bragai/bRAG-langchain

Everything you need to know to build your own RAG application

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Established

Structured as progressive Jupyter notebooks using LangChain, covering foundational vector storage with ChromaDB/Pinecone, multi-query retrieval, semantic routing, and advanced techniques like RAPTOR and ColBERT token-level indexing. Demonstrates end-to-end optimization strategies including reciprocal rank fusion, Cohere re-ranking, and self-RAG approaches, with integration points for OpenAI embeddings, LangSmith tracing, and metadata-filtered vector stores.

4,051 stars.

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Maintenance 6 / 25

Adoption 10 / 25

Maturity 16 / 25

Community 21 / 25

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4,051

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480

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Jupyter Notebook

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bRAG-langchain and rag-ecosystem bRAG-langchain and complex-RAG-guide bRAG-langchain and Learn_RAG_from_Scratch_LLM bRAG-langchain and RAG_local_tutorial

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