It is useful to frame LLM calls as pure (i.e., stateless) functions1.
Consider the following prompt:
What sounds does a chicken make?We would like the LLM to respond. But we could convert this into a callable function with an input argument so we can use it for other animals, e.g.
What sounds does a {{ animal }} make?We would also like to get our responses in a reliable format/following a predefined schema. We can define this schema as a pytantic BaseModel instance:
class AnimalSound(pydantic.BaseModel)
animal: str
sounds: list[str]And we can define our callable LLM function as:
@my_llm_client_wrapper.llm_function(output_schema=AnimalSound)
def foo_bar(animal='cow'):
'''
What sounds does a {{ animal }} make?
'''Note that there is no actual code in the function, only the signature, a docstring containing the prompt with (jinja2-compliant) variable placeholders, and a decorator declaration determining the desired output format.
This function can be called:
response_obj = foo_bar(animal='pig')And the results obtained:
response_obj.animal
# 'pig'
response_obj.sounds
# ['oink', 'sniff']This requires a wrapper class that implements the decorator,
my_llm_client_wrapper = MyLLMClientWrapper(
api_key,
llm_endpoint,
# ...
)which may be something like
from jinja2 import Template
import textwrap
class MyLLMClientWrapper:
# ...
def llm_function(
self,
model="gpt-oss-120b",
output_schema=None,
# ...
):
def decorator(func):
@functools.wraps(func)
def wrapper(**kwargs):
prompt_template = func.__doc__
if not prompt_template:
raise ValueError("The decorated function must have a docstring.")
# Construct prompt from template and arguments
dedented_template = textwrap.dedent(prompt_template).strip()
template = Template(dedented_template)
prompt = template.render(**kwargs)
# LLM call
llm_call_kwargs = dict(
messages=[{"role": "user", "content": prompt}],
function_name=func.__name__,
text_format=output_schema
model=model,
#...
)
response = self.invoke(**llm_call_kwargs)
response_content = (
response.output_parsed
if text_format is not None
else response.output_text
)
return response_content
return wrapper
return decorator