This documentation contains work-in-progress information for future Elastic Stack and Cloud releases. Use the version selector to view supported release docs. It also contains some Elastic Cloud serverless information. Check out our serverless docs for more details.
« Delete inference API Perform inference API »
Elastic Docs ›Elasticsearch Guide [8.16] ›REST APIs ›Inference APIs

Get inference API

edit

Get inference API

edit

Retrieves inference endpoint information.

The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the Machine learning trained model APIs.

Request

edit

GET /_inference

GET /_inference/_all

GET /_inference/<inference_id>

GET /_inference/<task_type>/_all

GET /_inference/<task_type>/<inference_id>

Prerequisites

edit
  • Requires the monitor_inference cluster privilege (the built-in inference_admin and inference_user roles grant this privilege)

Description

edit

You can get information in a single API request for:

  • a single inference endpoint by providing the task type and the inference ID,
  • all of the inference endpoints for a certain task type by providing the task type and a wildcard expression,
  • all of the inference endpoints by using a wildcard expression.

Path parameters

edit
<inference_id>
(Optional, string) The unique identifier of the inference endpoint.
<task_type>
(Optional, string) The type of inference task that the model performs.

Examples

edit

The following API call retrieves information about the my-elser-model inference model that can perform sparse_embedding tasks.

resp = client.inference.get(
    task_type="sparse_embedding",
    inference_id="my-elser-model",
)
print(resp)
response = client.inference.get_model(
  task_type: 'sparse_embedding',
  inference_id: 'my-elser-model'
)
puts response
const response = await client.inference.get({
  task_type: "sparse_embedding",
  inference_id: "my-elser-model",
});
console.log(response);
GET _inference/sparse_embedding/my-elser-model

The API returns the following response:

{
  "inference_id": "my-elser-model",
  "task_type": "sparse_embedding",
  "service": "elser",
  "service_settings": {
    "num_allocations": 1,
    "num_threads": 1
  },
  "task_settings": {}
}
« Delete inference API Perform inference API »