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Models

API Documentation

pydantic.main.BaseModel

One of the primary ways of defining schema in Pydantic is via models. Models are simply classes which inherit from BaseModel and define fields as annotated attributes.

You can think of models as similar to structs in languages like C, or as the requirements of a single endpoint in an API.

Models share many similarities with Python's dataclasses, but have been designed with some subtle-yet-important differences that streamline certain workflows related to validation, serialization, and JSON schema generation. You can find more discussion of this in the Dataclasses section of the docs.

Untrusted data can be passed to a model and, after parsing and validation, Pydantic guarantees that the fields of the resultant model instance will conform to the field types defined on the model.

Validation — a deliberate misnomer

TL;DR

We use the term "validation" to refer to the process of instantiating a model (or other type) that adheres to specified types and constraints. This task, which Pydantic is well known for, is most widely recognized as "validation" in colloquial terms, even though in other contexts the term "validation" may be more restrictive.

The long version

The potential confusion around the term "validation" arises from the fact that, strictly speaking, Pydantic's primary focus doesn't align precisely with the dictionary definition of "validation":

validation

noun the action of checking or proving the validity or accuracy of something.

In Pydantic, the term "validation" refers to the process of instantiating a model (or other type) that adheres to specified types and constraints. Pydantic guarantees the types and constraints of the output, not the input data. This distinction becomes apparent when considering that Pydantic's ValidationError is raised when data cannot be successfully parsed into a model instance.

While this distinction may initially seem subtle, it holds practical significance. In some cases, "validation" goes beyond just model creation, and can include the copying and coercion of data. This can involve copying arguments passed to the constructor in order to perform coercion to a new type without mutating the original input data. For a more in-depth understanding of the implications for your usage, refer to the Data Conversion and Attribute Copies sections below.

In essence, Pydantic's primary goal is to assure that the resulting structure post-processing (termed "validation") precisely conforms to the applied type hints. Given the widespread adoption of "validation" as the colloquial term for this process, we will consistently use it in our documentation.

While the terms "parse" and "validation" were previously used interchangeably, moving forward, we aim to exclusively employ "validate", with "parse" reserved specifically for discussions related to JSON parsing.

Basic model usage

Note

Pydantic relies heavily on the existing Python typing constructs to define models. If you are not familiar with those, the following resources can be useful:

from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str = 'Jane Doe'

In this example, User is a model with two fields:

  • id, which is an integer and is required
  • name, which is a string and is not required (it has a default value).

Fields can be customized in a number of ways using the Field() function. See the documentation on fields for more information.

The model can then be instantiated:

user = User(id='123')

user is an instance of User. Initialization of the object will perform all parsing and validation. If no ValidationError exception is raised, you know the resulting model instance is valid.

Fields of a model can be accessed as normal attributes of the user object:

assert user.name == 'Jane Doe'  # (1)!
assert user.id == 123  # (2)!
assert isinstance(user.id, int)
  1. name wasn't set when user was initialized, so the default value was used. The model_fields_set attribute can be inspected to check the field names explicitly set during instantiation.
  2. Note that the string '123' was coerced to an integer and its value is 123. More details on Pydantic's coercion logic can be found in the data conversion section.

The model instance can be serialized using the model_dump() method:

assert user.model_dump() == {'id': 123, 'name': 'Jane Doe'}

Calling dict on the instance will also provide a dictionary, but nested fields will not be recursively converted into dictionaries. model_dump() also provides numerous arguments to customize the serialization result.

By default, models are mutable and field values can be changed through attribute assignment:

user.id = 321
assert user.id == 321

Warning

When defining your models, watch out for naming collisions between your field name and its type annotation.

For example, the following will not behave as expected and would yield a validation error:

from typing import Optional

from pydantic import BaseModel


class Boo(BaseModel):
    int: Optional[int] = None


m = Boo(int=123)  # Will fail to validate.

Because of how Python evaluates annotated assignment statements, the statement is equivalent to int: None = None, thus leading to a validation error.

Model methods and properties

The example above only shows the tip of the iceberg of what models can do. Models possess the following methods and attributes:

Note

See the API documentation of BaseModel for the class definition including a full list of methods and attributes.

Tip

See Changes to pydantic.BaseModel in the Migration Guide for details on changes from Pydantic V1.

Data conversion

Pydantic may cast input data to force it to conform to model field types, and in some cases this may result in a loss of information. For example:

from pydantic import BaseModel


class Model(BaseModel):
    a: int
    b: float
    c: str


print(Model(a=3.000, b='2.72', c=b'binary data').model_dump())
#> {'a': 3, 'b': 2.72, 'c': 'binary data'}

This is a deliberate decision of Pydantic, and is frequently the most useful approach. See here for a longer discussion on the subject.

Nevertheless, Pydantic provides a strict mode, where no data conversion is performed. Values must be of the same type than the declared field type.

This is also the case for collections. In most cases, you shouldn't make use of abstract container classes and just use a concrete type, such as list:

from typing import List

from pydantic import BaseModel


class Model(BaseModel):
    items: List[int]  # (1)!


print(Model(items=(1, 2, 3)))
#> items=[1, 2, 3]
  1. In this case, you might be tempted to use the abstract Sequence type to allow both lists and tuples. But Pydantic takes care of converting the tuple input to a list, so in most cases this isn't necessary.
from pydantic import BaseModel


class Model(BaseModel):
    items: list[int]  # (1)!


print(Model(items=(1, 2, 3)))
#> items=[1, 2, 3]
  1. In this case, you might be tempted to use the abstract Sequence type to allow both lists and tuples. But Pydantic takes care of converting the tuple input to a list, so in most cases this isn't necessary.

Besides, using these abstract types can also lead to poor validation performance, and in general using concrete container types will avoid unnecessary checks.

Nested models

More complex hierarchical data structures can be defined using models themselves as types in annotations.

from typing import List, Optional

from pydantic import BaseModel


class Foo(BaseModel):
    count: int
    size: Optional[float] = None


class Bar(BaseModel):
    apple: str = 'x'
    banana: str = 'y'


class Spam(BaseModel):
    foo: Foo
    bars: List[Bar]


m = Spam(foo={'count': 4}, bars=[{'apple': 'x1'}, {'apple': 'x2'}])
print(m)
"""
foo=Foo(count=4, size=None) bars=[Bar(apple='x1', banana='y'), Bar(apple='x2', banana='y')]
"""
print(m.model_dump())
"""
{
    'foo': {'count': 4, 'size': None},
    'bars': [{'apple': 'x1', 'banana': 'y'}, {'apple': 'x2', 'banana': 'y'}],
}
"""

from typing import Optional

from pydantic import BaseModel


class Foo(BaseModel):
    count: int
    size: Optional[float] = None


class Bar(BaseModel):
    apple: str = 'x'
    banana: str = 'y'


class Spam(BaseModel):
    foo: Foo
    bars: list[Bar]


m = Spam(foo={'count': 4}, bars=[{'apple': 'x1'}, {'apple': 'x2'}])
print(m)
"""
foo=Foo(count=4, size=None) bars=[Bar(apple='x1', banana='y'), Bar(apple='x2', banana='y')]
"""
print(m.model_dump())
"""
{
    'foo': {'count': 4, 'size': None},
    'bars': [{'apple': 'x1', 'banana': 'y'}, {'apple': 'x2', 'banana': 'y'}],
}
"""

from pydantic import BaseModel


class Foo(BaseModel):
    count: int
    size: float | None = None


class Bar(BaseModel):
    apple: str = 'x'
    banana: str = 'y'


class Spam(BaseModel):
    foo: Foo
    bars: list[Bar]


m = Spam(foo={'count': 4}, bars=[{'apple': 'x1'}, {'apple': 'x2'}])
print(m)
"""
foo=Foo(count=4, size=None) bars=[Bar(apple='x1', banana='y'), Bar(apple='x2', banana='y')]
"""
print(m.model_dump())
"""
{
    'foo': {'count': 4, 'size': None},
    'bars': [{'apple': 'x1', 'banana': 'y'}, {'apple': 'x2', 'banana': 'y'}],
}
"""

Self-referencing models are supported. For more details, see the documentation related to forward annotations.

Rebuilding model schema

When you define a model class in your code, Pydantic will analyze the body of the class to collect a variety of information required to perform validation and serialization, gathered in a core schema. Notably, the model's type annotations are evaluated to understand the valid types for each field (more information can be found in the Architecture documentation). However, it might be the case that annotations refer to symbols not defined when the model class is being created. To circumvent this issue, the model_rebuild() method can be used:

from pydantic import BaseModel, PydanticUserError


class Foo(BaseModel):
    x: 'Bar'  # (1)!


try:
    Foo.model_json_schema()
except PydanticUserError as e:
    print(e)
    """
    `Foo` is not fully defined; you should define `Bar`, then call `Foo.model_rebuild()`.

    For further information visit https://errors.pydantic.dev/2/u/class-not-fully-defined
    """


class Bar(BaseModel):
    pass


Foo.model_rebuild()
print(Foo.model_json_schema())
"""
{
    '$defs': {'Bar': {'properties': {}, 'title': 'Bar', 'type': 'object'}},
    'properties': {'x': {'$ref': '#/$defs/Bar'}},
    'required': ['x'],
    'title': 'Foo',
    'type': 'object',
}
"""
  1. Bar is not yet defined when the Foo class is being created. For this reason, a forward annotation is being used.

Pydantic tries to determine when this is necessary automatically and error if it wasn't done, but you may want to call model_rebuild() proactively when dealing with recursive models or generics.

In V2, model_rebuild() replaced update_forward_refs() from V1. There are some slight differences with the new behavior. The biggest change is that when calling model_rebuild() on the outermost model, it builds a core schema used for validation of the whole model (nested models and all), so all types at all levels need to be ready before model_rebuild() is called.

Arbitrary class instances

(Formerly known as "ORM Mode"/from_orm).

Pydantic models can also be created from arbitrary class instances by reading the instance attributes corresponding to the model field names. One common application of this functionality is integration with object-relational mappings (ORMs).

To do this, set the from_attributes config value to True (see the documentation on Configuration for more details).

The example here uses SQLAlchemy, but the same approach should work for any ORM.

from typing import List

from sqlalchemy import ARRAY, String
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
from typing_extensions import Annotated

from pydantic import BaseModel, ConfigDict, StringConstraints


class Base(DeclarativeBase):
    pass


class CompanyOrm(Base):
    __tablename__ = 'companies'

    id: Mapped[int] = mapped_column(primary_key=True, nullable=False)
    public_key: Mapped[str] = mapped_column(
        String(20), index=True, nullable=False, unique=True
    )
    domains: Mapped[List[str]] = mapped_column(ARRAY(String(255)))


class CompanyModel(BaseModel):
    model_config = ConfigDict(from_attributes=True)

    id: int
    public_key: Annotated[str, StringConstraints(max_length=20)]
    domains: List[Annotated[str, StringConstraints(max_length=255)]]


co_orm = CompanyOrm(
    id=123,
    public_key='foobar',
    domains=['example.com', 'foobar.com'],
)
print(co_orm)
#> <__main__.CompanyOrm object at 0x0123456789ab>
co_model = CompanyModel.model_validate(co_orm)
print(co_model)
#> id=123 public_key='foobar' domains=['example.com', 'foobar.com']

from sqlalchemy import ARRAY, String
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
from typing import Annotated

from pydantic import BaseModel, ConfigDict, StringConstraints


class Base(DeclarativeBase):
    pass


class CompanyOrm(Base):
    __tablename__ = 'companies'

    id: Mapped[int] = mapped_column(primary_key=True, nullable=False)
    public_key: Mapped[str] = mapped_column(
        String(20), index=True, nullable=False, unique=True
    )
    domains: Mapped[list[str]] = mapped_column(ARRAY(String(255)))


class CompanyModel(BaseModel):
    model_config = ConfigDict(from_attributes=True)

    id: int
    public_key: Annotated[str, StringConstraints(max_length=20)]
    domains: list[Annotated[str, StringConstraints(max_length=255)]]


co_orm = CompanyOrm(
    id=123,
    public_key='foobar',
    domains=['example.com', 'foobar.com'],
)
print(co_orm)
#> <__main__.CompanyOrm object at 0x0123456789ab>
co_model = CompanyModel.model_validate(co_orm)
print(co_model)
#> id=123 public_key='foobar' domains=['example.com', 'foobar.com']

Nested attributes

When using attributes to parse models, model instances will be created from both top-level attributes and deeper-nested attributes as appropriate.

Here is an example demonstrating the principle:

from typing import List

from pydantic import BaseModel, ConfigDict


class PetCls:
    def __init__(self, *, name: str, species: str):
        self.name = name
        self.species = species


class PersonCls:
    def __init__(self, *, name: str, age: float = None, pets: List[PetCls]):
        self.name = name
        self.age = age
        self.pets = pets


class Pet(BaseModel):
    model_config = ConfigDict(from_attributes=True)

    name: str
    species: str


class Person(BaseModel):
    model_config = ConfigDict(from_attributes=True)

    name: str
    age: float = None
    pets: List[Pet]


bones = PetCls(name='Bones', species='dog')
orion = PetCls(name='Orion', species='cat')
anna = PersonCls(name='Anna', age=20, pets=[bones, orion])
anna_model = Person.model_validate(anna)
print(anna_model)
"""
name='Anna' age=20.0 pets=[Pet(name='Bones', species='dog'), Pet(name='Orion', species='cat')]
"""

from pydantic import BaseModel, ConfigDict


class PetCls:
    def __init__(self, *, name: str, species: str):
        self.name = name
        self.species = species


class PersonCls:
    def __init__(self, *, name: str, age: float = None, pets: list[PetCls]):
        self.name = name
        self.age = age
        self.pets = pets


class Pet(BaseModel):
    model_config = ConfigDict(from_attributes=True)

    name: str
    species: str


class Person(BaseModel):
    model_config = ConfigDict(from_attributes=True)

    name: str
    age: float = None
    pets: list[Pet]


bones = PetCls(name='Bones', species='dog')
orion = PetCls(name='Orion', species='cat')
anna = PersonCls(name='Anna', age=20, pets=[bones, orion])
anna_model = Person.model_validate(anna)
print(anna_model)
"""
name='Anna' age=20.0 pets=[Pet(name='Bones', species='dog'), Pet(name='Orion', species='cat')]
"""

Error handling

Pydantic will raise a ValidationError exception whenever it finds an error in the data it's validating.

A single exception will be raised regardless of the number of errors found, and that validation error will contain information about all of the errors and how they happened.

See Error Handling for details on standard and custom errors.

As a demonstration:

from typing import List

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    list_of_ints: List[int]
    a_float: float


data = dict(
    list_of_ints=['1', 2, 'bad'],
    a_float='not a float',
)

try:
    Model(**data)
except ValidationError as e:
    print(e)
    """
    2 validation errors for Model
    list_of_ints.2
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='bad', input_type=str]
    a_float
      Input should be a valid number, unable to parse string as a number [type=float_parsing, input_value='not a float', input_type=str]
    """

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    list_of_ints: list[int]
    a_float: float


data = dict(
    list_of_ints=['1', 2, 'bad'],
    a_float='not a float',
)

try:
    Model(**data)
except ValidationError as e:
    print(e)
    """
    2 validation errors for Model
    list_of_ints.2
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='bad', input_type=str]
    a_float
      Input should be a valid number, unable to parse string as a number [type=float_parsing, input_value='not a float', input_type=str]
    """

Validating data

Pydantic provides three methods on models classes for parsing data:

  • model_validate(): this is very similar to the __init__ method of the model, except it takes a dictionary or an object rather than keyword arguments. If the object passed cannot be validated, or if it's not a dictionary or instance of the model in question, a ValidationError will be raised.
  • model_validate_json(): this validates the provided data as a JSON string or bytes object. If your incoming data is a JSON payload, this is generally considered faster (instead of manually parsing the data as a dictionary). Learn more about JSON parsing in the JSON section of the docs.
  • model_validate_strings(): this takes a dictionary (can be nested) with string keys and values and validates the data in JSON mode so that said strings can be coerced into the correct types.
from datetime import datetime
from typing import Optional

from pydantic import BaseModel, ValidationError


class User(BaseModel):
    id: int
    name: str = 'John Doe'
    signup_ts: Optional[datetime] = None


m = User.model_validate({'id': 123, 'name': 'James'})
print(m)
#> id=123 name='James' signup_ts=None

try:
    User.model_validate(['not', 'a', 'dict'])
except ValidationError as e:
    print(e)
    """
    1 validation error for User
      Input should be a valid dictionary or instance of User [type=model_type, input_value=['not', 'a', 'dict'], input_type=list]
    """

m = User.model_validate_json('{"id": 123, "name": "James"}')
print(m)
#> id=123 name='James' signup_ts=None

try:
    m = User.model_validate_json('{"id": 123, "name": 123}')
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]
    """

try:
    m = User.model_validate_json('invalid JSON')
except ValidationError as e:
    print(e)
    """
    1 validation error for User
      Invalid JSON: expected value at line 1 column 1 [type=json_invalid, input_value='invalid JSON', input_type=str]
    """

m = User.model_validate_strings({'id': '123', 'name': 'James'})
print(m)
#> id=123 name='James' signup_ts=None

m = User.model_validate_strings(
    {'id': '123', 'name': 'James', 'signup_ts': '2024-04-01T12:00:00'}
)
print(m)
#> id=123 name='James' signup_ts=datetime.datetime(2024, 4, 1, 12, 0)

try:
    m = User.model_validate_strings(
        {'id': '123', 'name': 'James', 'signup_ts': '2024-04-01'}, strict=True
    )
except ValidationError as e:
    print(e)
    """
    1 validation error for User
    signup_ts
      Input should be a valid datetime, invalid datetime separator, expected `T`, `t`, `_` or space [type=datetime_parsing, input_value='2024-04-01', input_type=str]
    """

from datetime import datetime

from pydantic import BaseModel, ValidationError


class User(BaseModel):
    id: int
    name: str = 'John Doe'
    signup_ts: datetime | None = None


m = User.model_validate({'id': 123, 'name': 'James'})
print(m)
#> id=123 name='James' signup_ts=None

try:
    User.model_validate(['not', 'a', 'dict'])
except ValidationError as e:
    print(e)
    """
    1 validation error for User
      Input should be a valid dictionary or instance of User [type=model_type, input_value=['not', 'a', 'dict'], input_type=list]
    """

m = User.model_validate_json('{"id": 123, "name": "James"}')
print(m)
#> id=123 name='James' signup_ts=None

try:
    m = User.model_validate_json('{"id": 123, "name": 123}')
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]
    """

try:
    m = User.model_validate_json('invalid JSON')
except ValidationError as e:
    print(e)
    """
    1 validation error for User
      Invalid JSON: expected value at line 1 column 1 [type=json_invalid, input_value='invalid JSON', input_type=str]
    """

m = User.model_validate_strings({'id': '123', 'name': 'James'})
print(m)
#> id=123 name='James' signup_ts=None

m = User.model_validate_strings(
    {'id': '123', 'name': 'James', 'signup_ts': '2024-04-01T12:00:00'}
)
print(m)
#> id=123 name='James' signup_ts=datetime.datetime(2024, 4, 1, 12, 0)

try:
    m = User.model_validate_strings(
        {'id': '123', 'name': 'James', 'signup_ts': '2024-04-01'}, strict=True
    )
except ValidationError as e:
    print(e)
    """
    1 validation error for User
    signup_ts
      Input should be a valid datetime, invalid datetime separator, expected `T`, `t`, `_` or space [type=datetime_parsing, input_value='2024-04-01', input_type=str]
    """

If you want to validate serialized data in a format other than JSON, you should load the data into a dictionary yourself and then pass it to model_validate.

Note

Depending on the types and model configs involved, model_validate and model_validate_json may have different validation behavior. If you have data coming from a non-JSON source, but want the same validation behavior and errors you'd get from model_validate_json, our recommendation for now is to use either use model_validate_json(json.dumps(data)), or use model_validate_strings if the data takes the form of a (potentially nested) dictionary with string keys and values.

Note

If you're passing in an instance of a model to model_validate, you will want to consider setting revalidate_instances in the model's config. If you don't set this value, then validation will be skipped on model instances. See the below example:

from pydantic import BaseModel


class Model(BaseModel):
    a: int


m = Model(a=0)
# note: setting `validate_assignment` to `True` in the config can prevent this kind of misbehavior.
m.a = 'not an int'

# doesn't raise a validation error even though m is invalid
m2 = Model.model_validate(m)

from pydantic import BaseModel, ConfigDict, ValidationError


class Model(BaseModel):
    a: int

    model_config = ConfigDict(revalidate_instances='always')


m = Model(a=0)
# note: setting `validate_assignment` to `True` in the config can prevent this kind of misbehavior.
m.a = 'not an int'

try:
    m2 = Model.model_validate(m)
except ValidationError as e:
    print(e)
    """
    1 validation error for Model
    a
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='not an int', input_type=str]
    """

Creating models without validation

Pydantic also provides the model_construct() method, which allows models to be created without validation. This can be useful in at least a few cases:

  • when working with complex data that is already known to be valid (for performance reasons)
  • when one or more of the validator functions are non-idempotent
  • when one or more of the validator functions have side effects that you don't want to be triggered.

Warning

model_construct() does not do any validation, meaning it can create models which are invalid. You should only ever use the model_construct() method with data which has already been validated, or that you definitely trust.

Note

In Pydantic V2, the performance gap between validation (either with direct instantiation or the model_validate* methods) and model_construct() has been narrowed considerably. For simple models, going with validation may even be faster. If you are using model_construct() for performance reasons, you may want to profile your use case before assuming it is actually faster.

Note that for root models, the root value can be passed to model_construct() positionally, instead of using a keyword argument.

Here are some additional notes on the behavior of model_construct():

  • When we say "no validation is performed" — this includes converting dictionaries to model instances. So if you have a field referring to a model type, you will need to convert the inner dictionary to a model yourself.
  • If you do not pass keyword arguments for fields with defaults, the default values will still be used.
  • For models with private attributes, the __pydantic_private__ dictionary will be populated the same as it would be when creating the model with validation.
  • No __init__ method from the model or any of its parent classes will be called, even when a custom __init__ method is defined.

On extra fields behavior with model_construct()

  • For models with extra set to 'allow', data not corresponding to fields will be correctly stored in the __pydantic_extra__ dictionary and saved to the model's __dict__ attribute.
  • For models with extra set to 'ignore', data not corresponding to fields will be ignored — that is, not stored in __pydantic_extra__ or __dict__ on the instance.
  • Unlike when instiating the model with validation, a call to model_construct() with extra set to 'forbid' doesn't raise an error in the presence of data not corresponding to fields. Rather, said input data is simply ignored.

Generic models

Pydantic supports the creation of generic models to make it easier to reuse a common model structure.

In order to declare a generic model, you should follow the following steps:

  1. Declare one or more type variables to use to parameterize your model.
  2. Declare a pydantic model that inherits from BaseModel and typing.Generic (in this specific order), and add the list of type variables you declared previously as parameters to the Generic parent.
  3. Use the type variables as annotations where you will want to replace them with other types.

PEP 695 support

Pydantic does not support the new syntax for generic classes (introduced by PEP 695), available since Python 3.12. Progress can be tracked in this issue.

Here is an example using a generic Pydantic model to create an easily-reused HTTP response payload wrapper:

from typing import Generic, List, Optional, TypeVar

from pydantic import BaseModel, ValidationError

DataT = TypeVar('DataT')  # (1)!


class DataModel(BaseModel):
    numbers: List[int]
    people: List[str]


class Response(BaseModel, Generic[DataT]):  # (2)!
    data: Optional[DataT] = None  # (3)!


print(Response[int](data=1))
#> data=1
print(Response[str](data='value'))
#> data='value'
print(Response[str](data='value').model_dump())
#> {'data': 'value'}

data = DataModel(numbers=[1, 2, 3], people=[])
print(Response[DataModel](data=data).model_dump())
#> {'data': {'numbers': [1, 2, 3], 'people': []}}
try:
    Response[int](data='value')
except ValidationError as e:
    print(e)
    """
    1 validation error for Response[int]
    data
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='value', input_type=str]
    """
  1. Refers to step 1 described above.
  2. Refers to step 2 described above.
  3. Refers to step 3 described above.

Any configuration, validation or serialization logic set on the generic model will also be applied to the parametrized classes, in the same way as when inheriting from a model class. Any custom methods or attributes will also be inherited.

Generic models also integrate properly with type checkers, so you get all the type checking you would expect if you were to declare a distinct type for each parametrization.

Note

Internally, Pydantic creates subclasses of the generic model at runtime when the generic model class is parametrized. These classes are cached, so there should be minimal overhead introduced by the use of generics models.

To inherit from a generic model and preserve the fact that it is generic, the subclass must also inherit from Generic:

from typing import Generic, TypeVar

from pydantic import BaseModel

TypeX = TypeVar('TypeX')


class BaseClass(BaseModel, Generic[TypeX]):
    X: TypeX


class ChildClass(BaseClass[TypeX], Generic[TypeX]):
    pass


# Parametrize `TypeX` with `int`:
print(ChildClass[int](X=1))
#> X=1

You can also create a generic subclass of a model that partially or fully replaces the type variables in the superclass:

from typing import Generic, TypeVar

from pydantic import BaseModel

TypeX = TypeVar('TypeX')
TypeY = TypeVar('TypeY')
TypeZ = TypeVar('TypeZ')


class BaseClass(BaseModel, Generic[TypeX, TypeY]):
    x: TypeX
    y: TypeY


class ChildClass(BaseClass[int, TypeY], Generic[TypeY, TypeZ]):
    z: TypeZ


# Parametrize `TypeY` with `str`:
print(ChildClass[str, int](x='1', y='y', z='3'))
#> x=1 y='y' z=3

If the name of the concrete subclasses is important, you can also override the default name generation by overriding the model_parametrized_name() method:

from typing import Any, Generic, Tuple, Type, TypeVar

from pydantic import BaseModel

DataT = TypeVar('DataT')


class Response(BaseModel, Generic[DataT]):
    data: DataT

    @classmethod
    def model_parametrized_name(cls, params: Tuple[Type[Any], ...]) -> str:
        return f'{params[0].__name__.title()}Response'


print(repr(Response[int](data=1)))
#> IntResponse(data=1)
print(repr(Response[str](data='a')))
#> StrResponse(data='a')

from typing import Any, Generic, TypeVar

from pydantic import BaseModel

DataT = TypeVar('DataT')


class Response(BaseModel, Generic[DataT]):
    data: DataT

    @classmethod
    def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str:
        return f'{params[0].__name__.title()}Response'


print(repr(Response[int](data=1)))
#> IntResponse(data=1)
print(repr(Response[str](data='a')))
#> StrResponse(data='a')

You can use parametrized generic models as types in other models:

from typing import Generic, TypeVar

from pydantic import BaseModel

T = TypeVar('T')


class ResponseModel(BaseModel, Generic[T]):
    content: T


class Product(BaseModel):
    name: str
    price: float


class Order(BaseModel):
    id: int
    product: ResponseModel[Product]


product = Product(name='Apple', price=0.5)
response = ResponseModel[Product](content=product)
order = Order(id=1, product=response)
print(repr(order))
"""
Order(id=1, product=ResponseModel[Product](content=Product(name='Apple', price=0.5)))
"""

Using the same type variable in nested models allows you to enforce typing relationships at different points in your model:

from typing import Generic, TypeVar

from pydantic import BaseModel, ValidationError

T = TypeVar('T')


class InnerT(BaseModel, Generic[T]):
    inner: T


class OuterT(BaseModel, Generic[T]):
    outer: T
    nested: InnerT[T]


nested = InnerT[int](inner=1)
print(OuterT[int](outer=1, nested=nested))
#> outer=1 nested=InnerT[int](inner=1)
try:
    print(OuterT[int](outer='a', nested=InnerT(inner='a')))  # (1)!
except ValidationError as e:
    print(e)
    """
    2 validation errors for OuterT[int]
    outer
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    nested.inner
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    """
  1. The OuterT model is parametrized with int, but the data associated with the the T annotations during validation is of type str, leading to validation errors.

Warning

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 class).

If you need to perform isinstance() checks against parametrized generics, you can do this by subclassing the parametrized generic class:

class MyIntModel(MyGenericModel[int]): ...

isinstance(my_model, MyIntModel)

Implementation Details

When using nested generic models, Pydantic sometimes performs revalidation in an attempt to produce the most intuitive validation result. Specifically, if you have a field of type GenericModel[SomeType] and you validate data like GenericModel[SomeCompatibleType] against this field, we will inspect the data, recognize that the input data is sort of a "loose" subclass of GenericModel, and revalidate the contained SomeCompatibleType data.

This adds some validation overhead, but makes things more intuitive for cases like that shown below.

from typing import Any, Generic, TypeVar

from pydantic import BaseModel

T = TypeVar('T')


class GenericModel(BaseModel, Generic[T]):
    a: T


class Model(BaseModel):
    inner: GenericModel[Any]


print(repr(Model.model_validate(Model(inner=GenericModel[int](a=1)))))
#> Model(inner=GenericModel[Any](a=1))

Note, validation will still fail if you, for example are validating against GenericModel[int] and pass in an instance GenericModel[str](a='not an int').

It's also worth noting that this pattern will re-trigger any custom validation as well, like additional model validators and the like. Validators will be called once on the first pass, validating directly against GenericModel[Any]. That validation fails, as GenericModel[int] is not a subclass of GenericModel[Any]. This relates to the warning above about the complications of using parametrized generics in isinstance() and issubclass() checks. Then, the validators will be called again on the second pass, during more lax force-revalidation phase, which succeeds. To better understand this consequence, see below:

from typing import Any, Generic, Self, TypeVar

from pydantic import BaseModel, model_validator

T = TypeVar('T')


class GenericModel(BaseModel, Generic[T]):
    a: T

    @model_validator(mode='after')
    def validate_after(self: Self) -> Self:
        print('after validator running custom validation...')
        return self


class Model(BaseModel):
    inner: GenericModel[Any]


m = Model.model_validate(Model(inner=GenericModel[int](a=1)))
#> after validator running custom validation...
#> after validator running custom validation...
print(repr(m))
#> Model(inner=GenericModel[Any](a=1))

Validation of unparametrized type variables

When leaving type variables unparametrized, Pydantic treats generic models similarly to how it treats built-in generic types like list and dict:

  • If the type variable is bound or constrained to a specific type, it will be used.
  • If the type variable has a default type (as specified by PEP 696), it will be used.
  • For unbound or unconstrained type variables, Pydantic will fallback to Any.
from typing import Generic

from typing_extensions import TypeVar

from pydantic import BaseModel, ValidationError

T = TypeVar('T')
U = TypeVar('U', bound=int)
V = TypeVar('V', default=str)


class Model(BaseModel, Generic[T, U, V]):
    t: T
    u: U
    v: V


print(Model(t='t', u=1, v='v'))
#> t='t' u=1 v='v'

try:
    Model(t='t', u='u', v=1)
except ValidationError as exc:
    print(exc)
    """
    2 validation errors for Model
    u
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='u', input_type=str]
    v
      Input should be a valid string [type=string_type, input_value=1, input_type=int]
    """

Warning

In some cases, validation against an unparametrized generic model can lead to data loss. Specifically, if a subtype of the type variable upper bound, constraints, or default is being used and the model isn't explicitly parametrized, the resulting type will not be the one being provided:

from typing import Generic, TypeVar

from pydantic import BaseModel

ItemT = TypeVar('ItemT', bound='ItemBase')


class ItemBase(BaseModel): ...


class IntItem(ItemBase):
    value: int


class ItemHolder(BaseModel, Generic[ItemT]):
    item: ItemT


loaded_data = {'item': {'value': 1}}


print(ItemHolder(**loaded_data))  # (1)!
#> item=ItemBase()

print(ItemHolder[IntItem](**loaded_data))  # (2)!
#> item=IntItem(value=1)
  1. When the generic isn't parametrized, the input data is validated against the ItemT upper bound. Given that ItemBase has no fields, the item field information is lost.
  2. In this case, the type variable is explicitly parametrized, so the input data is validated against the IntItem class.

Serialization of unparametrized type variables

The behavior of serialization differs when using type variables with upper bounds, constraints, or a default value:

If a Pydantic model is used in a type variable upper bound and the type variable is never parametrized, then Pydantic will use the upper bound for validation but treat the value as Any in terms of serialization:

from typing import Generic, TypeVar

from pydantic import BaseModel


class ErrorDetails(BaseModel):
    foo: str


ErrorDataT = TypeVar('ErrorDataT', bound=ErrorDetails)


class Error(BaseModel, Generic[ErrorDataT]):
    message: str
    details: ErrorDataT


class MyErrorDetails(ErrorDetails):
    bar: str


# serialized as Any
error = Error(
    message='We just had an error',
    details=MyErrorDetails(foo='var', bar='var2'),
)
assert error.model_dump() == {
    'message': 'We just had an error',
    'details': {
        'foo': 'var',
        'bar': 'var2',
    },
}

# serialized using the concrete parametrization
# note that `'bar': 'var2'` is missing
error = Error[ErrorDetails](
    message='We just had an error',
    details=ErrorDetails(foo='var'),
)
assert error.model_dump() == {
    'message': 'We just had an error',
    'details': {
        'foo': 'var',
    },
}

Here's another example of the above behavior, enumerating all permutations regarding bound specification and generic type parametrization:

from typing import Generic, TypeVar

from pydantic import BaseModel

TBound = TypeVar('TBound', bound=BaseModel)
TNoBound = TypeVar('TNoBound')


class IntValue(BaseModel):
    value: int


class ItemBound(BaseModel, Generic[TBound]):
    item: TBound


class ItemNoBound(BaseModel, Generic[TNoBound]):
    item: TNoBound


item_bound_inferred = ItemBound(item=IntValue(value=3))
item_bound_explicit = ItemBound[IntValue](item=IntValue(value=3))
item_no_bound_inferred = ItemNoBound(item=IntValue(value=3))
item_no_bound_explicit = ItemNoBound[IntValue](item=IntValue(value=3))

# calling `print(x.model_dump())` on any of the above instances results in the following:
#> {'item': {'value': 3}}

However, if constraints or a default value (as per PEP 696) is being used, then the default type or constraints will be used for both validation and serialization if the type variable is not parametrized. You can override this behavior using SerializeAsAny:

from typing import Generic

from typing_extensions import TypeVar

from pydantic import BaseModel, SerializeAsAny


class ErrorDetails(BaseModel):
    foo: str


ErrorDataT = TypeVar('ErrorDataT', default=ErrorDetails)


class Error(BaseModel, Generic[ErrorDataT]):
    message: str
    details: ErrorDataT


class MyErrorDetails(ErrorDetails):
    bar: str


# serialized using the default's serializer
error = Error(
    message='We just had an error',
    details=MyErrorDetails(foo='var', bar='var2'),
)
assert error.model_dump() == {
    'message': 'We just had an error',
    'details': {
        'foo': 'var',
    },
}
# If `ErrorDataT` was using an upper bound, `bar` would be present in `details`.


class SerializeAsAnyError(BaseModel, Generic[ErrorDataT]):
    message: str
    details: SerializeAsAny[ErrorDataT]


# serialized as Any
error = SerializeAsAnyError(
    message='We just had an error',
    details=MyErrorDetails(foo='var', bar='baz'),
)
assert error.model_dump() == {
    'message': 'We just had an error',
    'details': {
        'foo': 'var',
        'bar': 'baz',
    },
}

Dynamic model creation

API Documentation

pydantic.main.create_model

There are some occasions where it is desirable to create a model using runtime information to specify the fields. Pydantic provides the create_model() function to allow models to be created dynamically:

from pydantic import BaseModel, create_model

DynamicFoobarModel = create_model('DynamicFoobarModel', foo=str, bar=(int, 123))

# Equivalent to:


class StaticFoobarModel(BaseModel):
    foo: str
    bar: int = 123

Field definitions are specified as keyword arguments, and should either be:

  • A single element, representing the type annotation of the field.
  • A two-tuple, the first element being the type and the second element the assigned value (either a default or the Field() function).

Here is a more advanced example:

from typing_extensions import Annotated

from pydantic import BaseModel, Field, PrivateAttr, create_model

DynamicModel = create_model(
    'DynamicModel',
    foo=(str, Field(alias='FOO')),
    bar=Annotated[str, Field(description='Bar field')],
    _private=(int, PrivateAttr(default=1)),
)


class StaticModel(BaseModel):
    foo: str = Field(alias='FOO')
    bar: Annotated[str, Field(description='Bar field')]
    _private: int = PrivateAttr(default=1)

from typing import Annotated

from pydantic import BaseModel, Field, PrivateAttr, create_model

DynamicModel = create_model(
    'DynamicModel',
    foo=(str, Field(alias='FOO')),
    bar=Annotated[str, Field(description='Bar field')],
    _private=(int, PrivateAttr(default=1)),
)


class StaticModel(BaseModel):
    foo: str = Field(alias='FOO')
    bar: Annotated[str, Field(description='Bar field')]
    _private: int = PrivateAttr(default=1)

The special keyword arguments __config__ and __base__ can be used to customize the new model. This includes extending a base model with extra fields.

from pydantic import BaseModel, create_model


class FooModel(BaseModel):
    foo: str
    bar: int = 123


BarModel = create_model(
    'BarModel',
    apple=(str, 'russet'),
    banana=(str, 'yellow'),
    __base__=FooModel,
)
print(BarModel)
#> <class '__main__.BarModel'>
print(BarModel.model_fields.keys())
#> dict_keys(['foo', 'bar', 'apple', 'banana'])

You can also add validators by passing a dictionary to the __validators__ argument.

from pydantic import ValidationError, create_model, field_validator


def alphanum(cls, v):
    assert v.isalnum(), 'must be alphanumeric'
    return v


validators = {
    'username_validator': field_validator('username')(alphanum)  # (1)!
}

UserModel = create_model(
    'UserModel', username=(str, ...), __validators__=validators
)

user = UserModel(username='scolvin')
print(user)
#> username='scolvin'

try:
    UserModel(username='scolvi%n')
except ValidationError as e:
    print(e)
    """
    1 validation error for UserModel
    username
      Assertion failed, must be alphanumeric [type=assertion_error, input_value='scolvi%n', input_type=str]
    """
  1. Make sure that the validators names do not clash with any of the field names as internally, Pydantic gathers all members into a namespace and mimics the normal creation of a class using the types module utilities.

Note

To pickle a dynamically created model:

  • the model must be defined globally
  • the __module__ argument must be provided

RootModel and custom root types

API Documentation

pydantic.root_model.RootModel

Pydantic models can be defined with a "custom root type" by subclassing pydantic.RootModel.

The root type can be any type supported by Pydantic, and is specified by the generic parameter to RootModel. The root value can be passed to the model __init__ or model_validate via the first and only argument.

Here's an example of how this works:

from typing import Dict, List

from pydantic import RootModel

Pets = RootModel[List[str]]
PetsByName = RootModel[Dict[str, str]]


print(Pets(['dog', 'cat']))
#> root=['dog', 'cat']
print(Pets(['dog', 'cat']).model_dump_json())
#> ["dog","cat"]
print(Pets.model_validate(['dog', 'cat']))
#> root=['dog', 'cat']
print(Pets.model_json_schema())
"""
{'items': {'type': 'string'}, 'title': 'RootModel[List[str]]', 'type': 'array'}
"""

print(PetsByName({'Otis': 'dog', 'Milo': 'cat'}))
#> root={'Otis': 'dog', 'Milo': 'cat'}
print(PetsByName({'Otis': 'dog', 'Milo': 'cat'}).model_dump_json())
#> {"Otis":"dog","Milo":"cat"}
print(PetsByName.model_validate({'Otis': 'dog', 'Milo': 'cat'}))
#> root={'Otis': 'dog', 'Milo': 'cat'}

from pydantic import RootModel

Pets = RootModel[list[str]]
PetsByName = RootModel[dict[str, str]]


print(Pets(['dog', 'cat']))
#> root=['dog', 'cat']
print(Pets(['dog', 'cat']).model_dump_json())
#> ["dog","cat"]
print(Pets.model_validate(['dog', 'cat']))
#> root=['dog', 'cat']
print(Pets.model_json_schema())
"""
{'items': {'type': 'string'}, 'title': 'RootModel[List[str]]', 'type': 'array'}
"""

print(PetsByName({'Otis': 'dog', 'Milo': 'cat'}))
#> root={'Otis': 'dog', 'Milo': 'cat'}
print(PetsByName({'Otis': 'dog', 'Milo': 'cat'}).model_dump_json())
#> {"Otis":"dog","Milo":"cat"}
print(PetsByName.model_validate({'Otis': 'dog', 'Milo': 'cat'}))
#> root={'Otis': 'dog', 'Milo': 'cat'}

If you want to access items in the root field directly or to iterate over the items, you can implement custom __iter__ and __getitem__ functions, as shown in the following example.

from typing import List

from pydantic import RootModel


class Pets(RootModel):
    root: List[str]

    def __iter__(self):
        return iter(self.root)

    def __getitem__(self, item):
        return self.root[item]


pets = Pets.model_validate(['dog', 'cat'])
print(pets[0])
#> dog
print([pet for pet in pets])
#> ['dog', 'cat']

from pydantic import RootModel


class Pets(RootModel):
    root: list[str]

    def __iter__(self):
        return iter(self.root)

    def __getitem__(self, item):
        return self.root[item]


pets = Pets.model_validate(['dog', 'cat'])
print(pets[0])
#> dog
print([pet for pet in pets])
#> ['dog', 'cat']

You can also create subclasses of the parametrized root model directly:

from typing import List

from pydantic import RootModel


class Pets(RootModel[List[str]]):
    def describe(self) -> str:
        return f'Pets: {", ".join(self.root)}'


my_pets = Pets.model_validate(['dog', 'cat'])

print(my_pets.describe())
#> Pets: dog, cat

from pydantic import RootModel


class Pets(RootModel[list[str]]):
    def describe(self) -> str:
        return f'Pets: {", ".join(self.root)}'


my_pets = Pets.model_validate(['dog', 'cat'])

print(my_pets.describe())
#> Pets: dog, cat

Faux immutability

Models can be configured to be immutable via model_config['frozen'] = True. When this is set, attempting to change the values of instance attributes will raise errors. See the API reference for more details.

Note

This behavior was achieved in Pydantic V1 via the config setting allow_mutation = False. This config flag is deprecated in Pydantic V2, and has been replaced with frozen.

Warning

In Python, immutability is not enforced. Developers have the ability to modify objects that are conventionally considered "immutable" if they choose to do so.

from pydantic import BaseModel, ConfigDict, ValidationError


class FooBarModel(BaseModel):
    model_config = ConfigDict(frozen=True)

    a: str
    b: dict


foobar = FooBarModel(a='hello', b={'apple': 'pear'})

try:
    foobar.a = 'different'
except ValidationError as e:
    print(e)
    """
    1 validation error for FooBarModel
    a
      Instance is frozen [type=frozen_instance, input_value='different', input_type=str]
    """

print(foobar.a)
#> hello
print(foobar.b)
#> {'apple': 'pear'}
foobar.b['apple'] = 'grape'
print(foobar.b)
#> {'apple': 'grape'}

Trying to change a caused an error, and a remains unchanged. However, the dict b is mutable, and the immutability of foobar doesn't stop b from being changed.

Abstract base classes

Pydantic models can be used alongside Python's Abstract Base Classes (ABCs).

import abc

from pydantic import BaseModel


class FooBarModel(BaseModel, abc.ABC):
    a: str
    b: int

    @abc.abstractmethod
    def my_abstract_method(self):
        pass

Field ordering

Field order affects models in the following ways:

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    a: int
    b: int = 2
    c: int = 1
    d: int = 0
    e: float


print(Model.model_fields.keys())
#> dict_keys(['a', 'b', 'c', 'd', 'e'])
m = Model(e=2, a=1)
print(m.model_dump())
#> {'a': 1, 'b': 2, 'c': 1, 'd': 0, 'e': 2.0}
try:
    Model(a='x', b='x', c='x', d='x', e='x')
except ValidationError as err:
    error_locations = [e['loc'] for e in err.errors()]

print(error_locations)
#> [('a',), ('b',), ('c',), ('d',), ('e',)]

Automatically excluded attributes

Class variables

Attributes annotated with ClassVar are properly treated by Pydantic as class variables, and will not become fields on model instances:

from typing import ClassVar

from pydantic import BaseModel


class Model(BaseModel):
    x: ClassVar[int] = 1

    y: int = 2


m = Model()
print(m)
#> y=2
print(Model.x)
#> 1

Private model attributes

API Documentation

pydantic.fields.PrivateAttr

Attributes whose name has a leading underscore are not treated as fields by Pydantic, and are not included in the model schema. Instead, these are converted into a "private attribute" which is not validated or even set during calls to __init__, model_validate, etc.

Here is an example of usage:

from datetime import datetime
from random import randint
from typing import Any

from pydantic import BaseModel, PrivateAttr


class TimeAwareModel(BaseModel):
    _processed_at: datetime = PrivateAttr(default_factory=datetime.now)
    _secret_value: str

    def model_post_init(self, context: Any) -> None:
        # this could also be done with `default_factory`:
        self._secret_value = randint(1, 5)


m = TimeAwareModel()
print(m._processed_at)
#> 2032-01-02 03:04:05.000006
print(m._secret_value)
#> 3

Private attribute names must start with underscore to prevent conflicts with model fields. However, dunder names (such as __attr__) are not supported, and will be completely ignored from the model definition.

Model signature

All Pydantic models will have their signature generated based on their fields:

import inspect

from pydantic import BaseModel, Field


class FooModel(BaseModel):
    id: int
    name: str = None
    description: str = 'Foo'
    apple: int = Field(alias='pear')


print(inspect.signature(FooModel))
#> (*, id: int, name: str = None, description: str = 'Foo', pear: int) -> None

An accurate signature is useful for introspection purposes and libraries like FastAPI or hypothesis.

The generated signature will also respect custom __init__ functions:

import inspect

from pydantic import BaseModel


class MyModel(BaseModel):
    id: int
    info: str = 'Foo'

    def __init__(self, id: int = 1, *, bar: str, **data) -> None:
        """My custom init!"""
        super().__init__(id=id, bar=bar, **data)


print(inspect.signature(MyModel))
#> (id: int = 1, *, bar: str, info: str = 'Foo') -> None

To be included in the signature, a field's alias or name must be a valid Python identifier. Pydantic will prioritize a field's alias over its name when generating the signature, but may use the field name if the alias is not a valid Python identifier.

If a field's alias and name are both not valid identifiers (which may be possible through exotic use of create_model), a **data argument will be added. In addition, the **data argument will always be present in the signature if model_config['extra'] == 'allow'.

Structural pattern matching

Pydantic supports structural pattern matching for models, as introduced by PEP 636 in Python 3.10.

from pydantic import BaseModel


class Pet(BaseModel):
    name: str
    species: str


a = Pet(name='Bones', species='dog')

match a:
    # match `species` to 'dog', declare and initialize `dog_name`
    case Pet(species='dog', name=dog_name):
        print(f'{dog_name} is a dog')
#> Bones is a dog
    # default case
    case _:
        print('No dog matched')

Note

A match-case statement may seem as if it creates a new model, but don't be fooled; it is just syntactic sugar for getting an attribute and either comparing it or declaring and initializing it.

Attribute copies

In many cases, arguments passed to the constructor will be copied in order to perform validation and, where necessary, coercion.

In this example, note that the ID of the list changes after the class is constructed because it has been copied during validation:

from typing import List

from pydantic import BaseModel


class C1:
    arr = []

    def __init__(self, in_arr):
        self.arr = in_arr


class C2(BaseModel):
    arr: List[int]


arr_orig = [1, 9, 10, 3]


c1 = C1(arr_orig)
c2 = C2(arr=arr_orig)
print(f'{id(c1.arr) == id(c2.arr)=}')
#> id(c1.arr) == id(c2.arr)=False

from pydantic import BaseModel


class C1:
    arr = []

    def __init__(self, in_arr):
        self.arr = in_arr


class C2(BaseModel):
    arr: list[int]


arr_orig = [1, 9, 10, 3]


c1 = C1(arr_orig)
c2 = C2(arr=arr_orig)
print(f'{id(c1.arr) == id(c2.arr)=}')
#> id(c1.arr) == id(c2.arr)=False

Note

There are some situations where Pydantic does not copy attributes, such as when passing models — we use the model as is. You can override this behaviour by setting model_config['revalidate_instances'] = 'always'.

Extra fields

By default, Pydantic models won't error when you provide data for unrecognized fields, they will just be ignored:

from pydantic import BaseModel


class Model(BaseModel):
    x: int


m = Model(x=1, y='a')
assert m.model_dump() == {'x': 1}

If you want this to raise an error, you can set the extra configuration value to 'forbid':

from pydantic import BaseModel, ConfigDict, ValidationError


class Model(BaseModel):
    x: int

    model_config = ConfigDict(extra='forbid')


try:
    Model(x=1, y='a')
except ValidationError as exc:
    print(exc)
    """
    1 validation error for Model
    y
      Extra inputs are not permitted [type=extra_forbidden, input_value='a', input_type=str]
    """

To instead preserve any extra data provided, you can set extra to 'allow'. The extra fields will then be stored in BaseModel.__pydantic_extra__:

from pydantic import BaseModel, ConfigDict


class Model(BaseModel):
    x: int

    model_config = ConfigDict(extra='allow')


m = Model(x=1, y='a')
assert m.__pydantic_extra__ == {'y': 'a'}

By default, no validation will be applied to these extra items, but you can set a type for the values by overriding the type annotation for __pydantic_extra__:

from typing import Dict

from pydantic import BaseModel, ConfigDict, Field, ValidationError


class Model(BaseModel):
    __pydantic_extra__: Dict[str, int] = Field(init=False)  # (1)!

    x: int

    model_config = ConfigDict(extra='allow')


try:
    Model(x=1, y='a')
except ValidationError as exc:
    print(exc)
    """
    1 validation error for Model
    y
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    """

m = Model(x=1, y='2')
assert m.x == 1
assert m.y == 2
assert m.model_dump() == {'x': 1, 'y': 2}
assert m.__pydantic_extra__ == {'y': 2}
  1. The = Field(init=False) does not have any effect at runtime, but prevents the __pydantic_extra__ field from being included as a parameter to the model's __init__ method by type checkers.
from pydantic import BaseModel, ConfigDict, Field, ValidationError


class Model(BaseModel):
    __pydantic_extra__: dict[str, int] = Field(init=False)  # (1)!

    x: int

    model_config = ConfigDict(extra='allow')


try:
    Model(x=1, y='a')
except ValidationError as exc:
    print(exc)
    """
    1 validation error for Model
    y
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    """

m = Model(x=1, y='2')
assert m.x == 1
assert m.y == 2
assert m.model_dump() == {'x': 1, 'y': 2}
assert m.__pydantic_extra__ == {'y': 2}
  1. The = Field(init=False) does not have any effect at runtime, but prevents the __pydantic_extra__ field from being included as a parameter to the model's __init__ method by type checkers.

The same configurations apply to TypedDict and dataclass' except the config is controlled by setting the __pydantic_config__ attribute of the class to a valid ConfigDict.