simpleaichat
gish
simpleaichat | gish | |
---|---|---|
22 | 4 | |
3,386 | 62 | |
- | - | |
8.7 | 7.9 | |
4 months ago | 3 months ago | |
Python | TypeScript | |
MIT License | MIT License |
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simpleaichat
- Efficient Coding Assistant with Simpleaichat
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Please Don't Ask If an Open Source Project Is Dead
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
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We Built an AI-Powered Magic the Gathering Card Generator
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... )
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LangChain Agent Simulation – Multi-Player Dungeons and Dragons
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:
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Custom Instructions for ChatGPT
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...
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The Problem with LangChain
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!
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How is Langchain's dev experience? Any alternatives?
https://github.com/minimaxir/simpleaichat bills itself as a simpler alternative to langchain. I have not tried it, but it looks interesting.
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Stanford A.I. Courses
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.
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Where is the engineering part in "prompt engineer"?
This notebook from the repo I linked to is a concise example, and the reason you would want to optimize prompts.
- Show HN: Python package for interfacing with ChatGPT with minimized complexity
gish
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Native JSON Output from GPT-4
I've had good luck with both:
https://github.com/drorm/gish/blob/main/tasks/coding.txt
and
https://github.com/drorm/gish/blob/main/tasks/webapp.txt
With the second one, I reliably generated half a dozen apps with one command.
Not to say that it won't fail sometimes.
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Show HN: A ChatGPT TUI with custom bots
Well, if you're interested in something more lite-weight, I wrote
https://github.com/drorm/gish
which is a shell command that lets you interact with GPT with flags, pipes, etc. in a much more unixy way.
This TUI has some impressive features, like the bots and plugins, but I feel gish covers most of the use cases, specifically for software development.
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Show HN: Promptr, let GPT operate on your codebase and other useful goodies
GPT is significantly better at modifying code when following this "all code in, all code out" pattern. This pattern has downsides: you can quickly exhaust the context window, it's slow waiting for GPT to re-type your code (most of which it hasn't modified) and of course you're running up token costs. But the ability of GPT to understand and execute high level changes to the code is far superior with this approach.
I have tried quite a large number of alternative workflows. Outside the "all code in/out" pattern, GPT gets confused, makes mistakes, implements the requested change in different ways in different sections of the code, or just plain fails.
If you're asking for self contained modifications to a single function, that's all the code that needs to go in/out. On the other side of the spectrum, I had GPT build an entire small webapp using this pattern by repeatedly feeding it all the html/css/js along with a series of feature requests. Many feature requests required coordinated changes across html/css/js.
https://github.com/paul-gauthier/easy-chat#created-by-chatgp...
Another HN user has also released a command line tool along these lines called gish:
https://github.com/drorm/gish
- ChatGPT Is a Calculator for Words
What are some alternatives?
lmql - A language for constraint-guided and efficient LLM programming.
promptr - Promptr is a CLI tool that lets you use plain English to instruct GPT3 or GPT4 to make changes to your codebase.
langroid - Harness LLMs with Multi-Agent Programming
leah - Leah combines voice recognition, voice synthesis and ChatGPT to provide an environment where you can improve your foreign language skills.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
gpt4all - gpt4all: run open-source LLMs anywhere
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
LLM-Repl - A REPL interface to interact with various LLMs like ChatGPT etc.
gchain - Composable LLM Application framework inspired by langchain
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
transynthetical-engine - Applied methods of analytical augmentation to build tools using large-language models.
openai-cookbook - Examples and guides for using the OpenAI API