Create an Azure OpenAI inference endpoint Added in 8.14.0

PUT /_inference/{task_type}/{azureopenai_inference_id}
Api key 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.

When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for "state": "fully_allocated" in the response and ensure that the "allocation_count" matches the "target_allocation_count". Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.

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
    • service string Required

      The service type

    • service_settings object Required
    • task_settings 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
    • Hide attributes Show 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"
    }
}