Custom request validation in FastAPI - Mini Project: Build & Apply

Custom request validation in FastAPI

📖 Scenario: You are building a simple web API to accept user registration data. You want to make sure the data is valid before processing it.

🎯 Goal: Create a FastAPI app that accepts a POST request with user data. Add custom validation to check that the username is at least 3 characters and the age is at least 18.

📋 What You'll Learn

Create a Pydantic model called User with fields username (string) and age (integer).

Add a custom validator to User that checks username length is at least 3 characters.

Add a custom validator to User that checks age is at least 18.

Create a POST endpoint /register that accepts a User model in the request body.

💡 Why This Matters

🌍 Real World

APIs often need to check that incoming data is correct before saving or processing it. Custom validation helps enforce business rules.

💼 Career

Backend developers use FastAPI and Pydantic validation to build reliable and secure APIs that handle user input safely.

1

Create the User model

Create a Pydantic model called User with two fields: username of type str and age of type int.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

# Create User model here

Hint

Use class User(BaseModel): and define the fields inside.

2

Add custom username validation

Add a custom validator method called validate_username inside the User model using the @validator('username') decorator. It should raise a ValueError if the username length is less than 3 characters.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel
from pydantic import validator

app = FastAPI()

class User(BaseModel):
    username: str
    age: int

    # Add username validator here

Hint

Use @validator('username') and define a method that checks length.

3

Add custom age validation

Add a custom validator method called validate_age inside the User model using the @validator('age') decorator. It should raise a ValueError if the age is less than 18.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel
from pydantic import validator

app = FastAPI()

class User(BaseModel):
    username: str
    age: int

    @validator('username')
    def validate_username(cls, v):
        if len(v) < 3:
            raise ValueError('username must be at least 3 characters')
        return v

    # Add age validator here

Hint

Use @validator('age') and define a method that checks the age value.

4

Create the POST /register endpoint

Create a POST endpoint /register in the FastAPI app that accepts a User model in the request body and returns the same user data.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel
from pydantic import validator

app = FastAPI()

class User(BaseModel):
    username: str
    age: int

    @validator('username')
    def validate_username(cls, v):
        if len(v) < 3:
            raise ValueError('username must be at least 3 characters')
        return v

    @validator('age')
    def validate_age(cls, v):
        if v < 18:
            raise ValueError('age must be at least 18')
        return v

# Create POST /register endpoint here

Hint

Use @app.post('/register') and define a function that takes user: User and returns it.

Practice

1. What is the main purpose of custom request validation in FastAPI?

easy

A. To handle database connections

B. To speed up the API response time

C. To check and enforce rules on incoming data before processing

D. To automatically generate API documentation

Solution

1. Step 1: Understand the role of validation

   Validation ensures data meets expected rules before using it.

2. Step 2: Identify FastAPI's validation purpose

   FastAPI uses validation to keep data clean and safe, preventing bad data.

3. Final Answer:

   To check and enforce rules on incoming data before processing -> Option C

4. Quick Check:

   Validation = Data rules check [OK]

Hint: Validation means checking data correctness before use [OK]

Common Mistakes:

• Confusing validation with performance optimization
• Thinking validation manages database connections
• Mixing validation with documentation generation

2. Which of the following is the correct way to add a custom validator in a Pydantic model for FastAPI?

easy

A. Override the __init__ method of the model

B. Use @validator decorator inside the model class

C. Add a method named validate() outside the model

D. Use @app.validator decorator in the FastAPI app

Solution

1. Step 1: Recall Pydantic validation method

   Pydantic uses @validator decorator inside model classes for custom checks.

2. Step 2: Check other options

   Overriding __init__ or external validate() methods are not standard; @app.validator does not exist.

3. Final Answer:

   Use @validator decorator inside the model class -> Option B

4. Quick Check:

   @validator = Custom check inside model [OK]

Hint: Custom validators use @validator inside Pydantic models [OK]

Common Mistakes:

• Trying to override __init__ for validation
• Using decorators outside the model class
• Confusing FastAPI app decorators with Pydantic validators

3. Given this Pydantic model with a custom validator:

from pydantic import BaseModel, validator

class User(BaseModel):
    age: int

    @validator('age')
    def age_must_be_positive(cls, v):
        if v <= 0:
            raise ValueError('Age must be positive')
        return v

What happens if a request sends {"age": -5} to a FastAPI endpoint using this model?

medium

A. The endpoint accepts the data and processes it

B. FastAPI ignores the validator and accepts the data

C. The server crashes with an unhandled exception

D. FastAPI returns a 422 error with validation details

Solution

1. Step 1: Understand the validator logic

   The validator raises ValueError if age is not positive.

2. Step 2: Know FastAPI's error handling

   FastAPI catches validation errors and returns a 422 response with details.

3. Final Answer:

   FastAPI returns a 422 error with validation details -> Option D

4. Quick Check:

   Invalid data triggers 422 error [OK]

Hint: Invalid data triggers 422 error in FastAPI [OK]

Common Mistakes:

• Assuming server crashes on validation error
• Thinking FastAPI accepts invalid data silently
• Confusing 422 with 500 server error

4. Identify the error in this FastAPI custom validation code:

from pydantic import BaseModel, validator

class Item(BaseModel):
    price: float

    @validator('price')
    def check_price(cls, value):
        if value < 0:
            return ValueError('Price must be non-negative')
        return value

medium

A. The validator should raise ValueError, not return it

B. The validator must be a staticmethod, not classmethod

C. The field price should be an int, not float

D. The validator name must start with 'validate_'

Solution

1. Step 1: Check validator error handling

   Validators must raise exceptions, not return them.

2. Step 2: Verify other parts

   Validator is correctly a classmethod; price type float is valid; no naming rule requires 'validate_' prefix.

3. Final Answer:

   The validator should raise ValueError, not return it -> Option A

4. Quick Check:

   Raise errors in validators, don't return [OK]

Hint: Raise exceptions in validators, don't return them [OK]

Common Mistakes:

• Returning exceptions instead of raising
• Confusing @validator with staticmethod
• Changing field types unnecessarily

5. You want to validate a request body in FastAPI where a user can submit either an email or a phone number, but not both. How can you implement this custom validation in a Pydantic model?

hard

A. Use a root_validator to check that exactly one of email or phone is provided

B. Add separate @validator for email and phone fields to check presence

C. Use FastAPI dependency to validate after model parsing

D. Make both fields required and let FastAPI handle conflicts automatically

Solution

1. Step 1: Understand the validation requirement

   We need to ensure exactly one of two fields is present, a cross-field check.

2. Step 2: Choose validation method

   root_validator runs after all fields are parsed and can check multiple fields together.

3. Step 3: Evaluate other options

   Field validators cannot check other fields; dependency validation is after parsing; making both required contradicts the rule.

4. Final Answer:

   Use a root_validator to check that exactly one of email or phone is provided -> Option A

5. Quick Check:

   Cross-field checks use root_validator [OK]

Hint: Use root_validator for checks involving multiple fields [OK]

Common Mistakes:

• Trying to check multiple fields in single field validators
• Relying on FastAPI dependencies for core validation
• Making both fields required when only one should be