great-ai/great_ai/parameters/parameter.py

83 lines
2.8 KiB
Python

from functools import wraps
from typing import Any, Callable, Dict, TypeVar, cast
from typeguard import check_type
from ..errors import ArgumentValidationError
from ..helper import get_arguments, get_function_metadata_store
from ..helper.assert_function_is_not_finalised import assert_function_is_not_finalised
from ..tracing.tracing_context import TracingContext
F = TypeVar("F", bound=Callable)
def parameter(
parameter_name: str,
*,
validate: Callable[[Any], bool] = lambda _: True,
disable_logging: bool = False,
) -> Callable[[F], F]:
"""Control the validation and logging of function parameters.
Examples:
>>> @parameter('a')
... def my_function(a: int):
... return a + 2
>>> my_function(4)
6
>>> my_function('3')
Traceback (most recent call last):
...
TypeError: type of a must be int; got str instead
>>> @parameter('positive_a', validate=lambda v: v > 0)
... def my_function(positive_a: int):
... return a + 2
>>> my_function(-1)
Traceback (most recent call last):
...
great_ai.errors.argument_validation_error.ArgumentValidationError: ...
Args:
parameter_name: Name of parameter to consider
validate: Optional validate to run against the concrete argument.
ArgumentValidationError is thrown when the return value is False.
disable_logging: Do not save the value in any active TracingContext.
Returns:
A decorator for argument validation.
"""
def decorator(func: F) -> F:
get_function_metadata_store(func).input_parameter_names.append(parameter_name)
assert_function_is_not_finalised(func)
actual_name = f"arg:{parameter_name}"
@wraps(func)
def wrapper(*args: Any, **kwargs: Dict[str, Any]) -> Any:
arguments = get_arguments(func, args, kwargs)
argument = arguments.get(parameter_name)
expected_type = func.__annotations__.get(parameter_name)
if expected_type is not None:
check_type(parameter_name, argument, expected_type)
if not validate(argument):
raise ArgumentValidationError(
f"""Argument {parameter_name} in {
func.__name__
} did not pass validation"""
)
context = TracingContext.get_current_tracing_context()
if context and not disable_logging:
context.log_value(name=f"{actual_name}:value", value=argument)
if isinstance(argument, str):
context.log_value(name=f"{actual_name}:length", value=len(argument))
return func(*args, **kwargs)
return cast(F, wrapper)
return decorator