revenium-middleware 0.3.1

A Python library that provides core functionality to send AI metering data to Revenium.

🔄 Revenium Core Middleware

A foundational library that provides core metering functionality shared across all Revenium AI provider-specific middleware implementations (OpenAI, Anthropic, Ollama, etc). 🐍✨

✨ Features

• 🧠 Shared Core Functionality: Provides the essential metering infrastructure used by all Revenium middleware implementations
• 🔄 Asynchronous Processing: Background thread management for non-blocking metering operations
• 🛑 Graceful Shutdown: Ensures all metering data is properly sent even during application shutdown
• 🔌 Provider Agnostic: Designed to work with any AI provider through specific middleware implementations

📥 Installation

pip install revenium-middleware

🔧 Usage

🔄 Direct Usage

While this package is primarily intended as a dependency for provider-specific middleware, you can use it directly:

from revenium_middleware import client, run_async_in_thread, shutdown_event

# Record usage directly
client.record_usage(
    model="gpt-4o",
    prompt_tokens=500,
    completion_tokens=200,
    user_id="user123",
    session_id="session456"
)

# Run async metering tasks in background threads
async def async_metering_task():
    await client.async_record_usage(
        model="gpt-3.5-turbo",
        prompt_tokens=300,
        completion_tokens=150,
        user_id="user789"
    )

thread = run_async_in_thread(async_metering_task())

# Application continues while metering happens in background

🏗️ Building Provider-Specific Middleware

This library is designed to be extended by provider-specific middleware implementations:

from revenium_middleware import client, run_async_in_thread

# Example of how a provider-specific middleware might use the core
def record_provider_usage(response_data, metadata):
    # Extract token counts from provider-specific response format
    prompt_tokens = response_data.usage.prompt_tokens
    completion_tokens = response_data.usage.completion_tokens
    
    # Use the core client to record the usage
    run_async_in_thread(
        client.async_record_usage(
            model=response_data.model,
            prompt_tokens=prompt_tokens,
            completion_tokens=completion_tokens,
            **metadata
        )
    )

🔄 Compatibility

• 🐍 Python 3.8+
• 🤝 Compatible with all Revenium provider-specific middleware implementations

🔍 Logging

This module uses Python's standard logging system. You can control the log level by setting the REVENIUM_LOG_LEVEL environment variable:

# Enable debug logging
export REVENIUM_LOG_LEVEL=DEBUG

# Or when running your script
REVENIUM_LOG_LEVEL=DEBUG python your_script.py

Available log levels:

• DEBUG: Detailed debugging information
• INFO: General information (default)
• WARNING: Warning messages only
• ERROR: Error messages only
• CRITICAL: Critical error messages only

📚 Documentation

For more detailed documentation, please refer to the docstrings in the code or visit our GitHub repository.

👥 Contributing

Contributions are welcome! Please check out our contributing guidelines for details.

1. 🍴 Fork the repository
2. 🌿 Create your 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.

🙏 Acknowledgments

• 💖 Built with ❤️ by the Revenium team