Pydantic Pro-Pydantic API Modeler

Example

from pydantic import BaseModel class User(BaseModel): name: str age: int = None # Example schema validation user = User(name='Alice', age='22')

Scenario

In a scenario where a web service needs to accept user data, Pydantic Pro provides insights into defining models like 'User', ensuring all incoming data adheres to the schema, and converts types where necessary (e.g., converting age from a string to an integer).

Example

from pydantic import BaseModel, validator class Product(BaseModel): name: str price: float @validator('price') def check_price(cls, value): if value <= 0: raise ValueError('Price must be positive') return value

Scenario

Consider an e-commerce backend where price integrity is vital. Pydantic Pro helps developers implement custom validators to check conditions not covered by standard type validations, such as ensuring prices are not negative or zero.

Example

from fastapi import FastAPI, Depends from pydantic import BaseModel class UserIdentity(BaseModel): username: str role: str app = FastAPI() @app.get('/items/') def read_items(user: UserIdentity = Depends()): return {'username': user.username, 'role': user.role}

Scenario

For API developers using FastAPI alongside Pydantic, Pydantic Pro can explain how to use dependency injection to manage user sessions or permissions dynamically, thereby enhancing API security and usability.