Gemini

Gemini is a family of multimodal large language models developed by Google DeepMind.

You can check out the Colab Notebook if you’d like to jump straight to the code:

Getting Started

First, ensure you have both opik and google-genai packages installed:

$
pip install opik google-genai

In addition, you can configure Opik using the opik configure command which will prompt you for the correct local server address or if you are using the Cloud platform your API key:

$
opik configure

Configuring Gemini

In order to configure Gemini, you will need to have:

• Your Gemini API Key: See the following documentation page how to retrieve it.

Once you have these, you can set them as environment variables:

1
import os
2
3
os.environ["GOOGLE_API_KEY"] = "" # Your Google AI Studio Gemini API Key

Tracking Gemini calls

1
from google import genai
2
from opik import track
3
from opik.integrations.genai import track_genai
4
5
client = genai.Client()
6
gemini_client = track_genai(client)
7
8
prompt = "Hello world"
9
10
response = gemini_client.models.generate_content(model="gemini-2.0-flash-001", contents=prompt)
11
print(response.text)

Cost Tracking

The track_genai wrapper automatically tracks token usage and cost for all supported Google AI models.

Cost information is automatically captured and displayed in the Opik UI, including:

• Token usage details
• Cost per request based on Google AI pricing
• Total trace cost