calibrate_relevance

The job fine-tunes the relevance metrics used during indexing to reject irrelevant content.

You will require a small relevance calibration dataset to be indexed first so that you can then call this job before indexing the full archive. The custom model will be exclusively accessible from your account and is linked to an archive.

You can use both real and synthetic ai generated data for your examples.

Required Account Privileges: "read-write"

Request JSON ["inputs"]:

    "archive":
       string (3 <= len <= 30) unique in account 
       null NOT allowed
       A unique string identifier for the archive within your account.
    
    "archive_content_ids_subset": 
       list of ints
       null allowed
       Optional. A list of integers representing the IDs of the specific contents to consider for filtering. If not provided, all contents in the archive will be considered.

Response JSON ["results"]

The job does not return results in the response JSON