simpleaichat VS transynthetical-engine

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.

Here’s an approach to return just JavaScript:

https://github.com/williamcotton/transynthetical-engine

The key is the addition of few-shot exemplars.

I think the two-layer approach is worthwhile if only for limiting tokens!

Here’s an example of what I mean:

https://github.com/williamcotton/transynthetical-engine#brow...

By keeping the main discourse between the user and the LLM from containing all of the generated code and instead just using that main “thread” to orchestrate instructions to write code it allows for more back-and-forth.

It’s a good technique in general!

I’m still too paranoid to execute instructions via email without a very limited set of abilities!

Here is an example of some prompt engineering in order to build augmentations for factual question-and-answer as well as building web applications:

https://github.com/williamcotton/transynthetical-engine

Hey Simon! I've been digging your writings on LLMs lately.

I've been having some decent luck with some of the approaches that I've discussed in the following articles and projects:

From Prompt Alchemy to Prompt Engineering: An Introduction to Analytic Augmentation: https://github.com/williamcotton/empirical-philosophy/blob/m...

https://www.williamcotton.com/articles/writing-web-applicati...

https://github.com/williamcotton/transynthetical-engine

I'd love to hear your thoughts on the matter!

Sure you can. The easiest way is to go to https://chat.openai.com/chat and paste in a Wikipedia article.

There are more involved manners like this: https://github.com/williamcotton/transynthetical-engine/blob...

Parsel : A (De-)compositional Framework for Algorithmic Reasoning with Language Models

https://arxiv.org/abs/2212.10561

Here's a notebook with an introduction:

https://github.com/ezelikman/parsel/blob/main/parsel.ipynb

And here's a GUI interface the author has been developing:

http://zelikman.me/parsel/interface.html

I've been working on an augmented large language model that given these few-shot exemplars can build the below fully-functional ToDo App: ==

https://github.com/williamcotton/transynthetical-engine/tree...

https://www.williamcotton.com/articles/junie-browser-builder...

All of this is still very rough around the edges, prone to errors of various kinds, and generally not ready for prime time, but anyone is welcome to play around with what is there!