I built and open-sourced a small Python library called PyFuncAI that allows LLMs to dynamically generate and execute Python functions from natural language. The idea is that instead of writing dozens of helper utilities for an AI system ahead of time, the model can generate the function it needs on demand. Example usage: from pyfuncai import create_function parse_log = create_function( “parse nginx log lines and return ip, path, and status” ) log_line = ‘127.0.0.1 - - [10/Oct/2024] “GET /index.html HTTP/1.1” 200’ print(parse_log(log_line)) # {‘ip’: ‘127.0.0.1’, ‘path’: ‘/index.html’, ‘status’: 200} Under the hood the model generates the Python function, compiles it, and injects it into the runtime. Curious what people think about this approach for dynamic tool generation in AI systems. I fully recognize this is kind of a meme idea, but the implementation is functional. submitted by /u/Kurumi_Shadowfall
Originally posted by u/Kurumi_Shadowfall on r/ArtificialInteligence