Nested models in FastAPI - Mini Project: Build & Apply

Nested models

📖 Scenario: You are building a simple API for a bookstore. Each book has a title and an author. The author has a name and an email. You want to organize this data using nested models.

🎯 Goal: Create nested Pydantic models in FastAPI to represent a book and its author. The author model should be nested inside the book model.

📋 What You'll Learn

Create a Pydantic model called Author with fields name (string) and email (string).

Create a Pydantic model called Book with fields title (string) and author (of type Author).

Create a FastAPI app instance called app.

Create a POST endpoint /books/ that accepts a Book model as input.

💡 Why This Matters

🌍 Real World

Nested models help organize complex data structures in APIs, like a book with an author inside it.

💼 Career

Understanding nested models is essential for building clear and maintainable APIs in FastAPI, a popular Python web framework.

1

Create the Author model

Create a Pydantic model called Author with two string fields: name and email.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

# Create the Author model here

Hint

Use class Author(BaseModel): and define name and email as string fields.

2

Create the Book model with nested Author

Create a Pydantic model called Book with a string field title and a field author of type Author.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

class Author(BaseModel):
    name: str
    email: str

# Create the Book model here

Hint

Define class Book(BaseModel): with title as string and author as type Author.

3

Create the FastAPI app instance

Create a FastAPI app instance called app.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

class Author(BaseModel):
    name: str
    email: str

class Book(BaseModel):
    title: str
    author: Author

# Create the FastAPI app instance here

Hint

Write app = FastAPI() to create the app instance.

4

Create POST endpoint to accept Book model

Create a POST endpoint /books/ in app that accepts a Book model as input parameter named book.

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel

class Author(BaseModel):
    name: str
    email: str

class Book(BaseModel):
    title: str
    author: Author

app = FastAPI()

# Create POST endpoint /books/ here

Hint

Use @app.post("/books/") decorator and define an async function create_book with parameter book: Book. Return the book object.

Practice

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

easy

A. To represent complex data structures by embedding one model inside another

B. To speed up the API response time

C. To automatically generate HTML pages

D. To replace database models with simpler ones

Solution

1. Step 1: Understand nested models concept

   Nested models allow you to include one Pydantic model inside another to represent complex or hierarchical data.

2. Step 2: Identify the main purpose in FastAPI

   FastAPI uses nested models to validate and structure complex JSON data clearly and maintainably.

3. Final Answer:

   To represent complex data structures by embedding one model inside another -> Option A

4. Quick Check:

   Nested models = complex data representation [OK]

Hint: Nested models = models inside models for complex data [OK]

Common Mistakes:

• Thinking nested models improve speed
• Confusing nested models with HTML generation
• Assuming nested models replace databases

2. Which of the following is the correct way to define a nested model in FastAPI using Pydantic?

easy

A. class User(BaseModel): name: str; address: str

B. class Address(BaseModel): street: str; class User(BaseModel): name: str; address: Address

C. class User(BaseModel): name: str; address = Address()

D. class Address: street: str; class User: name: str; address: Address

Solution

1. Step 1: Check Pydantic model syntax

   Models must inherit from BaseModel and use type annotations for fields.

2. Step 2: Verify nested model usage

   Nested models are declared by using the nested model class as a type annotation for a field.

3. Final Answer:

   class Address(BaseModel): street: str; class User(BaseModel): name: str; address: Address -> Option B

4. Quick Check:

   Nested model as type annotation = correct syntax [OK]

Hint: Use nested model class as field type with BaseModel inheritance [OK]

Common Mistakes:

• Not inheriting from BaseModel
• Assigning nested model instance instead of type
• Using plain classes without BaseModel

3. Given the following FastAPI models:

class Item(BaseModel): name: str; price: float
class Order(BaseModel): id: int; item: Item

What will be the output of Order(id=1, item={'name': 'Book', 'price': 12.5}).item.price?

medium

A. None

B. '12.5'

C. Error: item must be an Item instance

D. 12.5

Solution

1. Step 1: Understand nested model instantiation

   FastAPI/Pydantic automatically converts nested dicts to model instances when creating the parent model.

2. Step 2: Access nested attribute

   Accessing item.price returns the float value 12.5 from the nested Item model.

3. Final Answer:

   12.5 -> Option D

4. Quick Check:

   Nested dict converts to model, attribute accessed = 12.5 [OK]

Hint: Nested dicts auto-convert to models, access attributes normally [OK]

Common Mistakes:

• Expecting string instead of float
• Thinking nested dicts cause errors
• Assuming nested model must be instance, not dict

4. Identify the error in this FastAPI nested model code:

class Profile(BaseModel): age: int
class User(BaseModel): username: str; profile = {"age": 30}

medium

A. Missing type annotation for profile field

B. Profile class must inherit from BaseSettings, not BaseModel

C. Default value for nested model must be a Profile instance, not a dict

D. username field should be Optional[str]

Solution

1. Step 1: Check field type annotations

   All Pydantic model fields require type annotations (: Type) for proper validation, especially for nested models.

2. Step 2: Identify the missing annotation

   The profile field lacks type annotation: it should be profile: Profile = {"age": 30} or profile: Profile = Profile(age=30) to enable nested model validation.

3. Final Answer:

   Missing type annotation for profile field -> Option A

4. Quick Check:

   Type annotation required for nested model fields [OK]

Hint: All model fields must have type annotations: field: Type [OK]

Common Mistakes:

• Using dict instead of model instance as default
• Ignoring type annotations
• Confusing BaseModel with BaseSettings

5. You want to create a FastAPI model for a blog post with nested author info. The author has name and email. The post has title, content, and author. Which code correctly defines these nested models and validates that the author's email is a valid email address?

hard

A. class Author(BaseModel): name: str; email: str
class Post(BaseModel): title: str; content: str; author: Author

@validator('email')
def check_email(cls, v): return v

B. class Author(BaseModel): name: str; email: str
class Post(BaseModel): title: str; content: str; author: dict

C. class Author(BaseModel): name: str; email: EmailStr
class Post(BaseModel): title: str; content: str; author: Author

D. class Author(BaseModel): name: str; email: EmailStr
class Post(BaseModel): title: str; content: str; author: dict

Solution

1. Step 1: Use EmailStr for email validation

   Pydantic provides EmailStr type to validate email addresses automatically.

2. Step 2: Define nested models correctly

   Author model uses EmailStr, Post model nests Author as a field with correct type annotation.

3. Final Answer:

   class Author(BaseModel): name: str; email: EmailStr class Post(BaseModel): title: str; content: str; author: Author -> Option C

4. Quick Check:

   EmailStr + nested Author model = correct validation [OK]

Hint: Use EmailStr type and nested model as field type [OK]

Common Mistakes:

• Using plain str for email without validation
• Setting author field as dict instead of Author model
• Trying to validate email with incorrect validator placement