bragai/bRAG-langchain resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

4149 stars · 502 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of Jupyter notebooks teaching how to build Retrieval-Augmented Generation (RAG) applications, from basic pipelines to advanced techniques like multi-querying, routing, and custom RAG builds. It serves as a hands-on tutorial resource using LangChain, OpenAI embeddings, and vector stores such as ChromaDB and Pinecone.

Use cases

• learn how to build a rag application
• tutorial for retrieval augmented generation with langchain
• build a rag chatbot from scratch
• understand multi-query rag pipelines
• learn semantic and logical routing for rag
• set up vector store retrieval with chromadb or pinecone

When to choose

• you want a step-by-step, notebook-based introduction to RAG
• you are learning LangChain and vector store integration
• you need boilerplate starter code for a customizable RAG chatbot
• you want to explore advanced retrieval techniques like multi-querying and query routing

When to avoid

• you need a production-ready RAG framework rather than educational notebooks
• you want a maintained library with a stable API to depend on
• you need a non-Python or no-API-key solution
• you require a permissive license for commercial reuse

Facets

learning-resource · maturity active

rag llm-inference agent-framework chatbot machine-learning large-language-models artificial-intelligence machine-learning chatbots tutorials python cross-platform jupyter-notebooks langchain vector-databases educational hands-on-guide retrieval-augmented-generation

Member repositories

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markdown · JSON · MCP: product_card(name="bragai/bRAG-langchain")