Elasticsearch API

Base URL 
http://api.example.com

Elasticsearch provides REST APIs that are used by the UI components and can be called directly to configure and access Elasticsearch features.

Documentation source and versions

This documentation is derived from the 9.0 branch of the elasticsearch-specification repository. It is provided under license Attribution-NonCommercial-NoDerivatives 4.0 International. This documentation contains work-in-progress information for future Elastic Stack releases.

Last update on Jun 3, 2025.

This API is provided under license Apache 2.0.

Connector

The connector and sync jobs APIs provide a convenient way to create and manage Elastic connectors and sync jobs in an internal index. Connectors are Elasticsearch integrations for syncing content from third-party data sources, which can be deployed on Elastic Cloud or hosted on your own infrastructure. This API provides an alternative to relying solely on Kibana UI for connector and sync job management. The API comes with a set of validations and assertions to ensure that the state representation in the internal index remains valid. This API requires the manage_connector privilege or, for read-only endpoints, the monitor_connector privilege.

Check out the connector API tutorial

Create an Azure OpenAI inference endpoint Added in 8.14.0

PUT /_inference/{task_type}/{azureopenai_inference_id}
Api key auth Basic auth Bearer auth

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.

Path parameters

  • task_type string Required

    The type of the inference task that the model will perform. NOTE: The chat_completion task type only supports streaming and only through the _stream API.

    Values are completion or text_embedding.

  • azureopenai_inference_id string Required

    The unique identifier of the inference endpoint.

application/json

Body

  • chunking_settings 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).

    • 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.

    • 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.

    • strategy string

      The chunking strategy: sentence or word.

  • service string Required

    Value is azureopenai.

  • service_settings object Required
    Hide service_settings attributes Show service_settings attributes object
    • api_key string

      A valid API key for your Azure OpenAI account. You must specify either api_key or entra_id. If you do not provide either or you provide both, you will receive an error when you try to create your model.

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

      External documentation
    • api_version string Required

      The Azure API version ID to use. It is recommended to use the latest supported non-preview version.

    • deployment_id string Required

      The deployment name of your deployed models. Your Azure OpenAI deployments can be found though the Azure OpenAI Studio portal that is linked to your subscription.

      External documentation
    • entra_id string

      A valid Microsoft Entra token. You must specify either api_key or entra_id. If you do not provide either or you provide both, you will receive an error when you try to create your model.

      External documentation
    • rate_limit object
      Hide rate_limit attribute Show rate_limit attribute object
    • resource_name string Required

      The name of your Azure OpenAI resource. You can find this from the list of resources in the Azure Portal for your subscription.

      External documentation
  • task_settings object
    Hide task_settings attribute Show task_settings attribute object
    • user string

      For a completion or text_embedding task, specify the user issuing the request. This information can be used for abuse detection.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • chunking_settings 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).

      • 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.

      • 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.

      • strategy string

        The chunking strategy: sentence or word.

    • 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 sparse_embedding, text_embedding, rerank, completion, or chat_completion.
PUT /_inference/{task_type}/{azureopenai_inference_id} 
curl \
 --request PUT 'http://api.example.com/_inference/{task_type}/{azureopenai_inference_id}' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '"{\n    \"service\": \"azureopenai\",\n    \"service_settings\": {\n        \"api_key\": \"Api-Key\",\n        \"resource_name\": \"Resource-name\",\n        \"deployment_id\": \"Deployment-id\",\n        \"api_version\": \"2024-02-01\"\n    }\n}"'
Request examples
Run `PUT _inference/text_embedding/azure_openai_embeddings` to create an inference endpoint that performs a `text_embedding` task. You do not specify a model, as it is defined already in the Azure OpenAI deployment. 
{
    "service": "azureopenai",
    "service_settings": {
        "api_key": "Api-Key",
        "resource_name": "Resource-name",
        "deployment_id": "Deployment-id",
        "api_version": "2024-02-01"
    }
}
Run `PUT _inference/completion/azure_openai_completion` to create an inference endpoint that performs a `completion` task. 
{
    "service": "azureopenai",
    "service_settings": {
        "api_key": "Api-Key",
        "resource_name": "Resource-name",
        "deployment_id": "Deployment-id",
        "api_version": "2024-02-01"
    }
}