What is Insightflow

InsightFlow is an advanced analytics platform that utilizes real-time data processing and AI-powered insights through the Model Context Protocol (MCP). It integrates seamlessly with Claude AI for intelligent data analysis and decision-making support.

Use cases

Use cases for InsightFlow include analyzing datasets with configurable metrics, querying data with flexible capabilities, generating AI-powered insights, identifying trends, and detecting anomalies in data.

How to use

To use InsightFlow, clone the repository, set up a virtual environment, install dependencies, configure the environment variables, and start the server. Access the API documentation at http://localhost:8000/docs for further interaction.

Key features

Key features of InsightFlow include MCP integration for advanced AI capabilities, real-time analytics for data stream processing, AI-powered insights from Claude AI, flexible data processing from multiple sources, and comprehensive RESTful & WebSocket API support.

Where to use

InsightFlow can be used in various fields such as business intelligence, data science, real-time monitoring, and any domain requiring intelligent data analysis and decision support.

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:

git clone https://github.com/yourusername/insightflow.git
cd insightflow

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Configure environment:

cp config/config.example.yaml config/config.yaml
# Edit config.yaml with your settings

5. Set up environment variables:

cp .env.example .env
# Edit .env with your credentials

🚀 Quick Start

Running Locally

1. Start the server:

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:

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

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 ;