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title Document AI Analyst
emoji 🧠
colorFrom indigo
colorTo purple
sdk docker
app_port 7860
pinned true
license mit
short_description Enterprise Agentic RAG — upload PDFs and chat with AI

🧠 Document AI Analyst — Enterprise Agentic RAG System

Upload complex PDFs, financial reports, legal contracts, or research papers and chat with an AI agent that provides accurate, cited insights powered by Retrieval-Augmented Generation.

✨ Features

  • Multi-Format Upload — PDF, DOCX, TXT, Markdown with smart chunking
  • Semantic Search — Two-stage retrieval with cross-encoder reranking
  • Streaming Chat — Real-time AI responses with inline source citations
  • Data Isolation — Per-user vector collections for complete privacy
  • Open-Source LLMs — Powered by Mistral-7B and HuggingFace ecosystem

🏗️ Architecture

Layer Technology
Frontend Next.js 16, Tailwind CSS v4, Shadcn UI v2
Backend FastAPI, SQLAlchemy, JWT Auth
Embeddings sentence-transformers/all-MiniLM-L6-v2 (local)
Vector Store ChromaDB (persistent, per-user collections)
Reranker cross-encoder/ms-marco-MiniLM-L-6-v2
LLM Mistral-7B-Instruct via HuggingFace Inference API
Deployment Docker multi-stage build on HuggingFace Spaces

🚀 Quick Start

  1. Register an account
  2. Upload a PDF document
  3. Wait for processing (chunking + embedding)
  4. Ask questions and get cited answers!

🔧 Local Development

# Backend
cd backend && python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --port 7860

# Frontend
cd frontend && npm install && npm run dev

📦 Environment Variables

Variable Required Description
HF_TOKEN HuggingFace API token for LLM inference
SECRET_KEY JWT signing secret
DATABASE_URL SQLite path (default: sqlite:///./data/app.db)

🛠️ Tech Stack

Built with: FastAPILangChainChromaDBHuggingFaceNext.js 16Tailwind CSSShadcn UI

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