内容
# 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 ;
MCP 市场