Migration Guide

Pydantic V2 introduces a number of changes to the API, including some breaking changes.

This page provides a guide highlighting the most important changes to help you migrate your code from Pydantic V1 to Pydantic V2.

Install Pydantic V2

Pydantic V2 is now the current production release of Pydantic. You can install Pydantic V2 from PyPI:

pip install -U pydantic

If you encounter any issues, please create an issue in GitHub using the bug V2 label. This will help us to actively monitor and track errors, and to continue to improve the library's performance.

If you need to use latest Pydantic V1 for any reason, see the Continue using Pydantic V1 features section below for details on installation and imports from pydantic.v1.

Code transformation tool

We have created a tool to help you migrate your code. This tool is still in beta, but we hope it will help you to migrate your code more quickly.

You can install the tool from PyPI:

pip install bump-pydantic

The usage is simple. If your project structure is:

* repo_folder
    * my_package
        * <python source files> ...

Then you'll want to do:

cd /path/to/repo_folder
bump-pydantic my_package

See more about it on the Bump Pydantic repository.

Continue using Pydantic V1 features

Pydantic V1 is still available when you need it, though we recommend migrating to Pydantic V2 for its improvements and new features.

If you need to use latest Pydantic V1, you can install it with:

pip install "pydantic==1.*"

The Pydantic V2 package also continues to provide access to the Pydantic V1 API by importing through pydantic.v1. For example, you can use the BaseModel class from Pydantic V1 instead of the Pydantic V2 pydantic.BaseModel class:

from pydantic.v1 import BaseModel

You can also import functions that have been removed from Pydantic V2, such as lenient_isinstance:

from pydantic.v1.utils import lenient_isinstance

Pydantic V1 documentation is available at https://docs.pydantic.dev/1.10/.

Migration guide

The following sections provide details on the most important changes in Pydantic V2.

Changes to pydantic.BaseModel

Various method names have been changed; all non-deprecated BaseModel methods now have names matching either the format model_.* or __.*pydantic.*__. Where possible, we have retained the deprecated methods with their old names to help ease migration, but calling them will emit DeprecationWarnings.

Pydantic V1	Pydantic V2
__fields__	model_fields
__private_attributes__	__pydantic_private__
__validators__	__pydantic_validator__
construct()	model_construct()
copy()	model_copy()
dict()	model_dump()
json_schema()	model_json_schema()
json()	model_dump_json()
parse_obj()	model_validate()

• Some of the built-in data-loading functionality has been slated for removal. In particular, parse_raw and parse_file are now deprecated. In Pydantic V2, model_validate_json works like parse_raw. Otherwise, you should load the data and then pass it to model_validate.
• The from_orm method has been deprecated; you can now just use model_validate (equivalent to parse_obj from Pydantic V1) to achieve something similar, as long as you've set from_attributes=True in the model config.
• The __eq__ method has changed for models.
  • Models can only be equal to other BaseModel instances.
  • For two model instances to be equal, they must have the same:
    • Type (or, in the case of generic models, non-parametrized generic origin type)
    • Field values
    • Extra values (only relevant when model_config['extra'] == 'allow')
    • Private attribute values; models with different values of private attributes are no longer equal.
    • Models are no longer equal to the dicts containing their data.
    • Non-generic models of different types are never equal.
    • Generic models with different origin types are never equal. We don't require exact type equality so that, for example, instances of MyGenericModel[Any] could be equal to instances of MyGenericModel[int].
• We have replaced the use of the __root__ field to specify a "custom root model" with a new type called RootModel which is intended to replace the functionality of using a field called __root__ in Pydantic V1.
• We have significantly expanded Pydantic's capabilities related to customizing serialization. In particular, we have added the @field_serializer, @model_serializer, and @computed_field decorators, which each address various shortcomings from Pydantic V1.
  • See Custom serializers for the usage docs of these new decorators.
  • Due to performance overhead and implementation complexity, we have now deprecated support for specifying json_encoders in the model config. This functionality was originally added for the purpose of achieving custom serialization logic, and we think the new serialization decorators are a better choice in most common scenarios.
• We have changed the behavior related to serializing subclasses of models when they occur as nested fields in a parent model. In V1, we would always include all fields from the subclass instance. In V2, when we dump a model, we only include the fields that are defined on the annotated type of the field. This helps prevent some accidental security bugs. You can read more about this (including how to opt out of this behavior) in the Subclass instances for fields of BaseModel, dataclasses, TypedDict section of the model exporting docs.
• GetterDict has been removed as it was just an implementation detail of orm_mode, which has been removed.

Changes to pydantic.generics.GenericModel

The pydantic.generics.GenericModel class is no longer necessary, and has been removed. Instead, you can now create generic BaseModel subclasses by just adding Generic as a parent class on a BaseModel subclass directly. This looks like class MyGenericModel(BaseModel, Generic[T]): ....

While it may not raise an error, we strongly advise against using parametrized generics in isinstance checks.

• For example, you should not do isinstance(my_model, MyGenericModel[int]). However, it is fine to do isinstance(my_model, MyGenericModel). (Note that for standard generics, it would raise an error to do a subclass check with a parameterized generic.)
• If you need to perform isinstance checks against parametrized generics, you can do this by subclassing the parametrized generic class. This looks like class MyIntModel(MyGenericModel[int]): ... and isinstance(my_model, MyIntModel).

Find more information in the Generic models documentation.

Changes to pydantic.Field

Field no longer supports arbitrary keyword arguments to be added to the JSON schema. Instead, any extra data you want to add to the JSON schema should be passed as a dictionary to the json_schema_extra keyword argument.

In Pydantic V1, the alias property returns the field's name when no alias is set. In Pydantic V2, this behavior has changed to return None when no alias is set.

The following properties have been removed from or changed in Field:

• const
• min_items (use min_length instead)
• max_items (use max_length instead)
• unique_items
• allow_mutation (use frozen instead)
• regex (use pattern instead)

• final (use the typing.Final type hint instead)

• [TODO: Need to document any other backwards-incompatible changes to pydantic.Field]

Changes to dataclasses

Pydantic dataclasses continue to be useful for enabling the data validation on standard dataclasses without having to subclass BaseModel. Pydantic V2 introduces the following changes to this dataclass behavior:

• When used as fields, dataclasses (Pydantic or vanilla) no longer accept tuples as validation inputs; dicts should be used instead.
• The __post_init__ in Pydantic dataclasses will now be called after validation, rather than before.
  • As a result, the __post_init_post_parse__ method would have become redundant, so has been removed.
• Pydantic no longer supports extra='allow' for Pydantic dataclasses, where extra fields passed to the initializer would be stored as extra attributes on the dataclass. extra='ignore' is still supported for the purpose of ignoring unexpected fields while parsing data, they just won't be stored on the instance.
• Pydantic dataclasses no longer have an attribute __pydantic_model__, and no longer use an underlying BaseModel to perform validation or provide other functionality.
  • To perform validation, generate a JSON schema, or make use of any other functionality that may have required __pydantic_model__ in V1, you should now wrap the dataclass with a TypeAdapter (discussed more below) and make use of its methods.
• In Pydantic V1, if you used a vanilla (i.e., non-Pydantic) dataclass as a field, the config of the parent type would be used as though it was the config for the dataclass itself as well. In Pydantic V2, this is no longer the case.
  • In Pydantic V2, to override the config (like you would with model_config on a BaseModel), you can use the config parameter on the @dataclass decorator. See Dataclass Config for examples.

Changes to config

• In Pydantic V2, to specify config on a model, you should set a class attribute called model_config to be a dict with the key/value pairs you want to be used as the config. The Pydantic V1 behavior to create a class called Config in the namespace of the parent BaseModel subclass is now deprecated.

• The following config settings have been removed:

  • allow_mutation — this has been removed. You should be able to use frozen equivalently (inverse of current use).
  • error_msg_templates.
  • fields — this was the source of various bugs, so has been removed. You should be able to use Annotated on fields to modify them as desired.
  • getter_dict — orm_mode has been removed, and this implementation detail is no longer necessary.
  • smart_union.
  • underscore_attrs_are_private — the Pydantic V2 behavior is now the same as if this was always set to True in Pydantic V1.

• The following config settings have been renamed:

  • allow_population_by_field_name → populate_by_name
  • anystr_lower → str_to_lower
  • anystr_strip_whitespace → str_strip_whitespace
  • anystr_upper → str_to_upper
  • keep_untouched → ignored_types
  • max_anystr_length → str_max_length
  • min_anystr_length → str_min_length
  • orm_mode → from_attributes
  • schema_extra → json_schema_extra
  • validate_all → validate_default

See Model Config for more details.

Changes to validators

@validator and @root_validator are deprecated

• @validator has been deprecated, and should be replaced with @field_validator, which provides various new features and improvements.
  • The new @field_validator decorator does not have the each_item keyword argument; validators you want to apply to items within a generic container should be added by annotating the type argument. See validators in Annotated metadata for details. This looks like List[Annotated[int, Field(ge=0)]]
  • Even if you keep using the deprecated @validator decorator, you can no longer add the field or config arguments to the signature of validator functions. If you need access to these, you'll need to migrate to @field_validator — see the next section for more details.
  • If you use the always=True keyword argument to a validator function, note that standard validators for the annotated type will also be applied even to defaults, not just the custom validators. For example, despite the fact that the validator below will never error, the following code raises a ValidationError:

Note

To avoid this, you can use the validate_default argument in the Field function. When set to True, it mimics the behavior of always=True in Pydantic v1. However, the new way of using validate_default is encouraged as it provides more flexibility and control.

from pydantic import BaseModel, validator


class Model(BaseModel):
    x: str = 1

    @validator('x', always=True)
    @classmethod
    def validate_x(cls, v):
        return v


Model()

• @root_validator has been deprecated, and should be replaced with @model_validator, which also provides new features and improvements.
  • Under some circumstances (such as assignment when model_config['validate_assignment'] is True), the @model_validator decorator will receive an instance of the model, not a dict of values. You may need to be careful to handle this case.
  • Even if you keep using the deprecated @root_validator decorator, due to refactors in validation logic, you can no longer run with skip_on_failure=False (which is the default value of this keyword argument, so must be set explicitly to True).

Changes to @validator's allowed signatures

In Pydantic V1, functions wrapped by @validator could receive keyword arguments with metadata about what was being validated. Some of these arguments have been removed from @field_validator in Pydantic V2:

• config: Pydantic V2's config is now a dictionary instead of a class, which means this argument is no longer backwards compatible. If you need to access the configuration you should migrate to @field_validator and use info.config.
• field: this argument used to be a ModelField object, which was a quasi-internal class that no longer exists in Pydantic V2. Most of this information can still be accessed by using the field name from info.field_name to index into cls.model_fields

from pydantic import BaseModel, FieldValidationInfo, field_validator


class Model(BaseModel):
    x: int

    @field_validator('x')
    def val_x(cls, v: int, info: FieldValidationInfo) -> int:
        assert info.config is not None
        print(info.config.get('title'))
        #> Model
        print(cls.model_fields[info.field_name].is_required())
        #> True
        return v


Model(x=1)

TypeError is no longer converted to ValidationError in validators

Previously, when raising a TypeError within a validator function, that error would be wrapped into a ValidationError and, in some cases (such as with FastAPI), these errors might be displayed to end users. This led to a variety of undesirable behavior — for example, calling a function with the wrong signature might produce a user-facing ValidationError.

However, in Pydantic V2, when a TypeError is raised in a validator, it is no longer converted into a ValidationError:

import pytest

from pydantic import BaseModel, field_validator  # or validator


class Model(BaseModel):
    x: int

    @field_validator('x')
    def val_x(cls, v: int) -> int:
        return str.lower(v)  # raises a TypeError


with pytest.raises(TypeError):
    Model(x=1)

This applies to all validation decorators.

Validator behavior changes

Pydantic V2 includes some changes to type coercion. For example:

• int, float, and decimal values are no longer coerced to strings.
• iterable of pairs is no longer coerced to a dict.

See the Conversion table for details on Pydantic V2 type coercion defaults.

The allow_reuse keyword argument is no longer necessary

Previously, Pydantic tracked "reused" functions in decorators as this was a common source of mistakes. We did this by comparing the function's fully qualified name (module name + function name), which could result in false positives. The allow_reuse keyword argument could be used to disable this when it was intentional.

Our approach to detecting repeatedly defined functions has been overhauled to only error for redefinition within a single class, reducing false positives and bringing the behavior more in line with the errors that type checkers and linters would give for defining a method with the same name multiple times in a single class definition.

In nearly all cases, if you were using allow_reuse=True, you should be able to simply delete that keyword argument and have things keep working as expected.

@validate_arguments has been renamed to @validate_call

In Pydantic V2, the @validate_arguments decorator has been renamed to @validate_call.

In Pydantic V1, the decorated function had various attributes added, such as raw_function, and validate (which could be used to validate arguments without actually calling the decorated function). Due to limited use of these attributes, and performance-oriented changes in implementation, we have not preserved this functionality in @validate_call.

Input types are not preserved

In Pydantic V1 we made great efforts to preserve the types of all field inputs for generic collections when they were proper subtypes of the field annotations. For example, given the annotation Mapping[str, int] if you passed in a collection.Counter() you'd get a collection.Counter() as the value.

Supporting this behavior in V2 would have negative performance implications for the general case (we'd have to check types every time) and would add a lot of complexity to validation. Further, even in V1 this behavior was inconsistent and partially broken: it did not work for many types (str, UUID, etc.), and for generic collections it's impossible to re-build the original input correctly without a lot of special casing (consider ChainMap; rebuilding the input is necessary because we need to replace values after validation, e.g. if coercing strings to ints).

In Pydantic V2 we no longer attempt to preserve the input type in all cases; instead, we only promise that the output type will match the type annotations.

Going back to the Mapping example, we promise the output will be a valid Mapping, and in practice it will be a plain dict:

from typing import Mapping

from pydantic import TypeAdapter


class MyDict(dict):
    pass


ta = TypeAdapter(Mapping[str, int])
v = ta.validate_python(MyDict())
print(type(v))
#> <class 'dict'>

If you want the output type to be a specific type, consider annotating it as such or implementing a custom validator:

from typing import Any, Mapping, TypeVar

from typing_extensions import Annotated

from pydantic import (
    TypeAdapter,
    ValidationInfo,
    ValidatorFunctionWrapHandler,
    WrapValidator,
)


def restore_input_type(
    value: Any, handler: ValidatorFunctionWrapHandler, _info: ValidationInfo
) -> Any:
    return type(value)(handler(value))


T = TypeVar('T')
PreserveType = Annotated[T, WrapValidator(restore_input_type)]


ta = TypeAdapter(PreserveType[Mapping[str, int]])


class MyDict(dict):
    pass


v = ta.validate_python(MyDict())
assert type(v) is MyDict

While we don't promise to preserve input types everywhere, we do preserve them for subclasses of BaseModel, and for dataclasses:

import pydantic.dataclasses
from pydantic import BaseModel


class InnerModel(BaseModel):
    x: int


class OuterModel(BaseModel):
    inner: InnerModel


class SubInnerModel(InnerModel):
    y: int


m = OuterModel(inner=SubInnerModel(x=1, y=2))
print(m)
#> inner=SubInnerModel(x=1, y=2)


@pydantic.dataclasses.dataclass
class InnerDataclass:
    x: int


@pydantic.dataclasses.dataclass
class SubInnerDataclass(InnerDataclass):
    y: int


@pydantic.dataclasses.dataclass
class OuterDataclass:
    inner: InnerDataclass


d = OuterDataclass(inner=SubInnerDataclass(x=1, y=2))
print(d)
#> OuterDataclass(inner=SubInnerDataclass(x=1, y=2))

Changes to Handling of Standard Types

Dicts

Iterables of pairs (which include empty iterables) no longer pass validation for fields of type dict.

Unions

While union types will still attempt validation of each choice from left to right, they now preserve the type of the input whenever possible, even if the correct type is not the first choice for which the input would pass validation. As a demonstration, consider the following example:

from typing import Union

from pydantic import BaseModel


class Model(BaseModel):
    x: Union[int, str]


print(Model(x='1'))
#> x='1'

In Pydantic V1, the printed result would have been x=1, since the value would pass validation as an int. In Pydantic V2, we recognize that the value is an instance of one of the cases and short-circuit the standard union validation.

To revert to the non-short-circuiting left-to-right behavior of V1, annotate the union with Field(union_mode='left_to_right'). See Union Mode for more details.

Required, optional, and nullable fields

Pydantic V2 changes some of the logic for specifying whether a field annotated as Optional is required (i.e., has no default value) or not (i.e., has a default value of None or any other value of the corresponding type), and now more closely matches the behavior of dataclasses. Similarly, fields annotated as Any no longer have a default value of None.

The following table describes the behavior of field annotations in V2:

State	Field Definition
Required, cannot be None	f1: str
Not required, cannot be None, is 'abc' by default	f2: str = 'abc'
Required, can be None	f3: Optional[str]
Not required, can be None, is None by default	f4: Optional[str] = None
Not required, can be None, is 'abc' by default	f5: Optional[str] = 'abc'
Required, can be any type (including None)	f6: Any
Not required, can be any type (including None)	f7: Any = None

Note

A field annotated as typing.Optional[T] will be required, and will allow for a value of None. It does not mean that the field has a default value of None. (This is a breaking change from V1.)

Note

Any default value if provided makes a field not required.

Here is a code example demonstrating the above:

from typing import Optional

from pydantic import BaseModel, ValidationError


class Foo(BaseModel):
    f1: str  # required, cannot be None
    f2: Optional[str]  # required, can be None - same as str | None
    f3: Optional[str] = None  # not required, can be None
    f4: str = 'Foobar'  # not required, but cannot be None


try:
    Foo(f1=None, f2=None, f4='b')
except ValidationError as e:
    print(e)
    """
    1 validation error for Foo
    f1
      Input should be a valid string [type=string_type, input_value=None, input_type=NoneType]
    """

Patterns / regex on strings

Pydantic V1 used Python's regex library. Pydantic V2 uses the Rust regex crate. This crate is not just a "Rust version of regular expressions", it's a completely different approach to regular expressions. In particular, it promises linear time searching of strings in exchange for dropping a couple of features (namely look arounds and backreferences). We believe this is a tradeoff worth making, in particular because Pydantic is used to validate untrusted input where ensuring things don't accidentally run in exponential time depending on the untrusted input is important. On the flipside, for anyone not using these features complex regex validation should be orders of magnitude faster because it's done in Rust and in linear time. If you need those regex features you can create a custom validator that does the regex validation in Python:

import re
from dataclasses import dataclass
from typing import Any

from pydantic_core import CoreSchema, PydanticCustomError, core_schema
from typing_extensions import Annotated

from pydantic import (
    GetCoreSchemaHandler,
    GetJsonSchemaHandler,
    TypeAdapter,
    ValidationError,
)
from pydantic.json_schema import JsonSchemaValue


@dataclass
class Regex:
    pattern: str

    def __get_pydantic_core_schema__(
        self, source_type: Any, handler: GetCoreSchemaHandler
    ) -> CoreSchema:
        regex = re.compile(self.pattern)

        def match(v: str) -> str:
            if not regex.match(v):
                raise PydanticCustomError(
                    'string_pattern_mismatch',
                    "String should match pattern '{pattern}'",
                    {'pattern': self.pattern},
                )
            return v

        return core_schema.no_info_after_validator_function(
            match,
            handler(source_type),
        )

    def __get_pydantic_json_schema__(
        self, core_schema: CoreSchema, handler: GetJsonSchemaHandler
    ) -> JsonSchemaValue:
        json_schema = handler(core_schema)
        json_schema['pattern'] = self.pattern
        return json_schema


ta = TypeAdapter(Annotated[str, Regex('^(?!_)(?!.*__)[a-z_]{1,64}(?<!_)$')])

print(ta.json_schema())
#> {'pattern': '^(?!_)(?!.*__)[a-z_]{1,64}(?<!_)$', 'type': 'string'}

ta.validate_python('hello_world')

try:
    ta.validate_python('_invalid_')
except ValidationError as exc:
    print(exc)
    """
    1 validation error for function-after[match(), str]
      String should match pattern '^(?!_)(?!.*__)[a-z_]{1,64}(?<!_)$' [type=string_pattern_mismatch, input_value='_invalid_', input_type=str]
    """

Introduction of TypeAdapter

Pydantic V1 had weak support for validating or serializing non-BaseModel types.

To work with them, you had to either create a "root" model or use the utility functions in pydantic.tools (namely, parse_obj_as and schema_of).

In Pydantic V2 this is a lot easier: the TypeAdapter class lets you create an object with methods for validating, serializing, and producing JSON schemas for arbitrary types. This serves as a complete replacement for parse_obj_as and schema_of (which are now deprecated), and also covers some of the use cases of "root" models. (RootModel, discussed above, covers the others.)

from typing import List

from pydantic import TypeAdapter

adapter = TypeAdapter(List[int])
assert adapter.validate_python(['1', '2', '3']) == [1, 2, 3]
print(adapter.json_schema())
#> {'items': {'type': 'integer'}, 'type': 'array'}

Due to limitations of inferring generic types with common type checkers, to get proper typing in some scenarios, you may need to explicitly specify the generic parameter:

from pydantic import TypeAdapter

adapter: TypeAdapter[str | int] = TypeAdapter(str | int)
...

[TODO: Add link to TypeAdapter documentation. For now, you can find example usage in tests/test_type_adapter.py.]

Defining custom types

We have completely overhauled the way custom types are defined in pydantic.

We have exposed hooks for generating both pydantic-core and JSON schemas, allowing you to get all the performance benefits of Pydantic V2 even when using your own custom types.

We have also introduced ways to use typing.Annotated to add custom validation to your own types.

The main changes are:

• __get_validators__ should be replaced with __get_pydantic_core_schema__. See Custom Data Types for more information.
• __modify_schema__ becomes __get_pydantic_json_schema__. See JSON Schema Customization for more information.

Additionally, you can use typing.Annotated to modify or provide the __get_pydantic_core_schema__ and __get_pydantic_json_schema__ functions of a type by annotating it, rather than modifying the type itself. This provides a powerful and flexible mechanism for integrating third-party types with Pydantic, and in some cases may help you remove hacks from Pydantic V1 introduced to work around the limitations for custom types.

See Custom Data Types for more information.

Changes to JSON schema generation

We received many requests over the years to make changes to the JSON schemas that pydantic generates.

In Pydantic V2, we have tried to address many of the common requests:

• The JSON schema for Optional fields now indicates that the value null is allowed.
• The Decimal type is now exposed in JSON schema (and serialized) as a string.
• The JSON schema no longer preserves namedtuples as namedtuples.
• The JSON schema we generate by default now targets draft 2020-12 (with some OpenAPI extensions).
• When they differ, you can now specify if you want the JSON schema representing the inputs to validation, or the outputs from serialization.

However, there have been many reasonable requests over the years for changes which we have not chosen to implement.

In Pydantic V1, even if you were willing to implement changes yourself, it was very difficult because the JSON schema generation process involved various recursive function calls; to override one, you'd have to copy and modify the whole implementation.

In Pydantic V2, one of our design goals was to make it easier to customize JSON schema generation. To this end, we have introduced the class GenerateJsonSchema, which implements the translation of a type's pydantic-core schema into a JSON schema. By design, this class breaks the JSON schema generation process into smaller methods that can be easily overridden in subclasses to modify the "global" approach to generating JSON schema.

The various methods that can be used to produce JSON schema (such as BaseModel.model_json_schema or TypeAdapter.json_schema) accept a keyword argument schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, and you can pass your custom subclass to these methods in order to use your own approach to generating JSON schema.

Hopefully this means that if you disagree with any of the choices we've made, or if you are reliant on behaviors in Pydantic V1 that have changed in Pydantic V2, you can use a custom schema_generator, modifying the GenerateJsonSchema class as necessary for your application.

BaseSettings has moved to pydantic-settings

BaseSettings, the base object for Pydantic settings management, has been moved to a separate package, pydantic-settings.

Also, the parse_env_var classmethod has been removed. So, you need to customise settings sources to have your own parsing function.

Color and Payment Card Numbers moved to pydantic-extra-types

The following special-use types have been moved to the Pydantic Extra Types package, which may be installed separately if needed.

• Color Types
• Payment Card Numbers

Url and Dsn types in pydantic.networks no longer inherit from str

In Pydantic V1 the AnyUrl type inherited from str, and all the other Url and Dsn types inherited from these. In Pydantic V2 these types are built on two new Url and MultiHostUrl classes using Annotated.

Inheriting from str had upsides and downsides, and for V2 we decided it would be better to remove this. To use these types in APIs which expect str you'll now need to convert them (with str(url)).

Constrained types

The Constrained* classes were removed, and you should replace them by Annotated[<type>, Field(...)], for example:

from pydantic import BaseModel, ConstrainedInt


class MyInt(ConstrainedInt):
    ge = 0


class Model(BaseModel):
    x: MyInt

...becomes:

from typing_extensions import Annotated

from pydantic import BaseModel, Field

MyInt = Annotated[int, Field(ge=0)]


class Model(BaseModel):
    x: MyInt

Read more about it in the Composing types via Annotated docs.

For ConstrainedStr you can use StringConstraints instead.

Moved in Pydantic V2

Pydantic V1	Pydantic V2
pydantic.BaseSettings	pydantic-settings.BaseSettings
pydantic.color	pydantic_extra_types.color
pydantic.types.PaymentCardBrand	pydantic_extra_types.PaymentCardBrand
pydantic.types.PaymentCardNumber	pydantic_extra_types.PaymentCardNumber
pydantic.utils.version_info	pydantic.version.version_info
pydantic.error_wrappers.ValidationError	pydantic.ValidationError
pydantic.utils.to_camel	pydantic.alias_generators.to_pascal
pydantic.utils.to_lower_camel	pydantic.alias_generators.to_camel
pydantic.PyObject	pydantic.ImportString

Deprecated and moved in Pydantic V2

Pydantic V1	Pydantic V2
pydantic.tools.schema_of	pydantic.deprecated.tools.schema_of
pydantic.tools.parse_obj_as	pydantic.deprecated.tools.parse_obj_as
pydantic.tools.schema_json_of	pydantic.deprecated.tools.schema_json_of
pydantic.json.pydantic_encoder	pydantic.deprecated.json.pydantic_encoder
pydantic.validate_arguments	pydantic.deprecated.decorator.validate_arguments
pydantic.json.custom_pydantic_encoder	pydantic.deprecated.json.custom_pydantic_encoder
pydantic.json.timedelta_isoformat	pydantic.deprecated.json.timedelta_isoformat
pydantic.decorator.validate_arguments	pydantic.deprecated.decorator.validate_arguments
pydantic.class_validators.validator	pydantic.deprecated.class_validators.validator
pydantic.class_validators.root_validator	pydantic.deprecated.class_validators.root_validator
pydantic.utils.deep_update	pydantic.v1.utils.deep_update
pydantic.utils.GetterDict	pydantic.v1.utils.GetterDict
pydantic.utils.lenient_issubclass	pydantic.v1.utils.lenient_issubclass
pydantic.utils.lenient_isinstance	pydantic.v1.utils.lenient_isinstance
pydantic.utils.is_valid_field	pydantic.v1.utils.is_valid_field
pydantic.utils.update_not_none	pydantic.v1.utils.update_not_none
pydantic.utils.import_string	pydantic.v1.utils.import_string
pydantic.utils.Representation	pydantic.v1.utils.Representation
pydantic.utils.ROOT_KEY	pydantic.v1.utils.ROOT_KEY
pydantic.utils.smart_deepcopy	pydantic.v1.utils.smart_deepcopy
pydantic.utils.sequence_like	pydantic.v1.utils.sequence_like

Removed in Pydantic V2

• pydantic.ConstrainedBytes
• pydantic.ConstrainedDate
• pydantic.ConstrainedDecimal
• pydantic.ConstrainedFloat
• pydantic.ConstrainedFrozenSet
• pydantic.ConstrainedInt
• pydantic.ConstrainedList
• pydantic.ConstrainedSet
• pydantic.ConstrainedStr
• pydantic.JsonWrapper
• pydantic.NoneBytes
  • This was an alias to None | bytes.
• pydantic.NoneStr
  • This was an alias to None | str.
• pydantic.NoneStrBytes
  • This was an alias to None | str | bytes.
• pydantic.Protocol
• pydantic.Required
• pydantic.StrBytes
  • This was an alias to str | bytes.
• pydantic.compiled
• pydantic.config.get_config
• pydantic.config.inherit_config
• pydantic.config.prepare_config
• pydantic.create_model_from_namedtuple
• pydantic.create_model_from_typeddict
• pydantic.dataclasses.create_pydantic_model_from_dataclass
• pydantic.dataclasses.make_dataclass_validator
• pydantic.dataclasses.set_validation
• pydantic.datetime_parse.parse_date
• pydantic.datetime_parse.parse_time
• pydantic.datetime_parse.parse_datetime
• pydantic.datetime_parse.parse_duration
• pydantic.error_wrappers.ErrorWrapper
• pydantic.errors.AnyStrMaxLengthError
• pydantic.errors.AnyStrMinLengthError
• pydantic.errors.ArbitraryTypeError
• pydantic.errors.BoolError
• pydantic.errors.BytesError
• pydantic.errors.CallableError
• pydantic.errors.ClassError
• pydantic.errors.ColorError
• pydantic.errors.ConfigError
• pydantic.errors.DataclassTypeError
• pydantic.errors.DateError
• pydantic.errors.DateNotInTheFutureError
• pydantic.errors.DateNotInThePastError
• pydantic.errors.DateTimeError
• pydantic.errors.DecimalError
• pydantic.errors.DecimalIsNotFiniteError
• pydantic.errors.DecimalMaxDigitsError
• pydantic.errors.DecimalMaxPlacesError
• pydantic.errors.DecimalWholeDigitsError
• pydantic.errors.DictError
• pydantic.errors.DurationError
• pydantic.errors.EmailError
• pydantic.errors.EnumError
• pydantic.errors.EnumMemberError
• pydantic.errors.ExtraError
• pydantic.errors.FloatError
• pydantic.errors.FrozenSetError
• pydantic.errors.FrozenSetMaxLengthError
• pydantic.errors.FrozenSetMinLengthError
• pydantic.errors.HashableError
• pydantic.errors.IPv4AddressError
• pydantic.errors.IPv4InterfaceError
• pydantic.errors.IPv4NetworkError
• pydantic.errors.IPv6AddressError
• pydantic.errors.IPv6InterfaceError
• pydantic.errors.IPv6NetworkError
• pydantic.errors.IPvAnyAddressError
• pydantic.errors.IPvAnyInterfaceError
• pydantic.errors.IPvAnyNetworkError
• pydantic.errors.IntEnumError
• pydantic.errors.IntegerError
• pydantic.errors.InvalidByteSize
• pydantic.errors.InvalidByteSizeUnit
• pydantic.errors.InvalidDiscriminator
• pydantic.errors.InvalidLengthForBrand
• pydantic.errors.JsonError
• pydantic.errors.JsonTypeError
• pydantic.errors.ListError
• pydantic.errors.ListMaxLengthError
• pydantic.errors.ListMinLengthError
• pydantic.errors.ListUniqueItemsError
• pydantic.errors.LuhnValidationError
• pydantic.errors.MissingDiscriminator
• pydantic.errors.MissingError
• pydantic.errors.NoneIsAllowedError
• pydantic.errors.NoneIsNotAllowedError
• pydantic.errors.NotDigitError
• pydantic.errors.NotNoneError
• pydantic.errors.NumberNotGeError
• pydantic.errors.NumberNotGtError
• pydantic.errors.NumberNotLeError
• pydantic.errors.NumberNotLtError
• pydantic.errors.NumberNotMultipleError
• pydantic.errors.PathError
• pydantic.errors.PathNotADirectoryError
• pydantic.errors.PathNotAFileError
• pydantic.errors.PathNotExistsError
• pydantic.errors.PatternError
• pydantic.errors.PyObjectError
• pydantic.errors.PydanticTypeError
• pydantic.errors.PydanticValueError
• pydantic.errors.SequenceError
• pydantic.errors.SetError
• pydantic.errors.SetMaxLengthError
• pydantic.errors.SetMinLengthError
• pydantic.errors.StrError
• pydantic.errors.StrRegexError
• pydantic.errors.StrictBoolError
• pydantic.errors.SubclassError
• pydantic.errors.TimeError
• pydantic.errors.TupleError
• pydantic.errors.TupleLengthError
• pydantic.errors.UUIDError
• pydantic.errors.UUIDVersionError
• pydantic.errors.UrlError
• pydantic.errors.UrlExtraError
• pydantic.errors.UrlHostError
• pydantic.errors.UrlHostTldError
• pydantic.errors.UrlPortError
• pydantic.errors.UrlSchemeError
• pydantic.errors.UrlSchemePermittedError
• pydantic.errors.UrlUserInfoError
• pydantic.errors.WrongConstantError
• pydantic.main.validate_model
• pydantic.networks.stricturl
• pydantic.parse_file_as
• pydantic.parse_raw_as
• pydantic.stricturl
• pydantic.tools.parse_file_as
• pydantic.tools.parse_raw_as
• pydantic.types.JsonWrapper
• pydantic.types.NoneBytes
• pydantic.types.NoneStr
• pydantic.types.NoneStrBytes
• pydantic.types.PyObject
• pydantic.types.StrBytes
• pydantic.typing.evaluate_forwardref
• pydantic.typing.AbstractSetIntStr
• pydantic.typing.AnyCallable
• pydantic.typing.AnyClassMethod
• pydantic.typing.CallableGenerator
• pydantic.typing.DictAny
• pydantic.typing.DictIntStrAny
• pydantic.typing.DictStrAny
• pydantic.typing.IntStr
• pydantic.typing.ListStr
• pydantic.typing.MappingIntStrAny
• pydantic.typing.NoArgAnyCallable
• pydantic.typing.NoneType
• pydantic.typing.ReprArgs
• pydantic.typing.SetStr
• pydantic.typing.StrPath
• pydantic.typing.TupleGenerator
• pydantic.typing.WithArgsTypes
• pydantic.typing.all_literal_values
• pydantic.typing.display_as_type
• pydantic.typing.get_all_type_hints
• pydantic.typing.get_args
• pydantic.typing.get_origin
• pydantic.typing.get_sub_types
• pydantic.typing.is_callable_type
• pydantic.typing.is_classvar
• pydantic.typing.is_finalvar
• pydantic.typing.is_literal_type
• pydantic.typing.is_namedtuple
• pydantic.typing.is_new_type
• pydantic.typing.is_none_type
• pydantic.typing.is_typeddict
• pydantic.typing.is_typeddict_special
• pydantic.typing.is_union
• pydantic.typing.new_type_supertype
• pydantic.typing.resolve_annotations
• pydantic.typing.typing_base
• pydantic.typing.update_field_forward_refs
• pydantic.typing.update_model_forward_refs
• pydantic.utils.ClassAttribute
• pydantic.utils.DUNDER_ATTRIBUTES
• pydantic.utils.PyObjectStr
• pydantic.utils.ValueItems
• pydantic.utils.almost_equal_floats
• pydantic.utils.get_discriminator_alias_and_values
• pydantic.utils.get_model
• pydantic.utils.get_unique_discriminator_alias
• pydantic.utils.in_ipython
• pydantic.utils.is_valid_identifier
• pydantic.utils.path_type
• pydantic.utils.validate_field_name
• pydantic.validate_model