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Using Opik with OpenAI

apps/opik-documentation/documentation/docs/cookbook/openai.ipynb

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Using Opik with OpenAI

Opik integrates with OpenAI to provide a simple way to log traces for all OpenAI LLM calls. This works for all OpenAI models, including if you are using the streaming API.

Creating an account on Comet.com

Comet provides a hosted version of the Opik platform, simply create an account and grab your API Key.

You can also run the Opik platform locally, see the installation guide for more information.

python
%pip install --upgrade opik openai
python
import opik

opik.configure(use_local=False)

Preparing our environment

First, we will set up our OpenAI API keys.

python
import os
import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")

Logging traces

In order to log traces to Opik, we need to wrap our OpenAI calls with the track_openai function:

python
from opik.integrations.openai import track_openai
from openai import OpenAI

os.environ["OPIK_PROJECT_NAME"] = "openai-integration-demo"

client = OpenAI()
openai_client = track_openai(client)
python
prompt = """
Write a short two sentence story about Opik.
"""

completion = openai_client.chat.completions.create(
    model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}]
)

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

The prompt and response messages are automatically logged to Opik and can be viewed in the UI.

Using it with the track decorator

If you have multiple steps in your LLM pipeline, you can use the track decorator to log the traces for each step. If OpenAI is called within one of these steps, the LLM call with be associated with that corresponding step:

python
from opik import track
from opik.integrations.openai import track_openai
from openai import OpenAI

os.environ["OPIK_PROJECT_NAME"] = "openai-integration-demo"

client = OpenAI()
openai_client = track_openai(client)


@track
def generate_story(prompt):
    res = openai_client.chat.completions.create(
        model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}]
    )
    return res.choices[0].message.content


@track
def generate_topic():
    prompt = "Generate a topic for a story about Opik."
    res = openai_client.chat.completions.create(
        model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}]
    )
    return res.choices[0].message.content


@track
def generate_opik_story():
    topic = generate_topic()
    story = generate_story(topic)
    return story


generate_opik_story()

The trace can now be viewed in the UI: