Custom validators in FastAPI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a custom validator in FastAPI?

A custom validator is a user-defined function or method that checks if data meets specific rules before processing. It helps ensure data is correct and safe.

beginner

How do you create a custom validator using Pydantic in FastAPI?

You create a method decorated with @validator inside a Pydantic model class. This method receives the field value and returns the validated or transformed value.

beginner

What is the purpose of the @validator decorator in FastAPI?

The @validator decorator marks a method as a custom validator for one or more fields in a Pydantic model. It runs automatically during data validation.

intermediate

Can custom validators in FastAPI modify the input data? How?

Yes, custom validators can modify input data by returning a changed value. The returned value replaces the original field value during validation.

intermediate

What happens if a custom validator raises a ValueError in FastAPI?

If a ValueError is raised inside a custom validator, FastAPI returns a clear error response to the client explaining the validation failure.

Which decorator is used to create a custom validator in FastAPI's Pydantic models?

A @validator

B @validate

C @check

D @custom

The correct decorator to create custom validators in Pydantic models used by FastAPI is @validator.

What should a custom validator method return?

A Nothing (void)

B An error message string

C A boolean true or false

D The validated or transformed value

Custom validators must return the validated or transformed value to replace the original field value.

If a custom validator raises a ValueError, what does FastAPI do?

A Automatically fixes the data

B Ignores the error and continues

C Returns an error response to the client

D Logs the error but returns success

FastAPI returns an error response explaining the validation failure if a ValueError is raised.

Where do you define custom validators in FastAPI?

A In the main FastAPI app file

B Inside Pydantic model classes

C In the route function

D In the database schema

Custom validators are defined inside Pydantic model classes used for request or response data.

Can a custom validator check multiple fields at once?

A Yes, using <code>@root_validator</code>

B No, only one field at a time

C Yes, using <code>@multi_validator</code>

D No, you must write separate validators

To validate multiple fields together, Pydantic provides @root_validator.

Explain how to create and use a custom validator in FastAPI with Pydantic models.

Think about how Pydantic models check data before FastAPI processes it.

Describe what happens when a custom validator fails in FastAPI.

Consider how FastAPI handles errors to keep data safe.

Practice

1. What is the main purpose of using @validator in FastAPI models?

easy

A. To connect the model to a database

B. To automatically generate API documentation

C. To add custom rules that check if input data is valid

D. To style the API response format

Solution

1. Step 1: Understand the role of @validator

   The @validator decorator is used to add custom validation logic to model fields in FastAPI.

2. Step 2: Identify the purpose of validation

   Validation ensures that the data meets specific rules before the app processes it, helping keep input correct and safe.

3. Final Answer:

   To add custom rules that check if input data is valid -> Option C

4. Quick Check:

   Custom validation = check input rules [OK]

Hint: Validators check data rules before use [OK]

Common Mistakes:

• Confusing validators with database connectors
• Thinking validators generate docs
• Assuming validators change response style

2. Which of the following is the correct way to define a custom validator for a Pydantic model field named age?

easy

A. @validator('age')\ndef check_age(cls, value):

B. def validate_age(cls, value):

C. @validate('age')\ndef age_validator(cls, value):

D. @validate_field('age')\ndef age_check(cls, value):

Solution

1. Step 1: Recall the correct decorator syntax

   FastAPI uses Pydantic's @validator decorator with the field name as a string argument.

2. Step 2: Match the function definition style

   The validator function must be a class method with parameters cls and value.

3. Final Answer:

   @validator('age')\ndef check_age(cls, value): -> Option A

4. Quick Check:

   Use @validator('field') with cls, value [OK]

Hint: Use @validator('field') decorator for custom checks [OK]

Common Mistakes:

• Using @validate instead of @validator
• Missing the field name in decorator
• Wrong function parameters

3. Given this code snippet, what will be the output if age is set to 15?

from pydantic import BaseModel, validator

class User(BaseModel):
    age: int

    @validator('age')
    def check_age(cls, value):
        if value < 18:
            raise ValueError('Must be 18 or older')
        return value

try:
    user = User(age=15)
except Exception as e:
    print(e)

medium

A. Must be 18 or older

B. TypeError because age is int

C. User object with age 15 is created

D. No output, code runs silently

Solution

1. Step 1: Analyze the validator logic

   The validator checks if age is less than 18 and raises a ValueError if true.

2. Step 2: Check the input value

   Since age=15 is less than 18, the validator raises the error.

3. Final Answer:

   Must be 18 or older -> Option A

4. Quick Check:

   Age < 18 triggers ValueError [OK]

Hint: Validator raises error if condition fails [OK]

Common Mistakes:

• Assuming object creates despite validation error
• Confusing ValueError with TypeError
• Ignoring the exception handling block

4. Identify the error in this custom validator code:

from pydantic import BaseModel, validator

class Product(BaseModel):
    price: float

    @validator('price')
    def check_price(value):
        if value <= 0:
            raise ValueError('Price must be positive')
        return value

medium

A. Validator method must return None

B. Missing cls parameter in validator method

C. Price field should be int, not float

D. Validator decorator should be @validate

Solution

1. Step 1: Check validator method signature

   Validator methods must accept cls as the first parameter, then value.

2. Step 2: Identify missing parameter

   The method check_price only has value, missing cls.

3. Final Answer:

   Missing cls parameter in validator method -> Option B

4. Quick Check:

   Validator needs cls, value parameters [OK]

Hint: Validator methods need cls first, then value [OK]

Common Mistakes:

• Omitting cls parameter
• Using wrong decorator name
• Returning None instead of value

5. You want to create a custom validator that ensures a username is lowercase and at least 3 characters long. Which code correctly implements this in a FastAPI Pydantic model?

from pydantic import BaseModel, validator

class User(BaseModel):
    username: str

    @validator('username')
    def validate_username(cls, value):
        # Fill in validation logic here
        pass

hard

A. if len(value) < 3 and not value.islower():\n raise ValueError('Username must be lowercase and 3+ chars')\nreturn value

B. if len(value) < 3 and value.islower():\n raise ValueError('Username too short')\nreturn value

C. if len(value) > 3 and value.islower():\n return value\nelse:\n raise ValueError('Invalid username')

D. if value.islower() and len(value) >= 3:\n return value\nraise ValueError('Username invalid')

Solution

1. Step 1: Understand validation requirements

   The username must be lowercase and at least 3 characters long.

2. Step 2: Check each option logic

   if value.islower() and len(value) >= 3:\n return value\nraise ValueError('Username invalid') correctly checks both conditions and raises error if not met, returning value otherwise.

3. Final Answer:

   if value.islower() and len(value) >= 3:\n return value\nraise ValueError('Username invalid') -> Option D

4. Quick Check:

   Check both conditions, raise error if false [OK]

Hint: Check all conditions with 'and', raise error if false [OK]

Common Mistakes:

• Using 'or' instead of 'and' causing wrong logic
• Returning value inside else but missing return in if
• Incorrect length comparison operators