simpleaichat
clownfish
simpleaichat | clownfish | |
---|---|---|
22 | 11 | |
3,386 | 303 | |
- | - | |
8.7 | 4.3 | |
4 months ago | 12 months ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
simpleaichat
- Efficient Coding Assistant with Simpleaichat
-
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
-
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... )
-
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:
-
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...
-
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!
-
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.
-
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.
-
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
clownfish
-
Show HN: LLMs can generate valid JSON 100% of the time
I'm not sure how this is different than:
https://github.com/1rgs/jsonformer
or
https://github.com/newhouseb/clownfish
or
https://github.com/mkuchnik/relm
or
https://github.com/ggerganov/llama.cpp/pull/1773
or
https://github.com/Shopify/torch-grammar
Overall there are a ton of these logit based guidance systems, the reason they don't get tons of traction is the SOTA models are behind REST APIs that don't enable this fine-grained approach.
Those models perform so much better that people generally settle for just re-requesting until they get the correct format (and with GPT-4 that ends up being a fairly rare occurrence in my experience)
- OpenAI Function calling and API updates
-
Adding GPT to a web app. The real experience.
I can see some specific problems there, like malformed json (or json not matching intended schema being generated). Approaches like https://github.com/1rgs/jsonformer and https://github.com/newhouseb/clownfish could be interesting there, as well as approaches to validate outputs like https://medium.com/@markherhold/validating-json-patch-requests-44ca5981a7fc (references jsonpatch which could be interesting as well, but the approach is somewhat agnostic to how the changes actually get applied while still allowing you to enforce structure around what changes and how).
-
When you lose the ability to write, you also lose some of your ability to think
https://github.com/newhouseb/clownfish
Structural Alignment: Modifying Transformers (like GPT) to Follow a JSON Schema
- Clownfish: Constrained Decoding for LLMs Against JSON Schema
-
Jsonformer: A bulletproof way to generate structured output from LLMs
Oh nice! I built a similar system a few weeks ago: https://github.com/newhouseb/clownfish
I think the main differentiating factor here is that this is better if you have a simpler JSON schema without enums or oneOf constraints. If you do have these constraints, i.e. let's say you wanted an array of different types that represented a items on a menu { kind: pizza, toppings: [pepperoni] } or { kind: ice_cream, flavor: vanilla | strawberry } then you would need something more sophisticated like clownfish that can ask the LLM to pick specific properties.
-
Prompt injection: what’s the worst that can happen?
And on the other end, there's https://github.com/newhouseb/clownfish to force the model to produce structured output.
-
Teaching ChatGPT to Speak My Son’s Invented Language
It doesn't help with repetition, but when it comes to force structure on the output data, this approach looks interesting:
https://github.com/newhouseb/clownfish
TL;DR: it exploits the fact that the model returns probabilities for all the possible following tokens to enforce a JSON schema on the output as it is produced, backtracking as needed.
- Structural Alignment: Modifying Transformers (Like GPT) to Follow a JSON Schema
- Structural Alignment of LLMs with ControLogits
What are some alternatives?
lmql - A language for constraint-guided and efficient LLM programming.
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
langroid - Harness LLMs with Multi-Agent Programming
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
outlines - Structured Text Generation
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
evals - Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
gchain - Composable LLM Application framework inspired by langchain
ChatGPT_DAN - ChatGPT DAN, Jailbreaks prompt
transynthetical-engine - Applied methods of analytical augmentation to build tools using large-language models.
kodumisto - GitHub Issue as ChatGPT Prompt; ChatGPT's Response as a Pull Request