Body Required

• chunking_settings object

  The chunking configuration object. Applies only to the sparse_embedding or text_embedding task types. Not applicable to the rerank, completion, or chat_completion task types.

  External documentation
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  • max_chunk_size number

    The maximum size of a chunk in words. This value cannot be lower than 20 (for sentence strategy) or 10 (for word strategy). This value should not exceed the window size for the associated model.

    Default value is 250.

  • overlap number

    The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.

    Default value is 100.

  • sentence_overlap number

    The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

    Default value is 1.

  • separator_group string

    Only applicable to the recursive strategy and required when using it.

    Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.

    Using this parameter is an alternative to manually specifying a custom separators list.

  • separators array[string]

    Only applicable to the recursive strategy and required when using it.

    A list of strings used as possible split points when chunking text.

    Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.

    After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.

  • strategy string

    The chunking strategy: sentence, word, none or recursive.

    • If strategy is set to recursive, you must also specify:

      • max_chunk_size
      • either separators orseparator_group

    Learn more about different chunking strategies in the linked documentation.

    Default value is sentence.

    External documentation
• service string Required

  The type of service supported for the specified task type. In this case, amazon_sagemaker.

  Value is amazon_sagemaker.

• service_settings object Required

  Settings used to install the inference model. These settings are specific to the amazon_sagemaker service and service_settings.api you specified.

  Hide service_settings attributes Show service_settings attributes object

  • access_key string Required

    A valid AWS access key that has permissions to use Amazon SageMaker and access to models for invoking requests.

  • endpoint_name string Required

    The name of the SageMaker endpoint.

    External documentation
  • api string Required

    The API format to use when calling SageMaker. Elasticsearch will convert the POST _inference request to this data format when invoking the SageMaker endpoint.

    Values are openai or elastic.

  • region string Required

    The region that your endpoint or Amazon Resource Name (ARN) is deployed in. The list of available regions per model can be found in the Amazon SageMaker documentation.

    External documentation
  • secret_key string Required

    A valid AWS secret key that is paired with the access_key. For information about creating and managing access and secret keys, refer to the AWS documentation.

    External documentation
  • target_model string

    The model ID when calling a multi-model endpoint.

    External documentation
  • target_container_hostname string

    The container to directly invoke when calling a multi-container endpoint.

    External documentation
  • inference_component_name string

    The inference component to directly invoke when calling a multi-component endpoint.

    External documentation
  • batch_size number

    The maximum number of inputs in each batch. This value is used by inference ingestion pipelines when processing semantic values. It correlates to the number of times the SageMaker endpoint is invoked (one per batch of input).

    Default value is 256.

  • dimensions number

    The number of dimensions returned by the text embedding models. If this value is not provided, then it is guessed by making invoking the endpoint for the text_embedding task.

• task_settings object

  Settings to configure the inference task. These settings are specific to the task type and service_settings.api you specified.

  Hide task_settings attributes Show task_settings attributes object

  • custom_attributes string

    The AWS custom attributes passed verbatim through to the model running in the SageMaker Endpoint. Values will be returned in the X-elastic-sagemaker-custom-attributes header.

    External documentation
  • enable_explanations string

    The optional JMESPath expression used to override the EnableExplanations provided during endpoint creation.

    External documentation
  • inference_id string

    The capture data ID when enabled in the endpoint.

    External documentation
  • session_id string

    The stateful session identifier for a new or existing session. New sessions will be returned in the X-elastic-sagemaker-new-session-id header. Closed sessions will be returned in the X-elastic-sagemaker-closed-session-id header.

    External documentation
  • target_variant string

    Specifies the variant when running with multi-variant Endpoints.

    External documentation