Authenticate Vertex AI API requests

This page describes how to authenticate calls to Vertex AI services on Google Distributed Cloud (GDC) air-gapped. You must set up token authentication to secure your requests to the Vertex AI API within your air-gapped applications. This process validates your API requests by providing your identity and authorizing your interactions.

This page is for application developers within application operator groups responsible for setting up their application and development environments to enable AI features. For more information, see Audiences for GDC air-gapped documentation.

Before you begin

You must have your project set up for Vertex AI. For more information, see Set up a project for Vertex AI.

  • Make sure to update your local trust store before you set up authentication in your development environment.

Authenticating to Vertex AI services

Interactions with Vertex AI services are done through authentication tokens. Tokens are digital objects that verify your identity and authorization after you provide valid credentials. The token carries specific information about your account and the permissions it has to access and operate with services and resources.

There are two ways you can set up authentication:

Authenticate with your user account

The following guides you through getting an authentication token for your user account:

  1. Note the endpoint of the API you want to use.

  2. Gain access to the Vertex AI service or Generative AI model you want to use by granting your user account the corresponding role listed in Prepare IAM permissions.

  3. Sign in to Distributed Cloud with the user account you have to interact with the API:

    gdcloud auth login

  4. Get the authentication token:

    gdcloud auth print-identity-token --audiences=https://ENDPOINT

    Replace ENDPOINT with the API endpoint that you use for your organization. For more information, view service status and endpoints.

    Depending on the intended use of the authentication token, you might need to include the port after the service endpoint in the audiences path as follows:

    • If you use a client library for your request, you must include port :443 after the service endpoint in the audiences path. Therefore, the --audiences path in the command must be https://ENDPOINT:443.
    • If you use gRPC, curl, or programmatic REST calls for your request, don't include the port. Therefore, the --audiences path in the command must be https://ENDPOINT.

    The output displays the authentication token. Add the token to the header of the command-line requests you make, as in the following example:

     -H "Authorization: Bearer TOKEN"

    Replace TOKEN with the value for the authentication token that the output displays.

Authenticate with your service account

The following guides you through getting an authentication token for your service account:

  1. Note the endpoint of the API you want to use.

  2. Set up the service account you want to use to access the Vertex AI service or Generative AI model.

  3. Grant the service account the corresponding role listed in Prepare IAM permissions to let it gain access to the service or model you want to use.

  4. Get the service key pairs of your service account.

  5. Set the following environment variable:

    export GOOGLE_APPLICATION_CREDENTIALS=PATH_TO_SERVICE_KEY

    Replace PATH_TO_SERVICE_KEY with the path to the JSON file that contains the key pairs of your service account.

  6. Install the google-auth client library:

    pip install google-auth

  7. Add the following code to a Python script:

    import os
import google.auth
from google.auth.transport import requests
import requests as reqs

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "PATH_TO_SERVICE_KEY"
os.environ["GRPC_DEFAULT_SSL_ROOTS_FILE_PATH"] = "CERT_NAME"

# If you use a client library for your request,
# you must include port :443 after the service endpoint
# in the audience path.
audience = "https://ENDPOINT"

creds, project_id = google.auth.default()
print(project_id)
creds = creds.with_gdch_audience(audience)

def test_get_token():
  sesh = reqs.Session()
  req = requests.Request(session=sesh)
  creds.refresh(req)
  print(creds.token)

if __name__=="__main__":
  test_get_token()

    Replace the following:

    • PATH_TO_SERVICE_KEY: the path to the JSON file that contains the key pairs of your service account.
    • CERT_NAME: the name of the Certificate Authority (CA) certificate file, such as org-1-trust-bundle-ca.cert. You only need this value if you are in a development environment. Otherwise, omit it.

    • ENDPOINT: the API endpoint that you use for your organization. For more information, view service status and endpoints. Depending on the intended use of the authentication token, you might need to include the port after the service endpoint in the audience path as follows:

      • If you use a client library for your request, you must include port :443 after the service endpoint in the audience path. Therefore, the audience path in the script must be "https://ENDPOINT:443".
      • If you use gRPC, curl, or programmatic REST calls for your request, don't include the port. Therefore, the audience path in the script must be "https://ENDPOINT".

  8. Save the Python script.

  9. Run the Python script to fetch the token:

      python SCRIPT_NAME

    Replace SCRIPT_NAME with the name you gave to your Python script, such as token.py.

    The output displays the authentication token. Add the token to the header of the command-line requests you make, as in the following example:

     -H "Authorization: Bearer TOKEN"

    Replace TOKEN with the value for the authentication token that the output displays.