Config

The behaviour of Pydantic can be controlled via a variety of configuration values, documented on the ConfigDict class. This page describes how configuration can be specified for Pydantic’s supported types.

Configuration on Pydantic models

On Pydantic models, configuration can be specified in two ways:

Using the model_config class attribute:

from pydantic import BaseModel, ConfigDict, ValidationError


class Model(BaseModel):
  model_config = ConfigDict(str_max_length=5)  # (1)

  v: str


try:
  m = Model(v='abcdef')
except ValidationError as e:
  print(e)
  """
  1 validation error for Model
  v
    String should have at most 5 characters [type=string_too_long, input_value='abcdef', input_type=str]
  """

Using class arguments:
```
from pydantic import BaseModel


class Model(BaseModel, frozen=True):
    a: str
```
Unlike the model_config class attribute, static type checkers will recognize class arguments. For frozen, any instance mutation will be flagged as an type checking error.

Configuration on Pydantic dataclasses

Pydantic dataclasses also support configuration (read more in the dedicated section).

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


@dataclass(config=ConfigDict(str_max_length=10, validate_assignment=True))
class User:
    name: str


user = User(name='John Doe')
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]
    """

Configuration on `TypeAdapter`

Type adapters (using the TypeAdapter class) support configuration, by providing the config argument.

from pydantic import ConfigDict, TypeAdapter

ta = TypeAdapter(list[str], config=ConfigDict(coerce_numbers_to_str=True))

print(ta.validate_python([1, 2]))
#> ['1', '2']

Configuration can’t be provided if the type adapter directly wraps a type that support it, and a usage error is raised in this case. The configuration propagation rules also apply.

Configuration on other supported types

If you are using standard library dataclasses or TypedDict classes, the configuration can be set in two ways:

Using the __pydantic_config__ class attribute:

from dataclasses import dataclass

from pydantic import ConfigDict


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

    id: int
    name: str = 'John Doe'

Using the @with_config decorator (this avoids static type checking errors with TypedDict):

from typing_extensions import TypedDict

from pydantic import ConfigDict, with_config


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

Configuration on the `@validate_call` decorator

The @validate_call also supports setting custom configuration. See the dedicated section for more details.

Change behaviour globally

If you wish to change the behaviour of Pydantic globally, you can create your own custom parent class with a custom configuration, as the configuration is inherited:

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 provide configuration to the subclasses, it will be merged with the parent configuration:

from pydantic import BaseModel, ConfigDict


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


class Model(Parent):
    model_config = ConfigDict(str_to_lower=True)

    x: str


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

Configuration propagation

When using types that support configuration as field annotations, configuration may not be propagated:

For Pydantic models and dataclasses, configuration will not be propagated, each model has its own “configuration boundary”:

from pydantic import BaseModel, ConfigDict


class User(BaseModel):
    name: str


class Parent(BaseModel):
    user: User

    model_config = ConfigDict(str_to_lower=True)


print(Parent(user={'name': 'JOHN'}))
#> user=User(name='JOHN')

For stdlib types (dataclasses and typed dictionaries), configuration will be propagated, unless the type has its own configuration set:

from dataclasses import dataclass

from pydantic import BaseModel, ConfigDict, with_config


@dataclass
class UserWithoutConfig:
    name: str


@dataclass
@with_config(str_to_lower=False)
class UserWithConfig:
    name: str


class Parent(BaseModel):
    user_1: UserWithoutConfig
    user_2: UserWithConfig

    model_config = ConfigDict(str_to_lower=True)


print(Parent(user_1={'name': 'JOHN'}, user_2={'name': 'JOHN'}))
#> user_1=UserWithoutConfig(name='john') user_2=UserWithConfig(name='JOHN')