Create an Azure AI studio inference endpoint

PUT /_inference/{task_type}/{azureaistudio_inference_id}

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

Authentication

Api key

Authorization: <value>

Elasticsearch APIs support key-based authentication. You must create an API key and use the encoded value in the request header. For example:

		curl -X GET "${ES_URL}/_cat/indices?v=true" \
  -H "Authorization: ApiKey ${API_KEY}"

To get API keys, use the /_security/api_key APIs.

Basic

Authorization: Basic <credentials>

Bearer

Authorization: Bearer <token>

Elasticsearch APIs support the use of bearer tokens in the Authorization HTTP header to authenticate with the API. For examples, refer to Token-based authentication services

Request body

chunking_settings object: The chunking configuration object.; Reference
service enum required: The type of service supported for the specified task type. In this case, azureaistudio.; Values: azureaistudio
service_settings object required: Settings used to install the inference model. These settings are specific to the openai service.
task_settings object: Settings to configure the inference task. These settings are specific to the task type you specified.

Response

application/json

Examples

Request Console 1 / 8 languages

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.

Console Kibana Dev Tools

PUT _inference/text_embedding/azure_ai_studio_embeddings
{
    "service": "azureaistudio",
    "service_settings": {
        "api_key": "Azure-AI-Studio-API-key",
        "target": "Target-Uri",
        "provider": "openai",
        "endpoint_type": "token"
    }
}

JavaScript Elasticsearch JavaScript Client

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "azure_ai_studio_embeddings",
  inference_config: {
    service: "azureaistudio",
    service_settings: {
      api_key: "Azure-AI-Studio-API-key",
      target: "Target-Uri",
      provider: "openai",
      endpoint_type: "token",
    },
  },
});

Python Elasticsearch Python Client

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="azure_ai_studio_embeddings",
    inference_config={
        "service": "azureaistudio",
        "service_settings": {
            "api_key": "Azure-AI-Studio-API-key",
            "target": "Target-Uri",
            "provider": "openai",
            "endpoint_type": "token"
        }
    },
)

curl Shell

curl -X PUT "$ELASTICSEARCH_URL/_inference/text_embedding/azure_ai_studio_embeddings" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "service": "azureaistudio",
    "service_settings": {
      "api_key": "Azure-AI-Studio-API-key",
      "target": "Target-Uri",
      "provider": "openai",
      "endpoint_type": "token"
    }
  }'

Java Elasticsearch Java Client

client.inference().put(p -> p
    .inferenceId("azure_ai_studio_embeddings")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("azureaistudio")
        .serviceSettings(JsonData.fromJson("{\"api_key\":\"Azure-AI-Studio-API-key\",\"target\":\"Target-Uri\",\"provider\":\"openai\",\"endpoint_type\":\"token\"}"))
    )
);

C# Elasticsearch .NET Client

var response = await client.Inference
    .PutAsync(new PutInferenceRequest()
    {
        InferenceId = "azure_ai_studio_embeddings",
        TaskType = TaskType.TextEmbedding,
        InferenceConfig = new()
        {
            Service = "azureaistudio",
            ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
            {
              "api_key": "Azure-AI-Studio-API-key",
              "target": "Target-Uri",
              "provider": "openai",
              "endpoint_type": "token"
            }
            """)
        }
    });

PHP Elasticsearch PHP Client

$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "azure_ai_studio_embeddings",
    "body" => [
        "service" => "azureaistudio",
        "service_settings" => [
            "api_key" => "Azure-AI-Studio-API-key",
            "target" => "Target-Uri",
            "provider" => "openai",
            "endpoint_type" => "token",
        ],
    ],
]);

Ruby Elasticsearch Ruby Client

response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "azure_ai_studio_embeddings",
  body: {
    "service": "azureaistudio",
    "service_settings": {
      "api_key": "Azure-AI-Studio-API-key",
      "target": "Target-Uri",
      "provider": "openai",
      "endpoint_type": "token"
    }
  }
)

Response

No example

Request Console 1 / 2 languages

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

Console Kibana Dev Tools

PUT _inference/completion/azure_ai_studio_completion
{
    "service": "azureaistudio",
    "service_settings": {
        "api_key": "Azure-AI-Studio-API-key",
        "target": "Target-URI",
        "provider": "databricks",
        "endpoint_type": "realtime"
    }
}

Generated from the API spec

curl Shell

curl -X PUT "$ELASTICSEARCH_URL/_inference/completion/azure_ai_studio_completion" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
      "service": "azureaistudio",
      "service_settings": {
          "api_key": "Azure-AI-Studio-API-key",
          "target": "Target-URI",
          "provider": "databricks",
          "endpoint_type": "realtime"
      }
  }'

Generated from the API spec

Response

No example

Create an Azure AI studio inference endpoint

Required authorization

Authentication

Path parameters

Query parameters

Request body

Response

Required authorization

Authentication

Path parameters

Query parameters

Request body { }

Response

Request body