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Validation

Redis OM uses Pydantic behind the scenes to validate data at runtime, based on the model's type annotations.

Basic Type Validation

Validation works for basic type annotations like str. Thus, given the following model:

importdatetimefromtypingimportOptionalfrompydanticimportEmailStrfromredis_omimportHashModelclassCustomer(HashModel): first_name: strlast_name: stremail: EmailStrjoin_date: datetime.dateage: intbio: Optional[str]

... Redis OM will ensure that first_name is always a string.

But every Redis OM model is also a Pydantic model, so you can use existing Pydantic validators like EmailStr, Pattern, and many more for complex validation!

Complex Validation

Let's see what happens if we try to create a Customer object with an invalid email address.

importdatetimefromtypingimportOptionalfrompydanticimportEmailStr, ValidationErrorfromredis_omimportHashModelclassCustomer(HashModel): first_name: strlast_name: stremail: EmailStrjoin_date: datetime.dateage: intbio: Optional[str] # We'll get a validation error if we try to use an invalid email address!try: Customer( first_name="Andrew", last_name="Brookins", email="Not an email address!", join_date=datetime.date.today(), age=38, bio="Python developer, works at Redis, Inc." ) exceptValidationErrorase: print(e) """ pydantic.error_wrappers.ValidationError: 1 validation error for Customer email value is not a valid email address (type=value_error.email) """

As you can see, creating the Customer object generated the following error:

 Traceback: pydantic.error_wrappers.ValidationError: 1 validation error for Customer email value is not a valid email address (type=value_error.email)

We'll also get a validation error if we change a field on a model instance to an invalid value and then try to save the model:

importdatetimefromtypingimportOptionalfrompydanticimportEmailStr, ValidationErrorfromredis_omimportHashModelclassCustomer(HashModel): first_name: strlast_name: stremail: EmailStrjoin_date: datetime.dateage: intbio: Optional[str] andrew=Customer( first_name="Andrew", last_name="Brookins", email="andrew.brookins@example.com", join_date=datetime.date.today(), age=38, bio="Python developer, works at Redis, Inc." ) andrew.email="Not valid"try: andrew.save() exceptValidationErrorase: print(e) """ pydantic.error_wrappers.ValidationError: 1 validation error for Customer email value is not a valid email address (type=value_error.email) """

Once again, we get the validation error:

 Traceback: pydantic.error_wrappers.ValidationError: 1 validation error for Customer email value is not a valid email address (type=value_error.email)

Constrained Values

If you want to use any of the constraints.

Pydantic includes many type annotations to introduce constraints to your model field values.

The concept of "constraints" includes quite a few possibilities:

  • Strings that are always lowercase
  • Strings that must match a regular expression
  • Integers within a range
  • Integers that are a specific multiple
  • And many more...

All of these constraint types work with Redis OM models. Read the Pydantic documentation on constrained types to learn more.

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