Create a Google Vertex AI inference endpoint | Elasticsearch API documentation

Create a Google Vertex AI inference endpointAdded 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_typestring Required
The type of the inference task that the model will perform.
Values are rerank or text_embedding.
googlevertexai_inference_idstring Required
The unique identifier of the inference endpoint.

application/json

Body

chunking_settingsobject
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_sizenumber
  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).
- overlapnumber
  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_overlapnumber
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
- strategystring
  The chunking strategy: sentence or word.
servicestring Required
Value is googlevertexai.
service_settingsobject Required
Hide service_settings attributes Show service_settings attributes object
- locationstring 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_idstring 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_idstring Required
  The name of the project to use for the inference task.
- rate_limitobject
  Hide rate_limit attribute Show rate_limit attribute object
  requests_per_minutenumber
  The number of requests allowed per minute.
- service_account_jsonstring Required
  A valid service account in JSON format for the Google Vertex AI API.
task_settingsobject
Hide task_settings attributes Show task_settings attributes object
- auto_truncateboolean
  For a text_embedding task, truncate inputs longer than the maximum token length automatically.
- top_nnumber
  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_settingsobject
  Hide chunking_settings attributes Show chunking_settings attributes object
  max_chunk_sizenumber
  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).
  overlapnumber
  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_overlapnumber
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  strategystring
  The chunking strategy: sentence or word.
- servicestring Required
  The service type
- service_settingsobject Required
- task_settingsobject
- inference_idstring Required
  The inference Id
- task_typestring 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"
    }
}