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Google AI Studio inference service

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Google AI Studio inference service

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Creates an inference endpoint to perform an inference task with the googleaistudio service.

Request

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PUT /_inference/<task_type>/<inference_id>

Path parameters

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<inference_id>

(Required, string) The unique identifier of the inference endpoint.

<task_type>

(Required, string) The type of the inference task that the model will perform.

Available task types:

completion,
text_embedding.

Request body

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service

(Required, string) The type of service supported for the specified task type. In this case, googleaistudio.

service_settings

(Required, object) Settings used to install the inference model.

These settings are specific to the googleaistudio service.

api_key

(Required, string) A valid API key for the Google Gemini API.

model_id

(Required, string) The name of the model to use for the inference task. You can find the supported models at Gemini API models.

rate_limit

(Optional, object) By default, the googleaistudio service sets the number of requests allowed per minute to 360. This helps to minimize the number of rate limit errors returned from Google AI Studio. To modify this, set the requests_per_minute setting of this object in your service settings:

"rate_limit": {
    "requests_per_minute": <<number_of_requests>>
}

Google AI Studio service example

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The following example shows how to create an inference endpoint called google_ai_studio_completion to perform a completion task type.

resp = client.inference.put(
    task_type="completion",
    inference_id="google_ai_studio_completion",
    inference_config={
        "service": "googleaistudio",
        "service_settings": {
            "api_key": "<api_key>",
            "model_id": "<model_id>"
        }
    },
)
print(resp)

const response = await client.inference.put({
  task_type: "completion",
  inference_id: "google_ai_studio_completion",
  inference_config: {
    service: "googleaistudio",
    service_settings: {
      api_key: "<api_key>",
      model_id: "<model_id>",
    },
  },
});
console.log(response);

PUT _inference/completion/google_ai_studio_completion
{
    "service": "googleaistudio",
    "service_settings": {
        "api_key": "<api_key>",
        "model_id": "<model_id>"
    }
}

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