#6464th of 88 in Learning and cookbooks
brag-langchain
Jupyter notebooks teaching RAG with LangChain, from basics to reranking
- Stars
- 4.2k
- License
- custom license
- Last commit
- Oct 2026
- Language
- Jupyter Notebook
4,032 on 21 Apr 20264,198 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Five sequential Jupyter notebooks that build RAG pipelines with LangChain, OpenAI embeddings and a Chroma or Pinecone vector store. They cover multi-query retrieval, logical and semantic routing, multi-vector indexing, ColBERT, RAG-Fusion with reciprocal rank fusion, and Cohere reranking. It is tutorial code, not a deployable application.
Who it is for: Python developers learning RAG techniques with LangChain
Strengths
- Notebooks progress from basic pipeline to RRF, ColBERT and Cohere reranking
- Includes a starter notebook for a customizable RAG chatbot
- Covers routing and metadata-filter query construction, not just plain retrieval
- Pinned Python version (3.11.11) and an .env.example for setup
Weaknesses
- Requires external API keys: OpenAI, Pinecone, Cohere and LangSmith
- Notebooks only; no server, UI or packaged deployment
- CRAG, Self-RAG, RAPTOR and chunking are mostly links to outside resources
- License is not identified (NOASSERTION), which complicates reuse
What it needs
- no GPU
- Needs OpenAI API, Pinecone, Cohere API, LangSmith, ChromaDB
- Models: OpenAI
Quick answers
Is brag-langchain open source?
Its code is public on GitHub under its own license terms; read them before you use it at work.
Does brag-langchain need a GPU?
No, it runs without one.
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