Created
September 30, 2023 13:59
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buanzo's dyson.py - openai llm aided meta-functions
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import os | |
import openai | |
import inspect | |
from typing import Callable | |
import json | |
class Dyson: | |
def __init__(self, func: Callable, model='gpt-4'): | |
self.func = func | |
self.model = model | |
self.metadata = self._get_metadata() | |
def _get_metadata(self): | |
metadata = {} | |
metadata['name'] = self.func.__name__ | |
argspec = inspect.getfullargspec(self.func) | |
metadata['args'] = argspec.args | |
docstring = inspect.getdoc(self.func) | |
if docstring: | |
metadata['docstring'] = docstring | |
else: | |
raise ValueError("Function must have a docstring.") | |
return metadata | |
def _create_role(self): | |
role = f""" | |
You are an AI that calculates the result of this: {self.metadata['docstring']}. | |
User prompt indicates function arguments. | |
If user prompts 'EXECUTE', then no arguments are passed to the function.""" | |
return role | |
def __call__(self, *args, **kwargs): | |
role = self._create_role() | |
prompt = f"{args if args else self.metadata['args']}" | |
len_args = len(self.metadata['args']) | |
if len_args == 0: | |
prompt = 'EXECUTE' | |
ai_response = self.run_gpt_by_role(role, prompt) | |
return ai_response | |
def run_gpt_by_role(self, role, prompt): | |
response = openai.ChatCompletion.create(model=self.model, | |
messages=[{"role": "system", "content": role}, | |
{"role": "user", "content": prompt} | |
]) | |
r = response["choices"][0]["message"]["content"] | |
return r | |
@Dyson | |
def say_hello(): | |
""" | |
Generate a friendly greeting return as a python dictionary in english, spanish and klingon. | |
no explanation. no codeblocks. just json. | |
""" | |
pass | |
# lets test it | |
a = say_hello() | |
print(a) | |
print(type(a)) | |
print(json.loads(a)) |
Author
buanzo
commented
Sep 30, 2023
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