Package co.elastic.clients.elasticsearch.inference

Class PutWatsonxRequest

java.lang.Object
co.elastic.clients.elasticsearch._types.RequestBase
co.elastic.clients.elasticsearch.inference.PutWatsonxRequest
All Implemented Interfaces:
JsonpSerializable
@JsonpDeserializable public class PutWatsonxRequest extends RequestBase implements JsonpSerializable
Create a Watsonx inference endpoint.

Create an inference endpoint to perform an inference task with the watsonxai service. You need an IBM Cloud Databases for Elasticsearch deployment to use the watsonxai inference service. You can provision one through the IBM catalog, the Cloud Databases CLI plug-in, the Cloud Databases API, or Terraform.

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.

See Also: