Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window. Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window.

Method: projects.locations.extensions.import

Imports an Extension.

Endpoint

post


                https://{service-endpoint}/v1beta1/{parent}/extensions:import

Where {service-endpoint} is one of the supported service endpoints.

Path parameters

parent string

Required. The resource name of the Location to import the Extension in. Format: projects/{project}/locations/{location}

Request body

The request body contains an instance of Extension.

Example request

Python

import vertexai
from vertexai.preview import extensions

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

vertexai.init(project=project_id, location="us-central1")

extension = extensions.Extension.create(
    display_name="Code Interpreter",
    description="This extension generates and executes code in the specified language",
    manifest={
        "name": "code_interpreter_tool",
        "description": "Google Code Interpreter Extension",
        "api_spec": {
            "open_api_gcs_uri": "gs://vertex-extension-public/code_interpreter.yaml"
        },
        "auth_config": {
            "google_service_account_config": {},
            "auth_type": "GOOGLE_SERVICE_ACCOUNT_AUTH",
        },
    },
)gemini_extensions.py

Response body

If successful, the response body contains an instance of Operation.