OpenAI inference service
editOpenAI inference service
editCreates an inference endpoint to perform an inference task with the openai
service.
Request
editPUT /_inference/<task_type>/<inference_id>
Path parameters
edit-
<inference_id>
- (Required, string) The unique identifier of the inference endpoint.
-
<task_type>
-
(Required, string) The type of the inference task that the model will perform.
Available task types:
-
completion
, -
text_embedding
.
-
Request body
edit-
service
-
(Required, string)
The type of service supported for the specified task type. In this case,
openai
. -
service_settings
-
(Required, object) Settings used to install the inference model.
These settings are specific to the
openai
service.-
api_key
-
(Required, 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.
You need to provide the API key only once, during the inference model creation. The Get inference 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.
-
model_id
- (Required, string) The name of the model to use for the inference task. Refer to the OpenAI documentation for the list of available text embedding models.
-
organization_id
- (Optional, string) The unique identifier of your organization. You can find the Organization ID in your OpenAI account under Settings > Organizations.
-
url
-
(Optional, string)
The URL endpoint to use for the requests.
Can be changed for testing purposes.
Defaults to
https://api.openai.com/v1/embeddings
. -
rate_limit
-
(Optional, object) The
openai
service sets a default number of requests allowed per minute depending on the task type. Fortext_embedding
it is set to3000
. Forcompletion
it is set to500
. This helps to minimize the number of rate limit errors returned from OpenAI. To modify this, set therequests_per_minute
setting of this object in your service settings:"rate_limit": { "requests_per_minute": <<number_of_requests>> }
More information about the rate limits for OpenAI can be found in your Account limits.
-
-
task_settings
-
(Optional, object) Settings to configure the inference task. These settings are specific to the
<task_type>
you specified.task_settings
for thecompletion
task type-
user
- (Optional, string) Specifies the user issuing the request, which can be used for abuse detection.
task_settings
for thetext_embedding
task type-
user
- (optional, string) Specifies the user issuing the request, which can be used for abuse detection.
-
OpenAI service example
editThe following example shows how to create an inference endpoint called
openai-embeddings
to perform a text_embedding
task type.
resp = client.inference.put( task_type="text_embedding", inference_id="openai-embeddings", inference_config={ "service": "openai", "service_settings": { "api_key": "<api_key>", "model_id": "text-embedding-ada-002" } }, ) print(resp)
const response = await client.inference.put({ task_type: "text_embedding", inference_id: "openai-embeddings", inference_config: { service: "openai", service_settings: { api_key: "<api_key>", model_id: "text-embedding-ada-002", }, }, }); console.log(response);
PUT _inference/text_embedding/openai-embeddings { "service": "openai", "service_settings": { "api_key": "<api_key>", "model_id": "text-embedding-ada-002" } }
The next example shows how to create an inference endpoint called
openai-completion
to perform a completion
task type.
resp = client.inference.put( task_type="completion", inference_id="openai-completion", inference_config={ "service": "openai", "service_settings": { "api_key": "<api_key>", "model_id": "gpt-3.5-turbo" } }, ) print(resp)
const response = await client.inference.put({ task_type: "completion", inference_id: "openai-completion", inference_config: { service: "openai", service_settings: { api_key: "<api_key>", model_id: "gpt-3.5-turbo", }, }, }); console.log(response);
PUT _inference/completion/openai-completion { "service": "openai", "service_settings": { "api_key": "<api_key>", "model_id": "gpt-3.5-turbo" } }