Elasticsearch inference service

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

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

Request

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PUT /_inference/<task_type>/<inference_id>

Path parameters

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<inference_id>

(Required, string) The unique identifier of the inference endpoint.

<task_type>

(Required, string) The type of the inference task that the model will perform.

Available task types:

rerank,
text_embedding.

Request body

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service

(Required, string) The type of service supported for the specified task type. In this case, elasticsearch.

service_settings

(Required, object) Settings used to install the inference model.

These settings are specific to the elasticsearch service.

model_id: (Required, string) The name of the model to use for the inference task. It can be the ID of either a built-in model (for example, .multilingual-e5-small for E5) or a text embedding model already uploaded through Eland.
num_allocations: (Required, integer) The total number of allocations this model is assigned across machine learning nodes. Increasing this value generally increases the throughput.
num_threads: (Required, integer) Sets the number of threads used by each model allocation during inference. This 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. Must be a power of 2. Max allowed value is 32.

task_settings

(Optional, object) Settings to configure the inference task. These settings are specific to the <task_type> you specified.

task_settings for the rerank task type

return_documents: (Optional, Boolean) Returns the document instead of only the index. Defaults to true.

E5 via the `elasticsearch` service

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The following example shows how to create an inference endpoint called my-e5-model to perform a text_embedding task type.

The API request below will automatically download the E5 model if it isn’t already downloaded and then deploy the model.

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="my-e5-model",
    inference_config={
        "service": "elasticsearch",
        "service_settings": {
            "num_allocations": 1,
            "num_threads": 1,
            "model_id": ".multilingual-e5-small"
        }
    },
)
print(resp)

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "my-e5-model",
  inference_config: {
    service: "elasticsearch",
    service_settings: {
      num_allocations: 1,
      num_threads: 1,
      model_id: ".multilingual-e5-small",
    },
  },
});
console.log(response);

PUT _inference/text_embedding/my-e5-model
{
  "service": "elasticsearch",
  "service_settings": {
    "num_allocations": 1,
    "num_threads": 1,
    "model_id": ".multilingual-e5-small" 
  }
}

The model_id must be the ID of one of the built-in E5 models. Valid values are .multilingual-e5-small and .multilingual-e5-small_linux-x86_64. For further details, refer to the E5 model documentation.

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.

Models uploaded by Eland via the elasticsearch service

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The following example shows how to create an inference endpoint called my-msmarco-minilm-model to perform a text_embedding task type.

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="my-msmarco-minilm-model",
    inference_config={
        "service": "elasticsearch",
        "service_settings": {
            "num_allocations": 1,
            "num_threads": 1,
            "model_id": "msmarco-MiniLM-L12-cos-v5"
        }
    },
)
print(resp)

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "my-msmarco-minilm-model",
  inference_config: {
    service: "elasticsearch",
    service_settings: {
      num_allocations: 1,
      num_threads: 1,
      model_id: "msmarco-MiniLM-L12-cos-v5",
    },
  },
});
console.log(response);

PUT _inference/text_embedding/my-msmarco-minilm-model 
{
  "service": "elasticsearch",
  "service_settings": {
    "num_allocations": 1,
    "num_threads": 1,
    "model_id": "msmarco-MiniLM-L12-cos-v5" 
  }
}

	Provide an unique identifier for the inference endpoint. The `inference_id` must be unique and must not match the `model_id`.
	The `model_id` must be the ID of a text embedding model which has already been uploaded through Eland.

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Elasticsearch inference service