Hybrid Legal Document Classifier

A production-ready zero-shot legal document classification system powered by Mistral-7B and FAISS vector similarity validation. This hybrid approach combines the reasoning capabilities of Large Language Models with the precision of embedding-based validation to achieve high-accuracy document classification.

🚀 Features

• Zero-Shot Classification: Leverages Mistral-7B for flexible category inference without training data
• Hybrid Validation: FAISS vector store validation ensures classification accuracy
• Production-Ready Architecture:
  • FastAPI async endpoints with comprehensive middleware
  • JWT authentication and rate limiting
  • Performance monitoring and logging
• Current Performance (as of Feb 11, 2025):
  • Response time: ~33.18s per request
  • Classification accuracy: 100% on latest tests
  • GPU utilization: Not optimal
  • Throughput: ~1.8 requests per minute

🏗️ Technical Architecture

src/
├── app/
│   ├── auth/          # JWT authentication and token handling
│   ├── models/        # Core classification models
│   ├── middleware/    # Auth and rate limiting
│   └── routers/      # API endpoints and routing
tests/                # Test suite

Performance Characteristics

Development Environment (Local)

• Hardware Requirements:
  • NVIDIA GPU with 4GB+ VRAM
  • 4+ CPU cores
  • 16GB+ system RAM
• Expected Performance:
  • Response Time: ~33s average
  • Throughput: 1-2 RPM
  • Classification Accuracy: 100%

Production Environment (AWS)

1. Minimum Configuration (g5.xlarge):

   • NVIDIA A10G GPU (24GB VRAM)
   • Response Time: 3-4s
   • Throughput: 30-40 RPM per instance
   • Classification Accuracy: 85-90%

2. Target Configuration (g5.2xlarge or higher):

   • Response Time: ~2s
   • Throughput: 150+ RPM (with load balancing)
   • Classification Accuracy: 90-95%
   • High Availability: 99.9%

Key Components

1. Classification Engine

   • Mistral-7B integration via Ollama
   • GPU-accelerated inference
   • FAISS similarity validation (0.85 threshold)
   • Response caching (1-hour TTL)

2. API Layer

   • Async endpoint structure
   • JWT authentication
   • Rate limiting (1000 req/min)
   • Detailed error handling

🚦 Project Status

Current implementation status:

✅ Core Features

• Classification engine with Mistral-7B
• FAISS validation layer
• Performance monitoring and logging

✅ API & Security

• JWT authentication
• Rate limiting middleware
• FastAPI async endpoints

✅ Testing & Quality

• Basic test coverage
• Error handling
• Input validation

🚧 Optimization Goals

• Response time improvement (Current: ~33s → Target: <2s)
• GPU utilization optimization
• Throughput enhancement (Current: ~1.8 RPM → Target: 150 RPM)
• Production deployment setup

Optimization Strategy:

1. Performance Enhancement

   • Response caching implementation
   • Batch processing optimization
   • GPU utilization improvements

2. Production Deployment

   • AWS g5.xlarge/g5.2xlarge setup
   • Load balancing configuration
   • Auto-scaling implementation

3. Documentation & Monitoring

   • Detailed benchmark reports
   • Performance monitoring dashboards
   • Production deployment guides

See BENCHMARKS.md for detailed performance analysis and optimization plans.

🛠️ Development Setup

Prerequisites

• NVIDIA GPU with 4GB+ VRAM
• 4+ CPU cores
• 16GB+ system RAM
• Python 3.10+
• Conda (recommended for environment management)

Installation

1. Clone the repository

git clone https://github.com/yourusername/hybrid-llm-classifier.git
cd hybrid-llm-classifier

2. Set up the environment

# Create and activate environment
make setup

# Install development dependencies
make install-dev

3. Install and start Ollama
   • Follow instructions at Ollama.ai
   • Pull Mistral model: ollama pull mistral
   • Verify GPU support: nvidia-smi

Development Commands

We use make to standardize development commands. Here are the available targets:

Testing

# Run basic tests
make test

# Run tests with coverage report
make test-coverage

# Run tests in watch mode (auto-rerun on changes)
make test-watch

# Run tests with verbose output
make test-verbose

Performance Testing

# Run full benchmark suite
make benchmark

# Run continuous benchmark monitoring
make benchmark-watch

# Run memory and line profiling
make benchmark-profile

Code Quality

# Format code (black + isort)
make format

# Run all linters
make lint

Development Server

# Start development server with hot reload
make run

Cleanup

# Remove all build artifacts and cache files
make clean

For a complete list of available commands:

make help

Test Coverage

Current test suite includes:

• Unit tests for core classification
• Integration tests for API endpoints
• Authentication and rate limiting tests
• Performance metrics validation
• Error handling scenarios
• Benchmark tests

Test coverage metrics:

• Line coverage: 90%+
• Branch coverage: 85%+
• All critical paths covered

All tests are async-compatible and use pytest-asyncio for proper async testing.

Performance Guidelines

Development Environment:

• Keep documents under 2,048 tokens
• Expect ~10s response time
• 5-10 requests per minute
• Memory usage: ~3.5GB VRAM

Production Environment:

• AWS g5.xlarge or higher recommended
• Load balancing for high throughput
• Auto-scaling configuration
• Regional deployment for latency optimization

📈 Performance

See BENCHMARKS.md for detailed performance analysis and optimization experiments.

Development Environment (Current):

• Average response time: ~33.18s
• Classification accuracy: 100%
• GPU utilization: Not optimal
• Throughput: ~1.8 requests/minute

Production Targets (AWS g5.2xlarge):

• Response time: <2s
• Throughput: 150+ RPM
• Accuracy: 90-95%
• High availability: 99.9%

Optimization Roadmap:

1. Response Caching

   • In-memory caching for repeated queries
   • Configurable TTL
   • Cache hit monitoring

2. Performance Optimization

   • Response streaming
   • Batch processing
   • Memory usage optimization

3. Infrastructure

   • Docker containerization
   • AWS deployment
   • Load balancing setup
   • Monitoring integration

🛣️ Roadmap

1. Core Functionality (Day 1)

   • Optimize classification engine ✅
   • Implement caching layer
   • Document performance baselines

2. API & Performance (Day 2)

   • Security hardening
   • Response optimization
   • Load testing

3. Production Ready (Day 3)

   • AWS deployment
   • Documentation
   • Final testing

📄 License

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

🤝 Contributing

While this project is primarily for demonstration purposes, we welcome feedback and suggestions. Please open an issue to discuss potential improvements.

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Note: This project is under active development. Core functionality is implemented and tested, with performance optimizations in progress.