lmql VS simpleaichat

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.

the only time i've ever felt like it was anything other than guesswork was using LMQL . not coincidentally, LMQL works with LLMs as autocomplete engines rather than q&a ones.

lqml

This is also similar in spirit to LMQL

https://github.com/eth-sri/lmql

LLM calls are just function calls, so most functional composition is already afforded by any general-purpose language out there. If you need fancy stuff, use something like Python‘s functools.

Working on https://github.com/eth-sri/lmql (shameless plug, sorry), we have always found that compositional abstractions on top of LMQL are mostly there already, once you internalize prompts being functions.

LMQL is another.

I checked both the issues mentioned, people have been respectful and showing empathy to author's situation

https://github.com/minimaxir/simpleaichat/issues/91

https://github.com/minimaxir/simpleaichat/issues/92

ChatGPT's June updated added support for "function calling", which in practice is structured data I/O marketed very poorly: https://openai.com/blog/function-calling-and-other-api-updat...

Here's an example of using structured data for better output control (lightly leveraging my Python package to reduce LoC: https://github.com/minimaxir/simpleaichat/blob/main/examples... )

So what are the alternatives to LangChain that the HN crowd uses?

I see two contenders:

https://github.com/minimaxir/simpleaichat/tree/main/simpleai...

https://github.com/griptape-ai/griptape

There is also the llm command line utility that has a very thin underlying library, but which might grow eventually:

A fun note is that even with system prompt engineering it may not give the most efficient solution: ChatGPT still outputs the avergage case.

I tested around it and doing two passes (generate code and "make it more efficient") works best, with system prompt engineering to result in less code output: https://github.com/minimaxir/simpleaichat/blob/main/examples...

I played around with simpleaichat for a few minutes just now, and I really like it. Unlike LangChain, I can understand what it does in minutes, and it looks like its primitives are fairly powerful. It looks like it's going to replace the `openai` library for me, it seems like a nice wrapper.

I'm especially looking forward to playing with the structured data models bit: https://github.com/minimaxir/simpleaichat/blob/main/examples...

Well done, Max!

https://github.com/minimaxir/simpleaichat bills itself as a simpler alternative to langchain. I have not tried it, but it looks interesting.

I think you are asking specifically about practical LLM engineering and not the underlying science.

Honestly this is all moving so fast you can do well by reading the news, following a few reddits/substacks, and skimming the prompt engineering papers as they come out every week (!).

https://www.latent.space/p/ai-engineer provides an early manifesto for this nascent layer of the stack.

Zvi writes a good roundup (though he is concerned mostly with alignment so skip if you don’t like that angle): https://thezvi.substack.com/p/ai-18-the-great-debate-debates

Simon W has some good writeups too: https://simonwillison.net/

I strongly recommend playing with the OpenAI APIs and working with langchain in a Colab notebook to get a feel for how these all fit together. Also, the tools here are incredibly simple and easy to understand (very new) so looking at, say, https://github.com/minimaxir/simpleaichat/tree/main/simpleai... or https://github.com/smol-ai/developer and digging in to the prompts, what goes in system vs assistant roles, how you gourde the LLM, etc.

This notebook from the repo I linked to is a concise example, and the reason you would want to optimize prompts.