hof VS guidance

Many business analysts use SQL, have for a long time. They are probably not your target audience. With the problem being JAVA specific, you'd likely want to start there

This sounds similar to the goals of my hof tool (https://github.com/hofstadter-io/hof), lift type definitions out of code so they can be defined in one place, then generate the code for all the places. Is that sounding like what you are after?

Having built something similar, the biggest challenge for users is that they have to use a bespoke language, like WASP here. I suspect that it is also your biggest challenge as well.

Mine is built on CUE, which at least has the potential to become a more widely used language. CUE hasn't reached sufficient maturity for broader adoption yet, so I continue to face this same problem.

https://github.com/hofstadter-io/hof

Here's a big one I needed to get ChatGPT to do something more sophisticated with a JSON object response (predates functions and all that)

https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

It no longer worked after a model update some time ago, haven't tried recently.

I found codellama to be much better for this and require fewer instructions, an anecdotal validation for smaller, focussed models

I was surprised out how you can define arbitrary grammars using arbitrary formulation and it would follow it. Of course you have to redo the prompt every time there is an update... such a pain

https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

CUE is indeed a beautiful language, will get those mind juices flowing for sure!

There is more work to be done on the codec implementation, but if you just want to split yaml/json across files, CUE is a great option

You might also like my project, built on CUE: https://github.com/hofstadter-io/hof We have a TUI where you can explore and work with CUE, JSON, Yaml

You can pretty much make up any pseudo grammar like this one, which is a reduced JSON object that is close to CUE: https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

No need to be formal or use a standard format

Yea, in particular for this project, they have created a bespoke templating system.

You can get the same thing with Go text/templates by adding chat function(s) as custom a helper: https://github.com/hofstadter-io/hof/blob/_dev/lib/templates...

100% one of the best things about building a TUI is not having the pain of modern web development. I do think there is a way to have a CLI & TUI come from the same code, so you can get the best of both, or pick the best for the task at hand.

experiments in progress here: https://github.com/hofstadter-io/hof/tree/_dev/lib/tui

CUE is another interesting language to use from within Go, and is rather natural, given CUE is implemented in Go, but you can also do way more cool things with CUE via the Go API.

We're using CUE to validate and transform data, as input to code gen, the basis for a DAG task engine, and more

https://cuelang.org | https://pkg.go.dev/cuelang.org/[email protected]/cue | https://cuetorials.com/go-api (learn about CUE)

https://github.com/hofstadter-io/hof (where we are doing these things)

here is one of our early examples: https://github.com/hofstadter-io/hof/blob/_dev/flow/chat/pro...

[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.