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

Need a 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

Need a 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

Need a 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

Need a hint?

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