smartfunc. Vincent D. Warmerdam built this ingenious wrapper around my LLM Python library which lets you build LLM wrapper functions using a decorator and a docstring:
from smartfunc import backend @backend("gpt-4o") def generate_summary(text: str): """Generate a summary of the following text: {{ text }}""" pass summary = generate_summary(long_text)
It works with LLM plugins so the same pattern should work against Gemini, Claude and hundreds of others, including local models.
It integrates with more recent LLM features too, including async support and schemas, by introspecting the function signature:
class Summary(BaseModel): summary: str pros: list[str] cons: list[str] @async_backend("gpt-4o-mini") async def generate_poke_desc(text: str) -> Summary: "Describe the following pokemon: {{ text }}" pass pokemon = await generate_poke_desc("pikachu")
Vincent also recorded a 12 minute video walking through the implementation and showing how it uses Pydantic, Python's inspect module and typing.get_type_hints() function.
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