Just got my local RAG pipeline running with LangChain and FAISS! It's a fantastic way to prototype with your own docs without hitting API limits or worrying about data privacy. I'll walk through the key steps I used.
First, you'll need your docs in a simple `./docs` folder. LangChain's document loaders make this easy. The magic is in the embeddings and the FAISS vectorstore—keep everything on your machine. I used the `SentenceTransformerEmbeddings` for local embeddings. After splitting your text, you create the vectorstore with `FAISS.from_documents`. Then, you can query it with a simple `RetrievalQA` chain. The whole thing feels super responsive! Biggest tip: chunk size is everything for retrieval quality. Anyone else building local RAG? What embedding models are you using?
Hope this helps someone get started! 👋