kobold_assistant VS guidance

My project, https://github.com/lee-b/kobold_assistant, is similar to this: currently works locally, listens and responds in a loop, but doesn't function (well) remotely through a server or anything. There is a mumble branch that works over a mumble voice chat, but it has echo/feedback/noise reduction problems right now.

KoboldAI has text completion and text chat interface built in, and I built (or am building) https://github.com/lee-b/kobold_assistant to provide a voice chat interface to that, and I have an 'ai' command that I can run from a linux shell, for any time I want the same AI to assist with anything in a command pipeline, and some steam games already use it. LocalAI will provide a local adapter to make OpenAI-based stuff use a local model instead -- I don't think this talks to KoboldAI, but I plan to modify it to do so, if it doesn't already. Finally, I plan to run (or build) something similar to SillyTavern, but instead of "joke"/fun anime characters, make it like an advisory council, where there's a philosophy advisor, a scientific advisor, an economics advisor, and so on all in a chat room, and I can just post ideas or articles I'm interested in, to get feedback from multiple perspectives, all driven by one AI -- or maybe a small number of AIs, like a specialised medical model or science or law model, and then a general model for everything else.

kobold-assistant uses whisper. https://github.com/lee-b/kobold_assistant

This IS Microsoft Guidance, they seem to have spun off a separate GitHub organization for it.

https://github.com/microsoft/guidance redirects to https://github.com/guidance-ai/guidance now.

... and it sets the value of "armor" to "leather" so that you can use that value later in your code if you wish to. Guidance is pretty powerful, but I find the grammar hard to work with. I think the idea of being able to upload a bit of code or a context-free grammar to guide the model is super smart.

https://github.com/microsoft/guidance/blob/d2c5e3cbb730e337b...

Here's one thing I don't get.

Why all the rigamarole of hoping you get a valid response, adding last-mile validators to detect invalid responses, trying to beg the model to pretty please give me the syntax I'm asking for...

...when you can guarantee a valid JSON syntax by only sampling tokens that are valid? Instead of greedily picking the highest-scoring token every time, you select the highest-scoring token that conforms to the requested format.

This is what Guidance does already, also from Microsoft: https://github.com/microsoft/guidance

But OpenAI apparently does not expose the full scores of all tokens, it only exposes the highest-scoring token. Which is so odd, because if you run models locally, using Guidance is trivial, and you can guarantee your json is correct every time. It's faster to generate, too!

Perhaps something as simple as stating it was first built around OpenAI models and later expanded to local via plugins?

I've been meaning to ask you, have you seen/used MS Guidance[0] 'language' at all? I don't know if it's the right abstraction to interface as a plugin with what you've got in llm cli but there's a lot about Guidance that seems incredibly useful to local inference [token healing and acceleration especially].

[0]https://github.com/microsoft/guidance

LangChain is just too much, personal solutions are great, until you need to compare metrics or methodologies of prompt generation. Then the onus is on these n-parties who are sharing their resources to ensure that all of them used the same templates, they were generated the same way, with the only diff being the models these prompts were run on.

So maybe a simpler library like Microsoft's Guidance (https://github.com/microsoft/guidance)? It does this really well.

I am unclear on the status of the project but here is the conversation that seem to be tracking it: https://github.com/microsoft/guidance/discussions/201

Let's have a subreddit about Guidance!

You should look at guidance for an LLM to fill out a template. Define the output data structure and provide the real estate listing in the context (see the JSON template example here https://github.com/microsoft/guidance)