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bRAG langchain

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Basic Information

This repository is a hands-on collection of notebooks and resources for exploring and building Retrieval-Augmented Generation (RAG) systems using LangChain and common vector stores. It targets developers and researchers who want step-by-step, runnable examples that cover the full lifecycle of a RAG pipeline from environment setup to advanced retrieval strategies. The project includes a boilerplate starter notebook (full_basic_rag.ipynb) and five sequential notebooks that introduce basic RAG concepts, multi-query construction, routing and query structuring, multi-representation indexing and advanced retrieval methods, and retrieval plus reranking workflows. The README documents required Python version (3.11.11), virtual environment practices, dependency installation, and an example .env with keys for OpenAI, LangSmith, Pinecone and Cohere so users can connect to embeddings, vector stores and re-ranking services.

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App URL
https://github.com/BragAI/bRAG-langchain
Github Repository
https://github.com/BragAI/bRAG-langchain/blob/main/README.md

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App Details

Features
The repo is organized into five focused notebooks: an initial setup and baseline RAG pipeline, multi-query experimentation, logical and semantic routing with metadata filters, multi-vector indexing and in-memory summary stores, and retrieval with reranking and fusion. It demonstrates embedding generation with OpenAI-style models, vector store integration (ChromaDB and Pinecone), MultiVectorRetriever patterns, InMemoryByteStore for summaries, ColBERT token-level indexing, RAPTOR discussion, RAG-Fusion multi-query generation, Reciprocal Rank Fusion (RRF) and Cohere re-ranking. The project includes diagrams, a full_basic_rag.ipynb starter, a requirements.txt, an .env.example describing environment variables (OPENAI_API_KEY, LANGCHAIN keys, PINECONE and COHERE keys) and instructions to run notebooks in a controlled Python 3.11.11 virtual environment.
Use Cases
This repository provides practical, runnable examples to learn and prototype RAG systems, lowering the barrier to experiment with retrieval strategies, indexing schemes and re-ranking techniques. Beginners can follow the sequential notebooks to set up embeddings, vector stores, and a basic RAG pipeline, while advanced users can study multi-query fusion, semantic and logical routing, multi-vector indexing, ColBERT integration and re-ranking approaches like RRF and Cohere. The included environment and dependency guidance, boilerplate notebook, and explicit .env configuration help reproduce experiments and connect to services such as Pinecone and LangSmith. Overall it serves as a reference implementation and learning scaffold to accelerate building, comparing and adapting RAG pipelines for research or application development.

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