NirDiamant

    NirDiamant/RAG_Techniques

    This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

    30.2K stars
    3.7K forks
    250 GitHub watchers
    Updated Nov 6, 2026
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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    1 release in the last 90 days, the latest 10 days ago, with 40 contributors on GitHub.

    Use Cases & Benefits

    • Showcases advanced techniques and detailed tutorials for building and enhancing Retrieval-Augmented Generation (RAG) systems using Python and Jupyter notebooks.
    • Provides a comprehensive, practical, and regularly updated resource with step-by-step guides to improve RAG accuracy, efficiency, and contextual relevance.
    • Use for learning foundational RAG implementations with LangChain and LlamaIndex to build simple retrieval-augmented generative AI models.
    • Use for exploring advanced retrieval methods like fusion retrieval, reranking, and multi-faceted filtering to optimize information retrieval quality.
    • Use for experimenting with context enrichment techniques such as semantic chunking, contextual compression, and document augmentation to enhance response relevance.

    Should you depend on it?

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    License

    Other

    GitHub could not identify this license; read the LICENSE file before depending on it.

    Community files

    • README: present
    • Contributing guide: present

    Watch-outs

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    From the README

    🌟 Support This Project: Your sponsorship fuels innovation in RAG technologies. Become a sponsor to help maintain and expand this valuable resource!

    Excerpt from the NirDiamant/RAG_Techniques README on GitHub (NOASSERTION license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    3.7K+168

    Open issues

    7-7

    Compared with peers in AI

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth

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