FastAPI Day 3: Automating Data Validation with Pydantic Models (No Hand‑Written if‑else)

1. Pydantic models as data schema

Define a product schema using a Pydantic BaseModel with fields name, price, description, and tags. Validation rules such as min_length, max_length, and gt=0 are declared in the model, so the endpoint code does not need explicit checks.

from pydantic import BaseModel, Field

class ProductCreate(BaseModel):
    name: str = Field(..., min_length=1, max_length=100)
    price: float = Field(..., gt=0)
    description: str | None = None
    tags: list[str] = Field(default_factory=list)

In a FastAPI route the parameter product: ProductCreate is already validated and can be returned directly.

@app.post("/products")
def create_product(product: ProductCreate):
    # product is clean data after validation
    return {"id": 1, **product.model_dump()}

FastAPI automatically validates each field, applies the Field constraints, and returns a 422 response if validation fails, preventing the business logic from executing.

2. Nested models

When a product includes images, a separate Image model can be nested inside ProductCreate.

class Image(BaseModel):
    url: str
    alt: str | None = None

class ProductCreate(BaseModel):
    name: str
    price: float
    images: list[Image] = Field(default_factory=list)

Sending a JSON with a list of image objects triggers automatic parsing and validation, including URL format checks.

{
  "name": "Monitor",
  "price": 2999.0,
  "images": [
    {"url": "https://example.com/1.jpg", "alt": "Front"},
    {"url": "https://example.com/2.jpg", "alt": "Side"}
  ]
}

3. Field validation capabilities

The Field function not only defines defaults but also adds metadata such as description, which appears in the generated OpenAPI docs ( /docs).

class ProductCreate(BaseModel):
    name: str = Field(..., min_length=1, max_length=100,
                     description="Product name")
    price: float = Field(..., gt=0,
                     description="Product price (CNY)")

4. Separating input and output models

Separate models for creation and response prevent internal fields from leaking.

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

class Product(BaseModel):
    id: int          # only in responses
    name: str
    price: float

Declare the response type with -> Product in the route signature.

5. What was done on Day 3

Defined five Pydantic models: ProductCreate, Product, Image, OrderCreate, Order.

Implemented POST /products supporting nested image lists.

Implemented POST /orders with a required non‑empty product list.

All seven test cases passed, correctly catching missing fields, negative prices, and empty lists.

6. Next steps

Day 4 will cover response models ( response_model) and best practices for separating input and output schemas.

Related links

Official FastAPI documentation (Chinese):

https://fastapi.tiangolo.com/zh/tutorial/body/
https://fastapi.tiangolo.com/zh/tutorial/body-nested-models/