guidance VS langchainrb

[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.

8. LangChain RB | Github | tutorial

So I hooked up the Ruby on Rails langchainrb gem (https://github.com/andreibondarev/langchainrb) and it seems like the approach is to store the plane text entries as meta data on pinecone. I definitely DO NOT want to do this as the data is private and secure on my own DB.

langchain

I would suggest taking a look at Langchain.rb as well. Disclosure: I'm the core maintainer.

Is the emerging architecture made out to be more complicated than what most of the companies are currently building? Perhaps! But this is most likely the general direction where things will start trending towards as the auxiliary ecosystem matures.

Shameless plug: For fellow Ruby-ists we're building an orchestration layer for building LLM applications, inspired by the original, Langchain.rb: https://github.com/andreibondarev/langchainrb

I'm the author of Langchain.rb.

@dliteful23 I loved your super detailed lessons-learned article! I'm the author of Langchain.rb, I would love to hear what you think of it if you get a chance to check it out. If there's anything that you'd like to see in the framework, please do let us know and we'll make sure to build it out if it aligns with the vision.

We’re building “Langchain for Ruby” under the current working name of “Langchain.rb”: https://github.com/andreibondarev/langchainrb

People that have contributed on the project thus far each have at least a decade of experience programming in Ruby. We’re trying our best to build an abstraction layer on top all of the common emerging AI/ML techniques, tools, and providers. We’re also focusbig on building an excellent developer experience that Ruby developers love and have gotten to expect.

Unlike the Python project, as it’s been pointed out here a countless number of times, we’d like to avoid deeply nested class structures that make it incredibly difficult to track and extend.

We’ve been pondering over the “what does Rails for Machine Learning look like?” question, and we’re taking a stab at answering this question.

We’re hyper-focused on the open source community and the developer community at large. All feedback/ideas/contributions/criticism are welcome and encouraged!