Create an Google AI Studio inference endpoint

PUT /_inference/{task_type}/{googleaistudio_inference_id}

Auth

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

Required authorization

Cluster privileges manage_inference

Path parameters

task_type enum required: The type of the inference task that the model will perform.; Values: completion text_embedding
googleaistudio_inference_id string required: The unique identifier of the inference endpoint.

Query parameters

timeout string · default: 30s: Specifies the amount of time to wait for the inference endpoint to be created.

Request body

chunking_settings object: The chunking configuration object.; Reference
service enum required: The type of service supported for the specified task type. In this case, googleaistudio.; Values: googleaistudio
service_settings object required: Settings used to install the inference model. These settings are specific to the googleaistudio service.

Response

application/json

chunking_settings object: Chunking configuration object
service string required: The service type
service_settings object required: Settings specific to the service
task_settings object: Task settings specific to the service and task type
inference_id string required: The inference Id
task_type enum required: The task type; Values: text_embedding completion

Examples

Request Console 1 / 8 languages

Run PUT _inference/completion/google_ai_studio_completion to create an inference endpoint to perform a completion task type.

Console Kibana Dev Tools

PUT _inference/completion/google_ai_studio_completion
{
    "service": "googleaistudio",
    "service_settings": {
        "api_key": "api-key",
        "model_id": "model-id"
    }
}

JavaScript Elasticsearch JavaScript Client

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",
    },
  },
});

Python Elasticsearch Python Client

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"
        }
    },
)

curl Shell

curl -X PUT "$ELASTICSEARCH_URL/_inference/completion/google_ai_studio_completion" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "service": "googleaistudio",
    "service_settings": {
      "api_key": "api-key",
      "model_id": "model-id"
    }
  }'

Java Elasticsearch Java Client

client.inference().put(p -> p
    .inferenceId("google_ai_studio_completion")
    .taskType(TaskType.Completion)
    .inferenceConfig(i -> i
        .service("googleaistudio")
        .serviceSettings(JsonData.fromJson("{\"api_key\":\"api-key\",\"model_id\":\"model-id\"}"))
    )
);

C# Elasticsearch .NET Client

var response = await client.Inference
    .PutAsync(new PutInferenceRequest()
    {
        InferenceId = "google_ai_studio_completion",
        TaskType = TaskType.Completion,
        InferenceConfig = new()
        {
            Service = "googleaistudio",
            ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
            {
              "api_key": "api-key",
              "model_id": "model-id"
            }
            """)
        }
    });

PHP Elasticsearch PHP Client

$resp = $client->inference()->put([
    "task_type" => "completion",
    "inference_id" => "google_ai_studio_completion",
    "body" => [
        "service" => "googleaistudio",
        "service_settings" => [
            "api_key" => "api-key",
            "model_id" => "model-id",
        ],
    ],
]);

Ruby Elasticsearch Ruby Client

response = client.inference.put(
  task_type: "completion",
  inference_id: "google_ai_studio_completion",
  body: {
    "service": "googleaistudio",
    "service_settings": {
      "api_key": "api-key",
      "model_id": "model-id"
    }
  }
)

Response

{
  "chunking_settings": {
    "max_chunk_size": 250,
    "overlap": 100,
    "sentence_overlap": 1,
    "strategy": "sentence"
  },
  "service": "string",
  "service_settings": {},
  "task_settings": {},
  "inference_id": "string",
  "task_type": "text_embedding"
}

Generated from the API spec