Get an inference endpoint Generally available; Added in 8.11.0

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GET /_inference/{task_type}/{inference_id}
Api key auth Basic auth Bearer auth

All methods and paths for this operation:

GET /_inference

GET /_inference/{inference_id}
GET /_inference/{task_type}/{inference_id}

Path parameters

  • task_type string

    The task type

    Values are sparse_embedding, text_embedding, rerank, completion, or chat_completion.

  • inference_id string Required

    The inference Id

Responses

  • 200 application/json
    Hide response attribute Show response attribute object
    • endpoints array[object] Required

      Represents an inference endpoint as returned by the GET API

      Hide endpoints attributes Show endpoints attributes object

      Represents an inference endpoint as returned by the GET API

      • chunking_settings object

        The chunking configuration object. Applies only to the sparse_embedding and text_embedding task types. Not applicable to the rerank, completion, or chat_completion task types.

        Hide chunking_settings attributes Show chunking_settings attributes object
        • max_chunk_size number

          The maximum size of a chunk in words. This value cannot be lower than 20 (for sentence strategy) or 10 (for word strategy). This value should not exceed the window size for the associated model.

          Default value is 250.

        • overlap number

          The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.

          Default value is 100.

        • sentence_overlap number

          The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.

          Default value is 1.

        • separator_group string

          Only applicable to the recursive strategy and required when using it.

          Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.

          Using this parameter is an alternative to manually specifying a custom separators list.

        • separators array[string]

          Only applicable to the recursive strategy and required when using it.

          A list of strings used as possible split points when chunking text.

          Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.

          After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.

        • strategy string

          The chunking strategy: sentence, word, none or recursive.

          • If strategy is set to recursive, you must also specify:

            • max_chunk_size
            • either separators orseparator_group

          Learn more about different chunking strategies in the linked documentation.

          Default value is sentence.

      • service string Required

        The service type

      • service_settings object Required

        Settings specific to the service

      • task_settings object

        Task settings specific to the service and task type

      • inference_id string Required

        The inference Id

      • task_type string Required

        The task type

        Values are sparse_embedding, text_embedding, rerank, completion, or chat_completion.
GET /_inference/{task_type}/{inference_id} 
GET _inference/sparse_embedding/my-elser-model 
resp = client.inference.get( task_type="sparse_embedding", inference_id="my-elser-model", )
const response = await client.inference.get({ task_type: "sparse_embedding", inference_id: "my-elser-model", });
response = client.inference.get( task_type: "sparse_embedding", inference_id: "my-elser-model" )
$resp = $client->inference()->get([ "task_type" => "sparse_embedding", "inference_id" => "my-elser-model", ]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"
client.inference().get(g -> g .inferenceId("my-elser-model") .taskType(TaskType.SparseEmbedding) );