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Most chatbots give wrong answers because of retrieval, not the model: what gets pulled in before it ever answers. So I built a RAG chatbot that answers straight from your own documents, with sources you can check. Qdrant vector store, FastAPI + LangChain backend, clean chat UI. Get retrieval right and it stops making things up. Happy to build one of these on your docs. More of my work at kumarsomesh.com.
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Source references — every answer shows which document and section it came from
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One command to re-index after updating documents — documented, no programmer needed
LIVE DEMO
Ask it about my products, pricing, deployment and support — it answers from 6 real documents with sources:
https://kb-assistant-demo.dvc-ai.workers.dev
The 'I don't have that information' line is the part most RAG builds skip, and it's the one that matters. How do you set the cutoff? On mine, a fixed similarity threshold broke once I filtered results by user role. Scores shifted and it refused questions it could answer.
Problem: You spend too much time answering customers manually and lose track of conversations.
Which solution is best suited for your business?
3 votes
Ends in 20h