guidance VS chatbot-ui

[1]: https://github.com/guidance-ai/guidance/tree/main

> The most obvious usage of this is forcing a model to output valid JSON

Isn't this something that Outlines [0], Guidance [1] and others [2] already solve much more elegantly?

0. https://github.com/outlines-dev/outlines

1. https://github.com/guidance-ai/guidance

2. https://github.com/sgl-project/sglang

Or combine it with something like llama.cpp's grammer or microsoft's guidance-ai[0] (which I prefer) which would allow adding some react-style prompting and external tools. As others have mentioned, instruct tuning would help too.

[0] https://github.com/guidance-ai/guidance

have been experimenting with guidance and lmql. a bit too early to give any well formed opinions but really do like the idea of constraining llm output.

Thanks for your comment.

I did not know about "Betteridge's law of headlines", quite interesting. Thanks for sharing :)

You raise some interesting points.

1) Safety: It is true that LVMs and LLMs have unknown biases and could potentially create unsafe content. However, this is not necessarily unique to them, for example, Google had the same problem with their supervised learning model https://www.theverge.com/2018/1/12/16882408/google-racist-go.... It all depends on the original data. I believe we need systems on top of our models to ensure safety. It is also possible to restrict the output domain of our models (https://github.com/guidance-ai/guidance). Instead of allowing our LVMs to output any words, we could restrict it to only being able to answer "red, green, blue..." when giving the color of a car.

2) Cost: You are right right now LVMs are quite expensive to run. As you said are a great way to go to market faster but they cannot run on low-cost hardware for the moment. However, they could help with training those smaller models. Indeed, with see in the NLP domain that a lot of smaller models are trained on data created with GPT models. You can still distill the knowledge of your LVMs into a custom smaller model that can run on embedded devices. The advantage is that you can use your LVMs to generate data when it is scarce and use it as a fallback when your smaller device is uncertain of the answer.

3) Labelling data: I don't think labeling data is necessarily cheap. First, you have to collect the data, depending on the frequency of your events could take months of monitoring if you want to build a large-scale dataset. Lastly, not all labeling is necessarily cheap. I worked at a semiconductor company and labeled data was scarce as it required expert knowledge and could only be done by experienced employees. Indeed not all labelling can be done externally.

However, both approaches are indeed complementary and I think systems that will work the best will rely on both.

Thanks again for the thought-provoking discussion. I hope this answer some of the concerns you raised

I've had a bit of trouble getting function calling to work with cases that aren't just extracting some data from the input. The format is correct but it was harder to get the correct data if it wasn't a simple extraction.

Hopefully OpenAI and others will offer something like https://github.com/guidance-ai/guidance at some point to guarantee overall output structure.

Failed validations will retry, but from what I've seen JSONSchema + generated JSON examples are decently reliable in practice for gpt-3.5-turbo and extremely reliable on gpt-4.

One of the first things we did when GPT-4 became available was talk to our Azure rep and get access to the OpenAI models that they'd partnered with Microsoft to host in Azure. Now, we have our own private, not-datamined (so they claim, contractually) API endpoint and we use an OpenAI integration in VS Code[1] to connect to, allowing anyone in the company to use it to help them code.

I also spun up an internal chat UI[2] to replace ChatGPT so people can feel comfortable discussing proprietary data with the LLM endpoint.

The only thing that would make it more secure would be running inference engines internally, but I wouldn't have access to as good of models, and I'd need a _lot_ of hardware to match the speeds.

[1] - https://marketplace.visualstudio.com/items?itemName=AndrewBu...

[2] - https://github.com/mckaywrigley/chatbot-ui (legacy branch)

[3] https://github.com/mckaywrigley/chatbot-ui

Thank you for the work.

Please take this in a nice way: I can't see why I would use this over ChatbotUI+Ollama https://github.com/mckaywrigley/chatbot-ui

Seem the only advantage is having it as MacOS native app and only real distinction is maybe fast import and search - I've yet to try that though.

ChatbotUI (and other similar stuff) are cross-platform, customizable, private, debuggable. I'm easily able to see what it's trying to do.

You can make a privacy request for OpenAI to not train on your data here: https://privacy.openai.com/

Alternatively, you could also use your own UI/API token (API calls aren't trained on). Chatbot UI just got a major update released and has nice things like folders, and chat search: https://github.com/mckaywrigley/chatbot-ui

Also chatbotUI is great https://github.com/mckaywrigley/chatbot-ui it has a ui similar to chatgpt

> The comment I screenshotted is passive-aggressive at best, and there's no really good way to ask "is this repo dead" without being passive-aggressive. My day-to-day job that actually pays me a salary wouldn't ever provide a bulleted list of the reasons I suck, let alone a project I develop in my spare time.

There is nothing passive-aggressive about that comment. There is nothing problematic about it at all. Nobody's calling you slurs or making demands. I see one guy who might as well be a Mormon Boy Scout from Canada. "Is this repo dead" is not passive-aggressive, just ineloquent. Fuck my eyes until the jelly leaks out my ears if a courteous and professionally-written question constitutes "applying pressure and being rude" these days.

I don't know what a "bulleted list of the reasons [you] suck" has to do with anything (I don't see where anybody sent you one) but you're coming across as someone who invites people to your garage sale and then brandishes a shotgun and starts screaming when they set foot on your property.

> I’ve never seen any discussions or articles about whether it’s appropriate to ask if an open source repository is dead. Is there an implicit contract to actively maintain any open source software you publish? Are you obligated to provide free support if you hit a certain star amount on GitHub or ask for funding through GitHub Sponsorships/Patreon? After all, most permissive open source code licenses like the MIT License contain some variant of “the software is provided ‘as is’, without warranty of any kind.”

Here's an example of why everyone should ask if an open source project is dead:

https://github.com/mckaywrigley/chatbot-ui/issues

A number of issues complain about it leaking OpenAI keys. Nobody's figured out how, but it'd be nice to know if anybody's working on it, if it's worth submitting a PR, if it should be forked, if it's worth bothering with at all. This code is a massive liability in its current state. Its creator is absent. It warrants questions being asked about its future. Yeah, it's as-is software, but it's not an affront to your mother's virtue when someone asks if your shit still works or if you have plans to fix it.

> I’ve had an existential crisis about my work in open source AI on GitHub, particularly as there has been both increasingly toxic backlash against AI and because the AI industry has been evolving so rapidly that I flat-out don’t have enough bandwidth to keep up

Herein lies the problem? You sound overwhelmed. I've been there myself. I don't know what your year's been like but you genuinely might want to get away from the screen and get some fresh air. This is a good time of year to do it, since things generally slow down at work.