revenium-middleware-openai 0.3.9

A Python library that meters OpenAI usage to Revenium.

🤖 Revenium Middleware for OpenAI

A middleware library for metering and monitoring OpenAI API usage in Python applications. 🐍✨

✨ Features

• 📊 Precise Usage Tracking: Monitor tokens, costs, and request counts across all OpenAI API endpoints
• 🔌 Seamless Integration: Drop-in middleware that works with minimal code changes
• ⚙️ Flexible Configuration: Customize metering behavior to suit your application needs

📥 Installation

pip install revenium-middleware-openai

📥 Updating

pip install --upgrade revenium-middleware-openai

🔧 Usage

‼️ Setting Environment Variables ‼️

export OPENAI_API_KEY=your-key-value
export REVENIUM_METERING_API_KEY=your-key-value

That's it, now your OpenAI calls will be metered automatically:

import openai
import revenium_middleware_openai

response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {
            "role": "user",
            "content": "What is the answer to life, the universe and everything?",
        },
    ],
    max_tokens=500,
)

print(response.choices[0].message.content)

The middleware automatically intercepts OpenAI API calls and sends metering data to Revenium without requiring any changes to your existing code. Make sure to set the REVENIUM_METERING_API_KEY environment variable for authentication with the Revenium service.

📈 Enhanced Tracking with Metadata

For more granular usage tracking and detailed reporting, add the usage_metadata parameter:

import openai
import revenium_middleware_openai

response = openai.chat.completions.create(
    model="gpt-4o",  # You can change this to other models like "gpt-3.5-turbo"
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {
            "role": "user",
            "content": "What is the meaning of life, the universe and everything?",
        },
    ],
    max_tokens=500,
    usage_metadata={
         "trace_id": "conv-28a7e9d4",
         "task_type": "summarize-customer-issue",
         "subscriber": {
             "id": "subscriberid-1234567890",
             "email": "[e-mail]",
             "credential": {
                 "name": "engineering-api-key",
                 "value": "actual-api-key-value"
             }
         },
         "organization_id": "acme-corp",
         "subscription_id": "startup-plan-Q1",
         "product_id": "saas-app-gold-tier",
         "agent": "support-agent",
    },
)
print(response.choices[0].message.content)

🏷️ Metadata Fields

The usage_metadata parameter supports the following fields:

Field	Description	Use Case
trace_id	Unique identifier for a conversation or session	Group multi-turn conversations into single event for performance & cost tracking
task_type	Classification of the AI operation by type of work	Track cost & performance by purpose (e.g., classification, summarization)
subscriber	Nested object containing subscriber information	Track cost & performance by individual users (recommended structure)
organization_id	Customer or department ID from non-Revenium systems	Track cost & performance by customers or business units
subscription_id	Reference to a billing plan in non-Revenium systems	Track cost & performance by a specific subscription
product_id	Your product or feature making the AI call	Track cost & performance across different products
agent	Identifier for the specific AI agent	Track cost & performance performance by AI agent
response_quality_score	The quality of the AI response (0..1)	Track AI response quality

👤 Subscriber Object Structure

The subscriber field supports a nested structure for better organization:

usage_metadata = {
    "subscriber": {
        "id": "user-12345",
        "email": "[e-mail]", 
        "credential": {
            "name": "api-key-alias",
            "value": "actual-api-key-value"
        }
    },
    # ... other metadata fields
}

Subscriber fields:

• id: Unique identifier for the subscriber
• email: Email address of the subscriber
• credential: Nested object with API key information
  • name: Alias or name for the credential
  • value: The actual credential value

All metadata fields are optional. Adding them enables more detailed reporting and analytics in Revenium.

🔄 Compatibility

• 🐍 Python 3.8+
• 🤖 OpenAI Python SDK 1.0.0+
• 🌐 Works with all OpenAI models and endpoints

🔍 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

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• 💖 Built with ❤️ by the Revenium team