Perform text embedding inference on the service Generally available; Added in 8.11.0

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POST /_inference/text_embedding/{inference_id}
Api key auth Basic auth Bearer auth

Path parameters

  • inference_id string Required

    The inference Id

Query parameters

application/json

Body Required

  • input string | array[string] Required

    Inference input. Either a string or an array of strings.

    One of:
    string-1 string array-2 array[string]
  • 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.


    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 you specified and override the task settings specified when initializing the service.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • text_embedding_bytes array[object]

      The text embedding result object for byte representation

      Hide text_embedding_bytes attribute Show text_embedding_bytes attribute object
      • embedding array[number] Required

        Text Embedding results containing bytes are represented as Dense Vectors of bytes.

    • text_embedding_bits array[object]

      The text embedding result object for byte representation

      Hide text_embedding_bits attribute Show text_embedding_bits attribute object
      • embedding array[number] Required

        Text Embedding results containing bytes are represented as Dense Vectors of bytes.

    • text_embedding array[object]

      The text embedding result object

      Hide text_embedding attribute Show text_embedding attribute object
      • embedding array[number] Required

        Text Embedding results are represented as Dense Vectors of floats.
POST /_inference/text_embedding/{inference_id} 
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" }
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", )
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", });
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" } )
$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", ], ]);
curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"input":"The sky above the port was the color of television tuned to a dead channel.","input_type":"ingest"}' "$ELASTICSEARCH_URL/_inference/text_embedding/my-cohere-endpoint"
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") );
Request example
Run `POST _inference/text_embedding/my-cohere-endpoint` to perform text embedding on the example sentence using the Cohere integration, 
{ "input": "The sky above the port was the color of television tuned to a dead channel.", "input_type": "ingest" }
Response examples (200)
An abbreviated response from `POST _inference/text_embedding/my-cohere-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 } ] } ] }