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RAGatouille

a simple lib to use&train top retrieval models in RAG apps. Easily use and train state of the art late-interaction retrieval methods (ColBERT) in any RAG pipeline. Designed for modularity and ease-of-use, backed by research. If you’re at all interested in LLMs/LLM-apps, you’ve probably heard of RAG: Retrieval-Assisted Generation, i.e.

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  1. Demystifying Advanced RAG Pipelines

    github.com

    An LLM-powered advanced RAG pipeline built from scratch

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  2. Cognita

    github.com

    open-source RAG framework for modular applications

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  3. Open-Source Colab Notebooks to Implement Advanced RAG Techniques

    github.com

    This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems.

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  4. In-Browser Graph RAG with Kuzu-WASM and WebLLM

    blog.kuzudb.com

    We show the potential of modern, embedded graph databases in the browser by demonstrating a fully in-browser chatbot that can perform Graph RAG using Kuzu (the graph database we're building) and WebLLM, a popular in-browser inference engine for LLMs.

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  5. I built a browser-based tutorial for learning RAG for LLMs

    takehomes.com

    I wanted to share this project I've been working on that can maybe help you or your developer friends out.

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