AI Document Q&A

About This Blueprint

A retrieval-augmented (RAG) knowledge base you can point at your own documents. Users drag in PDFs or text, the app chunks and embeds them, and a chat panel answers questions grounded in the source — every answer shows the passages it came from so nothing is hallucinated. Includes a document list with upload status, a chat thread with streaming answers, inline source citations that expand to the original passage, and a clean empty state that walks a first-time user through adding their first document. Built with a provider-agnostic abstraction so you can swap the embedding and LLM backend.

What's Included

• Drag-and-drop document upload with parsing status
• Document list sidebar with per-file state
• Chat panel with streaming, grounded answers
• Inline source citations that expand to the original passage
• Chunking + embedding pipeline with a swappable provider
• Vector-search retrieval layer (provider-agnostic)
• First-run empty state that guides adding a document
• Graceful handling of unsupported files and empty results

Compatible AI Tools

This blueprint has been tested and produces reliable results with: