ez-openai VS rhasspy3

I did the same thing, but I went the easy way and used OpenAI's API. Half way through, I got fed up with all the boilerplate, so I wrote a really simple (but very Pythonic) wrapper around function calling with Python functions:

https://github.com/skorokithakis/ez-openai

Then my assistant is just a bunch of Python functions and a prompt. Very very simple.

This is just tangential, but I wouldn't call their APIs "nice", I'd be far less charitable. I spent a few hours (because that's how long it took to figure out the API, due to almost zero documentation) and wrote a nicer Python layer:

https://github.com/skorokithakis/ez-openai/

With all that money, I would have thought they'd be able to design more user-friendly APIs. Maybe they could even ask an LLM for help.

As a user of Home Assistant, I would want to easily be able to try out different AI models with a single click from the user interface.

Home Assistant allows users to install add-ons which are Docker containers + metadata. This is how today users install Whisper or Piper for STT and TTS. Both these engines have a wrapper that speaks Wyoming, our voice assistant standard to integrate such engines, among other things. (https://github.com/rhasspy/rhasspy3/blob/master/docs/wyoming...)

If we rely on just the ChatGPT API to allow interacting with a model, we wouldn't know what capabilities the model has and so can't know what features to use to get valid JSON actions out. Can we pass our function definitions or should we extend the prompt with instructions on how to generate JSON?

A typical Home Assistant pipeline is as follows: WWD -> VAD -> ASR -> Intent Classification -> Event Handler -> TTS. (see [rhasspy](https://github.com/rhasspy/rhasspy3 for reference).