simpleaichat

I was developing an open-source library for interfacing with LLMs agnostically (https://github.com/minimaxir/simpleaichat) and although it still works, I haven't had the time to maintain it unfortunately. Nowadays for writing code to interface with LLMs, I don't use client SDKs unless required, instead just hitting HTTP endpoints with libraries such as requests and httpx. It's also easier to upgrade to async if needed.

> Usually, if I don't get the answer I'm looking for from ChatGPT, I tell it it's not the answer I'm looking for, what the original answer was missing, and I usually get a better answer the second time around. This is an expected behavior, as by default even with RLHF ChatGPT will output statistically "average" content. It's also the reason why Chain of Thoughts prompting works very effectively. I have a (somewhat out of date) notebook demonstrating this, plus a function calling trick which allows you to get the improved result in a single API call: https://github.com/minimaxir/simpleaichat/blob/main/examples...

These examples are for more simple prompt engineering demos. With the ChatGPT system prompt, you can give the model a large and complex set of rules to account for and recent models of ChatGPT do a good job of accommodating them. Some of my best system prompts are >20 lines of text, and all of them are necessary to get the model to behave. The examples are also too polite and conversational: you can give more strict commands and in my experience it works better. There's also function calling/structured data support which is technically prompt engineering and requires similar skills, but is substantially more powerful than using the system prompt alone (I'm working on a blog post on it now and it unfortunately it is going to be a long post to address all of its power). Here's a fun demo example which compares system prompts and structured

If you look at the actual issues that ask about the development status, all the people who asked were really polite and gave the author of the tool best wishes as he works through his mental health. At no point would I describe those issues as “demanding,” just “inquiring.” The people who asked those questions were greeted by a software author who lashed out at them. See for yourself: https://github.com/minimaxir/simpleaichat/issues/91 https://github.com/minimaxir/simpleaichat/issues/92 One person who asked (rightfully, IMO) pointed out that all these questions are reasonable considering the project has sponsorship and Patreon links. The author is collecting $100 a month in Patreon and GitHub sponsorships, which is not nothing.

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 update 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: https://github.com/simonw/llm

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!

Thanks for making this: "simpleaichat is a Python package for easily interfacing with chat apps like ChatGPT and GPT-4 with robust features and minimal code complexity. This tool has many features optimized for working with ChatGPT as fast and as cheap as possible, but still much more capable of modern AI tricks than most implementations" https://github.com/minimaxir/simpleaichat Separately, in the article, typo expect->except here: "LangChain uses about the same amount of code as just using the official openai library, expect LangChain incorporates more object classes for not much obvious code benefit."

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 look

I've spent a lot of time experimenting with schema: https://github.com/minimaxir/simpleaichat/blob/main/examples...

Ah, I see I've been ninja'd by about 17 hours: https://github.com/minimaxir/simpleaichat/releases/tag/v0.2.... :-D

The project has an implementation for the function logic: https://github.com/minimaxir/simpleaichat#functions