// the find
hwchase17/chat-your-data
A minimal LangChain demo from early 2023 that shows how to do retrieval-augmented generation over a local document. It ships with the State of the Union address as sample data and a Streamlit frontend. This is a tutorial artifact, not a library.
- Extremely short — the whole thing is maybe 150 lines across four files, so you can read and understand every moving part in 20 minutes
- The RAG pipeline is laid out plainly: ingest → embed → FAISS → query, with no magic hiding the steps
- Good starting point if you want to strip it down and rebuild for your own use case rather than configure someone else's framework
- Abandoned — last meaningful change was 2023, and the LangChain APIs it uses have since been deprecated or renamed at least twice
- Persists the vector store as a pickle file, which is both fragile and a security risk if you ever load pickles from untrusted sources
- No chunking strategy beyond the default, no metadata filtering, no re-ranking — fine for a demo, a real problem the moment your document set grows
- Harrison Chase is LangChain's founder; this repo exists to drive blog traffic, not to be maintained or extended