Google AI Studio inference integration

Creates an inference endpoint to perform an inference task with the googleaistudio service.

PUT /_inference/<task_type>/<inference_id>

<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.

chunking_settings

(Optional, object) Chunking configuration object. Refer to Configuring chunking to learn more about chunking.

max_chunk_size

(Optional, integer) Specifies the maximum size of a chunk in words. Defaults to 250. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).

overlap

(Optional, integer) Only for word chunking strategy. Specifies the number of overlapping words for chunks. Defaults to 100. This value cannot be higher than the half of max_chunk_size.

sentence_overlap

(Optional, integer) Only for sentence chunking strategy. Specifies the numnber of overlapping sentences for chunks. It can be either 1 or 0. Defaults to 1.

strategy

(Optional, string) Specifies the chunking strategy. It could be either sentence or word.

service

(Required, string) The type of service supported for the specified task type. In this case, googleaistudio.

service_settings

(Required, object) Settings used to install the inference model.

These settings are specific to the googleaistudio service.

api_key

(Required, string) A valid API key for the Google Gemini API.

model_id

(Required, string) The name of the model to use for the inference task. You can find the supported models at Gemini API models.

rate_limit

(Optional, object) By default, the googleaistudio service sets the number of requests allowed per minute to 360. This helps to minimize the number of rate limit errors returned from Google AI Studio. To modify this, set the requests_per_minute setting of this object in your service settings:

text "rate_limit": { "requests_per_minute": <<number_of_requests>> }

The following example shows how to create an inference endpoint called google_ai_studio_completion to perform a completion task type.

 PUT _inference/completion/google_ai_studio_completion {
    "service": "googleaistudio",
    "service_settings": {
        "api_key": "<api_key>",
        "model_id": "<model_id>"
    }
}