Perform text embedding inference on the service

POST /_inference/text_embedding/{inference_id}

Auth

Required privileges

Cluster privileges monitor_inference

Path parameters

inference_id string required: The inference Id

Query parameters

timeout string · default: 30s: Specifies the amount of time to wait for the inference request to complete.

Request body

input union string | string[] required: Inference input. Either a string or an array of strings.
input_type string: The input data type for the text embedding model. Possible values include:

SEARCH

INGEST

CLASSIFICATION

CLUSTERING Not all services support all values. Unsupported values will trigger a validation exception. Accepted values depend on the configured inference service, refer to the relevant service-specific documentation for more info.

Note

The input_type parameter specified on the root level of the request body will take precedence over the input_type parameter specified in task_settings.
task_settings object: Task settings for the individual inference request. These settings are specific to the <task_type> you specified and override the task settings specified when initializing the service.

Response

application/json

text_embedding_bytes object[]
text_embedding_bits object[]
text_embedding object[]

View as Markdown

Examples

Request Console 1 / 8 languages

Run POST _inference/text_embedding/my-cohere-endpoint to perform text embedding on the example sentence using the Cohere integration,

Console Kibana Dev Tools

POST _inference/text_embedding/my-cohere-endpoint
{
  "input": "The sky above the port was the color of television tuned to a dead channel.",
  "input_type": "ingest"
}

JavaScript Elasticsearch JavaScript Client

const response = await client.inference.textEmbedding({
  inference_id: "my-cohere-endpoint",
  input:
    "The sky above the port was the color of television tuned to a dead channel.",
  input_type: "ingest",
});

Python Elasticsearch Python Client

resp = client.inference.text_embedding(
    inference_id="my-cohere-endpoint",
    input="The sky above the port was the color of television tuned to a dead channel.",
    input_type="ingest",
)

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_inference/text_embedding/my-cohere-endpoint" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "The sky above the port was the color of television tuned to a dead channel.",
    "input_type": "ingest"
  }'

Java Elasticsearch Java Client

client.inference().textEmbedding(t -> t
    .inferenceId("my-cohere-endpoint")
    .input("The sky above the port was the color of television tuned to a dead channel.")
    .inputType("ingest")
);

C# Elasticsearch .NET Client

var response = await client.Inference
    .TextEmbeddingAsync(inferenceId: "my-cohere-endpoint", d1 => d1
        .Input("The sky above the port was the color of television tuned to a dead channel.")
        .InputType("ingest")
    );

PHP Elasticsearch PHP Client

$resp = $client->inference()->textEmbedding([
    "inference_id" => "my-cohere-endpoint",
    "body" => [
        "input" => "The sky above the port was the color of television tuned to a dead channel.",
        "input_type" => "ingest",
    ],
]);

Ruby Elasticsearch Ruby Client

response = client.inference.text_embedding(
  inference_id: "my-cohere-endpoint",
  body: {
    "input": "The sky above the port was the color of television tuned to a dead channel.",
    "input_type": "ingest"
  }
)

Response

An abbreviated response from POST _inference/text_embedding/my-text-embedding-endpoint.

{
  "text_embedding": [
    {
      "embedding": [
        0.018569946,
        -0.036895752,
        0.01486969,
        -0.0045204163,
        -0.04385376,
        0.0075950623,
        0.04260254,
        -0.004005432,
        0.007865906,
        0.030792236,
        -0.050476074,
        0.011795044,
        -0.011642456,
        -0.010070801
      ]
    }
  ]
}