digital-twin-mcp

# Self-Evolving Digital Twin MCP Server A sophisticated digital twin system that simulates user-specific responses, maintains evolving personality profiles, and manages vectorized memories using advanced language models. ## 🌟 Features - **Personality Simulation**: Generate contextually appropriate responses based on personality traits and communication style - **Memory Management**: Store and retrieve vectorized memories with semantic search capabilities - **Self-Evolution**: Automatically update personality traits and knowledge based on interactions - **Reflection System**: Perform periodic self-reflection and summarization of experiences - **Modular Architecture**: Easily extensible system with clear separation of concerns ## 🚀 Quick Start ### Prerequisites - Python 3.8+ - OpenAI API key - HuggingFace API key (optional, for DialoGPT) ### Installation 1. Clone the repository: ```bash git clone https://github.com/yourusername/digital-twin-mcp.git cd digital-twin-mcp ``` 2. Create and activate a virtual environment: ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install dependencies: ```bash pip install -e . ``` 4. Create a `.env` file in the project root: ```env OPENAI_API_KEY=your_openai_api_key_here OPENAI_MODEL=gpt-4 HUGGINGFACE_API_KEY=your_huggingface_api_key_here DATABASE_URL=sqlite:///digital_twin.db ``` ## 📁 Project Structure ``` digital-twin-mcp/ ├── src/ │ └── fastmcp/ │ └── digital_twin/ │ ├── __init__.py │ ├── personality.py # Personality system │ ├── memory.py # Memory management │ ├── response_generator.py # Response generation │ ├── interaction.py # Interaction handling │ ├── server.py # FastAPI server │ └── prompts/ # Prompt templates │ ├── base_personality.txt │ ├── reply_simulation.txt │ ├── memory_update.txt │ └── reflection.txt ├── examples/ │ └── digital_twin_example.py # Usage examples ├── tests/ # Test suite ├── pyproject.toml # Project configuration └── README.md # This file ``` ## 💡 Usage ### Basic Example ```python from fastmcp.digital_twin import DigitalTwinInteraction, ResponseGenerator, Memory, Personality # Initialize components response_generator = ResponseGenerator() memory = Memory() personality = Personality() # Create interaction handler interaction = DigitalTwinInteraction( response_generator=response_generator, memory=memory, personality=personality ) # Simulate a response response, metadata = await interaction.simulate_response( user_input="Hello, how are you?", context={"mood": "casual"} ) # Perform reflection insights = await interaction.reflect_chain(time_period="1w") ``` ### Advanced Features 1. **Memory Management**: ```python # Add a memory await memory.add_memory( content="User prefers formal communication", memory_type="preference", metadata={"importance": 0.8} ) # Retrieve relevant memories relevant_memories = await memory.get_relevant_memories( query="communication style", limit=5 ) ``` 2. **Personality Evolution**: ```python # Get current personality traits = personality.get_traits() # Update personality await personality.process_update({ "trait": "formality", "value": 0.8, "reason": "User preference" }) ``` 3. **Interaction Routing**: ```python # Route through different chains result = await interaction.route_interaction( user_input="Tell me about yourself", interaction_type="simulate", context={"depth": "detailed"} ) ``` ## 🔧 Configuration The system can be configured through environment variables or a configuration file. Key settings include: - `OPENAI_MODEL`: Language model to use (default: "gpt-4") - `MAX_MEMORIES`: Maximum number of memories to store (default: 1000) - `PERSONALITY_UPDATE_INTERVAL`: How often to update personality (default: 3600s) - `MEMORY_CHUNK_SIZE`: Size of memory chunks (default: 1000) ## 📝 Contributing 1. Fork the repository 2. Create a feature branch 3. Commit your changes 4. Push to the branch 5. Create a Pull Request ## 📄 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## 🙏 Acknowledgments - OpenAI for GPT models - HuggingFace for DialoGPT - FastAPI for the web framework - SQLAlchemy for database management