Package co.elastic.clients.elasticsearch.inference

Class ElasticsearchInferenceClient

java.lang.Object

co.elastic.clients.ApiClient<ElasticsearchTransport,ElasticsearchInferenceClient>

co.elastic.clients.elasticsearch.inference.ElasticsearchInferenceClient

All Implemented Interfaces:

Closeable, AutoCloseable

public class ElasticsearchInferenceClient
extends ApiClient<ElasticsearchTransport,ElasticsearchInferenceClient>

Client for the inference namespace.

Field Summary

Fields inherited from class co.elastic.clients.ApiClient

transport, transportOptions

Constructor Summary

Constructors

Constructor	Description
ElasticsearchInferenceClient(ElasticsearchTransport transport)
ElasticsearchInferenceClient(ElasticsearchTransport transport, TransportOptions transportOptions)

Method Summary

Modifier and Type	Method	Description
BinaryResponse	chatCompletionUnified(ChatCompletionUnifiedRequest request)	Perform chat completion inference
final BinaryResponse	chatCompletionUnified(Function<ChatCompletionUnifiedRequest.Builder,ObjectBuilder<ChatCompletionUnifiedRequest>> fn)	Perform chat completion inference
CompletionResponse	completion(CompletionRequest request)	Perform completion inference on the service
final CompletionResponse	completion(Function<CompletionRequest.Builder,ObjectBuilder<CompletionRequest>> fn)	Perform completion inference on the service
DeleteInferenceResponse	delete(DeleteInferenceRequest request)	Delete an inference endpoint
final DeleteInferenceResponse	delete(Function<DeleteInferenceRequest.Builder,ObjectBuilder<DeleteInferenceRequest>> fn)	Delete an inference endpoint
GetInferenceResponse	get()	Get an inference endpoint
GetInferenceResponse	get(GetInferenceRequest request)	Get an inference endpoint
final GetInferenceResponse	get(Function<GetInferenceRequest.Builder,ObjectBuilder<GetInferenceRequest>> fn)	Get an inference endpoint
InferenceResponse	inference(InferenceRequest request)	Perform inference on the service.
final InferenceResponse	inference(Function<InferenceRequest.Builder,ObjectBuilder<InferenceRequest>> fn)	Perform inference on the service.
PutResponse	put(PutRequest request)	Create an inference endpoint.
final PutResponse	put(Function<PutRequest.Builder,ObjectBuilder<PutRequest>> fn)	Create an inference endpoint.
PutAi21Response	putAi21(PutAi21Request request)	Create a AI21 inference endpoint.
final PutAi21Response	putAi21(Function<PutAi21Request.Builder,ObjectBuilder<PutAi21Request>> fn)	Create a AI21 inference endpoint.
PutAlibabacloudResponse	putAlibabacloud(PutAlibabacloudRequest request)	Create an AlibabaCloud AI Search inference endpoint.
final PutAlibabacloudResponse	putAlibabacloud(Function<PutAlibabacloudRequest.Builder,ObjectBuilder<PutAlibabacloudRequest>> fn)	Create an AlibabaCloud AI Search inference endpoint.
PutAmazonbedrockResponse	putAmazonbedrock(PutAmazonbedrockRequest request)	Create an Amazon Bedrock inference endpoint.
final PutAmazonbedrockResponse	putAmazonbedrock(Function<PutAmazonbedrockRequest.Builder,ObjectBuilder<PutAmazonbedrockRequest>> fn)	Create an Amazon Bedrock inference endpoint.
PutAmazonsagemakerResponse	putAmazonsagemaker(PutAmazonsagemakerRequest request)	Create an Amazon SageMaker inference endpoint.
final PutAmazonsagemakerResponse	putAmazonsagemaker(Function<PutAmazonsagemakerRequest.Builder,ObjectBuilder<PutAmazonsagemakerRequest>> fn)	Create an Amazon SageMaker inference endpoint.
PutAnthropicResponse	putAnthropic(PutAnthropicRequest request)	Create an Anthropic inference endpoint.
final PutAnthropicResponse	putAnthropic(Function<PutAnthropicRequest.Builder,ObjectBuilder<PutAnthropicRequest>> fn)	Create an Anthropic inference endpoint.
PutAzureaistudioResponse	putAzureaistudio(PutAzureaistudioRequest request)	Create an Azure AI studio inference endpoint.
final PutAzureaistudioResponse	putAzureaistudio(Function<PutAzureaistudioRequest.Builder,ObjectBuilder<PutAzureaistudioRequest>> fn)	Create an Azure AI studio inference endpoint.
PutAzureopenaiResponse	putAzureopenai(PutAzureopenaiRequest request)	Create an Azure OpenAI inference endpoint.
final PutAzureopenaiResponse	putAzureopenai(Function<PutAzureopenaiRequest.Builder,ObjectBuilder<PutAzureopenaiRequest>> fn)	Create an Azure OpenAI inference endpoint.
PutCohereResponse	putCohere(PutCohereRequest request)	Create a Cohere inference endpoint.
final PutCohereResponse	putCohere(Function<PutCohereRequest.Builder,ObjectBuilder<PutCohereRequest>> fn)	Create a Cohere inference endpoint.
PutContextualaiResponse	putContextualai(PutContextualaiRequest request)	Create an Contextual AI inference endpoint.
final PutContextualaiResponse	putContextualai(Function<PutContextualaiRequest.Builder,ObjectBuilder<PutContextualaiRequest>> fn)	Create an Contextual AI inference endpoint.
PutCustomResponse	putCustom(PutCustomRequest request)	Create a custom inference endpoint.
final PutCustomResponse	putCustom(Function<PutCustomRequest.Builder,ObjectBuilder<PutCustomRequest>> fn)	Create a custom inference endpoint.
PutDeepseekResponse	putDeepseek(PutDeepseekRequest request)	Create a DeepSeek inference endpoint.
final PutDeepseekResponse	putDeepseek(Function<PutDeepseekRequest.Builder,ObjectBuilder<PutDeepseekRequest>> fn)	Create a DeepSeek inference endpoint.
PutElasticsearchResponse	putElasticsearch(PutElasticsearchRequest request)	Create an Elasticsearch inference endpoint.
final PutElasticsearchResponse	putElasticsearch(Function<PutElasticsearchRequest.Builder,ObjectBuilder<PutElasticsearchRequest>> fn)	Create an Elasticsearch inference endpoint.
PutElserResponse	putElser(PutElserRequest request)	Create an ELSER inference endpoint.
final PutElserResponse	putElser(Function<PutElserRequest.Builder,ObjectBuilder<PutElserRequest>> fn)	Create an ELSER inference endpoint.
PutGoogleaistudioResponse	putGoogleaistudio(PutGoogleaistudioRequest request)	Create an Google AI Studio inference endpoint.
final PutGoogleaistudioResponse	putGoogleaistudio(Function<PutGoogleaistudioRequest.Builder,ObjectBuilder<PutGoogleaistudioRequest>> fn)	Create an Google AI Studio inference endpoint.
PutGooglevertexaiResponse	putGooglevertexai(PutGooglevertexaiRequest request)	Create a Google Vertex AI inference endpoint.
final PutGooglevertexaiResponse	putGooglevertexai(Function<PutGooglevertexaiRequest.Builder,ObjectBuilder<PutGooglevertexaiRequest>> fn)	Create a Google Vertex AI inference endpoint.
PutHuggingFaceResponse	putHuggingFace(PutHuggingFaceRequest request)	Create a Hugging Face inference endpoint.
final PutHuggingFaceResponse	putHuggingFace(Function<PutHuggingFaceRequest.Builder,ObjectBuilder<PutHuggingFaceRequest>> fn)	Create a Hugging Face inference endpoint.
PutJinaaiResponse	putJinaai(PutJinaaiRequest request)	Create an JinaAI inference endpoint.
final PutJinaaiResponse	putJinaai(Function<PutJinaaiRequest.Builder,ObjectBuilder<PutJinaaiRequest>> fn)	Create an JinaAI inference endpoint.
PutLlamaResponse	putLlama(PutLlamaRequest request)	Create a Llama inference endpoint.
final PutLlamaResponse	putLlama(Function<PutLlamaRequest.Builder,ObjectBuilder<PutLlamaRequest>> fn)	Create a Llama inference endpoint.
PutMistralResponse	putMistral(PutMistralRequest request)	Create a Mistral inference endpoint.
final PutMistralResponse	putMistral(Function<PutMistralRequest.Builder,ObjectBuilder<PutMistralRequest>> fn)	Create a Mistral inference endpoint.
PutOpenaiResponse	putOpenai(PutOpenaiRequest request)	Create an OpenAI inference endpoint.
final PutOpenaiResponse	putOpenai(Function<PutOpenaiRequest.Builder,ObjectBuilder<PutOpenaiRequest>> fn)	Create an OpenAI inference endpoint.
PutVoyageaiResponse	putVoyageai(PutVoyageaiRequest request)	Create a VoyageAI inference endpoint.
final PutVoyageaiResponse	putVoyageai(Function<PutVoyageaiRequest.Builder,ObjectBuilder<PutVoyageaiRequest>> fn)	Create a VoyageAI inference endpoint.
PutWatsonxResponse	putWatsonx(PutWatsonxRequest request)	Create a Watsonx inference endpoint.
final PutWatsonxResponse	putWatsonx(Function<PutWatsonxRequest.Builder,ObjectBuilder<PutWatsonxRequest>> fn)	Create a Watsonx inference endpoint.
RerankResponse	rerank(RerankRequest request)	Perform reranking inference on the service
final RerankResponse	rerank(Function<RerankRequest.Builder,ObjectBuilder<RerankRequest>> fn)	Perform reranking inference on the service
SparseEmbeddingResponse	sparseEmbedding(SparseEmbeddingRequest request)	Perform sparse embedding inference on the service
final SparseEmbeddingResponse	sparseEmbedding(Function<SparseEmbeddingRequest.Builder,ObjectBuilder<SparseEmbeddingRequest>> fn)	Perform sparse embedding inference on the service
BinaryResponse	streamCompletion(StreamCompletionRequest request)	Perform streaming inference.
final BinaryResponse	streamCompletion(Function<StreamCompletionRequest.Builder,ObjectBuilder<StreamCompletionRequest>> fn)	Perform streaming inference.
TextEmbeddingResponse	textEmbedding(TextEmbeddingRequest request)	Perform text embedding inference on the service
final TextEmbeddingResponse	textEmbedding(Function<TextEmbeddingRequest.Builder,ObjectBuilder<TextEmbeddingRequest>> fn)	Perform text embedding inference on the service
UpdateInferenceResponse	update(UpdateInferenceRequest request)	Update an inference endpoint.
final UpdateInferenceResponse	update(Function<UpdateInferenceRequest.Builder,ObjectBuilder<UpdateInferenceRequest>> fn)	Update an inference endpoint.
ElasticsearchInferenceClient	withTransportOptions(TransportOptions transportOptions)	Creates a new client with some request options

Methods inherited from class co.elastic.clients.ApiClient

_jsonpMapper, _transport, _transportOptions, close, getDeserializer, withTransportOptions

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

ElasticsearchInferenceClient

public ElasticsearchInferenceClient(ElasticsearchTransport transport)

ElasticsearchInferenceClient

public ElasticsearchInferenceClient(ElasticsearchTransport transport,
 @Nullable
 TransportOptions transportOptions)

Method Details

withTransportOptions

public ElasticsearchInferenceClient withTransportOptions(@Nullable
 TransportOptions transportOptions)

Description copied from class: ApiClient

Creates a new client with some request options

Specified by:

withTransportOptions in class ApiClient<ElasticsearchTransport,ElasticsearchInferenceClient>

chatCompletionUnified

public BinaryResponse chatCompletionUnified(ChatCompletionUnifiedRequest request)
                                     throws IOException,
ElasticsearchException

Perform chat completion inference

The chat completion inference API enables real-time responses for chat completion tasks by delivering answers incrementally, reducing response times during computation. It only works with the chat_completion task type for openai and elastic inference services.

NOTE: The chat_completion task type is only available within the _stream API and only supports streaming. The Chat completion inference API and the Stream inference API differ in their response structure and capabilities. The Chat completion inference API provides more comprehensive customization options through more fields and function calling support. If you use the openai, hugging_face or the elastic service, use the Chat completion inference API.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

chatCompletionUnified

public final BinaryResponse chatCompletionUnified(Function<ChatCompletionUnifiedRequest.Builder,ObjectBuilder<ChatCompletionUnifiedRequest>> fn)
                                           throws IOException,
ElasticsearchException

Perform chat completion inference

The chat completion inference API enables real-time responses for chat completion tasks by delivering answers incrementally, reducing response times during computation. It only works with the chat_completion task type for openai and elastic inference services.

NOTE: The chat_completion task type is only available within the _stream API and only supports streaming. The Chat completion inference API and the Stream inference API differ in their response structure and capabilities. The Chat completion inference API provides more comprehensive customization options through more fields and function calling support. If you use the openai, hugging_face or the elastic service, use the Chat completion inference API.

Parameters:

fn - a function that initializes a builder to create the ChatCompletionUnifiedRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

completion

public CompletionResponse completion(CompletionRequest request)
                              throws IOException,
ElasticsearchException

Perform completion inference on the service

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

completion

public final CompletionResponse completion(Function<CompletionRequest.Builder,ObjectBuilder<CompletionRequest>> fn)
                                    throws IOException,
ElasticsearchException

Perform completion inference on the service

Parameters:

fn - a function that initializes a builder to create the CompletionRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

delete

public DeleteInferenceResponse delete(DeleteInferenceRequest request)
                               throws IOException,
ElasticsearchException

Delete an inference endpoint

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

delete

public final DeleteInferenceResponse delete(Function<DeleteInferenceRequest.Builder,ObjectBuilder<DeleteInferenceRequest>> fn)
                                     throws IOException,
ElasticsearchException

Delete an inference endpoint

Parameters:

fn - a function that initializes a builder to create the DeleteInferenceRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

get

public GetInferenceResponse get(GetInferenceRequest request)
                         throws IOException,
ElasticsearchException

Get an inference endpoint

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

get

public final GetInferenceResponse get(Function<GetInferenceRequest.Builder,ObjectBuilder<GetInferenceRequest>> fn)
                               throws IOException,
ElasticsearchException

Get an inference endpoint

Parameters:

fn - a function that initializes a builder to create the GetInferenceRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

get

public GetInferenceResponse get()
                         throws IOException,
ElasticsearchException

Get an inference endpoint

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

inference

public InferenceResponse inference(InferenceRequest request)
                            throws IOException,
ElasticsearchException

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.

For details about using this API with a service, such as Amazon Bedrock, Anthropic, or HuggingFace, refer to the service-specific documentation.

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.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

inference

public final InferenceResponse inference(Function<InferenceRequest.Builder,ObjectBuilder<InferenceRequest>> fn)
                                  throws IOException,
ElasticsearchException

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.

For details about using this API with a service, such as Amazon Bedrock, Anthropic, or HuggingFace, refer to the service-specific documentation.

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

put

public PutResponse put(PutRequest request)
                throws IOException,
ElasticsearchException

Create an inference endpoint.

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.

The following integrations are available through the inference API. You can find the available task types next to the integration name:

• AI21 (chat_completion, completion)
• AlibabaCloud AI Search (completion, rerank, sparse_embedding, text_embedding)
• Amazon Bedrock (completion, text_embedding)
• Amazon SageMaker (chat_completion, completion, rerank, sparse_embedding, text_embedding)
• Anthropic (completion)
• Azure AI Studio (completion, 'rerank', text_embedding)
• Azure OpenAI (completion, text_embedding)
• Cohere (completion, rerank, text_embedding)
• DeepSeek (chat_completion, completion)
• Elasticsearch (rerank, sparse_embedding, text_embedding - this service is for built-in models and models uploaded through Eland)
• ELSER (sparse_embedding)
• Google AI Studio (completion, text_embedding)
• Google Vertex AI (chat_completion, completion, rerank, text_embedding)
• Hugging Face (chat_completion, completion, rerank, text_embedding)
• JinaAI (rerank, text_embedding)
• Llama (chat_completion, completion, text_embedding)
• Mistral (chat_completion, completion, text_embedding)
• OpenAI (chat_completion, completion, text_embedding)
• VoyageAI (rerank, text_embedding)
• Watsonx inference integration (text_embedding)

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

put

public final PutResponse put(Function<PutRequest.Builder,ObjectBuilder<PutRequest>> fn)
                      throws IOException,
ElasticsearchException

Create an inference endpoint.

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.

The following integrations are available through the inference API. You can find the available task types next to the integration name:

• AI21 (chat_completion, completion)
• AlibabaCloud AI Search (completion, rerank, sparse_embedding, text_embedding)
• Amazon Bedrock (completion, text_embedding)
• Amazon SageMaker (chat_completion, completion, rerank, sparse_embedding, text_embedding)
• Anthropic (completion)
• Azure AI Studio (completion, 'rerank', text_embedding)
• Azure OpenAI (completion, text_embedding)
• Cohere (completion, rerank, text_embedding)
• DeepSeek (chat_completion, completion)
• Elasticsearch (rerank, sparse_embedding, text_embedding - this service is for built-in models and models uploaded through Eland)
• ELSER (sparse_embedding)
• Google AI Studio (completion, text_embedding)
• Google Vertex AI (chat_completion, completion, rerank, text_embedding)
• Hugging Face (chat_completion, completion, rerank, text_embedding)
• JinaAI (rerank, text_embedding)
• Llama (chat_completion, completion, text_embedding)
• Mistral (chat_completion, completion, text_embedding)
• OpenAI (chat_completion, completion, text_embedding)
• VoyageAI (rerank, text_embedding)
• Watsonx inference integration (text_embedding)

Parameters:

fn - a function that initializes a builder to create the PutRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAi21

public PutAi21Response putAi21(PutAi21Request request)
                        throws IOException,
ElasticsearchException

Create a AI21 inference endpoint.

Create an inference endpoint to perform an inference task with the ai21 service.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAi21

public final PutAi21Response putAi21(Function<PutAi21Request.Builder,ObjectBuilder<PutAi21Request>> fn)
                              throws IOException,
ElasticsearchException

Create a AI21 inference endpoint.

Create an inference endpoint to perform an inference task with the ai21 service.

Parameters:

fn - a function that initializes a builder to create the PutAi21Request

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAlibabacloud

public PutAlibabacloudResponse putAlibabacloud(PutAlibabacloudRequest request)
                                        throws IOException,
ElasticsearchException

Create an AlibabaCloud AI Search inference endpoint.

Create an inference endpoint to perform an inference task with the alibabacloud-ai-search service.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAlibabacloud

public final PutAlibabacloudResponse putAlibabacloud(Function<PutAlibabacloudRequest.Builder,ObjectBuilder<PutAlibabacloudRequest>> fn)
                                              throws IOException,
ElasticsearchException

Create an AlibabaCloud AI Search inference endpoint.

Create an inference endpoint to perform an inference task with the alibabacloud-ai-search service.

Parameters:

fn - a function that initializes a builder to create the PutAlibabacloudRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAmazonbedrock

public PutAmazonbedrockResponse putAmazonbedrock(PutAmazonbedrockRequest request)
                                          throws IOException,
ElasticsearchException

Create an Amazon Bedrock inference endpoint.

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

info You need to provide the access and secret keys only once, during the inference model creation. The get inference API does not retrieve your access or secret keys. After creating the inference model, you cannot change the associated key pairs. If you want to use a different access and secret key pair, delete the inference model and recreate it with the same name and the updated keys.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAmazonbedrock

public final PutAmazonbedrockResponse putAmazonbedrock(Function<PutAmazonbedrockRequest.Builder,ObjectBuilder<PutAmazonbedrockRequest>> fn)
                                                throws IOException,
ElasticsearchException

Create an Amazon Bedrock inference endpoint.

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

info You need to provide the access and secret keys only once, during the inference model creation. The get inference API does not retrieve your access or secret keys. After creating the inference model, you cannot change the associated key pairs. If you want to use a different access and secret key pair, delete the inference model and recreate it with the same name and the updated keys.

Parameters:

fn - a function that initializes a builder to create the PutAmazonbedrockRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAmazonsagemaker

public PutAmazonsagemakerResponse putAmazonsagemaker(PutAmazonsagemakerRequest request)
                                              throws IOException,
ElasticsearchException

Create an Amazon SageMaker inference endpoint.

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAmazonsagemaker

public final PutAmazonsagemakerResponse putAmazonsagemaker(Function<PutAmazonsagemakerRequest.Builder,ObjectBuilder<PutAmazonsagemakerRequest>> fn)
                                                    throws IOException,
ElasticsearchException

Create an Amazon SageMaker inference endpoint.

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

Parameters:

fn - a function that initializes a builder to create the PutAmazonsagemakerRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAnthropic

public PutAnthropicResponse putAnthropic(PutAnthropicRequest request)
                                  throws IOException,
ElasticsearchException

Create an Anthropic inference endpoint.

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAnthropic

public final PutAnthropicResponse putAnthropic(Function<PutAnthropicRequest.Builder,ObjectBuilder<PutAnthropicRequest>> fn)
                                        throws IOException,
ElasticsearchException

Create an Anthropic inference endpoint.

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

Parameters:

fn - a function that initializes a builder to create the PutAnthropicRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAzureaistudio

public PutAzureaistudioResponse putAzureaistudio(PutAzureaistudioRequest request)
                                          throws IOException,
ElasticsearchException

Create an Azure AI studio inference endpoint.

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAzureaistudio

public final PutAzureaistudioResponse putAzureaistudio(Function<PutAzureaistudioRequest.Builder,ObjectBuilder<PutAzureaistudioRequest>> fn)
                                                throws IOException,
ElasticsearchException

Create an Azure AI studio inference endpoint.

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

Parameters:

fn - a function that initializes a builder to create the PutAzureaistudioRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAzureopenai

public PutAzureopenaiResponse putAzureopenai(PutAzureopenaiRequest request)
                                      throws IOException,
ElasticsearchException

Create an Azure OpenAI inference endpoint.

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

The list of chat completion models that you can choose from in your Azure OpenAI deployment include:

• GPT-4 and GPT-4 Turbo models
• GPT-3.5

The list of embeddings models that you can choose from in your deployment can be found in the Azure models documentation.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putAzureopenai

public final PutAzureopenaiResponse putAzureopenai(Function<PutAzureopenaiRequest.Builder,ObjectBuilder<PutAzureopenaiRequest>> fn)
                                            throws IOException,
ElasticsearchException

Create an Azure OpenAI inference endpoint.

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

The list of chat completion models that you can choose from in your Azure OpenAI deployment include:

• GPT-4 and GPT-4 Turbo models
• GPT-3.5

The list of embeddings models that you can choose from in your deployment can be found in the Azure models documentation.

Parameters:

fn - a function that initializes a builder to create the PutAzureopenaiRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putCohere

public PutCohereResponse putCohere(PutCohereRequest request)
                            throws IOException,
ElasticsearchException

Create a Cohere inference endpoint.

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putCohere

public final PutCohereResponse putCohere(Function<PutCohereRequest.Builder,ObjectBuilder<PutCohereRequest>> fn)
                                  throws IOException,
ElasticsearchException

Create a Cohere inference endpoint.

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

Parameters:

fn - a function that initializes a builder to create the PutCohereRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putContextualai

public PutContextualaiResponse putContextualai(PutContextualaiRequest request)
                                        throws IOException,
ElasticsearchException

Create an Contextual AI inference endpoint.

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

To review the available rerank models, refer to https://docs.contextual.ai/api-reference/rerank/rerank#body-model.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putContextualai

public final PutContextualaiResponse putContextualai(Function<PutContextualaiRequest.Builder,ObjectBuilder<PutContextualaiRequest>> fn)
                                              throws IOException,
ElasticsearchException

Create an Contextual AI inference endpoint.

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

To review the available rerank models, refer to https://docs.contextual.ai/api-reference/rerank/rerank#body-model.

Parameters:

fn - a function that initializes a builder to create the PutContextualaiRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putCustom

public PutCustomResponse putCustom(PutCustomRequest request)
                            throws IOException,
ElasticsearchException

Create a custom inference endpoint.

The custom service gives more control over how to interact with external inference services that aren't explicitly supported through dedicated integrations. The custom service gives you the ability to define the headers, url, query parameters, request body, and secrets. The custom service supports the template replacement functionality, which enables you to define a template that can be replaced with the value associated with that key. Templates are portions of a string that start with ${ and end with }. The parameters secret_parameters and task_settings are checked for keys for template replacement. Template replacement is supported in the request, headers, url, and query_parameters. If the definition (key) is not found for a template, an error message is returned. In case of an endpoint definition like the following:

 PUT _inference/text_embedding/test-text-embedding
 {
   "service": "custom",
   "service_settings": {
      "secret_parameters": {
           "api_key": "<some api key>"
      },
      "url": "...endpoints.huggingface.cloud/v1/embeddings",
      "headers": {
          "Authorization": "Bearer ${api_key}",
          "Content-Type": "application/json"
      },
      "request": "{\"input\": ${input}}",
      "response": {
          "json_parser": {
              "text_embeddings":"$.data[*].embedding[*]"
          }
      }
   }
 }
 
 

To replace ${api_key} the secret_parameters and task_settings are checked for a key named api_key.

info Templates should not be surrounded by quotes.

Pre-defined templates:

• ${input} refers to the array of input strings that comes from the input field of the subsequent inference requests.
• ${input_type} refers to the input type translation values.
• ${query} refers to the query field used specifically for reranking tasks.
• ${top_n} refers to the top_n field available when performing rerank requests.
• ${return_documents} refers to the return_documents field available when performing rerank requests.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putCustom

public final PutCustomResponse putCustom(Function<PutCustomRequest.Builder,ObjectBuilder<PutCustomRequest>> fn)
                                  throws IOException,
ElasticsearchException

Create a custom inference endpoint.

The custom service gives more control over how to interact with external inference services that aren't explicitly supported through dedicated integrations. The custom service gives you the ability to define the headers, url, query parameters, request body, and secrets. The custom service supports the template replacement functionality, which enables you to define a template that can be replaced with the value associated with that key. Templates are portions of a string that start with ${ and end with }. The parameters secret_parameters and task_settings are checked for keys for template replacement. Template replacement is supported in the request, headers, url, and query_parameters. If the definition (key) is not found for a template, an error message is returned. In case of an endpoint definition like the following:

 PUT _inference/text_embedding/test-text-embedding
 {
   "service": "custom",
   "service_settings": {
      "secret_parameters": {
           "api_key": "<some api key>"
      },
      "url": "...endpoints.huggingface.cloud/v1/embeddings",
      "headers": {
          "Authorization": "Bearer ${api_key}",
          "Content-Type": "application/json"
      },
      "request": "{\"input\": ${input}}",
      "response": {
          "json_parser": {
              "text_embeddings":"$.data[*].embedding[*]"
          }
      }
   }
 }
 
 

To replace ${api_key} the secret_parameters and task_settings are checked for a key named api_key.

info Templates should not be surrounded by quotes.

Pre-defined templates:

• ${input} refers to the array of input strings that comes from the input field of the subsequent inference requests.
• ${input_type} refers to the input type translation values.
• ${query} refers to the query field used specifically for reranking tasks.
• ${top_n} refers to the top_n field available when performing rerank requests.
• ${return_documents} refers to the return_documents field available when performing rerank requests.

Parameters:

fn - a function that initializes a builder to create the PutCustomRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putDeepseek

public PutDeepseekResponse putDeepseek(PutDeepseekRequest request)
                                throws IOException,
ElasticsearchException

Create a DeepSeek inference endpoint.

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putDeepseek

public final PutDeepseekResponse putDeepseek(Function<PutDeepseekRequest.Builder,ObjectBuilder<PutDeepseekRequest>> fn)
                                      throws IOException,
ElasticsearchException

Create a DeepSeek inference endpoint.

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

Parameters:

fn - a function that initializes a builder to create the PutDeepseekRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putElasticsearch

public PutElasticsearchResponse putElasticsearch(PutElasticsearchRequest request)
                                          throws IOException,
ElasticsearchException

Create an Elasticsearch inference endpoint.

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

info Your Elasticsearch deployment contains preconfigured ELSER and E5 inference endpoints, you only need to create the enpoints using the API if you want to customize the settings.

If you use the ELSER or the E5 model through the elasticsearch service, the API request will automatically download and deploy the model if it isn't downloaded yet.

info You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.

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.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putElasticsearch

public final PutElasticsearchResponse putElasticsearch(Function<PutElasticsearchRequest.Builder,ObjectBuilder<PutElasticsearchRequest>> fn)
                                                throws IOException,
ElasticsearchException

Create an Elasticsearch inference endpoint.

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

info Your Elasticsearch deployment contains preconfigured ELSER and E5 inference endpoints, you only need to create the enpoints using the API if you want to customize the settings.

If you use the ELSER or the E5 model through the elasticsearch service, the API request will automatically download and deploy the model if it isn't downloaded yet.

info You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.

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.

Parameters:

fn - a function that initializes a builder to create the PutElasticsearchRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putElser

public PutElserResponse putElser(PutElserRequest request)
                          throws IOException,
ElasticsearchException

Create an ELSER inference endpoint.

Create an inference endpoint to perform an inference task with the elser service. You can also deploy ELSER by using the Elasticsearch inference integration.

info Your Elasticsearch deployment contains a preconfigured ELSER inference endpoint, you only need to create the enpoint using the API if you want to customize the settings.

The API request will automatically download and deploy the ELSER model if it isn't already downloaded.

info You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.

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.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putElser

public final PutElserResponse putElser(Function<PutElserRequest.Builder,ObjectBuilder<PutElserRequest>> fn)
                                throws IOException,
ElasticsearchException

Create an ELSER inference endpoint.

Create an inference endpoint to perform an inference task with the elser service. You can also deploy ELSER by using the Elasticsearch inference integration.

info Your Elasticsearch deployment contains a preconfigured ELSER inference endpoint, you only need to create the enpoint using the API if you want to customize the settings.

The API request will automatically download and deploy the ELSER model if it isn't already downloaded.

info You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.

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.

Parameters:

fn - a function that initializes a builder to create the PutElserRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putGoogleaistudio

public PutGoogleaistudioResponse putGoogleaistudio(PutGoogleaistudioRequest request)
                                            throws IOException,
ElasticsearchException

Create an Google AI Studio inference endpoint.

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putGoogleaistudio

public final PutGoogleaistudioResponse putGoogleaistudio(Function<PutGoogleaistudioRequest.Builder,ObjectBuilder<PutGoogleaistudioRequest>> fn)
                                                  throws IOException,
ElasticsearchException

Create an Google AI Studio inference endpoint.

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

Parameters:

fn - a function that initializes a builder to create the PutGoogleaistudioRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putGooglevertexai

public PutGooglevertexaiResponse putGooglevertexai(PutGooglevertexaiRequest request)
                                            throws IOException,
ElasticsearchException

Create a Google Vertex AI inference endpoint.

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putGooglevertexai

public final PutGooglevertexaiResponse putGooglevertexai(Function<PutGooglevertexaiRequest.Builder,ObjectBuilder<PutGooglevertexaiRequest>> fn)
                                                  throws IOException,
ElasticsearchException

Create a Google Vertex AI inference endpoint.

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

Parameters:

fn - a function that initializes a builder to create the PutGooglevertexaiRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putHuggingFace

public PutHuggingFaceResponse putHuggingFace(PutHuggingFaceRequest request)
                                      throws IOException,
ElasticsearchException

Create a Hugging Face inference endpoint.

Create an inference endpoint to perform an inference task with the hugging_face service. Supported tasks include: text_embedding, completion, and chat_completion.

To configure the endpoint, first visit the Hugging Face Inference Endpoints page and create a new endpoint. Select a model that supports the task you intend to use.

For Elastic's text_embedding task: The selected model must support the Sentence Embeddings task. On the new endpoint creation page, select the Sentence Embeddings task under the Advanced Configuration section. After the endpoint has initialized, copy the generated endpoint URL. Recommended models for text_embedding task:

• all-MiniLM-L6-v2
• all-MiniLM-L12-v2
• all-mpnet-base-v2
• e5-base-v2
• e5-small-v2
• multilingual-e5-base
• multilingual-e5-small

For Elastic's chat_completion and completion tasks: The selected model must support the Text Generation task and expose OpenAI API. HuggingFace supports both serverless and dedicated endpoints for Text Generation. When creating dedicated endpoint select the Text Generation task. After the endpoint is initialized (for dedicated) or ready (for serverless), ensure it supports the OpenAI API and includes /v1/chat/completions part in URL. Then, copy the full endpoint URL for use. Recommended models for chat_completion and completion tasks:

• Mistral-7B-Instruct-v0.2
• QwQ-32B
• Phi-3-mini-128k-instruct

For Elastic's rerank task: The selected model must support the sentence-ranking task and expose OpenAI API. HuggingFace supports only dedicated (not serverless) endpoints for Rerank so far. After the endpoint is initialized, copy the full endpoint URL for use. Tested models for rerank task:

• bge-reranker-base
• jina-reranker-v1-turbo-en-GGUF

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putHuggingFace

public final PutHuggingFaceResponse putHuggingFace(Function<PutHuggingFaceRequest.Builder,ObjectBuilder<PutHuggingFaceRequest>> fn)
                                            throws IOException,
ElasticsearchException

Create a Hugging Face inference endpoint.

Create an inference endpoint to perform an inference task with the hugging_face service. Supported tasks include: text_embedding, completion, and chat_completion.

To configure the endpoint, first visit the Hugging Face Inference Endpoints page and create a new endpoint. Select a model that supports the task you intend to use.

For Elastic's text_embedding task: The selected model must support the Sentence Embeddings task. On the new endpoint creation page, select the Sentence Embeddings task under the Advanced Configuration section. After the endpoint has initialized, copy the generated endpoint URL. Recommended models for text_embedding task:

• all-MiniLM-L6-v2
• all-MiniLM-L12-v2
• all-mpnet-base-v2
• e5-base-v2
• e5-small-v2
• multilingual-e5-base
• multilingual-e5-small

For Elastic's chat_completion and completion tasks: The selected model must support the Text Generation task and expose OpenAI API. HuggingFace supports both serverless and dedicated endpoints for Text Generation. When creating dedicated endpoint select the Text Generation task. After the endpoint is initialized (for dedicated) or ready (for serverless), ensure it supports the OpenAI API and includes /v1/chat/completions part in URL. Then, copy the full endpoint URL for use. Recommended models for chat_completion and completion tasks:

• Mistral-7B-Instruct-v0.2
• QwQ-32B
• Phi-3-mini-128k-instruct

For Elastic's rerank task: The selected model must support the sentence-ranking task and expose OpenAI API. HuggingFace supports only dedicated (not serverless) endpoints for Rerank so far. After the endpoint is initialized, copy the full endpoint URL for use. Tested models for rerank task:

• bge-reranker-base
• jina-reranker-v1-turbo-en-GGUF

Parameters:

fn - a function that initializes a builder to create the PutHuggingFaceRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putJinaai

public PutJinaaiResponse putJinaai(PutJinaaiRequest request)
                            throws IOException,
ElasticsearchException

Create an JinaAI inference endpoint.

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

To review the available rerank models, refer to https://jina.ai/reranker. To review the available text_embedding models, refer to the https://jina.ai/embeddings/.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putJinaai

public final PutJinaaiResponse putJinaai(Function<PutJinaaiRequest.Builder,ObjectBuilder<PutJinaaiRequest>> fn)
                                  throws IOException,
ElasticsearchException

Create an JinaAI inference endpoint.

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

To review the available rerank models, refer to https://jina.ai/reranker. To review the available text_embedding models, refer to the https://jina.ai/embeddings/.

Parameters:

fn - a function that initializes a builder to create the PutJinaaiRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putLlama

public PutLlamaResponse putLlama(PutLlamaRequest request)
                          throws IOException,
ElasticsearchException

Create a Llama inference endpoint.

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putLlama

public final PutLlamaResponse putLlama(Function<PutLlamaRequest.Builder,ObjectBuilder<PutLlamaRequest>> fn)
                                throws IOException,
ElasticsearchException

Create a Llama inference endpoint.

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

Parameters:

fn - a function that initializes a builder to create the PutLlamaRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putMistral

public PutMistralResponse putMistral(PutMistralRequest request)
                              throws IOException,
ElasticsearchException

Create a Mistral inference endpoint.

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putMistral

public final PutMistralResponse putMistral(Function<PutMistralRequest.Builder,ObjectBuilder<PutMistralRequest>> fn)
                                    throws IOException,
ElasticsearchException

Create a Mistral inference endpoint.

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

Parameters:

fn - a function that initializes a builder to create the PutMistralRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putOpenai

public PutOpenaiResponse putOpenai(PutOpenaiRequest request)
                            throws IOException,
ElasticsearchException

Create an OpenAI inference endpoint.

Create an inference endpoint to perform an inference task with the openai service or openai compatible APIs.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putOpenai

public final PutOpenaiResponse putOpenai(Function<PutOpenaiRequest.Builder,ObjectBuilder<PutOpenaiRequest>> fn)
                                  throws IOException,
ElasticsearchException

Create an OpenAI inference endpoint.

Create an inference endpoint to perform an inference task with the openai service or openai compatible APIs.

Parameters:

fn - a function that initializes a builder to create the PutOpenaiRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putVoyageai

public PutVoyageaiResponse putVoyageai(PutVoyageaiRequest request)
                                throws IOException,
ElasticsearchException

Create a VoyageAI inference endpoint.

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

Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putVoyageai

public final PutVoyageaiResponse putVoyageai(Function<PutVoyageaiRequest.Builder,ObjectBuilder<PutVoyageaiRequest>> fn)
                                      throws IOException,
ElasticsearchException

Create a VoyageAI inference endpoint.

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

Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.

Parameters:

fn - a function that initializes a builder to create the PutVoyageaiRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putWatsonx

public PutWatsonxResponse putWatsonx(PutWatsonxRequest request)
                              throws IOException,
ElasticsearchException

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.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

putWatsonx

public final PutWatsonxResponse putWatsonx(Function<PutWatsonxRequest.Builder,ObjectBuilder<PutWatsonxRequest>> fn)
                                    throws IOException,
ElasticsearchException

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.

Parameters:

fn - a function that initializes a builder to create the PutWatsonxRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

rerank

public RerankResponse rerank(RerankRequest request)
                      throws IOException,
ElasticsearchException

Perform reranking inference on the service

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

rerank

public final RerankResponse rerank(Function<RerankRequest.Builder,ObjectBuilder<RerankRequest>> fn)
                            throws IOException,
ElasticsearchException

Perform reranking inference on the service

Parameters:

fn - a function that initializes a builder to create the RerankRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

sparseEmbedding

public SparseEmbeddingResponse sparseEmbedding(SparseEmbeddingRequest request)
                                        throws IOException,
ElasticsearchException

Perform sparse embedding inference on the service

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

sparseEmbedding

public final SparseEmbeddingResponse sparseEmbedding(Function<SparseEmbeddingRequest.Builder,ObjectBuilder<SparseEmbeddingRequest>> fn)
                                              throws IOException,
ElasticsearchException

Perform sparse embedding inference on the service

Parameters:

fn - a function that initializes a builder to create the SparseEmbeddingRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

streamCompletion

public BinaryResponse streamCompletion(StreamCompletionRequest request)
                                throws IOException,
ElasticsearchException

Perform streaming inference. Get real-time responses for completion tasks by delivering answers incrementally, reducing response times during computation. This API works only with the completion 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.

This API requires the monitor_inference cluster privilege (the built-in inference_admin and inference_user roles grant this privilege). You must use a client that supports streaming.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

streamCompletion

public final BinaryResponse streamCompletion(Function<StreamCompletionRequest.Builder,ObjectBuilder<StreamCompletionRequest>> fn)
                                      throws IOException,
ElasticsearchException

Perform streaming inference. Get real-time responses for completion tasks by delivering answers incrementally, reducing response times during computation. This API works only with the completion 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.

This API requires the monitor_inference cluster privilege (the built-in inference_admin and inference_user roles grant this privilege). You must use a client that supports streaming.

Parameters:

fn - a function that initializes a builder to create the StreamCompletionRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

textEmbedding

public TextEmbeddingResponse textEmbedding(TextEmbeddingRequest request)
                                    throws IOException,
ElasticsearchException

Perform text embedding inference on the service

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

textEmbedding

public final TextEmbeddingResponse textEmbedding(Function<TextEmbeddingRequest.Builder,ObjectBuilder<TextEmbeddingRequest>> fn)
                                          throws IOException,
ElasticsearchException

Perform text embedding inference on the service

Parameters:

fn - a function that initializes a builder to create the TextEmbeddingRequest

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

update

public UpdateInferenceResponse update(UpdateInferenceRequest request)
                               throws IOException,
ElasticsearchException

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.

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co

update

public final UpdateInferenceResponse update(Function<UpdateInferenceRequest.Builder,ObjectBuilder<UpdateInferenceRequest>> fn)
                                     throws IOException,
ElasticsearchException

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

Throws:

IOException

ElasticsearchException

See Also:

• Documentation on elastic.co