Create an Amazon SageMaker inference endpoint | Elasticsearch Serverless API documentation

Create an Amazon SageMaker inference endpoint Generally available

PUT /_inference/{task_type}/{amazonsagemaker_inference_id}

Api key auth

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

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string

The type of the inference task that the model will perform.

Values are text_embedding, completion, chat_completion, sparse_embedding, or rerank.
amazonsagemaker_inference_id string Required

The unique identifier of the inference endpoint.

Query parameters

timeout string

Specifies the amount of time to wait for the inference endpoint to be created.

Values are -1 or 0.

application/json

Body

chunking_settings object

Chunking configuration object
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  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 Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  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] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  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

Value is amazon_sagemaker.
service_settings object Required
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
  
  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
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

Responses

200 application/json
Hide response attributes Show response attributes object
- chunking_settings object
  
  Chunking configuration object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  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 Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  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] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  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 service type
- service_settings object Required
- task_settings object
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  Values are text_embedding, completion, chat_completion, sparse_embedding, or rerank.

PUT /_inference/{task_type}/{amazonsagemaker_inference_id}

PUT _inference/text_embedding/amazon_sagemaker_embeddings
{
    "service": "amazon_sagemaker",
    "service_settings": {
        "access_key": "AWS-access-key",
        "secret_key": "AWS-secret-key",
        "region": "us-east-1",
        "api": "elastic",
        "endpoint_name": "my-endpoint",
        "dimensions": 384,
        "element_type": "float"
    }
}

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="amazon_sagemaker_embeddings",
    inference_config={
        "service": "amazon_sagemaker",
        "service_settings": {
            "access_key": "AWS-access-key",
            "secret_key": "AWS-secret-key",
            "region": "us-east-1",
            "api": "elastic",
            "endpoint_name": "my-endpoint",
            "dimensions": 384,
            "element_type": "float"
        }
    },
)

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "amazon_sagemaker_embeddings",
  inference_config: {
    service: "amazon_sagemaker",
    service_settings: {
      access_key: "AWS-access-key",
      secret_key: "AWS-secret-key",
      region: "us-east-1",
      api: "elastic",
      endpoint_name: "my-endpoint",
      dimensions: 384,
      element_type: "float",
    },
  },
});

response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "amazon_sagemaker_embeddings",
  body: {
    "service": "amazon_sagemaker",
    "service_settings": {
      "access_key": "AWS-access-key",
      "secret_key": "AWS-secret-key",
      "region": "us-east-1",
      "api": "elastic",
      "endpoint_name": "my-endpoint",
      "dimensions": 384,
      "element_type": "float"
    }
  }
)

$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "amazon_sagemaker_embeddings",
    "body" => [
        "service" => "amazon_sagemaker",
        "service_settings" => [
            "access_key" => "AWS-access-key",
            "secret_key" => "AWS-secret-key",
            "region" => "us-east-1",
            "api" => "elastic",
            "endpoint_name" => "my-endpoint",
            "dimensions" => 384,
            "element_type" => "float",
        ],
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"amazon_sagemaker","service_settings":{"access_key":"AWS-access-key","secret_key":"AWS-secret-key","region":"us-east-1","api":"elastic","endpoint_name":"my-endpoint","dimensions":384,"element_type":"float"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/amazon_sagemaker_embeddings"

Request examples

Run `PUT _inference/text_embedding/amazon_sagemaker_embeddings` to create an inference endpoint that performs a text embedding task.

{
    "service": "amazon_sagemaker",
    "service_settings": {
        "access_key": "AWS-access-key",
        "secret_key": "AWS-secret-key",
        "region": "us-east-1",
        "api": "elastic",
        "endpoint_name": "my-endpoint",
        "dimensions": 384,
        "element_type": "float"
    }
}

Run `PUT _inference/completion/amazon_sagemaker_completion` to create an inference endpoint that performs a completion task.

{
    "service": "amazon_sagemaker",
    "service_settings": {
        "access_key": "AWS-access-key",
        "secret_key": "AWS-secret-key",
        "region": "us-east-1",
        "api": "elastic",
        "endpoint_name": "my-endpoint"
    }
}

Run `PUT _inference/chat_completion/amazon_sagemaker_chat_completion` to create an inference endpoint that performs a chat completion task.

{
    "service": "amazon_sagemaker",
    "service_settings": {
        "access_key": "AWS-access-key",
        "secret_key": "AWS-secret-key",
        "region": "us-east-1",
        "api": "elastic",
        "endpoint_name": "my-endpoint"
    }
}

Run `PUT _inference/sparse_embedding/amazon_sagemaker_sparse_embedding` to create an inference endpoint that performs a sparse embedding task.

{
    "service": "amazon_sagemaker",
    "service_settings": {
        "access_key": "AWS-access-key",
        "secret_key": "AWS-secret-key",
        "region": "us-east-1",
        "api": "elastic",
        "endpoint_name": "my-endpoint"
    }
}

Run `PUT _inference/rerank/amazon_sagemaker_rerank` to create an inference endpoint that performs a rerank task.

{
    "service": "amazon_sagemaker",
    "service_settings": {
        "access_key": "AWS-access-key",
        "secret_key": "AWS-secret-key",
        "region": "us-east-1",
        "api": "elastic",
        "endpoint_name": "my-endpoint"
    }
}