validate-call-safe 0.2.0

Safe, non-error-raising, alternative to Pydantic validate_call decorator

validate-call-safe

validate_call_safe is a safe, non-error-raising alternative to Pydantic's validate_call decorator. It allows you to validate function arguments while gracefully handling validation errors through an error model, inspired by effects handlers, returning them as structured data models instead of raising exceptions.

This therefore means that side effects ('erroring') are transformed into return types. The return type annotation of a decorated function is modified accordingly as the Union of the existing return type with the provided error model type.

Features

• Validates function arguments using Pydantic's existing validate_call decorator
• Returns a custom error model instead of raising exceptions when validation fails
• Configurable error information, including JSON representation and string representation of errors
• Compatible with Pydantic v2, tested back to version 2.0.1
• Optional model config and return value validation, as in the original Pydantic @validate_call decorator

Installation

pip install validate-call-safe

Usage

Basic Usage

Here's a basic example using a custom error model:

from pydantic import BaseModel, Json
from validate_call_safe import validate_call_safe

class CustomErrorModel(BaseModel):
    error_type: str
    error_json: Json
    error_str: str
    error_repr: str

@validate_call_safe(CustomErrorModel)
def int_noop(a: int) -> int:
    return a

success = int_noop(a=1)  # 1
failure = int_noop(a="A")  # CustomErrorModel(error_type='ValidationError', ...)

Return Value Validation

You can enable return value validation using the validate_return parameter:

@validate_call_safe(validate_return=True)
def int_noop(a: int) -> int:
    return "foo"  # This will cause a validation error

result = int_noop(a=1)  # ErrorModel(error_type='ValidationError', ...)

Error Model Configuration

validate_call_safe offers flexibility in specifying the error model:

1. No brackets:

   @validate_call_safe
   def int_noop(a: int) -> int:
       return a
   

2. Empty brackets:

   @validate_call_safe()
   def int_noop(a: int) -> int:
       return a
   

3. Custom error model:

   @validate_call_safe(CustomErrorModel)
   def int_noop(a: int) -> int:
       return a
   

4. With validation parameters:

   @validate_call_safe(validate_return=True)
   def int_noop(a: int) -> int:
       return a
   

Comparison with validate_call

With validate_call_safe you don't have to catch the expected ValidationError from Pydantic's validate_call:

# Using validate_call
from pydantic import validate_call

@validate_call
def unsafe_int_noop(a: int) -> int:
    return a

try:
    unsafe_int_noop(a="A")
except ValidationError as e:
    print(f"Error: {e}")

# Using validate_call_safe
from validate_call_safe import validate_call_safe

@validate_call_safe(CustomErrorModel)
def safe_int_noop(a: int) -> int:
    return a

result = safe_int_noop(a="A")
match result:
    case CustomErrorModel():
        print(f"Error: {result.error_type}")
    case int():
        ...  # Regular business logic here

Ideas for Future Development

• Optionally restrict to ValidationError
• Specify non-ValidationError exceptions to capture
• Capture tracebacks