law_ai

LegalAI - Legal Document Search & Analysis Platform

A comprehensive AI-powered legal document search and analysis platform built with FastAPI, React, and advanced AI models. This system provides semantic search, legal reasoning, document analysis, and intelligent categorization for legal professionals.

🏗️ System Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Frontend (React)                         │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐           │
│  │   Search    │ │   Upload    │ │  Analysis   │           │
│  │   Interface │ │   System    │ │  Dashboard  │           │
│  └─────────────┘ └─────────────┘ └─────────────┘           │
└─────────────────────────────────────────────────────────────┘
                            │
                     ┌──────┴──────┐
                     │             │
┌────────────────────▼─────────────▼───────────────────────────┐
│                Backend (FastAPI)                            │
│  ┌──────────────┐ ┌──────────────┐ ┌──────────────┐        │
│  │  Document    │ │   Vector     │ │      AI      │        │
│  │  Processor   │ │   Store      │ │   Analyzer   │        │
│  └──────────────┘ └──────────────┘ └──────────────┘        │
└─────────────────────────────────────────────────────────────┘
                            │
                 ┌──────────┼──────────┐
                 │          │          │
          ┌──────▼───┐ ┌────▼───┐ ┌───▼────┐
          │PostgreSQL│ │ChromaDB│ │OpenAI  │
          │Database  │ │Vectors │ │Models  │
          └──────────┘ └────────┘ └────────┘

✨ Features

🔍 Advanced Search

Semantic Search: Find documents by meaning, not just keywords
Keyword Search: Traditional full-text search with filters
Hybrid Search: Combine semantic and keyword approaches
Citation Search: Find documents by legal citations
Advanced Filters: Filter by jurisdiction, document type, date range

🧠 AI-Powered Analysis

Legal Reasoning: Get comprehensive analysis of legal issues
Document Summarization: AI-generated summaries and key points
Citation Extraction: Automatic identification of legal citations
Precedent Analysis: Find related cases and precedents
Counterargument Detection: Identify alternative legal perspectives

📄 Document Processing

Multi-Format Support: PDF, DOCX, TXT files
OCR Capability: Extract text from scanned documents
Intelligent Chunking: Break documents into semantic sections
Metadata Extraction: Automatic categorization and tagging
Legal Entity Recognition: Identify legal concepts and entities

📊 Analytics & Insights

Document Analytics: Readability scores, complexity analysis
Search Analytics: Track popular queries and trends
Usage Statistics: Monitor system performance and user behavior
Similar Document Detection: Find related documents automatically

🚀 Quick Start

Prerequisites

Python 3.9+
Node.js 16+
PostgreSQL 12+ (production) or SQLite (development)
Git

1. Clone the Repository

git clone <repository-url>
cd law_ai

2. Backend Setup

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp env.example .env
# Edit .env with your configuration

3. Frontend Setup

cd frontend
npm install

4. Database Setup

# For development (SQLite)
cd backend
python -m app.core.database

# For production (PostgreSQL)
# Ensure PostgreSQL is running and create database
createdb legal_ai

5. Start Development Servers

# Terminal 1: Backend
cd backend
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

# Terminal 2: Frontend
cd frontend
npm start

Visit http://localhost:3000 to access the application.

🔧 Configuration

Environment Variables

Create a .env file in the root directory:

# OpenRouter AI Configuration
OPENROUTER_API_KEY=your_openrouter_api_key_here
OPENROUTER_BASE_URL=https://openrouter.ai/api/v1

# AI Model Configuration
DOCUMENT_ANALYSIS_MODEL=anthropic/claude-3-sonnet
LEGAL_REASONING_MODEL=anthropic/claude-3-opus
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

# Database Configuration
DATABASE_URL=sqlite:///./legal_ai.db  # Development
# DATABASE_URL=postgresql://user:password@localhost/legal_ai  # Production

# Vector Database
CHROMA_HOST=localhost
CHROMA_PORT=8001
CHROMA_COLLECTION_NAME=legal_documents

# File Processing
MAX_FILE_SIZE_MB=50
SUPPORTED_FILE_TYPES=.pdf,.docx,.txt
CHUNK_SIZE=1000
CHUNK_OVERLAP=200

# Search Configuration
MAX_SEARCH_RESULTS=50
SIMILARITY_THRESHOLD=0.7
ENABLE_HYBRID_SEARCH=true

# Optional: Advanced Features
REDIS_URL=redis://localhost:6379  # For caching
ELASTICSEARCH_URL=http://localhost:9200  # For advanced search

Model Configuration

The system supports various AI models through OpenRouter:

Document Analysis: anthropic/claude-3-sonnet (default)
Legal Reasoning: anthropic/claude-3-opus (default)
Embeddings: sentence-transformers/all-MiniLM-L6-v2

📁 Project Structure

law_ai/
├── backend/                    # FastAPI backend
│   ├── app/
│   │   ├── api/               # API endpoints
│   │   ├── core/              # Core configuration
│   │   ├── models/            # Database models
│   │   ├── schemas/           # Pydantic schemas
│   │   └── services/          # Business logic
│   └── requirements.txt
├── frontend/                   # React frontend
│   ├── src/
│   │   ├── components/        # React components
│   │   ├── pages/             # Page components
│   │   └── styles/            # CSS/Tailwind
│   └── package.json
├── pdf/                       # Legal documents
└── README.md

🎯 Usage Guide

1. Document Upload

Navigate to the Upload page
Drag and drop PDF, DOCX, or TXT files
Wait for processing to complete
Documents are automatically indexed for search

2. Search Documents

Semantic Search:

"What are the requirements for establishing breach of contract?"

Keyword Search:

contract AND breach AND damages

Citation Search:

cite:"42 U.S.C. § 1983"

3. AI Analysis

Go to the Analysis page
Enter your legal question
Select analysis type (General, Case Law, Statutory, Precedent)
Choose options (citations, counterarguments)
Get comprehensive AI-powered analysis

4. Document Viewing

View document content in organized chunks
Access metadata and legal concepts
See AI-generated analysis and key points
Find similar documents automatically

🔌 API Documentation

Authentication

Currently using API key authentication. Include your API key in the header:

curl -H "Authorization: Bearer YOUR_API_KEY" \
     http://localhost:8000/api/documents

Key Endpoints

POST /api/documents/upload - Upload documents
GET /api/search/semantic - Semantic search
GET /api/search/keyword - Keyword search
POST /api/analysis/analyze - AI analysis
GET /api/documents/{id} - Get document details

Full API documentation is available at http://localhost:8000/docs when running the backend.

🐳 Docker Deployment

Using Docker Compose

# Build and start all services
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

Individual Container Build

# Backend
docker build -t legal-ai-backend ./backend

# Frontend
docker build -t legal-ai-frontend ./frontend

📊 Performance & Scalability

Database Optimization

Indexed fields for fast search queries
Chunked document storage for efficient retrieval
Vector embeddings for semantic similarity

Caching Strategy

Redis for search result caching
Application-level caching for expensive operations
CDN for static assets in production

Monitoring

Built-in logging for all operations
Performance metrics collection
Error tracking and alerting

🧪 Testing

Backend Tests

cd backend
pytest tests/ -v

Frontend Tests

cd frontend
npm test

Integration Tests

# Run full test suite
npm run test:integration

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Development Guidelines

Follow PEP 8 for Python code
Use TypeScript for frontend development
Write tests for new features
Update documentation as needed

📋 Troubleshooting

Common Issues

“Module not found” errors:

# Ensure virtual environment is activated
source venv/bin/activate
pip install -r requirements.txt

Database connection errors:

# Check database configuration in .env
# Ensure PostgreSQL is running (production)

Frontend build errors:

cd frontend
rm -rf node_modules package-lock.json
npm install

Logs and Debugging

Backend logs: Check console output or log files
Frontend logs: Open browser developer tools
Database logs: Check PostgreSQL/SQLite logs

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

OpenRouter for AI model access
ChromaDB for vector database
FastAPI and React communities
Legal professionals who provided domain expertise

📞 Support

For support, please:

Check the documentation
Search existing issues
Create a new issue with detailed information
Contact the development team

Built with ❤️ for the legal community