tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb

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      "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,
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      "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:"
      ]
    },
    {
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          "base_uri": "https://localhost:8080/"
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        "outputId": "584fbb90-714a-4016-d21f-ca9b16d2a307"
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      "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"
      ]
    },
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      "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)"
      ]
    },
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      "source": [
        "### 2) Create an ExtractiveQAPipeline:\n",
        "Let's define our retriever and reader to to use in an `ExtractiveQAPipeline`:"
      ]
    },
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      "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"
      ]
    },
    {
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      "cell_type": "markdown",
      "metadata": {
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      "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?\""
      ]
    },
    {
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      "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\")"
      ]
    },
    {
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      "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"
      ]
    },
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