bragai/bRAG-langchain
A collection of Jupyter notebooks teaching how to build Retrieval-Augmented Generation applications using LangChain with various embedding models and vector stores.
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This repository offers a comprehensive tutorial series on building RAG applications from introductory to advanced levels. It includes notebooks covering environment setup, document loading, embedding generation with OpenAI and other models, and vector store configuration using ChromaDB and Pinecone. The project provides starter code in a fully customizable RAG chatbot notebook and explores advanced techniques like multi-query retrieval and custom RAG builds.
Frequently asked
- What is bragai/bRAG-langchain?
- A collection of Jupyter notebooks teaching how to build Retrieval-Augmented Generation applications using LangChain with various embedding models and vector stores.
- Is bRAG-langchain open source?
- Yes — bragai/bRAG-langchain is an open-source project tracked on heatdrop.
- What language is bRAG-langchain written in?
- bragai/bRAG-langchain is primarily written in Jupyter Notebook.
- How popular is bRAG-langchain?
- bragai/bRAG-langchain has 4.1k stars on GitHub.
- Where can I find bRAG-langchain?
- bragai/bRAG-langchain is on GitHub at https://github.com/bragai/bRAG-langchain.