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OpenAI

Lack of transparency

OpenAI 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 OpenAI official python client .

Official links:

Installation

To install EcoLogits along with all necessary dependencies for compatibility with the OpenAI client, please use the openai extra-dependency option as follows:

pip install ecologits[openai]

This installation command ensures that EcoLogits is set up with the specific libraries required to interface seamlessly with OpenAI's 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
from openai import OpenAI

# Initialize EcoLogits
EcoLogits.init()

client = OpenAI(api_key="<OPENAI_API_KEY>")

response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "user", "content": "Tell me a funny joke!"}
    ]
)

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

import asyncio
from ecologits import EcoLogits
from openai import AsyncOpenAI

# Initialize EcoLogits
EcoLogits.init()

client = AsyncOpenAI(api_key="<OPENAI_API_KEY>")

async def main() -> None:
    response = await client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "user", "content": "Tell me a funny joke!"}
        ]
    )

    # 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
from openai import OpenAI

# Initialize EcoLogits
EcoLogits.init()

client = OpenAI(api_key="<OPENAI_API_KEY>")

stream = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello World!"}],
    stream=True
)

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

import asyncio
from ecologits import EcoLogits
from openai import AsyncOpenAI

# Initialize EcoLogits
EcoLogits.init()

client = AsyncOpenAI(api_key="<OPENAI_API_KEY>")

async def main() -> None:
    stream = await client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "user", "content": "Tell me a funny joke!"}
        ]
    )

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


asyncio.run(main())

Compatibility with Azure OpenAI

EcoLogits is also compatible with Azure OpenAI .

import os
from ecologits import EcoLogits
from openai import AzureOpenAI

# Initialize EcoLogits
EcoLogits.init()

client = AzureOpenAI(
    azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
    api_version="2024-02-01"
)


response = client.chat.completions.create(
    model="gpt-35-turbo",
    messages=[
        {"role": "user", "content": "Tell me a funny joke!"}
    ]
)

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