Path parameters

  • task_typestring Required

    The type of the inference task that the model will perform. NOTE: The chat_completion task type only supports and only through the _stream API.

    Values are chat_completion, completion, or text_embedding.

  • openai_inference_idstring Required

    The unique identifier of the inference endpoint.

application/json

Body

  • chunking_settingsobject

    Chunking configuration object

    Hide chunking_settings attributes Show chunking_settings attributes object
    • max_chunk_sizenumber

      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).

    • overlapnumber

      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.

    • sentence_overlapnumber

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

    • strategystring

      The chunking strategy: sentence or word.

  • servicestring Required

    Value is openai.

  • service_settingsobject Required
    Hide service_settings attributes Show service_settings attributes object
    • api_keystring Required

      A valid API key of your OpenAI account. You can find your OpenAI API keys in your OpenAI account under the API keys section.

      IMPORTANT: You need to provide the API key only once, during the inference model creation. The get inference endpoint API does not retrieve your API key. After creating the inference model, you cannot change the associated API key. If you want to use a different API key, delete the inference model and recreate it with the same name and the updated API key.

      External documentation
    • dimensionsnumber

      The number of dimensions the resulting output embeddings should have. It is supported only in text-embedding-3 and later models. If it is not set, the OpenAI defined default for the model is used.

    • model_idstring Required

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

      External documentation
    • organization_idstring

      The unique identifier for your organization. You can find the Organization ID in your OpenAI account under Settings > Organizations.

    • rate_limitobject

      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_minutenumber

        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
        • mistral service: 240
        • openai service and task type text_embedding: 3000
        • openai service and task type completion: 500
        • voyageai service: 2000
        • watsonxai service: 120
    • urlstring

      The URL endpoint to use for the requests. It can be changed for testing purposes.

  • task_settingsobject
    Hide task_settings attribute Show task_settings attribute object
    • userstring

      For a completion or text_embedding task, specify the user issuing the request. This information can be used for abuse detection.

Responses

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

      Chunking configuration object

      Hide chunking_settings attributes Show chunking_settings attributes object
      • max_chunk_sizenumber

        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).

      • overlapnumber

        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.

      • sentence_overlapnumber

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

      • strategystring

        The chunking strategy: sentence or word.

    • servicestring Required

      The service type

    • service_settingsobject Required
    • task_settingsobject
    • inference_idstring Required

      The inference Id

    • task_typestring Required

      Values are text_embedding, chat_completion, or completion.

PUT /_inference/{task_type}/{openai_inference_id}
PUT _inference/text_embedding/openai-embeddings
{
    "service": "openai",
    "service_settings": {
        "api_key": "OpenAI-API-Key",
        "model_id": "text-embedding-3-small",
        "dimensions": 128
    }
}
resp = client.inference.put(
    task_type="text_embedding",
    inference_id="openai-embeddings",
    inference_config={
        "service": "openai",
        "service_settings": {
            "api_key": "OpenAI-API-Key",
            "model_id": "text-embedding-3-small",
            "dimensions": 128
        }
    },
)
const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "openai-embeddings",
  inference_config: {
    service: "openai",
    service_settings: {
      api_key: "OpenAI-API-Key",
      model_id: "text-embedding-3-small",
      dimensions: 128,
    },
  },
});
response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "openai-embeddings",
  body: {
    "service": "openai",
    "service_settings": {
      "api_key": "OpenAI-API-Key",
      "model_id": "text-embedding-3-small",
      "dimensions": 128
    }
  }
)
$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "openai-embeddings",
    "body" => [
        "service" => "openai",
        "service_settings" => [
            "api_key" => "OpenAI-API-Key",
            "model_id" => "text-embedding-3-small",
            "dimensions" => 128,
        ],
    ],
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"openai","service_settings":{"api_key":"OpenAI-API-Key","model_id":"text-embedding-3-small","dimensions":128}}' "$ELASTICSEARCH_URL/_inference/text_embedding/openai-embeddings"
Request examples
Run `PUT _inference/text_embedding/openai-embeddings` to create an inference endpoint that performs a `text_embedding` task. The embeddings created by requests to this endpoint will have 128 dimensions.
{
    "service": "openai",
    "service_settings": {
        "api_key": "OpenAI-API-Key",
        "model_id": "text-embedding-3-small",
        "dimensions": 128
    }
}
Run `PUT _inference/completion/amazon_bedrock_completion` to create an inference endpoint to perform a completion task.
{
    "service": "amazonbedrock",
    "service_settings": {
        "access_key": "AWS-access-key",
        "secret_key": "AWS-secret-key",
        "region": "us-east-1",
        "provider": "amazontitan",
        "model": "amazon.titan-text-premier-v1:0"
    }
}