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Change Mixtral to Mistral #59

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Feb 27, 2024
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Change Mixtral to Mistral #59

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -19,7 +19,7 @@ For more examples, you may also find our [Blog](https://haystack.deepset.ai/blog
| Hacker News RAG with Custom Component | <a href="https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/hackernews-custom-component-rag.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>|
| Use Chroma for RAG and Indexing | <a href="https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/chroma-indexing-and-rag-examples.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>|
| Using the Jina-embeddings-v2-base-en model in a Haystack RAG pipeline for legal document analsysis| <a href="https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/jina-embeddings-v2-legal-analysis-rag.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>|
| Multilingual RAG from a podcast with Whisper, Qdrant and Mixtral| <a href="https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/multilingual_rag_podcast.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>|
| Multilingual RAG from a podcast with Whisper, Qdrant and Mistral| <a href="https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/multilingual_rag_podcast.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>|
| Improve retrieval by embedding meaningful metadata| <a href="https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/improve-retrieval-by-embedding-metadata.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>|
| Information extraction via LLMs (Gorilla OpenFunctions)| <a href="https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/information-extraction-gorilla.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>|
| Using AstraDB as a data store in your Haystack pipelines| <a href="https://colab.research.google.com/github/deepset-ai/haystack-cookbook/blob/main/notebooks/astradb_haystack_integration.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>|
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4 changes: 2 additions & 2 deletions notebooks/chroma-indexing-and-rag-examples.ipynb
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Expand Up @@ -118,7 +118,7 @@
"\n",
"\n",
"In this example we are using:\n",
"- The `HuggingFaceTGIGenerator` with the Mistral 8x7B model. (You will need a Hugging Face token to use this model). You can repleace this with any of the other [`Generators`](https://docs.haystack.deepset.ai/v2.0/docs/generators)\n",
"- The `HuggingFaceTGIGenerator` with the Mistral-7B-Instruct-v0.1. (You will need a Hugging Face token to use this model). You can repleace this with any of the other [`Generators`](https://docs.haystack.deepset.ai/v2.0/docs/generators)\n",
"- The `PromptBuilder` which holds the prompt template. You can adjust this to a prompt of your choice\n",
"- The `ChromaQueryRetriver` which expects a list of queries and retieves the `top_k` most relevant documents from your Chroma collection."
]
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"\"\"\"\n",
"prompt_builder = PromptBuilder(template=prompt)\n",
"\n",
"llm = HuggingFaceTGIGenerator(model=\"mistralai/Mixtral-8x7B-Instruct-v0.1\")\n",
"llm = HuggingFaceTGIGenerator(model=\"mistralai/Mistral-7B-Instruct-v0.1\")\n",
"llm.warm_up()\n",
"retriever = ChromaQueryTextRetriever(document_store)\n",
"\n",
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6 changes: 3 additions & 3 deletions notebooks/jina-embeddings-v2-legal-analysis-rag.ipynb
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Expand Up @@ -16,7 +16,7 @@
"- the [`jina-embeddings-v2-base-en`](https://arxiv.org/abs/2310.19923) model\n",
"- [Haystack](https://haystack.deepset.ai/), the open source LLM orchestration framework, version [2.0-beta](https://docs.haystack.deepset.ai/v2.0/docs)\n",
"- [Chroma](https://docs.trychroma.com/getting-started) to store our vector embeddings, via the [Chroma Document Store Haystack integration](https://haystack.deepset.ai/integrations/chroma-documentstore)\n",
"- the open source [Mixtral 8x7B LLM](https://huggingface.co/docs/transformers/model_doc/mixtral)\n",
"- the open source [Mistral 7B Instruct LLM](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)\n",
"\n",
"\n",
"## Prerequisites:\n",
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"source": [
"# Query pipeline\n",
"\n",
"Now the real fun begins. Let's create a query pipeline so we can actually start asking questions. We write a prompt allowing us to pass our documents to the Mixtral-8x7B LLM. Then we initiatialize the LLM via the `HuggingFaceTGIGenerator`.\n",
"Now the real fun begins. Let's create a query pipeline so we can actually start asking questions. We write a prompt allowing us to pass our documents to the Mistral-7B LLM. Then we initiatialize the LLM via the `HuggingFaceTGIGenerator`.\n",
"\n",
"In Haystack 2.0 `retriever`s are tightly coupled to `DocumentStores`. If we pass in the `retriever` we initialized earlier, this pipeline can access those embeddings we generated, and pass them to the LLM."
]
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"\"\"\"\n",
"\n",
"text_embedder = JinaTextEmbedder(model=\"jina-embeddings-v2-base-en\")\n",
"generator = HuggingFaceTGIGenerator(\"mistralai/Mixtral-8x7B-Instruct-v0.1\")\n",
"generator = HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.1\")\n",
"generator.warm_up()\n",
"\n",
"prompt_builder = PromptBuilder(template=prompt)\n",
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3 changes: 2 additions & 1 deletion notebooks/mixtral-8x7b-for-web-qa.ipynb
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Expand Up @@ -47,7 +47,8 @@
"source": [
"## Prompt the Model - Standalone\n",
"\n",
"We are using Hugging Face [Text Generation interface](https://github.com/huggingface/text-generation-inference) (TGI) here. This required an API key 👇"
"We are using Hugging Face [Text Generation interface](https://github.com/huggingface/text-generation-inference) (TGI) here. This requires an API key 👇 \n",
"> Keep in mind that `mistralai/Mixtral-8x7B-Instruct-v0.1` model is not available on the free tier of Hugging Face Inference API."
]
},
{
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6 changes: 3 additions & 3 deletions notebooks/multilingual_rag_podcast.ipynb
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Expand Up @@ -21,7 +21,7 @@
"- OpenAI Whisper model for audio transcription\n",
"- Qdrant vector database\n",
"- multilingual embedding model: multilingual-e5-large\n",
"- multilingual LLM: Mixtral-8x7B-Instruct-v0.1"
"- multilingual LLM: Mistral-7B-Instruct-v0.1"
]
},
{
Expand Down Expand Up @@ -991,9 +991,9 @@
}
],
"source": [
"# load the model (https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) and try the Generator\n",
"# load the model (https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) and try the Generator\n",
"\n",
"generator = HuggingFaceTGIGenerator(\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
"generator = HuggingFaceTGIGenerator(\"mistralai/Mistral-7B-Instruct-v0.1\",\n",
" generation_kwargs={\"max_new_tokens\":500})\n",
"generator.warm_up()\n",
"\n",
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