Custom validators in FastAPI

Introduction

Custom validators help check if data fits special rules you make. This keeps your app safe and correct.

You want to check if a username has no spaces.

You need to ensure a password has a mix of letters and numbers.

You want to confirm an email ends with a certain domain.

You want to make sure a date is not in the past.

You want to validate a custom ID format.

Syntax

FastAPI

from pydantic import BaseModel, validator

class MyModel(BaseModel):
    field_name: str

    @validator('field_name')
    def check_field(cls, value):
        # your custom check here
        if not value.isalpha():
            raise ValueError('Only letters allowed')
        return value

Use @validator('field_name') above a method to check that field.

The method gets the value and must return it if valid or raise ValueError if not.

Examples

This checks the username has no spaces.

FastAPI

from pydantic import BaseModel, validator

class User(BaseModel):
    username: str

    @validator('username')
    def no_spaces(cls, v):
        if ' ' in v:
            raise ValueError('No spaces allowed')
        return v

This ensures the price is more than zero.

FastAPI

from pydantic import BaseModel, validator

class Product(BaseModel):
    price: float

    @validator('price')
    def positive_price(cls, v):
        if v <= 0:
            raise ValueError('Price must be positive')
        return v

This forces the email to end with '@gmail.com'.

FastAPI

from pydantic import BaseModel, validator

class EmailCheck(BaseModel):
    email: str

    @validator('email')
    def must_be_gmail(cls, v):
        if not v.endswith('@gmail.com'):
            raise ValueError('Email must be gmail')
        return v

Sample Program

This FastAPI app has a User model with two custom checks: username cannot have spaces, and age must be 18 or older. If data fails, FastAPI returns an error automatically.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel, validator
from fastapi.responses import JSONResponse

app = FastAPI()

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

    @validator('username')
    def username_no_spaces(cls, v):
        if ' ' in v:
            raise ValueError('Username cannot contain spaces')
        return v

    @validator('age')
    def age_must_be_adult(cls, v):
        if v < 18:
            raise ValueError('Must be at least 18 years old')
        return v

@app.post('/users')
async def create_user(user: User):
    return JSONResponse(content={'message': f'User {user.username} created, age {user.age}'})

Important Notes

Custom validators run automatically when data is received or created.

Raise ValueError with a clear message to help users fix input.

You can add multiple validators for different fields or the same field.

Summary

Custom validators check data with your own rules.

They help keep your app safe and user input correct.

Use @validator on model fields to add these checks.

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