NOVA (Neuro-symbolic Optimized Versatile Agent)

NOVA is a knowledge distillation system that extracts domain-specific knowledge and trains compact models while maintaining high performance.

Features

• 🧠 Knowledge Extraction: Extract structured knowledge from any domain
• 🎯 Model Distillation: Train compact models that preserve essential knowledge
• 📊 Performance Validation: Ensure models meet accuracy and efficiency targets
• 💾 Caching System: Optimize performance with intelligent caching
• 🔄 Streaming Support: Process large-scale data efficiently

Installation

Using Docker

# Build the Docker image
docker build -t nova .

# Run NOVA with Docker
docker run -it --rm \
  -v $(pwd)/.nova_cache:/app/.nova_cache \
  -e OPENROUTER_API_KEY=your_key_here \
  nova distill \
  --domain "computer_science" \
  --prompt "Explain distributed systems" \
  --architecture transformer_tiny

Manual Installation

# Clone the repository
git clone <repository-url>
cd nova

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
export OPENROUTER_API_KEY=your_key_here

# Run NOVA
python -m nova.distillation.cli distill \
  --domain "computer_science" \
  --prompt "Explain distributed systems" \
  --architecture transformer_tiny

Configuration

Environment Variables

• OPENROUTER_API_KEY: Your OpenRouter API key for LLM access
• E2B_API_KEY: (Optional) E2B API key for sandbox environments
• SLACK_BOT_TOKEN: (Optional) Slack integration for notifications
• EMAIL_SMTP_SERVER: (Optional) SMTP server for email notifications

Model Architectures

• transformer_tiny: 60M parameters, optimized for efficiency

  • Quantization: int8
  • Pruning: magnitude-based
  • Distillation: progressive

• transformer_small: 250M parameters, balanced performance

  • Quantization: float16
  • Pruning: structured
  • Distillation: vanilla

Usage Examples

Basic Knowledge Distillation

python -m nova.distillation.cli distill \
  --domain "computer_science" \
  --prompt "Explain distributed systems" \
  --architecture transformer_tiny

List Available Models

python -m nova.distillation.cli list

Show Model Information

python -m nova.distillation.cli info transformer_tiny

Performance Thresholds

• Accuracy: Minimum 0.7 (target 0.85)
• Latency: Maximum 200ms (target 100ms)
• Memory Usage: Maximum 2000MB (target 1000MB)
• Knowledge Coverage: Minimum 0.6 (target 0.8)

Development

Running Tests

pytest tests/

Code Style

# Install development dependencies
pip install -r requirements-dev.txt

# Run linters
flake8 nova/
black nova/
isort nova/

Contributing

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

License

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