target-synthesis-agent 1.0.6

AI-powered target variable creation and synthesis agent

Target Synthesis Agent

An intelligent AI-powered agent for generating and synthesizing target variables for machine learning tasks. This tool analyzes data characteristics and business context to create optimal target variables for various ML applications.

🚀 Features

• AI-Powered Analysis: Leverages advanced LLM models to analyze data and business context
• Multiple Data Sources: Works with both SQL databases and pandas DataFrames
• Customizable Workflows: Supports various ML approaches and synthesis strategies
• Comprehensive Testing: Includes a complete test suite for reliability
• Extensible Architecture: Easy to extend with custom components and integrations

📦 Installation

Prerequisites

• Python 3.10+
• Git
• uv package manager (recommended)

Setup

1. Clone the repository

   git clone https://github.com/stepfnAI/target_synthesis_agent.git
   cd target_synthesis_agent/
   git checkout review
   

2. Set up the virtual environment and install dependencies

   uv venv --python=3.10 venv
   source venv/bin/activate
   uv pip install -e ".[dev]"
   

3. Clone and install the blueprint dependency

   cd ..
   git clone https://github.com/stepfnAI/sfn_blueprint.git
   cd sfn_blueprint
   git switch dev
   uv pip install -e .
   cd ../target_synthesis_agent
   

4. Set up environment variables

   # Optional: Configure LLM provider (default: openai)
   export LLM_PROVIDER="your_llm_provider"
   
   # Optional: Configure LLM model (default: gpt-4)
   export LLM_MODEL="your_llm_model"
   
   # Required: Your LLM API key
   export LLM_API_KEY="your_llm_api_key"
   

🛠️ Usage

Basic SQL Usage

python examples/sql_basic_usage.py

🧪 Testing

Run the complete test suite:

pytest tests/ -s

Or run individual test files:

pytest tests/conftest.py -s
pytest tests/test_agent.py -s
pytest tests/test_utils.py -s

🏗️ Architecture

The Target Synthesis Agent is built with a modular architecture:

• Core Components:

  • agent.py: Main SQL-based implementation
  • models.py: Data models and schemas
  • utils.py: Utility functions and helpers
  • constants.py: Configuration and prompts

• Dependencies:

  • sfn-blueprint: Core framework and utilities
  • pandas: Data manipulation
  • sqlalchemy: Database interactions
  • scikit-learn: ML utilities

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some 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.

📧 Contact

For questions or support, please contact [e-mail]

🙏 Acknowledgments

• Built with ❤️ by StepFunction AI
• Uses sfn-blueprint for core functionality
• Inspired by modern MLOps best practices