Configuration

Configuration for Pydantic models.

ConfigDict

Bases: TypedDict

A TypedDict for configuring Pydantic behaviour.

title instance-attribute

title: str | None

The title for the generated JSON schema, defaults to the model's name

model_title_generator instance-attribute

model_title_generator: Callable[[type], str] | None

A callable that takes a model class and returns the title for it. Defaults to None.

field_title_generator instance-attribute

field_title_generator: (
    Callable[[str, FieldInfo | ComputedFieldInfo], str]
    | None
)

A callable that takes a field's name and info and returns title for it. Defaults to None.

str_to_lower instance-attribute

str_to_lower: bool

Whether to convert all characters to lowercase for str types. Defaults to False.

str_to_upper instance-attribute

str_to_upper: bool

Whether to convert all characters to uppercase for str types. Defaults to False.

str_strip_whitespace instance-attribute

str_strip_whitespace: bool

Whether to strip leading and trailing whitespace for str types.

str_min_length instance-attribute

str_min_length: int

The minimum length for str types. Defaults to None.

str_max_length instance-attribute

str_max_length: int | None

The maximum length for str types. Defaults to None.

extra instance-attribute

extra: ExtraValues | None

Whether to ignore, allow, or forbid extra attributes during model initialization. Defaults to 'ignore'.

You can configure how pydantic handles the attributes that are not defined in the model:

• allow - Allow any extra attributes.
• forbid - Forbid any extra attributes.
• ignore - Ignore any extra attributes.

from pydantic import BaseModel, ConfigDict


class User(BaseModel):
    model_config = ConfigDict(extra='ignore')  # (1)!

    name: str


user = User(name='John Doe', age=20)  # (2)!
print(user)
#> name='John Doe'

1. This is the default behaviour.
2. The age argument is ignored.

Instead, with extra='allow', the age argument is included:

from pydantic import BaseModel, ConfigDict


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

    name: str


user = User(name='John Doe', age=20)  # (1)!
print(user)
#> name='John Doe' age=20

1. The age argument is included.

With extra='forbid', an error is raised:

from pydantic import BaseModel, ConfigDict, ValidationError


class User(BaseModel):
    model_config = ConfigDict(extra='forbid')

    name: str


try:
    User(name='John Doe', age=20)
except ValidationError as e:
    print(e)
    '''
    1 validation error for User
    age
    Extra inputs are not permitted [type=extra_forbidden, input_value=20, input_type=int]
    '''

frozen instance-attribute

frozen: bool

Whether models are 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. Defaults to False.

Note

On V1, the inverse of this setting was called allow_mutation, and was True by default.

populate_by_name instance-attribute

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.

Note

The name of this configuration setting was changed in v2.0 from allow_population_by_field_name to populate_by_name.

from pydantic import BaseModel, ConfigDict, Field


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

    name: str = Field(alias='full_name')  # (1)!
    age: int


user = User(full_name='John Doe', age=20)  # (2)!
print(user)
#> name='John Doe' age=20
user = User(name='John Doe', age=20)  # (3)!
print(user)
#> name='John Doe' age=20

1. The field 'name' has an alias 'full_name'.
2. The model is populated by the alias 'full_name'.
3. The model is populated by the field name 'name'.

use_enum_values instance-attribute

use_enum_values: bool

Whether to populate models with the value property of enums, rather than the raw enum. This may be useful if you want to serialize model.model_dump() later. Defaults to False.

Note

If you have an Optional[Enum] value that you set a default for, you need to use validate_default=True for said Field to ensure that the use_enum_values flag takes effect on the default, as extracting an enum's value occurs during validation, not serialization.

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'}

validate_assignment instance-attribute

validate_assignment: bool

Whether to validate the data when the model is changed. Defaults to False.

The default behavior of Pydantic is to validate the data when the model is created.

In case the user changes the data after the model is created, the model is not revalidated.

from pydantic import BaseModel

class User(BaseModel):
    name: str

user = User(name='John Doe')  # (1)!
print(user)
#> name='John Doe'
user.name = 123  # (1)!
print(user)
#> name=123

1. The validation happens only when the model is created.
2. The validation does not happen when the data is changed.

In case you want to revalidate the model when the data is changed, you can use validate_assignment=True:

from pydantic import BaseModel, ValidationError

class User(BaseModel, validate_assignment=True):  # (1)!
    name: str

user = User(name='John Doe')  # (2)!
print(user)
#> name='John Doe'
try:
    user.name = 123  # (3)!
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]
    '''

1. You can either use class keyword arguments, or model_config to set validate_assignment=True.
2. The validation happens when the model is created.
3. The validation also happens when the data is changed.

arbitrary_types_allowed instance-attribute

arbitrary_types_allowed: bool

Whether arbitrary types are allowed for field types. Defaults to False.

from pydantic import BaseModel, ConfigDict, ValidationError

# 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

pet = Pet(name='Hedwig')
# A simple check of instance type is used to validate the data
model = Model(owner='Harry', pet=pet)
print(model)
#> pet=<__main__.Pet object at 0x0123456789ab> owner='Harry'
print(model.pet)
#> <__main__.Pet object at 0x0123456789ab>
print(model.pet.name)
#> Hedwig
print(type(model.pet))
#> <class '__main__.Pet'>
try:
    # If the value is not an instance of the type, it's invalid
    Model(owner='Harry', pet='Hedwig')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    pet
      Input should be an instance of Pet [type=is_instance_of, input_value='Hedwig', input_type=str]
    '''

# Nothing in the instance of the arbitrary type is checked
# Here name probably should have been a str, but it's not validated
pet2 = Pet(name=42)
model2 = Model(owner='Harry', pet=pet2)
print(model2)
#> pet=<__main__.Pet object at 0x0123456789ab> owner='Harry'
print(model2.pet)
#> <__main__.Pet object at 0x0123456789ab>
print(model2.pet.name)
#> 42
print(type(model2.pet))
#> <class '__main__.Pet'>

from_attributes instance-attribute

from_attributes: bool

Whether to build models and look up discriminators of tagged unions using python object attributes.

loc_by_alias instance-attribute

loc_by_alias: bool

Whether to use the actual key provided in the data (e.g. alias) for error locs rather than the field's name. Defaults to True.

alias_generator instance-attribute

alias_generator: (
    Callable[[str], str] | AliasGenerator | None
)

A callable that takes a field name and returns an alias for it or an instance of AliasGenerator. Defaults to None.

When using a callable, the alias generator is used for both validation and serialization. If you want to use different alias generators for validation and serialization, you can use AliasGenerator instead.

If data source field names do not match your code style (e. g. CamelCase fields), you can automatically generate aliases using alias_generator. Here's an example with a basic callable:

from pydantic import BaseModel, ConfigDict
from pydantic.alias_generators import to_pascal

class Voice(BaseModel):
    model_config = ConfigDict(alias_generator=to_pascal)

    name: str
    language_code: str

voice = Voice(Name='Filiz', LanguageCode='tr-TR')
print(voice.language_code)
#> tr-TR
print(voice.model_dump(by_alias=True))
#> {'Name': 'Filiz', 'LanguageCode': 'tr-TR'}

If you want to use different alias generators for validation and serialization, you can use AliasGenerator.

from pydantic import AliasGenerator, BaseModel, ConfigDict
from pydantic.alias_generators import to_camel, to_pascal

class Athlete(BaseModel):
    first_name: str
    last_name: str
    sport: str

    model_config = ConfigDict(
        alias_generator=AliasGenerator(
            validation_alias=to_camel,
            serialization_alias=to_pascal,
        )
    )

athlete = Athlete(firstName='John', lastName='Doe', sport='track')
print(athlete.model_dump(by_alias=True))
#> {'FirstName': 'John', 'LastName': 'Doe', 'Sport': 'track'}

Note

Pydantic offers three built-in alias generators: to_pascal, to_camel, and to_snake.

ignored_types instance-attribute

ignored_types: tuple[type, ...]

A tuple of types that may occur as values of class attributes without annotations. This is typically used for custom descriptors (classes that behave like property). If an attribute is set on a class without an annotation and has a type that is not in this tuple (or otherwise recognized by pydantic), an error will be raised. Defaults to ().

allow_inf_nan instance-attribute

allow_inf_nan: bool

Whether to allow infinity (+inf an -inf) and NaN values to float fields. Defaults to True.

json_schema_extra instance-attribute

json_schema_extra: JsonDict | JsonSchemaExtraCallable | None

A dict or callable to provide extra JSON schema properties. Defaults to None.

json_encoders instance-attribute

json_encoders: dict[type[object], JsonEncoder] | None

A dict of custom JSON encoders for specific types. Defaults to None.

Deprecated

This config option is a carryover from v1. We originally planned to remove it in v2 but didn't have a 1:1 replacement so we are keeping it for now. It is still deprecated and will likely be removed in the future.

strict instance-attribute

strict: bool

(new in V2) If True, strict validation is applied to all fields on the model.

By default, Pydantic attempts to coerce values to the correct type, when possible.

There are situations in which you may want to disable this behavior, and instead raise an error if a value's type does not match the field's type annotation.

To configure strict mode for all fields on a model, you can set strict=True on the model.

from pydantic import BaseModel, ConfigDict

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

    name: str
    age: int

See Strict Mode for more details.

See the Conversion Table for more details on how Pydantic converts data in both strict and lax modes.

revalidate_instances instance-attribute

revalidate_instances: Literal[
    "always", "never", "subclass-instances"
]

When and how to revalidate models and dataclasses during validation. Accepts the string values of 'never', 'always' and 'subclass-instances'. Defaults to 'never'.

• 'never' will not revalidate models and dataclasses during validation
• 'always' will revalidate models and dataclasses during validation
• 'subclass-instances' will revalidate models and dataclasses during validation if the instance is a subclass of the model or dataclass

By default, model and dataclass instances are not revalidated during validation.

from typing import List

from pydantic import BaseModel

class User(BaseModel, revalidate_instances='never'):  # (1)!
    hobbies: List[str]

class SubUser(User):
    sins: List[str]

class Transaction(BaseModel):
    user: User

my_user = User(hobbies=['reading'])
t = Transaction(user=my_user)
print(t)
#> user=User(hobbies=['reading'])

my_user.hobbies = [1]  # (2)!
t = Transaction(user=my_user)  # (3)!
print(t)
#> user=User(hobbies=[1])

my_sub_user = SubUser(hobbies=['scuba diving'], sins=['lying'])
t = Transaction(user=my_sub_user)
print(t)
#> user=SubUser(hobbies=['scuba diving'], sins=['lying'])

1. revalidate_instances is set to 'never' by **default.
2. The assignment is not validated, unless you set validate_assignment to True in the model's config.
3. Since revalidate_instances is set to never, this is not revalidated.

If you want to revalidate instances during validation, you can set revalidate_instances to 'always' in the model's config.

from typing import List

from pydantic import BaseModel, ValidationError

class User(BaseModel, revalidate_instances='always'):  # (1)!
    hobbies: List[str]

class SubUser(User):
    sins: List[str]

class Transaction(BaseModel):
    user: User

my_user = User(hobbies=['reading'])
t = Transaction(user=my_user)
print(t)
#> user=User(hobbies=['reading'])

my_user.hobbies = [1]
try:
    t = Transaction(user=my_user)  # (2)!
except ValidationError as e:
    print(e)
    '''
    1 validation error for Transaction
    user.hobbies.0
      Input should be a valid string [type=string_type, input_value=1, input_type=int]
    '''

my_sub_user = SubUser(hobbies=['scuba diving'], sins=['lying'])
t = Transaction(user=my_sub_user)
print(t)  # (3)!
#> user=User(hobbies=['scuba diving'])

1. revalidate_instances is set to 'always'.
2. The model is revalidated, since revalidate_instances is set to 'always'.
3. Using 'never' we would have gotten user=SubUser(hobbies=['scuba diving'], sins=['lying']).

It's also possible to set revalidate_instances to 'subclass-instances' to only revalidate instances of subclasses of the model.

from typing import List

from pydantic import BaseModel

class User(BaseModel, revalidate_instances='subclass-instances'):  # (1)!
    hobbies: List[str]

class SubUser(User):
    sins: List[str]

class Transaction(BaseModel):
    user: User

my_user = User(hobbies=['reading'])
t = Transaction(user=my_user)
print(t)
#> user=User(hobbies=['reading'])

my_user.hobbies = [1]
t = Transaction(user=my_user)  # (2)!
print(t)
#> user=User(hobbies=[1])

my_sub_user = SubUser(hobbies=['scuba diving'], sins=['lying'])
t = Transaction(user=my_sub_user)
print(t)  # (3)!
#> user=User(hobbies=['scuba diving'])

1. revalidate_instances is set to 'subclass-instances'.
2. This is not revalidated, since my_user is not a subclass of User.
3. Using 'never' we would have gotten user=SubUser(hobbies=['scuba diving'], sins=['lying']).

ser_json_timedelta instance-attribute

ser_json_timedelta: Literal['iso8601', 'float']

The format of JSON serialized timedeltas. Accepts the string values of 'iso8601' and 'float'. Defaults to 'iso8601'.

• 'iso8601' will serialize timedeltas to ISO 8601 durations.
• 'float' will serialize timedeltas to the total number of seconds.

ser_json_bytes instance-attribute

ser_json_bytes: Literal['utf8', 'base64']

The encoding of JSON serialized bytes. Accepts the string values of 'utf8' and 'base64'. Defaults to 'utf8'.

• 'utf8' will serialize bytes to UTF-8 strings.
• 'base64' will serialize bytes to URL safe base64 strings.

ser_json_inf_nan instance-attribute

ser_json_inf_nan: Literal['null', 'constants', 'strings']

The encoding of JSON serialized infinity and NaN float values. Defaults to 'null'.

• 'null' will serialize infinity and NaN values as null.
• 'constants' will serialize infinity and NaN values as Infinity and NaN.
• 'strings' will serialize infinity as string "Infinity" and NaN as string "NaN".

validate_default instance-attribute

validate_default: bool

Whether to validate default values during validation. Defaults to False.

validate_return instance-attribute

validate_return: bool

whether to validate the return value from call validators. Defaults to False.

protected_namespaces instance-attribute

protected_namespaces: tuple[str, ...]

A tuple of strings that prevent model to have field which conflict with them. Defaults to ('model_', )).

Pydantic prevents collisions between model attributes and BaseModel's own methods by namespacing them with the prefix model_.

import warnings

from pydantic import BaseModel

warnings.filterwarnings('error')  # Raise warnings as errors

try:

    class Model(BaseModel):
        model_prefixed_field: str

except UserWarning as e:
    print(e)
    '''
    Field "model_prefixed_field" has conflict with protected namespace "model_".

    You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
    '''

You can customize this behavior using the protected_namespaces setting:

import warnings

from pydantic import BaseModel, ConfigDict

warnings.filterwarnings('error')  # Raise warnings as errors

try:

    class Model(BaseModel):
        model_prefixed_field: str
        also_protect_field: str

        model_config = ConfigDict(
            protected_namespaces=('protect_me_', 'also_protect_')
        )

except UserWarning as e:
    print(e)
    '''
    Field "also_protect_field" has conflict with protected namespace "also_protect_".

    You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ('protect_me_',)`.
    '''

While Pydantic will only emit a warning when an item is in a protected namespace but does not actually have a collision, an error is raised if there is an actual collision with an existing attribute:

from pydantic import BaseModel

try:

    class Model(BaseModel):
        model_validate: str

except NameError as e:
    print(e)
    '''
    Field "model_validate" conflicts with member <bound method BaseModel.model_validate of <class 'pydantic.main.BaseModel'>> of protected namespace "model_".
    '''

hide_input_in_errors instance-attribute

hide_input_in_errors: bool

Whether to hide inputs when printing errors. Defaults to False.

Pydantic shows the input value and type when it raises ValidationError during the validation.

from pydantic import BaseModel, ValidationError

class Model(BaseModel):
    a: str

try:
    Model(a=123)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    a
      Input should be a valid string [type=string_type, input_value=123, input_type=int]
    '''

You can hide the input value and type by setting the hide_input_in_errors config to True.

from pydantic import BaseModel, ConfigDict, ValidationError

class Model(BaseModel):
    a: str
    model_config = ConfigDict(hide_input_in_errors=True)

try:
    Model(a=123)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    a
      Input should be a valid string [type=string_type]
    '''

defer_build instance-attribute

defer_build: bool

Whether to defer model validator and serializer construction until the first model validation. Defaults to False.

This can be useful to avoid the overhead of building models which are only used nested within other models, or when you want to manually define type namespace via Model.model_rebuild(_types_namespace=...).

See also experimental_defer_build_mode.

Note

defer_build does not work by default with FastAPI Pydantic models. By default, the validator and serializer for said models is constructed immediately for FastAPI routes. You also need to define experimental_defer_build_mode=('model', 'type_adapter') with FastAPI models in order for defer_build=True to take effect. This additional (experimental) parameter is required for the deferred building due to FastAPI relying on TypeAdapters.

experimental_defer_build_mode instance-attribute

experimental_defer_build_mode: tuple[
    Literal["model", "type_adapter"], ...
]

Controls when defer_build is applicable. Defaults to ('model',).

Due to backwards compatibility reasons TypeAdapter does not by default respect defer_build. Meaning when defer_build is True and experimental_defer_build_mode is the default ('model',) then TypeAdapter immediately constructs its validator and serializer instead of postponing said construction until the first model validation. Set this to ('model', 'type_adapter') to make TypeAdapter respect the defer_build so it postpones validator and serializer construction until the first validation or serialization.

Note

The experimental_defer_build_mode parameter is named with an underscore to suggest this is an experimental feature. It may be removed or changed in the future in a minor release.

plugin_settings instance-attribute

plugin_settings: dict[str, object] | None

A dict of settings for plugins. Defaults to None.

See Pydantic Plugins for details.

schema_generator instance-attribute

schema_generator: type[GenerateSchema] | None

A custom core schema generator class to use when generating JSON schemas. Useful if you want to change the way types are validated across an entire model/schema. Defaults to None.

The GenerateSchema interface is subject to change, currently only the string_schema method is public.

See #6737 for details.

json_schema_serialization_defaults_required instance-attribute

json_schema_serialization_defaults_required: bool

Whether fields with default values should be marked as required in the serialization schema. Defaults to False.

This ensures that the serialization schema will reflect the fact a field with a default will always be present when serializing the model, even though it is not required for validation.

However, there are scenarios where this may be undesirable — in particular, if you want to share the schema between validation and serialization, and don't mind fields with defaults being marked as not required during serialization. See #7209 for more details.

from pydantic import BaseModel, ConfigDict

class Model(BaseModel):
    a: str = 'a'

    model_config = ConfigDict(json_schema_serialization_defaults_required=True)

print(Model.model_json_schema(mode='validation'))
'''
{
    'properties': {'a': {'default': 'a', 'title': 'A', 'type': 'string'}},
    'title': 'Model',
    'type': 'object',
}
'''
print(Model.model_json_schema(mode='serialization'))
'''
{
    'properties': {'a': {'default': 'a', 'title': 'A', 'type': 'string'}},
    'required': ['a'],
    'title': 'Model',
    'type': 'object',
}
'''

json_schema_mode_override instance-attribute

json_schema_mode_override: Literal[
    "validation", "serialization", None
]

If not None, the specified mode will be used to generate the JSON schema regardless of what mode was passed to the function call. Defaults to None.

This provides a way to force the JSON schema generation to reflect a specific mode, e.g., to always use the validation schema.

It can be useful when using frameworks (such as FastAPI) that may generate different schemas for validation and serialization that must both be referenced from the same schema; when this happens, we automatically append -Input to the definition reference for the validation schema and -Output to the definition reference for the serialization schema. By specifying a json_schema_mode_override though, this prevents the conflict between the validation and serialization schemas (since both will use the specified schema), and so prevents the suffixes from being added to the definition references.

from pydantic import BaseModel, ConfigDict, Json

class Model(BaseModel):
    a: Json[int]  # requires a string to validate, but will dump an int

print(Model.model_json_schema(mode='serialization'))
'''
{
    'properties': {'a': {'title': 'A', 'type': 'integer'}},
    'required': ['a'],
    'title': 'Model',
    'type': 'object',
}
'''

class ForceInputModel(Model):
    # the following ensures that even with mode='serialization', we
    # will get the schema that would be generated for validation.
    model_config = ConfigDict(json_schema_mode_override='validation')

print(ForceInputModel.model_json_schema(mode='serialization'))
'''
{
    'properties': {
        'a': {
            'contentMediaType': 'application/json',
            'contentSchema': {'type': 'integer'},
            'title': 'A',
            'type': 'string',
        }
    },
    'required': ['a'],
    'title': 'ForceInputModel',
    'type': 'object',
}
'''

coerce_numbers_to_str instance-attribute

coerce_numbers_to_str: bool

If True, enables automatic coercion of any Number type to str in "lax" (non-strict) mode. Defaults to False.

Pydantic doesn't allow number types (int, float, Decimal) to be coerced as type str by default.

from decimal import Decimal

from pydantic import BaseModel, ConfigDict, ValidationError

class Model(BaseModel):
    value: str

try:
    print(Model(value=42))
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    value
      Input should be a valid string [type=string_type, input_value=42, input_type=int]
    '''

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

    value: str

repr(Model(value=42).value)
#> "42"
repr(Model(value=42.13).value)
#> "42.13"
repr(Model(value=Decimal('42.13')).value)
#> "42.13"

regex_engine instance-attribute

regex_engine: Literal['rust-regex', 'python-re']

The regex engine to be used for pattern validation. Defaults to 'rust-regex'.

• rust-regex uses the regex Rust crate, which is non-backtracking and therefore more DDoS resistant, but does not support all regex features.
• python-re use the re module, which supports all regex features, but may be slower.

Note

If you use a compiled regex pattern, the python-re engine will be used regardless of this setting. This is so that flags such as re.IGNORECASE are respected.

from pydantic import BaseModel, ConfigDict, Field, ValidationError

class Model(BaseModel):
    model_config = ConfigDict(regex_engine='python-re')

    value: str = Field(pattern=r'^abc(?=def)')

print(Model(value='abcdef').value)
#> abcdef

try:
    print(Model(value='abxyzcdef'))
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    value
      String should match pattern '^abc(?=def)' [type=string_pattern_mismatch, input_value='abxyzcdef', input_type=str]
    '''

validation_error_cause instance-attribute

validation_error_cause: bool

If True, Python exceptions that were part of a validation failure will be shown as an exception group as a cause. Can be useful for debugging. Defaults to False.

Note

Python 3.10 and older don't support exception groups natively. <=3.10, backport must be installed: pip install exceptiongroup.

Note

The structure of validation errors are likely to change in future Pydantic versions. Pydantic offers no guarantees about their structure. Should be used for visual traceback debugging only.

use_attribute_docstrings instance-attribute

use_attribute_docstrings: bool

Whether docstrings of attributes (bare string literals immediately following the attribute declaration) should be used for field descriptions. Defaults to False.

Available in Pydantic v2.7+.

from pydantic import BaseModel, ConfigDict, Field


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

    x: str
    """
    Example of an attribute docstring
    """

    y: int = Field(description="Description in Field")
    """
    Description in Field overrides attribute docstring
    """


print(Model.model_fields["x"].description)
# > Example of an attribute docstring
print(Model.model_fields["y"].description)
# > Description in Field

This requires the source code of the class to be available at runtime.

Usage with TypedDict

Due to current limitations, attribute docstrings detection may not work as expected when using TypedDict (in particular when multiple TypedDict classes have the same name in the same source file). The behavior can be different depending on the Python version used.

cache_strings instance-attribute

cache_strings: bool | Literal['all', 'keys', 'none']

Whether to cache strings to avoid constructing new Python objects. Defaults to True.

Enabling this setting should significantly improve validation performance while increasing memory usage slightly.

• True or 'all' (the default): cache all strings
• 'keys': cache only dictionary keys
• False or 'none': no caching

Note

True or 'all' is required to cache strings during general validation because validators don't know if they're in a key or a value.

Tip

If repeated strings are rare, it's recommended to use 'keys' or 'none' to reduce memory usage, as the performance difference is minimal if repeated strings are rare.

with_config

with_config(
    config: ConfigDict,
) -> Callable[[_TypeT], _TypeT]

Usage Documentation

Configuration with dataclass from the standard library or TypedDict

A convenience decorator to set a Pydantic configuration on a TypedDict or a dataclass from the standard library.

Although the configuration can be set using the __pydantic_config__ attribute, it does not play well with type checkers, especially with TypedDict.

Usage

from typing_extensions import TypedDict

from pydantic import ConfigDict, TypeAdapter, with_config

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

ta = TypeAdapter(Model)

print(ta.validate_python({'x': 'ABC'}))
#> {'x': 'abc'}

Source code in pydantic/config.py

def with_config(config: ConfigDict) -> Callable[[_TypeT], _TypeT]:
    """Usage docs: https://docs.pydantic.dev/2.8/concepts/config/#configuration-with-dataclass-from-the-standard-library-or-typeddict

    A convenience decorator to set a [Pydantic configuration](config.md) on a `TypedDict` or a `dataclass` from the standard library.

    Although the configuration can be set using the `__pydantic_config__` attribute, it does not play well with type checkers,
    especially with `TypedDict`.

    !!! example "Usage"

        ```py
        from typing_extensions import TypedDict

        from pydantic import ConfigDict, TypeAdapter, with_config

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

        ta = TypeAdapter(Model)

        print(ta.validate_python({'x': 'ABC'}))
        #> {'x': 'abc'}
        ```
    """

    def inner(class_: _TypeT, /) -> _TypeT:
        # Ideally, we would check for `class_` to either be a `TypedDict` or a stdlib dataclass.
        # However, the `@with_config` decorator can be applied *after* `@dataclass`. To avoid
        # common mistakes, we at least check for `class_` to not be a Pydantic model.
        from ._internal._utils import is_model_class

        if is_model_class(class_):
            raise PydanticUserError(
                f'Cannot use `with_config` on {class_.__name__} as it is a Pydantic model',
                code='with-config-on-model',
            )
        class_.__pydantic_config__ = config
        return class_

    return inner

ExtraValues module-attribute

ExtraValues = Literal['allow', 'ignore', 'forbid']

pydantic.alias_generators

Alias generators for converting between different capitalization conventions.

to_pascal

to_pascal(snake: str) -> str

Convert a snake_case string to PascalCase.

Parameters:

Name	Type	Description	Default
snake	str	The string to convert.	required

Returns:

Type	Description
str	The PascalCase string.

Source code in pydantic/alias_generators.py

def to_pascal(snake: str) -> str:
    """Convert a snake_case string to PascalCase.

    Args:
        snake: The string to convert.

    Returns:
        The PascalCase string.
    """
    camel = snake.title()
    return re.sub('([0-9A-Za-z])_(?=[0-9A-Z])', lambda m: m.group(1), camel)

to_camel

to_camel(snake: str) -> str

Convert a snake_case string to camelCase.

Parameters:

Name	Type	Description	Default
snake	str	The string to convert.	required

Returns:

Type	Description
str	The converted camelCase string.

Source code in pydantic/alias_generators.py

def to_camel(snake: str) -> str:
    """Convert a snake_case string to camelCase.

    Args:
        snake: The string to convert.

    Returns:
        The converted camelCase string.
    """
    # If the string is already in camelCase and does not contain a digit followed
    # by a lowercase letter, return it as it is
    if re.match('^[a-z]+[A-Za-z0-9]*$', snake) and not re.search(r'\d[a-z]', snake):
        return snake

    camel = to_pascal(snake)
    return re.sub('(^_*[A-Z])', lambda m: m.group(1).lower(), camel)

to_snake

to_snake(camel: str) -> str

Convert a PascalCase, camelCase, or kebab-case string to snake_case.

Parameters:

Name	Type	Description	Default
camel	str	The string to convert.	required

Returns:

Type	Description
str	The converted string in snake_case.

Source code in pydantic/alias_generators.py

def to_snake(camel: str) -> str:
    """Convert a PascalCase, camelCase, or kebab-case string to snake_case.

    Args:
        camel: The string to convert.

    Returns:
        The converted string in snake_case.
    """
    # Handle the sequence of uppercase letters followed by a lowercase letter
    snake = re.sub(r'([A-Z]+)([A-Z][a-z])', lambda m: f'{m.group(1)}_{m.group(2)}', camel)
    # Insert an underscore between a lowercase letter and an uppercase letter
    snake = re.sub(r'([a-z])([A-Z])', lambda m: f'{m.group(1)}_{m.group(2)}', snake)
    # Insert an underscore between a digit and an uppercase letter
    snake = re.sub(r'([0-9])([A-Z])', lambda m: f'{m.group(1)}_{m.group(2)}', snake)
    # Insert an underscore between a lowercase letter and a digit
    snake = re.sub(r'([a-z])([0-9])', lambda m: f'{m.group(1)}_{m.group(2)}', snake)
    # Replace hyphens with underscores to handle kebab-case
    snake = snake.replace('-', '_')
    return snake.lower()