Create a Google Vertex AI inference endpoint Generally available; Added in 8.15.0

PUT /_inference/{task_type}/{googlevertexai_inference_id}
Api key auth Basic auth Bearer auth

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

Required authorization

  • Cluster privileges: manage_inference

Path parameters

  • task_type string

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

    Values are rerank, text_embedding, completion, or chat_completion.

  • googlevertexai_inference_id string Required

    The unique identifier of the inference endpoint.

Query parameters

  • timeout string

    Specifies the amount of time to wait for the inference endpoint to be created.

    Values are -1 or 0.

application/json

Body

  • chunking_settings object

    The chunking configuration object.

    Hide chunking_settings attributes Show chunking_settings 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).

      Default value is 250.

    • 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.

      Default value is 100.

    • 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.

      Default value is 1.

    • strategy string

      The chunking strategy: sentence or word.

      Default value is sentence.

  • service string Required

    The type of service supported for the specified task type. In this case, googlevertexai.

    Value is googlevertexai.

  • service_settings object Required

    Settings used to install the inference model. These settings are specific to the googlevertexai service.

    Hide service_settings attributes Show service_settings attributes object
    • location string Required

      The name of the location to use for the inference task. Refer to the Google documentation for the list of supported locations.

      External documentation
    • 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
    • project_id string Required

      The name of the project to use for the inference task.

    • rate_limit object

      This setting helps to minimize the number of rate limit errors returned from Google Vertex AI. By default, the googlevertexai service sets the number of requests allowed per minute to 30.000.

      Hide rate_limit attribute Show rate_limit attribute object
      • requests_per_minute number

        The number of requests allowed per minute.

    • service_account_json string Required

      A valid service account in JSON format for the Google Vertex AI API.

  • task_settings object

    Settings to configure the inference task. These settings are specific to the task type you specified.

    Hide task_settings attributes Show task_settings attributes object
    • auto_truncate boolean

      For a text_embedding task, truncate inputs longer than the maximum token length automatically.

    • top_n number

      For a rerank task, the number of the top N documents that should be returned.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • chunking_settings object

      Chunking configuration object

      Hide chunking_settings attributes Show chunking_settings 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).

        Default value is 250.

      • 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.

        Default value is 100.

      • 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.

        Default value is 1.

      • strategy string

        The chunking strategy: sentence or word.

        Default value is sentence.

    • 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 string Required

      The task type

      Values are text_embedding or rerank.
PUT /_inference/{task_type}/{googlevertexai_inference_id} 
PUT _inference/text_embedding/google_vertex_ai_embeddingss
{
    "service": "googlevertexai",
    "service_settings": {
        "service_account_json": "service-account-json",
        "model_id": "model-id",
        "location": "location",
        "project_id": "project-id"
    }
}
resp = client.inference.put(
    task_type="text_embedding",
    inference_id="google_vertex_ai_embeddingss",
    inference_config={
        "service": "googlevertexai",
        "service_settings": {
            "service_account_json": "service-account-json",
            "model_id": "model-id",
            "location": "location",
            "project_id": "project-id"
        }
    },
)
const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "google_vertex_ai_embeddingss",
  inference_config: {
    service: "googlevertexai",
    service_settings: {
      service_account_json: "service-account-json",
      model_id: "model-id",
      location: "location",
      project_id: "project-id",
    },
  },
});
response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "google_vertex_ai_embeddingss",
  body: {
    "service": "googlevertexai",
    "service_settings": {
      "service_account_json": "service-account-json",
      "model_id": "model-id",
      "location": "location",
      "project_id": "project-id"
    }
  }
)
$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "google_vertex_ai_embeddingss",
    "body" => [
        "service" => "googlevertexai",
        "service_settings" => [
            "service_account_json" => "service-account-json",
            "model_id" => "model-id",
            "location" => "location",
            "project_id" => "project-id",
        ],
    ],
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"googlevertexai","service_settings":{"service_account_json":"service-account-json","model_id":"model-id","location":"location","project_id":"project-id"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/google_vertex_ai_embeddingss"
client.inference().put(p -> p
    .inferenceId("google_vertex_ai_embeddingss")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("googlevertexai")
        .serviceSettings(JsonData.fromJson("{\"service_account_json\":\"service-account-json\",\"model_id\":\"model-id\",\"location\":\"location\",\"project_id\":\"project-id\"}"))
    )
);
Request examples
Run `PUT _inference/text_embedding/google_vertex_ai_embeddings` to create an inference endpoint to perform a `text_embedding` task type. 
{
    "service": "googlevertexai",
    "service_settings": {
        "service_account_json": "service-account-json",
        "model_id": "model-id",
        "location": "location",
        "project_id": "project-id"
    }
}
Run `PUT _inference/rerank/google_vertex_ai_rerank` to create an inference endpoint to perform a `rerank` task type. 
{
    "service": "googlevertexai",
    "service_settings": {
        "service_account_json": "service-account-json",
        "project_id": "project-id"
    }
}