Perform sparse embedding inference on the service

POST /_inference/sparse_embedding/{inference_id}

Auth

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.
task_settings object: Optional task settings

Response

application/json

sparse_embedding object[] required

Examples

Request Console 1 / 8 languages

Run POST _inference/sparse_embedding/my-elser-model to perform sparse embedding on the example sentence.

Console Kibana Dev Tools

POST _inference/sparse_embedding/my-elser-model
{
  "input": "The sky above the port was the color of television tuned to a dead channel."
}

JavaScript Elasticsearch JavaScript Client

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

Python Elasticsearch Python Client

resp = client.inference.sparse_embedding(
    inference_id="my-elser-model",
    input="The sky above the port was the color of television tuned to a dead channel.",
)

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model" \
  -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."
  }'

Java Elasticsearch Java Client

client.inference().sparseEmbedding(s -> s
    .inferenceId("my-elser-model")
    .input("The sky above the port was the color of television tuned to a dead channel.")
);

C# Elasticsearch .NET Client

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

PHP Elasticsearch PHP Client

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

Ruby Elasticsearch Ruby Client

response = client.inference.sparse_embedding(
  inference_id: "my-elser-model",
  body: {
    "input": "The sky above the port was the color of television tuned to a dead channel."
  }
)

Response

An abbreviated response from POST _inference/sparse_embedding/my-elser-model.

{
  "sparse_embedding": [
    {
      "is_truncated": false,
      "embedding": {
        "port": 2.1259406,
        "sky": 1.7073475,
        "color": 1.6922266,
        "dead": 1.6247464,
        "television": 1.3525393,
        "above": 1.2425821,
        "tuned": 1.1440028,
        "colors": 1.1218185,
        "tv": 1.0111054,
        "ports": 1.0067928,
        "poem": 1.0042328,
        "channel": 0.99471164,
        "tune": 0.96235967,
        "scene": 0.9020516
      }
    }
  ]
}

Path parameters

Query parameters

Request body { }

Response

Request body