LocalRAG Docs
GitHub →A fully local, privacy-first RAG system for technical document Q&A. Users upload PDFs and ask natural-language questions, the system retrieves relevant passages and generates grounded, source-attributed answers entirely on-device, with no data sent to third-party APIs.
The challenge: keeping every answer verifiable and grounded — not just generating a plausible response, but returning the exact source passages, page numbers, and relevance scores behind it, using a multi-stage retrieval pipeline with zero cloud dependency.