Create an Amazon SageMaker inference endpoint

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.

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.

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

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.

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.

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

The name of the SageMaker endpoint.

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

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.

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.

Required when api is elastic and task type is text_embedding. The similarity measure used when invoking the text_embedding task type.

Required when api is elastic and task type is text_embedding. The data type returned by the text embedding model. This value is used when parsing the response back to Elasticsearch data structures.

The model ID when calling a multi-model endpoint.

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

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

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).