Delete an inference endpoint

DELETE /_inference/{task_type}/{inference_id}
DELETE /_inference/{inference_id}

Auth

Path parameters

task_type enum optional

The task type

Leave it out to use /_inference/{inference_id}.

Values: sparse_embedding text_embedding rerank completion chat_completion

inference_id string required

The inference identifier.

Query parameters

dry_run boolean · default: false: When true, the endpoint is not deleted and a list of ingest processors which reference this endpoint is returned.
force boolean · default: false: When true, the inference endpoint is forcefully deleted even if it is still being used by ingest processors or semantic text fields.

Response

application/json

acknowledged boolean required: For a successful response, this value is always true. On failure, an exception is returned instead.
pipelines string[] required

View as Markdown

Examples

Request Console 1 / 8 languages

Console Kibana Dev Tools

DELETE /_inference/sparse_embedding/my-elser-model

JavaScript Elasticsearch JavaScript Client

const response = await client.inference.delete({
  task_type: "sparse_embedding",
  inference_id: "my-elser-model",
});

Python Elasticsearch Python Client

resp = client.inference.delete(
    task_type="sparse_embedding",
    inference_id="my-elser-model",
)

curl Shell

curl -X DELETE "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY"

Java Elasticsearch Java Client

client.inference().delete(d -> d
    .inferenceId("my-elser-model")
    .taskType(TaskType.SparseEmbedding)
);

C# Elasticsearch .NET Client

var response = await client.Inference
    .DeleteAsync(new DeleteInferenceRequest()
    {
        InferenceId = "my-elser-model",
        TaskType = TaskType.SparseEmbedding
    });

PHP Elasticsearch PHP Client

$resp = $client->inference()->delete([
    "task_type" => "sparse_embedding",
    "inference_id" => "my-elser-model",
]);

Ruby Elasticsearch Ruby Client

response = client.inference.delete(
  task_type: "sparse_embedding",
  inference_id: "my-elser-model"
)

Response

{
  "acknowledged": false,
  "pipelines": [
    "string"
  ]
}

Generated from the API spec