simpleaichat
llm-gpt4all
simpleaichat | llm-gpt4all | |
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22 | 3 | |
3,386 | 180 | |
- | - | |
8.7 | 6.4 | |
4 months ago | 10 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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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
llm-gpt4all
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LLM now provides tools for working with embeddings
I'm still iterating on that. Plugins get complete control over the prompts, so they can handle the various weirdnesses of them. Here's some relevant code:
https://github.com/simonw/llm-gpt4all/blob/0046e2bf5d0a9c369...
https://github.com/simonw/llm-mlc/blob/b05eec9ba008e700ecc42...
https://github.com/simonw/llm-llama-cpp/blob/29ee8d239f5cfbf...
I'm not completely happy with this yet. Part of the problem is that different models on the same architecture may have completely different prompting styles.
I expect I'll eventually evolve the plugins to allow them to be configured in an easier and more flexible way. Ideally I'd like you to be able to run new models on existing architectures using an existing plugin.
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Accessing Llama 2 from the command-line with the LLM-replicate plugin
My LLM tool can be used for both. That's what the plugins are for.
It can talk to OpenAI, PaLM 2 and Llama / other models on Replicate via API, using API keys.
It can run local models on your own machine using these two plugins: https://github.com/simonw/llm-gpt4all and https://github.com/simonw/llm-mpt30b
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The Problem with LangChain
Yeah I haven't figured out how to have it reuse the models from the desktop GPT4All installation yet, issue here: https://github.com/simonw/llm-gpt4all/issues/5
What are some alternatives?
lmql - A language for constraint-guided and efficient LLM programming.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
langroid - Harness LLMs with Multi-Agent Programming
gchain - Composable LLM Application framework inspired by langchain
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
llm-mlc - LLM plugin for running models using MLC
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
multi-gpt - A Clojure interface into the GPT API with advanced tools like conversational memory, task management, and more
llama.cpp - LLM inference in C/C++
transynthetical-engine - Applied methods of analytical augmentation to build tools using large-language models.
aipl - Array-Inspired Pipeline Language