Path parameters

  • task_typestring Required

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

    Values are completion or text_embedding.

  • azureopenai_inference_idstring Required

    The unique identifier of the inference endpoint.

application/json

Body

  • chunking_settingsobject

    Chunking configuration object

    Hide chunking_settings attributes Show chunking_settings attributes object
    • max_chunk_sizenumber

      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).

    • overlapnumber

      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_overlapnumber

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

    • strategystring

      The chunking strategy: sentence or word.

  • servicestring Required

    Value is azureopenai.

  • service_settingsobject Required
    Hide service_settings attributes Show service_settings attributes object
    • api_keystring

      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_versionstring Required

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

    • deployment_idstring 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_idstring

      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_limitobject

      This setting helps to minimize the number of rate limit errors returned from the service.

      Hide rate_limit attribute Show rate_limit attribute object
      • requests_per_minutenumber

        The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:

        • alibabacloud-ai-search service: 1000
        • anthropic service: 50
        • azureaistudio service: 240
        • azureopenai service and task type text_embedding: 1440
        • azureopenai service and task type completion: 120
        • cohere service: 10000
        • elastic service and task type chat_completion: 240
        • googleaistudio service: 360
        • googlevertexai service: 30000
        • hugging_face service: 3000
        • jinaai service: 2000
        • mistral service: 240
        • openai service and task type text_embedding: 3000
        • openai service and task type completion: 500
        • voyageai service: 2000
        • watsonxai service: 120
    • resource_namestring 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_settingsobject
    Hide task_settings attribute Show task_settings attribute object
    • userstring

      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_settingsobject

      Chunking configuration object

      Hide chunking_settings attributes Show chunking_settings attributes object
      • max_chunk_sizenumber

        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).

      • overlapnumber

        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_overlapnumber

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

      • strategystring

        The chunking strategy: sentence or word.

    • servicestring Required

      The service type

    • service_settingsobject Required
    • task_settingsobject
    • inference_idstring Required

      The inference Id

    • task_typestring Required

      Values are text_embedding or completion.

PUT /_inference/{task_type}/{azureopenai_inference_id}
PUT _inference/text_embedding/azure_openai_embeddings
{
    "service": "azureopenai",
    "service_settings": {
        "api_key": "Api-Key",
        "resource_name": "Resource-name",
        "deployment_id": "Deployment-id",
        "api_version": "2024-02-01"
    }
}
resp = client.inference.put(
    task_type="text_embedding",
    inference_id="azure_openai_embeddings",
    inference_config={
        "service": "azureopenai",
        "service_settings": {
            "api_key": "Api-Key",
            "resource_name": "Resource-name",
            "deployment_id": "Deployment-id",
            "api_version": "2024-02-01"
        }
    },
)
const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "azure_openai_embeddings",
  inference_config: {
    service: "azureopenai",
    service_settings: {
      api_key: "Api-Key",
      resource_name: "Resource-name",
      deployment_id: "Deployment-id",
      api_version: "2024-02-01",
    },
  },
});
response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "azure_openai_embeddings",
  body: {
    "service": "azureopenai",
    "service_settings": {
      "api_key": "Api-Key",
      "resource_name": "Resource-name",
      "deployment_id": "Deployment-id",
      "api_version": "2024-02-01"
    }
  }
)
$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "azure_openai_embeddings",
    "body" => [
        "service" => "azureopenai",
        "service_settings" => [
            "api_key" => "Api-Key",
            "resource_name" => "Resource-name",
            "deployment_id" => "Deployment-id",
            "api_version" => "2024-02-01",
        ],
    ],
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"azureopenai","service_settings":{"api_key":"Api-Key","resource_name":"Resource-name","deployment_id":"Deployment-id","api_version":"2024-02-01"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/azure_openai_embeddings"
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"
    }
}