Authenticate

To use the OpenAI Python libraries, install the OpenAI SDK:

pip install openai

To authenticate with the Chat Completions API, you can either modify your client setup or change your environment configuration to use Google authentication and a Vertex AI endpoint. Choose whichever method that's easier, and follow the steps for setting up depending on whether you want to call Gemini models or self-deployed Model Garden models.

Certain models in Model Garden and supported Hugging Face models need to be deployed to a Vertex AI endpoint first before they can serve requests. When calling these self-deployed models from the Chat Completions API, you need to specify the endpoint ID. To list your existing Vertex AI endpoints, use the gcloud ai endpoints list command.

Client setup

To programmatically get Google credentials in Python, you can use the google-auth Python SDK:

pip install google-auth requests

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

import openai

from google.auth import default
import google.auth.transport.requests

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())
# Note: the credential lives for 1 hour by default (https://cloud.google.com/docs/authentication/token-types#at-lifetime); after expiration, it must be refreshed.

##############################
# Choose one of the following:
##############################

# If you are calling a Gemini model, set the ENDPOINT_ID variable to use openapi.
ENDPOINT_ID = "openapi"

# If you are calling a self-deployed model from Model Garden, set the
# ENDPOINT_ID variable and set the client's base URL to use your endpoint.
# ENDPOINT_ID = "YOUR_ENDPOINT_ID"

# OpenAI Client
client = openai.OpenAI(
    base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/{ENDPOINT_ID}",
    api_key=credentials.token,
)

By default, access tokens last for 1 hour. You can extend the life of your access token or periodically refresh your token and update the openai.api_key variable.

Environment variables

Install the Google Cloud CLI. The OpenAI library can read the OPENAI_API_KEY and OPENAI_BASE_URL environment variables to change the authentication and endpoint in their default client. Set the following variables:

$ export PROJECT_ID=PROJECT_ID
$ export LOCATION=LOCATION
$ export OPENAI_API_KEY="$(gcloud auth application-default print-access-token)"

To call a Gemini model, set the MODEL_ID variable and use the openapi endpoint:

$ export MODEL_ID=MODEL_ID
$ export OPENAI_BASE_URL="https://${LOCATION}-aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/endpoints/openapi"

To call a self-deployed model from Model Garden, set the ENDPOINT variable and use that in your URL instead:

$ export ENDPOINT=ENDPOINT_ID
$ export OPENAI_BASE_URL="https://${LOCATION}-aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/endpoints/${ENDPOINT}"

Next, initialize the client:

client = openai.OpenAI()

The Gemini Chat Completions API uses OAuth to authenticate with a short-lived access token. By default, access tokens last for 1 hour. You can extend the life of your access token or periodically refresh your token and update the OPENAI_API_KEY environment variable.

Refresh your credentials

The following example shows how to refresh your credentials automatically as needed:

Python 

from typing import Any

import google.auth
import google.auth.transport.requests
import openai


class OpenAICredentialsRefresher:
    def __init__(self, **kwargs: Any) -> None:
        # Set a placeholder key here
        self.client = openai.OpenAI(**kwargs, api_key="PLACEHOLDER")
        self.creds, self.project = google.auth.default(
            scopes=["https://www.googleapis.com/auth/cloud-platform"]
        )

    def __getattr__(self, name: str) -> Any:
        if not self.creds.valid:
            self.creds.refresh(google.auth.transport.requests.Request())

            if not self.creds.valid:
                raise RuntimeError("Unable to refresh auth")

            self.client.api_key = self.creds.token
        return getattr(self.client, name)



    # TODO(developer): Update and un-comment below lines
    # project_id = "PROJECT_ID"
    # location = "us-central1"

    client = OpenAICredentialsRefresher(
        base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
    )

    response = client.chat.completions.create(
        model="google/gemini-2.0-flash-001",
        messages=[{"role": "user", "content": "Why is the sky blue?"}],
    )

    print(response)

What's next