python, pydantic的基本用法

1、基本模型

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    age: int
    email: str

# 自动类型转换
user = User(id="1", name="Alice", age="25", email="alice@example.com")
print(user)
# id=1 name='Alice' age=25 email='alice@example.com'

print(user.id, type(user.id))  # 1 <class 'int'>

2、字段校验与默认值

from pydantic import BaseModel, Field
from typing import Optional

class Product(BaseModel):
    name: str = Field(..., min_length=1, max_length=100)
    price: float = Field(..., gt=0, description="价格必须大于0")
    quantity: int = Field(default=0, ge=0)
    description: Optional[str] = None

p = Product(name="苹果", price=5.5)
print(p)
# name='苹果' price=5.5 quantity=0 description=None

# 校验失败会抛出 ValidationError
try:
    Product(name="", price=-1)
except Exception as e:
    print(e)

3、嵌套模型

from pydantic import BaseModel
from typing import List

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

class Person(BaseModel):
    name: str
    age: int
    addresses: List[Address]

data = {
    "name": "Bob",
    "age": 30,
    "addresses": [
        {"city": "北京", "street": "长安街"},
        {"city": "上海", "street": "南京路"},
    ]
}

person = Person(**data)
print(person.addresses[0].city)  # 北京
print(person.model_dump())       # 转 dict
print(person.model_dump_json())  # 转 JSON 字符串

4、自定义校验器

from pydantic import BaseModel, field_validator, model_validator

class User(BaseModel):
    username: str
    password: str
    confirm_password: str

    @field_validator("username")
    @classmethod
    def username_alphanumeric(cls, v: str) -> str:
        if not v.isalnum():
            raise ValueError("用户名必须为字母或数字")
        return v.lower()

    @model_validator(mode="after")
    def check_passwords_match(self):
        if self.password != self.confirm_password:
            raise ValueError("两次密码不一致")
        return self

u = User(username="Alice123", password="abc", confirm_password="abc")
print(u.username)  # alice123

5、枚举与leteral

from pydantic import BaseModel
from enum import Enum
from typing import Literal

class Status(str, Enum):
    active = "active"
    inactive = "inactive"

class Account(BaseModel):
    status: Status
    role: Literal["admin", "user", "guest"]

a = Account(status="active", role="admin")
print(a.status)  # Status.active

6、环境变量配置（baseSettings）

from pydantic_settings import BaseSettings

class Settings(BaseSettings):
    app_name: str = "MyApp"
    debug: bool = False
    database_url: str

    model_config = {
        "env_file": ".env",
        "env_prefix": "APP_",
    }

# .env 文件内容:
# APP_DATABASE_URL=postgresql://localhost/db
# APP_DEBUG=true

settings = Settings()
print(settings.database_url)

7、序列化与模型转换

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    password: str

u = User(id=1, name="Alice", password="secret")

# 导出时排除敏感字段
print(u.model_dump(exclude={"password"}))
# {'id': 1, 'name': 'Alice'}

# 从 ORM 对象创建（需开启 from_attributes）
class UserORM:
    def __init__(self, id, name, password):
        self.id = id
        self.name = name
        self.password = password

class UserSchema(BaseModel):
    id: int
    name: str
    model_config = {"from_attributes": True}

orm_user = UserORM(1, "Bob", "xxx")
print(UserSchema.model_validate(orm_user))

8、泛型模型

from pydantic import BaseModel
from typing import Generic, TypeVar

T = TypeVar("T")

class Response(BaseModel, Generic[T]):
    code: int
    message: str
    data: T

class UserInfo(BaseModel):
    id: int
    name: str

resp = Response[UserInfo](
    code=200,
    message="ok",
    data={"id": 1, "name": "Alice"}
)
print(resp.data.name)  # Alice