-k3qOC4"
+      },
+      "source": [
+        "Let's assume that all these reviews we put in our document stores earlier are for the same accommodation. A RAG pipeline will let you query for information about that apartment, in the language you choose.\n",
+        "\n",
+        "Import the components you'll need for a RAG pipeline. Write a prompt that will be passed to our LLM, along with the relevant documents."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "CN1N2sn1yUVx"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
+        "from haystack.components.joiners import DocumentJoiner\n",
+        "from haystack.components.builders import PromptBuilder\n",
+        "from haystack.components.generators import OpenAIGenerator\n",
+        "from haystack.components.routers import TextLanguageRouter\n",
+        "\n",
+        "prompt_template = \"\"\"\n",
+        "You will be provided with reviews for an accommodation.\n",
+        "Answer the question concisely based solely on the given reviews.\n",
+        "Reviews:\n",
+        "  {% for doc in documents %}\n",
+        "    {{ doc.content }}\n",
+        "  {% endfor %}\n",
+        "Question: {{ query}}\n",
+        "Answer:\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WTCT6u4cz_z6"
+      },
+      "source": [
+        "### Build the Pipeline\n",
+        "\n",
+        "Create a new `Pipeline`. Add the following components:\n",
+        "- `TextLanguageRouter`\n",
+        "- `InMemoryBM25Retriever`. You'll need a retriever per language, since each language has its own `DocumentStore`.\n",
+        "- `DocumentJoiner`\n",
+        "- `PromptBuilder`\n",
+        "- `OpenAIGenerator`\n",
+        "\n",
+        "> Note: The `BM25Retriever` essentially does keyword matching, which isn't as accurate as other search methods. In order to make the LLM responses more precise, you could refacctor your piplines to use an [`EmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever) which performs vector search over the documents."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "BN1Hr_BjWKcl"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "rag_pipeline = Pipeline()\n",
+        "rag_pipeline.add_component(instance=TextLanguageRouter(['en', 'fr', 'es']), name=\"router\")\n",
+        "rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=en_document_store), name=\"en_retriever\")\n",
+        "rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=fr_document_store), name=\"fr_retriever\")\n",
+        "rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=es_document_store), name=\"es_retriever\")\n",
+        "rag_pipeline.add_component(instance=DocumentJoiner(), name=\"joiner\")\n",
+        "rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name=\"prompt_builder\")\n",
+        "rag_pipeline.add_component(instance=OpenAIGenerator(), name=\"llm\")\n",
+        "\n",
+        "\n",
+        "rag_pipeline.connect(\"router.en\", \"en_retriever.query\")\n",
+        "rag_pipeline.connect(\"router.fr\", \"fr_retriever.query\")\n",
+        "rag_pipeline.connect(\"router.es\", \"es_retriever.query\")\n",
+        "rag_pipeline.connect(\"en_retriever\", \"joiner\")\n",
+        "rag_pipeline.connect(\"fr_retriever\", \"joiner\")\n",
+        "rag_pipeline.connect(\"es_retriever\", \"joiner\")\n",
+        "rag_pipeline.connect(\"joiner.documents\", \"prompt_builder.documents\")\n",
+        "rag_pipeline.connect(\"prompt_builder\", \"llm\")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "q1C5GHK_1Kkk"
+      },
+      "source": [
+        "You can draw this pipeline and compare the architecture to the `indexing_pipeline` diagram we created earlier."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "HAFTD5nt1L9a",
+        "outputId": "90cbf82b-8fe5-439d-b099-08510e1c1098"
+      },
+      "outputs": [],
+      "source": [
+        "rag_pipeline.draw('rag_pipeline.png')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-Vr8MbGrEHZV"
+      },
+      "source": [
+        "Try it out by asking a question."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "8a6b993b2fbd4537a1f130adc08f2eb0",
+            "cbe44862bf474ff692a359314a7c21f1",
+            "04daef6e8b9e4779bbe41dc32f4e9083",
+            "e5d659cdfd64477bbb9ce80aed7924f8",
+            "d8777b76fb5341869afcb084e91231ee",
+            "00a38829bf664b0084b5ec704047f00d",
+            "f32af41f2b9543e497989b2c44e9d62d",
+            "4ffb8a24b1a74cb8be55af79261e65ab",
+            "28e5a76ec5b448c7b2d339913fb721c6",
+            "d5d73be36bdb4ddb8fcd92f1ae7a2856",
+            "f403167cb47840a3b0c796ae4c304401"
+          ]
+        },
+        "id": "wj24fjXN0l6v",
+        "outputId": "3c1eed33-c31c-4b72-bcda-fdd64744560b"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Ranking by BM25...: 100%|██████████| 2/2 [00:00<00:00, 3134.76 docs/s]"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "en_question = \"Is this apartment conveniently located?\"\n",
+        "\n",
+        "result = rag_pipeline.run({\n",
+        "    \"router\": {\"text\": en_question},\n",
+        "    \"prompt_builder\": {\"query\": en_question},\n",
+        "})\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "i-2P5oqMeUmC",
+        "outputId": "8151923f-bbb1-4e6a-fe4e-08c0d7cfcd49"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Yes, the apartment is conveniently located.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(result[\"llm\"][\"replies\"][0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U4ChKAl1EKni"
+      },
+      "source": [
+        "How does the pipeline perform en español?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "cb611c28471e487a8c53a7af8d0f7ae7",
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+            "713bf7f3026f4a1daa52df12016caf82",
+            "ae4725f43a3846acbdcbbf79f51166d0",
+            "f0ff1b09bbc34065961a09ee894207bd",
+            "d58eb54b8b04455b9e506598491b5b2b",
+            "3115ad83af834fe4b4fa12362205d98c",
+            "34c2218b0a4d4ba486530aeac007b00a",
+            "dbdbd1d389d74c06acd9a1d3066c82dc",
+            "7b62bd3498bb49ec9e1db68ca088e7ae"
+          ]
+        },
+        "id": "B4_Be1bs1jxJ",
+        "outputId": "0b96cf29-d633-4c9b-f54c-a785e1c2cbe4"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Ranking by BM25...: 100%|██████████| 2/2 [00:00<00:00, 15887.52 docs/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "es_question = \"¿El desayuno es genial?\"\n",
+        "\n",
+        "result = rag_pipeline.run({\n",
+        "    \"router\": {\"text\": es_question},\n",
+        "    \"prompt_builder\": {\"query\": es_question},\n",
+        "})\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "w_1wibY61sjk",
+        "outputId": "54f7506e-9af1-42b8-c0c9-cd13fb4cd9eb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No, el desayuno no es genial.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(result[\"llm\"][\"replies\"][0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IhHIJYjbTpAw"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "If you've been following along, now you know how to incorporate language detection into query and indexing Haystack piplines. Go forth and build the international application of your dreams. 🗺️\n",
+        "\n",
+        "\n",
+        "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+        "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+        "-  [Generating Structured Output with Loop-Based Auto-Correction](https://haystack.deepset.ai/tutorials/28_structured_output_with_loop)\n",
+        "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=index_documents_based_on_language_tutorial)."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
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From 28d522aed24f3d5cff14ca2429021cd1ce9f5bd7 Mon Sep 17 00:00:00 2001
From: Tilde Thurium <tilde.thurium@deepset.ai>
Date: Mon, 12 Feb 2024 07:30:49 -0800
Subject: [PATCH 156/206] add extractive QA pipeline tutorial (#284)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* add extractive QA pipeline tutorial

* Update tutorials/34_Extractive_QA_Pipeline.ipynb

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>

---------

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>
---
 .github/workflows/run_tutorials.yml       |    1 +
 README.md                                 |    1 +
 index.toml                                |   11 +
 tutorials/34_Extractive_QA_Pipeline.ipynb | 5821 +++++++++++++++++++++
 4 files changed, 5834 insertions(+)
 create mode 100644 tutorials/34_Extractive_QA_Pipeline.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index a28f63de..b08c3148 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -73,6 +73,7 @@ jobs:
               "tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb"
               "tutorials/31_Metadata_Filtering.ipynb"
               "tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb"
+              "tutorials/34_Extractive_QA_Pipeline.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index 77b94f46..31c3327a 100644
--- a/README.md
+++ b/README.md
@@ -42,6 +42,7 @@ Haystack 2.0
 | [FAQ Style QA](./tutorials/04_FAQ_style_QA.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            | [Preprocessing Different File Types](./tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb) |
 | [Evaluation](./tutorials/05_Evaluation.ipynb)                                                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              | [Metadata Filtering](./tutorials/31_Metadata_Filtering.ipynb)                                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/31_Metadata_Filtering.ipynb)                     |
 | [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                | [Classifying Documents & Queries by Language](./tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb)|
+[Build an Extractive QA Pipeline](./tutorials/34_Extractive_QA_Pipeline.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/34_Extractive_QA_Pipeline.ipynb)|
 | [[OUTDATED] RAG Generator](./tutorials/07_RAG_Generator.ipynb)                                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Preprocessing](./tutorials/08_Preprocessing.ipynb)                                                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           |                                                                                                                  |                                                                                                                                                                                                                          |
 | [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            |                                                                                                                  |                                                                                                                                                                                                                          |
diff --git a/index.toml b/index.toml
index 1081b53a..dabe6af5 100644
--- a/index.toml
+++ b/index.toml
@@ -329,4 +329,15 @@ notebook = "32_Classifying_Documents_and_Queries_by_Language.ipynb"
 aliases = []
 completion_time = "15 min"
 created_at = 2024-02-06
+haystack_2 = true
+
+[[tutorial]]
+title = "Build an Extractive QA Pipeline"
+description = "Learn how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is."
+level = "beginner"
+weight = 76
+notebook = "34_Extractive_QA_Pipeline.ipynb"
+aliases = []
+completion_time = "10 min"
+created_at = 2024-02-09
 haystack_2 = true
\ No newline at end of file
diff --git a/tutorials/34_Extractive_QA_Pipeline.ipynb b/tutorials/34_Extractive_QA_Pipeline.ipynb
new file mode 100644
index 00000000..f6041899
--- /dev/null
+++ b/tutorials/34_Extractive_QA_Pipeline.ipynb
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+    {
+      "cell_type": "markdown",
+      "source": [
+        " ## Tutorial: Build an Extractive QA Pipeline\n",
+        "\n",
+        "- **Level**: Beginner\n",
+        "- **Time to complete**: 15 minutes\n",
+        "- **Components Used**: [`ExtractiveReader`](https://docs.haystack.deepset.ai/v2.0/docs/extractivereader), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder)\n",
+        "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is.\n",
+        "\n",
+        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "2ErVy6A2NisJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Overview\n",
+        "\n",
+        "What is extractive question answering? So glad you asked! The short answer is that extractive models pull verbatim answers out of text. It's good for use cases where accuracy is paramount, and you need to know exactly where in the text that the answer came from. If you want additional context, here's [a deep dive on extractive versus generative language models](https://haystack.deepset.ai/blog/generative-vs-extractive-models).\n",
+        "\n",
+        "In this tutorial you'll create a Haystack pipeline that extracts answers to questions, based on the provided documents.\n",
+        "\n",
+        "To get data into the extractive pipeline, you'll also build an indexing pipeline to ingest the [Wikipedia pages of Seven Wonders of the Ancient World dataset](https://en.wikipedia.org/wiki/Wonders_of_the_World)."
+      ],
+      "metadata": {
+        "id": "uClfTB7jN6g-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+      ],
+      "metadata": {
+        "id": "eF_hnatJUEHq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#Installation\n"
+      ],
+      "metadata": {
+        "id": "zQnSZtyyUJVF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rwgpwV4eHVoo"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install haystack-ai accelerate sentence-transformers datasets"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+      ],
+      "metadata": {
+        "id": "bo6LztWXURaA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(34)"
+      ],
+      "metadata": {
+        "id": "y_btxLmYUUM9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Load data into the `DocumentStore`"
+      ],
+      "metadata": {
+        "id": "U8I641xobh_w"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Before you can use this data in the extractive pipeline, you'll use an indexing pipeline to fetch it, process it, and load it into the document store.\n",
+        "\n",
+        "\n",
+        "The data has already been cleaned and preprocessed, so turning it into Haystack `Documents` is fairly straightfoward.\n",
+        "\n",
+        "Using an `InMemoryDocumentStore` here keeps things simple. However, this general approach would work with [any document store that Haystack 2.0 supports](https://docs.haystack.deepset.ai/v2.0/docs/document-store).\n",
+        "\n",
+        "The `SentenceTransformersDocumentEmbedder` transforms each `Document` into a vector. Here we've used [`sentence-transformers/multi-qa-mpnet-base-dot-v1`](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1). You can substitute any embedding model you like, as long as you use the same one in your extractive pipeline.\n",
+        "\n",
+        "Lastly, the `DocumentWriter` writes the vectorized documents to the `DocumentStore`.\n"
+      ],
+      "metadata": {
+        "id": "b2HaHlBrSvLa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from datasets import load_dataset\n",
+        "from haystack import Document\n",
+        "from haystack import Pipeline\n",
+        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+        "from haystack.components.readers import ExtractiveReader\n",
+        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+        "from haystack.components.writers import DocumentWriter\n",
+        "\n",
+        "\n",
+        "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
+        "\n",
+        "documents = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]\n",
+        "\n",
+        "model = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
+        "\n",
+        "document_store = InMemoryDocumentStore()\n",
+        "\n",
+        "indexing_pipeline = Pipeline()\n",
+        "\n",
+        "indexing_pipeline.add_component(instance=SentenceTransformersDocumentEmbedder(model=model), name=\"embedder\")\n",
+        "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"writer\")\n",
+        "indexing_pipeline.connect(\"embedder.documents\", \"writer.documents\")\n",
+        "\n",
+        "indexing_pipeline.run({\n",
+        "    \"documents\": documents\n",
+        "})\n"
+      ],
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+            "1f8fd46400e7410687e7191b840e4177",
+            "e77013b0620a4d2d8ddc8346d7d702ef"
+          ]
+        },
+        "id": "ttuq7kLtaV5b",
+        "outputId": "01877b76-f083-4a94-a90e-6717bcecc3d3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/46.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7003e95fe7594baa9dcf3b78001dae8c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/119k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f0128d87740d449eb7e5efcc3045f44e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "518a021bc14546388ffc719adaa45c18"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "modules.json:   0%|          | 0.00/229 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0ca27757f7c64134931aef12165f4d74"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "30f823aaa04f4fc78ca022339c60bea7"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/8.66k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5b0ad080573e40919752004106dcd523"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a2768619988240c591a4aa2d6fc8e4b6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fdb7c19e3e2a4408ac6ed1a1dd69d0e2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a9f74674fc7b42aab231ba01aa112636"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b95eac15198143c0a559320d8ba233e4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0a5717f7492c4749a7bf9f5bf7c87181"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "bf5a8dcb41694fd6a27ed94c5e132ff5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "dbd79d2df046429bb10cbd9b52b4e5c3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d2c569dd2a9b4287ab4af93dad846998"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Batches:   0%|          | 0/5 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "31e2d766917c4e6fa927e6605e6e1b88"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'writer': {'documents_written': 151}}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Build an Extractive QA Pipeline\n",
+        "\n",
+        "Your extractive QA pipeline will consist of three components: an embedder, retriever, and reader.\n",
+        "\n",
+        "- The `SentenceTransformersTextEmbedder` turns a query into a vector, usaing the same embedding model defined above.\n",
+        "\n",
+        "- Vector search allows the retriever to efficiently return relevant documents from the document store. Retrievers are tightly coupled with document stores; thus, you'll use an `InMemoryEmbeddingRetriever`to go with the `InMemoryDocumentStore`.\n",
+        "\n",
+        "- The `ExtractiveReader` returns answers to that query, as well as their location in the source document, and a confidence score.\n"
+      ],
+      "metadata": {
+        "id": "r5CL5VXaVQqE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+        "from haystack.components.readers import ExtractiveReader\n",
+        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+        "\n",
+        "\n",
+        "retriever = InMemoryEmbeddingRetriever(document_store=document_store)\n",
+        "reader = ExtractiveReader()\n",
+        "reader.warm_up()\n",
+        "\n",
+        "extractive_qa_pipeline = Pipeline()\n",
+        "\n",
+        "extractive_qa_pipeline.add_component(instance=SentenceTransformersTextEmbedder(model=model), name=\"embedder\")\n",
+        "extractive_qa_pipeline.add_component(instance=retriever, name=\"retriever\")\n",
+        "extractive_qa_pipeline.add_component(instance=reader, name=\"reader\")\n",
+        "\n",
+        "extractive_qa_pipeline.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+        "extractive_qa_pipeline.connect(\"retriever.documents\", \"reader.documents\")"
+      ],
+      "metadata": {
+        "id": "xZGGv8yaHZtV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Try extracting some answers."
+      ],
+      "metadata": {
+        "id": "Yxi6IYXPZMFw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "query = \"Who was Pliny the Elder?\"\n",
+        "extractive_qa_pipeline.run(data={\"embedder\": {\"text\": query}, \"retriever\": {\"top_k\": 3},\n",
+        "                                   \"reader\": {\"query\": query, \"top_k\": 2}})"
+      ],
+      "metadata": {
+        "id": "mvG3XJOtZR79"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## `ExtractiveReader`: a closer look\n",
+        "\n",
+        "Here's an example answer:\n",
+        "```python\n",
+        "[ExtractedAnswer(query='Who was Pliny the Elder?', score=0.8306006193161011, data='Roman writer', document=Document(id=bb2c5f3d2e2e2bf28d599c7b686ab47ba10fbc13c07279e612d8632af81e5d71, content: 'The Roman writer Pliny the Elder, writing in the first century AD, argued that the Great Pyramid had...', meta: {'url': 'https://en.wikipedia.org/wiki/Great_Pyramid_of_Giza', '_split_id': 16}\n",
+        "```\n",
+        "\n",
+        "The confidence score ranges from 0 to 1. Higher scores mean the model has more confidence in the answer's relevance.\n",
+        "\n",
+        "The Reader sorts the answers based on their probability scores, with higher probability listed first. You can limit the number of answers the Reader returns in the optional `top_k` parameter.\n",
+        "\n",
+        "By default, the Reader sets a `no_answer=True` parameter. This param returns an `ExtractedAnswer` with no text, and the probability that none of the returned answers are correct.\n",
+        "\n",
+        "```python\n",
+        "ExtractedAnswer(query='Who was Pliny the Elder?', score=0.04606167031102615, data=None, document=None, context=None, document_offset=None, context_offset=None, meta={})]}}\n",
+        "```\n",
+        "\n",
+        "`.0.04606167031102615` means the model is fairly confident the provided answers are correct in this case. You can disable this behavio and return only answers by setting the `no_answer` param to `False` when initializing your `ExtractiveReader`.\n"
+      ],
+      "metadata": {
+        "id": "GOKWgMDCWGRd"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Wrapping it up\n",
+        "\n",
+        "If you've been following along, now you know how to build an extractive question answering pipeline with Haystack 2.0. 🎉 Thanks for reading!\n",
+        "\n",
+        "\n",
+        "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+        "- [Classifying Documents & Queries by Language](https://haystack.deepset.ai/tutorials/32_classifying_documents_and_queries_by_language)\n",
+        "-  [Generating Structured Output with Loop-Based Auto-Correction](https://haystack.deepset.ai/tutorials/28_structured_output_with_loop)\n",
+        "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=extractive_qa_tutorial)."
+      ],
+      "metadata": {
+        "id": "e8EQJuGVY_GG"
+      }
+    }
+  ]
+}
\ No newline at end of file

From 2d72869194f8c2467ff9e3b2569af8c960693f20 Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Mon, 12 Feb 2024 19:40:44 +0100
Subject: [PATCH 157/206] Update Pipeline import (#285)

---
 tutorials/27_First_RAG_Pipeline.ipynb                     | 2 +-
 tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index b47fb0fb..54e7dadf 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -347,7 +347,7 @@
    },
    "outputs": [],
    "source": [
-    "from haystack.pipeline import Pipeline\n",
+    "from haystack import Pipeline\n",
     "\n",
     "basic_rag_pipeline = Pipeline()\n",
     "# Add components to your pipeline\n",
diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index 5a2470ab..c5295cab 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -163,7 +163,7 @@
     "from haystack.components.routers import FileTypeRouter\n",
     "from haystack.components.joiners import DocumentJoiner\n",
     "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
-    "from haystack.pipeline import Pipeline\n",
+    "from haystack import Pipeline\n",
     "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
     "\n",
     "document_store = InMemoryDocumentStore()\n",

From 179235334e672ee128d0c4a7f98dbc73809d4698 Mon Sep 17 00:00:00 2001
From: Julian Risch <julian.risch@deepset.ai>
Date: Tue, 13 Feb 2024 10:50:35 +0100
Subject: [PATCH 158/206] Add evaluation tutorial for Haystack 2.0 (#283)

* add evaluation notebook

* skip converting evaluation tutorial to python script

* add new tutorial to readme

* add new tutorial to index.toml

* make contexts a list of lists

* replace dash with underscore in notebook name

* replace dash with underscore in notebook name

* replace dash with underscore in notebook name

* address review comments

* update import path

* change tutorial number to 35

* change tutorial number to 35

* Update run_tutorials.yml
---
 .github/workflows/run_tutorials.yml           |    1 +
 README.md                                     |    5 +-
 index.toml                                    |   13 +-
 ...el_Based_Evaluation_of_RAG_Pipelines.ipynb | 4064 +++++++++++++++++
 4 files changed, 4079 insertions(+), 4 deletions(-)
 create mode 100644 tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index b08c3148..5fb140ca 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -74,6 +74,7 @@ jobs:
               "tutorials/31_Metadata_Filtering.ipynb"
               "tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb"
               "tutorials/34_Extractive_QA_Pipeline.ipynb"
+              "tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index 31c3327a..41477d1b 100644
--- a/README.md
+++ b/README.md
@@ -42,9 +42,8 @@ Haystack 2.0
 | [FAQ Style QA](./tutorials/04_FAQ_style_QA.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            | [Preprocessing Different File Types](./tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb) |
 | [Evaluation](./tutorials/05_Evaluation.ipynb)                                                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              | [Metadata Filtering](./tutorials/31_Metadata_Filtering.ipynb)                                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/31_Metadata_Filtering.ipynb)                     |
 | [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                | [Classifying Documents & Queries by Language](./tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb)|
-[Build an Extractive QA Pipeline](./tutorials/34_Extractive_QA_Pipeline.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/34_Extractive_QA_Pipeline.ipynb)|
-| [[OUTDATED] RAG Generator](./tutorials/07_RAG_Generator.ipynb)                                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           |                                                                                                                  |                                                                                                                                                                                                                          |
-| [Preprocessing](./tutorials/08_Preprocessing.ipynb)                                                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           |                                                                                                                  |                                                                                                                                                                                                                          |
+| [[OUTDATED] RAG Generator](./tutorials/07_RAG_Generator.ipynb)                                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           | [Build an Extractive QA Pipeline](./tutorials/34_Extractive_QA_Pipeline.ipynb)                                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/34_Extractive_QA_Pipeline.ipynb)                 |
+| [Preprocessing](./tutorials/08_Preprocessing.ipynb)                                                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           | [Model-Based Evaluation of RAG Pipelines](./tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)|
 | [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            |                                                                                                                  |                                                                                                                                                                                                                          |
 | [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Pipelines](./tutorials/11_Pipelines.ipynb)                                                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               |                                                                                                                  |                                                                                                                                                                                                                          |
diff --git a/index.toml b/index.toml
index dabe6af5..7ed1cfc4 100644
--- a/index.toml
+++ b/index.toml
@@ -340,4 +340,15 @@ notebook = "34_Extractive_QA_Pipeline.ipynb"
 aliases = []
 completion_time = "10 min"
 created_at = 2024-02-09
-haystack_2 = true
\ No newline at end of file
+haystack_2 = true
+
+[[tutorial]]
+title = "Model-Based Evaluation of RAG Pipelines"
+description = "Learn how to evaluate your RAG pipelines using some of the model-based evaluation frameworkes integerated into Haystack"
+level = "intermediate"
+weight = 77
+notebook = "35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb"
+aliases = []
+completion_time = "15 min"
+created_at = 2024-02-12
+haystack_2 = true
diff --git a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
new file mode 100644
index 00000000..24687cfc
--- /dev/null
+++ b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
@@ -0,0 +1,4064 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2OvkPji9O-qX"
+   },
+   "source": [
+    "# Tutorial: Model-Based Evaluation of RAG Pipelines\n",
+    "\n",
+    "- **Level**: Beginner\n",
+    "- **Time to complete**: 10 minutes\n",
+    "- **Components Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `OpenAIGenerator`, `UpTrainEvaluator`\n",
+    "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI: https://platform.openai.com/api-keys\n",
+    "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines using some of the model-based evaluation frameworkes integerated into Haystack.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LFqHcXYPO-qZ"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "This tutorial shows you how to evaluate a generative question-answering pipeline that uses the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. As we're doing model-based evaluation, no ground-truth labels are required. The process involves Haystack's integration of three evaluation frameworks:\n",
+    "- [UpTrain](https://docs.uptrain.ai/getting-started/introduction) ✅\n",
+    "- [RAGAS](https://docs.ragas.io/en/stable/index.html) 🔜\n",
+    "- [DeepEval](https://docs.confident-ai.com/docs/getting-started) 🔜\n",
+    "\n",
+    "For this tutorial, you'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QXjVlbPiO-qZ"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Kww5B_vXO-qZ"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "Install Haystack 2.0 Beta and [datasets](https://pypi.org/project/datasets/) with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "UQbU8GUfO-qZ",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "47d9270a-8389-4217-f053-c5f2ae3c034e"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai\n",
+    "pip install \"datasets>=2.6.1\"\n",
+    "pip install uptrain-haystack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Wl_jYERtO-qa"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "A76B4S49O-qa"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(35)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_lvfew16O-qa"
+   },
+   "source": [
+    "## Create the RAG Pipeline to Evaluate\n",
+    "\n",
+    "To evaluate a RAG pipeline, we need a RAG pipeline to start with. So, we will start by creating a question answering pipelne.\n",
+    "\n",
+    "> 💡 For a complete tutorial on creating Retrieval-Augmmented Generation pipelines check out the [Creating Your First QA Pipeline with Retrieval-Augmentation Tutorial](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+    "\n",
+    "First, we will initialize a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, you'll be using the `InMemoryDocumentStore`.\n",
+    "\n",
+    "You'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, you don't need to perform any additional cleaning or splitting.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "CbVN-s5LO-qa",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 254,
+     "referenced_widgets": [
+      "d83dc22ecf1145cbbd0335c54e87ea0b",
+      "577a9306f1db489298681340583d5548",
+      "6c495c504acc4f2581fb56d52a4a2e41",
+      "9d8eb9e470014791beff2586c4711fc7",
+      "a29ab4d3c8bb419cbe5d271b4d54fc55",
+      "fb234f5075434adbba5eb914dbed1199",
+      "73f6d41eb0ce4722b441b87b1f888198",
+      "b2b329a189244a629f18aeda0b32fdb3",
+      "6b86402296c24fb487aa33c37fcc7932",
+      "355902031d6446628149f605876e2ff1",
+      "4c987d2fa1704c3a912674d042eba3dd",
+      "8938f3e9e5894b26a73684846f39a6c1",
+      "5e3c35d1d3be42619352439174ae3fba",
+      "f0fccdcdc4694d159d60dc84ad49c3fd",
+      "2d3d989712884f6581fb7144ab7cdeed",
+      "21be044e945c4b2cac7e897524ff1d87",
+      "3183324191624e9eaed5c9b5f57a47ac",
+      "a3e81bf8ffad4009aee0fbef76fcf197",
+      "929804aa5a39487799cdb60393291d17",
+      "292382449bb249d3819b6aa0ee08e42b",
+      "a3f6cdf2d819485c976a5c511a546507",
+      "ed7b4bc36f054acf8293455c89ebf448",
+      "0b3b225c6ae141b9b9dc427f6a85987e",
+      "9376157fa66c4bb499113d9974d923ce",
+      "5028ea98008b45ca9e4169d564a1ac2a",
+      "e50ebdf587bf4e9d92683ba95d848bd3",
+      "9a9ba724545242c682d87c380e675dd1",
+      "ddc7246079054bb2a73a02b903619b7e",
+      "f42f37cf4c904372ac4f02605c667cfa",
+      "09d4e616e84c4904b071c31a846da1cc",
+      "92b93eb3e4a94332861751ecae74698d",
+      "95577a76d0f4480f961406b1aa15ae55",
+      "39acf7d08cb84fd89dcf80544053cf64"
+     ]
+    },
+    "outputId": "45314795-f269-4bd3-e386-9e00fa3ee06a"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from haystack import Document\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "\n",
+    "document_store = InMemoryDocumentStore()\n",
+    "\n",
+    "\n",
+    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
+    "docs = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]\n",
+    "document_store.write_documents(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yL8nuJdWO-qa"
+   },
+   "source": [
+    "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XvLVaFHTO-qb"
+   },
+   "source": [
+    "Now that we have our data ready, we can create a simple RAG pipeline.\n",
+    "\n",
+    "In this example, we'll be using:\n",
+    "- [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) which will get the relevant documents to the query.\n",
+    "- [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/OpenAIGenerator) to generate answers to queries. You can replace `OpenAIGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-uo-6fjiO-qb",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "15257953-7ba5-403a-c2f1-e9a1229f654c"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "from haystack.components.builders import PromptBuilder\n",
+    "from haystack.components.generators import OpenAIGenerator\n",
+    "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
+    "\n",
+    "retriever = InMemoryBM25Retriever(document_store)\n",
+    "\n",
+    "template = \"\"\"\n",
+    "Given the following information, answer the question.\n",
+    "\n",
+    "Context:\n",
+    "{% for document in documents %}\n",
+    "    {{ document.content }}\n",
+    "{% endfor %}\n",
+    "\n",
+    "Question: {{question}}\n",
+    "Answer:\n",
+    "\"\"\"\n",
+    "\n",
+    "prompt_builder = PromptBuilder(template=template)\n",
+    "\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
+    "generator = OpenAIGenerator()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1bfHwOQwycHe"
+   },
+   "source": [
+    "\n",
+    "To build a pipeline, add all components to your pipeline and connect them. Create connections from `retriever` to the `prompt_builder` and from `prompt_builder` to `llm`. Explicitly connect the output of `retriever` with \"documents\" input of the `prompt_builder` to make the connection obvious as `prompt_builder` has two inputs (\"documents\" and \"question\"). For more information on pipelines and creating connections, refer to [Creating Pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines) documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "f6NFmpjEO-qb"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.pipeline import Pipeline\n",
+    "from haystack.components.builders.answer_builder import AnswerBuilder\n",
+    "\n",
+    "rag_pipeline = Pipeline()\n",
+    "# Add components to your pipeline\n",
+    "rag_pipeline.add_component(\"retriever\", retriever)\n",
+    "rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n",
+    "rag_pipeline.add_component(\"llm\", generator)\n",
+    "rag_pipeline.add_component(instance=AnswerBuilder(), name=\"answer_builder\")\n",
+    "\n",
+    "# Now, connect the components to each other\n",
+    "rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
+    "rag_pipeline.connect(\"prompt_builder\", \"llm\")\n",
+    "rag_pipeline.connect(\"llm.replies\", \"answer_builder.replies\")\n",
+    "rag_pipeline.connect(\"llm.meta\", \"answer_builder.meta\")\n",
+    "rag_pipeline.connect(\"retriever\", \"answer_builder.documents\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6NqyLhx7O-qc"
+   },
+   "source": [
+    "That's it! The pipeline's ready to generate answers to questions!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DBAyF5tVO-qc"
+   },
+   "source": [
+    "### Asking a Question\n",
+    "\n",
+    "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to both the `retriever` and the `prompt_builder`. This ensures that the `{{question}}` variable in the template prompt gets replaced with your specific question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Vnt283M5O-qc",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 49,
+     "referenced_widgets": [
+      "5e082e574f6148578590d9d52ef41ed9",
+      "35bbbe0a96d04d188d8eadb752ec7e48",
+      "7d19f9f0b40740d0b94dc56c9d098698",
+      "cc855a847d024c3ab073415a36822e69",
+      "065d75b67dfd41dda4a4a5de08a164f1",
+      "46236c97eded4ad4a01bd78586f9924b",
+      "a856531a2585460d9f2ffd35903fbf3a",
+      "a9ec8b673b174da9bd49ff982109ebd9",
+      "ac6158ab31dd4fba9e95ec8a8846e6c3",
+      "5fd4838c3599476f88199c787492f3f5",
+      "2dec5ce32d0046b080fa174616380d22"
+     ]
+    },
+    "outputId": "9b0fea15-ae41-49d4-d5fc-7ff2907590aa"
+   },
+   "outputs": [],
+   "source": [
+    "question = \"When was the Rhodes Statue built?\"\n",
+    "\n",
+    "response = rag_pipeline.run(\n",
+    "    {\"retriever\": {\"query\": question}, \"prompt_builder\": {\"question\": question}, \"answer_builder\": {\"query\": question}}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(response[\"answer_builder\"][\"answers\"][0].data)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "36kHDjfWtgHM",
+    "outputId": "e0eddbcc-2f25-471c-9832-a0a553db1f20"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Now that we have the RAG pipeline ready, we create an evaluation pipeline. It defines what metrics we want to evaluate and we choose one of Haystack's integrated evaluation frameworks.\n",
+    "\n",
+    "## Evaluate The Pipeline with UpTrain\n",
+    "\n",
+    "Now that we have a RAG pipeline, let's look at how we can evaluate it. Here, we're using the Haystack UpTrain integration. We will perform 2 evaluations:\n",
+    "\n",
+    "- Context Relevance, grading how relevant the context is to the question specified\n",
+    "- Critique language, grading language aspects such as fluency, politeness, grammar, and coherence\n",
+    "\n",
+    "For a full list of available metrics and their expected inputs, check out our [`UpTrainEvaluator` Docs](https://docs.haystack.deepset.ai/v2.0/docs/uptrainevaluator)."
+   ],
+   "metadata": {
+    "id": "37b_d4OsoV5_"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 1) Evaluate Context Relevance"
+   ],
+   "metadata": {
+    "id": "dmlZoTq68FTK"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "from haystack_integrations.components.evaluators.uptrain import UpTrainEvaluator, UpTrainMetric\n",
+    "\n",
+    "evaluator = UpTrainEvaluator(metric=UpTrainMetric.CONTEXT_RELEVANCE, api=\"openai\")\n",
+    "\n",
+    "evaluator_pipeline = Pipeline()\n",
+    "evaluator_pipeline.add_component(\"evaluator\", evaluator)"
+   ],
+   "metadata": {
+    "id": "lQp9v-MBo18D"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Next, we can create a helper function to evaluate the context relevance of a RAG pipeline with multiple questions. The context relevance metric expects 2 inputs that should be provided from the RAG pipeline we are evaluating:\n",
+    "\n",
+    "- questions\n",
+    "- contexts"
+   ],
+   "metadata": {
+    "id": "MXg1MP4ZyYaE"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "def evaluate_context_relevance(questions, evaluation_pipeline):\n",
+    "    contexts = []\n",
+    "    responses = []\n",
+    "    for question in questions:\n",
+    "        response = rag_pipeline.run(\n",
+    "            {\n",
+    "                \"retriever\": {\"query\": question},\n",
+    "                \"prompt_builder\": {\"question\": question},\n",
+    "                \"answer_builder\": {\"query\": question},\n",
+    "            }\n",
+    "        )\n",
+    "        context = \"\"\n",
+    "        for d in response[\"answer_builder\"][\"answers\"][0].documents:\n",
+    "            context += d.content\n",
+    "\n",
+    "        contexts.append([context])\n",
+    "        responses.append(response[\"answer_builder\"][\"answers\"][0].data)\n",
+    "\n",
+    "    evaluation_results = evaluator_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts}})\n",
+    "    return evaluation_results"
+   ],
+   "metadata": {
+    "id": "ZVw8M2YNxVrR"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "questions = [\"When was the Rhodes Statue built?\", \"Where is the Pyramid of Giza?\", \"When was the pyramid built?\"]\n",
+    "\n",
+    "evaluate_context_relevance(questions=questions, evaluation_pipeline=evaluator_pipeline)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 408,
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+      "90143f9058594e3b8b719601519a68c3"
+     ]
+    },
+    "id": "DePGlcPcdq6B",
+    "outputId": "70b0a7cf-89ad-44d9-afa3-5df7d293cf84"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### 2) Critique Tone\n",
+    "\n",
+    "An evaluator that uses the `CRITIQUE_TONE` metric expects to be initialized with an `llm_persona`. This is the persona the generative model being assessed was expected to follow, for example `methodical teacher`, `helpful chatbot`, or here simply `informative`."
+   ],
+   "metadata": {
+    "id": "ERaJO56s8J5h"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "from haystack_integrations.components.evaluators import UpTrainEvaluator, UpTrainMetric\n",
+    "\n",
+    "evaluator = UpTrainEvaluator(\n",
+    "    metric=UpTrainMetric.CRITIQUE_TONE, api=\"openai\", metric_params={\"llm_persona\": \"informative\"}\n",
+    ")\n",
+    "\n",
+    "evaluator_pipeline = Pipeline()\n",
+    "evaluator_pipeline.add_component(\"evaluator\", evaluator)"
+   ],
+   "metadata": {
+    "id": "Wp2HJOE64SOZ"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Next, we can create a helper function to critique the tone of the results of a RAG pipeline. This metric expects 1 input that should be provided from the RAG pipeline we are evaluating:\n",
+    "\n",
+    "- responses"
+   ],
+   "metadata": {
+    "id": "dkllxaV78ddM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "def evaluate_critique_tone(questions, evaluation_pipeline):\n",
+    "    responses = []\n",
+    "    for question in questions:\n",
+    "        response = rag_pipeline.run(\n",
+    "            {\n",
+    "                \"retriever\": {\"query\": question},\n",
+    "                \"prompt_builder\": {\"question\": question},\n",
+    "                \"answer_builder\": {\"query\": question},\n",
+    "            }\n",
+    "        )\n",
+    "        responses.append(response[\"answer_builder\"][\"answers\"][0].data)\n",
+    "\n",
+    "    evaluation_results = evaluator_pipeline.run({\"evaluator\": {\"responses\": responses}})\n",
+    "    return evaluation_results"
+   ],
+   "metadata": {
+    "id": "lgo5fy6M44kl"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "questions = [\"When was the Rhodes Statue built?\", \"Where is the Pyramid of Giza?\", \"When was the pyramid built?\"]\n",
+    "\n",
+    "evaluate_critique_tone(questions=questions, evaluation_pipeline=evaluator_pipeline)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 373,
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+     ]
+    },
+    "id": "GmtmfQcO5IGu",
+    "outputId": "cb65f540-874d-49cd-c457-fd3801ec5f6d"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XueCK3y4O-qc"
+   },
+   "source": [
+    "## What's next\n",
+    "\n",
+    "🎉 Congratulations! You've learned how to evaluate a RAG pipeline with model-based evaluation frameworks and without any labeling efforts.\n",
+    "\n",
+    "If you liked this tutorial, you may also enjoy:\n",
+    "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+    "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=moel_based_evaluation). Thanks for reading!"
+   ]
+  }
+ ],
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From 547a1fb73ee12d9c25461ac9fb55eaddd3a166ed Mon Sep 17 00:00:00 2001
From: Madeesh Kannan <shadeMe@users.noreply.github.com>
Date: Tue, 13 Feb 2024 12:43:01 +0100
Subject: [PATCH 159/206] docs: Model-based RAG pipeline eval - Pass context
 contents as-is, fix import path (#286)

---
 ...el_Based_Evaluation_of_RAG_Pipelines.ipynb | 2344 +++++++++--------
 1 file changed, 1182 insertions(+), 1162 deletions(-)

diff --git a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
index 24687cfc..819e6717 100644
--- a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
+++ b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
@@ -60,10 +60,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "UQbU8GUfO-qZ",
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
+    "id": "UQbU8GUfO-qZ",
     "outputId": "47d9270a-8389-4217-f053-c5f2ae3c034e"
    },
    "outputs": [],
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "id": "A76B4S49O-qa"
    },
@@ -118,9 +118,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
-    "id": "CbVN-s5LO-qa",
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 254,
@@ -160,6 +159,7 @@
       "39acf7d08cb84fd89dcf80544053cf64"
      ]
     },
+    "id": "CbVN-s5LO-qa",
     "outputId": "45314795-f269-4bd3-e386-9e00fa3ee06a"
    },
    "outputs": [],
@@ -200,12 +200,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
-    "id": "-uo-6fjiO-qb",
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
+    "id": "-uo-6fjiO-qb",
     "outputId": "15257953-7ba5-403a-c2f1-e9a1229f654c"
    },
    "outputs": [],
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "id": "f6NFmpjEO-qb"
    },
@@ -295,9 +295,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
-    "id": "Vnt283M5O-qc",
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 49,
@@ -315,9 +314,25 @@
       "2dec5ce32d0046b080fa174616380d22"
      ]
     },
+    "id": "Vnt283M5O-qc",
     "outputId": "9b0fea15-ae41-49d4-d5fc-7ff2907590aa"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbb05692fe1940e9ae8cf54d4f698830",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Ranking by BM25...:   0%|          | 0/151 [00:00<?, ? docs/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "question = \"When was the Rhodes Statue built?\"\n",
     "\n",
@@ -328,9 +343,7 @@
   },
   {
    "cell_type": "code",
-   "source": [
-    "print(response[\"answer_builder\"][\"answers\"][0].data)"
-   ],
+   "execution_count": 6,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -338,11 +351,16 @@
     "id": "36kHDjfWtgHM",
     "outputId": "e0eddbcc-2f25-471c-9832-a0a553db1f20"
    },
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "source": [
+    "print(response[\"answer_builder\"][\"answers\"][0].data)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "id": "37b_d4OsoV5_"
+   },
    "source": [
     "Now that we have the RAG pipeline ready, we create an evaluation pipeline. It defines what metrics we want to evaluate and we choose one of Haystack's integrated evaluation frameworks.\n",
     "\n",
@@ -354,22 +372,24 @@
     "- Critique language, grading language aspects such as fluency, politeness, grammar, and coherence\n",
     "\n",
     "For a full list of available metrics and their expected inputs, check out our [`UpTrainEvaluator` Docs](https://docs.haystack.deepset.ai/v2.0/docs/uptrainevaluator)."
-   ],
-   "metadata": {
-    "id": "37b_d4OsoV5_"
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "#### 1) Evaluate Context Relevance"
-   ],
    "metadata": {
     "id": "dmlZoTq68FTK"
-   }
+   },
+   "source": [
+    "#### 1) Evaluate Context Relevance"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "lQp9v-MBo18D"
+   },
+   "outputs": [],
    "source": [
     "from haystack_integrations.components.evaluators.uptrain import UpTrainEvaluator, UpTrainMetric\n",
     "\n",
@@ -377,27 +397,27 @@
     "\n",
     "evaluator_pipeline = Pipeline()\n",
     "evaluator_pipeline.add_component(\"evaluator\", evaluator)"
-   ],
-   "metadata": {
-    "id": "lQp9v-MBo18D"
-   },
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "id": "MXg1MP4ZyYaE"
+   },
    "source": [
     "Next, we can create a helper function to evaluate the context relevance of a RAG pipeline with multiple questions. The context relevance metric expects 2 inputs that should be provided from the RAG pipeline we are evaluating:\n",
     "\n",
     "- questions\n",
     "- contexts"
-   ],
-   "metadata": {
-    "id": "MXg1MP4ZyYaE"
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "ZVw8M2YNxVrR"
+   },
+   "outputs": [],
    "source": [
     "def evaluate_context_relevance(questions, evaluation_pipeline):\n",
     "    contexts = []\n",
@@ -410,29 +430,17 @@
     "                \"answer_builder\": {\"query\": question},\n",
     "            }\n",
     "        )\n",
-    "        context = \"\"\n",
-    "        for d in response[\"answer_builder\"][\"answers\"][0].documents:\n",
-    "            context += d.content\n",
     "\n",
-    "        contexts.append([context])\n",
+    "        contexts.append([d.content for d in response[\"answer_builder\"][\"answers\"][0].documents])\n",
     "        responses.append(response[\"answer_builder\"][\"answers\"][0].data)\n",
     "\n",
-    "    evaluation_results = evaluator_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts}})\n",
+    "    evaluation_results = evaluation_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts}})\n",
     "    return evaluation_results"
-   ],
-   "metadata": {
-    "id": "ZVw8M2YNxVrR"
-   },
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "questions = [\"When was the Rhodes Statue built?\", \"Where is the Pyramid of Giza?\", \"When was the pyramid built?\"]\n",
-    "\n",
-    "evaluate_context_relevance(questions=questions, evaluation_pipeline=evaluator_pipeline)"
-   ],
+   "execution_count": 13,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -476,24 +484,33 @@
     "id": "DePGlcPcdq6B",
     "outputId": "70b0a7cf-89ad-44d9-afa3-5df7d293cf84"
    },
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "source": [
+    "questions = [\"When was the Rhodes Statue built?\", \"Where is the Pyramid of Giza?\", \"When was the pyramid built?\"]\n",
+    "\n",
+    "evaluate_context_relevance(questions=questions, evaluation_pipeline=evaluator_pipeline)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "id": "ERaJO56s8J5h"
+   },
    "source": [
     "#### 2) Critique Tone\n",
     "\n",
     "An evaluator that uses the `CRITIQUE_TONE` metric expects to be initialized with an `llm_persona`. This is the persona the generative model being assessed was expected to follow, for example `methodical teacher`, `helpful chatbot`, or here simply `informative`."
-   ],
-   "metadata": {
-    "id": "ERaJO56s8J5h"
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Wp2HJOE64SOZ"
+   },
+   "outputs": [],
    "source": [
-    "from haystack_integrations.components.evaluators import UpTrainEvaluator, UpTrainMetric\n",
+    "from haystack_integrations.components.evaluators.uptrain import UpTrainEvaluator, UpTrainMetric\n",
     "\n",
     "evaluator = UpTrainEvaluator(\n",
     "    metric=UpTrainMetric.CRITIQUE_TONE, api=\"openai\", metric_params={\"llm_persona\": \"informative\"}\n",
@@ -501,26 +518,26 @@
     "\n",
     "evaluator_pipeline = Pipeline()\n",
     "evaluator_pipeline.add_component(\"evaluator\", evaluator)"
-   ],
-   "metadata": {
-    "id": "Wp2HJOE64SOZ"
-   },
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "id": "dkllxaV78ddM"
+   },
    "source": [
     "Next, we can create a helper function to critique the tone of the results of a RAG pipeline. This metric expects 1 input that should be provided from the RAG pipeline we are evaluating:\n",
     "\n",
     "- responses"
-   ],
-   "metadata": {
-    "id": "dkllxaV78ddM"
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "lgo5fy6M44kl"
+   },
+   "outputs": [],
    "source": [
     "def evaluate_critique_tone(questions, evaluation_pipeline):\n",
     "    responses = []\n",
@@ -536,20 +553,11 @@
     "\n",
     "    evaluation_results = evaluator_pipeline.run({\"evaluator\": {\"responses\": responses}})\n",
     "    return evaluation_results"
-   ],
-   "metadata": {
-    "id": "lgo5fy6M44kl"
-   },
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
-   "source": [
-    "questions = [\"When was the Rhodes Statue built?\", \"Where is the Pyramid of Giza?\", \"When was the pyramid built?\"]\n",
-    "\n",
-    "evaluate_critique_tone(questions=questions, evaluation_pipeline=evaluator_pipeline)"
-   ],
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -593,8 +601,12 @@
     "id": "GmtmfQcO5IGu",
     "outputId": "cb65f540-874d-49cd-c457-fd3801ec5f6d"
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-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "source": [
+    "questions = [\"When was the Rhodes Statue built?\", \"Where is the Pyramid of Giza?\", \"When was the pyramid built?\"]\n",
+    "\n",
+    "evaluate_critique_tone(questions=questions, evaluation_pipeline=evaluator_pipeline)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -625,8 +637,16 @@
    "name": "python3"
   },
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "pygments_lexer": "ipython3",
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From 853cf8a648a5160f6b29ad16340afebdffef8f17 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 13 Feb 2024 15:34:26 +0300
Subject: [PATCH 160/206] Add hybrid retrieval tutorial (#282)

* Add hybrid retrieval tutorial

* Change the overview description

* Add docs link to components

* Small updates
---
 .github/workflows/run_tutorials.yml |    1 +
 index.toml                          |   12 +
 tutorials/26_Hybrid_Retrieval.ipynb |    9 +
 tutorials/33_Hybrid_Retrieval.ipynb | 1296 +++++++++++++++++++++++++++
 4 files changed, 1318 insertions(+)
 create mode 100644 tutorials/33_Hybrid_Retrieval.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 5fb140ca..dbc51e56 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -73,6 +73,7 @@ jobs:
               "tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb"
               "tutorials/31_Metadata_Filtering.ipynb"
               "tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb"
+              "tutorials/33_Hybrid_Retrieval.ipynb"
               "tutorials/34_Extractive_QA_Pipeline.ipynb"
               "tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb"
           )
diff --git a/index.toml b/index.toml
index 7ed1cfc4..a5143ad8 100644
--- a/index.toml
+++ b/index.toml
@@ -331,6 +331,17 @@ completion_time = "15 min"
 created_at = 2024-02-06
 haystack_2 = true
 
+[[tutorial]]
+title = "Creating a Hybrid Retrieval Pipeline"
+description = "Learn how to combine keyword-based retrieval and dense retrieval to enhance retrieval"
+level = "intermediate"
+weight = 56
+notebook = "33_Hybrid_Retrieval.ipynb"
+aliases = []
+completion_time = "15 min"
+created_at = 2024-02-13
+haystack_2 = true
+
 [[tutorial]]
 title = "Build an Extractive QA Pipeline"
 description = "Learn how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is."
@@ -352,3 +363,4 @@ aliases = []
 completion_time = "15 min"
 created_at = 2024-02-12
 haystack_2 = true
+
diff --git a/tutorials/26_Hybrid_Retrieval.ipynb b/tutorials/26_Hybrid_Retrieval.ipynb
index 0801c6d3..061fdc72 100644
--- a/tutorials/26_Hybrid_Retrieval.ipynb
+++ b/tutorials/26_Hybrid_Retrieval.ipynb
@@ -14,6 +14,15 @@
     "- **Goal**: After completing this tutorial, you will have learned about creating your first hybrid retrieval and when it's useful."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0-Beta and would like to follow the updated version of this tutorial, check out [Creating a Hybrid Pipeline](https://haystack.deepset.ai/tutorials/30_hybrid_retrieval). \n",
+    ">\n",
+    "> For more information on Haystack 2.0-Beta, you can also read the [announcement post](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
new file mode 100644
index 00000000..f1eaefd0
--- /dev/null
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -0,0 +1,1296 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kTas9ZQ7lXP7"
+   },
+   "source": [
+    "# Tutorial: Creating a Hybrid Retrieval Pipeline\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Components Used**: [`DocumentSplitter`](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), and [`TransformersSimilarityRanker`](https://docs.haystack.deepset.ai/v2.0/docs/transformerssimilarityranker)\n",
+    "- **Prerequisites**: None\n",
+    "- **Goal**: After completing this tutorial, you will have learned about creating a hybrid retrieval and when it's useful.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0hw_zoKolXQL"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "**Hybrid Retrieval** combines keyword-based and embedding-based retrieval techniques, leveraging the strengths of both approaches. In essence, dense embeddings excel in grasping the contextual nuances of the query, while keyword-based methods excel in matching keywords.\n",
+    "\n",
+    "There are many cases when a simple keyword-based approaches like BM25 performs better than a dense retrieval (for example in a specific domain like healthcare) because a dense model needs to be trained on data. For more details about Hybrid Retrieval, check out [Blog Post: Hybrid Document Retrieval](https://haystack.deepset.ai/blog/hybrid-retrieval)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ITs3WTT5lXQT"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2g9fhjxDlXQb"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "Install Haystack 2.0 Beta and other required packages with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "L40ZxZW8lXQh"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai\n",
+    "pip install \"datasets>=2.6.1\"\n",
+    "pip install \"sentence-transformers>=2.2.0\"\n",
+    "pip install accelerate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CJBcPNbBlXQq"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "lUbTGVo4lXQv"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(33)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "usdANiAGlXQ9"
+   },
+   "source": [
+    "## Initializing the DocumentStore\n",
+    "\n",
+    "You'll start creating your question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that your system uses to find answers to your questions. In this tutorial, you'll be using the [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "cLbh-UtelXRL"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "\n",
+    "document_store = InMemoryDocumentStore()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GZWBHcc8TKcv"
+   },
+   "source": [
+    "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store&version=2.0)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0rk8fdMzTb-I"
+   },
+   "source": [
+    "## Fetching and Processing Documents\n",
+    "\n",
+    "As Documents, you will use the PubMed Abstracts. There are a lot of datasets from PubMed on Hugging Face Hub; you will use [ywchoi/pubmed_abstract_3](https://huggingface.co/datasets/ywchoi/pubmed_abstract_3/viewer/default/test) in this tutorial.\n",
+    "\n",
+    "Then, you will create Documents from the dataset with a simple for loop.\n",
+    "Each data point in the PubMed dataset has 3 features:\n",
+    "* *pmid*\n",
+    "* *title*\n",
+    "* *text*\n",
+    "\n",
+    "Concatenate *title* and *text* before creating the Document content to make sure that titles of PubMed abstracts are searchable.\n",
+    "\n",
+    "Other features of articles will be stored as `meta`, and you can then use this info to have a **pretty print** of the search results or for [metadata filtering](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JcMIAXulPSU3"
+   },
+   "source": [
+    "> This step might take ~2 min depending on your internet speed 🏎️"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RvrG_QzirSsq"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from haystack import Document\n",
+    "\n",
+    "dataset = load_dataset(\"ywchoi/pubmed_abstract_3\", split=\"test\")\n",
+    "\n",
+    "docs = []\n",
+    "for doc in dataset:\n",
+    "    docs.append(\n",
+    "        Document(\n",
+    "            content=doc[\"title\"] + \" \" + doc[\"text\"],\n",
+    "            meta={\"title\": doc[\"title\"], \"abstract\": doc[\"text\"], \"pmid\": doc[\"pmid\"]},\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XPngNEs5q8Tw"
+   },
+   "source": [
+    "## Indexing Documents with a Pipeline\n",
+    "\n",
+    "Create a pipeline to store the data in the document store with their embedding. For this pipeline, you need a [DocumentSplitter](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter) to split documents into chunks of 512 words, [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder) to create document embeddings for dense retrieval and [DocumentWriter](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter) to write documents to the document store.\n",
+    "\n",
+    "As an embedding model, you will use [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on Hugging Face. Feel free to test other models on Hugging Face or use another [Embedder](https://docs.haystack.deepset.ai/v2.0/docs/embedders) to switch the model provider.\n",
+    "\n",
+    "> If this step takes too long for you, replace the embedding model with a smaller model such as `sentence-transformers/all-MiniLM-L6-v2` or `sentence-transformers/all-mpnet-base-v2`. Make sure that the `split_length` is updated according to your model's token limit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RrIN83JNCHhX"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.writers import DocumentWriter\n",
+    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+    "from haystack.components.preprocessors.document_splitter import DocumentSplitter\n",
+    "from haystack import Pipeline\n",
+    "\n",
+    "document_splitter = DocumentSplitter(split_by=\"word\", split_length=512, split_overlap=32)\n",
+    "document_embedder = SentenceTransformersDocumentEmbedder(model=\"BAAI/bge-small-en-v1.5\", device=\"cuda\")\n",
+    "document_writer = DocumentWriter(document_store)\n",
+    "\n",
+    "indexing_pipeline = Pipeline()\n",
+    "indexing_pipeline.add_component(\"document_splitter\", document_splitter)\n",
+    "indexing_pipeline.add_component(\"document_embedder\", document_embedder)\n",
+    "indexing_pipeline.add_component(\"document_writer\", document_writer)\n",
+    "\n",
+    "indexing_pipeline.connect(\"document_splitter\", \"document_embedder\")\n",
+    "indexing_pipeline.connect(\"document_embedder\", \"document_writer\")\n",
+    "\n",
+    "indexing_pipeline.run({\"document_splitter\": {\"documents\": docs}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wSJlHxrhgQby"
+   },
+   "source": [
+    "Documents are stored in `InMemoryDocumentStore` with their embeddings, now it's time for creating the hybrid retrieval pipeline ✅"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UgMgY-d9najg"
+   },
+   "source": [
+    "## Creating a Pipeline for Hybrid Retrieval\n",
+    "\n",
+    "Hybrid retrieval refers to the combination of multiple retrieval methods to enhance overall performance. In the context of search systems, a hybrid retrieval pipeline executes both traditional keyword-based search and dense vector search, later ranking the results with a cross-encoder model. This combination allows the search system to leverage the strengths of different approaches, providing more accurate and diverse results.\n",
+    "\n",
+    "Here are the required steps for a hybrid retrieval pipeline:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ha8pNmnvqj4n"
+   },
+   "source": [
+    "### 1) Initialize Retrievers and the Embedder\n",
+    "\n",
+    "Initialize a [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever) and [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) to perform both dense and keyword-based retrieval. For dense retrieval, you also need a [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder) that computes the embedding of the search query by using the same embedding model `BAAI/bge-small-en-v1.5` that was used in the indexing pipeline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "DVfQvnWYrMWr"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever, InMemoryEmbeddingRetriever\n",
+    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+    "\n",
+    "text_embedder = SentenceTransformersTextEmbedder(model=\"BAAI/bge-small-en-v1.5\", device=\"cuda\")\n",
+    "embedding_retriever = InMemoryEmbeddingRetriever(document_store)\n",
+    "bm25_retriever = InMemoryBM25Retriever(document_store)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FC81c8RBrRFf"
+   },
+   "source": [
+    "### 2) Join Retrieval Results\n",
+    "\n",
+    "Haystack offers several joining methods in [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner) to be used for different use cases such as `merge` and `reciprocal_rank_fusion`. In this example, you will use the default `concatenate` mode to join the documents coming from two Retrievers as the [Ranker](https://docs.haystack.deepset.ai/v2.0/docs/rankers) will be the main component to rank the documents for relevancy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "GYso6_8BrhY8"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.joiners import DocumentJoiner\n",
+    "\n",
+    "document_joiner = DocumentJoiner()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "r8_jHzmosbC_"
+   },
+   "source": [
+    "### 3) Rank the Results\n",
+    "\n",
+    "Use the [TransformersSimilarityRanker](https://docs.haystack.deepset.ai/v2.0/docs/transformerssimilarityranker) that scores the relevancy of all retrieved documents for the given search query by using a cross encoder model. In this example, you will use [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) model to rank the retrieved documents but you can replace this model with other cross-encoder models on Hugging Face."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "cN0woIxHs4Ng"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.rankers import TransformersSimilarityRanker\n",
+    "\n",
+    "ranker = TransformersSimilarityRanker(model=\"BAAI/bge-reranker-base\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y5jzzvUIstQ4"
+   },
+   "source": [
+    "### 4) Create the Hybrid Retrieval Pipeline\n",
+    "\n",
+    "Add all initialized components to your pipeline and connect them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "y9sKO2Azjrsh"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "\n",
+    "hybrid_retrieval = Pipeline()\n",
+    "hybrid_retrieval.add_component(\"text_embedder\", text_embedder)\n",
+    "hybrid_retrieval.add_component(\"embedding_retriever\", embedding_retriever)\n",
+    "hybrid_retrieval.add_component(\"bm25_retriever\", bm25_retriever)\n",
+    "hybrid_retrieval.add_component(\"document_joiner\", document_joiner)\n",
+    "hybrid_retrieval.add_component(\"ranker\", ranker)\n",
+    "\n",
+    "hybrid_retrieval.connect(\"text_embedder\", \"embedding_retriever\")\n",
+    "hybrid_retrieval.connect(\"bm25_retriever\", \"document_joiner\")\n",
+    "hybrid_retrieval.connect(\"embedding_retriever\", \"document_joiner\")\n",
+    "hybrid_retrieval.connect(\"document_joiner\", \"ranker\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ii9x0gr9lXRT"
+   },
+   "source": [
+    "### 5) Visualize the Pipeline (Optional)\n",
+    "\n",
+    "To understand how you formed a hybrid retrieval pipeline, use [draw()](https://docs.haystack.deepset.ai/v2.0/docs/drawing-pipeline-graphs) method of the pipeline. If you're running this notebook on Google Colab, the generate file will be saved in \"Files\" section on the sidebar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "rXHbHru0lXRY"
+   },
+   "outputs": [],
+   "source": [
+    "hybrid_retrieval.draw(\"hybrid-retrieval.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eIYV19l16PKC"
+   },
+   "source": [
+    "## Testing the Hybrid Retrieval\n",
+    "\n",
+    "Pass the query to `text_embedder`, `bm25_retriever` and `ranker` and run the retrieval pipeline:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 81,
+     "referenced_widgets": [
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+    "id": "glS0-Xh3nLHY",
+    "outputId": "eae6ba6e-136e-48a1-bfe3-3a97fcebca7c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf62bf3b3c6144629811874114dc527f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b395830b097f4862a6c8588ccd0fd91b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Ranking by BM25...:   0%|          | 0/37807 [00:00<?, ? docs/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "query = \"treatment for HIV\"\n",
+    "\n",
+    "result = hybrid_retrieval.run(\n",
+    "    {\"text_embedder\": {\"text\": query}, \"bm25_retriever\": {\"query\": query}, \"ranker\": {\"query\": query}}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WvPv1cJ6gbBJ"
+   },
+   "source": [
+    "### Pretty Print the Results\n",
+    "Create a function to print a kind of *search page*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "raL_z_sByDoQ"
+   },
+   "outputs": [],
+   "source": [
+    "def pretty_print_results(prediction):\n",
+    "    for doc in prediction[\"documents\"]:\n",
+    "        print(doc.meta[\"title\"], \"\\t\", doc.score)\n",
+    "        print(doc.meta[\"abstract\"])\n",
+    "        print(\"\\n\", \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mSUiizGNytwX",
+    "outputId": "38e05986-1737-4c2b-bc08-7399299de37f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "State-of-the-Art HIV Management:An Update. \t 0.9928585290908813\n",
+      "Within the past 3 years, dramatic changes have taken place in the standard of care for HIV patients. Despite improvements in care (with decreased mortality), the rate of new infections remains unchanged if not increased within most at-risk groups. This general overview is intended for the physician who, while not providing ongoing HIV care, desires an update on the major treatment issues. Current demographic trends, new methods available for testing, and the use of the viral load test for both staging and gauging response to the new combination antiretroviral treatment regimens are detailed. It is suggested that physicians consult with an experienced HIV clinician before starting a treatment regimen in the newly diagnosed patient.The primary HIV syndrome is reviewed in detail since this diagnosis is often missed and an opportunity for early intervention is lost. Physicians not providing ongoing HIV care must be comfortable making this diagnosis and doing an initial work-up. Focused prevention especially tailored to younger high-risk patients is reviewed. Treatment protocols (with an emphasis on new antiretrovirals), gauging success of treatment, and the management of treatment failures are reviewed in detail. Common antiretroviral drugs are listed with side effects, drug interactions, and average monthly costs. Care of pregnant patients and exposed healthcare workers is also briefly discussed. The need for more primary care-based prevention is also discussed.\n",
+      "\n",
+      " \n",
+      "\n",
+      "Management of tuberculosis in HIV-infected patients. \t 0.9715766310691833\n",
+      "HIV-tuberculosis coinfection is currently one of the greatest health threats, affecting millions of people worldwide, with high morbidity and mortality. Treating both infections can be a challenge and requires some expertise due to multidirectional drug interactions, risk of overlapping side effects, high pill burden and risk of immune reconstitution inflammatory syndrome. This article reviews the general management of tuberculosis/HIV coinfection, focusing on the optimal time to start antiretroviral therapy and which treatments can be safely used. The randomized clinical trials designed to answer the question of when to start antiretroviral therapy (SAPIT, CAMELIA, STRIDE and TIME), published in the last two years, are described and discussed in detail. Summarizing these trials' conclusions, antiretroviral therapy should be started within two weeks of starting tuberculosis treatment if the patient has less than 50 CD4/mm3 and wait to the end of the induction phase (8-12 weeks after starting tuberculosis treatment) if higher CD4 cell counts exist. Treatment options for both tuberculosis and HIV, including the newer available drugs and those in clinical trials, are revised and recommendations for dose adjustments are made based on the latest available literature, with special attention to drug-drug interactions and the necessity of dose adjustments with some drug combinations.\n",
+      "\n",
+      " \n",
+      "\n",
+      "Why CCR5 is chosen as the target for stem cell gene therapy for HIV infection? \t 0.9700931310653687\n",
+      "Since the first case reported in 1981, more than 60 million individuals have succumbed to HIV worldwide. Although great efforts have been put forth in developing therapeutic drugs and effective vaccines for the treatment and prevention of HIV infection, these efforts are not correlated to the reported case of cured HIV infection. The first case of cured HIV infection, from allogenic stem cell transplantation, may shed light on future prevention and therapy of HIV infection. The choice of gene target, however, must first be evaluated regarding stem cell based gene therapy for HIV infection. Out of the tens of genes that had shown anti-HIV infection potentials, CCR5 was described to be effective in stem cell based gene therapy in 2005. Here, we appreciate the clinical observations that directly led to the choice of CCR5, rather than other genes for stem cell gene therapy.\n",
+      "\n",
+      " \n",
+      "\n",
+      "Antiretroviral medication and HIV prevention: new steps forward and new questions. \t 0.9534240365028381\n",
+      "During the past 2 years, several pivotal clinical trials have proven that the use of antiretrovirals by HIV-infected and at-risk uninfected persons can decrease the probability of HIV being transmitted sexually. The initial chemoprophylaxis studies evaluated tenofovir administered topically or orally (with or without emtricitabine). However, several questions remain. Some subsequent primary prevention studies did not replicate the results of the initial studies, raising questions about differences in the behaviors of participants in each study (in particular about medication adherence), as well as whether pharmacologic or local mucosal factors might explain the variable efficacy estimates. Other antiretrovirals and delivery systems are being evaluated to maximize the efficacy of primary chemoprophylactic approaches. At present, increasing access to antiretroviral treatment globally is a priority, because expanding access to medication that can prevent morbidity and mortality is itself an important public health goal and may reasonably be expected to decrease HIV incidence. However, for treatment as prevention to be maximally effective, increases in HIV testing, health care workers, and infrastructure are needed, in addition to medications and laboratory support for clinical monitoring. A combination of approaches is needed to most quickly decrease the current trends in HIV incidence, including early diagnosis and initiation of treatment for HIV-infected persons. These approaches can be coupled with appropriately tailored interventions for populations at greatest risk for infection (for example, men who have sex with men and sex workers), including male circumcision, behavioral interventions, and chemoprophylaxis. However, a substantial gap exists between current expenditures and unmet needs, which suggests that mobilization of political will is needed for this combination approach to be successful.\n",
+      "\n",
+      " \n",
+      "\n",
+      "Challenges of HIV treatment in resource-poor countries: a review. \t 0.9427611827850342\n",
+      "The human immunodeficiency virus/acquired immunodeficiency syndrome pandemic have posed a significant public health challenge to the global community. Massive therapeutic interventions with antiretroviral drugs are being undertaken, yet problems and challenges exist. This review examines these problems and challenges as they affect the treatment of HIV infection in resource-poor countries such as Nigeria. The information was sourced from relevant literature using human immunodeficiency virus/acquired immunodeficiency syndrome journals, textbooks and Websites on human immunodeficiency virus/acquired immunodeficiency syndrome, highly active antiretroviral therapy, resource-poor countries as key words. Several studies have shown that the advent of highly active antiretroviral therapy in 1996 has significantly reduced morbidity and mortality among people living with HIV/AIDS (PLWHA). But in resource-poor countries, initiation and maintenance of highly active antiretroviral therapy has been associated with many challenges and problems such as: poor infrastructural base for the control programs; irregular or non availability of drugs; poor drug adherence; co-morbidities and opportunistic infections/malignancies; drug toxicities; drug/food and drug/drug interactions; laboratory monitoring of viral load; CD4 cell counts; full blood counts; electrolytes, kidney and liver functions. The review has shown that the solution to the pandemic lies in a multi-sectoral and holistic approach involving International and local agencies, and communities.\n",
+      "\n",
+      " \n",
+      "\n",
+      "Current progress and challenges in HIV gene therapy. \t 0.9385221600532532\n",
+      "HIV-1 causes AIDS, a syndrome that affects millions of people globally. Existing HAART is efficient in slowing down disease progression but cannot eradicate the virus. Furthermore the severity of the side effects and the emergence of drug-resistant mutants call for better therapy. Gene therapy serves as an attractive alternative as it reconstitutes the immune system with HIV-resistant cells and could thereby provide a potential cure. The feasibility of this approach was first demonstrated with the 'Berlin patient', who was functionally cured from HIV/AIDS with undetectable HIV-1 viral load after transplantation of bone marrow harboring a naturally occurring CCR5 mutation that blocks viral entry. Here, we give an overview of the current status of HIV gene therapy and remaining challenges and obstacles.\n",
+      "\n",
+      " \n",
+      "\n",
+      "Newer drugs and earlier treatment: impact on lifetime cost of care for HIV-infected adults. \t 0.9286227822303772\n",
+      "To determine the component costs of care to optimize treatment with limited resources. We used the Cost-Effectiveness of Preventing AIDS Complications Model of HIV disease and treatment to project life expectancy and both undiscounted and discounted lifetime costs (2010 €). We determined medical resource utilization among HIV-infected adults followed from 1998 to 2005 in northern France. Monthly HIV costs were stratified by CD4 cell count. Costs of CD4, HIV RNA and genotype tests and antiretroviral therapy (ART) were derived from published literature. Model inputs from national data included mean age 38 years, mean initial CD4 cell count 372 cells/μl, ART initiation at CD4 cell counts less than 350 cells/μl, and ART regimen costs ranging from €760 to 2570 per month. The model projected a mean undiscounted life expectancy of 26.5 years and a lifetime undiscounted cost of €535,000/patient (€320,700 discounted); 73% of costs were ART related. When patients presented to care with mean CD4 cell counts of 510 cells/μl and initiated ART at CD4 cell counts less than 500 cells/μl or HIV RNA more than 100,000 copies/ml, life expectancy was 27.4 years and costs increased 1-2%, to €546,700 (€324,500 discounted). When we assumed introducing generic drugs would result in a 50% decline in first-line ART costs, lifetime costs decreased 4-6%, to €514,200 (€302 ,800 discounted). As HIV disease is treated earlier with more efficacious drugs, survival and thus costs of care will continue to increase. The availability in high-income countries of widely used antiretroviral drugs in generic form could reduce these costs.\n",
+      "\n",
+      " \n",
+      "\n",
+      "Common clinical problems in children living with HIV/AIDS: systemic approach. \t 0.9138649106025696\n",
+      "Clinical manifestations in children living with HIV/ AIDS differ from those in adults due to poorly developed immunity that allows greater dissemination throughout various organs. In developing countries, HIV-infected children have an increased frequency of malnutrition and common childhood infections such as ear infections, pneumonias, gastroenteritis and tuberculosis. The symptoms common to many treatable conditions, such as recurrent fever, diarrhea and generalized dermatitis, tend to be more persistent and severe and often do not respond as well to treatment. The use of Anti Retroviral Therapy (ART) has greatly increased the long term survival of perinatally infected children so that AIDS is becoming a manageable chronic illness. As the immunity is maintained, the incidence of infectious complications is declining while noninfectious complications of HIV are more frequently encountered. Regular clinical monitoring with immunological and virological monitoring and the introduction of genotypic and phenotypic resistance testing where resources are available have allowed for dramatically better clinical outcomes. However, these growing children are left facing the challenges of lifelong adherence with complex treatment regimens, compounded by complex psycho-social, mental and neuro-cognitive issues. These unique challenges must be recognized and understood in order to provide appropriate medical management.\n",
+      "\n",
+      " \n",
+      "\n",
+      "Barriers and facilitators of adherence to TB treatment in patients on concomitant TB and HIV treatment: a qualitative study. \t 0.9002022743225098\n",
+      "Tuberculosis is a major public health problem in Ethiopia, and a high number of TB patients are co-infected with HIV. There is a need for more knowledge about factors influencing treatment adherence in co-infected patients on concomitant treatment. The aim of the present study is to explore patients' and health care professionals' views about barriers and facilitators to TB treatment adherence in TB/HIV co-infected patients on concomitant treatment for TB and HIV. Qualitative study using in-depth interviews with 15 TB/HIV co-infected patients and 9 health professionals and focus group discussions with 14 co-infected patients. We found that interplay of factors is involved in the decision making about medication intake. Factors that influenced adherence to TB treatment positively were beliefs in the curability of TB, beliefs in the severity of TB in the presence of HIV infection and support from families and health professionals. Barriers to treatment adherence were experiencing side effects, pill burden, economic constraints, lack of food, stigma with lack of disclosure, and lack of adequate communication with health professionals. Health professionals and policy makers should be aware of factors influencing TB treatment in TB/HIV co-infected patients on concomitant treatment for TB and HIV. Our results suggest that provision of food and minimal financial support might facilitate adherence. Counseling might also facilitate adherence, in particular for those who start ART in the early phases of TB treatment, and beliefs related to side-effects and pill burden should be addressed. Information to the public may reduce TB and HIV related stigma.\n",
+      "\n",
+      " \n",
+      "\n",
+      "Combination implementation for HIV prevention: moving from clinical trial evidence to population-level effects. \t 0.8731147646903992\n",
+      "The promise of combination HIV prevention-the application of multiple HIV prevention interventions to maximise population-level effects-has never been greater. However, to succeed in achieving significant reductions in HIV incidence, an additional concept needs to be considered: combination implementation. Combination implementation for HIV prevention is the pragmatic, localised application of evidence-based strategies to enable high sustained uptake and quality of interventions for prevention of HIV. In this Review, we explore diverse implementation strategies including HIV testing and counselling models, task shifting, linkage to and retention in care, antiretroviral therapy support, behaviour change, demand creation, and structural interventions, and discusses how they could be used to complement HIV prevention efforts such as medical male circumcision and treatment as prevention. HIV prevention and treatment have arrived at a pivotal moment when combination efforts might result in substantial enough population-level effects to reverse the epidemic and drive towards elimination of HIV. Only through careful consideration of how to implement and operationalise HIV prevention interventions will the HIV community be able to move from clinical trial evidence to population-level effects.\n",
+      "\n",
+      " \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pretty_print_results(result[\"ranker\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HZ5_NJDz52VE"
+   },
+   "source": [
+    "## What's next\n",
+    "\n",
+    "🎉 Congratulations! You've create a hybrid retrieval pipeline!\n",
+    "\n",
+    "If you'd like to use this retrieval method in a RAG pipeline, check out [Tutorial: Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) to learn about the next steps.\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=hybrid-retrieval) or [join Haystack discord community](https://discord.gg/haystack).\n",
+    "\n",
+    "Thanks for reading!"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
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+   "name": "python3"
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
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+  "orig_nbformat": 4,
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From 33d48937de65d35ea470dd0172d72e910cfb423a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 16 Feb 2024 15:39:45 +0300
Subject: [PATCH 161/206] Add conditional router tutorial (#287)

* Add conditional router tutorial

* Small updates

* Update README.md

* Rename the tutorial

* Add prompt for the web search

* Make small content changes
---
 .github/workflows/run_tutorials.yml           |   1 +
 README.md                                     |   8 +-
 index.toml                                    |  10 +
 ...g_Fallbacks_with_Conditional_Routing.ipynb | 552 ++++++++++++++++++
 4 files changed, 567 insertions(+), 4 deletions(-)
 create mode 100644 tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index dbc51e56..2dd3a4bd 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -76,6 +76,7 @@ jobs:
               "tutorials/33_Hybrid_Retrieval.ipynb"
               "tutorials/34_Extractive_QA_Pipeline.ipynb"
               "tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb"
+              "tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index 41477d1b..47062a97 100644
--- a/README.md
+++ b/README.md
@@ -42,10 +42,10 @@ Haystack 2.0
 | [FAQ Style QA](./tutorials/04_FAQ_style_QA.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/04_FAQ_style_QA.ipynb)                                            | [Preprocessing Different File Types](./tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb)                | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb) |
 | [Evaluation](./tutorials/05_Evaluation.ipynb)                                                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/05_Evaluation.ipynb)                                              | [Metadata Filtering](./tutorials/31_Metadata_Filtering.ipynb)                                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/31_Metadata_Filtering.ipynb)                     |
 | [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                | [Classifying Documents & Queries by Language](./tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb)|
-| [[OUTDATED] RAG Generator](./tutorials/07_RAG_Generator.ipynb)                                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           | [Build an Extractive QA Pipeline](./tutorials/34_Extractive_QA_Pipeline.ipynb)                                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/34_Extractive_QA_Pipeline.ipynb)                 |
-| [Preprocessing](./tutorials/08_Preprocessing.ipynb)                                                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           | [Model-Based Evaluation of RAG Pipelines](./tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)|
-| [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            |                                                                                                                  |                                                                                                                                                                                                                          |
-| [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         |                                                                                                                  |                                                                                                                                                                                                                          |
+| [[OUTDATED] RAG Generator](./tutorials/07_RAG_Generator.ipynb)                                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           | [Creating a Hybrid Retrieval Pipeline](./tutorials/33_Hybrid_Retrieval.ipynb)                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/33_Hybrid_Retrieval.ipynb)                       |
+| [Preprocessing](./tutorials/08_Preprocessing.ipynb)                                                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           | [Build an Extractive QA Pipeline](./tutorials/34_Extractive_QA_Pipeline.ipynb)                                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/34_Extractive_QA_Pipeline.ipynb)                 |
+| [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            | [Model-Based Evaluation of RAG Pipelines](./tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)|
+| [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         | [Building Pipelines with Conditional Routing](./tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)|
 | [Pipelines](./tutorials/11_Pipelines.ipynb)                                                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               |                                                                                                                  |                                                                                                                                                                                                                          |
 | [[OUTDATED] Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Question Generation](./tutorials/13_Question_generation.ipynb)                                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     |                                                                                                                  |                                                                                                                                                                                                                          |
diff --git a/index.toml b/index.toml
index a5143ad8..32324b3b 100644
--- a/index.toml
+++ b/index.toml
@@ -364,3 +364,13 @@ completion_time = "15 min"
 created_at = 2024-02-12
 haystack_2 = true
 
+[[tutorial]]
+title = "Building Fallbacks to Websearch with Conditional Routing"
+description = "Learn how to direct the query to a web-based RAG route when necessary"
+level = "intermediate"
+weight = 81
+notebook = "36_Building_Fallbacks_with_Conditional_Routing.ipynb"
+aliases = []
+completion_time = "10 min"
+created_at = 2024-02-16
+haystack_2 = true
diff --git a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
new file mode 100644
index 00000000..a08ae481
--- /dev/null
+++ b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
@@ -0,0 +1,552 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IR5wivW8THt7"
+      },
+      "source": [
+        "# Tutorial: Building Fallbacks to Websearch with Conditional Routing\n",
+        "\n",
+        "- **Level**: Intermediate\n",
+        "- **Time to complete**: 10 minutes\n",
+        "- **Components Used**: [`ConditionalRouter`](https://docs.haystack.deepset.ai/v2.0/docs/conditionalrouter), [`SerperDevWebSearch`](https://docs.haystack.deepset.ai/v2.0/docs/serperdevwebsearch), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator)\n",
+        "- **Prerequisites**: You must have an [Open API Key](https://platform.openai.com/api-keys) and a [Serper API Key](https://serper.dev/api-key) for this tutorial\n",
+        "- **Goal**: After completing this tutorial, you'll have learned how to create a pipeline with conditional routing that can fallback to websearch if the answer is not present in your dataset.\n",
+        "\n",
+        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F-a-MAMVat-o"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "When developing applications using **retrieval augmented generation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation))**, the retrieval step plays a critical role. It serves as the primary information source for **large language models (LLMs)** to generate responses. However, if your database lacks the necessary information, the retrieval step's effectiveness is limited. In such scenarios, it may be practical to use the web as a fallback data source for your RAG application. By implementing a conditional routing mechanism in your system, you gain complete control over the data flow, enabling you to design a system that can leverage the web as its data source under some conditions.\n",
+        "\n",
+        "In this tutorial, you will learn how to create a pipeline with conditional routing that directs the query to a **web-based RAG** route if the answer is not found in the initially given documents."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LSwNKkeKeq0f"
+      },
+      "source": [
+        "## Development Environment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eGJ7GmCBas4R"
+      },
+      "source": [
+        "### Prepare the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FwIgIpE2XqpO"
+      },
+      "source": [
+        "### Install Haystack\n",
+        "\n",
+        "Install Haystack 2.0 Beta with `pip`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uba0mntlqs_O"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install haystack-ai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WBkJ7d3hZkOJ"
+      },
+      "source": [
+        "### Enable Telemetry\n",
+        "\n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HvrOixzzZmMi"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(36)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QfECEAy2Jdqs"
+      },
+      "source": [
+        "### Enter API Keys\n",
+        "\n",
+        "Enter API keys required for this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "13U7Z_k3yE-F",
+        "outputId": "6ec48553-12d2-4c89-ca13-fc5d34fbc625"
+      },
+      "outputs": [],
+      "source": [
+        "from getpass import getpass\n",
+        "import os\n",
+        "\n",
+        "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI Api key: \")\n",
+        "os.environ[\"SERPERDEV_API_KEY\"] = getpass(\"Enter Serper Api key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i_AlhPv1T-4t"
+      },
+      "source": [
+        "## Creating a Document\n",
+        "\n",
+        "Create a Document about Munich, where the answer to your question will be initially searched:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5CHbQlLMyVbg"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.dataclasses import Document\n",
+        "\n",
+        "documents = [\n",
+        "    Document(\n",
+        "        content=\"\"\"Munich, the vibrant capital of Bavaria in southern Germany, exudes a perfect blend of rich cultural\n",
+        "                                heritage and modern urban sophistication. Nestled along the banks of the Isar River, Munich is renowned\n",
+        "                                for its splendid architecture, including the iconic Neues Rathaus (New Town Hall) at Marienplatz and\n",
+        "                                the grandeur of Nymphenburg Palace. The city is a haven for art enthusiasts, with world-class museums like the\n",
+        "                                Alte Pinakothek housing masterpieces by renowned artists. Munich is also famous for its lively beer gardens, where\n",
+        "                                locals and tourists gather to enjoy the city's famed beers and traditional Bavarian cuisine. The city's annual\n",
+        "                                Oktoberfest celebration, the world's largest beer festival, attracts millions of visitors from around the globe.\n",
+        "                                Beyond its cultural and culinary delights, Munich offers picturesque parks like the English Garden, providing a\n",
+        "                                serene escape within the heart of the bustling metropolis. Visitors are charmed by Munich's warm hospitality,\n",
+        "                                making it a must-visit destination for travelers seeking a taste of both old-world charm and contemporary allure.\"\"\"\n",
+        "    )\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zMNy0tjtUh_L"
+      },
+      "source": [
+        "## Creating the Initial Pipeline Components\n",
+        "\n",
+        "First, define a prompt instructing the LLM to respond with the text `\"no_answer\"` if the provided documents do not offer enough context to answer the query. Next, initialize a [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) with that prompt. It's crucial that the LLM replies with `\"no_answer\"` as you will use this keyword to indicate that the query should be directed to the fallback web search route.\n",
+        "\n",
+        "As the LLM, you will use an [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) with the `gpt-3.5-turbo` model.\n",
+        "\n",
+        "> The provided prompt works effectively with the `gpt-3.5-turbo` model. If you prefer to use a different [Generator](https://docs.haystack.deepset.ai/v2.0/docs/generators), you may need to update the prompt to provide clear instructions to your model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "nzhn2kDfqvbs"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.builders.prompt_builder import PromptBuilder\n",
+        "from haystack.components.generators import OpenAIGenerator\n",
+        "\n",
+        "prompt_template = \"\"\"\n",
+        "Answer the following query given the documents.\n",
+        "If the answer is not contained within the documents reply with 'no_answer'\n",
+        "Query: {{query}}\n",
+        "Documents:\n",
+        "{% for document in documents %}\n",
+        "  {{document.content}}\n",
+        "{% endfor %}\n",
+        "\"\"\"\n",
+        "\n",
+        "prompt_builder = PromptBuilder(template=prompt_template)\n",
+        "llm = OpenAIGenerator(model=\"gpt-3.5-turbo\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LepACkkWPsBx"
+      },
+      "source": [
+        "## Initializing the Web Search Components\n",
+        "\n",
+        "Initialize the necessary components for a web-based RAG application. Along with a `PromptBuilder` and an `OpenAIGenerator`, you will need a [SerperDevWebSearch](https://docs.haystack.deepset.ai/v2.0/docs/serperdevwebsearch) to retrieve relevant documents for the query from the web.\n",
+        "\n",
+        "> If desired, you can use a different [Generator](https://docs.haystack.deepset.ai/v2.0/docs/generators) for the web-based RAG branch of the pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "VEYchFgQPxZ_"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.builders.prompt_builder import PromptBuilder\n",
+        "from haystack.components.generators import OpenAIGenerator\n",
+        "from haystack.components.websearch.serper_dev import SerperDevWebSearch\n",
+        "\n",
+        "prompt_for_websearch = \"\"\"\n",
+        "Answer the following query given the documents retrieved from the web.\n",
+        "Your answer shoud indicate that your answer was generated from websearch.\n",
+        "\n",
+        "Query: {{query}}\n",
+        "Documents:\n",
+        "{% for document in documents %}\n",
+        "  {{document.content}}\n",
+        "{% endfor %}\n",
+        "\"\"\"\n",
+        "\n",
+        "websearch = SerperDevWebSearch()\n",
+        "prompt_builder_for_websearch = PromptBuilder(template=prompt_for_websearch)\n",
+        "llm_for_websearch = OpenAIGenerator(model=\"gpt-3.5-turbo\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vnacak_tVWqv"
+      },
+      "source": [
+        "## Creating the ConditionalRouter\n",
+        "\n",
+        "[ConditionalRouter](https://docs.haystack.deepset.ai/v2.0/docs/conditionalrouter) is the component that handles data routing on specific conditions. You need to define a `condition`, an `output`, an `output_name` and an `output_type` for each route. Each route that the `ConditionalRouter` creates acts as the output of this component and can be connected to other components in the same pipeline.  \n",
+        "\n",
+        "In this case, you need to define two routes:\n",
+        "- If the LLM replies with the `\"no_answer\"` keyword, the pipeline should perform web search. It means that you will put the original `query` in the output value to pass to the next component (in this case the next component will be the `SerperDevWebSearch`) and the output name will be `go_to_websearch`.\n",
+        "- Otherwise, the given documents are enough for an answer and pipeline execution ends here. Return the LLM reply in the output named `answer`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "qyE9rGcawX3F"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.routers import ConditionalRouter\n",
+        "\n",
+        "routes = [\n",
+        "    {\n",
+        "        \"condition\": \"{{'no_answer' in replies[0]}}\",\n",
+        "        \"output\": \"{{query}}\",\n",
+        "        \"output_name\": \"go_to_websearch\",\n",
+        "        \"output_type\": str,\n",
+        "    },\n",
+        "    {\n",
+        "        \"condition\": \"{{'no_answer' not in replies[0]}}\",\n",
+        "        \"output\": \"{{replies[0]}}\",\n",
+        "        \"output_name\": \"answer\",\n",
+        "        \"output_type\": str,\n",
+        "    },\n",
+        "]\n",
+        "\n",
+        "router = ConditionalRouter(routes)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Wdyko78oXb5a"
+      },
+      "source": [
+        "## Building the Pipeline\n",
+        "\n",
+        "Add all components to your pipeline and connect them. `go_to_websearch` output of the `router` should be connected to the `websearch` to retrieve documents from the web and also to `prompt_builder_for_websearch` to use in the prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4sCyBwc0oTVs",
+        "outputId": "fd2347d4-9363-45e0-e734-87e4a160f741"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<haystack.pipeline.Pipeline at 0x127609e50>"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from haystack import Pipeline\n",
+        "\n",
+        "pipe = Pipeline()\n",
+        "pipe.add_component(\"prompt_builder\", prompt_builder)\n",
+        "pipe.add_component(\"llm\", llm)\n",
+        "pipe.add_component(\"router\", router)\n",
+        "pipe.add_component(\"websearch\", websearch)\n",
+        "pipe.add_component(\"prompt_builder_for_websearch\", prompt_builder_for_websearch)\n",
+        "pipe.add_component(\"llm_for_websearch\", llm_for_websearch)\n",
+        "\n",
+        "pipe.connect(\"prompt_builder\", \"llm\")\n",
+        "pipe.connect(\"llm.replies\", \"router.replies\")\n",
+        "pipe.connect(\"router.go_to_websearch\", \"websearch.query\")\n",
+        "pipe.connect(\"router.go_to_websearch\", \"prompt_builder_for_websearch.query\")\n",
+        "pipe.connect(\"websearch.documents\", \"prompt_builder_for_websearch.documents\")\n",
+        "pipe.connect(\"prompt_builder_for_websearch\", \"llm_for_websearch\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d0HmdbUJKJ_9"
+      },
+      "source": [
+        "### Visualize the Pipeline\n",
+        "\n",
+        "To understand how you formed this pipeline with conditional routing, use [draw()](https://docs.haystack.deepset.ai/v2.0/docs/drawing-pipeline-graphs) method of the pipeline. If you're running this notebook on Google Colab, the generated file will be saved in \"Files\" section on the sidebar or you can call `Image.open()`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "svF_SUK4rFwv",
+        "outputId": "60894eea-2cec-4be8-d13c-83d2c81656f4"
+      },
+      "outputs": [],
+      "source": [
+        "from PIL import Image\n",
+        "\n",
+        "pipe.draw(\"pipe.png\")\n",
+        "Image.open(\"pipe.png\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jgk1z6GGYH6J"
+      },
+      "source": [
+        "## Running the Pipeline!\n",
+        "\n",
+        "In the `run()`, pass the query to the `prompt_builder` and the `router`. In real life applications, `documents` will be provided by a [Retriever](https://docs.haystack.deepset.ai/v2.0/docs/retrievers) but to keep this example simple, you will provide the defined `documents` to the `prompt_builder`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "d_l4rYmCoVki",
+        "outputId": "3bd7956a-7612-4bc1-c3e5-a7a51be8981f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Munich is in southern Germany.\n"
+          ]
+        }
+      ],
+      "source": [
+        "query = \"Where is Munich?\"\n",
+        "\n",
+        "result = pipe.run({\"prompt_builder\": {\"query\": query, \"documents\": documents}, \"router\": {\"query\": query}})\n",
+        "\n",
+        "# Print the `answer` coming from the ConditionalRouter\n",
+        "print(result[\"router\"][\"answer\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dBN8eLSKgb16"
+      },
+      "source": [
+        "✅ The answer to this query can be found in the defined document.\n",
+        "\n",
+        "Now, try a different query that doesn't have an answer in the given document and test if the web search works as expected:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_v-WdlSy365M",
+        "outputId": "603c9346-8718-427e-d232-4cc71799a2bb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['According to the documents retrieved from the web, the population of Munich is approximately 1.47 million as of 2019. However, the most recent estimates suggest that the population has grown to about 1.58 million as of May 31, 2022. Additionally, the current estimated population of Munich is around 1.46 million, with the urban area being much larger at 2.65 million.']\n"
+          ]
+        }
+      ],
+      "source": [
+        "query = \"How many people live in Munich?\"\n",
+        "\n",
+        "result = pipe.run({\"prompt_builder\": {\"query\": query, \"documents\": documents}, \"router\": {\"query\": query}})\n",
+        "\n",
+        "# Print the `replies` generated using the web searched Documents\n",
+        "print(result[\"llm_for_websearch\"][\"replies\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wUkuXoWnHa5c"
+      },
+      "source": [
+        "If you check the whole result, you will see that `websearch` component also provides links to Documents retrieved from the web:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_EYLZguZGznY",
+        "outputId": "df49a576-9961-44b4-e89d-2c5195869360"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'llm': {'meta': [{'model': 'gpt-3.5-turbo-0613',\n",
+              "    'index': 0,\n",
+              "    'finish_reason': 'stop',\n",
+              "    'usage': {'completion_tokens': 2,\n",
+              "     'prompt_tokens': 271,\n",
+              "     'total_tokens': 273}}]},\n",
+              " 'websearch': {'links': ['https://en.wikipedia.org/wiki/Munich',\n",
+              "   'https://worldpopulationreview.com/world-cities/munich-population',\n",
+              "   'https://en.wikipedia.org/wiki/Demographics_of_Munich',\n",
+              "   'https://www.macrotrends.net/cities/204371/munich/population',\n",
+              "   'https://www.britannica.com/place/Munich-Bavaria-Germany',\n",
+              "   'https://www.statista.com/statistics/519723/munich-population-by-age-group/',\n",
+              "   'https://www.citypopulation.de/en/germany/bayern/m%C3%BCnchen_stadt/09162000__m%C3%BCnchen/',\n",
+              "   'https://www.quora.com/How-many-people-live-in-Munich',\n",
+              "   'https://earth.esa.int/web/earth-watching/image-of-the-week/content/-/article/munich-germany/']},\n",
+              " 'llm_for_websearch': {'replies': ['According to the documents retrieved from the web, the population of Munich is approximately 1.47 million as of 2019. However, the most recent estimates suggest that the population has grown to about 1.58 million as of May 31, 2022. Additionally, the current estimated population of Munich is around 1.46 million, with the urban area being much larger at 2.65 million.'],\n",
+              "  'meta': [{'model': 'gpt-3.5-turbo-0613',\n",
+              "    'index': 0,\n",
+              "    'finish_reason': 'stop',\n",
+              "    'usage': {'completion_tokens': 85,\n",
+              "     'prompt_tokens': 436,\n",
+              "     'total_tokens': 521}}]}}"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "result"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6nhdYK-vHpNM"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "🎉 Congratulations! You've built a pipeline with conditional routing! You can now customize the condition for your specific use case and create a custom Haystack 2.0 pipeline to meet your needs.\n",
+        "\n",
+        "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+        "- [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+        "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=conditional-router) or [join Haystack discord community](https://discord.gg/haystack).\n",
+        "\n",
+        "Thanks for reading!"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From 6419b3e69dc1995f075076c653ada96ef985cadd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 20 Feb 2024 12:04:24 +0300
Subject: [PATCH 162/206] Update 26_Hybrid_Retrieval.ipynb (#292)

---
 tutorials/26_Hybrid_Retrieval.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/26_Hybrid_Retrieval.ipynb b/tutorials/26_Hybrid_Retrieval.ipynb
index 061fdc72..fb943482 100644
--- a/tutorials/26_Hybrid_Retrieval.ipynb
+++ b/tutorials/26_Hybrid_Retrieval.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0-Beta and would like to follow the updated version of this tutorial, check out [Creating a Hybrid Pipeline](https://haystack.deepset.ai/tutorials/30_hybrid_retrieval). \n",
+    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0-Beta and would like to follow the updated version of this tutorial, check out [Creating a Hybrid Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval). \n",
     ">\n",
     "> For more information on Haystack 2.0-Beta, you can also read the [announcement post](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent)."
    ]

From 27cbcce9df85a6b309d2667f78ef7b5d1ffa28ed Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Tue, 20 Feb 2024 12:37:03 +0100
Subject: [PATCH 163/206] Extending eval tutorial (#291)

* adding ragas and deepeval

* fixed ragas imports

* add evaluator names to top

* typo fix

* correction of level

* fixing typos

* changing aspect critique
---
 ...el_Based_Evaluation_of_RAG_Pipelines.ipynb | 532 ++++++++++++------
 1 file changed, 352 insertions(+), 180 deletions(-)

diff --git a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
index 819e6717..d3494fde 100644
--- a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
+++ b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
@@ -8,9 +8,9 @@
    "source": [
     "# Tutorial: Model-Based Evaluation of RAG Pipelines\n",
     "\n",
-    "- **Level**: Beginner\n",
-    "- **Time to complete**: 10 minutes\n",
-    "- **Components Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `OpenAIGenerator`, `UpTrainEvaluator`\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Components Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `OpenAIGenerator`, `DeepEvalEvaluator`, `RagasEvaluator`, `UpTrainEvaluator`\n",
     "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI: https://platform.openai.com/api-keys\n",
     "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines using some of the model-based evaluation frameworkes integerated into Haystack.\n",
     "\n",
@@ -26,9 +26,9 @@
     "## Overview\n",
     "\n",
     "This tutorial shows you how to evaluate a generative question-answering pipeline that uses the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. As we're doing model-based evaluation, no ground-truth labels are required. The process involves Haystack's integration of three evaluation frameworks:\n",
-    "- [UpTrain](https://docs.uptrain.ai/getting-started/introduction) ✅\n",
-    "- [RAGAS](https://docs.ragas.io/en/stable/index.html) 🔜\n",
-    "- [DeepEval](https://docs.confident-ai.com/docs/getting-started) 🔜\n",
+    "- [DeepEval](#evaluate-the-pipeline-with-deepeval) \n",
+    "- [RAGAS](#evaluate-the-pipeline-with-ragas)\n",
+    "- [UpTrain](#evaluate-the-pipeline-with-uptrain)\n",
     "\n",
     "For this tutorial, you'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"
    ]
@@ -70,9 +70,9 @@
    "source": [
     "%%bash\n",
     "\n",
+    "pip install \"pydantic<1.10.10\"\n",
     "pip install haystack-ai\n",
-    "pip install \"datasets>=2.6.1\"\n",
-    "pip install uptrain-haystack"
+    "pip install \"datasets>=2.6.1\""
    ]
   },
   {
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "id": "A76B4S49O-qa"
    },
@@ -162,7 +162,26 @@
     "id": "CbVN-s5LO-qa",
     "outputId": "45314795-f269-4bd3-e386-9e00fa3ee06a"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "151"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from datasets import load_dataset\n",
     "from haystack import Document\n",
@@ -253,7 +272,18 @@
    "metadata": {
     "id": "f6NFmpjEO-qb"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<haystack.pipeline.Pipeline at 0x19518d010>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from haystack.pipeline import Pipeline\n",
     "from haystack.components.builders.answer_builder import AnswerBuilder\n",
@@ -295,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -317,22 +347,7 @@
     "id": "Vnt283M5O-qc",
     "outputId": "9b0fea15-ae41-49d4-d5fc-7ff2907590aa"
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bbb05692fe1940e9ae8cf54d4f698830",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Ranking by BM25...:   0%|          | 0/151 [00:00<?, ? docs/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "question = \"When was the Rhodes Statue built?\"\n",
     "\n",
@@ -343,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -356,22 +371,311 @@
     "print(response[\"answer_builder\"][\"answers\"][0].data)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate the Pipeline\n",
+    "\n",
+    "In the following sections, we will evaluate the RAG pipeline. You can do this section with one of the following integations: DeepEval, Ragas or UpTrain.\n",
+    "\n",
+    "For most metrics, we will need to provide not only the questions that we're evaluating our RAG pipeline with, but also the generated `responses` and the `contexts` that were porvided to the pipeline. To make this easier, we create a helper function 👇"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_contexts_and_responses(questions, pipeline):\n",
+    "    contexts = []\n",
+    "    responses = []\n",
+    "    for question in questions:\n",
+    "        response = pipeline.run(\n",
+    "            {\n",
+    "                \"retriever\": {\"query\": question},\n",
+    "                \"prompt_builder\": {\"question\": question},\n",
+    "                \"answer_builder\": {\"query\": question},\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "        contexts.append([d.content for d in response[\"answer_builder\"][\"answers\"][0].documents])\n",
+    "        responses.append(response[\"answer_builder\"][\"answers\"][0].data)\n",
+    "    return contexts, responses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we create our evaluation pipeline. It defines what metrics we want to evaluate and we choose one of Haystack's integrated evaluation frameworks. We will run the evaluation pipelines on the following `questions`, `contexts` and `responses`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Ranking by BM25...: 100%|██████████| 151/151 [00:00<00:00, 14439.05 docs/s]\n",
+      "Ranking by BM25...: 100%|██████████| 151/151 [00:00<00:00, 14076.72 docs/s]\n",
+      "Ranking by BM25...: 100%|██████████| 151/151 [00:00<00:00, 9893.77 docs/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "questions = [\"When was the Rhodes Statue built?\", \"Where is the Pyramid of Giza?\", \"When was the pyramid built?\"]\n",
+    "contexts, responses = get_contexts_and_responses(questions, rag_pipeline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## Evaluate the Pipeline with DeepEval\n",
+    "We will perform 2 evaluations with DeepEval\n",
+    "- Faithfullness, grading how factual the generated responses were.\n",
+    "- Contextual relevance, grading how relevant the context was to the question.\n",
+    "\n",
+    "For a full list of available metrics and their expected inputs, check out the [`DeepEvalEvaluator` Docs](https://docs.haystack.deepset.ai/v2.0/docs/deepevalevaluator)\n",
+    "\n",
+    "First, we install the `deepeval-haystack` integration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install deepeval-haystack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1) Evaluate Faithfulness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack_integrations.components.evaluators.deepeval import DeepEvalEvaluator, DeepEvalMetric\n",
+    "\n",
+    "faithfulness_evlautator = Pipeline()\n",
+    "evaluator = DeepEvalEvaluator(\n",
+    "    metric=DeepEvalMetric.FAITHFULNESS,\n",
+    "    metric_params={\"model\": \"gpt-4\"},\n",
+    ")\n",
+    "faithfulness_evlautator.add_component(\"evaluator\", evaluator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluation_results = faithfulness_evlautator.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\":responses}})\n",
+    "print(evaluation_results['evaluator']['results'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2) Evaluate Contextual Relevance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "contetural_relevance_evaluator = Pipeline()\n",
+    "evaluator = DeepEvalEvaluator(\n",
+    "    metric=DeepEvalMetric.CONTEXTUAL_RELEVANCE,\n",
+    "    metric_params={\"model\": \"gpt-4\"},\n",
+    ")\n",
+    "contetural_relevance_evaluator.add_component(\"evaluator\", evaluator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluation_results = contetural_relevance_evaluator.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\":responses}})\n",
+    "print(evaluation_results['evaluator']['results'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate the Pipeline with Ragas\n",
+    "\n",
+    "Here, we're using the Haystack Ragas integration. We will perform 2 evaluations:\n",
+    "\n",
+    "- Context Utilization, grading to what extent the generated answer uses the provided context.\n",
+    "- Aspect critique, grading generated answers based on custom aspects on a binary scale.\n",
+    "\n",
+    "For a full list of available metrics and their expected inputs, check out our [`RagasEvaluator` Docs](https://docs.haystack.deepset.ai/v2.0/docs/ragasevalutor).\n",
+    "\n",
+    "First, we install the `ragas-haystack` integration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install ragas-haystack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1) Evaluate Context Utilizaton"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "from haystack_integrations.components.evaluators.ragas import RagasEvaluator, RagasMetric\n",
+    "\n",
+    "context_utilization_pipeline = Pipeline()\n",
+    "evaluator = RagasEvaluator(\n",
+    "    metric=RagasMetric.CONTEXT_UTILIZATION,\n",
+    ")\n",
+    "context_utilization_pipeline.add_component(\"evaluator\", evaluator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using 'context_precision' without ground truth will be soon depreciated. Use 'context_utilization' instead\n",
+      "Using 'context_precision' without ground truth will be soon depreciated. Use 'context_utilization' instead\n",
+      "Using 'context_precision' without ground truth will be soon depreciated. Use 'context_utilization' instead\n",
+      "Evaluating: 100%|██████████| 3/3 [00:10<00:00,  3.59s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[{'name': 'context_utilization', 'score': 0.9028571428390857}], [{'name': 'context_utilization', 'score': 0.99999999999}], [{'name': 'context_utilization', 'score': 0.3313492063381614}]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "evaluation_results = context_utilization_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}})\n",
+    "print(evaluation_results['evaluator']['results'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2) Evaluate Aspect Critique\n",
+    "A `RagasEvaluator` that uses the `ASPECT_CRITIQUE` metric expects to be initialized with the `name`, `defintion` and optionally the `strictness` of the aspect to critique the reponse with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "from haystack_integrations.components.evaluators.ragas import RagasEvaluator, RagasMetric\n",
+    "\n",
+    "aspect_critique_pipeline = Pipeline()\n",
+    "evaluator = RagasEvaluator(\n",
+    "    metric=RagasMetric.ASPECT_CRITIQUE,\n",
+    "    metric_params={\"name\": \"maliciousness\", \"definition\": \"Is the submission intends to harm, deceive, or exploit users?\", \"strictness\": 2}\n",
+    ")\n",
+    "aspect_critique_pipeline.add_component(\"evaluator\", evaluator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating: 100%|██████████| 3/3 [00:01<00:00,  2.89it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[{'name': 'child-friendliness', 'score': 0}], [{'name': 'child-friendliness', 'score': 1}], [{'name': 'child-friendliness', 'score': 0}]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "evaluation_results = aspect_critique_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}})\n",
+    "print(evaluation_results['evaluator']['results'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "37b_d4OsoV5_"
    },
    "source": [
-    "Now that we have the RAG pipeline ready, we create an evaluation pipeline. It defines what metrics we want to evaluate and we choose one of Haystack's integrated evaluation frameworks.\n",
-    "\n",
-    "## Evaluate The Pipeline with UpTrain\n",
+    "## Evaluate the Pipeline with UpTrain\n",
     "\n",
-    "Now that we have a RAG pipeline, let's look at how we can evaluate it. Here, we're using the Haystack UpTrain integration. We will perform 2 evaluations:\n",
+    "Here, we're using the Haystack UpTrain integration. We will perform 2 evaluations:\n",
     "\n",
     "- Context Relevance, grading how relevant the context is to the question specified\n",
     "- Critique language, grading language aspects such as fluency, politeness, grammar, and coherence\n",
     "\n",
-    "For a full list of available metrics and their expected inputs, check out our [`UpTrainEvaluator` Docs](https://docs.haystack.deepset.ai/v2.0/docs/uptrainevaluator)."
+    "For a full list of available metrics and their expected inputs, check out our [`UpTrainEvaluator` Docs](https://docs.haystack.deepset.ai/v2.0/docs/uptrainevaluator).\n",
+    "\n",
+    "First, we install the `uptrain-haystack` integration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install uptrain-haystack"
    ]
   },
   {
@@ -385,18 +689,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "id": "lQp9v-MBo18D"
    },
    "outputs": [],
    "source": [
+    "from haystack import Pipeline\n",
     "from haystack_integrations.components.evaluators.uptrain import UpTrainEvaluator, UpTrainMetric\n",
     "\n",
     "evaluator = UpTrainEvaluator(metric=UpTrainMetric.CONTEXT_RELEVANCE, api=\"openai\")\n",
     "\n",
-    "evaluator_pipeline = Pipeline()\n",
-    "evaluator_pipeline.add_component(\"evaluator\", evaluator)"
+    "contect_relevanve_evaluator_pipeline = Pipeline()\n",
+    "contect_relevanve_evaluator_pipeline.add_component(\"evaluator\", evaluator)"
    ]
   },
   {
@@ -405,7 +710,7 @@
     "id": "MXg1MP4ZyYaE"
    },
    "source": [
-    "Next, we can create a helper function to evaluate the context relevance of a RAG pipeline with multiple questions. The context relevance metric expects 2 inputs that should be provided from the RAG pipeline we are evaluating:\n",
+    "Next, we can evaluate the context relevance of a RAG pipeline with multiple questions. The context relevance metric expects 2 inputs that should be provided from the RAG pipeline we are evaluating:\n",
     "\n",
     "- questions\n",
     "- contexts"
@@ -413,82 +718,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "id": "ZVw8M2YNxVrR"
    },
    "outputs": [],
    "source": [
-    "def evaluate_context_relevance(questions, evaluation_pipeline):\n",
-    "    contexts = []\n",
-    "    responses = []\n",
-    "    for question in questions:\n",
-    "        response = rag_pipeline.run(\n",
-    "            {\n",
-    "                \"retriever\": {\"query\": question},\n",
-    "                \"prompt_builder\": {\"question\": question},\n",
-    "                \"answer_builder\": {\"query\": question},\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "        contexts.append([d.content for d in response[\"answer_builder\"][\"answers\"][0].documents])\n",
-    "        responses.append(response[\"answer_builder\"][\"answers\"][0].data)\n",
-    "\n",
-    "    evaluation_results = evaluation_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts}})\n",
-    "    return evaluation_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 408,
-     "referenced_widgets": [
-      "35b9c342a696457ca9912934af042762",
-      "4fd08ab23d0b47b4adbb8404ce7150e0",
-      "0ecfef6d203b4a3ca174a45d20438b0e",
-      "aab594e4e445408e8b4cb320c11f7e13",
-      "9c9adf86cd3d4c5580b7c8dd3dca86f2",
-      "88e8eafb2ad2426faae9c768a32d8bde",
-      "59f7abb97ddd47778fa500fd04c6c954",
-      "2dedf340dab64a3d8207f912a18f7e92",
-      "667d8a7743cc466884a46e430c794fc5",
-      "d15d9ceaf16c4b6d9f6839e1928341fd",
-      "1bfcf351881f41d897fe15b949720580",
-      "f5098389f80945acb824eefdba90a2cb",
-      "242aee91d302467382e308c7b55b88aa",
-      "a441139a727e4945b5ab9fabff4df43e",
-      "dc4cb3fbf0d64406a94b5e6cd16a0e89",
-      "bbebb06137524c9293087b6006488a1a",
-      "ebdd4c0275b74ce8a307243ea23f0249",
-      "5f738a9438e24f03975d45ceec8b4428",
-      "1714540726014c268acd47d8ec1c5da7",
-      "c2ce2fc0020443df9dafeef5f896bce3",
-      "5ef8065d72ba4f63a693252a7f482a5f",
-      "4f2affa3fcab4ba1958a23075d11938e",
-      "8fb884e599034e36bb062338f68fa823",
-      "5c08186637b9483694fed56e99317795",
-      "3b0963d13bc2461692dcc1d14cc3d48d",
-      "610c7453276d4b82b838a75eea62e5b9",
-      "d199c429729a481cb5c9b4825eb87396",
-      "4861c9ba88b740b4968992115eb42a63",
-      "b69286d4b44a4b2aa9291b636a8e6417",
-      "7cd0f0830a994e058c8a5b819f49b7c1",
-      "bb3ce998288f4c148cfa770f12542d6f",
-      "fe0763bbcf884fab83cedb356f3b8728",
-      "90143f9058594e3b8b719601519a68c3"
-     ]
-    },
-    "id": "DePGlcPcdq6B",
-    "outputId": "70b0a7cf-89ad-44d9-afa3-5df7d293cf84"
-   },
-   "outputs": [],
-   "source": [
-    "questions = [\"When was the Rhodes Statue built?\", \"Where is the Pyramid of Giza?\", \"When was the pyramid built?\"]\n",
-    "\n",
-    "evaluate_context_relevance(questions=questions, evaluation_pipeline=evaluator_pipeline)"
+    "evaluation_results = contect_relevanve_evaluator_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts}})\n",
+    "print(evaluation_results['evaluator']['results'])"
    ]
   },
   {
@@ -516,8 +753,8 @@
     "    metric=UpTrainMetric.CRITIQUE_TONE, api=\"openai\", metric_params={\"llm_persona\": \"informative\"}\n",
     ")\n",
     "\n",
-    "evaluator_pipeline = Pipeline()\n",
-    "evaluator_pipeline.add_component(\"evaluator\", evaluator)"
+    "critique_tone_pipeline = Pipeline()\n",
+    "critique_tone_pipeline.add_component(\"evaluator\", evaluator)"
    ]
   },
   {
@@ -526,7 +763,7 @@
     "id": "dkllxaV78ddM"
    },
    "source": [
-    "Next, we can create a helper function to critique the tone of the results of a RAG pipeline. This metric expects 1 input that should be provided from the RAG pipeline we are evaluating:\n",
+    "Next, we can critique the tone of the results of a RAG pipeline. This metric expects 1 input that should be provided from the RAG pipeline we are evaluating:\n",
     "\n",
     "- responses"
    ]
@@ -539,73 +776,8 @@
    },
    "outputs": [],
    "source": [
-    "def evaluate_critique_tone(questions, evaluation_pipeline):\n",
-    "    responses = []\n",
-    "    for question in questions:\n",
-    "        response = rag_pipeline.run(\n",
-    "            {\n",
-    "                \"retriever\": {\"query\": question},\n",
-    "                \"prompt_builder\": {\"question\": question},\n",
-    "                \"answer_builder\": {\"query\": question},\n",
-    "            }\n",
-    "        )\n",
-    "        responses.append(response[\"answer_builder\"][\"answers\"][0].data)\n",
-    "\n",
-    "    evaluation_results = evaluator_pipeline.run({\"evaluator\": {\"responses\": responses}})\n",
-    "    return evaluation_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 373,
-     "referenced_widgets": [
-      "471713771ccf46b6aefce7ea0f6dc85a",
-      "096a6986744b4d5aa0ddd4f3ea5928a2",
-      "0d0be3d81cfd40aeb1135782eb6e5a30",
-      "150b3c1c084a4a09bea3346741abc53b",
-      "d5fb0854f30544f1a6d98b1b9aec0375",
-      "ef52f2df645f41b19c4802736d379e26",
-      "ffbd1e28c95f4470a24c7e7f4c866e4c",
-      "29424463c4934501a8290b9fb5d82967",
-      "5248269173cd4001a17c769e436a3173",
-      "4c3bf766252d45a7a2d50ea13f999d4b",
-      "07a799395c9c45fb9e43ba16793e2ccc",
-      "597e9f63d5564421966ef1ce456edc66",
-      "48438c57fbcb43f1a5b73335d84a4361",
-      "2531168ca5b849a8ab71017d441421eb",
-      "0d6cb5e9741e46eca55942e61368203d",
-      "6ab9c6de5f454c10bfa8b118f62d5afe",
-      "8957ad58c41d4f31a9c01f3ab8029f98",
-      "9aff0087de7848e28a15456dca2876e6",
-      "7eb08bfda53a44a28ad66739883be51f",
-      "b39ff0324ca248bd8dd2746e152e7338",
-      "29a434266a194715ad855ac608fb8ae0",
-      "25ea5807cf424ceeab4f17d3d27d55f8",
-      "9268e70e5fca48a8ae088f542382df89",
-      "6cbd03ef092c4f0b8ddb172fe4354c76",
-      "865e2bfbb0144493976c0b25aa16a218",
-      "c5d4c495ca0440619f4a7c542dd72140",
-      "a35478f7428c4a2f90f3c5d011e1f58f",
-      "8f8c3437a62142fd85b9f4f8ea32cf7c",
-      "63f476aaffa14dd9ad16af6d35d6fd26",
-      "fd9b3d8db9f841b5a350f70e4358f7c9",
-      "4c27fcc5b6b047ebb760f4dbda21c55c",
-      "d247f27cda324ad59a0cc7ceafd6d3e6",
-      "fccea9d69eba4c5599ce70555cb955df"
-     ]
-    },
-    "id": "GmtmfQcO5IGu",
-    "outputId": "cb65f540-874d-49cd-c457-fd3801ec5f6d"
-   },
-   "outputs": [],
-   "source": [
-    "questions = [\"When was the Rhodes Statue built?\", \"Where is the Pyramid of Giza?\", \"When was the pyramid built?\"]\n",
-    "\n",
-    "evaluate_critique_tone(questions=questions, evaluation_pipeline=evaluator_pipeline)"
+    "evaluation_results = critique_tone_pipeline.run({\"evaluator\": {\"responses\": responses}})\n",
+    "print(evaluation_results['evaluator']['results'])"
    ]
   },
   {
@@ -646,7 +818,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.1"
   },
   "orig_nbformat": 4,
   "vscode": {

From fec38b287a2a522319f3695d703764894f02931d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 20 Feb 2024 15:10:32 +0300
Subject: [PATCH 164/206] Tut 37: Simplifying Pipeline Inputs with Multiplexer
 (#288)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add a tutorial explaning a basic use case of a Multiplexer

* Small changes

* Update README.md

* Update index.toml

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>

* Update tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>

---------

Co-authored-by: Tuana Çelik <tuana.celik@deepset.ai>
---
 .github/workflows/run_tutorials.yml           |    1 +
 README.md                                     |    2 +-
 index.toml                                    |   11 +
 ...ing_Pipeline_Inputs_with_Multiplexer.ipynb | 2010 +++++++++++++++++
 4 files changed, 2023 insertions(+), 1 deletion(-)
 create mode 100644 tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 2dd3a4bd..8075519b 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -77,6 +77,7 @@ jobs:
               "tutorials/34_Extractive_QA_Pipeline.ipynb"
               "tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb"
               "tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb"
+              "tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index 47062a97..f701f6b8 100644
--- a/README.md
+++ b/README.md
@@ -46,7 +46,7 @@ Haystack 2.0
 | [Preprocessing](./tutorials/08_Preprocessing.ipynb)                                                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           | [Build an Extractive QA Pipeline](./tutorials/34_Extractive_QA_Pipeline.ipynb)                                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/34_Extractive_QA_Pipeline.ipynb)                 |
 | [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            | [Model-Based Evaluation of RAG Pipelines](./tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)|
 | [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         | [Building Pipelines with Conditional Routing](./tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)|
-| [Pipelines](./tutorials/11_Pipelines.ipynb)                                                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Pipelines](./tutorials/11_Pipelines.ipynb)                                                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               | [Simplifying Pipeline Inputs with Multiplexer](./tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)|
 | [[OUTDATED] Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Question Generation](./tutorials/13_Question_generation.ipynb)                                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Query Classifier](./tutorials/14_Query_Classifier.ipynb)                                                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)                                        |                                                                                                                  |                                                                                                                                                                                                                          |
diff --git a/index.toml b/index.toml
index 32324b3b..130da1ac 100644
--- a/index.toml
+++ b/index.toml
@@ -374,3 +374,14 @@ aliases = []
 completion_time = "10 min"
 created_at = 2024-02-16
 haystack_2 = true
+
+[[tutorial]]
+title = "Simplifying Pipeline Inputs with Multiplexer"
+description = "Learn how to declutter the inputs of complex pipelines"
+level = "intermediate"
+weight = 84
+notebook = "37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb"
+aliases = []
+completion_time = "10 min"
+created_at = 2024-02-19
+haystack_2 = true
diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
new file mode 100644
index 00000000..9a79d5d1
--- /dev/null
+++ b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
@@ -0,0 +1,2010 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JFAFUa7BECmK"
+   },
+   "source": [
+    "# Tutorial: Simplifying Pipeline Inputs with Multiplexer\n",
+    "\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 10 minutes\n",
+    "- **Components Used**: [Multiplexer](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer), [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [HuggingFaceTEIDocumentEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_document_embedder.py), [HuggingFaceTEITextEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_text_embedder.py), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [HuggingFaceTGIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) and [AnswerBuilder](https://docs.haystack.deepset.ai/v2.0/docs/answerbuilder)\n",
+    "- **Prerequisites**: You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines)\n",
+    "- **Goal**: After completing this tutorial, you'll have learned how to use a Multiplexer to simplify the inputs that `Pipeline.run()` get\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jy3ZkDzu9-CW"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "If you've ever built a Haystack pipeline with more than 3-4 components, you probably noticed that the number of inputs to pass to the `run()` method of the pipeline grow endlessly. New components take some of their input from the other components of a pipeline, but many of them also require additional input from the user. As a result, the `data` input of `Pipeline.run()` grows and becomes very repetitive.\n",
+    "\n",
+    "There is one component that can help managing this repetition in a more effective manner, and it's called [`Multiplexer`](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer).\n",
+    "\n",
+    "In this tutorial, you will learn how to drastically simplify the `Pipeline.run()` of a RAG pipeline using a Multiplexer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RJPsjBXZKWnb"
+   },
+   "source": [
+    "## Setup\n",
+    "### Prepare the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CcK-dK--G5ng"
+   },
+   "source": [
+    "### Install Haystack\n",
+    "\n",
+    "Install Haystack 2.0 Beta and other required packages with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0hwJTyV5HARC"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3N_97P0OV9cx"
+   },
+   "source": [
+    "### Enable Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BKilNUd8V_Uc"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(37)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uTNEeEcBJc_4"
+   },
+   "source": [
+    "### Enter a Hugging Face API key\n",
+    "\n",
+    "Set a Hugging Face API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "aiHltCF7JgaV"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"HF_API_KEY\"] = getpass(\"HF_API_KEY key:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "e57ugQB7dYsQ"
+   },
+   "source": [
+    "## Indexing Documents with a Pipeline\n",
+    "\n",
+    "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore) with their embeddings. You will use [HuggingFaceTEIDocumentEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_document_embedder.py) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter).\n",
+    "\n",
+    "After adding these components to your pipeline, connect them and run the pipeline.\n",
+    "\n",
+    "> If you'd like to learn about preprocessing files before you index them to your document store, follow the [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline) tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "My_fx0lNJUVb",
+    "outputId": "1987b9b8-0ada-4127-d4c3-80ef53a1b9d0"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+      "You will be able to reuse this secret in all of your notebooks.\n",
+      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+      "  warnings.warn(\n",
+      "Calculating embeddings: 100%|██████████| 1/1 [00:00<00:00,  3.86it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'doc_writer': {'documents_written': 5}}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from haystack import Pipeline, Document\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "from haystack.components.writers import DocumentWriter\n",
+    "from haystack.components.embedders import HuggingFaceTEIDocumentEmbedder\n",
+    "\n",
+    "documents = [\n",
+    "    Document(content=\"My name is Jean and I live in Paris.\"),\n",
+    "    Document(content=\"My name is Mark and I live in Berlin.\"),\n",
+    "    Document(content=\"My name is Giorgio and I live in Rome.\"),\n",
+    "    Document(content=\"My name is Giorgio and I live in Milan.\"),\n",
+    "    Document(content=\"My name is Giorgio and I lived in many cities, but I settled in Naples eventually.\"),\n",
+    "]\n",
+    "\n",
+    "document_store = InMemoryDocumentStore()\n",
+    "\n",
+    "indexing_pipeline = Pipeline()\n",
+    "indexing_pipeline.add_component(\n",
+    "    instance=HuggingFaceTEIDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"), name=\"doc_embedder\"\n",
+    ")\n",
+    "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"doc_writer\")\n",
+    "\n",
+    "indexing_pipeline.connect(\"doc_embedder.documents\", \"doc_writer.documents\")\n",
+    "\n",
+    "indexing_pipeline.run({\"doc_embedder\": {\"documents\": documents}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "e9hOmQx4L2Lw"
+   },
+   "source": [
+    "## Building a RAG Pipeline\n",
+    "\n",
+    "Build a basic retrieval augmented generative pipeline with [HuggingFaceTEITextEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_text_embedder.py), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) and [HuggingFaceTGIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator). Additionally, add [AnswerBuilder](https://docs.haystack.deepset.ai/v2.0/docs/answerbuilder) to help you enrich the generated answer with `meta` info and the `query` input.\n",
+    "\n",
+    "> For a step-by-step guide to create a RAG pipeline with Haystack, follow the [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) tutorial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ueu5W07IWyXa",
+    "outputId": "fa94ac9e-0a88-49b1-8483-31a7590b8060"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<haystack.pipeline.Pipeline at 0x7da6badb2e90>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from haystack.components.embedders import HuggingFaceTEITextEmbedder\n",
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
+    "from haystack.components.generators import HuggingFaceTGIGenerator\n",
+    "\n",
+    "template = \"\"\"\n",
+    "<|system|>\n",
+    "Answer the questions based on the given context.\n",
+    "</s>\n",
+    "<|user|>\n",
+    "Context:\n",
+    "{% for document in documents %}\n",
+    "    {{ document.content }}\n",
+    "{% endfor %}\n",
+    "\n",
+    "Question: {{ question }}\n",
+    "Answer:</s>\n",
+    "<|assistant|>\n",
+    "\"\"\"\n",
+    "pipe = Pipeline()\n",
+    "pipe.add_component(\"embedder\", HuggingFaceTEITextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+    "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+    "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+    "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.2\"))\n",
+    "pipe.add_component(\"answer_builder\", AnswerBuilder())\n",
+    "\n",
+    "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+    "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+    "pipe.connect(\"prompt_builder\", \"llm\")\n",
+    "pipe.connect(\"llm.replies\", \"answer_builder.replies\")\n",
+    "pipe.connect(\"llm.meta\", \"answer_builder.meta\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5xxvPqyurZTi"
+   },
+   "source": [
+    "## Running the Pipeline\n",
+    "Pass the `query` to `embedder`, `prompt_builder` and `answer_builder` and run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 180,
+     "referenced_widgets": [
+      "ce3dbf88776644ddaaf5230103a0c8a4",
+      "1618506e91914b6fa6608f1111d25c07",
+      "8d42264e1210400497ec742963469cac",
+      "f13aef9432424dcaa90f2a738eab10ff",
+      "b43e437fa6914c0a8da32ebd733040d5",
+      "024250df6331459f8ab6b16ca87072ab",
+      "a048537d05ea43558674f6c9eef26704",
+      "83e4ec7bf8524955a21eb70894ae2798",
+      "85f9c49e9acc4e0f9daf8101d662232c",
+      "7125ddab3dc94d588350aab66e3e091e",
+      "c889f54c4b1945869a32e550b894a99a",
+      "90c696ac0e4d42ccae76c7bf3580badb",
+      "7c7cf639dd53403ea6d466b695c81158",
+      "373cead3c35845cfbb35721cf4da6640",
+      "5b64c6ec2e4949c4b599c016f430a0f4",
+      "11ff7ab5eb044e9f90cea4bc05d2876f",
+      "d37cb0ad69814b9fa0b2dc0cdbe7af01",
+      "d71124d6341e42f29fb57103868d1265",
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+      "384775b89b0a47eb86668e990fd7fe76",
+      "d4e61d665db74798bc7d4ccbcdabf1bd",
+      "dbaca5fb30db43b485a94efb9646805f",
+      "8b089617c8804f3688349656bfc3d696",
+      "c6813d416ae742d6bb2b3148359994cf",
+      "c1741ef7dbd8440b8c30e34cfba734a4",
+      "4b707740fe3d4a159eec4cd78a2a9534",
+      "27ff0db46763416fa4921db6f8036e4e",
+      "ff47fff35492433e83250315d64c9794",
+      "63fec26043e948e092c43b8593241144",
+      "5563ab711bfc4602bd6f034ee6511a9d",
+      "5136122faba64a83864745fdc04bbdd7",
+      "173bef0a6eef42abbdb77302ddd48342",
+      "648b01abb78248b1af06d556b2fe8972",
+      "950e9cbd6aac4f70bc0f77821bdb1565",
+      "395c6900afd745c3b6713db543f400d3",
+      "ce8109a20f07499eb8cc077031fb2086",
+      "f9dc45c6a5294991a68167c94dce4874",
+      "77be128c10b44268ae3939171a24cfb0",
+      "03ab731b50c94976b78492fb2d4d3a9f",
+      "1960dc3979a24764be602be9f09b0ddf",
+      "ced7ae4acb104091bd59e707b9b9e679",
+      "968e3c5d74fd4b04bfa823d7d54422a0",
+      "185c58a7243e4df7aa444b766df54a50",
+      "ccc37a05521446cbb67b4dc8c47c6c1f"
+     ]
+    },
+    "id": "AIsphy4hJDpE",
+    "outputId": "9eff163e-e9bc-4883-d53f-111fbece8b5d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce3dbf88776644ddaaf5230103a0c8a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/1.46k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90c696ac0e4d42ccae76c7bf3580badb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b089617c8804f3688349656bfc3d696",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "950e9cbd6aac4f70bc0f77821bdb1565",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/72.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'answer_builder': {'answers': [GeneratedAnswer(data='\\nMark lives in Berlin.', query='Where does Mark live?', documents=[], meta={'model': 'mistralai/Mistral-7B-Instruct-v0.2', 'index': 0, 'finish_reason': 'eos_token', 'usage': {'completion_tokens': 7, 'prompt_tokens': 133, 'total_tokens': 140}})]}}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"Where does Mark live?\"\n",
+    "pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}, \"answer_builder\": {\"query\": query}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wrH2MGSBLvVC"
+   },
+   "source": [
+    "In this basic RAG pipeline, components require a `query` to operate are `embedder`, `prompt_builder`, and `answer_builder`. However, as you extend the pipeline with additional components like Retrievers and Rankers, the number of components needing a `query` can increase indefinitely. This leads to repetitive and increasingly complex `Pipeline.run()` calls. In such cases, using a Multiplexer can help simplify and declutter `Pipeline.run()`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ewDXDrw9N0CG"
+   },
+   "source": [
+    "## Introducing a Multiplexer\n",
+    "\n",
+    "The [Multiplexer](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer) is a component that can accept multiple input connections and then distributes the first value it receives to all components connected to its output. In this seeting, you can use this component by connecting it to other pipeline components that expect a `query` during runtime.\n",
+    "\n",
+    "Now, initialize the Multiplexer with the expected input type (in this case, `str` since the `query` is a string):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "kArO87EKN3N-"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.others import Multiplexer\n",
+    "\n",
+    "multiplexer = Multiplexer(str)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vBGC2wO5LWIL"
+   },
+   "source": [
+    "## Adding the `Multiplexer` to the Pipeline\n",
+    "\n",
+    "Create the same RAG pipeline but this time with the Multiplexer. Add the Multiplexer to the pipeline and connect it to all the components that need the `query` as an input:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "CTmnCZvgEAut",
+    "outputId": "69794020-6d67-4b46-e67b-67fecc98e100"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<haystack.pipeline.Pipeline at 0x7da66d82a260>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from haystack.components.embedders import HuggingFaceTEITextEmbedder\n",
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
+    "from haystack.components.generators import HuggingFaceTGIGenerator\n",
+    "\n",
+    "template = \"\"\"\n",
+    "<|system|>\n",
+    "Answer the questions based on the given context.\n",
+    "</s>\n",
+    "<|user|>\n",
+    "Context:\n",
+    "{% for document in documents %}\n",
+    "    {{ document.content }}\n",
+    "{% endfor %}\n",
+    "\n",
+    "Question: {{ question }}\n",
+    "Answer:</s>\n",
+    "<|assistant|>\n",
+    "\"\"\"\n",
+    "pipe = Pipeline()\n",
+    "\n",
+    "pipe.add_component(\"multiplexer\", multiplexer)\n",
+    "\n",
+    "pipe.add_component(\"embedder\", HuggingFaceTEITextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+    "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+    "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+    "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.2\"))\n",
+    "pipe.add_component(\"answer_builder\", AnswerBuilder())\n",
+    "\n",
+    "# Connect the Multiplexer to all the components that need the query\n",
+    "pipe.connect(\"multiplexer.value\", \"embedder.text\")\n",
+    "pipe.connect(\"multiplexer.value\", \"prompt_builder.question\")\n",
+    "pipe.connect(\"multiplexer.value\", \"answer_builder.query\")\n",
+    "\n",
+    "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+    "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+    "pipe.connect(\"prompt_builder\", \"llm\")\n",
+    "pipe.connect(\"llm.replies\", \"answer_builder.replies\")\n",
+    "pipe.connect(\"llm.meta\", \"answer_builder.meta\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cJ0TjMWn-3gj"
+   },
+   "source": [
+    "### Visualize the Pipeline\n",
+    "\n",
+    "To understand how you formed this pipeline with a Multiplexer, use [draw()](https://docs.haystack.deepset.ai/v2.0/docs/drawing-pipeline-graphs) method of the pipeline. If you're running this notebook on Google Colab, the generated file will be saved in \"Files\" section on the sidebar or you can call `Image.open()`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 784
+    },
+    "id": "cOh_4u3jMI9O",
+    "outputId": "33b8ffd9-3f2d-48ae-a3ec-5a668c30587e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {
+      "image/png": {
+       "width": 300
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "\n",
+    "pipe.draw(\"pipeline_with_multiplexer.png\")\n",
+    "Image(\"pipeline_with_multiplexer.png\", width=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i2wW4nbEQKhJ"
+   },
+   "source": [
+    "## Running the Pipeline with a Multiplexer\n",
+    "\n",
+    "Run the pipeline that you updated with a Multiplexer. This time, instead of passing the query to `prompt_builder`, `retriever` and `answer_builder` seperately, you only need to pass it to the `multiplexer`. As a result, you will get the same answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "YbIHBCKPQF4f",
+    "outputId": "f1c0a91f-777b-4163-982b-f9a9f77ab549"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer_builder': {'answers': [GeneratedAnswer(data='\\nMark lives in Berlin.', query='Where does Mark live?', documents=[], meta={'model': 'mistralai/Mistral-7B-Instruct-v0.2', 'index': 0, 'finish_reason': 'eos_token', 'usage': {'completion_tokens': 7, 'prompt_tokens': 133, 'total_tokens': 140}})]}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.run({\"multiplexer\": {\"value\": \"Where does Mark live?\"}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kPiSU2xoKmio"
+   },
+   "source": [
+    "## What's next\n",
+    "\n",
+    "🎉 Congratulations! You've simplified your pipeline run with a Multiplexer!\n",
+    "\n",
+    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+    "- [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)\n",
+    "- [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)\n",
+    "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=multiplexer) or [join Haystack discord community](https://discord.gg/haystack).\n",
+    "\n",
+    "Thanks for reading!"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  },
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+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From 488d0f71d9d88a7b499093a6d5ae13ed6c7e58b8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 20 Feb 2024 16:52:21 +0300
Subject: [PATCH 165/206] Update
 37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb

---
 tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
index 9a79d5d1..b3a95aef 100644
--- a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
+++ b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
@@ -116,7 +116,7 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"HF_API_KEY\"] = getpass(\"HF_API_KEY key:\")"
+    "os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter a Hugging Face API Token:\")"
    ]
   },
   {

From f7a7357b81ccdbe171799ee34b471c76551e21d6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 20 Feb 2024 18:12:40 +0300
Subject: [PATCH 166/206] Change the prompt for mistral (#294)

---
 ...ing_Pipeline_Inputs_with_Multiplexer.ipynb | 20 ++++++-------------
 1 file changed, 6 insertions(+), 14 deletions(-)

diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
index b3a95aef..6ec12fe0 100644
--- a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
+++ b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
@@ -238,18 +238,14 @@
     "from haystack.components.generators import HuggingFaceTGIGenerator\n",
     "\n",
     "template = \"\"\"\n",
-    "<|system|>\n",
-    "Answer the questions based on the given context.\n",
-    "</s>\n",
-    "<|user|>\n",
+    "<s>[INST] Answer the questions based on the given context. Only return the answer\n",
+    "\n",
     "Context:\n",
     "{% for document in documents %}\n",
     "    {{ document.content }}\n",
     "{% endfor %}\n",
-    "\n",
     "Question: {{ question }}\n",
-    "Answer:</s>\n",
-    "<|assistant|>\n",
+    "Answer: [/INST]\n",
     "\"\"\"\n",
     "pipe = Pipeline()\n",
     "pipe.add_component(\"embedder\", HuggingFaceTEITextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
@@ -480,18 +476,14 @@
     "from haystack.components.generators import HuggingFaceTGIGenerator\n",
     "\n",
     "template = \"\"\"\n",
-    "<|system|>\n",
-    "Answer the questions based on the given context.\n",
-    "</s>\n",
-    "<|user|>\n",
+    "<s>[INST] Answer the questions based on the given context. Only return the answer\n",
+    "\n",
     "Context:\n",
     "{% for document in documents %}\n",
     "    {{ document.content }}\n",
     "{% endfor %}\n",
-    "\n",
     "Question: {{ question }}\n",
-    "Answer:</s>\n",
-    "<|assistant|>\n",
+    "Answer: [/INST]\n",
     "\"\"\"\n",
     "pipe = Pipeline()\n",
     "\n",

From 8c5c758e1a28c2dee30379809f6a8eb750b40040 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Tue, 20 Feb 2024 16:49:04 +0100
Subject: [PATCH 167/206] The embedding metadata for retrieval tutorial (#293)

* adding the embedding metadata for retrieval tutorial

* adding docs

* Update tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

* Update tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

* Update tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

* remove api key block

* typo fix

* Update README.md

Adding to readme

* fix

* Update tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

* Update tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

* Update tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

* Update tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

* Update tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

* add a small explanation

* Update tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

* typo

---------

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>
---
 .github/workflows/run_tutorials.yml           |   1 +
 README.md                                     |   2 +-
 index.toml                                    |  17 +-
 ...ding_Metadata_for_Improved_Retrieval.ipynb | 379 ++++++++++++++++++
 4 files changed, 395 insertions(+), 4 deletions(-)
 create mode 100644 tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 8075519b..91ddef26 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -78,6 +78,7 @@ jobs:
               "tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb"
               "tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb"
               "tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb"
+              "tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index f701f6b8..7a10a4f7 100644
--- a/README.md
+++ b/README.md
@@ -47,7 +47,7 @@ Haystack 2.0
 | [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            | [Model-Based Evaluation of RAG Pipelines](./tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)|
 | [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         | [Building Pipelines with Conditional Routing](./tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)|
 | [Pipelines](./tutorials/11_Pipelines.ipynb)                                                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               | [Simplifying Pipeline Inputs with Multiplexer](./tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)|
-| [[OUTDATED] Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    |                                                                                                                  |                                                                                                                                                                                                                          |
+| [[OUTDATED] Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    | [Embedding Metadata for Improved Retrieval](./tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb)|
 | [Question Generation](./tutorials/13_Question_generation.ipynb)                                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Query Classifier](./tutorials/14_Query_Classifier.ipynb)                                                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)                                        |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Table QA](./tutorials/15_TableQA.ipynb)                                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)                                                 |                                                                                                                  |                                                                                                                                                                                                                          |
diff --git a/index.toml b/index.toml
index 130da1ac..0d38fab8 100644
--- a/index.toml
+++ b/index.toml
@@ -291,7 +291,7 @@ haystack_2 = true
 title = "Serializing LLM Pipelines"
 description = "Learn how to serialize and deserialize your pipelines between YAML and Python"
 level = "beginner"
-weight = 7
+weight = 9
 notebook = "29_Serializing_Pipelines.ipynb"
 aliases = []
 completion_time = "10 min"
@@ -302,7 +302,7 @@ haystack_2 = true
 title = "Preprocessing Different File Types"
 description = "Learn how to build an indexing pipeline that will preprocess files based on their file type"
 level = "beginner"
-weight = 72
+weight = 7
 notebook = "30_File_Type_Preprocessing_Index_Pipeline.ipynb"
 aliases = []
 completion_time = "15 min"
@@ -346,7 +346,7 @@ haystack_2 = true
 title = "Build an Extractive QA Pipeline"
 description = "Learn how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is."
 level = "beginner"
-weight = 76
+weight = 15
 notebook = "34_Extractive_QA_Pipeline.ipynb"
 aliases = []
 completion_time = "10 min"
@@ -385,3 +385,14 @@ aliases = []
 completion_time = "10 min"
 created_at = 2024-02-19
 haystack_2 = true
+
+[[tutorial]]
+title = "Embedding Metadata for Improved Retrieval"
+description = "Learn how to embed metadata while indexing, to improve the quality of retrieval results"
+level = "beginner"
+weight = 8
+notebook = "39_Embedding_Metadata_for_Improved_Retrieval.ipynb"
+aliases = []
+completion_time = "10 min"
+created_at = 2024-02-20
+haystack_2 = true
diff --git a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
new file mode 100644
index 00000000..73e8eeee
--- /dev/null
+++ b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
@@ -0,0 +1,379 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: Embedding Metadata for Improved Retrieval\n",
+    "\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 10 minutes\n",
+    "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder)\n",
+    "- **Goal**: After completing this tutorial, you'll have learned how to embed metadata information while indexing documents, to improve retrieval.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
+    "\n",
+    "> ⚠️ Note of caution: The method showcased in this tutorial is not always the right approach for all types of metadata. This method works best when the embedded metadata is meaningful. For example, here we're showcasing embedding the \"title\" meta field, which can also provide good context for the embedding model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "While indexing documents into a document store, we have 2 options: embed the text for that document or embed the text alongside some meaningful metadata. In some cases, embedding meaningful metadata alongside the contents of a document may improve retrieval down the line. \n",
+    "\n",
+    "In this tutorial, we will see how we can embed metadata as well as the text of a document. We will fetch various pages from Wikipedia and index them into an `InMemoryDocumentStore` with metadata information that includes their title, and URL. Next, we will see how retrieval with and without this metadata."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "### Prepare the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install Haystack\n",
+    "\n",
+    "Install Haystack 2.0 Beta and other required packages with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: haystack-ai in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (2.0.0b7)\n",
+      "Collecting wikipedia\n",
+      "  Downloading wikipedia-1.4.0.tar.gz (27 kB)\n",
+      "  Preparing metadata (setup.py): started\n",
+      "  Preparing metadata (setup.py): finished with status 'done'\n",
+      "Requirement already satisfied: boilerpy3 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (1.0.7)\n",
+      "Requirement already satisfied: haystack-bm25 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (1.0.2)\n",
+      "Requirement already satisfied: jinja2 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (3.1.3)\n",
+      "Requirement already satisfied: lazy-imports in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (0.3.1)\n",
+      "Requirement already satisfied: more-itertools in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (10.2.0)\n",
+      "Requirement already satisfied: networkx in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (3.2.1)\n",
+      "Requirement already satisfied: openai>=1.1.0 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (1.12.0)\n",
+      "Requirement already satisfied: pandas in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (2.2.0)\n",
+      "Requirement already satisfied: posthog in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (3.4.1)\n",
+      "Requirement already satisfied: pyyaml in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (6.0.1)\n",
+      "Requirement already satisfied: tenacity in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (8.2.3)\n",
+      "Requirement already satisfied: tqdm in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (4.66.2)\n",
+      "Requirement already satisfied: typing-extensions in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-ai) (4.9.0)\n",
+      "Collecting beautifulsoup4 (from wikipedia)\n",
+      "  Using cached beautifulsoup4-4.12.3-py3-none-any.whl.metadata (3.8 kB)\n",
+      "Requirement already satisfied: requests<3.0.0,>=2.0.0 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from wikipedia) (2.31.0)\n",
+      "Requirement already satisfied: anyio<5,>=3.5.0 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (4.2.0)\n",
+      "Requirement already satisfied: distro<2,>=1.7.0 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (1.9.0)\n",
+      "Requirement already satisfied: httpx<1,>=0.23.0 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (0.26.0)\n",
+      "Requirement already satisfied: pydantic<3,>=1.9.0 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (1.10.9)\n",
+      "Requirement already satisfied: sniffio in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (1.3.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2.2.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2024.2.2)\n",
+      "Collecting soupsieve>1.2 (from beautifulsoup4->wikipedia)\n",
+      "  Using cached soupsieve-2.5-py3-none-any.whl.metadata (4.7 kB)\n",
+      "Requirement already satisfied: numpy in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from haystack-bm25->haystack-ai) (1.26.4)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from jinja2->haystack-ai) (2.1.5)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from pandas->haystack-ai) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from pandas->haystack-ai) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from pandas->haystack-ai) (2024.1)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from posthog->haystack-ai) (1.16.0)\n",
+      "Requirement already satisfied: monotonic>=1.5 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from posthog->haystack-ai) (1.6)\n",
+      "Requirement already satisfied: backoff>=1.10.0 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from posthog->haystack-ai) (2.2.1)\n",
+      "Requirement already satisfied: httpcore==1.* in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (1.0.2)\n",
+      "Requirement already satisfied: h11<0.15,>=0.13 in /Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (0.14.0)\n",
+      "Using cached beautifulsoup4-4.12.3-py3-none-any.whl (147 kB)\n",
+      "Using cached soupsieve-2.5-py3-none-any.whl (36 kB)\n",
+      "Building wheels for collected packages: wikipedia\n",
+      "  Building wheel for wikipedia (setup.py): started\n",
+      "  Building wheel for wikipedia (setup.py): finished with status 'done'\n",
+      "  Created wheel for wikipedia: filename=wikipedia-1.4.0-py3-none-any.whl size=11678 sha256=17926b00d77f1d294e927f20b0e52e7a137fcd6b219ca85e63570f3b5c7d58f3\n",
+      "  Stored in directory: /Users/tuanacelik/Library/Caches/pip/wheels/63/47/7c/a9688349aa74d228ce0a9023229c6c0ac52ca2a40fe87679b8\n",
+      "Successfully built wikipedia\n",
+      "Installing collected packages: soupsieve, beautifulsoup4, wikipedia\n",
+      "Successfully installed beautifulsoup4-4.12.3 soupsieve-2.5 wikipedia-1.4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai wikipedia sentence-transformers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enable Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(39)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing Documents with Metadata\n",
+    "\n",
+    "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore) with their embeddings. We will use [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter).\n",
+    "\n",
+    "After adding these components to your pipeline, connect them and run the pipeline.\n",
+    "\n",
+    "> 💡 The `InMemoryDocumentStore` is the simplest document store to run tutorials with and comes with no additional requirements. This can be changed to any of the other available document stores such as **Weaviate, AstraDB, Qdrant, Pinecone and more**. Check out the [full list of document stores](https://haystack.deepset.ai/integrations?type=Document+Store) with instructions on how to run them. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we'll create a helper function that can create indexing pipelines. We will optionally provide this function with `meta_fields_to_embed`. If provided, the `SentenceTransformersDocumentEmbedder` will be initialized with metadata to embed alongside the content of the document.\n",
+    "\n",
+    "For example, the embedder below will be embedding the \"url\" field as well as the contents of documents:\n",
+    "\n",
+    "```python\n",
+    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+    "\n",
+    "embedder = SentenceTransformersDocumentEmbedder(meta_fields_to_embed=[\"url\"])\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter\n",
+    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+    "from haystack.components.writers import DocumentWriter\n",
+    "from haystack.document_stores.types import DuplicatePolicy\n",
+    "\n",
+    "def create_indexing_pipeline(document_store, metadata_fields_to_embed = None):\n",
+    "   document_cleaner = DocumentCleaner()\n",
+    "   document_splitter = DocumentSplitter(split_by='sentence', split_length=2)\n",
+    "   document_embedder = SentenceTransformersDocumentEmbedder(model=\"thenlper/gte-large\", meta_fields_to_embed=metadata_fields_to_embed, device=\"cuda\")\n",
+    "   document_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE)\n",
+    "\n",
+    "   indexing_pipeline = Pipeline()\n",
+    "   indexing_pipeline.add_component(\"cleaner\", document_cleaner)\n",
+    "   indexing_pipeline.add_component(\"splitter\", document_splitter)\n",
+    "   indexing_pipeline.add_component(\"embedder\", document_embedder)\n",
+    "   indexing_pipeline.add_component(\"writer\", document_writer)\n",
+    "   \n",
+    "   indexing_pipeline.connect(\"cleaner\", \"splitter\")\n",
+    "   indexing_pipeline.connect(\"splitter\", \"embedder\")\n",
+    "   indexing_pipeline.connect(\"embedder\", \"writer\")\n",
+    "   \n",
+    "   return indexing_pipeline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we can index our documents from various wikipedia articles. We will create 2 indexing pipelines:\n",
+    "\n",
+    "- The `indexing_pipeline`: which indexes only the contents of the documents. We will index these documents into `document_store`.\n",
+    "- The `indexing_with_metadata_pipeline`: which indexes meta fields alongside the contents of the documents. We will index these documents into `document_store_with_embedded_metadata`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "modules.json: 100%|██████████| 349/349 [00:00<00:00, 561kB/s]\n",
+      "config_sentence_transformers.json: 100%|██████████| 124/124 [00:00<00:00, 360kB/s]\n",
+      "README.md: 100%|██████████| 93.0k/93.0k [00:00<00:00, 970kB/s]\n",
+      "sentence_bert_config.json: 100%|██████████| 52.0/52.0 [00:00<00:00, 156kB/s]\n",
+      "config.json: 100%|██████████| 743/743 [00:00<00:00, 2.25MB/s]\n",
+      "model.safetensors: 100%|██████████| 133M/133M [02:41<00:00, 826kB/s]  \n",
+      "tokenizer_config.json: 100%|██████████| 366/366 [00:00<00:00, 1.92MB/s]\n",
+      "vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 1.09MB/s]\n",
+      "tokenizer.json: 100%|██████████| 711k/711k [00:00<00:00, 1.11MB/s]\n",
+      "special_tokens_map.json: 100%|██████████| 125/125 [00:00<00:00, 374kB/s]\n",
+      "1_Pooling/config.json: 100%|██████████| 190/190 [00:00<00:00, 596kB/s]\n",
+      "Batches: 100%|██████████| 2/2 [00:40<00:00, 20.03s/it]\n",
+      "Batches: 100%|██████████| 2/2 [00:38<00:00, 19.09s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'writer': {'documents_written': 47}}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import wikipedia\n",
+    "from haystack import Document\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "\n",
+    "some_bands=\"\"\"The Beatles,The Cure\"\"\".split(\",\")\n",
+    "\n",
+    "raw_docs=[]\n",
+    "\n",
+    "for title in some_bands:\n",
+    "    page = wikipedia.page(title=title, auto_suggest=False)\n",
+    "    doc = Document(content=page.content, meta={\"title\": page.title, \"url\": page.url})\n",
+    "    raw_docs.append(doc)\n",
+    "\n",
+    "document_store = InMemoryDocumentStore(embedding_similarity_function=\"cosine\")\n",
+    "document_store_with_embedded_metadata = InMemoryDocumentStore(embedding_similarity_function=\"cosine\")\n",
+    "\n",
+    "indexing_pipeline = create_indexing_pipeline(document_store=document_store)\n",
+    "indexing_with_metadata_pipeline = create_indexing_pipeline(document_store=document_store_with_embedded_metadata, metadata_fields_to_embed=[\"title\"])\n",
+    "\n",
+    "indexing_pipeline.run({\"cleaner\":{\"documents\": raw_docs}})\n",
+    "indexing_with_metadata_pipeline.run({\"cleaner\":{\"documents\": raw_docs}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing Retrieval With and Without Embedded Metadata\n",
+    "\n",
+    "As a final step, we will be creating a retrieval pipeline that will have 2 retrievers:\n",
+    "- First: retrieving from the `document_store`, where we have not embedded metadata.\n",
+    "- Second: retrieving from the `document_store_with_embedded_metadata`, where we have embedded metadata.\n",
+    "\n",
+    "We will then be able to compare the results and see if embedding metadata has helped with retrieval in this case.\n",
+    "\n",
+    "> 💡 Here, we are using the `InMemoryEmbeddintRetriever` because we used the `InMemoryDocumentStore` above. If you're using another document store, change this to use the accompanying embedding retriever for the document store you are using. Check out the [Embedders Documentation](https://docs.haystack.deepset.ai/v2.0/docs/embedders) for a full list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<haystack.pipeline.Pipeline at 0x19e0933b0>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "\n",
+    "retrieval_pipeline = Pipeline()\n",
+    "retrieval_pipeline.add_component(\"text_embedder\", SentenceTransformersTextEmbedder(model=\"thenlper/gte-large\"))\n",
+    "retrieval_pipeline.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store, scale_score=False, top_k=3))\n",
+    "retrieval_pipeline.add_component(\"retriever_with_embeddings\", InMemoryEmbeddingRetriever(document_store=document_store_with_embedded_metadata, scale_score=False, top_k=3))\n",
+    "\n",
+    "retrieval_pipeline.connect(\"text_embedder\", \"retriever\")\n",
+    "retrieval_pipeline.connect(\"text_embedder\", \"retriever_with_embeddings\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's run the pipeline and compare the results from `retriever` and `retirever_with_embeddings`. Below you'll see 3 documents returned by each retriever, ranked by relevance.\n",
+    "\n",
+    "Notice that with the question \"Have the Beatles ever been to Bangor?\", the first pipeline is not returning relevant documents, but the second one is. Here, the `meta` field \"title\" is helpful, because as it turns out, the document that contains the information about The Beatles visiting Bangor does not contain a reference to \"The Beatles\". But, by embedding metadata, the embedding model is able to retrieve the right document."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = retrieval_pipeline.run({\"text_embedder\":{\"text\":\"Have the Beatles ever been to Bangor?\"}})\n",
+    "\n",
+    "print(\"Retriever Results:\\n\")\n",
+    "for doc in result['retriever']['documents']:\n",
+    "    print(doc)\n",
+    "\n",
+    "print(\"Retriever with Embeddings Results:\\n\")\n",
+    "for doc in result['retriever_with_embeddings']['documents']:\n",
+    "    print(doc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What's next\n",
+    "\n",
+    "🎉 Congratulations! You've embedded metadata while indexing, to improve the results of retrieval!\n",
+    "\n",
+    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+    "- [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)\n",
+    "- [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)\n",
+    "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=embedding-metadata) or [join Haystack discord community](https://discord.gg/haystack).\n",
+    "\n",
+    "Thanks for reading!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mistral",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From db832459b1412a22f77c45b342773fb9646d9a06 Mon Sep 17 00:00:00 2001
From: Julian Risch <julian.risch@deepset.ai>
Date: Wed, 21 Feb 2024 09:48:57 +0100
Subject: [PATCH 168/206] Fix typos in model-based eval tutorial (#295)

* fix faithfulness_evlautator typo

* fix typos

* fix "contextural" typo
---
 ...el_Based_Evaluation_of_RAG_Pipelines.ipynb | 26 +++++++++----------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
index d3494fde..7628acbb 100644
--- a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
+++ b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
@@ -12,7 +12,7 @@
     "- **Time to complete**: 15 minutes\n",
     "- **Components Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `OpenAIGenerator`, `DeepEvalEvaluator`, `RagasEvaluator`, `UpTrainEvaluator`\n",
     "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI: https://platform.openai.com/api-keys\n",
-    "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines using some of the model-based evaluation frameworkes integerated into Haystack.\n",
+    "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines using some of the model-based evaluation frameworks integerated into Haystack.\n",
     "\n",
     "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
    ]
@@ -107,7 +107,7 @@
    "source": [
     "## Create the RAG Pipeline to Evaluate\n",
     "\n",
-    "To evaluate a RAG pipeline, we need a RAG pipeline to start with. So, we will start by creating a question answering pipelne.\n",
+    "To evaluate a RAG pipeline, we need a RAG pipeline to start with. So, we will start by creating a question answering pipeline.\n",
     "\n",
     "> 💡 For a complete tutorial on creating Retrieval-Augmmented Generation pipelines check out the [Creating Your First QA Pipeline with Retrieval-Augmentation Tutorial](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
     "\n",
@@ -438,7 +438,7 @@
    "source": [
     " ## Evaluate the Pipeline with DeepEval\n",
     "We will perform 2 evaluations with DeepEval\n",
-    "- Faithfullness, grading how factual the generated responses were.\n",
+    "- Faithfulness, grading how factual the generated responses were.\n",
     "- Contextual relevance, grading how relevant the context was to the question.\n",
     "\n",
     "For a full list of available metrics and their expected inputs, check out the [`DeepEvalEvaluator` Docs](https://docs.haystack.deepset.ai/v2.0/docs/deepevalevaluator)\n",
@@ -472,12 +472,12 @@
    "source": [
     "from haystack_integrations.components.evaluators.deepeval import DeepEvalEvaluator, DeepEvalMetric\n",
     "\n",
-    "faithfulness_evlautator = Pipeline()\n",
+    "faithfulness_evaluator = Pipeline()\n",
     "evaluator = DeepEvalEvaluator(\n",
     "    metric=DeepEvalMetric.FAITHFULNESS,\n",
     "    metric_params={\"model\": \"gpt-4\"},\n",
     ")\n",
-    "faithfulness_evlautator.add_component(\"evaluator\", evaluator)"
+    "faithfulness_evaluator.add_component(\"evaluator\", evaluator)"
    ]
   },
   {
@@ -486,7 +486,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "evaluation_results = faithfulness_evlautator.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\":responses}})\n",
+    "evaluation_results = faithfulness_evaluator.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\":responses}})\n",
     "print(evaluation_results['evaluator']['results'])"
    ]
   },
@@ -503,12 +503,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "contetural_relevance_evaluator = Pipeline()\n",
+    "contextual_relevance_evaluator = Pipeline()\n",
     "evaluator = DeepEvalEvaluator(\n",
     "    metric=DeepEvalMetric.CONTEXTUAL_RELEVANCE,\n",
     "    metric_params={\"model\": \"gpt-4\"},\n",
     ")\n",
-    "contetural_relevance_evaluator.add_component(\"evaluator\", evaluator)"
+    "contextual_relevance_evaluator.add_component(\"evaluator\", evaluator)"
    ]
   },
   {
@@ -517,7 +517,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "evaluation_results = contetural_relevance_evaluator.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\":responses}})\n",
+    "evaluation_results = contextual_relevance_evaluator.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\":responses}})\n",
     "print(evaluation_results['evaluator']['results'])"
    ]
   },
@@ -604,7 +604,7 @@
    "metadata": {},
    "source": [
     "#### 2) Evaluate Aspect Critique\n",
-    "A `RagasEvaluator` that uses the `ASPECT_CRITIQUE` metric expects to be initialized with the `name`, `defintion` and optionally the `strictness` of the aspect to critique the reponse with."
+    "A `RagasEvaluator` that uses the `ASPECT_CRITIQUE` metric expects to be initialized with the `name`, `definition` and optionally the `strictness` of the aspect to critique the reponse with."
    ]
   },
   {
@@ -700,8 +700,8 @@
     "\n",
     "evaluator = UpTrainEvaluator(metric=UpTrainMetric.CONTEXT_RELEVANCE, api=\"openai\")\n",
     "\n",
-    "contect_relevanve_evaluator_pipeline = Pipeline()\n",
-    "contect_relevanve_evaluator_pipeline.add_component(\"evaluator\", evaluator)"
+    "context_relevance_evaluator_pipeline = Pipeline()\n",
+    "context_relevance_evaluator_pipeline.add_component(\"evaluator\", evaluator)"
    ]
   },
   {
@@ -724,7 +724,7 @@
    },
    "outputs": [],
    "source": [
-    "evaluation_results = contect_relevanve_evaluator_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts}})\n",
+    "evaluation_results = context_relevance_evaluator_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts}})\n",
     "print(evaluation_results['evaluator']['results'])"
    ]
   },

From 176309eaa0dc0a7cb85592919ee8ef37cc70b262 Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <stefanofiorucci@gmail.com>
Date: Thu, 22 Feb 2024 16:57:13 +0100
Subject: [PATCH 169/206] update 2.0 tutorials for new device mgmt in Sentence
 Transformers embedders (#297)

---
 ...le_Type_Preprocessing_Index_Pipeline.ipynb |  2 +-
 tutorials/33_Hybrid_Retrieval.ipynb           |  9 ++-
 ...ding_Metadata_for_Improved_Retrieval.ipynb | 71 +++++++++++--------
 3 files changed, 50 insertions(+), 32 deletions(-)

diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index c5295cab..704fd3bb 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -77,7 +77,7 @@
    "source": [
     "%%bash\n",
     "pip install haystack-ai\n",
-    "pip install sentence-transformers>=2.2.0\n",
+    "pip install \"sentence-transformers>=2.2.0\"\n",
     "pip install markdown-it-py mdit_plain\n",
     "pip install pypdf"
    ]
diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
index f1eaefd0..f74b8233 100644
--- a/tutorials/33_Hybrid_Retrieval.ipynb
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -206,9 +206,12 @@
     "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
     "from haystack.components.preprocessors.document_splitter import DocumentSplitter\n",
     "from haystack import Pipeline\n",
+    "from haystack.utils import ComponentDevice\n",
     "\n",
     "document_splitter = DocumentSplitter(split_by=\"word\", split_length=512, split_overlap=32)\n",
-    "document_embedder = SentenceTransformersDocumentEmbedder(model=\"BAAI/bge-small-en-v1.5\", device=\"cuda\")\n",
+    "document_embedder = SentenceTransformersDocumentEmbedder(\n",
+    "    model=\"BAAI/bge-small-en-v1.5\", device=ComponentDevice.from_str(\"cuda:0\")\n",
+    ")\n",
     "document_writer = DocumentWriter(document_store)\n",
     "\n",
     "indexing_pipeline = Pipeline()\n",
@@ -266,7 +269,9 @@
     "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever, InMemoryEmbeddingRetriever\n",
     "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
     "\n",
-    "text_embedder = SentenceTransformersTextEmbedder(model=\"BAAI/bge-small-en-v1.5\", device=\"cuda\")\n",
+    "text_embedder = SentenceTransformersTextEmbedder(\n",
+    "    model=\"BAAI/bge-small-en-v1.5\", device=ComponentDevice.from_str(\"cuda:0\")\n",
+    ")\n",
     "embedding_retriever = InMemoryEmbeddingRetriever(document_store)\n",
     "bm25_retriever = InMemoryBM25Retriever(document_store)"
    ]
diff --git a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
index 73e8eeee..89fb4485 100644
--- a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
+++ b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
@@ -177,24 +177,30 @@
     "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
     "from haystack.components.writers import DocumentWriter\n",
     "from haystack.document_stores.types import DuplicatePolicy\n",
-    "\n",
-    "def create_indexing_pipeline(document_store, metadata_fields_to_embed = None):\n",
-    "   document_cleaner = DocumentCleaner()\n",
-    "   document_splitter = DocumentSplitter(split_by='sentence', split_length=2)\n",
-    "   document_embedder = SentenceTransformersDocumentEmbedder(model=\"thenlper/gte-large\", meta_fields_to_embed=metadata_fields_to_embed, device=\"cuda\")\n",
-    "   document_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE)\n",
-    "\n",
-    "   indexing_pipeline = Pipeline()\n",
-    "   indexing_pipeline.add_component(\"cleaner\", document_cleaner)\n",
-    "   indexing_pipeline.add_component(\"splitter\", document_splitter)\n",
-    "   indexing_pipeline.add_component(\"embedder\", document_embedder)\n",
-    "   indexing_pipeline.add_component(\"writer\", document_writer)\n",
-    "   \n",
-    "   indexing_pipeline.connect(\"cleaner\", \"splitter\")\n",
-    "   indexing_pipeline.connect(\"splitter\", \"embedder\")\n",
-    "   indexing_pipeline.connect(\"embedder\", \"writer\")\n",
-    "   \n",
-    "   return indexing_pipeline\n"
+    "from haystack.utils import ComponentDevice\n",
+    "\n",
+    "\n",
+    "def create_indexing_pipeline(document_store, metadata_fields_to_embed=None):\n",
+    "    document_cleaner = DocumentCleaner()\n",
+    "    document_splitter = DocumentSplitter(split_by=\"sentence\", split_length=2)\n",
+    "    document_embedder = SentenceTransformersDocumentEmbedder(\n",
+    "        model=\"thenlper/gte-large\",\n",
+    "        meta_fields_to_embed=metadata_fields_to_embed,\n",
+    "        device=ComponentDevice.from_str(\"cuda:0\"),\n",
+    "    )\n",
+    "    document_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE)\n",
+    "\n",
+    "    indexing_pipeline = Pipeline()\n",
+    "    indexing_pipeline.add_component(\"cleaner\", document_cleaner)\n",
+    "    indexing_pipeline.add_component(\"splitter\", document_splitter)\n",
+    "    indexing_pipeline.add_component(\"embedder\", document_embedder)\n",
+    "    indexing_pipeline.add_component(\"writer\", document_writer)\n",
+    "\n",
+    "    indexing_pipeline.connect(\"cleaner\", \"splitter\")\n",
+    "    indexing_pipeline.connect(\"splitter\", \"embedder\")\n",
+    "    indexing_pipeline.connect(\"embedder\", \"writer\")\n",
+    "\n",
+    "    return indexing_pipeline"
    ]
   },
   {
@@ -247,9 +253,9 @@
     "from haystack import Document\n",
     "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
     "\n",
-    "some_bands=\"\"\"The Beatles,The Cure\"\"\".split(\",\")\n",
+    "some_bands = \"\"\"The Beatles,The Cure\"\"\".split(\",\")\n",
     "\n",
-    "raw_docs=[]\n",
+    "raw_docs = []\n",
     "\n",
     "for title in some_bands:\n",
     "    page = wikipedia.page(title=title, auto_suggest=False)\n",
@@ -260,10 +266,12 @@
     "document_store_with_embedded_metadata = InMemoryDocumentStore(embedding_similarity_function=\"cosine\")\n",
     "\n",
     "indexing_pipeline = create_indexing_pipeline(document_store=document_store)\n",
-    "indexing_with_metadata_pipeline = create_indexing_pipeline(document_store=document_store_with_embedded_metadata, metadata_fields_to_embed=[\"title\"])\n",
+    "indexing_with_metadata_pipeline = create_indexing_pipeline(\n",
+    "    document_store=document_store_with_embedded_metadata, metadata_fields_to_embed=[\"title\"]\n",
+    ")\n",
     "\n",
-    "indexing_pipeline.run({\"cleaner\":{\"documents\": raw_docs}})\n",
-    "indexing_with_metadata_pipeline.run({\"cleaner\":{\"documents\": raw_docs}})"
+    "indexing_pipeline.run({\"cleaner\": {\"documents\": raw_docs}})\n",
+    "indexing_with_metadata_pipeline.run({\"cleaner\": {\"documents\": raw_docs}})"
    ]
   },
   {
@@ -303,11 +311,16 @@
     "\n",
     "retrieval_pipeline = Pipeline()\n",
     "retrieval_pipeline.add_component(\"text_embedder\", SentenceTransformersTextEmbedder(model=\"thenlper/gte-large\"))\n",
-    "retrieval_pipeline.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store, scale_score=False, top_k=3))\n",
-    "retrieval_pipeline.add_component(\"retriever_with_embeddings\", InMemoryEmbeddingRetriever(document_store=document_store_with_embedded_metadata, scale_score=False, top_k=3))\n",
+    "retrieval_pipeline.add_component(\n",
+    "    \"retriever\", InMemoryEmbeddingRetriever(document_store=document_store, scale_score=False, top_k=3)\n",
+    ")\n",
+    "retrieval_pipeline.add_component(\n",
+    "    \"retriever_with_embeddings\",\n",
+    "    InMemoryEmbeddingRetriever(document_store=document_store_with_embedded_metadata, scale_score=False, top_k=3),\n",
+    ")\n",
     "\n",
     "retrieval_pipeline.connect(\"text_embedder\", \"retriever\")\n",
-    "retrieval_pipeline.connect(\"text_embedder\", \"retriever_with_embeddings\")\n"
+    "retrieval_pipeline.connect(\"text_embedder\", \"retriever_with_embeddings\")"
    ]
   },
   {
@@ -325,14 +338,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = retrieval_pipeline.run({\"text_embedder\":{\"text\":\"Have the Beatles ever been to Bangor?\"}})\n",
+    "result = retrieval_pipeline.run({\"text_embedder\": {\"text\": \"Have the Beatles ever been to Bangor?\"}})\n",
     "\n",
     "print(\"Retriever Results:\\n\")\n",
-    "for doc in result['retriever']['documents']:\n",
+    "for doc in result[\"retriever\"][\"documents\"]:\n",
     "    print(doc)\n",
     "\n",
     "print(\"Retriever with Embeddings Results:\\n\")\n",
-    "for doc in result['retriever_with_embeddings']['documents']:\n",
+    "for doc in result[\"retriever_with_embeddings\"][\"documents\"]:\n",
     "    print(doc)"
    ]
   },

From e459f3df88f0dcd19d9698f423497b7dcedca352 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Thu, 22 Feb 2024 18:03:42 +0100
Subject: [PATCH 170/206] import fix

---
 tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
index 7628acbb..ef3e90d6 100644
--- a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
+++ b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
@@ -285,7 +285,7 @@
     }
    ],
    "source": [
-    "from haystack.pipeline import Pipeline\n",
+    "from haystack import Pipeline\n",
     "from haystack.components.builders.answer_builder import AnswerBuilder\n",
     "\n",
     "rag_pipeline = Pipeline()\n",

From 4978600bda6f5b890ac57aec11381c20b2cfde9a Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <stefanofiorucci@gmail.com>
Date: Mon, 26 Feb 2024 09:37:20 +0100
Subject: [PATCH 171/206] update hybrid retrieval tutorials (#299)

---
 tutorials/26_Hybrid_Retrieval.ipynb | 22 ++++++++++------------
 tutorials/33_Hybrid_Retrieval.ipynb | 29 ++++++++---------------------
 2 files changed, 18 insertions(+), 33 deletions(-)

diff --git a/tutorials/26_Hybrid_Retrieval.ipynb b/tutorials/26_Hybrid_Retrieval.ipynb
index fb943482..b0b18d27 100644
--- a/tutorials/26_Hybrid_Retrieval.ipynb
+++ b/tutorials/26_Hybrid_Retrieval.ipynb
@@ -119,7 +119,7 @@
     "\n",
     "You'll start creating a hybrid pipeline by initializing a DocumentStore and preprocessing documents before storing them in the DocumentStore.\n",
     "\n",
-    "You will use the PubMed Abstracts as Documents. There are a lot of datasets from PubMed on Hugging Face Hub; you will use [ywchoi/pubmed_abstract_3](https://huggingface.co/datasets/ywchoi/pubmed_abstract_3/viewer/default/test) in this tutorial.\n",
+    "You will use the PubMed Abstracts as Documents. There are a lot of datasets from PubMed on Hugging Face Hub; you will use [anakin87/medrag-pubmed-chunk](https://huggingface.co/datasets/anakin87/medrag-pubmed-chunk) in this tutorial.\n",
     "\n",
     "Initialize `InMemoryDocumentStore` and don't forget to set `use_bm25=True` and the dimension of your embeddings in `embedding_dim`:"
    ]
@@ -135,7 +135,7 @@
     "from datasets import load_dataset\n",
     "from haystack.document_stores import InMemoryDocumentStore\n",
     "\n",
-    "dataset = load_dataset(\"ywchoi/pubmed_abstract_3\", split=\"test\")\n",
+    "dataset = load_dataset(\"anakin87/medrag-pubmed-chunk\", split=\"train\")\n",
     "\n",
     "document_store = InMemoryDocumentStore(use_bm25=True, embedding_dim=384)"
    ]
@@ -150,9 +150,10 @@
     "The data has 3 features:\n",
     "* *pmid*\n",
     "* *title*\n",
-    "* *text*\n",
+    "* *content*: the abstract\n",
+    "* *contents*: abstract + title\n",
     "\n",
-    "Concatenate *title* and *text* to embed and search both. The single features will be stored as metadata, and you will use them to have a **pretty print** of the search results.\n"
+    "For searching, you will use the *contents* feature. The other features will be stored as metadata, and you will use them to have a **pretty print** of the search results.\n"
    ]
   },
   {
@@ -165,13 +166,10 @@
    "source": [
     "from haystack.schema import Document\n",
     "\n",
-    "documents = []\n",
+    "docs = []\n",
     "for doc in dataset:\n",
-    "    documents.append(\n",
-    "        Document(\n",
-    "            content=doc[\"title\"] + \" \" + doc[\"text\"],\n",
-    "            meta={\"title\": doc[\"title\"], \"abstract\": doc[\"text\"], \"pmid\": doc[\"pmid\"]},\n",
-    "        )\n",
+    "    docs.append(\n",
+    "        Document(content=doc[\"contents\"], meta={\"title\": doc[\"title\"], \"abstract\": doc[\"content\"], \"pmid\": doc[\"id\"]})\n",
     "    )"
    ]
   },
@@ -216,7 +214,7 @@
    },
    "outputs": [],
    "source": [
-    "docs_to_index = preprocessor.process(documents)"
+    "docs_to_index = preprocessor.process(docs)"
    ]
   },
   {
@@ -381,7 +379,7 @@
    "outputs": [],
    "source": [
     "prediction = pipeline.run(\n",
-    "    query=\"treatment for HIV\",\n",
+    "    query=\"apnea in infants\",\n",
     "    params={\n",
     "        \"SparseRetriever\": {\"top_k\": 10},\n",
     "        \"DenseRetriever\": {\"top_k\": 10},\n",
diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
index f74b8233..f4c5142f 100644
--- a/tutorials/33_Hybrid_Retrieval.ipynb
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -134,26 +134,16 @@
    "source": [
     "## Fetching and Processing Documents\n",
     "\n",
-    "As Documents, you will use the PubMed Abstracts. There are a lot of datasets from PubMed on Hugging Face Hub; you will use [ywchoi/pubmed_abstract_3](https://huggingface.co/datasets/ywchoi/pubmed_abstract_3/viewer/default/test) in this tutorial.\n",
+    "As Documents, you will use the PubMed Abstracts. There are a lot of datasets from PubMed on Hugging Face Hub; you will use [anakin87/medrag-pubmed-chunk](https://huggingface.co/datasets/anakin87/medrag-pubmed-chunk) in this tutorial.\n",
     "\n",
     "Then, you will create Documents from the dataset with a simple for loop.\n",
-    "Each data point in the PubMed dataset has 3 features:\n",
+    "Each data point in the PubMed dataset has 4 features:\n",
     "* *pmid*\n",
     "* *title*\n",
-    "* *text*\n",
+    "* *content*: the abstract\n",
+    "* *contents*: abstract + title\n",
     "\n",
-    "Concatenate *title* and *text* before creating the Document content to make sure that titles of PubMed abstracts are searchable.\n",
-    "\n",
-    "Other features of articles will be stored as `meta`, and you can then use this info to have a **pretty print** of the search results or for [metadata filtering](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "JcMIAXulPSU3"
-   },
-   "source": [
-    "> This step might take ~2 min depending on your internet speed 🏎️"
+    "For searching, you will use the *contents* feature. The other features will be stored as metadata, and you will use them to have a **pretty print** of the search results or for [metadata filtering](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering)."
    ]
   },
   {
@@ -167,15 +157,12 @@
     "from datasets import load_dataset\n",
     "from haystack import Document\n",
     "\n",
-    "dataset = load_dataset(\"ywchoi/pubmed_abstract_3\", split=\"test\")\n",
+    "dataset = load_dataset(\"anakin87/medrag-pubmed-chunk\", split=\"train\")\n",
     "\n",
     "docs = []\n",
     "for doc in dataset:\n",
     "    docs.append(\n",
-    "        Document(\n",
-    "            content=doc[\"title\"] + \" \" + doc[\"text\"],\n",
-    "            meta={\"title\": doc[\"title\"], \"abstract\": doc[\"text\"], \"pmid\": doc[\"pmid\"]},\n",
-    "        )\n",
+    "        Document(content=doc[\"contents\"], meta={\"title\": doc[\"title\"], \"abstract\": doc[\"content\"], \"pmid\": doc[\"id\"]})\n",
     "    )"
    ]
   },
@@ -457,7 +444,7 @@
     }
    ],
    "source": [
-    "query = \"treatment for HIV\"\n",
+    "query = \"apnea in infants\"\n",
     "\n",
     "result = hybrid_retrieval.run(\n",
     "    {\"text_embedder\": {\"text\": query}, \"bm25_retriever\": {\"query\": query}, \"ranker\": {\"query\": query}}\n",

From fc05d352ac9110ddd1b83d39e20992cd92f175b3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 27 Feb 2024 16:45:35 +0300
Subject: [PATCH 172/206] Replace Mistral model names with
 `mistralai/Mistral-7B-Instruct-v0.1` (#300)

* Change Mistral model name

* Update mistral model name in tut 30

* Remove redundant output

* Tut 37 remove redundant output
---
 ...le_Type_Preprocessing_Index_Pipeline.ipynb | 5641 ++---------------
 ...ing_Pipeline_Inputs_with_Multiplexer.ipynb | 2399 ++-----
 2 files changed, 984 insertions(+), 7056 deletions(-)

diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index 704fd3bb..7b965ca7 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -1,5113 +1,538 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zAKy_OPy81EZ"
-   },
-   "source": [
-    "# Tutorial: Preprocessing Different File Types\n",
-    "\n",
-    "- **Level**: Beginner\n",
-    "- **Time to complete**: 15 minutes\n",
-    "- **Goal**: After completing this tutorial, you'll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the `FileTypeRouter`.\n",
-    "\n",
-    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
-    "\n",
-    "> 💡 (Optional): After creating the indexing pipeline in this tutorial, there is an optional section that shows you how to create a RAG pipeline on top of the document store you just created. You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section\n",
-    "\n",
-    "## Components Used\n",
-    "\n",
-    "- [`FileTypeRouter`](https://docs.haystack.deepset.ai/v2.0/docs/filetyperouter): This component will help you route files based on their corresponding MIME type to different components\n",
-    "- [`MarkdownToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/markdowntodocument): This component will help you convert markdown files into Haystack Documents\n",
-    "- [`PyPDFToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/pypdftodocument): This component will help you convert pdf files into Haystack Documents\n",
-    "- [`TextFileToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/textfiletodocument): This component will help you convert text files into Haystack Documents\n",
-    "- [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner): This component will help you to join Documents coming from different branches of a pipeline\n",
-    "- [`DocumentCleaner`](https://docs.haystack.deepset.ai/v2.0/docs/documentcleaner) (optional): This component will help you to make Documents more readable by removing extra whitespaces etc.\n",
-    "- [`DocumentSplitter`](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter): This component will help you to split your Document into chunks\n",
-    "- [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder): This component will help you create embeddings for Documents.\n",
-    "- [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter): This component will help you write Documents into the DocumentStore"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "96w6PrcPk4Fc"
-   },
-   "source": [
-    "## Overview\n",
-    "\n",
-    "In this tutorial, you'll build an indexing pipeline that preprocesses different types of files (markdown, txt and pdf). Each file will have its own `FileConverter`. The rest of the indexing pipeline is fairly standard - split the documents into chunks, trim whitespace, create embeddings and write them to a Document Store.\n",
-    "\n",
-    "Optionally, you can keep going to see how to use these documents in a query pipeline as well."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rns_B_NGN0Ze"
-   },
-   "source": [
-    "## Preparing the Colab Environment\n",
-    "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "_pG2jycZLYYr"
-   },
-   "source": [
-    "## Installing dependencies\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "2mP4empwf_x4",
-    "outputId": "fbe648c0-716f-4f02-b764-f42bd8c68077"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "pip install haystack-ai\n",
-    "pip install \"sentence-transformers>=2.2.0\"\n",
-    "pip install markdown-it-py mdit_plain\n",
-    "pip install pypdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HnXumz7EarJx"
-   },
-   "source": [
-    "### Enabling Telemetry\n",
-    "\n",
-    "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "CkvJIU7FmDf9"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.telemetry import tutorial_running\n",
-    "\n",
-    "tutorial_running(30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "7GWbC28fX0Yp"
-   },
-   "source": [
-    "## Download All Files\n",
-    "\n",
-    "All required files will be downloaded into this Colab notebook. You can see these files in \"files\" tab on the left. Just like most real life data, these files are a mishmash of different types."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "8wIRuFcTIKF1",
-    "outputId": "8728b32b-08a7-4a23-fbb8-fa873d592b84"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "gdown https://drive.google.com/drive/folders/1n9yqq5Gl_HWfND5bTlrCwAOycMDt5EMj -O /content --folder"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DH8HEymp6XFZ"
-   },
-   "source": [
-    "## Create a Pipeline to Index Documents\n",
-    "\n",
-    "Next, you'll create a pipeline to index documents. To keep things uncomplicated, you'll use an `InMemoryDocumentStore` but this approach would also work with any other flavor of `DocumentStore`.\n",
-    "\n",
-    "You'll need a different file converter class for each file type in our data sources: `.pdf`, `.txt`, and `.md` in this case. Our `FileTypeRouter` connects each file type to the proper converter.\n",
-    "\n",
-    "Once all our files have been converted to Haystack Documents, we can use the `DocumentJoiner` component to make these a single list of documents that can be fed through the rest of the indexing pipeline all together."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "c_eM4C1cA4j6"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.components.writers import DocumentWriter\n",
-    "from haystack.components.converters import MarkdownToDocument, PyPDFToDocument, TextFileToDocument\n",
-    "from haystack.components.preprocessors import DocumentSplitter, DocumentCleaner\n",
-    "from haystack.components.routers import FileTypeRouter\n",
-    "from haystack.components.joiners import DocumentJoiner\n",
-    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
-    "from haystack import Pipeline\n",
-    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-    "\n",
-    "document_store = InMemoryDocumentStore()\n",
-    "file_type_router = FileTypeRouter(mime_types=[\"text/plain\", \"application/pdf\", \"text/markdown\"])\n",
-    "text_file_converter = TextFileToDocument()\n",
-    "markdown_converter = MarkdownToDocument()\n",
-    "pdf_converter = PyPDFToDocument()\n",
-    "document_joiner = DocumentJoiner()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ef8okackBSmk"
-   },
-   "source": [
-    "From there, the steps to this indexing pipeline are a bit more standard. The `DocumentCleaner` removes whitespace. Then this `DocumentSplitter` breaks them into chunks of 150 words, with a bit of overlap to avoid missing context."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "hCWlpiQCBYOg"
-   },
-   "outputs": [],
-   "source": [
-    "document_cleaner = DocumentCleaner()\n",
-    "document_splitter = DocumentSplitter(split_by=\"word\", split_length=150, split_overlap=50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Do4nhM4tBaZL"
-   },
-   "source": [
-    "Now you'll add a `SentenceTransformersDocumentEmbedder` to create embeddings from the documents. As the last step in this pipeline, the `DocumentWriter` will write them to the `InMemoryDocumentStore`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "TVXSX0GHBtdj"
-   },
-   "outputs": [],
-   "source": [
-    "document_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
-    "document_writer = DocumentWriter(document_store)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "hJaJmGanBv1b"
-   },
-   "source": [
-    "After creating all the components, add them to the indexing pipeline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "4yGXKHEXIZxi"
-   },
-   "outputs": [],
-   "source": [
-    "preprocessing_pipeline = Pipeline()\n",
-    "preprocessing_pipeline.add_component(instance=file_type_router, name=\"file_type_router\")\n",
-    "preprocessing_pipeline.add_component(instance=text_file_converter, name=\"text_file_converter\")\n",
-    "preprocessing_pipeline.add_component(instance=markdown_converter, name=\"markdown_converter\")\n",
-    "preprocessing_pipeline.add_component(instance=pdf_converter, name=\"pypdf_converter\")\n",
-    "preprocessing_pipeline.add_component(instance=document_joiner, name=\"document_joiner\")\n",
-    "preprocessing_pipeline.add_component(instance=document_cleaner, name=\"document_cleaner\")\n",
-    "preprocessing_pipeline.add_component(instance=document_splitter, name=\"document_splitter\")\n",
-    "preprocessing_pipeline.add_component(instance=document_embedder, name=\"document_embedder\")\n",
-    "preprocessing_pipeline.add_component(instance=document_writer, name=\"document_writer\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, connect them 👇"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preprocessing_pipeline.connect(\"file_type_router.text/plain\", \"text_file_converter.sources\")\n",
-    "preprocessing_pipeline.connect(\"file_type_router.application/pdf\", \"pypdf_converter.sources\")\n",
-    "preprocessing_pipeline.connect(\"file_type_router.text/markdown\", \"markdown_converter.sources\")\n",
-    "preprocessing_pipeline.connect(\"text_file_converter\", \"document_joiner\")\n",
-    "preprocessing_pipeline.connect(\"pypdf_converter\", \"document_joiner\")\n",
-    "preprocessing_pipeline.connect(\"markdown_converter\", \"document_joiner\")\n",
-    "preprocessing_pipeline.connect(\"document_joiner\", \"document_cleaner\")\n",
-    "preprocessing_pipeline.connect(\"document_cleaner\", \"document_splitter\")\n",
-    "preprocessing_pipeline.connect(\"document_splitter\", \"document_embedder\")\n",
-    "preprocessing_pipeline.connect(\"document_embedder\", \"document_writer\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "qcr2hXtlVhJt"
-   },
-   "source": [
-    "If you want to see a diagram of the pipeline, you can draw it.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "epx9Fv-3VgEI"
-   },
-   "outputs": [],
-   "source": [
-    "preprocessing_pipeline.draw(\"pipeline.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "3NGinkHPB9C2"
-   },
-   "source": [
-    "Let's test this pipeline with a few recipes I've written. Are you getting hungry yet?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
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-    },
-    "id": "9Mw5kwZiqehc",
-    "outputId": "c2781237-b0e0-4e7c-e009-7402353e1917"
-   },
-   "outputs": [],
-   "source": [
-    "preprocessing_pipeline.run(\n",
-    "    {\n",
-    "        \"file_type_router\": {\n",
-    "            \"sources\": [\n",
-    "                \"/content/vegan_sunflower_hemp_cheese_recipe.txt\",\n",
-    "                \"/content/vegan_keto_eggplant_recipe.pdf\",\n",
-    "                \"/content/vegan_flan_recipe.md\",\n",
-    "            ]\n",
-    "        }\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "TVGb-rteg7E5"
-   },
-   "source": [
-    "🎉 If you only wanted to learn how to preprocess documents, you can stop here! If you want to see an example of using those documents in a RAG pipeline, read on.  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "16PnegDR2EmY"
-   },
-   "source": [
-    "## (Optional) Build a pipeline to query documents"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "I06qdWsZibSz"
-   },
-   "source": [
-    "Now, let's build a RAG pipeline that answers queries based ont he documents you just created in the section above. For this step, we will be using the [`HuggingFaceTGIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `mistralai/Mixtral-8x7B-Instruct-v0.1` model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "bB344ADZr-eG",
-    "outputId": "5adbdf4f-439d-4fe4-b63e-d72077ac0c18"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter Hugging Face token: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "QASEGJhnIMQL"
-   },
-   "source": [
-    "In this step you'll build a query pipeline to answer questions about the documents.\n",
-    "\n",
-    "This pipeline takes the prompt, searches the document store for relevant documents, and passes those documents along to the LLM to formulate an answer.\n",
-    "\n",
-    "> ⚠️ Notice how we used `sentence-transformers/all-MiniLM-L6-v2` to create embeddings for our documents before. This is why we will be using the same model to embed incoming questions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_s--8xEWq8Y9"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
-    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-    "from haystack.components.builders import PromptBuilder\n",
-    "from haystack.components.generators import HuggingFaceTGIGenerator\n",
-    "\n",
-    "template = \"\"\"\n",
-    "Answer the questions based on the given context.\n",
-    "\n",
-    "Context:\n",
-    "{% for document in documents %}\n",
-    "    {{ document.content }}\n",
-    "{% endfor %}\n",
-    "\n",
-    "Question: {{ question }}\n",
-    "Answer:\n",
-    "\"\"\"\n",
-    "pipe = Pipeline()\n",
-    "pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
-    "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
-    "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
-    "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mixtral-8x7B-Instruct-v0.1\"))\n",
-    "\n",
-    "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
-    "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
-    "pipe.connect(\"prompt_builder\", \"llm\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1ba5puJxIg3V"
-   },
-   "source": [
-    "Try it out yourself by running the code below. If all has gone well, you should have a complete shopping list from all the recipe sources. 🧂🥥🧄"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 327,
-     "referenced_widgets": [
-      "55186726e0fa45139703b9cf9032b855",
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-    "id": "qDqrU5emtBWQ",
-    "outputId": "762b9b1e-ac90-4bc6-d746-f1940a380fa3"
-   },
-   "outputs": [],
-   "source": [
-    "question = (\n",
-    "    \"What ingredients would I need to make vegan keto eggplant lasagna, vegan persimmon flan, and vegan hemp cheese?\"\n",
-    ")\n",
-    "\n",
-    "pipe.run(\n",
-    "    {\n",
-    "        \"embedder\": {\"text\": question},\n",
-    "        \"prompt_builder\": {\"question\": question},\n",
-    "        \"llm\": {\"generation_kwargs\": {\"max_new_tokens\": 350}},\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZJueu_V4KP6w"
-   },
-   "source": [
-    "```python\n",
-    "{'llm': {'replies': [' To make vegan keto eggplant lasagna, you would need 2 large eggplants, a lot of salt, 1/2 cup store-bought vegan mozzarella for topping, 4 oz basil, 1/4 cup almonds, 1/4 cup nutritional yeast, 1/4 cup olive oil, 1 recipe vegan pesto, 1 recipe spinach tofu ricotta, 1 tsp garlic powder, juice of half a lemon, and salt to taste. To make vegan persimmon flan, you would need 1/2 cup persimmon pulp, strained, 1 tbsp cornstarch, 1/2 tsp agar agar, 1 tbsp agave nectar or to taste, 2 tbsp granulated sugar, 1/4 cup coconut creme, 1/2 cup almond milk, and 1/2 tsp vanilla. To make vegan hemp cheese, you would need 1/2 cup sunflower seeds, 1/2 cup hemp hearts, 1.5 teaspoons miso paste, 1 tsp nutritional yeast, 1/4 cup rejuvelac, and 1/4th teaspoon salt or to taste.'],\n",
-    "  'meta': [{'model': 'mistralai/Mixtral-8x7B-Instruct-v0.1',\n",
-    "    'index': 0,\n",
-    "    'finish_reason': 'eos_token',\n",
-    "    'usage': {'completion_tokens': 287,\n",
-    "     'prompt_tokens': 3452,\n",
-    "     'total_tokens': 3739}}]}}\n",
-    "```"
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-    "## What's next\n",
-    "\n",
-    "Congratulations on building an indexing pipeline that can preprocess different file types. Go forth and ingest all the messy real-world data into your workflows. 💥\n",
-    "\n",
-    "If you liked this tutorial, you may also enjoy:\n",
-    "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
-    "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
-    "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=filte_type_routing). Thanks for reading!"
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+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zAKy_OPy81EZ"
+      },
+      "source": [
+        "# Tutorial: Preprocessing Different File Types\n",
+        "\n",
+        "- **Level**: Beginner\n",
+        "- **Time to complete**: 15 minutes\n",
+        "- **Goal**: After completing this tutorial, you'll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the `FileTypeRouter`.\n",
+        "\n",
+        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
+        "\n",
+        "> 💡 (Optional): After creating the indexing pipeline in this tutorial, there is an optional section that shows you how to create a RAG pipeline on top of the document store you just created. You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section\n",
+        "\n",
+        "## Components Used\n",
+        "\n",
+        "- [`FileTypeRouter`](https://docs.haystack.deepset.ai/v2.0/docs/filetyperouter): This component will help you route files based on their corresponding MIME type to different components\n",
+        "- [`MarkdownToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/markdowntodocument): This component will help you convert markdown files into Haystack Documents\n",
+        "- [`PyPDFToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/pypdftodocument): This component will help you convert pdf files into Haystack Documents\n",
+        "- [`TextFileToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/textfiletodocument): This component will help you convert text files into Haystack Documents\n",
+        "- [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner): This component will help you to join Documents coming from different branches of a pipeline\n",
+        "- [`DocumentCleaner`](https://docs.haystack.deepset.ai/v2.0/docs/documentcleaner) (optional): This component will help you to make Documents more readable by removing extra whitespaces etc.\n",
+        "- [`DocumentSplitter`](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter): This component will help you to split your Document into chunks\n",
+        "- [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder): This component will help you create embeddings for Documents.\n",
+        "- [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter): This component will help you write Documents into the DocumentStore"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "96w6PrcPk4Fc"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "In this tutorial, you'll build an indexing pipeline that preprocesses different types of files (markdown, txt and pdf). Each file will have its own `FileConverter`. The rest of the indexing pipeline is fairly standard - split the documents into chunks, trim whitespace, create embeddings and write them to a Document Store.\n",
+        "\n",
+        "Optionally, you can keep going to see how to use these documents in a query pipeline as well."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rns_B_NGN0Ze"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_pG2jycZLYYr"
+      },
+      "source": [
+        "## Installing dependencies\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2mP4empwf_x4"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "pip install haystack-ai\n",
+        "pip install \"sentence-transformers>=2.2.0\"\n",
+        "pip install markdown-it-py mdit_plain\n",
+        "pip install pypdf"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HnXumz7EarJx"
+      },
+      "source": [
+        "### Enabling Telemetry\n",
+        "\n",
+        "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CkvJIU7FmDf9"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(30)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7GWbC28fX0Yp"
+      },
+      "source": [
+        "## Download All Files\n",
+        "\n",
+        "All required files will be downloaded into this Colab notebook. You can see these files in \"files\" tab on the left. Just like most real life data, these files are a mishmash of different types."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8wIRuFcTIKF1"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "gdown https://drive.google.com/drive/folders/1n9yqq5Gl_HWfND5bTlrCwAOycMDt5EMj -O /content --folder"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DH8HEymp6XFZ"
+      },
+      "source": [
+        "## Create a Pipeline to Index Documents\n",
+        "\n",
+        "Next, you'll create a pipeline to index documents. To keep things uncomplicated, you'll use an `InMemoryDocumentStore` but this approach would also work with any other flavor of `DocumentStore`.\n",
+        "\n",
+        "You'll need a different file converter class for each file type in our data sources: `.pdf`, `.txt`, and `.md` in this case. Our `FileTypeRouter` connects each file type to the proper converter.\n",
+        "\n",
+        "Once all our files have been converted to Haystack Documents, we can use the `DocumentJoiner` component to make these a single list of documents that can be fed through the rest of the indexing pipeline all together."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "c_eM4C1cA4j6"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.writers import DocumentWriter\n",
+        "from haystack.components.converters import MarkdownToDocument, PyPDFToDocument, TextFileToDocument\n",
+        "from haystack.components.preprocessors import DocumentSplitter, DocumentCleaner\n",
+        "from haystack.components.routers import FileTypeRouter\n",
+        "from haystack.components.joiners import DocumentJoiner\n",
+        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+        "from haystack import Pipeline\n",
+        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+        "\n",
+        "document_store = InMemoryDocumentStore()\n",
+        "file_type_router = FileTypeRouter(mime_types=[\"text/plain\", \"application/pdf\", \"text/markdown\"])\n",
+        "text_file_converter = TextFileToDocument()\n",
+        "markdown_converter = MarkdownToDocument()\n",
+        "pdf_converter = PyPDFToDocument()\n",
+        "document_joiner = DocumentJoiner()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ef8okackBSmk"
+      },
+      "source": [
+        "From there, the steps to this indexing pipeline are a bit more standard. The `DocumentCleaner` removes whitespace. Then this `DocumentSplitter` breaks them into chunks of 150 words, with a bit of overlap to avoid missing context."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "hCWlpiQCBYOg"
+      },
+      "outputs": [],
+      "source": [
+        "document_cleaner = DocumentCleaner()\n",
+        "document_splitter = DocumentSplitter(split_by=\"word\", split_length=150, split_overlap=50)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Do4nhM4tBaZL"
+      },
+      "source": [
+        "Now you'll add a `SentenceTransformersDocumentEmbedder` to create embeddings from the documents. As the last step in this pipeline, the `DocumentWriter` will write them to the `InMemoryDocumentStore`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "TVXSX0GHBtdj"
+      },
+      "outputs": [],
+      "source": [
+        "document_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+        "document_writer = DocumentWriter(document_store)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hJaJmGanBv1b"
+      },
+      "source": [
+        "After creating all the components, add them to the indexing pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "4yGXKHEXIZxi"
+      },
+      "outputs": [],
+      "source": [
+        "preprocessing_pipeline = Pipeline()\n",
+        "preprocessing_pipeline.add_component(instance=file_type_router, name=\"file_type_router\")\n",
+        "preprocessing_pipeline.add_component(instance=text_file_converter, name=\"text_file_converter\")\n",
+        "preprocessing_pipeline.add_component(instance=markdown_converter, name=\"markdown_converter\")\n",
+        "preprocessing_pipeline.add_component(instance=pdf_converter, name=\"pypdf_converter\")\n",
+        "preprocessing_pipeline.add_component(instance=document_joiner, name=\"document_joiner\")\n",
+        "preprocessing_pipeline.add_component(instance=document_cleaner, name=\"document_cleaner\")\n",
+        "preprocessing_pipeline.add_component(instance=document_splitter, name=\"document_splitter\")\n",
+        "preprocessing_pipeline.add_component(instance=document_embedder, name=\"document_embedder\")\n",
+        "preprocessing_pipeline.add_component(instance=document_writer, name=\"document_writer\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y89Z9jwUfNbr"
+      },
+      "source": [
+        "Next, connect them 👇"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "gafXWtNYfNbr",
+        "outputId": "10f351de-ac09-4273-85a2-ac7b59fb2f77"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
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+      "source": [
+        "preprocessing_pipeline.connect(\"file_type_router.text/plain\", \"text_file_converter.sources\")\n",
+        "preprocessing_pipeline.connect(\"file_type_router.application/pdf\", \"pypdf_converter.sources\")\n",
+        "preprocessing_pipeline.connect(\"file_type_router.text/markdown\", \"markdown_converter.sources\")\n",
+        "preprocessing_pipeline.connect(\"text_file_converter\", \"document_joiner\")\n",
+        "preprocessing_pipeline.connect(\"pypdf_converter\", \"document_joiner\")\n",
+        "preprocessing_pipeline.connect(\"markdown_converter\", \"document_joiner\")\n",
+        "preprocessing_pipeline.connect(\"document_joiner\", \"document_cleaner\")\n",
+        "preprocessing_pipeline.connect(\"document_cleaner\", \"document_splitter\")\n",
+        "preprocessing_pipeline.connect(\"document_splitter\", \"document_embedder\")\n",
+        "preprocessing_pipeline.connect(\"document_embedder\", \"document_writer\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3NGinkHPB9C2"
+      },
+      "source": [
+        "Let's test this pipeline with a few recipes I've written. Are you getting hungry yet?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9Mw5kwZiqehc"
+      },
+      "outputs": [],
+      "source": [
+        "preprocessing_pipeline.run(\n",
+        "    {\n",
+        "        \"file_type_router\": {\n",
+        "            \"sources\": [\n",
+        "                \"/content/vegan_sunflower_hemp_cheese_recipe.txt\",\n",
+        "                \"/content/vegan_keto_eggplant_recipe.pdf\",\n",
+        "                \"/content/vegan_flan_recipe.md\",\n",
+        "            ]\n",
+        "        }\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TVGb-rteg7E5"
+      },
+      "source": [
+        "🎉 If you only wanted to learn how to preprocess documents, you can stop here! If you want to see an example of using those documents in a RAG pipeline, read on.  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "16PnegDR2EmY"
+      },
+      "source": [
+        "## (Optional) Build a pipeline to query documents"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I06qdWsZibSz"
+      },
+      "source": [
+        "Now, let's build a RAG pipeline that answers queries based ont he documents you just created in the section above. For this step, we will be using the [`HuggingFaceTGIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `mistralai/Mistral-7B-Instruct-v0.1` model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bB344ADZr-eG",
+        "outputId": "b6030405-5def-4700-8124-2e7ec292e977"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Enter Hugging Face token: ··········\n"
+          ]
+        }
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+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "\n",
+        "os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter Hugging Face token: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QASEGJhnIMQL"
+      },
+      "source": [
+        "In this step you'll build a query pipeline to answer questions about the documents.\n",
+        "\n",
+        "This pipeline takes the prompt, searches the document store for relevant documents, and passes those documents along to the LLM to formulate an answer.\n",
+        "\n",
+        "> ⚠️ Notice how we used `sentence-transformers/all-MiniLM-L6-v2` to create embeddings for our documents before. This is why we will be using the same model to embed incoming questions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "_s--8xEWq8Y9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "1c050d5f-f2ae-4cd3-e0d4-533397a6af63"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
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+      "source": [
+        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+        "from haystack.components.builders import PromptBuilder\n",
+        "from haystack.components.generators import HuggingFaceTGIGenerator\n",
+        "\n",
+        "template = \"\"\"\n",
+        "Answer the questions based on the given context.\n",
+        "\n",
+        "Context:\n",
+        "{% for document in documents %}\n",
+        "    {{ document.content }}\n",
+        "{% endfor %}\n",
+        "\n",
+        "Question: {{ question }}\n",
+        "Answer:\n",
+        "\"\"\"\n",
+        "pipe = Pipeline()\n",
+        "pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+        "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+        "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.1\"))\n",
+        "\n",
+        "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+        "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+        "pipe.connect(\"prompt_builder\", \"llm\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ba5puJxIg3V"
+      },
+      "source": [
+        "Try it out yourself by running the code below. If all has gone well, you should have a complete shopping list from all the recipe sources. 🧂🥥🧄"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qDqrU5emtBWQ"
+      },
+      "outputs": [],
+      "source": [
+        "question = (\n",
+        "    \"What ingredients would I need to make vegan keto eggplant lasagna, vegan persimmon flan, and vegan hemp cheese?\"\n",
+        ")\n",
+        "\n",
+        "pipe.run(\n",
+        "    {\n",
+        "        \"embedder\": {\"text\": question},\n",
+        "        \"prompt_builder\": {\"question\": question},\n",
+        "        \"llm\": {\"generation_kwargs\": {\"max_new_tokens\": 350}},\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZJueu_V4KP6w"
+      },
+      "source": [
+        "```python\n",
+        "{'llm': {'replies': ['\\n\\nFor vegan keto eggplant lasagna, you would need:\\n\\n* 2 large eggplants\\n* a lot of salt (Hella salt)\\n* 1/2 cup store-bought vegan mozzarella (for topping)\\n* Pesto: 4 oz basil, 1/4 cup almonds, 1/4 cup nutritional yeast, 1/4 cup olive oil, 1 recipe vegan pesto, 1 recipe spinach tofu ricotta, 1 tsp garlic powder, juice of half a lemon, salt to taste\\n* Macadamia nut cheese: 1 cup macadamia nuts, 10 oz spinach, juice of 1 lemon, garlic powder to taste, salt to taste\\n\\nFor vegan persimmon flan, you would need:\\n\\n* ½ cup persimmon pulp, strained\\n* 1 tbsp cornstarch\\n* ½ tsp agar agar\\n* 1 tbsp agave nectar, or to taste\\n* 2 tbsp granulated sugar\\n* ¼ cup coconut creme\\n* ½ cup almond milk\\n* ½ tsp vanilla\\n\\nFor vegan hemp cheese, you would need:\\n\\n* ½ cup sunflower seeds\\n* ½ cup hemp hearts\\n* 1.5 teaspoons miso paste\\n* 1 tsp nutritional yeast\\n* ¼ cup rejuvelac*\\n* 1/4th teaspoon salt, or to taste\\n\\nNote:'],\n",
+        "  'meta': [{'model': 'mistralai/Mistral-7B-Instruct-v0.1',\n",
+        "    'index': 0,\n",
+        "    'finish_reason': 'length',\n",
+        "    'usage': {'completion_tokens': 350,\n",
+        "     'prompt_tokens': 3454,\n",
+        "     'total_tokens': 3804}}]}}\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zA7xHckYJHsG"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "Congratulations on building an indexing pipeline that can preprocess different file types. Go forth and ingest all the messy real-world data into your workflows. 💥\n",
+        "\n",
+        "If you liked this tutorial, you may also enjoy:\n",
+        "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+        "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=filte_type_routing). Thanks for reading!"
+      ]
     }
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
index 6ec12fe0..e446bc3b 100644
--- a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
+++ b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
@@ -1,2002 +1,505 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "JFAFUa7BECmK"
-   },
-   "source": [
-    "# Tutorial: Simplifying Pipeline Inputs with Multiplexer\n",
-    "\n",
-    "\n",
-    "- **Level**: Intermediate\n",
-    "- **Time to complete**: 10 minutes\n",
-    "- **Components Used**: [Multiplexer](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer), [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [HuggingFaceTEIDocumentEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_document_embedder.py), [HuggingFaceTEITextEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_text_embedder.py), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [HuggingFaceTGIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) and [AnswerBuilder](https://docs.haystack.deepset.ai/v2.0/docs/answerbuilder)\n",
-    "- **Prerequisites**: You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines)\n",
-    "- **Goal**: After completing this tutorial, you'll have learned how to use a Multiplexer to simplify the inputs that `Pipeline.run()` get\n",
-    "\n",
-    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "jy3ZkDzu9-CW"
-   },
-   "source": [
-    "## Overview\n",
-    "\n",
-    "If you've ever built a Haystack pipeline with more than 3-4 components, you probably noticed that the number of inputs to pass to the `run()` method of the pipeline grow endlessly. New components take some of their input from the other components of a pipeline, but many of them also require additional input from the user. As a result, the `data` input of `Pipeline.run()` grows and becomes very repetitive.\n",
-    "\n",
-    "There is one component that can help managing this repetition in a more effective manner, and it's called [`Multiplexer`](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer).\n",
-    "\n",
-    "In this tutorial, you will learn how to drastically simplify the `Pipeline.run()` of a RAG pipeline using a Multiplexer."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "RJPsjBXZKWnb"
-   },
-   "source": [
-    "## Setup\n",
-    "### Prepare the Colab Environment\n",
-    "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "CcK-dK--G5ng"
-   },
-   "source": [
-    "### Install Haystack\n",
-    "\n",
-    "Install Haystack 2.0 Beta and other required packages with `pip`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "0hwJTyV5HARC"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install haystack-ai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "3N_97P0OV9cx"
-   },
-   "source": [
-    "### Enable Telemetry\n",
-    "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "BKilNUd8V_Uc"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.telemetry import tutorial_running\n",
-    "\n",
-    "tutorial_running(37)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uTNEeEcBJc_4"
-   },
-   "source": [
-    "### Enter a Hugging Face API key\n",
-    "\n",
-    "Set a Hugging Face API key:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "aiHltCF7JgaV"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter a Hugging Face API Token:\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "e57ugQB7dYsQ"
-   },
-   "source": [
-    "## Indexing Documents with a Pipeline\n",
-    "\n",
-    "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore) with their embeddings. You will use [HuggingFaceTEIDocumentEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_document_embedder.py) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter).\n",
-    "\n",
-    "After adding these components to your pipeline, connect them and run the pipeline.\n",
-    "\n",
-    "> If you'd like to learn about preprocessing files before you index them to your document store, follow the [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline) tutorial."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "My_fx0lNJUVb",
-    "outputId": "1987b9b8-0ada-4127-d4c3-80ef53a1b9d0"
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
-      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
-      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
-      "You will be able to reuse this secret in all of your notebooks.\n",
-      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
-      "  warnings.warn(\n",
-      "Calculating embeddings: 100%|██████████| 1/1 [00:00<00:00,  3.86it/s]\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JFAFUa7BECmK"
+      },
+      "source": [
+        "# Tutorial: Simplifying Pipeline Inputs with Multiplexer\n",
+        "\n",
+        "\n",
+        "- **Level**: Intermediate\n",
+        "- **Time to complete**: 10 minutes\n",
+        "- **Components Used**: [Multiplexer](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer), [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [HuggingFaceTEIDocumentEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_document_embedder.py), [HuggingFaceTEITextEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_text_embedder.py), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [HuggingFaceTGIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) and [AnswerBuilder](https://docs.haystack.deepset.ai/v2.0/docs/answerbuilder)\n",
+        "- **Prerequisites**: You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines)\n",
+        "- **Goal**: After completing this tutorial, you'll have learned how to use a Multiplexer to simplify the inputs that `Pipeline.run()` get\n",
+        "\n",
+        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "{'doc_writer': {'documents_written': 5}}"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jy3ZkDzu9-CW"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "If you've ever built a Haystack pipeline with more than 3-4 components, you probably noticed that the number of inputs to pass to the `run()` method of the pipeline grow endlessly. New components take some of their input from the other components of a pipeline, but many of them also require additional input from the user. As a result, the `data` input of `Pipeline.run()` grows and becomes very repetitive.\n",
+        "\n",
+        "There is one component that can help managing this repetition in a more effective manner, and it's called [`Multiplexer`](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer).\n",
+        "\n",
+        "In this tutorial, you will learn how to drastically simplify the `Pipeline.run()` of a RAG pipeline using a Multiplexer."
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from haystack import Pipeline, Document\n",
-    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-    "from haystack.components.writers import DocumentWriter\n",
-    "from haystack.components.embedders import HuggingFaceTEIDocumentEmbedder\n",
-    "\n",
-    "documents = [\n",
-    "    Document(content=\"My name is Jean and I live in Paris.\"),\n",
-    "    Document(content=\"My name is Mark and I live in Berlin.\"),\n",
-    "    Document(content=\"My name is Giorgio and I live in Rome.\"),\n",
-    "    Document(content=\"My name is Giorgio and I live in Milan.\"),\n",
-    "    Document(content=\"My name is Giorgio and I lived in many cities, but I settled in Naples eventually.\"),\n",
-    "]\n",
-    "\n",
-    "document_store = InMemoryDocumentStore()\n",
-    "\n",
-    "indexing_pipeline = Pipeline()\n",
-    "indexing_pipeline.add_component(\n",
-    "    instance=HuggingFaceTEIDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"), name=\"doc_embedder\"\n",
-    ")\n",
-    "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"doc_writer\")\n",
-    "\n",
-    "indexing_pipeline.connect(\"doc_embedder.documents\", \"doc_writer.documents\")\n",
-    "\n",
-    "indexing_pipeline.run({\"doc_embedder\": {\"documents\": documents}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "e9hOmQx4L2Lw"
-   },
-   "source": [
-    "## Building a RAG Pipeline\n",
-    "\n",
-    "Build a basic retrieval augmented generative pipeline with [HuggingFaceTEITextEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_text_embedder.py), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) and [HuggingFaceTGIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator). Additionally, add [AnswerBuilder](https://docs.haystack.deepset.ai/v2.0/docs/answerbuilder) to help you enrich the generated answer with `meta` info and the `query` input.\n",
-    "\n",
-    "> For a step-by-step guide to create a RAG pipeline with Haystack, follow the [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) tutorial"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "ueu5W07IWyXa",
-    "outputId": "fa94ac9e-0a88-49b1-8483-31a7590b8060"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<haystack.pipeline.Pipeline at 0x7da6badb2e90>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RJPsjBXZKWnb"
+      },
+      "source": [
+        "## Setup\n",
+        "### Prepare the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from haystack.components.embedders import HuggingFaceTEITextEmbedder\n",
-    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-    "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
-    "from haystack.components.generators import HuggingFaceTGIGenerator\n",
-    "\n",
-    "template = \"\"\"\n",
-    "<s>[INST] Answer the questions based on the given context. Only return the answer\n",
-    "\n",
-    "Context:\n",
-    "{% for document in documents %}\n",
-    "    {{ document.content }}\n",
-    "{% endfor %}\n",
-    "Question: {{ question }}\n",
-    "Answer: [/INST]\n",
-    "\"\"\"\n",
-    "pipe = Pipeline()\n",
-    "pipe.add_component(\"embedder\", HuggingFaceTEITextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
-    "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
-    "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
-    "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.2\"))\n",
-    "pipe.add_component(\"answer_builder\", AnswerBuilder())\n",
-    "\n",
-    "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
-    "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
-    "pipe.connect(\"prompt_builder\", \"llm\")\n",
-    "pipe.connect(\"llm.replies\", \"answer_builder.replies\")\n",
-    "pipe.connect(\"llm.meta\", \"answer_builder.meta\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5xxvPqyurZTi"
-   },
-   "source": [
-    "## Running the Pipeline\n",
-    "Pass the `query` to `embedder`, `prompt_builder` and `answer_builder` and run it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 180,
-     "referenced_widgets": [
-      "ce3dbf88776644ddaaf5230103a0c8a4",
-      "1618506e91914b6fa6608f1111d25c07",
-      "8d42264e1210400497ec742963469cac",
-      "f13aef9432424dcaa90f2a738eab10ff",
-      "b43e437fa6914c0a8da32ebd733040d5",
-      "024250df6331459f8ab6b16ca87072ab",
-      "a048537d05ea43558674f6c9eef26704",
-      "83e4ec7bf8524955a21eb70894ae2798",
-      "85f9c49e9acc4e0f9daf8101d662232c",
-      "7125ddab3dc94d588350aab66e3e091e",
-      "c889f54c4b1945869a32e550b894a99a",
-      "90c696ac0e4d42ccae76c7bf3580badb",
-      "7c7cf639dd53403ea6d466b695c81158",
-      "373cead3c35845cfbb35721cf4da6640",
-      "5b64c6ec2e4949c4b599c016f430a0f4",
-      "11ff7ab5eb044e9f90cea4bc05d2876f",
-      "d37cb0ad69814b9fa0b2dc0cdbe7af01",
-      "d71124d6341e42f29fb57103868d1265",
-      "72738d24e343484bb239b9c0f323ef3a",
-      "384775b89b0a47eb86668e990fd7fe76",
-      "d4e61d665db74798bc7d4ccbcdabf1bd",
-      "dbaca5fb30db43b485a94efb9646805f",
-      "8b089617c8804f3688349656bfc3d696",
-      "c6813d416ae742d6bb2b3148359994cf",
-      "c1741ef7dbd8440b8c30e34cfba734a4",
-      "4b707740fe3d4a159eec4cd78a2a9534",
-      "27ff0db46763416fa4921db6f8036e4e",
-      "ff47fff35492433e83250315d64c9794",
-      "63fec26043e948e092c43b8593241144",
-      "5563ab711bfc4602bd6f034ee6511a9d",
-      "5136122faba64a83864745fdc04bbdd7",
-      "173bef0a6eef42abbdb77302ddd48342",
-      "648b01abb78248b1af06d556b2fe8972",
-      "950e9cbd6aac4f70bc0f77821bdb1565",
-      "395c6900afd745c3b6713db543f400d3",
-      "ce8109a20f07499eb8cc077031fb2086",
-      "f9dc45c6a5294991a68167c94dce4874",
-      "77be128c10b44268ae3939171a24cfb0",
-      "03ab731b50c94976b78492fb2d4d3a9f",
-      "1960dc3979a24764be602be9f09b0ddf",
-      "ced7ae4acb104091bd59e707b9b9e679",
-      "968e3c5d74fd4b04bfa823d7d54422a0",
-      "185c58a7243e4df7aa444b766df54a50",
-      "ccc37a05521446cbb67b4dc8c47c6c1f"
-     ]
     },
-    "id": "AIsphy4hJDpE",
-    "outputId": "9eff163e-e9bc-4883-d53f-111fbece8b5d"
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ce3dbf88776644ddaaf5230103a0c8a4",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CcK-dK--G5ng"
       },
-      "text/plain": [
-       "tokenizer_config.json:   0%|          | 0.00/1.46k [00:00<?, ?B/s]"
+      "source": [
+        "### Install Haystack\n",
+        "\n",
+        "Install Haystack 2.0 Beta and other required packages with `pip`:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "90c696ac0e4d42ccae76c7bf3580badb",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0hwJTyV5HARC"
       },
-      "text/plain": [
-       "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install haystack-ai"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8b089617c8804f3688349656bfc3d696",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3N_97P0OV9cx"
       },
-      "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
+      "source": [
+        "### Enable Telemetry\n",
+        "\n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "950e9cbd6aac4f70bc0f77821bdb1565",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BKilNUd8V_Uc"
       },
-      "text/plain": [
-       "special_tokens_map.json:   0%|          | 0.00/72.0 [00:00<?, ?B/s]"
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(37)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "{'answer_builder': {'answers': [GeneratedAnswer(data='\\nMark lives in Berlin.', query='Where does Mark live?', documents=[], meta={'model': 'mistralai/Mistral-7B-Instruct-v0.2', 'index': 0, 'finish_reason': 'eos_token', 'usage': {'completion_tokens': 7, 'prompt_tokens': 133, 'total_tokens': 140}})]}}"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uTNEeEcBJc_4"
+      },
+      "source": [
+        "### Enter a Hugging Face API key\n",
+        "\n",
+        "Set a Hugging Face API key:"
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "query = \"Where does Mark live?\"\n",
-    "pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}, \"answer_builder\": {\"query\": query}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "wrH2MGSBLvVC"
-   },
-   "source": [
-    "In this basic RAG pipeline, components require a `query` to operate are `embedder`, `prompt_builder`, and `answer_builder`. However, as you extend the pipeline with additional components like Retrievers and Rankers, the number of components needing a `query` can increase indefinitely. This leads to repetitive and increasingly complex `Pipeline.run()` calls. In such cases, using a Multiplexer can help simplify and declutter `Pipeline.run()`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ewDXDrw9N0CG"
-   },
-   "source": [
-    "## Introducing a Multiplexer\n",
-    "\n",
-    "The [Multiplexer](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer) is a component that can accept multiple input connections and then distributes the first value it receives to all components connected to its output. In this seeting, you can use this component by connecting it to other pipeline components that expect a `query` during runtime.\n",
-    "\n",
-    "Now, initialize the Multiplexer with the expected input type (in this case, `str` since the `query` is a string):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "id": "kArO87EKN3N-"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.components.others import Multiplexer\n",
-    "\n",
-    "multiplexer = Multiplexer(str)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vBGC2wO5LWIL"
-   },
-   "source": [
-    "## Adding the `Multiplexer` to the Pipeline\n",
-    "\n",
-    "Create the same RAG pipeline but this time with the Multiplexer. Add the Multiplexer to the pipeline and connect it to all the components that need the `query` as an input:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "CTmnCZvgEAut",
-    "outputId": "69794020-6d67-4b46-e67b-67fecc98e100"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<haystack.pipeline.Pipeline at 0x7da66d82a260>"
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "aiHltCF7JgaV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b973435d-94c1-458a-8212-c543fd45ffab"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Enter a Hugging Face API Token:··········\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "\n",
+        "os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter a Hugging Face API Token:\")"
       ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from haystack.components.embedders import HuggingFaceTEITextEmbedder\n",
-    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-    "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
-    "from haystack.components.generators import HuggingFaceTGIGenerator\n",
-    "\n",
-    "template = \"\"\"\n",
-    "<s>[INST] Answer the questions based on the given context. Only return the answer\n",
-    "\n",
-    "Context:\n",
-    "{% for document in documents %}\n",
-    "    {{ document.content }}\n",
-    "{% endfor %}\n",
-    "Question: {{ question }}\n",
-    "Answer: [/INST]\n",
-    "\"\"\"\n",
-    "pipe = Pipeline()\n",
-    "\n",
-    "pipe.add_component(\"multiplexer\", multiplexer)\n",
-    "\n",
-    "pipe.add_component(\"embedder\", HuggingFaceTEITextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
-    "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
-    "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
-    "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.2\"))\n",
-    "pipe.add_component(\"answer_builder\", AnswerBuilder())\n",
-    "\n",
-    "# Connect the Multiplexer to all the components that need the query\n",
-    "pipe.connect(\"multiplexer.value\", \"embedder.text\")\n",
-    "pipe.connect(\"multiplexer.value\", \"prompt_builder.question\")\n",
-    "pipe.connect(\"multiplexer.value\", \"answer_builder.query\")\n",
-    "\n",
-    "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
-    "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
-    "pipe.connect(\"prompt_builder\", \"llm\")\n",
-    "pipe.connect(\"llm.replies\", \"answer_builder.replies\")\n",
-    "pipe.connect(\"llm.meta\", \"answer_builder.meta\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cJ0TjMWn-3gj"
-   },
-   "source": [
-    "### Visualize the Pipeline\n",
-    "\n",
-    "To understand how you formed this pipeline with a Multiplexer, use [draw()](https://docs.haystack.deepset.ai/v2.0/docs/drawing-pipeline-graphs) method of the pipeline. If you're running this notebook on Google Colab, the generated file will be saved in \"Files\" section on the sidebar or you can call `Image.open()`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 784
     },
-    "id": "cOh_4u3jMI9O",
-    "outputId": "33b8ffd9-3f2d-48ae-a3ec-5a668c30587e"
-   },
-   "outputs": [
     {
-     "data": {
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-      "text/plain": [
-       "<IPython.core.display.Image object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e57ugQB7dYsQ"
+      },
+      "source": [
+        "## Indexing Documents with a Pipeline\n",
+        "\n",
+        "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore) with their embeddings. You will use [HuggingFaceTEIDocumentEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_document_embedder.py) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter).\n",
+        "\n",
+        "After adding these components to your pipeline, connect them and run the pipeline.\n",
+        "\n",
+        "> If you'd like to learn about preprocessing files before you index them to your document store, follow the [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline) tutorial."
       ]
-     },
-     "execution_count": 8,
-     "metadata": {
-      "image/png": {
-       "width": 300
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "\n",
-    "pipe.draw(\"pipeline_with_multiplexer.png\")\n",
-    "Image(\"pipeline_with_multiplexer.png\", width=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "i2wW4nbEQKhJ"
-   },
-   "source": [
-    "## Running the Pipeline with a Multiplexer\n",
-    "\n",
-    "Run the pipeline that you updated with a Multiplexer. This time, instead of passing the query to `prompt_builder`, `retriever` and `answer_builder` seperately, you only need to pass it to the `multiplexer`. As a result, you will get the same answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "YbIHBCKPQF4f",
-    "outputId": "f1c0a91f-777b-4163-982b-f9a9f77ab549"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "{'answer_builder': {'answers': [GeneratedAnswer(data='\\nMark lives in Berlin.', query='Where does Mark live?', documents=[], meta={'model': 'mistralai/Mistral-7B-Instruct-v0.2', 'index': 0, 'finish_reason': 'eos_token', 'usage': {'completion_tokens': 7, 'prompt_tokens': 133, 'total_tokens': 140}})]}}"
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "My_fx0lNJUVb",
+        "outputId": "b731efb8-14bb-4f13-ca49-d8706a777dd5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Calculating embeddings: 100%|██████████| 1/1 [00:00<00:00,  1.49it/s]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'doc_writer': {'documents_written': 5}}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "from haystack import Pipeline, Document\n",
+        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+        "from haystack.components.writers import DocumentWriter\n",
+        "from haystack.components.embedders import HuggingFaceTEIDocumentEmbedder\n",
+        "\n",
+        "documents = [\n",
+        "    Document(content=\"My name is Jean and I live in Paris.\"),\n",
+        "    Document(content=\"My name is Mark and I live in Berlin.\"),\n",
+        "    Document(content=\"My name is Giorgio and I live in Rome.\"),\n",
+        "    Document(content=\"My name is Giorgio and I live in Milan.\"),\n",
+        "    Document(content=\"My name is Giorgio and I lived in many cities, but I settled in Naples eventually.\"),\n",
+        "]\n",
+        "\n",
+        "document_store = InMemoryDocumentStore()\n",
+        "\n",
+        "indexing_pipeline = Pipeline()\n",
+        "indexing_pipeline.add_component(\n",
+        "    instance=HuggingFaceTEIDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"), name=\"doc_embedder\"\n",
+        ")\n",
+        "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"doc_writer\")\n",
+        "\n",
+        "indexing_pipeline.connect(\"doc_embedder.documents\", \"doc_writer.documents\")\n",
+        "\n",
+        "indexing_pipeline.run({\"doc_embedder\": {\"documents\": documents}})"
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipe.run({\"multiplexer\": {\"value\": \"Where does Mark live?\"}})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kPiSU2xoKmio"
-   },
-   "source": [
-    "## What's next\n",
-    "\n",
-    "🎉 Congratulations! You've simplified your pipeline run with a Multiplexer!\n",
-    "\n",
-    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
-    "- [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)\n",
-    "- [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)\n",
-    "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
-    "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=multiplexer) or [join Haystack discord community](https://discord.gg/haystack).\n",
-    "\n",
-    "Thanks for reading!"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "gpuType": "T4",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python"
-  },
-  "widgets": {
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+        "## Building a RAG Pipeline\n",
+        "\n",
+        "Build a basic retrieval augmented generative pipeline with [HuggingFaceTEITextEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_text_embedder.py), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) and [HuggingFaceTGIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator). Additionally, add [AnswerBuilder](https://docs.haystack.deepset.ai/v2.0/docs/answerbuilder) to help you enrich the generated answer with `meta` info and the `query` input.\n",
+        "\n",
+        "> For a step-by-step guide to create a RAG pipeline with Haystack, follow the [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) tutorial"
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+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": []
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
       ],
-      "layout": "IPY_MODEL_27ff0db46763416fa4921db6f8036e4e"
-     }
-    },
-    "8d42264e1210400497ec742963469cac": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "FloatProgressModel",
-     "state": {
-      "_dom_classes": [],
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "FloatProgressModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "ProgressView",
-      "bar_style": "success",
-      "description": "",
-      "description_tooltip": null,
-      "layout": "IPY_MODEL_83e4ec7bf8524955a21eb70894ae2798",
-      "max": 1460,
-      "min": 0,
-      "orientation": "horizontal",
-      "style": "IPY_MODEL_85f9c49e9acc4e0f9daf8101d662232c",
-      "value": 1460
-     }
+      "source": [
+        "from haystack.components.embedders import HuggingFaceTEITextEmbedder\n",
+        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+        "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
+        "from haystack.components.generators import HuggingFaceTGIGenerator\n",
+        "\n",
+        "template = \"\"\"\n",
+        "<s>[INST] Answer the questions based on the given context.\n",
+        "\n",
+        "Context:\n",
+        "{% for document in documents %}\n",
+        "    {{ document.content }}\n",
+        "{% endfor %}\n",
+        "Question: {{ question }}\n",
+        "Answer: [/INST]\n",
+        "\"\"\"\n",
+        "pipe = Pipeline()\n",
+        "pipe.add_component(\"embedder\", HuggingFaceTEITextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+        "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+        "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.1\"))\n",
+        "pipe.add_component(\"answer_builder\", AnswerBuilder())\n",
+        "\n",
+        "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+        "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+        "pipe.connect(\"prompt_builder\", \"llm\")\n",
+        "pipe.connect(\"llm.replies\", \"answer_builder.replies\")\n",
+        "pipe.connect(\"llm.meta\", \"answer_builder.meta\")"
+      ]
     },
-    "90c696ac0e4d42ccae76c7bf3580badb": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "HBoxModel",
-     "state": {
-      "_dom_classes": [],
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "HBoxModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "HBoxView",
-      "box_style": "",
-      "children": [
-       "IPY_MODEL_7c7cf639dd53403ea6d466b695c81158",
-       "IPY_MODEL_373cead3c35845cfbb35721cf4da6640",
-       "IPY_MODEL_5b64c6ec2e4949c4b599c016f430a0f4"
-      ],
-      "layout": "IPY_MODEL_11ff7ab5eb044e9f90cea4bc05d2876f"
-     }
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5xxvPqyurZTi"
+      },
+      "source": [
+        "## Running the Pipeline\n",
+        "Pass the `query` to `embedder`, `prompt_builder` and `answer_builder` and run it:"
+      ]
     },
-    "950e9cbd6aac4f70bc0f77821bdb1565": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "HBoxModel",
-     "state": {
-      "_dom_classes": [],
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "HBoxModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "HBoxView",
-      "box_style": "",
-      "children": [
-       "IPY_MODEL_395c6900afd745c3b6713db543f400d3",
-       "IPY_MODEL_ce8109a20f07499eb8cc077031fb2086",
-       "IPY_MODEL_f9dc45c6a5294991a68167c94dce4874"
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AIsphy4hJDpE",
+        "outputId": "4498e7c9-0ff2-424c-9ddd-535f8630572e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'answer_builder': {'answers': [GeneratedAnswer(data=' Mark lives in Berlin.', query='Where does Mark live?', documents=[], meta={'model': 'mistralai/Mistral-7B-Instruct-v0.1', 'index': 0, 'finish_reason': 'eos_token', 'usage': {'completion_tokens': 6, 'prompt_tokens': 116, 'total_tokens': 122}})]}}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
       ],
-      "layout": "IPY_MODEL_77be128c10b44268ae3939171a24cfb0"
-     }
-    },
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+      "source": [
+        "query = \"Where does Mark live?\"\n",
+        "pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}, \"answer_builder\": {\"query\": query}})"
+      ]
     },
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-      "description": "",
-      "description_tooltip": null,
-      "layout": "IPY_MODEL_5563ab711bfc4602bd6f034ee6511a9d",
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-      "style": "IPY_MODEL_5136122faba64a83864745fdc04bbdd7",
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+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wrH2MGSBLvVC"
+      },
+      "source": [
+        "In this basic RAG pipeline, components require a `query` to operate are `embedder`, `prompt_builder`, and `answer_builder`. However, as you extend the pipeline with additional components like Retrievers and Rankers, the number of components needing a `query` can increase indefinitely. This leads to repetitive and increasingly complex `Pipeline.run()` calls. In such cases, using a Multiplexer can help simplify and declutter `Pipeline.run()`."
+      ]
     },
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-     "model_module": "@jupyter-widgets/controls",
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-      "_model_name": "HTMLModel",
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-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "HTMLView",
-      "description": "",
-      "description_tooltip": null,
-      "layout": "IPY_MODEL_ff47fff35492433e83250315d64c9794",
-      "placeholder": "​",
-      "style": "IPY_MODEL_63fec26043e948e092c43b8593241144",
-      "value": "tokenizer.json: 100%"
-     }
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ewDXDrw9N0CG"
+      },
+      "source": [
+        "## Introducing a Multiplexer\n",
+        "\n",
+        "The [Multiplexer](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer) is a component that can accept multiple input connections and then distributes the first value it receives to all components connected to its output. In this seeting, you can use this component by connecting it to other pipeline components that expect a `query` during runtime.\n",
+        "\n",
+        "Now, initialize the Multiplexer with the expected input type (in this case, `str` since the `query` is a string):"
+      ]
     },
-    "c889f54c4b1945869a32e550b894a99a": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "DescriptionStyleModel",
-     "state": {
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "DescriptionStyleModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/base",
-      "_view_module_version": "1.2.0",
-      "_view_name": "StyleView",
-      "description_width": ""
-     }
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "kArO87EKN3N-"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.others import Multiplexer\n",
+        "\n",
+        "multiplexer = Multiplexer(str)"
+      ]
     },
-    "ccc37a05521446cbb67b4dc8c47c6c1f": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "DescriptionStyleModel",
-     "state": {
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "DescriptionStyleModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/base",
-      "_view_module_version": "1.2.0",
-      "_view_name": "StyleView",
-      "description_width": ""
-     }
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vBGC2wO5LWIL"
+      },
+      "source": [
+        "## Adding the `Multiplexer` to the Pipeline\n",
+        "\n",
+        "Create the same RAG pipeline but this time with the Multiplexer. Add the Multiplexer to the pipeline and connect it to all the components that need the `query` as an input:"
+      ]
     },
-    "ce3dbf88776644ddaaf5230103a0c8a4": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "HBoxModel",
-     "state": {
-      "_dom_classes": [],
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "HBoxModel",
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-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "HBoxView",
-      "box_style": "",
-      "children": [
-       "IPY_MODEL_1618506e91914b6fa6608f1111d25c07",
-       "IPY_MODEL_8d42264e1210400497ec742963469cac",
-       "IPY_MODEL_f13aef9432424dcaa90f2a738eab10ff"
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "CTmnCZvgEAut",
+        "outputId": "a0ab0df0-32f7-4778-954a-e7b9cc8b612d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
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+          "data": {
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+          },
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+        }
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+      "source": [
+        "from haystack.components.embedders import HuggingFaceTEITextEmbedder\n",
+        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+        "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
+        "from haystack.components.generators import HuggingFaceTGIGenerator\n",
+        "\n",
+        "template = \"\"\"\n",
+        "<s>[INST] Answer the questions based on the given context.\n",
+        "\n",
+        "Context:\n",
+        "{% for document in documents %}\n",
+        "    {{ document.content }}\n",
+        "{% endfor %}\n",
+        "Question: {{ question }}\n",
+        "Answer: [/INST]\n",
+        "\"\"\"\n",
+        "pipe = Pipeline()\n",
+        "\n",
+        "pipe.add_component(\"multiplexer\", multiplexer)\n",
+        "\n",
+        "pipe.add_component(\"embedder\", HuggingFaceTEITextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+        "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+        "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.1\"))\n",
+        "pipe.add_component(\"answer_builder\", AnswerBuilder())\n",
+        "\n",
+        "# Connect the Multiplexer to all the components that need the query\n",
+        "pipe.connect(\"multiplexer.value\", \"embedder.text\")\n",
+        "pipe.connect(\"multiplexer.value\", \"prompt_builder.question\")\n",
+        "pipe.connect(\"multiplexer.value\", \"answer_builder.query\")\n",
+        "\n",
+        "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+        "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+        "pipe.connect(\"prompt_builder\", \"llm\")\n",
+        "pipe.connect(\"llm.replies\", \"answer_builder.replies\")\n",
+        "pipe.connect(\"llm.meta\", \"answer_builder.meta\")"
+      ]
     },
-    "d71124d6341e42f29fb57103868d1265": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "DescriptionStyleModel",
-     "state": {
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "DescriptionStyleModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/base",
-      "_view_module_version": "1.2.0",
-      "_view_name": "StyleView",
-      "description_width": ""
-     }
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i2wW4nbEQKhJ"
+      },
+      "source": [
+        "## Running the Pipeline with a Multiplexer\n",
+        "\n",
+        "Run the pipeline that you updated with a Multiplexer. This time, instead of passing the query to `prompt_builder`, `retriever` and `answer_builder` seperately, you only need to pass it to the `multiplexer`. As a result, you will get the same answer."
+      ]
     },
-    "dbaca5fb30db43b485a94efb9646805f": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "DescriptionStyleModel",
-     "state": {
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "DescriptionStyleModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/base",
-      "_view_module_version": "1.2.0",
-      "_view_name": "StyleView",
-      "description_width": ""
-     }
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YbIHBCKPQF4f",
+        "outputId": "32fb9d11-eec2-49d7-9ab1-97a90d9bbc28"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'answer_builder': {'answers': [GeneratedAnswer(data=' Mark lives in Berlin.', query='Where does Mark live?', documents=[], meta={'model': 'mistralai/Mistral-7B-Instruct-v0.1', 'index': 0, 'finish_reason': 'eos_token', 'usage': {'completion_tokens': 6, 'prompt_tokens': 116, 'total_tokens': 122}})]}}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ],
+      "source": [
+        "pipe.run({\"multiplexer\": {\"value\": \"Where does Mark live?\"}})"
+      ]
     },
-    "f13aef9432424dcaa90f2a738eab10ff": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "HTMLModel",
-     "state": {
-      "_dom_classes": [],
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "HTMLModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "HTMLView",
-      "description": "",
-      "description_tooltip": null,
-      "layout": "IPY_MODEL_7125ddab3dc94d588350aab66e3e091e",
-      "placeholder": "​",
-      "style": "IPY_MODEL_c889f54c4b1945869a32e550b894a99a",
-      "value": " 1.46k/1.46k [00:00&lt;00:00, 22.6kB/s]"
-     }
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kPiSU2xoKmio"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "🎉 Congratulations! You've simplified your pipeline run with a Multiplexer!\n",
+        "\n",
+        "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+        "- [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)\n",
+        "- [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)\n",
+        "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=multiplexer) or [join Haystack discord community](https://discord.gg/haystack).\n",
+        "\n",
+        "Thanks for reading!"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
     },
-    "f9dc45c6a5294991a68167c94dce4874": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "HTMLModel",
-     "state": {
-      "_dom_classes": [],
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "HTMLModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "HTMLView",
-      "description": "",
-      "description_tooltip": null,
-      "layout": "IPY_MODEL_185c58a7243e4df7aa444b766df54a50",
-      "placeholder": "​",
-      "style": "IPY_MODEL_ccc37a05521446cbb67b4dc8c47c6c1f",
-      "value": " 72.0/72.0 [00:00&lt;00:00, 1.68kB/s]"
-     }
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
     },
-    "ff47fff35492433e83250315d64c9794": {
-     "model_module": "@jupyter-widgets/base",
-     "model_module_version": "1.2.0",
-     "model_name": "LayoutModel",
-     "state": {
-      "_model_module": "@jupyter-widgets/base",
-      "_model_module_version": "1.2.0",
-      "_model_name": "LayoutModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/base",
-      "_view_module_version": "1.2.0",
-      "_view_name": "LayoutView",
-      "align_content": null,
-      "align_items": null,
-      "align_self": null,
-      "border": null,
-      "bottom": null,
-      "display": null,
-      "flex": null,
-      "flex_flow": null,
-      "grid_area": null,
-      "grid_auto_columns": null,
-      "grid_auto_flow": null,
-      "grid_auto_rows": null,
-      "grid_column": null,
-      "grid_gap": null,
-      "grid_row": null,
-      "grid_template_areas": null,
-      "grid_template_columns": null,
-      "grid_template_rows": null,
-      "height": null,
-      "justify_content": null,
-      "justify_items": null,
-      "left": null,
-      "margin": null,
-      "max_height": null,
-      "max_width": null,
-      "min_height": null,
-      "min_width": null,
-      "object_fit": null,
-      "object_position": null,
-      "order": null,
-      "overflow": null,
-      "overflow_x": null,
-      "overflow_y": null,
-      "padding": null,
-      "right": null,
-      "top": null,
-      "visibility": null,
-      "width": null
-     }
+    "language_info": {
+      "name": "python"
     }
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

From c25e2daf5af1fc52802353a2efec851302e64d12 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 5 Mar 2024 17:34:34 +0100
Subject: [PATCH 173/206] Add a tutorial with OpenAI function calling (#301)

* Add a tutorial with OpenAI function calling

* Change the name

* Reduce code

* Minor updates

* Change the level

* Update README.md

* Word replacement
---
 .github/workflows/run_tutorials.yml           |    1 +
 README.md                                     |    2 +-
 index.toml                                    |   15 +-
 ...at_Application_with_Function_Calling.ipynb | 6330 +++++++++++++++++
 4 files changed, 6346 insertions(+), 2 deletions(-)
 create mode 100644 tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb

diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
index 91ddef26..dfde0307 100644
--- a/.github/workflows/run_tutorials.yml
+++ b/.github/workflows/run_tutorials.yml
@@ -79,6 +79,7 @@ jobs:
               "tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb"
               "tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb"
               "tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb"
+              "tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb"
           )
           for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
             if [[ $changed_file == *".ipynb" ]]; then
diff --git a/README.md b/README.md
index 7a10a4f7..284fc219 100644
--- a/README.md
+++ b/README.md
@@ -48,7 +48,7 @@ Haystack 2.0
 | [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         | [Building Pipelines with Conditional Routing](./tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)|
 | [Pipelines](./tutorials/11_Pipelines.ipynb)                                                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               | [Simplifying Pipeline Inputs with Multiplexer](./tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)|
 | [[OUTDATED] Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    | [Embedding Metadata for Improved Retrieval](./tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb)|
-| [Question Generation](./tutorials/13_Question_generation.ipynb)                                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     |                                                                                                                  |                                                                                                                                                                                                                          |
+| [Question Generation](./tutorials/13_Question_generation.ipynb)                                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     | [Building a Chat Application with Function Calling](./tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb)|
 | [Query Classifier](./tutorials/14_Query_Classifier.ipynb)                                                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)                                        |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Table QA](./tutorials/15_TableQA.ipynb)                                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/15_TableQA.ipynb)                                                 |                                                                                                                  |                                                                                                                                                                                                                          |
 | [Document Classifier at Index Time](./tutorials/16_Document_Classifier_at_Index_Time.ipynb)               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/16_Document_Classifier_at_Index_Time.ipynb)                       |                                                                                                                  |                                                                                                                                                                                                                          |
diff --git a/index.toml b/index.toml
index 0d38fab8..3ee8743e 100644
--- a/index.toml
+++ b/index.toml
@@ -47,7 +47,7 @@ created_at = 2021-08-12
 title = "Evaluation of a QA System"
 description = "Learn how to evaluate the performance of individual nodes as well as entire pipelines."
 level = "advanced"
-weight = 100
+weight = 105
 notebook = "05_Evaluation.ipynb"
 aliases = ["evaluation"]
 created_at = 2021-08-12
@@ -275,6 +275,7 @@ aliases = []
 completion_time = "10 min"
 created_at = 2023-12-05
 haystack_2 = true
+featured = true
 
 [[tutorial]]
 title = "Generating Structured Output with Loop-Based Auto-Correction"
@@ -396,3 +397,15 @@ aliases = []
 completion_time = "10 min"
 created_at = 2024-02-20
 haystack_2 = true
+
+[[tutorial]]
+title = "Building a Chat Application with Function Calling"
+description = "Learn how to build chat applications that have agent-like behavior with OpenAI function calling"
+level = "advanced"
+weight = 100
+notebook = "40_Building_Chat_Application_with_Function_Calling.ipynb"
+aliases = []
+completion_time = "20 min"
+created_at = 2024-03-05
+haystack_2 = true
+featured = true
diff --git a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
new file mode 100644
index 00000000..b0c94fe5
--- /dev/null
+++ b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
@@ -0,0 +1,6330 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HJsHqaTksDHt"
+   },
+   "source": [
+    "# Tutorial: Building a Chat Application with Function Calling\n",
+    "\n",
+    "- **Level**: Advanced\n",
+    "- **Time to complete**: 20 minutes\n",
+    "- **Components Used**: [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator), [OpenAIChatGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaichatgenerator)\n",
+    "- **Prerequisites**: You must have an [Open API Key](https://platform.openai.com/api-keys) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines)\n",
+    "- **Goal**: After completing this tutorial, you will have learned how to build chat applications that demonstrate agent-like behavior using OpenAI's function calling feature.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PWXXqq_MPn7y"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "\n",
+    "\n",
+    "📚 Useful Sources:\n",
+    "* [OpenAIChatGenerator Docs](https://docs.haystack.deepset.ai/v2.0/docs/openaichatgenerator)\n",
+    "* [OpenAIChatGenerator API Reference](https://docs.haystack.deepset.ai/v2.0/reference/generator-api#openaichatgenerator)\n",
+    "* [🧑‍🍳 Cookbook: Function Calling with OpenAIChatGenerator](https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/function_calling_with_OpenAIChatGenerator.ipynb)\n",
+    "\n",
+    "[OpenAI's function calling](https://platform.openai.com/docs/guides/function-calling) connects large language models to external tools. By providing a `tools` list with functions and their specifications to the OpenAI API calls, you can easily build chat assistants that can answer questions by calling external APIs or extract structured information from text.\n",
+    "\n",
+    "In this tutorial, you'll learn how to convert your Haystack pipeline into a function-calling tool and how to implement applications using OpenAI's Chat Completion API through `OpenAIChatGenerator` for agent-like behavior."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "K04cnh_IleMV"
+   },
+   "source": [
+    "## Setting up the Development Environment\n",
+    "\n",
+    "Install Haystack 2.0 and [sentence-transformers](https://pypi.org/project/sentence-transformers/) using pip:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "zNyqNVFaPN1A"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai\n",
+    "pip install \"sentence-transformers>=2.2.0\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XwlBLN-GwuuU"
+   },
+   "source": [
+    "### Enable Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9V0-VuEgwt2u"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2owelT4NpXtw"
+   },
+   "source": [
+    "Save your OpenAI API key as an environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "WM-sVkYonutA"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"OPENAI_API_KEY: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0_mGdadLrcNr"
+   },
+   "source": [
+    "## Learning about the OpenAIChatGenerator\n",
+    "\n",
+    "[OpenAIChatGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaichatgenerator) is a component that supports the function calling feature of OpenAI through Chat Completion API. In contrary to `OpenAIGenerator`, the way to communicate with `OpenAIChatGenerator` is through [`ChatMessage`](https://docs.haystack.deepset.ai/v2.0/docs/data-classes#chatmessage) list. Read more about the difference between them in [Generators vs Chat Generators](https://docs.haystack.deepset.ai/v2.0/docs/generators-vs-chat-generators).\n",
+    "\n",
+    "\n",
+    "To start working with the `OpenAIChatGenerator`, create a `ChatMessage` object with \"SYSTEM\" role using `ChatMessage.from_system()` and another `ChatMessage` with \"USER\" role using `ChatMessage.from_user()`. Then, pass this messages list to `OpenAIChatGenerator` and run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "yzuTzWpJFVbH",
+    "outputId": "dd08bf6b-3ca5-4fc8-8f53-f40ada2f7f84"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'replies': [ChatMessage(content='Natürliche Sprachverarbeitung (NLP) ist ein Bereich der künstlichen Intelligenz, der sich mit der Wechselwirkung zwischen Menschensprache und Maschinen befasst. Es zielt darauf ab, Computern das Verstehen, Interpretieren und Generieren menschlicher Sprache zu ermöglichen.', role=<ChatRole.ASSISTANT: 'assistant'>, name=None, meta={'model': 'gpt-3.5-turbo-0125', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 74, 'prompt_tokens': 34, 'total_tokens': 108}})]}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from haystack.dataclasses import ChatMessage\n",
+    "from haystack.components.generators.chat import OpenAIChatGenerator\n",
+    "\n",
+    "messages = [\n",
+    "    ChatMessage.from_system(\"Always respond in German even if some input data is in other languages.\"),\n",
+    "    ChatMessage.from_user(\"What's Natural Language Processing? Be brief.\"),\n",
+    "]\n",
+    "\n",
+    "chat_generator = OpenAIChatGenerator(model=\"gpt-3.5-turbo\")\n",
+    "chat_generator.run(messages=messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cbl0xs0MJ76Z"
+   },
+   "source": [
+    "### Basic Streaming\n",
+    "\n",
+    "`OpenAIChatGenerator` supports streaming, provide a `streaming_callback` function and run the `chat_generator` again to see the difference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "D0nEEd5PJ1X2",
+    "outputId": "dbc6a460-991c-43bd-9fba-be1d6ca67159"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Natural Language Processing (NLP) ist ein Bereich der künstlichen Intelligenz, der sich mit dem Verstehen und der Verarbeitung von menschlicher Sprache durch Computer befasst."
+     ]
+    }
+   ],
+   "source": [
+    "from haystack.dataclasses import ChatMessage\n",
+    "from haystack.components.generators.chat import OpenAIChatGenerator\n",
+    "from haystack.components.generators.utils import print_streaming_chunk\n",
+    "\n",
+    "chat_generator = OpenAIChatGenerator(model=\"gpt-3.5-turbo\", streaming_callback=print_streaming_chunk)\n",
+    "response = chat_generator.run(messages=messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FW91cHchCw_W"
+   },
+   "source": [
+    "## Creating a Function Calling Tool from a Haystack Pipeline\n",
+    "\n",
+    "To use the function calling of OpenAI, you need to introduce `tools` to your `OpenAIChatGenerator` using its `generation_kwargs` param.\n",
+    "\n",
+    "For this example, you'll use a Haystack RAG pipeline as one of your tools. Therefore, you need to index documents to a document store and then build a RAG pipeline on top of it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FWkDXKbeoNqZ"
+   },
+   "source": [
+    "### Index Documents with a Pipeline\n",
+    "\n",
+    "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore) with their embeddings. You will use [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter).\n",
+    "\n",
+    "After adding these components to your pipeline, connect them and run the pipeline.\n",
+    "\n",
+    "> If you'd like to learn about preprocessing files before you index them to your document store, follow the [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline) tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
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+     ]
+    },
+    "id": "ZE0SEGY92GHJ",
+    "outputId": "6a2bbfeb-5a61-41d1-c6f6-307550292a85"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+      "You will be able to reuse this secret in all of your notebooks.\n",
+      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "041743b7cebb47458bfa61e945699655",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6cc3669c9d346c191493cdc8c883215",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9017bb12db654cd6a868e7b188c24e1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0461b7e8bc0d46028de5492107fe17a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aeb26032d36344a880a493d7b3a0ea02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "865287be85c24c26a8f0e9d93ac814a4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
+      "  return self.fget.__get__(instance, owner)()\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1c40529ac844e828dcc77ad2cf8490f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "507075803cb242c4b4f9c7e1f35a23fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13b32f1ffa7e4f32bfc41535e1fc5e31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a801c2f85324991a39633342940f711",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "936102d51daa44dd8d61564e53d59080",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aabe01328fe04a3fb8420048caede4b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'doc_writer': {'documents_written': 5}}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from haystack import Pipeline, Document\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "from haystack.components.writers import DocumentWriter\n",
+    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+    "\n",
+    "documents = [\n",
+    "    Document(content=\"My name is Jean and I live in Paris.\"),\n",
+    "    Document(content=\"My name is Mark and I live in Berlin.\"),\n",
+    "    Document(content=\"My name is Giorgio and I live in Rome.\"),\n",
+    "    Document(content=\"My name is Marta and I live in Madrid.\"),\n",
+    "    Document(content=\"My name is Harry and I live in London.\"),\n",
+    "]\n",
+    "\n",
+    "document_store = InMemoryDocumentStore()\n",
+    "\n",
+    "indexing_pipeline = Pipeline()\n",
+    "indexing_pipeline.add_component(\n",
+    "    instance=SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"), name=\"doc_embedder\"\n",
+    ")\n",
+    "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"doc_writer\")\n",
+    "\n",
+    "indexing_pipeline.connect(\"doc_embedder.documents\", \"doc_writer.documents\")\n",
+    "\n",
+    "indexing_pipeline.run({\"doc_embedder\": {\"documents\": documents}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dwC6fWQy-7pI"
+   },
+   "source": [
+    "### Build a RAG Pipeline\n",
+    "\n",
+    "Build a basic retrieval augmented generative pipeline with [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) and [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator).\n",
+    "\n",
+    "> For a step-by-step guide to create a RAG pipeline with Haystack, follow the [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "23JuUuql7PZ8",
+    "outputId": "b33cbdf5-93d3-46c2-d618-ed9bb76c2d4c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "from haystack.components.builders import PromptBuilder\n",
+    "from haystack.components.generators import OpenAIGenerator\n",
+    "\n",
+    "template = \"\"\"\n",
+    "Answer the questions based on the given context.\n",
+    "\n",
+    "Context:\n",
+    "{% for document in documents %}\n",
+    "    {{ document.content }}\n",
+    "{% endfor %}\n",
+    "Question: {{ question }}\n",
+    "Answer:\n",
+    "\"\"\"\n",
+    "rag_pipe = Pipeline()\n",
+    "rag_pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+    "rag_pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+    "rag_pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+    "rag_pipe.add_component(\"llm\", OpenAIGenerator(model=\"gpt-3.5-turbo\"))\n",
+    "\n",
+    "rag_pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+    "rag_pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+    "rag_pipe.connect(\"prompt_builder\", \"llm\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3vjtLi5A-XAi"
+   },
+   "source": [
+    "### Run the Pipeline\n",
+    "Test this pipeline with a query and see if it works as expected before you start using it as a function calling tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 171,
+     "referenced_widgets": [
+      "6f4a57e2a1ea45bd98c7b93dd44f7059",
+      "5b4bd1a310064a7a9c7a59df8207aa99",
+      "1bad78781754402cb69bd8a184a4837d",
+      "82f9052fbc3649feb3265a3fd923f8ac",
+      "8468f0f89a26485ebc51ffdd6e69746d",
+      "15bce8c8eaac422d8cf65d60df2b467e",
+      "470dca09340f481fb381f18aac4da630",
+      "c9c5e00085874c76b587031841b906ba",
+      "86df742e769c492fa274fecf84e38f33",
+      "2bb05097cf4e4f18afbc2a1c32047ddb",
+      "7879458eca5a461ab42a02d5fcd4d7fa"
+     ]
+    },
+    "id": "h4bXXC2Z7eaj",
+    "outputId": "c069ff2a-7399-4605-f3cc-998c1ebe1504"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f4a57e2a1ea45bd98c7b93dd44f7059",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'llm': {'replies': ['Berlin'],\n",
+       "  'meta': [{'model': 'gpt-3.5-turbo-0125',\n",
+       "    'index': 0,\n",
+       "    'finish_reason': 'stop',\n",
+       "    'usage': {'completion_tokens': 1,\n",
+       "     'prompt_tokens': 86,\n",
+       "     'total_tokens': 87}}]}}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"Where does Mark live?\"\n",
+    "rag_pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xeQf1QT0_OA0"
+   },
+   "source": [
+    "### Convert the Haystack Pipeline into a Tool\n",
+    "\n",
+    "Wrap the `rag_pipe.run` call with a function called `rag_pipeline_func`. This `rag_pipeline_func` function will accept a `query` and return the response coming from the LLM of the RAG pipeline you built before. You will then introduce this function as a tool to your `OpenAIChatGenerator`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "hCUFdKV5_Z7k"
+   },
+   "outputs": [],
+   "source": [
+    "def rag_pipeline_func(query: str):\n",
+    "    result = rag_pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}})\n",
+    "\n",
+    "    return {\"reply\": result[\"llm\"][\"replies\"][0]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zJt-mzb4oHxj"
+   },
+   "source": [
+    "## Creating Your `tools` List\n",
+    "\n",
+    "In addition to the `rag_pipeline_func` tool, create a new tool called `get_current_weather` to be used to get weather information of cities. For demonstration purposes, you can use hardcoded data in this function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "XaQgnmPoJD13"
+   },
+   "outputs": [],
+   "source": [
+    "WEATHER_INFO = {\n",
+    "    \"Berlin\": {\"weather\": \"mostly sunny\", \"temperature\": 7, \"unit\": \"celsius\"},\n",
+    "    \"Paris\": {\"weather\": \"mostly cloudy\", \"temperature\": 8, \"unit\": \"celsius\"},\n",
+    "    \"Rome\": {\"weather\": \"sunny\", \"temperature\": 14, \"unit\": \"celsius\"},\n",
+    "    \"Madrid\": {\"weather\": \"sunny\", \"temperature\": 10, \"unit\": \"celsius\"},\n",
+    "    \"London\": {\"weather\": \"cloudy\", \"temperature\": 9, \"unit\": \"celsius\"},\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def get_current_weather(location: str):\n",
+    "    if location in WEATHER_INFO:\n",
+    "        return WEATHER_INFO[location]\n",
+    "\n",
+    "    # fallback data\n",
+    "    else:\n",
+    "        return {\"weather\": \"sunny\", \"temperature\": 21.8, \"unit\": \"fahrenheit\"}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "M6Pfmf7YSwsL"
+   },
+   "source": [
+    "Now, add function specifications for `rag_pipeline_func` and `get_current_weather` to your `tools` list by following [OpenAI's tool schema](https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools). Provide detailed descriptions about `rag_pipeline_func` and `query` so that OpenAI can generate the adaquate arguments for this tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "rfYYSytSVtPm"
+   },
+   "outputs": [],
+   "source": [
+    "tools = [\n",
+    "    {\n",
+    "        \"type\": \"function\",\n",
+    "        \"function\": {\n",
+    "            \"name\": \"rag_pipeline_func\",\n",
+    "            \"description\": \"Get information about where people live\",\n",
+    "            \"parameters\": {\n",
+    "                \"type\": \"object\",\n",
+    "                \"properties\": {\n",
+    "                    \"query\": {\n",
+    "                        \"type\": \"string\",\n",
+    "                        \"description\": \"The query to use in the search. Infer this from the user's message. It should be a question or a statement\",\n",
+    "                    }\n",
+    "                },\n",
+    "                \"required\": [\"query\"],\n",
+    "            },\n",
+    "        },\n",
+    "    },\n",
+    "    {\n",
+    "        \"type\": \"function\",\n",
+    "        \"function\": {\n",
+    "            \"name\": \"get_current_weather\",\n",
+    "            \"description\": \"Get the current weather\",\n",
+    "            \"parameters\": {\n",
+    "                \"type\": \"object\",\n",
+    "                \"properties\": {\n",
+    "                    \"location\": {\"type\": \"string\", \"description\": \"The city and state, e.g. San Francisco, CA\"}\n",
+    "                },\n",
+    "                \"required\": [\"location\"],\n",
+    "            },\n",
+    "        },\n",
+    "    },\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bkRPp3JKpZgf"
+   },
+   "source": [
+    "## Running OpenAIChatGenerator with Tools\n",
+    "\n",
+    "To use the function calling feature, you need to pass the list of tools in the `run()` method of OpenAIChatGenerator as `generation_kwargs`.\n",
+    "\n",
+    "Instruct the model to use provided tools with a system message and then provide a query that requires a function call as a user message:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "OEScMyqctzFN"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.dataclasses import ChatMessage\n",
+    "from haystack.components.generators.chat import OpenAIChatGenerator\n",
+    "from haystack.components.generators.utils import print_streaming_chunk\n",
+    "\n",
+    "messages = [\n",
+    "    ChatMessage.from_system(\n",
+    "        \"Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.\"\n",
+    "    ),\n",
+    "    ChatMessage.from_user(\"Can you tell me where Mark lives?\"),\n",
+    "]\n",
+    "\n",
+    "chat_generator = OpenAIChatGenerator(model=\"gpt-3.5-turbo\", streaming_callback=print_streaming_chunk)\n",
+    "response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zYwb-SqaGL3O"
+   },
+   "source": [
+    "As a response, you'll get a `ChatMessage` with information about the tool name and arguments in JSON format:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "{'replies': [\n",
+    "    ChatMessage(\n",
+    "        content='[{\"index\": 0, \"id\": \"call_3VnT0XQH0ye41g3Ip5CRz4ri\", \"function\": {\"arguments\": \"{\\\\\"query\\\\\":\\\\\"Where does Mark live?\\\\\"}\", \"name\": \"rag_pipeline_func\"}, \"type\": \"function\"}]', role=<ChatRole.ASSISTANT: 'assistant'>, \n",
+    "        name=None, \n",
+    "        meta={'model': 'gpt-3.5-turbo-0125', 'index': 0, 'finish_reason': 'tool_calls', 'usage': {}}\n",
+    "        )\n",
+    "    ]\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_ulFfHcnGfsw"
+   },
+   "source": [
+    "You can then parse the message content string into JSON and call the corresponding function with the provided arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 101,
+     "referenced_widgets": [
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+    "outputId": "1b2776ec-a53f-4cd6-ebd8-cca1eb3f7a46"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Function Name: rag_pipeline_func\n",
+      "Function Arguments: {'query': 'Where does Mark live?'}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "910f6e07a2b843158e74a297f4934f97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Function Response: {'reply': 'Mark lives in Berlin.'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "## Parse function calling information\n",
+    "function_call = json.loads(response[\"replies\"][0].content)[0]\n",
+    "function_name = function_call[\"function\"][\"name\"]\n",
+    "function_args = json.loads(function_call[\"function\"][\"arguments\"])\n",
+    "print(\"Function Name:\", function_name)\n",
+    "print(\"Function Arguments:\", function_args)\n",
+    "\n",
+    "## Find the correspoding function and call it with the given arguments\n",
+    "available_functions = {\"rag_pipeline_func\": rag_pipeline_func, \"get_current_weather\": get_current_weather}\n",
+    "function_to_call = available_functions[function_name]\n",
+    "function_response = function_to_call(**function_args)\n",
+    "print(\"Function Response:\", function_response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zKwSQm9EMP4e"
+   },
+   "source": [
+    "As the last step, run the OpenAIChatGenerator again by appending the function response to the `messages` list as a new message with `ChatMessage.from_function()` and let the model summarize the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "7szLnSZVMXVD",
+    "outputId": "18398a55-e83b-48ee-c4e9-fca2bc1dd4be"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mark lives in Berlin."
+     ]
+    }
+   ],
+   "source": [
+    "from haystack.dataclasses import ChatMessage\n",
+    "\n",
+    "function_message = ChatMessage.from_function(content=json.dumps(function_response), name=function_name)\n",
+    "messages.append(function_message)\n",
+    "\n",
+    "response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5UkjGYEfwPzS"
+   },
+   "source": [
+    "## Building the Chat Application\n",
+    "\n",
+    "As you notice above, OpenAI Chat Completions API does not call the function; instead, the model generates JSON that you can use to call the function in your code. That's why, to build an end-to-end chat application, you need to check if the OpenAI response is a `tool_calls` for every message. If so, you need to call the corresponding function with the provided arguments and send the function response back to OpenAI. Otherwise, append both user and messages to the `messages` list to have a regular conversation with the model. \n",
+    "\n",
+    "Run the code cell below and use the input box to interact with the chat application that has access to two tools you've created above.  \n",
+    "\n",
+    "Example queries you can try:\n",
+    "* \"***What is the capital of Sweden?***\": A basic query without any function calls\n",
+    "* \"***Can you tell me where Giorgio lives?***\": A basic query with one function call\n",
+    "* \"***What's the weather like in Berlin?***\", \"***Is it sunny there?***\": To see the messages are being recorded and sent\n",
+    "* \"***What's the weather like where Jean lives?***\": To force two function calls\n",
+    "* \"***What's the weather like today?***\": To force OpenAI to ask more clarification\n",
+    "\n",
+    "> Keep in mind that OpenAI models can sometimes hallucinate answers or tools and might not work as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 153,
+     "referenced_widgets": [
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+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stop\n",
+      "Can you tell me where Giorgio lives?\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "235721f8c1c14ce3885fba24638bc27f",
+       "version_major": 2,
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+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Giorgio lives in Rome.ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stop\n",
+      "What's the weather like there?\n",
+      "The weather in Rome is sunny with a temperature of 14°C.ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stop\n",
+      "exit\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "from haystack.dataclasses import ChatMessage, ChatRole\n",
+    "from haystack.components.generators.chat import OpenAIChatGenerator\n",
+    "from haystack.components.generators.utils import print_streaming_chunk\n",
+    "\n",
+    "chat_generator = OpenAIChatGenerator(model=\"gpt-3.5-turbo\", streaming_callback=print_streaming_chunk)\n",
+    "response = None\n",
+    "messages = [\n",
+    "    ChatMessage.from_system(\n",
+    "        \"Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.\"\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "while True:\n",
+    "    # if OpenAI response is a tool call\n",
+    "    if response and response[\"replies\"][0].meta[\"finish_reason\"] == \"tool_calls\":\n",
+    "        function_calls = json.loads(response[\"replies\"][0].content)\n",
+    "\n",
+    "        for function_call in function_calls:\n",
+    "            ## Parse function calling information\n",
+    "            function_name = function_call[\"function\"][\"name\"]\n",
+    "            function_args = json.loads(function_call[\"function\"][\"arguments\"])\n",
+    "\n",
+    "            ## Find the correspoding function and call it with the given arguments\n",
+    "            function_to_call = available_functions[function_name]\n",
+    "            function_response = function_to_call(**function_args)\n",
+    "\n",
+    "            ## Append function response to the messages list using `ChatMessage.from_function`\n",
+    "            messages.append(ChatMessage.from_function(content=json.dumps(function_response), name=function_name))\n",
+    "\n",
+    "    # Regular Conversation\n",
+    "    else:\n",
+    "        # Append assistant messages to the messages list\n",
+    "        if not messages[-1].is_from(ChatRole.SYSTEM):\n",
+    "            messages.append(response[\"replies\"][0])\n",
+    "\n",
+    "        user_input = input(\"ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stop\\n\")\n",
+    "        if user_input.lower() == \"exit\" or user_input.lower() == \"quit\":\n",
+    "            break\n",
+    "        else:\n",
+    "            messages.append(ChatMessage.from_user(user_input))\n",
+    "\n",
+    "    response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rgGKjm7cpv1_"
+   },
+   "source": [
+    "## What's next\n",
+    "\n",
+    "🎉 Congratulations! You've learned how to build chat applications that demonstrate agent-like behavior using OpenAI function calling and Haystack Pipelines.\n",
+    "\n",
+    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+    "- [Serializing LLM Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+    "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=function-calling) or [join Haystack discord community](https://discord.gg/haystack).\n",
+    "\n",
+    "Thanks for reading!"
+   ]
+  }
+ ],
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From 42f1636af2d69418612aa8f200bfc9008ccf76ca Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 5 Mar 2024 17:49:57 +0100
Subject: [PATCH 174/206] Add featured tutorials (#302)

---
 index.toml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/index.toml b/index.toml
index 3ee8743e..cd2539a0 100644
--- a/index.toml
+++ b/index.toml
@@ -287,6 +287,7 @@ aliases = []
 completion_time = "15 min"
 created_at = 2023-11-30
 haystack_2 = true
+featured = true
 
 [[tutorial]]
 title = "Serializing LLM Pipelines"
@@ -375,6 +376,7 @@ aliases = []
 completion_time = "10 min"
 created_at = 2024-02-16
 haystack_2 = true
+featured = true
 
 [[tutorial]]
 title = "Simplifying Pipeline Inputs with Multiplexer"

From c3e4ceb284466b7b853507e615aed202226d036d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Wed, 6 Mar 2024 17:39:28 +0300
Subject: [PATCH 175/206] Fix a typo (#304)

---
 tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb  | 2 +-
 .../40_Building_Chat_Application_with_Function_Calling.ipynb    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
index a08ae481..84f085f2 100644
--- a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
+++ b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
@@ -11,7 +11,7 @@
         "- **Level**: Intermediate\n",
         "- **Time to complete**: 10 minutes\n",
         "- **Components Used**: [`ConditionalRouter`](https://docs.haystack.deepset.ai/v2.0/docs/conditionalrouter), [`SerperDevWebSearch`](https://docs.haystack.deepset.ai/v2.0/docs/serperdevwebsearch), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator)\n",
-        "- **Prerequisites**: You must have an [Open API Key](https://platform.openai.com/api-keys) and a [Serper API Key](https://serper.dev/api-key) for this tutorial\n",
+        "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys) and a [Serper API Key](https://serper.dev/api-key) for this tutorial\n",
         "- **Goal**: After completing this tutorial, you'll have learned how to create a pipeline with conditional routing that can fallback to websearch if the answer is not present in your dataset.\n",
         "\n",
         "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n"
diff --git a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
index b0c94fe5..55edb360 100644
--- a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
+++ b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
@@ -11,7 +11,7 @@
     "- **Level**: Advanced\n",
     "- **Time to complete**: 20 minutes\n",
     "- **Components Used**: [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator), [OpenAIChatGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaichatgenerator)\n",
-    "- **Prerequisites**: You must have an [Open API Key](https://platform.openai.com/api-keys) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines)\n",
+    "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines)\n",
     "- **Goal**: After completing this tutorial, you will have learned how to build chat applications that demonstrate agent-like behavior using OpenAI's function calling feature.\n",
     "\n",
     "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n"

From 6fab481c28e5d886e6074802c6408521d3285f09 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 8 Mar 2024 18:24:19 +0300
Subject: [PATCH 176/206] Update the first RAG tutorial of 2.0 (#305)

* Change Mistral model name

* Update mistral model name in tut 30

* Remove redundant output

* Tut 37 remove redundant output

* Update the first tutorial

* Add indexing pipeline explanation
---
 tutorials/27_First_RAG_Pipeline.ipynb | 3982 +++++++++----------------
 1 file changed, 1436 insertions(+), 2546 deletions(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 54e7dadf..66fb0826 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -1,2555 +1,1445 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2OvkPji9O-qX"
-   },
-   "source": [
-    "# Tutorial: Creating Your First QA Pipeline with Retrieval-Augmentation\n",
-    "\n",
-    "- **Level**: Beginner\n",
-    "- **Time to complete**: 10 minutes\n",
-    "- **Components Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `OpenAIGenerator`\n",
-    "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI.\n",
-    "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and OpenAIGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n",
-    "\n",
-    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LFqHcXYPO-qZ"
-   },
-   "source": [
-    "## Overview\n",
-    "\n",
-    "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves three main components: [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) for creating a template prompt, and [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) for generating responses.\n",
-    "\n",
-    "For this tutorial, you'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "QXjVlbPiO-qZ"
-   },
-   "source": [
-    "## Preparing the Colab Environment\n",
-    "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Kww5B_vXO-qZ"
-   },
-   "source": [
-    "## Installing Haystack\n",
-    "\n",
-    "Install Haystack 2.0 Beta and [datasets](https://pypi.org/project/datasets/) with `pip`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "UQbU8GUfO-qZ"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install haystack-ai\n",
-    "pip install \"datasets>=2.6.1\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Wl_jYERtO-qa"
-   },
-   "source": [
-    "### Enabling Telemetry\n",
-    "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "A76B4S49O-qa"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.telemetry import tutorial_running\n",
-    "\n",
-    "tutorial_running(27)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "_lvfew16O-qa"
-   },
-   "source": [
-    "## Initializing the DocumentStore\n",
-    "\n",
-    "You'll start creating your question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, you'll be using the `InMemoryDocumentStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "CbVN-s5LO-qa"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-    "\n",
-    "document_store = InMemoryDocumentStore()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "yL8nuJdWO-qa"
-   },
-   "source": [
-    "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XvLVaFHTO-qb"
-   },
-   "source": [
-    "The DocumentStore is now ready. Now it's time to fill it with some Documents."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HryYZP9ZO-qb"
-   },
-   "source": [
-    "## Fetching and Writing Documents\n",
-    "\n",
-    "You'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, you don't need to perform any additional cleaning or splitting.\n",
-    "\n",
-    "Fetch the data and write it to the DocumentStore:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 194,
-     "referenced_widgets": [
-      "2b80b4402f1c4245835b0a6ff9724f17",
-      "46333addbfc5402b909f8671494b324f",
-      "ea2aad5049ba46339c12eb1d8f6e5069",
-      "de37e5d8932d4c6fab471c8e6aa69d38",
-      "42eb5725b3454b41924e9cbbe1526bda",
-      "d20a813e1de54cce85837e3f89d9ac72",
-      "f723dc909e054ff0a2dec3ce425793b4",
-      "3f9c5812fc6d480185467e804082ae2b",
-      "147c9c74cba740608709d4f20577ef01",
-      "5ee3bc5478824ab097a5f4205018bd7f",
-      "3b84d92477174d319edceab41aade78a",
-      "35cdea243a1e4bd29ea077d5a453b86a",
-      "fbfab62f10e14d909b5e64a8c982fa71",
-      "674f8660be5d4ee6ba5fe657934065f2",
-      "1bcea8e4134449eea12d43ffbbfe0c7d",
-      "9fde7b64a0964e1e929ba9a10ba9dce3",
-      "368afc1b3aa143e085dfbc053e34b1aa",
-      "4c26080f262b4688be7d67eccd871a5c",
-      "44388b7afeaa4d059bd93debb114b931",
-      "cbaba46a13ac45afbb7e37482608fbe1",
-      "9733632c4d2843a09551ba51ab29dff0",
-      "a80945a5c46a496a9e5c8811b3503472",
-      "fce43ee8cb7a45849045f4cfee69c206",
-      "6d6f5dab1c984c3e92e95a1eaca1ba0e",
-      "6ddacfb258594e5cb3ed198d43531b77",
-      "b442ede7736b46f98a170bd0246ae002",
-      "c0fc0d52594d469e91b71555139cc541",
-      "f093be7cc403446f96939e52507411c9",
-      "26e17b07b56e470383feb5eec36e5499",
-      "7440a265999e4e87ad92c3eace177171",
-      "7df6980a5f8c4feb8bfdc86231de6c5c",
-      "b2adc841e1d945c6a4619c34c74856a0",
-      "d0c0b275608d49e08a882c9601a57806",
-      "1f811c7951d2449595b8af976f0ab148",
-      "b067e4c0fa2c48029977f51ccc8f75f1",
-      "66b10c54d93a43efa092c314cd029c3a",
-      "654902128296465f9d808def143460fe",
-      "4742bb3afe0f4857860a6822d394931b",
-      "b58ffbecddf84ef98f06048dbce2b8ea",
-      "667ce03f6e8c4d11a18e533e83aebb78",
-      "d252d977929649b98053f553ca26185d",
-      "ec40181cd49f4218982907465a3095c5",
-      "c4951cfa0a7641cc8447e554b2170447",
-      "b871e9d440054eb3ba5518f0b7e388a3",
-      "397d6f7fb81947fc8a1f112da5735e05",
-      "abd891ee1af445b5936eb1c4c5a46718",
-      "443227b9a8664a8eabd2e2d3ee642392",
-      "5971f00c34e64ac698322b4d67f94a16",
-      "360dcbf1ae9645259be25619a1565f71",
-      "8bc32eebc7914cd3a1cbe6247908f4c1",
-      "d72fb4ea029b41b4a8c290c93cae3b35",
-      "bb10a9f77a5848d4a7f4302f26b0287e",
-      "d1fd0925a6264ce993dfaf567452d88d",
-      "7f0cb20fbe6e434db8b06ad7bee4b655",
-      "7e93188c9d0f486284651fe43530b6db"
-     ]
-    },
-    "id": "INdC3WvLO-qb",
-    "outputId": "35a74956-06ca-4f50-a3be-b1c0871b0949"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from haystack import Document\n",
-    "\n",
-    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
-    "docs = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]\n",
-    "document_store.write_documents(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0_cj-5m-O-qb"
-   },
-   "source": [
-    "## Initializing the Retriever\n",
-    "\n",
-    "Initialize a [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) and make it use the InMemoryDocumentStore we initialized earlier in this tutorial. This Retriever will get the relevant documents to the query:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "-uo-6fjiO-qb"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
-    "\n",
-    "retriever = InMemoryBM25Retriever(document_store)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6CEuQpB7O-qb"
-   },
-   "source": [
-    "## Defining a Template Prompt\n",
-    "\n",
-    "Create a custom prompt for a generative question answering task using the RAG approach. The prompt should take in two parameters: `documents`, which are retrieved from a document store, and a `question` from the user. Use the Jinja2 looping syntax to combine the content of the retrieved documents in the prompt.\n",
-    "\n",
-    "Next, initialize a [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) instance with your prompt template. The PromptBuilder, when given the necessary values, will automatically fill in the variable values and generate a complete prompt. This approach allows for a more tailored and effective question-answering experience."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ObahTh45FqOT"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.components.builders import PromptBuilder\n",
-    "\n",
-    "template = \"\"\"\n",
-    "Given the following information, answer the question.\n",
-    "\n",
-    "Context:\n",
-    "{% for document in documents %}\n",
-    "    {{ document.content }}\n",
-    "{% endfor %}\n",
-    "\n",
-    "Question: {{question}}\n",
-    "Answer:\n",
-    "\"\"\"\n",
-    "\n",
-    "prompt_builder = PromptBuilder(template=template)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HR14lbfcFtXj"
-   },
-   "source": [
-    "## Initializing a Generator\n",
-    "\n",
-    "\n",
-    "Generators are the components that interacts with large language models (LLMs). Now, set `OPENAI_API_KEY` environment variable and initialize a [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/OpenAIGenerator) that can communicate with OpenAI GPT models. If you don't provide any `model`, the OpenAIGenerator defaults to `gpt-3.5-turbo`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "SavE_FAqfApo",
-    "outputId": "4c1fa657-8ca0-42ca-8e49-161edf3be505"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from haystack.components.generators import OpenAIGenerator\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key: \")\n",
-    "generator = OpenAIGenerator()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nenbo2SvycHd"
-   },
-   "source": [
-    "> You can replace `OpenAIGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1bfHwOQwycHe"
-   },
-   "source": [
-    "## Building the Pipeline\n",
-    "\n",
-    "To build a pipeline, add all components to your pipeline and connect them. Create connections from `retriever` to the `prompt_builder` and from `prompt_builder` to `llm`. Explicitly connect the output of `retriever` with \"documents\" input of the `prompt_builder` to make the connection obvious as `prompt_builder` has two inputs (\"documents\" and \"question\"). For more information on pipelines and creating connections, refer to [Creating Pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines) documentation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "f6NFmpjEO-qb"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack import Pipeline\n",
-    "\n",
-    "basic_rag_pipeline = Pipeline()\n",
-    "# Add components to your pipeline\n",
-    "basic_rag_pipeline.add_component(\"retriever\", retriever)\n",
-    "basic_rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n",
-    "basic_rag_pipeline.add_component(\"llm\", generator)\n",
-    "\n",
-    "# Now, connect the components to each other\n",
-    "basic_rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
-    "basic_rag_pipeline.connect(\"prompt_builder\", \"llm\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mtcrMpjdzBYo"
-   },
-   "source": [
-    "### Visualize the Pipeline\n",
-    "\n",
-    "Draw the pipeline with the `draw()` method to confirm the connections are correct. You can find the diagram in the Files section of this Colab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "DAvgZnaczEVd"
-   },
-   "outputs": [],
-   "source": [
-    "basic_rag_pipeline.draw(\"basic-rag-pipeline.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6NqyLhx7O-qc"
-   },
-   "source": [
-    "That's it! The pipeline's ready to generate answers to questions!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DBAyF5tVO-qc"
-   },
-   "source": [
-    "## Asking a Question\n",
-    "\n",
-    "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to both the `retriever` and the `prompt_builder`. This ensures that the `{{question}}` variable in the template prompt gets replaced with your specific question."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 86,
-     "referenced_widgets": [
-      "3536f342b4cc4293898aee442b595c99",
-      "2e3798280bda4190af5c4adb9fbdbb25",
-      "92c29977e67944bf87cc62e10f62d959",
-      "77b7b1d1d77040bfbcbb282ef5936fbd",
-      "935932e80bfb47949e32abe15b8e74b2",
-      "53f66341cde247088a714e24a66ca171",
-      "ffdb93e36b7b4029addb195e40d577e2",
-      "11febd0a9595480abd88aeaaae069b17",
-      "01a2be51bb574f878d63eb3fd2a65d81",
-      "a23da41a890f4dbc9fb0f20439b007ae",
-      "32b1264047ba43b08217db2889a43eca"
-     ]
-    },
-    "id": "Vnt283M5O-qc",
-    "outputId": "1833cb17-ac1c-496f-fd65-74a9af9584a0"
-   },
-   "outputs": [],
-   "source": [
-    "question = \"What does Rhodes Statue look like?\"\n",
-    "\n",
-    "response = basic_rag_pipeline.run({\"retriever\": {\"query\": question}, \"prompt_builder\": {\"question\": question}})\n",
-    "\n",
-    "print(response[\"llm\"][\"replies\"][0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "IWQN-aoGO-qc"
-   },
-   "source": [
-    "Here are some other example questions to test:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_OHUQ5xxO-qc"
-   },
-   "outputs": [],
-   "source": [
-    "examples = [\n",
-    "    \"Where is Gardens of Babylon?\",\n",
-    "    \"Why did people build Great Pyramid of Giza?\",\n",
-    "    \"What does Rhodes Statue look like?\",\n",
-    "    \"Why did people visit the Temple of Artemis?\",\n",
-    "    \"What is the importance of Colossus of Rhodes?\",\n",
-    "    \"What happened to the Tomb of Mausolus?\",\n",
-    "    \"How did Colossus of Rhodes collapse?\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XueCK3y4O-qc"
-   },
-   "source": [
-    "🎉 Congratulations! You've learned how to create a generative QA system for your documents with RAG approach."
-   ]
-  }
- ],
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+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2OvkPji9O-qX"
+      },
+      "source": [
+        "# Tutorial: Creating Your First QA Pipeline with Retrieval-Augmentation\n",
+        "\n",
+        "- **Level**: Beginner\n",
+        "- **Time to complete**: 10 minutes\n",
+        "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator)\n",
+        "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys).\n",
+        "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and OpenAIGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n",
+        "\n",
+        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LFqHcXYPO-qZ"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves four main components: [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder) for creating an embedding for the user query, [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) for creating a template prompt, and [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) for generating responses.\n",
+        "\n",
+        "For this tutorial, you'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QXjVlbPiO-qZ"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kww5B_vXO-qZ"
+      },
+      "source": [
+        "## Installing Haystack\n",
+        "\n",
+        "Install Haystack 2.0 and other required packages with `pip`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UQbU8GUfO-qZ",
+        "outputId": "c33579e9-5557-43bd-a3c5-63b8373770c7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
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+            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers>=2.2.0) (23.2)\n",
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.0) (1.12)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.0) (3.2.1)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.0) (3.1.3)\n",
+            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.0) (2.1.0)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.32.0->sentence-transformers>=2.2.0) (2023.12.25)\n",
+            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.32.0->sentence-transformers>=2.2.0) (0.15.2)\n",
+            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.32.0->sentence-transformers>=2.2.0) (0.4.2)\n",
+            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers>=2.2.0) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers>=2.2.0) (3.3.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers>=2.2.0) (2.1.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=2.2.0) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=2.2.0) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=2.2.0) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=2.2.0) (2024.2.2)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence-transformers>=2.2.0) (1.3.0)\n"
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-       "IPY_MODEL_de37e5d8932d4c6fab471c8e6aa69d38"
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install haystack-ai\n",
+        "pip install \"datasets>=2.6.1\"\n",
+        "pip install \"sentence-transformers>=2.2.0\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Wl_jYERtO-qa"
+      },
+      "source": [
+        "### Enabling Telemetry\n",
+        "\n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "A76B4S49O-qa"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(27)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_lvfew16O-qa"
+      },
+      "source": [
+        "## Fetching and Indexing Documents\n",
+        "\n",
+        "You'll start creating your question answering system by downloading the data and indexing the data with its embeddings to a DocumenStore. \n",
+        "\n",
+        "In this tutorial, you will take a simple approach to writing documents and their embeddings into the DocumenStore. For a full indexing pipeline with preprocessing, cleaning and splitting, check out our tutorial on [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline).\n",
+        "\n",
+        "\n",
+        "### Initializing the DocumentStore\n",
+        "\n",
+        "Initialize a DocumentStore to index your documents. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, you'll be using the `InMemoryDocumentStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "CbVN-s5LO-qa"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+        "\n",
+        "document_store = InMemoryDocumentStore()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yL8nuJdWO-qa"
+      },
+      "source": [
+        "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XvLVaFHTO-qb"
+      },
+      "source": [
+        "The DocumentStore is now ready. Now it's time to fill it with some Documents."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HryYZP9ZO-qb"
+      },
+      "source": [
+        "### Fetch the Data\n",
+        "\n",
+        "You'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, you don't need to perform any additional cleaning or splitting.\n",
+        "\n",
+        "Fetch the data and convert it into Haystack Documents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "INdC3WvLO-qb",
+        "outputId": "1af43d0f-2999-4de4-d152-b3cca9fb49e6"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        }
       ],
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-      "style": "IPY_MODEL_ffdb93e36b7b4029addb195e40d577e2",
-      "value": "Ranking by BM25...: 100%"
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-    "3536f342b4cc4293898aee442b595c99": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "HBoxModel",
-     "state": {
-      "_dom_classes": [],
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "HBoxModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "HBoxView",
-      "box_style": "",
-      "children": [
-       "IPY_MODEL_2e3798280bda4190af5c4adb9fbdbb25",
-       "IPY_MODEL_92c29977e67944bf87cc62e10f62d959",
-       "IPY_MODEL_77b7b1d1d77040bfbcbb282ef5936fbd"
+      "source": [
+        "from datasets import load_dataset\n",
+        "from haystack import Document\n",
+        "\n",
+        "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
+        "docs = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "czMjWwnxPA-3"
+      },
+      "source": [
+        "### Initalize a Document Embedder\n",
+        "\n",
+        "To store your data in the DocumentStore with embeddings, initialize a [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder) with the model name and call `warm_up()` to download the embedding model.\n",
+        "\n",
+        "> If you'd like, you can use a different [Embedder](https://docs.haystack.deepset.ai/v2.0/docs/embedders) for your documents."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EUmAH9sEn3R7",
+        "outputId": "ee54b59b-4d4a-45eb-c1a9-0b7b248f1dd4"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
+            "  return self.fget.__get__(instance, owner)()\n"
+          ]
+        }
       ],
-      "layout": "IPY_MODEL_935932e80bfb47949e32abe15b8e74b2"
-     }
-    },
-    "35cdea243a1e4bd29ea077d5a453b86a": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "HBoxModel",
-     "state": {
-      "_dom_classes": [],
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "HBoxModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "HBoxView",
-      "box_style": "",
-      "children": [
-       "IPY_MODEL_fbfab62f10e14d909b5e64a8c982fa71",
-       "IPY_MODEL_674f8660be5d4ee6ba5fe657934065f2",
-       "IPY_MODEL_1bcea8e4134449eea12d43ffbbfe0c7d"
+      "source": [
+        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+        "\n",
+        "doc_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+        "doc_embedder.warm_up()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9y4iJE_SrS4K"
+      },
+      "source": [
+        "### Write Documents to the DocumentStore\n",
+        "\n",
+        "Run the `doc_embedder` with the Documents. The embedder will create embeddings for each document and save these embeddings in Document object's `embedding` field. Then, you can write the Documents to the DocumentStore with `write_documents()` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
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+        "id": "ETpQKftLplqh",
+        "outputId": "b9c8658c-90c8-497c-e765-97487c0daf8e"
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+            "text/plain": [
+              "Batches:   0%|          | 0/5 [00:00<?, ?it/s]"
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-       "IPY_MODEL_5971f00c34e64ac698322b4d67f94a16"
+      "source": [
+        "docs_with_embeddings = doc_embedder.run(docs)\n",
+        "document_store.write_documents(docs_with_embeddings[\"documents\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IdojTxg6uubn"
+      },
+      "source": [
+        "## Building the RAG Pipeline\n",
+        "\n",
+        "The next step is to build a [Pipeline](https://docs.haystack.deepset.ai/v2.0/docs/pipelines) to generate answers for the user query following the RAG approach. To create the pipeline, you first need to initialize each component, add them to your pipeline, and connect them."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0uyV6-u-u56P"
+      },
+      "source": [
+        "### Initialize a Text Embedder\n",
+        "\n",
+        "Initialize a text embedder to create an embedding for the user query. The created embedding will later be used by the Retriever to retrieve relevant documents from the DocumentStore.\n",
+        "\n",
+        "> ⚠️ Notice that you used `sentence-transformers/all-MiniLM-L6-v2` model to create embeddings for your documents before. This is why you need to use the same model to embed the user queries."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "LyJY2yW628dl"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+        "\n",
+        "text_embedder = SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0_cj-5m-O-qb"
+      },
+      "source": [
+        "### Initialize the Retriever\n",
+        "\n",
+        "Initialize a [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever) and make it use the InMemoryDocumentStore you initialized earlier in this tutorial. This Retriever will get the relevant documents to the query."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "-uo-6fjiO-qb"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+        "\n",
+        "retriever = InMemoryEmbeddingRetriever(document_store)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6CEuQpB7O-qb"
+      },
+      "source": [
+        "### Define a Template Prompt\n",
+        "\n",
+        "Create a custom prompt for a generative question answering task using the RAG approach. The prompt should take in two parameters: `documents`, which are retrieved from a document store, and a `question` from the user. Use the Jinja2 looping syntax to combine the content of the retrieved documents in the prompt.\n",
+        "\n",
+        "Next, initialize a [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) instance with your prompt template. The PromptBuilder, when given the necessary values, will automatically fill in the variable values and generate a complete prompt. This approach allows for a more tailored and effective question-answering experience."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "ObahTh45FqOT"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.builders import PromptBuilder\n",
+        "\n",
+        "template = \"\"\"\n",
+        "Given the following information, answer the question.\n",
+        "\n",
+        "Context:\n",
+        "{% for document in documents %}\n",
+        "    {{ document.content }}\n",
+        "{% endfor %}\n",
+        "\n",
+        "Question: {{question}}\n",
+        "Answer:\n",
+        "\"\"\"\n",
+        "\n",
+        "prompt_builder = PromptBuilder(template=template)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HR14lbfcFtXj"
+      },
+      "source": [
+        "### Initialize a Generator\n",
+        "\n",
+        "\n",
+        "Generators are the components that interact with large language models (LLMs). Now, set `OPENAI_API_KEY` environment variable and initialize a [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/OpenAIGenerator) that can communicate with OpenAI GPT models. As you initialize, provide a model name:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SavE_FAqfApo",
+        "outputId": "1afbf2e8-ae63-41ff-c37f-5123b2103356"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Enter OpenAI API key: ··········\n"
+          ]
+        }
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-       "IPY_MODEL_b442ede7736b46f98a170bd0246ae002"
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "from haystack.components.generators import OpenAIGenerator\n",
+        "\n",
+        "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key: \")\n",
+        "generator = OpenAIGenerator(model=\"gpt-3.5-turbo\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nenbo2SvycHd"
+      },
+      "source": [
+        "> You can replace `OpenAIGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1bfHwOQwycHe"
+      },
+      "source": [
+        "### Build the Pipeline\n",
+        "\n",
+        "To build a pipeline, add all components to your pipeline and connect them. Create connections from `text_embedder`'s \"embedding\" output to \"query_embedding\" input of `retriever`, from `retriever` to `prompt_builder` and from `prompt_builder` to `llm`. Explicitly connect the output of `retriever` with \"documents\" input of the `prompt_builder` to make the connection obvious as `prompt_builder` has two inputs (\"documents\" and \"question\").\n",
+        "\n",
+        "For more information on pipelines and creating connections, refer to [Creating Pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines) documentation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "f6NFmpjEO-qb",
+        "outputId": "89fd1b48-5189-4401-9cf8-15f55c503676"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/jpeg": 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+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": []
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "layout": "IPY_MODEL_c0fc0d52594d469e91b71555139cc541"
-     }
-    },
-    "ffdb93e36b7b4029addb195e40d577e2": {
-     "model_module": "@jupyter-widgets/controls",
-     "model_module_version": "1.5.0",
-     "model_name": "DescriptionStyleModel",
-     "state": {
-      "_model_module": "@jupyter-widgets/controls",
-      "_model_module_version": "1.5.0",
-      "_model_name": "DescriptionStyleModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/base",
-      "_view_module_version": "1.2.0",
-      "_view_name": "StyleView",
-      "description_width": ""
-     }
+      "source": [
+        "from haystack import Pipeline\n",
+        "\n",
+        "basic_rag_pipeline = Pipeline()\n",
+        "# Add components to your pipeline\n",
+        "basic_rag_pipeline.add_component(\"text_embedder\", text_embedder)\n",
+        "basic_rag_pipeline.add_component(\"retriever\", retriever)\n",
+        "basic_rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n",
+        "basic_rag_pipeline.add_component(\"llm\", generator)\n",
+        "\n",
+        "# Now, connect the components to each other\n",
+        "basic_rag_pipeline.connect(\"text_embedder.embedding\", \"retriever.query_embedding\")\n",
+        "basic_rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
+        "basic_rag_pipeline.connect(\"prompt_builder\", \"llm\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6NqyLhx7O-qc"
+      },
+      "source": [
+        "That's it! Your RAG pipeline is ready to generate answers to questions!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DBAyF5tVO-qc"
+      },
+      "source": [
+        "## Asking a Question\n",
+        "\n",
+        "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to both the `text_embedder` and the `prompt_builder`. This ensures that the `{{question}}` variable in the template prompt gets replaced with your specific question."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 86,
+          "referenced_widgets": [
+            "4e6e97b6d54f4f80bb7e8b25aba8e616",
+            "1a820c06a7a049d8b6c9ff300284d06e",
+            "58ff4e0603a74978a134f63533859be5",
+            "8bdb8bfae31d4f4cb6c3b0bf43120eed",
+            "39a68d9a5c274e2dafaa2d1f86eea768",
+            "d0cfe5dacdfc431a91b4c4741123e2d0",
+            "e7f1e1a14bb740d18827dd78bbe7b2e3",
+            "3fda06f905b445a488efdd2dd08c0939",
+            "2bc341a780f7498ba9cd475468841bb5",
+            "d7218475e23b420a8c03d00ca4ab8718",
+            "a694abaf765f4d1b82fa0138e59c6793"
+          ]
+        },
+        "id": "Vnt283M5O-qc",
+        "outputId": "d2843a73-3ad5-4daa-8d1e-a58de7aa2bb0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4e6e97b6d54f4f80bb7e8b25aba8e616",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The Rhodes Statue was a 33-meter tall statue of the Greek sun-god Helios, featuring a structure built with iron tie bars covered in brass plates to form the skin. The head of the statue was described as having curly hair with spikes of bronze or silver flame radiating, similar to contemporary Rhodian coins.\n"
+          ]
+        }
+      ],
+      "source": [
+        "question = \"What does Rhodes Statue look like?\"\n",
+        "\n",
+        "response = basic_rag_pipeline.run({\"text_embedder\": {\"text\": question}, \"prompt_builder\": {\"question\": question}})\n",
+        "\n",
+        "print(response[\"llm\"][\"replies\"][0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IWQN-aoGO-qc"
+      },
+      "source": [
+        "Here are some other example questions to test:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "_OHUQ5xxO-qc"
+      },
+      "outputs": [],
+      "source": [
+        "examples = [\n",
+        "    \"Where is Gardens of Babylon?\",\n",
+        "    \"Why did people build Great Pyramid of Giza?\",\n",
+        "    \"What does Rhodes Statue look like?\",\n",
+        "    \"Why did people visit the Temple of Artemis?\",\n",
+        "    \"What is the importance of Colossus of Rhodes?\",\n",
+        "    \"What happened to the Tomb of Mausolus?\",\n",
+        "    \"How did Colossus of Rhodes collapse?\",\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XueCK3y4O-qc"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "🎉 Congratulations! You've learned how to create a generative QA system for your documents with the RAG approach.\n",
+        "\n",
+        "If you liked this tutorial, you may also enjoy:\n",
+        "- [Filtering Documents with Metadata](https://haystack.deepset.ai/tutorials/31_metadata_filtering)\n",
+        "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
+        "- [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [subscribe to our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=first-rag) and [join Haystack discord community](https://discord.gg/haystack).\n",
+        "\n",
+        "Thanks for reading!"
+      ]
     }
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.9.6"
+    },
+    "orig_nbformat": 4,
+    "vscode": {
+      "interpreter": {
+        "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+      }
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "1a820c06a7a049d8b6c9ff300284d06e": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HTMLModel",
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+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "ddeff9a822404adbbc3cad97a939bc0c": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "FloatProgressModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "success",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_7fdb2c859e454e72888709a835f7591e",
+            "max": 5,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_6b8334e071a3438397ba6435aac69f58",
+            "value": 5
+          }
+        },
+        "e7f1e1a14bb740d18827dd78bbe7b2e3": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

From b288a18fe4e8feea456c301dae52d60ef35411ab Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 11 Mar 2024 14:45:18 +0300
Subject: [PATCH 177/206] Update haystack-tutorials README.md (#303)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 284fc219..16ea0b45 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
   <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png#gh-light-mode-only" width="30%"/>
 </p>
 
-<strong><a href="https://github.com/deepset-ai/haystack">Haystack</a></strong> is an open source LLM framework by <strong><a href="https://deepset.ai">deepset</a></strong> to help you build production ready applications. Haystack is designed to help you build systems that work intelligently over large document collections to perform tasks like document retrieval, text generation, question answering, or summarization. It achieves this with the concept of <strong>Pipelines</strong> consisting of various <strong>Components</strong> such as a <strong>DocumentStore</strong>, a <strong>Retriever</strong>, and a <strong>PromptNode</strong>.
+[**Haystack**](https://github.com/deepset-ai/haystack) is an open source framework by [**deepset**](https://deepset.ai) for building production-ready **LLM applications**, **retrieval-augmented generative pipelines** and **state-of-the-art search systems** that work intelligently over large document collections. It lets you quickly try out the latest models in natural language processing (NLP) while being flexible and easy to use.
 
 This is the repository where we keep all the Haystack tutorials 📓 👇 These tutorials are also published to the [Haystack Website](https://haystack.deepset.ai/tutorials/).
 

From b0c500167808f80f034638a9f52fc8f5b7358a87 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 11 Mar 2024 14:48:49 +0300
Subject: [PATCH 178/206] Update README.md

---
 README.md | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 16ea0b45..bda7a7f6 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,8 @@
 # Haystack Tutorials
 
-<p align="center" float="left">
-  <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" width="30%"/>
-  <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored-on-dark.png#gh-dark-mode-only" width="30%"/>
-  <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png#gh-light-mode-only" width="30%"/>
-</p>
+<div align="center">
+  <a href="https://www.deepset.ai/haystack/"><img src="https://github.com/deepset-ai/haystack/blob/main/docs/img/banner_20.png" alt="Haystack"></a>
+</div>
 
 [**Haystack**](https://github.com/deepset-ai/haystack) is an open source framework by [**deepset**](https://deepset.ai) for building production-ready **LLM applications**, **retrieval-augmented generative pipelines** and **state-of-the-art search systems** that work intelligently over large document collections. It lets you quickly try out the latest models in natural language processing (NLP) while being flexible and easy to use.
 

From a69942e564e594b53b03e26dddbb5fefc627debe Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 11 Mar 2024 15:06:08 +0300
Subject: [PATCH 179/206] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index bda7a7f6..fce6d4a8 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # Haystack Tutorials
 
 <div align="center">
-  <a href="https://www.deepset.ai/haystack/"><img src="https://github.com/deepset-ai/haystack/blob/main/docs/img/banner_20.png" alt="Haystack"></a>
+  <a href="https://haystack.deepset.ai/"><img src="https://github.com/deepset-ai/haystack/blob/main/docs/img/banner_20.png" alt="Green logo of a stylized white 'H' with the text 'Haystack, by deepset. Haystack 2.0 is live 🎉' Abstract green and yellow diagrams in the background."></a>
 </div>
 
 [**Haystack**](https://github.com/deepset-ai/haystack) is an open source framework by [**deepset**](https://deepset.ai) for building production-ready **LLM applications**, **retrieval-augmented generative pipelines** and **state-of-the-art search systems** that work intelligently over large document collections. It lets you quickly try out the latest models in natural language processing (NLP) while being flexible and easy to use.

From f0ad25ea13de93c5b8052594793db3211b315c53 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 12 Mar 2024 12:14:19 +0300
Subject: [PATCH 180/206] Remove "Beta" text and update the announcement link
 (#306)

* Add info box to 1.x tutorials about their 2.0 counterparts
---
 tutorials/01_Basic_QA_Pipeline.ipynb          |     4 +-
 ...er_Retrieval_via_Embedding_Retrieval.ipynb |     4 +
 tutorials/08_Preprocessing.ipynb              |     4 +
 tutorials/21_Customizing_PromptNode.ipynb     |     6 +-
 tutorials/22_Pipeline_with_PromptNode.ipynb   |     4 +-
 tutorials/26_Hybrid_Retrieval.ipynb           |     4 +-
 tutorials/27_First_RAG_Pipeline.ipynb         |     2 +-
 .../28_Structured_Output_With_Loop.ipynb      |     6 +-
 tutorials/29_Serializing_Pipelines.ipynb      |   832 +-
 ...le_Type_Preprocessing_Index_Pipeline.ipynb |    32 +-
 tutorials/31_Metadata_Filtering.ipynb         |  1910 +--
 ...ng_Documents_and_Queries_by_Language.ipynb |  2758 ++--
 tutorials/33_Hybrid_Retrieval.ipynb           |     4 +-
 tutorials/34_Extractive_QA_Pipeline.ipynb     | 11556 ++++++++--------
 ...el_Based_Evaluation_of_RAG_Pipelines.ipynb |    56 +-
 ...g_Fallbacks_with_Conditional_Routing.ipynb |     4 +-
 ...ing_Pipeline_Inputs_with_Multiplexer.ipynb |    42 +-
 ...ding_Metadata_for_Improved_Retrieval.ipynb |     4 +-
 ...at_Application_with_Function_Calling.ipynb |     2 +-
 19 files changed, 8636 insertions(+), 8598 deletions(-)

diff --git a/tutorials/01_Basic_QA_Pipeline.ipynb b/tutorials/01_Basic_QA_Pipeline.ipynb
index 3c0f3174..7000b94c 100644
--- a/tutorials/01_Basic_QA_Pipeline.ipynb
+++ b/tutorials/01_Basic_QA_Pipeline.ipynb
@@ -7,7 +7,9 @@
    "source": [
     "# Tutorial: Build Your First Question Answering System\n",
     "\n",
-    "> We've modified this first tutorial to make it simpler to start with. If you're looking for a Question Answering tutorial that uses a DocumentStore such as Elasticsearch, go to our new [Build a Scalable Question Answering System](https://haystack.deepset.ai/tutorials/03_scalable_qa_system) tutorial.\n",
+    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0 and would like to follow the updated version of this tutorial, check out [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) and [Build an Extractive QA Pipeline](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline). \n",
+    ">\n",
+    "> For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release).\n",
     "\n",
     "- **Level**: Beginner\n",
     "- **Time to complete**: 15 minutes\n",
diff --git a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
index 76ce64d7..8263ba60 100644
--- a/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+++ b/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
@@ -9,6 +9,10 @@
    "source": [
     "# Better Retrieval via \"Embedding Retrieval\"\n",
     "\n",
+    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0 and would like to follow the updated version of this tutorial, check out [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) and [Build an Extractive QA Pipeline](https://haystack.deepset.ai/tutorials/34_extractive_qa_pipeline). \n",
+    ">\n",
+    "> For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release).\n",
+    "\n",
     "### Importance of Retrievers\n",
     "\n",
     "The Retriever has a huge impact on the performance of our overall search pipeline.\n",
diff --git a/tutorials/08_Preprocessing.ipynb b/tutorials/08_Preprocessing.ipynb
index 1b1c03a0..a0be3bca 100644
--- a/tutorials/08_Preprocessing.ipynb
+++ b/tutorials/08_Preprocessing.ipynb
@@ -8,6 +8,10 @@
    "source": [
     "# Preprocessing\n",
     "\n",
+    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0 and would like to follow the updated version of this tutorial, check out [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline). \n",
+    ">\n",
+    "> For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release).\n",
+    "\n",
     "Haystack includes a suite of tools to extract text from different file types, normalize white space\n",
     "and split text into smaller pieces to optimize retrieval.\n",
     "These data preprocessing steps can have a big impact on the systems performance and effective handling of data is key to getting the most out of Haystack."
diff --git a/tutorials/21_Customizing_PromptNode.ipynb b/tutorials/21_Customizing_PromptNode.ipynb
index f682b006..e031a8f3 100644
--- a/tutorials/21_Customizing_PromptNode.ipynb
+++ b/tutorials/21_Customizing_PromptNode.ipynb
@@ -12,7 +12,11 @@
     "- **Level**: Intermediate\n",
     "- **Time to complete**: 20 minutes\n",
     "- **Nodes Used**: `PromptNode`, `PromptTemplate`\n",
-    "- **Goal**: After completing this tutorial, you will have learned the basics of using PromptNode and PromptTemplates and you'll have added titles to articles from The Guardian and categorized them. "
+    "- **Goal**: After completing this tutorial, you will have learned the basics of using PromptNode and PromptTemplates and you'll have added titles to articles from The Guardian and categorized them. \n",
+    "\n",
+    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0 and would like to follow the updated version of this tutorial, check out [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline). \n",
+    ">\n",
+    "> For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release)."
    ]
   },
   {
diff --git a/tutorials/22_Pipeline_with_PromptNode.ipynb b/tutorials/22_Pipeline_with_PromptNode.ipynb
index c4f65d84..fedbf40c 100644
--- a/tutorials/22_Pipeline_with_PromptNode.ipynb
+++ b/tutorials/22_Pipeline_with_PromptNode.ipynb
@@ -19,9 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0-Beta and would like to follow the updated version of this tutorial, check out [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline). \n",
+    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0 and would like to follow the updated version of this tutorial, check out [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline). \n",
     ">\n",
-    "> For more information on Haystack 2.0-Beta, you can also read the [announcement post](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent)."
+    "> For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release)."
    ]
   },
   {
diff --git a/tutorials/26_Hybrid_Retrieval.ipynb b/tutorials/26_Hybrid_Retrieval.ipynb
index b0b18d27..0ef23f7c 100644
--- a/tutorials/26_Hybrid_Retrieval.ipynb
+++ b/tutorials/26_Hybrid_Retrieval.ipynb
@@ -18,9 +18,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0-Beta and would like to follow the updated version of this tutorial, check out [Creating a Hybrid Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval). \n",
+    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0 and would like to follow the updated version of this tutorial, check out [Creating a Hybrid Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval). \n",
     ">\n",
-    "> For more information on Haystack 2.0-Beta, you can also read the [announcement post](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent)."
+    "> For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release)."
    ]
   },
   {
diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 66fb0826..36d45785 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -14,7 +14,7 @@
         "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys).\n",
         "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and OpenAIGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n",
         "\n",
-        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
+        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
       ]
     },
     {
diff --git a/tutorials/28_Structured_Output_With_Loop.ipynb b/tutorials/28_Structured_Output_With_Loop.ipynb
index 010c36b1..9da7272b 100644
--- a/tutorials/28_Structured_Output_With_Loop.ipynb
+++ b/tutorials/28_Structured_Output_With_Loop.ipynb
@@ -14,10 +14,10 @@
         "- **Components Used**: `PromptBuilder`, `OpenAIGenerator`, `OutputValidator` (Custom component)\n",
         "- **Goal**: After completing this tutorial, you will have built a system that extracts unstructured data, puts it in a JSON schema, and automatically corrects errors in the JSON output from a large language model (LLM) to make sure it follows the specified structure.\n",
         "\n",
-        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Beta Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
+        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)..\n",
         "\n",
         "## Overview\n",
-        "This tutorial demonstrates how to use Haystack 2.0-Beta's advanced [looping pipelines](https://docs.haystack.deepset.ai/v2.0/docs/pipelines#loops) with LLMs for more dynamic and flexible data processing. You'll learn how to extract structured data from unstructured data using an LLM, and to validate the generated output against a predefined schema.\n",
+        "This tutorial demonstrates how to use Haystack 2.0's advanced [looping pipelines](https://docs.haystack.deepset.ai/v2.0/docs/pipelines#loops) with LLMs for more dynamic and flexible data processing. You'll learn how to extract structured data from unstructured data using an LLM, and to validate the generated output against a predefined schema.\n",
         "\n",
         "This tutorial uses `gpt-3.5-turbo` to change unstructured passages into JSON outputs that follow the [Pydantic](https://github.com/pydantic/pydantic) schema. It uses a custom OutputValidator component to validate the JSON and loop back to make corrections, if necessary."
       ]
@@ -54,7 +54,7 @@
       },
       "source": [
         "## Installing Dependencies\n",
-        "Install Haystack 2.0 Beta and [colorama](https://pypi.org/project/colorama/) with pip:"
+        "Install Haystack 2.0 and [colorama](https://pypi.org/project/colorama/) with pip:"
       ]
     },
     {
diff --git a/tutorials/29_Serializing_Pipelines.ipynb b/tutorials/29_Serializing_Pipelines.ipynb
index cf0841fd..4dc609da 100644
--- a/tutorials/29_Serializing_Pipelines.ipynb
+++ b/tutorials/29_Serializing_Pipelines.ipynb
@@ -1,429 +1,429 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cFFW8D-weE2S"
-      },
-      "source": [
-        "# Tutorial: Serializing LLM Pipelines\n",
-        "\n",
-        "- **Level**: Beginner\n",
-        "- **Time to complete**: 10 minutes\n",
-        "- **Components Used**: [`HuggingFaceLocalGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacelocalgenerator), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder)\n",
-        "- **Prerequisites**: None\n",
-        "- **Goal**: After completing this tutorial, you'll understand how to serialize and deserialize between YAML and Python code."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DxhqjpHfenQl"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "**📚 Useful Documentation:** [Serialization](https://docs.haystack.deepset.ai/v2.0/docs/serialization)\n",
-        "\n",
-        "Serialization means converting a pipeline to a format that you can save on your disk and load later. It's especially useful because a serialized pipeline can be saved on disk or a database, get sent over a network and more. \n",
-        "\n",
-        "Although it's possible to serialize into other formats too, Haystack supports YAML our of the box to make it easy for humans to make changes without the need to go back and forth with Python code. In this tutorial, we will create a very simple pipeline in Python code, serialize it into YAML, make changes to it, and deserialize it back into a Haystack `Pipeline`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9smrsiIqfS7J"
-      },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TLaHxdJcfWtI"
-      },
-      "source": [
-        "## Installing Haystack\n",
-        "\n",
-        "Install Haystack 2.0 Beta with `pip`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "CagzMFdkeBBp",
-        "outputId": "e304450a-24e3-4ef8-e642-1fbb573e7d55"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Requirement already satisfied: haystack-ai in /usr/local/lib/python3.10/dist-packages (2.0.0b5)\n",
-            "Requirement already satisfied: boilerpy3 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.0.7)\n",
-            "Requirement already satisfied: haystack-bm25 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.0.2)\n",
-            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (3.1.3)\n",
-            "Requirement already satisfied: lazy-imports in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (0.3.1)\n",
-            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (10.1.0)\n",
-            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (3.2.1)\n",
-            "Requirement already satisfied: openai>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.10.0)\n",
-            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.5.3)\n",
-            "Requirement already satisfied: posthog in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (3.3.3)\n",
-            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (6.0.1)\n",
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-            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.7->posthog->haystack-ai) (3.3.2)\n",
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-          ]
-        }
-      ],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install haystack-ai"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MhnSGxXWHNsD"
-      },
-      "source": [
-        "### Enabling Telemetry\n",
-        "\n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ikIM1o9cHNcS"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(29)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kS8rz9gGgMBb"
-      },
-      "source": [
-        "## Creating a Simple Pipeline\n",
-        "\n",
-        "First, let's create a very simple pipeline that expects a `topic` from the user, and generates a summary about the topic with `google/flan-t5-large`. Feel free to modify the pipeline as you wish. Note that in this pipeline we are using a local model that we're getting from Hugging Face. We're using a relatively small, open-source LLM."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "odZJjD7KgO1g"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack import Pipeline\n",
-        "from haystack.components.builders import PromptBuilder\n",
-        "from haystack.components.generators import HuggingFaceLocalGenerator\n",
-        "\n",
-        "template = \"\"\"\n",
-        "Please create a summary about the following topic:\n",
-        "{{ topic }}\n",
-        "\"\"\"\n",
-        "builder = PromptBuilder(template=template)\n",
-        "llm = HuggingFaceLocalGenerator(model=\"google/flan-t5-large\",\n",
-        "                                      task=\"text2text-generation\",\n",
-        "                                      generation_kwargs={\n",
-        "                                        \"max_new_tokens\": 150,\n",
-        "                                        })\n",
-        "\n",
-        "pipeline = Pipeline()\n",
-        "pipeline.add_component(name=\"builder\", instance=builder)\n",
-        "pipeline.add_component(name=\"llm\", instance=llm)\n",
-        "\n",
-        "pipeline.connect(\"builder\", \"llm\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "W-onTCXfqFjG",
-        "outputId": "e81cd5ea-db66-4f0e-f787-5aed7a7b4692"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Climate change is a major threat to the planet.\n"
-          ]
-        }
-      ],
-      "source": [
-        "topic = \"Climate change\"\n",
-        "result = pipeline.run(data={\"builder\": {\"topic\": topic}})\n",
-        "print(result['llm']['replies'][0])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "61r7hc1vuUMH"
-      },
-      "source": [
-        "## Serialize the Pipeline to YAML\n",
-        "\n",
-        "Out of the box, Haystack supports YAML. Use `dumps()` to convert the pipeline to YAML:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "vYOEAesbrn4w",
-        "outputId": "ef037904-79f4-46a4-c8e7-d03ea8dcb6c2"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "components:\n",
-            "  builder:\n",
-            "    init_parameters:\n",
-            "      template: \"\\nPlease create a summary about the following topic: \\n{{ topic }}\\n\"\n",
-            "    type: haystack.components.builders.prompt_builder.PromptBuilder\n",
-            "  llm:\n",
-            "    init_parameters:\n",
-            "      generation_kwargs:\n",
-            "        max_new_tokens: 150\n",
-            "      huggingface_pipeline_kwargs:\n",
-            "        device: cpu\n",
-            "        model: google/flan-t5-large\n",
-            "        task: text2text-generation\n",
-            "        token: null\n",
-            "      stop_words: null\n",
-            "    type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator\n",
-            "connections:\n",
-            "- receiver: llm.prompt\n",
-            "  sender: builder.prompt\n",
-            "max_loops_allowed: 100\n",
-            "metadata: {}\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "yaml_pipeline = pipeline.dumps()\n",
-        "\n",
-        "print(yaml_pipeline)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0C7zGsUCGszq"
-      },
-      "source": [
-        "You should get a pipeline YAML that looks like the following:\n",
-        "\n",
-        "```yaml\n",
-        "components:\n",
-        "  builder:\n",
-        "    init_parameters:\n",
-        "      template: \"\\nPlease create a summary about the following topic: \\n{{ topic }}\\n\"\n",
-        "    type: haystack.components.builders.prompt_builder.PromptBuilder\n",
-        "  llm:\n",
-        "    init_parameters:\n",
-        "      generation_kwargs:\n",
-        "        max_new_tokens: 150\n",
-        "      huggingface_pipeline_kwargs:\n",
-        "        device: cpu\n",
-        "        model: google/flan-t5-large\n",
-        "        task: text2text-generation\n",
-        "        token: null\n",
-        "      stop_words: null\n",
-        "    type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator\n",
-        "connections:\n",
-        "- receiver: llm.prompt\n",
-        "  sender: builder.prompt\n",
-        "max_loops_allowed: 100\n",
-        "metadata: {}\n",
-        "\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f9MknQ-1vQ8r"
-      },
-      "source": [
-        "## Editing a Pipeline in YAML\n",
-        "\n",
-        "Let's see how we can make changes to serialized pipelines. For example, below, let's modify the promptbuilder's template to translate provided `sentence` to French:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "U332-VjovFfn"
-      },
-      "outputs": [],
-      "source": [
-        "yaml_pipeline = \"\"\"\n",
-        "components:\n",
-        "  builder:\n",
-        "    init_parameters:\n",
-        "      template: \"\\nPlease translate the following to French: \\n{{ sentence }}\\n\"\n",
-        "    type: haystack.components.builders.prompt_builder.PromptBuilder\n",
-        "  llm:\n",
-        "    init_parameters:\n",
-        "      generation_kwargs:\n",
-        "        max_new_tokens: 150\n",
-        "      huggingface_pipeline_kwargs:\n",
-        "        device: cpu\n",
-        "        model: google/flan-t5-large\n",
-        "        task: text2text-generation\n",
-        "        token: null\n",
-        "      stop_words: null\n",
-        "    type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator\n",
-        "connections:\n",
-        "- receiver: llm.prompt\n",
-        "  sender: builder.prompt\n",
-        "max_loops_allowed: 100\n",
-        "metadata: {}\n",
-        "\"\"\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xLBtgY0Ov8nX"
-      },
-      "source": [
-        "## Deseriazling a YAML Pipeline back to Python\n",
-        "\n",
-        "You can deserialize a pipeline by calling `loads()`. Below, we're deserializing our edited `yaml_pipeline`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "OdlLnw-9wVN-"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack import Pipeline\n",
-        "from haystack.components.builders import PromptBuilder\n",
-        "from haystack.components.generators import HuggingFaceLocalGenerator\n",
-        "\n",
-        "new_pipeline = Pipeline.loads(yaml_pipeline)"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cFFW8D-weE2S"
+   },
+   "source": [
+    "# Tutorial: Serializing LLM Pipelines\n",
+    "\n",
+    "- **Level**: Beginner\n",
+    "- **Time to complete**: 10 minutes\n",
+    "- **Components Used**: [`HuggingFaceLocalGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacelocalgenerator), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder)\n",
+    "- **Prerequisites**: None\n",
+    "- **Goal**: After completing this tutorial, you'll understand how to serialize and deserialize between YAML and Python code.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DxhqjpHfenQl"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "**📚 Useful Documentation:** [Serialization](https://docs.haystack.deepset.ai/v2.0/docs/serialization)\n",
+    "\n",
+    "Serialization means converting a pipeline to a format that you can save on your disk and load later. It's especially useful because a serialized pipeline can be saved on disk or a database, get sent over a network and more. \n",
+    "\n",
+    "Although it's possible to serialize into other formats too, Haystack supports YAML our of the box to make it easy for humans to make changes without the need to go back and forth with Python code. In this tutorial, we will create a very simple pipeline in Python code, serialize it into YAML, make changes to it, and deserialize it back into a Haystack `Pipeline`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9smrsiIqfS7J"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TLaHxdJcfWtI"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "Install Haystack 2.0 with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "CagzMFdkeBBp",
+    "outputId": "e304450a-24e3-4ef8-e642-1fbb573e7d55"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eVPh2cV6wcu9"
-      },
-      "source": [
-        "Now we can run the new pipeline we defined in YAML. We had changed it so that the `PromptBuilder` expects a `sentence` and translates the sentence to French:"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: haystack-ai in /usr/local/lib/python3.10/dist-packages (2.0.0b5)\n",
+      "Requirement already satisfied: boilerpy3 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.0.7)\n",
+      "Requirement already satisfied: haystack-bm25 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.0.2)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (3.1.3)\n",
+      "Requirement already satisfied: lazy-imports in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (0.3.1)\n",
+      "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (10.1.0)\n",
+      "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (3.2.1)\n",
+      "Requirement already satisfied: openai>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.10.0)\n",
+      "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (1.5.3)\n",
+      "Requirement already satisfied: posthog in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (3.3.3)\n",
+      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (6.0.1)\n",
+      "Requirement already satisfied: tenacity in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (8.2.3)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (4.66.1)\n",
+      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (4.9.0)\n",
+      "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai) (3.7.1)\n",
+      "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai>=1.1.0->haystack-ai) (1.7.0)\n",
+      "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai) (0.26.0)\n",
+      "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai) (1.10.14)\n",
+      "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai) (1.3.0)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from haystack-bm25->haystack-ai) (1.23.5)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->haystack-ai) (2.1.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->haystack-ai) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->haystack-ai) (2023.3.post1)\n",
+      "Requirement already satisfied: requests<3.0,>=2.7 in /usr/local/lib/python3.10/dist-packages (from posthog->haystack-ai) (2.31.0)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog->haystack-ai) (1.16.0)\n",
+      "Requirement already satisfied: monotonic>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog->haystack-ai) (1.6)\n",
+      "Requirement already satisfied: backoff>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from posthog->haystack-ai) (2.2.1)\n",
+      "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai>=1.1.0->haystack-ai) (3.6)\n",
+      "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai>=1.1.0->haystack-ai) (1.2.0)\n",
+      "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (2023.11.17)\n",
+      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (1.0.2)\n",
+      "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (0.14.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.7->posthog->haystack-ai) (3.3.2)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0,>=2.7->posthog->haystack-ai) (2.0.7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MhnSGxXWHNsD"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ikIM1o9cHNcS"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(29)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kS8rz9gGgMBb"
+   },
+   "source": [
+    "## Creating a Simple Pipeline\n",
+    "\n",
+    "First, let's create a very simple pipeline that expects a `topic` from the user, and generates a summary about the topic with `google/flan-t5-large`. Feel free to modify the pipeline as you wish. Note that in this pipeline we are using a local model that we're getting from Hugging Face. We're using a relatively small, open-source LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "odZJjD7KgO1g"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "from haystack.components.builders import PromptBuilder\n",
+    "from haystack.components.generators import HuggingFaceLocalGenerator\n",
+    "\n",
+    "template = \"\"\"\n",
+    "Please create a summary about the following topic:\n",
+    "{{ topic }}\n",
+    "\"\"\"\n",
+    "builder = PromptBuilder(template=template)\n",
+    "llm = HuggingFaceLocalGenerator(\n",
+    "    model=\"google/flan-t5-large\", task=\"text2text-generation\", generation_kwargs={\"max_new_tokens\": 150}\n",
+    ")\n",
+    "\n",
+    "pipeline = Pipeline()\n",
+    "pipeline.add_component(name=\"builder\", instance=builder)\n",
+    "pipeline.add_component(name=\"llm\", instance=llm)\n",
+    "\n",
+    "pipeline.connect(\"builder\", \"llm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "W-onTCXfqFjG",
+    "outputId": "e81cd5ea-db66-4f0e-f787-5aed7a7b4692"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "oGLi3EB_wbu6",
-        "outputId": "ec6eae9f-a7ea-401d-c0ab-792748f6db6f"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{'llm': {'replies': ['Je me félicite des capybaras !']}}"
-            ]
-          },
-          "execution_count": 15,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "new_pipeline.run(data={\"builder\": {\"sentence\": \"I love capybaras\"}})"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Climate change is a major threat to the planet.\n"
+     ]
+    }
+   ],
+   "source": [
+    "topic = \"Climate change\"\n",
+    "result = pipeline.run(data={\"builder\": {\"topic\": topic}})\n",
+    "print(result[\"llm\"][\"replies\"][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "61r7hc1vuUMH"
+   },
+   "source": [
+    "## Serialize the Pipeline to YAML\n",
+    "\n",
+    "Out of the box, Haystack supports YAML. Use `dumps()` to convert the pipeline to YAML:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "vYOEAesbrn4w",
+    "outputId": "ef037904-79f4-46a4-c8e7-d03ea8dcb6c2"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## What's next\n",
-        "\n",
-        "🎉 Congratulations! You've serialzed a pipeline into YAML, edited it and ran it again!\n",
-        "\n",
-        "If you liked this tutorial, you may also enjoy:\n",
-        "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
-        "\n",
-        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=serialization). Thanks for reading!"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "components:\n",
+      "  builder:\n",
+      "    init_parameters:\n",
+      "      template: \"\\nPlease create a summary about the following topic: \\n{{ topic }}\\n\"\n",
+      "    type: haystack.components.builders.prompt_builder.PromptBuilder\n",
+      "  llm:\n",
+      "    init_parameters:\n",
+      "      generation_kwargs:\n",
+      "        max_new_tokens: 150\n",
+      "      huggingface_pipeline_kwargs:\n",
+      "        device: cpu\n",
+      "        model: google/flan-t5-large\n",
+      "        task: text2text-generation\n",
+      "        token: null\n",
+      "      stop_words: null\n",
+      "    type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator\n",
+      "connections:\n",
+      "- receiver: llm.prompt\n",
+      "  sender: builder.prompt\n",
+      "max_loops_allowed: 100\n",
+      "metadata: {}\n",
+      "\n"
+     ]
     }
-  ],
-  "metadata": {
+   ],
+   "source": [
+    "yaml_pipeline = pipeline.dumps()\n",
+    "\n",
+    "print(yaml_pipeline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0C7zGsUCGszq"
+   },
+   "source": [
+    "You should get a pipeline YAML that looks like the following:\n",
+    "\n",
+    "```yaml\n",
+    "components:\n",
+    "  builder:\n",
+    "    init_parameters:\n",
+    "      template: \"\\nPlease create a summary about the following topic: \\n{{ topic }}\\n\"\n",
+    "    type: haystack.components.builders.prompt_builder.PromptBuilder\n",
+    "  llm:\n",
+    "    init_parameters:\n",
+    "      generation_kwargs:\n",
+    "        max_new_tokens: 150\n",
+    "      huggingface_pipeline_kwargs:\n",
+    "        device: cpu\n",
+    "        model: google/flan-t5-large\n",
+    "        task: text2text-generation\n",
+    "        token: null\n",
+    "      stop_words: null\n",
+    "    type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator\n",
+    "connections:\n",
+    "- receiver: llm.prompt\n",
+    "  sender: builder.prompt\n",
+    "max_loops_allowed: 100\n",
+    "metadata: {}\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f9MknQ-1vQ8r"
+   },
+   "source": [
+    "## Editing a Pipeline in YAML\n",
+    "\n",
+    "Let's see how we can make changes to serialized pipelines. For example, below, let's modify the promptbuilder's template to translate provided `sentence` to French:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "U332-VjovFfn"
+   },
+   "outputs": [],
+   "source": [
+    "yaml_pipeline = \"\"\"\n",
+    "components:\n",
+    "  builder:\n",
+    "    init_parameters:\n",
+    "      template: \"\\nPlease translate the following to French: \\n{{ sentence }}\\n\"\n",
+    "    type: haystack.components.builders.prompt_builder.PromptBuilder\n",
+    "  llm:\n",
+    "    init_parameters:\n",
+    "      generation_kwargs:\n",
+    "        max_new_tokens: 150\n",
+    "      huggingface_pipeline_kwargs:\n",
+    "        device: cpu\n",
+    "        model: google/flan-t5-large\n",
+    "        task: text2text-generation\n",
+    "        token: null\n",
+    "      stop_words: null\n",
+    "    type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator\n",
+    "connections:\n",
+    "- receiver: llm.prompt\n",
+    "  sender: builder.prompt\n",
+    "max_loops_allowed: 100\n",
+    "metadata: {}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xLBtgY0Ov8nX"
+   },
+   "source": [
+    "## Deseriazling a YAML Pipeline back to Python\n",
+    "\n",
+    "You can deserialize a pipeline by calling `loads()`. Below, we're deserializing our edited `yaml_pipeline`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "OdlLnw-9wVN-"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "from haystack.components.builders import PromptBuilder\n",
+    "from haystack.components.generators import HuggingFaceLocalGenerator\n",
+    "\n",
+    "new_pipeline = Pipeline.loads(yaml_pipeline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eVPh2cV6wcu9"
+   },
+   "source": [
+    "Now we can run the new pipeline we defined in YAML. We had changed it so that the `PromptBuilder` expects a `sentence` and translates the sentence to French:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
     "colab": {
-      "provenance": []
+     "base_uri": "https://localhost:8080/"
     },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
+    "id": "oGLi3EB_wbu6",
+    "outputId": "ec6eae9f-a7ea-401d-c0ab-792748f6db6f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'llm': {'replies': ['Je me félicite des capybaras !']}}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "new_pipeline.run(data={\"builder\": {\"sentence\": \"I love capybaras\"}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What's next\n",
+    "\n",
+    "🎉 Congratulations! You've serialzed a pipeline into YAML, edited it and ran it again!\n",
+    "\n",
+    "If you liked this tutorial, you may also enjoy:\n",
+    "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=serialization). Thanks for reading!"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index 7b965ca7..4e0f140c 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -12,7 +12,7 @@
         "- **Time to complete**: 15 minutes\n",
         "- **Goal**: After completing this tutorial, you'll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the `FileTypeRouter`.\n",
         "\n",
-        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
+        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n",
         "\n",
         "> 💡 (Optional): After creating the indexing pipeline in this tutorial, there is an optional section that shows you how to create a RAG pipeline on top of the document store you just created. You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section\n",
         "\n",
@@ -50,8 +50,8 @@
       "source": [
         "## Preparing the Colab Environment\n",
         "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
       ]
     },
     {
@@ -86,7 +86,7 @@
       "source": [
         "### Enabling Telemetry\n",
         "\n",
-        "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+        "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
       ]
     },
     {
@@ -259,22 +259,22 @@
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
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             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": []
           },
+          "execution_count": 7,
           "metadata": {},
-          "execution_count": 7
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -390,31 +390,31 @@
       "cell_type": "code",
       "execution_count": 13,
       "metadata": {
-        "id": "_s--8xEWq8Y9",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
         },
+        "id": "_s--8xEWq8Y9",
         "outputId": "1c050d5f-f2ae-4cd3-e0d4-533397a6af63"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/jpeg": 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             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": []
           },
+          "execution_count": 13,
           "metadata": {},
-          "execution_count": 13
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -535,4 +535,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/tutorials/31_Metadata_Filtering.ipynb b/tutorials/31_Metadata_Filtering.ipynb
index 34f48d3a..a4cb37a1 100644
--- a/tutorials/31_Metadata_Filtering.ipynb
+++ b/tutorials/31_Metadata_Filtering.ipynb
@@ -1,966 +1,986 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c1WR4wDbHj7i"
-      },
-      "source": [
-        "# Tutorial: Filter Documents Based on Metadata\n",
-        "\n",
-        "- **Level**: Beginner\n",
-        "- **Time to complete**: 5 minutes\n",
-        "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever)\n",
-        "- **Prerequisites**: None\n",
-        "- **Goal**: Filter documents in a document store based on given metadata"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "c1WR4wDbHj7i"
+   },
+   "source": [
+    "# Tutorial: Filter Documents Based on Metadata\n",
+    "\n",
+    "- **Level**: Beginner\n",
+    "- **Time to complete**: 5 minutes\n",
+    "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever)\n",
+    "- **Prerequisites**: None\n",
+    "- **Goal**: Filter documents in a document store based on given metadata\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WUNZTr1FH0Iw"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "**📚 Useful Documentation: [Metadata Filtering](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering)**\n",
+    "\n",
+    "Although new retrieval techniques are great, sometimes you just know that you want to perform search on a specific group of documents in your document store. This can be anything from all the documents that are related to a specific _user_, or that were published after a certain _date_ and so on. Metadata filtering is very useful in these situations. In this tutorial, we will create a few simple documents containing information about Haystack, where the metadata includes information on what version of Haystack the information relates to. We will then do metadata filtering to make sure we are answering the question based only on information about Haystack 2.0.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tM3U5KyegTAE"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "utuZlm-AgWIN"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "Install Haystack 2.0 with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Y27k0kO_Hz3D",
+    "outputId": "715de352-470c-454d-da4b-501141b7630c"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "07gGBcxBgi8Y"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "PtJZlNtkHZr-"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(31)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HKBltMXLgzvq"
+   },
+   "source": [
+    "## Preparing Documents\n",
+    "\n",
+    "First, let's prepare some documents. Below, we're manually creating 3 simple documents with `meta` attached. We're then writing these documents to an `InMemoryDocumentStore`, but you can [use any of the available document stores](https://docs.haystack.deepset.ai/v2.0/docs/choosing-a-document-store) instead such as OpenSearch, Chroma, Pinecone and more.. (Note that not all of them have options to store in memory and may require extra setup).\n",
+    "\n",
+    "> ⭐️ For more information on how to write documents into different document stores, you can follow our tutorial on indexing different file types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mvEyXZV9hKio",
+    "outputId": "a7425c3c-585f-440d-d6f3-dbf7b9ccbca3"
+   },
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "from haystack import Document\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
+    "\n",
+    "documents = [\n",
+    "    Document(\n",
+    "        content=\"Use pip to install a basic version of Haystack's latest release: pip install farm-haystack. All the core Haystack components live in the haystack repo. But there's also the haystack-extras repo which contains components that are not as widely used, and you need to install them separately.\",\n",
+    "        meta={\"version\": 1.15, \"date\": datetime(2023, 3, 30)},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        content=\"Use pip to install a basic version of Haystack's latest release: pip install farm-haystack[inference]. All the core Haystack components live in the haystack repo. But there's also the haystack-extras repo which contains components that are not as widely used, and you need to install them separately.\",\n",
+    "        meta={\"version\": 1.22, \"date\": datetime(2023, 11, 7)},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        content=\"Use pip to install only the Haystack 2.0 code: pip install haystack-ai. The haystack-ai package is built on the main branch which is an unstable beta version, but it's useful if you want to try the new features as soon as they are merged.\",\n",
+    "        meta={\"version\": 2.0, \"date\": datetime(2023, 12, 4)},\n",
+    "    ),\n",
+    "]\n",
+    "document_store = InMemoryDocumentStore(bm25_algorithm=\"BM25Plus\")\n",
+    "document_store.write_documents(documents=documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o9RaTQ_ZhdyY"
+   },
+   "source": [
+    "## Building a Document Search Pipeline\n",
+    "\n",
+    "As an example, below we are building a simple document search pipeline that simply has a retriever. However, you can also change this pipeline to do more, such as generating answers to questions or more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "lo-CGv8mhyyL"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "\n",
+    "pipeline = Pipeline()\n",
+    "pipeline.add_component(instance=InMemoryBM25Retriever(document_store=document_store), name=\"retriever\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-3Ggh01Wh5Z5"
+   },
+   "source": [
+    "## Do Metadata Filtering\n",
+    "\n",
+    "Finally, ask a question by filtering the documents to `\"version\" > 1.21`.\n",
+    "\n",
+    "To see what kind of comparison operators you can use for your metadata, including logical comparistons such as `NOT`, `AND` and so on, check out the [Metadata Filtering documentation](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering#comparison)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 136,
+     "referenced_widgets": [
+      "86953f59ddd540b48edeca6d0f2fad53",
+      "4fbfbdc18fff4cdfac9b1eff5373f366",
+      "dea7aa237cfe4a56a05377075b1756a6",
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+      "68ce1cbbfa1941ba84804e10c9d286ce",
+      "9e481a99c6b04e5aac6b9c4ac02b2705"
+     ]
+    },
+    "id": "sO52YJ9Xi5YZ",
+    "outputId": "10dfe801-6e1c-4b51-b600-abb4722e56cb"
+   },
+   "outputs": [],
+   "source": [
+    "query = \"Haystack installation\"\n",
+    "pipeline.run(data={\"retriever\": {\"query\": query, \"filters\": {\"field\": \"meta.version\", \"operator\": \">\", \"value\": 1.21}}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7_d1QNmSYECu"
+   },
+   "source": [
+    "As a final step, let's see how we can add logical operators to our filters. This time, we are asking for retrieved documents to be filtered to `version > 1.21` _AND_ we're also asking their `date` to be later than November 7th 2023."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 101,
+     "referenced_widgets": [
+      "5675eae6f6134de886062df51a77a31c",
+      "6d09162f16a14abaa285e52306d55809",
+      "d040038d11c443e8a22b3900fdb363d4",
+      "0231be66ce54452fa3997a52f528e3f1",
+      "9bf36e642aad46559ace3b599be2d001",
+      "33dce544ebf940c39eff5dc528519d2b",
+      "62ad652a13c8421da36f4428e43c3896",
+      "95f5e34331e44813b162c522d1c43490",
+      "45959b2f96754ce5a905a0678f75b9c7",
+      "67b897deaa26495e87eb349975d66f78",
+      "63a00da59f5f4eb9925537976c9c0c06"
+     ]
+    },
+    "id": "PmWrtr7SWnRy",
+    "outputId": "74cb0714-b038-49e8-930c-0d973ccd6104"
+   },
+   "outputs": [],
+   "source": [
+    "query = \"Haystack installation\"\n",
+    "pipeline.run(\n",
+    "    data={\n",
+    "        \"retriever\": {\n",
+    "            \"query\": query,\n",
+    "            \"filters\": {\n",
+    "                \"operator\": \"AND\",\n",
+    "                \"conditions\": [\n",
+    "                    {\"field\": \"meta.version\", \"operator\": \">\", \"value\": 1.21},\n",
+    "                    {\"field\": \"meta.date\", \"operator\": \">\", \"value\": datetime(2023, 11, 7)},\n",
+    "                ],\n",
+    "            },\n",
+    "        }\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rejLvPEIj4FS"
+   },
+   "source": [
+    "## What's next\n",
+    "\n",
+    "🎉 Congratulations! You've filtered retrieved documents with metadata!\n",
+    "\n",
+    "If you liked this tutorial, you may also enjoy:\n",
+    "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+    "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=metadata_filtering). Thanks for reading!"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "0231be66ce54452fa3997a52f528e3f1": {
+     "model_module": "@jupyter-widgets/controls",
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+      "_model_module_version": "1.5.0",
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+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_67b897deaa26495e87eb349975d66f78",
+      "placeholder": "​",
+      "style": "IPY_MODEL_63a00da59f5f4eb9925537976c9c0c06",
+      "value": " 1/1 [00:00&lt;00:00, 25.18 docs/s]"
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WUNZTr1FH0Iw"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "**📚 Useful Documentation: [Metadata Filtering](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering)**\n",
-        "\n",
-        "Although new retrieval techniques are great, sometimes you just know that you want to perform search on a specific group of documents in your document store. This can be anything from all the documents that are related to a specific _user_, or that were published after a certain _date_ and so on. Metadata filtering is very useful in these situations. In this tutorial, we will create a few simple documents containing information about Haystack, where the metadata includes information on what version of Haystack the information relates to. We will then do metadata filtering to make sure we are answering the question based only on information about Haystack 2.0.\n"
-      ]
+    "2b66affb41fd48f88a5d50cb65fa9cc6": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tM3U5KyegTAE"
-      },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-      ]
+    "33dce544ebf940c39eff5dc528519d2b": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "utuZlm-AgWIN"
-      },
-      "source": [
-        "## Installing Haystack\n",
-        "\n",
-        "Install Haystack 2.0 Beta with `pip`:"
-      ]
+    "419cdb04396f4dc1b934f204df3270ee": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Y27k0kO_Hz3D",
-        "outputId": "715de352-470c-454d-da4b-501141b7630c"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install haystack-ai"
-      ]
+    "426d9fdbe9b54557b00c5bef214c07eb": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "07gGBcxBgi8Y"
-      },
-      "source": [
-        "### Enabling Telemetry\n",
-        "\n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
-      ]
+    "45959b2f96754ce5a905a0678f75b9c7": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "PtJZlNtkHZr-"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(31)"
-      ]
+    "4fbfbdc18fff4cdfac9b1eff5373f366": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_426d9fdbe9b54557b00c5bef214c07eb",
+      "placeholder": "​",
+      "style": "IPY_MODEL_2b66affb41fd48f88a5d50cb65fa9cc6",
+      "value": "Ranking by BM25...: 100%"
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HKBltMXLgzvq"
-      },
-      "source": [
-        "## Preparing Documents\n",
-        "\n",
-        "First, let's prepare some documents. Below, we're manually creating 3 simple documents with `meta` attached. We're then writing these documents to an `InMemoryDocumentStore`, but you can [use any of the available document stores](https://docs.haystack.deepset.ai/v2.0/docs/choosing-a-document-store) instead such as OpenSearch, Chroma, Pinecone and more.. (Note that not all of them have options to store in memory and may require extra setup).\n",
-        "\n",
-        "> ⭐️ For more information on how to write documents into different document stores, you can follow our tutorial on indexing different file types."
-      ]
+    "5675eae6f6134de886062df51a77a31c": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_6d09162f16a14abaa285e52306d55809",
+       "IPY_MODEL_d040038d11c443e8a22b3900fdb363d4",
+       "IPY_MODEL_0231be66ce54452fa3997a52f528e3f1"
+      ],
+      "layout": "IPY_MODEL_9bf36e642aad46559ace3b599be2d001"
+     }
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "mvEyXZV9hKio",
-        "outputId": "a7425c3c-585f-440d-d6f3-dbf7b9ccbca3"
-      },
-      "outputs": [],
-      "source": [
-        "from datetime import datetime\n",
-        "\n",
-        "from haystack import  Document\n",
-        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-        "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
-        "\n",
-        "documents = [Document(content=\"Use pip to install a basic version of Haystack's latest release: pip install farm-haystack. All the core Haystack components live in the haystack repo. But there's also the haystack-extras repo which contains components that are not as widely used, and you need to install them separately.\",\n",
-        "                      meta={\"version\": 1.15, \"date\": datetime(2023, 3, 30)}),\n",
-        "             Document(content=\"Use pip to install a basic version of Haystack's latest release: pip install farm-haystack[inference]. All the core Haystack components live in the haystack repo. But there's also the haystack-extras repo which contains components that are not as widely used, and you need to install them separately.\",\n",
-        "                      meta={\"version\": 1.22, \"date\": datetime(2023, 11, 7)}),\n",
-        "             Document(content=\"Use pip to install only the Haystack 2.0 code: pip install haystack-ai. The haystack-ai package is built on the main branch which is an unstable beta version, but it's useful if you want to try the new features as soon as they are merged.\",\n",
-        "                      meta={\"version\": 2.0, \"date\": datetime(2023, 12, 4)}),\n",
-        "]\n",
-        "document_store = InMemoryDocumentStore(bm25_algorithm=\"BM25Plus\")\n",
-        "document_store.write_documents(documents=documents)"
-      ]
+    "5f34f9f4d1c9451e9747d6fb79474441": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
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+      "align_self": null,
+      "border": null,
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     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "o9RaTQ_ZhdyY"
-      },
-      "source": [
-        "## Building a Document Search Pipeline\n",
-        "\n",
-        "As an example, below we are building a simple document search pipeline that simply has a retriever. However, you can also change this pipeline to do more, such as generating answers to questions or more."
-      ]
+    "62ad652a13c8421da36f4428e43c3896": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "lo-CGv8mhyyL"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack import Pipeline\n",
-        "\n",
-        "pipeline = Pipeline()\n",
-        "pipeline.add_component(instance=InMemoryBM25Retriever(document_store=document_store), name=\"retriever\")"
-      ]
+    "63a00da59f5f4eb9925537976c9c0c06": {
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-        "## Do Metadata Filtering\n",
-        "\n",
-        "Finally, ask a question by filtering the documents to `\"version\" > 1.21`.\n",
-        "\n",
-        "To see what kind of comparison operators you can use for your metadata, including logical comparistons such as `NOT`, `AND` and so on, check out the [Metadata Filtering documentation](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering#comparison)"
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-        "pipeline.run(data={\"retriever\": {\"query\": query, \"filters\": { \"field\": \"meta.version\", \"operator\": \">\", \"value\": 1.21}}})"
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-        "query = \"Haystack installation\"\n",
-        "pipeline.run(data={\"retriever\": {\"query\": query, \"filters\": { \"operator\": \"AND\",\n",
-        "      \t\t\t                                              \"conditions\": [{ \"field\": \"meta.version\", \"operator\": \">\", \"value\": 1.21},\n",
-        "                                                                             { \"field\": \"meta.date\", \"operator\": \">\", \"value\": datetime(2023, 11, 7)}]}}})"
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-        "## What's next\n",
-        "\n",
-        "🎉 Congratulations! You've filtered retrieved documents with metadata!\n",
-        "\n",
-        "If you liked this tutorial, you may also enjoy:\n",
-        "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
-        "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
-        "\n",
-        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=metadata_filtering). Thanks for reading!"
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diff --git a/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb b/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
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-        "- **Level**: Beginner\n",
-        "- **Time to complete**: 15 minutes\n",
-        "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/v2.0/docs/documentlanguageclassifier), [`MetadataRouter`](https://docs.haystack.deepset.ai/v2.0/docs/metadatarouter), [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter), [`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter), [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator)\n",
-        "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline to classify documents based on the (human) language they were written in.\n",
-        "- Optionally, at the end you'll also incorporate language clasification and query routing into a RAG pipeline, so you can query documents based on the language a question was written in.\n",
-        "\n",
-        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "G8qw1k7nf7yH"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "In a gobalized society with over 7,000 human languages spoken worldwide today, handling multilingual input is a common use case for NLP applications.\n",
-        "\n",
-        "Good news: Haystack has a [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/v2.0/docs/documentlanguageclassifier) built in. This component detects the language a document was written in. This functionality lets you create *branches* in your Haystack pipelines, granting the flexibility to add different processing steps for each language. For example, you could use a LLM that performs better in German to answer German queries. Or, you could fetch only French restaurant reviews for your French users.\n",
-        "\n",
-        "In this tutorial, you'll take a text samples from hotel reviews, written in different languages. The text samples will be made into Haystack documents and classified by language. Then each document will be written to a language-specific `DocumentStore`. To validate that the language detection is working correctly, you'll filter the document stores to display their contents.\n",
-        "\n",
-        "In the last section, you'll build a multi-lingual RAG pipeline. The language of a question is detected, and only documents in that language are used to generate the answer. For this section, the [`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter) will come in handy.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "oBa4Q25cGTr6"
-      },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sZVlYSmRjzuk"
+   },
+   "source": [
+    "# Tutorial: Classifying Documents & Queries by Language\n",
+    "\n",
+    "- **Level**: Beginner\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/v2.0/docs/documentlanguageclassifier), [`MetadataRouter`](https://docs.haystack.deepset.ai/v2.0/docs/metadatarouter), [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter), [`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter), [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator)\n",
+    "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline to classify documents based on the (human) language they were written in.\n",
+    "- Optionally, at the end you'll also incorporate language clasification and query routing into a RAG pipeline, so you can query documents based on the language a question was written in.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "G8qw1k7nf7yH"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In a gobalized society with over 7,000 human languages spoken worldwide today, handling multilingual input is a common use case for NLP applications.\n",
+    "\n",
+    "Good news: Haystack has a [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/v2.0/docs/documentlanguageclassifier) built in. This component detects the language a document was written in. This functionality lets you create *branches* in your Haystack pipelines, granting the flexibility to add different processing steps for each language. For example, you could use a LLM that performs better in German to answer German queries. Or, you could fetch only French restaurant reviews for your French users.\n",
+    "\n",
+    "In this tutorial, you'll take a text samples from hotel reviews, written in different languages. The text samples will be made into Haystack documents and classified by language. Then each document will be written to a language-specific `DocumentStore`. To validate that the language detection is working correctly, you'll filter the document stores to display their contents.\n",
+    "\n",
+    "In the last section, you'll build a multi-lingual RAG pipeline. The language of a question is detected, and only documents in that language are used to generate the answer. For this section, the [`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter) will come in handy.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oBa4Q25cGTr6"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oC7ff5x0XTfN"
+   },
+   "source": [
+    "# Installing Haystack\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "lxgAfuxcdftS",
+    "outputId": "36339d6b-f7a8-4686-911a-60642a8adbe6"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "oC7ff5x0XTfN"
-      },
-      "source": [
-        "# Installing Haystack\n"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: haystack-ai in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (2.0.0b6)\n",
+      "Requirement already satisfied: boilerpy3 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (1.0.7)\n",
+      "Requirement already satisfied: haystack-bm25 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (1.0.2)\n",
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+      "Requirement already satisfied: lazy-imports in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (0.3.1)\n",
+      "Requirement already satisfied: more-itertools in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (10.2.0)\n",
+      "Requirement already satisfied: networkx in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (3.2.1)\n",
+      "Requirement already satisfied: openai>=1.1.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (1.10.0)\n",
+      "Requirement already satisfied: pandas in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (2.2.0)\n",
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+      "Requirement already satisfied: tenacity in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (8.2.3)\n",
+      "Requirement already satisfied: tqdm in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (4.66.1)\n",
+      "Requirement already satisfied: typing-extensions in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (4.9.0)\n",
+      "Requirement already satisfied: anyio<5,>=3.5.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (4.2.0)\n",
+      "Requirement already satisfied: distro<2,>=1.7.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (1.9.0)\n",
+      "Requirement already satisfied: httpx<1,>=0.23.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (0.26.0)\n",
+      "Requirement already satisfied: pydantic<3,>=1.9.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (2.6.0)\n",
+      "Requirement already satisfied: sniffio in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from openai>=1.1.0->haystack-ai) (1.3.0)\n",
+      "Requirement already satisfied: numpy in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-bm25->haystack-ai) (1.26.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from jinja2->haystack-ai) (2.1.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from pandas->haystack-ai) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from pandas->haystack-ai) (2023.4)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from pandas->haystack-ai) (2023.4)\n",
+      "Requirement already satisfied: requests<3.0,>=2.7 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from posthog->haystack-ai) (2.31.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from posthog->haystack-ai) (1.16.0)\n",
+      "Requirement already satisfied: monotonic>=1.5 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from posthog->haystack-ai) (1.6)\n",
+      "Requirement already satisfied: backoff>=1.10.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from posthog->haystack-ai) (2.2.1)\n",
+      "Requirement already satisfied: idna>=2.8 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from anyio<5,>=3.5.0->openai>=1.1.0->haystack-ai) (3.6)\n",
+      "Requirement already satisfied: certifi in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (2023.11.17)\n",
+      "Requirement already satisfied: httpcore==1.* in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (1.0.2)\n",
+      "Requirement already satisfied: h11<0.15,>=0.13 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai) (0.14.0)\n",
+      "Requirement already satisfied: annotated-types>=0.4.0 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->haystack-ai) (0.6.0)\n",
+      "Requirement already satisfied: pydantic-core==2.16.1 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->haystack-ai) (2.16.1)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from requests<3.0,>=2.7->posthog->haystack-ai) (3.3.2)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from requests<3.0,>=2.7->posthog->haystack-ai) (2.2.0)\n",
+      "Collecting langdetect\n",
+      "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25h  Preparing metadata (setup.py): started\n",
+      "  Preparing metadata (setup.py): finished with status 'done'\n",
+      "Requirement already satisfied: six in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from langdetect) (1.16.0)\n",
+      "Building wheels for collected packages: langdetect\n",
+      "  Building wheel for langdetect (setup.py): started\n",
+      "  Building wheel for langdetect (setup.py): finished with status 'done'\n",
+      "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993224 sha256=8ec8e722a5a05de5f2df58f42eeb655dad2d4dce611f88b04c375fd435f3f36a\n",
+      "  Stored in directory: /Users/tuanacelik/Library/Caches/pip/wheels/c1/67/88/e844b5b022812e15a52e4eaa38a1e709e99f06f6639d7e3ba7\n",
+      "Successfully built langdetect\n",
+      "Installing collected packages: langdetect\n",
+      "Successfully installed langdetect-1.0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai\n",
+    "pip install langdetect"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "32oB-HJlGXmY"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "Ubr7yVt6Gbnj"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "K0wRwkyvkV3Z"
+   },
+   "source": [
+    "## Write Documents Into `InMemoryDocumentStore`\n",
+    "\n",
+    "The following indexing pipeline writes French and English documents into their own `InMemoryDocumentStores` based on language.\n",
+    "\n",
+    "Import the modules you'll need. Then instantiate a list of Haystack `Documents` that are snippets of hotel reviews in various languages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "mN2fFuWWP_8D"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "lxgAfuxcdftS",
-        "outputId": "36339d6b-f7a8-4686-911a-60642a8adbe6"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Requirement already satisfied: haystack-ai in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (2.0.0b6)\n",
-            "Requirement already satisfied: boilerpy3 in /Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages (from haystack-ai) (1.0.7)\n",
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
-            "\u001b[?25h  Preparing metadata (setup.py): started\n",
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-            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993224 sha256=8ec8e722a5a05de5f2df58f42eeb655dad2d4dce611f88b04c375fd435f3f36a\n",
-            "  Stored in directory: /Users/tuanacelik/Library/Caches/pip/wheels/c1/67/88/e844b5b022812e15a52e4eaa38a1e709e99f06f6639d7e3ba7\n",
-            "Successfully built langdetect\n",
-            "Installing collected packages: langdetect\n",
-            "Successfully installed langdetect-1.0.9\n"
-          ]
-        }
-      ],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install haystack-ai\n",
-        "pip install langdetect"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from haystack import Document, Pipeline\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "from haystack.components.classifiers import DocumentLanguageClassifier\n",
+    "from haystack.components.routers import MetadataRouter\n",
+    "from haystack.components.writers import DocumentWriter\n",
+    "\n",
+    "\n",
+    "documents = [\n",
+    "    Document(\n",
+    "        content=\"Super appartement. Juste au dessus de plusieurs bars qui ferment très tard. A savoir à l'avance. (Bouchons d'oreilles fournis !)\"\n",
+    "    ),\n",
+    "    Document(\n",
+    "        content=\"El apartamento estaba genial y muy céntrico, todo a mano. Al lado de la librería Lello y De la Torre de los clérigos. Está situado en una zona de marcha, así que si vais en fin de semana , habrá ruido, aunque a nosotros no nos molestaba para dormir\"\n",
+    "    ),\n",
+    "    Document(\n",
+    "        content=\"The keypad with a code is convenient and the location is convenient. Basically everything else, very noisy, wi-fi didn't work, check-in person didn't explain anything about facilities, shower head was broken, there's no cleaning and everything else one may need is charged.\"\n",
+    "    ),\n",
+    "    Document(\n",
+    "        content=\"It is very central and appartement has a nice appearance (even though a lot IKEA stuff), *W A R N I N G** the appartement presents itself as a elegant and as a place to relax, very wrong place to relax - you cannot sleep in this appartement, even the beds are vibrating from the bass of the clubs in the same building - you get ear plugs from the hotel -> now I understand why -> I missed a trip as it was so loud and I could not hear the alarm next day due to the ear plugs.- there is a green light indicating 'emergency exit' just above the bed, which shines very bright at night - during the arrival process, you felt the urge of the agent to leave as soon as possible. - try to go to 'RVA clerigos appartements' -> same price, super quiet, beautiful, city center and very nice staff (not an agency)- you are basically sleeping next to the fridge, which makes a lot of noise, when the compressor is running -> had to switch it off - but then had no cool food and drinks. - the bed was somehow broken down - the wooden part behind the bed was almost falling appart and some hooks were broken before- when the neighbour room is cooking you hear the fan very loud. I initially thought that I somehow activated the kitchen fan\"\n",
+    "    ),\n",
+    "    Document(content=\"Un peu salé surtout le sol. Manque de service et de souplesse\"),\n",
+    "    Document(\n",
+    "        content=\"Nous avons passé un séjour formidable. Merci aux personnes , le bonjours à Ricardo notre taxi man, très sympathique. Je pense refaire un séjour parmi vous, après le confinement, tout était parfait, surtout leur gentillesse, aucune chaude négative. Je n'ai rien à redire de négative, Ils étaient a notre écoute, un gentil message tout les matins, pour nous demander si nous avions besoins de renseignement et savoir si tout allait bien pendant notre séjour.\"\n",
+    "    ),\n",
+    "    Document(\n",
+    "        content=\"Céntrico. Muy cómodo para moverse y ver Oporto. Edificio con terraza propia en la última planta. Todo reformado y nuevo. Te traen un estupendo desayuno todas las mañanas al apartamento. Solo que se puede escuchar algo de ruido de la calle a primeras horas de la noche. Es un zona de ocio nocturno. Pero respetan los horarios.\"\n",
+    "    ),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TcZbAvjbRJLA"
+   },
+   "source": [
+    "Each language gets its own `DocumentStore`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "rfC1ZCigQJgI"
+   },
+   "outputs": [],
+   "source": [
+    "en_document_store = InMemoryDocumentStore()\n",
+    "fr_document_store = InMemoryDocumentStore()\n",
+    "es_document_store = InMemoryDocumentStore()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d9fyP-ThRTue"
+   },
+   "source": [
+    "The `DocumentLanguageClassifier` takes a list of languages. The `MetadataRouter` needs a dictionary of rules.  These rules specify which node to route a document to (in this case, which language-specific `DocumentWriter`), based on the document's metadata.\n",
+    "\n",
+    "The keys of the dictionary are the names of the output connections, and the values are dictionaries that follow the format of [filtering expressions in Haystack.](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "FlqGdbuxQNKk"
+   },
+   "outputs": [],
+   "source": [
+    "language_classifier = DocumentLanguageClassifier(languages=[\"en\", \"fr\", \"es\"])\n",
+    "router_rules = {\"en\": {\"language\": {\"$eq\": \"en\"}}, \"fr\": {\"language\": {\"$eq\": \"fr\"}}, \"es\": {\"language\": {\"$eq\": \"es\"}}}\n",
+    "router = MetadataRouter(rules=router_rules)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "FEw5pfmBQRBB"
+   },
+   "outputs": [],
+   "source": [
+    "en_writer = DocumentWriter(document_store=en_document_store)\n",
+    "fr_writer = DocumentWriter(document_store=fr_document_store)\n",
+    "es_writer = DocumentWriter(document_store=es_document_store)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kAQvRdtESq_J"
+   },
+   "source": [
+    "Now that all the components have been created, instantiate the `Pipeline`. Add the components to the pipeline. Connect the outputs of one component to the input of the following component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "BdvO_fEfcVAY"
+   },
+   "outputs": [],
+   "source": [
+    "indexing_pipeline = Pipeline()\n",
+    "indexing_pipeline.add_component(instance=language_classifier, name=\"language_classifier\")\n",
+    "indexing_pipeline.add_component(instance=router, name=\"router\")\n",
+    "indexing_pipeline.add_component(instance=en_writer, name=\"en_writer\")\n",
+    "indexing_pipeline.add_component(instance=fr_writer, name=\"fr_writer\")\n",
+    "indexing_pipeline.add_component(instance=es_writer, name=\"es_writer\")\n",
+    "\n",
+    "\n",
+    "indexing_pipeline.connect(\"language_classifier\", \"router\")\n",
+    "indexing_pipeline.connect(\"router.en\", \"en_writer\")\n",
+    "indexing_pipeline.connect(\"router.fr\", \"fr_writer\")\n",
+    "indexing_pipeline.connect(\"router.es\", \"es_writer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ulAiCB1vTIbr"
+   },
+   "source": [
+    "Draw a diagram of the pipeline to see what the graph looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "598ZTa7RzNeR"
+   },
+   "outputs": [],
+   "source": [
+    "indexing_pipeline.draw(\"indexing_pipeline.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UzQX7zFLS_Bk"
+   },
+   "source": [
+    "Run the pipeline and it will tell you how many documents were written in each language. Voila!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "lE5XE8cPXN5-",
+    "outputId": "43017d9b-65f8-48ad-dadb-66ad0de3af43"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "32oB-HJlGXmY"
-      },
-      "source": [
-        "### Enabling Telemetry\n",
-        "\n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+     "data": {
+      "text/plain": [
+       "{'router': {'unmatched': []},\n",
+       " 'en_writer': {'documents_written': 2},\n",
+       " 'fr_writer': {'documents_written': 3},\n",
+       " 'es_writer': {'documents_written': 2}}"
       ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indexing_pipeline.run(data={\"language_classifier\": {\"documents\": documents}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "R-Q2SxDnu3v-"
+   },
+   "source": [
+    "### Check the Contents of Your Document Stores\n",
+    "\n",
+    "You can check the contents of your document stores. Each one should only contain documents in the correct language."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "LNHzxz52uxZV",
+    "outputId": "d0459677-73c0-4bb6-f5d3-87c0c00b1552"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "Ubr7yVt6Gbnj"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(32)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "English documents:  [Document(id=8f64ab234c6a5d5652d02bed144d069ec6e988903b071d16fffbf400abfc1047, content: 'The keypad with a code is convenient and the location is convenient. Basically everything else, very...', meta: {'language': 'en'}), Document(id=d4d878288efba5e28a43ae0195e43dadd0298fe36d3d9b3075c5c5120d27763e, content: 'It is very central and appartement has a nice appearance (even though a lot IKEA stuff), *W A R N I ...', meta: {'language': 'en'})]\n",
+      "French documents:  [Document(id=ea7ea338874232de2d8105a258813f50345db82772e21ad2c4549dbb7adce8a3, content: 'Super appartement. Juste au dessus de plusieurs bars qui ferment très tard. A savoir à l'avance. (Bo...', meta: {'language': 'fr'}), Document(id=6b64c8a60543ee32b81cd39bc8d6e09fae4bff1b22c6ccdcf414db26fa354e7a, content: 'Un peu salé surtout le sol. Manque de service et de souplesse', meta: {'language': 'fr'}), Document(id=b1be23526f19a8af80a190e775bfd05e65878e585529037cb45b47267a4eaa98, content: 'Nous avons passé un séjour formidable. Merci aux personnes , le bonjours à Ricardo notre taxi man, t...', meta: {'language': 'fr'})]\n",
+      "Spanish documents:  [Document(id=72b094c163b22a660528bc5adbdf0fecf96b4b4d753c1b117f15dba482d2f948, content: 'El apartamento estaba genial y muy céntrico, todo a mano. Al lado de la librería Lello y De la Torre...', meta: {'language': 'es'}), Document(id=4b37b8bdfffccfb3211ea167b4fdc5121ca51fc5f869b4f834e8da473f0d3353, content: 'Céntrico. Muy cómodo para moverse y ver Oporto. Edificio con terraza propia en la última planta. Tod...', meta: {'language': 'es'})]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"English documents: \", en_document_store.filter_documents())\n",
+    "print(\"French documents: \", fr_document_store.filter_documents())\n",
+    "print(\"Spanish documents: \", es_document_store.filter_documents())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "A6J0ac9UWdrT"
+   },
+   "source": [
+    "## (Optional) Create a Multi-Lingual RAG pipeline\n",
+    "\n",
+    "To build a multi-lingual RAG pipeline, you can use the[`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter) to detect the language of the query. Then, fetch documents in that same language from the correct `DocumentStore`.\n",
+    "\n",
+    "In order to do this you'll need an [OpenAI access token](https://help.openai.com/en/articles/4936850-where-do-i-find-my-api-key), although this approach would also work with any other [generator Haystack supports](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "K0wRwkyvkV3Z"
-      },
-      "source": [
-        "## Write Documents Into `InMemoryDocumentStore`\n",
-        "\n",
-        "The following indexing pipeline writes French and English documents into their own `InMemoryDocumentStores` based on language.\n",
-        "\n",
-        "Import the modules you'll need. Then instantiate a list of Haystack `Documents` that are snippets of hotel reviews in various languages."
-      ]
+    "id": "hVJaARodWezy",
+    "outputId": "d9bdcb42-bd50-4fd9-f4d8-a69e8b4b64f8"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ei8up-k3qOC4"
+   },
+   "source": [
+    "Let's assume that all these reviews we put in our document stores earlier are for the same accommodation. A RAG pipeline will let you query for information about that apartment, in the language you choose.\n",
+    "\n",
+    "Import the components you'll need for a RAG pipeline. Write a prompt that will be passed to our LLM, along with the relevant documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "id": "CN1N2sn1yUVx"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
+    "from haystack.components.joiners import DocumentJoiner\n",
+    "from haystack.components.builders import PromptBuilder\n",
+    "from haystack.components.generators import OpenAIGenerator\n",
+    "from haystack.components.routers import TextLanguageRouter\n",
+    "\n",
+    "prompt_template = \"\"\"\n",
+    "You will be provided with reviews for an accommodation.\n",
+    "Answer the question concisely based solely on the given reviews.\n",
+    "Reviews:\n",
+    "  {% for doc in documents %}\n",
+    "    {{ doc.content }}\n",
+    "  {% endfor %}\n",
+    "Question: {{ query}}\n",
+    "Answer:\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WTCT6u4cz_z6"
+   },
+   "source": [
+    "### Build the Pipeline\n",
+    "\n",
+    "Create a new `Pipeline`. Add the following components:\n",
+    "- `TextLanguageRouter`\n",
+    "- `InMemoryBM25Retriever`. You'll need a retriever per language, since each language has its own `DocumentStore`.\n",
+    "- `DocumentJoiner`\n",
+    "- `PromptBuilder`\n",
+    "- `OpenAIGenerator`\n",
+    "\n",
+    "> Note: The `BM25Retriever` essentially does keyword matching, which isn't as accurate as other search methods. In order to make the LLM responses more precise, you could refacctor your piplines to use an [`EmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever) which performs vector search over the documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "id": "BN1Hr_BjWKcl"
+   },
+   "outputs": [],
+   "source": [
+    "rag_pipeline = Pipeline()\n",
+    "rag_pipeline.add_component(instance=TextLanguageRouter([\"en\", \"fr\", \"es\"]), name=\"router\")\n",
+    "rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=en_document_store), name=\"en_retriever\")\n",
+    "rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=fr_document_store), name=\"fr_retriever\")\n",
+    "rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=es_document_store), name=\"es_retriever\")\n",
+    "rag_pipeline.add_component(instance=DocumentJoiner(), name=\"joiner\")\n",
+    "rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name=\"prompt_builder\")\n",
+    "rag_pipeline.add_component(instance=OpenAIGenerator(), name=\"llm\")\n",
+    "\n",
+    "\n",
+    "rag_pipeline.connect(\"router.en\", \"en_retriever.query\")\n",
+    "rag_pipeline.connect(\"router.fr\", \"fr_retriever.query\")\n",
+    "rag_pipeline.connect(\"router.es\", \"es_retriever.query\")\n",
+    "rag_pipeline.connect(\"en_retriever\", \"joiner\")\n",
+    "rag_pipeline.connect(\"fr_retriever\", \"joiner\")\n",
+    "rag_pipeline.connect(\"es_retriever\", \"joiner\")\n",
+    "rag_pipeline.connect(\"joiner.documents\", \"prompt_builder.documents\")\n",
+    "rag_pipeline.connect(\"prompt_builder\", \"llm\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "q1C5GHK_1Kkk"
+   },
+   "source": [
+    "You can draw this pipeline and compare the architecture to the `indexing_pipeline` diagram we created earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "mN2fFuWWP_8D"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/Users/tuanacelik/opt/anaconda3/envs/tutorials/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-            "  from .autonotebook import tqdm as notebook_tqdm\n"
-          ]
-        }
-      ],
-      "source": [
-        "from haystack import Document, Pipeline\n",
-        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-        "from haystack.components.classifiers import DocumentLanguageClassifier\n",
-        "from haystack.components.routers import MetadataRouter\n",
-        "from haystack.components.writers import DocumentWriter\n",
-        "\n",
-        "\n",
-        "documents = [\n",
-        "             Document(content = \"Super appartement. Juste au dessus de plusieurs bars qui ferment très tard. A savoir à l'avance. (Bouchons d'oreilles fournis !)\"),\n",
-        "             Document(content = \"El apartamento estaba genial y muy céntrico, todo a mano. Al lado de la librería Lello y De la Torre de los clérigos. Está situado en una zona de marcha, así que si vais en fin de semana , habrá ruido, aunque a nosotros no nos molestaba para dormir\"),\n",
-        "             Document(content = \"The keypad with a code is convenient and the location is convenient. Basically everything else, very noisy, wi-fi didn't work, check-in person didn't explain anything about facilities, shower head was broken, there's no cleaning and everything else one may need is charged.\"),\n",
-        "             Document(content = \"It is very central and appartement has a nice appearance (even though a lot IKEA stuff), *W A R N I N G** the appartement presents itself as a elegant and as a place to relax, very wrong place to relax - you cannot sleep in this appartement, even the beds are vibrating from the bass of the clubs in the same building - you get ear plugs from the hotel -> now I understand why -> I missed a trip as it was so loud and I could not hear the alarm next day due to the ear plugs.- there is a green light indicating 'emergency exit' just above the bed, which shines very bright at night - during the arrival process, you felt the urge of the agent to leave as soon as possible. - try to go to 'RVA clerigos appartements' -> same price, super quiet, beautiful, city center and very nice staff (not an agency)- you are basically sleeping next to the fridge, which makes a lot of noise, when the compressor is running -> had to switch it off - but then had no cool food and drinks. - the bed was somehow broken down - the wooden part behind the bed was almost falling appart and some hooks were broken before- when the neighbour room is cooking you hear the fan very loud. I initially thought that I somehow activated the kitchen fan\"),\n",
-        "             Document(content = \"Un peu salé surtout le sol. Manque de service et de souplesse\"),\n",
-        "             Document(content = \"Nous avons passé un séjour formidable. Merci aux personnes , le bonjours à Ricardo notre taxi man, très sympathique. Je pense refaire un séjour parmi vous, après le confinement, tout était parfait, surtout leur gentillesse, aucune chaude négative. Je n'ai rien à redire de négative, Ils étaient a notre écoute, un gentil message tout les matins, pour nous demander si nous avions besoins de renseignement et savoir si tout allait bien pendant notre séjour.\"),\n",
-        "             Document(content = \"Céntrico. Muy cómodo para moverse y ver Oporto. Edificio con terraza propia en la última planta. Todo reformado y nuevo. Te traen un estupendo desayuno todas las mañanas al apartamento. Solo que se puede escuchar algo de ruido de la calle a primeras horas de la noche. Es un zona de ocio nocturno. Pero respetan los horarios.\")]"
-      ]
+    "id": "HAFTD5nt1L9a",
+    "outputId": "90cbf82b-8fe5-439d-b099-08510e1c1098"
+   },
+   "outputs": [],
+   "source": [
+    "rag_pipeline.draw(\"rag_pipeline.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-Vr8MbGrEHZV"
+   },
+   "source": [
+    "Try it out by asking a question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 49,
+     "referenced_widgets": [
+      "8a6b993b2fbd4537a1f130adc08f2eb0",
+      "cbe44862bf474ff692a359314a7c21f1",
+      "04daef6e8b9e4779bbe41dc32f4e9083",
+      "e5d659cdfd64477bbb9ce80aed7924f8",
+      "d8777b76fb5341869afcb084e91231ee",
+      "00a38829bf664b0084b5ec704047f00d",
+      "f32af41f2b9543e497989b2c44e9d62d",
+      "4ffb8a24b1a74cb8be55af79261e65ab",
+      "28e5a76ec5b448c7b2d339913fb721c6",
+      "d5d73be36bdb4ddb8fcd92f1ae7a2856",
+      "f403167cb47840a3b0c796ae4c304401"
+     ]
     },
+    "id": "wj24fjXN0l6v",
+    "outputId": "3c1eed33-c31c-4b72-bcda-fdd64744560b"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TcZbAvjbRJLA"
-      },
-      "source": [
-        "Each language gets its own `DocumentStore`."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Ranking by BM25...: 100%|██████████| 2/2 [00:00<00:00, 3134.76 docs/s]"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "id": "rfC1ZCigQJgI"
-      },
-      "outputs": [],
-      "source": [
-        "en_document_store = InMemoryDocumentStore()\n",
-        "fr_document_store = InMemoryDocumentStore()\n",
-        "es_document_store = InMemoryDocumentStore()"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "en_question = \"Is this apartment conveniently located?\"\n",
+    "\n",
+    "result = rag_pipeline.run({\"router\": {\"text\": en_question}, \"prompt_builder\": {\"query\": en_question}})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "i-2P5oqMeUmC",
+    "outputId": "8151923f-bbb1-4e6a-fe4e-08c0d7cfcd49"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d9fyP-ThRTue"
-      },
-      "source": [
-        "The `DocumentLanguageClassifier` takes a list of languages. The `MetadataRouter` needs a dictionary of rules.  These rules specify which node to route a document to (in this case, which language-specific `DocumentWriter`), based on the document's metadata.\n",
-        "\n",
-        "The keys of the dictionary are the names of the output connections, and the values are dictionaries that follow the format of [filtering expressions in Haystack.](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering).\n"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Yes, the apartment is conveniently located.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(result[\"llm\"][\"replies\"][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "U4ChKAl1EKni"
+   },
+   "source": [
+    "How does the pipeline perform en español?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 49,
+     "referenced_widgets": [
+      "cb611c28471e487a8c53a7af8d0f7ae7",
+      "c3876ec6082f466c89d14ea2dccd8207",
+      "f4f835630d074a618f4389c69d1a75d2",
+      "713bf7f3026f4a1daa52df12016caf82",
+      "ae4725f43a3846acbdcbbf79f51166d0",
+      "f0ff1b09bbc34065961a09ee894207bd",
+      "d58eb54b8b04455b9e506598491b5b2b",
+      "3115ad83af834fe4b4fa12362205d98c",
+      "34c2218b0a4d4ba486530aeac007b00a",
+      "dbdbd1d389d74c06acd9a1d3066c82dc",
+      "7b62bd3498bb49ec9e1db68ca088e7ae"
+     ]
     },
+    "id": "B4_Be1bs1jxJ",
+    "outputId": "0b96cf29-d633-4c9b-f54c-a785e1c2cbe4"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "FlqGdbuxQNKk"
-      },
-      "outputs": [],
-      "source": [
-        "\n",
-        "language_classifier = DocumentLanguageClassifier(languages = [\"en\", \"fr\", \"es\"])\n",
-        "router_rules = {\"en\": {\"language\": {\"$eq\": \"en\"}},\n",
-        "                \"fr\": {\"language\": {\"$eq\": \"fr\"}},\n",
-        "                \"es\": {\"language\": {\"$eq\": \"es\"}}}\n",
-        "router = MetadataRouter(rules=router_rules)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Ranking by BM25...: 100%|██████████| 2/2 [00:00<00:00, 15887.52 docs/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "es_question = \"¿El desayuno es genial?\"\n",
+    "\n",
+    "result = rag_pipeline.run({\"router\": {\"text\": es_question}, \"prompt_builder\": {\"query\": es_question}})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "w_1wibY61sjk",
+    "outputId": "54f7506e-9af1-42b8-c0c9-cd13fb4cd9eb"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "id": "FEw5pfmBQRBB"
-      },
-      "outputs": [],
-      "source": [
-        "en_writer = DocumentWriter(document_store = en_document_store)\n",
-        "fr_writer = DocumentWriter(document_store = fr_document_store)\n",
-        "es_writer = DocumentWriter(document_store = es_document_store)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No, el desayuno no es genial.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(result[\"llm\"][\"replies\"][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IhHIJYjbTpAw"
+   },
+   "source": [
+    "## What's next\n",
+    "\n",
+    "If you've been following along, now you know how to incorporate language detection into query and indexing Haystack piplines. Go forth and build the international application of your dreams. 🗺️\n",
+    "\n",
+    "\n",
+    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+    "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+    "-  [Generating Structured Output with Loop-Based Auto-Correction](https://haystack.deepset.ai/tutorials/28_structured_output_with_loop)\n",
+    "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=index_documents_based_on_language_tutorial)."
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
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+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
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+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
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     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kAQvRdtESq_J"
-      },
-      "source": [
-        "Now that all the components have been created, instantiate the `Pipeline`. Add the components to the pipeline. Connect the outputs of one component to the input of the following component."
-      ]
+    "04daef6e8b9e4779bbe41dc32f4e9083": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
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+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_4ffb8a24b1a74cb8be55af79261e65ab",
+      "max": 2,
+      "min": 0,
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+      "style": "IPY_MODEL_28e5a76ec5b448c7b2d339913fb721c6",
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     },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "id": "BdvO_fEfcVAY"
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-      "outputs": [],
-      "source": [
-        "\n",
-        "indexing_pipeline = Pipeline()\n",
-        "indexing_pipeline.add_component(instance=language_classifier, name=\"language_classifier\")\n",
-        "indexing_pipeline.add_component(instance=router, name=\"router\")\n",
-        "indexing_pipeline.add_component(instance=en_writer, name=\"en_writer\")\n",
-        "indexing_pipeline.add_component(instance=fr_writer, name=\"fr_writer\")\n",
-        "indexing_pipeline.add_component(instance=es_writer, name=\"es_writer\")\n",
-        "\n",
-        "\n",
-        "indexing_pipeline.connect(\"language_classifier\", \"router\")\n",
-        "indexing_pipeline.connect(\"router.en\", \"en_writer\")\n",
-        "indexing_pipeline.connect(\"router.fr\", \"fr_writer\")\n",
-        "indexing_pipeline.connect(\"router.es\", \"es_writer\")\n",
-        "\n"
-      ]
+    "28e5a76ec5b448c7b2d339913fb721c6": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
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+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
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+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ulAiCB1vTIbr"
-      },
-      "source": [
-        "Draw a diagram of the pipeline to see what the graph looks like."
-      ]
+    "3115ad83af834fe4b4fa12362205d98c": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
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-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "id": "598ZTa7RzNeR"
-      },
-      "outputs": [],
-      "source": [
-        "indexing_pipeline.draw('indexing_pipeline.png')"
-      ]
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-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UzQX7zFLS_Bk"
-      },
-      "source": [
-        "Run the pipeline and it will tell you how many documents were written in each language. Voila!"
-      ]
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-      "execution_count": 13,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "lE5XE8cPXN5-",
-        "outputId": "43017d9b-65f8-48ad-dadb-66ad0de3af43"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{'router': {'unmatched': []},\n",
-              " 'en_writer': {'documents_written': 2},\n",
-              " 'fr_writer': {'documents_written': 3},\n",
-              " 'es_writer': {'documents_written': 2}}"
-            ]
-          },
-          "execution_count": 13,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "indexing_pipeline.run(data={\"language_classifier\": {\"documents\": documents}})"
-      ]
+    "713bf7f3026f4a1daa52df12016caf82": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_dbdbd1d389d74c06acd9a1d3066c82dc",
+      "placeholder": "​",
+      "style": "IPY_MODEL_7b62bd3498bb49ec9e1db68ca088e7ae",
+      "value": " 2/2 [00:00&lt;00:00, 108.06 docs/s]"
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "R-Q2SxDnu3v-"
-      },
-      "source": [
-        "### Check the Contents of Your Document Stores\n",
-        "\n",
-        "You can check the contents of your document stores. Each one should only contain documents in the correct language."
-      ]
+    "7b62bd3498bb49ec9e1db68ca088e7ae": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
     },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "LNHzxz52uxZV",
-        "outputId": "d0459677-73c0-4bb6-f5d3-87c0c00b1552"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "English documents:  [Document(id=8f64ab234c6a5d5652d02bed144d069ec6e988903b071d16fffbf400abfc1047, content: 'The keypad with a code is convenient and the location is convenient. Basically everything else, very...', meta: {'language': 'en'}), Document(id=d4d878288efba5e28a43ae0195e43dadd0298fe36d3d9b3075c5c5120d27763e, content: 'It is very central and appartement has a nice appearance (even though a lot IKEA stuff), *W A R N I ...', meta: {'language': 'en'})]\n",
-            "French documents:  [Document(id=ea7ea338874232de2d8105a258813f50345db82772e21ad2c4549dbb7adce8a3, content: 'Super appartement. Juste au dessus de plusieurs bars qui ferment très tard. A savoir à l'avance. (Bo...', meta: {'language': 'fr'}), Document(id=6b64c8a60543ee32b81cd39bc8d6e09fae4bff1b22c6ccdcf414db26fa354e7a, content: 'Un peu salé surtout le sol. Manque de service et de souplesse', meta: {'language': 'fr'}), Document(id=b1be23526f19a8af80a190e775bfd05e65878e585529037cb45b47267a4eaa98, content: 'Nous avons passé un séjour formidable. Merci aux personnes , le bonjours à Ricardo notre taxi man, t...', meta: {'language': 'fr'})]\n",
-            "Spanish documents:  [Document(id=72b094c163b22a660528bc5adbdf0fecf96b4b4d753c1b117f15dba482d2f948, content: 'El apartamento estaba genial y muy céntrico, todo a mano. Al lado de la librería Lello y De la Torre...', meta: {'language': 'es'}), Document(id=4b37b8bdfffccfb3211ea167b4fdc5121ca51fc5f869b4f834e8da473f0d3353, content: 'Céntrico. Muy cómodo para moverse y ver Oporto. Edificio con terraza propia en la última planta. Tod...', meta: {'language': 'es'})]\n"
-          ]
-        }
+    "8a6b993b2fbd4537a1f130adc08f2eb0": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_cbe44862bf474ff692a359314a7c21f1",
+       "IPY_MODEL_04daef6e8b9e4779bbe41dc32f4e9083",
+       "IPY_MODEL_e5d659cdfd64477bbb9ce80aed7924f8"
       ],
-      "source": [
-        "print(\"English documents: \", en_document_store.filter_documents())\n",
-        "print(\"French documents: \", fr_document_store.filter_documents())\n",
-        "print(\"Spanish documents: \", es_document_store.filter_documents())"
-      ]
+      "layout": "IPY_MODEL_d8777b76fb5341869afcb084e91231ee"
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "A6J0ac9UWdrT"
-      },
-      "source": [
-        "## (Optional) Create a Multi-Lingual RAG pipeline\n",
-        "\n",
-        "To build a multi-lingual RAG pipeline, you can use the[`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter) to detect the language of the query. Then, fetch documents in that same language from the correct `DocumentStore`.\n",
-        "\n",
-        "In order to do this you'll need an [OpenAI access token](https://help.openai.com/en/articles/4936850-where-do-i-find-my-api-key), although this approach would also work with any other [generator Haystack supports](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "hVJaARodWezy",
-        "outputId": "d9bdcb42-bd50-4fd9-f4d8-a69e8b4b64f8"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from getpass import getpass\n",
-        "\n",
-        "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")"
-      ]
+    "ae4725f43a3846acbdcbbf79f51166d0": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Ei8up-k3qOC4"
-      },
-      "source": [
-        "Let's assume that all these reviews we put in our document stores earlier are for the same accommodation. A RAG pipeline will let you query for information about that apartment, in the language you choose.\n",
-        "\n",
-        "Import the components you'll need for a RAG pipeline. Write a prompt that will be passed to our LLM, along with the relevant documents."
-      ]
+    "c3876ec6082f466c89d14ea2dccd8207": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_f0ff1b09bbc34065961a09ee894207bd",
+      "placeholder": "​",
+      "style": "IPY_MODEL_d58eb54b8b04455b9e506598491b5b2b",
+      "value": "Ranking by BM25...: 100%"
+     }
     },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {
-        "id": "CN1N2sn1yUVx"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
-        "from haystack.components.joiners import DocumentJoiner\n",
-        "from haystack.components.builders import PromptBuilder\n",
-        "from haystack.components.generators import OpenAIGenerator\n",
-        "from haystack.components.routers import TextLanguageRouter\n",
-        "\n",
-        "prompt_template = \"\"\"\n",
-        "You will be provided with reviews for an accommodation.\n",
-        "Answer the question concisely based solely on the given reviews.\n",
-        "Reviews:\n",
-        "  {% for doc in documents %}\n",
-        "    {{ doc.content }}\n",
-        "  {% endfor %}\n",
-        "Question: {{ query}}\n",
-        "Answer:\n",
-        "\"\"\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WTCT6u4cz_z6"
-      },
-      "source": [
-        "### Build the Pipeline\n",
-        "\n",
-        "Create a new `Pipeline`. Add the following components:\n",
-        "- `TextLanguageRouter`\n",
-        "- `InMemoryBM25Retriever`. You'll need a retriever per language, since each language has its own `DocumentStore`.\n",
-        "- `DocumentJoiner`\n",
-        "- `PromptBuilder`\n",
-        "- `OpenAIGenerator`\n",
-        "\n",
-        "> Note: The `BM25Retriever` essentially does keyword matching, which isn't as accurate as other search methods. In order to make the LLM responses more precise, you could refacctor your piplines to use an [`EmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever) which performs vector search over the documents."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {
-        "id": "BN1Hr_BjWKcl"
-      },
-      "outputs": [],
-      "source": [
-        "\n",
-        "rag_pipeline = Pipeline()\n",
-        "rag_pipeline.add_component(instance=TextLanguageRouter(['en', 'fr', 'es']), name=\"router\")\n",
-        "rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=en_document_store), name=\"en_retriever\")\n",
-        "rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=fr_document_store), name=\"fr_retriever\")\n",
-        "rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=es_document_store), name=\"es_retriever\")\n",
-        "rag_pipeline.add_component(instance=DocumentJoiner(), name=\"joiner\")\n",
-        "rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name=\"prompt_builder\")\n",
-        "rag_pipeline.add_component(instance=OpenAIGenerator(), name=\"llm\")\n",
-        "\n",
-        "\n",
-        "rag_pipeline.connect(\"router.en\", \"en_retriever.query\")\n",
-        "rag_pipeline.connect(\"router.fr\", \"fr_retriever.query\")\n",
-        "rag_pipeline.connect(\"router.es\", \"es_retriever.query\")\n",
-        "rag_pipeline.connect(\"en_retriever\", \"joiner\")\n",
-        "rag_pipeline.connect(\"fr_retriever\", \"joiner\")\n",
-        "rag_pipeline.connect(\"es_retriever\", \"joiner\")\n",
-        "rag_pipeline.connect(\"joiner.documents\", \"prompt_builder.documents\")\n",
-        "rag_pipeline.connect(\"prompt_builder\", \"llm\")\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "q1C5GHK_1Kkk"
-      },
-      "source": [
-        "You can draw this pipeline and compare the architecture to the `indexing_pipeline` diagram we created earlier."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "HAFTD5nt1L9a",
-        "outputId": "90cbf82b-8fe5-439d-b099-08510e1c1098"
-      },
-      "outputs": [],
-      "source": [
-        "rag_pipeline.draw('rag_pipeline.png')"
-      ]
+    "cb611c28471e487a8c53a7af8d0f7ae7": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_c3876ec6082f466c89d14ea2dccd8207",
+       "IPY_MODEL_f4f835630d074a618f4389c69d1a75d2",
+       "IPY_MODEL_713bf7f3026f4a1daa52df12016caf82"
+      ],
+      "layout": "IPY_MODEL_ae4725f43a3846acbdcbbf79f51166d0"
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "-Vr8MbGrEHZV"
-      },
-      "source": [
-        "Try it out by asking a question."
-      ]
+    "cbe44862bf474ff692a359314a7c21f1": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_00a38829bf664b0084b5ec704047f00d",
+      "placeholder": "​",
+      "style": "IPY_MODEL_f32af41f2b9543e497989b2c44e9d62d",
+      "value": "Ranking by BM25...: 100%"
+     }
     },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 49,
-          "referenced_widgets": [
-            "8a6b993b2fbd4537a1f130adc08f2eb0",
-            "cbe44862bf474ff692a359314a7c21f1",
-            "04daef6e8b9e4779bbe41dc32f4e9083",
-            "e5d659cdfd64477bbb9ce80aed7924f8",
-            "d8777b76fb5341869afcb084e91231ee",
-            "00a38829bf664b0084b5ec704047f00d",
-            "f32af41f2b9543e497989b2c44e9d62d",
-            "4ffb8a24b1a74cb8be55af79261e65ab",
-            "28e5a76ec5b448c7b2d339913fb721c6",
-            "d5d73be36bdb4ddb8fcd92f1ae7a2856",
-            "f403167cb47840a3b0c796ae4c304401"
-          ]
-        },
-        "id": "wj24fjXN0l6v",
-        "outputId": "3c1eed33-c31c-4b72-bcda-fdd64744560b"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Ranking by BM25...: 100%|██████████| 2/2 [00:00<00:00, 3134.76 docs/s]"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "en_question = \"Is this apartment conveniently located?\"\n",
-        "\n",
-        "result = rag_pipeline.run({\n",
-        "    \"router\": {\"text\": en_question},\n",
-        "    \"prompt_builder\": {\"query\": en_question},\n",
-        "})\n"
-      ]
+    "d58eb54b8b04455b9e506598491b5b2b": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
     },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "i-2P5oqMeUmC",
-        "outputId": "8151923f-bbb1-4e6a-fe4e-08c0d7cfcd49"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Yes, the apartment is conveniently located.\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(result[\"llm\"][\"replies\"][0])"
-      ]
+    "d5d73be36bdb4ddb8fcd92f1ae7a2856": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "U4ChKAl1EKni"
-      },
-      "source": [
-        "How does the pipeline perform en español?"
-      ]
+    "d8777b76fb5341869afcb084e91231ee": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
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-            "Ranking by BM25...: 100%|██████████| 2/2 [00:00<00:00, 15887.52 docs/s]\n"
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-        "es_question = \"¿El desayuno es genial?\"\n",
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-        "## What's next\n",
-        "\n",
-        "If you've been following along, now you know how to incorporate language detection into query and indexing Haystack piplines. Go forth and build the international application of your dreams. 🗺️\n",
-        "\n",
-        "\n",
-        "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
-        "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
-        "-  [Generating Structured Output with Loop-Based Auto-Correction](https://haystack.deepset.ai/tutorials/28_structured_output_with_loop)\n",
-        "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
-        "\n",
-        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=index_documents_based_on_language_tutorial)."
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diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
index f4c5142f..829d13fc 100644
--- a/tutorials/33_Hybrid_Retrieval.ipynb
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -14,7 +14,7 @@
     "- **Prerequisites**: None\n",
     "- **Goal**: After completing this tutorial, you will have learned about creating a hybrid retrieval and when it's useful.\n",
     "\n",
-    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
    ]
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    "source": [
     "## Installing Haystack\n",
     "\n",
-    "Install Haystack 2.0 Beta and other required packages with `pip`:"
+    "Install Haystack 2.0 and other required packages with `pip`:"
    ]
   },
   {
diff --git a/tutorials/34_Extractive_QA_Pipeline.ipynb b/tutorials/34_Extractive_QA_Pipeline.ipynb
index f6041899..610cbbd7 100644
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+    " ## Tutorial: Build an Extractive QA Pipeline\n",
+    "\n",
+    "- **Level**: Beginner\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Components Used**: [`ExtractiveReader`](https://docs.haystack.deepset.ai/v2.0/docs/extractivereader), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder)\n",
+    "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uClfTB7jN6g-"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "What is extractive question answering? So glad you asked! The short answer is that extractive models pull verbatim answers out of text. It's good for use cases where accuracy is paramount, and you need to know exactly where in the text that the answer came from. If you want additional context, here's [a deep dive on extractive versus generative language models](https://haystack.deepset.ai/blog/generative-vs-extractive-models).\n",
+    "\n",
+    "In this tutorial you'll create a Haystack pipeline that extracts answers to questions, based on the provided documents.\n",
+    "\n",
+    "To get data into the extractive pipeline, you'll also build an indexing pipeline to ingest the [Wikipedia pages of Seven Wonders of the Ancient World dataset](https://en.wikipedia.org/wiki/Wonders_of_the_World)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eF_hnatJUEHq"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zQnSZtyyUJVF"
+   },
+   "source": [
+    "#Installation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "rwgpwV4eHVoo"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install haystack-ai accelerate sentence-transformers datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bo6LztWXURaA"
+   },
+   "source": [
+    "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "y_btxLmYUUM9"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(34)"
+   ]
   },
-  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "U8I641xobh_w"
+   },
+   "source": [
+    "## Load data into the `DocumentStore`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b2HaHlBrSvLa"
+   },
+   "source": [
+    "Before you can use this data in the extractive pipeline, you'll use an indexing pipeline to fetch it, process it, and load it into the document store.\n",
+    "\n",
+    "\n",
+    "The data has already been cleaned and preprocessed, so turning it into Haystack `Documents` is fairly straightfoward.\n",
+    "\n",
+    "Using an `InMemoryDocumentStore` here keeps things simple. However, this general approach would work with [any document store that Haystack 2.0 supports](https://docs.haystack.deepset.ai/v2.0/docs/document-store).\n",
+    "\n",
+    "The `SentenceTransformersDocumentEmbedder` transforms each `Document` into a vector. Here we've used [`sentence-transformers/multi-qa-mpnet-base-dot-v1`](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1). You can substitute any embedding model you like, as long as you use the same one in your extractive pipeline.\n",
+    "\n",
+    "Lastly, the `DocumentWriter` writes the vectorized documents to the `DocumentStore`.\n"
+   ]
+  },
+  {
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+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        " ## Tutorial: Build an Extractive QA Pipeline\n",
-        "\n",
-        "- **Level**: Beginner\n",
-        "- **Time to complete**: 15 minutes\n",
-        "- **Components Used**: [`ExtractiveReader`](https://docs.haystack.deepset.ai/v2.0/docs/extractivereader), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder)\n",
-        "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is.\n",
-        "\n",
-        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "2ErVy6A2NisJ"
-      }
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+      "You will be able to reuse this secret in all of your notebooks.\n",
+      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+      "  warnings.warn(\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Overview\n",
-        "\n",
-        "What is extractive question answering? So glad you asked! The short answer is that extractive models pull verbatim answers out of text. It's good for use cases where accuracy is paramount, and you need to know exactly where in the text that the answer came from. If you want additional context, here's [a deep dive on extractive versus generative language models](https://haystack.deepset.ai/blog/generative-vs-extractive-models).\n",
-        "\n",
-        "In this tutorial you'll create a Haystack pipeline that extracts answers to questions, based on the provided documents.\n",
-        "\n",
-        "To get data into the extractive pipeline, you'll also build an indexing pipeline to ingest the [Wikipedia pages of Seven Wonders of the Ancient World dataset](https://en.wikipedia.org/wiki/Wonders_of_the_World)."
-      ],
-      "metadata": {
-        "id": "uClfTB7jN6g-"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7003e95fe7594baa9dcf3b78001dae8c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/46.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
-      ],
-      "metadata": {
-        "id": "eF_hnatJUEHq"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0128d87740d449eb7e5efcc3045f44e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/119k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "#Installation\n"
-      ],
-      "metadata": {
-        "id": "zQnSZtyyUJVF"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "518a021bc14546388ffc719adaa45c18",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "rwgpwV4eHVoo"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ca27757f7c64134931aef12165f4d74",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "!pip install haystack-ai accelerate sentence-transformers datasets"
+      "text/plain": [
+       "modules.json:   0%|          | 0.00/229 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
-      ],
-      "metadata": {
-        "id": "bo6LztWXURaA"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30f823aaa04f4fc78ca022339c60bea7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(34)"
-      ],
-      "metadata": {
-        "id": "y_btxLmYUUM9"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b0ad080573e40919752004106dcd523",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count": null,
-      "outputs": []
+      "text/plain": [
+       "README.md:   0%|          | 0.00/8.66k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Load data into the `DocumentStore`"
-      ],
-      "metadata": {
-        "id": "U8I641xobh_w"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2768619988240c591a4aa2d6fc8e4b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "Before you can use this data in the extractive pipeline, you'll use an indexing pipeline to fetch it, process it, and load it into the document store.\n",
-        "\n",
-        "\n",
-        "The data has already been cleaned and preprocessed, so turning it into Haystack `Documents` is fairly straightfoward.\n",
-        "\n",
-        "Using an `InMemoryDocumentStore` here keeps things simple. However, this general approach would work with [any document store that Haystack 2.0 supports](https://docs.haystack.deepset.ai/v2.0/docs/document-store).\n",
-        "\n",
-        "The `SentenceTransformersDocumentEmbedder` transforms each `Document` into a vector. Here we've used [`sentence-transformers/multi-qa-mpnet-base-dot-v1`](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1). You can substitute any embedding model you like, as long as you use the same one in your extractive pipeline.\n",
-        "\n",
-        "Lastly, the `DocumentWriter` writes the vectorized documents to the `DocumentStore`.\n"
-      ],
-      "metadata": {
-        "id": "b2HaHlBrSvLa"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fdb7c19e3e2a4408ac6ed1a1dd69d0e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "source": [
-        "from datasets import load_dataset\n",
-        "from haystack import Document\n",
-        "from haystack import Pipeline\n",
-        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-        "from haystack.components.readers import ExtractiveReader\n",
-        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
-        "from haystack.components.writers import DocumentWriter\n",
-        "\n",
-        "\n",
-        "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
-        "\n",
-        "documents = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]\n",
-        "\n",
-        "model = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-        "\n",
-        "document_store = InMemoryDocumentStore()\n",
-        "\n",
-        "indexing_pipeline = Pipeline()\n",
-        "\n",
-        "indexing_pipeline.add_component(instance=SentenceTransformersDocumentEmbedder(model=model), name=\"embedder\")\n",
-        "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"writer\")\n",
-        "indexing_pipeline.connect(\"embedder.documents\", \"writer.documents\")\n",
-        "\n",
-        "indexing_pipeline.run({\n",
-        "    \"documents\": documents\n",
-        "})\n"
-      ],
-      "metadata": {
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-        },
-        "id": "ttuq7kLtaV5b",
-        "outputId": "01877b76-f083-4a94-a90e-6717bcecc3d3"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9f74674fc7b42aab231ba01aa112636",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
-            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
-            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
-            "You will be able to reuse this secret in all of your notebooks.\n",
-            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
-            "  warnings.warn(\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading readme:   0%|          | 0.00/46.0 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "7003e95fe7594baa9dcf3b78001dae8c"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading data:   0%|          | 0.00/119k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
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-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating train split: 0 examples [00:00, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
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-        {
-          "output_type": "display_data",
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-              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
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-        },
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-          "data": {
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-              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
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-              "Batches:   0%|          | 0/5 [00:00<?, ?it/s]"
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-          "metadata": {}
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "{'writer': {'documents_written': 151}}"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Build an Extractive QA Pipeline\n",
-        "\n",
-        "Your extractive QA pipeline will consist of three components: an embedder, retriever, and reader.\n",
-        "\n",
-        "- The `SentenceTransformersTextEmbedder` turns a query into a vector, usaing the same embedding model defined above.\n",
-        "\n",
-        "- Vector search allows the retriever to efficiently return relevant documents from the document store. Retrievers are tightly coupled with document stores; thus, you'll use an `InMemoryEmbeddingRetriever`to go with the `InMemoryDocumentStore`.\n",
-        "\n",
-        "- The `ExtractiveReader` returns answers to that query, as well as their location in the source document, and a confidence score.\n"
-      ],
-      "metadata": {
-        "id": "r5CL5VXaVQqE"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b95eac15198143c0a559320d8ba233e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "source": [
-        "\n",
-        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-        "from haystack.components.readers import ExtractiveReader\n",
-        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
-        "\n",
-        "\n",
-        "retriever = InMemoryEmbeddingRetriever(document_store=document_store)\n",
-        "reader = ExtractiveReader()\n",
-        "reader.warm_up()\n",
-        "\n",
-        "extractive_qa_pipeline = Pipeline()\n",
-        "\n",
-        "extractive_qa_pipeline.add_component(instance=SentenceTransformersTextEmbedder(model=model), name=\"embedder\")\n",
-        "extractive_qa_pipeline.add_component(instance=retriever, name=\"retriever\")\n",
-        "extractive_qa_pipeline.add_component(instance=reader, name=\"reader\")\n",
-        "\n",
-        "extractive_qa_pipeline.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
-        "extractive_qa_pipeline.connect(\"retriever.documents\", \"reader.documents\")"
-      ],
-      "metadata": {
-        "id": "xZGGv8yaHZtV"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a5717f7492c4749a7bf9f5bf7c87181",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count": null,
-      "outputs": []
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "Try extracting some answers."
-      ],
-      "metadata": {
-        "id": "Yxi6IYXPZMFw"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf5a8dcb41694fd6a27ed94c5e132ff5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "source": [
-        "query = \"Who was Pliny the Elder?\"\n",
-        "extractive_qa_pipeline.run(data={\"embedder\": {\"text\": query}, \"retriever\": {\"top_k\": 3},\n",
-        "                                   \"reader\": {\"query\": query, \"top_k\": 2}})"
-      ],
-      "metadata": {
-        "id": "mvG3XJOtZR79"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbd79d2df046429bb10cbd9b52b4e5c3",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count": null,
-      "outputs": []
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## `ExtractiveReader`: a closer look\n",
-        "\n",
-        "Here's an example answer:\n",
-        "```python\n",
-        "[ExtractedAnswer(query='Who was Pliny the Elder?', score=0.8306006193161011, data='Roman writer', document=Document(id=bb2c5f3d2e2e2bf28d599c7b686ab47ba10fbc13c07279e612d8632af81e5d71, content: 'The Roman writer Pliny the Elder, writing in the first century AD, argued that the Great Pyramid had...', meta: {'url': 'https://en.wikipedia.org/wiki/Great_Pyramid_of_Giza', '_split_id': 16}\n",
-        "```\n",
-        "\n",
-        "The confidence score ranges from 0 to 1. Higher scores mean the model has more confidence in the answer's relevance.\n",
-        "\n",
-        "The Reader sorts the answers based on their probability scores, with higher probability listed first. You can limit the number of answers the Reader returns in the optional `top_k` parameter.\n",
-        "\n",
-        "By default, the Reader sets a `no_answer=True` parameter. This param returns an `ExtractedAnswer` with no text, and the probability that none of the returned answers are correct.\n",
-        "\n",
-        "```python\n",
-        "ExtractedAnswer(query='Who was Pliny the Elder?', score=0.04606167031102615, data=None, document=None, context=None, document_offset=None, context_offset=None, meta={})]}}\n",
-        "```\n",
-        "\n",
-        "`.0.04606167031102615` means the model is fairly confident the provided answers are correct in this case. You can disable this behavio and return only answers by setting the `no_answer` param to `False` when initializing your `ExtractiveReader`.\n"
-      ],
-      "metadata": {
-        "id": "GOKWgMDCWGRd"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2c569dd2a9b4287ab4af93dad846998",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31e2d766917c4e6fa927e6605e6e1b88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Wrapping it up\n",
-        "\n",
-        "If you've been following along, now you know how to build an extractive question answering pipeline with Haystack 2.0. 🎉 Thanks for reading!\n",
-        "\n",
-        "\n",
-        "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
-        "- [Classifying Documents & Queries by Language](https://haystack.deepset.ai/tutorials/32_classifying_documents_and_queries_by_language)\n",
-        "-  [Generating Structured Output with Loop-Based Auto-Correction](https://haystack.deepset.ai/tutorials/28_structured_output_with_loop)\n",
-        "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
-        "\n",
-        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=extractive_qa_tutorial)."
+     "data": {
+      "text/plain": [
+       "{'writer': {'documents_written': 151}}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from haystack import Document\n",
+    "from haystack import Pipeline\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "from haystack.components.readers import ExtractiveReader\n",
+    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+    "from haystack.components.writers import DocumentWriter\n",
+    "\n",
+    "\n",
+    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
+    "\n",
+    "documents = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]\n",
+    "\n",
+    "model = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
+    "\n",
+    "document_store = InMemoryDocumentStore()\n",
+    "\n",
+    "indexing_pipeline = Pipeline()\n",
+    "\n",
+    "indexing_pipeline.add_component(instance=SentenceTransformersDocumentEmbedder(model=model), name=\"embedder\")\n",
+    "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"writer\")\n",
+    "indexing_pipeline.connect(\"embedder.documents\", \"writer.documents\")\n",
+    "\n",
+    "indexing_pipeline.run({\"documents\": documents})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "r5CL5VXaVQqE"
+   },
+   "source": [
+    "## Build an Extractive QA Pipeline\n",
+    "\n",
+    "Your extractive QA pipeline will consist of three components: an embedder, retriever, and reader.\n",
+    "\n",
+    "- The `SentenceTransformersTextEmbedder` turns a query into a vector, usaing the same embedding model defined above.\n",
+    "\n",
+    "- Vector search allows the retriever to efficiently return relevant documents from the document store. Retrievers are tightly coupled with document stores; thus, you'll use an `InMemoryEmbeddingRetriever`to go with the `InMemoryDocumentStore`.\n",
+    "\n",
+    "- The `ExtractiveReader` returns answers to that query, as well as their location in the source document, and a confidence score.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xZGGv8yaHZtV"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "from haystack.components.readers import ExtractiveReader\n",
+    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+    "\n",
+    "\n",
+    "retriever = InMemoryEmbeddingRetriever(document_store=document_store)\n",
+    "reader = ExtractiveReader()\n",
+    "reader.warm_up()\n",
+    "\n",
+    "extractive_qa_pipeline = Pipeline()\n",
+    "\n",
+    "extractive_qa_pipeline.add_component(instance=SentenceTransformersTextEmbedder(model=model), name=\"embedder\")\n",
+    "extractive_qa_pipeline.add_component(instance=retriever, name=\"retriever\")\n",
+    "extractive_qa_pipeline.add_component(instance=reader, name=\"reader\")\n",
+    "\n",
+    "extractive_qa_pipeline.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+    "extractive_qa_pipeline.connect(\"retriever.documents\", \"reader.documents\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Yxi6IYXPZMFw"
+   },
+   "source": [
+    "Try extracting some answers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "mvG3XJOtZR79"
+   },
+   "outputs": [],
+   "source": [
+    "query = \"Who was Pliny the Elder?\"\n",
+    "extractive_qa_pipeline.run(\n",
+    "    data={\"embedder\": {\"text\": query}, \"retriever\": {\"top_k\": 3}, \"reader\": {\"query\": query, \"top_k\": 2}}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GOKWgMDCWGRd"
+   },
+   "source": [
+    "## `ExtractiveReader`: a closer look\n",
+    "\n",
+    "Here's an example answer:\n",
+    "```python\n",
+    "[ExtractedAnswer(query='Who was Pliny the Elder?', score=0.8306006193161011, data='Roman writer', document=Document(id=bb2c5f3d2e2e2bf28d599c7b686ab47ba10fbc13c07279e612d8632af81e5d71, content: 'The Roman writer Pliny the Elder, writing in the first century AD, argued that the Great Pyramid had...', meta: {'url': 'https://en.wikipedia.org/wiki/Great_Pyramid_of_Giza', '_split_id': 16}\n",
+    "```\n",
+    "\n",
+    "The confidence score ranges from 0 to 1. Higher scores mean the model has more confidence in the answer's relevance.\n",
+    "\n",
+    "The Reader sorts the answers based on their probability scores, with higher probability listed first. You can limit the number of answers the Reader returns in the optional `top_k` parameter.\n",
+    "\n",
+    "By default, the Reader sets a `no_answer=True` parameter. This param returns an `ExtractedAnswer` with no text, and the probability that none of the returned answers are correct.\n",
+    "\n",
+    "```python\n",
+    "ExtractedAnswer(query='Who was Pliny the Elder?', score=0.04606167031102615, data=None, document=None, context=None, document_offset=None, context_offset=None, meta={})]}}\n",
+    "```\n",
+    "\n",
+    "`.0.04606167031102615` means the model is fairly confident the provided answers are correct in this case. You can disable this behavio and return only answers by setting the `no_answer` param to `False` when initializing your `ExtractiveReader`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "e8EQJuGVY_GG"
+   },
+   "source": [
+    "## Wrapping it up\n",
+    "\n",
+    "If you've been following along, now you know how to build an extractive question answering pipeline with Haystack 2.0. 🎉 Thanks for reading!\n",
+    "\n",
+    "\n",
+    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+    "- [Classifying Documents & Queries by Language](https://haystack.deepset.ai/tutorials/32_classifying_documents_and_queries_by_language)\n",
+    "-  [Generating Structured Output with Loop-Based Auto-Correction](https://haystack.deepset.ai/tutorials/28_structured_output_with_loop)\n",
+    "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=extractive_qa_tutorial)."
+   ]
+  }
+ ],
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+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
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+  },
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+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
     }
-  ]
-}
\ No newline at end of file
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
index ef3e90d6..a3ca030a 100644
--- a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
+++ b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
@@ -14,7 +14,7 @@
     "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI: https://platform.openai.com/api-keys\n",
     "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines using some of the model-based evaluation frameworks integerated into Haystack.\n",
     "\n",
-    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
    ]
   },
   {
@@ -53,7 +53,7 @@
    "source": [
     "## Installing Haystack\n",
     "\n",
-    "Install Haystack 2.0 Beta and [datasets](https://pypi.org/project/datasets/) with `pip`:"
+    "Install Haystack 2.0 and [datasets](https://pypi.org/project/datasets/) with `pip`:"
    ]
   },
   {
@@ -473,10 +473,7 @@
     "from haystack_integrations.components.evaluators.deepeval import DeepEvalEvaluator, DeepEvalMetric\n",
     "\n",
     "faithfulness_evaluator = Pipeline()\n",
-    "evaluator = DeepEvalEvaluator(\n",
-    "    metric=DeepEvalMetric.FAITHFULNESS,\n",
-    "    metric_params={\"model\": \"gpt-4\"},\n",
-    ")\n",
+    "evaluator = DeepEvalEvaluator(metric=DeepEvalMetric.FAITHFULNESS, metric_params={\"model\": \"gpt-4\"})\n",
     "faithfulness_evaluator.add_component(\"evaluator\", evaluator)"
    ]
   },
@@ -486,8 +483,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "evaluation_results = faithfulness_evaluator.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\":responses}})\n",
-    "print(evaluation_results['evaluator']['results'])"
+    "evaluation_results = faithfulness_evaluator.run(\n",
+    "    {\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}}\n",
+    ")\n",
+    "print(evaluation_results[\"evaluator\"][\"results\"])"
    ]
   },
   {
@@ -504,10 +503,7 @@
    "outputs": [],
    "source": [
     "contextual_relevance_evaluator = Pipeline()\n",
-    "evaluator = DeepEvalEvaluator(\n",
-    "    metric=DeepEvalMetric.CONTEXTUAL_RELEVANCE,\n",
-    "    metric_params={\"model\": \"gpt-4\"},\n",
-    ")\n",
+    "evaluator = DeepEvalEvaluator(metric=DeepEvalMetric.CONTEXTUAL_RELEVANCE, metric_params={\"model\": \"gpt-4\"})\n",
     "contextual_relevance_evaluator.add_component(\"evaluator\", evaluator)"
    ]
   },
@@ -517,8 +513,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "evaluation_results = contextual_relevance_evaluator.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\":responses}})\n",
-    "print(evaluation_results['evaluator']['results'])"
+    "evaluation_results = contextual_relevance_evaluator.run(\n",
+    "    {\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}}\n",
+    ")\n",
+    "print(evaluation_results[\"evaluator\"][\"results\"])"
    ]
   },
   {
@@ -565,9 +563,7 @@
     "from haystack_integrations.components.evaluators.ragas import RagasEvaluator, RagasMetric\n",
     "\n",
     "context_utilization_pipeline = Pipeline()\n",
-    "evaluator = RagasEvaluator(\n",
-    "    metric=RagasMetric.CONTEXT_UTILIZATION,\n",
-    ")\n",
+    "evaluator = RagasEvaluator(metric=RagasMetric.CONTEXT_UTILIZATION)\n",
     "context_utilization_pipeline.add_component(\"evaluator\", evaluator)"
    ]
   },
@@ -595,8 +591,10 @@
     }
    ],
    "source": [
-    "evaluation_results = context_utilization_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}})\n",
-    "print(evaluation_results['evaluator']['results'])"
+    "evaluation_results = context_utilization_pipeline.run(\n",
+    "    {\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}}\n",
+    ")\n",
+    "print(evaluation_results[\"evaluator\"][\"results\"])"
    ]
   },
   {
@@ -619,7 +617,11 @@
     "aspect_critique_pipeline = Pipeline()\n",
     "evaluator = RagasEvaluator(\n",
     "    metric=RagasMetric.ASPECT_CRITIQUE,\n",
-    "    metric_params={\"name\": \"maliciousness\", \"definition\": \"Is the submission intends to harm, deceive, or exploit users?\", \"strictness\": 2}\n",
+    "    metric_params={\n",
+    "        \"name\": \"maliciousness\",\n",
+    "        \"definition\": \"Is the submission intends to harm, deceive, or exploit users?\",\n",
+    "        \"strictness\": 2,\n",
+    "    },\n",
     ")\n",
     "aspect_critique_pipeline.add_component(\"evaluator\", evaluator)"
    ]
@@ -645,8 +647,10 @@
     }
    ],
    "source": [
-    "evaluation_results = aspect_critique_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}})\n",
-    "print(evaluation_results['evaluator']['results'])"
+    "evaluation_results = aspect_critique_pipeline.run(\n",
+    "    {\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}}\n",
+    ")\n",
+    "print(evaluation_results[\"evaluator\"][\"results\"])"
    ]
   },
   {
@@ -724,8 +728,10 @@
    },
    "outputs": [],
    "source": [
-    "evaluation_results = context_relevance_evaluator_pipeline.run({\"evaluator\": {\"questions\": questions, \"contexts\": contexts}})\n",
-    "print(evaluation_results['evaluator']['results'])"
+    "evaluation_results = context_relevance_evaluator_pipeline.run(\n",
+    "    {\"evaluator\": {\"questions\": questions, \"contexts\": contexts}}\n",
+    ")\n",
+    "print(evaluation_results[\"evaluator\"][\"results\"])"
    ]
   },
   {
@@ -777,7 +783,7 @@
    "outputs": [],
    "source": [
     "evaluation_results = critique_tone_pipeline.run({\"evaluator\": {\"responses\": responses}})\n",
-    "print(evaluation_results['evaluator']['results'])"
+    "print(evaluation_results[\"evaluator\"][\"results\"])"
    ]
   },
   {
diff --git a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
index 84f085f2..4ec26eed 100644
--- a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
+++ b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
@@ -14,7 +14,7 @@
         "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys) and a [Serper API Key](https://serper.dev/api-key) for this tutorial\n",
         "- **Goal**: After completing this tutorial, you'll have learned how to create a pipeline with conditional routing that can fallback to websearch if the answer is not present in your dataset.\n",
         "\n",
-        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n"
+        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n"
       ]
     },
     {
@@ -59,7 +59,7 @@
       "source": [
         "### Install Haystack\n",
         "\n",
-        "Install Haystack 2.0 Beta with `pip`:"
+        "Install Haystack 2.0 with `pip`:"
       ]
     },
     {
diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
index e446bc3b..4d8715c2 100644
--- a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
+++ b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
@@ -15,7 +15,7 @@
         "- **Prerequisites**: You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines)\n",
         "- **Goal**: After completing this tutorial, you'll have learned how to use a Multiplexer to simplify the inputs that `Pipeline.run()` get\n",
         "\n",
-        "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)."
+        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
       ]
     },
     {
@@ -54,7 +54,7 @@
       "source": [
         "### Install Haystack\n",
         "\n",
-        "Install Haystack 2.0 Beta and other required packages with `pip`:"
+        "Install Haystack 2.0 with `pip`:"
       ]
     },
     {
@@ -109,10 +109,10 @@
       "cell_type": "code",
       "execution_count": 2,
       "metadata": {
-        "id": "aiHltCF7JgaV",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "aiHltCF7JgaV",
         "outputId": "b973435d-94c1-458a-8212-c543fd45ffab"
       },
       "outputs": [
@@ -158,21 +158,21 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "Calculating embeddings: 100%|██████████| 1/1 [00:00<00:00,  1.49it/s]\n"
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'doc_writer': {'documents_written': 5}}"
             ]
           },
+          "execution_count": 3,
           "metadata": {},
-          "execution_count": 3
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -228,22 +228,22 @@
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
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             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": []
           },
+          "execution_count": 6,
           "metadata": {},
-          "execution_count": 6
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -298,14 +298,14 @@
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'answer_builder': {'answers': [GeneratedAnswer(data=' Mark lives in Berlin.', query='Where does Mark live?', documents=[], meta={'model': 'mistralai/Mistral-7B-Instruct-v0.1', 'index': 0, 'finish_reason': 'eos_token', 'usage': {'completion_tokens': 6, 'prompt_tokens': 116, 'total_tokens': 122}})]}}"
             ]
           },
+          "execution_count": 7,
           "metadata": {},
-          "execution_count": 7
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -372,22 +372,22 @@
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/jpeg": 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hUhHF1xLhJTzStaiXsXIjH96c9qzsaaSyG38UzWtq3Wq1mnaUbdk/3UNpbjiGUd4hXBPN1aj47GozI1bmRbBs2lmumZZzgugtNhr9C3ZZJhb9tb2N3GelohnETDYUpLbTrfPk++tOxrTvtvyLjsr+cL7T2e5anSCZYQqLFKLMa6K+u5lV0qZFnznJC0eRMLQ8koi1soS62p/mSzdJBbmk99YxTtTdjLCOPtJm8GDxr5NWgy9tFmiNIkrkvNp5JPjyeWpW5bGXQiMkpSReOo7SPYro7PH5sTOI6ToGYrFZEdft3YcYozRMx1pjL3ZNxCCIidNBr9fLfqAzUft9LoZdYxktG2qPXzLeoyqxrmlk1Bso7k0oUNlBrUanZCYKz47nt3zBf+1SP7te1Jq8u3tcYqsThPZnj0KH7YwY+OWdhFn2chhEnyBqU0tLENDTTzKVSJDhkZqNRNkkth/iu2H2OlxHYyswo1MO3hZI4k6+YfOyJwnSlGfdbmslpSfwbJItti2H4ye132QJOoR5v9kNmNkjndd/Ihu2sdmUbRcWjkMNklp80F0SbiFGRERF4EAmzT7Ps41C1UzqMyqgr8Gxe5RTp3hvPT57hQWXnyJzvkobJDz5JJXdr5ElSeKTLkfBjtcKp1doPNfc/Ww6ijRbTWIMOvYSywhlqW80g0ISRERGTZK6F6x2Ac7e/ZzwLGr49P8AOMefu7Ge7aKi27llHjSJTzhKfcceKK+tHTkZJSgy9FKSJJdS43douRSPZ/FbosghZQwxUwW5VrXJdTHkTO5SqUpHeoQs098pzY1JIz8dgEXgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJD0c6+7gvhxed/+4I8FptKtDqusrJ1ki9etIt3WKiIU0wljgy6RGo+ql+l0Tt8Gx7kfqiPW3Tyi04s66BUzZcqS82t59EpaFcE7kSNuKS8TJz+wv8AqEagAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANs0r0ryXWfN67E8Trl2VvNXslJdENI/lOOK/koTv1P8hFuZkRhLnZu1JZgUNvT2sjuota0qc084fottGoiWnf1empOxes1mXwCEs1yh/M8osbiRulUlwzQgz34ILohP/RJEQ7pdmLsTYT2f9LJeNy4EXI7W6Y7u9sJbJK8r3Lq0RH71tO57JL8u5mZmOa/bw7B9j2crp7KcWZesdO5rvor2NTlasz6NOH/ADf5qv8AofUBToAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAG2aV6V5LrPm9dieJ1y7K3mr2SkuiGkfynHFfyUJ36n+Qi3MyIwaV6V5LrPm9dieJ1y7K3mr2SkuiGkfynHFfyUJ36n+Qi3MyI+6HZD7IeNdlbCEw4aUWWUTUJVa3S0bLeX/ADEfzW07nsn/AKnuZmZuyH2Q8a7K2EJhw0ossomoSq1ulo2W8v8AmI/mtp3PZP8A1PczMzn4AHgv6Cuymlm1FvCZsaya0piRFkIJTbqDLYyMjEXa3dpOq0QybE6awprKxXeyDb72JHdWSEcVkXd8EK7x03SZbJozT/n0q5bFscwAOJXbw7B9j2crp7KcWZesdO5rvor2NTlasz6NOH/N/mq/6H1FOh9Nd/QV2U0s2ot4TNjWTWlMSIshBKbdQZbGRkY4s9vDsH2PZyunspxZl6x07mu+ivY1OVqzPo04f83+ar1+B9QFOgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHSv2JnW/TTGHJ+EWFZHo86s3TW1dPL39skFuaWSUfvDSW/oF0PqrxNW3NQfrFlPQZTMmM85HkMrJxp5pRpWhRHuSkmXUjIy3IyAfTqA5/wDsffsgzGqEWFp5qJNbj5cygm4Fo6ZJRZJIveq9ROkRdS9fiXrIugADGW2LUt8+h6zqIFi82jukOS4yHVJRzQ5xI1Eexc2m1bfzm0n4pLbJgAAIn7T2r2A6N6R3NpqGlibSymlRU07iSW5ZLUXRhCD8TP1n4JLqZlsMhr9r9ifZx09l5Xlcvu2UbtxILRkb81/bdLTSfWZ+s/BJbmfQcI+0j2kcs7TeoL+S5NI7thG7ddVMqM2IDO/RCC9Zn0NSj6qP8WxEEc5FPhWl9YTK2vTU17763I8FLpuFHbNRmlHM+qti2Lf1/iGPAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAfpFlPQZLMmM85HkMrJxt5pRpWhRHuSkmXUjIy3IyHXT2Pv2QZjU6LC071FnNx8tZQTdfaPGSUWKSLolR+BOkXq9fiXrIuQ4/SLKehSWZEd5yPIZWTjbrSjStCiPclJMupGR9SMgH07CNtftfsT7OOnsvK8rl92yjduJBaMu/mv7bpaaT6zP1n4JLczFFeyv7KjVVGltjWarqkP5BRxOcGXGRuu1ItiS38CXNzIjM9i26+BGRUO7SPaRyztN6hSMmyaR3bCN266qaUZsQGN+iEF6zPoalH1Uf4tiIHaR7SOWdpvUKRk2TSO7YRu3XVTKjNiAxv0QgvWZ9DUo+qj/ABbEUUgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA//2Q==",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": []
           },
+          "execution_count": 13,
           "metadata": {},
-          "execution_count": 13
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -451,14 +451,14 @@
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'answer_builder': {'answers': [GeneratedAnswer(data=' Mark lives in Berlin.', query='Where does Mark live?', documents=[], meta={'model': 'mistralai/Mistral-7B-Instruct-v0.1', 'index': 0, 'finish_reason': 'eos_token', 'usage': {'completion_tokens': 6, 'prompt_tokens': 116, 'total_tokens': 122}})]}}"
             ]
           },
+          "execution_count": 14,
           "metadata": {},
-          "execution_count": 14
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -502,4 +502,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
index 89fb4485..6d89cb3e 100644
--- a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
+++ b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
@@ -12,7 +12,7 @@
     "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder)\n",
     "- **Goal**: After completing this tutorial, you'll have learned how to embed metadata information while indexing documents, to improve retrieval.\n",
     "\n",
-    "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n",
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n",
     "\n",
     "> ⚠️ Note of caution: The method showcased in this tutorial is not always the right approach for all types of metadata. This method works best when the embedded metadata is meaningful. For example, here we're showcasing embedding the \"title\" meta field, which can also provide good context for the embedding model."
    ]
@@ -45,7 +45,7 @@
    "source": [
     "### Install Haystack\n",
     "\n",
-    "Install Haystack 2.0 Beta and other required packages with `pip`:"
+    "Install Haystack 2.0 and other required packages with `pip`:"
    ]
   },
   {
diff --git a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
index 55edb360..e246d339 100644
--- a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
+++ b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
@@ -14,7 +14,7 @@
     "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines)\n",
     "- **Goal**: After completing this tutorial, you will have learned how to build chat applications that demonstrate agent-like behavior using OpenAI's function calling feature.\n",
     "\n",
-    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs).\n"
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n"
    ]
   },
   {

From 9b2c7f9029c2b1ed07b391d3d3596630627446df Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 15 Mar 2024 12:09:58 +0300
Subject: [PATCH 181/206] hardcode 1.0 version to fix the nighlty tutorial
 tests (#308)

---
 .github/workflows/nightly.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index c8571fba..2ad6b007 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -19,7 +19,7 @@ jobs:
         env:
           GH_TOKEN: ${{ github.token }}  
         run: |
-          VERSION=$(gh api /repos/deepset-ai/haystack/releases/latest | jq -r .tag_name)
+          VERSION=v1.25.0
           NOTEBOOKS=$(python ./scripts/generate_matrix.py --haystack-version "$VERSION" --include-main)
           echo "matrix={\"include\":$NOTEBOOKS}" >> "$GITHUB_OUTPUT"
 

From 05e037adf12407ffc9a56905acbb94b918b0ae2b Mon Sep 17 00:00:00 2001
From: mgosal <m@5bricks.co.uk>
Date: Wed, 3 Apr 2024 12:35:12 +0100
Subject: [PATCH 182/206] Update
 30_File_Type_Preprocessing_Index_Pipeline.ipynb (#310)

---
 tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index 4e0f140c..c2d09b23 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -344,7 +344,7 @@
         "id": "I06qdWsZibSz"
       },
       "source": [
-        "Now, let's build a RAG pipeline that answers queries based ont he documents you just created in the section above. For this step, we will be using the [`HuggingFaceTGIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `mistralai/Mistral-7B-Instruct-v0.1` model."
+        "Now, let's build a RAG pipeline that answers queries based on the documents you just created in the section above. For this step, we will be using the [`HuggingFaceTGIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `mistralai/Mistral-7B-Instruct-v0.1` model."
       ]
     },
     {

From bfe4db4b0fa1e38aa710ac7df51e78dab93f4892 Mon Sep 17 00:00:00 2001
From: absnormal <37442154+absnormal@users.noreply.github.com>
Date: Wed, 3 Apr 2024 19:36:08 +0800
Subject: [PATCH 183/206] tutorial typo fix (#311)

DocumenStore -> DocumentStore
---
 tutorials/27_First_RAG_Pipeline.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 36d45785..1b8e6658 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -207,9 +207,9 @@
       "source": [
         "## Fetching and Indexing Documents\n",
         "\n",
-        "You'll start creating your question answering system by downloading the data and indexing the data with its embeddings to a DocumenStore. \n",
+        "You'll start creating your question answering system by downloading the data and indexing the data with its embeddings to a DocumentStore. \n",
         "\n",
-        "In this tutorial, you will take a simple approach to writing documents and their embeddings into the DocumenStore. For a full indexing pipeline with preprocessing, cleaning and splitting, check out our tutorial on [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline).\n",
+        "In this tutorial, you will take a simple approach to writing documents and their embeddings into the DocumentStore. For a full indexing pipeline with preprocessing, cleaning and splitting, check out our tutorial on [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline).\n",
         "\n",
         "\n",
         "### Initializing the DocumentStore\n",

From e96d58e79c3f8bb51ba0c5cd1235dde4dbd0f86f Mon Sep 17 00:00:00 2001
From: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
Date: Fri, 5 Apr 2024 16:09:25 +0200
Subject: [PATCH 184/206] Update nightly tests for Haystack 2 (#312)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Correctly pick latest 1.x version for nightly

* Adapt matrix generation script to handle 2.x

* Add Haystack 2 tutorial nightly testing

* CI trigger

* Fix matrix generation

* Fix jq parsing

* Use raw jq output

* Add dependencies installation

* Add dependencies in index for all Haystack 2 tutorials

* Update matrix generator to add tutorial dependencies

* Install tutorial dependencies if they exist

* Add some env var in Haystack 2 tests

* Cache common dependencies

* Change how secrets are handled in Haystack 2 tutorials

* Add missing env var secret

* Remove caching for the time being

* Install other stuffs too

* Try again with cache

* Remove caching

* Re enable testing for Haystack 1 tutorials

* Remove CI trigger

* Fix tutorials deps install

* Trigger CI

* Fix if

* Fix if again

* Try to fix the install if again

* Yet another attempt trying to fix if

* Restore max-parallel

* Remove CI trigger

* Update tutorials to run tests

* Fix more errors
* Add missing deps
* Change api key name for 2.0 tutorials
* Introduce a new way of file upload that works for both colab and local envs 🤞

* Update how to download files in tut 30

* Add new dependencies

* Update `run_tutorials.yml` for 2.x tutorials (#314)

* Update run_tutorials.yml for 2.x tutorials

* Update .github/workflows/run_tutorials.yml

---------

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>

* Rename the workflow for v2 tutorials

* Rework workflow to test Haystack 1.x tutorials in PR (#313)

* Rework workflow to test Haystack 1.x tutorials in PR

* Update .github/workflows/run_tutorials_v1.yml

* Update .github/workflows/run_tutorials_v1.yml

---------

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>

---------

Co-authored-by: bilgeyucel <bilgeyucel96@gmail.com>
---
 .github/workflows/nightly.yml                 |   88 +-
 .github/workflows/run_tutorials.yml           |   96 --
 .github/workflows/run_tutorials_v1.yml        |  154 +++
 .github/workflows/run_tutorials_v2.yml        |  156 +++
 index.toml                                    |   30 +-
 scripts/generate_matrix.py                    |   33 +-
 tutorials/24_Building_Chat_App.ipynb          |    2 +-
 tutorials/27_First_RAG_Pipeline.ipynb         |    3 +-
 .../28_Structured_Output_With_Loop.ipynb      |    3 +-
 ...le_Type_Preprocessing_Index_Pipeline.ipynb | 1020 ++++++++---------
 ...ng_Documents_and_Queries_by_Language.ipynb |    3 +-
 tutorials/34_Extractive_QA_Pipeline.ipynb     |    4 +-
 ...el_Based_Evaluation_of_RAG_Pipelines.ipynb |    3 +-
 ...g_Fallbacks_with_Conditional_Routing.ipynb |   13 +-
 ...ing_Pipeline_Inputs_with_Multiplexer.ipynb |    5 +-
 ...ding_Metadata_for_Improved_Retrieval.ipynb |    4 +-
 ...at_Application_with_Function_Calling.ipynb |  118 +-
 17 files changed, 1006 insertions(+), 729 deletions(-)
 delete mode 100644 .github/workflows/run_tutorials.yml
 create mode 100644 .github/workflows/run_tutorials_v1.yml
 create mode 100644 .github/workflows/run_tutorials_v2.yml

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index 2ad6b007..2d419f25 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -9,21 +9,30 @@ jobs:
   generate-matrix:
     runs-on: ubuntu-latest
     outputs:
-      matrix: ${{ steps.generator.outputs.matrix }}
+      matrix_v1: ${{ steps.generator.outputs.matrix_v1 }}
+      matrix_v2: ${{ steps.generator.outputs.matrix_v2 }}
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
         with:
-          python-version: '3.11'
+          python-version: "3.11"
       - id: generator
         env:
-          GH_TOKEN: ${{ github.token }}  
+          GH_TOKEN: ${{ github.token }}
         run: |
-          VERSION=v1.25.0
+          # Get tutorial notebooks for v1
+          VERSION=$(gh api /repos/deepset-ai/haystack/releases | \
+            jq -r '[.[].tag_name | select(test("^v1.[0-9]+.[0-9]+$"))] | first')
           NOTEBOOKS=$(python ./scripts/generate_matrix.py --haystack-version "$VERSION" --include-main)
-          echo "matrix={\"include\":$NOTEBOOKS}" >> "$GITHUB_OUTPUT"
+          echo "matrix_v1={\"include\":$NOTEBOOKS}" >> "$GITHUB_OUTPUT"
 
-  run-tutorials:
+          # Get tutorial notebooks for v2
+          VERSION=$(gh api /repos/deepset-ai/haystack/releases | \
+            jq -r '[.[].tag_name | select(test("^v2.[0-9]+.[0-9]+$"))] | first')
+          NOTEBOOKS=$(python ./scripts/generate_matrix.py --haystack-version "$VERSION" --include-main)
+          echo "matrix_v2={\"include\":$NOTEBOOKS}" >> "$GITHUB_OUTPUT"
+
+  run-tutorials-v1:
     needs: generate-matrix
     runs-on: ubuntu-latest
     container: deepset/haystack:base-cpu-${{ matrix.haystack_version }}
@@ -38,7 +47,7 @@ jobs:
     strategy:
       max-parallel: 2
       fail-fast: false
-      matrix: ${{ fromJSON(needs.generate-matrix.outputs.matrix) }}
+      matrix: ${{ fromJSON(needs.generate-matrix.outputs.matrix_v1) }}
 
     env:
       HAYSTACK_TELEMETRY_ENABLED: "False"
@@ -66,10 +75,71 @@ jobs:
       - name: Install Hugging Face datasets
         run: |
           pip install "datasets>=2.6.1"
-      
+
       - name: Install ipywidgets for tutorial 24
         run: |
-          pip install ipywidgets 
+          pip install ipywidgets
+
+      - name: Convert notebook to Python
+        run: |
+          jupyter nbconvert --to python --RegexRemovePreprocessor.patterns '%%bash' ./tutorials/${{ matrix.notebook }}.ipynb
+
+      - name: Run the converted notebook
+        run: |
+          python ./tutorials/${{ matrix.notebook }}.py
+
+      - name: Send Failure to Datadog
+        if: failure()
+        uses: masci/datadog@v1
+        with:
+          api-key: ${{ secrets.CORE_DATADOG_API_KEY }}
+          api-url: https://api.datadoghq.eu
+          events: |
+            - title: "Tutorial ${{ matrix.notebook }} failed"
+              text: "Branch ${{ github.ref_name }} tests failed"
+              alert_type: "error"
+              source_type_name: "Github"
+              host: ${{ github.repository_owner }}
+              tags:
+                - "project:${{ github.repository }}"
+                - "name:${{ matrix.notebook }}"
+                - "url:https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}"
+
+  run-tutorials-v2:
+    needs: generate-matrix
+    runs-on: ubuntu-latest
+    container: deepset/haystack:base-${{ matrix.haystack_version }}
+
+    strategy:
+      max-parallel: 2
+      fail-fast: false
+      matrix: ${{ fromJSON(needs.generate-matrix.outputs.matrix_v2) }}
+
+    env:
+      HAYSTACK_TELEMETRY_ENABLED: "False"
+      HF_API_TOKEN: ${{ secrets.HF_API_KEY }}
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      SERPERDEV_API_KEY: ${{ secrets.SERPERDEV_API_KEY }}
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Install common dependencies
+        run: |
+          apt-get update && apt-get install -y \
+              build-essential \
+              gcc \
+              libsndfile1 \
+              ffmpeg
+
+          pip install nbconvert ipython
+          pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
+
+      - name: Install tutorial dependencies
+        if: toJSON(matrix.dependencies) != '[]'
+        run: |
+          pip install "${{ join(matrix.dependencies, '" "')}}"
 
       - name: Convert notebook to Python
         run: |
diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml
deleted file mode 100644
index dfde0307..00000000
--- a/.github/workflows/run_tutorials.yml
+++ /dev/null
@@ -1,96 +0,0 @@
-name: Run Tutorials
-
-on:
-  workflow_dispatch:  # Activate this workflow manually
-  pull_request:
-    paths:
-      - 'tutorials/*.ipynb'
-
-jobs:
-  run-tutorials:
-    runs-on: ubuntu-latest
-    container: deepset/haystack:base-cpu-main
-
-    services:
-      elasticsearch:
-        image: elasticsearch:7.9.2
-        env:
-          discovery.type: "single-node"
-          ES_JAVA_OPTS: "-Xms128m -Xmx256m"
-
-    env:
-      HAYSTACK_TELEMETRY_ENABLED: "False"
-      ELASTICSEARCH_HOST: "elasticsearch"
-
-    steps:
-      - name: Install dependencies
-        run: |
-          apt-get update && apt-get install -y git build-essential gcc libsndfile1 ffmpeg && rm -rf /var/lib/apt/lists/*
-          pip install nbconvert ipython
-          pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
-          pip install farm-haystack[pdf]
-
-      - name: Install Haystack Extras text2speech dependencies
-        run: |
-          pip install farm-haystack-text2speech
-      
-      - name: Install Hugging Face datasets 
-        run: |
-          pip install "datasets>=2.6.1"
-
-      - name: Checkout
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 2
-
-      # See https://github.com/actions/runner-images/issues/6775
-      - name: Change Owner of Container Working Directory
-        run: chown root:root .
-
-      - name: Get changed files
-        id: files
-        uses: tj-actions/changed-files@v41
-
-      - name: Convert notebooks to Python
-        shell: bash
-        run: |
-          skiplist=(
-              "tutorials/02_Finetune_a_model_on_your_data.ipynb"
-              "tutorials/07_RAG_Generator.ipynb"
-              "tutorials/09_DPR_training.ipynb"
-              "tutorials/10_Knowledge_Graph.ipynb"
-              "tutorials/12_LFQA.ipynb"
-              "tutorials/13_Question_generation.ipynb"
-              "tutorials/14_Query_Classifier.ipynb"
-              "tutorials/18_GPL.ipynb"
-              "tutorials/22_Pipeline_with_PromptNode.ipynb"
-              "tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb"
-              "tutorials/24_Building_Chat_App.ipynb"
-              "tutorials/25_Customizing_Agent.ipynb"
-              "tutorials/27_First_RAG_Pipeline.ipynb"
-              "tutorials/28_Structured_Output_With_Loop.ipynb"
-              "tutorials/29_Serializing_Pipelines.ipynb"
-              "tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb"
-              "tutorials/31_Metadata_Filtering.ipynb"
-              "tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb"
-              "tutorials/33_Hybrid_Retrieval.ipynb"
-              "tutorials/34_Extractive_QA_Pipeline.ipynb"
-              "tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb"
-              "tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb"
-              "tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb"
-              "tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb"
-              "tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb"
-          )
-          for changed_file in ${{ steps.files.outputs.all_changed_files }}; do
-            if [[ $changed_file == *".ipynb" ]]; then
-              if [[ ! " ${skiplist[*]} " =~ " ${changed_file} " ]]; then
-                jupyter nbconvert --to python --RegexRemovePreprocessor.patterns '%%bash' ${changed_file}
-              fi
-            fi
-          done
-
-      - name: Run the converted notebooks
-        # Note: the `+` at the end of the `find` invocation will make it fail if any
-        # of the execs failed, otherwise `find` returns 0 even when the execs fail.
-        run: |
-          find ./tutorials -name "*.py" -execdir python {} +;
diff --git a/.github/workflows/run_tutorials_v1.yml b/.github/workflows/run_tutorials_v1.yml
new file mode 100644
index 00000000..1807d116
--- /dev/null
+++ b/.github/workflows/run_tutorials_v1.yml
@@ -0,0 +1,154 @@
+name: Run Tutorials for v1.x
+
+on:
+  workflow_dispatch: # Activate this workflow manually
+  pull_request:
+    paths:
+      # This workflow must run only for v1.x tutorials
+      # Some v1 tutorials are ignored in any case as we can't run those
+      # for different reasons
+      - "tutorials/01_Basic_QA_Pipeline.ipynb"
+      - "tutorials/03_Scalable_QA_System.ipynb"
+      - "tutorials/04_FAQ_style_QA.ipynb"
+      - "tutorials/05_Evaluation.ipynb"
+      - "tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb"
+      - "tutorials/07_RAG_Generator.ipynb"
+      - "tutorials/08_Preprocessing.ipynb"
+      - "tutorials/10_Knowledge_Graph.ipynb"
+      - "tutorials/11_Pipelines.ipynb"
+      - "tutorials/14_Query_Classifier.ipynb"
+      - "tutorials/15_TableQA.ipynb"
+      - "tutorials/16_Document_Classifier_at_Index_Time.ipynb"
+      - "tutorials/17_Audio.ipynb"
+      - "tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
+      - "tutorials/20_Using_Haystack_with_REST_API.ipynb"
+      - "tutorials/21_Customizing_PromptNode.ipynb"
+      - "tutorials/22_Pipeline_with_PromptNode.ipynb"
+      - "tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb"
+      - "tutorials/24_Building_Chat_App.ipynb"
+      - "tutorials/25_Customizing_Agents.ipynb"
+      - "tutorials/26_Hybrid_Retrieval.ipynb"
+
+jobs:
+  generate-matrix:
+    runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.filter.outputs.matrix }}
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+
+      - id: generator
+        env:
+          GH_TOKEN: ${{ github.token }}
+        run: |
+          # Get tutorial notebooks for 1.x
+          VERSION=$(gh api /repos/deepset-ai/haystack/releases | \
+            jq -r '[.[].tag_name | select(test("^v1.[0-9]+.[0-9]+$"))] | first')
+          NOTEBOOKS=$(python ./scripts/generate_matrix.py --haystack-version "$VERSION" --include-main)
+          echo "matrix={\"include\":$NOTEBOOKS}" >> "$GITHUB_OUTPUT"
+
+      - name: Get changed files
+        id: files
+        uses: tj-actions/changed-files@v44
+        with:
+          # We only want v1 tutorials, this is a necessary duplication
+          files: |
+            tutorials/01_Basic_QA_Pipeline.ipynb
+            tutorials/03_Scalable_QA_System.ipynb
+            tutorials/04_FAQ_style_QA.ipynb
+            tutorials/05_Evaluation.ipynb
+            tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+            tutorials/07_RAG_Generator.ipynb
+            tutorials/08_Preprocessing.ipynb
+            tutorials/10_Knowledge_Graph.ipynb
+            tutorials/11_Pipelines.ipynb
+            tutorials/14_Query_Classifier.ipynb
+            tutorials/15_TableQA.ipynb
+            tutorials/16_Document_Classifier_at_Index_Time.ipynb
+            tutorials/17_Audio.ipynb
+            tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
+            tutorials/20_Using_Haystack_with_REST_API.ipynb
+            tutorials/21_Customizing_PromptNode.ipynb
+            tutorials/22_Pipeline_with_PromptNode.ipynb
+            tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
+            tutorials/24_Building_Chat_App.ipynb
+            tutorials/25_Customizing_Agents.ipynb
+            tutorials/26_Hybrid_Retrieval.ipynb
+
+      - name: Filter non changed notebooks
+        id: filter
+        shell: python
+        env:
+          MATRIX: ${{ steps.generator.outputs.matrix }}
+          CHANGED_FILES: ${{ steps.files.outputs.all_changed_files }}
+        run: |
+          import os
+          import json
+
+          matrix = json.loads(os.environ["MATRIX"])
+          changed_files = json.loads(os.environ["CHANGED_FILES"])
+          new_matrix = {"include": []}
+          for item in matrix:
+            notebook = item["notebook"]
+            if f"tutorials/{notebook}" not in changed_files:
+              continue
+            new_matrix["include"].append(item)
+
+          new_matrix = json.dumps(new_matrix)
+          with open(os.environ["GITHUB_OUTPUT"], "a") as f:
+            print(f"matrix={new_matrix}", file=f)
+
+  run-tutorials:
+    runs-on: ubuntu-latest
+    needs: get-latest-version
+    container: deepset/haystack:base-cpu-${{ matrix.version }}
+
+    strategy:
+      fail-fast: false
+      matrix: ${{ fromJSON(needs.generate-matrix.outputs.matrix) }}
+
+    services:
+      elasticsearch:
+        image: elasticsearch:7.9.2
+        env:
+          discovery.type: "single-node"
+          ES_JAVA_OPTS: "-Xms128m -Xmx256m"
+
+    env:
+      HAYSTACK_TELEMETRY_ENABLED: "False"
+      ELASTICSEARCH_HOST: "elasticsearch"
+
+    steps:
+      - name: Install dependencies
+        run: |
+          apt-get update && apt-get install -y git build-essential gcc libsndfile1 ffmpeg && rm -rf /var/lib/apt/lists/*
+          pip install nbconvert ipython
+          pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
+          pip install farm-haystack[pdf]
+
+      - name: Install Haystack Extras text2speech dependencies
+        run: |
+          pip install farm-haystack-text2speech
+
+      - name: Install Hugging Face datasets
+        run: |
+          pip install "datasets>=2.6.1"
+
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      # See https://github.com/actions/runner-images/issues/6775
+      - name: Change Owner of Container Working Directory
+        run: chown root:root .
+
+      - name: Convert notebook to Python
+        run: |
+          jupyter nbconvert --to python --RegexRemovePreprocessor.patterns '%%bash' ./tutorials/${{ matrix.notebook }}.ipynb
+
+      - name: Run the converted notebook
+        run: |
+          python ./tutorials/${{ matrix.notebook }}.py
diff --git a/.github/workflows/run_tutorials_v2.yml b/.github/workflows/run_tutorials_v2.yml
new file mode 100644
index 00000000..8c4e2199
--- /dev/null
+++ b/.github/workflows/run_tutorials_v2.yml
@@ -0,0 +1,156 @@
+name: Run Haystack 2.x Tutorials
+
+on:
+  pull_request:
+    paths:
+      - "tutorials/*.ipynb"
+      # Exclude all v1.x tutorials, we can remove this when we drop support for it
+      - "!tutorials/01_Basic_QA_Pipeline.ipynb"
+      - "!tutorials/02_Finetune_a_model_on_your_data.ipynb"
+      - "!tutorials/03_Scalable_QA_System.ipynb"
+      - "!tutorials/04_FAQ_style_QA.ipynb"
+      - "!tutorials/05_Evaluation.ipynb"
+      - "!tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb"
+      - "!tutorials/07_RAG_Generator.ipynb"
+      - "!tutorials/08_Preprocessing.ipynb"
+      - "!tutorials/09_DPR_training.ipynb"
+      - "!tutorials/10_Knowledge_Graph.ipynb"
+      - "!tutorials/11_Pipelines.ipynb"
+      - "!tutorials/12_LFQA.ipynb"
+      - "!tutorials/13_Question_generation.ipynb"
+      - "!tutorials/14_Query_Classifier.ipynb"
+      - "!tutorials/15_TableQA.ipynb"
+      - "!tutorials/16_Document_Classifier_at_Index_Time.ipynb"
+      - "!tutorials/17_Audio.ipynb"
+      - "!tutorials/18_GPL.ipynb"
+      - "!tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
+      - "!tutorials/20_Using_Haystack_with_REST_API.ipynb"
+      - "!tutorials/21_Customizing_PromptNode.ipynb"
+      - "!tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb"
+      - "!tutorials/22_Pipeline_with_PromptNode.ipynb"
+      - "!tutorials/24_Building_Chat_App.ipynb"
+      - "!tutorials/25_Customizing_Agent.ipynb"
+      - "!tutorials/26_Hybrid_Retrieval.ipynb"
+
+jobs:
+  generate-matrix:
+    runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.filter.outputs.matrix }}
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+
+      - id: generator
+        env:
+          GH_TOKEN: ${{ github.token }}
+        run: |
+          # Get tutorial notebooks for v2
+          VERSION=$(gh api /repos/deepset-ai/haystack/releases | \
+            jq -r '[.[].tag_name | select(test("^v2.[0-9]+.[0-9]+$"))] | first')
+          NOTEBOOKS=$(python ./scripts/generate_matrix.py --haystack-version "$VERSION" --include-main)
+          echo "matrix={\"include\":$NOTEBOOKS}" >> "$GITHUB_OUTPUT"
+
+      - name: Get changed files
+        id: files
+        uses: tj-actions/changed-files@v44
+        with:
+          json: true
+          files: tutorials/*.ipynb
+          # Ignore all 1.x tutorials, we can remove this as soon as we drop support for it
+          files_ignore: |
+            tutorials/01_Basic_QA_Pipeline.ipynb
+            tutorials/02_Finetune_a_model_on_your_data.ipynb
+            tutorials/03_Scalable_QA_System.ipynb
+            tutorials/04_FAQ_style_QA.ipynb
+            tutorials/05_Evaluation.ipynb
+            tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb
+            tutorials/07_RAG_Generator.ipynb
+            tutorials/08_Preprocessing.ipynb
+            tutorials/09_DPR_training.ipynb
+            tutorials/10_Knowledge_Graph.ipynb
+            tutorials/11_Pipelines.ipynb
+            tutorials/12_LFQA.ipynb
+            tutorials/13_Question_generation.ipynb
+            tutorials/14_Query_Classifier.ipynb
+            tutorials/15_TableQA.ipynb
+            tutorials/16_Document_Classifier_at_Index_Time.ipynb
+            tutorials/17_Audio.ipynb
+            tutorials/18_GPL.ipynb
+            tutorials/19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb
+            tutorials/20_Using_Haystack_with_REST_API.ipynb
+            tutorials/21_Customizing_PromptNode.ipynb
+            tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb
+            tutorials/22_Pipeline_with_PromptNode.ipynb
+            tutorials/24_Building_Chat_App.ipynb
+            tutorials/25_Customizing_Agent.ipynb
+            tutorials/26_Hybrid_Retrieval.ipynb
+
+      - name: Filter non changed notebooks
+        id: filter
+        shell: python
+        env:
+          MATRIX: ${{ steps.generator.outputs.matrix }}
+          CHANGED_FILES: ${{ steps.files.outputs.all_changed_files }}
+        run: |
+          import os
+          import json
+
+          matrix = json.loads(os.environ["MATRIX"])
+          changed_files = json.loads(os.environ["CHANGED_FILES"])
+          new_matrix = {"include": []}
+          for item in matrix:
+            notebook = item["notebook"]
+            if f"tutorials/{notebook}" not in changed_files:
+              continue
+            new_matrix["include"].append(item)
+
+          new_matrix = json.dumps(new_matrix)
+          with open(os.environ["GITHUB_OUTPUT"], "a") as f:
+            print(f"matrix={new_matrix}", file=f)
+
+  run-tutorials:
+    runs-on: ubuntu-latest
+    needs: generate-matrix
+    container: deepset/haystack:base-${{ matrix.version }}
+
+    strategy:
+      fail-fast: false
+      matrix: ${{ fromJSON(needs.generate-matrix.outputs.matrix) }}
+
+    env:
+      HAYSTACK_TELEMETRY_ENABLED: "False"
+      HF_API_TOKEN: ${{ secrets.HF_API_KEY }}
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      SERPERDEV_API_KEY: ${{ secrets.SERPERDEV_API_KEY }}
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Install common dependencies
+        run: |
+          apt-get update && apt-get install -y \
+              build-essential \
+              gcc \
+              libsndfile1 \
+              ffmpeg
+
+          pip install nbconvert ipython
+          pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
+
+      - name: Install tutorial dependencies
+        if: toJSON(matrix.dependencies) != '[]'
+        run: |
+          pip install "${{ join(matrix.dependencies, '" "')}}"
+
+      - name: Convert notebook to Python
+        run: |
+          jupyter nbconvert --to python --RegexRemovePreprocessor.patterns '%%bash' ./tutorials/${{ matrix.notebook }}.ipynb
+
+      - name: Run the converted notebook
+        run: |
+          python ./tutorials/${{ matrix.notebook }}.py
diff --git a/index.toml b/index.toml
index cd2539a0..01125687 100644
--- a/index.toml
+++ b/index.toml
@@ -1,6 +1,6 @@
 [config]
 layout = "tutorial"
-toc = true 
+toc = true
 colab = "https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/"
 
 [[tutorial]]
@@ -9,7 +9,11 @@ description = "Get Started by creating a Retriever Reader pipeline."
 level = "beginner"
 weight = 10
 notebook = "01_Basic_QA_Pipeline.ipynb"
-aliases = ["first-qa-system", "without-elasticsearch", "03_basic_qa_pipeline_without_elasticsearch"]
+aliases = [
+    "first-qa-system",
+    "without-elasticsearch",
+    "03_basic_qa_pipeline_without_elasticsearch",
+]
 completion_time = "15 min"
 created_at = 2023-01-11
 
@@ -275,6 +279,7 @@ aliases = []
 completion_time = "10 min"
 created_at = 2023-12-05
 haystack_2 = true
+dependencies = ["datasets>=2.6.1", "sentence-transformers>=2.2.0"]
 featured = true
 
 [[tutorial]]
@@ -287,6 +292,7 @@ aliases = []
 completion_time = "15 min"
 created_at = 2023-11-30
 haystack_2 = true
+dependencies = ["colorama"]
 featured = true
 
 [[tutorial]]
@@ -299,6 +305,7 @@ aliases = []
 completion_time = "10 min"
 created_at = 2024-01-29
 haystack_2 = true
+dependencies = ["transformers[torch]"]
 
 [[tutorial]]
 title = "Preprocessing Different File Types"
@@ -310,6 +317,15 @@ aliases = []
 completion_time = "15 min"
 created_at = 2024-01-30
 haystack_2 = true
+dependencies = [
+    "sentence-transformers>=2.2.0",
+    "huggingface_hub>=0.22.0",
+    "transformers",
+    "markdown-it-py",
+    "mdit_plain",
+    "pypdf",
+    "gdown"
+]
 
 [[tutorial]]
 title = "Filtering Documents with Metadata"
@@ -321,6 +337,7 @@ aliases = []
 completion_time = "5 min"
 created_at = 2024-01-30
 haystack_2 = true
+dependencies = []
 
 [[tutorial]]
 title = "Classifying Documents & Queries by Language"
@@ -332,6 +349,7 @@ aliases = []
 completion_time = "15 min"
 created_at = 2024-02-06
 haystack_2 = true
+dependencies = ["langdetect"]
 
 [[tutorial]]
 title = "Creating a Hybrid Retrieval Pipeline"
@@ -343,6 +361,8 @@ aliases = []
 completion_time = "15 min"
 created_at = 2024-02-13
 haystack_2 = true
+dependencies = ["datasets>=2.6.1", "sentence-transformers>=2.2.0", "accelerate"]
+needs_gpu = true
 
 [[tutorial]]
 title = "Build an Extractive QA Pipeline"
@@ -354,6 +374,7 @@ aliases = []
 completion_time = "10 min"
 created_at = 2024-02-09
 haystack_2 = true
+dependencies = ["accelerate", "sentence-transformers", "datasets"]
 
 [[tutorial]]
 title = "Model-Based Evaluation of RAG Pipelines"
@@ -365,6 +386,7 @@ aliases = []
 completion_time = "15 min"
 created_at = 2024-02-12
 haystack_2 = true
+dependencies = ["pydantic<1.10.10", "datasets>=2.6.1", "deepeval-haystack", "ragas-haystack", "uptrain-haystack"]
 
 [[tutorial]]
 title = "Building Fallbacks to Websearch with Conditional Routing"
@@ -376,6 +398,7 @@ aliases = []
 completion_time = "10 min"
 created_at = 2024-02-16
 haystack_2 = true
+dependencies = []
 featured = true
 
 [[tutorial]]
@@ -388,6 +411,7 @@ aliases = []
 completion_time = "10 min"
 created_at = 2024-02-19
 haystack_2 = true
+dependencies = ["transformers", "huggingface_hub>=0.22.0"]
 
 [[tutorial]]
 title = "Embedding Metadata for Improved Retrieval"
@@ -399,6 +423,7 @@ aliases = []
 completion_time = "10 min"
 created_at = 2024-02-20
 haystack_2 = true
+dependencies = ["wikipedia", "sentence-transformers"]
 
 [[tutorial]]
 title = "Building a Chat Application with Function Calling"
@@ -410,4 +435,5 @@ aliases = []
 completion_time = "20 min"
 created_at = 2024-03-05
 haystack_2 = true
+dependencies = ["sentence-transformers>=2.2.0", "gradio"]
 featured = true
diff --git a/scripts/generate_matrix.py b/scripts/generate_matrix.py
index 4f9ca07d..84011b2e 100644
--- a/scripts/generate_matrix.py
+++ b/scripts/generate_matrix.py
@@ -1,6 +1,8 @@
-import tomllib
 import argparse
 import json
+import re
+
+import tomllib
 
 
 def read_index(path):
@@ -9,7 +11,9 @@ def read_index(path):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(usage="""python generate_matrix.py --haystack-version v1.18.1""")
+    parser = argparse.ArgumentParser(
+        usage="""python generate_matrix.py --haystack-version v1.18.1"""
+    )
     parser.add_argument("--index", dest="index", default="index.toml")
     parser.add_argument("--notebooks", dest="notebooks", nargs="+", default=[])
     parser.add_argument("--haystack-version", dest="version", required=True)
@@ -18,6 +22,8 @@ def read_index(path):
     args = parser.parse_args()
     index = read_index(args.index)
 
+    is_haystack2 = re.match("^v?2", args.version) is not None
+
     matrix = []
     for tutorial in index["tutorial"]:
         notebook = tutorial["notebook"]
@@ -37,18 +43,33 @@ def read_index(path):
             # so there's nothing to test
             continue
 
-        if tutorial.get("haystack_2", False):
-            # Haystack 2.0 tutorials should be skipped for now
+        if is_haystack2 and not tutorial.get("haystack_2", False):
+            # Skip Haystack 1.0 tutorials when testing Haystack 2.0
+            continue
+        elif not is_haystack2 and tutorial.get("haystack_2", False):
+            # Skip Haystack 2.0 tutorials when testing Haystack 1.0
             continue
 
         version = tutorial.get("haystack_version", args.version)
         if version[0] != "v":
             version = f"v{version}"
 
-        matrix.append({"notebook": notebook[:-6], "haystack_version": version})
+        matrix.append(
+            {
+                "notebook": notebook[:-6],
+                "haystack_version": version,
+                "dependencies": tutorial.get("dependencies", []),
+            }
+        )
 
         if args.main and "haystack_version" not in tutorial:
             # If a tutorial doesn't specify a version, we also test it on main
-            matrix.append({"notebook": notebook[:-6], "haystack_version": "main"})
+            matrix.append(
+                {
+                    "notebook": notebook[:-6],
+                    "haystack_version": "main",
+                    "dependencies": tutorial.get("dependencies", []),
+                }
+            )
 
     print(json.dumps(matrix))
diff --git a/tutorials/24_Building_Chat_App.ipynb b/tutorials/24_Building_Chat_App.ipynb
index ee15a6a9..215dbac8 100644
--- a/tutorials/24_Building_Chat_App.ipynb
+++ b/tutorials/24_Building_Chat_App.ipynb
@@ -124,7 +124,7 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "model_api_key = os.getenv(\"HF_API_KEY\", None) or getpass(\"Enter HF API key:\")"
+    "model_api_key = os.getenv(\"HF_API_TOKEN\", None) or getpass(\"Enter HF API key:\")"
    ]
   },
   {
diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 1b8e6658..3eba9d1b 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -534,7 +534,8 @@
         "from getpass import getpass\n",
         "from haystack.components.generators import OpenAIGenerator\n",
         "\n",
-        "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key: \")\n",
+        "if \"OPENAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
         "generator = OpenAIGenerator(model=\"gpt-3.5-turbo\")"
       ]
     },
diff --git a/tutorials/28_Structured_Output_With_Loop.ipynb b/tutorials/28_Structured_Output_With_Loop.ipynb
index 9da7272b..c3e76764 100644
--- a/tutorials/28_Structured_Output_With_Loop.ipynb
+++ b/tutorials/28_Structured_Output_With_Loop.ipynb
@@ -309,7 +309,8 @@
         "\n",
         "from haystack.components.generators import OpenAIGenerator\n",
         "\n",
-        "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key: \")\n",
+        "if \"OPENAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
         "generator = OpenAIGenerator()"
       ]
     },
diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index c2d09b23..688f1de5 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -1,538 +1,488 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zAKy_OPy81EZ"
-      },
-      "source": [
-        "# Tutorial: Preprocessing Different File Types\n",
-        "\n",
-        "- **Level**: Beginner\n",
-        "- **Time to complete**: 15 minutes\n",
-        "- **Goal**: After completing this tutorial, you'll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the `FileTypeRouter`.\n",
-        "\n",
-        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n",
-        "\n",
-        "> 💡 (Optional): After creating the indexing pipeline in this tutorial, there is an optional section that shows you how to create a RAG pipeline on top of the document store you just created. You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section\n",
-        "\n",
-        "## Components Used\n",
-        "\n",
-        "- [`FileTypeRouter`](https://docs.haystack.deepset.ai/v2.0/docs/filetyperouter): This component will help you route files based on their corresponding MIME type to different components\n",
-        "- [`MarkdownToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/markdowntodocument): This component will help you convert markdown files into Haystack Documents\n",
-        "- [`PyPDFToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/pypdftodocument): This component will help you convert pdf files into Haystack Documents\n",
-        "- [`TextFileToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/textfiletodocument): This component will help you convert text files into Haystack Documents\n",
-        "- [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner): This component will help you to join Documents coming from different branches of a pipeline\n",
-        "- [`DocumentCleaner`](https://docs.haystack.deepset.ai/v2.0/docs/documentcleaner) (optional): This component will help you to make Documents more readable by removing extra whitespaces etc.\n",
-        "- [`DocumentSplitter`](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter): This component will help you to split your Document into chunks\n",
-        "- [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder): This component will help you create embeddings for Documents.\n",
-        "- [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter): This component will help you write Documents into the DocumentStore"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "96w6PrcPk4Fc"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "In this tutorial, you'll build an indexing pipeline that preprocesses different types of files (markdown, txt and pdf). Each file will have its own `FileConverter`. The rest of the indexing pipeline is fairly standard - split the documents into chunks, trim whitespace, create embeddings and write them to a Document Store.\n",
-        "\n",
-        "Optionally, you can keep going to see how to use these documents in a query pipeline as well."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rns_B_NGN0Ze"
-      },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_pG2jycZLYYr"
-      },
-      "source": [
-        "## Installing dependencies\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2mP4empwf_x4"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "pip install haystack-ai\n",
-        "pip install \"sentence-transformers>=2.2.0\"\n",
-        "pip install markdown-it-py mdit_plain\n",
-        "pip install pypdf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HnXumz7EarJx"
-      },
-      "source": [
-        "### Enabling Telemetry\n",
-        "\n",
-        "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "CkvJIU7FmDf9"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(30)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7GWbC28fX0Yp"
-      },
-      "source": [
-        "## Download All Files\n",
-        "\n",
-        "All required files will be downloaded into this Colab notebook. You can see these files in \"files\" tab on the left. Just like most real life data, these files are a mishmash of different types."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8wIRuFcTIKF1"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "gdown https://drive.google.com/drive/folders/1n9yqq5Gl_HWfND5bTlrCwAOycMDt5EMj -O /content --folder"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DH8HEymp6XFZ"
-      },
-      "source": [
-        "## Create a Pipeline to Index Documents\n",
-        "\n",
-        "Next, you'll create a pipeline to index documents. To keep things uncomplicated, you'll use an `InMemoryDocumentStore` but this approach would also work with any other flavor of `DocumentStore`.\n",
-        "\n",
-        "You'll need a different file converter class for each file type in our data sources: `.pdf`, `.txt`, and `.md` in this case. Our `FileTypeRouter` connects each file type to the proper converter.\n",
-        "\n",
-        "Once all our files have been converted to Haystack Documents, we can use the `DocumentJoiner` component to make these a single list of documents that can be fed through the rest of the indexing pipeline all together."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "id": "c_eM4C1cA4j6"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.components.writers import DocumentWriter\n",
-        "from haystack.components.converters import MarkdownToDocument, PyPDFToDocument, TextFileToDocument\n",
-        "from haystack.components.preprocessors import DocumentSplitter, DocumentCleaner\n",
-        "from haystack.components.routers import FileTypeRouter\n",
-        "from haystack.components.joiners import DocumentJoiner\n",
-        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
-        "from haystack import Pipeline\n",
-        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-        "\n",
-        "document_store = InMemoryDocumentStore()\n",
-        "file_type_router = FileTypeRouter(mime_types=[\"text/plain\", \"application/pdf\", \"text/markdown\"])\n",
-        "text_file_converter = TextFileToDocument()\n",
-        "markdown_converter = MarkdownToDocument()\n",
-        "pdf_converter = PyPDFToDocument()\n",
-        "document_joiner = DocumentJoiner()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ef8okackBSmk"
-      },
-      "source": [
-        "From there, the steps to this indexing pipeline are a bit more standard. The `DocumentCleaner` removes whitespace. Then this `DocumentSplitter` breaks them into chunks of 150 words, with a bit of overlap to avoid missing context."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "hCWlpiQCBYOg"
-      },
-      "outputs": [],
-      "source": [
-        "document_cleaner = DocumentCleaner()\n",
-        "document_splitter = DocumentSplitter(split_by=\"word\", split_length=150, split_overlap=50)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Do4nhM4tBaZL"
-      },
-      "source": [
-        "Now you'll add a `SentenceTransformersDocumentEmbedder` to create embeddings from the documents. As the last step in this pipeline, the `DocumentWriter` will write them to the `InMemoryDocumentStore`.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "id": "TVXSX0GHBtdj"
-      },
-      "outputs": [],
-      "source": [
-        "document_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
-        "document_writer = DocumentWriter(document_store)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hJaJmGanBv1b"
-      },
-      "source": [
-        "After creating all the components, add them to the indexing pipeline."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "id": "4yGXKHEXIZxi"
-      },
-      "outputs": [],
-      "source": [
-        "preprocessing_pipeline = Pipeline()\n",
-        "preprocessing_pipeline.add_component(instance=file_type_router, name=\"file_type_router\")\n",
-        "preprocessing_pipeline.add_component(instance=text_file_converter, name=\"text_file_converter\")\n",
-        "preprocessing_pipeline.add_component(instance=markdown_converter, name=\"markdown_converter\")\n",
-        "preprocessing_pipeline.add_component(instance=pdf_converter, name=\"pypdf_converter\")\n",
-        "preprocessing_pipeline.add_component(instance=document_joiner, name=\"document_joiner\")\n",
-        "preprocessing_pipeline.add_component(instance=document_cleaner, name=\"document_cleaner\")\n",
-        "preprocessing_pipeline.add_component(instance=document_splitter, name=\"document_splitter\")\n",
-        "preprocessing_pipeline.add_component(instance=document_embedder, name=\"document_embedder\")\n",
-        "preprocessing_pipeline.add_component(instance=document_writer, name=\"document_writer\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "y89Z9jwUfNbr"
-      },
-      "source": [
-        "Next, connect them 👇"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "gafXWtNYfNbr",
-        "outputId": "10f351de-ac09-4273-85a2-ac7b59fb2f77"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/jpeg": 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-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": []
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "preprocessing_pipeline.connect(\"file_type_router.text/plain\", \"text_file_converter.sources\")\n",
-        "preprocessing_pipeline.connect(\"file_type_router.application/pdf\", \"pypdf_converter.sources\")\n",
-        "preprocessing_pipeline.connect(\"file_type_router.text/markdown\", \"markdown_converter.sources\")\n",
-        "preprocessing_pipeline.connect(\"text_file_converter\", \"document_joiner\")\n",
-        "preprocessing_pipeline.connect(\"pypdf_converter\", \"document_joiner\")\n",
-        "preprocessing_pipeline.connect(\"markdown_converter\", \"document_joiner\")\n",
-        "preprocessing_pipeline.connect(\"document_joiner\", \"document_cleaner\")\n",
-        "preprocessing_pipeline.connect(\"document_cleaner\", \"document_splitter\")\n",
-        "preprocessing_pipeline.connect(\"document_splitter\", \"document_embedder\")\n",
-        "preprocessing_pipeline.connect(\"document_embedder\", \"document_writer\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3NGinkHPB9C2"
-      },
-      "source": [
-        "Let's test this pipeline with a few recipes I've written. Are you getting hungry yet?"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "9Mw5kwZiqehc"
-      },
-      "outputs": [],
-      "source": [
-        "preprocessing_pipeline.run(\n",
-        "    {\n",
-        "        \"file_type_router\": {\n",
-        "            \"sources\": [\n",
-        "                \"/content/vegan_sunflower_hemp_cheese_recipe.txt\",\n",
-        "                \"/content/vegan_keto_eggplant_recipe.pdf\",\n",
-        "                \"/content/vegan_flan_recipe.md\",\n",
-        "            ]\n",
-        "        }\n",
-        "    }\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TVGb-rteg7E5"
-      },
-      "source": [
-        "🎉 If you only wanted to learn how to preprocess documents, you can stop here! If you want to see an example of using those documents in a RAG pipeline, read on.  "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "16PnegDR2EmY"
-      },
-      "source": [
-        "## (Optional) Build a pipeline to query documents"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "I06qdWsZibSz"
-      },
-      "source": [
-        "Now, let's build a RAG pipeline that answers queries based on the documents you just created in the section above. For this step, we will be using the [`HuggingFaceTGIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `mistralai/Mistral-7B-Instruct-v0.1` model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bB344ADZr-eG",
-        "outputId": "b6030405-5def-4700-8124-2e7ec292e977"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Enter Hugging Face token: ··········\n"
-          ]
-        }
-      ],
-      "source": [
-        "import os\n",
-        "from getpass import getpass\n",
-        "\n",
-        "os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter Hugging Face token: \")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QASEGJhnIMQL"
-      },
-      "source": [
-        "In this step you'll build a query pipeline to answer questions about the documents.\n",
-        "\n",
-        "This pipeline takes the prompt, searches the document store for relevant documents, and passes those documents along to the LLM to formulate an answer.\n",
-        "\n",
-        "> ⚠️ Notice how we used `sentence-transformers/all-MiniLM-L6-v2` to create embeddings for our documents before. This is why we will be using the same model to embed incoming questions."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "_s--8xEWq8Y9",
-        "outputId": "1c050d5f-f2ae-4cd3-e0d4-533397a6af63"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/jpeg": 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-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": []
-          },
-          "execution_count": 13,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
-        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-        "from haystack.components.builders import PromptBuilder\n",
-        "from haystack.components.generators import HuggingFaceTGIGenerator\n",
-        "\n",
-        "template = \"\"\"\n",
-        "Answer the questions based on the given context.\n",
-        "\n",
-        "Context:\n",
-        "{% for document in documents %}\n",
-        "    {{ document.content }}\n",
-        "{% endfor %}\n",
-        "\n",
-        "Question: {{ question }}\n",
-        "Answer:\n",
-        "\"\"\"\n",
-        "pipe = Pipeline()\n",
-        "pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
-        "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
-        "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
-        "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.1\"))\n",
-        "\n",
-        "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
-        "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
-        "pipe.connect(\"prompt_builder\", \"llm\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1ba5puJxIg3V"
-      },
-      "source": [
-        "Try it out yourself by running the code below. If all has gone well, you should have a complete shopping list from all the recipe sources. 🧂🥥🧄"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qDqrU5emtBWQ"
-      },
-      "outputs": [],
-      "source": [
-        "question = (\n",
-        "    \"What ingredients would I need to make vegan keto eggplant lasagna, vegan persimmon flan, and vegan hemp cheese?\"\n",
-        ")\n",
-        "\n",
-        "pipe.run(\n",
-        "    {\n",
-        "        \"embedder\": {\"text\": question},\n",
-        "        \"prompt_builder\": {\"question\": question},\n",
-        "        \"llm\": {\"generation_kwargs\": {\"max_new_tokens\": 350}},\n",
-        "    }\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZJueu_V4KP6w"
-      },
-      "source": [
-        "```python\n",
-        "{'llm': {'replies': ['\\n\\nFor vegan keto eggplant lasagna, you would need:\\n\\n* 2 large eggplants\\n* a lot of salt (Hella salt)\\n* 1/2 cup store-bought vegan mozzarella (for topping)\\n* Pesto: 4 oz basil, 1/4 cup almonds, 1/4 cup nutritional yeast, 1/4 cup olive oil, 1 recipe vegan pesto, 1 recipe spinach tofu ricotta, 1 tsp garlic powder, juice of half a lemon, salt to taste\\n* Macadamia nut cheese: 1 cup macadamia nuts, 10 oz spinach, juice of 1 lemon, garlic powder to taste, salt to taste\\n\\nFor vegan persimmon flan, you would need:\\n\\n* ½ cup persimmon pulp, strained\\n* 1 tbsp cornstarch\\n* ½ tsp agar agar\\n* 1 tbsp agave nectar, or to taste\\n* 2 tbsp granulated sugar\\n* ¼ cup coconut creme\\n* ½ cup almond milk\\n* ½ tsp vanilla\\n\\nFor vegan hemp cheese, you would need:\\n\\n* ½ cup sunflower seeds\\n* ½ cup hemp hearts\\n* 1.5 teaspoons miso paste\\n* 1 tsp nutritional yeast\\n* ¼ cup rejuvelac*\\n* 1/4th teaspoon salt, or to taste\\n\\nNote:'],\n",
-        "  'meta': [{'model': 'mistralai/Mistral-7B-Instruct-v0.1',\n",
-        "    'index': 0,\n",
-        "    'finish_reason': 'length',\n",
-        "    'usage': {'completion_tokens': 350,\n",
-        "     'prompt_tokens': 3454,\n",
-        "     'total_tokens': 3804}}]}}\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zA7xHckYJHsG"
-      },
-      "source": [
-        "## What's next\n",
-        "\n",
-        "Congratulations on building an indexing pipeline that can preprocess different file types. Go forth and ingest all the messy real-world data into your workflows. 💥\n",
-        "\n",
-        "If you liked this tutorial, you may also enjoy:\n",
-        "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
-        "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
-        "\n",
-        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=filte_type_routing). Thanks for reading!"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zAKy_OPy81EZ"
+   },
+   "source": [
+    "# Tutorial: Preprocessing Different File Types\n",
+    "\n",
+    "- **Level**: Beginner\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Goal**: After completing this tutorial, you'll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the `FileTypeRouter`.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n",
+    "\n",
+    "> 💡 (Optional): After creating the indexing pipeline in this tutorial, there is an optional section that shows you how to create a RAG pipeline on top of the document store you just created. You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section\n",
+    "\n",
+    "## Components Used\n",
+    "\n",
+    "- [`FileTypeRouter`](https://docs.haystack.deepset.ai/v2.0/docs/filetyperouter): This component will help you route files based on their corresponding MIME type to different components\n",
+    "- [`MarkdownToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/markdowntodocument): This component will help you convert markdown files into Haystack Documents\n",
+    "- [`PyPDFToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/pypdftodocument): This component will help you convert pdf files into Haystack Documents\n",
+    "- [`TextFileToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/textfiletodocument): This component will help you convert text files into Haystack Documents\n",
+    "- [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner): This component will help you to join Documents coming from different branches of a pipeline\n",
+    "- [`DocumentCleaner`](https://docs.haystack.deepset.ai/v2.0/docs/documentcleaner) (optional): This component will help you to make Documents more readable by removing extra whitespaces etc.\n",
+    "- [`DocumentSplitter`](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter): This component will help you to split your Document into chunks\n",
+    "- [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder): This component will help you create embeddings for Documents.\n",
+    "- [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter): This component will help you write Documents into the DocumentStore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "96w6PrcPk4Fc"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In this tutorial, you'll build an indexing pipeline that preprocesses different types of files (markdown, txt and pdf). Each file will have its own `FileConverter`. The rest of the indexing pipeline is fairly standard - split the documents into chunks, trim whitespace, create embeddings and write them to a Document Store.\n",
+    "\n",
+    "Optionally, you can keep going to see how to use these documents in a query pipeline as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rns_B_NGN0Ze"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_pG2jycZLYYr"
+   },
+   "source": [
+    "## Installing dependencies\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2mP4empwf_x4"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "pip install haystack-ai\n",
+    "pip install \"sentence-transformers>=2.2.0\" \"huggingface_hub>=0.22.0\" transformers\n",
+    "pip install markdown-it-py mdit_plain pypdf\n",
+    "pip install gdown"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HnXumz7EarJx"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "CkvJIU7FmDf9"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7GWbC28fX0Yp"
+   },
+   "source": [
+    "## Download All Files\n",
+    "\n",
+    "Files that you will use in this tutorial are stored in a [GDrive folder](https://drive.google.com/drive/folders/1n9yqq5Gl_HWfND5bTlrCwAOycMDt5EMj). Either download files directly from the GDrive folder or run the code below. If you're running this tutorial on colab, you'll find the downloaded files under \"/recipe_files\" folder in \"files\" tab on the left.\n",
+    "\n",
+    "Just like most real life data, these files are a mishmash of different types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gdown\n",
+    "\n",
+    "url = \"https://drive.google.com/drive/folders/1n9yqq5Gl_HWfND5bTlrCwAOycMDt5EMj\"\n",
+    "output_dir = \"recipe_files\"\n",
+    "\n",
+    "gdown.download_folder(url, quiet=True, output=output_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DH8HEymp6XFZ"
+   },
+   "source": [
+    "## Create a Pipeline to Index Documents\n",
+    "\n",
+    "Next, you'll create a pipeline to index documents. To keep things uncomplicated, you'll use an `InMemoryDocumentStore` but this approach would also work with any other flavor of `DocumentStore`.\n",
+    "\n",
+    "You'll need a different file converter class for each file type in our data sources: `.pdf`, `.txt`, and `.md` in this case. Our `FileTypeRouter` connects each file type to the proper converter.\n",
+    "\n",
+    "Once all our files have been converted to Haystack Documents, we can use the `DocumentJoiner` component to make these a single list of documents that can be fed through the rest of the indexing pipeline all together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "c_eM4C1cA4j6"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.writers import DocumentWriter\n",
+    "from haystack.components.converters import MarkdownToDocument, PyPDFToDocument, TextFileToDocument\n",
+    "from haystack.components.preprocessors import DocumentSplitter, DocumentCleaner\n",
+    "from haystack.components.routers import FileTypeRouter\n",
+    "from haystack.components.joiners import DocumentJoiner\n",
+    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+    "from haystack import Pipeline\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "\n",
+    "document_store = InMemoryDocumentStore()\n",
+    "file_type_router = FileTypeRouter(mime_types=[\"text/plain\", \"application/pdf\", \"text/markdown\"])\n",
+    "text_file_converter = TextFileToDocument()\n",
+    "markdown_converter = MarkdownToDocument()\n",
+    "pdf_converter = PyPDFToDocument()\n",
+    "document_joiner = DocumentJoiner()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ef8okackBSmk"
+   },
+   "source": [
+    "From there, the steps to this indexing pipeline are a bit more standard. The `DocumentCleaner` removes whitespace. Then this `DocumentSplitter` breaks them into chunks of 150 words, with a bit of overlap to avoid missing context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "hCWlpiQCBYOg"
+   },
+   "outputs": [],
+   "source": [
+    "document_cleaner = DocumentCleaner()\n",
+    "document_splitter = DocumentSplitter(split_by=\"word\", split_length=150, split_overlap=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Do4nhM4tBaZL"
+   },
+   "source": [
+    "Now you'll add a `SentenceTransformersDocumentEmbedder` to create embeddings from the documents. As the last step in this pipeline, the `DocumentWriter` will write them to the `InMemoryDocumentStore`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "TVXSX0GHBtdj"
+   },
+   "outputs": [],
+   "source": [
+    "document_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+    "document_writer = DocumentWriter(document_store)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hJaJmGanBv1b"
+   },
+   "source": [
+    "After creating all the components, add them to the indexing pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "4yGXKHEXIZxi"
+   },
+   "outputs": [],
+   "source": [
+    "preprocessing_pipeline = Pipeline()\n",
+    "preprocessing_pipeline.add_component(instance=file_type_router, name=\"file_type_router\")\n",
+    "preprocessing_pipeline.add_component(instance=text_file_converter, name=\"text_file_converter\")\n",
+    "preprocessing_pipeline.add_component(instance=markdown_converter, name=\"markdown_converter\")\n",
+    "preprocessing_pipeline.add_component(instance=pdf_converter, name=\"pypdf_converter\")\n",
+    "preprocessing_pipeline.add_component(instance=document_joiner, name=\"document_joiner\")\n",
+    "preprocessing_pipeline.add_component(instance=document_cleaner, name=\"document_cleaner\")\n",
+    "preprocessing_pipeline.add_component(instance=document_splitter, name=\"document_splitter\")\n",
+    "preprocessing_pipeline.add_component(instance=document_embedder, name=\"document_embedder\")\n",
+    "preprocessing_pipeline.add_component(instance=document_writer, name=\"document_writer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y89Z9jwUfNbr"
+   },
+   "source": [
+    "Next, connect them 👇"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
     "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "gafXWtNYfNbr",
+    "outputId": "10f351de-ac09-4273-85a2-ac7b59fb2f77"
+   },
+   "outputs": [],
+   "source": [
+    "preprocessing_pipeline.connect(\"file_type_router.text/plain\", \"text_file_converter.sources\")\n",
+    "preprocessing_pipeline.connect(\"file_type_router.application/pdf\", \"pypdf_converter.sources\")\n",
+    "preprocessing_pipeline.connect(\"file_type_router.text/markdown\", \"markdown_converter.sources\")\n",
+    "preprocessing_pipeline.connect(\"text_file_converter\", \"document_joiner\")\n",
+    "preprocessing_pipeline.connect(\"pypdf_converter\", \"document_joiner\")\n",
+    "preprocessing_pipeline.connect(\"markdown_converter\", \"document_joiner\")\n",
+    "preprocessing_pipeline.connect(\"document_joiner\", \"document_cleaner\")\n",
+    "preprocessing_pipeline.connect(\"document_cleaner\", \"document_splitter\")\n",
+    "preprocessing_pipeline.connect(\"document_splitter\", \"document_embedder\")\n",
+    "preprocessing_pipeline.connect(\"document_embedder\", \"document_writer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3NGinkHPB9C2"
+   },
+   "source": [
+    "Let's test this pipeline with a few recipes I've written. Are you getting hungry yet?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9Mw5kwZiqehc"
+   },
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "preprocessing_pipeline.run({\"file_type_router\": {\"sources\": list(Path(output_dir).glob(\"**/*\"))}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TVGb-rteg7E5"
+   },
+   "source": [
+    "🎉 If you only wanted to learn how to preprocess documents, you can stop here! If you want to see an example of using those documents in a RAG pipeline, read on.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "16PnegDR2EmY"
+   },
+   "source": [
+    "## (Optional) Build a pipeline to query documents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "I06qdWsZibSz"
+   },
+   "source": [
+    "Now, let's build a RAG pipeline that answers queries based on the documents you just created in the section above. For this step, we will be using the [`HuggingFaceTGIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `mistralai/Mistral-7B-Instruct-v0.1` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "bB344ADZr-eG",
+    "outputId": "b6030405-5def-4700-8124-2e7ec292e977"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "if \"HF_API_TOKEN\" not in os.environ:\n",
+    "    os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter Hugging Face token:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QASEGJhnIMQL"
+   },
+   "source": [
+    "In this step you'll build a query pipeline to answer questions about the documents.\n",
+    "\n",
+    "This pipeline takes the prompt, searches the document store for relevant documents, and passes those documents along to the LLM to formulate an answer.\n",
+    "\n",
+    "> ⚠️ Notice how we used `sentence-transformers/all-MiniLM-L6-v2` to create embeddings for our documents before. This is why we will be using the same model to embed incoming questions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "_s--8xEWq8Y9",
+    "outputId": "1c050d5f-f2ae-4cd3-e0d4-533397a6af63"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "from haystack.components.builders import PromptBuilder\n",
+    "from haystack.components.generators import HuggingFaceTGIGenerator\n",
+    "\n",
+    "template = \"\"\"\n",
+    "Answer the questions based on the given context.\n",
+    "\n",
+    "Context:\n",
+    "{% for document in documents %}\n",
+    "    {{ document.content }}\n",
+    "{% endfor %}\n",
+    "\n",
+    "Question: {{ question }}\n",
+    "Answer:\n",
+    "\"\"\"\n",
+    "pipe = Pipeline()\n",
+    "pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+    "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+    "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+    "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.1\"))\n",
+    "\n",
+    "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+    "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+    "pipe.connect(\"prompt_builder\", \"llm\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1ba5puJxIg3V"
+   },
+   "source": [
+    "Try it out yourself by running the code below. If all has gone well, you should have a complete shopping list from all the recipe sources. 🧂🥥🧄"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "qDqrU5emtBWQ"
+   },
+   "outputs": [],
+   "source": [
+    "question = (\n",
+    "    \"What ingredients would I need to make vegan keto eggplant lasagna, vegan persimmon flan, and vegan hemp cheese?\"\n",
+    ")\n",
+    "\n",
+    "pipe.run(\n",
+    "    {\n",
+    "        \"embedder\": {\"text\": question},\n",
+    "        \"prompt_builder\": {\"question\": question},\n",
+    "        \"llm\": {\"generation_kwargs\": {\"max_new_tokens\": 350}},\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZJueu_V4KP6w"
+   },
+   "source": [
+    "```python\n",
+    "{'llm': {'replies': ['\\n\\nFor vegan keto eggplant lasagna, you would need:\\n\\n* 2 large eggplants\\n* a lot of salt (Hella salt)\\n* 1/2 cup store-bought vegan mozzarella (for topping)\\n* Pesto: 4 oz basil, 1/4 cup almonds, 1/4 cup nutritional yeast, 1/4 cup olive oil, 1 recipe vegan pesto, 1 recipe spinach tofu ricotta, 1 tsp garlic powder, juice of half a lemon, salt to taste\\n* Macadamia nut cheese: 1 cup macadamia nuts, 10 oz spinach, juice of 1 lemon, garlic powder to taste, salt to taste\\n\\nFor vegan persimmon flan, you would need:\\n\\n* ½ cup persimmon pulp, strained\\n* 1 tbsp cornstarch\\n* ½ tsp agar agar\\n* 1 tbsp agave nectar, or to taste\\n* 2 tbsp granulated sugar\\n* ¼ cup coconut creme\\n* ½ cup almond milk\\n* ½ tsp vanilla\\n\\nFor vegan hemp cheese, you would need:\\n\\n* ½ cup sunflower seeds\\n* ½ cup hemp hearts\\n* 1.5 teaspoons miso paste\\n* 1 tsp nutritional yeast\\n* ¼ cup rejuvelac*\\n* 1/4th teaspoon salt, or to taste\\n\\nNote:'],\n",
+    "  'meta': [{'model': 'mistralai/Mistral-7B-Instruct-v0.1',\n",
+    "    'index': 0,\n",
+    "    'finish_reason': 'length',\n",
+    "    'usage': {'completion_tokens': 350,\n",
+    "     'prompt_tokens': 3454,\n",
+    "     'total_tokens': 3804}}]}}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zA7xHckYJHsG"
+   },
+   "source": [
+    "## What's next\n",
+    "\n",
+    "Congratulations on building an indexing pipeline that can preprocess different file types. Go forth and ingest all the messy real-world data into your workflows. 💥\n",
+    "\n",
+    "If you liked this tutorial, you may also enjoy:\n",
+    "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+    "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=filte_type_routing). Thanks for reading!"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb b/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
index 94d2e88a..06cd631d 100644
--- a/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
+++ b/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
@@ -428,7 +428,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")"
    ]
   },
   {
diff --git a/tutorials/34_Extractive_QA_Pipeline.ipynb b/tutorials/34_Extractive_QA_Pipeline.ipynb
index 610cbbd7..11c76ef1 100644
--- a/tutorials/34_Extractive_QA_Pipeline.ipynb
+++ b/tutorials/34_Extractive_QA_Pipeline.ipynb
@@ -61,7 +61,9 @@
    },
    "outputs": [],
    "source": [
-    "!pip install haystack-ai accelerate sentence-transformers datasets"
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai accelerate \"sentence-transformers>=2.2.0\" \"datasets>=2.6.1\""
    ]
   },
   {
diff --git a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
index a3ca030a..dc4b2e5d 100644
--- a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
+++ b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
@@ -252,7 +252,8 @@
     "prompt_builder = PromptBuilder(template=template)\n",
     "\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
     "generator = OpenAIGenerator()"
    ]
   },
diff --git a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
index 4ec26eed..fbdf9d0a 100644
--- a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
+++ b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
@@ -125,8 +125,10 @@
         "from getpass import getpass\n",
         "import os\n",
         "\n",
-        "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI Api key: \")\n",
-        "os.environ[\"SERPERDEV_API_KEY\"] = getpass(\"Enter Serper Api key: \")"
+        "if \"OPENAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
+        "if \"SERPERDEV_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"SERPERDEV_API_KEY\"] = getpass(\"Enter Serper Api key: \")"
       ]
     },
     {
@@ -350,7 +352,7 @@
       "source": [
         "### Visualize the Pipeline\n",
         "\n",
-        "To understand how you formed this pipeline with conditional routing, use [draw()](https://docs.haystack.deepset.ai/v2.0/docs/drawing-pipeline-graphs) method of the pipeline. If you're running this notebook on Google Colab, the generated file will be saved in \"Files\" section on the sidebar or you can call `Image.open()`:"
+        "To understand how you formed this pipeline with conditional routing, use [draw()](https://docs.haystack.deepset.ai/docs/drawing-pipeline-graphs) method of the pipeline. If you're running this notebook on Google Colab, the generated file will be saved in \\\"Files\\\" section on the sidebar."
       ]
     },
     {
@@ -366,10 +368,7 @@
       },
       "outputs": [],
       "source": [
-        "from PIL import Image\n",
-        "\n",
-        "pipe.draw(\"pipe.png\")\n",
-        "Image.open(\"pipe.png\")"
+        "pipe.draw(\"pipe.png\")"
       ]
     },
     {
diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
index 4d8715c2..b44fdd2f 100644
--- a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
+++ b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
@@ -67,7 +67,7 @@
       "source": [
         "%%bash\n",
         "\n",
-        "pip install haystack-ai"
+        "pip install haystack-ai \"huggingface_hub>=0.22.0\" transformers"
       ]
     },
     {
@@ -128,7 +128,8 @@
         "import os\n",
         "from getpass import getpass\n",
         "\n",
-        "os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter a Hugging Face API Token:\")"
+        "if \"HF_API_TOKEN\" not in os.environ:\n",
+        "    os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter Hugging Face token:\")"
       ]
     },
     {
diff --git a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
index 6d89cb3e..ecf39cd2 100644
--- a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
+++ b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
@@ -184,9 +184,7 @@
     "    document_cleaner = DocumentCleaner()\n",
     "    document_splitter = DocumentSplitter(split_by=\"sentence\", split_length=2)\n",
     "    document_embedder = SentenceTransformersDocumentEmbedder(\n",
-    "        model=\"thenlper/gte-large\",\n",
-    "        meta_fields_to_embed=metadata_fields_to_embed,\n",
-    "        device=ComponentDevice.from_str(\"cuda:0\"),\n",
+    "        model=\"thenlper/gte-large\", meta_fields_to_embed=metadata_fields_to_embed\n",
     "    )\n",
     "    document_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE)\n",
     "\n",
diff --git a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
index e246d339..853e12ed 100644
--- a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
+++ b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
@@ -106,7 +106,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"OPENAI_API_KEY: \")"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")"
    ]
   },
   {
@@ -1042,7 +1043,7 @@
     "\n",
     "As you notice above, OpenAI Chat Completions API does not call the function; instead, the model generates JSON that you can use to call the function in your code. That's why, to build an end-to-end chat application, you need to check if the OpenAI response is a `tool_calls` for every message. If so, you need to call the corresponding function with the provided arguments and send the function response back to OpenAI. Otherwise, append both user and messages to the `messages` list to have a regular conversation with the model. \n",
     "\n",
-    "Run the code cell below and use the input box to interact with the chat application that has access to two tools you've created above.  \n",
+    "To build a nice UI for your application, you can use [Gradio](https://www.gradio.app/) that comes with a chat interface. Install `gradio`, run the code cell below and use the input box to interact with the chat application that has access to two tools you've created above.  \n",
     "\n",
     "Example queries you can try:\n",
     "* \"***What is the capital of Sweden?***\": A basic query without any function calls\n",
@@ -1056,7 +1057,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1078,48 +1090,15 @@
     "id": "sK_JeKZLhXcy",
     "outputId": "b0c8c776-4151-44a0-9acd-b12f53119af8"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stop\n",
-      "Can you tell me where Giorgio lives?\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "235721f8c1c14ce3885fba24638bc27f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Giorgio lives in Rome.ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stop\n",
-      "What's the weather like there?\n",
-      "The weather in Rome is sunny with a temperature of 14°C.ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stop\n",
-      "exit\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "import gradio as gr\n",
     "import json\n",
     "\n",
-    "from haystack.dataclasses import ChatMessage, ChatRole\n",
+    "from haystack.dataclasses import ChatMessage\n",
     "from haystack.components.generators.chat import OpenAIChatGenerator\n",
-    "from haystack.components.generators.utils import print_streaming_chunk\n",
     "\n",
-    "chat_generator = OpenAIChatGenerator(model=\"gpt-3.5-turbo\", streaming_callback=print_streaming_chunk)\n",
+    "chat_generator = OpenAIChatGenerator(model=\"gpt-3.5-turbo\")\n",
     "response = None\n",
     "messages = [\n",
     "    ChatMessage.from_system(\n",
@@ -1127,36 +1106,49 @@
     "    )\n",
     "]\n",
     "\n",
-    "while True:\n",
-    "    # if OpenAI response is a tool call\n",
-    "    if response and response[\"replies\"][0].meta[\"finish_reason\"] == \"tool_calls\":\n",
-    "        function_calls = json.loads(response[\"replies\"][0].content)\n",
     "\n",
-    "        for function_call in function_calls:\n",
-    "            ## Parse function calling information\n",
-    "            function_name = function_call[\"function\"][\"name\"]\n",
-    "            function_args = json.loads(function_call[\"function\"][\"arguments\"])\n",
+    "def chatbot_with_fc(message, history):\n",
+    "    messages.append(ChatMessage.from_user(message))\n",
+    "    response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})\n",
     "\n",
-    "            ## Find the correspoding function and call it with the given arguments\n",
-    "            function_to_call = available_functions[function_name]\n",
-    "            function_response = function_to_call(**function_args)\n",
+    "    while True:\n",
+    "        # if OpenAI response is a tool call\n",
+    "        if response and response[\"replies\"][0].meta[\"finish_reason\"] == \"tool_calls\":\n",
+    "            function_calls = json.loads(response[\"replies\"][0].content)\n",
+    "            print(response[\"replies\"][0])\n",
+    "            for function_call in function_calls:\n",
+    "                ## Parse function calling information\n",
+    "                function_name = function_call[\"function\"][\"name\"]\n",
+    "                function_args = json.loads(function_call[\"function\"][\"arguments\"])\n",
     "\n",
-    "            ## Append function response to the messages list using `ChatMessage.from_function`\n",
-    "            messages.append(ChatMessage.from_function(content=json.dumps(function_response), name=function_name))\n",
+    "                ## Find the correspoding function and call it with the given arguments\n",
+    "                function_to_call = available_functions[function_name]\n",
+    "                function_response = function_to_call(**function_args)\n",
     "\n",
-    "    # Regular Conversation\n",
-    "    else:\n",
-    "        # Append assistant messages to the messages list\n",
-    "        if not messages[-1].is_from(ChatRole.SYSTEM):\n",
-    "            messages.append(response[\"replies\"][0])\n",
+    "                ## Append function response to the messages list using `ChatMessage.from_function`\n",
+    "                messages.append(ChatMessage.from_function(content=json.dumps(function_response), name=function_name))\n",
+    "                response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})\n",
     "\n",
-    "        user_input = input(\"ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stop\\n\")\n",
-    "        if user_input.lower() == \"exit\" or user_input.lower() == \"quit\":\n",
-    "            break\n",
+    "        # Regular Conversation\n",
     "        else:\n",
-    "            messages.append(ChatMessage.from_user(user_input))\n",
+    "            messages.append(response[\"replies\"][0])\n",
+    "            break\n",
+    "    return response[\"replies\"][0].content\n",
+    "\n",
+    "\n",
+    "demo = gr.ChatInterface(\n",
+    "    fn=chatbot_with_fc,\n",
+    "    examples=[\n",
+    "        \"Can you tell me where Giorgio lives?\",\n",
+    "        \"What's the weather like in Madrid?\",\n",
+    "        \"Who lives in London?\",\n",
+    "        \"What's the weather like where Mark lives?\",\n",
+    "    ],\n",
+    "    title=\"Ask me about weather or where people live!\",\n",
+    ")\n",
     "\n",
-    "    response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})"
+    "## Uncomment the line below to launch the chat app with UI\n",
+    "# demo.launch()"
    ]
   },
   {

From ef278dfd59f15136c49abf1802f709854a34bb18 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Mon, 8 Apr 2024 11:00:05 +0300
Subject: [PATCH 185/206] Change the api key name (#315)

---
 tutorials/24_Building_Chat_App.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/24_Building_Chat_App.ipynb b/tutorials/24_Building_Chat_App.ipynb
index 215dbac8..ee15a6a9 100644
--- a/tutorials/24_Building_Chat_App.ipynb
+++ b/tutorials/24_Building_Chat_App.ipynb
@@ -124,7 +124,7 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "model_api_key = os.getenv(\"HF_API_TOKEN\", None) or getpass(\"Enter HF API key:\")"
+    "model_api_key = os.getenv(\"HF_API_KEY\", None) or getpass(\"Enter HF API key:\")"
    ]
   },
   {

From 54d048ae3ea56cffdab0a1a642444fd0bc3358ac Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Tue, 23 Apr 2024 12:46:42 +0200
Subject: [PATCH 186/206] Update 28_Structured_Output_With_Loop.ipynb

Install from main till 2.1.0 is released
---
 tutorials/28_Structured_Output_With_Loop.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/28_Structured_Output_With_Loop.ipynb b/tutorials/28_Structured_Output_With_Loop.ipynb
index c3e76764..1b9f204d 100644
--- a/tutorials/28_Structured_Output_With_Loop.ipynb
+++ b/tutorials/28_Structured_Output_With_Loop.ipynb
@@ -71,7 +71,7 @@
       "source": [
         "%%bash\n",
         "\n",
-        "pip install haystack-ai\n",
+        "pip install git+https://github.com/deepset-ai/haystack.git@main\n",
         "pip install colorama"
       ]
     },

From 87d45c2d2a0546f78fd8630a1d365160f54dd033 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Thu, 25 Apr 2024 18:00:11 +0200
Subject: [PATCH 187/206] remove UTMs

---
 tutorials/27_First_RAG_Pipeline.ipynb                     | 2 +-
 tutorials/28_Structured_Output_With_Loop.ipynb            | 8 +++++++-
 tutorials/29_Serializing_Pipelines.ipynb                  | 2 +-
 tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb | 2 +-
 tutorials/31_Metadata_Filtering.ipynb                     | 2 +-
 ...32_Classifying_Documents_and_Queries_by_Language.ipynb | 2 +-
 tutorials/33_Hybrid_Retrieval.ipynb                       | 2 +-
 tutorials/34_Extractive_QA_Pipeline.ipynb                 | 2 +-
 .../35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb      | 2 +-
 .../36_Building_Fallbacks_with_Conditional_Routing.ipynb  | 2 +-
 .../37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb | 2 +-
 .../39_Embedding_Metadata_for_Improved_Retrieval.ipynb    | 2 +-
 ..._Building_Chat_Application_with_Function_Calling.ipynb | 2 +-
 13 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 3eba9d1b..c2350fb7 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -726,7 +726,7 @@
         "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
         "- [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)\n",
         "\n",
-        "To stay up to date on the latest Haystack developments, you can [subscribe to our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=first-rag) and [join Haystack discord community](https://discord.gg/haystack).\n",
+        "To stay up to date on the latest Haystack developments, you can [subscribe to our newsletter](https://landing.deepset.ai/haystack-community-updates) and [join Haystack discord community](https://discord.gg/haystack).\n",
         "\n",
         "Thanks for reading!"
       ]
diff --git a/tutorials/28_Structured_Output_With_Loop.ipynb b/tutorials/28_Structured_Output_With_Loop.ipynb
index 1b9f204d..6938125d 100644
--- a/tutorials/28_Structured_Output_With_Loop.ipynb
+++ b/tutorials/28_Structured_Output_With_Loop.ipynb
@@ -463,7 +463,13 @@
         "id": "Egz_4h2vI_QL"
       },
       "source": [
-        "🎉 Congratulations! You've built a system that generates structured JSON out of unstructured text passages, and auto-corrects it by using the looping functionality of Haystack pipelines."
+        "## What's next\n",
+        "\n",
+        "🎉 Congratulations! You've built a system that generates structured JSON out of unstructured text passages, and auto-corrects it by using the looping functionality of Haystack pipelines.\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [subscribe to our newsletter](https://landing.deepset.ai/haystack-community-updates) and [join Haystack discord community](https://discord.gg/haystack).\n",
+        "\n",
+        "Thanks for reading!"
       ]
     }
   ],
diff --git a/tutorials/29_Serializing_Pipelines.ipynb b/tutorials/29_Serializing_Pipelines.ipynb
index 4dc609da..711a0f14 100644
--- a/tutorials/29_Serializing_Pipelines.ipynb
+++ b/tutorials/29_Serializing_Pipelines.ipynb
@@ -408,7 +408,7 @@
     "If you liked this tutorial, you may also enjoy:\n",
     "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
     "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=serialization). Thanks for reading!"
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates). Thanks for reading!"
    ]
   }
  ],
diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index 688f1de5..e369a2f0 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -456,7 +456,7 @@
     "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
     "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
     "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=filte_type_routing). Thanks for reading!"
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates). Thanks for reading!"
    ]
   }
  ],
diff --git a/tutorials/31_Metadata_Filtering.ipynb b/tutorials/31_Metadata_Filtering.ipynb
index a4cb37a1..ba67c0ea 100644
--- a/tutorials/31_Metadata_Filtering.ipynb
+++ b/tutorials/31_Metadata_Filtering.ipynb
@@ -277,7 +277,7 @@
     "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
     "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
     "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=metadata_filtering). Thanks for reading!"
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates). Thanks for reading!"
    ]
   }
  ],
diff --git a/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb b/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
index 06cd631d..4b178f42 100644
--- a/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
+++ b/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
@@ -705,7 +705,7 @@
     "-  [Generating Structured Output with Loop-Based Auto-Correction](https://haystack.deepset.ai/tutorials/28_structured_output_with_loop)\n",
     "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
     "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=index_documents_based_on_language_tutorial)."
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates)."
    ]
   }
  ],
diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
index 829d13fc..85757127 100644
--- a/tutorials/33_Hybrid_Retrieval.ipynb
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -560,7 +560,7 @@
     "\n",
     "If you'd like to use this retrieval method in a RAG pipeline, check out [Tutorial: Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) to learn about the next steps.\n",
     "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=hybrid-retrieval) or [join Haystack discord community](https://discord.gg/haystack).\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates) or [join Haystack discord community](https://discord.gg/haystack).\n",
     "\n",
     "Thanks for reading!"
    ]
diff --git a/tutorials/34_Extractive_QA_Pipeline.ipynb b/tutorials/34_Extractive_QA_Pipeline.ipynb
index 11c76ef1..cb528502 100644
--- a/tutorials/34_Extractive_QA_Pipeline.ipynb
+++ b/tutorials/34_Extractive_QA_Pipeline.ipynb
@@ -664,7 +664,7 @@
     "-  [Generating Structured Output with Loop-Based Auto-Correction](https://haystack.deepset.ai/tutorials/28_structured_output_with_loop)\n",
     "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
     "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=extractive_qa_tutorial)."
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates)."
    ]
   }
  ],
diff --git a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
index dc4b2e5d..eb5c6ad6 100644
--- a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
+++ b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
@@ -801,7 +801,7 @@
     "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
     "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
     "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=moel_based_evaluation). Thanks for reading!"
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates). Thanks for reading!"
    ]
   }
  ],
diff --git a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
index fbdf9d0a..d5d79d5f 100644
--- a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
+++ b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
@@ -519,7 +519,7 @@
         "- [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
         "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
         "\n",
-        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=conditional-router) or [join Haystack discord community](https://discord.gg/haystack).\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates) or [join Haystack discord community](https://discord.gg/haystack).\n",
         "\n",
         "Thanks for reading!"
       ]
diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
index b44fdd2f..4aa66f34 100644
--- a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
+++ b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
@@ -481,7 +481,7 @@
         "- [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)\n",
         "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
         "\n",
-        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=multiplexer) or [join Haystack discord community](https://discord.gg/haystack).\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates) or [join Haystack discord community](https://discord.gg/haystack).\n",
         "\n",
         "Thanks for reading!"
       ]
diff --git a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
index ecf39cd2..166d26a0 100644
--- a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
+++ b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
@@ -360,7 +360,7 @@
     "- [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)\n",
     "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
     "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=embedding-metadata) or [join Haystack discord community](https://discord.gg/haystack).\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates) or [join Haystack discord community](https://discord.gg/haystack).\n",
     "\n",
     "Thanks for reading!"
    ]
diff --git a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
index 853e12ed..007f3176 100644
--- a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
+++ b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
@@ -1165,7 +1165,7 @@
     "- [Serializing LLM Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
     "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
     "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates?utm_campaign=developer-relations&utm_source=tutorial&utm_medium=function-calling) or [join Haystack discord community](https://discord.gg/haystack).\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates) or [join Haystack discord community](https://discord.gg/haystack).\n",
     "\n",
     "Thanks for reading!"
    ]

From 4e7a1eb80e520ed1c26624169eb48a26e0c444ea Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Tue, 7 May 2024 10:15:53 +0200
Subject: [PATCH 188/206] Update the eval tutorial (#318)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* update the eval tutorial

* correcting file name

* some structural changes

* add image

* fix typos

* Update index.toml

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>

* resolving all comments

* installation update

---------

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>
---
 README.md                                     |     2 +-
 index.toml                                    |     8 +-
 tutorials/35_Evaluating_RAG_Pipelines.ipynb   | 18096 ++++++++++++++++
 ...el_Based_Evaluation_of_RAG_Pipelines.ipynb |  4263 ----
 tutorials/img/tutorial35_rag.png              |   Bin 0 -> 62076 bytes
 5 files changed, 18101 insertions(+), 4268 deletions(-)
 create mode 100644 tutorials/35_Evaluating_RAG_Pipelines.ipynb
 delete mode 100644 tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
 create mode 100644 tutorials/img/tutorial35_rag.png

diff --git a/README.md b/README.md
index fce6d4a8..915e2599 100644
--- a/README.md
+++ b/README.md
@@ -42,7 +42,7 @@ Haystack 2.0
 | [Better Retrieval via Embedding Retrieval](./tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/06_Better_Retrieval_via_Embedding_Retrieval.ipynb)                | [Classifying Documents & Queries by Language](./tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb)|
 | [[OUTDATED] RAG Generator](./tutorials/07_RAG_Generator.ipynb)                                            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/07_RAG_Generator.ipynb)                                           | [Creating a Hybrid Retrieval Pipeline](./tutorials/33_Hybrid_Retrieval.ipynb)                                    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/33_Hybrid_Retrieval.ipynb)                       |
 | [Preprocessing](./tutorials/08_Preprocessing.ipynb)                                                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           | [Build an Extractive QA Pipeline](./tutorials/34_Extractive_QA_Pipeline.ipynb)                                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/34_Extractive_QA_Pipeline.ipynb)                 |
-| [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            | [Model-Based Evaluation of RAG Pipelines](./tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb)|
+| [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            | [Evaluating RAG Pipelines](./tutorials/35_Evaluating_RAG_Pipelines.ipynb)          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/35_Evaluating_RAG_Pipelines.ipynb)|
 | [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         | [Building Pipelines with Conditional Routing](./tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)|
 | [Pipelines](./tutorials/11_Pipelines.ipynb)                                                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               | [Simplifying Pipeline Inputs with Multiplexer](./tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)|
 | [[OUTDATED] Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    | [Embedding Metadata for Improved Retrieval](./tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb)|
diff --git a/index.toml b/index.toml
index 01125687..7d495451 100644
--- a/index.toml
+++ b/index.toml
@@ -377,12 +377,12 @@ haystack_2 = true
 dependencies = ["accelerate", "sentence-transformers", "datasets"]
 
 [[tutorial]]
-title = "Model-Based Evaluation of RAG Pipelines"
-description = "Learn how to evaluate your RAG pipelines using some of the model-based evaluation frameworkes integerated into Haystack"
+title = "Evaluating RAG Pipelines"
+description = "Learn how to evaluate your RAG pipelines using statistical and model-based evaluation metrics"
 level = "intermediate"
 weight = 77
-notebook = "35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb"
-aliases = []
+notebook = "35_Evaluating_RAG_Pipelines.ipynb"
+aliases = ["35_Model_Based_Evaluation_of_RAG_Pipelines"]
 completion_time = "15 min"
 created_at = 2024-02-12
 haystack_2 = true
diff --git a/tutorials/35_Evaluating_RAG_Pipelines.ipynb b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
new file mode 100644
index 00000000..b591d1ac
--- /dev/null
+++ b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
@@ -0,0 +1,18096 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2OvkPji9O-qX"
+      },
+      "source": [
+        "# Tutorial: Evaluating RAG Pipelines\n",
+        "\n",
+        "- **Level**: Intermediate\n",
+        "- **Time to complete**: 15 minutes\n",
+        "- **Components Used**: `InMemoryDocumentStore`, `InMemoryEmbeddingRetriever`, `PromptBuilder`, `OpenAIGenerator`, `DocumentMRREvaluator`, `FaithfulnessEvaluator`, `SASEvaluator`\n",
+        "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI: https://platform.openai.com/api-keys\n",
+        "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines both with model-based, and statistical metrics available in the Haystack evaluation offering. You'll also see which other evaluation frameworks are integrated with Haystack.\n",
+        "\n",
+        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LFqHcXYPO-qZ"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "In this tutorial, you will learn how to evaluate Haystack pipelines, in particular, Retriaval-Augmented Generation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) pipelines.\n",
+        "1. You will first build a pipeline that answers medical questions based on PubMed data.\n",
+        "2. You will build an evaluation pipeline that makes use of some metrics like Document MRR and Answer Faithfulness.\n",
+        "3. You will run your RAG pipeline and evaluated the output with your evaluation pipeline.\n",
+        "\n",
+        "Haystack provides a wide range of [`Evaluators`](https://docs.haystack.deepset.ai/docs/evaluators) which can perform 2 types of evaluations:\n",
+        "- [Model-Based evaluation](https://docs.haystack.deepset.ai/docs/model-based-evaluation)\n",
+        "- [Statistical evaluation](https://docs.haystack.deepset.ai/docs/statistical-evaluation)\n",
+        "\n",
+        "We will use some of these evalution techniques in this tutorial to evaluate a RAG pipeline that is designed to answer questions on PubMed data.\n",
+        "\n",
+        ">🧑‍🍳 As well as Haystack's own evaluation metrics, you can also integrate with a number of evaluation frameworks. See the integrations and examples below 👇\n",
+        "> - [Evaluate with DeepEval](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_deep_eval.ipynb)\n",
+        "> - [Evaluate with RAGAS](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_ragas.ipynb)\n",
+        "> - [Evaluate with UpTrain](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_uptrain.ipynb)\n",
+        "\n",
+        "### Evaluating RAG Pipelines\n",
+        "RAG pipelines ultimately consist of at least 2 steps:\n",
+        "- Retrieval\n",
+        "- Generation\n",
+        "\n",
+        "To evaluate a full RAG pipeline, we have to evaluate each of these steps in isolation, as well as a full unit. While retrieval can in some cases be evaluated with some statistical metrics that require labels, it's not a straight-forward task to do the same for the generation step. Instead, we often rely on model-based metrics to evaluate the generation step, where an LLM is used as the 'evaluator'.\n",
+        "\n",
+        "![Steps or RAG](img/tutorial35_rag.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QXjVlbPiO-qZ"
+      },
+      "source": [
+        "## Preparing the Colab Environment\n",
+        "\n",
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/setting-the-log-level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kww5B_vXO-qZ"
+      },
+      "source": [
+        "## Installing Haystack\n",
+        "\n",
+        "Install Haystack 2.0 and [datasets](https://pypi.org/project/datasets/) with `pip`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UQbU8GUfO-qZ",
+        "outputId": "80fe52ea-108b-4bb4-cb1d-fe79373c86f3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting git+https://github.com/deepset-ai/haystack.git@main\n",
+            "  Cloning https://github.com/deepset-ai/haystack.git (to revision main) to /tmp/pip-req-build-83hiigdl\n",
+            "  Resolved https://github.com/deepset-ai/haystack.git to commit 2509eeea7e82ef52ef65ccce00bfdcc6c1e8c1c2\n",
+            "  Installing build dependencies: started\n",
+            "  Installing build dependencies: finished with status 'done'\n",
+            "  Getting requirements to build wheel: started\n",
+            "  Getting requirements to build wheel: finished with status 'done'\n",
+            "  Preparing metadata (pyproject.toml): started\n",
+            "  Preparing metadata (pyproject.toml): finished with status 'done'\n",
+            "Collecting boilerpy3 (from haystack-ai==2.1.0rc0)\n",
+            "  Downloading boilerpy3-1.0.7-py3-none-any.whl (22 kB)\n",
+            "Collecting haystack-bm25 (from haystack-ai==2.1.0rc0)\n",
+            "  Downloading haystack_bm25-1.0.2-py2.py3-none-any.whl (8.8 kB)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from haystack-ai==2.1.0rc0) (3.1.3)\n",
+            "Collecting lazy-imports (from haystack-ai==2.1.0rc0)\n",
+            "  Downloading lazy_imports-0.3.1-py3-none-any.whl (12 kB)\n",
+            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from haystack-ai==2.1.0rc0) (10.1.0)\n",
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+            "  Downloading openai-1.25.0-py3-none-any.whl (312 kB)\n",
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+            "Building wheels for collected packages: haystack-ai\n",
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+            "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets\n",
+            "  Attempting uninstall: huggingface-hub\n",
+            "    Found existing installation: huggingface-hub 0.20.3\n",
+            "    Uninstalling huggingface-hub-0.20.3:\n",
+            "      Successfully uninstalled huggingface-hub-0.20.3\n",
+            "Successfully installed datasets-2.19.0 dill-0.3.8 huggingface-hub-0.22.2 multiprocess-0.70.16 xxhash-3.4.1\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  Running command git clone --filter=blob:none --quiet https://github.com/deepset-ai/haystack.git /tmp/pip-req-build-83hiigdl\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install haystack-ai\n",
+        "pip install \"datasets>=2.6.1\"\n",
+        "pip install sentence-transformers>=2.2.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Wl_jYERtO-qa"
+      },
+      "source": [
+        "### Enabling Telemetry\n",
+        "\n",
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "A76B4S49O-qa"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.telemetry import tutorial_running\n",
+        "\n",
+        "tutorial_running(35)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_lvfew16O-qa"
+      },
+      "source": [
+        "## Create the RAG Pipeline to Evaluate\n",
+        "\n",
+        "To evaluate a RAG pipeline, we need a RAG pipeline to start with. So, we will start by creating a question answering pipeline.\n",
+        "\n",
+        "> 💡 For a complete tutorial on creating Retrieval-Augmmented Generation pipelines check out the [Creating Your First QA Pipeline with Retrieval-Augmentation Tutorial](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+        "\n",
+        "For this tutorial, we will be using [a labeled PubMed dataset](https://huggingface.co/datasets/vblagoje/PubMedQA_instruction/viewer/default/train?row=0) with questions, contexts and answers. This way, we can use the contexts as Documents, and we also have the required labeled data that we need for some of the evaluation metrics we will be using.\n",
+        "\n",
+        "First, let's fetch the prepared dataset and extract `all_documents`, `all_questions` and `all_ground_truth_answers`:\n",
+        "\n",
+        "> ℹ️ The dataset is quite large, we're using the first 1000 rows in this example, but you can increase this if you want to\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 177,
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+        "id": "CbVN-s5LO-qa",
+        "outputId": "199392b0-f51d-4148-a486-5e797c049d9f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f4dcb30b5589434f9faa18eb9563c738",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/498 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f7f86d8d1e5e403fae730c43cd99ab1d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/274M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b6978ebb1d574a658ba65d8d5dfa4342",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5889178c30ee4d36b67059f3b3f406a1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/272458 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "18b53d4a7a404ec2aca100308c4c8036",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "from haystack import Document\n",
+        "\n",
+        "dataset = load_dataset(\"vblagoje/PubMedQA_instruction\", split=\"train\")\n",
+        "dataset = dataset.select(range(1000))\n",
+        "all_documents = [Document(content=doc[\"context\"]) for doc in dataset]\n",
+        "all_questions = [doc[\"instruction\"] for doc in dataset]\n",
+        "all_ground_truth_answers = [doc[\"response\"] for doc in dataset]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yL8nuJdWO-qa"
+      },
+      "source": [
+        "Next, let's build a simple indexing pipeline and write the `documents` into a DocumentStore. Here, we're using the `InMemoryDocumentStore`.\n",
+        "\n",
+        "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
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+          "height": 418,
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+        "outputId": "f66883c5-3d09-4610-f9b3-0f5495799ad5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c372271358e844fbacd24f184ffc821a",
+              "version_major": 2,
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+            },
+            "text/plain": [
+              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b6726d348126452a88b5b70c444198b3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7235dd65564a4af883bbfa28ab83f692",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "cd36e63787aa45879d7dbc5b3467e829",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
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+            },
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+            },
+            "text/plain": [
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "82b49fa6b2fb4ddba29f7b10feab6b2a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "54cba43df68b4b22bd77a259a2b730a1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'document_writer': {'documents_written': 1000}}"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from typing import List\n",
+        "from haystack import Pipeline\n",
+        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+        "from haystack.components.writers import DocumentWriter\n",
+        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+        "from haystack.document_stores.types import DuplicatePolicy\n",
+        "\n",
+        "document_store = InMemoryDocumentStore()\n",
+        "\n",
+        "document_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+        "document_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP)\n",
+        "\n",
+        "indexing = Pipeline()\n",
+        "indexing.add_component(instance=document_embedder, name=\"document_embedder\")\n",
+        "indexing.add_component(instance=document_writer, name=\"document_writer\")\n",
+        "\n",
+        "indexing.connect(\"document_embedder.documents\", \"document_writer.documents\")\n",
+        "\n",
+        "indexing.run({\"document_embedder\": {\"documents\": all_documents}})\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XvLVaFHTO-qb"
+      },
+      "source": [
+        "Now that we have our data ready, we can create a simple RAG pipeline.\n",
+        "\n",
+        "In this example, we'll be using:\n",
+        "- [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever) which will get the relevant documents to the query.\n",
+        "- [`OpenAIGenerator`](https://docs.haystack.deepset.ai/docs/OpenAIGenerator) to generate answers to queries. You can replace `OpenAIGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/docs/generators)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-uo-6fjiO-qb",
+        "outputId": "12f00bd6-fc05-40fd-db96-429658039c32"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Enter OpenAI API key:··········\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<haystack.core.pipeline.pipeline.Pipeline object at 0x7b698ec37d60>\n",
+              "🚅 Components\n",
+              "  - query_embedder: SentenceTransformersTextEmbedder\n",
+              "  - retriever: InMemoryEmbeddingRetriever\n",
+              "  - prompt_builder: PromptBuilder\n",
+              "  - generator: OpenAIGenerator\n",
+              "  - answer_builder: AnswerBuilder\n",
+              "🛤️ Connections\n",
+              "  - query_embedder.embedding -> retriever.query_embedding (List[float])\n",
+              "  - retriever.documents -> prompt_builder.documents (List[Document])\n",
+              "  - retriever.documents -> answer_builder.documents (List[Document])\n",
+              "  - prompt_builder.prompt -> generator.prompt (str)\n",
+              "  - generator.replies -> answer_builder.replies (List[str])\n",
+              "  - generator.meta -> answer_builder.meta (List[Dict[str, Any]])"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "from haystack.components.builders import AnswerBuilder, PromptBuilder\n",
+        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+        "from haystack.components.generators import OpenAIGenerator\n",
+        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+        "\n",
+        "if \"OPENAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
+        "\n",
+        "template = \"\"\"\n",
+        "        You have to answer the following question based on the given context information only.\n",
+        "\n",
+        "        Context:\n",
+        "        {% for document in documents %}\n",
+        "            {{ document.content }}\n",
+        "        {% endfor %}\n",
+        "\n",
+        "        Question: {{question}}\n",
+        "        Answer:\n",
+        "        \"\"\"\n",
+        "\n",
+        "rag_pipeline = Pipeline()\n",
+        "rag_pipeline.add_component(\n",
+        "    \"query_embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+        ")\n",
+        "rag_pipeline.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store, top_k=3))\n",
+        "rag_pipeline.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+        "rag_pipeline.add_component(\"generator\", OpenAIGenerator(model=\"gpt-3.5-turbo\"))\n",
+        "rag_pipeline.add_component(\"answer_builder\", AnswerBuilder())\n",
+        "\n",
+        "rag_pipeline.connect(\"query_embedder\", \"retriever.query_embedding\")\n",
+        "rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
+        "rag_pipeline.connect(\"prompt_builder\", \"generator\")\n",
+        "rag_pipeline.connect(\"generator.replies\", \"answer_builder.replies\")\n",
+        "rag_pipeline.connect(\"generator.meta\", \"answer_builder.meta\")\n",
+        "rag_pipeline.connect(\"retriever\", \"answer_builder.documents\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DBAyF5tVO-qc"
+      },
+      "source": [
+        "### Asking a Question\n",
+        "\n",
+        "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to all components that require it as input. In this case these are the `query_embedder`, the `prompt_builder` and the `answer_builder`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 86,
+          "referenced_widgets": [
+            "f92db6040c414987acb2e855b5efe29a",
+            "a5e5e17fa90941c8820424bcb8a64461",
+            "f42b858a75334567b6e21d4491d8bf9a",
+            "2451c140c32f46f68fa59d949b198c81",
+            "927fd891c86f4098bb4b32717231d547",
+            "997e1beb4a714368b583b812698db5bd",
+            "e8419341d2c94b2c879671184da63da1",
+            "5f56cc1fdb13480fae8838f613b79d9e",
+            "d2b53a8ababf4744beca8bae4abea6ca",
+            "076a7c8e3a7747ec928a0d5853e92e88",
+            "298f01846fd4442cbcea6149a74c979d"
+          ]
+        },
+        "id": "Vnt283M5O-qc",
+        "outputId": "4c8f1c3e-d8c0-4d1c-d336-09df4b70544d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f92db6040c414987acb2e855b5efe29a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Yes, high levels of procalcitonin in the early phase after pediatric liver transplantation indicated a poor postoperative outcome. Patients with high procalcitonin levels on postoperative day 2 had higher International Normalized Ratio values, suffered more often from primary graft non-function, had longer stays in the pediatric intensive care unit and on mechanical ventilation. However, there was no correlation between procalcitonin elevation and systemic infection.\n"
+          ]
+        }
+      ],
+      "source": [
+        "question = \"Do high levels of procalcitonin in the early phase after pediatric liver transplantation indicate poor postoperative outcome?\"\n",
+        "\n",
+        "response = rag_pipeline.run(\n",
+        "    {\"query_embedder\": {\"text\": question}, \"prompt_builder\": {\"question\": question}, \"answer_builder\": {\"query\": question}}\n",
+        ")\n",
+        "print(response[\"answer_builder\"][\"answers\"][0].data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Bl6Q0PyoDV-E"
+      },
+      "source": [
+        "## Evaluate the Pipeline\n",
+        "\n",
+        "For this tutorial, let's evaluate the pipeline with the following metrics:\n",
+        "\n",
+        "- [Document Mean Reciprocal Rank](https://docs.haystack.deepset.ai/docs/documentmrrevaluator): Evaluates retrieved documents using ground truth labels. It checks at what rank ground truth documents appear in the list of retrieved documents.\n",
+        "- [Semantic Answer Similarity](https://docs.haystack.deepset.ai/docs/sasevaluator): Evaluates predicted answers using ground truth labels. It checks the semantic similarity of a predicted answer and the ground truth answer using a fine-tuned language model.\n",
+        "- [Faithfulness](https://docs.haystack.deepset.ai/docs/faithfulnessevaluator): Uses an LLM to evaluate whether a generated answer can be inferred from the provided contexts. Does not require ground truth labels.\n",
+        "\n",
+        "\n",
+        "Firt, let's actually run our RAG pipeline with a set of questions, and make sure we have the ground truth labels (both answers and documents) for these questions. Let's start with 25 random questions and labels 👇\n",
+        "\n",
+        "> 📝 **Some Notes:**\n",
+        "> 1. For a full list of available metrics, check out the [Haystack Evaluators](https://docs.haystack.deepset.ai/docs/evaluators).\n",
+        "> 2. In our dataset, for each example question, we have 1 ground truth document as labels. However, in some scenarios more than 1 ground truth document may be provided as labels. You will notice that this is why we provide a list of `ground_truth_documents` for each question."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "qgOwnuQLMKLk"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "\n",
+        "questions, ground_truth_answers, ground_truth_docs = zip(*random.sample(list(zip(all_questions, all_ground_truth_answers, all_documents)), 25))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6SO8oX7mMaGC"
+      },
+      "source": [
+        "Next, let's run our pipeline and make sure to track what our pipeline returns as answers, and which documents it retrieves:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
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+          "height": 1000,
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+        "id": "SknPWiKQMZpy",
+        "outputId": "8b06916b-bffe-498b-9d03-e4831d97aa40"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "baef63eb04e8453bb5b935e953cb38c3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: 's it only what you say , it 's also how you say it : communicating nipah virus prevention messages during an outbreak in Bangladesh?\n",
+            "Answer from pipeline:\n",
+            "During the Nipah virus outbreak in Bangladesh, it was not only important to convey prevention messages but also how they were communicated. Field anthropologists played a crucial role in bridging the gap between biomedical explanations and local beliefs about the outbreak. Through interactive sessions with residents and using photos to illustrate how the virus could be transmitted, they were able to successfully convey the message. Prior to this intervention, residents believed in supernatural causes and continued risky behaviors like consuming raw date palm sap. However, after the intervention, residents understood the importance of abstaining from such practices and adopting safer behaviors. This shows that the manner in which prevention messages are communicated can greatly impact their effectiveness during an outbreak.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "78fe7cd499bb461088be730ba13fc50d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Does relieving dyspnoea by non-invasive ventilation decrease pain thresholds in amyotrophic lateral sclerosis?\n",
+            "Answer from pipeline:\n",
+            "Yes, relieving dyspnoea by non-invasive ventilation decreases pain thresholds in amyotrophic lateral sclerosis (ALS) patients. The pressure pain thresholds measured in the deltoid muscle during unassisted breathing decreased significantly by a median of 24.5%-33.0% of baseline during non-invasive ventilation at 30 and 60 minutes (NIV30 and NIV60) in ALS patients.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f44587fa7ba24a98bfd3c321ee63a931",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Is patient satisfaction biased by renovations to the interior of a primary care office : a pretest-posttest assessment?\n",
+            "Answer from pipeline:\n",
+            "Based on the information provided, patient satisfaction is not biased by renovations to the interior of a primary care office. The study conducted a pretest-posttest assessment and found that patient satisfaction was higher for all domains after the office was renovated, with statistical significance. Additionally, the results did not change when potential confounders were included in the analysis. Therefore, it can be concluded that patient satisfaction was genuinely influenced by the interior redesign of the primary care office.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c94e26dd87544b5f960fe1eb06621616",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Is cD30 expression a novel prognostic indicator in extranodal natural killer/T-cell lymphoma , nasal type?\n",
+            "Answer from pipeline:\n",
+            "Based on the provided context information, CD30 expression is not a novel prognostic indicator in extranodal natural killer/T-cell lymphoma, nasal type. The study found that CD30 expression was significantly correlated with certain clinical features, treatment response, and prognosis in ENKTL patients. CD30 positivity was associated with shorter 5-year overall survival and progression-free survival rates in specific patient groups. Additionally, CD30 expression was identified as an independent prognostic factor for overall survival and progression-free survival in a multivariate Cox regression model. Therefore, while CD30 expression is a significant factor in predicting the prognosis of ENKTL patients, it is not considered a novel prognostic indicator.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "474e5bcffcb84de78c4ec36fc7ba16f2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Is obesity associated with increased postoperative complications after operative management of proximal humerus fractures?\n",
+            "Answer from pipeline:\n",
+            "Yes, according to the first context provided, obesity was associated with a substantial increase in local and systemic complications following operative management of proximal humerus fractures.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d2165726ba2c4900bc76a2ee93dd9acb",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Does deep Sequencing the microRNA profile in rhabdomyosarcoma reveal down-regulation of miR-378 family members?\n",
+            "Answer from pipeline:\n",
+            "Yes, deep sequencing of the microRNA profile in rhabdomyosarcoma (RMS) revealed the down-regulation of miR-378 family members in RMS tumour tissue and cell lines.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9d4ab8f056d04a17880d6ddfcb8836c4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Is dorsal plication without degloving safe and effective for correcting ventral penile deformities?\n",
+            "Answer from pipeline:\n",
+            "Based on the context information provided, dorsal plication without degloving was not specifically mentioned as a method for correcting ventral penile deformities. The study focused on comparing the safety and efficacy of patients undergoing dorsal penile plication, ventral plication, and lateral plication. Therefore, based on the information provided, it is not clear whether dorsal plication without degloving is safe and effective for correcting ventral penile deformities.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a3de895bf14f4161b084e1546477b4eb",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Does mental fatigue affect maximal anaerobic exercise performance?\n",
+            "Answer from pipeline:\n",
+            "Based on the given context information, it can be concluded that mental fatigue does not affect maximal anaerobic exercise performance. The study mentioned in the context found no difference in any performance or physiological variable between participants who were mentally fatigued and those who were not. Therefore, mental fatigue does not seem to have a significant impact on maximal anaerobic exercise performance.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b07451ecd6d945469905b9b73a1b931f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Are women using bleach for home cleaning at increased risk of non-allergic asthma?\n",
+            "Answer from pipeline:\n",
+            "Yes, women using bleach for home cleaning are at an increased risk of non-allergic asthma. The study showed that bleach use was significantly associated with non-allergic asthma, particularly non-allergic adult-onset asthma. Women using bleach frequently were more likely to have current asthma compared to non-users, and there were positive associations found between bleach use and bronchial hyperresponsiveness, asthma-like symptoms, and chronic cough among women without allergic sensitization.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "87610ad009084c1191c57b028f6bdd6a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Does trichostatin A inhibit Retinal Pigmented Epithelium Activation in an In Vitro Model of Proliferative Vitreoretinopathy?\n",
+            "Answer from pipeline:\n",
+            "Yes, trichostatin A inhibits Retinal Pigmented Epithelium Activation in an In Vitro Model of Proliferative Vitreoretinopathy as shown in the study where it was observed that cells treated with transforming growth factor beta 2 (TGFβ2) alone or in the presence of trichostatin A showed inhibited contraction and migration of RPE cells, indicating a role of acetylation in RPE activation and progression of Proliferative Vitreoretinopathy.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0d947864ef3a46ea8b95a89dbfe1244a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Are vitamin D levels and bone turnover markers related to non-alcoholic fatty liver disease in severely obese patients?\n",
+            "Answer from pipeline:\n",
+            "Based on the first context provided, the study concluded that there was no association between liver histology and levels of vitamin D or bone turnover parameters in severely obese patients. Therefore, vitamin D levels and bone turnover markers were not found to be related to non-alcoholic fatty liver disease in this specific group of patients.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ab4bff71c4224b20b78d0bd20648e723",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Does alcohol disrupt levels and function of the cystic fibrosis transmembrane conductance regulator to promote development of pancreatitis?\n",
+            "Answer from pipeline:\n",
+            "Yes, alcohol disrupts levels and function of the cystic fibrosis transmembrane conductance regulator (CFTR) to promote the development of pancreatitis. Studies have shown that alcohol inhibits CFTR activity in pancreatic ductal epithelial cells, reduces CFTR expression and stability, and disrupts CFTR folding, leading to lower levels of CFTR in pancreatic tissues from patients with acute or chronic pancreatitis induced by alcohol. Additionally, CFTR knockout mice given ethanol developed more severe pancreatitis than mice not given ethanol.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7cee12908a374d0d8de02d8cf4954d61",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Do genome-wide ancestry patterns in Rapanui suggest pre-European admixture with Native Americans?\n",
+            "Answer from pipeline:\n",
+            "Yes, genome-wide ancestry patterns in Rapanui suggest pre-European admixture with Native Americans, as evidenced by statistical support for Native American admixture dating to AD 1280-1495.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2df0f6a05652407caedd1be9d3091db3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Is termination of Nociceptive Bahaviour at the End of Phase 2 of Formalin Test Attributable to Endogenous Inhibitory Mechanisms , but not by Opioid Receptors Activation?\n",
+            "Answer from pipeline:\n",
+            "Yes, termination of nociceptive behavior at the end of phase 2 of the Formalin test appears to be attributable to endogenous inhibitory mechanisms rather than opioid receptors activation. This is supported by the observation that naloxone, a non-selective antagonist of opioid receptors, decreased nociception in phase 2A but had no effect on the delayed termination of the Formalin test. Additionally, the study specifically investigated active inhibitory mechanisms that lead to termination of nociceptive response in phase II, suggesting that other mechanisms besides opioid receptors may be involved.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b87f7df83e974a109bb3ac5e638063b6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Is real-time three-dimensional transesophageal echocardiography useful for percutaneous closure of multiple secundum atrial septal defects?\n",
+            "Answer from pipeline:\n",
+            "Yes, real-time three-dimensional transesophageal echocardiography (RT-3D-TEE) was found to be useful for percutaneous closure of multiple secundum atrial septal defects in the study described in the context information. It was used to clarify the diagnosis, determine the operation scheme, monitor and guide the operation during the procedure, and evaluate the result shortly after the procedure.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c3311fa8553b4e8592e0a52cac8f7ca4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Does thalidomide control adipose tissue inflammation associated with high-fat diet-induced obesity in mice?\n",
+            "Answer from pipeline:\n",
+            "Yes, thalidomide has been shown to control adipose tissue inflammation associated with high-fat diet-induced obesity in mice. Thalidomide administration in obese mice resulted in a reduction in adiposity, decreased production of pro-inflammatory adipokines such as tumor necrosis factor-α (TNF-α), leptin, and monocyte chemoattractant protein-1 (MCP-1) in adipose tissue, reduced macrophage infiltration, and inhibition of c-Jun N-terminal kinase (JNK) activation. Additionally, thalidomide treatment lowered TNF-α and leptin serum levels in obese mice and inhibited the release of TNF-α and MCP-1 in adipocytes.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "17da0b1a782f4499b2f8e3e8e35133f3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Does puerarin inhibit the inflammatory response in atherosclerosis via modulation of the NF-κB pathway in a rabbit model?\n",
+            "Answer from pipeline:\n",
+            "Yes, puerarin inhibits the inflammatory response in atherosclerosis via modulation of the NF-κB pathway in a rabbit model. The study found that puerarin reduced the protein and mRNA levels of adhesion molecules (AMs) in the rabbit model of atherosclerosis. It was also noted that the reduced AM levels were due to inhibition of the phosphorylation and degradation of inhibitor-κB (I-κB), resulting in reduced p65 NF-κB nuclear translocation. This indicates that puerarin has a modulatory effect on the NF-κB pathway, which plays a crucial role in the inflammatory response in atherosclerosis.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "660a0c8430f24839ab363446150dcd7b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Is serum free 1,25-dihydroxy-vitamin D more closely associated with fibroblast growth factor 23 than other vitamin D forms in chronic dialysis patients?\n",
+            "Answer from pipeline:\n",
+            "Yes, according to the information provided in the context, serum free 1,25-dihydroxy-vitamin D was found to outweigh all other vitamin D forms regarding its association with fibroblast growth factor 23 (FGF-23) in chronic dialysis patients, as indicated by a p-value of 0.03 in the regression analysis.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b5d3a873bed04e8cbd1852444fa05030",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Do a critical analysis of secondary overtriage to a Level I trauma center?\n",
+            "Answer from pipeline:\n",
+            "Secondary overtriage to a Level I trauma center refers to the transfer of trauma patients to a higher-level trauma center who do not require the specialized resources and care available at that level of facility. In the context provided, the study analyzed the incidence and pattern of secondary overtriage to a Level I trauma center by assessing trauma patients transferred and discharged within 24 hours of admission.\n",
+            "\n",
+            "The study found that 24% of transferred trauma patients were discharged within 24 hours of admission, indicating a significant proportion of patients who may not have required the level of care provided at the Level I trauma center. The most common reasons for referral were extremity fractures, head injuries, and soft tissue injuries, which are conditions that may not always necessitate treatment at a higher-level trauma center.\n",
+            "\n",
+            "Furthermore, the majority of patients discharged within 24 hours required only observation or minor procedures, such as fracture management and wound care, which could potentially have been managed at a lower-level trauma center or non-trauma hospital. The study also highlighted the average interfacility transfer distance and hospital charges per transfer, indicating the potential resource utilization associated with secondary overtriage.\n",
+            "\n",
+            "Overall, the findings suggest that there may be a significant incidence of secondary overtriage to Level I trauma centers, leading to unnecessary utilization of resources and potentially impacting the efficiency and capacity of these specialized facilities. Further evaluation and protocols may be needed to determine appropriate triage criteria and optimize the transfer of trauma patients to the most suitable level of care based on their needs.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4ecbd5b99db641488e4b9b6001c2b1ec",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Is methylation of the FGFR2 gene associated with high birth weight centile in humans?\n",
+            "Answer from pipeline:\n",
+            "Yes, methylation of the FGFR2 gene is significantly associated with high birth weight centile in humans (p = 0.004-0.027).\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ad2e9edf0dc3472d9dc0639b07328638",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Do two decades of British newspaper coverage regarding attempt cardiopulmonary resuscitation decisions : Lessons for clinicians?\n",
+            "Answer from pipeline:\n",
+            "Yes, the two decades of British newspaper coverage regarding Do Not Attempt Cardiopulmonary Resuscitation (DNACPR) decisions provide important lessons for clinicians. The coverage highlights the need for adequate patient and family consultation when making DNACPR decisions, as well as the importance of avoiding ageism and discrimination against the disabled in these decisions. Additionally, the association of DNACPR decisions with euthanasia and patients receiving CPR against their wishes should be taken into consideration by clinicians. These lessons can help clinicians make more informed and ethical decisions regarding CPR in the future.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "62da147141b84d56a593db7cf8472831",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Are phospholipase C epsilon 1 ( PLCE1 ) haplotypes associated with increased risk of gastric cancer in Kashmir Valley?\n",
+            "Answer from pipeline:\n",
+            "Yes, the PLCE1 haplotypes (A2274223C3765524T7922612, G2274223C3765524T7922612, and G2274223T3765524C7922612) were found to be associated with an increased risk of gastric cancer in patients from Kashmir Valley. The frequencies of these haplotypes were higher in patients compared to controls and conferred a high risk for gastric cancer.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "29f9b903c4b547edb70ea5e68b845ff0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Are reclassification rates higher among African American men than Caucasians on active surveillance?\n",
+            "Answer from pipeline:\n",
+            "Yes, reclassification rates are higher among African American men than Caucasians on active surveillance. The study found that African American men on active surveillance were more likely to experience upgrading on serial biopsy compared to Caucasians (36% vs 16%). Adjusting for various factors, African American race was an independent predictor of biopsy reclassification.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b3e515210b6b4369aaeb267c20ffe456",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Does health indicators associated with fall among middle-aged and older women enrolled in an evidence-based program?\n",
+            "Answer from pipeline:\n",
+            "No, the context information provided focuses on older women participating in a fall prevention program, not middle-aged women. The study examines the relationship between older female participants' baseline health status and self-reported falls during the fall prevention interventions.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "14ab52345e1b4db1a442ebda71c61e91",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: Do maternal and childhood psychological factors predict chronic disabling fatigue at age 13 years?\n",
+            "Answer from pipeline:\n",
+            "Yes, maternal and childhood psychological factors do predict chronic disabling fatigue at age 13 years. Maternal anxiety, maternal depression, child psychological problems, and upsetting life events were all associated with chronic disabling fatigue in children at age 13 years in the Avon Longitudinal Study of Parents and Children birth cohort. Specifically, maternal anxiety and depression, as well as child psychological problems and upsetting events, were all found to be risk factors for chronic disabling fatigue at age 13 years.\n",
+            "\n",
+            "-----------------------------------\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "rag_answers = []\n",
+        "retrieved_docs = []\n",
+        "\n",
+        "for question in list(questions):\n",
+        "  response = rag_pipeline.run({\"query_embedder\": {\"text\": question},\n",
+        "                              \"prompt_builder\": {\"question\": question},\n",
+        "                              \"answer_builder\": {\"query\": question}})\n",
+        "  print(f\"Question: {question}\")\n",
+        "  print(\"Answer from pipeline:\")\n",
+        "  print(response[\"answer_builder\"][\"answers\"][0].data)\n",
+        "  print(\"\\n-----------------------------------\\n\")\n",
+        "\n",
+        "  rag_answers.append(response[\"answer_builder\"][\"answers\"][0].data)\n",
+        "  retrieved_docs.append(response[\"answer_builder\"][\"answers\"][0].documents)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dZSvAl9fZAQ1"
+      },
+      "source": [
+        "While each evaluator is a component that can be run individually in Haystack, they can also be added into a pipeline. This way, we can construct an `eval_pipeline` that includes all evaluators for the metrics we want to evaluate our pipeline on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "5Z4to-q3OxSV"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.evaluators.document_mrr import DocumentMRREvaluator\n",
+        "from haystack.components.evaluators.faithfulness import FaithfulnessEvaluator\n",
+        "from haystack.components.evaluators.sas_evaluator import SASEvaluator\n",
+        "\n",
+        "eval_pipeline = Pipeline()\n",
+        "eval_pipeline.add_component(\"doc_mrr_evaluator\", DocumentMRREvaluator())\n",
+        "eval_pipeline.add_component(\"faithfulness\", FaithfulnessEvaluator())\n",
+        "eval_pipeline.add_component(\"sas_evaluator\", SASEvaluator(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "\n",
+        "results = eval_pipeline.run({\n",
+        "    \"doc_mrr_evaluator\": {\"ground_truth_documents\": list([d] for d in ground_truth_docs) , \"retrieved_documents\": retrieved_docs},\n",
+        "    \"faithfulness\": {\"questions\": list(questions), \"contexts\": list([d.content] for d in ground_truth_docs), \"predicted_answers\": rag_answers},\n",
+        "    \"sas_evaluator\": {\"predicted_answers\": rag_answers, \"ground_truth_answers\": list(ground_truth_answers)}\n",
+        "})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0rIL855wZa6C"
+      },
+      "source": [
+        "### Constructing an Evaluation Report\n",
+        "\n",
+        "Once we've run our evaluation pipeline, we can also create a full evaluation report. Haystac provides an `EvaluationRunResult` which we can use to display a `score_report` 👇"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        },
+        "id": "ZO5jzX7uQi0i",
+        "outputId": "cb3f6554-47f9-47f9-ce2d-a7b6aeda2236"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"evaluation_result\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16276986554268258,\n        \"min\": 0.718074322938919,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.718074322938919,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-39810713-7480-4309-ab03-552b6f692b45\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>doc_mrr_evaluator</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>faithfulness</th>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sas_evaluator</th>\n",
+              "      <td>0.718074</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39810713-7480-4309-ab03-552b6f692b45')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-39810713-7480-4309-ab03-552b6f692b45 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-39810713-7480-4309-ab03-552b6f692b45');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-12ef0863-1683-4398-8475-a534dd06b97b\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-12ef0863-1683-4398-8475-a534dd06b97b')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-12ef0863-1683-4398-8475-a534dd06b97b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                      score\n",
+              "doc_mrr_evaluator  1.000000\n",
+              "faithfulness       1.000000\n",
+              "sas_evaluator      0.718074"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from haystack.evaluation.eval_run_result import EvaluationRunResult\n",
+        "\n",
+        "inputs= {\n",
+        "        \"question\": list(questions),\n",
+        "        \"contexts\": list([d.content] for d in ground_truth_docs),\n",
+        "        \"answer\": list(ground_truth_answers),\n",
+        "        \"predicted_answer\": rag_answers,\n",
+        "        }\n",
+        "\n",
+        "evaluation_result = EvaluationRunResult(run_name=\"pubmed_rag_pipeline\", inputs=inputs, results=results)\n",
+        "evaluation_result.score_report()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "q8rvbj5rZsW9"
+      },
+      "source": [
+        "#### Extra: Convert the Report into a Pandas DataFrame\n",
+        "\n",
+        "In addition, you can display your evaluation results as a pandas dataframe 👇"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "P0hxWyTMTsbq",
+        "outputId": "3e5693dc-10a3-4ad5-a630-f2da0085db7d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 25,\n  \"fields\": [\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Are women using bleach for home cleaning at increased risk of non-allergic asthma?\",\n          \"Does puerarin inhibit the inflammatory response in atherosclerosis via modulation of the NF-\\u03baB pathway in a rabbit model?\",\n          \"'s it only what you say , it 's also how you say it : communicating nipah virus prevention messages during an outbreak in Bangladesh?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contexts\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Frequent use of bleach for home-cleaning is associated with non-allergic adult-onset asthma, elevated neutrophil counts and lower-airway symptoms in women.\",\n          \"This study indicates that the effect of puerarin on the suppression of atherosclerosis was connected with an inhibited inflammatory response and reduced NF-\\u03baB activation.\",\n          \"During outbreaks, one-way behaviour change communication without meaningful causal explanations is unlikely to be effective. Based on the cultural context, interactive communication strategies in lay language with supporting evidence can make biomedical prevention messages credible in affected communities, even among those who initially invoke supernatural causal explanations.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Yes, women using bleach for home cleaning are at an increased risk of non-allergic asthma. The study showed that bleach use was significantly associated with non-allergic asthma, particularly non-allergic adult-onset asthma. Women using bleach frequently were more likely to have current asthma compared to non-users, and there were positive associations found between bleach use and bronchial hyperresponsiveness, asthma-like symptoms, and chronic cough among women without allergic sensitization.\",\n          \"Yes, puerarin inhibits the inflammatory response in atherosclerosis via modulation of the NF-\\u03baB pathway in a rabbit model. The study found that puerarin reduced the protein and mRNA levels of adhesion molecules (AMs) in the rabbit model of atherosclerosis. It was also noted that the reduced AM levels were due to inhibition of the phosphorylation and degradation of inhibitor-\\u03baB (I-\\u03baB), resulting in reduced p65 NF-\\u03baB nuclear translocation. This indicates that puerarin has a modulatory effect on the NF-\\u03baB pathway, which plays a crucial role in the inflammatory response in atherosclerosis.\",\n          \"During the Nipah virus outbreak in Bangladesh, it was not only important to convey prevention messages but also how they were communicated. Field anthropologists played a crucial role in bridging the gap between biomedical explanations and local beliefs about the outbreak. Through interactive sessions with residents and using photos to illustrate how the virus could be transmitted, they were able to successfully convey the message. Prior to this intervention, residents believed in supernatural causes and continued risky behaviors like consuming raw date palm sap. However, after the intervention, residents understood the importance of abstaining from such practices and adopting safer behaviors. This shows that the manner in which prevention messages are communicated can greatly impact their effectiveness during an outbreak.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"doc_mrr_evaluator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sas_evaluator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13475112832644295,\n        \"min\": 0.46613821387290955,\n        \"max\": 0.9011739492416382,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          0.8999284505844116\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>question</th>\n",
+              "      <th>contexts</th>\n",
+              "      <th>answer</th>\n",
+              "      <th>predicted_answer</th>\n",
+              "      <th>doc_mrr_evaluator</th>\n",
+              "      <th>faithfulness</th>\n",
+              "      <th>sas_evaluator</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>'s it only what you say , it 's also how you s...</td>\n",
+              "      <td>[During a fatal Nipah virus (NiV) outbreak in ...</td>\n",
+              "      <td>During outbreaks, one-way behaviour change com...</td>\n",
+              "      <td>During the Nipah virus outbreak in Bangladesh,...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.688929</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Does relieving dyspnoea by non-invasive ventil...</td>\n",
+              "      <td>[Dyspnoea is a threatening sensation of respir...</td>\n",
+              "      <td>Relieving dyspnoea by NIV in patients with ALS...</td>\n",
+              "      <td>Yes, relieving dyspnoea by non-invasive ventil...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.811266</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Is patient satisfaction biased by renovations ...</td>\n",
+              "      <td>[Measuring quality of care is essential to imp...</td>\n",
+              "      <td>Renovating the interior of a primary care offi...</td>\n",
+              "      <td>Based on the information provided, patient sat...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.849888</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Is cD30 expression a novel prognostic indicato...</td>\n",
+              "      <td>[Extranodal natural killer/T-cell lymphoma, na...</td>\n",
+              "      <td>Our results showed that expression of CD30 was...</td>\n",
+              "      <td>Based on the provided context information, CD3...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.775011</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Is obesity associated with increased postopera...</td>\n",
+              "      <td>[Obesity has become a significant public healt...</td>\n",
+              "      <td>Obesity and its resultant medical comorbiditie...</td>\n",
+              "      <td>Yes, according to the first context provided, ...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.845495</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Does deep Sequencing the microRNA profile in r...</td>\n",
+              "      <td>[Rhabdomyosarcoma (RMS) is a highly malignant ...</td>\n",
+              "      <td>MiR-378a-3p may function as a tumour suppresso...</td>\n",
+              "      <td>Yes, deep sequencing of the microRNA profile i...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.661563</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Is dorsal plication without degloving safe and...</td>\n",
+              "      <td>[To compare the safety and efficacy of patient...</td>\n",
+              "      <td>Penile plication is a safe and effective techn...</td>\n",
+              "      <td>Based on the context information provided, dor...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.804615</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Does mental fatigue affect maximal anaerobic e...</td>\n",
+              "      <td>[Mental fatigue can negatively impact on subma...</td>\n",
+              "      <td>Near identical responses in performance and ph...</td>\n",
+              "      <td>Based on the given context information, it can...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.849995</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Are women using bleach for home cleaning at in...</td>\n",
+              "      <td>[Bleach is widely used for household cleaning....</td>\n",
+              "      <td>Frequent use of bleach for home-cleaning is as...</td>\n",
+              "      <td>Yes, women using bleach for home cleaning are ...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.899928</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Does trichostatin A inhibit Retinal Pigmented ...</td>\n",
+              "      <td>[Proliferative vitreoretinopathy (PVR) is a bl...</td>\n",
+              "      <td>Our findings indicate a role of acetylation in...</td>\n",
+              "      <td>Yes, trichostatin A inhibits Retinal Pigmented...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.466138</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Are vitamin D levels and bone turnover markers...</td>\n",
+              "      <td>[Morbidly obese patients usually present vitam...</td>\n",
+              "      <td>Low levels of vitamin D or hyperparathyroidism...</td>\n",
+              "      <td>Based on the first context provided, the study...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.747388</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Does alcohol disrupt levels and function of th...</td>\n",
+              "      <td>[Excessive consumption of ethanol is one of th...</td>\n",
+              "      <td>Based on studies of human, mouse, and guinea p...</td>\n",
+              "      <td>Yes, alcohol disrupts levels and function of t...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.863766</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Do genome-wide ancestry patterns in Rapanui su...</td>\n",
+              "      <td>[Rapa Nui (Easter Island), located in the east...</td>\n",
+              "      <td>These genetic results can be explained by one ...</td>\n",
+              "      <td>Yes, genome-wide ancestry patterns in Rapanui ...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.517162</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Is termination of Nociceptive Bahaviour at the...</td>\n",
+              "      <td>[Formalin injection induces nociceptive bahavi...</td>\n",
+              "      <td>The results of this study suggest the existenc...</td>\n",
+              "      <td>Yes, termination of nociceptive behavior at th...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.901174</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>Is real-time three-dimensional transesophageal...</td>\n",
+              "      <td>[The purpose of this study was to investigate ...</td>\n",
+              "      <td>RT-3D-TEE provides reliable diagnostic evidenc...</td>\n",
+              "      <td>Yes, real-time three-dimensional transesophage...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.596325</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>Does thalidomide control adipose tissue inflam...</td>\n",
+              "      <td>[Immunosuppressant agents modulate the activit...</td>\n",
+              "      <td>Our results suggest that drugs that can modula...</td>\n",
+              "      <td>Yes, thalidomide has been shown to control adi...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.692221</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>Does puerarin inhibit the inflammatory respons...</td>\n",
+              "      <td>[The isoflavone puerarin [7-hydroxy-3-(4-hydro...</td>\n",
+              "      <td>This study indicates that the effect of puerar...</td>\n",
+              "      <td>Yes, puerarin inhibits the inflammatory respon...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.894604</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>Is serum free 1,25-dihydroxy-vitamin D more cl...</td>\n",
+              "      <td>[Mineral bone disorder (MBD) is prevalent amon...</td>\n",
+              "      <td>The relationship between FGF-23 and vitamin D ...</td>\n",
+              "      <td>Yes, according to the information provided in ...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.730452</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>Do a critical analysis of secondary overtriage...</td>\n",
+              "      <td>[Trauma centers often receive transfers from l...</td>\n",
+              "      <td>A significant number of patients transferred t...</td>\n",
+              "      <td>Secondary overtriage to a Level I trauma cente...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.709596</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>Is methylation of the FGFR2 gene associated wi...</td>\n",
+              "      <td>[This study examined links between DNA methyla...</td>\n",
+              "      <td>We identified a novel biologically plausible c...</td>\n",
+              "      <td>Yes, methylation of the FGFR2 gene is signific...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.490618</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>Do two decades of British newspaper coverage r...</td>\n",
+              "      <td>[To review UK newspaper reports relating to Do...</td>\n",
+              "      <td>Regarding DNACPR decision-making, the predomin...</td>\n",
+              "      <td>Yes, the two decades of British newspaper cove...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.608133</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>Are phospholipase C epsilon 1 ( PLCE1 ) haplot...</td>\n",
+              "      <td>[Phospholipase C epsilon 1 (PLCE1) plays a cru...</td>\n",
+              "      <td>These results suggest that variation in PLCE1 ...</td>\n",
+              "      <td>Yes, the PLCE1 haplotypes (A2274223C3765524T79...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.613439</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>Are reclassification rates higher among Africa...</td>\n",
+              "      <td>[To evaluate the risk of reclassification on s...</td>\n",
+              "      <td>AA men with VLR prostate cancer followed on AS...</td>\n",
+              "      <td>Yes, reclassification rates are higher among A...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.573658</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>Does health indicators associated with fall am...</td>\n",
+              "      <td>[Evidence-based fall prevention programs prima...</td>\n",
+              "      <td>Findings have implications for identifying at-...</td>\n",
+              "      <td>No, the context information provided focuses o...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.572017</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>Do maternal and childhood psychological factor...</td>\n",
+              "      <td>[To investigate whether premorbid maternal and...</td>\n",
+              "      <td>Pediatricians need to be aware that children w...</td>\n",
+              "      <td>Yes, maternal and childhood psychological fact...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.788478</td>\n",
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+              "                                                    [key], {});\n",
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+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
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+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
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+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
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+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "    (() => {\n",
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+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                             question  \\\n",
+              "0   's it only what you say , it 's also how you s...   \n",
+              "1   Does relieving dyspnoea by non-invasive ventil...   \n",
+              "2   Is patient satisfaction biased by renovations ...   \n",
+              "3   Is cD30 expression a novel prognostic indicato...   \n",
+              "4   Is obesity associated with increased postopera...   \n",
+              "5   Does deep Sequencing the microRNA profile in r...   \n",
+              "6   Is dorsal plication without degloving safe and...   \n",
+              "7   Does mental fatigue affect maximal anaerobic e...   \n",
+              "8   Are women using bleach for home cleaning at in...   \n",
+              "9   Does trichostatin A inhibit Retinal Pigmented ...   \n",
+              "10  Are vitamin D levels and bone turnover markers...   \n",
+              "11  Does alcohol disrupt levels and function of th...   \n",
+              "12  Do genome-wide ancestry patterns in Rapanui su...   \n",
+              "13  Is termination of Nociceptive Bahaviour at the...   \n",
+              "14  Is real-time three-dimensional transesophageal...   \n",
+              "15  Does thalidomide control adipose tissue inflam...   \n",
+              "16  Does puerarin inhibit the inflammatory respons...   \n",
+              "17  Is serum free 1,25-dihydroxy-vitamin D more cl...   \n",
+              "18  Do a critical analysis of secondary overtriage...   \n",
+              "19  Is methylation of the FGFR2 gene associated wi...   \n",
+              "20  Do two decades of British newspaper coverage r...   \n",
+              "21  Are phospholipase C epsilon 1 ( PLCE1 ) haplot...   \n",
+              "22  Are reclassification rates higher among Africa...   \n",
+              "23  Does health indicators associated with fall am...   \n",
+              "24  Do maternal and childhood psychological factor...   \n",
+              "\n",
+              "                                             contexts  \\\n",
+              "0   [During a fatal Nipah virus (NiV) outbreak in ...   \n",
+              "1   [Dyspnoea is a threatening sensation of respir...   \n",
+              "2   [Measuring quality of care is essential to imp...   \n",
+              "3   [Extranodal natural killer/T-cell lymphoma, na...   \n",
+              "4   [Obesity has become a significant public healt...   \n",
+              "5   [Rhabdomyosarcoma (RMS) is a highly malignant ...   \n",
+              "6   [To compare the safety and efficacy of patient...   \n",
+              "7   [Mental fatigue can negatively impact on subma...   \n",
+              "8   [Bleach is widely used for household cleaning....   \n",
+              "9   [Proliferative vitreoretinopathy (PVR) is a bl...   \n",
+              "10  [Morbidly obese patients usually present vitam...   \n",
+              "11  [Excessive consumption of ethanol is one of th...   \n",
+              "12  [Rapa Nui (Easter Island), located in the east...   \n",
+              "13  [Formalin injection induces nociceptive bahavi...   \n",
+              "14  [The purpose of this study was to investigate ...   \n",
+              "15  [Immunosuppressant agents modulate the activit...   \n",
+              "16  [The isoflavone puerarin [7-hydroxy-3-(4-hydro...   \n",
+              "17  [Mineral bone disorder (MBD) is prevalent amon...   \n",
+              "18  [Trauma centers often receive transfers from l...   \n",
+              "19  [This study examined links between DNA methyla...   \n",
+              "20  [To review UK newspaper reports relating to Do...   \n",
+              "21  [Phospholipase C epsilon 1 (PLCE1) plays a cru...   \n",
+              "22  [To evaluate the risk of reclassification on s...   \n",
+              "23  [Evidence-based fall prevention programs prima...   \n",
+              "24  [To investigate whether premorbid maternal and...   \n",
+              "\n",
+              "                                               answer  \\\n",
+              "0   During outbreaks, one-way behaviour change com...   \n",
+              "1   Relieving dyspnoea by NIV in patients with ALS...   \n",
+              "2   Renovating the interior of a primary care offi...   \n",
+              "3   Our results showed that expression of CD30 was...   \n",
+              "4   Obesity and its resultant medical comorbiditie...   \n",
+              "5   MiR-378a-3p may function as a tumour suppresso...   \n",
+              "6   Penile plication is a safe and effective techn...   \n",
+              "7   Near identical responses in performance and ph...   \n",
+              "8   Frequent use of bleach for home-cleaning is as...   \n",
+              "9   Our findings indicate a role of acetylation in...   \n",
+              "10  Low levels of vitamin D or hyperparathyroidism...   \n",
+              "11  Based on studies of human, mouse, and guinea p...   \n",
+              "12  These genetic results can be explained by one ...   \n",
+              "13  The results of this study suggest the existenc...   \n",
+              "14  RT-3D-TEE provides reliable diagnostic evidenc...   \n",
+              "15  Our results suggest that drugs that can modula...   \n",
+              "16  This study indicates that the effect of puerar...   \n",
+              "17  The relationship between FGF-23 and vitamin D ...   \n",
+              "18  A significant number of patients transferred t...   \n",
+              "19  We identified a novel biologically plausible c...   \n",
+              "20  Regarding DNACPR decision-making, the predomin...   \n",
+              "21  These results suggest that variation in PLCE1 ...   \n",
+              "22  AA men with VLR prostate cancer followed on AS...   \n",
+              "23  Findings have implications for identifying at-...   \n",
+              "24  Pediatricians need to be aware that children w...   \n",
+              "\n",
+              "                                     predicted_answer  doc_mrr_evaluator  \\\n",
+              "0   During the Nipah virus outbreak in Bangladesh,...                1.0   \n",
+              "1   Yes, relieving dyspnoea by non-invasive ventil...                1.0   \n",
+              "2   Based on the information provided, patient sat...                1.0   \n",
+              "3   Based on the provided context information, CD3...                1.0   \n",
+              "4   Yes, according to the first context provided, ...                1.0   \n",
+              "5   Yes, deep sequencing of the microRNA profile i...                1.0   \n",
+              "6   Based on the context information provided, dor...                1.0   \n",
+              "7   Based on the given context information, it can...                1.0   \n",
+              "8   Yes, women using bleach for home cleaning are ...                1.0   \n",
+              "9   Yes, trichostatin A inhibits Retinal Pigmented...                1.0   \n",
+              "10  Based on the first context provided, the study...                1.0   \n",
+              "11  Yes, alcohol disrupts levels and function of t...                1.0   \n",
+              "12  Yes, genome-wide ancestry patterns in Rapanui ...                1.0   \n",
+              "13  Yes, termination of nociceptive behavior at th...                1.0   \n",
+              "14  Yes, real-time three-dimensional transesophage...                1.0   \n",
+              "15  Yes, thalidomide has been shown to control adi...                1.0   \n",
+              "16  Yes, puerarin inhibits the inflammatory respon...                1.0   \n",
+              "17  Yes, according to the information provided in ...                1.0   \n",
+              "18  Secondary overtriage to a Level I trauma cente...                1.0   \n",
+              "19  Yes, methylation of the FGFR2 gene is signific...                1.0   \n",
+              "20  Yes, the two decades of British newspaper cove...                1.0   \n",
+              "21  Yes, the PLCE1 haplotypes (A2274223C3765524T79...                1.0   \n",
+              "22  Yes, reclassification rates are higher among A...                1.0   \n",
+              "23  No, the context information provided focuses o...                1.0   \n",
+              "24  Yes, maternal and childhood psychological fact...                1.0   \n",
+              "\n",
+              "    faithfulness  sas_evaluator  \n",
+              "0            1.0       0.688929  \n",
+              "1            1.0       0.811266  \n",
+              "2            1.0       0.849888  \n",
+              "3            1.0       0.775011  \n",
+              "4            1.0       0.845495  \n",
+              "5            1.0       0.661563  \n",
+              "6            1.0       0.804615  \n",
+              "7            1.0       0.849995  \n",
+              "8            1.0       0.899928  \n",
+              "9            1.0       0.466138  \n",
+              "10           1.0       0.747388  \n",
+              "11           1.0       0.863766  \n",
+              "12           1.0       0.517162  \n",
+              "13           1.0       0.901174  \n",
+              "14           1.0       0.596325  \n",
+              "15           1.0       0.692221  \n",
+              "16           1.0       0.894604  \n",
+              "17           1.0       0.730452  \n",
+              "18           1.0       0.709596  \n",
+              "19           1.0       0.490618  \n",
+              "20           1.0       0.608133  \n",
+              "21           1.0       0.613439  \n",
+              "22           1.0       0.573658  \n",
+              "23           1.0       0.572017  \n",
+              "24           1.0       0.788478  "
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "results_df = evaluation_result.to_pandas()\n",
+        "results_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TidpbS5NwuIA"
+      },
+      "source": [
+        "Having our evaluation results as a dataframe can be quite useful. For example, below we can use the pandas dataframe to filter the results to the top 3 best scores for semantic answer similarity (`sas_evaluator`) as well as the bottom 3 👇\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 341
+        },
+        "id": "d6PuFgcnwt1i",
+        "outputId": "0dbe0769-f2d9-43e5-8cbb-a0ceea6c8d55"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Is termination of Nociceptive Bahaviour at the End of Phase 2 of Formalin Test Attributable to Endogenous Inhibitory Mechanisms , but not by Opioid Receptors Activation?\",\n          \"Are women using bleach for home cleaning at increased risk of non-allergic asthma?\",\n          \"Do genome-wide ancestry patterns in Rapanui suggest pre-European admixture with Native Americans?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contexts\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"The results of this study suggest the existence of an active inhibitory mechanism, other than the endogenous opioids, that is responsible for termination of nociceptive behaviour at the end of formalin test.\",\n          \"Frequent use of bleach for home-cleaning is associated with non-allergic adult-onset asthma, elevated neutrophil counts and lower-airway symptoms in women.\",\n          \"These genetic results can be explained by one or more pre-European trans-Pacific contacts.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Yes, termination of nociceptive behavior at the end of phase 2 of the Formalin test appears to be attributable to endogenous inhibitory mechanisms rather than opioid receptors activation. This is supported by the observation that naloxone, a non-selective antagonist of opioid receptors, decreased nociception in phase 2A but had no effect on the delayed termination of the Formalin test. Additionally, the study specifically investigated active inhibitory mechanisms that lead to termination of nociceptive response in phase II, suggesting that other mechanisms besides opioid receptors may be involved.\",\n          \"Yes, women using bleach for home cleaning are at an increased risk of non-allergic asthma. The study showed that bleach use was significantly associated with non-allergic asthma, particularly non-allergic adult-onset asthma. Women using bleach frequently were more likely to have current asthma compared to non-users, and there were positive associations found between bleach use and bronchial hyperresponsiveness, asthma-like symptoms, and chronic cough among women without allergic sensitization.\",\n          \"Yes, genome-wide ancestry patterns in Rapanui suggest pre-European admixture with Native Americans, as evidenced by statistical support for Native American admixture dating to AD 1280-1495.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"doc_mrr_evaluator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sas_evaluator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22366097741438715,\n        \"min\": 0.46613821387290955,\n        \"max\": 0.9011739492416382,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9011739492416382\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-96973a5e-1fd0-4574-bf0b-1aad2b044f6f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>question</th>\n",
+              "      <th>contexts</th>\n",
+              "      <th>answer</th>\n",
+              "      <th>predicted_answer</th>\n",
+              "      <th>doc_mrr_evaluator</th>\n",
+              "      <th>faithfulness</th>\n",
+              "      <th>sas_evaluator</th>\n",
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+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Is termination of Nociceptive Bahaviour at the...</td>\n",
+              "      <td>[Formalin injection induces nociceptive bahavi...</td>\n",
+              "      <td>The results of this study suggest the existenc...</td>\n",
+              "      <td>Yes, termination of nociceptive behavior at th...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.901174</td>\n",
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+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Are women using bleach for home cleaning at in...</td>\n",
+              "      <td>[Bleach is widely used for household cleaning....</td>\n",
+              "      <td>Frequent use of bleach for home-cleaning is as...</td>\n",
+              "      <td>Yes, women using bleach for home cleaning are ...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.899928</td>\n",
+              "    </tr>\n",
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+              "      <th>16</th>\n",
+              "      <td>Does puerarin inhibit the inflammatory respons...</td>\n",
+              "      <td>[The isoflavone puerarin [7-hydroxy-3-(4-hydro...</td>\n",
+              "      <td>This study indicates that the effect of puerar...</td>\n",
+              "      <td>Yes, puerarin inhibits the inflammatory respon...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.894604</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Does trichostatin A inhibit Retinal Pigmented ...</td>\n",
+              "      <td>[Proliferative vitreoretinopathy (PVR) is a bl...</td>\n",
+              "      <td>Our findings indicate a role of acetylation in...</td>\n",
+              "      <td>Yes, trichostatin A inhibits Retinal Pigmented...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.466138</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>Is methylation of the FGFR2 gene associated wi...</td>\n",
+              "      <td>[This study examined links between DNA methyla...</td>\n",
+              "      <td>We identified a novel biologically plausible c...</td>\n",
+              "      <td>Yes, methylation of the FGFR2 gene is signific...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.490618</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Do genome-wide ancestry patterns in Rapanui su...</td>\n",
+              "      <td>[Rapa Nui (Easter Island), located in the east...</td>\n",
+              "      <td>These genetic results can be explained by one ...</td>\n",
+              "      <td>Yes, genome-wide ancestry patterns in Rapanui ...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.517162</td>\n",
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+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                             question  \\\n",
+              "13  Is termination of Nociceptive Bahaviour at the...   \n",
+              "8   Are women using bleach for home cleaning at in...   \n",
+              "16  Does puerarin inhibit the inflammatory respons...   \n",
+              "9   Does trichostatin A inhibit Retinal Pigmented ...   \n",
+              "19  Is methylation of the FGFR2 gene associated wi...   \n",
+              "12  Do genome-wide ancestry patterns in Rapanui su...   \n",
+              "\n",
+              "                                             contexts  \\\n",
+              "13  [Formalin injection induces nociceptive bahavi...   \n",
+              "8   [Bleach is widely used for household cleaning....   \n",
+              "16  [The isoflavone puerarin [7-hydroxy-3-(4-hydro...   \n",
+              "9   [Proliferative vitreoretinopathy (PVR) is a bl...   \n",
+              "19  [This study examined links between DNA methyla...   \n",
+              "12  [Rapa Nui (Easter Island), located in the east...   \n",
+              "\n",
+              "                                               answer  \\\n",
+              "13  The results of this study suggest the existenc...   \n",
+              "8   Frequent use of bleach for home-cleaning is as...   \n",
+              "16  This study indicates that the effect of puerar...   \n",
+              "9   Our findings indicate a role of acetylation in...   \n",
+              "19  We identified a novel biologically plausible c...   \n",
+              "12  These genetic results can be explained by one ...   \n",
+              "\n",
+              "                                     predicted_answer  doc_mrr_evaluator  \\\n",
+              "13  Yes, termination of nociceptive behavior at th...                1.0   \n",
+              "8   Yes, women using bleach for home cleaning are ...                1.0   \n",
+              "16  Yes, puerarin inhibits the inflammatory respon...                1.0   \n",
+              "9   Yes, trichostatin A inhibits Retinal Pigmented...                1.0   \n",
+              "19  Yes, methylation of the FGFR2 gene is signific...                1.0   \n",
+              "12  Yes, genome-wide ancestry patterns in Rapanui ...                1.0   \n",
+              "\n",
+              "    faithfulness  sas_evaluator  \n",
+              "13           1.0       0.901174  \n",
+              "8            1.0       0.899928  \n",
+              "16           1.0       0.894604  \n",
+              "9            1.0       0.466138  \n",
+              "19           1.0       0.490618  \n",
+              "12           1.0       0.517162  "
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "top_3 = results_df.nlargest(3, 'sas_evaluator')\n",
+        "bottom_3 = results_df.nsmallest(3, 'sas_evaluator')\n",
+        "pd.concat([top_3, bottom_3])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XueCK3y4O-qc"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "🎉 Congratulations! You've learned how to evaluate a RAG pipeline with model-based evaluation frameworks and without any labeling efforts.\n",
+        "\n",
+        "If you liked this tutorial, you may also enjoy:\n",
+        "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+        "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates). Thanks for reading!"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
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diff --git a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb b/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
deleted file mode 100644
index eb5c6ad6..00000000
--- a/tutorials/35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb
+++ /dev/null
@@ -1,4263 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2OvkPji9O-qX"
-   },
-   "source": [
-    "# Tutorial: Model-Based Evaluation of RAG Pipelines\n",
-    "\n",
-    "- **Level**: Intermediate\n",
-    "- **Time to complete**: 15 minutes\n",
-    "- **Components Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `OpenAIGenerator`, `DeepEvalEvaluator`, `RagasEvaluator`, `UpTrainEvaluator`\n",
-    "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI: https://platform.openai.com/api-keys\n",
-    "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines using some of the model-based evaluation frameworks integerated into Haystack.\n",
-    "\n",
-    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LFqHcXYPO-qZ"
-   },
-   "source": [
-    "## Overview\n",
-    "\n",
-    "This tutorial shows you how to evaluate a generative question-answering pipeline that uses the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. As we're doing model-based evaluation, no ground-truth labels are required. The process involves Haystack's integration of three evaluation frameworks:\n",
-    "- [DeepEval](#evaluate-the-pipeline-with-deepeval) \n",
-    "- [RAGAS](#evaluate-the-pipeline-with-ragas)\n",
-    "- [UpTrain](#evaluate-the-pipeline-with-uptrain)\n",
-    "\n",
-    "For this tutorial, you'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "QXjVlbPiO-qZ"
-   },
-   "source": [
-    "## Preparing the Colab Environment\n",
-    "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Kww5B_vXO-qZ"
-   },
-   "source": [
-    "## Installing Haystack\n",
-    "\n",
-    "Install Haystack 2.0 and [datasets](https://pypi.org/project/datasets/) with `pip`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "UQbU8GUfO-qZ",
-    "outputId": "47d9270a-8389-4217-f053-c5f2ae3c034e"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install \"pydantic<1.10.10\"\n",
-    "pip install haystack-ai\n",
-    "pip install \"datasets>=2.6.1\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Wl_jYERtO-qa"
-   },
-   "source": [
-    "### Enabling Telemetry\n",
-    "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "A76B4S49O-qa"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.telemetry import tutorial_running\n",
-    "\n",
-    "tutorial_running(35)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "_lvfew16O-qa"
-   },
-   "source": [
-    "## Create the RAG Pipeline to Evaluate\n",
-    "\n",
-    "To evaluate a RAG pipeline, we need a RAG pipeline to start with. So, we will start by creating a question answering pipeline.\n",
-    "\n",
-    "> 💡 For a complete tutorial on creating Retrieval-Augmmented Generation pipelines check out the [Creating Your First QA Pipeline with Retrieval-Augmentation Tutorial](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
-    "\n",
-    "First, we will initialize a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, you'll be using the `InMemoryDocumentStore`.\n",
-    "\n",
-    "You'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, you don't need to perform any additional cleaning or splitting.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
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-     ]
-    },
-    "id": "CbVN-s5LO-qa",
-    "outputId": "45314795-f269-4bd3-e386-9e00fa3ee06a"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/tuanacelik/opt/anaconda3/envs/mistral/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "151"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from haystack import Document\n",
-    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-    "\n",
-    "document_store = InMemoryDocumentStore()\n",
-    "\n",
-    "\n",
-    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
-    "docs = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]\n",
-    "document_store.write_documents(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "yL8nuJdWO-qa"
-   },
-   "source": [
-    "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XvLVaFHTO-qb"
-   },
-   "source": [
-    "Now that we have our data ready, we can create a simple RAG pipeline.\n",
-    "\n",
-    "In this example, we'll be using:\n",
-    "- [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) which will get the relevant documents to the query.\n",
-    "- [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/OpenAIGenerator) to generate answers to queries. You can replace `OpenAIGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "-uo-6fjiO-qb",
-    "outputId": "15257953-7ba5-403a-c2f1-e9a1229f654c"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "from haystack.components.builders import PromptBuilder\n",
-    "from haystack.components.generators import OpenAIGenerator\n",
-    "from haystack.components.retrievers.in_memory import InMemoryBM25Retriever\n",
-    "\n",
-    "retriever = InMemoryBM25Retriever(document_store)\n",
-    "\n",
-    "template = \"\"\"\n",
-    "Given the following information, answer the question.\n",
-    "\n",
-    "Context:\n",
-    "{% for document in documents %}\n",
-    "    {{ document.content }}\n",
-    "{% endfor %}\n",
-    "\n",
-    "Question: {{question}}\n",
-    "Answer:\n",
-    "\"\"\"\n",
-    "\n",
-    "prompt_builder = PromptBuilder(template=template)\n",
-    "\n",
-    "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
-    "generator = OpenAIGenerator()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1bfHwOQwycHe"
-   },
-   "source": [
-    "\n",
-    "To build a pipeline, add all components to your pipeline and connect them. Create connections from `retriever` to the `prompt_builder` and from `prompt_builder` to `llm`. Explicitly connect the output of `retriever` with \"documents\" input of the `prompt_builder` to make the connection obvious as `prompt_builder` has two inputs (\"documents\" and \"question\"). For more information on pipelines and creating connections, refer to [Creating Pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines) documentation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "id": "f6NFmpjEO-qb"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<haystack.pipeline.Pipeline at 0x19518d010>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from haystack import Pipeline\n",
-    "from haystack.components.builders.answer_builder import AnswerBuilder\n",
-    "\n",
-    "rag_pipeline = Pipeline()\n",
-    "# Add components to your pipeline\n",
-    "rag_pipeline.add_component(\"retriever\", retriever)\n",
-    "rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n",
-    "rag_pipeline.add_component(\"llm\", generator)\n",
-    "rag_pipeline.add_component(instance=AnswerBuilder(), name=\"answer_builder\")\n",
-    "\n",
-    "# Now, connect the components to each other\n",
-    "rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
-    "rag_pipeline.connect(\"prompt_builder\", \"llm\")\n",
-    "rag_pipeline.connect(\"llm.replies\", \"answer_builder.replies\")\n",
-    "rag_pipeline.connect(\"llm.meta\", \"answer_builder.meta\")\n",
-    "rag_pipeline.connect(\"retriever\", \"answer_builder.documents\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6NqyLhx7O-qc"
-   },
-   "source": [
-    "That's it! The pipeline's ready to generate answers to questions!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DBAyF5tVO-qc"
-   },
-   "source": [
-    "### Asking a Question\n",
-    "\n",
-    "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to both the `retriever` and the `prompt_builder`. This ensures that the `{{question}}` variable in the template prompt gets replaced with your specific question."
-   ]
-  },
-  {
-   "cell_type": "code",
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-      "2dec5ce32d0046b080fa174616380d22"
-     ]
-    },
-    "id": "Vnt283M5O-qc",
-    "outputId": "9b0fea15-ae41-49d4-d5fc-7ff2907590aa"
-   },
-   "outputs": [],
-   "source": [
-    "question = \"When was the Rhodes Statue built?\"\n",
-    "\n",
-    "response = rag_pipeline.run(\n",
-    "    {\"retriever\": {\"query\": question}, \"prompt_builder\": {\"question\": question}, \"answer_builder\": {\"query\": question}}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "36kHDjfWtgHM",
-    "outputId": "e0eddbcc-2f25-471c-9832-a0a553db1f20"
-   },
-   "outputs": [],
-   "source": [
-    "print(response[\"answer_builder\"][\"answers\"][0].data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluate the Pipeline\n",
-    "\n",
-    "In the following sections, we will evaluate the RAG pipeline. You can do this section with one of the following integations: DeepEval, Ragas or UpTrain.\n",
-    "\n",
-    "For most metrics, we will need to provide not only the questions that we're evaluating our RAG pipeline with, but also the generated `responses` and the `contexts` that were porvided to the pipeline. To make this easier, we create a helper function 👇"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_contexts_and_responses(questions, pipeline):\n",
-    "    contexts = []\n",
-    "    responses = []\n",
-    "    for question in questions:\n",
-    "        response = pipeline.run(\n",
-    "            {\n",
-    "                \"retriever\": {\"query\": question},\n",
-    "                \"prompt_builder\": {\"question\": question},\n",
-    "                \"answer_builder\": {\"query\": question},\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "        contexts.append([d.content for d in response[\"answer_builder\"][\"answers\"][0].documents])\n",
-    "        responses.append(response[\"answer_builder\"][\"answers\"][0].data)\n",
-    "    return contexts, responses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we create our evaluation pipeline. It defines what metrics we want to evaluate and we choose one of Haystack's integrated evaluation frameworks. We will run the evaluation pipelines on the following `questions`, `contexts` and `responses`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Ranking by BM25...: 100%|██████████| 151/151 [00:00<00:00, 14439.05 docs/s]\n",
-      "Ranking by BM25...: 100%|██████████| 151/151 [00:00<00:00, 14076.72 docs/s]\n",
-      "Ranking by BM25...: 100%|██████████| 151/151 [00:00<00:00, 9893.77 docs/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "questions = [\"When was the Rhodes Statue built?\", \"Where is the Pyramid of Giza?\", \"When was the pyramid built?\"]\n",
-    "contexts, responses = get_contexts_and_responses(questions, rag_pipeline)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " ## Evaluate the Pipeline with DeepEval\n",
-    "We will perform 2 evaluations with DeepEval\n",
-    "- Faithfulness, grading how factual the generated responses were.\n",
-    "- Contextual relevance, grading how relevant the context was to the question.\n",
-    "\n",
-    "For a full list of available metrics and their expected inputs, check out the [`DeepEvalEvaluator` Docs](https://docs.haystack.deepset.ai/v2.0/docs/deepevalevaluator)\n",
-    "\n",
-    "First, we install the `deepeval-haystack` integration:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install deepeval-haystack"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1) Evaluate Faithfulness"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack_integrations.components.evaluators.deepeval import DeepEvalEvaluator, DeepEvalMetric\n",
-    "\n",
-    "faithfulness_evaluator = Pipeline()\n",
-    "evaluator = DeepEvalEvaluator(metric=DeepEvalMetric.FAITHFULNESS, metric_params={\"model\": \"gpt-4\"})\n",
-    "faithfulness_evaluator.add_component(\"evaluator\", evaluator)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "evaluation_results = faithfulness_evaluator.run(\n",
-    "    {\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}}\n",
-    ")\n",
-    "print(evaluation_results[\"evaluator\"][\"results\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2) Evaluate Contextual Relevance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "contextual_relevance_evaluator = Pipeline()\n",
-    "evaluator = DeepEvalEvaluator(metric=DeepEvalMetric.CONTEXTUAL_RELEVANCE, metric_params={\"model\": \"gpt-4\"})\n",
-    "contextual_relevance_evaluator.add_component(\"evaluator\", evaluator)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "evaluation_results = contextual_relevance_evaluator.run(\n",
-    "    {\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}}\n",
-    ")\n",
-    "print(evaluation_results[\"evaluator\"][\"results\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluate the Pipeline with Ragas\n",
-    "\n",
-    "Here, we're using the Haystack Ragas integration. We will perform 2 evaluations:\n",
-    "\n",
-    "- Context Utilization, grading to what extent the generated answer uses the provided context.\n",
-    "- Aspect critique, grading generated answers based on custom aspects on a binary scale.\n",
-    "\n",
-    "For a full list of available metrics and their expected inputs, check out our [`RagasEvaluator` Docs](https://docs.haystack.deepset.ai/v2.0/docs/ragasevalutor).\n",
-    "\n",
-    "First, we install the `ragas-haystack` integration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install ragas-haystack"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 1) Evaluate Context Utilizaton"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack import Pipeline\n",
-    "from haystack_integrations.components.evaluators.ragas import RagasEvaluator, RagasMetric\n",
-    "\n",
-    "context_utilization_pipeline = Pipeline()\n",
-    "evaluator = RagasEvaluator(metric=RagasMetric.CONTEXT_UTILIZATION)\n",
-    "context_utilization_pipeline.add_component(\"evaluator\", evaluator)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using 'context_precision' without ground truth will be soon depreciated. Use 'context_utilization' instead\n",
-      "Using 'context_precision' without ground truth will be soon depreciated. Use 'context_utilization' instead\n",
-      "Using 'context_precision' without ground truth will be soon depreciated. Use 'context_utilization' instead\n",
-      "Evaluating: 100%|██████████| 3/3 [00:10<00:00,  3.59s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[{'name': 'context_utilization', 'score': 0.9028571428390857}], [{'name': 'context_utilization', 'score': 0.99999999999}], [{'name': 'context_utilization', 'score': 0.3313492063381614}]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "evaluation_results = context_utilization_pipeline.run(\n",
-    "    {\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}}\n",
-    ")\n",
-    "print(evaluation_results[\"evaluator\"][\"results\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2) Evaluate Aspect Critique\n",
-    "A `RagasEvaluator` that uses the `ASPECT_CRITIQUE` metric expects to be initialized with the `name`, `definition` and optionally the `strictness` of the aspect to critique the reponse with."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from haystack import Pipeline\n",
-    "from haystack_integrations.components.evaluators.ragas import RagasEvaluator, RagasMetric\n",
-    "\n",
-    "aspect_critique_pipeline = Pipeline()\n",
-    "evaluator = RagasEvaluator(\n",
-    "    metric=RagasMetric.ASPECT_CRITIQUE,\n",
-    "    metric_params={\n",
-    "        \"name\": \"maliciousness\",\n",
-    "        \"definition\": \"Is the submission intends to harm, deceive, or exploit users?\",\n",
-    "        \"strictness\": 2,\n",
-    "    },\n",
-    ")\n",
-    "aspect_critique_pipeline.add_component(\"evaluator\", evaluator)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Evaluating: 100%|██████████| 3/3 [00:01<00:00,  2.89it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[{'name': 'child-friendliness', 'score': 0}], [{'name': 'child-friendliness', 'score': 1}], [{'name': 'child-friendliness', 'score': 0}]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "evaluation_results = aspect_critique_pipeline.run(\n",
-    "    {\"evaluator\": {\"questions\": questions, \"contexts\": contexts, \"responses\": responses}}\n",
-    ")\n",
-    "print(evaluation_results[\"evaluator\"][\"results\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "37b_d4OsoV5_"
-   },
-   "source": [
-    "## Evaluate the Pipeline with UpTrain\n",
-    "\n",
-    "Here, we're using the Haystack UpTrain integration. We will perform 2 evaluations:\n",
-    "\n",
-    "- Context Relevance, grading how relevant the context is to the question specified\n",
-    "- Critique language, grading language aspects such as fluency, politeness, grammar, and coherence\n",
-    "\n",
-    "For a full list of available metrics and their expected inputs, check out our [`UpTrainEvaluator` Docs](https://docs.haystack.deepset.ai/v2.0/docs/uptrainevaluator).\n",
-    "\n",
-    "First, we install the `uptrain-haystack` integration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install uptrain-haystack"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "dmlZoTq68FTK"
-   },
-   "source": [
-    "#### 1) Evaluate Context Relevance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "lQp9v-MBo18D"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack import Pipeline\n",
-    "from haystack_integrations.components.evaluators.uptrain import UpTrainEvaluator, UpTrainMetric\n",
-    "\n",
-    "evaluator = UpTrainEvaluator(metric=UpTrainMetric.CONTEXT_RELEVANCE, api=\"openai\")\n",
-    "\n",
-    "context_relevance_evaluator_pipeline = Pipeline()\n",
-    "context_relevance_evaluator_pipeline.add_component(\"evaluator\", evaluator)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "MXg1MP4ZyYaE"
-   },
-   "source": [
-    "Next, we can evaluate the context relevance of a RAG pipeline with multiple questions. The context relevance metric expects 2 inputs that should be provided from the RAG pipeline we are evaluating:\n",
-    "\n",
-    "- questions\n",
-    "- contexts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ZVw8M2YNxVrR"
-   },
-   "outputs": [],
-   "source": [
-    "evaluation_results = context_relevance_evaluator_pipeline.run(\n",
-    "    {\"evaluator\": {\"questions\": questions, \"contexts\": contexts}}\n",
-    ")\n",
-    "print(evaluation_results[\"evaluator\"][\"results\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ERaJO56s8J5h"
-   },
-   "source": [
-    "#### 2) Critique Tone\n",
-    "\n",
-    "An evaluator that uses the `CRITIQUE_TONE` metric expects to be initialized with an `llm_persona`. This is the persona the generative model being assessed was expected to follow, for example `methodical teacher`, `helpful chatbot`, or here simply `informative`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Wp2HJOE64SOZ"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack_integrations.components.evaluators.uptrain import UpTrainEvaluator, UpTrainMetric\n",
-    "\n",
-    "evaluator = UpTrainEvaluator(\n",
-    "    metric=UpTrainMetric.CRITIQUE_TONE, api=\"openai\", metric_params={\"llm_persona\": \"informative\"}\n",
-    ")\n",
-    "\n",
-    "critique_tone_pipeline = Pipeline()\n",
-    "critique_tone_pipeline.add_component(\"evaluator\", evaluator)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "dkllxaV78ddM"
-   },
-   "source": [
-    "Next, we can critique the tone of the results of a RAG pipeline. This metric expects 1 input that should be provided from the RAG pipeline we are evaluating:\n",
-    "\n",
-    "- responses"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "lgo5fy6M44kl"
-   },
-   "outputs": [],
-   "source": [
-    "evaluation_results = critique_tone_pipeline.run({\"evaluator\": {\"responses\": responses}})\n",
-    "print(evaluation_results[\"evaluator\"][\"results\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XueCK3y4O-qc"
-   },
-   "source": [
-    "## What's next\n",
-    "\n",
-    "🎉 Congratulations! You've learned how to evaluate a RAG pipeline with model-based evaluation frameworks and without any labeling efforts.\n",
-    "\n",
-    "If you liked this tutorial, you may also enjoy:\n",
-    "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
-    "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
-    "\n",
-    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates). Thanks for reading!"
-   ]
-  }
- ],
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HcmV?d00001


From 94a2ae2ff7c0214007d747910c4966b5cd7e0564 Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Tue, 7 May 2024 10:27:38 +0200
Subject: [PATCH 189/206] fix image

---
 tutorials/35_Evaluating_RAG_Pipelines.ipynb |   2 +-
 tutorials/img/tutorial35_rag.png            | Bin 62076 -> 87970 bytes
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/35_Evaluating_RAG_Pipelines.ipynb b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
index b591d1ac..54e2a3ff 100644
--- a/tutorials/35_Evaluating_RAG_Pipelines.ipynb
+++ b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
@@ -48,7 +48,7 @@
         "\n",
         "To evaluate a full RAG pipeline, we have to evaluate each of these steps in isolation, as well as a full unit. While retrieval can in some cases be evaluated with some statistical metrics that require labels, it's not a straight-forward task to do the same for the generation step. Instead, we often rely on model-based metrics to evaluate the generation step, where an LLM is used as the 'evaluator'.\n",
         "\n",
-        "![Steps or RAG](img/tutorial35_rag.png)"
+        "![Steps or RAG](https://raw.githubusercontent.com/deepset-ai/haystack-tutorials/main/tutorials/img/tutorial35_rag.png)"
       ]
     },
     {
diff --git a/tutorials/img/tutorial35_rag.png b/tutorials/img/tutorial35_rag.png
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From 45f0734329ddb508ee03f92baa0fd01fa5453c21 Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <stefanofiorucci@gmail.com>
Date: Fri, 10 May 2024 07:52:28 +0200
Subject: [PATCH 190/206] squash (#319)

---
 ...le_Type_Preprocessing_Index_Pipeline.ipynb |   22 +-
 ...ing_Pipeline_Inputs_with_Multiplexer.ipynb | 1012 +++++++++--------
 2 files changed, 540 insertions(+), 494 deletions(-)

diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index e369a2f0..dfaaeaa9 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -73,7 +73,7 @@
    "source": [
     "%%bash\n",
     "pip install haystack-ai\n",
-    "pip install \"sentence-transformers>=2.2.0\" \"huggingface_hub>=0.22.0\" transformers\n",
+    "pip install \"sentence-transformers>=2.2.0\" \"huggingface_hub>=0.22.0\"\n",
     "pip install markdown-it-py mdit_plain pypdf\n",
     "pip install gdown"
    ]
@@ -320,7 +320,7 @@
     "id": "I06qdWsZibSz"
    },
    "source": [
-    "Now, let's build a RAG pipeline that answers queries based on the documents you just created in the section above. For this step, we will be using the [`HuggingFaceTGIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `mistralai/Mistral-7B-Instruct-v0.1` model."
+    "Now, let's build a RAG pipeline that answers queries based on the documents you just created in the section above. For this step, we will be using the [`HuggingFaceAPIGenerator`](https://docs.haystack.deepset.ai/docs/huggingfaceapigenerator) so must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section. We will be using the `HuggingFaceH4/zephyr-7b-beta` model."
    ]
   },
   {
@@ -371,7 +371,7 @@
     "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
     "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
     "from haystack.components.builders import PromptBuilder\n",
-    "from haystack.components.generators import HuggingFaceTGIGenerator\n",
+    "from haystack.components.generators import HuggingFaceAPIGenerator\n",
     "\n",
     "template = \"\"\"\n",
     "Answer the questions based on the given context.\n",
@@ -388,7 +388,10 @@
     "pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
     "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
     "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
-    "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.1\"))\n",
+    "pipe.add_component(\n",
+    "    \"llm\",\n",
+    "    HuggingFaceAPIGenerator(api_type=\"serverless_inference_api\", api_params={\"model\": \"HuggingFaceH4/zephyr-7b-beta\"}),\n",
+    ")\n",
     "\n",
     "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
     "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
@@ -432,13 +435,10 @@
    },
    "source": [
     "```python\n",
-    "{'llm': {'replies': ['\\n\\nFor vegan keto eggplant lasagna, you would need:\\n\\n* 2 large eggplants\\n* a lot of salt (Hella salt)\\n* 1/2 cup store-bought vegan mozzarella (for topping)\\n* Pesto: 4 oz basil, 1/4 cup almonds, 1/4 cup nutritional yeast, 1/4 cup olive oil, 1 recipe vegan pesto, 1 recipe spinach tofu ricotta, 1 tsp garlic powder, juice of half a lemon, salt to taste\\n* Macadamia nut cheese: 1 cup macadamia nuts, 10 oz spinach, juice of 1 lemon, garlic powder to taste, salt to taste\\n\\nFor vegan persimmon flan, you would need:\\n\\n* ½ cup persimmon pulp, strained\\n* 1 tbsp cornstarch\\n* ½ tsp agar agar\\n* 1 tbsp agave nectar, or to taste\\n* 2 tbsp granulated sugar\\n* ¼ cup coconut creme\\n* ½ cup almond milk\\n* ½ tsp vanilla\\n\\nFor vegan hemp cheese, you would need:\\n\\n* ½ cup sunflower seeds\\n* ½ cup hemp hearts\\n* 1.5 teaspoons miso paste\\n* 1 tsp nutritional yeast\\n* ¼ cup rejuvelac*\\n* 1/4th teaspoon salt, or to taste\\n\\nNote:'],\n",
-    "  'meta': [{'model': 'mistralai/Mistral-7B-Instruct-v0.1',\n",
-    "    'index': 0,\n",
-    "    'finish_reason': 'length',\n",
-    "    'usage': {'completion_tokens': 350,\n",
-    "     'prompt_tokens': 3454,\n",
-    "     'total_tokens': 3804}}]}}\n",
+    "{'llm': {'replies': [\"\\n\\nVegan Keto Eggplant Lasagna:\\n\\nIngredients:\\n- 2 large eggplants\\n- A lot of salt (you should have this in your house already)\\n- 1/2 cup store-bought vegan mozzarella (for topping)\\n\\nPesto:\\n- 4 oz basil (generally one large clamshell or 2 small ones)\\n- 1/4 cup almonds\\n- 1/4 cup nutritional yeast\\n- 1/4 cup olive oil\\n- 1 recipe vegan pesto (you can find this in the recipe)\\n- 1 recipe spinach tofu ricotta (you can find this in the recipe)\\n- 1 tsp garlic powder\\n- Juice of half a lemon\\n- Salt to taste\\n\\nSpinach Tofu Ricotta:\\n- 10 oz firm or extra firm tofu\\n- Juice of 1 lemon\\n- Garlic powder to taste\\n- Salt to taste\\n\\nInstructions:\\n1. Slice the eggplants into 1/4 inch thick slices. Some slices will need to be scrapped because it's difficult to get them all uniformly thin. Use them in soup or something, IDK, man.\\n2. Take the eggplant slices and rub both sides with salt. Don't be shy about how much, you're gonna rinse it off anyway.\\n3. Put them in a colander with something underneath it and let them sit for half an hour. This draws the water out so that the egg\"],\n",
+    "  'meta': [{'model': 'HuggingFaceH4/zephyr-7b-beta',\n",
+    "    ...\n",
+    "    }]}}\n",
     "```"
    ]
   },
diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
index 4aa66f34..6c579b8b 100644
--- a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
+++ b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
@@ -1,506 +1,552 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JFAFUa7BECmK"
-      },
-      "source": [
-        "# Tutorial: Simplifying Pipeline Inputs with Multiplexer\n",
-        "\n",
-        "\n",
-        "- **Level**: Intermediate\n",
-        "- **Time to complete**: 10 minutes\n",
-        "- **Components Used**: [Multiplexer](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer), [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [HuggingFaceTEIDocumentEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_document_embedder.py), [HuggingFaceTEITextEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_text_embedder.py), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [HuggingFaceTGIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator) and [AnswerBuilder](https://docs.haystack.deepset.ai/v2.0/docs/answerbuilder)\n",
-        "- **Prerequisites**: You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines)\n",
-        "- **Goal**: After completing this tutorial, you'll have learned how to use a Multiplexer to simplify the inputs that `Pipeline.run()` get\n",
-        "\n",
-        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jy3ZkDzu9-CW"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "If you've ever built a Haystack pipeline with more than 3-4 components, you probably noticed that the number of inputs to pass to the `run()` method of the pipeline grow endlessly. New components take some of their input from the other components of a pipeline, but many of them also require additional input from the user. As a result, the `data` input of `Pipeline.run()` grows and becomes very repetitive.\n",
-        "\n",
-        "There is one component that can help managing this repetition in a more effective manner, and it's called [`Multiplexer`](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer).\n",
-        "\n",
-        "In this tutorial, you will learn how to drastically simplify the `Pipeline.run()` of a RAG pipeline using a Multiplexer."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RJPsjBXZKWnb"
-      },
-      "source": [
-        "## Setup\n",
-        "### Prepare the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CcK-dK--G5ng"
-      },
-      "source": [
-        "### Install Haystack\n",
-        "\n",
-        "Install Haystack 2.0 with `pip`:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0hwJTyV5HARC"
-      },
-      "outputs": [],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install haystack-ai \"huggingface_hub>=0.22.0\" transformers"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3N_97P0OV9cx"
-      },
-      "source": [
-        "### Enable Telemetry\n",
-        "\n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "BKilNUd8V_Uc"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(37)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uTNEeEcBJc_4"
-      },
-      "source": [
-        "### Enter a Hugging Face API key\n",
-        "\n",
-        "Set a Hugging Face API key:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "aiHltCF7JgaV",
-        "outputId": "b973435d-94c1-458a-8212-c543fd45ffab"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Enter a Hugging Face API Token:··········\n"
-          ]
-        }
-      ],
-      "source": [
-        "import os\n",
-        "from getpass import getpass\n",
-        "\n",
-        "if \"HF_API_TOKEN\" not in os.environ:\n",
-        "    os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter Hugging Face token:\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "e57ugQB7dYsQ"
-      },
-      "source": [
-        "## Indexing Documents with a Pipeline\n",
-        "\n",
-        "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore) with their embeddings. You will use [HuggingFaceTEIDocumentEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_document_embedder.py) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter).\n",
-        "\n",
-        "After adding these components to your pipeline, connect them and run the pipeline.\n",
-        "\n",
-        "> If you'd like to learn about preprocessing files before you index them to your document store, follow the [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline) tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "My_fx0lNJUVb",
-        "outputId": "b731efb8-14bb-4f13-ca49-d8706a777dd5"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Calculating embeddings: 100%|██████████| 1/1 [00:00<00:00,  1.49it/s]\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "{'doc_writer': {'documents_written': 5}}"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from haystack import Pipeline, Document\n",
-        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-        "from haystack.components.writers import DocumentWriter\n",
-        "from haystack.components.embedders import HuggingFaceTEIDocumentEmbedder\n",
-        "\n",
-        "documents = [\n",
-        "    Document(content=\"My name is Jean and I live in Paris.\"),\n",
-        "    Document(content=\"My name is Mark and I live in Berlin.\"),\n",
-        "    Document(content=\"My name is Giorgio and I live in Rome.\"),\n",
-        "    Document(content=\"My name is Giorgio and I live in Milan.\"),\n",
-        "    Document(content=\"My name is Giorgio and I lived in many cities, but I settled in Naples eventually.\"),\n",
-        "]\n",
-        "\n",
-        "document_store = InMemoryDocumentStore()\n",
-        "\n",
-        "indexing_pipeline = Pipeline()\n",
-        "indexing_pipeline.add_component(\n",
-        "    instance=HuggingFaceTEIDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"), name=\"doc_embedder\"\n",
-        ")\n",
-        "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"doc_writer\")\n",
-        "\n",
-        "indexing_pipeline.connect(\"doc_embedder.documents\", \"doc_writer.documents\")\n",
-        "\n",
-        "indexing_pipeline.run({\"doc_embedder\": {\"documents\": documents}})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "e9hOmQx4L2Lw"
-      },
-      "source": [
-        "## Building a RAG Pipeline\n",
-        "\n",
-        "Build a basic retrieval augmented generative pipeline with [HuggingFaceTEITextEmbedder](https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/hugging_face_tei_text_embedder.py), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) and [HuggingFaceTGIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacetgigenerator). Additionally, add [AnswerBuilder](https://docs.haystack.deepset.ai/v2.0/docs/answerbuilder) to help you enrich the generated answer with `meta` info and the `query` input.\n",
-        "\n",
-        "> For a step-by-step guide to create a RAG pipeline with Haystack, follow the [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) tutorial"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "ueu5W07IWyXa",
-        "outputId": "51419b90-14d8-4e4a-cd24-8884053b9688"
-      },
-      "outputs": [
-        {
-          "data": {
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-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": []
-          },
-          "execution_count": 6,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from haystack.components.embedders import HuggingFaceTEITextEmbedder\n",
-        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-        "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
-        "from haystack.components.generators import HuggingFaceTGIGenerator\n",
-        "\n",
-        "template = \"\"\"\n",
-        "<s>[INST] Answer the questions based on the given context.\n",
-        "\n",
-        "Context:\n",
-        "{% for document in documents %}\n",
-        "    {{ document.content }}\n",
-        "{% endfor %}\n",
-        "Question: {{ question }}\n",
-        "Answer: [/INST]\n",
-        "\"\"\"\n",
-        "pipe = Pipeline()\n",
-        "pipe.add_component(\"embedder\", HuggingFaceTEITextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
-        "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
-        "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
-        "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.1\"))\n",
-        "pipe.add_component(\"answer_builder\", AnswerBuilder())\n",
-        "\n",
-        "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
-        "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
-        "pipe.connect(\"prompt_builder\", \"llm\")\n",
-        "pipe.connect(\"llm.replies\", \"answer_builder.replies\")\n",
-        "pipe.connect(\"llm.meta\", \"answer_builder.meta\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5xxvPqyurZTi"
-      },
-      "source": [
-        "## Running the Pipeline\n",
-        "Pass the `query` to `embedder`, `prompt_builder` and `answer_builder` and run it:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "AIsphy4hJDpE",
-        "outputId": "4498e7c9-0ff2-424c-9ddd-535f8630572e"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{'answer_builder': {'answers': [GeneratedAnswer(data=' Mark lives in Berlin.', query='Where does Mark live?', documents=[], meta={'model': 'mistralai/Mistral-7B-Instruct-v0.1', 'index': 0, 'finish_reason': 'eos_token', 'usage': {'completion_tokens': 6, 'prompt_tokens': 116, 'total_tokens': 122}})]}}"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "query = \"Where does Mark live?\"\n",
-        "pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}, \"answer_builder\": {\"query\": query}})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wrH2MGSBLvVC"
-      },
-      "source": [
-        "In this basic RAG pipeline, components require a `query` to operate are `embedder`, `prompt_builder`, and `answer_builder`. However, as you extend the pipeline with additional components like Retrievers and Rankers, the number of components needing a `query` can increase indefinitely. This leads to repetitive and increasingly complex `Pipeline.run()` calls. In such cases, using a Multiplexer can help simplify and declutter `Pipeline.run()`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ewDXDrw9N0CG"
-      },
-      "source": [
-        "## Introducing a Multiplexer\n",
-        "\n",
-        "The [Multiplexer](https://docs.haystack.deepset.ai/v2.0/docs/multiplexer) is a component that can accept multiple input connections and then distributes the first value it receives to all components connected to its output. In this seeting, you can use this component by connecting it to other pipeline components that expect a `query` during runtime.\n",
-        "\n",
-        "Now, initialize the Multiplexer with the expected input type (in this case, `str` since the `query` is a string):"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JFAFUa7BECmK"
+   },
+   "source": [
+    "# Tutorial: Simplifying Pipeline Inputs with Multiplexer\n",
+    "\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 10 minutes\n",
+    "- **Components Used**: [Multiplexer](https://docs.haystack.deepset.ai/docs/multiplexer), [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [HuggingFaceAPIDocumentEmbedder](https://docs.haystack.deepset.ai/docs/huggingfaceapidocumentembedder), [HuggingFaceAPITextEmbedder](https://docs.haystack.deepset.ai/docs/huggingfaceapitextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder), [HuggingFaceAPIGenerator](https://docs.haystack.deepset.ai/docs/huggingfaceapigenerator) and [AnswerBuilder](https://docs.haystack.deepset.ai/docs/answerbuilder)\n",
+    "- **Prerequisites**: You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/docs/creating-pipelines)\n",
+    "- **Goal**: After completing this tutorial, you'll have learned how to use a Multiplexer to simplify the inputs that `Pipeline.run()` get\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jy3ZkDzu9-CW"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "If you've ever built a Haystack pipeline with more than 3-4 components, you probably noticed that the number of inputs to pass to the `run()` method of the pipeline grow endlessly. New components take some of their input from the other components of a pipeline, but many of them also require additional input from the user. As a result, the `data` input of `Pipeline.run()` grows and becomes very repetitive.\n",
+    "\n",
+    "There is one component that can help managing this repetition in a more effective manner, and it's called [`Multiplexer`](https://docs.haystack.deepset.ai/docs/multiplexer).\n",
+    "\n",
+    "In this tutorial, you will learn how to drastically simplify the `Pipeline.run()` of a RAG pipeline using a Multiplexer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RJPsjBXZKWnb"
+   },
+   "source": [
+    "## Setup\n",
+    "### Prepare the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/setting-the-log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CcK-dK--G5ng"
+   },
+   "source": [
+    "### Install Haystack\n",
+    "\n",
+    "Install Haystack 2.0 with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0hwJTyV5HARC"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai \"huggingface_hub>=0.22.0\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3N_97P0OV9cx"
+   },
+   "source": [
+    "### Enable Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BKilNUd8V_Uc"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(37)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uTNEeEcBJc_4"
+   },
+   "source": [
+    "### Enter a Hugging Face API key\n",
+    "\n",
+    "Set a Hugging Face API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "aiHltCF7JgaV",
+    "outputId": "b973435d-94c1-458a-8212-c543fd45ffab"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "id": "kArO87EKN3N-"
-      },
-      "outputs": [],
-      "source": [
-        "from haystack.components.others import Multiplexer\n",
-        "\n",
-        "multiplexer = Multiplexer(str)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter a Hugging Face API Token:··········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "if \"HF_API_TOKEN\" not in os.environ:\n",
+    "    os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter Hugging Face token:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "e57ugQB7dYsQ"
+   },
+   "source": [
+    "## Indexing Documents with a Pipeline\n",
+    "\n",
+    "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore) with their embeddings. You will use [HuggingFaceAPIDocumentEmbedder](https://docs.haystack.deepset.ai/docs/huggingfaceapidocumentembedder) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/docs/documentwriter).\n",
+    "\n",
+    "After adding these components to your pipeline, connect them and run the pipeline.\n",
+    "\n",
+    "> If you'd like to learn about preprocessing files before you index them to your document store, follow the [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline) tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "My_fx0lNJUVb",
+    "outputId": "b731efb8-14bb-4f13-ca49-d8706a777dd5"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vBGC2wO5LWIL"
-      },
-      "source": [
-        "## Adding the `Multiplexer` to the Pipeline\n",
-        "\n",
-        "Create the same RAG pipeline but this time with the Multiplexer. Add the Multiplexer to the pipeline and connect it to all the components that need the `query` as an input:"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/anakin87/.virtualenvs/tutorials/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "Calculating embeddings: 100%|██████████| 1/1 [00:00<00:00,  1.13it/s]\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "CTmnCZvgEAut",
-        "outputId": "a0ab0df0-32f7-4778-954a-e7b9cc8b612d"
-      },
-      "outputs": [
-        {
-          "data": {
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-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": []
-          },
-          "execution_count": 13,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from haystack.components.embedders import HuggingFaceTEITextEmbedder\n",
-        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-        "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
-        "from haystack.components.generators import HuggingFaceTGIGenerator\n",
-        "\n",
-        "template = \"\"\"\n",
-        "<s>[INST] Answer the questions based on the given context.\n",
-        "\n",
-        "Context:\n",
-        "{% for document in documents %}\n",
-        "    {{ document.content }}\n",
-        "{% endfor %}\n",
-        "Question: {{ question }}\n",
-        "Answer: [/INST]\n",
-        "\"\"\"\n",
-        "pipe = Pipeline()\n",
-        "\n",
-        "pipe.add_component(\"multiplexer\", multiplexer)\n",
-        "\n",
-        "pipe.add_component(\"embedder\", HuggingFaceTEITextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
-        "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
-        "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
-        "pipe.add_component(\"llm\", HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.1\"))\n",
-        "pipe.add_component(\"answer_builder\", AnswerBuilder())\n",
-        "\n",
-        "# Connect the Multiplexer to all the components that need the query\n",
-        "pipe.connect(\"multiplexer.value\", \"embedder.text\")\n",
-        "pipe.connect(\"multiplexer.value\", \"prompt_builder.question\")\n",
-        "pipe.connect(\"multiplexer.value\", \"answer_builder.query\")\n",
-        "\n",
-        "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
-        "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
-        "pipe.connect(\"prompt_builder\", \"llm\")\n",
-        "pipe.connect(\"llm.replies\", \"answer_builder.replies\")\n",
-        "pipe.connect(\"llm.meta\", \"answer_builder.meta\")"
+     "data": {
+      "text/plain": [
+       "{'doc_writer': {'documents_written': 5}}"
       ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from haystack import Pipeline, Document\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "from haystack.components.writers import DocumentWriter\n",
+    "from haystack.components.embedders import HuggingFaceAPIDocumentEmbedder\n",
+    "\n",
+    "documents = [\n",
+    "    Document(content=\"My name is Jean and I live in Paris.\"),\n",
+    "    Document(content=\"My name is Mark and I live in Berlin.\"),\n",
+    "    Document(content=\"My name is Giorgio and I live in Rome.\"),\n",
+    "    Document(content=\"My name is Giorgio and I live in Milan.\"),\n",
+    "    Document(content=\"My name is Giorgio and I lived in many cities, but I settled in Naples eventually.\"),\n",
+    "]\n",
+    "\n",
+    "document_store = InMemoryDocumentStore()\n",
+    "\n",
+    "indexing_pipeline = Pipeline()\n",
+    "indexing_pipeline.add_component(\n",
+    "    instance=HuggingFaceAPIDocumentEmbedder(\n",
+    "        api_type=\"serverless_inference_api\", api_params={\"model\": \"sentence-transformers/all-MiniLM-L6-v2\"}\n",
+    "    ),\n",
+    "    name=\"doc_embedder\",\n",
+    ")\n",
+    "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"doc_writer\")\n",
+    "\n",
+    "indexing_pipeline.connect(\"doc_embedder.documents\", \"doc_writer.documents\")\n",
+    "\n",
+    "indexing_pipeline.run({\"doc_embedder\": {\"documents\": documents}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "e9hOmQx4L2Lw"
+   },
+   "source": [
+    "## Building a RAG Pipeline\n",
+    "\n",
+    "Build a basic retrieval augmented generative pipeline with [HuggingFaceAPITextEmbedder](https://docs.haystack.deepset.ai/docs/huggingfaceapitextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) and [HuggingFaceAPIGenerator](https://docs.haystack.deepset.ai/docs/huggingfaceapigenerator). Additionally, add [AnswerBuilder](https://docs.haystack.deepset.ai/docs/answerbuilder) to help you enrich the generated answer with `meta` info and the `query` input.\n",
+    "\n",
+    "> For a step-by-step guide to create a RAG pipeline with Haystack, follow the [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) tutorial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "ueu5W07IWyXa",
+    "outputId": "51419b90-14d8-4e4a-cd24-8884053b9688"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "i2wW4nbEQKhJ"
-      },
-      "source": [
-        "## Running the Pipeline with a Multiplexer\n",
-        "\n",
-        "Run the pipeline that you updated with a Multiplexer. This time, instead of passing the query to `prompt_builder`, `retriever` and `answer_builder` seperately, you only need to pass it to the `multiplexer`. As a result, you will get the same answer."
+     "data": {
+      "text/plain": [
+       "<haystack.core.pipeline.pipeline.Pipeline object at 0x7f78bb41feb0>\n",
+       "🚅 Components\n",
+       "  - embedder: HuggingFaceAPITextEmbedder\n",
+       "  - retriever: InMemoryEmbeddingRetriever\n",
+       "  - prompt_builder: PromptBuilder\n",
+       "  - llm: HuggingFaceAPIGenerator\n",
+       "  - answer_builder: AnswerBuilder\n",
+       "🛤️ Connections\n",
+       "  - embedder.embedding -> retriever.query_embedding (List[float])\n",
+       "  - retriever.documents -> prompt_builder.documents (List[Document])\n",
+       "  - prompt_builder.prompt -> llm.prompt (str)\n",
+       "  - llm.replies -> answer_builder.replies (List[str])\n",
+       "  - llm.meta -> answer_builder.meta (List[Dict[str, Any]])"
       ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from haystack.components.embedders import HuggingFaceAPITextEmbedder\n",
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
+    "from haystack.components.generators import HuggingFaceAPIGenerator\n",
+    "\n",
+    "template = \"\"\"\n",
+    " <|user|>\n",
+    " Answer the question based on the given context.\n",
+    "\n",
+    "Context:\n",
+    "{% for document in documents %}\n",
+    "    {{ document.content }}\n",
+    "{% endfor %}\n",
+    "Question: {{ question }}</s>\n",
+    "<|assistant|>\n",
+    "Answer:\n",
+    "\"\"\"\n",
+    "pipe = Pipeline()\n",
+    "pipe.add_component(\n",
+    "    \"embedder\",\n",
+    "    HuggingFaceAPITextEmbedder(\n",
+    "        api_type=\"serverless_inference_api\", api_params={\"model\": \"sentence-transformers/all-MiniLM-L6-v2\"}\n",
+    "    ),\n",
+    ")\n",
+    "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+    "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+    "pipe.add_component(\n",
+    "    \"llm\",\n",
+    "    HuggingFaceAPIGenerator(api_type=\"serverless_inference_api\", api_params={\"model\": \"HuggingFaceH4/zephyr-7b-beta\"}),\n",
+    ")\n",
+    "pipe.add_component(\"answer_builder\", AnswerBuilder())\n",
+    "\n",
+    "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+    "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+    "pipe.connect(\"prompt_builder\", \"llm\")\n",
+    "pipe.connect(\"llm.replies\", \"answer_builder.replies\")\n",
+    "pipe.connect(\"llm.meta\", \"answer_builder.meta\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5xxvPqyurZTi"
+   },
+   "source": [
+    "## Running the Pipeline\n",
+    "Pass the `query` to `embedder`, `prompt_builder` and `answer_builder` and run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "AIsphy4hJDpE",
+    "outputId": "4498e7c9-0ff2-424c-9ddd-535f8630572e"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "YbIHBCKPQF4f",
-        "outputId": "32fb9d11-eec2-49d7-9ab1-97a90d9bbc28"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{'answer_builder': {'answers': [GeneratedAnswer(data=' Mark lives in Berlin.', query='Where does Mark live?', documents=[], meta={'model': 'mistralai/Mistral-7B-Instruct-v0.1', 'index': 0, 'finish_reason': 'eos_token', 'usage': {'completion_tokens': 6, 'prompt_tokens': 116, 'total_tokens': 122}})]}}"
-            ]
-          },
-          "execution_count": 14,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "pipe.run({\"multiplexer\": {\"value\": \"Where does Mark live?\"}})"
+     "data": {
+      "text/plain": [
+       "{'answer_builder': {'answers': [GeneratedAnswer(data=' Mark lives in Berlin, as stated in the first sentence of the context provided.', query='Where does Mark live?', documents=[], meta={'model': 'HuggingFaceH4/zephyr-7b-beta', 'finish_reason': None, 'usage': {'completion_tokens': 0}})]}}"
       ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"Where does Mark live?\"\n",
+    "pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}, \"answer_builder\": {\"query\": query}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wrH2MGSBLvVC"
+   },
+   "source": [
+    "In this basic RAG pipeline, components require a `query` to operate are `embedder`, `prompt_builder`, and `answer_builder`. However, as you extend the pipeline with additional components like Retrievers and Rankers, the number of components needing a `query` can increase indefinitely. This leads to repetitive and increasingly complex `Pipeline.run()` calls. In such cases, using a Multiplexer can help simplify and declutter `Pipeline.run()`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ewDXDrw9N0CG"
+   },
+   "source": [
+    "## Introducing a Multiplexer\n",
+    "\n",
+    "The [Multiplexer](https://docs.haystack.deepset.ai/docs/multiplexer) is a component that can accept multiple input connections and then distributes the first value it receives to all components connected to its output. In this seeting, you can use this component by connecting it to other pipeline components that expect a `query` during runtime.\n",
+    "\n",
+    "Now, initialize the Multiplexer with the expected input type (in this case, `str` since the `query` is a string):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "kArO87EKN3N-"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.others import Multiplexer\n",
+    "\n",
+    "multiplexer = Multiplexer(str)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vBGC2wO5LWIL"
+   },
+   "source": [
+    "## Adding the `Multiplexer` to the Pipeline\n",
+    "\n",
+    "Create the same RAG pipeline but this time with the Multiplexer. Add the Multiplexer to the pipeline and connect it to all the components that need the `query` as an input:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "CTmnCZvgEAut",
+    "outputId": "a0ab0df0-32f7-4778-954a-e7b9cc8b612d"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kPiSU2xoKmio"
-      },
-      "source": [
-        "## What's next\n",
-        "\n",
-        "🎉 Congratulations! You've simplified your pipeline run with a Multiplexer!\n",
-        "\n",
-        "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
-        "- [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)\n",
-        "- [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)\n",
-        "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
-        "\n",
-        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates) or [join Haystack discord community](https://discord.gg/haystack).\n",
-        "\n",
-        "Thanks for reading!"
+     "data": {
+      "text/plain": [
+       "<haystack.core.pipeline.pipeline.Pipeline object at 0x7f78bb4cdf90>\n",
+       "🚅 Components\n",
+       "  - multiplexer: Multiplexer\n",
+       "  - embedder: HuggingFaceAPITextEmbedder\n",
+       "  - retriever: InMemoryEmbeddingRetriever\n",
+       "  - prompt_builder: PromptBuilder\n",
+       "  - llm: HuggingFaceAPIGenerator\n",
+       "  - answer_builder: AnswerBuilder\n",
+       "🛤️ Connections\n",
+       "  - multiplexer.value -> embedder.text (str)\n",
+       "  - multiplexer.value -> prompt_builder.question (str)\n",
+       "  - multiplexer.value -> answer_builder.query (str)\n",
+       "  - embedder.embedding -> retriever.query_embedding (List[float])\n",
+       "  - retriever.documents -> prompt_builder.documents (List[Document])\n",
+       "  - prompt_builder.prompt -> llm.prompt (str)\n",
+       "  - llm.replies -> answer_builder.replies (List[str])\n",
+       "  - llm.meta -> answer_builder.meta (List[Dict[str, Any]])"
       ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
+   ],
+   "source": [
+    "from haystack.components.embedders import HuggingFaceAPITextEmbedder\n",
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "from haystack.components.builders import PromptBuilder, AnswerBuilder\n",
+    "from haystack.components.generators import HuggingFaceAPIGenerator\n",
+    "\n",
+    "template = \"\"\"\n",
+    " <|user|>\n",
+    " Answer the question based on the given context.\n",
+    "\n",
+    "Context:\n",
+    "{% for document in documents %}\n",
+    "    {{ document.content }}\n",
+    "{% endfor %}\n",
+    "Question: {{ question }}</s>\n",
+    "<|assistant|>\n",
+    "Answer:\n",
+    "\"\"\"\n",
+    "pipe = Pipeline()\n",
+    "\n",
+    "pipe.add_component(\"multiplexer\", multiplexer)\n",
+    "\n",
+    "pipe.add_component(\n",
+    "    \"embedder\",\n",
+    "    HuggingFaceAPITextEmbedder(\n",
+    "        api_type=\"serverless_inference_api\", api_params={\"model\": \"sentence-transformers/all-MiniLM-L6-v2\"}\n",
+    "    ),\n",
+    ")\n",
+    "pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
+    "pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+    "pipe.add_component(\n",
+    "    \"llm\",\n",
+    "    HuggingFaceAPIGenerator(api_type=\"serverless_inference_api\", api_params={\"model\": \"HuggingFaceH4/zephyr-7b-beta\"}),\n",
+    ")\n",
+    "pipe.add_component(\"answer_builder\", AnswerBuilder())\n",
+    "\n",
+    "# Connect the Multiplexer to all the components that need the query\n",
+    "pipe.connect(\"multiplexer.value\", \"embedder.text\")\n",
+    "pipe.connect(\"multiplexer.value\", \"prompt_builder.question\")\n",
+    "pipe.connect(\"multiplexer.value\", \"answer_builder.query\")\n",
+    "\n",
+    "pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
+    "pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
+    "pipe.connect(\"prompt_builder\", \"llm\")\n",
+    "pipe.connect(\"llm.replies\", \"answer_builder.replies\")\n",
+    "pipe.connect(\"llm.meta\", \"answer_builder.meta\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i2wW4nbEQKhJ"
+   },
+   "source": [
+    "## Running the Pipeline with a Multiplexer\n",
+    "\n",
+    "Run the pipeline that you updated with a Multiplexer. This time, instead of passing the query to `prompt_builder`, `retriever` and `answer_builder` seperately, you only need to pass it to the `multiplexer`. As a result, you will get the same answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
     "colab": {
-      "gpuType": "T4",
-      "provenance": []
+     "base_uri": "https://localhost:8080/"
     },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
+    "id": "YbIHBCKPQF4f",
+    "outputId": "32fb9d11-eec2-49d7-9ab1-97a90d9bbc28"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer_builder': {'answers': [GeneratedAnswer(data=' Mark lives in Berlin, as stated in the first sentence of the context provided.', query='Where does Mark live?', documents=[], meta={'model': 'HuggingFaceH4/zephyr-7b-beta', 'finish_reason': None, 'usage': {'completion_tokens': 0}})]}}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "pipe.run({\"multiplexer\": {\"value\": \"Where does Mark live?\"}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kPiSU2xoKmio"
+   },
+   "source": [
+    "## What's next\n",
+    "\n",
+    "🎉 Congratulations! You've simplified your pipeline run with a Multiplexer!\n",
+    "\n",
+    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+    "- [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)\n",
+    "- [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)\n",
+    "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates) or [join Haystack discord community](https://discord.gg/haystack).\n",
+    "\n",
+    "Thanks for reading!"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }

From ff691378fc45313e573cb936b431873758380fae Mon Sep 17 00:00:00 2001
From: Tuana Celik <tuana.celik@deepset.ai>
Date: Fri, 10 May 2024 16:30:09 +0200
Subject: [PATCH 191/206] add video to tutorial

---
 tutorials/35_Evaluating_RAG_Pipelines.ipynb | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tutorials/35_Evaluating_RAG_Pipelines.ipynb b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
index 54e2a3ff..4bb6b70d 100644
--- a/tutorials/35_Evaluating_RAG_Pipelines.ipynb
+++ b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
@@ -48,7 +48,11 @@
         "\n",
         "To evaluate a full RAG pipeline, we have to evaluate each of these steps in isolation, as well as a full unit. While retrieval can in some cases be evaluated with some statistical metrics that require labels, it's not a straight-forward task to do the same for the generation step. Instead, we often rely on model-based metrics to evaluate the generation step, where an LLM is used as the 'evaluator'.\n",
         "\n",
-        "![Steps or RAG](https://raw.githubusercontent.com/deepset-ai/haystack-tutorials/main/tutorials/img/tutorial35_rag.png)"
+        "![Steps or RAG](https://raw.githubusercontent.com/deepset-ai/haystack-tutorials/main/tutorials/img/tutorial35_rag.png)\n",
+        "\n",
+        "#### 📺 Code Along\n",
+        "\n",
+        "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/5PrzXaZ0-qk?si=lgBSfHatbV2i59J-\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n"
       ]
     },
     {

From 5194adf18d7ce2807f3f632f8c7febb0b27fb6d4 Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Wed, 22 May 2024 09:17:24 +0200
Subject: [PATCH 192/206] tentative fix for 17

---
 .github/workflows/nightly.yml | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index 2d419f25..8f3690be 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -67,17 +67,9 @@ jobs:
           pip install nbconvert ipython
           pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
           pip install farm-haystack[pdf]
-
-      - name: Install Haystack Extras text2speech dependencies
-        run: |
+          pip install scipy --upgrade
           pip install farm-haystack-text2speech
-
-      - name: Install Hugging Face datasets
-        run: |
           pip install "datasets>=2.6.1"
-
-      - name: Install ipywidgets for tutorial 24
-        run: |
           pip install ipywidgets
 
       - name: Convert notebook to Python

From bfb9c04c3288458874cbb5249a513f8d7bf7e18a Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Wed, 22 May 2024 09:26:09 +0200
Subject: [PATCH 193/206] remove old deps

---
 .github/workflows/nightly.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index 8f3690be..f14873a1 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -126,7 +126,6 @@ jobs:
               ffmpeg
 
           pip install nbconvert ipython
-          pip install "pyworld<=0.2.12" espnet espnet-model-zoo pydub
 
       - name: Install tutorial dependencies
         if: toJSON(matrix.dependencies) != '[]'

From 98dbcf4cc2a0e0739af0f0ed1c992b1953e555ac Mon Sep 17 00:00:00 2001
From: Massimiliano Pippi <mpippi@gmail.com>
Date: Wed, 22 May 2024 10:51:42 +0200
Subject: [PATCH 194/206] do not test hidden tutorials

---
 scripts/generate_matrix.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/scripts/generate_matrix.py b/scripts/generate_matrix.py
index 84011b2e..4c781172 100644
--- a/scripts/generate_matrix.py
+++ b/scripts/generate_matrix.py
@@ -26,11 +26,8 @@ def read_index(path):
 
     matrix = []
     for tutorial in index["tutorial"]:
-        notebook = tutorial["notebook"]
-
-        if args.notebooks and notebook not in args.notebooks:
-            # If the user specified a list of notebooks to run, only run those
-            # otherwise run all of them
+        if tutorial.get("hidden", False):
+            # Do not waste CI time on hidden tutorials
             continue
 
         if tutorial.get("needs_gpu", False):
@@ -46,10 +43,17 @@ def read_index(path):
         if is_haystack2 and not tutorial.get("haystack_2", False):
             # Skip Haystack 1.0 tutorials when testing Haystack 2.0
             continue
-        elif not is_haystack2 and tutorial.get("haystack_2", False):
+
+        if not is_haystack2 and tutorial.get("haystack_2", False):
             # Skip Haystack 2.0 tutorials when testing Haystack 1.0
             continue
 
+        notebook = tutorial["notebook"]
+        if args.notebooks and notebook not in args.notebooks:
+            # If the user specified a list of notebooks to run, only run those
+            # otherwise run all of them
+            continue
+
         version = tutorial.get("haystack_version", args.version)
         if version[0] != "v":
             version = f"v{version}"

From c3ecf587fdd3cded7f0ba9db2a8fd4078ae8bb0e Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <stefanofiorucci@gmail.com>
Date: Fri, 7 Jun 2024 09:04:59 +0200
Subject: [PATCH 195/206] fix outdated link to squad_to_dpr script (#328)

---
 tutorials/09_DPR_training.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/09_DPR_training.ipynb b/tutorials/09_DPR_training.ipynb
index ffe0dd7b..611450ae 100644
--- a/tutorials/09_DPR_training.ipynb
+++ b/tutorials/09_DPR_training.ipynb
@@ -164,7 +164,7 @@
     "Passages which contain the answer text are filtered out.\n",
     "\n",
     "If you'd like to convert your SQuAD format data into something that can train a DPR model,\n",
-    "check out the utility script at [`haystack/utils/squad_to_dpr.py`](https://github.com/deepset-ai/haystack/blob/main/haystack/utils/squad_to_dpr.py)"
+    "check out the [SQuAD to DPR utility script](https://github.com/deepset-ai/haystack/blob/v1.26.x/haystack/utils/squad_to_dpr.py)."
    ]
   },
   {

From 3078011e1cce86488602deeb1caf1128d17c2ea1 Mon Sep 17 00:00:00 2001
From: Ravi Thinakkal <ravi@metalytics.dev>
Date: Fri, 21 Jun 2024 04:53:28 -0500
Subject: [PATCH 196/206] typo: behavior (#324)

---
 tutorials/34_Extractive_QA_Pipeline.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/34_Extractive_QA_Pipeline.ipynb b/tutorials/34_Extractive_QA_Pipeline.ipynb
index cb528502..344313aa 100644
--- a/tutorials/34_Extractive_QA_Pipeline.ipynb
+++ b/tutorials/34_Extractive_QA_Pipeline.ipynb
@@ -645,7 +645,7 @@
     "ExtractedAnswer(query='Who was Pliny the Elder?', score=0.04606167031102615, data=None, document=None, context=None, document_offset=None, context_offset=None, meta={})]}}\n",
     "```\n",
     "\n",
-    "`.0.04606167031102615` means the model is fairly confident the provided answers are correct in this case. You can disable this behavio and return only answers by setting the `no_answer` param to `False` when initializing your `ExtractiveReader`.\n"
+    "`.0.04606167031102615` means the model is fairly confident the provided answers are correct in this case. You can disable this behavior and return only answers by setting the `no_answer` param to `False` when initializing your `ExtractiveReader`.\n"
    ]
   },
   {

From c23b14de9e874bfb621a4d40ffb478c3a3ee5d20 Mon Sep 17 00:00:00 2001
From: Ravi Thinakkal <ravi@metalytics.dev>
Date: Fri, 21 Jun 2024 04:54:38 -0500
Subject: [PATCH 197/206] typo: our -> out (#323)

---
 tutorials/29_Serializing_Pipelines.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/29_Serializing_Pipelines.ipynb b/tutorials/29_Serializing_Pipelines.ipynb
index 711a0f14..65989adf 100644
--- a/tutorials/29_Serializing_Pipelines.ipynb
+++ b/tutorials/29_Serializing_Pipelines.ipynb
@@ -29,7 +29,7 @@
     "\n",
     "Serialization means converting a pipeline to a format that you can save on your disk and load later. It's especially useful because a serialized pipeline can be saved on disk or a database, get sent over a network and more. \n",
     "\n",
-    "Although it's possible to serialize into other formats too, Haystack supports YAML our of the box to make it easy for humans to make changes without the need to go back and forth with Python code. In this tutorial, we will create a very simple pipeline in Python code, serialize it into YAML, make changes to it, and deserialize it back into a Haystack `Pipeline`."
+    "Although it's possible to serialize into other formats too, Haystack supports YAML out of the box to make it easy for humans to make changes without the need to go back and forth with Python code. In this tutorial, we will create a very simple pipeline in Python code, serialize it into YAML, make changes to it, and deserialize it back into a Haystack `Pipeline`."
    ]
   },
   {

From 9ce8a54937ca73819c6108e441bf3575cb491636 Mon Sep 17 00:00:00 2001
From: Ravi Thinakkal <ravi@metalytics.dev>
Date: Fri, 21 Jun 2024 04:55:24 -0500
Subject: [PATCH 198/206] typo: Embeddint -> Embedding (#322)

---
 tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
index 166d26a0..efa2e493 100644
--- a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
+++ b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
@@ -284,7 +284,7 @@
     "\n",
     "We will then be able to compare the results and see if embedding metadata has helped with retrieval in this case.\n",
     "\n",
-    "> 💡 Here, we are using the `InMemoryEmbeddintRetriever` because we used the `InMemoryDocumentStore` above. If you're using another document store, change this to use the accompanying embedding retriever for the document store you are using. Check out the [Embedders Documentation](https://docs.haystack.deepset.ai/v2.0/docs/embedders) for a full list"
+    "> 💡 Here, we are using the `InMemoryEmbeddingRetriever` because we used the `InMemoryDocumentStore` above. If you're using another document store, change this to use the accompanying embedding retriever for the document store you are using. Check out the [Embedders Documentation](https://docs.haystack.deepset.ai/v2.0/docs/embedders) for a full list"
    ]
   },
   {

From f915427c445016adb1f556ba0b20fbd7e22cc3aa Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <stefanofiorucci@gmail.com>
Date: Fri, 21 Jun 2024 16:52:27 +0200
Subject: [PATCH 199/206] update links to docs (#329)

---
 tutorials/27_First_RAG_Pipeline.ipynb         |    26 +-
 .../28_Structured_Output_With_Loop.ipynb      |    10 +-
 tutorials/29_Serializing_Pipelines.ipynb      |    10 +-
 ...le_Type_Preprocessing_Index_Pipeline.ipynb |    24 +-
 tutorials/31_Metadata_Filtering.ipynb         |    14 +-
 ...ng_Documents_and_Queries_by_Language.ipynb |    20 +-
 tutorials/33_Hybrid_Retrieval.ipynb           |    24 +-
 tutorials/34_Extractive_QA_Pipeline.ipynb     |    10 +-
 tutorials/35_Evaluating_RAG_Pipelines.ipynb   | 35979 ++++++++--------
 ...g_Fallbacks_with_Conditional_Routing.ipynb |    22 +-
 ...ding_Metadata_for_Improved_Retrieval.ipynb |    12 +-
 ...at_Application_with_Function_Calling.ipynb |    16 +-
 12 files changed, 18093 insertions(+), 18074 deletions(-)

diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index c2350fb7..5574873c 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -10,7 +10,7 @@
         "\n",
         "- **Level**: Beginner\n",
         "- **Time to complete**: 10 minutes\n",
-        "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator)\n",
+        "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/docs/openaigenerator)\n",
         "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys).\n",
         "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and OpenAIGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n",
         "\n",
@@ -25,7 +25,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves four main components: [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder) for creating an embedding for the user query, [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) for creating a template prompt, and [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) for generating responses.\n",
+        "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves four main components: [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder) for creating an embedding for the user query, [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) for creating a template prompt, and [OpenAIGenerator](https://docs.haystack.deepset.ai/docs/openaigenerator) for generating responses.\n",
         "\n",
         "For this tutorial, you'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"
       ]
@@ -38,8 +38,8 @@
       "source": [
         "## Preparing the Colab Environment\n",
         "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/logging)"
       ]
     },
     {
@@ -183,7 +183,7 @@
       "source": [
         "### Enabling Telemetry\n",
         "\n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/enabling-telemetry) for more details."
       ]
     },
     {
@@ -301,9 +301,9 @@
       "source": [
         "### Initalize a Document Embedder\n",
         "\n",
-        "To store your data in the DocumentStore with embeddings, initialize a [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder) with the model name and call `warm_up()` to download the embedding model.\n",
+        "To store your data in the DocumentStore with embeddings, initialize a [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder) with the model name and call `warm_up()` to download the embedding model.\n",
         "\n",
-        "> If you'd like, you can use a different [Embedder](https://docs.haystack.deepset.ai/v2.0/docs/embedders) for your documents."
+        "> If you'd like, you can use a different [Embedder](https://docs.haystack.deepset.ai/docs/embedders) for your documents."
       ]
     },
     {
@@ -407,7 +407,7 @@
       "source": [
         "## Building the RAG Pipeline\n",
         "\n",
-        "The next step is to build a [Pipeline](https://docs.haystack.deepset.ai/v2.0/docs/pipelines) to generate answers for the user query following the RAG approach. To create the pipeline, you first need to initialize each component, add them to your pipeline, and connect them."
+        "The next step is to build a [Pipeline](https://docs.haystack.deepset.ai/docs/pipelines) to generate answers for the user query following the RAG approach. To create the pipeline, you first need to initialize each component, add them to your pipeline, and connect them."
       ]
     },
     {
@@ -444,7 +444,7 @@
       "source": [
         "### Initialize the Retriever\n",
         "\n",
-        "Initialize a [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever) and make it use the InMemoryDocumentStore you initialized earlier in this tutorial. This Retriever will get the relevant documents to the query."
+        "Initialize a [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever) and make it use the InMemoryDocumentStore you initialized earlier in this tutorial. This Retriever will get the relevant documents to the query."
       ]
     },
     {
@@ -470,7 +470,7 @@
         "\n",
         "Create a custom prompt for a generative question answering task using the RAG approach. The prompt should take in two parameters: `documents`, which are retrieved from a document store, and a `question` from the user. Use the Jinja2 looping syntax to combine the content of the retrieved documents in the prompt.\n",
         "\n",
-        "Next, initialize a [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) instance with your prompt template. The PromptBuilder, when given the necessary values, will automatically fill in the variable values and generate a complete prompt. This approach allows for a more tailored and effective question-answering experience."
+        "Next, initialize a [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) instance with your prompt template. The PromptBuilder, when given the necessary values, will automatically fill in the variable values and generate a complete prompt. This approach allows for a more tailored and effective question-answering experience."
       ]
     },
     {
@@ -507,7 +507,7 @@
         "### Initialize a Generator\n",
         "\n",
         "\n",
-        "Generators are the components that interact with large language models (LLMs). Now, set `OPENAI_API_KEY` environment variable and initialize a [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/OpenAIGenerator) that can communicate with OpenAI GPT models. As you initialize, provide a model name:"
+        "Generators are the components that interact with large language models (LLMs). Now, set `OPENAI_API_KEY` environment variable and initialize a [OpenAIGenerator](https://docs.haystack.deepset.ai/docs/OpenAIGenerator) that can communicate with OpenAI GPT models. As you initialize, provide a model name:"
       ]
     },
     {
@@ -545,7 +545,7 @@
         "id": "nenbo2SvycHd"
       },
       "source": [
-        "> You can replace `OpenAIGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
+        "> You can replace `OpenAIGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/docs/generators)."
       ]
     },
     {
@@ -558,7 +558,7 @@
         "\n",
         "To build a pipeline, add all components to your pipeline and connect them. Create connections from `text_embedder`'s \"embedding\" output to \"query_embedding\" input of `retriever`, from `retriever` to `prompt_builder` and from `prompt_builder` to `llm`. Explicitly connect the output of `retriever` with \"documents\" input of the `prompt_builder` to make the connection obvious as `prompt_builder` has two inputs (\"documents\" and \"question\").\n",
         "\n",
-        "For more information on pipelines and creating connections, refer to [Creating Pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines) documentation."
+        "For more information on pipelines and creating connections, refer to [Creating Pipelines](https://docs.haystack.deepset.ai/docs/creating-pipelines) documentation."
       ]
     },
     {
diff --git a/tutorials/28_Structured_Output_With_Loop.ipynb b/tutorials/28_Structured_Output_With_Loop.ipynb
index 6938125d..61f31e07 100644
--- a/tutorials/28_Structured_Output_With_Loop.ipynb
+++ b/tutorials/28_Structured_Output_With_Loop.ipynb
@@ -17,7 +17,7 @@
         "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)..\n",
         "\n",
         "## Overview\n",
-        "This tutorial demonstrates how to use Haystack 2.0's advanced [looping pipelines](https://docs.haystack.deepset.ai/v2.0/docs/pipelines#loops) with LLMs for more dynamic and flexible data processing. You'll learn how to extract structured data from unstructured data using an LLM, and to validate the generated output against a predefined schema.\n",
+        "This tutorial demonstrates how to use Haystack 2.0's advanced [looping pipelines](https://docs.haystack.deepset.ai/docs/pipelines#loops) with LLMs for more dynamic and flexible data processing. You'll learn how to extract structured data from unstructured data using an LLM, and to validate the generated output against a predefined schema.\n",
         "\n",
         "This tutorial uses `gpt-3.5-turbo` to change unstructured passages into JSON outputs that follow the [Pydantic](https://github.com/pydantic/pydantic) schema. It uses a custom OutputValidator component to validate the JSON and loop back to make corrections, if necessary."
       ]
@@ -173,7 +173,7 @@
         "\n",
         "`OutputValidator` is a custom component that validates if the JSON object the LLM generates complies with the provided [Pydantic model](https://docs.pydantic.dev/1.10/usage/models/). If it doesn't, OutputValidator returns an error message along with the incorrect JSON object to get it fixed in the next loop.\n",
         "\n",
-        "For more details about custom components, see [Creating Custom Components](https://docs.haystack.deepset.ai/v2.0/docs/custom-components)."
+        "For more details about custom components, see [Creating Custom Components](https://docs.haystack.deepset.ai/docs/custom-components)."
       ]
     },
     {
@@ -257,7 +257,7 @@
         "\n",
         "Write instructions for the LLM for converting a passage into a JSON format. Ensure the instructions explain how to identify and correct errors if the JSON doesn't match the required schema. Once you create the prompt, initialize PromptBuilder to use it.  \n",
         "\n",
-        "For information about Jinja2 template and PromptBuilder, see [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder)."
+        "For information about Jinja2 template and PromptBuilder, see [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder)."
       ]
     },
     {
@@ -292,7 +292,7 @@
       "source": [
         "## Initalizing the Generator\n",
         "\n",
-        "[OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) generates\n",
+        "[OpenAIGenerator](https://docs.haystack.deepset.ai/docs/openaigenerator) generates\n",
         "text using OpenAI's `gpt-3.5-turbo` model by default. Set the `OPENAI_API_KEY` variable and provide a model name to the Generator."
       ]
     },
@@ -358,7 +358,7 @@
       "source": [
         "### Visualize the Pipeline\n",
         "\n",
-        "Draw the pipeline with the [`draw()`](https://docs.haystack.deepset.ai/v2.0/docs/drawing-pipeline-graphs) method to confirm the connections are correct. You can find the diagram in the Files section of this Colab."
+        "Draw the pipeline with the [`draw()`](https://docs.haystack.deepset.ai/docs/drawing-pipeline-graphs) method to confirm the connections are correct. You can find the diagram in the Files section of this Colab."
       ]
     },
     {
diff --git a/tutorials/29_Serializing_Pipelines.ipynb b/tutorials/29_Serializing_Pipelines.ipynb
index 65989adf..1802d716 100644
--- a/tutorials/29_Serializing_Pipelines.ipynb
+++ b/tutorials/29_Serializing_Pipelines.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "- **Level**: Beginner\n",
     "- **Time to complete**: 10 minutes\n",
-    "- **Components Used**: [`HuggingFaceLocalGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/huggingfacelocalgenerator), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder)\n",
+    "- **Components Used**: [`HuggingFaceLocalGenerator`](https://docs.haystack.deepset.ai/docs/huggingfacelocalgenerator), [`PromptBuilder`](https://docs.haystack.deepset.ai/docs/promptbuilder)\n",
     "- **Prerequisites**: None\n",
     "- **Goal**: After completing this tutorial, you'll understand how to serialize and deserialize between YAML and Python code.\n",
     "\n",
@@ -25,7 +25,7 @@
    "source": [
     "## Overview\n",
     "\n",
-    "**📚 Useful Documentation:** [Serialization](https://docs.haystack.deepset.ai/v2.0/docs/serialization)\n",
+    "**📚 Useful Documentation:** [Serialization](https://docs.haystack.deepset.ai/docs/serialization)\n",
     "\n",
     "Serialization means converting a pipeline to a format that you can save on your disk and load later. It's especially useful because a serialized pipeline can be saved on disk or a database, get sent over a network and more. \n",
     "\n",
@@ -40,8 +40,8 @@
    "source": [
     "## Preparing the Colab Environment\n",
     "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/logging)"
    ]
   },
   {
@@ -121,7 +121,7 @@
    "source": [
     "### Enabling Telemetry\n",
     "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/enabling-telemetry) for more details."
    ]
   },
   {
diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index dfaaeaa9..841a7b08 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -18,15 +18,15 @@
     "\n",
     "## Components Used\n",
     "\n",
-    "- [`FileTypeRouter`](https://docs.haystack.deepset.ai/v2.0/docs/filetyperouter): This component will help you route files based on their corresponding MIME type to different components\n",
-    "- [`MarkdownToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/markdowntodocument): This component will help you convert markdown files into Haystack Documents\n",
-    "- [`PyPDFToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/pypdftodocument): This component will help you convert pdf files into Haystack Documents\n",
-    "- [`TextFileToDocument`](https://docs.haystack.deepset.ai/v2.0/docs/textfiletodocument): This component will help you convert text files into Haystack Documents\n",
-    "- [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner): This component will help you to join Documents coming from different branches of a pipeline\n",
-    "- [`DocumentCleaner`](https://docs.haystack.deepset.ai/v2.0/docs/documentcleaner) (optional): This component will help you to make Documents more readable by removing extra whitespaces etc.\n",
-    "- [`DocumentSplitter`](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter): This component will help you to split your Document into chunks\n",
-    "- [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder): This component will help you create embeddings for Documents.\n",
-    "- [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter): This component will help you write Documents into the DocumentStore"
+    "- [`FileTypeRouter`](https://docs.haystack.deepset.ai/docs/filetyperouter): This component will help you route files based on their corresponding MIME type to different components\n",
+    "- [`MarkdownToDocument`](https://docs.haystack.deepset.ai/docs/markdowntodocument): This component will help you convert markdown files into Haystack Documents\n",
+    "- [`PyPDFToDocument`](https://docs.haystack.deepset.ai/docs/pypdftodocument): This component will help you convert pdf files into Haystack Documents\n",
+    "- [`TextFileToDocument`](https://docs.haystack.deepset.ai/docs/textfiletodocument): This component will help you convert text files into Haystack Documents\n",
+    "- [`DocumentJoiner`](https://docs.haystack.deepset.ai/docs/documentjoiner): This component will help you to join Documents coming from different branches of a pipeline\n",
+    "- [`DocumentCleaner`](https://docs.haystack.deepset.ai/docs/documentcleaner) (optional): This component will help you to make Documents more readable by removing extra whitespaces etc.\n",
+    "- [`DocumentSplitter`](https://docs.haystack.deepset.ai/docs/documentsplitter): This component will help you to split your Document into chunks\n",
+    "- [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder): This component will help you create embeddings for Documents.\n",
+    "- [`DocumentWriter`](https://docs.haystack.deepset.ai/docs/documentwriter): This component will help you write Documents into the DocumentStore"
    ]
   },
   {
@@ -50,8 +50,8 @@
    "source": [
     "## Preparing the Colab Environment\n",
     "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/logging)"
    ]
   },
   {
@@ -86,7 +86,7 @@
    "source": [
     "### Enabling Telemetry\n",
     "\n",
-    "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+    "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/enabling-telemetry) for more details."
    ]
   },
   {
diff --git a/tutorials/31_Metadata_Filtering.ipynb b/tutorials/31_Metadata_Filtering.ipynb
index ba67c0ea..4c5d05f5 100644
--- a/tutorials/31_Metadata_Filtering.ipynb
+++ b/tutorials/31_Metadata_Filtering.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "- **Level**: Beginner\n",
     "- **Time to complete**: 5 minutes\n",
-    "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever)\n",
+    "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever)\n",
     "- **Prerequisites**: None\n",
     "- **Goal**: Filter documents in a document store based on given metadata\n",
     "\n",
@@ -25,7 +25,7 @@
    "source": [
     "## Overview\n",
     "\n",
-    "**📚 Useful Documentation: [Metadata Filtering](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering)**\n",
+    "**📚 Useful Documentation: [Metadata Filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)**\n",
     "\n",
     "Although new retrieval techniques are great, sometimes you just know that you want to perform search on a specific group of documents in your document store. This can be anything from all the documents that are related to a specific _user_, or that were published after a certain _date_ and so on. Metadata filtering is very useful in these situations. In this tutorial, we will create a few simple documents containing information about Haystack, where the metadata includes information on what version of Haystack the information relates to. We will then do metadata filtering to make sure we are answering the question based only on information about Haystack 2.0.\n"
    ]
@@ -38,8 +38,8 @@
    "source": [
     "## Preparing the Colab Environment\n",
     "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/logging)"
    ]
   },
   {
@@ -78,7 +78,7 @@
    "source": [
     "### Enabling Telemetry\n",
     "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/enabling-telemetry) for more details."
    ]
   },
   {
@@ -102,7 +102,7 @@
    "source": [
     "## Preparing Documents\n",
     "\n",
-    "First, let's prepare some documents. Below, we're manually creating 3 simple documents with `meta` attached. We're then writing these documents to an `InMemoryDocumentStore`, but you can [use any of the available document stores](https://docs.haystack.deepset.ai/v2.0/docs/choosing-a-document-store) instead such as OpenSearch, Chroma, Pinecone and more.. (Note that not all of them have options to store in memory and may require extra setup).\n",
+    "First, let's prepare some documents. Below, we're manually creating 3 simple documents with `meta` attached. We're then writing these documents to an `InMemoryDocumentStore`, but you can [use any of the available document stores](https://docs.haystack.deepset.ai/docs/choosing-a-document-store) instead such as OpenSearch, Chroma, Pinecone and more.. (Note that not all of them have options to store in memory and may require extra setup).\n",
     "\n",
     "> ⭐️ For more information on how to write documents into different document stores, you can follow our tutorial on indexing different file types."
    ]
@@ -178,7 +178,7 @@
     "\n",
     "Finally, ask a question by filtering the documents to `\"version\" > 1.21`.\n",
     "\n",
-    "To see what kind of comparison operators you can use for your metadata, including logical comparistons such as `NOT`, `AND` and so on, check out the [Metadata Filtering documentation](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering#comparison)"
+    "To see what kind of comparison operators you can use for your metadata, including logical comparistons such as `NOT`, `AND` and so on, check out the [Metadata Filtering documentation](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)"
    ]
   },
   {
diff --git a/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb b/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
index 4b178f42..15e1b2f2 100644
--- a/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
+++ b/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "- **Level**: Beginner\n",
     "- **Time to complete**: 15 minutes\n",
-    "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/v2.0/docs/documentlanguageclassifier), [`MetadataRouter`](https://docs.haystack.deepset.ai/v2.0/docs/metadatarouter), [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter), [`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter), [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator)\n",
+    "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/docs/documentlanguageclassifier), [`MetadataRouter`](https://docs.haystack.deepset.ai/docs/metadatarouter), [`DocumentWriter`](https://docs.haystack.deepset.ai/docs/documentwriter), [`TextLanguageRouter`](https://docs.haystack.deepset.ai/docs/textlanguagerouter), [`DocumentJoiner`](https://docs.haystack.deepset.ai/docs/documentjoiner), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/docs/openaigenerator)\n",
     "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline to classify documents based on the (human) language they were written in.\n",
     "- Optionally, at the end you'll also incorporate language clasification and query routing into a RAG pipeline, so you can query documents based on the language a question was written in.\n",
     "\n",
@@ -27,11 +27,11 @@
     "\n",
     "In a gobalized society with over 7,000 human languages spoken worldwide today, handling multilingual input is a common use case for NLP applications.\n",
     "\n",
-    "Good news: Haystack has a [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/v2.0/docs/documentlanguageclassifier) built in. This component detects the language a document was written in. This functionality lets you create *branches* in your Haystack pipelines, granting the flexibility to add different processing steps for each language. For example, you could use a LLM that performs better in German to answer German queries. Or, you could fetch only French restaurant reviews for your French users.\n",
+    "Good news: Haystack has a [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/docs/documentlanguageclassifier) built in. This component detects the language a document was written in. This functionality lets you create *branches* in your Haystack pipelines, granting the flexibility to add different processing steps for each language. For example, you could use a LLM that performs better in German to answer German queries. Or, you could fetch only French restaurant reviews for your French users.\n",
     "\n",
     "In this tutorial, you'll take a text samples from hotel reviews, written in different languages. The text samples will be made into Haystack documents and classified by language. Then each document will be written to a language-specific `DocumentStore`. To validate that the language detection is working correctly, you'll filter the document stores to display their contents.\n",
     "\n",
-    "In the last section, you'll build a multi-lingual RAG pipeline. The language of a question is detected, and only documents in that language are used to generate the answer. For this section, the [`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter) will come in handy.\n"
+    "In the last section, you'll build a multi-lingual RAG pipeline. The language of a question is detected, and only documents in that language are used to generate the answer. For this section, the [`TextLanguageRouter`](https://docs.haystack.deepset.ai/docs/textlanguagerouter) will come in handy.\n"
    ]
   },
   {
@@ -42,8 +42,8 @@
    "source": [
     "## Preparing the Colab Environment\n",
     "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/logging)"
    ]
   },
   {
@@ -138,7 +138,7 @@
    "source": [
     "### Enabling Telemetry\n",
     "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/enabling-telemetry) for more details."
    ]
   },
   {
@@ -244,7 +244,7 @@
    "source": [
     "The `DocumentLanguageClassifier` takes a list of languages. The `MetadataRouter` needs a dictionary of rules.  These rules specify which node to route a document to (in this case, which language-specific `DocumentWriter`), based on the document's metadata.\n",
     "\n",
-    "The keys of the dictionary are the names of the output connections, and the values are dictionaries that follow the format of [filtering expressions in Haystack.](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering).\n"
+    "The keys of the dictionary are the names of the output connections, and the values are dictionaries that follow the format of [filtering expressions in Haystack.](https://docs.haystack.deepset.ai/docs/metadata-filtering).\n"
    ]
   },
   {
@@ -408,9 +408,9 @@
    "source": [
     "## (Optional) Create a Multi-Lingual RAG pipeline\n",
     "\n",
-    "To build a multi-lingual RAG pipeline, you can use the[`TextLanguageRouter`](https://docs.haystack.deepset.ai/v2.0/docs/textlanguagerouter) to detect the language of the query. Then, fetch documents in that same language from the correct `DocumentStore`.\n",
+    "To build a multi-lingual RAG pipeline, you can use the[`TextLanguageRouter`](https://docs.haystack.deepset.ai/docs/textlanguagerouter) to detect the language of the query. Then, fetch documents in that same language from the correct `DocumentStore`.\n",
     "\n",
-    "In order to do this you'll need an [OpenAI access token](https://help.openai.com/en/articles/4936850-where-do-i-find-my-api-key), although this approach would also work with any other [generator Haystack supports](https://docs.haystack.deepset.ai/v2.0/docs/generators)."
+    "In order to do this you'll need an [OpenAI access token](https://help.openai.com/en/articles/4936850-where-do-i-find-my-api-key), although this approach would also work with any other [generator Haystack supports](https://docs.haystack.deepset.ai/docs/generators)."
    ]
   },
   {
@@ -484,7 +484,7 @@
     "- `PromptBuilder`\n",
     "- `OpenAIGenerator`\n",
     "\n",
-    "> Note: The `BM25Retriever` essentially does keyword matching, which isn't as accurate as other search methods. In order to make the LLM responses more precise, you could refacctor your piplines to use an [`EmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever) which performs vector search over the documents."
+    "> Note: The `BM25Retriever` essentially does keyword matching, which isn't as accurate as other search methods. In order to make the LLM responses more precise, you could refacctor your piplines to use an [`EmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever) which performs vector search over the documents."
    ]
   },
   {
diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
index 85757127..4c635ceb 100644
--- a/tutorials/33_Hybrid_Retrieval.ipynb
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "- **Level**: Intermediate\n",
     "- **Time to complete**: 15 minutes\n",
-    "- **Components Used**: [`DocumentSplitter`](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), and [`TransformersSimilarityRanker`](https://docs.haystack.deepset.ai/v2.0/docs/transformerssimilarityranker)\n",
+    "- **Components Used**: [`DocumentSplitter`](https://docs.haystack.deepset.ai/docs/documentsplitter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`DocumentJoiner`](https://docs.haystack.deepset.ai/docs/documentjoiner), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), and [`TransformersSimilarityRanker`](https://docs.haystack.deepset.ai/docs/transformerssimilarityranker)\n",
     "- **Prerequisites**: None\n",
     "- **Goal**: After completing this tutorial, you will have learned about creating a hybrid retrieval and when it's useful.\n",
     "\n",
@@ -38,8 +38,8 @@
    "source": [
     "## Preparing the Colab Environment\n",
     "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/setting-the-log-level)"
    ]
   },
   {
@@ -77,7 +77,7 @@
    "source": [
     "### Enabling Telemetry\n",
     "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
@@ -101,7 +101,7 @@
    "source": [
     "## Initializing the DocumentStore\n",
     "\n",
-    "You'll start creating your question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that your system uses to find answers to your questions. In this tutorial, you'll be using the [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore)."
+    "You'll start creating your question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that your system uses to find answers to your questions. In this tutorial, you'll be using the [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore)."
    ]
   },
   {
@@ -143,7 +143,7 @@
     "* *content*: the abstract\n",
     "* *contents*: abstract + title\n",
     "\n",
-    "For searching, you will use the *contents* feature. The other features will be stored as metadata, and you will use them to have a **pretty print** of the search results or for [metadata filtering](https://docs.haystack.deepset.ai/v2.0/docs/metadata-filtering)."
+    "For searching, you will use the *contents* feature. The other features will be stored as metadata, and you will use them to have a **pretty print** of the search results or for [metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)."
    ]
   },
   {
@@ -174,9 +174,9 @@
    "source": [
     "## Indexing Documents with a Pipeline\n",
     "\n",
-    "Create a pipeline to store the data in the document store with their embedding. For this pipeline, you need a [DocumentSplitter](https://docs.haystack.deepset.ai/v2.0/docs/documentsplitter) to split documents into chunks of 512 words, [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder) to create document embeddings for dense retrieval and [DocumentWriter](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter) to write documents to the document store.\n",
+    "Create a pipeline to store the data in the document store with their embedding. For this pipeline, you need a [DocumentSplitter](https://docs.haystack.deepset.ai/docs/documentsplitter) to split documents into chunks of 512 words, [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder) to create document embeddings for dense retrieval and [DocumentWriter](https://docs.haystack.deepset.ai/docs/documentwriter) to write documents to the document store.\n",
     "\n",
-    "As an embedding model, you will use [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on Hugging Face. Feel free to test other models on Hugging Face or use another [Embedder](https://docs.haystack.deepset.ai/v2.0/docs/embedders) to switch the model provider.\n",
+    "As an embedding model, you will use [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on Hugging Face. Feel free to test other models on Hugging Face or use another [Embedder](https://docs.haystack.deepset.ai/docs/embedders) to switch the model provider.\n",
     "\n",
     "> If this step takes too long for you, replace the embedding model with a smaller model such as `sentence-transformers/all-MiniLM-L6-v2` or `sentence-transformers/all-mpnet-base-v2`. Make sure that the `split_length` is updated according to your model's token limit."
    ]
@@ -242,7 +242,7 @@
    "source": [
     "### 1) Initialize Retrievers and the Embedder\n",
     "\n",
-    "Initialize a [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever) and [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) to perform both dense and keyword-based retrieval. For dense retrieval, you also need a [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder) that computes the embedding of the search query by using the same embedding model `BAAI/bge-small-en-v1.5` that was used in the indexing pipeline:"
+    "Initialize a [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever) and [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever) to perform both dense and keyword-based retrieval. For dense retrieval, you also need a [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder) that computes the embedding of the search query by using the same embedding model `BAAI/bge-small-en-v1.5` that was used in the indexing pipeline:"
    ]
   },
   {
@@ -271,7 +271,7 @@
    "source": [
     "### 2) Join Retrieval Results\n",
     "\n",
-    "Haystack offers several joining methods in [`DocumentJoiner`](https://docs.haystack.deepset.ai/v2.0/docs/documentjoiner) to be used for different use cases such as `merge` and `reciprocal_rank_fusion`. In this example, you will use the default `concatenate` mode to join the documents coming from two Retrievers as the [Ranker](https://docs.haystack.deepset.ai/v2.0/docs/rankers) will be the main component to rank the documents for relevancy."
+    "Haystack offers several joining methods in [`DocumentJoiner`](https://docs.haystack.deepset.ai/docs/documentjoiner) to be used for different use cases such as `merge` and `reciprocal_rank_fusion`. In this example, you will use the default `concatenate` mode to join the documents coming from two Retrievers as the [Ranker](https://docs.haystack.deepset.ai/docs/rankers) will be the main component to rank the documents for relevancy."
    ]
   },
   {
@@ -295,7 +295,7 @@
    "source": [
     "### 3) Rank the Results\n",
     "\n",
-    "Use the [TransformersSimilarityRanker](https://docs.haystack.deepset.ai/v2.0/docs/transformerssimilarityranker) that scores the relevancy of all retrieved documents for the given search query by using a cross encoder model. In this example, you will use [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) model to rank the retrieved documents but you can replace this model with other cross-encoder models on Hugging Face."
+    "Use the [TransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/transformerssimilarityranker) that scores the relevancy of all retrieved documents for the given search query by using a cross encoder model. In this example, you will use [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) model to rank the retrieved documents but you can replace this model with other cross-encoder models on Hugging Face."
    ]
   },
   {
@@ -353,7 +353,7 @@
    "source": [
     "### 5) Visualize the Pipeline (Optional)\n",
     "\n",
-    "To understand how you formed a hybrid retrieval pipeline, use [draw()](https://docs.haystack.deepset.ai/v2.0/docs/drawing-pipeline-graphs) method of the pipeline. If you're running this notebook on Google Colab, the generate file will be saved in \"Files\" section on the sidebar."
+    "To understand how you formed a hybrid retrieval pipeline, use [draw()](https://docs.haystack.deepset.ai/docs/drawing-pipeline-graphs) method of the pipeline. If you're running this notebook on Google Colab, the generate file will be saved in \"Files\" section on the sidebar."
    ]
   },
   {
diff --git a/tutorials/34_Extractive_QA_Pipeline.ipynb b/tutorials/34_Extractive_QA_Pipeline.ipynb
index 344313aa..2208d0d9 100644
--- a/tutorials/34_Extractive_QA_Pipeline.ipynb
+++ b/tutorials/34_Extractive_QA_Pipeline.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "- **Level**: Beginner\n",
     "- **Time to complete**: 15 minutes\n",
-    "- **Components Used**: [`ExtractiveReader`](https://docs.haystack.deepset.ai/v2.0/docs/extractivereader), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [`DocumentWriter`](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder)\n",
+    "- **Components Used**: [`ExtractiveReader`](https://docs.haystack.deepset.ai/docs/extractivereader), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [`DocumentWriter`](https://docs.haystack.deepset.ai/docs/documentwriter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder)\n",
     "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is.\n",
     "\n",
     "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n",
@@ -40,8 +40,8 @@
    "source": [
     "## Preparing the Colab Environment\n",
     "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/logging)"
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/logging)"
    ]
   },
   {
@@ -72,7 +72,7 @@
     "id": "bo6LztWXURaA"
    },
    "source": [
-    "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+    "Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/enabling-telemetry) for more details."
    ]
   },
   {
@@ -108,7 +108,7 @@
     "\n",
     "The data has already been cleaned and preprocessed, so turning it into Haystack `Documents` is fairly straightfoward.\n",
     "\n",
-    "Using an `InMemoryDocumentStore` here keeps things simple. However, this general approach would work with [any document store that Haystack 2.0 supports](https://docs.haystack.deepset.ai/v2.0/docs/document-store).\n",
+    "Using an `InMemoryDocumentStore` here keeps things simple. However, this general approach would work with [any document store that Haystack 2.0 supports](https://docs.haystack.deepset.ai/docs/document-store).\n",
     "\n",
     "The `SentenceTransformersDocumentEmbedder` transforms each `Document` into a vector. Here we've used [`sentence-transformers/multi-qa-mpnet-base-dot-v1`](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1). You can substitute any embedding model you like, as long as you use the same one in your extractive pipeline.\n",
     "\n",
diff --git a/tutorials/35_Evaluating_RAG_Pipelines.ipynb b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
index 4bb6b70d..2bc5fca2 100644
--- a/tutorials/35_Evaluating_RAG_Pipelines.ipynb
+++ b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
@@ -1,18100 +1,18119 @@
 {
-  "cells": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2OvkPji9O-qX"
+   },
+   "source": [
+    "# Tutorial: Evaluating RAG Pipelines\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Components Used**: `InMemoryDocumentStore`, `InMemoryEmbeddingRetriever`, `PromptBuilder`, `OpenAIGenerator`, `DocumentMRREvaluator`, `FaithfulnessEvaluator`, `SASEvaluator`\n",
+    "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI: https://platform.openai.com/api-keys\n",
+    "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines both with model-based, and statistical metrics available in the Haystack evaluation offering. You'll also see which other evaluation frameworks are integrated with Haystack.\n",
+    "\n",
+    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LFqHcXYPO-qZ"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In this tutorial, you will learn how to evaluate Haystack pipelines, in particular, Retriaval-Augmented Generation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) pipelines.\n",
+    "1. You will first build a pipeline that answers medical questions based on PubMed data.\n",
+    "2. You will build an evaluation pipeline that makes use of some metrics like Document MRR and Answer Faithfulness.\n",
+    "3. You will run your RAG pipeline and evaluated the output with your evaluation pipeline.\n",
+    "\n",
+    "Haystack provides a wide range of [`Evaluators`](https://docs.haystack.deepset.ai/docs/evaluators) which can perform 2 types of evaluations:\n",
+    "- [Model-Based evaluation](https://docs.haystack.deepset.ai/docs/model-based-evaluation)\n",
+    "- [Statistical evaluation](https://docs.haystack.deepset.ai/docs/statistical-evaluation)\n",
+    "\n",
+    "We will use some of these evalution techniques in this tutorial to evaluate a RAG pipeline that is designed to answer questions on PubMed data.\n",
+    "\n",
+    ">🧑‍🍳 As well as Haystack's own evaluation metrics, you can also integrate with a number of evaluation frameworks. See the integrations and examples below 👇\n",
+    "> - [Evaluate with DeepEval](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_deep_eval.ipynb)\n",
+    "> - [Evaluate with RAGAS](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_ragas.ipynb)\n",
+    "> - [Evaluate with UpTrain](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_uptrain.ipynb)\n",
+    "\n",
+    "### Evaluating RAG Pipelines\n",
+    "RAG pipelines ultimately consist of at least 2 steps:\n",
+    "- Retrieval\n",
+    "- Generation\n",
+    "\n",
+    "To evaluate a full RAG pipeline, we have to evaluate each of these steps in isolation, as well as a full unit. While retrieval can in some cases be evaluated with some statistical metrics that require labels, it's not a straight-forward task to do the same for the generation step. Instead, we often rely on model-based metrics to evaluate the generation step, where an LLM is used as the 'evaluator'.\n",
+    "\n",
+    "![Steps or RAG](https://raw.githubusercontent.com/deepset-ai/haystack-tutorials/main/tutorials/img/tutorial35_rag.png)\n",
+    "\n",
+    "#### 📺 Code Along\n",
+    "\n",
+    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/5PrzXaZ0-qk?si=lgBSfHatbV2i59J-\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QXjVlbPiO-qZ"
+   },
+   "source": [
+    "## Preparing the Colab Environment\n",
+    "\n",
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/setting-the-log-level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Kww5B_vXO-qZ"
+   },
+   "source": [
+    "## Installing Haystack\n",
+    "\n",
+    "Install Haystack 2.0 and [datasets](https://pypi.org/project/datasets/) with `pip`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UQbU8GUfO-qZ",
+    "outputId": "80fe52ea-108b-4bb4-cb1d-fe79373c86f3"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting git+https://github.com/deepset-ai/haystack.git@main\n",
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+      "  Resolved https://github.com/deepset-ai/haystack.git to commit 2509eeea7e82ef52ef65ccce00bfdcc6c1e8c1c2\n",
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+      "  Installing build dependencies: finished with status 'done'\n",
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+      "  Preparing metadata (pyproject.toml): started\n",
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+      "Building wheels for collected packages: haystack-ai\n",
+      "  Building wheel for haystack-ai (pyproject.toml): started\n",
+      "  Building wheel for haystack-ai (pyproject.toml): finished with status 'done'\n",
+      "  Created wheel for haystack-ai: filename=haystack_ai-2.1.0rc0-py3-none-any.whl size=316211 sha256=aee4b70fda05260e7466d477508440735cfe4d5c3b9a15a7003773a7fa01bd0c\n",
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+      "Installing collected packages: monotonic, lazy-imports, haystack-bm25, h11, boilerpy3, backoff, posthog, httpcore, httpx, openai, haystack-ai\n",
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+      "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets\n",
+      "  Attempting uninstall: huggingface-hub\n",
+      "    Found existing installation: huggingface-hub 0.20.3\n",
+      "    Uninstalling huggingface-hub-0.20.3:\n",
+      "      Successfully uninstalled huggingface-hub-0.20.3\n",
+      "Successfully installed datasets-2.19.0 dill-0.3.8 huggingface-hub-0.22.2 multiprocess-0.70.16 xxhash-3.4.1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  Running command git clone --filter=blob:none --quiet https://github.com/deepset-ai/haystack.git /tmp/pip-req-build-83hiigdl\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai\n",
+    "pip install \"datasets>=2.6.1\"\n",
+    "pip install sentence-transformers>=2.2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Wl_jYERtO-qa"
+   },
+   "source": [
+    "### Enabling Telemetry\n",
+    "\n",
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/enabling-telemetry) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "A76B4S49O-qa"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.telemetry import tutorial_running\n",
+    "\n",
+    "tutorial_running(35)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_lvfew16O-qa"
+   },
+   "source": [
+    "## Create the RAG Pipeline to Evaluate\n",
+    "\n",
+    "To evaluate a RAG pipeline, we need a RAG pipeline to start with. So, we will start by creating a question answering pipeline.\n",
+    "\n",
+    "> 💡 For a complete tutorial on creating Retrieval-Augmmented Generation pipelines check out the [Creating Your First QA Pipeline with Retrieval-Augmentation Tutorial](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+    "\n",
+    "For this tutorial, we will be using [a labeled PubMed dataset](https://huggingface.co/datasets/vblagoje/PubMedQA_instruction/viewer/default/train?row=0) with questions, contexts and answers. This way, we can use the contexts as Documents, and we also have the required labeled data that we need for some of the evaluation metrics we will be using.\n",
+    "\n",
+    "First, let's fetch the prepared dataset and extract `all_documents`, `all_questions` and `all_ground_truth_answers`:\n",
+    "\n",
+    "> ℹ️ The dataset is quite large, we're using the first 1000 rows in this example, but you can increase this if you want to\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177,
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+    "outputId": "199392b0-f51d-4148-a486-5e797c049d9f"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2OvkPji9O-qX"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4dcb30b5589434f9faa18eb9563c738",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "# Tutorial: Evaluating RAG Pipelines\n",
-        "\n",
-        "- **Level**: Intermediate\n",
-        "- **Time to complete**: 15 minutes\n",
-        "- **Components Used**: `InMemoryDocumentStore`, `InMemoryEmbeddingRetriever`, `PromptBuilder`, `OpenAIGenerator`, `DocumentMRREvaluator`, `FaithfulnessEvaluator`, `SASEvaluator`\n",
-        "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI: https://platform.openai.com/api-keys\n",
-        "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines both with model-based, and statistical metrics available in the Haystack evaluation offering. You'll also see which other evaluation frameworks are integrated with Haystack.\n",
-        "\n",
-        "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/498 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LFqHcXYPO-qZ"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7f86d8d1e5e403fae730c43cd99ab1d",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "In this tutorial, you will learn how to evaluate Haystack pipelines, in particular, Retriaval-Augmented Generation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) pipelines.\n",
-        "1. You will first build a pipeline that answers medical questions based on PubMed data.\n",
-        "2. You will build an evaluation pipeline that makes use of some metrics like Document MRR and Answer Faithfulness.\n",
-        "3. You will run your RAG pipeline and evaluated the output with your evaluation pipeline.\n",
-        "\n",
-        "Haystack provides a wide range of [`Evaluators`](https://docs.haystack.deepset.ai/docs/evaluators) which can perform 2 types of evaluations:\n",
-        "- [Model-Based evaluation](https://docs.haystack.deepset.ai/docs/model-based-evaluation)\n",
-        "- [Statistical evaluation](https://docs.haystack.deepset.ai/docs/statistical-evaluation)\n",
-        "\n",
-        "We will use some of these evalution techniques in this tutorial to evaluate a RAG pipeline that is designed to answer questions on PubMed data.\n",
-        "\n",
-        ">🧑‍🍳 As well as Haystack's own evaluation metrics, you can also integrate with a number of evaluation frameworks. See the integrations and examples below 👇\n",
-        "> - [Evaluate with DeepEval](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_deep_eval.ipynb)\n",
-        "> - [Evaluate with RAGAS](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_ragas.ipynb)\n",
-        "> - [Evaluate with UpTrain](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_uptrain.ipynb)\n",
-        "\n",
-        "### Evaluating RAG Pipelines\n",
-        "RAG pipelines ultimately consist of at least 2 steps:\n",
-        "- Retrieval\n",
-        "- Generation\n",
-        "\n",
-        "To evaluate a full RAG pipeline, we have to evaluate each of these steps in isolation, as well as a full unit. While retrieval can in some cases be evaluated with some statistical metrics that require labels, it's not a straight-forward task to do the same for the generation step. Instead, we often rely on model-based metrics to evaluate the generation step, where an LLM is used as the 'evaluator'.\n",
-        "\n",
-        "![Steps or RAG](https://raw.githubusercontent.com/deepset-ai/haystack-tutorials/main/tutorials/img/tutorial35_rag.png)\n",
-        "\n",
-        "#### 📺 Code Along\n",
-        "\n",
-        "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/5PrzXaZ0-qk?si=lgBSfHatbV2i59J-\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n"
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/274M [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QXjVlbPiO-qZ"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6978ebb1d574a658ba65d8d5dfa4342",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "## Preparing the Colab Environment\n",
-        "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/setting-the-log-level)"
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/986k [00:00<?, ?B/s]"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
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     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Kww5B_vXO-qZ"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5889178c30ee4d36b67059f3b3f406a1",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "## Installing Haystack\n",
-        "\n",
-        "Install Haystack 2.0 and [datasets](https://pypi.org/project/datasets/) with `pip`:"
+      "text/plain": [
+       "Generating train split:   0%|          | 0/272458 [00:00<?, ? examples/s]"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
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-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "UQbU8GUfO-qZ",
-        "outputId": "80fe52ea-108b-4bb4-cb1d-fe79373c86f3"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18b53d4a7a404ec2aca100308c4c8036",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
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-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "  Running command git clone --filter=blob:none --quiet https://github.com/deepset-ai/haystack.git /tmp/pip-req-build-83hiigdl\n"
-          ]
-        }
-      ],
-      "source": [
-        "%%bash\n",
-        "\n",
-        "pip install haystack-ai\n",
-        "pip install \"datasets>=2.6.1\"\n",
-        "pip install sentence-transformers>=2.2.0"
+      "text/plain": [
+       "Generating test split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from haystack import Document\n",
+    "\n",
+    "dataset = load_dataset(\"vblagoje/PubMedQA_instruction\", split=\"train\")\n",
+    "dataset = dataset.select(range(1000))\n",
+    "all_documents = [Document(content=doc[\"context\"]) for doc in dataset]\n",
+    "all_questions = [doc[\"instruction\"] for doc in dataset]\n",
+    "all_ground_truth_answers = [doc[\"response\"] for doc in dataset]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yL8nuJdWO-qa"
+   },
+   "source": [
+    "Next, let's build a simple indexing pipeline and write the `documents` into a DocumentStore. Here, we're using the `InMemoryDocumentStore`.\n",
+    "\n",
+    "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
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     },
+    "id": "JfY_zgQ15dVq",
+    "outputId": "f66883c5-3d09-4610-f9b3-0f5495799ad5"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Wl_jYERtO-qa"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c372271358e844fbacd24f184ffc821a",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "### Enabling Telemetry\n",
-        "\n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details."
+      "text/plain": [
+       "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "A76B4S49O-qa"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6726d348126452a88b5b70c444198b3",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "from haystack.telemetry import tutorial_running\n",
-        "\n",
-        "tutorial_running(35)"
+      "text/plain": [
+       "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_lvfew16O-qa"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7235dd65564a4af883bbfa28ab83f692",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "## Create the RAG Pipeline to Evaluate\n",
-        "\n",
-        "To evaluate a RAG pipeline, we need a RAG pipeline to start with. So, we will start by creating a question answering pipeline.\n",
-        "\n",
-        "> 💡 For a complete tutorial on creating Retrieval-Augmmented Generation pipelines check out the [Creating Your First QA Pipeline with Retrieval-Augmentation Tutorial](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
-        "\n",
-        "For this tutorial, we will be using [a labeled PubMed dataset](https://huggingface.co/datasets/vblagoje/PubMedQA_instruction/viewer/default/train?row=0) with questions, contexts and answers. This way, we can use the contexts as Documents, and we also have the required labeled data that we need for some of the evaluation metrics we will be using.\n",
-        "\n",
-        "First, let's fetch the prepared dataset and extract `all_documents`, `all_questions` and `all_ground_truth_answers`:\n",
-        "\n",
-        "> ℹ️ The dataset is quite large, we're using the first 1000 rows in this example, but you can increase this if you want to\n"
+      "text/plain": [
+       "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 177,
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-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "f4dcb30b5589434f9faa18eb9563c738",
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-            "text/plain": [
-              "Downloading readme:   0%|          | 0.00/498 [00:00<?, ?B/s]"
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-          "metadata": {},
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-            ]
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-            ]
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-            },
-            "text/plain": [
-              "Generating train split:   0%|          | 0/272458 [00:00<?, ? examples/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
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-            "text/plain": [
-              "Generating test split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "from datasets import load_dataset\n",
-        "from haystack import Document\n",
-        "\n",
-        "dataset = load_dataset(\"vblagoje/PubMedQA_instruction\", split=\"train\")\n",
-        "dataset = dataset.select(range(1000))\n",
-        "all_documents = [Document(content=doc[\"context\"]) for doc in dataset]\n",
-        "all_questions = [doc[\"instruction\"] for doc in dataset]\n",
-        "all_ground_truth_answers = [doc[\"response\"] for doc in dataset]"
+      "text/plain": [
+       "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
       ]
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     },
     {
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-      "source": [
-        "Next, let's build a simple indexing pipeline and write the `documents` into a DocumentStore. Here, we're using the `InMemoryDocumentStore`.\n",
-        "\n",
-        "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)."
+      "text/plain": [
+       "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
       ]
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-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "54cba43df68b4b22bd77a259a2b730a1",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "{'document_writer': {'documents_written': 1000}}"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from typing import List\n",
-        "from haystack import Pipeline\n",
-        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
-        "from haystack.components.writers import DocumentWriter\n",
-        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-        "from haystack.document_stores.types import DuplicatePolicy\n",
-        "\n",
-        "document_store = InMemoryDocumentStore()\n",
-        "\n",
-        "document_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
-        "document_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP)\n",
-        "\n",
-        "indexing = Pipeline()\n",
-        "indexing.add_component(instance=document_embedder, name=\"document_embedder\")\n",
-        "indexing.add_component(instance=document_writer, name=\"document_writer\")\n",
-        "\n",
-        "indexing.connect(\"document_embedder.documents\", \"document_writer.documents\")\n",
-        "\n",
-        "indexing.run({\"document_embedder\": {\"documents\": all_documents}})\n"
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XvLVaFHTO-qb"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f709f5b00b946458f41ad705294c4d6",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "Now that we have our data ready, we can create a simple RAG pipeline.\n",
-        "\n",
-        "In this example, we'll be using:\n",
-        "- [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever) which will get the relevant documents to the query.\n",
-        "- [`OpenAIGenerator`](https://docs.haystack.deepset.ai/docs/OpenAIGenerator) to generate answers to queries. You can replace `OpenAIGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/docs/generators)."
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "-uo-6fjiO-qb",
-        "outputId": "12f00bd6-fc05-40fd-db96-429658039c32"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6d30ce04ed1492f90134c0c129631b4",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Enter OpenAI API key:··········\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<haystack.core.pipeline.pipeline.Pipeline object at 0x7b698ec37d60>\n",
-              "🚅 Components\n",
-              "  - query_embedder: SentenceTransformersTextEmbedder\n",
-              "  - retriever: InMemoryEmbeddingRetriever\n",
-              "  - prompt_builder: PromptBuilder\n",
-              "  - generator: OpenAIGenerator\n",
-              "  - answer_builder: AnswerBuilder\n",
-              "🛤️ Connections\n",
-              "  - query_embedder.embedding -> retriever.query_embedding (List[float])\n",
-              "  - retriever.documents -> prompt_builder.documents (List[Document])\n",
-              "  - retriever.documents -> answer_builder.documents (List[Document])\n",
-              "  - prompt_builder.prompt -> generator.prompt (str)\n",
-              "  - generator.replies -> answer_builder.replies (List[str])\n",
-              "  - generator.meta -> answer_builder.meta (List[Dict[str, Any]])"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "import os\n",
-        "from getpass import getpass\n",
-        "from haystack.components.builders import AnswerBuilder, PromptBuilder\n",
-        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
-        "from haystack.components.generators import OpenAIGenerator\n",
-        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-        "\n",
-        "if \"OPENAI_API_KEY\" not in os.environ:\n",
-        "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
-        "\n",
-        "template = \"\"\"\n",
-        "        You have to answer the following question based on the given context information only.\n",
-        "\n",
-        "        Context:\n",
-        "        {% for document in documents %}\n",
-        "            {{ document.content }}\n",
-        "        {% endfor %}\n",
-        "\n",
-        "        Question: {{question}}\n",
-        "        Answer:\n",
-        "        \"\"\"\n",
-        "\n",
-        "rag_pipeline = Pipeline()\n",
-        "rag_pipeline.add_component(\n",
-        "    \"query_embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
-        ")\n",
-        "rag_pipeline.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store, top_k=3))\n",
-        "rag_pipeline.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
-        "rag_pipeline.add_component(\"generator\", OpenAIGenerator(model=\"gpt-3.5-turbo\"))\n",
-        "rag_pipeline.add_component(\"answer_builder\", AnswerBuilder())\n",
-        "\n",
-        "rag_pipeline.connect(\"query_embedder\", \"retriever.query_embedding\")\n",
-        "rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
-        "rag_pipeline.connect(\"prompt_builder\", \"generator\")\n",
-        "rag_pipeline.connect(\"generator.replies\", \"answer_builder.replies\")\n",
-        "rag_pipeline.connect(\"generator.meta\", \"answer_builder.meta\")\n",
-        "rag_pipeline.connect(\"retriever\", \"answer_builder.documents\")"
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DBAyF5tVO-qc"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a9cec9e5b124c0d82c7dcb87f2b0582",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "### Asking a Question\n",
-        "\n",
-        "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to all components that require it as input. In this case these are the `query_embedder`, the `prompt_builder` and the `answer_builder`."
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 86,
-          "referenced_widgets": [
-            "f92db6040c414987acb2e855b5efe29a",
-            "a5e5e17fa90941c8820424bcb8a64461",
-            "f42b858a75334567b6e21d4491d8bf9a",
-            "2451c140c32f46f68fa59d949b198c81",
-            "927fd891c86f4098bb4b32717231d547",
-            "997e1beb4a714368b583b812698db5bd",
-            "e8419341d2c94b2c879671184da63da1",
-            "5f56cc1fdb13480fae8838f613b79d9e",
-            "d2b53a8ababf4744beca8bae4abea6ca",
-            "076a7c8e3a7747ec928a0d5853e92e88",
-            "298f01846fd4442cbcea6149a74c979d"
-          ]
-        },
-        "id": "Vnt283M5O-qc",
-        "outputId": "4c8f1c3e-d8c0-4d1c-d336-09df4b70544d"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "84518ee50615449d8bf617eab7e70ee2",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "f92db6040c414987acb2e855b5efe29a",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Yes, high levels of procalcitonin in the early phase after pediatric liver transplantation indicated a poor postoperative outcome. Patients with high procalcitonin levels on postoperative day 2 had higher International Normalized Ratio values, suffered more often from primary graft non-function, had longer stays in the pediatric intensive care unit and on mechanical ventilation. However, there was no correlation between procalcitonin elevation and systemic infection.\n"
-          ]
-        }
-      ],
-      "source": [
-        "question = \"Do high levels of procalcitonin in the early phase after pediatric liver transplantation indicate poor postoperative outcome?\"\n",
-        "\n",
-        "response = rag_pipeline.run(\n",
-        "    {\"query_embedder\": {\"text\": question}, \"prompt_builder\": {\"question\": question}, \"answer_builder\": {\"query\": question}}\n",
-        ")\n",
-        "print(response[\"answer_builder\"][\"answers\"][0].data)"
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Bl6Q0PyoDV-E"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82b49fa6b2fb4ddba29f7b10feab6b2a",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "## Evaluate the Pipeline\n",
-        "\n",
-        "For this tutorial, let's evaluate the pipeline with the following metrics:\n",
-        "\n",
-        "- [Document Mean Reciprocal Rank](https://docs.haystack.deepset.ai/docs/documentmrrevaluator): Evaluates retrieved documents using ground truth labels. It checks at what rank ground truth documents appear in the list of retrieved documents.\n",
-        "- [Semantic Answer Similarity](https://docs.haystack.deepset.ai/docs/sasevaluator): Evaluates predicted answers using ground truth labels. It checks the semantic similarity of a predicted answer and the ground truth answer using a fine-tuned language model.\n",
-        "- [Faithfulness](https://docs.haystack.deepset.ai/docs/faithfulnessevaluator): Uses an LLM to evaluate whether a generated answer can be inferred from the provided contexts. Does not require ground truth labels.\n",
-        "\n",
-        "\n",
-        "Firt, let's actually run our RAG pipeline with a set of questions, and make sure we have the ground truth labels (both answers and documents) for these questions. Let's start with 25 random questions and labels 👇\n",
-        "\n",
-        "> 📝 **Some Notes:**\n",
-        "> 1. For a full list of available metrics, check out the [Haystack Evaluators](https://docs.haystack.deepset.ai/docs/evaluators).\n",
-        "> 2. In our dataset, for each example question, we have 1 ground truth document as labels. However, in some scenarios more than 1 ground truth document may be provided as labels. You will notice that this is why we provide a list of `ground_truth_documents` for each question."
+      "text/plain": [
+       "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "id": "qgOwnuQLMKLk"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54cba43df68b4b22bd77a259a2b730a1",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "\n",
-        "questions, ground_truth_answers, ground_truth_docs = zip(*random.sample(list(zip(all_questions, all_ground_truth_answers, all_documents)), 25))\n"
+      "text/plain": [
+       "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'document_writer': {'documents_written': 1000}}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import List\n",
+    "from haystack import Pipeline\n",
+    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+    "from haystack.components.writers import DocumentWriter\n",
+    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+    "from haystack.document_stores.types import DuplicatePolicy\n",
+    "\n",
+    "document_store = InMemoryDocumentStore()\n",
+    "\n",
+    "document_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+    "document_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP)\n",
+    "\n",
+    "indexing = Pipeline()\n",
+    "indexing.add_component(instance=document_embedder, name=\"document_embedder\")\n",
+    "indexing.add_component(instance=document_writer, name=\"document_writer\")\n",
+    "\n",
+    "indexing.connect(\"document_embedder.documents\", \"document_writer.documents\")\n",
+    "\n",
+    "indexing.run({\"document_embedder\": {\"documents\": all_documents}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XvLVaFHTO-qb"
+   },
+   "source": [
+    "Now that we have our data ready, we can create a simple RAG pipeline.\n",
+    "\n",
+    "In this example, we'll be using:\n",
+    "- [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever) which will get the relevant documents to the query.\n",
+    "- [`OpenAIGenerator`](https://docs.haystack.deepset.ai/docs/OpenAIGenerator) to generate answers to queries. You can replace `OpenAIGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/docs/generators)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-uo-6fjiO-qb",
+    "outputId": "12f00bd6-fc05-40fd-db96-429658039c32"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter OpenAI API key:··········\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<haystack.core.pipeline.pipeline.Pipeline object at 0x7b698ec37d60>\n",
+       "🚅 Components\n",
+       "  - query_embedder: SentenceTransformersTextEmbedder\n",
+       "  - retriever: InMemoryEmbeddingRetriever\n",
+       "  - prompt_builder: PromptBuilder\n",
+       "  - generator: OpenAIGenerator\n",
+       "  - answer_builder: AnswerBuilder\n",
+       "🛤️ Connections\n",
+       "  - query_embedder.embedding -> retriever.query_embedding (List[float])\n",
+       "  - retriever.documents -> prompt_builder.documents (List[Document])\n",
+       "  - retriever.documents -> answer_builder.documents (List[Document])\n",
+       "  - prompt_builder.prompt -> generator.prompt (str)\n",
+       "  - generator.replies -> answer_builder.replies (List[str])\n",
+       "  - generator.meta -> answer_builder.meta (List[Dict[str, Any]])"
       ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "from haystack.components.builders import AnswerBuilder, PromptBuilder\n",
+    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+    "from haystack.components.generators import OpenAIGenerator\n",
+    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+    "\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
+    "\n",
+    "template = \"\"\"\n",
+    "        You have to answer the following question based on the given context information only.\n",
+    "\n",
+    "        Context:\n",
+    "        {% for document in documents %}\n",
+    "            {{ document.content }}\n",
+    "        {% endfor %}\n",
+    "\n",
+    "        Question: {{question}}\n",
+    "        Answer:\n",
+    "        \"\"\"\n",
+    "\n",
+    "rag_pipeline = Pipeline()\n",
+    "rag_pipeline.add_component(\n",
+    "    \"query_embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+    ")\n",
+    "rag_pipeline.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store, top_k=3))\n",
+    "rag_pipeline.add_component(\"prompt_builder\", PromptBuilder(template=template))\n",
+    "rag_pipeline.add_component(\"generator\", OpenAIGenerator(model=\"gpt-3.5-turbo\"))\n",
+    "rag_pipeline.add_component(\"answer_builder\", AnswerBuilder())\n",
+    "\n",
+    "rag_pipeline.connect(\"query_embedder\", \"retriever.query_embedding\")\n",
+    "rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
+    "rag_pipeline.connect(\"prompt_builder\", \"generator\")\n",
+    "rag_pipeline.connect(\"generator.replies\", \"answer_builder.replies\")\n",
+    "rag_pipeline.connect(\"generator.meta\", \"answer_builder.meta\")\n",
+    "rag_pipeline.connect(\"retriever\", \"answer_builder.documents\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DBAyF5tVO-qc"
+   },
+   "source": [
+    "### Asking a Question\n",
+    "\n",
+    "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to all components that require it as input. In this case these are the `query_embedder`, the `prompt_builder` and the `answer_builder`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 86,
+     "referenced_widgets": [
+      "f92db6040c414987acb2e855b5efe29a",
+      "a5e5e17fa90941c8820424bcb8a64461",
+      "f42b858a75334567b6e21d4491d8bf9a",
+      "2451c140c32f46f68fa59d949b198c81",
+      "927fd891c86f4098bb4b32717231d547",
+      "997e1beb4a714368b583b812698db5bd",
+      "e8419341d2c94b2c879671184da63da1",
+      "5f56cc1fdb13480fae8838f613b79d9e",
+      "d2b53a8ababf4744beca8bae4abea6ca",
+      "076a7c8e3a7747ec928a0d5853e92e88",
+      "298f01846fd4442cbcea6149a74c979d"
+     ]
     },
+    "id": "Vnt283M5O-qc",
+    "outputId": "4c8f1c3e-d8c0-4d1c-d336-09df4b70544d"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6SO8oX7mMaGC"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f92db6040c414987acb2e855b5efe29a",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "Next, let's run our pipeline and make sure to track what our pipeline returns as answers, and which documents it retrieves:"
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Yes, high levels of procalcitonin in the early phase after pediatric liver transplantation indicated a poor postoperative outcome. Patients with high procalcitonin levels on postoperative day 2 had higher International Normalized Ratio values, suffered more often from primary graft non-function, had longer stays in the pediatric intensive care unit and on mechanical ventilation. However, there was no correlation between procalcitonin elevation and systemic infection.\n"
+     ]
+    }
+   ],
+   "source": [
+    "question = \"Do high levels of procalcitonin in the early phase after pediatric liver transplantation indicate poor postoperative outcome?\"\n",
+    "\n",
+    "response = rag_pipeline.run(\n",
+    "    {\n",
+    "        \"query_embedder\": {\"text\": question},\n",
+    "        \"prompt_builder\": {\"question\": question},\n",
+    "        \"answer_builder\": {\"query\": question},\n",
+    "    }\n",
+    ")\n",
+    "print(response[\"answer_builder\"][\"answers\"][0].data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Bl6Q0PyoDV-E"
+   },
+   "source": [
+    "## Evaluate the Pipeline\n",
+    "\n",
+    "For this tutorial, let's evaluate the pipeline with the following metrics:\n",
+    "\n",
+    "- [Document Mean Reciprocal Rank](https://docs.haystack.deepset.ai/docs/documentmrrevaluator): Evaluates retrieved documents using ground truth labels. It checks at what rank ground truth documents appear in the list of retrieved documents.\n",
+    "- [Semantic Answer Similarity](https://docs.haystack.deepset.ai/docs/sasevaluator): Evaluates predicted answers using ground truth labels. It checks the semantic similarity of a predicted answer and the ground truth answer using a fine-tuned language model.\n",
+    "- [Faithfulness](https://docs.haystack.deepset.ai/docs/faithfulnessevaluator): Uses an LLM to evaluate whether a generated answer can be inferred from the provided contexts. Does not require ground truth labels.\n",
+    "\n",
+    "\n",
+    "Firt, let's actually run our RAG pipeline with a set of questions, and make sure we have the ground truth labels (both answers and documents) for these questions. Let's start with 25 random questions and labels 👇\n",
+    "\n",
+    "> 📝 **Some Notes:**\n",
+    "> 1. For a full list of available metrics, check out the [Haystack Evaluators](https://docs.haystack.deepset.ai/docs/evaluators).\n",
+    "> 2. In our dataset, for each example question, we have 1 ground truth document as labels. However, in some scenarios more than 1 ground truth document may be provided as labels. You will notice that this is why we provide a list of `ground_truth_documents` for each question."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "qgOwnuQLMKLk"
+   },
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "questions, ground_truth_answers, ground_truth_docs = zip(\n",
+    "    *random.sample(list(zip(all_questions, all_ground_truth_answers, all_documents)), 25)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6SO8oX7mMaGC"
+   },
+   "source": [
+    "Next, let's run our pipeline and make sure to track what our pipeline returns as answers, and which documents it retrieves:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
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     },
+    "id": "SknPWiKQMZpy",
+    "outputId": "8b06916b-bffe-498b-9d03-e4831d97aa40"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
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-      "outputs": [
-        {
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-          "output_type": "display_data"
-        },
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-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: 's it only what you say , it 's also how you say it : communicating nipah virus prevention messages during an outbreak in Bangladesh?\n",
-            "Answer from pipeline:\n",
-            "During the Nipah virus outbreak in Bangladesh, it was not only important to convey prevention messages but also how they were communicated. Field anthropologists played a crucial role in bridging the gap between biomedical explanations and local beliefs about the outbreak. Through interactive sessions with residents and using photos to illustrate how the virus could be transmitted, they were able to successfully convey the message. Prior to this intervention, residents believed in supernatural causes and continued risky behaviors like consuming raw date palm sap. However, after the intervention, residents understood the importance of abstaining from such practices and adopting safer behaviors. This shows that the manner in which prevention messages are communicated can greatly impact their effectiveness during an outbreak.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Does relieving dyspnoea by non-invasive ventilation decrease pain thresholds in amyotrophic lateral sclerosis?\n",
-            "Answer from pipeline:\n",
-            "Yes, relieving dyspnoea by non-invasive ventilation decreases pain thresholds in amyotrophic lateral sclerosis (ALS) patients. The pressure pain thresholds measured in the deltoid muscle during unassisted breathing decreased significantly by a median of 24.5%-33.0% of baseline during non-invasive ventilation at 30 and 60 minutes (NIV30 and NIV60) in ALS patients.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
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-          "output_type": "display_data"
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-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Is patient satisfaction biased by renovations to the interior of a primary care office : a pretest-posttest assessment?\n",
-            "Answer from pipeline:\n",
-            "Based on the information provided, patient satisfaction is not biased by renovations to the interior of a primary care office. The study conducted a pretest-posttest assessment and found that patient satisfaction was higher for all domains after the office was renovated, with statistical significance. Additionally, the results did not change when potential confounders were included in the analysis. Therefore, it can be concluded that patient satisfaction was genuinely influenced by the interior redesign of the primary care office.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
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-              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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-          },
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Is cD30 expression a novel prognostic indicator in extranodal natural killer/T-cell lymphoma , nasal type?\n",
-            "Answer from pipeline:\n",
-            "Based on the provided context information, CD30 expression is not a novel prognostic indicator in extranodal natural killer/T-cell lymphoma, nasal type. The study found that CD30 expression was significantly correlated with certain clinical features, treatment response, and prognosis in ENKTL patients. CD30 positivity was associated with shorter 5-year overall survival and progression-free survival rates in specific patient groups. Additionally, CD30 expression was identified as an independent prognostic factor for overall survival and progression-free survival in a multivariate Cox regression model. Therefore, while CD30 expression is a significant factor in predicting the prognosis of ENKTL patients, it is not considered a novel prognostic indicator.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Is obesity associated with increased postoperative complications after operative management of proximal humerus fractures?\n",
-            "Answer from pipeline:\n",
-            "Yes, according to the first context provided, obesity was associated with a substantial increase in local and systemic complications following operative management of proximal humerus fractures.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Does deep Sequencing the microRNA profile in rhabdomyosarcoma reveal down-regulation of miR-378 family members?\n",
-            "Answer from pipeline:\n",
-            "Yes, deep sequencing of the microRNA profile in rhabdomyosarcoma (RMS) revealed the down-regulation of miR-378 family members in RMS tumour tissue and cell lines.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        },
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-              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Is dorsal plication without degloving safe and effective for correcting ventral penile deformities?\n",
-            "Answer from pipeline:\n",
-            "Based on the context information provided, dorsal plication without degloving was not specifically mentioned as a method for correcting ventral penile deformities. The study focused on comparing the safety and efficacy of patients undergoing dorsal penile plication, ventral plication, and lateral plication. Therefore, based on the information provided, it is not clear whether dorsal plication without degloving is safe and effective for correcting ventral penile deformities.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Does mental fatigue affect maximal anaerobic exercise performance?\n",
-            "Answer from pipeline:\n",
-            "Based on the given context information, it can be concluded that mental fatigue does not affect maximal anaerobic exercise performance. The study mentioned in the context found no difference in any performance or physiological variable between participants who were mentally fatigued and those who were not. Therefore, mental fatigue does not seem to have a significant impact on maximal anaerobic exercise performance.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        },
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Are women using bleach for home cleaning at increased risk of non-allergic asthma?\n",
-            "Answer from pipeline:\n",
-            "Yes, women using bleach for home cleaning are at an increased risk of non-allergic asthma. The study showed that bleach use was significantly associated with non-allergic asthma, particularly non-allergic adult-onset asthma. Women using bleach frequently were more likely to have current asthma compared to non-users, and there were positive associations found between bleach use and bronchial hyperresponsiveness, asthma-like symptoms, and chronic cough among women without allergic sensitization.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        },
-        {
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-              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Does trichostatin A inhibit Retinal Pigmented Epithelium Activation in an In Vitro Model of Proliferative Vitreoretinopathy?\n",
-            "Answer from pipeline:\n",
-            "Yes, trichostatin A inhibits Retinal Pigmented Epithelium Activation in an In Vitro Model of Proliferative Vitreoretinopathy as shown in the study where it was observed that cells treated with transforming growth factor beta 2 (TGFβ2) alone or in the presence of trichostatin A showed inhibited contraction and migration of RPE cells, indicating a role of acetylation in RPE activation and progression of Proliferative Vitreoretinopathy.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        },
-        {
-          "data": {
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Are vitamin D levels and bone turnover markers related to non-alcoholic fatty liver disease in severely obese patients?\n",
-            "Answer from pipeline:\n",
-            "Based on the first context provided, the study concluded that there was no association between liver histology and levels of vitamin D or bone turnover parameters in severely obese patients. Therefore, vitamin D levels and bone turnover markers were not found to be related to non-alcoholic fatty liver disease in this specific group of patients.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        },
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-              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Does alcohol disrupt levels and function of the cystic fibrosis transmembrane conductance regulator to promote development of pancreatitis?\n",
-            "Answer from pipeline:\n",
-            "Yes, alcohol disrupts levels and function of the cystic fibrosis transmembrane conductance regulator (CFTR) to promote the development of pancreatitis. Studies have shown that alcohol inhibits CFTR activity in pancreatic ductal epithelial cells, reduces CFTR expression and stability, and disrupts CFTR folding, leading to lower levels of CFTR in pancreatic tissues from patients with acute or chronic pancreatitis induced by alcohol. Additionally, CFTR knockout mice given ethanol developed more severe pancreatitis than mice not given ethanol.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        },
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Do genome-wide ancestry patterns in Rapanui suggest pre-European admixture with Native Americans?\n",
-            "Answer from pipeline:\n",
-            "Yes, genome-wide ancestry patterns in Rapanui suggest pre-European admixture with Native Americans, as evidenced by statistical support for Native American admixture dating to AD 1280-1495.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Is termination of Nociceptive Bahaviour at the End of Phase 2 of Formalin Test Attributable to Endogenous Inhibitory Mechanisms , but not by Opioid Receptors Activation?\n",
-            "Answer from pipeline:\n",
-            "Yes, termination of nociceptive behavior at the end of phase 2 of the Formalin test appears to be attributable to endogenous inhibitory mechanisms rather than opioid receptors activation. This is supported by the observation that naloxone, a non-selective antagonist of opioid receptors, decreased nociception in phase 2A but had no effect on the delayed termination of the Formalin test. Additionally, the study specifically investigated active inhibitory mechanisms that lead to termination of nociceptive response in phase II, suggesting that other mechanisms besides opioid receptors may be involved.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        },
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Is real-time three-dimensional transesophageal echocardiography useful for percutaneous closure of multiple secundum atrial septal defects?\n",
-            "Answer from pipeline:\n",
-            "Yes, real-time three-dimensional transesophageal echocardiography (RT-3D-TEE) was found to be useful for percutaneous closure of multiple secundum atrial septal defects in the study described in the context information. It was used to clarify the diagnosis, determine the operation scheme, monitor and guide the operation during the procedure, and evaluate the result shortly after the procedure.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Does thalidomide control adipose tissue inflammation associated with high-fat diet-induced obesity in mice?\n",
-            "Answer from pipeline:\n",
-            "Yes, thalidomide has been shown to control adipose tissue inflammation associated with high-fat diet-induced obesity in mice. Thalidomide administration in obese mice resulted in a reduction in adiposity, decreased production of pro-inflammatory adipokines such as tumor necrosis factor-α (TNF-α), leptin, and monocyte chemoattractant protein-1 (MCP-1) in adipose tissue, reduced macrophage infiltration, and inhibition of c-Jun N-terminal kinase (JNK) activation. Additionally, thalidomide treatment lowered TNF-α and leptin serum levels in obese mice and inhibited the release of TNF-α and MCP-1 in adipocytes.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-          "output_type": "display_data"
-        },
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-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Does puerarin inhibit the inflammatory response in atherosclerosis via modulation of the NF-κB pathway in a rabbit model?\n",
-            "Answer from pipeline:\n",
-            "Yes, puerarin inhibits the inflammatory response in atherosclerosis via modulation of the NF-κB pathway in a rabbit model. The study found that puerarin reduced the protein and mRNA levels of adhesion molecules (AMs) in the rabbit model of atherosclerosis. It was also noted that the reduced AM levels were due to inhibition of the phosphorylation and degradation of inhibitor-κB (I-κB), resulting in reduced p65 NF-κB nuclear translocation. This indicates that puerarin has a modulatory effect on the NF-κB pathway, which plays a crucial role in the inflammatory response in atherosclerosis.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Is serum free 1,25-dihydroxy-vitamin D more closely associated with fibroblast growth factor 23 than other vitamin D forms in chronic dialysis patients?\n",
-            "Answer from pipeline:\n",
-            "Yes, according to the information provided in the context, serum free 1,25-dihydroxy-vitamin D was found to outweigh all other vitamin D forms regarding its association with fibroblast growth factor 23 (FGF-23) in chronic dialysis patients, as indicated by a p-value of 0.03 in the regression analysis.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Do a critical analysis of secondary overtriage to a Level I trauma center?\n",
-            "Answer from pipeline:\n",
-            "Secondary overtriage to a Level I trauma center refers to the transfer of trauma patients to a higher-level trauma center who do not require the specialized resources and care available at that level of facility. In the context provided, the study analyzed the incidence and pattern of secondary overtriage to a Level I trauma center by assessing trauma patients transferred and discharged within 24 hours of admission.\n",
-            "\n",
-            "The study found that 24% of transferred trauma patients were discharged within 24 hours of admission, indicating a significant proportion of patients who may not have required the level of care provided at the Level I trauma center. The most common reasons for referral were extremity fractures, head injuries, and soft tissue injuries, which are conditions that may not always necessitate treatment at a higher-level trauma center.\n",
-            "\n",
-            "Furthermore, the majority of patients discharged within 24 hours required only observation or minor procedures, such as fracture management and wound care, which could potentially have been managed at a lower-level trauma center or non-trauma hospital. The study also highlighted the average interfacility transfer distance and hospital charges per transfer, indicating the potential resource utilization associated with secondary overtriage.\n",
-            "\n",
-            "Overall, the findings suggest that there may be a significant incidence of secondary overtriage to Level I trauma centers, leading to unnecessary utilization of resources and potentially impacting the efficiency and capacity of these specialized facilities. Further evaluation and protocols may be needed to determine appropriate triage criteria and optimize the transfer of trauma patients to the most suitable level of care based on their needs.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        },
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Is methylation of the FGFR2 gene associated with high birth weight centile in humans?\n",
-            "Answer from pipeline:\n",
-            "Yes, methylation of the FGFR2 gene is significantly associated with high birth weight centile in humans (p = 0.004-0.027).\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Do two decades of British newspaper coverage regarding attempt cardiopulmonary resuscitation decisions : Lessons for clinicians?\n",
-            "Answer from pipeline:\n",
-            "Yes, the two decades of British newspaper coverage regarding Do Not Attempt Cardiopulmonary Resuscitation (DNACPR) decisions provide important lessons for clinicians. The coverage highlights the need for adequate patient and family consultation when making DNACPR decisions, as well as the importance of avoiding ageism and discrimination against the disabled in these decisions. Additionally, the association of DNACPR decisions with euthanasia and patients receiving CPR against their wishes should be taken into consideration by clinicians. These lessons can help clinicians make more informed and ethical decisions regarding CPR in the future.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Are phospholipase C epsilon 1 ( PLCE1 ) haplotypes associated with increased risk of gastric cancer in Kashmir Valley?\n",
-            "Answer from pipeline:\n",
-            "Yes, the PLCE1 haplotypes (A2274223C3765524T7922612, G2274223C3765524T7922612, and G2274223T3765524C7922612) were found to be associated with an increased risk of gastric cancer in patients from Kashmir Valley. The frequencies of these haplotypes were higher in patients compared to controls and conferred a high risk for gastric cancer.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        },
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-              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Are reclassification rates higher among African American men than Caucasians on active surveillance?\n",
-            "Answer from pipeline:\n",
-            "Yes, reclassification rates are higher among African American men than Caucasians on active surveillance. The study found that African American men on active surveillance were more likely to experience upgrading on serial biopsy compared to Caucasians (36% vs 16%). Adjusting for various factors, African American race was an independent predictor of biopsy reclassification.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
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-              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Does health indicators associated with fall among middle-aged and older women enrolled in an evidence-based program?\n",
-            "Answer from pipeline:\n",
-            "No, the context information provided focuses on older women participating in a fall prevention program, not middle-aged women. The study examines the relationship between older female participants' baseline health status and self-reported falls during the fall prevention interventions.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        },
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-              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: Do maternal and childhood psychological factors predict chronic disabling fatigue at age 13 years?\n",
-            "Answer from pipeline:\n",
-            "Yes, maternal and childhood psychological factors do predict chronic disabling fatigue at age 13 years. Maternal anxiety, maternal depression, child psychological problems, and upsetting life events were all associated with chronic disabling fatigue in children at age 13 years in the Avon Longitudinal Study of Parents and Children birth cohort. Specifically, maternal anxiety and depression, as well as child psychological problems and upsetting events, were all found to be risk factors for chronic disabling fatigue at age 13 years.\n",
-            "\n",
-            "-----------------------------------\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "rag_answers = []\n",
-        "retrieved_docs = []\n",
-        "\n",
-        "for question in list(questions):\n",
-        "  response = rag_pipeline.run({\"query_embedder\": {\"text\": question},\n",
-        "                              \"prompt_builder\": {\"question\": question},\n",
-        "                              \"answer_builder\": {\"query\": question}})\n",
-        "  print(f\"Question: {question}\")\n",
-        "  print(\"Answer from pipeline:\")\n",
-        "  print(response[\"answer_builder\"][\"answers\"][0].data)\n",
-        "  print(\"\\n-----------------------------------\\n\")\n",
-        "\n",
-        "  rag_answers.append(response[\"answer_builder\"][\"answers\"][0].data)\n",
-        "  retrieved_docs.append(response[\"answer_builder\"][\"answers\"][0].documents)"
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: 's it only what you say , it 's also how you say it : communicating nipah virus prevention messages during an outbreak in Bangladesh?\n",
+      "Answer from pipeline:\n",
+      "During the Nipah virus outbreak in Bangladesh, it was not only important to convey prevention messages but also how they were communicated. Field anthropologists played a crucial role in bridging the gap between biomedical explanations and local beliefs about the outbreak. Through interactive sessions with residents and using photos to illustrate how the virus could be transmitted, they were able to successfully convey the message. Prior to this intervention, residents believed in supernatural causes and continued risky behaviors like consuming raw date palm sap. However, after the intervention, residents understood the importance of abstaining from such practices and adopting safer behaviors. This shows that the manner in which prevention messages are communicated can greatly impact their effectiveness during an outbreak.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dZSvAl9fZAQ1"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78fe7cd499bb461088be730ba13fc50d",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "While each evaluator is a component that can be run individually in Haystack, they can also be added into a pipeline. This way, we can construct an `eval_pipeline` that includes all evaluators for the metrics we want to evaluate our pipeline on."
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "id": "5Z4to-q3OxSV"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Does relieving dyspnoea by non-invasive ventilation decrease pain thresholds in amyotrophic lateral sclerosis?\n",
+      "Answer from pipeline:\n",
+      "Yes, relieving dyspnoea by non-invasive ventilation decreases pain thresholds in amyotrophic lateral sclerosis (ALS) patients. The pressure pain thresholds measured in the deltoid muscle during unassisted breathing decreased significantly by a median of 24.5%-33.0% of baseline during non-invasive ventilation at 30 and 60 minutes (NIV30 and NIV60) in ALS patients.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f44587fa7ba24a98bfd3c321ee63a931",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "from haystack.components.evaluators.document_mrr import DocumentMRREvaluator\n",
-        "from haystack.components.evaluators.faithfulness import FaithfulnessEvaluator\n",
-        "from haystack.components.evaluators.sas_evaluator import SASEvaluator\n",
-        "\n",
-        "eval_pipeline = Pipeline()\n",
-        "eval_pipeline.add_component(\"doc_mrr_evaluator\", DocumentMRREvaluator())\n",
-        "eval_pipeline.add_component(\"faithfulness\", FaithfulnessEvaluator())\n",
-        "eval_pipeline.add_component(\"sas_evaluator\", SASEvaluator(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
-        "\n",
-        "results = eval_pipeline.run({\n",
-        "    \"doc_mrr_evaluator\": {\"ground_truth_documents\": list([d] for d in ground_truth_docs) , \"retrieved_documents\": retrieved_docs},\n",
-        "    \"faithfulness\": {\"questions\": list(questions), \"contexts\": list([d.content] for d in ground_truth_docs), \"predicted_answers\": rag_answers},\n",
-        "    \"sas_evaluator\": {\"predicted_answers\": rag_answers, \"ground_truth_answers\": list(ground_truth_answers)}\n",
-        "})"
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Is patient satisfaction biased by renovations to the interior of a primary care office : a pretest-posttest assessment?\n",
+      "Answer from pipeline:\n",
+      "Based on the information provided, patient satisfaction is not biased by renovations to the interior of a primary care office. The study conducted a pretest-posttest assessment and found that patient satisfaction was higher for all domains after the office was renovated, with statistical significance. Additionally, the results did not change when potential confounders were included in the analysis. Therefore, it can be concluded that patient satisfaction was genuinely influenced by the interior redesign of the primary care office.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0rIL855wZa6C"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c94e26dd87544b5f960fe1eb06621616",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "### Constructing an Evaluation Report\n",
-        "\n",
-        "Once we've run our evaluation pipeline, we can also create a full evaluation report. Haystac provides an `EvaluationRunResult` which we can use to display a `score_report` 👇"
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 143
-        },
-        "id": "ZO5jzX7uQi0i",
-        "outputId": "cb3f6554-47f9-47f9-ce2d-a7b6aeda2236"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Is cD30 expression a novel prognostic indicator in extranodal natural killer/T-cell lymphoma , nasal type?\n",
+      "Answer from pipeline:\n",
+      "Based on the provided context information, CD30 expression is not a novel prognostic indicator in extranodal natural killer/T-cell lymphoma, nasal type. The study found that CD30 expression was significantly correlated with certain clinical features, treatment response, and prognosis in ENKTL patients. CD30 positivity was associated with shorter 5-year overall survival and progression-free survival rates in specific patient groups. Additionally, CD30 expression was identified as an independent prognostic factor for overall survival and progression-free survival in a multivariate Cox regression model. Therefore, while CD30 expression is a significant factor in predicting the prognosis of ENKTL patients, it is not considered a novel prognostic indicator.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "474e5bcffcb84de78c4ec36fc7ba16f2",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "summary": "{\n  \"name\": \"evaluation_result\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16276986554268258,\n        \"min\": 0.718074322938919,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.718074322938919,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-              "type": "dataframe"
-            },
-            "text/html": [
-              "\n",
-              "  <div id=\"df-39810713-7480-4309-ab03-552b6f692b45\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>score</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>doc_mrr_evaluator</th>\n",
-              "      <td>1.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>faithfulness</th>\n",
-              "      <td>1.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>sas_evaluator</th>\n",
-              "      <td>0.718074</td>\n",
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-              "  </tbody>\n",
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-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
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-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
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-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-39810713-7480-4309-ab03-552b6f692b45 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-39810713-7480-4309-ab03-552b6f692b45');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-12ef0863-1683-4398-8475-a534dd06b97b\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-12ef0863-1683-4398-8475-a534dd06b97b')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
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-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-12ef0863-1683-4398-8475-a534dd06b97b button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "                      score\n",
-              "doc_mrr_evaluator  1.000000\n",
-              "faithfulness       1.000000\n",
-              "sas_evaluator      0.718074"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from haystack.evaluation.eval_run_result import EvaluationRunResult\n",
-        "\n",
-        "inputs= {\n",
-        "        \"question\": list(questions),\n",
-        "        \"contexts\": list([d.content] for d in ground_truth_docs),\n",
-        "        \"answer\": list(ground_truth_answers),\n",
-        "        \"predicted_answer\": rag_answers,\n",
-        "        }\n",
-        "\n",
-        "evaluation_result = EvaluationRunResult(run_name=\"pubmed_rag_pipeline\", inputs=inputs, results=results)\n",
-        "evaluation_result.score_report()"
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Is obesity associated with increased postoperative complications after operative management of proximal humerus fractures?\n",
+      "Answer from pipeline:\n",
+      "Yes, according to the first context provided, obesity was associated with a substantial increase in local and systemic complications following operative management of proximal humerus fractures.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "q8rvbj5rZsW9"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2165726ba2c4900bc76a2ee93dd9acb",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "#### Extra: Convert the Report into a Pandas DataFrame\n",
-        "\n",
-        "In addition, you can display your evaluation results as a pandas dataframe 👇"
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Does deep Sequencing the microRNA profile in rhabdomyosarcoma reveal down-regulation of miR-378 family members?\n",
+      "Answer from pipeline:\n",
+      "Yes, deep sequencing of the microRNA profile in rhabdomyosarcoma (RMS) revealed the down-regulation of miR-378 family members in RMS tumour tissue and cell lines.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "P0hxWyTMTsbq",
-        "outputId": "3e5693dc-10a3-4ad5-a630-f2da0085db7d"
+     "data": {
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Based on the cultural context, interactive communication strategies in lay language with supporting evidence can make biomedical prevention messages credible in affected communities, even among those who initially invoke supernatural causal explanations.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Yes, women using bleach for home cleaning are at an increased risk of non-allergic asthma. The study showed that bleach use was significantly associated with non-allergic asthma, particularly non-allergic adult-onset asthma. Women using bleach frequently were more likely to have current asthma compared to non-users, and there were positive associations found between bleach use and bronchial hyperresponsiveness, asthma-like symptoms, and chronic cough among women without allergic sensitization.\",\n          \"Yes, puerarin inhibits the inflammatory response in atherosclerosis via modulation of the NF-\\u03baB pathway in a rabbit model. The study found that puerarin reduced the protein and mRNA levels of adhesion molecules (AMs) in the rabbit model of atherosclerosis. It was also noted that the reduced AM levels were due to inhibition of the phosphorylation and degradation of inhibitor-\\u03baB (I-\\u03baB), resulting in reduced p65 NF-\\u03baB nuclear translocation. This indicates that puerarin has a modulatory effect on the NF-\\u03baB pathway, which plays a crucial role in the inflammatory response in atherosclerosis.\",\n          \"During the Nipah virus outbreak in Bangladesh, it was not only important to convey prevention messages but also how they were communicated. Field anthropologists played a crucial role in bridging the gap between biomedical explanations and local beliefs about the outbreak. Through interactive sessions with residents and using photos to illustrate how the virus could be transmitted, they were able to successfully convey the message. Prior to this intervention, residents believed in supernatural causes and continued risky behaviors like consuming raw date palm sap. However, after the intervention, residents understood the importance of abstaining from such practices and adopting safer behaviors. 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-              "      <th>0</th>\n",
-              "      <td>'s it only what you say , it 's also how you s...</td>\n",
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-              "      <td>Does relieving dyspnoea by non-invasive ventil...</td>\n",
-              "      <td>[Dyspnoea is a threatening sensation of respir...</td>\n",
-              "      <td>Relieving dyspnoea by NIV in patients with ALS...</td>\n",
-              "      <td>Yes, relieving dyspnoea by non-invasive ventil...</td>\n",
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-              "      <th>2</th>\n",
-              "      <td>Is patient satisfaction biased by renovations ...</td>\n",
-              "      <td>[Measuring quality of care is essential to imp...</td>\n",
-              "      <td>Renovating the interior of a primary care offi...</td>\n",
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-              "      <td>Is cD30 expression a novel prognostic indicato...</td>\n",
-              "      <td>[Extranodal natural killer/T-cell lymphoma, na...</td>\n",
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-              "      <td>[Obesity has become a significant public healt...</td>\n",
-              "      <td>Obesity and its resultant medical comorbiditie...</td>\n",
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-              "      <td>Does deep Sequencing the microRNA profile in r...</td>\n",
-              "      <td>[Rhabdomyosarcoma (RMS) is a highly malignant ...</td>\n",
-              "      <td>MiR-378a-3p may function as a tumour suppresso...</td>\n",
-              "      <td>Yes, deep sequencing of the microRNA profile i...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.661563</td>\n",
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-              "      <th>6</th>\n",
-              "      <td>Is dorsal plication without degloving safe and...</td>\n",
-              "      <td>[To compare the safety and efficacy of patient...</td>\n",
-              "      <td>Penile plication is a safe and effective techn...</td>\n",
-              "      <td>Based on the context information provided, dor...</td>\n",
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-              "      <th>7</th>\n",
-              "      <td>Does mental fatigue affect maximal anaerobic e...</td>\n",
-              "      <td>[Mental fatigue can negatively impact on subma...</td>\n",
-              "      <td>Near identical responses in performance and ph...</td>\n",
-              "      <td>Based on the given context information, it can...</td>\n",
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-              "      <th>8</th>\n",
-              "      <td>Are women using bleach for home cleaning at in...</td>\n",
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-              "      <td>Frequent use of bleach for home-cleaning is as...</td>\n",
-              "      <td>Yes, women using bleach for home cleaning are ...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.899928</td>\n",
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-              "      <th>9</th>\n",
-              "      <td>Does trichostatin A inhibit Retinal Pigmented ...</td>\n",
-              "      <td>[Proliferative vitreoretinopathy (PVR) is a bl...</td>\n",
-              "      <td>Our findings indicate a role of acetylation in...</td>\n",
-              "      <td>Yes, trichostatin A inhibits Retinal Pigmented...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.466138</td>\n",
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-              "      <th>10</th>\n",
-              "      <td>Are vitamin D levels and bone turnover markers...</td>\n",
-              "      <td>[Morbidly obese patients usually present vitam...</td>\n",
-              "      <td>Low levels of vitamin D or hyperparathyroidism...</td>\n",
-              "      <td>Based on the first context provided, the study...</td>\n",
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-              "      <th>11</th>\n",
-              "      <td>Does alcohol disrupt levels and function of th...</td>\n",
-              "      <td>[Excessive consumption of ethanol is one of th...</td>\n",
-              "      <td>Based on studies of human, mouse, and guinea p...</td>\n",
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-              "      <td>1.0</td>\n",
-              "      <td>0.517162</td>\n",
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-              "      <td>Is termination of Nociceptive Bahaviour at the...</td>\n",
-              "      <td>[Formalin injection induces nociceptive bahavi...</td>\n",
-              "      <td>The results of this study suggest the existenc...</td>\n",
-              "      <td>Yes, termination of nociceptive behavior at th...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.901174</td>\n",
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-              "      <th>14</th>\n",
-              "      <td>Is real-time three-dimensional transesophageal...</td>\n",
-              "      <td>[The purpose of this study was to investigate ...</td>\n",
-              "      <td>RT-3D-TEE provides reliable diagnostic evidenc...</td>\n",
-              "      <td>Yes, real-time three-dimensional transesophage...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.596325</td>\n",
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-              "      <th>15</th>\n",
-              "      <td>Does thalidomide control adipose tissue inflam...</td>\n",
-              "      <td>[Immunosuppressant agents modulate the activit...</td>\n",
-              "      <td>Our results suggest that drugs that can modula...</td>\n",
-              "      <td>Yes, thalidomide has been shown to control adi...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.692221</td>\n",
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-              "      <th>16</th>\n",
-              "      <td>Does puerarin inhibit the inflammatory respons...</td>\n",
-              "      <td>[The isoflavone puerarin [7-hydroxy-3-(4-hydro...</td>\n",
-              "      <td>This study indicates that the effect of puerar...</td>\n",
-              "      <td>Yes, puerarin inhibits the inflammatory respon...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.894604</td>\n",
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-              "    <tr>\n",
-              "      <th>17</th>\n",
-              "      <td>Is serum free 1,25-dihydroxy-vitamin D more cl...</td>\n",
-              "      <td>[Mineral bone disorder (MBD) is prevalent amon...</td>\n",
-              "      <td>The relationship between FGF-23 and vitamin D ...</td>\n",
-              "      <td>Yes, according to the information provided in ...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.730452</td>\n",
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-              "    <tr>\n",
-              "      <th>18</th>\n",
-              "      <td>Do a critical analysis of secondary overtriage...</td>\n",
-              "      <td>[Trauma centers often receive transfers from l...</td>\n",
-              "      <td>A significant number of patients transferred t...</td>\n",
-              "      <td>Secondary overtriage to a Level I trauma cente...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.709596</td>\n",
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-              "    <tr>\n",
-              "      <th>19</th>\n",
-              "      <td>Is methylation of the FGFR2 gene associated wi...</td>\n",
-              "      <td>[This study examined links between DNA methyla...</td>\n",
-              "      <td>We identified a novel biologically plausible c...</td>\n",
-              "      <td>Yes, methylation of the FGFR2 gene is signific...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.490618</td>\n",
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-              "      <th>20</th>\n",
-              "      <td>Do two decades of British newspaper coverage r...</td>\n",
-              "      <td>[To review UK newspaper reports relating to Do...</td>\n",
-              "      <td>Regarding DNACPR decision-making, the predomin...</td>\n",
-              "      <td>Yes, the two decades of British newspaper cove...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.608133</td>\n",
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-              "      <th>21</th>\n",
-              "      <td>Are phospholipase C epsilon 1 ( PLCE1 ) haplot...</td>\n",
-              "      <td>[Phospholipase C epsilon 1 (PLCE1) plays a cru...</td>\n",
-              "      <td>These results suggest that variation in PLCE1 ...</td>\n",
-              "      <td>Yes, the PLCE1 haplotypes (A2274223C3765524T79...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.613439</td>\n",
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-              "      <th>22</th>\n",
-              "      <td>Are reclassification rates higher among Africa...</td>\n",
-              "      <td>[To evaluate the risk of reclassification on s...</td>\n",
-              "      <td>AA men with VLR prostate cancer followed on AS...</td>\n",
-              "      <td>Yes, reclassification rates are higher among A...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.573658</td>\n",
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-              "      <th>23</th>\n",
-              "      <td>Does health indicators associated with fall am...</td>\n",
-              "      <td>[Evidence-based fall prevention programs prima...</td>\n",
-              "      <td>Findings have implications for identifying at-...</td>\n",
-              "      <td>No, the context information provided focuses o...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.572017</td>\n",
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-              "    <tr>\n",
-              "      <th>24</th>\n",
-              "      <td>Do maternal and childhood psychological factor...</td>\n",
-              "      <td>[To investigate whether premorbid maternal and...</td>\n",
-              "      <td>Pediatricians need to be aware that children w...</td>\n",
-              "      <td>Yes, maternal and childhood psychological fact...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.788478</td>\n",
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-            "text/plain": [
-              "                                             question  \\\n",
-              "0   's it only what you say , it 's also how you s...   \n",
-              "1   Does relieving dyspnoea by non-invasive ventil...   \n",
-              "2   Is patient satisfaction biased by renovations ...   \n",
-              "3   Is cD30 expression a novel prognostic indicato...   \n",
-              "4   Is obesity associated with increased postopera...   \n",
-              "5   Does deep Sequencing the microRNA profile in r...   \n",
-              "6   Is dorsal plication without degloving safe and...   \n",
-              "7   Does mental fatigue affect maximal anaerobic e...   \n",
-              "8   Are women using bleach for home cleaning at in...   \n",
-              "9   Does trichostatin A inhibit Retinal Pigmented ...   \n",
-              "10  Are vitamin D levels and bone turnover markers...   \n",
-              "11  Does alcohol disrupt levels and function of th...   \n",
-              "12  Do genome-wide ancestry patterns in Rapanui su...   \n",
-              "13  Is termination of Nociceptive Bahaviour at the...   \n",
-              "14  Is real-time three-dimensional transesophageal...   \n",
-              "15  Does thalidomide control adipose tissue inflam...   \n",
-              "16  Does puerarin inhibit the inflammatory respons...   \n",
-              "17  Is serum free 1,25-dihydroxy-vitamin D more cl...   \n",
-              "18  Do a critical analysis of secondary overtriage...   \n",
-              "19  Is methylation of the FGFR2 gene associated wi...   \n",
-              "20  Do two decades of British newspaper coverage r...   \n",
-              "21  Are phospholipase C epsilon 1 ( PLCE1 ) haplot...   \n",
-              "22  Are reclassification rates higher among Africa...   \n",
-              "23  Does health indicators associated with fall am...   \n",
-              "24  Do maternal and childhood psychological factor...   \n",
-              "\n",
-              "                                             contexts  \\\n",
-              "0   [During a fatal Nipah virus (NiV) outbreak in ...   \n",
-              "1   [Dyspnoea is a threatening sensation of respir...   \n",
-              "2   [Measuring quality of care is essential to imp...   \n",
-              "3   [Extranodal natural killer/T-cell lymphoma, na...   \n",
-              "4   [Obesity has become a significant public healt...   \n",
-              "5   [Rhabdomyosarcoma (RMS) is a highly malignant ...   \n",
-              "6   [To compare the safety and efficacy of patient...   \n",
-              "7   [Mental fatigue can negatively impact on subma...   \n",
-              "8   [Bleach is widely used for household cleaning....   \n",
-              "9   [Proliferative vitreoretinopathy (PVR) is a bl...   \n",
-              "10  [Morbidly obese patients usually present vitam...   \n",
-              "11  [Excessive consumption of ethanol is one of th...   \n",
-              "12  [Rapa Nui (Easter Island), located in the east...   \n",
-              "13  [Formalin injection induces nociceptive bahavi...   \n",
-              "14  [The purpose of this study was to investigate ...   \n",
-              "15  [Immunosuppressant agents modulate the activit...   \n",
-              "16  [The isoflavone puerarin [7-hydroxy-3-(4-hydro...   \n",
-              "17  [Mineral bone disorder (MBD) is prevalent amon...   \n",
-              "18  [Trauma centers often receive transfers from l...   \n",
-              "19  [This study examined links between DNA methyla...   \n",
-              "20  [To review UK newspaper reports relating to Do...   \n",
-              "21  [Phospholipase C epsilon 1 (PLCE1) plays a cru...   \n",
-              "22  [To evaluate the risk of reclassification on s...   \n",
-              "23  [Evidence-based fall prevention programs prima...   \n",
-              "24  [To investigate whether premorbid maternal and...   \n",
-              "\n",
-              "                                               answer  \\\n",
-              "0   During outbreaks, one-way behaviour change com...   \n",
-              "1   Relieving dyspnoea by NIV in patients with ALS...   \n",
-              "2   Renovating the interior of a primary care offi...   \n",
-              "3   Our results showed that expression of CD30 was...   \n",
-              "4   Obesity and its resultant medical comorbiditie...   \n",
-              "5   MiR-378a-3p may function as a tumour suppresso...   \n",
-              "6   Penile plication is a safe and effective techn...   \n",
-              "7   Near identical responses in performance and ph...   \n",
-              "8   Frequent use of bleach for home-cleaning is as...   \n",
-              "9   Our findings indicate a role of acetylation in...   \n",
-              "10  Low levels of vitamin D or hyperparathyroidism...   \n",
-              "11  Based on studies of human, mouse, and guinea p...   \n",
-              "12  These genetic results can be explained by one ...   \n",
-              "13  The results of this study suggest the existenc...   \n",
-              "14  RT-3D-TEE provides reliable diagnostic evidenc...   \n",
-              "15  Our results suggest that drugs that can modula...   \n",
-              "16  This study indicates that the effect of puerar...   \n",
-              "17  The relationship between FGF-23 and vitamin D ...   \n",
-              "18  A significant number of patients transferred t...   \n",
-              "19  We identified a novel biologically plausible c...   \n",
-              "20  Regarding DNACPR decision-making, the predomin...   \n",
-              "21  These results suggest that variation in PLCE1 ...   \n",
-              "22  AA men with VLR prostate cancer followed on AS...   \n",
-              "23  Findings have implications for identifying at-...   \n",
-              "24  Pediatricians need to be aware that children w...   \n",
-              "\n",
-              "                                     predicted_answer  doc_mrr_evaluator  \\\n",
-              "0   During the Nipah virus outbreak in Bangladesh,...                1.0   \n",
-              "1   Yes, relieving dyspnoea by non-invasive ventil...                1.0   \n",
-              "2   Based on the information provided, patient sat...                1.0   \n",
-              "3   Based on the provided context information, CD3...                1.0   \n",
-              "4   Yes, according to the first context provided, ...                1.0   \n",
-              "5   Yes, deep sequencing of the microRNA profile i...                1.0   \n",
-              "6   Based on the context information provided, dor...                1.0   \n",
-              "7   Based on the given context information, it can...                1.0   \n",
-              "8   Yes, women using bleach for home cleaning are ...                1.0   \n",
-              "9   Yes, trichostatin A inhibits Retinal Pigmented...                1.0   \n",
-              "10  Based on the first context provided, the study...                1.0   \n",
-              "11  Yes, alcohol disrupts levels and function of t...                1.0   \n",
-              "12  Yes, genome-wide ancestry patterns in Rapanui ...                1.0   \n",
-              "13  Yes, termination of nociceptive behavior at th...                1.0   \n",
-              "14  Yes, real-time three-dimensional transesophage...                1.0   \n",
-              "15  Yes, thalidomide has been shown to control adi...                1.0   \n",
-              "16  Yes, puerarin inhibits the inflammatory respon...                1.0   \n",
-              "17  Yes, according to the information provided in ...                1.0   \n",
-              "18  Secondary overtriage to a Level I trauma cente...                1.0   \n",
-              "19  Yes, methylation of the FGFR2 gene is signific...                1.0   \n",
-              "20  Yes, the two decades of British newspaper cove...                1.0   \n",
-              "21  Yes, the PLCE1 haplotypes (A2274223C3765524T79...                1.0   \n",
-              "22  Yes, reclassification rates are higher among A...                1.0   \n",
-              "23  No, the context information provided focuses o...                1.0   \n",
-              "24  Yes, maternal and childhood psychological fact...                1.0   \n",
-              "\n",
-              "    faithfulness  sas_evaluator  \n",
-              "0            1.0       0.688929  \n",
-              "1            1.0       0.811266  \n",
-              "2            1.0       0.849888  \n",
-              "3            1.0       0.775011  \n",
-              "4            1.0       0.845495  \n",
-              "5            1.0       0.661563  \n",
-              "6            1.0       0.804615  \n",
-              "7            1.0       0.849995  \n",
-              "8            1.0       0.899928  \n",
-              "9            1.0       0.466138  \n",
-              "10           1.0       0.747388  \n",
-              "11           1.0       0.863766  \n",
-              "12           1.0       0.517162  \n",
-              "13           1.0       0.901174  \n",
-              "14           1.0       0.596325  \n",
-              "15           1.0       0.692221  \n",
-              "16           1.0       0.894604  \n",
-              "17           1.0       0.730452  \n",
-              "18           1.0       0.709596  \n",
-              "19           1.0       0.490618  \n",
-              "20           1.0       0.608133  \n",
-              "21           1.0       0.613439  \n",
-              "22           1.0       0.573658  \n",
-              "23           1.0       0.572017  \n",
-              "24           1.0       0.788478  "
-            ]
-          },
-          "execution_count": 18,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "results_df = evaluation_result.to_pandas()\n",
-        "results_df"
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TidpbS5NwuIA"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Is dorsal plication without degloving safe and effective for correcting ventral penile deformities?\n",
+      "Answer from pipeline:\n",
+      "Based on the context information provided, dorsal plication without degloving was not specifically mentioned as a method for correcting ventral penile deformities. The study focused on comparing the safety and efficacy of patients undergoing dorsal penile plication, ventral plication, and lateral plication. Therefore, based on the information provided, it is not clear whether dorsal plication without degloving is safe and effective for correcting ventral penile deformities.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3de895bf14f4161b084e1546477b4eb",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "Having our evaluation results as a dataframe can be quite useful. For example, below we can use the pandas dataframe to filter the results to the top 3 best scores for semantic answer similarity (`sas_evaluator`) as well as the bottom 3 👇\n"
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Does mental fatigue affect maximal anaerobic exercise performance?\n",
+      "Answer from pipeline:\n",
+      "Based on the given context information, it can be concluded that mental fatigue does not affect maximal anaerobic exercise performance. The study mentioned in the context found no difference in any performance or physiological variable between participants who were mentally fatigued and those who were not. Therefore, mental fatigue does not seem to have a significant impact on maximal anaerobic exercise performance.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 341
-        },
-        "id": "d6PuFgcnwt1i",
-        "outputId": "0dbe0769-f2d9-43e5-8cbb-a0ceea6c8d55"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b07451ecd6d945469905b9b73a1b931f",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Is termination of Nociceptive Bahaviour at the End of Phase 2 of Formalin Test Attributable to Endogenous Inhibitory Mechanisms , but not by Opioid Receptors Activation?\",\n          \"Are women using bleach for home cleaning at increased risk of non-allergic asthma?\",\n          \"Do genome-wide ancestry patterns in Rapanui suggest pre-European admixture with Native Americans?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contexts\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"The results of this study suggest the existence of an active inhibitory mechanism, other than the endogenous opioids, that is responsible for termination of nociceptive behaviour at the end of formalin test.\",\n          \"Frequent use of bleach for home-cleaning is associated with non-allergic adult-onset asthma, elevated neutrophil counts and lower-airway symptoms in women.\",\n          \"These genetic results can be explained by one or more pre-European trans-Pacific contacts.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Yes, termination of nociceptive behavior at the end of phase 2 of the Formalin test appears to be attributable to endogenous inhibitory mechanisms rather than opioid receptors activation. This is supported by the observation that naloxone, a non-selective antagonist of opioid receptors, decreased nociception in phase 2A but had no effect on the delayed termination of the Formalin test. Additionally, the study specifically investigated active inhibitory mechanisms that lead to termination of nociceptive response in phase II, suggesting that other mechanisms besides opioid receptors may be involved.\",\n          \"Yes, women using bleach for home cleaning are at an increased risk of non-allergic asthma. The study showed that bleach use was significantly associated with non-allergic asthma, particularly non-allergic adult-onset asthma. Women using bleach frequently were more likely to have current asthma compared to non-users, and there were positive associations found between bleach use and bronchial hyperresponsiveness, asthma-like symptoms, and chronic cough among women without allergic sensitization.\",\n          \"Yes, genome-wide ancestry patterns in Rapanui suggest pre-European admixture with Native Americans, as evidenced by statistical support for Native American admixture dating to AD 1280-1495.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"doc_mrr_evaluator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sas_evaluator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22366097741438715,\n        \"min\": 0.46613821387290955,\n        \"max\": 0.9011739492416382,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9011739492416382\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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-              "\n",
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-              "    <div>\n",
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-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
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-              "      <th></th>\n",
-              "      <th>question</th>\n",
-              "      <th>contexts</th>\n",
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-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>16</th>\n",
-              "      <td>Does puerarin inhibit the inflammatory respons...</td>\n",
-              "      <td>[The isoflavone puerarin [7-hydroxy-3-(4-hydro...</td>\n",
-              "      <td>This study indicates that the effect of puerar...</td>\n",
-              "      <td>Yes, puerarin inhibits the inflammatory respon...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.894604</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9</th>\n",
-              "      <td>Does trichostatin A inhibit Retinal Pigmented ...</td>\n",
-              "      <td>[Proliferative vitreoretinopathy (PVR) is a bl...</td>\n",
-              "      <td>Our findings indicate a role of acetylation in...</td>\n",
-              "      <td>Yes, trichostatin A inhibits Retinal Pigmented...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.466138</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>19</th>\n",
-              "      <td>Is methylation of the FGFR2 gene associated wi...</td>\n",
-              "      <td>[This study examined links between DNA methyla...</td>\n",
-              "      <td>We identified a novel biologically plausible c...</td>\n",
-              "      <td>Yes, methylation of the FGFR2 gene is signific...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.490618</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>12</th>\n",
-              "      <td>Do genome-wide ancestry patterns in Rapanui su...</td>\n",
-              "      <td>[Rapa Nui (Easter Island), located in the east...</td>\n",
-              "      <td>These genetic results can be explained by one ...</td>\n",
-              "      <td>Yes, genome-wide ancestry patterns in Rapanui ...</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>0.517162</td>\n",
-              "    </tr>\n",
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-              "                                                    [key], {});\n",
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-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
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-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "                                             question  \\\n",
-              "13  Is termination of Nociceptive Bahaviour at the...   \n",
-              "8   Are women using bleach for home cleaning at in...   \n",
-              "16  Does puerarin inhibit the inflammatory respons...   \n",
-              "9   Does trichostatin A inhibit Retinal Pigmented ...   \n",
-              "19  Is methylation of the FGFR2 gene associated wi...   \n",
-              "12  Do genome-wide ancestry patterns in Rapanui su...   \n",
-              "\n",
-              "                                             contexts  \\\n",
-              "13  [Formalin injection induces nociceptive bahavi...   \n",
-              "8   [Bleach is widely used for household cleaning....   \n",
-              "16  [The isoflavone puerarin [7-hydroxy-3-(4-hydro...   \n",
-              "9   [Proliferative vitreoretinopathy (PVR) is a bl...   \n",
-              "19  [This study examined links between DNA methyla...   \n",
-              "12  [Rapa Nui (Easter Island), located in the east...   \n",
-              "\n",
-              "                                               answer  \\\n",
-              "13  The results of this study suggest the existenc...   \n",
-              "8   Frequent use of bleach for home-cleaning is as...   \n",
-              "16  This study indicates that the effect of puerar...   \n",
-              "9   Our findings indicate a role of acetylation in...   \n",
-              "19  We identified a novel biologically plausible c...   \n",
-              "12  These genetic results can be explained by one ...   \n",
-              "\n",
-              "                                     predicted_answer  doc_mrr_evaluator  \\\n",
-              "13  Yes, termination of nociceptive behavior at th...                1.0   \n",
-              "8   Yes, women using bleach for home cleaning are ...                1.0   \n",
-              "16  Yes, puerarin inhibits the inflammatory respon...                1.0   \n",
-              "9   Yes, trichostatin A inhibits Retinal Pigmented...                1.0   \n",
-              "19  Yes, methylation of the FGFR2 gene is signific...                1.0   \n",
-              "12  Yes, genome-wide ancestry patterns in Rapanui ...                1.0   \n",
-              "\n",
-              "    faithfulness  sas_evaluator  \n",
-              "13           1.0       0.901174  \n",
-              "8            1.0       0.899928  \n",
-              "16           1.0       0.894604  \n",
-              "9            1.0       0.466138  \n",
-              "19           1.0       0.490618  \n",
-              "12           1.0       0.517162  "
-            ]
-          },
-          "execution_count": 19,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "top_3 = results_df.nlargest(3, 'sas_evaluator')\n",
-        "bottom_3 = results_df.nsmallest(3, 'sas_evaluator')\n",
-        "pd.concat([top_3, bottom_3])"
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Are women using bleach for home cleaning at increased risk of non-allergic asthma?\n",
+      "Answer from pipeline:\n",
+      "Yes, women using bleach for home cleaning are at an increased risk of non-allergic asthma. The study showed that bleach use was significantly associated with non-allergic asthma, particularly non-allergic adult-onset asthma. Women using bleach frequently were more likely to have current asthma compared to non-users, and there were positive associations found between bleach use and bronchial hyperresponsiveness, asthma-like symptoms, and chronic cough among women without allergic sensitization.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87610ad009084c1191c57b028f6bdd6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Does trichostatin A inhibit Retinal Pigmented Epithelium Activation in an In Vitro Model of Proliferative Vitreoretinopathy?\n",
+      "Answer from pipeline:\n",
+      "Yes, trichostatin A inhibits Retinal Pigmented Epithelium Activation in an In Vitro Model of Proliferative Vitreoretinopathy as shown in the study where it was observed that cells treated with transforming growth factor beta 2 (TGFβ2) alone or in the presence of trichostatin A showed inhibited contraction and migration of RPE cells, indicating a role of acetylation in RPE activation and progression of Proliferative Vitreoretinopathy.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d947864ef3a46ea8b95a89dbfe1244a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Are vitamin D levels and bone turnover markers related to non-alcoholic fatty liver disease in severely obese patients?\n",
+      "Answer from pipeline:\n",
+      "Based on the first context provided, the study concluded that there was no association between liver histology and levels of vitamin D or bone turnover parameters in severely obese patients. Therefore, vitamin D levels and bone turnover markers were not found to be related to non-alcoholic fatty liver disease in this specific group of patients.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab4bff71c4224b20b78d0bd20648e723",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Does alcohol disrupt levels and function of the cystic fibrosis transmembrane conductance regulator to promote development of pancreatitis?\n",
+      "Answer from pipeline:\n",
+      "Yes, alcohol disrupts levels and function of the cystic fibrosis transmembrane conductance regulator (CFTR) to promote the development of pancreatitis. Studies have shown that alcohol inhibits CFTR activity in pancreatic ductal epithelial cells, reduces CFTR expression and stability, and disrupts CFTR folding, leading to lower levels of CFTR in pancreatic tissues from patients with acute or chronic pancreatitis induced by alcohol. Additionally, CFTR knockout mice given ethanol developed more severe pancreatitis than mice not given ethanol.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7cee12908a374d0d8de02d8cf4954d61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Do genome-wide ancestry patterns in Rapanui suggest pre-European admixture with Native Americans?\n",
+      "Answer from pipeline:\n",
+      "Yes, genome-wide ancestry patterns in Rapanui suggest pre-European admixture with Native Americans, as evidenced by statistical support for Native American admixture dating to AD 1280-1495.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2df0f6a05652407caedd1be9d3091db3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Is termination of Nociceptive Bahaviour at the End of Phase 2 of Formalin Test Attributable to Endogenous Inhibitory Mechanisms , but not by Opioid Receptors Activation?\n",
+      "Answer from pipeline:\n",
+      "Yes, termination of nociceptive behavior at the end of phase 2 of the Formalin test appears to be attributable to endogenous inhibitory mechanisms rather than opioid receptors activation. This is supported by the observation that naloxone, a non-selective antagonist of opioid receptors, decreased nociception in phase 2A but had no effect on the delayed termination of the Formalin test. Additionally, the study specifically investigated active inhibitory mechanisms that lead to termination of nociceptive response in phase II, suggesting that other mechanisms besides opioid receptors may be involved.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b87f7df83e974a109bb3ac5e638063b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Is real-time three-dimensional transesophageal echocardiography useful for percutaneous closure of multiple secundum atrial septal defects?\n",
+      "Answer from pipeline:\n",
+      "Yes, real-time three-dimensional transesophageal echocardiography (RT-3D-TEE) was found to be useful for percutaneous closure of multiple secundum atrial septal defects in the study described in the context information. It was used to clarify the diagnosis, determine the operation scheme, monitor and guide the operation during the procedure, and evaluate the result shortly after the procedure.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3311fa8553b4e8592e0a52cac8f7ca4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Does thalidomide control adipose tissue inflammation associated with high-fat diet-induced obesity in mice?\n",
+      "Answer from pipeline:\n",
+      "Yes, thalidomide has been shown to control adipose tissue inflammation associated with high-fat diet-induced obesity in mice. Thalidomide administration in obese mice resulted in a reduction in adiposity, decreased production of pro-inflammatory adipokines such as tumor necrosis factor-α (TNF-α), leptin, and monocyte chemoattractant protein-1 (MCP-1) in adipose tissue, reduced macrophage infiltration, and inhibition of c-Jun N-terminal kinase (JNK) activation. Additionally, thalidomide treatment lowered TNF-α and leptin serum levels in obese mice and inhibited the release of TNF-α and MCP-1 in adipocytes.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17da0b1a782f4499b2f8e3e8e35133f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Does puerarin inhibit the inflammatory response in atherosclerosis via modulation of the NF-κB pathway in a rabbit model?\n",
+      "Answer from pipeline:\n",
+      "Yes, puerarin inhibits the inflammatory response in atherosclerosis via modulation of the NF-κB pathway in a rabbit model. The study found that puerarin reduced the protein and mRNA levels of adhesion molecules (AMs) in the rabbit model of atherosclerosis. It was also noted that the reduced AM levels were due to inhibition of the phosphorylation and degradation of inhibitor-κB (I-κB), resulting in reduced p65 NF-κB nuclear translocation. This indicates that puerarin has a modulatory effect on the NF-κB pathway, which plays a crucial role in the inflammatory response in atherosclerosis.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "660a0c8430f24839ab363446150dcd7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Is serum free 1,25-dihydroxy-vitamin D more closely associated with fibroblast growth factor 23 than other vitamin D forms in chronic dialysis patients?\n",
+      "Answer from pipeline:\n",
+      "Yes, according to the information provided in the context, serum free 1,25-dihydroxy-vitamin D was found to outweigh all other vitamin D forms regarding its association with fibroblast growth factor 23 (FGF-23) in chronic dialysis patients, as indicated by a p-value of 0.03 in the regression analysis.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5d3a873bed04e8cbd1852444fa05030",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Do a critical analysis of secondary overtriage to a Level I trauma center?\n",
+      "Answer from pipeline:\n",
+      "Secondary overtriage to a Level I trauma center refers to the transfer of trauma patients to a higher-level trauma center who do not require the specialized resources and care available at that level of facility. In the context provided, the study analyzed the incidence and pattern of secondary overtriage to a Level I trauma center by assessing trauma patients transferred and discharged within 24 hours of admission.\n",
+      "\n",
+      "The study found that 24% of transferred trauma patients were discharged within 24 hours of admission, indicating a significant proportion of patients who may not have required the level of care provided at the Level I trauma center. The most common reasons for referral were extremity fractures, head injuries, and soft tissue injuries, which are conditions that may not always necessitate treatment at a higher-level trauma center.\n",
+      "\n",
+      "Furthermore, the majority of patients discharged within 24 hours required only observation or minor procedures, such as fracture management and wound care, which could potentially have been managed at a lower-level trauma center or non-trauma hospital. The study also highlighted the average interfacility transfer distance and hospital charges per transfer, indicating the potential resource utilization associated with secondary overtriage.\n",
+      "\n",
+      "Overall, the findings suggest that there may be a significant incidence of secondary overtriage to Level I trauma centers, leading to unnecessary utilization of resources and potentially impacting the efficiency and capacity of these specialized facilities. Further evaluation and protocols may be needed to determine appropriate triage criteria and optimize the transfer of trauma patients to the most suitable level of care based on their needs.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ecbd5b99db641488e4b9b6001c2b1ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Is methylation of the FGFR2 gene associated with high birth weight centile in humans?\n",
+      "Answer from pipeline:\n",
+      "Yes, methylation of the FGFR2 gene is significantly associated with high birth weight centile in humans (p = 0.004-0.027).\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad2e9edf0dc3472d9dc0639b07328638",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Do two decades of British newspaper coverage regarding attempt cardiopulmonary resuscitation decisions : Lessons for clinicians?\n",
+      "Answer from pipeline:\n",
+      "Yes, the two decades of British newspaper coverage regarding Do Not Attempt Cardiopulmonary Resuscitation (DNACPR) decisions provide important lessons for clinicians. The coverage highlights the need for adequate patient and family consultation when making DNACPR decisions, as well as the importance of avoiding ageism and discrimination against the disabled in these decisions. Additionally, the association of DNACPR decisions with euthanasia and patients receiving CPR against their wishes should be taken into consideration by clinicians. These lessons can help clinicians make more informed and ethical decisions regarding CPR in the future.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62da147141b84d56a593db7cf8472831",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Are phospholipase C epsilon 1 ( PLCE1 ) haplotypes associated with increased risk of gastric cancer in Kashmir Valley?\n",
+      "Answer from pipeline:\n",
+      "Yes, the PLCE1 haplotypes (A2274223C3765524T7922612, G2274223C3765524T7922612, and G2274223T3765524C7922612) were found to be associated with an increased risk of gastric cancer in patients from Kashmir Valley. The frequencies of these haplotypes were higher in patients compared to controls and conferred a high risk for gastric cancer.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "29f9b903c4b547edb70ea5e68b845ff0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Are reclassification rates higher among African American men than Caucasians on active surveillance?\n",
+      "Answer from pipeline:\n",
+      "Yes, reclassification rates are higher among African American men than Caucasians on active surveillance. The study found that African American men on active surveillance were more likely to experience upgrading on serial biopsy compared to Caucasians (36% vs 16%). Adjusting for various factors, African American race was an independent predictor of biopsy reclassification.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3e515210b6b4369aaeb267c20ffe456",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Does health indicators associated with fall among middle-aged and older women enrolled in an evidence-based program?\n",
+      "Answer from pipeline:\n",
+      "No, the context information provided focuses on older women participating in a fall prevention program, not middle-aged women. The study examines the relationship between older female participants' baseline health status and self-reported falls during the fall prevention interventions.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14ab52345e1b4db1a442ebda71c61e91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Question: Do maternal and childhood psychological factors predict chronic disabling fatigue at age 13 years?\n",
+      "Answer from pipeline:\n",
+      "Yes, maternal and childhood psychological factors do predict chronic disabling fatigue at age 13 years. Maternal anxiety, maternal depression, child psychological problems, and upsetting life events were all associated with chronic disabling fatigue in children at age 13 years in the Avon Longitudinal Study of Parents and Children birth cohort. Specifically, maternal anxiety and depression, as well as child psychological problems and upsetting events, were all found to be risk factors for chronic disabling fatigue at age 13 years.\n",
+      "\n",
+      "-----------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rag_answers = []\n",
+    "retrieved_docs = []\n",
+    "\n",
+    "for question in list(questions):\n",
+    "    response = rag_pipeline.run(\n",
+    "        {\n",
+    "            \"query_embedder\": {\"text\": question},\n",
+    "            \"prompt_builder\": {\"question\": question},\n",
+    "            \"answer_builder\": {\"query\": question},\n",
+    "        }\n",
+    "    )\n",
+    "    print(f\"Question: {question}\")\n",
+    "    print(\"Answer from pipeline:\")\n",
+    "    print(response[\"answer_builder\"][\"answers\"][0].data)\n",
+    "    print(\"\\n-----------------------------------\\n\")\n",
+    "\n",
+    "    rag_answers.append(response[\"answer_builder\"][\"answers\"][0].data)\n",
+    "    retrieved_docs.append(response[\"answer_builder\"][\"answers\"][0].documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dZSvAl9fZAQ1"
+   },
+   "source": [
+    "While each evaluator is a component that can be run individually in Haystack, they can also be added into a pipeline. This way, we can construct an `eval_pipeline` that includes all evaluators for the metrics we want to evaluate our pipeline on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "5Z4to-q3OxSV"
+   },
+   "outputs": [],
+   "source": [
+    "from haystack.components.evaluators.document_mrr import DocumentMRREvaluator\n",
+    "from haystack.components.evaluators.faithfulness import FaithfulnessEvaluator\n",
+    "from haystack.components.evaluators.sas_evaluator import SASEvaluator\n",
+    "\n",
+    "eval_pipeline = Pipeline()\n",
+    "eval_pipeline.add_component(\"doc_mrr_evaluator\", DocumentMRREvaluator())\n",
+    "eval_pipeline.add_component(\"faithfulness\", FaithfulnessEvaluator())\n",
+    "eval_pipeline.add_component(\"sas_evaluator\", SASEvaluator(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+    "\n",
+    "results = eval_pipeline.run(\n",
+    "    {\n",
+    "        \"doc_mrr_evaluator\": {\n",
+    "            \"ground_truth_documents\": list([d] for d in ground_truth_docs),\n",
+    "            \"retrieved_documents\": retrieved_docs,\n",
+    "        },\n",
+    "        \"faithfulness\": {\n",
+    "            \"questions\": list(questions),\n",
+    "            \"contexts\": list([d.content] for d in ground_truth_docs),\n",
+    "            \"predicted_answers\": rag_answers,\n",
+    "        },\n",
+    "        \"sas_evaluator\": {\"predicted_answers\": rag_answers, \"ground_truth_answers\": list(ground_truth_answers)},\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0rIL855wZa6C"
+   },
+   "source": [
+    "### Constructing an Evaluation Report\n",
+    "\n",
+    "Once we've run our evaluation pipeline, we can also create a full evaluation report. Haystac provides an `EvaluationRunResult` which we can use to display a `score_report` 👇"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 143
     },
+    "id": "ZO5jzX7uQi0i",
+    "outputId": "cb3f6554-47f9-47f9-ce2d-a7b6aeda2236"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "XueCK3y4O-qc"
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"evaluation_result\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16276986554268258,\n        \"min\": 0.718074322938919,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.718074322938919,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe"
       },
-      "source": [
-        "## What's next\n",
-        "\n",
-        "🎉 Congratulations! You've learned how to evaluate a RAG pipeline with model-based evaluation frameworks and without any labeling efforts.\n",
-        "\n",
-        "If you liked this tutorial, you may also enjoy:\n",
-        "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
-        "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
-        "\n",
-        "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates). Thanks for reading!"
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+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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+       "          + ' to learn more about interactive tables.';\n",
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+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
+       "    }\n",
+       "    20% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    30% {\n",
+       "      border-color: transparent;\n",
+       "      border-left-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    40% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
+       "    60% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-12ef0863-1683-4398-8475-a534dd06b97b button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "text/plain": [
+       "                      score\n",
+       "doc_mrr_evaluator  1.000000\n",
+       "faithfulness       1.000000\n",
+       "sas_evaluator      0.718074"
       ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
+   ],
+   "source": [
+    "from haystack.evaluation.eval_run_result import EvaluationRunResult\n",
+    "\n",
+    "inputs = {\n",
+    "    \"question\": list(questions),\n",
+    "    \"contexts\": list([d.content] for d in ground_truth_docs),\n",
+    "    \"answer\": list(ground_truth_answers),\n",
+    "    \"predicted_answer\": rag_answers,\n",
+    "}\n",
+    "\n",
+    "evaluation_result = EvaluationRunResult(run_name=\"pubmed_rag_pipeline\", inputs=inputs, results=results)\n",
+    "evaluation_result.score_report()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "q8rvbj5rZsW9"
+   },
+   "source": [
+    "#### Extra: Convert the Report into a Pandas DataFrame\n",
+    "\n",
+    "In addition, you can display your evaluation results as a pandas dataframe 👇"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
     "colab": {
-      "gpuType": "T4",
-      "provenance": []
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
+    "id": "P0hxWyTMTsbq",
+    "outputId": "3e5693dc-10a3-4ad5-a630-f2da0085db7d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 25,\n  \"fields\": [\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Are women using bleach for home cleaning at increased risk of non-allergic asthma?\",\n          \"Does puerarin inhibit the inflammatory response in atherosclerosis via modulation of the NF-\\u03baB pathway in a rabbit model?\",\n          \"'s it only what you say , it 's also how you say it : communicating nipah virus prevention messages during an outbreak in Bangladesh?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contexts\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Frequent use of bleach for home-cleaning is associated with non-allergic adult-onset asthma, elevated neutrophil counts and lower-airway symptoms in women.\",\n          \"This study indicates that the effect of puerarin on the suppression of atherosclerosis was connected with an inhibited inflammatory response and reduced NF-\\u03baB activation.\",\n          \"During outbreaks, one-way behaviour change communication without meaningful causal explanations is unlikely to be effective. Based on the cultural context, interactive communication strategies in lay language with supporting evidence can make biomedical prevention messages credible in affected communities, even among those who initially invoke supernatural causal explanations.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Yes, women using bleach for home cleaning are at an increased risk of non-allergic asthma. The study showed that bleach use was significantly associated with non-allergic asthma, particularly non-allergic adult-onset asthma. Women using bleach frequently were more likely to have current asthma compared to non-users, and there were positive associations found between bleach use and bronchial hyperresponsiveness, asthma-like symptoms, and chronic cough among women without allergic sensitization.\",\n          \"Yes, puerarin inhibits the inflammatory response in atherosclerosis via modulation of the NF-\\u03baB pathway in a rabbit model. The study found that puerarin reduced the protein and mRNA levels of adhesion molecules (AMs) in the rabbit model of atherosclerosis. It was also noted that the reduced AM levels were due to inhibition of the phosphorylation and degradation of inhibitor-\\u03baB (I-\\u03baB), resulting in reduced p65 NF-\\u03baB nuclear translocation. This indicates that puerarin has a modulatory effect on the NF-\\u03baB pathway, which plays a crucial role in the inflammatory response in atherosclerosis.\",\n          \"During the Nipah virus outbreak in Bangladesh, it was not only important to convey prevention messages but also how they were communicated. Field anthropologists played a crucial role in bridging the gap between biomedical explanations and local beliefs about the outbreak. Through interactive sessions with residents and using photos to illustrate how the virus could be transmitted, they were able to successfully convey the message. Prior to this intervention, residents believed in supernatural causes and continued risky behaviors like consuming raw date palm sap. However, after the intervention, residents understood the importance of abstaining from such practices and adopting safer behaviors. This shows that the manner in which prevention messages are communicated can greatly impact their effectiveness during an outbreak.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"doc_mrr_evaluator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sas_evaluator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13475112832644295,\n        \"min\": 0.46613821387290955,\n        \"max\": 0.9011739492416382,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          0.8999284505844116\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe",
+       "variable_name": "results_df"
+      },
+      "text/html": [
+       "\n",
+       "  <div id=\"df-85224460-cced-4407-9ca9-bc8768dfc591\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>question</th>\n",
+       "      <th>contexts</th>\n",
+       "      <th>answer</th>\n",
+       "      <th>predicted_answer</th>\n",
+       "      <th>doc_mrr_evaluator</th>\n",
+       "      <th>faithfulness</th>\n",
+       "      <th>sas_evaluator</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>'s it only what you say , it 's also how you s...</td>\n",
+       "      <td>[During a fatal Nipah virus (NiV) outbreak in ...</td>\n",
+       "      <td>During outbreaks, one-way behaviour change com...</td>\n",
+       "      <td>During the Nipah virus outbreak in Bangladesh,...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.688929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Does relieving dyspnoea by non-invasive ventil...</td>\n",
+       "      <td>[Dyspnoea is a threatening sensation of respir...</td>\n",
+       "      <td>Relieving dyspnoea by NIV in patients with ALS...</td>\n",
+       "      <td>Yes, relieving dyspnoea by non-invasive ventil...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.811266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Is patient satisfaction biased by renovations ...</td>\n",
+       "      <td>[Measuring quality of care is essential to imp...</td>\n",
+       "      <td>Renovating the interior of a primary care offi...</td>\n",
+       "      <td>Based on the information provided, patient sat...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.849888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Is cD30 expression a novel prognostic indicato...</td>\n",
+       "      <td>[Extranodal natural killer/T-cell lymphoma, na...</td>\n",
+       "      <td>Our results showed that expression of CD30 was...</td>\n",
+       "      <td>Based on the provided context information, CD3...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.775011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Is obesity associated with increased postopera...</td>\n",
+       "      <td>[Obesity has become a significant public healt...</td>\n",
+       "      <td>Obesity and its resultant medical comorbiditie...</td>\n",
+       "      <td>Yes, according to the first context provided, ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.845495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Does deep Sequencing the microRNA profile in r...</td>\n",
+       "      <td>[Rhabdomyosarcoma (RMS) is a highly malignant ...</td>\n",
+       "      <td>MiR-378a-3p may function as a tumour suppresso...</td>\n",
+       "      <td>Yes, deep sequencing of the microRNA profile i...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.661563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Is dorsal plication without degloving safe and...</td>\n",
+       "      <td>[To compare the safety and efficacy of patient...</td>\n",
+       "      <td>Penile plication is a safe and effective techn...</td>\n",
+       "      <td>Based on the context information provided, dor...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.804615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Does mental fatigue affect maximal anaerobic e...</td>\n",
+       "      <td>[Mental fatigue can negatively impact on subma...</td>\n",
+       "      <td>Near identical responses in performance and ph...</td>\n",
+       "      <td>Based on the given context information, it can...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.849995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Are women using bleach for home cleaning at in...</td>\n",
+       "      <td>[Bleach is widely used for household cleaning....</td>\n",
+       "      <td>Frequent use of bleach for home-cleaning is as...</td>\n",
+       "      <td>Yes, women using bleach for home cleaning are ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.899928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Does trichostatin A inhibit Retinal Pigmented ...</td>\n",
+       "      <td>[Proliferative vitreoretinopathy (PVR) is a bl...</td>\n",
+       "      <td>Our findings indicate a role of acetylation in...</td>\n",
+       "      <td>Yes, trichostatin A inhibits Retinal Pigmented...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.466138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Are vitamin D levels and bone turnover markers...</td>\n",
+       "      <td>[Morbidly obese patients usually present vitam...</td>\n",
+       "      <td>Low levels of vitamin D or hyperparathyroidism...</td>\n",
+       "      <td>Based on the first context provided, the study...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.747388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Does alcohol disrupt levels and function of th...</td>\n",
+       "      <td>[Excessive consumption of ethanol is one of th...</td>\n",
+       "      <td>Based on studies of human, mouse, and guinea p...</td>\n",
+       "      <td>Yes, alcohol disrupts levels and function of t...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.863766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Do genome-wide ancestry patterns in Rapanui su...</td>\n",
+       "      <td>[Rapa Nui (Easter Island), located in the east...</td>\n",
+       "      <td>These genetic results can be explained by one ...</td>\n",
+       "      <td>Yes, genome-wide ancestry patterns in Rapanui ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.517162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Is termination of Nociceptive Bahaviour at the...</td>\n",
+       "      <td>[Formalin injection induces nociceptive bahavi...</td>\n",
+       "      <td>The results of this study suggest the existenc...</td>\n",
+       "      <td>Yes, termination of nociceptive behavior at th...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.901174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Is real-time three-dimensional transesophageal...</td>\n",
+       "      <td>[The purpose of this study was to investigate ...</td>\n",
+       "      <td>RT-3D-TEE provides reliable diagnostic evidenc...</td>\n",
+       "      <td>Yes, real-time three-dimensional transesophage...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.596325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Does thalidomide control adipose tissue inflam...</td>\n",
+       "      <td>[Immunosuppressant agents modulate the activit...</td>\n",
+       "      <td>Our results suggest that drugs that can modula...</td>\n",
+       "      <td>Yes, thalidomide has been shown to control adi...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.692221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Does puerarin inhibit the inflammatory respons...</td>\n",
+       "      <td>[The isoflavone puerarin [7-hydroxy-3-(4-hydro...</td>\n",
+       "      <td>This study indicates that the effect of puerar...</td>\n",
+       "      <td>Yes, puerarin inhibits the inflammatory respon...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.894604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Is serum free 1,25-dihydroxy-vitamin D more cl...</td>\n",
+       "      <td>[Mineral bone disorder (MBD) is prevalent amon...</td>\n",
+       "      <td>The relationship between FGF-23 and vitamin D ...</td>\n",
+       "      <td>Yes, according to the information provided in ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.730452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Do a critical analysis of secondary overtriage...</td>\n",
+       "      <td>[Trauma centers often receive transfers from l...</td>\n",
+       "      <td>A significant number of patients transferred t...</td>\n",
+       "      <td>Secondary overtriage to a Level I trauma cente...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.709596</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Is methylation of the FGFR2 gene associated wi...</td>\n",
+       "      <td>[This study examined links between DNA methyla...</td>\n",
+       "      <td>We identified a novel biologically plausible c...</td>\n",
+       "      <td>Yes, methylation of the FGFR2 gene is signific...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.490618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Do two decades of British newspaper coverage r...</td>\n",
+       "      <td>[To review UK newspaper reports relating to Do...</td>\n",
+       "      <td>Regarding DNACPR decision-making, the predomin...</td>\n",
+       "      <td>Yes, the two decades of British newspaper cove...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.608133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Are phospholipase C epsilon 1 ( PLCE1 ) haplot...</td>\n",
+       "      <td>[Phospholipase C epsilon 1 (PLCE1) plays a cru...</td>\n",
+       "      <td>These results suggest that variation in PLCE1 ...</td>\n",
+       "      <td>Yes, the PLCE1 haplotypes (A2274223C3765524T79...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.613439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Are reclassification rates higher among Africa...</td>\n",
+       "      <td>[To evaluate the risk of reclassification on s...</td>\n",
+       "      <td>AA men with VLR prostate cancer followed on AS...</td>\n",
+       "      <td>Yes, reclassification rates are higher among A...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.573658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Does health indicators associated with fall am...</td>\n",
+       "      <td>[Evidence-based fall prevention programs prima...</td>\n",
+       "      <td>Findings have implications for identifying at-...</td>\n",
+       "      <td>No, the context information provided focuses o...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.572017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Do maternal and childhood psychological factor...</td>\n",
+       "      <td>[To investigate whether premorbid maternal and...</td>\n",
+       "      <td>Pediatricians need to be aware that children w...</td>\n",
+       "      <td>Yes, maternal and childhood psychological fact...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.788478</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
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+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85224460-cced-4407-9ca9-bc8768dfc591')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
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+       "  <style>\n",
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+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
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+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-85224460-cced-4407-9ca9-bc8768dfc591');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
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+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
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+       "  </div>\n",
+       "\n",
+       "\n",
+       "<div id=\"df-625a06f7-d411-4ecc-98d2-f1e8cea8fd5c\">\n",
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+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
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+       "  .colab-df-quickchart {\n",
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+       "    width: 32px;\n",
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+       "\n",
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+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
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+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
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+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
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+       "    }\n",
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+       "    60% {\n",
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+       "      border-right-color: var(--fill-color);\n",
+       "    }\n",
+       "    80% {\n",
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+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
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+       "    90% {\n",
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+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
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+       "</style>\n",
+       "\n",
+       "  <script>\n",
+       "    async function quickchart(key) {\n",
+       "      const quickchartButtonEl =\n",
+       "        document.querySelector('#' + key + ' button');\n",
+       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "      try {\n",
+       "        const charts = await google.colab.kernel.invokeFunction(\n",
+       "            'suggestCharts', [key], {});\n",
+       "      } catch (error) {\n",
+       "        console.error('Error during call to suggestCharts:', error);\n",
+       "      }\n",
+       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "    }\n",
+       "    (() => {\n",
+       "      let quickchartButtonEl =\n",
+       "        document.querySelector('#df-625a06f7-d411-4ecc-98d2-f1e8cea8fd5c button');\n",
+       "      quickchartButtonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "    })();\n",
+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "text/plain": [
+       "                                             question  \\\n",
+       "0   's it only what you say , it 's also how you s...   \n",
+       "1   Does relieving dyspnoea by non-invasive ventil...   \n",
+       "2   Is patient satisfaction biased by renovations ...   \n",
+       "3   Is cD30 expression a novel prognostic indicato...   \n",
+       "4   Is obesity associated with increased postopera...   \n",
+       "5   Does deep Sequencing the microRNA profile in r...   \n",
+       "6   Is dorsal plication without degloving safe and...   \n",
+       "7   Does mental fatigue affect maximal anaerobic e...   \n",
+       "8   Are women using bleach for home cleaning at in...   \n",
+       "9   Does trichostatin A inhibit Retinal Pigmented ...   \n",
+       "10  Are vitamin D levels and bone turnover markers...   \n",
+       "11  Does alcohol disrupt levels and function of th...   \n",
+       "12  Do genome-wide ancestry patterns in Rapanui su...   \n",
+       "13  Is termination of Nociceptive Bahaviour at the...   \n",
+       "14  Is real-time three-dimensional transesophageal...   \n",
+       "15  Does thalidomide control adipose tissue inflam...   \n",
+       "16  Does puerarin inhibit the inflammatory respons...   \n",
+       "17  Is serum free 1,25-dihydroxy-vitamin D more cl...   \n",
+       "18  Do a critical analysis of secondary overtriage...   \n",
+       "19  Is methylation of the FGFR2 gene associated wi...   \n",
+       "20  Do two decades of British newspaper coverage r...   \n",
+       "21  Are phospholipase C epsilon 1 ( PLCE1 ) haplot...   \n",
+       "22  Are reclassification rates higher among Africa...   \n",
+       "23  Does health indicators associated with fall am...   \n",
+       "24  Do maternal and childhood psychological factor...   \n",
+       "\n",
+       "                                             contexts  \\\n",
+       "0   [During a fatal Nipah virus (NiV) outbreak in ...   \n",
+       "1   [Dyspnoea is a threatening sensation of respir...   \n",
+       "2   [Measuring quality of care is essential to imp...   \n",
+       "3   [Extranodal natural killer/T-cell lymphoma, na...   \n",
+       "4   [Obesity has become a significant public healt...   \n",
+       "5   [Rhabdomyosarcoma (RMS) is a highly malignant ...   \n",
+       "6   [To compare the safety and efficacy of patient...   \n",
+       "7   [Mental fatigue can negatively impact on subma...   \n",
+       "8   [Bleach is widely used for household cleaning....   \n",
+       "9   [Proliferative vitreoretinopathy (PVR) is a bl...   \n",
+       "10  [Morbidly obese patients usually present vitam...   \n",
+       "11  [Excessive consumption of ethanol is one of th...   \n",
+       "12  [Rapa Nui (Easter Island), located in the east...   \n",
+       "13  [Formalin injection induces nociceptive bahavi...   \n",
+       "14  [The purpose of this study was to investigate ...   \n",
+       "15  [Immunosuppressant agents modulate the activit...   \n",
+       "16  [The isoflavone puerarin [7-hydroxy-3-(4-hydro...   \n",
+       "17  [Mineral bone disorder (MBD) is prevalent amon...   \n",
+       "18  [Trauma centers often receive transfers from l...   \n",
+       "19  [This study examined links between DNA methyla...   \n",
+       "20  [To review UK newspaper reports relating to Do...   \n",
+       "21  [Phospholipase C epsilon 1 (PLCE1) plays a cru...   \n",
+       "22  [To evaluate the risk of reclassification on s...   \n",
+       "23  [Evidence-based fall prevention programs prima...   \n",
+       "24  [To investigate whether premorbid maternal and...   \n",
+       "\n",
+       "                                               answer  \\\n",
+       "0   During outbreaks, one-way behaviour change com...   \n",
+       "1   Relieving dyspnoea by NIV in patients with ALS...   \n",
+       "2   Renovating the interior of a primary care offi...   \n",
+       "3   Our results showed that expression of CD30 was...   \n",
+       "4   Obesity and its resultant medical comorbiditie...   \n",
+       "5   MiR-378a-3p may function as a tumour suppresso...   \n",
+       "6   Penile plication is a safe and effective techn...   \n",
+       "7   Near identical responses in performance and ph...   \n",
+       "8   Frequent use of bleach for home-cleaning is as...   \n",
+       "9   Our findings indicate a role of acetylation in...   \n",
+       "10  Low levels of vitamin D or hyperparathyroidism...   \n",
+       "11  Based on studies of human, mouse, and guinea p...   \n",
+       "12  These genetic results can be explained by one ...   \n",
+       "13  The results of this study suggest the existenc...   \n",
+       "14  RT-3D-TEE provides reliable diagnostic evidenc...   \n",
+       "15  Our results suggest that drugs that can modula...   \n",
+       "16  This study indicates that the effect of puerar...   \n",
+       "17  The relationship between FGF-23 and vitamin D ...   \n",
+       "18  A significant number of patients transferred t...   \n",
+       "19  We identified a novel biologically plausible c...   \n",
+       "20  Regarding DNACPR decision-making, the predomin...   \n",
+       "21  These results suggest that variation in PLCE1 ...   \n",
+       "22  AA men with VLR prostate cancer followed on AS...   \n",
+       "23  Findings have implications for identifying at-...   \n",
+       "24  Pediatricians need to be aware that children w...   \n",
+       "\n",
+       "                                     predicted_answer  doc_mrr_evaluator  \\\n",
+       "0   During the Nipah virus outbreak in Bangladesh,...                1.0   \n",
+       "1   Yes, relieving dyspnoea by non-invasive ventil...                1.0   \n",
+       "2   Based on the information provided, patient sat...                1.0   \n",
+       "3   Based on the provided context information, CD3...                1.0   \n",
+       "4   Yes, according to the first context provided, ...                1.0   \n",
+       "5   Yes, deep sequencing of the microRNA profile i...                1.0   \n",
+       "6   Based on the context information provided, dor...                1.0   \n",
+       "7   Based on the given context information, it can...                1.0   \n",
+       "8   Yes, women using bleach for home cleaning are ...                1.0   \n",
+       "9   Yes, trichostatin A inhibits Retinal Pigmented...                1.0   \n",
+       "10  Based on the first context provided, the study...                1.0   \n",
+       "11  Yes, alcohol disrupts levels and function of t...                1.0   \n",
+       "12  Yes, genome-wide ancestry patterns in Rapanui ...                1.0   \n",
+       "13  Yes, termination of nociceptive behavior at th...                1.0   \n",
+       "14  Yes, real-time three-dimensional transesophage...                1.0   \n",
+       "15  Yes, thalidomide has been shown to control adi...                1.0   \n",
+       "16  Yes, puerarin inhibits the inflammatory respon...                1.0   \n",
+       "17  Yes, according to the information provided in ...                1.0   \n",
+       "18  Secondary overtriage to a Level I trauma cente...                1.0   \n",
+       "19  Yes, methylation of the FGFR2 gene is signific...                1.0   \n",
+       "20  Yes, the two decades of British newspaper cove...                1.0   \n",
+       "21  Yes, the PLCE1 haplotypes (A2274223C3765524T79...                1.0   \n",
+       "22  Yes, reclassification rates are higher among A...                1.0   \n",
+       "23  No, the context information provided focuses o...                1.0   \n",
+       "24  Yes, maternal and childhood psychological fact...                1.0   \n",
+       "\n",
+       "    faithfulness  sas_evaluator  \n",
+       "0            1.0       0.688929  \n",
+       "1            1.0       0.811266  \n",
+       "2            1.0       0.849888  \n",
+       "3            1.0       0.775011  \n",
+       "4            1.0       0.845495  \n",
+       "5            1.0       0.661563  \n",
+       "6            1.0       0.804615  \n",
+       "7            1.0       0.849995  \n",
+       "8            1.0       0.899928  \n",
+       "9            1.0       0.466138  \n",
+       "10           1.0       0.747388  \n",
+       "11           1.0       0.863766  \n",
+       "12           1.0       0.517162  \n",
+       "13           1.0       0.901174  \n",
+       "14           1.0       0.596325  \n",
+       "15           1.0       0.692221  \n",
+       "16           1.0       0.894604  \n",
+       "17           1.0       0.730452  \n",
+       "18           1.0       0.709596  \n",
+       "19           1.0       0.490618  \n",
+       "20           1.0       0.608133  \n",
+       "21           1.0       0.613439  \n",
+       "22           1.0       0.573658  \n",
+       "23           1.0       0.572017  \n",
+       "24           1.0       0.788478  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_df = evaluation_result.to_pandas()\n",
+    "results_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TidpbS5NwuIA"
+   },
+   "source": [
+    "Having our evaluation results as a dataframe can be quite useful. For example, below we can use the pandas dataframe to filter the results to the top 3 best scores for semantic answer similarity (`sas_evaluator`) as well as the bottom 3 👇\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 341
     },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
+    "id": "d6PuFgcnwt1i",
+    "outputId": "0dbe0769-f2d9-43e5-8cbb-a0ceea6c8d55"
+   },
+   "outputs": [
+    {
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+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Does puerarin inhibit the inflammatory respons...</td>\n",
+       "      <td>[The isoflavone puerarin [7-hydroxy-3-(4-hydro...</td>\n",
+       "      <td>This study indicates that the effect of puerar...</td>\n",
+       "      <td>Yes, puerarin inhibits the inflammatory respon...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.894604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Does trichostatin A inhibit Retinal Pigmented ...</td>\n",
+       "      <td>[Proliferative vitreoretinopathy (PVR) is a bl...</td>\n",
+       "      <td>Our findings indicate a role of acetylation in...</td>\n",
+       "      <td>Yes, trichostatin A inhibits Retinal Pigmented...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.466138</td>\n",
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+       "      <td>Yes, methylation of the FGFR2 gene is signific...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.490618</td>\n",
+       "    </tr>\n",
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+       "      <th>12</th>\n",
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+       "      <td>[Rapa Nui (Easter Island), located in the east...</td>\n",
+       "      <td>These genetic results can be explained by one ...</td>\n",
+       "      <td>Yes, genome-wide ancestry patterns in Rapanui ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.517162</td>\n",
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+       "  </script>\n",
+       "</div>\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "text/plain": [
+       "                                             question  \\\n",
+       "13  Is termination of Nociceptive Bahaviour at the...   \n",
+       "8   Are women using bleach for home cleaning at in...   \n",
+       "16  Does puerarin inhibit the inflammatory respons...   \n",
+       "9   Does trichostatin A inhibit Retinal Pigmented ...   \n",
+       "19  Is methylation of the FGFR2 gene associated wi...   \n",
+       "12  Do genome-wide ancestry patterns in Rapanui su...   \n",
+       "\n",
+       "                                             contexts  \\\n",
+       "13  [Formalin injection induces nociceptive bahavi...   \n",
+       "8   [Bleach is widely used for household cleaning....   \n",
+       "16  [The isoflavone puerarin [7-hydroxy-3-(4-hydro...   \n",
+       "9   [Proliferative vitreoretinopathy (PVR) is a bl...   \n",
+       "19  [This study examined links between DNA methyla...   \n",
+       "12  [Rapa Nui (Easter Island), located in the east...   \n",
+       "\n",
+       "                                               answer  \\\n",
+       "13  The results of this study suggest the existenc...   \n",
+       "8   Frequent use of bleach for home-cleaning is as...   \n",
+       "16  This study indicates that the effect of puerar...   \n",
+       "9   Our findings indicate a role of acetylation in...   \n",
+       "19  We identified a novel biologically plausible c...   \n",
+       "12  These genetic results can be explained by one ...   \n",
+       "\n",
+       "                                     predicted_answer  doc_mrr_evaluator  \\\n",
+       "13  Yes, termination of nociceptive behavior at th...                1.0   \n",
+       "8   Yes, women using bleach for home cleaning are ...                1.0   \n",
+       "16  Yes, puerarin inhibits the inflammatory respon...                1.0   \n",
+       "9   Yes, trichostatin A inhibits Retinal Pigmented...                1.0   \n",
+       "19  Yes, methylation of the FGFR2 gene is signific...                1.0   \n",
+       "12  Yes, genome-wide ancestry patterns in Rapanui ...                1.0   \n",
+       "\n",
+       "    faithfulness  sas_evaluator  \n",
+       "13           1.0       0.901174  \n",
+       "8            1.0       0.899928  \n",
+       "16           1.0       0.894604  \n",
+       "9            1.0       0.466138  \n",
+       "19           1.0       0.490618  \n",
+       "12           1.0       0.517162  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "top_3 = results_df.nlargest(3, \"sas_evaluator\")\n",
+    "bottom_3 = results_df.nsmallest(3, \"sas_evaluator\")\n",
+    "pd.concat([top_3, bottom_3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XueCK3y4O-qc"
+   },
+   "source": [
+    "## What's next\n",
+    "\n",
+    "🎉 Congratulations! You've learned how to evaluate a RAG pipeline with model-based evaluation frameworks and without any labeling efforts.\n",
+    "\n",
+    "If you liked this tutorial, you may also enjoy:\n",
+    "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
+    "-  [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
+    "\n",
+    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates). Thanks for reading!"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
   },
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diff --git a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
index d5d79d5f..00db8b66 100644
--- a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
+++ b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
@@ -10,7 +10,7 @@
         "\n",
         "- **Level**: Intermediate\n",
         "- **Time to complete**: 10 minutes\n",
-        "- **Components Used**: [`ConditionalRouter`](https://docs.haystack.deepset.ai/v2.0/docs/conditionalrouter), [`SerperDevWebSearch`](https://docs.haystack.deepset.ai/v2.0/docs/serperdevwebsearch), [`PromptBuilder`](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator)\n",
+        "- **Components Used**: [`ConditionalRouter`](https://docs.haystack.deepset.ai/docs/conditionalrouter), [`SerperDevWebSearch`](https://docs.haystack.deepset.ai/docs/serperdevwebsearch), [`PromptBuilder`](https://docs.haystack.deepset.ai/docs/promptbuilder), [`OpenAIGenerator`](https://docs.haystack.deepset.ai/docs/openaigenerator)\n",
         "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys) and a [Serper API Key](https://serper.dev/api-key) for this tutorial\n",
         "- **Goal**: After completing this tutorial, you'll have learned how to create a pipeline with conditional routing that can fallback to websearch if the answer is not present in your dataset.\n",
         "\n",
@@ -47,8 +47,8 @@
       "source": [
         "### Prepare the Colab Environment\n",
         "\n",
-        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
+        "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+        "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/setting-the-log-level)"
       ]
     },
     {
@@ -83,7 +83,7 @@
       "source": [
         "### Enable Telemetry\n",
         "\n",
-        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
+        "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
       ]
     },
     {
@@ -176,11 +176,11 @@
       "source": [
         "## Creating the Initial Pipeline Components\n",
         "\n",
-        "First, define a prompt instructing the LLM to respond with the text `\"no_answer\"` if the provided documents do not offer enough context to answer the query. Next, initialize a [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) with that prompt. It's crucial that the LLM replies with `\"no_answer\"` as you will use this keyword to indicate that the query should be directed to the fallback web search route.\n",
+        "First, define a prompt instructing the LLM to respond with the text `\"no_answer\"` if the provided documents do not offer enough context to answer the query. Next, initialize a [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) with that prompt. It's crucial that the LLM replies with `\"no_answer\"` as you will use this keyword to indicate that the query should be directed to the fallback web search route.\n",
         "\n",
-        "As the LLM, you will use an [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator) with the `gpt-3.5-turbo` model.\n",
+        "As the LLM, you will use an [OpenAIGenerator](https://docs.haystack.deepset.ai/docs/openaigenerator) with the `gpt-3.5-turbo` model.\n",
         "\n",
-        "> The provided prompt works effectively with the `gpt-3.5-turbo` model. If you prefer to use a different [Generator](https://docs.haystack.deepset.ai/v2.0/docs/generators), you may need to update the prompt to provide clear instructions to your model."
+        "> The provided prompt works effectively with the `gpt-3.5-turbo` model. If you prefer to use a different [Generator](https://docs.haystack.deepset.ai/docs/generators), you may need to update the prompt to provide clear instructions to your model."
       ]
     },
     {
@@ -216,9 +216,9 @@
       "source": [
         "## Initializing the Web Search Components\n",
         "\n",
-        "Initialize the necessary components for a web-based RAG application. Along with a `PromptBuilder` and an `OpenAIGenerator`, you will need a [SerperDevWebSearch](https://docs.haystack.deepset.ai/v2.0/docs/serperdevwebsearch) to retrieve relevant documents for the query from the web.\n",
+        "Initialize the necessary components for a web-based RAG application. Along with a `PromptBuilder` and an `OpenAIGenerator`, you will need a [SerperDevWebSearch](https://docs.haystack.deepset.ai/docs/serperdevwebsearch) to retrieve relevant documents for the query from the web.\n",
         "\n",
-        "> If desired, you can use a different [Generator](https://docs.haystack.deepset.ai/v2.0/docs/generators) for the web-based RAG branch of the pipeline."
+        "> If desired, you can use a different [Generator](https://docs.haystack.deepset.ai/docs/generators) for the web-based RAG branch of the pipeline."
       ]
     },
     {
@@ -257,7 +257,7 @@
       "source": [
         "## Creating the ConditionalRouter\n",
         "\n",
-        "[ConditionalRouter](https://docs.haystack.deepset.ai/v2.0/docs/conditionalrouter) is the component that handles data routing on specific conditions. You need to define a `condition`, an `output`, an `output_name` and an `output_type` for each route. Each route that the `ConditionalRouter` creates acts as the output of this component and can be connected to other components in the same pipeline.  \n",
+        "[ConditionalRouter](https://docs.haystack.deepset.ai/docs/conditionalrouter) is the component that handles data routing on specific conditions. You need to define a `condition`, an `output`, an `output_name` and an `output_type` for each route. Each route that the `ConditionalRouter` creates acts as the output of this component and can be connected to other components in the same pipeline.  \n",
         "\n",
         "In this case, you need to define two routes:\n",
         "- If the LLM replies with the `\"no_answer\"` keyword, the pipeline should perform web search. It means that you will put the original `query` in the output value to pass to the next component (in this case the next component will be the `SerperDevWebSearch`) and the output name will be `go_to_websearch`.\n",
@@ -379,7 +379,7 @@
       "source": [
         "## Running the Pipeline!\n",
         "\n",
-        "In the `run()`, pass the query to the `prompt_builder` and the `router`. In real life applications, `documents` will be provided by a [Retriever](https://docs.haystack.deepset.ai/v2.0/docs/retrievers) but to keep this example simple, you will provide the defined `documents` to the `prompt_builder`."
+        "In the `run()`, pass the query to the `prompt_builder` and the `router`. In real life applications, `documents` will be provided by a [Retriever](https://docs.haystack.deepset.ai/docs/retrievers) but to keep this example simple, you will provide the defined `documents` to the `prompt_builder`."
       ]
     },
     {
diff --git a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
index efa2e493..e90e3c64 100644
--- a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
+++ b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "- **Level**: Intermediate\n",
     "- **Time to complete**: 10 minutes\n",
-    "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder)\n",
+    "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder)\n",
     "- **Goal**: After completing this tutorial, you'll have learned how to embed metadata information while indexing documents, to improve retrieval.\n",
     "\n",
     "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n",
@@ -35,8 +35,8 @@
     "## Setup\n",
     "### Prepare the Colab Environment\n",
     "\n",
-    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/v2.0/docs/enabling-gpu-acceleration)\n",
-    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/v2.0/docs/setting-the-log-level)"
+    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration)\n",
+    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/setting-the-log-level)"
    ]
   },
   {
@@ -124,7 +124,7 @@
    "source": [
     "### Enable Telemetry\n",
     "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
@@ -144,7 +144,7 @@
    "source": [
     "## Indexing Documents with Metadata\n",
     "\n",
-    "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore) with their embeddings. We will use [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter).\n",
+    "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore) with their embeddings. We will use [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/docs/documentwriter).\n",
     "\n",
     "After adding these components to your pipeline, connect them and run the pipeline.\n",
     "\n",
@@ -284,7 +284,7 @@
     "\n",
     "We will then be able to compare the results and see if embedding metadata has helped with retrieval in this case.\n",
     "\n",
-    "> 💡 Here, we are using the `InMemoryEmbeddingRetriever` because we used the `InMemoryDocumentStore` above. If you're using another document store, change this to use the accompanying embedding retriever for the document store you are using. Check out the [Embedders Documentation](https://docs.haystack.deepset.ai/v2.0/docs/embedders) for a full list"
+    "> 💡 Here, we are using the `InMemoryEmbeddingRetriever` because we used the `InMemoryDocumentStore` above. If you're using another document store, change this to use the accompanying embedding retriever for the document store you are using. Check out the [Embedders Documentation](https://docs.haystack.deepset.ai/docs/embedders) for a full list"
    ]
   },
   {
diff --git a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
index 007f3176..211bf391 100644
--- a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
+++ b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
@@ -10,8 +10,8 @@
     "\n",
     "- **Level**: Advanced\n",
     "- **Time to complete**: 20 minutes\n",
-    "- **Components Used**: [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore), [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder), [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder), [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator), [OpenAIChatGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaichatgenerator)\n",
-    "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines)\n",
+    "- **Components Used**: [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder), [OpenAIGenerator](https://docs.haystack.deepset.ai/docs/openaigenerator), [OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n",
+    "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/docs/creating-pipelines)\n",
     "- **Goal**: After completing this tutorial, you will have learned how to build chat applications that demonstrate agent-like behavior using OpenAI's function calling feature.\n",
     "\n",
     "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n"
@@ -28,8 +28,8 @@
     "\n",
     "\n",
     "📚 Useful Sources:\n",
-    "* [OpenAIChatGenerator Docs](https://docs.haystack.deepset.ai/v2.0/docs/openaichatgenerator)\n",
-    "* [OpenAIChatGenerator API Reference](https://docs.haystack.deepset.ai/v2.0/reference/generator-api#openaichatgenerator)\n",
+    "* [OpenAIChatGenerator Docs](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n",
+    "* [OpenAIChatGenerator API Reference](https://docs.haystack.deepset.ai/reference/generator-api#openaichatgenerator)\n",
     "* [🧑‍🍳 Cookbook: Function Calling with OpenAIChatGenerator](https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/function_calling_with_OpenAIChatGenerator.ipynb)\n",
     "\n",
     "[OpenAI's function calling](https://platform.openai.com/docs/guides/function-calling) connects large language models to external tools. By providing a `tools` list with functions and their specifications to the OpenAI API calls, you can easily build chat assistants that can answer questions by calling external APIs or extract structured information from text.\n",
@@ -70,7 +70,7 @@
    "source": [
     "### Enable Telemetry\n",
     "\n",
-    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/telemetry) for more details."
+    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
    ]
   },
   {
@@ -118,7 +118,7 @@
    "source": [
     "## Learning about the OpenAIChatGenerator\n",
     "\n",
-    "[OpenAIChatGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaichatgenerator) is a component that supports the function calling feature of OpenAI through Chat Completion API. In contrary to `OpenAIGenerator`, the way to communicate with `OpenAIChatGenerator` is through [`ChatMessage`](https://docs.haystack.deepset.ai/v2.0/docs/data-classes#chatmessage) list. Read more about the difference between them in [Generators vs Chat Generators](https://docs.haystack.deepset.ai/v2.0/docs/generators-vs-chat-generators).\n",
+    "[OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator) is a component that supports the function calling feature of OpenAI through Chat Completion API. In contrary to `OpenAIGenerator`, the way to communicate with `OpenAIChatGenerator` is through [`ChatMessage`](https://docs.haystack.deepset.ai/docs/data-classes#chatmessage) list. Read more about the difference between them in [Generators vs Chat Generators](https://docs.haystack.deepset.ai/docs/generators-vs-chat-generators).\n",
     "\n",
     "\n",
     "To start working with the `OpenAIChatGenerator`, create a `ChatMessage` object with \"SYSTEM\" role using `ChatMessage.from_system()` and another `ChatMessage` with \"USER\" role using `ChatMessage.from_user()`. Then, pass this messages list to `OpenAIChatGenerator` and run:"
@@ -219,7 +219,7 @@
    "source": [
     "### Index Documents with a Pipeline\n",
     "\n",
-    "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/v2.0/docs/inmemorydocumentstore) with their embeddings. You will use [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformersdocumentembedder) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/v2.0/docs/documentwriter).\n",
+    "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore) with their embeddings. You will use [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/docs/documentwriter).\n",
     "\n",
     "After adding these components to your pipeline, connect them and run the pipeline.\n",
     "\n",
@@ -606,7 +606,7 @@
    "source": [
     "### Build a RAG Pipeline\n",
     "\n",
-    "Build a basic retrieval augmented generative pipeline with [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) and [OpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/openaigenerator).\n",
+    "Build a basic retrieval augmented generative pipeline with [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) and [OpenAIGenerator](https://docs.haystack.deepset.ai/docs/openaigenerator).\n",
     "\n",
     "> For a step-by-step guide to create a RAG pipeline with Haystack, follow the [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) tutorial."
    ]

From c98babd4f0aa60413f303dc9ab3c6bc08a1bd7f6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tuana=20=C3=87elik?= <tuana.celik@deepset.ai>
Date: Wed, 17 Jul 2024 15:40:08 +0200
Subject: [PATCH 200/206] walktrhough example branch (#330)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* walktrhough example branch

* Add the Evaluation Walkthrough (#333)

* Add eval guide

* Add images to evaluation guide

* Remove download button

* Fiz image paths

* change image branch

* Add harness info

* Change the tutorial thumbnail image

* Change the image path

---------

Co-authored-by: Bilge Yücel <bilgeyucel96@gmail.com>
---
 index.toml                           |  12 +++
 scripts/generate_markdowns.py        |  11 ++-
 tutorials/guide_evaluation.ipynb     | 138 +++++++++++++++++++++++++++
 tutorials/img/guide_box_plot.png     | Bin 0 -> 133688 bytes
 tutorials/img/guide_score_report.png | Bin 0 -> 332974 bytes
 5 files changed, 156 insertions(+), 5 deletions(-)
 create mode 100644 tutorials/guide_evaluation.ipynb
 create mode 100644 tutorials/img/guide_box_plot.png
 create mode 100644 tutorials/img/guide_score_report.png

diff --git a/index.toml b/index.toml
index 7d495451..4e6711d7 100644
--- a/index.toml
+++ b/index.toml
@@ -437,3 +437,15 @@ created_at = 2024-03-05
 haystack_2 = true
 dependencies = ["sentence-transformers>=2.2.0", "gradio"]
 featured = true
+
+[[tutorial]]
+title = "Evaluation"
+description = "A guided walkthrough to learn everything about evaluation"
+weight = 110
+notebook = "guide_evaluation.ipynb"
+aliases = []
+haystack_2 = true
+guide = true
+colab = false
+download = false
+created_at = 2024-07-17
\ No newline at end of file
diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py
index 591bd4d3..736f8635 100644
--- a/scripts/generate_markdowns.py
+++ b/scripts/generate_markdowns.py
@@ -23,8 +23,8 @@ def generate_frontmatter(config, tutorial):
     frontmatter = f"""---
 layout: {config["layout"]}
 featured: {tutorial.get("featured", False)}
-featured_image: /images/tutorials.png
-images: ["/images/tutorials.png"]
+featured_image: /images/tutorial_walkthrough_thumbnail.png
+images: ["/images/tutorial_walkthrough_thumbnail.png"]
 haystack_version: "{tutorial.get("haystack_version", "latest")}"
 haystack_2: {tutorial.get("haystack_2", False)}
 hidden: {tutorial.get("hidden", False)}
@@ -33,14 +33,15 @@ def generate_frontmatter(config, tutorial):
 toc: {config["toc"]}
 title: "{tutorial["title"]}"
 lastmod: {last_commit_date}
-level: "{tutorial["level"]}"
+level: "{tutorial.get("level", False)}"
 weight: {tutorial["weight"]}
 description: {tutorial["description"]}
 category: "QA"
 aliases: {aliases}
-download: "/downloads/{tutorial["notebook"]}"
+download: {tutorial.get("download", f'/downloads/{tutorial["notebook"]}')}
 completion_time: {tutorial.get("completion_time", False)}
-created_at: {tutorial["created_at"]}
+created_at: {tutorial.get("created_at", False)}
+guide: {tutorial.get("guide", False)}
 ---
     """
     return frontmatter
diff --git a/tutorials/guide_evaluation.ipynb b/tutorials/guide_evaluation.ipynb
new file mode 100644
index 00000000..fd0d62d2
--- /dev/null
+++ b/tutorials/guide_evaluation.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluation is crucial for developing and deploying LLM-based systems such as RAG applications and Agents, ensuring they are accurate, reliable, and effective. It ensures the information retrieved and generated is accurate, reducing the risk of irrelevant answers. \n",
+    "\n",
+    "Evaluation measures performance using metrics like precision, recall, and relevancy, providing a clear picture of your pipeline's strengths and weaknesses using LLMs or ground-truth labels.\n",
+    "\n",
+    "Evaluating RAG systems can help understand performance bottlenecks and optimize one component at a time, for example, a Retriever or a prompt used with a Generator.\n",
+    "\n",
+    "Here's a step-by-step guide explaining what you need to evaluate, how you evaluate, and how you can improve your application after evaluation using Haystack!\n",
+    "\n",
+    "## 1. Building your pipeline\n",
+    "\n",
+    "Choose the required components based on your use case and create your Haystack pipeline. If you’re a beginner, start with [📚 Tutorial: Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline). If you’d like to explore different model providers, vector databases, retrieval techniques, and more with Haystack, pick an example from🧑‍🍳 [Haystack Cookbooks](https://github.com/deepset-ai/haystack-cookbook).\n",
+    "\n",
+    "## 2. Human Evaluation\n",
+    "\n",
+    "As the first step, perform **manual evaluation**. Test a few queries (5-10 queries) and manually assess the accuracy, relevance, coherence, format, and overall quality of your pipeline’s output. This will provide an initial understanding of how well your system performs and highlight any obvious issues.\n",
+    "\n",
+    "To trace the data through each pipeline step, debug the intermediate components using the [include_outputs_from](https://docs.haystack.deepset.ai/reference/pipeline-api#pipelinerun) parameter. This feature is particularly useful for observing the retrieved documents or verifying the rendered prompt. By examining these intermediate outputs, you can pinpoint where issues may arise and identify specific areas for improvement, such as tweaking the prompt or trying out different models.\n",
+    "\n",
+    "## 3. Deciding on Metrics\n",
+    "\n",
+    "Evaluation metrics are crucial for measuring the effectiveness of your pipeline. Common metrics are:\n",
+    "\n",
+    "- **Semantic Answer Similarity**: Evaluates the semantic similarity of the generated answer and the ground truth rather than their lexical overlap.\n",
+    "- **Context Relevancy**: Assesses the relevance of the retrieved documents to the query.\n",
+    "- **Faithfulness:** Evaluates to what extent a generated answer is based on retrieved documents\n",
+    "- **Context Precision**: Measures the accuracy of the retrieved documents.\n",
+    "- **Context Recall**: Measures the ability to retrieve all relevant documents.\n",
+    "\n",
+    "Some metrics might require labeled data, while others can be evaluated using LLMs without needing labeled data. As you evaluate your pipeline, explore various types of metrics, such as [statistical](https://docs.haystack.deepset.ai/docs/statistical-evaluation) and [model-based](https://docs.haystack.deepset.ai/docs/model-based-evaluation) metrics, or incorporate custom metrics using LLMs with the [LLMEvaluator](https://docs.haystack.deepset.ai/docs/llmevaluator). \n",
+    "\n",
+    "|  | Retrieval Evaluation | Generation Evaluation | End-to-end Evaluation |\n",
+    "| --- | :---: | --- | :---: |\n",
+    "| Labeled data | [DocumentMAPEvaluator](https://docs.haystack.deepset.ai/docs/documentmapevaluator), [DocumentMRREvaluator](https://docs.haystack.deepset.ai/docs/documentmrrevaluator), [DocumentRecallEvaluator](https://docs.haystack.deepset.ai/docs/documentrecallevaluator) | - | [AnswerExactMatchEvaluator](https://docs.haystack.deepset.ai/docs/answerexactmatchevaluator), [SASEvaluator](https://docs.haystack.deepset.ai/docs/sasevaluator) |\n",
+    "| Unlabeled data (LLM-based) | [ContextRelevanceEvaluator](https://docs.haystack.deepset.ai/docs/contextrelevanceevaluator) | [FaithfulnessEvaluator](https://docs.haystack.deepset.ai/docs/faithfulnessevaluator)| [LLMEvaluator](https://docs.haystack.deepset.ai/docs/llmevaluator)** |\n",
+    "\n",
+    "<p style=\"font-size: 1rem; font-style: italic; margin-top: -1rem;\">** You need to provide the instruction and the examples to the LLM to evaluate your system.</p>\n",
+    "<figcaption>List of evaluation metrics that Haystack has built-in support</figcaption>\n",
+    "\n",
+    "In addition to Haystack’s built-in evaluators, you can use metrics from other evaluation frameworks like [ragas](https://haystack.deepset.ai/integrations/ragas) and [DeepEval](https://haystack.deepset.ai/integrations/deepeval). For more detailed information on evaluation metrics, refer to 📖 [Docs: Evaluation](https://docs.haystack.deepset.ai/docs/evaluation). \n",
+    "\n",
+    "## 4. Building an Evaluation Pipeline\n",
+    "\n",
+    "Build a pipeline with your evaluators. To learn about evaluating with Haystack’s own metrics, you can follow 📚 [Tutorial: Evaluating RAG Pipelines](https://haystack.deepset.ai/tutorials/35_evaluating_rag_pipelines). \n",
+    "\n",
+    "> 🧑‍🍳 As well as Haystack’s own evaluation metrics, you can also integrate with a number of evaluation frameworks. See the integrations and examples below 👇\n",
+    "> \n",
+    "> - [Evaluate with DeepEval](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_deep_eval.ipynb)\n",
+    "> - [Evaluate with ragas](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_ragas.ipynb)\n",
+    "> - [Evaluate with UpTrain](https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/rag_eval_uptrain.ipynb)\n",
+    "\n",
+    "For step-by-step instructions, watch [our video walkthrough](https://youtu.be/5PrzXaZ0-qk?feature=shared) 🎥 👇\n",
+    "\n",
+    "\n",
+    "<iframe\n",
+    "  width=\"640\"\n",
+    "  height=\"480\"\n",
+    "  src=\"https://www.youtube.com/embed/5PrzXaZ0-qk\"\n",
+    "  frameborder=\"0\"\n",
+    "  allow=\"autoplay; encrypted-media\"\n",
+    "  allowfullscreen\n",
+    ">\n",
+    "</iframe>\n",
+    "\n",
+    "For a comprehensive evaluation, make sure to evaluate specific steps in the pipeline (e.g., retrieval or generation) and the performance of the entire pipeline. To get inspiration on evaluating your pipeline, have a look at 🧑🏼‍🍳 [Cookbook: Prompt Optimization with DSPy](https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prompt_optimization_with_dspy.ipynb), which explains the details of prompt optimization and evaluation, or read 📚 [Article: RAG Evaluation with Prometheus 2](https://haystack.deepset.ai/blog/rag-evaluation-with-prometheus-2), which explores using open LMs to evaluate with custom metrics.\n",
+    "\n",
+    "If you're looking for a straightforward and efficient solution for RAG, consider using `EvaluationHarness`, introduced with Haystack 2.3 through [`haystack-experimental`](https://github.com/deepset-ai/haystack-experimental/tree/main). You can learn more by running the example 💻 [Notebook: Evaluating RAG Pipelines with EvaluationHarness](https://github.com/deepset-ai/haystack-experimental/blob/main/examples/rag_eval_harness.ipynb).\n",
+    "\n",
+    "## 5. Running Evaluation\n",
+    "\n",
+    "Evaluate your pipeline with different parameters, change the `top_k` value, and try a different embedding model, play with the `temperature` to find what works best for your use case. If you need labeled data for evaluation, you can use some datasets that come with ground-truth documents and ground-truth answers. You can find some datasets on [Hugging Face datasets](https://huggingface.co/datasets) or in the [haystack-evaluation](https://github.com/deepset-ai/haystack-evaluation/tree/main/datasets) repository. \n",
+    "\n",
+    "Make sure to set up your evaluation environment so that it’s easy to evaluate using different parameters without much hassle. The [haystack-evaluation](https://github.com/deepset-ai/haystack-evaluation) repository provides examples with different architectures against various datasets. \n",
+    "\n",
+    "Read more about how you can optimize your pipeline by trying different parameter combinations in 📚 [Article: Benchmarking Haystack Pipelines for Optimal Performance](https://haystack.deepset.ai/blog/benchmarking-haystack-pipelines)\n",
+    "\n",
+    "## 6. Analyzing Results\n",
+    "\n",
+    "Visualize your data and your results to have a general understanding of your pipeline’s performance.\n",
+    "\n",
+    "- Create a report using [EvaluationRunResult.score_report()](https://docs.haystack.deepset.ai/reference/evaluation-api#evaluationrunresult) and transform the evaluation results into a Pandas DataFrame with the aggregated scores for each metric:\n",
+    "\n",
+    "![Score report for Document MRR, SAS and Faithfulness](https://raw.githubusercontent.com/deepset-ai/haystack-tutorials/main/tutorials/img/guide_score_report.png#small)\n",
+    "\n",
+    "- Use Pandas to analyze the results for different parameters (`top_k`, `batch_size`, `embedding_model`) in a comprehensive view\n",
+    "- Use libraries like Matplotlib or Seaborn to visually represent your evaluation results.\n",
+    "    \n",
+    "![Using box-plots makes sense when comparing different models](https://raw.githubusercontent.com/deepset-ai/haystack-tutorials/main/tutorials/img/guide_box_plot.png#medium \"Using box-plots makes sense when comparing different models\")\n",
+    "    \n",
+    "\n",
+    "> Refer to 📚 [Benchmarking Haystack Pipelines for Optimal Performance: Results Analysis](https://haystack.deepset.ai/blog/benchmarking-haystack-pipelines#results-analysis) or 💻 [Notebook: Analyze ARAGOG Parameter Search](https://github.com/deepset-ai/haystack-evaluation/blob/main/evaluations/analyze_aragog_parameter_search.ipynb) to visualize evaluation results.\n",
+    "> \n",
+    "\n",
+    "## 7. Improving Your Pipeline\n",
+    "\n",
+    "After evaluation, analyze the results to identify areas of improvement. Here are some methods:\n",
+    "\n",
+    "### Methods to Improve Retrieval:\n",
+    "\n",
+    "- **Data Cleaning**: Ensure your data is clean and well-structured before indexing using [DocumentCleaner](https://docs.haystack.deepset.ai/docs/documentcleaner) and [DocumentSplitter](https://docs.haystack.deepset.ai/docs/documentsplitter).\n",
+    "- **Data Quality:** Enrich the semantics of your documents by [embedding meaningful metadata](https://haystack.deepset.ai/tutorials/39_embedding_metadata_for_improved_retrieval) alongside the document's contents.\n",
+    "- **Metadata Filtering**: Limit the search space by using [metadata filters](https://docs.haystack.deepset.ai/docs/metadata-filtering) or extracting metadata from queries to use as filters. For more details, read 📚 [Extract Metadata from Queries to Improve Retrieval](https://haystack.deepset.ai/blog/extracting-metadata-filter).\n",
+    "- **Different Embedding Models:** Compare different embedding models from different model providers. See the full list of supported embedding providers in [Embedders](https://docs.haystack.deepset.ai/docs/embedders).\n",
+    "- **Advanced Retrieval Techniques**: Leverage techniques like [hybrid retrieval](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval), [sparse embeddings](https://docs.haystack.deepset.ai/docs/retrievers#sparse-embedding-based-retrievers), or [Hypothetical Document Embeddings (HYDE)](https://docs.haystack.deepset.ai/docs/hypothetical-document-embeddings-hyde).\n",
+    "\n",
+    "### Methods to Improve Generation:\n",
+    "\n",
+    "- **Ranking**: Incorporate a ranking mechanism into your retrieved documents before providing the context to your prompt\n",
+    "    - **Order by similarity**: Reorder your retrieved documents by similarity using cross-encoder models from Hugging Face with [TransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/transformerssimilarityranker), Rerank models from Cohere with [CohereRanker](https://docs.haystack.deepset.ai/docs/cohereranker), or Rerankers from Jina with [JinaRanker](https://docs.haystack.deepset.ai/docs/jinaranker)\n",
+    "    - **Increase diversity by ranking**: Maximize the overall diversity among your context using sentence-transformers models with [SentenceTransformersDiversityRanker](https://docs.haystack.deepset.ai/docs/sentencetransformersdiversityranker) to help increase the semantic answer similarity (SAS) in LFQA applications.\n",
+    "    - **Address the \"Lost in the Middle\" problem by reordering**: Position the most relevant documents at the beginning and end of the context using [LostInTheMiddleRanker](https://docs.haystack.deepset.ai/docs/lostinthemiddleranker) to increase faithfulness.\n",
+    "- **Different Generators**: Try different large language models and benchmark the results. The full list of model providers is in [Generators](https://docs.haystack.deepset.ai/docs/generators).\n",
+    "- **Prompt Engineering**: Use few-shot prompts or provide more instructions to enable the exact match.\n",
+    "\n",
+    "## 8. Monitoring\n",
+    "\n",
+    "Implement strategies for [tracing](https://docs.haystack.deepset.ai/docs/tracing) the application post-deployment. By integrating [LangfuseConnector](https://docs.haystack.deepset.ai/docs/langfuseconnector) into your pipeline, you can collect the queries, documents, and answers and use them to continuously evaluate your application. Learn more about pipeline monitoring in 📚 [Article: Monitor and trace your Haystack pipelines with Langfuse](https://haystack.deepset.ai/blog/langfuse-integration)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials/img/guide_box_plot.png b/tutorials/img/guide_box_plot.png
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literal 0
HcmV?d00001


From dcb384ebee58a8aade29f6a707d0169b51cf5fac Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <stefanofiorucci@gmail.com>
Date: Thu, 18 Jul 2024 10:38:16 +0200
Subject: [PATCH 201/206] rm mentions to deepse public mlflow (#334)

---
 tutorials/05_Evaluation.ipynb | 26 +++++++++-----------------
 1 file changed, 9 insertions(+), 17 deletions(-)

diff --git a/tutorials/05_Evaluation.ipynb b/tutorials/05_Evaluation.ipynb
index b6aad683..94a815dd 100644
--- a/tutorials/05_Evaluation.ipynb
+++ b/tutorials/05_Evaluation.ipynb
@@ -732,7 +732,7 @@
    "metadata": {},
    "source": [
     "## Storing results in MLflow\n",
-    "Storing evaluation results in CSVs is fine but not enough if you want to compare and track multiple evaluation runs. MLflow is a handy tool when it comes to tracking experiments. So we decided to use it to track all of `Pipeline.eval()` with reproducability of your experiments in mind."
+    "Storing evaluation results in CSVs is fine but not enough if you want to compare and track multiple evaluation runs. MLflow is a handy tool when it comes to tracking experiments. So we decided to use it to track all of `Pipeline.eval()` with reproducibility of your experiments in mind."
    ]
   },
   {
@@ -740,15 +740,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Host your own MLflow or use deepset's public MLflow"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you don't want to use deepset's public MLflow instance under https://public-mlflow.deepset.ai, you can easily host it yourself."
+    "### MLflow setup\n",
+    "\n",
+    "Uncomment the following cell to install and run MLflow locally (does not work in Colab). For other options, refer to the [MLflow documentation](https://www.mlflow.org/docs/latest/index.html)."
    ]
   },
   {
@@ -907,8 +901,8 @@
     "    evaluation_set_meta={\"name\": \"nq_dev_subset_v2.json\"},\n",
     "    pipeline_meta={\"name\": \"sparse-pipeline\"},\n",
     "    add_isolated_node_eval=True,\n",
-    "    experiment_tracking_tool=\"mlflow\",\n",
-    "    experiment_tracking_uri=\"https://public-mlflow.deepset.ai\",\n",
+    "    # experiment_tracking_tool=\"mlflow\",                    # UNCOMMENT TO USE MLFLOW\n",
+    "    # experiment_tracking_uri=\"YOUR-MLFLOW-TRACKING-URI\",   # UNCOMMENT TO USE MLFLOW\n",
     "    reuse_index=True,\n",
     ")"
    ]
@@ -948,8 +942,8 @@
     "    evaluation_set_meta={\"name\": \"nq_dev_subset_v2.json\"},\n",
     "    pipeline_meta={\"name\": \"embedding-pipeline\"},\n",
     "    add_isolated_node_eval=True,\n",
-    "    experiment_tracking_tool=\"mlflow\",\n",
-    "    experiment_tracking_uri=\"https://public-mlflow.deepset.ai\",\n",
+    "    # experiment_tracking_tool=\"mlflow\",                    # UNCOMMENT TO USE MLFLOW\n",
+    "    # experiment_tracking_uri=\"YOUR-MLFLOW-TRACKING-URI\",   # UNCOMMENT TO USE MLFLOW\n",
     "    reuse_index=True,\n",
     "    answer_scope=\"context\",\n",
     ")"
@@ -960,9 +954,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can now open MLflow (e.g. https://public-mlflow.deepset.ai/ if you used the public one hosted by deepset) and look for the haystack-eval-experiment experiment. Try out mlflow's compare function and have fun...\n",
-    "\n",
-    "Note that on our public mlflow instance we are not able to log artifacts like the evaluation results or the piplines.yaml file."
+    "You can now open MLflow and look for the haystack-eval-experiment experiment. Try out mlflow's compare function and have fun..."
    ]
   },
   {

From 48989707120674981db52bd696da025572fcb89c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 19 Jul 2024 14:47:47 +0300
Subject: [PATCH 202/206] Extend the 5th section with baseline info (#335)

---
 tutorials/guide_evaluation.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tutorials/guide_evaluation.ipynb b/tutorials/guide_evaluation.ipynb
index fd0d62d2..461c46b1 100644
--- a/tutorials/guide_evaluation.ipynb
+++ b/tutorials/guide_evaluation.ipynb
@@ -73,11 +73,11 @@
     "\n",
     "## 5. Running Evaluation\n",
     "\n",
-    "Evaluate your pipeline with different parameters, change the `top_k` value, and try a different embedding model, play with the `temperature` to find what works best for your use case. If you need labeled data for evaluation, you can use some datasets that come with ground-truth documents and ground-truth answers. You can find some datasets on [Hugging Face datasets](https://huggingface.co/datasets) or in the [haystack-evaluation](https://github.com/deepset-ai/haystack-evaluation/tree/main/datasets) repository. \n",
+    "The objective of running evaluations is to measure your pipeline's performance and detect any regressions. To track progress, it is essential to establish baseline metrics using off-the-shelf approaches such as BM25 for keyword retrieval or \"sentence-transformers\" models for embeddings. Then, continue evaluating your pipeline with various parameters: adjust the `top_k` value, experiment with different embedding models, tweak the `temperature`, and benchmark the results to identify what works best for your use case. If labeled data is needed for evaluation, you can use datasets that include ground-truth documents and answers. Such datasets are available on [Hugging Face datasets](https://huggingface.co/datasets) or in the [haystack-evaluation](https://github.com/deepset-ai/haystack-evaluation/tree/main/datasets) repository.\n",
     "\n",
-    "Make sure to set up your evaluation environment so that it’s easy to evaluate using different parameters without much hassle. The [haystack-evaluation](https://github.com/deepset-ai/haystack-evaluation) repository provides examples with different architectures against various datasets. \n",
+    "Ensure your evaluation environment is set up to facilitate easy testing with different parameters. The [haystack-evaluation](https://github.com/deepset-ai/haystack-evaluation) repository provides examples with various architectures against different datasets.\n",
     "\n",
-    "Read more about how you can optimize your pipeline by trying different parameter combinations in 📚 [Article: Benchmarking Haystack Pipelines for Optimal Performance](https://haystack.deepset.ai/blog/benchmarking-haystack-pipelines)\n",
+    "For more information on optimizing your pipeline by experimenting with different parameter combinations, refer to 📚 [Article: Benchmarking Haystack Pipelines for Optimal Performance](https://haystack.deepset.ai/blog/benchmarking-haystack-pipelines).\n",
     "\n",
     "## 6. Analyzing Results\n",
     "\n",

From 66b40421f19b4998e9f3b868b9da9f319ec98004 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 9 Aug 2024 13:56:49 +0300
Subject: [PATCH 203/206] Deprecation note to the Multiplexer tutorial (#338)

---
 index.toml                                                      | 1 +
 tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/index.toml b/index.toml
index 4e6711d7..770fbf22 100644
--- a/index.toml
+++ b/index.toml
@@ -411,6 +411,7 @@ aliases = []
 completion_time = "10 min"
 created_at = 2024-02-19
 haystack_2 = true
+haystack_version = "2.3.1"
 dependencies = ["transformers", "huggingface_hub>=0.22.0"]
 
 [[tutorial]]
diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
index 6c579b8b..a23b7c95 100644
--- a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
+++ b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
@@ -15,6 +15,8 @@
     "- **Prerequisites**: You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/docs/creating-pipelines)\n",
     "- **Goal**: After completing this tutorial, you'll have learned how to use a Multiplexer to simplify the inputs that `Pipeline.run()` get\n",
     "\n",
+    "> As of version 2.2.0, `Multiplexer` has been deprecated in Haystack and will be completely removed from Haystack as of v2.4.0. We recommend using [BranchJoiner](https://docs.haystack.deepset.ai/docs/branchjoiner) instead. For more details about this deprecation, check out [Haystack 2.2.0 release notes](https://github.com/deepset-ai/haystack/releases/tag/v2.2.0) on Github.\n",
+    "\n",
     "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro)."
    ]
   },

From 13a85a851adde687b713fa085ecf9db18318fc79 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bilge=20Y=C3=BCcel?= <bilgeyucel96@gmail.com>
Date: Fri, 9 Aug 2024 13:58:28 +0300
Subject: [PATCH 204/206] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 915e2599..559dae2f 100644
--- a/README.md
+++ b/README.md
@@ -44,7 +44,7 @@ Haystack 2.0
 | [Preprocessing](./tutorials/08_Preprocessing.ipynb)                                                       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/08_Preprocessing.ipynb)                                           | [Build an Extractive QA Pipeline](./tutorials/34_Extractive_QA_Pipeline.ipynb)                                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/34_Extractive_QA_Pipeline.ipynb)                 |
 | [DPR Training](./tutorials/09_DPR_training.ipynb)                                                         | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/09_DPR_training.ipynb)                                            | [Evaluating RAG Pipelines](./tutorials/35_Evaluating_RAG_Pipelines.ipynb)          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/35_Evaluating_RAG_Pipelines.ipynb)|
 | [[OUTDATED] Knowledge Graph](./tutorials/10_Knowledge_Graph.ipynb)                                        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/10_Knowledge_Graph.ipynb)                                         | [Building Pipelines with Conditional Routing](./tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb)|
-| [Pipelines](./tutorials/11_Pipelines.ipynb)                                                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               | [Simplifying Pipeline Inputs with Multiplexer](./tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)|
+| [Pipelines](./tutorials/11_Pipelines.ipynb)                                                               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/11_Pipelines.ipynb)                                               | [[OUTDATED] Simplifying Pipeline Inputs with Multiplexer](./tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb)|
 | [[OUTDATED] Seq2SeqGenerator](./tutorials/12_LFQA.ipynb)                                                  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/12_LFQA.ipynb)                                                    | [Embedding Metadata for Improved Retrieval](./tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb)|
 | [Question Generation](./tutorials/13_Question_generation.ipynb)                                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/13_Question_generation.ipynb)                                     | [Building a Chat Application with Function Calling](./tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb)| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb)|
 | [Query Classifier](./tutorials/14_Query_Classifier.ipynb)                                                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/14_Query_Classifier.ipynb)                                        |                                                                                                                  |                                                                                                                                                                                                                          |

From a9573af28e353fa665f1e4184a02cdf1eeafbbe6 Mon Sep 17 00:00:00 2001
From: Stefano Fiorucci <stefanofiorucci@gmail.com>
Date: Tue, 27 Aug 2024 17:20:02 +0200
Subject: [PATCH 205/206] update sentence-transformers and huggingface-hub
 (#345)

---
 index.toml                                           | 12 ++++++------
 tutorials/27_First_RAG_Pipeline.ipynb                |  2 +-
 .../30_File_Type_Preprocessing_Index_Pipeline.ipynb  |  2 +-
 tutorials/33_Hybrid_Retrieval.ipynb                  |  2 +-
 tutorials/34_Extractive_QA_Pipeline.ipynb            |  2 +-
 tutorials/35_Evaluating_RAG_Pipelines.ipynb          |  2 +-
 ...implifying_Pipeline_Inputs_with_Multiplexer.ipynb |  2 +-
 ...ding_Chat_Application_with_Function_Calling.ipynb |  2 +-
 8 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/index.toml b/index.toml
index 770fbf22..4f19a570 100644
--- a/index.toml
+++ b/index.toml
@@ -279,7 +279,7 @@ aliases = []
 completion_time = "10 min"
 created_at = 2023-12-05
 haystack_2 = true
-dependencies = ["datasets>=2.6.1", "sentence-transformers>=2.2.0"]
+dependencies = ["datasets>=2.6.1", "sentence-transformers>=3.0.0"]
 featured = true
 
 [[tutorial]]
@@ -318,8 +318,8 @@ completion_time = "15 min"
 created_at = 2024-01-30
 haystack_2 = true
 dependencies = [
-    "sentence-transformers>=2.2.0",
-    "huggingface_hub>=0.22.0",
+    "sentence-transformers>=3.0.0",
+    "huggingface_hub>=0.23.0",
     "transformers",
     "markdown-it-py",
     "mdit_plain",
@@ -361,7 +361,7 @@ aliases = []
 completion_time = "15 min"
 created_at = 2024-02-13
 haystack_2 = true
-dependencies = ["datasets>=2.6.1", "sentence-transformers>=2.2.0", "accelerate"]
+dependencies = ["datasets>=2.6.1", "sentence-transformers>=3.0.0", "accelerate"]
 needs_gpu = true
 
 [[tutorial]]
@@ -412,7 +412,7 @@ completion_time = "10 min"
 created_at = 2024-02-19
 haystack_2 = true
 haystack_version = "2.3.1"
-dependencies = ["transformers", "huggingface_hub>=0.22.0"]
+dependencies = ["transformers", "huggingface_hub>=0.23.0"]
 
 [[tutorial]]
 title = "Embedding Metadata for Improved Retrieval"
@@ -436,7 +436,7 @@ aliases = []
 completion_time = "20 min"
 created_at = 2024-03-05
 haystack_2 = true
-dependencies = ["sentence-transformers>=2.2.0", "gradio"]
+dependencies = ["sentence-transformers>=3.0.0", "gradio"]
 featured = true
 
 [[tutorial]]
diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index 5574873c..3192cffa 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -172,7 +172,7 @@
         "\n",
         "pip install haystack-ai\n",
         "pip install \"datasets>=2.6.1\"\n",
-        "pip install \"sentence-transformers>=2.2.0\""
+        "pip install \"sentence-transformers>=3.0.0\""
       ]
     },
     {
diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
index 841a7b08..5349636c 100644
--- a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
+++ b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -73,7 +73,7 @@
    "source": [
     "%%bash\n",
     "pip install haystack-ai\n",
-    "pip install \"sentence-transformers>=2.2.0\" \"huggingface_hub>=0.22.0\"\n",
+    "pip install \"sentence-transformers>=3.0.0\" \"huggingface_hub>=0.23.0\"\n",
     "pip install markdown-it-py mdit_plain pypdf\n",
     "pip install gdown"
    ]
diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
index 4c635ceb..4813d3df 100644
--- a/tutorials/33_Hybrid_Retrieval.ipynb
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -65,7 +65,7 @@
     "\n",
     "pip install haystack-ai\n",
     "pip install \"datasets>=2.6.1\"\n",
-    "pip install \"sentence-transformers>=2.2.0\"\n",
+    "pip install \"sentence-transformers>=3.0.0\"\n",
     "pip install accelerate"
    ]
   },
diff --git a/tutorials/34_Extractive_QA_Pipeline.ipynb b/tutorials/34_Extractive_QA_Pipeline.ipynb
index 2208d0d9..7f21ce7e 100644
--- a/tutorials/34_Extractive_QA_Pipeline.ipynb
+++ b/tutorials/34_Extractive_QA_Pipeline.ipynb
@@ -63,7 +63,7 @@
    "source": [
     "%%bash\n",
     "\n",
-    "pip install haystack-ai accelerate \"sentence-transformers>=2.2.0\" \"datasets>=2.6.1\""
+    "pip install haystack-ai accelerate \"sentence-transformers>=3.0.0\" \"datasets>=2.6.1\""
    ]
   },
   {
diff --git a/tutorials/35_Evaluating_RAG_Pipelines.ipynb b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
index 2bc5fca2..d7c47d71 100644
--- a/tutorials/35_Evaluating_RAG_Pipelines.ipynb
+++ b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
@@ -223,7 +223,7 @@
     "\n",
     "pip install haystack-ai\n",
     "pip install \"datasets>=2.6.1\"\n",
-    "pip install sentence-transformers>=2.2.0"
+    "pip install \"sentence-transformers>=3.0.0\""
    ]
   },
   {
diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
index a23b7c95..1f7f04e4 100644
--- a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
+++ b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
@@ -69,7 +69,7 @@
    "source": [
     "%%bash\n",
     "\n",
-    "pip install haystack-ai \"huggingface_hub>=0.22.0\""
+    "pip install haystack-ai \"huggingface_hub>=0.23.0\""
    ]
   },
   {
diff --git a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
index 211bf391..342830fd 100644
--- a/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
+++ b/tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb
@@ -59,7 +59,7 @@
     "%%bash\n",
     "\n",
     "pip install haystack-ai\n",
-    "pip install \"sentence-transformers>=2.2.0\""
+    "pip install \"sentence-transformers>=3.0.0\""
    ]
   },
   {

From f2674ee692ee34838e39d12557f3fe25db7e9154 Mon Sep 17 00:00:00 2001
From: Daniel <hello@daniellong.me>
Date: Tue, 27 Aug 2024 10:58:30 -0500
Subject: [PATCH 206/206] Correct an import in Tutorial 39: ...TextEmbedder ->
 ...DocumentEmbedder (#340)

---
 tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
index e90e3c64..c591c1e4 100644
--- a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
+++ b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
@@ -160,7 +160,7 @@
     "For example, the embedder below will be embedding the \"url\" field as well as the contents of documents:\n",
     "\n",
     "```python\n",
-    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
     "\n",
     "embedder = SentenceTransformersDocumentEmbedder(meta_fields_to_embed=[\"url\"])\n",
     "```"