That's a good point! We're actually working on integrating this as well, but in practice, what we've found is that LLM's in general don't like to respond with empty strings for example.

My hypothesis here is that due to RLFH, there's likely some implicit learning that tangentially related content is better than no content.

Given that, you'd likely still get better results with your schema being:

"string | null" so the LLM can output a null instead of "" since there is probably not as much training data that gives "" high log prob values.

But we're looking forward to evaluating the functions call, and seeing what the metrics show!