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Using Opik with Gemini

Opik integrates with Gemini to provide a simple way to log traces for all Gemini LLM calls. This works for all Gemini models.

Creating an account on Comet.com​

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

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

%pip install --upgrade opik google-generativeai litellm
import opik

opik.configure(use_local=False)

Preparing our environment​

First, we will set up our OpenAI API keys.

import os
import getpass
import google.generativeai as genai

if "GEMINI_API_KEY" not in os.environ:
    genai.configure(api_key=getpass.getpass("Enter your Gemini API key: "))

Configure LiteLLM​

Add the LiteLLM OpikTracker to log traces and steps to Opik:

import litellm
import os
from litellm.integrations.opik.opik import OpikLogger
from opik import track
from opik.opik_context import get_current_span_data

os.environ["OPIK_PROJECT_NAME"] = "gemini-integration-demo"
opik_logger = OpikLogger()
litellm.callbacks = [opik_logger]

Logging traces​

Now each completion will logs a separate trace to LiteLLM:

prompt = """
Write a short two sentence story about Opik.
"""

response = litellm.completion(
    model="gemini/gemini-pro",
    messages=[{"role": "user", "content": prompt}],
)

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

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

Gemini Cookbook

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 Gemini is called within one of these steps, the LLM call with be associated with that corresponding step:

@track
def generate_story(prompt):
    response = litellm.completion(
        model="gemini/gemini-pro",
        messages=[{"role": "user", "content": prompt}],
        metadata={
            "opik": {
                "current_span_data": get_current_span_data(),
            },
        },
    )
    return response.choices[0].message.content


@track
def generate_topic():
    prompt = "Generate a topic for a story about Opik."
    response = litellm.completion(
        model="gemini/gemini-pro",
        messages=[{"role": "user", "content": prompt}],
        metadata={
            "opik": {
                "current_span_data": get_current_span_data(),
            },
        },
    )
    return response.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:

Gemini Cookbook