apps/opik-documentation/documentation/fern/docs-v2/integrations/openai.mdx
This guide explains how to integrate Opik with the OpenAI Python SDK. By using the track_openai method provided by opik, you can easily track and evaluate your OpenAI API calls within your Opik projects as Opik will automatically log the input prompt, model used, token usage, and response generated.
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.
First, ensure you have both opik and openai packages installed:
pip install opik openai
Configure the Opik Python SDK for your deployment type. See the Python SDK Configuration guide for detailed instructions on:
opik configureopik.configure()In order to configure OpenAI, you will need to have your OpenAI API Key. You can find or create your OpenAI API Key in this page.
You can set it as an environment variable:
export OPENAI_API_KEY="YOUR_API_KEY"
Or set it programmatically:
import os
import getpass
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
In order to log the LLM calls to Opik, you will need to wrap the OpenAI client with track_openai. When making calls with that wrapped client, all calls will be logged to Opik:
from opik.integrations.openai import track_openai
from openai import OpenAI
import os
os.environ["OPIK_PROJECT_NAME"] = "openai-integration-demo"
client = OpenAI()
openai_client = track_openai(client)
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)
@track decoratorIf 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 will be associated with that corresponding step:
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
# Execute the multi-step pipeline
generate_opik_story()
The trace can now be viewed in the UI with hierarchical spans showing the relationship between different steps:
<Frame> </Frame>The OpenAI integration also supports Azure OpenAI Services. To use Azure OpenAI, initialize your client with Azure configuration and use it with track_openai just like the standard OpenAI client:
from opik.integrations.openai import track_openai
from openai import AzureOpenAI
# gets the API Key from environment variable AZURE_OPENAI_API_KEY
azure_client = AzureOpenAI(
# https://learn.microsoft.com/azure/ai-services/openai/reference#rest-api-versioning
api_version="2023-07-01-preview",
# https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource
azure_endpoint="https://example-endpoint.openai.azure.com",
)
azure_client = track_openai(azure_client)
completion = azure_client.chat.completions.create(
model="deployment-name", # e.g. gpt-35-instant
messages=[
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
],
)
The track_openai wrapper automatically tracks token usage and cost for all supported OpenAI models.
Cost information is automatically captured and displayed in the Opik UI, including:
thread_idThreads in Opik are collections of traces that are grouped together using a unique thread_id.
The thread_id can be passed to the OpenAI client as a parameter, which will be used to group all traces into a single thread.
import openai
from opik.integrations.openai import track_openai
client = openai.OpenAI()
wrapped_client = track_openai(
openai_client=client,
project_name="opik_args demo",
)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is Opik?"},
]
_ = wrapped_client.responses.create(
model="gpt-4o-mini",
input=messages,
opik_args={"trace": {"thread_id": "f174a"}}
)
More information on logging chat conversations can be found in the Log conversations section.
The track_openai wrapper also supports OpenAI's video generation API (Sora). When you generate videos, Opik automatically tracks the video creation process and logs the generated video as an attachment when you download it via the client API.
import os
from opik import track
from opik.integrations.openai import track_openai
from openai import OpenAI
os.environ["OPIK_PROJECT_NAME"] = "openai-video-demo"
client = OpenAI()
tracked_client = track_openai(client)
@track
def generate_video(prompt: str) -> dict:
"""Generate a video using OpenAI's Sora model."""
# Create video and wait for completion
video = tracked_client.videos.create_and_poll(
model="sora-2",
prompt=prompt,
)
result = {"id": video.id, "status": video.status}
# Download the video if generation succeeded
# If OpenAI moderation rejected your request - you'll see it in the create_and_poll span output
if video.status == "completed":
content = tracked_client.videos.download_content(video_id=video.id)
content.write_to_file("output_video.mp4")
result["output_path"] = "output_video.mp4"
return result
# Generate a video
generate_video("A golden retriever playing in the snow")
The trace will show the full video generation workflow including the video creation, polling, download, and the generated video as an attachment:
<Frame> </Frame> <Tip> The `videos.retrieve` method is intentionally not tracked because the `poll` method makes many retrieve calls internally, which would create excessive noise in your traces. If you need to track retrieve calls (e.g. if you don't use `poll` and check the progress manually), you can decorate the method yourself:tracked_client.videos.retrieve = opik.track(name="videos.retrieve")(
tracked_client.videos.retrieve
)
The track_openai wrapper supports the following OpenAI methods:
openai_client.chat.completions.create(), including support for stream=True mode.openai_client.beta.chat.completions.parse()openai_client.beta.chat.completions.stream()openai_client.responses.create()openai_client.videos.create()openai_client.videos.create_and_poll()openai_client.videos.poll()openai_client.videos.download_content()openai_client.videos.list()openai_client.videos.delete()If you would like to track another OpenAI method, please let us know by opening an issue on GitHub.