Configuration
Configuration for Pydantic models.
Bases: TypedDict
A TypedDict for configuring Pydantic behaviour.
The title for the generated JSON schema, defaults to the model’s name
Type: str | None
Whether to convert all characters to lowercase for str types. Defaults to False.
Type: bool
Whether to convert all characters to uppercase for str types. Defaults to False.
Type: bool
Whether to strip leading and trailing whitespace for str types.
Type: bool
The minimum length for str types. Defaults to None.
Type: int
The maximum length for str types. Defaults to None.
Type: int | 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'This is the default behaviour.
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=20The 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]
'''
Type: ExtraValues | None
Whether or not 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.
Type: 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.
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=20The field 'name' has an alias 'full_name'.
The model is populated by the alias 'full_name'.
The model is populated by the field name 'name'.
Type: 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.
Type: 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=123The validation happens only when the model is created.
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]
'''You can either use class keyword arguments, or model_config to set validate_assignment=True.
The validation happens when the model is created.
The validation also happens when the data is changed.
Type: 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'>
Type: bool
Whether to build models and look up discriminators of tagged unions using python object attributes.
Type: 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.
Type: bool
A callable that takes a field name and returns an alias for it.
If data source field names do not match your code style (e. g. CamelCase fields),
you can automatically generate aliases using alias_generator:
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'}
Type: Callable[[str], str] | None
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 ().
Type: tuple[type, ...]
Whether to allow infinity (+inf an -inf) and NaN values to float fields. Defaults to True.
Type: bool
A dict or callable to provide extra JSON schema properties. Defaults to None.
Type: dict[str, object] | JsonSchemaExtraCallable | None
A dict of custom JSON encoders for specific types. Defaults to None.
Type: dict[type[object], JsonEncoder] | None
(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.
Type: bool
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'])revalidate_instances is set to 'never' by **default.
The assignment is not validated, unless you set validate_assignment to True in the model's config.
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'])revalidate_instances is set to 'always'.
The model is revalidated, since revalidate_instances is set to 'always'.
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'])revalidate_instances is set to 'subclass-instances'.
This is not revalidated, since my_user is not a subclass of User.
Using 'never' we would have gotten user=SubUser(hobbies=['scuba diving'], sins=['lying']).
Type: Literal['always', 'never', 'subclass-instances']
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.
Type: Literal['iso8601', 'float']
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.
Type: Literal['utf8', 'base64']
Whether to validate default values during validation. Defaults to False.
Type: bool
whether to validate the return value from call validators. Defaults to False.
Type: bool
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_".
'''
Type: tuple[str, ...]
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]
'''
Type: bool
Whether to defer model validator and serializer construction until the first model validation.
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=...). Defaults to False.
Type: bool
A dict of settings for plugins. Defaults to None.
See Pydantic Plugins for details.
Type: dict[str, object] | 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.
Type: type[_GenerateSchema] | None
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',
}
'''
Type: bool
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',
}
'''
Type: Literal['validation', 'serialization', None]
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"
Type: bool
Default: Literal['allow', 'ignore', 'forbid']
Alias generators for converting between different capitalization conventions.
def to_pascal(snake: str) -> str
Convert a snake_case string to PascalCase.
str — The PascalCase string.
The string to convert.
def to_camel(snake: str) -> str
Convert a snake_case string to camelCase.
str — The converted camelCase string.
The string to convert.
def to_snake(camel: str) -> str
Convert a PascalCase or camelCase string to snake_case.
str — The converted string in snake_case.
The string to convert.