InsightFlow

# InsightFlow InsightFlow is an advanced analytics platform that combines real-time data processing with AI-powered insights using the Model Context Protocol (MCP). It provides seamless integration with Claude AI for intelligent data analysis and decision support. ## 🚀 Features - **MCP Integration**: Full support for Model Context Protocol, enabling advanced AI capabilities - **Real-time Analytics**: Process and analyze data streams in real-time - **AI-Powered Insights**: Leverage Claude AI for intelligent data interpretation - **Flexible Data Processing**: Support for multiple data sources and formats - **RESTful & WebSocket APIs**: Comprehensive API support for various integration needs ## 🛠️ Technology Stack - **Backend**: Python 3.9+, FastAPI - **AI Integration**: Anthropic Claude API - **Data Processing**: Pandas, NumPy - **Database**: SQLAlchemy (supports multiple databases) - **API**: REST + WebSocket - **Protocol**: Model Context Protocol (MCP) ## 📋 Prerequisites - Python 3.9 or higher - Anthropic API key - Redis (for caching and message queuing) ## 🔧 Installation 1. Clone the repository: ```bash git clone https://github.com/yourusername/insightflow.git cd insightflow ``` 2. Create and activate virtual environment: ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` 4. Configure environment: ```bash cp config/config.example.yaml config/config.yaml # Edit config.yaml with your settings ``` 5. Set up environment variables: ```bash cp .env.example .env # Edit .env with your credentials ``` ## 🚀 Quick Start ### Running Locally 1. Start the server: ```bash python app/main.py ``` 2. Access the API documentation: ``` http://localhost:8000/docs ``` ## 📚 API Documentation ### REST API Endpoints - `GET /tools` - List available MCP tools - `POST /tool/{tool_name}` - Execute specific tool - `WS /ws` - WebSocket endpoint for real-time communication ### MCP Tools 1. **Data Analysis** - Analyze datasets with configurable metrics - Generate statistical insights - Support for time-series analysis 2. **Query Data** - Flexible data querying capabilities - Filter and aggregate data - Export results in multiple formats 3. **Generate Insight** - AI-powered data interpretation - Trend identification - Anomaly detection ## 🔧 Configuration The system can be configured through `config.yaml` or environment variables: ```yaml server: host: "0.0.0.0" port: 8000 debug: false mcp: enabled: true websocket_path: "/ws" max_connections: 100 ai: model_name: "claude-2" temperature: 0.7 max_tokens: 2000 ``` ## 🔍 Development ### Project Structure ``` insightflow/ ├── app/ │ ├── main.py # Application entry point │ ├── config.py # Configuration management │ ├── core/ # Core MCP and server logic │ ├── data/ # Data processing modules │ ├── analytics/ # Analytics engine │ ├── ai/ # AI integration │ ├── api/ # API endpoints │ └── models/ # Data models └── requirements.txt # Python dependencies ``` ### Running Tests ```bash pytest tests/ ``` ### Contributing 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/AmazingFeature`) 3. Commit your changes (`git commit -m 'Add some AmazingFeature'`) 4. Push to the branch (`git push origin feature/AmazingFeature`) 5. Open a Pull Request ## 📄 License This project is licensed under the MIT License - see the LICENSE file for details. ## 🤝 Support For support and questions, please open an issue in the GitHub repository or contact the maintainers. ## 🙏 Acknowledgments - Anthropic for Claude AI integration - Model Context Protocol community - All contributors and users of InsightFlow --- Made with ❤️ by the Ilias RAFIK ;