Field types and constraints in FastAPI - Mini Project: Build & Apply

Field types and constraints in FastAPI

📖 Scenario: You are building a simple API to register users. Each user has a name, age, and email. You want to make sure the data received is correct and follows some rules.

🎯 Goal: Create a FastAPI app with a User model that uses field types and constraints to validate incoming data.

📋 What You'll Learn

Use Pydantic BaseModel to define a User model

Add a name field as a string with a minimum length of 3

Add an age field as an integer with a minimum value of 18

Add an email field as a string that must be a valid email

Create a POST endpoint /users/ that accepts a User and returns it

💡 Why This Matters

🌍 Real World

APIs often need to validate incoming data to ensure it is correct and safe before processing. Using field types and constraints helps catch errors early and improves reliability.

💼 Career

Backend developers use FastAPI and Pydantic models to build robust APIs that validate user input and enforce data rules, which is essential for professional web services.

1

Create the User model with basic fields

Create a Pydantic model called User with these fields: name as str, age as int, and email as str.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

# Create User model with name, age, email fields
# Your code here

Hint

Define a class User that inherits from BaseModel. Add the fields with their types inside.

2

Add constraints to the User model fields

Modify the User model to add these constraints: name must have a minimum length of 3, age must be at least 18, and email must be a valid email using EmailStr from pydantic.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel, EmailStr, Field

app = FastAPI()

class User(BaseModel):
    # Add constraints to fields below
    name: str
    age: int
    email: EmailStr
# Your code here

Hint

Use Field to add min_length=3 for name and ge=18 for age. Use EmailStr type for email.

3

Create the POST endpoint to receive User data

Create a POST endpoint at /users/ that accepts a User object in the request body and returns the same User object.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel, EmailStr, Field

app = FastAPI()

class User(BaseModel):
    name: str = Field(..., min_length=3)
    age: int = Field(..., ge=18)
    email: EmailStr

# Create POST endpoint /users/ below
# Your code here

Hint

Use @app.post('/users/') decorator and define an async function create_user that takes a User parameter and returns it.

4

Add response model and run the FastAPI app

Modify the POST endpoint to specify the response model as User. Add the code to run the FastAPI app with uvicorn if the script is run directly.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel, EmailStr, Field

app = FastAPI()

class User(BaseModel):
    name: str = Field(..., min_length=3)
    age: int = Field(..., ge=18)
    email: EmailStr

@app.post('/users/', response_model=User)
async def create_user(user: User):
    return user

# Add code to run app with uvicorn below
# Your code here

Hint

Add response_model=User to the POST decorator. Use if __name__ == '__main__': block to run uvicorn.run() with host and port.

Practice

1. In FastAPI, what is the main purpose of using Field() in a Pydantic model?

easy

A. To define API routes

B. To create database tables automatically

C. To add validation rules and metadata to model fields

D. To handle HTTP requests directly

Solution

1. Step 1: Understand the role of Pydantic models in FastAPI

   Pydantic models define the data structure and validation for request and response data.

2. Step 2: Identify what Field() adds

   Field() allows adding constraints like min/max length, default values, and descriptions to model fields.

3. Final Answer:

   To add validation rules and metadata to model fields -> Option C

4. Quick Check:

   Field() = validation and metadata [OK]

Hint: Field() is for validation and extra info on model fields [OK]

Common Mistakes:

• Thinking Field() creates routes or handles requests
• Confusing Field() with database table creation
• Assuming Field() is for HTTP methods

2. Which of the following is the correct way to require a string field with a minimum length of 3 in a Pydantic model using Field()?

easy

A. name: str = Field(required=True, length=3)

B. name: str = Field(min=3)

C. name: str = Field(length=3)

D. name: str = Field(min_length=3)

Solution

1. Step 1: Recall the correct parameter for minimum length in Field()

   The correct parameter to set minimum string length is min_length.

2. Step 2: Check each option for correct syntax

   name: str = Field(min_length=3) uses min_length=3, which is correct. Others use invalid or incorrect parameters.

3. Final Answer:

   name: str = Field(min_length=3) -> Option D

4. Quick Check:

   Minimum string length uses min_length [OK]

Hint: Use min_length for string length constraints in Field() [OK]

Common Mistakes:

• Using min instead of min_length for strings
• Using length instead of min_length
• Adding required=True which is not a Field() parameter

3. Given this Pydantic model snippet:

from pydantic import BaseModel, Field

class Item(BaseModel):
    price: float = Field(gt=0)
    name: str = Field(min_length=2, max_length=10)

What happens if you create Item(price=-5, name='A')?

medium

A. Validation error because price is not greater than 0 and name is too short

B. Item is created successfully with given values

C. Validation error only for price, name is accepted

D. Validation error only for name, price is accepted

Solution

1. Step 1: Check the constraints on price

   Price must be greater than 0 (gt=0). Given price is -5, which fails this.

2. Step 2: Check the constraints on name

   Name must have length between 2 and 10. Given name is 'A' (length 1), which fails min_length=2.

3. Final Answer:

   Validation error because price is not greater than 0 and name is too short -> Option A

4. Quick Check:

   Both price and name fail constraints [OK]

Hint: Check all Field constraints; both price and name fail here [OK]

Common Mistakes:

• Assuming only one field causes error
• Ignoring min_length constraint on strings
• Thinking negative price passes gt=0

4. What is wrong with this FastAPI Pydantic model field definition?

from pydantic import BaseModel, Field

class User(BaseModel):
    age: int = Field(min_length=18)

medium

A. Field cannot be used with integers

B. min_length is invalid for integer fields

C. Missing default value for age

D. Age should be a string, not int

Solution

1. Step 1: Understand Field constraints for int type

   Integer fields use gt or ge for minimum value, not min_length.

2. Step 2: Identify the error in the code

   Using min_length on an int field causes a validation error because it applies only to strings or collections.

3. Final Answer:

   min_length is invalid for integer fields -> Option B

4. Quick Check:

   min_length applies to strings, not ints [OK]

Hint: Use gt/ge for numbers, min_length for strings only [OK]

Common Mistakes:

• Using string constraints on numeric fields
• Thinking Field() can't be used with int
• Confusing default value requirement

5. You want to create a FastAPI model for a product with a tags field that must be a list of strings, each string at least 2 characters long. Which is the best way to enforce this using Pydantic Field()?

hard

A. from typing import List
from pydantic import BaseModel, Field, conlist, constr

class Product(BaseModel):
tags: conlist(constr(min_length=2))

B. from typing import List
from pydantic import BaseModel, Field

class Product(BaseModel):
tags: List[str] = Field(min_length=2)

C. from typing import List
from pydantic import BaseModel, Field

class Product(BaseModel):
tags: List[str] = Field(min_items=2)

D. from typing import List
from pydantic import BaseModel, Field

class Product(BaseModel):
tags: List[str] = Field(min_length=2, max_length=10)

Solution

1. Step 1: Understand the requirement for list of strings with string length constraints

   We need to enforce each string in the list to have at least 2 characters.

2. Step 2: Identify how to apply constraints on list items

   Pydantic's constr can constrain strings, and conlist can constrain lists of constrained types.

3. Step 3: Evaluate options

   from typing import List from pydantic import BaseModel, Field, conlist, constr class Product(BaseModel): tags: conlist(constr(min_length=2)) uses conlist(constr(min_length=2)) which correctly enforces each string's min length inside the list. Others either apply min_length to the list itself or incorrectly.

4. Final Answer:

   Use conlist with constr to enforce string length inside list -> Option A

5. Quick Check:

   conlist(constr(min_length=2)) = correct nested constraints [OK]

Hint: Use conlist with constr for list of strings with length limits [OK]

Common Mistakes:

• Applying min_length to the list instead of strings inside
• Not using constr for string constraints
• Confusing min_items with min_length