Create an Azure AI studio inference endpoint Added in 8.14.0

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

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

Path parameters

  • task_type string Required

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

    Values are completion or text_embedding.

  • azureaistudio_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 azureaistudio.

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

      A valid API key of your Azure AI Studio model deployment. This key can be found on the overview page for your deployment in the management section of your Azure AI Studio account.

      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
    • endpoint_type string Required

      The type of endpoint that is available for deployment through Azure AI Studio: token or realtime. The token endpoint type is for "pay as you go" endpoints that are billed per token. The realtime endpoint type is for "real-time" endpoints that are billed per hour of usage.

      External documentation
    • target string Required

      The target URL of your Azure AI Studio model deployment. This can be found on the overview page for your deployment in the management section of your Azure AI Studio account.

    • provider string Required

      The model provider for your deployment. Note that some providers may support only certain task types. Supported providers include:

      • cohere - available for text_embedding and completion task types
      • databricks - available for completion task type only
      • meta - available for completion task type only
      • microsoft_phi - available for completion task type only
      • mistral - available for completion task type only
      • openai - available for text_embedding and completion task types
    • rate_limit object
      Hide rate_limit attribute Show rate_limit attribute object
  • task_settings object
    Hide task_settings attributes Show task_settings attributes object
    • do_sample number

      For a completion task, instruct the inference process to perform sampling. It has no effect unless temperature or top_p is specified.

    • max_new_tokens number

      For a completion task, provide a hint for the maximum number of output tokens to be generated.

    • temperature number

      For a completion task, control the apparent creativity of generated completions with a sampling temperature. It must be a number in the range of 0.0 to 2.0. It should not be used if top_p is specified.

    • top_p number

      For a completion task, make the model consider the results of the tokens with nucleus sampling probability. It is an alternative value to temperature and must be a number in the range of 0.0 to 2.0. It should not be used if temperature is specified.

    • user string

      For a 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}/{azureaistudio_inference_id} 
curl \
 --request PUT 'http://api.example.com/_inference/{task_type}/{azureaistudio_inference_id}' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '"{\n    \"service\": \"azureaistudio\",\n    \"service_settings\": {\n        \"api_key\": \"Azure-AI-Studio-API-key\",\n        \"target\": \"Target-Uri\",\n        \"provider\": \"openai\",\n        \"endpoint_type\": \"token\"\n    }\n}"'
Request examples
Run `PUT _inference/text_embedding/azure_ai_studio_embeddings` to create an inference endpoint that performs a text_embedding task. Note that you do not specify a model here, as it is defined already in the Azure AI Studio deployment. 
{
    "service": "azureaistudio",
    "service_settings": {
        "api_key": "Azure-AI-Studio-API-key",
        "target": "Target-Uri",
        "provider": "openai",
        "endpoint_type": "token"
    }
}
Run `PUT _inference/completion/azure_ai_studio_completion` to create an inference endpoint that performs a completion task. 
{
    "service": "azureaistudio",
    "service_settings": {
        "api_key": "Azure-AI-Studio-API-key",
        "target": "Target-URI",
        "provider": "databricks",
        "endpoint_type": "realtime"
    }
}