pydantic-validation-formatter 0.1.2

This is a package that allows to customize pydantic built-in validation error messages

Pydantic Validation Formatter

Installation

Install package using pip -> pip install pydantic-validation-formatter

Usage

Use @customize_validation_message decorator on pydantic class to apply message templates on specific validation error message.

from pydantic_validation_formatter import customize_validation_message
from pydantic import BaseModel, Field, ValidationError

@customize_validation_message
class Hero(BaseModel):
    id: int = Field(gt=0)
    name: str
    class Config:
        validation_message_template = {
            "id": {
                "greater_than": "id value should be greater than {gt} but received {input}",
                "missing": "id field is required",
            },
        }

try:
    Hero(id=-1, name="hero")
except ValidationError as exc:
    print(exc.errors())

This customize the msg field of validation error as follows -

[
    {
        'type': 'greater_than',
        'loc': ('id',),
        'msg': 'id value should be greater than 0 but received -1',     # The default generated message will be 'Input should be greater than 0' but it customize the message.
        'input': -1,
        'ctx': {'gt': 0},
        'url': 'https://errors.pydantic.dev/2.6/v/greater_than'
    }
]

The validation error message can be templated with following variables

• input - The input value in validation error payload
• field - The last item in loc key value from validation error payload
• error_type - The type key value from validation error payload

If any other keys found in ctx dict, then you can use those values in templated validation error message.

To provide custom validation templated message, you need to define validation_message_template attribute in Config class.
This should be a dict value which contains field name as keys (same as attribute name defined in pydantic class) and values should be dict of validation error type and customize templated error message mapping.
To know what kind of error type available, follow the official docs -> https://docs.pydantic.dev/latest/errors/validation_errors/