08. Configuration

BaseModel.model_config 用于配置 BaseModel 的行为

from pydantic import BaseModel, ConfigDict, ValidationError

class Model(BaseModel):
    model_config = ConfigDict(str_max_length=10)
    
    v: str

try:
    m = Model(v='x' * 20)
except ValidationError as e:
    print(e)
    """
    1 validation error for Model
    v
      String should have at most 10 characters [type=string_too_long, input_value='xxxxxxxxxxxxxxxxxxxx', input_type=str]
    """

当然，直接在类参数中传入配置项也是可行的

from pydantic import BaseModel, ValidationError


class Model(BaseModel, extra='forbid'):  
    a: str


try:
    Model(a='spam', b='oh no')
except ValidationError as e:
    print(e)
    """
    1 validation error for Model
    b
      Extra inputs are not permitted [type=extra_forbidden, input_value='oh no', input_type=str]
    """

使用 dataclass 的时候，也能在装饰器中传入设置项

from datetime import datetime

from pydantic import ConfigDict, ValidationError
from pydantic.dataclasses import dataclass

config = ConfigDict(str_max_length=10, validate_assignment=True)


@dataclass(config=config)
class User:
    id: int
    name: str = 'John Doe'
    signup_ts: datetime = None


user = User(id='42', signup_ts='2032-06-21T12:00')
try:
    user.name = 'x' * 20
except ValidationError as e:
    print(e)
    """
    1 validation error for User
    name
      String should have at most 10 characters [type=string_too_long, input_value='xxxxxxxxxxxxxxxxxxxx', input_type=str]
    """

在 `dataclass` 或 `TypedDict` 中使用设置项

使用 __pydantic_config__

from dataclasses import dataclass
from datetime import datetime

from pydantic import ConfigDict

@dataclass
class User:
    __pydantic_config__ = ConfigDict(strict=True)

    id: int
    name: str = 'John Doe'
    signup_ts: datetime = None

Alternatively, the with_config decorator can be used to comply with type checkers.

from typing_extensions import TypedDict

from pydantic import ConfigDict, with_config


@with_config(ConfigDict(str_to_lower=True))
class Model(TypedDict):
    x: str

全局性配置

可以创建一个继承自 BaseModel 的基础 model 类，达成全局性配置的目的

from pydantic import BaseModel, ConfigDict


class Parent(BaseModel):
    model_config = ConfigDict(extra='allow')

class Model(Parent):
    x: str

m = Model(x='foo', y='bar')
print(m.model_dump())
#> {'x': 'foo', 'y': 'bar'}

If you add a model_config to the Model class, it will merge with the model_config from Parent:

from pydantic import BaseModel, ConfigDict

class Parent(BaseModel):
    model_config = ConfigDict(extra='allow')

class Model(Parent):
    model_config = ConfigDict(str_to_lower=True)  # (1)!

    x: str


m = Model(x='FOO', y='bar')
print(m.model_dump())
#> {'x': 'foo', 'y': 'bar'}
print(m.model_config)
#> {'extra': 'allow', 'str_to_lower': True}

配置项

Configuration - Pydantic

title
- str，The title for the generated JSON schema, defaults to the model's name
str_to_lower、str_to_upper
- bool，是否将所有字符串大写 / 小写
str_strip_whitespace
- bool，是否移除前后空白字符
str_min_length、str_max_length
- int，字符串最短 / 最长长度
extra
- ExtraValues，在初始化 model 时，针对额外传入的参数的行为
- allow - Allow any extra attributes.
- forbid - Forbid any extra attributes. 遇到额外字段会报错
- ignore - Ignore any extra attributes. Default value.
frozen
- bool，model 是否不变（faux-immutable）
- i.e. whether __setattr__ is allowed, and also generates a __hash__() method for the model. This makes instances of the model potentially hashable if all the attributes are hashable.
populate_by_name
- bool，Whether an aliased field may be populated by its name as given by the model attribute, as well as the alias. Defaults to False.
- bool，针对别名字段，能否使用原名来填充 model

from pydantic import BaseModel, ConfigDict, Field

class User(BaseModel):
    model_config = ConfigDict(populate_by_name=True)

    name: str = Field(alias='full_name')  
    age: int


user = User(full_name='John Doe', age=20)  # The model is populated by the alias 'full_name'.
print(user)
#> name='John Doe' age=20
user = User(name='John Doe', age=20)  # 如果 populate_by_name=False，则会报错
print(user)
#> name='John Doe' age=20

use_enum_values
- bool，是否使用枚举的值本身来填充 models，而不是枚举值
- This may be useful if you want to serialize model.model_dump() later. Defaults to False.

from enum import Enum
from typing import Optional

from pydantic import BaseModel, ConfigDict, Field

class SomeEnum(Enum):
    FOO = 'foo'
    BAR = 'bar'
    BAZ = 'baz'

class SomeModel(BaseModel):
    model_config = ConfigDict(use_enum_values=True)

    some_enum: SomeEnum
    another_enum: Optional[SomeEnum] = Field(default=SomeEnum.FOO, validate_default=True)

model1 = SomeModel(some_enum=SomeEnum.BAR)
print(model1.model_dump())
# {'some_enum': 'bar', 'another_enum': 'foo'}

model2 = SomeModel(some_enum=SomeEnum.BAR, another_enum=SomeEnum.BAZ)
print(model2.model_dump())
#> {'some_enum': 'bar', 'another_enum': 'baz'}

# 如果设置为False的话，则会输出
#> {'some_enum': <SomeEnum.BAR: 'bar'>, 'another_enum': <SomeEnum.FOO: 'foo'>}
#> {'some_enum': <SomeEnum.BAR: 'bar'>, 'another_enum': <SomeEnum.BAZ: 'baz'>}

validate_assignment
- bool，当 model 值改变时，是否重新 validate 一遍
- Pydantic 默认的行为是：只在创建时验证

from pydantic import BaseModel, ValidationError

class User(BaseModel, validate_assignment=True):  
    name: str

user = User(name='John Doe')  
print(user)
#> name='John Doe'
try:
    user.name = 123  
except ValidationError as e:
    print(e)
    '''
    1 validation error for User
    name
      Input should be a valid string [type=string_type, input_value=123, input_type=int]
    '''

arbitrary_types_allowed
- bool，字段类型是否允许使用任意类型

from pydantic import BaseModel, ConfigDict

# This is not a pydantic model, it's an arbitrary class
class Pet:
    def __init__(self, name: str):
        self.name = name

class Model(BaseModel):
    model_config = ConfigDict(arbitrary_types_allowed=True)

    pet: Pet
    owner: str

TBD

08. Configuration

在 dataclass 或 TypedDict 中使用设置项​

全局性配置​

配置项​

在 `dataclass` 或 `TypedDict` 中使用设置项

全局性配置

配置项