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mistral-haystack
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Mistral + Haystack: build RAG pipelines that rock ๐Ÿค˜

Last updated Jul 22, 2026
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๐Ÿ“Œ mistral-haystack collection

Mistral + Haystack Collection: build RAG pipelines that rock ๐Ÿค˜

Collection of notebooks and resources to build Retrieval Augmented Generation pipelines using: - Mistral models ๐Ÿค– - Haystack LLM orchestration framework ๐Ÿ—๏ธ.

๐Ÿ’ป For other great Haystack Notebooks, check out the ๐Ÿ‘ฉ๐Ÿปโ€๐Ÿณ Haystack Cookbook

๐Ÿ““ Notebooks

| Model | Haystack version | Link | Details | Author | |--------------------------|----------------------|----------|---------------------------------------------------------|------------| | Mistral-7B-Instruct-v0.1 | 1.x | ๐ŸŽธ Notebook | RAG on a collection of Rock music resources, using the free Hugging Face Inference API | @anakin87 | | Mixtral-8x7B-Instruct-v0.1 | 1.x | ๐Ÿ“„๐Ÿš€ Notebook | RAG on a PDF File, using the free Hugging Face Inference API (using the free Hugging Face Inference API) | @AlessandroDiLauro | | Mixtral-8x7B-Instruct-v0.1 | 1.x | ๐Ÿ›’ Notebook
๐Ÿ“Š๐Ÿ” Blog post | RAG from CSV, Product description analysis | @AlessandroDiLauro | | Mixtral-8x7B-Instruct-v0.1 | 2.x | ๐Ÿ•ธ๏ธ๐Ÿ’ฌ Notebook | RAG on the Web, using the free Hugging Face Inference API | @TuanaCelik | | Zephyr-7B Beta | 2.x | ๐Ÿช Article and notebook | Article on how make this great model (fine-tuned from Mistral) run locally on Colab | @TuanaCelik @anakin87 | | Mixtral-8x7B-Instruct-v0.1 | 2.x | ๐Ÿฉบ๐Ÿ’ฌ Article and notebook | Healthcare chatbot with Mixtral, Haystack, and PubMed | @annthurium | | | Mixtral-8x7B-Instruct-v0.1 | 2.x | ๐Ÿ‡ฎ๐Ÿ‡น๐Ÿ‡ฌ๐Ÿ‡ง๐ŸŽง Notebook | Multilingual RAG from a podcast | @anakin87 | | | Mixtral-8x7B-Instruct-v0.1 | 2.x | ๐Ÿ“ฐ Notebook | Building a Hacker News Top Stories TL;DR | @TuanaCelik | |

๐Ÿ“š Resources

Great and deep blog post by Hugging Face on the MoE architecture, which is the basis of Mistral 8x7B. Technical report by the Hugging Face H4 team. They explain how they trained Zephyr, a strong 7B model fine-tuned from Mistral. The main topic is: โš—๏ธ how to effectively distill the capabilities of GPT-4 into smaller models? The report is insightful and well worth reading. I have summarized it here.
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