Create a VoyageAI inference endpoint Generally available; Added in 8.19.0

PUT /_inference/{task_type}/{voyageai_inference_id}

Create an inference endpoint to perform an inference task with the voyageai service.

Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.

Required authorization

  • Cluster privileges: manage_inference

Path parameters

  • task_type string

    The type of the inference task that the model will perform.

    Values are text_embedding or rerank.

  • voyageai_inference_id string Required

    The unique identifier of the inference endpoint.

Query parameters

  • timeout string

    Specifies the amount of time to wait for the inference endpoint to be created.

    Values are -1 or 0.

application/json

Body

  • chunking_settings object

    Chunking configuration object

    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 higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

      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 Required

      This parameter is only applicable when using the recursive chunking strategy.

      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] Required

      A list of strings used as possible split points when chunking text with the recursive strategy.

      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.

      External documentation
  • service string Required

    Value is voyageai.

  • service_settings object Required
    Hide service_settings attributes Show service_settings attributes object
    • dimensions number

      The number of dimensions for resulting output embeddings. This setting maps to output_dimension in the VoyageAI documentation. Only for the text_embedding task type.

      External documentation
    • model_id string Required

      The name of the model to use for the inference task. Refer to the VoyageAI documentation for the list of available text embedding and rerank models.

      External documentation
    • rate_limit object

      This setting helps to minimize the number of rate limit errors returned from the service.

      Hide rate_limit attribute Show rate_limit attribute object
      • requests_per_minute number

        The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:

        • alibabacloud-ai-search service: 1000
        • anthropic service: 50
        • azureaistudio service: 240
        • azureopenai service and task type text_embedding: 1440
        • azureopenai service and task type completion: 120
        • cohere service: 10000
        • elastic service and task type chat_completion: 240
        • googleaistudio service: 360
        • googlevertexai service: 30000
        • hugging_face service: 3000
        • jinaai service: 2000
        • llama service: 3000
        • mistral service: 240
        • openai service and task type text_embedding: 3000
        • openai service and task type completion: 500
        • voyageai service: 2000
        • watsonxai service: 120
    • embedding_type number

      The data type for the embeddings to be returned. This setting maps to output_dtype in the VoyageAI documentation. Permitted values: float, int8, bit. int8 is a synonym of byte in the VoyageAI documentation. bit is a synonym of binary in the VoyageAI documentation. Only for the text_embedding task type.

      External documentation
  • task_settings object
    Hide task_settings attributes Show task_settings attributes object
    • input_type string

      Type of the input text. Permitted values: ingest (maps to document in the VoyageAI documentation), search (maps to query in the VoyageAI documentation). Only for the text_embedding task type.

    • return_documents boolean

      Whether to return the source documents in the response. Only for the rerank task type.

      Default value is false.

    • top_k number

      The number of most relevant documents to return. If not specified, the reranking results of all documents will be returned. Only for the rerank task type.

    • truncation boolean

      Whether to truncate the input texts to fit within the context length.

      Default value is true.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • chunking_settings object

      Chunking configuration object

      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 higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

        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 Required

        This parameter is only applicable when using the recursive chunking strategy.

        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] Required

        A list of strings used as possible split points when chunking text with the recursive strategy.

        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.

        External documentation
    • service string Required

      The service type

    • service_settings object Required
    • task_settings object
    • inference_id string Required

      The inference Id

    • task_type string Required

      Values are text_embedding or rerank.

PUT /_inference/{task_type}/{voyageai_inference_id}
PUT _inference/text_embedding/openai-embeddings
{
    "service": "voyageai",
    "service_settings": {
        "model_id": "voyage-3-large",
        "dimensions": 512
    }
}
resp = client.inference.put(
    task_type="text_embedding",
    inference_id="openai-embeddings",
    inference_config={
        "service": "voyageai",
        "service_settings": {
            "model_id": "voyage-3-large",
            "dimensions": 512
        }
    },
)
const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "openai-embeddings",
  inference_config: {
    service: "voyageai",
    service_settings: {
      model_id: "voyage-3-large",
      dimensions: 512,
    },
  },
});
response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "openai-embeddings",
  body: {
    "service": "voyageai",
    "service_settings": {
      "model_id": "voyage-3-large",
      "dimensions": 512
    }
  }
)
$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "openai-embeddings",
    "body" => [
        "service" => "voyageai",
        "service_settings" => [
            "model_id" => "voyage-3-large",
            "dimensions" => 512,
        ],
    ],
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"voyageai","service_settings":{"model_id":"voyage-3-large","dimensions":512}}' "$ELASTICSEARCH_URL/_inference/text_embedding/openai-embeddings"
client.inference().put(p -> p
    .inferenceId("openai-embeddings")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("voyageai")
        .serviceSettings(JsonData.fromJson("{\"model_id\":\"voyage-3-large\",\"dimensions\":512}"))
    )
);
Request examples
Run `PUT _inference/text_embedding/voyageai-embeddings` to create an inference endpoint that performs a `text_embedding` task. The embeddings created by requests to this endpoint will have 512 dimensions.
{
    "service": "voyageai",
    "service_settings": {
        "model_id": "voyage-3-large",
        "dimensions": 512
    }
}
Run `PUT _inference/rerank/voyageai-rerank` to create an inference endpoint that performs a `rerank` task.
{
    "service": "voyageai",
    "service_settings": {
        "model_id": "rerank-2"
    }
}