DotChat
PDF-to-cited-answer RAG product. Kimi K2.6 handles the chat, DeepSeek V4 Pro powers retrieval, Supabase pgvector stores the index. Compare mode runs both models in parallel on the same retrieved context.
The brief.
PDF-to-cited-answer RAG product. Kimi K2.6 handles the chat, DeepSeek V4 Pro powers retrieval, Supabase pgvector stores the index. Compare mode runs both models in parallel on the same retrieved context.
The problem
Most "chat with your PDF" tools either hallucinate or hide which model and retrieval choices actually drive answer quality.
What I built
A PDF-to-cited-answer RAG product where every answer is grounded in specific pages. Kimi K2.6 runs the chat, DeepSeek V4 Pro powers retrieval, and Supabase pgvector stores the index — no Pinecone, no LangChain. A compare mode runs two models in parallel on the same retrieved context, so you can see exactly how model choice changes the answer.
The result
Every answer is page-grounded and citable, two models can be compared side by side, and the whole thing runs on pgvector instead of a managed vector vendor. A working demonstration that retrieval quality — not model hype — decides RAG quality.
I wrote about this approach in building AI agents that don't hallucinate. Want a grounded knowledge agent? Book a call.
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