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:

import datetime
from typing import Optional

from pydantic import EmailStr

from redis_om import HashModel


class Customer(HashModel):
    first_name: str
    last_name: str
    email: EmailStr
    join_date: datetime.date
    age: int
    bio: 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.

import datetime
from typing import Optional

from pydantic import EmailStr, ValidationError

from redis_om import HashModel


class Customer(HashModel):
    first_name: str
    last_name: str
    email: EmailStr
    join_date: datetime.date
    age: int
    bio: 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."
    )
except ValidationError as e:
    print(e)
    """
    1 validation error for Customer
    email
      value is not a valid email address: An email address must have an @-sign. [type=value_error, ...]
    """

As you can see, creating the Customer object generated a validation error indicating that the email address is invalid.

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:

import datetime
from typing import Optional

from pydantic import EmailStr, ValidationError

from redis_om import HashModel


class Customer(HashModel):
    first_name: str
    last_name: str
    email: EmailStr
    join_date: datetime.date
    age: int
    bio: 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()
except ValidationError as e:
    print(e)
    """
    1 validation error for Customer
    email
      value is not a valid email address: An email address must have an @-sign. [type=value_error, ...]
    """

Once again, we get a validation error indicating the email address is invalid.

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.