grafana-openai-monitoring 0.0.9

Library to monitor your OpenAI usage and send metrics and logs to Grafana Cloud

OpenAI Monitoring: Monitor OpenAI API Usage with Grafana Cloud

grafana-openai-monitoring is a Python library that provides a decorators to monitor chat completions and Completions endpoints of the OpenAI API. It facilitates sending metrics and logs to Grafana Cloud, allowing you to track and analyze OpenAI API usage and responses.

Installation

You can install grafana-openai-monitoring using pip:

pip install grafana-openai-monitoring

Usage

The following tables shows which OpenAI function correspons to which monitoing function in this library

OpenAI Function	Monitoring Function
ChatCompletion.create	chat_v2.monitor
Completion.create	chat_v1.monitor

ChatCompletions

To monitor ChatCompletions using the OpenAI API, you can use the chat_v2.monitor decorator. This decorator automatically tracks API calls and sends metrics and logs to the specified Grafana Cloud endpoints.

Here's how to set it up:

from openai import OpenAI
from grafana_openai_monitoring import chat_v2

client = OpenAI(
    api_key="YOUR_OPENAI_API_KEY",
)

# Apply the custom decorator to the OpenAI API function. To use with AsyncOpenAI, Pass `use_async` = True in this function.
client.chat.completions.create = chat_v2.monitor(
    client.chat.completions.create,
    metrics_url="YOUR_PROMETHEUS_METRICS_URL",  # Example: "https://prometheus.grafana.net/api/prom"
    logs_url="YOUR_LOKI_LOGS_URL",  # Example: "https://logs.example.com/loki/api/v1/push/"
    metrics_username="YOUR_METRICS_USERNAME",  # Example: "123456"
    logs_username="YOUR_LOGS_USERNAME",  # Example: "987654"
    access_token="YOUR_ACCESS_TOKEN"  # Example: "glc_eyasdansdjnaxxxxxxxxxxx"
)

# Now any call to client.chat.completions.create will be automatically tracked
response = client.chat.completions.create(model="gpt-4", max_tokens=100, messages=[{"role": "user", "content": "What is Grafana?"}])
print(response)

Completions

To monitor completions using the OpenAI API, you can use the chat_v1.monitor decorator. This decorator adds monitoring capabilities to the OpenAI API function and sends metrics and logs to the specified Grafana Cloud endpoints.

Here's how to apply it:

from openai import OpenAI
from grafana_openai_monitoring import chat_v1

client = OpenAI(
    api_key="YOUR_OPENAI_API_KEY",
)

# Apply the custom decorator to the OpenAI API function
client.completions.create = chat_v1.monitor(
    client.completions.create,
    metrics_url="YOUR_PROMETHEUS_METRICS_URL",  # Example: "https://prometheus.grafana.net/api/prom"
    logs_url="YOUR_LOKI_LOGS_URL",  # Example: "https://logs.example.com/loki/api/v1/push/"
    metrics_username="YOUR_METRICS_USERNAME",  # Example: "123456"
    logs_username="YOUR_LOGS_USERNAME",  # Example: "987654"
    access_token="YOUR_ACCESS_TOKEN"  # Example: "glc_eyasdansdjnaxxxxxxxxxxx"
)

# Now any call to client.completions.create will be automatically tracked
response = client.completions.create(model="davinci", max_tokens=100, prompt="Isn't Grafana the best?")
print(response)

Configuration

To use the grafana-openai-monitoring library effectively, you need to provide the following information:

• YOUR_OPENAI_API_KEY: Replace this with your actual OpenAI API key.
• YOUR_PROMETHEUS_METRICS_URL: Replace the URL with your Prometheus URL.
• YOUR_LOKI_LOGS_URL: Replace with the URL where you want to send Loki logs.
• YOUR_METRICS_USERNAME: Replace with the username for Prometheus.
• YOUR_LOGS_USERNAME: Replace with the username for Loki.
• YOUR_ACCESS_TOKEN: Replace with the Cloud Access Policy token required for authentication.

After configuring the parameters, the monitored API function will automatically log and track the requests and responses to the specified endpoints.

Compatibility

Python 3.7.1 and above

Dependencies

• OpenAI
• requests

License

This project is licensed under the GPL-3.0 license - see the LICENSE for details.