comet-ml--opik/sdks/python/examples/openai_integration_example.py

from openai import OpenAI
from opik import flush_tracker, track
from opik.integrations.openai import opik_tracker
from pydantic import BaseModel

# os.environ["OPENAI_ORG_ID"] = "YOUR OPENAI ORG ID"
# os.environ["OPENAI_API_KEY"] = "YOUR OPENAI API KEY"

client = OpenAI()

client = opik_tracker.track_openai(client)


@track()
def f_with_structured_output_openai_call():
    class CalendarEvent(BaseModel):
        name: str
        date: str
        participants: list[str]

    completion = client.beta.chat.completions.parse(
        model="gpt-4o-2024-08-06",
        messages=[
            {"role": "system", "content": "Extract the event information."},
            {
                "role": "user",
                "content": "Alice and Bob are going to a science fair on Friday.",
            },
        ],
        response_format=CalendarEvent,
    )

    print(completion)


@track()
def f_with_streamed_openai_call():
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell a fact"},
    ]

    # will create one more nested span, its output will
    # be updated once stream generator is exhausted
    stream = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=messages,
        max_tokens=10,
        stream=True,
        stream_options={"include_usage": True},
    )

    for item in stream:
        print(item)


@track()
def f_with_usual_chat_completion_call():
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell a fact"},
    ]

    # will create one more nested span
    _ = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=messages,
        max_tokens=10,
    )


f_with_streamed_openai_call()  # trace 1
f_with_usual_chat_completion_call()  # trace 2
f_with_structured_output_openai_call()  # trace 3

_ = client.chat.completions.create(  # trace 4
    model="gpt-3.5-turbo",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell a fact"},
    ],
    max_tokens=10,
)

flush_tracker()