bRAG-langchain

Why we included this project

This notebook collection is a practical walkthrough for anyone building a first RAG pipeline. It starts with the basics, document loading, embeddings, and vector stores, then moves to advanced techniques like multi-query retrieval, RAG-Fusion, query routing, and Cohere re-ranking. Each notebook is self-contained and pairs LangChain code with the reasoning behind the approach, so you can lift patterns such as CRAG, Self-RAG, RAPTOR, or ColBERT into your own project. A starter file offers boilerplate for a customizable RAG chatbot. It is a learning resource rather than a turnkey production system, but a useful map for teams weighing retrieval strategies and quality trade-offs.