Pydantic Validating Functions
validate_arguments decorator allows the arguments passed to a function to be parsed and validated using the function's annotations before the function is called. While under the hood this uses the same approach of model creation and initialisation; it provides an extremely easy way to apply validation to your code with minimal boilerplate.
validate_arguments decorator is in beta, it has been added to pydantic in v1.5 on a provisional basis. It may change significantly in future releases and its interface will not be concrete until v2. Feedback from the community while it's still provisional would be extremely useful; either comment on #1205 or create a new issue.
Be sure you understand it's limitations.
Example of usage:
from pydantic import validate_arguments, ValidationError @validate_arguments def repeat(s: str, count: int, *, separator: bytes = b'') -> bytes: b = s.encode() return separator.join(b for _ in range(count)) a = repeat('hello', 3) print(a) #> b'hellohellohello' b = repeat('x', '4', separator=' ') print(b) #> b'x x x x' try: c = repeat('hello', 'wrong') except ValidationError as exc: print(exc) """ 1 validation error for Repeat count value is not a valid integer (type=type_error.integer) """
Usage with mypy⚑
validate_arguments decorator should work "out of the box" with mypy since it's defined to return a function with the same signature as the function it decorates. The only limitation is that since we trick mypy into thinking the function returned by the decorator is the same as the function being decorated; access to the raw function or other attributes will require