DocChat - RAG over your documents.
A production-style AI demo that turns uploaded PDFs into cited answers using a practical two-model architecture.
page text extracted and indexed into pgvector
retrieved for every grounded answer by default
Kimi primary chat plus DeepSeek comparison
demo-scale API cost target
Internal documents are useful only if teams can query them.
SMB teams often have policies, manuals, onboarding docs, and client instructions scattered across PDFs. A useful workflow needs ingestion, retrieval, source citations, and an honest fallback when the answer is not in the document.
A lean RAG pipeline without extra infrastructure.
Ingest
A PDF upload is parsed page by page, normalized, and split into overlapping token chunks.
Retrieve
DeepSeek-compatible embeddings are stored in Supabase and queried through a pgvector RPC.
Answer
Kimi receives only the retrieved chunks and must cite each factual claim with source markers.
Compare
A separate compare endpoint runs Kimi and DeepSeek over the same context for side-by-side evaluation.
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