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


try:
    # this doesn't work, see raised error
    foo = Foo(a={'b': {'a': None}})
except PydanticUserError as exc_info:
    assert exc_info.code == 'class-not-fully-defined'


class Bar(BaseModel):
    b: 'Foo'


# this works, though
foo = Foo(a={'b': {'a': None}})

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:

Old way

from pydantic import BaseModel, PydanticUserError

try:

    class Model(BaseModel):
        @classmethod
        def __modify_schema__(cls, field_schema):
            field_schema.update(examples=['example'])

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:

New way

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=['example'])
        return json_schema


print(Model.model_json_schema())
"""
{'examples': ['example'], '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 Literal, Union

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 Literal, Union

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 Literal, Union

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 Literal, Union

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 Literal, Union

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 Literal, Union

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'

Callable discriminator case with no tag

This error is raised when a Union that uses a callable Discriminator doesn't have Tag annotations for all cases.

from typing import Union

from typing_extensions import Annotated

from pydantic import BaseModel, Discriminator, PydanticUserError, Tag


def model_x_discriminator(v):
    if isinstance(v, str):
        return 'str'
    if isinstance(v, (dict, BaseModel)):
        return 'model'


# tag missing for both union choices
try:

    class DiscriminatedModel(BaseModel):
        x: Annotated[
            Union[str, 'DiscriminatedModel'],
            Discriminator(model_x_discriminator),
        ]

except PydanticUserError as exc_info:
    assert exc_info.code == 'callable-discriminator-no-tag'

# tag missing for `'DiscriminatedModel'` union choice
try:

    class DiscriminatedModel(BaseModel):
        x: Annotated[
            Union[Annotated[str, Tag('str')], 'DiscriminatedModel'],
            Discriminator(model_x_discriminator),
        ]

except PydanticUserError as exc_info:
    assert exc_info.code == 'callable-discriminator-no-tag'

# tag missing for `str` union choice
try:

    class DiscriminatedModel(BaseModel):
        x: Annotated[
            Union[str, Annotated['DiscriminatedModel', Tag('model')]],
            Discriminator(model_x_discriminator),
        ]

except PydanticUserError as exc_info:
    assert exc_info.code == 'callable-discriminator-no-tag'

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'

Or when you use typing.Annotated with invalid input

from typing_extensions import Annotated

from pydantic import PydanticUserError, create_model

try:
    create_model('FooModel', foo=Annotated[str, 'NotFieldInfoValue'])
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, value):
            return value

except PydanticUserError as exc_info:
    assert exc_info.code == 'validator-instance-method'

json_schema_input_type used with the wrong mode

This error is raised when you explicitly specify a value for the json_schema_input_type argument and mode isn't set to either 'before', 'plain' or 'wrap'.

from pydantic import BaseModel, PydanticUserError, field_validator

try:

    class Model(BaseModel):
        a: int = 1

        @field_validator('a', mode='after', json_schema_input_type=int)
        @classmethod
        def check_a(self, value):
            return value

except PydanticUserError as exc_info:
    assert exc_info.code == 'validator-input-type'

Documenting the JSON Schema input type is only possible for validators where the given value can be anything. That is why it isn't available for after validators, where the value is first validated against the type annotation.

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 field serializer signatures are:

from pydantic import FieldSerializationInfo, SerializerFunctionWrapHandler, field_serializer

# an instance method with the default mode or `mode='plain'`
@field_serializer('x')  # or @field_serializer('x', mode='plain')
def ser_x(self, value: Any, info: FieldSerializationInfo): ...

# a static method or function with the default mode or `mode='plain'`
@field_serializer('x')  # or @field_serializer('x', mode='plain')
@staticmethod
def ser_x(value: Any, info: FieldSerializationInfo): ...

# equivalent to
def ser_x(value: Any, info: FieldSerializationInfo): ...
serializer('x')(ser_x)

# an instance method with `mode='wrap'`
@field_serializer('x', mode='wrap')
def ser_x(self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo): ...

# a static method or function with `mode='wrap'`
@field_serializer('x', mode='wrap')
@staticmethod
def ser_x(value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo): ...

# equivalent to
def ser_x(value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo): ...
serializer('x')(ser_x)

# For all of these, you can also choose to omit the `info` argument, for example:
@field_serializer('x')
def ser_x(self, value: Any): ...

@field_serializer('x', mode='wrap')
def ser_x(self, value: Any, handler: 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'

Valid model serializer signatures are:

from pydantic import SerializerFunctionWrapHandler, SerializationInfo, model_serializer

# an instance method with the default mode or `mode='plain'`
@model_serializer  # or model_serializer(mode='plain')
def mod_ser(self, info: SerializationInfo): ...

# an instance method with `mode='wrap'`
@model_serializer(mode='wrap')
def mod_ser(self, handler: SerializerFunctionWrapHandler, info: SerializationInfo):

# For all of these, you can also choose to omit the `info` argument, for example:
@model_serializer(mode='plain')
def mod_ser(self): ...

@model_serializer(mode='wrap')
def mod_ser(self, handler: SerializerFunctionWrapHandler): ...

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 its 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

Cannot specify model_config['extra'] with RootModel

Because RootModel is not capable of storing or even accepting extra fields during initialization, we raise an error if you try to specify a value for the config setting 'extra' when creating a subclass of RootModel:

from pydantic import PydanticUserError, RootModel

try:

    class MyRootModel(RootModel):
        model_config = {'extra': 'allow'}
        root: int

except PydanticUserError as exc_info:
    assert exc_info.code == 'root-model-extra'

Cannot evaluate type annotation

Because type annotations are evaluated after assignments, you might get unexpected results when using a type annotation name that clashes with one of your fields. We raise an error in the following case:

from datetime import date

from pydantic import BaseModel, Field


class Model(BaseModel):
    date: date = Field(description='A date')

As a workaround, you can either use an alias or change your import:

import datetime
# Or `from datetime import date as _date`

from pydantic import BaseModel, Field


class Model(BaseModel):
    date: datetime.date = Field(description='A date')

Incompatible dataclass init and extra settings

Pydantic does not allow the specification of the extra='allow' setting on a dataclass while any of the fields have init=False set.

Thus, you may not do something like the following:

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


@dataclass(config=ConfigDict(extra='allow'))
class A:
    a: int = Field(init=False, default=1)

The above snippet results in the following error during schema building for the A dataclass:

pydantic.errors.PydanticUserError: Field a has `init=False` and dataclass has config setting `extra="allow"`.
This combination is not allowed.

Incompatible init and init_var settings on dataclass field

The init=False and init_var=True settings are mutually exclusive. Doing so results in the PydanticUserError shown in the example below.

from pydantic import Field
from pydantic.dataclasses import dataclass


@dataclass
class Foo:
    bar: str = Field(..., init=False, init_var=True)


"""
pydantic.errors.PydanticUserError: Dataclass field bar has init=False and init_var=True, but these are mutually exclusive.
"""

model_config is used as a model field

This error is raised when model_config is used as the name of a field.

from pydantic import BaseModel, PydanticUserError

try:

    class Model(BaseModel):
        model_config: str

except PydanticUserError as exc_info:
    assert exc_info.code == 'model-config-invalid-field-name'

with_config is used on a BaseModel subclass

This error is raised when the with_config decorator is used on a class which is already a Pydantic model (use the model_config attribute instead).

from pydantic import BaseModel, PydanticUserError, with_config

try:

    @with_config({'allow_inf_nan': True})
    class Model(BaseModel):
        bar: str

except PydanticUserError as exc_info:
    assert exc_info.code == 'with-config-on-model'

dataclass is used on a BaseModel subclass

This error is raised when the Pydantic dataclass decorator is used on a class which is already a Pydantic model.

from pydantic import BaseModel, PydanticUserError
from pydantic.dataclasses import dataclass

try:

    @dataclass
    class Model(BaseModel):
        bar: str

except PydanticUserError as exc_info:
    assert exc_info.code == 'dataclass-on-model'