Create a Google Vertex AI inference endpoint | Elasticsearch Serverless API documentation

Create a Google Vertex AI inference endpoint Added in 8.15.0

PUT /_inference/{task_type}/{googlevertexai_inference_id}

Create an inference endpoint to perform an inference task with the googlevertexai 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 rerank or text_embedding.
googlevertexai_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 googlevertexai.
service_settings object Required
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
  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
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
- 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}/{googlevertexai_inference_id}

curl \
 --request PUT 'http://api.example.com/_inference/{task_type}/{googlevertexai_inference_id}' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '"{\n    \"service\": \"googlevertexai\",\n    \"service_settings\": {\n        \"service_account_json\": \"service-account-json\",\n        \"model_id\": \"model-id\",\n        \"location\": \"location\",\n        \"project_id\": \"project-id\"\n    }\n}"'

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"
    }
}