BonusLockSMith/rag-citations

RAG Q&A with Citations 📚

Ask a question and get an answer grounded only in a curated knowledge base, with inline `[n]` citations to the exact sources — and an honest "I don't know" when the answer isn't in the KB. No hallucinating past the sources. Demo knowledge base: construction & home remodeling.

Project #10 of my "30 AI Projects in 15 Days" build-in-public challenge.

▶ Live demo: https://ask.gritai.solutions

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Why it's different

Most chatbots will confidently make things up. This one is retrieval-grounded and attributed: it answers only from the sources it retrieved, cites each claim, and refuses to guess when the KB doesn't cover the question. That's the pattern real production RAG needs — trust and traceability.

How it works

• —Chunk → embed → retrieve → answer. The KB is embedded with fastembed (BAAI/bge-small-en-v1.5, ONNX/CPU — no GPU, no torch). A question is embedded and matched by cosine similarity to the top sources.
• —Grounded answer with forced tool-use: Claude answers using only the retrieved sources, returns inline [n] citations, and a found flag — if the sources don't answer the question, it says so.
• —The UI shows the answer with clickable citations and the exact source passages below.

Run locally

pip install -r requirements.txt
export ANTHROPIC_API_KEY=sk-ant-...
python app.py                            # http://127.0.0.1:7860

Deploy (always-on)

Ships with a Dockerfile — works on Hugging Face Spaces, Render, Railway, or Fly.io. Set ANTHROPIC_API_KEY as a secret. Embeddings are local (no other keys).

Pro tier (roadmap): point it at your own private document set — hosted, grounded, cited.

Built by Robert Lucyk · GritAI Solutions · part of the 30-in-15 challenge.