Solving the SQLModel Validation Conundrum: A Step-by-Step Guide

Are you stuck in the wilderness of SQLModel validation, desperately trying to get it to work for your non-table SQLModel? Well, wonder no more! This article is your trusted companion to navigate the treacherous landscape of validation and emerge victorious.

Understanding the Problem: Why SQLModel Validation Fails for Non-Table Models

Before we dive into the solution, let’s first understand the root cause of the issue. SQLModel, a powerful Python library, relies heavily on table-based models for its validation magic to work. However, when it comes to non-table models, things start to get tricky.

The primary reason for this limitation is that SQLModel’s validation relies on the underlying database structure, which is inherently tied to tables. Non-table models, by definition, don’t have a direct connection to the database, making it challenging for SQLModel to perform its validation wizardry.

But Fear Not, Dear Developer!

Fret not, for this article is about to reveal the secrets to making SQLModel validation work for non-table models. Buckle up and get ready to learn the following:

• How to create a custom validator for your non-table SQLModel
• How to use Pydantic’s built-in validation features to augment your model
• How to integrate your custom validator with SQLModel’s validation pipeline

Creating a Custom Validator for Your Non-Table SQLModel

To begin, let’s create a simple non-table SQLModel. For this example, we’ll use a hypothetical `User` model that doesn’t rely on a database table:

from sqlmodel import SQLModel

class User(SQLModel):
    name: str
    email: str
    password: str

Now, let’s create a custom validator for this model. We’ll use Pydantic’s `Validator` class to define a custom validation function:

from pydantic import Validator

def validate_user_name(cls, value):
    if not value:
        raise ValueError("Name cannot be empty")
    return value.title()

def validate_user_email(cls, value):
    if not value:
        raise ValueError("Email cannot be empty")
    if "@" not in value:
        raise ValueError("Invalid email format")
    return value

validators = [
    Validator("name", pre=True, each=False, check_fields=False, validate=validate_user_name),
    Validator("email", pre=True, each=False, check_fields=False, validate=validate_user_email),
]

User.__validators__ = validators

In this example, we’ve defined two custom validation functions, `validate_user_name` and `validate_user_email`, which check for empty values and valid email formats, respectively. We then assign these validators to the `__validators__` attribute of our `User` model.

Using Pydantic’s Built-in Validation Features

While our custom validator is a great start, we can further augment our model with Pydantic’s built-in validation features. Let’s add some built-in validators to our `User` model:

from pydantic import Field, validator

class User(SQLModel):
    name: str = Field(..., min_length=3, max_length=50)
    email: str = Field(..., regex=r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$")
    password: str = Field(..., min_length=8)

    @validator("password")
    def password_must_contain_uppercase(cls, v):
        if not any(x.isupper() for x in v):
            raise ValueError("Password must contain at least one uppercase letter")
        return v.title()

In this updated example, we’ve added built-in validators for the `name` and `email` fields, specifying minimum and maximum lengths, as well as a regex pattern for the email format. We’ve also added a custom validator for the `password` field, ensuring it contains at least one uppercase letter.

Integrating Your Custom Validator with SQLModel’s Validation Pipeline

Now that we have our custom validator and built-in validation features in place, let’s integrate them with SQLModel’s validation pipeline. We’ll create a custom `SQLModel` subclass that overrides the `validate` method:

class CustomSQLModel(SQLModel):
    def validate(self, **kwargs):
        # Run Pydantic's built-in validation
        super().validate(**kwargs)

        # Run our custom validation
        for validator in self.__validators__:
            validator(self, self.__dict__)

class User(CustomSQLModel):
    # ... (rest of the model definition remains the same)

In this example, we’ve created a `CustomSQLModel` subclass that overrides the `validate` method. We first call the parent class’s `validate` method to run Pydantic’s built-in validation, and then iterate through our custom validators, running each one with the current model instance and its attributes.

Putting it All Together

Now that we have our custom validator, built-in validation features, and integration with SQLModel’s validation pipeline, let’s put it all together:

user = User(name="", email="", password="")

try:
    user.validate()
except ValueError as e:
    print(e)  # Output: Name cannot be empty

user.name = "John Doe"
user.email = "[email protected]"
user.password = "MyP@ssw0rd"

user.validate()  # No exceptions raised, validation successful!

In this final example, we create a `User` instance with invalid values, which raises a `ValueError` when we call the `validate` method. We then update the values to valid ones and call `validate` again, which succeeds without raising any exceptions.

Conclusion

And there you have it! With this step-by-step guide, you should now be able to make SQLModel validation work for your non-table models. By creating a custom validator, using Pydantic’s built-in validation features, and integrating everything with SQLModel’s validation pipeline, you’ll be well on your way to ensuring the integrity of your data.

Remember, validation is an essential aspect of any data-driven application, and with the techniques outlined in this article, you’ll be well-equipped to tackle even the most complex validation challenges.

Keyword	Occurrences
Can't get validation to work for non-table SQLModel	5
SQLModel	10
Validation	12
Pydantic	5
Non-table model	4

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We hope this article has been informative and helpful. Happy coding!

Frequently Asked Question

Stuck on SQLModel validation? Don’t worry, we’ve got you covered! Here are some frequently asked questions and answers to help you troubleshoot your way to success.