Package co.elastic.clients.elasticsearch.inference

Class ElasticsearchInferenceAsyncClient

java.lang.Object
co.elastic.clients.ApiClient<ElasticsearchTransport,ElasticsearchInferenceAsyncClient>
co.elastic.clients.elasticsearch.inference.ElasticsearchInferenceAsyncClient
All Implemented Interfaces:
Closeable, AutoCloseable
public class ElasticsearchInferenceAsyncClient extends ApiClient<ElasticsearchTransport,ElasticsearchInferenceAsyncClient>
Client for the inference namespace.

  • Constructor Details

  • Method Details

    • withTransportOptions

      public ElasticsearchInferenceAsyncClient withTransportOptions(@Nullable TransportOptions transportOptions)
      Description copied from class: ApiClient
      Creates a new client with some request options
      Specified by:
      withTransportOptions in class ApiClient<ElasticsearchTransport,ElasticsearchInferenceAsyncClient>

    • delete

      public CompletableFuture<DeleteInferenceResponse> delete(DeleteInferenceRequest request)
      Delete an inference endpoint
      See Also:

    • delete

      public final CompletableFuture<DeleteInferenceResponse> delete(Function<DeleteInferenceRequest.Builder,ObjectBuilder<DeleteInferenceRequest>> fn)
      Delete an inference endpoint
      Parameters:
      fn - a function that initializes a builder to create the DeleteInferenceRequest
      See Also:

    • get

      public CompletableFuture<GetInferenceResponse> get(GetInferenceRequest request)
      Get an inference endpoint
      See Also:

    • get

      public final CompletableFuture<GetInferenceResponse> get(Function<GetInferenceRequest.Builder,ObjectBuilder<GetInferenceRequest>> fn)
      Get an inference endpoint
      Parameters:
      fn - a function that initializes a builder to create the GetInferenceRequest
      See Also:

    • get

      public CompletableFuture<GetInferenceResponse> get()
      Get an inference endpoint
      See Also:

    • inference

      public CompletableFuture<InferenceResponse> inference(InferenceRequest request)
      Perform inference on the service.

      This API enables you to use machine learning models to perform specific tasks on data that you provide as an input. It returns a response with the results of the tasks. The inference endpoint you use can perform one specific task that has been defined when the endpoint was created with the create inference API.

      info The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

      See Also:

    • inference

      public final CompletableFuture<InferenceResponse> inference(Function<InferenceRequest.Builder,ObjectBuilder<InferenceRequest>> fn)
      Perform inference on the service.

      This API enables you to use machine learning models to perform specific tasks on data that you provide as an input. It returns a response with the results of the tasks. The inference endpoint you use can perform one specific task that has been defined when the endpoint was created with the create inference API.

      info The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

      Parameters:
      fn - a function that initializes a builder to create the InferenceRequest
      See Also:

    • put

      public CompletableFuture<PutResponse> put(PutRequest request)
      Create an inference endpoint. 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.

      IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

      See Also:

    • put

      public final CompletableFuture<PutResponse> put(Function<PutRequest.Builder,ObjectBuilder<PutRequest>> fn)
      Create an inference endpoint. 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.

      IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

      Parameters:
      fn - a function that initializes a builder to create the PutRequest
      See Also:

    • update

      public CompletableFuture<UpdateInferenceResponse> update(UpdateInferenceRequest request)
      Update an inference endpoint.

      Modify task_settings, secrets (within service_settings), or num_allocations for an inference endpoint, depending on the specific endpoint service and task_type.

      IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

      See Also:

    • update

      public final CompletableFuture<UpdateInferenceResponse> update(Function<UpdateInferenceRequest.Builder,ObjectBuilder<UpdateInferenceRequest>> fn)
      Update an inference endpoint.

      Modify task_settings, secrets (within service_settings), or num_allocations for an inference endpoint, depending on the specific endpoint service and task_type.

      IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

      Parameters:
      fn - a function that initializes a builder to create the UpdateInferenceRequest
      See Also: