Create an Google AI Studio inference endpoint Added in 8.15.0

PUT /_inference/{task_type}/{googleaistudio_inference_id}
Api key auth

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

When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for "state": "fully_allocated" in the response and ensure that the "allocation_count" matches the "target_allocation_count". Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.

Path parameters

  • task_type string Required

    The type of the inference task that the model will perform.

    Values are completion or text_embedding.

  • googleaistudio_inference_id string Required

    The unique identifier of the inference endpoint.

application/json

Body

  • chunking_settings object
    Hide chunking_settings attributes Show chunking_settings attributes object
    • service string Required

      The service type

    • service_settings object Required
    • task_settings object
    • max_chunk_size number

      The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

    • overlap number

      The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.

    • sentence_overlap number

      The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

    • strategy string

      The chunking strategy: sentence or word.

  • service string Required

    Value is googleaistudio.

  • service_settings object Required
    Hide service_settings attributes Show service_settings attributes object
    • api_key string Required

      A valid API key of your Google Gemini account.

    • model_id string Required

      The name of the model to use for the inference task. Refer to the Google documentation for the list of supported models.

      External documentation
    • rate_limit object
      Hide rate_limit attribute Show rate_limit attribute object

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • Hide attributes Show attributes object
      • max_chunk_size number

        The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

      • overlap number

        The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.

      • sentence_overlap number

        The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

      • strategy string

        The chunking strategy: sentence or word.

    • service string Required

      The service type

    • service_settings object Required
    • task_settings object
    • inference_id string Required

      The inference Id

    • task_type string Required

      Values are sparse_embedding, text_embedding, rerank, completion, or chat_completion.
PUT /_inference/{task_type}/{googleaistudio_inference_id} 
curl \
 --request PUT 'http://api.example.com/_inference/{task_type}/{googleaistudio_inference_id}' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '"{\n    \"service\": \"googleaistudio\",\n    \"service_settings\": {\n        \"api_key\": \"api-key\",\n        \"model_id\": \"model-id\"\n    }\n}"'
Request example
Run `PUT _inference/completion/google_ai_studio_completion` to create an inference endpoint to perform a `completion` task type. 
{
    "service": "googleaistudio",
    "service_settings": {
        "api_key": "api-key",
        "model_id": "model-id"
    }
}