Create an Azure OpenAI inference endpoint

PUT /_inference/{task_type}/{azureopenai_inference_id}

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.

Required authorization

Cluster privileges manage_inference

Path parameters

task_type enum 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: completion text_embedding
azureopenai_inference_id string required: The unique identifier of the inference endpoint.

Query parameters

timeout string · default: 30s: Specifies the amount of time to wait for the inference endpoint to be created.

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, azureopenai.; Values: azureopenai
service_settings object required: Settings used to install the inference model. These settings are specific to the azureopenai service.
task_settings object: Settings to configure the inference task. These settings are specific to the task type you specified.

Response

application/json

chunking_settings object: Chunking configuration object
service string required: The service type
service_settings object required: Settings specific to the service
task_settings object: Task settings specific to the service and task type
inference_id string required: The inference Id
task_type enum required: The task type; Values: text_embedding completion

Examples

Request Console 1 / 8 languages

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.

Console Kibana Dev Tools

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"
    }
}

JavaScript Elasticsearch JavaScript Client

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",
    },
  },
});

Python Elasticsearch Python Client

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"
        }
    },
)

curl Shell

curl -X PUT "$ELASTICSEARCH_URL/_inference/text_embedding/azure_openai_embeddings" \
  -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"
    }
  }'

Java Elasticsearch Java Client

client.inference().put(p -> p
    .inferenceId("azure_openai_embeddings")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("azureopenai")
        .serviceSettings(JsonData.fromJson("{\"api_key\":\"Api-Key\",\"resource_name\":\"Resource-name\",\"deployment_id\":\"Deployment-id\",\"api_version\":\"2024-02-01\"}"))
    )
);

C# Elasticsearch .NET Client

var response = await client.Inference
    .PutAsync(new PutInferenceRequest()
    {
        InferenceId = "azure_openai_embeddings",
        TaskType = TaskType.TextEmbedding,
        InferenceConfig = new()
        {
            Service = "azureopenai",
            ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
            {
              "api_key": "Api-Key",
              "resource_name": "Resource-name",
              "deployment_id": "Deployment-id",
              "api_version": "2024-02-01"
            }
            """)
        }
    });

PHP Elasticsearch PHP Client

$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",
        ],
    ],
]);

Ruby Elasticsearch Ruby Client

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"
    }
  }
)

Response

{
  "chunking_settings": {
    "max_chunk_size": 250,
    "overlap": 100,
    "sentence_overlap": 1,
    "strategy": "sentence"
  },
  "service": "string",
  "service_settings": {},
  "task_settings": {},
  "inference_id": "string",
  "task_type": "text_embedding"
}

Generated from the API spec

Request Console 1 / 8 languages

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

Console Kibana Dev Tools

PUT _inference/completion/azure_openai_completion
{
    "service": "azureopenai",
    "service_settings": {
        "api_key": "Api-Key",
        "resource_name": "Resource-name",
        "deployment_id": "Deployment-id",
        "api_version": "2024-02-01"
    }
}

JavaScript Elasticsearch JavaScript Client

const response = await client.inference.put({
  task_type: "completion",
  inference_id: "azure_openai_completion",
  inference_config: {
    service: "azureopenai",
    service_settings: {
      api_key: "Api-Key",
      resource_name: "Resource-name",
      deployment_id: "Deployment-id",
      api_version: "2024-02-01",
    },
  },
});

Python Elasticsearch Python Client

resp = client.inference.put(
    task_type="completion",
    inference_id="azure_openai_completion",
    inference_config={
        "service": "azureopenai",
        "service_settings": {
            "api_key": "Api-Key",
            "resource_name": "Resource-name",
            "deployment_id": "Deployment-id",
            "api_version": "2024-02-01"
        }
    },
)

curl Shell

curl -X PUT "$ELASTICSEARCH_URL/_inference/completion/azure_openai_completion" \
  -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"
    }
  }'

Java Elasticsearch Java Client

client.inference().put(p -> p
    .inferenceId("azure_openai_completion")
    .taskType(TaskType.Completion)
    .inferenceConfig(i -> i
        .service("azureopenai")
        .serviceSettings(JsonData.fromJson("{\"api_key\":\"Api-Key\",\"resource_name\":\"Resource-name\",\"deployment_id\":\"Deployment-id\",\"api_version\":\"2024-02-01\"}"))
    )
);

C# Elasticsearch .NET Client

var response = await client.Inference
    .PutAsync(new PutInferenceRequest()
    {
        InferenceId = "azure_openai_completion",
        TaskType = TaskType.Completion,
        InferenceConfig = new()
        {
            Service = "azureopenai",
            ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
            {
              "api_key": "Api-Key",
              "resource_name": "Resource-name",
              "deployment_id": "Deployment-id",
              "api_version": "2024-02-01"
            }
            """)
        }
    });

PHP Elasticsearch PHP Client

$resp = $client->inference()->put([
    "task_type" => "completion",
    "inference_id" => "azure_openai_completion",
    "body" => [
        "service" => "azureopenai",
        "service_settings" => [
            "api_key" => "Api-Key",
            "resource_name" => "Resource-name",
            "deployment_id" => "Deployment-id",
            "api_version" => "2024-02-01",
        ],
    ],
]);

Ruby Elasticsearch Ruby Client

response = client.inference.put(
  task_type: "completion",
  inference_id: "azure_openai_completion",
  body: {
    "service": "azureopenai",
    "service_settings": {
      "api_key": "Api-Key",
      "resource_name": "Resource-name",
      "deployment_id": "Deployment-id",
      "api_version": "2024-02-01"
    }
  }
)

Response

{
  "chunking_settings": {
    "max_chunk_size": 250,
    "overlap": 100,
    "sentence_overlap": 1,
    "strategy": "sentence"
  },
  "service": "string",
  "service_settings": {},
  "task_settings": {},
  "inference_id": "string",
  "task_type": "text_embedding"
}

Generated from the API spec