# 🤖 Revenium Middleware for OpenAI
[](https://pypi.org/project/revenium-middleware-openai/)
[](https://pypi.org/project/revenium-middleware-openai/)
[](https://revenium-middleware-openai.readthedocs.io/en/latest/?badge=latest)
[](https://www.gnu.org/licenses/lgpl-3.0)
[//]: # ([](https://github.com/revenium/revenium-middleware-openai/actions))
A production-grade 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
```bash
pip install revenium-middleware-openai
```
## 🔧 Usage
### 🔄 Zero-Config Integration
Simply export your REVENIUM_METERING_API_KEY and import the middleware.
Your OpenAI calls will be metered automatically:
```python
import openai
import revenium_middleware_openai
# Ensure REVENIUM_METERING_API_KEY environment variable is set
response = openai.chat.completions.create(
model="gpt-4",
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:
```python
import openai
import revenium_middleware_openai
response = openai.chat.completions.create(
model="gpt-4", # 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-28a7e9d4-1c3b-4e5f-8a9b-7d1e3f2c1b4a",
"task_id": "chat-summary-af23c910",
"task_type": "text-classification",
"subscriber_identity": "customer-email@example.com",
"organization_id": "acme-corporation-12345",
"subscription_id": "startup-plan-quarterly-2025-Q1",
"product_id": "intelligent-document-processor-v3",
"source_id": "mobile-app-ios-v4.2",
"ai_provider_key_name": "openai-production-key1",
"agent": "customer-support-assistant-v2",
},
)
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 | Track multi-turn conversations |
| `task_id` | Identifier for a specific AI task | Group related API calls for a single task |
| `task_type` | Classification of the AI operation | Categorize usage by purpose (e.g., classification, summarization) |
| `subscriber_identity` | End-user identifier | Track usage by individual users |
| `organization_id` | Customer or department identifier | Allocate costs to business units |
| `subscription_id` | Reference to a billing plan | Associate usage with specific subscriptions |
| `product_id` | The product or feature using AI | Track usage across different products |
| `source_id` | Origin of the request | Monitor usage by platform or app version |
| `ai_provider_key_name` | Identifier for the API key used | Track usage by different API keys |
| `agent` | Identifier for the specific AI agent | Compare performance across different AI agents |
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
## 📚 Documentation
Full documentation is available at [https://revenium-middleware-openai.readthedocs.io/](https://revenium-middleware-openai.readthedocs.io/)
## 👥 Contributing
Contributions are welcome! Please check out our [contributing guidelines](CONTRIBUTING.md) 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 GNU Lesser General Public License v3.0 (LGPL-3.0) - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- 🔥 Thanks to the OpenAI team for creating an excellent API
- 💖 Built with ❤️ by the Revenium team
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"description": "# \ud83e\udd16 Revenium Middleware for OpenAI\n\n[](https://pypi.org/project/revenium-middleware-openai/)\n[](https://pypi.org/project/revenium-middleware-openai/)\n[](https://revenium-middleware-openai.readthedocs.io/en/latest/?badge=latest)\n[](https://www.gnu.org/licenses/lgpl-3.0)\n\n[//]: # ([](https://github.com/revenium/revenium-middleware-openai/actions))\n\nA production-grade middleware library for metering and monitoring OpenAI API usage in Python applications. \ud83d\udc0d\u2728\n\n## \u2728 Features\n\n- **\ud83d\udcca Precise Usage Tracking**: Monitor tokens, costs, and request counts across all OpenAI API endpoints\n- **\ud83d\udd0c Seamless Integration**: Drop-in middleware that works with minimal code changes\n- **\u2699\ufe0f Flexible Configuration**: Customize metering behavior to suit your application needs\n\n\n## \ud83d\udce5 Installation\n\n```bash\npip install revenium-middleware-openai\n```\n\n## \ud83d\udd27 Usage\n\n### \ud83d\udd04 Zero-Config Integration\n\nSimply export your REVENIUM_METERING_API_KEY and import the middleware. \nYour OpenAI calls will be metered automatically:\n\n```python\nimport openai\nimport revenium_middleware_openai\n\n# Ensure REVENIUM_METERING_API_KEY environment variable is set\n\nresponse = openai.chat.completions.create(\n model=\"gpt-4\", \n messages=[\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\n \"role\": \"user\",\n \"content\": \"What is the answer to life, the universe and everything?\",\n },\n ],\n max_tokens=500,\n)\n\nprint(response.choices[0].message.content)\n```\n\nThe 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.\n\n### \ud83d\udcc8 Enhanced Tracking with Metadata\n\nFor more granular usage tracking and detailed reporting, add the `usage_metadata` parameter:\n\n```python\nimport openai\nimport revenium_middleware_openai\n\nresponse = openai.chat.completions.create(\n model=\"gpt-4\", # You can change this to other models like \"gpt-3.5-turbo\"\n messages=[\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\n \"role\": \"user\",\n \"content\": \"What is the meaning of life, the universe and everything?\",\n },\n ],\n max_tokens=500,\n usage_metadata={\n \"trace_id\": \"conv-28a7e9d4-1c3b-4e5f-8a9b-7d1e3f2c1b4a\",\n \"task_id\": \"chat-summary-af23c910\",\n \"task_type\": \"text-classification\",\n \"subscriber_identity\": \"customer-email@example.com\",\n \"organization_id\": \"acme-corporation-12345\",\n \"subscription_id\": \"startup-plan-quarterly-2025-Q1\",\n \"product_id\": \"intelligent-document-processor-v3\",\n \"source_id\": \"mobile-app-ios-v4.2\",\n \"ai_provider_key_name\": \"openai-production-key1\",\n \"agent\": \"customer-support-assistant-v2\",\n },\n)\nprint(response.choices[0].message.content)\n```\n\n#### \ud83c\udff7\ufe0f Metadata Fields\n\nThe `usage_metadata` parameter supports the following fields:\n\n| Field | Description | Use Case |\n|-------|-------------|----------|\n| `trace_id` | Unique identifier for a conversation or session | Track multi-turn conversations |\n| `task_id` | Identifier for a specific AI task | Group related API calls for a single task |\n| `task_type` | Classification of the AI operation | Categorize usage by purpose (e.g., classification, summarization) |\n| `subscriber_identity` | End-user identifier | Track usage by individual users |\n| `organization_id` | Customer or department identifier | Allocate costs to business units |\n| `subscription_id` | Reference to a billing plan | Associate usage with specific subscriptions |\n| `product_id` | The product or feature using AI | Track usage across different products |\n| `source_id` | Origin of the request | Monitor usage by platform or app version |\n| `ai_provider_key_name` | Identifier for the API key used | Track usage by different API keys |\n| `agent` | Identifier for the specific AI agent | Compare performance across different AI agents |\n\nAll metadata fields are optional. Adding them enables more detailed reporting and analytics in Revenium.\n\n## \ud83d\udd04 Compatibility\n\n- \ud83d\udc0d Python 3.8+\n- \ud83e\udd16 OpenAI Python SDK 1.0.0+\n- \ud83c\udf10 Works with all OpenAI models and endpoints\n\n## \ud83d\udcda Documentation\n\nFull documentation is available at [https://revenium-middleware-openai.readthedocs.io/](https://revenium-middleware-openai.readthedocs.io/)\n\n## \ud83d\udc65 Contributing\n\nContributions are welcome! 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