Evaluate a trained model

POST /_ml/trained_models/{model_id}/deployment/_infer
POST /_ml/trained_models/{model_id}/_infer

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

Path parameters

Query parameters

Request body

docs string to object[] required: An array of objects to pass to the model for inference. The objects should contain a fields matching your configured trained model input. Typically, for NLP models, the field name is text_field. Currently, for NLP models, only a single value is allowed.
inference_config object: The inference configuration updates to apply on the API call

Response

application/json

inference_results object[] required

Examples

Request Console 1 / 8 languages

An example body for a POST _ml/trained_models/lang_ident_model_1/_infer request.

Console Kibana Dev Tools

POST _ml/trained_models/lang_ident_model_1/_infer
{
  "docs":[{"text": "The fool doth think he is wise, but the wise man knows himself to be a fool."}]
}

JavaScript Elasticsearch JavaScript Client

const response = await client.ml.inferTrainedModel({
  model_id: "lang_ident_model_1",
  docs: [
    {
      text: "The fool doth think he is wise, but the wise man knows himself to be a fool.",
    },
  ],
});

Python Elasticsearch Python Client

resp = client.ml.infer_trained_model(
    model_id="lang_ident_model_1",
    docs=[
        {
            "text": "The fool doth think he is wise, but the wise man knows himself to be a fool."
        }
    ],
)

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_ml/trained_models/lang_ident_model_1/_infer" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "docs": [
      {
        "text": "The fool doth think he is wise, but the wise man knows himself to be a fool."
      }
    ]
  }'

Java Elasticsearch Java Client

client.ml().inferTrainedModel(i -> i
    .docs(Map.of("text", JsonData.fromJson("\"The fool doth think he is wise, but the wise man knows himself to be a fool.\"")))
    .modelId("lang_ident_model_1")
);

C# Elasticsearch .NET Client

var response = await client.MachineLearning
    .InferTrainedModelAsync(modelId: "lang_ident_model_1", d1 => d1
        .Docs([new Dictionary<string, object>()
        {
            { "text", "The fool doth think he is wise, but the wise man knows himself to be a fool." }
        }])
    );

PHP Elasticsearch PHP Client

$resp = $client->ml()->inferTrainedModel([
    "model_id" => "lang_ident_model_1",
    "body" => [
        "docs" => array(
            [
                "text" => "The fool doth think he is wise, but the wise man knows himself to be a fool.",
            ],
        ),
    ],
]);

Ruby Elasticsearch Ruby Client

response = client.ml.infer_trained_model(
  model_id: "lang_ident_model_1",
  body: {
    "docs": [
      {
        "text": "The fool doth think he is wise, but the wise man knows himself to be a fool."
      }
    ]
  }
)

Response

No example

Authentication

Path parameters

Query parameters

Request body { }

Response

Request body