DocIntel — AI-Powered Document Intelligence Hub

Upload any business document. Ask questions. Get cited answers. All running locally on your machine.

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What it does

DocIntel lets you upload PDFs, contracts, and reports and interact with them through four features:

• Q&A — ask natural language questions, get answers grounded in the document with page-level citations
• Summary — automatically extracts purpose, parties, key dates, obligations, and risks into a structured card view
• Entity Extraction — pulls out people, organizations, dates, monetary values, and contract clauses as tagged pills
• Comparison — side-by-side diff table between two documents with similarity verdicts

Zero external API calls. Everything runs locally via Ollama.

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Tech Stack

Layer	Technology
LLM	Llama 3.1 8B via Ollama
Embeddings	nomic-embed-text via Ollama
Vector Store	ChromaDB
Retrieval	Hybrid dense + BM25 with Reciprocal Rank Fusion
Backend	FastAPI + SQLAlchemy + PostgreSQL
Frontend	React + Vite + Zustand
Parsing	PyMuPDF + python-docx
NER	spaCy + LLM fallback

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Architecture

React (Vite)
    ↓
FastAPI
    ├── Document Parser (PyMuPDF / python-docx)
    ├── Embedder (nomic-embed-text via Ollama)
    ├── Hybrid Retriever (ChromaDB + BM25 + RRF)
    ├── LLM (Llama 3.1 8B via Ollama)
    └── Entity Extractor (spaCy + LLM)
         ↓
PostgreSQL (metadata + sessions) + ChromaDB (vectors)

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Setup

Prerequisites

• Python 3.11
• Node.js 18+
• Docker
• Ollama

1. Pull models

ollama pull llama3.1:8b
ollama pull nomic-embed-text

2. Start infrastructure

docker compose up -d

3. Backend

cd backend
python3.11 -m venv venv
source venv/bin/activate
pip install --only-binary=pymupdf -r requirements.txt
python -m spacy download en_core_web_sm
uvicorn main:app --reload --port 8000

4. Frontend

cd frontend
npm install
npm run dev

Open http://localhost:5173

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API Reference

Method	Endpoint	Description
POST	/documents/upload	Upload and process a document
GET	/documents/	List all documents
DELETE	/documents/{id}	Delete a document
POST	/qa/ask	Ask a question over documents
GET	/analysis/summary/{id}	Get structured summary
GET	/analysis/entities/{id}	Get extracted entities
POST	/compare/	Compare two documents

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Why hybrid retrieval

Most RAG tutorials use pure vector search. This project uses Reciprocal Rank Fusion to combine dense vector search (ChromaDB cosine similarity) with BM25 keyword search. The fused ranking consistently outperforms either method alone, especially on short documents and contract-specific terminology — exactly the kind of content this app is built for.