Usage Errors
Pydantic attempts to provide useful errors. The following sections provide details on common errors developers may encounter when working with Pydantic, along with suggestions for addressing the error condition.
Class not fully defined¶
This error is raised when a type referenced in an annotation of a pydantic-validated type
(such as a subclass of BaseModel
, or a pydantic dataclass
) is not defined:
from typing import ForwardRef
from pydantic import BaseModel, PydanticUserError
UndefinedType = ForwardRef('UndefinedType')
class Foobar(BaseModel):
a: UndefinedType
try:
Foobar(a=1)
except PydanticUserError as exc_info:
assert exc_info.code == 'class-not-fully-defined'
Or when the type has been defined after usage:
from typing import Optional
from pydantic import BaseModel, PydanticUserError
class Foo(BaseModel):
a: Optional['Bar'] = None
class Bar(BaseModel):
b: 'Foo'
try:
foo = Foo(a={'b': {'a': None}})
except PydanticUserError as exc_info:
assert exc_info.code == 'class-not-fully-defined'
For BaseModel subclasses, it can be fixed by defining the type and then calling .model_rebuild()
:
from typing import Optional
from pydantic import BaseModel
class Foo(BaseModel):
a: Optional['Bar'] = None
class Bar(BaseModel):
b: 'Foo'
Foo.model_rebuild()
foo = Foo(a={'b': {'a': None}})
In other cases, the error message should indicate how to rebuild the class with the appropriate type defined.
Custom JSON Schema¶
The __modify_schema__
method is no longer supported in V2. You should use the __get_pydantic_json_schema__
method instead.
The __modify_schema__
used to receive a single argument representing the JSON schema. See the example below:
from pydantic import BaseModel, PydanticUserError
try:
class Model(BaseModel):
@classmethod
def __modify_schema__(cls, field_schema):
field_schema.update(examples='examples')
except PydanticUserError as exc_info:
assert exc_info.code == 'custom-json-schema'
The new method __get_pydantic_json_schema__
receives two arguments: the first is a dictionary denoted as CoreSchema
,
and the second a callable handler
that receives a CoreSchema
as parameter, and returns a JSON schema. See the example
below:
from typing import Any, Dict
from pydantic_core import CoreSchema
from pydantic import BaseModel, GetJsonSchemaHandler
class Model(BaseModel):
@classmethod
def __get_pydantic_json_schema__(
cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler
) -> Dict[str, Any]:
json_schema = super().__get_pydantic_json_schema__(core_schema, handler)
json_schema = handler.resolve_ref_schema(json_schema)
json_schema.update(examples='examples')
return json_schema
print(Model.model_json_schema())
"""
{'examples': 'examples', 'properties': {}, 'title': 'Model', 'type': 'object'}
"""
Decorator on missing field¶
This error is raised when you define a decorator with a field that is not valid.
from typing import Any
from pydantic import BaseModel, PydanticUserError, field_validator
try:
class Model(BaseModel):
a: str
@field_validator('b')
def check_b(cls, v: Any):
return v
except PydanticUserError as exc_info:
assert exc_info.code == 'decorator-missing-field'
You can use check_fields=False
if you're inheriting from the model and intended this.
from typing import Any
from pydantic import BaseModel, create_model, field_validator
class Model(BaseModel):
@field_validator('a', check_fields=False)
def check_a(cls, v: Any):
return v
model = create_model('FooModel', a=(str, 'cake'), __base__=Model)
Discriminator no field¶
This error is raised when a model in discriminated unions doesn't define a discriminator field.
from typing import Union
from typing_extensions import Literal
from pydantic import BaseModel, Field, PydanticUserError
class Cat(BaseModel):
c: str
class Dog(BaseModel):
pet_type: Literal['dog']
d: str
try:
class Model(BaseModel):
pet: Union[Cat, Dog] = Field(..., discriminator='pet_type')
number: int
except PydanticUserError as exc_info:
assert exc_info.code == 'discriminator-no-field'
Discriminator alias type¶
This error is raised when you define a non-string alias on a discriminator field.
from typing import Union
from typing_extensions import Literal
from pydantic import AliasChoices, BaseModel, Field, PydanticUserError
class Cat(BaseModel):
pet_type: Literal['cat'] = Field(
validation_alias=AliasChoices('Pet', 'PET')
)
c: str
class Dog(BaseModel):
pet_type: Literal['dog']
d: str
try:
class Model(BaseModel):
pet: Union[Cat, Dog] = Field(..., discriminator='pet_type')
number: int
except PydanticUserError as exc_info:
assert exc_info.code == 'discriminator-alias-type'
Discriminator needs literal¶
This error is raised when you define a non-Literal
type on a discriminator field.
from typing import Union
from typing_extensions import Literal
from pydantic import BaseModel, Field, PydanticUserError
class Cat(BaseModel):
pet_type: int
c: str
class Dog(BaseModel):
pet_type: Literal['dog']
d: str
try:
class Model(BaseModel):
pet: Union[Cat, Dog] = Field(..., discriminator='pet_type')
number: int
except PydanticUserError as exc_info:
assert exc_info.code == 'discriminator-needs-literal'
Discriminator alias¶
This error is raised when you define different aliases on discriminator fields.
from typing import Union
from typing_extensions import Literal
from pydantic import BaseModel, Field, PydanticUserError
class Cat(BaseModel):
pet_type: Literal['cat'] = Field(validation_alias='PET')
c: str
class Dog(BaseModel):
pet_type: Literal['dog'] = Field(validation_alias='Pet')
d: str
try:
class Model(BaseModel):
pet: Union[Cat, Dog] = Field(..., discriminator='pet_type')
number: int
except PydanticUserError as exc_info:
assert exc_info.code == 'discriminator-alias'
Invalid discriminator validator¶
This error is raised when you use a before, wrap, or plain validator on a discriminator field.
This is disallowed because the discriminator field is used to determine the type of the model to use for validation, so you can't use a validator that might change its value.
from typing import Union
from typing_extensions import Literal
from pydantic import BaseModel, Field, PydanticUserError, field_validator
class Cat(BaseModel):
pet_type: Literal['cat']
@field_validator('pet_type', mode='before')
@classmethod
def validate_pet_type(cls, v):
if v == 'kitten':
return 'cat'
return v
class Dog(BaseModel):
pet_type: Literal['dog']
try:
class Model(BaseModel):
pet: Union[Cat, Dog] = Field(..., discriminator='pet_type')
number: int
except PydanticUserError as exc_info:
assert exc_info.code == 'discriminator-validator'
This can be worked around by using a standard Union
, dropping the discriminator:
from typing import Union
from typing_extensions import Literal
from pydantic import BaseModel, field_validator
class Cat(BaseModel):
pet_type: Literal['cat']
@field_validator('pet_type', mode='before')
@classmethod
def validate_pet_type(cls, v):
if v == 'kitten':
return 'cat'
return v
class Dog(BaseModel):
pet_type: Literal['dog']
class Model(BaseModel):
pet: Union[Cat, Dog]
assert Model(pet={'pet_type': 'kitten'}).pet.pet_type == 'cat'
TypedDict
version¶
This error is raised when you use typing.TypedDict
instead of typing_extensions.TypedDict
on Python < 3.12.
Model parent field overridden¶
This error is raised when a field defined on a base class was overridden by a non-annotated attribute.
from pydantic import BaseModel, PydanticUserError
class Foo(BaseModel):
a: float
try:
class Bar(Foo):
x: float = 12.3
a = 123.0
except PydanticUserError as exc_info:
assert exc_info.code == 'model-field-overridden'
Model field missing annotation¶
This error is raised when a field doesn't have an annotation.
from pydantic import BaseModel, Field, PydanticUserError
try:
class Model(BaseModel):
a = Field('foobar')
b = None
except PydanticUserError as exc_info:
assert exc_info.code == 'model-field-missing-annotation'
If the field is not meant to be a field, you may be able to resolve the error
by annotating it as a ClassVar
:
from typing import ClassVar
from pydantic import BaseModel
class Model(BaseModel):
a: ClassVar[str]
Or updating model_config['ignored_types']
:
from pydantic import BaseModel, ConfigDict
class IgnoredType:
pass
class MyModel(BaseModel):
model_config = ConfigDict(ignored_types=(IgnoredType,))
_a = IgnoredType()
_b: int = IgnoredType()
_c: IgnoredType
_d: IgnoredType = IgnoredType()
Config
and model_config
both defined¶
This error is raised when class Config
and model_config
are used together.
from pydantic import BaseModel, ConfigDict, PydanticUserError
try:
class Model(BaseModel):
model_config = ConfigDict(from_attributes=True)
a: str
class Config:
from_attributes = True
except PydanticUserError as exc_info:
assert exc_info.code == 'config-both'
Keyword arguments removed¶
This error is raised when the keyword arguments are not available in Pydantic V2.
For example, regex
is removed from Pydantic V2:
from pydantic import BaseModel, Field, PydanticUserError
try:
class Model(BaseModel):
x: str = Field(regex='test')
except PydanticUserError as exc_info:
assert exc_info.code == 'removed-kwargs'
JSON schema invalid type¶
This error is raised when Pydantic fails to generate a JSON schema for some CoreSchema
.
from pydantic import BaseModel, ImportString, PydanticUserError
class Model(BaseModel):
a: ImportString
try:
Model.model_json_schema()
except PydanticUserError as exc_info:
assert exc_info.code == 'invalid-for-json-schema'
JSON schema already used¶
This error is raised when the JSON schema generator has already been used to generate a JSON schema. You must create a new instance to generate a new JSON schema.
BaseModel instantiated¶
This error is raised when you instantiate BaseModel
directly. Pydantic models should inherit from BaseModel
.
from pydantic import BaseModel, PydanticUserError
try:
BaseModel()
except PydanticUserError as exc_info:
assert exc_info.code == 'base-model-instantiated'
Undefined annotation¶
This error is raised when handling undefined annotations during CoreSchema
generation.
from pydantic import BaseModel, PydanticUndefinedAnnotation
class Model(BaseModel):
a: 'B' # noqa F821
try:
Model.model_rebuild()
except PydanticUndefinedAnnotation as exc_info:
assert exc_info.code == 'undefined-annotation'
Schema for unknown type¶
This error is raised when Pydantic fails to generate a CoreSchema
for some type.
from pydantic import BaseModel, PydanticUserError
try:
class Model(BaseModel):
x: 43 = 123
except PydanticUserError as exc_info:
assert exc_info.code == 'schema-for-unknown-type'
Import error¶
This error is raised when you try to import an object that was available in Pydantic V1, but has been removed in Pydantic V2.
See the Migration Guide for more information.
create_model
field definitions¶
This error is raised when you provide field definitions input in create_model
that is not valid.
from pydantic import PydanticUserError, create_model
try:
create_model('FooModel', foo=(str, 'default value', 'more'))
except PydanticUserError as exc_info:
assert exc_info.code == 'create-model-field-definitions'
create_model
config base¶
This error is raised when you use both __config__
and __base__
together in create_model
.
from pydantic import BaseModel, ConfigDict, PydanticUserError, create_model
try:
config = ConfigDict(frozen=True)
model = create_model(
'FooModel', foo=(int, ...), __config__=config, __base__=BaseModel
)
except PydanticUserError as exc_info:
assert exc_info.code == 'create-model-config-base'
Validator with no fields¶
This error is raised when you use validator bare (with no fields).
from pydantic import BaseModel, PydanticUserError, field_validator
try:
class Model(BaseModel):
a: str
@field_validator
def checker(cls, v):
return v
except PydanticUserError as exc_info:
assert exc_info.code == 'validator-no-fields'
Validators should be used with fields and keyword arguments.
from pydantic import BaseModel, field_validator
class Model(BaseModel):
a: str
@field_validator('a')
def checker(cls, v):
return v
Invalid validator fields¶
This error is raised when you use a validator with non-string fields.
from pydantic import BaseModel, PydanticUserError, field_validator
try:
class Model(BaseModel):
a: str
b: str
@field_validator(['a', 'b'])
def check_fields(cls, v):
return v
except PydanticUserError as exc_info:
assert exc_info.code == 'validator-invalid-fields'
Fields should be passed as separate string arguments:
from pydantic import BaseModel, field_validator
class Model(BaseModel):
a: str
b: str
@field_validator('a', 'b')
def check_fields(cls, v):
return v
Validator on instance method¶
This error is raised when you apply a validator on an instance method.
from pydantic import BaseModel, PydanticUserError, field_validator
try:
class Model(BaseModel):
a: int = 1
@field_validator('a')
def check_a(self, values):
return values
except PydanticUserError as exc_info:
assert exc_info.code == 'validator-instance-method'
Root validator, pre
, skip_on_failure
¶
If you use @root_validator
with pre=False
(the default) you MUST specify skip_on_failure=True
.
The skip_on_failure=False
option is no longer available.
If you were not trying to set skip_on_failure=False
, you can safely set skip_on_failure=True
.
If you do, this root validator will no longer be called if validation fails for any of the fields.
Please see the Migration Guide for more details.
model_serializer
instance methods¶
@model_serializer
must be applied to instance methods.
This error is raised when you apply model_serializer
on an instance method without self
:
from pydantic import BaseModel, PydanticUserError, model_serializer
try:
class MyModel(BaseModel):
a: int
@model_serializer
def _serialize(slf, x, y, z):
return slf
except PydanticUserError as exc_info:
assert exc_info.code == 'model-serializer-instance-method'
Or on a class method:
from pydantic import BaseModel, PydanticUserError, model_serializer
try:
class MyModel(BaseModel):
a: int
@model_serializer
@classmethod
def _serialize(self, x, y, z):
return self
except PydanticUserError as exc_info:
assert exc_info.code == 'model-serializer-instance-method'
validator
, field
, config
, and info
¶
The field
and config
parameters are not available in Pydantic V2.
Please use the info
parameter instead.
You can access the configuration via info.config
,
but it is a dictionary instead of an object like it was in Pydantic V1.
The field
argument is no longer available.
Pydantic V1 validator signature¶
This error is raised when you use an unsupported signature for Pydantic V1-style validator.
import warnings
from pydantic import BaseModel, PydanticUserError, validator
warnings.filterwarnings('ignore', category=DeprecationWarning)
try:
class Model(BaseModel):
a: int
@validator('a')
def check_a(cls, value, foo):
return value
except PydanticUserError as exc_info:
assert exc_info.code == 'validator-v1-signature'
Unrecognized field_validator
signature¶
This error is raised when a field_validator
or model_validator
function has the wrong signature.
from pydantic import BaseModel, PydanticUserError, field_validator
try:
class Model(BaseModel):
a: str
@field_validator('a')
@classmethod
def check_a(cls):
return 'a'
except PydanticUserError as exc_info:
assert exc_info.code == 'validator-signature'
Unrecognized field_serializer
signature¶
This error is raised when the field_serializer
function has the wrong signature.
from pydantic import BaseModel, PydanticUserError, field_serializer
try:
class Model(BaseModel):
x: int
@field_serializer('x')
def no_args():
return 'x'
except PydanticUserError as exc_info:
assert exc_info.code == 'field-serializer-signature'
Valid serializer signatures are:
from pydantic import model_serializer
# an instance method with the default mode or `mode='plain'`
@model_serializer('x') # or @serialize('x', mode='plain')
def ser_x(self, value: Any, info: pydantic.FieldSerializationInfo): ...
# a static method or free-standing function with the default mode or `mode='plain'`
@model_serializer('x') # or @serialize('x', mode='plain')
@staticmethod
def ser_x(value: Any, info: pydantic.FieldSerializationInfo): ...
# equivalent to
def ser_x(value: Any, info: pydantic.FieldSerializationInfo): ...
serializer('x')(ser_x)
# an instance method with `mode='wrap'`
@model_serializer('x', mode='wrap')
def ser_x(self, value: Any, nxt: pydantic.SerializerFunctionWrapHandler, info: pydantic.FieldSerializationInfo): ...
# a static method or free-standing function with `mode='wrap'`
@model_serializer('x', mode='wrap')
@staticmethod
def ser_x(value: Any, nxt: pydantic.SerializerFunctionWrapHandler, info: pydantic.FieldSerializationInfo): ...
# equivalent to
def ser_x(value: Any, nxt: pydantic.SerializerFunctionWrapHandler, info: pydantic.FieldSerializationInfo): ...
serializer('x')(ser_x)
For all of these, you can also choose to omit the `info` argument, for example:
@model_serializer('x')
def ser_x(self, value: Any): ...
@model_serializer('x', mode='wrap')
def ser_x(self, value: Any, handler: pydantic.SerializerFunctionWrapHandler): ...
Unrecognized model_serializer
signature¶
This error is raised when the model_serializer
function has the wrong signature.
from pydantic import BaseModel, PydanticUserError, model_serializer
try:
class MyModel(BaseModel):
a: int
@model_serializer
def _serialize(self, x, y, z):
return self
except PydanticUserError as exc_info:
assert exc_info.code == 'model-serializer-signature'
Multiple field serializers¶
This error is raised when multiple model_serializer
functions are defined for a field.
from pydantic import BaseModel, PydanticUserError, field_serializer
try:
class MyModel(BaseModel):
x: int
y: int
@field_serializer('x', 'y')
def serializer1(v):
return f'{v:,}'
@field_serializer('x')
def serializer2(v):
return v
except PydanticUserError as exc_info:
assert exc_info.code == 'multiple-field-serializers'
Invalid annotated type¶
This error is raised when an annotation cannot annotate a type.
from typing_extensions import Annotated
from pydantic import BaseModel, FutureDate, PydanticUserError
try:
class Model(BaseModel):
foo: Annotated[str, FutureDate()]
except PydanticUserError as exc_info:
assert exc_info.code == 'invalid_annotated_type'
config
is unused with TypeAdapter¶
You will get this error if you try to pass config
to TypeAdapter
when the type is a type that
has it's own config that cannot be overridden (currently this is only BaseModel
, TypedDict
and dataclass
):
from typing_extensions import TypedDict
from pydantic import ConfigDict, PydanticUserError, TypeAdapter
class MyTypedDict(TypedDict):
x: int
try:
TypeAdapter(MyTypedDict, config=ConfigDict(strict=True))
except PydanticUserError as exc_info:
assert exc_info.code == 'type-adapter-config-unused'
Instead you'll need to subclass the type and override or set the config on it:
from typing_extensions import TypedDict
from pydantic import ConfigDict, TypeAdapter
class MyTypedDict(TypedDict):
x: int
# or `model_config = ...` for BaseModel
__pydantic_config__ = ConfigDict(strict=True)
TypeAdapter(MyTypedDict) # ok