Authenticate API requests

Note the endpoint of the API you want to use.

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.

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

gdcloud auth login

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.

Note the endpoint of the API you want to use.

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

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.

Get the service key pairs of your service account.

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.

Install the google-auth client library:

pip install google-auth

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".

Save the Python script.

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.