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>

    • chatCompletionUnified

      public CompletableFuture<BinaryResponse> chatCompletionUnified(ChatCompletionUnifiedRequest request)
      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.

      See Also:

    • chatCompletionUnified

      public final CompletableFuture<BinaryResponse> chatCompletionUnified(Function<ChatCompletionUnifiedRequest.Builder,ObjectBuilder<ChatCompletionUnifiedRequest>> fn)
      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
      See Also:

    • completion

      public CompletableFuture<CompletionResponse> completion(CompletionRequest request)
      Perform completion inference on the service
      See Also:

    • completion

      public final CompletableFuture<CompletionResponse> completion(Function<CompletionRequest.Builder,ObjectBuilder<CompletionRequest>> fn)
      Perform completion inference on the service
      Parameters:
      fn - a function that initializes a builder to create the CompletionRequest
      See Also:

    • 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.

      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.

      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.

      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
      See Also:

    • put

      public CompletableFuture<PutResponse> put(PutRequest request)
      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:

      • 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, 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)
      See Also:

    • put

      public final CompletableFuture<PutResponse> put(Function<PutRequest.Builder,ObjectBuilder<PutRequest>> fn)
      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:

      • 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, 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
      See Also:

    • putAlibabacloud

      public CompletableFuture<PutAlibabacloudResponse> putAlibabacloud(PutAlibabacloudRequest request)
      Create an AlibabaCloud AI Search inference endpoint.

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

      See Also:

    • putAlibabacloud

      public final CompletableFuture<PutAlibabacloudResponse> putAlibabacloud(Function<PutAlibabacloudRequest.Builder,ObjectBuilder<PutAlibabacloudRequest>> fn)
      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
      See Also:

    • putAmazonbedrock

      public CompletableFuture<PutAmazonbedrockResponse> putAmazonbedrock(PutAmazonbedrockRequest request)
      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.

      See Also:

    • putAmazonbedrock

      public final CompletableFuture<PutAmazonbedrockResponse> putAmazonbedrock(Function<PutAmazonbedrockRequest.Builder,ObjectBuilder<PutAmazonbedrockRequest>> fn)
      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
      See Also:

    • putAmazonsagemaker

      public CompletableFuture<PutAmazonsagemakerResponse> putAmazonsagemaker(PutAmazonsagemakerRequest request)
      Create an Amazon SageMaker inference endpoint.

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

      See Also:

    • putAmazonsagemaker

      public final CompletableFuture<PutAmazonsagemakerResponse> putAmazonsagemaker(Function<PutAmazonsagemakerRequest.Builder,ObjectBuilder<PutAmazonsagemakerRequest>> fn)
      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
      See Also:

    • putAnthropic

      public CompletableFuture<PutAnthropicResponse> putAnthropic(PutAnthropicRequest request)
      Create an Anthropic inference endpoint.

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

      See Also:

    • putAnthropic

      public final CompletableFuture<PutAnthropicResponse> putAnthropic(Function<PutAnthropicRequest.Builder,ObjectBuilder<PutAnthropicRequest>> fn)
      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
      See Also:

    • putAzureaistudio

      public CompletableFuture<PutAzureaistudioResponse> putAzureaistudio(PutAzureaistudioRequest request)
      Create an Azure AI studio inference endpoint.

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

      See Also:

    • putAzureaistudio

      public final CompletableFuture<PutAzureaistudioResponse> putAzureaistudio(Function<PutAzureaistudioRequest.Builder,ObjectBuilder<PutAzureaistudioRequest>> fn)
      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
      See Also:

    • putAzureopenai

      public CompletableFuture<PutAzureopenaiResponse> putAzureopenai(PutAzureopenaiRequest request)
      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:

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

      See Also:

    • putAzureopenai

      public final CompletableFuture<PutAzureopenaiResponse> putAzureopenai(Function<PutAzureopenaiRequest.Builder,ObjectBuilder<PutAzureopenaiRequest>> fn)
      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:

      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
      See Also:

    • putCohere

      public CompletableFuture<PutCohereResponse> putCohere(PutCohereRequest request)
      Create a Cohere inference endpoint.

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

      See Also:

    • putCohere

      public final CompletableFuture<PutCohereResponse> putCohere(Function<PutCohereRequest.Builder,ObjectBuilder<PutCohereRequest>> fn)
      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
      See Also:

    • putCustom

      public CompletableFuture<PutCustomResponse> putCustom(PutCustomRequest request)
      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.
      See Also:

    • putCustom

      public final CompletableFuture<PutCustomResponse> putCustom(Function<PutCustomRequest.Builder,ObjectBuilder<PutCustomRequest>> fn)
      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
      See Also:

    • putDeepseek

      public CompletableFuture<PutDeepseekResponse> putDeepseek(PutDeepseekRequest request)
      Create a DeepSeek inference endpoint.

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

      See Also:

    • putDeepseek

      public final CompletableFuture<PutDeepseekResponse> putDeepseek(Function<PutDeepseekRequest.Builder,ObjectBuilder<PutDeepseekRequest>> fn)
      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
      See Also:

    • putElasticsearch

      public CompletableFuture<PutElasticsearchResponse> putElasticsearch(PutElasticsearchRequest request)
      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.

      See Also:

    • putElasticsearch

      public final CompletableFuture<PutElasticsearchResponse> putElasticsearch(Function<PutElasticsearchRequest.Builder,ObjectBuilder<PutElasticsearchRequest>> fn)
      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
      See Also:

    • putElser

      public CompletableFuture<PutElserResponse> putElser(PutElserRequest request)
      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.

      See Also:

    • putElser

      public final CompletableFuture<PutElserResponse> putElser(Function<PutElserRequest.Builder,ObjectBuilder<PutElserRequest>> fn)
      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
      See Also:

    • putGoogleaistudio

      public CompletableFuture<PutGoogleaistudioResponse> putGoogleaistudio(PutGoogleaistudioRequest request)
      Create an Google AI Studio inference endpoint.

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

      See Also:

    • putGoogleaistudio

      public final CompletableFuture<PutGoogleaistudioResponse> putGoogleaistudio(Function<PutGoogleaistudioRequest.Builder,ObjectBuilder<PutGoogleaistudioRequest>> fn)
      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
      See Also:

    • putGooglevertexai

      public CompletableFuture<PutGooglevertexaiResponse> putGooglevertexai(PutGooglevertexaiRequest request)
      Create a Google Vertex AI inference endpoint.

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

      See Also:

    • putGooglevertexai

      public final CompletableFuture<PutGooglevertexaiResponse> putGooglevertexai(Function<PutGooglevertexaiRequest.Builder,ObjectBuilder<PutGooglevertexaiRequest>> fn)
      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
      See Also:

    • putHuggingFace

      public CompletableFuture<PutHuggingFaceResponse> putHuggingFace(PutHuggingFaceRequest request)
      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
      See Also:

    • putHuggingFace

      public final CompletableFuture<PutHuggingFaceResponse> putHuggingFace(Function<PutHuggingFaceRequest.Builder,ObjectBuilder<PutHuggingFaceRequest>> fn)
      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
      See Also:

    • putJinaai

      public CompletableFuture<PutJinaaiResponse> putJinaai(PutJinaaiRequest request)
      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/.

      See Also:

    • putJinaai

      public final CompletableFuture<PutJinaaiResponse> putJinaai(Function<PutJinaaiRequest.Builder,ObjectBuilder<PutJinaaiRequest>> fn)
      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
      See Also:

    • putMistral

      public CompletableFuture<PutMistralResponse> putMistral(PutMistralRequest request)
      Create a Mistral inference endpoint.

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

      See Also:

    • putMistral

      public final CompletableFuture<PutMistralResponse> putMistral(Function<PutMistralRequest.Builder,ObjectBuilder<PutMistralRequest>> fn)
      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
      See Also:

    • putOpenai

      public CompletableFuture<PutOpenaiResponse> putOpenai(PutOpenaiRequest request)
      Create an OpenAI inference endpoint.

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

      See Also:

    • putOpenai

      public final CompletableFuture<PutOpenaiResponse> putOpenai(Function<PutOpenaiRequest.Builder,ObjectBuilder<PutOpenaiRequest>> fn)
      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
      See Also:

    • putVoyageai

      public CompletableFuture<PutVoyageaiResponse> putVoyageai(PutVoyageaiRequest request)
      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.

      See Also:

    • putVoyageai

      public final CompletableFuture<PutVoyageaiResponse> putVoyageai(Function<PutVoyageaiRequest.Builder,ObjectBuilder<PutVoyageaiRequest>> fn)
      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
      See Also:

    • putWatsonx

      public CompletableFuture<PutWatsonxResponse> putWatsonx(PutWatsonxRequest request)
      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.

      See Also:

    • putWatsonx

      public final CompletableFuture<PutWatsonxResponse> putWatsonx(Function<PutWatsonxRequest.Builder,ObjectBuilder<PutWatsonxRequest>> fn)
      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
      See Also:

    • rerank

      public CompletableFuture<RerankResponse> rerank(RerankRequest request)
      Perform reranking inference on the service
      See Also:

    • rerank

      public final CompletableFuture<RerankResponse> rerank(Function<RerankRequest.Builder,ObjectBuilder<RerankRequest>> fn)
      Perform reranking inference on the service
      Parameters:
      fn - a function that initializes a builder to create the RerankRequest
      See Also:

    • sparseEmbedding

      public CompletableFuture<SparseEmbeddingResponse> sparseEmbedding(SparseEmbeddingRequest request)
      Perform sparse embedding inference on the service
      See Also:

    • sparseEmbedding

      public final CompletableFuture<SparseEmbeddingResponse> sparseEmbedding(Function<SparseEmbeddingRequest.Builder,ObjectBuilder<SparseEmbeddingRequest>> fn)
      Perform sparse embedding inference on the service
      Parameters:
      fn - a function that initializes a builder to create the SparseEmbeddingRequest
      See Also:

    • streamCompletion

      public CompletableFuture<BinaryResponse> streamCompletion(StreamCompletionRequest request)
      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.

      See Also:

    • streamCompletion

      public final CompletableFuture<BinaryResponse> streamCompletion(Function<StreamCompletionRequest.Builder,ObjectBuilder<StreamCompletionRequest>> fn)
      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
      See Also:

    • textEmbedding

      public CompletableFuture<TextEmbeddingResponse> textEmbedding(TextEmbeddingRequest request)
      Perform text embedding inference on the service
      See Also:

    • textEmbedding

      public final CompletableFuture<TextEmbeddingResponse> textEmbedding(Function<TextEmbeddingRequest.Builder,ObjectBuilder<TextEmbeddingRequest>> fn)
      Perform text embedding inference on the service
      Parameters:
      fn - a function that initializes a builder to create the TextEmbeddingRequest
      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: