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Google Gemini

Lack of transparency

Google does not disclose any information about model architecture and inference infrastrucure. Thus, the environmental impacts are estimated with a very low precision.

This guide focuses on the integration of 🌱 EcoLogits with the Google Gemini official python client .

Official links:

Installation

To install EcoLogits along with all necessary dependencies for compatibility with the Google Gemini client, please use the google-generativeai extra-dependency option as follows:

pip install ecologits[google-generativeai]

This installation command ensures that EcoLogits is set up with the specific libraries required to interface seamlessly with Google Gemini Python client.

Chat Completions

Example

Integrating EcoLogits with your applications does not alter the standard outputs from the API responses. Instead, it enriches them by adding the Impacts object, which contains detailed environmental impact data.

from ecologits import EcoLogits
import google.generativeai as genai

# Initialize EcoLogits
EcoLogits.init()

# Ask something to Google Gemini
genai.configure(api_key="<GOOGLE_API_KEY>")
model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content("Write a story about a magic backpack.")

# Get estimated environmental impacts of the inference
print(response.impacts)

import asyncio
from ecologits import EcoLogits
import google.generativeai as genai

# Initialize EcoLogits
EcoLogits.init()

# Ask something to Google Gemini in async mode
async def main() -> None:
    genai.configure(api_key="<GOOGLE_API_KEY>")
    model = genai.GenerativeModel("gemini-1.5-flash")
    response = await model.generate_content_async(
        "Write a story about a magic backpack."
    )

    # Get estimated environmental impacts of the inference
    print(response.impacts)

asyncio.run(main())

Streaming example

In streaming mode, the impacts are calculated incrementally, which means you don't need to sum the impacts from each data chunk. Instead, the impact information in the last chunk reflects the total cumulative environmental impacts for the entire request.

from ecologits import EcoLogits
import google.generativeai as genai

# Initialize EcoLogits
EcoLogits.init()

# Ask something to Google Gemini in streaming mode
genai.configure(api_key="<GOOGLE_API_KEY>")
model = genai.GenerativeModel("gemini-1.5-flash")
stream = model.generate_content(
    "Write a story about a magic backpack.", 
    stream=True
)

# Get cumulative estimated environmental impacts of the inference
for chunk in stream:
    print(chunk.impacts)

import asyncio
from ecologits import EcoLogits
import google.generativeai as genai

# Initialize EcoLogits
EcoLogits.init()

# Ask something to Google Gemini in streaming and async mode
async def main() -> None:
    genai.configure(api_key="<GOOGLE_API_KEY>")
    model = genai.GenerativeModel("gemini-1.5-flash")
    stream = await model.generate_content_async(
        "Write a story about a magic backpack.", 
        stream=True
    )

    # Get cumulative estimated environmental impacts of the inference
    async for chunk in stream:
        print(chunk.impacts)

asyncio.run(main())