symbolicai

> I'm surprised that they haven't revamped it No worries! I can't find it right now, but Wolfram had a stream (or short?) where he introduced "Function". We liked it so much we implemented it after one day. Usage: https://github.com/ExtensityAI/symbolicai/blob/main/tests/en...

We've been working on some exciting things with SymbolicAI and here a few things which might interest the HN community. Two years ago, we built a benchmark to evaluate multistep reasoning, tool use, and logical capabilities in language models. It includes a quality measure to assess performance and is built on a plugin system we developed for SymbolicAI. - Benchmark & Plugin System: https://github.com/ExtensityAI/benchmark - Example Eval: https://github.com/ExtensityAI/benchmark/blob/main/src/evals... We've also implemented some interesting concepts in our framework: - C#-style Extension Methods in Python: Using GlobalSymbolPrimitive to extend functionalities. - https://github.com/ExtensityAI/benchmark/blob/main/src/func.py#L146 - Symbolic <> Sub-symbolic Conversion: And using this for quality metrics, like a reward signal from the p

Probably linking the paper and examples notebook here makes sense as they are pretty explanatory: https://github.com/ExtensityAI/symbolicai/blob/main/examples... https://arxiv.org/pdf/2402.00854

The “around” part of your question hints that you may want to see the range of options possible with LangChain. Instead, for brainstorming, I’d recommend the description of what Symbolic AI aims to support: https://github.com/ExtensityAI/symbolicai?tab=readme-ov-file... I think what you’ll find is that some applications are very capable locally, like Whisper. A lot of plugins expect to work with the llama.cpp family. Nowadays, that’s HuggingFace TGI: https://huggingface.co/blog/tgi-messages-api So your application could speak OpenAI api, and you’d run HuggingFace TGI on your hardware for testing and comparison.