AI-powered legal automation platform with LLM integration, RAG, and document analysis

Full-featured legal automation platform demonstrating modern full-stack development with AI/ML integration. Built with Python FastAPI backend, React frontend, and comprehensive LLM capabilities for document analysis, contract generation, and intelligent client communication.

• AI Legal Assistant - RAG-powered chatbot for legal consultations
• Document Analysis - Automated contract analysis with risk detection
• Contract Generator - Template-based document generation with LLM
• CRM Integration - AmoCRM/Bitrix24 connectors
• Telegram Bot - Automated client communication
• Payment Integration - YooKassa support
• Knowledge Base - Semantic search with vector embeddings

Backend

• FastAPI 0.110 (async/await)
• SQLAlchemy 2.0 (async ORM)
• MySQL 8.0
• Redis (caching)
• LangChain + OpenAI/YandexGPT
• ChromaDB (vector storage)

Frontend

• React 18 + TypeScript
• Vite
• TanStack Query
• Zustand
• Tailwind CSS

Infrastructure

• Docker + Docker Compose
• Alembic (migrations)
• pytest (testing)
• Nginx

┌─────────────────┐
│  React Frontend │
└────────┬────────┘
         │
    ┌────▼────┐
    │ FastAPI │
    └────┬────┘
         │
    ┌────┼─────┬──────┐
    │    │     │      │
┌───▼┐ ┌─▼──┐ ┌▼───┐ ┌▼─────┐
│MySQL│ │Redis│ │LLM│ │CRM   │
└─────┘ └────┘ └───┘ └──────┘

• Python 3.11+
• Node.js 20+
• MySQL 8.0
• Redis 7+

# Clone repository
git clone https://github.com/shuldeshoff/legalflow-ai.git
cd legalflow-ai

# Configure environment
cp .env.example .env
# Edit .env with your API keys

# Run with Docker
docker-compose up -d

# Or manually:

# Backend
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
alembic upgrade head
uvicorn app.main:app --reload

# Frontend
cd frontend
npm install
npm run dev

• Frontend: http://localhost:5173
• Backend API: http://localhost:8000
• API Docs: http://localhost:8000/docs

POST /api/v1/documents/analyze
{
  "file": "contract.pdf",
  "type": "contract"
}

Response:
{
  "summary": "Service agreement...",
  "key_points": ["Payment terms", "Delivery date"],
  "risks": ["Missing penalty clause"],
  "recommendations": "Add termination conditions"
}

POST /api/v1/llm/chat
{
  "messages": [{"role": "user", "content": "How to terminate a lease?"}],
  "model": "gpt-3.5-turbo"
}

Response (streaming):
{
  "content": "To terminate a lease agreement...",
  "sources": ["Knowledge base"],
  "model": "gpt-3.5-turbo"
}

POST /api/v1/knowledge/rag
{
  "query": "What are client rights?",
  "limit": 3
}

Response:
{
  "answer": "Based on available knowledge...",
  "sources": [{"title": "Civil Code", "score": 0.89}],
  "model": "gpt-3.5-turbo"
}

# Run tests
cd backend
pytest tests/ -v

# With coverage
pytest tests/ --cov=app --cov-report=html

# Results
13/13 tests passing
Coverage: 51%

legalflow-ai/
├── backend/
│   ├── app/
│   │   ├── api/              # API endpoints
│   │   ├── core/             # Configuration
│   │   ├── models/           # Database models
│   │   ├── schemas/          # Pydantic schemas
│   │   └── services/         # Business logic
│   ├── tests/                # Test suite
│   └── requirements.txt
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   └── services/
│   └── package.json
├── docs/                     # Documentation
└── docker-compose.yml

Backend Development

• FastAPI with async/await patterns
• SQLAlchemy 2.0 async ORM
• Pydantic validation
• JWT authentication
• File processing (PDF, DOCX)
• Background tasks
• Unit & integration tests

AI/ML Integration

• LLM integration (OpenAI, YandexGPT)
• RAG (Retrieval Augmented Generation)
• LangChain orchestration
• Vector database (ChromaDB)
• Semantic search
• Streaming responses
• Prompt engineering

Frontend Development

• React 18 with TypeScript
• State management (Zustand + React Query)
• Real-time updates
• Responsive design
• Form validation

Database & Performance

• MySQL schema design
• Complex queries & JOINs
• Indexes & optimization
• Migrations (Alembic)
• Connection pooling
• Redis caching

Integrations

• REST API clients
• OAuth 2.0
• Webhook handling
• CRM systems
• Telegram Bot API
• Payment systems
• Error handling & retry logic

DevOps

• Docker containerization
• Docker Compose orchestration
• Nginx configuration
• Environment management
• Structured logging

• API response time: < 200ms (p95)
• LLM response time: 2-5s (streaming)
• Database queries: < 50ms (indexed)
• Test execution: < 3s
• Code coverage: 51%

• JWT tokens with refresh mechanism
• SQL injection protection (ORM)
• Password hashing (bcrypt)
• Environment variables for secrets
• CORS configuration
• Input validation

• API Documentation: /docs (Swagger UI)
• Alternative docs: /redoc (ReDoc)
• Architecture: docs/ARCHITECTURE.md
• API Reference: docs/API.md
• Testing Guide: docs/TESTING.md

# Production deployment
docker-compose -f docker-compose.prod.yml up -d

# Or manual:
# 1. Set up server (Ubuntu 22.04+)
# 2. Install Docker
# 3. Configure environment
# 4. Run containers
# 5. Set up Nginx + SSL

MIT License

Yuri Shuldeshov

Full-stack developer specializing in Python, AI/ML, and legal tech automation

• GitHub: @shuldeshoff
• Telegram: @shuldeshoff
• Repository: legalflow-ai

Technologies: Python · FastAPI · React · TypeScript · MySQL · Redis · OpenAI GPT-4 · LangChain · Docker