Rule Validation

Rules let you attach declarative validation to managers. They execute before data is written and generate detailed error messages when conditions fail.

Defining rules

Create rules with the general_manager.rule.Rule helper. Rules accept a callable that receives the manager instance under validation and returns a boolean:

from general_manager.rule import Rule

positive_price = Rule(lambda product: product.price > 0)

Use the generic form to declare the target manager type so that type checkers and IDEs understand the lambda body:

Rule["Project"](lambda project: project.start_date < project.end_date)

Attaching rules to interfaces

Attach rules inside the Meta class of an interface. Each rule runs during create and update operations.

class Project(GeneralManager):
    ...

    class Interface(DatabaseInterface):
        ...

        class Meta:
            rules = [
                Rule["Project"](lambda x: x.total_capex >= "0 EUR"),
                Rule["Project"](
                    lambda x: x.end_date is None or x.start_date <= x.end_date
                ),
            ]

If a rule returns False, the interface raises ValidationError. Errors propagate to the GraphQL mutation response and include messages per attribute involved.

Custom messages and None handling

Rules automatically generate helpful error messages by analysing the expression. Override the text with custom_error_message and use placeholders that match variable names:

Rule(
    lambda order: order.quantity <= order.stock,
    custom_error_message="Ordered quantity ({quantity}) exceeds stock ({stock}).",
)

By default, rules ignore None values and return None. Set ignore_if_none=False when None should fail the validation.

Manual evaluation

You can evaluate a rule in isolation, which is useful for unit tests:

result = positive_price.evaluate(product)
if not result:
    print(positive_price.get_error_message())

For advanced scenarios, register additional rule handlers via the RULE_HANDLERS Django setting. Each handler customises error message extraction for custom or built-in functions such as len(), sum(), max(), or min().