Create an Elasticsearch inference endpoint

Auth

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

Note

Your Elasticsearch deployment contains preconfigured ELSER and E5 inference endpoints, you only need to create the enpoints using the API if you want to customize the settings.

If you use the ELSER or the E5 model through the elasticsearch service, the API request will automatically download and deploy the model if it isn't downloaded yet.

Note

You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.

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.

Required privileges

Cluster privileges manage_inference

Path parameters

task_type enum required

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

Values: rerank sparse_embedding text_embedding
elasticsearch_inference_id string required

The unique identifier of the inference endpoint. The must not match the model_id.

Query parameters

timeout union oneOf · default: 30s

Specifies the amount of time to wait for the inference endpoint to be created.

Values: -1 0

Request body

service enum required

The type of service supported for the specified task type. In this case, elasticsearch.

Values: elasticsearch
service_settings object required

Settings used to install the inference model. These settings are specific to the elasticsearch service.

deployment_id string

The deployment identifier for a trained model deployment. When deployment_id is used the model_id is optional.

model_id string required

The name of the model to use for the inference task. It can be the ID of a built-in model (for example, .multilingual-e5-small for E5) or a text embedding model that was uploaded by using the Eland client.

Reference
num_allocations number

The total number of allocations that are assigned to the model across machine learning nodes. Increasing this value generally increases the throughput. If adaptive allocations are enabled, do not set this value because it's automatically set.

num_threads number required

The number of threads used by each model allocation during inference. This setting generally increases the speed per inference request. The inference process is a compute-bound process; threads_per_allocations must not exceed the number of available allocated processors per node. The value must be a power of 2. The maximum value is 32.

Response

application/json
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: sparse_embedding text_embedding rerank