At first I was excited -- this is the second time i'm.seeing this advertised, and we are thinking through reliable API call-out strategies for louie.ai -- but t

At first I was excited -- this is the second time i'm.seeing this advertised, and we are thinking through reliable API call-out strategies for louie.ai -- but then I got confused by the paper: Is this really just tested against 95 API calls, and I'm guessing largely from just a small number of libriaries like pytorch? More importantly, if anywhere near true, is there any reason to (so far) use this for use cases like OpenAI's around calling generic OpenAPI style libs (zapier scenario), known specific tools, or random python libs not in that dataset? I'm really thinking 3 scenarios for our users: -- python libraries we know they'll want to use ahead of time, like pandas and pygraphistry -- Same for CLI, like AWS and az, and OpenAPI from and index -- Long-tail that we don't expect, esp in python + js, so on the fly, with limited time budget for inspecting GitHub/Google/etc So far, we generally find auto approaches too unreliable for non-hobbyists, and have to tune a bunch for each tool and database we teach louie. This line of research is def interesting to us...