FareedKhan-dev/rag-with-raptor
A Step-by-Step Implementation of RAPTOR based RAG implementation
This project helps developers build more effective Retrieval Augmented Generation (RAG) systems by creating a smarter, hierarchical index of their documents. It takes raw source documents, processes them into detailed chunks, then clusters and summarizes these chunks recursively to build a 'knowledge tree' of concepts. The output is a highly optimized vector store that improves the accuracy and relevance of AI-generated answers for a wide range of queries.
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Use this if you are a developer struggling with your RAG system providing irrelevant or low-quality answers from complex, extensive documentation.
Not ideal if you need a simple RAG setup for small, straightforward datasets where basic retrieval is sufficient.
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38
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Language
Jupyter Notebook
License
MIT
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Last pushed
Sep 01, 2025
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