Ritu Singh
Question:
Python- Pydantic v2 custom type validators with info
In Pydantic, you can create custom validators for your data models by using the `@validator` decorator. These validators allow you to perform custom validation logic on the fields of your Pydantic models. You can also use the `pre` and `always` keyword arguments to access additional information about the validation context.
Here's a general example of how to create a custom validator with Pydantic:
from pydantic import BaseModel, validator
class MyModel(BaseModel):
value: int
@validator("value")
def validate_value(cls, value, values):
# The `value` argument contains the field value being validated.
# The `values` argument contains all the field values in the model.
if value < 0:
raise ValueError("Value must be non-negative")
# You can access other fields using `values`.
other_field_value = values.get("other_field")
if other_field_value is not None and value > other_field_value:
raise ValueError("Value must be less than or equal to 'other_field'")
return value
In the example above:
- We define a Pydantic model `MyModel` with a field `value`.
- We use the `@validator` decorator to create a custom validator for the `value` field.
- Inside the `validate_value` method, we can perform custom validation logic. We have access to the `value` being validated and all other field values in the `values` dictionary.
- We raise a `ValueError` if the validation conditions are not met.
- The `validate_value` method should return the validated value if it passes validation.
You can also use the `pre` and `always` keyword arguments to access additional information about the validation context, but the specific usage may depend on the version of Pydantic you are using. Be sure to check the Pydantic documentation or release notes for version-specific details when working with Pydantic 2 or newer versions.
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