Nested Models

Learn the fundamentals of nested models in FastAPI.

🎯 Core Concept

Nested Pydantic models allow building complex, hierarchical data structures. Inner models are automatically validated and documented, creating clean APIs for structured data.

📖 What You'll Learn

In this section, you'll understand:

• Defining nested model hierarchies

• Multiple levels of nesting

• List of nested models

• Automatic validation of nested data

• Real-world structured requests

💡 Simple Nested Model Example

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Address(BaseModel):
    street: str
    city: str
    zip_code: str

class User(BaseModel):
    name: str
    email: str
    address: Address  # Nested model

@app.post("/users/")
async def create_user(user: User):
    return user

# Request: {
#   "name": "John",
#   "email": "john@example.com",
#   "address": {
#     "street": "123 Main St",
#     "city": "Boston",
#     "zip_code": "02101"
#   }
# }

🔍 List of Nested Models

class Item(BaseModel):
    name: str
    price: float

class Order(BaseModel):
    order_id: str
    items: list[Item]  # List of nested models

@app.post("/orders/")
async def create_order(order: Order):
    return order

Real-World Usage

Nested models are essential for:

• **User profiles**: User + address + phone

• **Orders**: Order + items + shipping info

• **Blog posts**: Post + author + comments

• **API hierarchies**: Complex domain models

🔑 Key Takeaways

• ✅ Understand the purpose of nested models

• ✅ Know when to apply this pattern

• ✅ Recognize its benefits in real-world scenarios

• ✅ Be prepared to use it in your projects

Ready to explore more? Check out the advanced section for production patterns and edge cases.