Create an OpenAI inference endpoint

Auth

Create an inference endpoint to perform an inference task with the openai service or openai compatible APIs.

Required authorization

Cluster privileges manage_inference

Path parameters

task_type enum required

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

Values: chat_completion completion text_embedding embedding
openai_inference_id string required

The unique identifier of the inference endpoint.

Query parameters

timeout string · default: 30s

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

Request body

chunking_settings object

The chunking configuration object. Applies only to the text_embedding task type. Not applicable to the completion or chat_completion task types.

Reference
max_chunk_size number · default: 250

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.

overlap number · default: 100

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_overlap number · default: 1

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

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 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 · default: sentence

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.

Reference
service enum required

The type of service supported for the specified task type. In this case, openai.

Values: openai
service_settings object required

Settings used to install the inference model. These settings are specific to the openai service.

api_key string

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 must specify either api_key or client_secret. If you do not provide one or you provide more than one of them, you will receive an error when you try to create your endpoint.

External docs
client_id string

For OAuth 2.0 authorization using the client credentials grant flow. The application ID that's assigned to your app.

Important

To configure OAuth 2.0, you must specify client_id, scopes, token_url, and client_secret together. If one of the fields is missing, you will receive an error when you try to create your endpoint.

client_secret string

For OAuth 2.0 authorization using the client credentials grant flow. The application secret that you created for your app.

Important

You must specify either api_key or client_secret. If you do not provide one or you provide more than one of them, you will receive an error when you try to create your endpoint.

Important

To configure OAuth 2.0, you must specify client_id, scopes, token_url, and client_secret together. If one of the fields is missing, you will receive an error when you try to create your endpoint.

dimensions number

For a text_embedding or embedding task, 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_id string 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 docs
organization_id string

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

rate_limit object

This setting helps to minimize the number of rate limit errors returned from OpenAI. The openai service sets a default number of requests allowed per minute depending on the task type. For text_embedding and embedding, it is set to 3000. For completion and chat_completion, it is set to 500.

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
  • amazonbedrock service: 240
  • anthropic service: 50
  • azureaistudio service: 240
  • azureopenai service and task type text_embedding: 1440
  • azureopenai service and task types completion or chat_completion: 120
  • cohere service: 10000
  • contextualai service: 1000
  • elastic service and task type chat_completion: 240
  • fireworksai service: 6000
  • 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
  • openshift_ai service: 3000
  • voyageai service: 2000
  • watsonxai service: 120
scopes string[]

For OAuth 2.0 authorization using the client credentials grant flow. The resource identifier of the resource you want. For example:

"scopes": [
  "scope1",
  "scope2"
]
Important

To configure OAuth 2.0, you must specify client_id, scopes, token_url, and client_secret together. If one of the fields is missing, you will receive an error when you try to create your endpoint.

similarity enum

For a text_embedding or embedding task, the similarity measure. One of cosine, dot_product, l2_norm. Defaults to dot_product.

Values: cosine dot_product l2_norm
token_url string

For OAuth 2.0 authorization using the client credentials grant flow. An OAuth2 token endpoint where Elasticsearch sends a request to exchange client credentials for an access token.

Important

To configure OAuth 2.0, you must specify client_id, scopes, token_url, and client_secret together. If one of the fields is missing, you will receive an error when you try to create your endpoint.

url string

The URL endpoint to use for the requests. It can be changed for testing purposes. Default value is https://api.openai.com/v1/embeddings for a text_embedding or embedding task, https://api.openai.com/v1/chat/completions for a completion or chat_completion task.

task_settings object

Settings to configure the inference task. These settings are specific to the task type you specified.

user string

Specifies the user issuing the request. This information can be used for abuse detection.

headers map string to string

Specifies custom HTTP header parameters. For example:

"headers": {
  "Custom-Header": "Some-Value",
  "Another-Custom-Header": "Another-Value"
}

Response

application/json
chunking_settings object

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

max_chunk_size number · default: 250

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.

overlap number · default: 100

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_overlap number · default: 1

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

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 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 · default: sentence

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.

Reference
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 enum required

The task type

Values: text_embedding chat_completion completion embedding