Document QA Agent (RAG)
A production RAG service that answers questions with citations from private document corpora.
Architecture & workflows
How this product is built, deployed, and operated in production.
Delivery flow
End-to-end delivery flow for this project, from intake through the final handoff.
Challenge
Teams could not search long PDFs and policies reliably. Keyword search missed meaning and chatbots invented answers.
How we did it
Built an ingest pipeline (chunk, embed, index) and a query path with hybrid retrieval plus answer generation grounded in sources.
Outcome
Support and ops teams got cited answers with measurable groundedness, served behind a FastAPI microservice.
Tech stack
Step-by-step
- 1
Ingest
Parse PDFs/docs, clean text, split into chunks
- 2
Embed
Create vectors and metadata (source, page, tenant)
- 3
Index
Upsert into the vector store with ACLs
- 4
Retrieve
Hybrid search + rerank top passages
- 5
Generate
LLM answers with citations only from retrieved context
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