README.md

metadata

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: FastAPI • LangChain • ChromaDB • HuggingFace • Next.js 16 • Tailwind CSS • Shadcn UI