Authenticate
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To use the OpenAI Python libraries, install the OpenAI SDK:
pipinstallopenai
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:
pipinstallgoogle-authrequests
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.
importopenai
fromgoogle.authimport default
importgoogle.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, service account access tokens last for 1 hour. You can
extend the life of service account access tokens
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:
$exportPROJECT_ID=PROJECT_ID
$exportLOCATION=LOCATION
$exportOPENAI_API_KEY="$(gcloudauthapplication-defaultprint-access-token)"
To call a Gemini model, set the MODEL_ID
variable and use the openapi endpoint:
$exportMODEL_ID=MODEL_ID
$exportOPENAI_BASE_URL="https://${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:
$exportENDPOINT=ENDPOINT_ID
$exportOPENAI_BASE_URL="https://${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, service account access tokens last for 1 hour. You can
extend the life of service account access tokens
or periodically refresh your token and update the openai.api_key variable.
Refresh your credentials
The following example shows how to refresh your credentials automatically as needed:
Python
fromtypingimport Any
importgoogle.auth
importgoogle.auth.transport.requests
importopenai
classOpenAICredentialsRefresher:
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
- See examples of calling the Chat Completions API with the OpenAI-compatible syntax.
- See examples of calling the Inference API with the OpenAI-compatible syntax.
- See examples of calling the Function Calling API with OpenAI-compatible syntax.
- Learn more about the Gemini API.
- Learn more about migrating from Azure OpenAI to the Gemini API.