pydantic_core.core_schema

This module contains definitions to build schemas which pydantic_core can validate and serialize.

WhenUsed `module-attribute` ¶

WhenUsed = Literal[
    "always", "unless-none", "json", "json-unless-none"
]

Values have the following meanings:

'always' means always use
'unless-none' means use unless the value is None
'json' means use when serializing to JSON
'json-unless-none' means use when serializing to JSON and the value is not None

CoreConfig ¶

Bases: TypedDict

Base class for schema configuration options.

Attributes:

Name	Type	Description
`title`	`str`	The name of the configuration.
`strict`	`bool`	Whether the configuration should strictly adhere to specified rules.
`extra_fields_behavior`	`ExtraBehavior`	The behavior for handling extra fields.
`typed_dict_total`	`bool`	Whether the TypedDict should be considered total. Default is `True`.
`from_attributes`	`bool`	Whether to use attributes for models, dataclasses, and tagged union keys.
`loc_by_alias`	`bool`	Whether to use the used alias (or first alias for "field required" errors) instead of `field_names` to construct error `loc`s. Default is `True`.
`revalidate_instances`	`Literal['always', 'never', 'subclass-instances']`	Whether instances of models and dataclasses should re-validate. Default is 'never'.
`validate_default`	`bool`	Whether to validate default values during validation. Default is `False`.
`populate_by_name`	`bool`	Whether an aliased field may be populated by its name as given by the model attribute, as well as the alias. (Replaces 'allow_population_by_field_name' in Pydantic v1.) Default is `False`.
`str_max_length`	`int`	The maximum length for string fields.
`str_min_length`	`int`	The minimum length for string fields.
`str_strip_whitespace`	`bool`	Whether to strip whitespace from string fields.
`str_to_lower`	`bool`	Whether to convert string fields to lowercase.
`str_to_upper`	`bool`	Whether to convert string fields to uppercase.
`allow_inf_nan`	`bool`	Whether to allow infinity and NaN values for float fields. Default is `True`.
`ser_json_timedelta`	`Literal['iso8601', 'float']`	The serialization option for `timedelta` values. Default is 'iso8601'.
`ser_json_bytes`	`Literal['utf8', 'base64', 'hex']`	The serialization option for `bytes` values. Default is 'utf8'.
`ser_json_inf_nan`	`Literal['null', 'constants']`	The serialization option for infinity and NaN values in float fields. Default is 'null'.
`hide_input_in_errors`	`bool`	Whether to hide input data from `ValidationError` representation.
`validation_error_cause`	`bool`	Whether to add user-python excs to the cause of a ValidationError. Requires exceptiongroup backport pre Python 3.11.
`coerce_numbers_to_str`	`bool`	Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode).
`regex_engine`	`Literal['rust-regex', 'python-re']`	The regex engine to use for regex pattern validation. Default is 'rust-regex'. See `StringSchema`.
`cache_strings`	`Union[bool, Literal['all', 'keys', 'none']]`	Whether to cache strings. Default is `True`, `True` or `'all'` is required to cache strings during general validation since validators don't know if they're in a key or a value.

SerializationInfo ¶

Bases: Protocol

context `property` ¶

context: Any | None

Current serialization context.

ValidationInfo ¶

Bases: Protocol

Argument passed to validation functions.

context `property` ¶

context: Any | None

Current validation context.

config `property` ¶

config: CoreConfig | None

The CoreConfig that applies to this validation.

mode `property` ¶

mode: Literal['python', 'json']

The type of input data we are currently validating

data `property` ¶

data: Dict[str, Any]

The data being validated for this model.

field_name `property` ¶

field_name: str | None

The name of the current field being validated if this validator is attached to a model field.

simple_ser_schema ¶

simple_ser_schema(
    type: ExpectedSerializationTypes,
) -> SimpleSerSchema

Returns a schema for serialization with a custom type.

Parameters:

Name	Type	Description	Default
`type`	`ExpectedSerializationTypes`	The type to use for serialization	required

Source code in pydantic_core/core_schema.py

def simple_ser_schema(type: ExpectedSerializationTypes) -> SimpleSerSchema:
    """
    Returns a schema for serialization with a custom type.

    Args:
        type: The type to use for serialization
    """
    return SimpleSerSchema(type=type)

plain_serializer_function_ser_schema ¶

plain_serializer_function_ser_schema(
    function: SerializerFunction,
    *,
    is_field_serializer: bool | None = None,
    info_arg: bool | None = None,
    return_schema: CoreSchema | None = None,
    when_used: WhenUsed = "always"
) -> PlainSerializerFunctionSerSchema

Returns a schema for serialization with a function, can be either a "general" or "field" function.

Parameters:

Name	Type	Description	Default
`function`	`SerializerFunction`	The function to use for serialization	required
`is_field_serializer`	`bool \| None`	Whether the serializer is for a field, e.g. takes `model` as the first argument, and `info` includes `field_name`	`None`
`info_arg`	`bool \| None`	Whether the function takes an `info` argument	`None`
`return_schema`	`CoreSchema \| None`	Schema to use for serializing return value	`None`
`when_used`	`WhenUsed`	When the function should be called	`'always'`

Source code in pydantic_core/core_schema.py

def plain_serializer_function_ser_schema(
    function: SerializerFunction,
    *,
    is_field_serializer: bool | None = None,
    info_arg: bool | None = None,
    return_schema: CoreSchema | None = None,
    when_used: WhenUsed = 'always',
) -> PlainSerializerFunctionSerSchema:
    """
    Returns a schema for serialization with a function, can be either a "general" or "field" function.

    Args:
        function: The function to use for serialization
        is_field_serializer: Whether the serializer is for a field, e.g. takes `model` as the first argument,
            and `info` includes `field_name`
        info_arg: Whether the function takes an `info` argument
        return_schema: Schema to use for serializing return value
        when_used: When the function should be called
    """
    if when_used == 'always':
        # just to avoid extra elements in schema, and to use the actual default defined in rust
        when_used = None  # type: ignore
    return _dict_not_none(
        type='function-plain',
        function=function,
        is_field_serializer=is_field_serializer,
        info_arg=info_arg,
        return_schema=return_schema,
        when_used=when_used,
    )

wrap_serializer_function_ser_schema ¶

wrap_serializer_function_ser_schema(
    function: WrapSerializerFunction,
    *,
    is_field_serializer: bool | None = None,
    info_arg: bool | None = None,
    schema: CoreSchema | None = None,
    return_schema: CoreSchema | None = None,
    when_used: WhenUsed = "always"
) -> WrapSerializerFunctionSerSchema

Returns a schema for serialization with a wrap function, can be either a "general" or "field" function.

Parameters:

Name	Type	Description	Default
`function`	`WrapSerializerFunction`	The function to use for serialization	required
`is_field_serializer`	`bool \| None`	Whether the serializer is for a field, e.g. takes `model` as the first argument, and `info` includes `field_name`	`None`
`info_arg`	`bool \| None`	Whether the function takes an `info` argument	`None`
`schema`	`CoreSchema \| None`	The schema to use for the inner serialization	`None`
`return_schema`	`CoreSchema \| None`	Schema to use for serializing return value	`None`
`when_used`	`WhenUsed`	When the function should be called	`'always'`

Source code in pydantic_core/core_schema.py

def wrap_serializer_function_ser_schema(
    function: WrapSerializerFunction,
    *,
    is_field_serializer: bool | None = None,
    info_arg: bool | None = None,
    schema: CoreSchema | None = None,
    return_schema: CoreSchema | None = None,
    when_used: WhenUsed = 'always',
) -> WrapSerializerFunctionSerSchema:
    """
    Returns a schema for serialization with a wrap function, can be either a "general" or "field" function.

    Args:
        function: The function to use for serialization
        is_field_serializer: Whether the serializer is for a field, e.g. takes `model` as the first argument,
            and `info` includes `field_name`
        info_arg: Whether the function takes an `info` argument
        schema: The schema to use for the inner serialization
        return_schema: Schema to use for serializing return value
        when_used: When the function should be called
    """
    if when_used == 'always':
        # just to avoid extra elements in schema, and to use the actual default defined in rust
        when_used = None  # type: ignore
    return _dict_not_none(
        type='function-wrap',
        function=function,
        is_field_serializer=is_field_serializer,
        info_arg=info_arg,
        schema=schema,
        return_schema=return_schema,
        when_used=when_used,
    )

format_ser_schema ¶

format_ser_schema(
    formatting_string: str,
    *,
    when_used: WhenUsed = "json-unless-none"
) -> FormatSerSchema

Returns a schema for serialization using python's format method.

Parameters:

Name	Type	Description	Default
`formatting_string`	`str`	String defining the format to use	required
`when_used`	`WhenUsed`	Same meaning as for [general_function_plain_ser_schema], but with a different default	`'json-unless-none'`

Source code in pydantic_core/core_schema.py

def format_ser_schema(formatting_string: str, *, when_used: WhenUsed = 'json-unless-none') -> FormatSerSchema:
    """
    Returns a schema for serialization using python's `format` method.

    Args:
        formatting_string: String defining the format to use
        when_used: Same meaning as for [general_function_plain_ser_schema], but with a different default
    """
    if when_used == 'json-unless-none':
        # just to avoid extra elements in schema, and to use the actual default defined in rust
        when_used = None  # type: ignore
    return _dict_not_none(type='format', formatting_string=formatting_string, when_used=when_used)

to_string_ser_schema ¶

to_string_ser_schema(
    *, when_used: WhenUsed = "json-unless-none"
) -> ToStringSerSchema

Returns a schema for serialization using python's str() / __str__ method.

Parameters:

Name	Type	Description	Default
`when_used`	`WhenUsed`	Same meaning as for [general_function_plain_ser_schema], but with a different default	`'json-unless-none'`

Source code in pydantic_core/core_schema.py

def to_string_ser_schema(*, when_used: WhenUsed = 'json-unless-none') -> ToStringSerSchema:
    """
    Returns a schema for serialization using python's `str()` / `__str__` method.

    Args:
        when_used: Same meaning as for [general_function_plain_ser_schema], but with a different default
    """
    s = dict(type='to-string')
    if when_used != 'json-unless-none':
        # just to avoid extra elements in schema, and to use the actual default defined in rust
        s['when_used'] = when_used
    return s  # type: ignore

model_ser_schema ¶

model_ser_schema(
    cls: Type[Any], schema: CoreSchema
) -> ModelSerSchema

Returns a schema for serialization using a model.

Parameters:

Name	Type	Description	Default
`cls`	`Type[Any]`	The expected class type, used to generate warnings if the wrong type is passed	required
`schema`	`CoreSchema`	Internal schema to use to serialize the model dict	required

Source code in pydantic_core/core_schema.py

def model_ser_schema(cls: Type[Any], schema: CoreSchema) -> ModelSerSchema:
    """
    Returns a schema for serialization using a model.

    Args:
        cls: The expected class type, used to generate warnings if the wrong type is passed
        schema: Internal schema to use to serialize the model dict
    """
    return ModelSerSchema(type='model', cls=cls, schema=schema)

computed_field ¶

computed_field(
    property_name: str,
    return_schema: CoreSchema,
    *,
    alias: str | None = None,
    metadata: Any = None
) -> ComputedField

ComputedFields are properties of a model or dataclass that are included in serialization.

Parameters:

Name	Type	Description	Default
`property_name`	`str`	The name of the property on the model or dataclass	required
`return_schema`	`CoreSchema`	The schema used for the type returned by the computed field	required
`alias`	`str \| None`	The name to use in the serialized output	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`

Source code in pydantic_core/core_schema.py

def computed_field(
    property_name: str, return_schema: CoreSchema, *, alias: str | None = None, metadata: Any = None
) -> ComputedField:
    """
    ComputedFields are properties of a model or dataclass that are included in serialization.

    Args:
        property_name: The name of the property on the model or dataclass
        return_schema: The schema used for the type returned by the computed field
        alias: The name to use in the serialized output
        metadata: Any other information you want to include with the schema, not used by pydantic-core
    """
    return _dict_not_none(
        type='computed-field', property_name=property_name, return_schema=return_schema, alias=alias, metadata=metadata
    )

any_schema ¶

any_schema(
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> AnySchema

Returns a schema that matches any value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.any_schema()
v = SchemaValidator(schema)
assert v.validate_python(1) == 1

Parameters:

Name	Type	Description	Default
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def any_schema(*, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None) -> AnySchema:
    """
    Returns a schema that matches any value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.any_schema()
    v = SchemaValidator(schema)
    assert v.validate_python(1) == 1
    ```

    Args:
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(type='any', ref=ref, metadata=metadata, serialization=serialization)

none_schema ¶

none_schema(
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> NoneSchema

Returns a schema that matches a None value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.none_schema()
v = SchemaValidator(schema)
assert v.validate_python(None) is None

Parameters:

Name	Type	Description	Default
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def none_schema(*, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None) -> NoneSchema:
    """
    Returns a schema that matches a None value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.none_schema()
    v = SchemaValidator(schema)
    assert v.validate_python(None) is None
    ```

    Args:
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(type='none', ref=ref, metadata=metadata, serialization=serialization)

bool_schema ¶

bool_schema(
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> BoolSchema

Returns a schema that matches a bool value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.bool_schema()
v = SchemaValidator(schema)
assert v.validate_python('True') is True

Parameters:

Name	Type	Description	Default
`strict`	`bool \| None`	Whether the value should be a bool or a value that can be converted to a bool	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def bool_schema(
    strict: bool | None = None, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None
) -> BoolSchema:
    """
    Returns a schema that matches a bool value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.bool_schema()
    v = SchemaValidator(schema)
    assert v.validate_python('True') is True
    ```

    Args:
        strict: Whether the value should be a bool or a value that can be converted to a bool
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(type='bool', strict=strict, ref=ref, metadata=metadata, serialization=serialization)

int_schema ¶

int_schema(
    *,
    multiple_of: int | None = None,
    le: int | None = None,
    ge: int | None = None,
    lt: int | None = None,
    gt: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> IntSchema

Returns a schema that matches a int value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.int_schema(multiple_of=2, le=6, ge=2)
v = SchemaValidator(schema)
assert v.validate_python('4') == 4

Parameters:

Name	Type	Description	Default
`multiple_of`	`int \| None`	The value must be a multiple of this number	`None`
`le`	`int \| None`	The value must be less than or equal to this number	`None`
`ge`	`int \| None`	The value must be greater than or equal to this number	`None`
`lt`	`int \| None`	The value must be strictly less than this number	`None`
`gt`	`int \| None`	The value must be strictly greater than this number	`None`
`strict`	`bool \| None`	Whether the value should be a int or a value that can be converted to a int	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def int_schema(
    *,
    multiple_of: int | None = None,
    le: int | None = None,
    ge: int | None = None,
    lt: int | None = None,
    gt: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> IntSchema:
    """
    Returns a schema that matches a int value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.int_schema(multiple_of=2, le=6, ge=2)
    v = SchemaValidator(schema)
    assert v.validate_python('4') == 4
    ```

    Args:
        multiple_of: The value must be a multiple of this number
        le: The value must be less than or equal to this number
        ge: The value must be greater than or equal to this number
        lt: The value must be strictly less than this number
        gt: The value must be strictly greater than this number
        strict: Whether the value should be a int or a value that can be converted to a int
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='int',
        multiple_of=multiple_of,
        le=le,
        ge=ge,
        lt=lt,
        gt=gt,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

float_schema ¶

float_schema(
    *,
    allow_inf_nan: bool | None = None,
    multiple_of: float | None = None,
    le: float | None = None,
    ge: float | None = None,
    lt: float | None = None,
    gt: float | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> FloatSchema

Returns a schema that matches a float value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.float_schema(le=0.8, ge=0.2)
v = SchemaValidator(schema)
assert v.validate_python('0.5') == 0.5

Parameters:

Name	Type	Description	Default
`allow_inf_nan`	`bool \| None`	Whether to allow inf and nan values	`None`
`multiple_of`	`float \| None`	The value must be a multiple of this number	`None`
`le`	`float \| None`	The value must be less than or equal to this number	`None`
`ge`	`float \| None`	The value must be greater than or equal to this number	`None`
`lt`	`float \| None`	The value must be strictly less than this number	`None`
`gt`	`float \| None`	The value must be strictly greater than this number	`None`
`strict`	`bool \| None`	Whether the value should be a float or a value that can be converted to a float	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def float_schema(
    *,
    allow_inf_nan: bool | None = None,
    multiple_of: float | None = None,
    le: float | None = None,
    ge: float | None = None,
    lt: float | None = None,
    gt: float | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> FloatSchema:
    """
    Returns a schema that matches a float value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.float_schema(le=0.8, ge=0.2)
    v = SchemaValidator(schema)
    assert v.validate_python('0.5') == 0.5
    ```

    Args:
        allow_inf_nan: Whether to allow inf and nan values
        multiple_of: The value must be a multiple of this number
        le: The value must be less than or equal to this number
        ge: The value must be greater than or equal to this number
        lt: The value must be strictly less than this number
        gt: The value must be strictly greater than this number
        strict: Whether the value should be a float or a value that can be converted to a float
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='float',
        allow_inf_nan=allow_inf_nan,
        multiple_of=multiple_of,
        le=le,
        ge=ge,
        lt=lt,
        gt=gt,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

decimal_schema ¶

decimal_schema(
    *,
    allow_inf_nan: bool = None,
    multiple_of: Decimal | None = None,
    le: Decimal | None = None,
    ge: Decimal | None = None,
    lt: Decimal | None = None,
    gt: Decimal | None = None,
    max_digits: int | None = None,
    decimal_places: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> DecimalSchema

Returns a schema that matches a decimal value, e.g.:

from decimal import Decimal
from pydantic_core import SchemaValidator, core_schema

schema = core_schema.decimal_schema(le=0.8, ge=0.2)
v = SchemaValidator(schema)
assert v.validate_python('0.5') == Decimal('0.5')

Parameters:

Name	Type	Description	Default
`allow_inf_nan`	`bool`	Whether to allow inf and nan values	`None`
`multiple_of`	`Decimal \| None`	The value must be a multiple of this number	`None`
`le`	`Decimal \| None`	The value must be less than or equal to this number	`None`
`ge`	`Decimal \| None`	The value must be greater than or equal to this number	`None`
`lt`	`Decimal \| None`	The value must be strictly less than this number	`None`
`gt`	`Decimal \| None`	The value must be strictly greater than this number	`None`
`max_digits`	`int \| None`	The maximum number of decimal digits allowed	`None`
`decimal_places`	`int \| None`	The maximum number of decimal places allowed	`None`
`strict`	`bool \| None`	Whether the value should be a float or a value that can be converted to a float	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def decimal_schema(
    *,
    allow_inf_nan: bool = None,
    multiple_of: Decimal | None = None,
    le: Decimal | None = None,
    ge: Decimal | None = None,
    lt: Decimal | None = None,
    gt: Decimal | None = None,
    max_digits: int | None = None,
    decimal_places: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> DecimalSchema:
    """
    Returns a schema that matches a decimal value, e.g.:

    ```py
    from decimal import Decimal
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.decimal_schema(le=0.8, ge=0.2)
    v = SchemaValidator(schema)
    assert v.validate_python('0.5') == Decimal('0.5')
    ```

    Args:
        allow_inf_nan: Whether to allow inf and nan values
        multiple_of: The value must be a multiple of this number
        le: The value must be less than or equal to this number
        ge: The value must be greater than or equal to this number
        lt: The value must be strictly less than this number
        gt: The value must be strictly greater than this number
        max_digits: The maximum number of decimal digits allowed
        decimal_places: The maximum number of decimal places allowed
        strict: Whether the value should be a float or a value that can be converted to a float
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='decimal',
        gt=gt,
        ge=ge,
        lt=lt,
        le=le,
        max_digits=max_digits,
        decimal_places=decimal_places,
        multiple_of=multiple_of,
        allow_inf_nan=allow_inf_nan,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

str_schema ¶

str_schema(
    *,
    pattern: str | None = None,
    max_length: int | None = None,
    min_length: int | None = None,
    strip_whitespace: bool | None = None,
    to_lower: bool | None = None,
    to_upper: bool | None = None,
    regex_engine: Literal["rust-regex", "python-re"]
    | None = None,
    strict: bool | None = None,
    coerce_numbers_to_str: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> StringSchema

Returns a schema that matches a string value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.str_schema(max_length=10, min_length=2)
v = SchemaValidator(schema)
assert v.validate_python('hello') == 'hello'

Parameters:

Name	Type	Description	Default
`pattern`	`str \| None`	A regex pattern that the value must match	`None`
`max_length`	`int \| None`	The value must be at most this length	`None`
`min_length`	`int \| None`	The value must be at least this length	`None`
`strip_whitespace`	`bool \| None`	Whether to strip whitespace from the value	`None`
`to_lower`	`bool \| None`	Whether to convert the value to lowercase	`None`
`to_upper`	`bool \| None`	Whether to convert the value to uppercase	`None`
`regex_engine`	`Literal['rust-regex', 'python-re'] \| None`	The regex engine to use for pattern validation. Default is 'rust-regex'. - `rust-regex` uses the `regex` Rust crate, which is non-backtracking and therefore more DDoS resistant, but does not support all regex features. - `python-re` use the `re` module, which supports all regex features, but may be slower.	`None`
`strict`	`bool \| None`	Whether the value should be a string or a value that can be converted to a string	`None`
`coerce_numbers_to_str`	`bool \| None`	Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode).	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def str_schema(
    *,
    pattern: str | None = None,
    max_length: int | None = None,
    min_length: int | None = None,
    strip_whitespace: bool | None = None,
    to_lower: bool | None = None,
    to_upper: bool | None = None,
    regex_engine: Literal['rust-regex', 'python-re'] | None = None,
    strict: bool | None = None,
    coerce_numbers_to_str: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> StringSchema:
    """
    Returns a schema that matches a string value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.str_schema(max_length=10, min_length=2)
    v = SchemaValidator(schema)
    assert v.validate_python('hello') == 'hello'
    ```

    Args:
        pattern: A regex pattern that the value must match
        max_length: The value must be at most this length
        min_length: The value must be at least this length
        strip_whitespace: Whether to strip whitespace from the value
        to_lower: Whether to convert the value to lowercase
        to_upper: Whether to convert the value to uppercase
        regex_engine: The regex engine to use for pattern validation. Default is 'rust-regex'.
            - `rust-regex` uses the [`regex`](https://docs.rs/regex) Rust
              crate, which is non-backtracking and therefore more DDoS
              resistant, but does not support all regex features.
            - `python-re` use the [`re`](https://docs.python.org/3/library/re.html) module,
              which supports all regex features, but may be slower.
        strict: Whether the value should be a string or a value that can be converted to a string
        coerce_numbers_to_str: Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode).
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='str',
        pattern=pattern,
        max_length=max_length,
        min_length=min_length,
        strip_whitespace=strip_whitespace,
        to_lower=to_lower,
        to_upper=to_upper,
        regex_engine=regex_engine,
        strict=strict,
        coerce_numbers_to_str=coerce_numbers_to_str,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

bytes_schema ¶

bytes_schema(
    *,
    max_length: int | None = None,
    min_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> BytesSchema

Returns a schema that matches a bytes value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.bytes_schema(max_length=10, min_length=2)
v = SchemaValidator(schema)
assert v.validate_python(b'hello') == b'hello'

Parameters:

Name	Type	Description	Default
`max_length`	`int \| None`	The value must be at most this length	`None`
`min_length`	`int \| None`	The value must be at least this length	`None`
`strict`	`bool \| None`	Whether the value should be a bytes or a value that can be converted to a bytes	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def bytes_schema(
    *,
    max_length: int | None = None,
    min_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> BytesSchema:
    """
    Returns a schema that matches a bytes value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.bytes_schema(max_length=10, min_length=2)
    v = SchemaValidator(schema)
    assert v.validate_python(b'hello') == b'hello'
    ```

    Args:
        max_length: The value must be at most this length
        min_length: The value must be at least this length
        strict: Whether the value should be a bytes or a value that can be converted to a bytes
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='bytes',
        max_length=max_length,
        min_length=min_length,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

date_schema ¶

date_schema(
    *,
    strict: bool | None = None,
    le: date | None = None,
    ge: date | None = None,
    lt: date | None = None,
    gt: date | None = None,
    now_op: Literal["past", "future"] | None = None,
    now_utc_offset: int | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> DateSchema

Returns a schema that matches a date value, e.g.:

from datetime import date
from pydantic_core import SchemaValidator, core_schema

schema = core_schema.date_schema(le=date(2020, 1, 1), ge=date(2019, 1, 1))
v = SchemaValidator(schema)
assert v.validate_python(date(2019, 6, 1)) == date(2019, 6, 1)

Parameters:

Name	Type	Description	Default
`strict`	`bool \| None`	Whether the value should be a date or a value that can be converted to a date	`None`
`le`	`date \| None`	The value must be less than or equal to this date	`None`
`ge`	`date \| None`	The value must be greater than or equal to this date	`None`
`lt`	`date \| None`	The value must be strictly less than this date	`None`
`gt`	`date \| None`	The value must be strictly greater than this date	`None`
`now_op`	`Literal['past', 'future'] \| None`	The value must be in the past or future relative to the current date	`None`
`now_utc_offset`	`int \| None`	The value must be in the past or future relative to the current date with this utc offset	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def date_schema(
    *,
    strict: bool | None = None,
    le: date | None = None,
    ge: date | None = None,
    lt: date | None = None,
    gt: date | None = None,
    now_op: Literal['past', 'future'] | None = None,
    now_utc_offset: int | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> DateSchema:
    """
    Returns a schema that matches a date value, e.g.:

    ```py
    from datetime import date
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.date_schema(le=date(2020, 1, 1), ge=date(2019, 1, 1))
    v = SchemaValidator(schema)
    assert v.validate_python(date(2019, 6, 1)) == date(2019, 6, 1)
    ```

    Args:
        strict: Whether the value should be a date or a value that can be converted to a date
        le: The value must be less than or equal to this date
        ge: The value must be greater than or equal to this date
        lt: The value must be strictly less than this date
        gt: The value must be strictly greater than this date
        now_op: The value must be in the past or future relative to the current date
        now_utc_offset: The value must be in the past or future relative to the current date with this utc offset
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='date',
        strict=strict,
        le=le,
        ge=ge,
        lt=lt,
        gt=gt,
        now_op=now_op,
        now_utc_offset=now_utc_offset,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

time_schema ¶

time_schema(
    *,
    strict: bool | None = None,
    le: time | None = None,
    ge: time | None = None,
    lt: time | None = None,
    gt: time | None = None,
    tz_constraint: Literal["aware", "naive"]
    | int
    | None = None,
    microseconds_precision: Literal[
        "truncate", "error"
    ] = "truncate",
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> TimeSchema

Returns a schema that matches a time value, e.g.:

from datetime import time
from pydantic_core import SchemaValidator, core_schema

schema = core_schema.time_schema(le=time(12, 0, 0), ge=time(6, 0, 0))
v = SchemaValidator(schema)
assert v.validate_python(time(9, 0, 0)) == time(9, 0, 0)

Parameters:

Name	Type	Description	Default
`strict`	`bool \| None`	Whether the value should be a time or a value that can be converted to a time	`None`
`le`	`time \| None`	The value must be less than or equal to this time	`None`
`ge`	`time \| None`	The value must be greater than or equal to this time	`None`
`lt`	`time \| None`	The value must be strictly less than this time	`None`
`gt`	`time \| None`	The value must be strictly greater than this time	`None`
`tz_constraint`	`Literal['aware', 'naive'] \| int \| None`	The value must be timezone aware or naive, or an int to indicate required tz offset	`None`
`microseconds_precision`	`Literal['truncate', 'error']`	The behavior when seconds have more than 6 digits or microseconds is too large	`'truncate'`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def time_schema(
    *,
    strict: bool | None = None,
    le: time | None = None,
    ge: time | None = None,
    lt: time | None = None,
    gt: time | None = None,
    tz_constraint: Literal['aware', 'naive'] | int | None = None,
    microseconds_precision: Literal['truncate', 'error'] = 'truncate',
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> TimeSchema:
    """
    Returns a schema that matches a time value, e.g.:

    ```py
    from datetime import time
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.time_schema(le=time(12, 0, 0), ge=time(6, 0, 0))
    v = SchemaValidator(schema)
    assert v.validate_python(time(9, 0, 0)) == time(9, 0, 0)
    ```

    Args:
        strict: Whether the value should be a time or a value that can be converted to a time
        le: The value must be less than or equal to this time
        ge: The value must be greater than or equal to this time
        lt: The value must be strictly less than this time
        gt: The value must be strictly greater than this time
        tz_constraint: The value must be timezone aware or naive, or an int to indicate required tz offset
        microseconds_precision: The behavior when seconds have more than 6 digits or microseconds is too large
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='time',
        strict=strict,
        le=le,
        ge=ge,
        lt=lt,
        gt=gt,
        tz_constraint=tz_constraint,
        microseconds_precision=microseconds_precision,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

datetime_schema ¶

datetime_schema(
    *,
    strict: bool | None = None,
    le: datetime | None = None,
    ge: datetime | None = None,
    lt: datetime | None = None,
    gt: datetime | None = None,
    now_op: Literal["past", "future"] | None = None,
    tz_constraint: Literal["aware", "naive"]
    | int
    | None = None,
    now_utc_offset: int | None = None,
    microseconds_precision: Literal[
        "truncate", "error"
    ] = "truncate",
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> DatetimeSchema

Returns a schema that matches a datetime value, e.g.:

from datetime import datetime
from pydantic_core import SchemaValidator, core_schema

schema = core_schema.datetime_schema()
v = SchemaValidator(schema)
now = datetime.now()
assert v.validate_python(str(now)) == now

Parameters:

Name	Type	Description	Default
`strict`	`bool \| None`	Whether the value should be a datetime or a value that can be converted to a datetime	`None`
`le`	`datetime \| None`	The value must be less than or equal to this datetime	`None`
`ge`	`datetime \| None`	The value must be greater than or equal to this datetime	`None`
`lt`	`datetime \| None`	The value must be strictly less than this datetime	`None`
`gt`	`datetime \| None`	The value must be strictly greater than this datetime	`None`
`now_op`	`Literal['past', 'future'] \| None`	The value must be in the past or future relative to the current datetime	`None`
`tz_constraint`	`Literal['aware', 'naive'] \| int \| None`	The value must be timezone aware or naive, or an int to indicate required tz offset TODO: use of a tzinfo where offset changes based on the datetime is not yet supported	`None`
`now_utc_offset`	`int \| None`	The value must be in the past or future relative to the current datetime with this utc offset	`None`
`microseconds_precision`	`Literal['truncate', 'error']`	The behavior when seconds have more than 6 digits or microseconds is too large	`'truncate'`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def datetime_schema(
    *,
    strict: bool | None = None,
    le: datetime | None = None,
    ge: datetime | None = None,
    lt: datetime | None = None,
    gt: datetime | None = None,
    now_op: Literal['past', 'future'] | None = None,
    tz_constraint: Literal['aware', 'naive'] | int | None = None,
    now_utc_offset: int | None = None,
    microseconds_precision: Literal['truncate', 'error'] = 'truncate',
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> DatetimeSchema:
    """
    Returns a schema that matches a datetime value, e.g.:

    ```py
    from datetime import datetime
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.datetime_schema()
    v = SchemaValidator(schema)
    now = datetime.now()
    assert v.validate_python(str(now)) == now
    ```

    Args:
        strict: Whether the value should be a datetime or a value that can be converted to a datetime
        le: The value must be less than or equal to this datetime
        ge: The value must be greater than or equal to this datetime
        lt: The value must be strictly less than this datetime
        gt: The value must be strictly greater than this datetime
        now_op: The value must be in the past or future relative to the current datetime
        tz_constraint: The value must be timezone aware or naive, or an int to indicate required tz offset
            TODO: use of a tzinfo where offset changes based on the datetime is not yet supported
        now_utc_offset: The value must be in the past or future relative to the current datetime with this utc offset
        microseconds_precision: The behavior when seconds have more than 6 digits or microseconds is too large
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='datetime',
        strict=strict,
        le=le,
        ge=ge,
        lt=lt,
        gt=gt,
        now_op=now_op,
        tz_constraint=tz_constraint,
        now_utc_offset=now_utc_offset,
        microseconds_precision=microseconds_precision,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

timedelta_schema ¶

timedelta_schema(
    *,
    strict: bool | None = None,
    le: timedelta | None = None,
    ge: timedelta | None = None,
    lt: timedelta | None = None,
    gt: timedelta | None = None,
    microseconds_precision: Literal[
        "truncate", "error"
    ] = "truncate",
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> TimedeltaSchema

Returns a schema that matches a timedelta value, e.g.:

from datetime import timedelta
from pydantic_core import SchemaValidator, core_schema

schema = core_schema.timedelta_schema(le=timedelta(days=1), ge=timedelta(days=0))
v = SchemaValidator(schema)
assert v.validate_python(timedelta(hours=12)) == timedelta(hours=12)

Parameters:

Name	Type	Description	Default
`strict`	`bool \| None`	Whether the value should be a timedelta or a value that can be converted to a timedelta	`None`
`le`	`timedelta \| None`	The value must be less than or equal to this timedelta	`None`
`ge`	`timedelta \| None`	The value must be greater than or equal to this timedelta	`None`
`lt`	`timedelta \| None`	The value must be strictly less than this timedelta	`None`
`gt`	`timedelta \| None`	The value must be strictly greater than this timedelta	`None`
`microseconds_precision`	`Literal['truncate', 'error']`	The behavior when seconds have more than 6 digits or microseconds is too large	`'truncate'`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def timedelta_schema(
    *,
    strict: bool | None = None,
    le: timedelta | None = None,
    ge: timedelta | None = None,
    lt: timedelta | None = None,
    gt: timedelta | None = None,
    microseconds_precision: Literal['truncate', 'error'] = 'truncate',
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> TimedeltaSchema:
    """
    Returns a schema that matches a timedelta value, e.g.:

    ```py
    from datetime import timedelta
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.timedelta_schema(le=timedelta(days=1), ge=timedelta(days=0))
    v = SchemaValidator(schema)
    assert v.validate_python(timedelta(hours=12)) == timedelta(hours=12)
    ```

    Args:
        strict: Whether the value should be a timedelta or a value that can be converted to a timedelta
        le: The value must be less than or equal to this timedelta
        ge: The value must be greater than or equal to this timedelta
        lt: The value must be strictly less than this timedelta
        gt: The value must be strictly greater than this timedelta
        microseconds_precision: The behavior when seconds have more than 6 digits or microseconds is too large
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='timedelta',
        strict=strict,
        le=le,
        ge=ge,
        lt=lt,
        gt=gt,
        microseconds_precision=microseconds_precision,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

literal_schema ¶

literal_schema(
    expected: list[Any],
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> LiteralSchema

Returns a schema that matches a literal value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.literal_schema(['hello', 'world'])
v = SchemaValidator(schema)
assert v.validate_python('hello') == 'hello'

Parameters:

Name	Type	Description	Default
`expected`	`list[Any]`	The value must be one of these values	required
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def literal_schema(
    expected: list[Any], *, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None
) -> LiteralSchema:
    """
    Returns a schema that matches a literal value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.literal_schema(['hello', 'world'])
    v = SchemaValidator(schema)
    assert v.validate_python('hello') == 'hello'
    ```

    Args:
        expected: The value must be one of these values
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(type='literal', expected=expected, ref=ref, metadata=metadata, serialization=serialization)

enum_schema ¶

enum_schema(
    cls: Any,
    members: list[Any],
    *,
    sub_type: Literal["str", "int", "float"] | None = None,
    missing: Callable[[Any], Any] | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> EnumSchema

Returns a schema that matches an enum value, e.g.:

from enum import Enum
from pydantic_core import SchemaValidator, core_schema

class Color(Enum):
    RED = 1
    GREEN = 2
    BLUE = 3

schema = core_schema.enum_schema(Color, list(Color.__members__.values()))
v = SchemaValidator(schema)
assert v.validate_python(2) is Color.GREEN

Parameters:

Name	Type	Description	Default
`cls`	`Any`	The enum class	required
`members`	`list[Any]`	The members of the enum, generally `list(MyEnum.__members__.values())`	required
`sub_type`	`Literal['str', 'int', 'float'] \| None`	The type of the enum, either 'str' or 'int' or None for plain enums	`None`
`missing`	`Callable[[Any], Any] \| None`	A function to use when the value is not found in the enum, from `_missing_`	`None`
`strict`	`bool \| None`	Whether to use strict mode, defaults to False	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def enum_schema(
    cls: Any,
    members: list[Any],
    *,
    sub_type: Literal['str', 'int', 'float'] | None = None,
    missing: Callable[[Any], Any] | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> EnumSchema:
    """
    Returns a schema that matches an enum value, e.g.:

    ```py
    from enum import Enum
    from pydantic_core import SchemaValidator, core_schema

    class Color(Enum):
        RED = 1
        GREEN = 2
        BLUE = 3

    schema = core_schema.enum_schema(Color, list(Color.__members__.values()))
    v = SchemaValidator(schema)
    assert v.validate_python(2) is Color.GREEN
    ```

    Args:
        cls: The enum class
        members: The members of the enum, generally `list(MyEnum.__members__.values())`
        sub_type: The type of the enum, either 'str' or 'int' or None for plain enums
        missing: A function to use when the value is not found in the enum, from `_missing_`
        strict: Whether to use strict mode, defaults to False
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='enum',
        cls=cls,
        members=members,
        sub_type=sub_type,
        missing=missing,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

is_instance_schema ¶

is_instance_schema(
    cls: Any,
    *,
    cls_repr: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> IsInstanceSchema

Returns a schema that checks if a value is an instance of a class, equivalent to python's isinstance method, e.g.:

from pydantic_core import SchemaValidator, core_schema

class A:
    pass

schema = core_schema.is_instance_schema(cls=A)
v = SchemaValidator(schema)
v.validate_python(A())

Parameters:

Name	Type	Description	Default
`cls`	`Any`	The value must be an instance of this class	required
`cls_repr`	`str \| None`	If provided this string is used in the validator name instead of `repr(cls)`	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def is_instance_schema(
    cls: Any,
    *,
    cls_repr: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> IsInstanceSchema:
    """
    Returns a schema that checks if a value is an instance of a class, equivalent to python's `isinstance` method, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    class A:
        pass

    schema = core_schema.is_instance_schema(cls=A)
    v = SchemaValidator(schema)
    v.validate_python(A())
    ```

    Args:
        cls: The value must be an instance of this class
        cls_repr: If provided this string is used in the validator name instead of `repr(cls)`
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='is-instance', cls=cls, cls_repr=cls_repr, ref=ref, metadata=metadata, serialization=serialization
    )

is_subclass_schema ¶

is_subclass_schema(
    cls: Type[Any],
    *,
    cls_repr: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> IsInstanceSchema

Returns a schema that checks if a value is a subtype of a class, equivalent to python's issubclass method, e.g.:

from pydantic_core import SchemaValidator, core_schema

class A:
    pass

class B(A):
    pass

schema = core_schema.is_subclass_schema(cls=A)
v = SchemaValidator(schema)
v.validate_python(B)

Parameters:

Name	Type	Description	Default
`cls`	`Type[Any]`	The value must be a subclass of this class	required
`cls_repr`	`str \| None`	If provided this string is used in the validator name instead of `repr(cls)`	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def is_subclass_schema(
    cls: Type[Any],
    *,
    cls_repr: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> IsInstanceSchema:
    """
    Returns a schema that checks if a value is a subtype of a class, equivalent to python's `issubclass` method, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    class A:
        pass

    class B(A):
        pass

    schema = core_schema.is_subclass_schema(cls=A)
    v = SchemaValidator(schema)
    v.validate_python(B)
    ```

    Args:
        cls: The value must be a subclass of this class
        cls_repr: If provided this string is used in the validator name instead of `repr(cls)`
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='is-subclass', cls=cls, cls_repr=cls_repr, ref=ref, metadata=metadata, serialization=serialization
    )

callable_schema ¶

callable_schema(
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> CallableSchema

Returns a schema that checks if a value is callable, equivalent to python's callable method, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.callable_schema()
v = SchemaValidator(schema)
v.validate_python(min)

Parameters:

Name	Type	Description	Default
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def callable_schema(
    *, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None
) -> CallableSchema:
    """
    Returns a schema that checks if a value is callable, equivalent to python's `callable` method, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.callable_schema()
    v = SchemaValidator(schema)
    v.validate_python(min)
    ```

    Args:
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(type='callable', ref=ref, metadata=metadata, serialization=serialization)

list_schema ¶

list_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None
) -> ListSchema

Returns a schema that matches a list value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.list_schema(core_schema.int_schema(), min_length=0, max_length=10)
v = SchemaValidator(schema)
assert v.validate_python(['4']) == [4]

Parameters:

Name	Type	Description	Default
`items_schema`	`CoreSchema \| None`	The value must be a list of items that match this schema	`None`
`min_length`	`int \| None`	The value must be a list with at least this many items	`None`
`max_length`	`int \| None`	The value must be a list with at most this many items	`None`
`strict`	`bool \| None`	The value must be a list with exactly this many items	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`IncExSeqOrElseSerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def list_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None,
) -> ListSchema:
    """
    Returns a schema that matches a list value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.list_schema(core_schema.int_schema(), min_length=0, max_length=10)
    v = SchemaValidator(schema)
    assert v.validate_python(['4']) == [4]
    ```

    Args:
        items_schema: The value must be a list of items that match this schema
        min_length: The value must be a list with at least this many items
        max_length: The value must be a list with at most this many items
        strict: The value must be a list with exactly this many items
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='list',
        items_schema=items_schema,
        min_length=min_length,
        max_length=max_length,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

tuple_positional_schema ¶

tuple_positional_schema(
    items_schema: list[CoreSchema],
    *,
    extras_schema: CoreSchema | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None
) -> TupleSchema

Returns a schema that matches a tuple of schemas, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.tuple_positional_schema(
    [core_schema.int_schema(), core_schema.str_schema()]
)
v = SchemaValidator(schema)
assert v.validate_python((1, 'hello')) == (1, 'hello')

Parameters:

Name	Type	Description	Default
`items_schema`	`list[CoreSchema]`	The value must be a tuple with items that match these schemas	required
`extras_schema`	`CoreSchema \| None`	The value must be a tuple with items that match this schema This was inspired by JSON schema's `prefixItems` and `items` fields. In python's `typing.Tuple`, you can't specify a type for "extra" items -- they must all be the same type if the length is variable. So this field won't be set from a `typing.Tuple` annotation on a pydantic model.	`None`
`strict`	`bool \| None`	The value must be a tuple with exactly this many items	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`IncExSeqOrElseSerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def tuple_positional_schema(
    items_schema: list[CoreSchema],
    *,
    extras_schema: CoreSchema | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None,
) -> TupleSchema:
    """
    Returns a schema that matches a tuple of schemas, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.tuple_positional_schema(
        [core_schema.int_schema(), core_schema.str_schema()]
    )
    v = SchemaValidator(schema)
    assert v.validate_python((1, 'hello')) == (1, 'hello')
    ```

    Args:
        items_schema: The value must be a tuple with items that match these schemas
        extras_schema: The value must be a tuple with items that match this schema
            This was inspired by JSON schema's `prefixItems` and `items` fields.
            In python's `typing.Tuple`, you can't specify a type for "extra" items -- they must all be the same type
            if the length is variable. So this field won't be set from a `typing.Tuple` annotation on a pydantic model.
        strict: The value must be a tuple with exactly this many items
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    if extras_schema is not None:
        variadic_item_index = len(items_schema)
        items_schema = items_schema + [extras_schema]
    else:
        variadic_item_index = None
    return tuple_schema(
        items_schema=items_schema,
        variadic_item_index=variadic_item_index,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

tuple_variable_schema ¶

tuple_variable_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None
) -> TupleSchema

Returns a schema that matches a tuple of a given schema, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.tuple_variable_schema(
    items_schema=core_schema.int_schema(), min_length=0, max_length=10
)
v = SchemaValidator(schema)
assert v.validate_python(('1', 2, 3)) == (1, 2, 3)

Parameters:

Name	Type	Description	Default
`items_schema`	`CoreSchema \| None`	The value must be a tuple with items that match this schema	`None`
`min_length`	`int \| None`	The value must be a tuple with at least this many items	`None`
`max_length`	`int \| None`	The value must be a tuple with at most this many items	`None`
`strict`	`bool \| None`	The value must be a tuple with exactly this many items	`None`
`ref`	`str \| None`	Optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`IncExSeqOrElseSerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def tuple_variable_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None,
) -> TupleSchema:
    """
    Returns a schema that matches a tuple of a given schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.tuple_variable_schema(
        items_schema=core_schema.int_schema(), min_length=0, max_length=10
    )
    v = SchemaValidator(schema)
    assert v.validate_python(('1', 2, 3)) == (1, 2, 3)
    ```

    Args:
        items_schema: The value must be a tuple with items that match this schema
        min_length: The value must be a tuple with at least this many items
        max_length: The value must be a tuple with at most this many items
        strict: The value must be a tuple with exactly this many items
        ref: Optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return tuple_schema(
        items_schema=[items_schema or any_schema()],
        variadic_item_index=0,
        min_length=min_length,
        max_length=max_length,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

tuple_schema ¶

tuple_schema(
    items_schema: list[CoreSchema],
    *,
    variadic_item_index: int | None = None,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None
) -> TupleSchema

Returns a schema that matches a tuple of schemas, with an optional variadic item, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.tuple_schema(
    [core_schema.int_schema(), core_schema.str_schema(), core_schema.float_schema()],
    variadic_item_index=1,
)
v = SchemaValidator(schema)
assert v.validate_python((1, 'hello', 'world', 1.5)) == (1, 'hello', 'world', 1.5)

Parameters:

Name	Type	Description	Default
`items_schema`	`list[CoreSchema]`	The value must be a tuple with items that match these schemas	required
`variadic_item_index`	`int \| None`	The index of the schema in `items_schema` to be treated as variadic (following PEP 646)	`None`
`min_length`	`int \| None`	The value must be a tuple with at least this many items	`None`
`max_length`	`int \| None`	The value must be a tuple with at most this many items	`None`
`strict`	`bool \| None`	The value must be a tuple with exactly this many items	`None`
`ref`	`str \| None`	Optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`IncExSeqOrElseSerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def tuple_schema(
    items_schema: list[CoreSchema],
    *,
    variadic_item_index: int | None = None,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None,
) -> TupleSchema:
    """
    Returns a schema that matches a tuple of schemas, with an optional variadic item, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.tuple_schema(
        [core_schema.int_schema(), core_schema.str_schema(), core_schema.float_schema()],
        variadic_item_index=1,
    )
    v = SchemaValidator(schema)
    assert v.validate_python((1, 'hello', 'world', 1.5)) == (1, 'hello', 'world', 1.5)
    ```

    Args:
        items_schema: The value must be a tuple with items that match these schemas
        variadic_item_index: The index of the schema in `items_schema` to be treated as variadic (following PEP 646)
        min_length: The value must be a tuple with at least this many items
        max_length: The value must be a tuple with at most this many items
        strict: The value must be a tuple with exactly this many items
        ref: Optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='tuple',
        items_schema=items_schema,
        variadic_item_index=variadic_item_index,
        min_length=min_length,
        max_length=max_length,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

set_schema ¶

set_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> SetSchema

Returns a schema that matches a set of a given schema, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.set_schema(
    items_schema=core_schema.int_schema(), min_length=0, max_length=10
)
v = SchemaValidator(schema)
assert v.validate_python({1, '2', 3}) == {1, 2, 3}

Parameters:

Name	Type	Description	Default
`items_schema`	`CoreSchema \| None`	The value must be a set with items that match this schema	`None`
`min_length`	`int \| None`	The value must be a set with at least this many items	`None`
`max_length`	`int \| None`	The value must be a set with at most this many items	`None`
`strict`	`bool \| None`	The value must be a set with exactly this many items	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def set_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> SetSchema:
    """
    Returns a schema that matches a set of a given schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.set_schema(
        items_schema=core_schema.int_schema(), min_length=0, max_length=10
    )
    v = SchemaValidator(schema)
    assert v.validate_python({1, '2', 3}) == {1, 2, 3}
    ```

    Args:
        items_schema: The value must be a set with items that match this schema
        min_length: The value must be a set with at least this many items
        max_length: The value must be a set with at most this many items
        strict: The value must be a set with exactly this many items
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='set',
        items_schema=items_schema,
        min_length=min_length,
        max_length=max_length,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

frozenset_schema ¶

frozenset_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> FrozenSetSchema

Returns a schema that matches a frozenset of a given schema, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.frozenset_schema(
    items_schema=core_schema.int_schema(), min_length=0, max_length=10
)
v = SchemaValidator(schema)
assert v.validate_python(frozenset(range(3))) == frozenset({0, 1, 2})

Parameters:

Name	Type	Description	Default
`items_schema`	`CoreSchema \| None`	The value must be a frozenset with items that match this schema	`None`
`min_length`	`int \| None`	The value must be a frozenset with at least this many items	`None`
`max_length`	`int \| None`	The value must be a frozenset with at most this many items	`None`
`strict`	`bool \| None`	The value must be a frozenset with exactly this many items	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def frozenset_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> FrozenSetSchema:
    """
    Returns a schema that matches a frozenset of a given schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.frozenset_schema(
        items_schema=core_schema.int_schema(), min_length=0, max_length=10
    )
    v = SchemaValidator(schema)
    assert v.validate_python(frozenset(range(3))) == frozenset({0, 1, 2})
    ```

    Args:
        items_schema: The value must be a frozenset with items that match this schema
        min_length: The value must be a frozenset with at least this many items
        max_length: The value must be a frozenset with at most this many items
        strict: The value must be a frozenset with exactly this many items
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='frozenset',
        items_schema=items_schema,
        min_length=min_length,
        max_length=max_length,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

generator_schema ¶

generator_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None
) -> GeneratorSchema

Returns a schema that matches a generator value, e.g.:

from typing import Iterator
from pydantic_core import SchemaValidator, core_schema

def gen() -> Iterator[int]:
    yield 1

schema = core_schema.generator_schema(items_schema=core_schema.int_schema())
v = SchemaValidator(schema)
v.validate_python(gen())

Unlike other types, validated generators do not raise ValidationErrors eagerly, but instead will raise a ValidationError when a violating value is actually read from the generator. This is to ensure that "validated" generators retain the benefit of lazy evaluation.

Parameters:

Name	Type	Description	Default
`items_schema`	`CoreSchema \| None`	The value must be a generator with items that match this schema	`None`
`min_length`	`int \| None`	The value must be a generator that yields at least this many items	`None`
`max_length`	`int \| None`	The value must be a generator that yields at most this many items	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`IncExSeqOrElseSerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def generator_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None,
) -> GeneratorSchema:
    """
    Returns a schema that matches a generator value, e.g.:

    ```py
    from typing import Iterator
    from pydantic_core import SchemaValidator, core_schema

    def gen() -> Iterator[int]:
        yield 1

    schema = core_schema.generator_schema(items_schema=core_schema.int_schema())
    v = SchemaValidator(schema)
    v.validate_python(gen())
    ```

    Unlike other types, validated generators do not raise ValidationErrors eagerly,
    but instead will raise a ValidationError when a violating value is actually read from the generator.
    This is to ensure that "validated" generators retain the benefit of lazy evaluation.

    Args:
        items_schema: The value must be a generator with items that match this schema
        min_length: The value must be a generator that yields at least this many items
        max_length: The value must be a generator that yields at most this many items
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='generator',
        items_schema=items_schema,
        min_length=min_length,
        max_length=max_length,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

dict_schema ¶

dict_schema(
    keys_schema: CoreSchema | None = None,
    values_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> DictSchema

Returns a schema that matches a dict value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.dict_schema(
    keys_schema=core_schema.str_schema(), values_schema=core_schema.int_schema()
)
v = SchemaValidator(schema)
assert v.validate_python({'a': '1', 'b': 2}) == {'a': 1, 'b': 2}

Parameters:

Name	Type	Description	Default
`keys_schema`	`CoreSchema \| None`	The value must be a dict with keys that match this schema	`None`
`values_schema`	`CoreSchema \| None`	The value must be a dict with values that match this schema	`None`
`min_length`	`int \| None`	The value must be a dict with at least this many items	`None`
`max_length`	`int \| None`	The value must be a dict with at most this many items	`None`
`strict`	`bool \| None`	Whether the keys and values should be validated with strict mode	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def dict_schema(
    keys_schema: CoreSchema | None = None,
    values_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> DictSchema:
    """
    Returns a schema that matches a dict value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.dict_schema(
        keys_schema=core_schema.str_schema(), values_schema=core_schema.int_schema()
    )
    v = SchemaValidator(schema)
    assert v.validate_python({'a': '1', 'b': 2}) == {'a': 1, 'b': 2}
    ```

    Args:
        keys_schema: The value must be a dict with keys that match this schema
        values_schema: The value must be a dict with values that match this schema
        min_length: The value must be a dict with at least this many items
        max_length: The value must be a dict with at most this many items
        strict: Whether the keys and values should be validated with strict mode
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='dict',
        keys_schema=keys_schema,
        values_schema=values_schema,
        min_length=min_length,
        max_length=max_length,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

no_info_before_validator_function ¶

no_info_before_validator_function(
    function: NoInfoValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> BeforeValidatorFunctionSchema

Returns a schema that calls a validator function before validating, no info argument is provided, e.g.:

from pydantic_core import SchemaValidator, core_schema

def fn(v: bytes) -> str:
    return v.decode() + 'world'

func_schema = core_schema.no_info_before_validator_function(
    function=fn, schema=core_schema.str_schema()
)
schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)})

v = SchemaValidator(schema)
assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'}

Parameters:

Name	Type	Description	Default
`function`	`NoInfoValidatorFunction`	The validator function to call	required
`schema`	`CoreSchema`	The schema to validate the output of the validator function	required
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def no_info_before_validator_function(
    function: NoInfoValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> BeforeValidatorFunctionSchema:
    """
    Returns a schema that calls a validator function before validating, no `info` argument is provided, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    def fn(v: bytes) -> str:
        return v.decode() + 'world'

    func_schema = core_schema.no_info_before_validator_function(
        function=fn, schema=core_schema.str_schema()
    )
    schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)})

    v = SchemaValidator(schema)
    assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'}
    ```

    Args:
        function: The validator function to call
        schema: The schema to validate the output of the validator function
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='function-before',
        function={'type': 'no-info', 'function': function},
        schema=schema,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

with_info_before_validator_function ¶

with_info_before_validator_function(
    function: WithInfoValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> BeforeValidatorFunctionSchema

Returns a schema that calls a validator function before validation, the function is called with an info argument, e.g.:

from pydantic_core import SchemaValidator, core_schema

def fn(v: bytes, info: core_schema.ValidationInfo) -> str:
    assert info.data is not None
    assert info.field_name is not None
    return v.decode() + 'world'

func_schema = core_schema.with_info_before_validator_function(
    function=fn, schema=core_schema.str_schema(), field_name='a'
)
schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)})

v = SchemaValidator(schema)
assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'}

Parameters:

Name	Type	Description	Default
`function`	`WithInfoValidatorFunction`	The validator function to call	required
`field_name`	`str \| None`	The name of the field	`None`
`schema`	`CoreSchema`	The schema to validate the output of the validator function	required
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def with_info_before_validator_function(
    function: WithInfoValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> BeforeValidatorFunctionSchema:
    """
    Returns a schema that calls a validator function before validation, the function is called with
    an `info` argument, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    def fn(v: bytes, info: core_schema.ValidationInfo) -> str:
        assert info.data is not None
        assert info.field_name is not None
        return v.decode() + 'world'

    func_schema = core_schema.with_info_before_validator_function(
        function=fn, schema=core_schema.str_schema(), field_name='a'
    )
    schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)})

    v = SchemaValidator(schema)
    assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'}
    ```

    Args:
        function: The validator function to call
        field_name: The name of the field
        schema: The schema to validate the output of the validator function
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='function-before',
        function=_dict_not_none(type='with-info', function=function, field_name=field_name),
        schema=schema,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

no_info_after_validator_function ¶

no_info_after_validator_function(
    function: NoInfoValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> AfterValidatorFunctionSchema

Returns a schema that calls a validator function after validating, no info argument is provided, e.g.:

from pydantic_core import SchemaValidator, core_schema

def fn(v: str) -> str:
    return v + 'world'

func_schema = core_schema.no_info_after_validator_function(fn, core_schema.str_schema())
schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)})

v = SchemaValidator(schema)
assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'}

Parameters:

Name	Type	Description	Default
`function`	`NoInfoValidatorFunction`	The validator function to call after the schema is validated	required
`schema`	`CoreSchema`	The schema to validate before the validator function	required
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def no_info_after_validator_function(
    function: NoInfoValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> AfterValidatorFunctionSchema:
    """
    Returns a schema that calls a validator function after validating, no `info` argument is provided, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    def fn(v: str) -> str:
        return v + 'world'

    func_schema = core_schema.no_info_after_validator_function(fn, core_schema.str_schema())
    schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)})

    v = SchemaValidator(schema)
    assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'}
    ```

    Args:
        function: The validator function to call after the schema is validated
        schema: The schema to validate before the validator function
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='function-after',
        function={'type': 'no-info', 'function': function},
        schema=schema,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

with_info_after_validator_function ¶

with_info_after_validator_function(
    function: WithInfoValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> AfterValidatorFunctionSchema

Returns a schema that calls a validator function after validation, the function is called with an info argument, e.g.:

from pydantic_core import SchemaValidator, core_schema

def fn(v: str, info: core_schema.ValidationInfo) -> str:
    assert info.data is not None
    assert info.field_name is not None
    return v + 'world'

func_schema = core_schema.with_info_after_validator_function(
    function=fn, schema=core_schema.str_schema(), field_name='a'
)
schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)})

v = SchemaValidator(schema)
assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'}

Parameters:

Name	Type	Description	Default
`function`	`WithInfoValidatorFunction`	The validator function to call after the schema is validated	required
`schema`	`CoreSchema`	The schema to validate before the validator function	required
`field_name`	`str \| None`	The name of the field this validators is applied to, if any	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def with_info_after_validator_function(
    function: WithInfoValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> AfterValidatorFunctionSchema:
    """
    Returns a schema that calls a validator function after validation, the function is called with
    an `info` argument, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    def fn(v: str, info: core_schema.ValidationInfo) -> str:
        assert info.data is not None
        assert info.field_name is not None
        return v + 'world'

    func_schema = core_schema.with_info_after_validator_function(
        function=fn, schema=core_schema.str_schema(), field_name='a'
    )
    schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)})

    v = SchemaValidator(schema)
    assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'}
    ```

    Args:
        function: The validator function to call after the schema is validated
        schema: The schema to validate before the validator function
        field_name: The name of the field this validators is applied to, if any
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='function-after',
        function=_dict_not_none(type='with-info', function=function, field_name=field_name),
        schema=schema,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

no_info_wrap_validator_function ¶

no_info_wrap_validator_function(
    function: NoInfoWrapValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> WrapValidatorFunctionSchema

Returns a schema which calls a function with a validator callable argument which can optionally be used to call inner validation with the function logic, this is much like the "onion" implementation of middleware in many popular web frameworks, no info argument is passed, e.g.:

from pydantic_core import SchemaValidator, core_schema

def fn(
    v: str,
    validator: core_schema.ValidatorFunctionWrapHandler,
) -> str:
    return validator(input_value=v) + 'world'

schema = core_schema.no_info_wrap_validator_function(
    function=fn, schema=core_schema.str_schema()
)
v = SchemaValidator(schema)
assert v.validate_python('hello ') == 'hello world'

Parameters:

Name	Type	Description	Default
`function`	`NoInfoWrapValidatorFunction`	The validator function to call	required
`schema`	`CoreSchema`	The schema to validate the output of the validator function	required
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def no_info_wrap_validator_function(
    function: NoInfoWrapValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> WrapValidatorFunctionSchema:
    """
    Returns a schema which calls a function with a `validator` callable argument which can
    optionally be used to call inner validation with the function logic, this is much like the
    "onion" implementation of middleware in many popular web frameworks, no `info` argument is passed, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    def fn(
        v: str,
        validator: core_schema.ValidatorFunctionWrapHandler,
    ) -> str:
        return validator(input_value=v) + 'world'

    schema = core_schema.no_info_wrap_validator_function(
        function=fn, schema=core_schema.str_schema()
    )
    v = SchemaValidator(schema)
    assert v.validate_python('hello ') == 'hello world'
    ```

    Args:
        function: The validator function to call
        schema: The schema to validate the output of the validator function
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='function-wrap',
        function={'type': 'no-info', 'function': function},
        schema=schema,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

with_info_wrap_validator_function ¶

with_info_wrap_validator_function(
    function: WithInfoWrapValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> WrapValidatorFunctionSchema

Returns a schema which calls a function with a validator callable argument which can optionally be used to call inner validation with the function logic, this is much like the "onion" implementation of middleware in many popular web frameworks, an info argument is also passed, e.g.:

from pydantic_core import SchemaValidator, core_schema

def fn(
    v: str,
    validator: core_schema.ValidatorFunctionWrapHandler,
    info: core_schema.ValidationInfo,
) -> str:
    return validator(input_value=v) + 'world'

schema = core_schema.with_info_wrap_validator_function(
    function=fn, schema=core_schema.str_schema()
)
v = SchemaValidator(schema)
assert v.validate_python('hello ') == 'hello world'

Parameters:

Name	Type	Description	Default
`function`	`WithInfoWrapValidatorFunction`	The validator function to call	required
`schema`	`CoreSchema`	The schema to validate the output of the validator function	required
`field_name`	`str \| None`	The name of the field this validators is applied to, if any	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def with_info_wrap_validator_function(
    function: WithInfoWrapValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> WrapValidatorFunctionSchema:
    """
    Returns a schema which calls a function with a `validator` callable argument which can
    optionally be used to call inner validation with the function logic, this is much like the
    "onion" implementation of middleware in many popular web frameworks, an `info` argument is also passed, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    def fn(
        v: str,
        validator: core_schema.ValidatorFunctionWrapHandler,
        info: core_schema.ValidationInfo,
    ) -> str:
        return validator(input_value=v) + 'world'

    schema = core_schema.with_info_wrap_validator_function(
        function=fn, schema=core_schema.str_schema()
    )
    v = SchemaValidator(schema)
    assert v.validate_python('hello ') == 'hello world'
    ```

    Args:
        function: The validator function to call
        schema: The schema to validate the output of the validator function
        field_name: The name of the field this validators is applied to, if any
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='function-wrap',
        function=_dict_not_none(type='with-info', function=function, field_name=field_name),
        schema=schema,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

no_info_plain_validator_function ¶

no_info_plain_validator_function(
    function: NoInfoValidatorFunction,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> PlainValidatorFunctionSchema

Returns a schema that uses the provided function for validation, no info argument is passed, e.g.:

from pydantic_core import SchemaValidator, core_schema

def fn(v: str) -> str:
    assert 'hello' in v
    return v + 'world'

schema = core_schema.no_info_plain_validator_function(function=fn)
v = SchemaValidator(schema)
assert v.validate_python('hello ') == 'hello world'

Parameters:

Name	Type	Description	Default
`function`	`NoInfoValidatorFunction`	The validator function to call	required
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def no_info_plain_validator_function(
    function: NoInfoValidatorFunction,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> PlainValidatorFunctionSchema:
    """
    Returns a schema that uses the provided function for validation, no `info` argument is passed, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    def fn(v: str) -> str:
        assert 'hello' in v
        return v + 'world'

    schema = core_schema.no_info_plain_validator_function(function=fn)
    v = SchemaValidator(schema)
    assert v.validate_python('hello ') == 'hello world'
    ```

    Args:
        function: The validator function to call
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='function-plain',
        function={'type': 'no-info', 'function': function},
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

with_info_plain_validator_function ¶

with_info_plain_validator_function(
    function: WithInfoValidatorFunction,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> PlainValidatorFunctionSchema

Returns a schema that uses the provided function for validation, an info argument is passed, e.g.:

from pydantic_core import SchemaValidator, core_schema

def fn(v: str, info: core_schema.ValidationInfo) -> str:
    assert 'hello' in v
    return v + 'world'

schema = core_schema.with_info_plain_validator_function(function=fn)
v = SchemaValidator(schema)
assert v.validate_python('hello ') == 'hello world'

Parameters:

Name	Type	Description	Default
`function`	`WithInfoValidatorFunction`	The validator function to call	required
`field_name`	`str \| None`	The name of the field this validators is applied to, if any	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def with_info_plain_validator_function(
    function: WithInfoValidatorFunction,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> PlainValidatorFunctionSchema:
    """
    Returns a schema that uses the provided function for validation, an `info` argument is passed, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    def fn(v: str, info: core_schema.ValidationInfo) -> str:
        assert 'hello' in v
        return v + 'world'

    schema = core_schema.with_info_plain_validator_function(function=fn)
    v = SchemaValidator(schema)
    assert v.validate_python('hello ') == 'hello world'
    ```

    Args:
        function: The validator function to call
        field_name: The name of the field this validators is applied to, if any
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='function-plain',
        function=_dict_not_none(type='with-info', function=function, field_name=field_name),
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

with_default_schema ¶

with_default_schema(
    schema: CoreSchema,
    *,
    default: Any = PydanticUndefined,
    default_factory: Callable[[], Any] | None = None,
    on_error: Literal["raise", "omit", "default"]
    | None = None,
    validate_default: bool | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> WithDefaultSchema

Returns a schema that adds a default value to the given schema, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.with_default_schema(core_schema.str_schema(), default='hello')
wrapper_schema = core_schema.typed_dict_schema(
    {'a': core_schema.typed_dict_field(schema)}
)
v = SchemaValidator(wrapper_schema)
assert v.validate_python({}) == v.validate_python({'a': 'hello'})

Parameters:

Name	Type	Description	Default
`schema`	`CoreSchema`	The schema to add a default value to	required
`default`	`Any`	The default value to use	`PydanticUndefined`
`default_factory`	`Callable[[], Any] \| None`	A function that returns the default value to use	`None`
`on_error`	`Literal['raise', 'omit', 'default'] \| None`	What to do if the schema validation fails. One of 'raise', 'omit', 'default'	`None`
`validate_default`	`bool \| None`	Whether the default value should be validated	`None`
`strict`	`bool \| None`	Whether the underlying schema should be validated with strict mode	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def with_default_schema(
    schema: CoreSchema,
    *,
    default: Any = PydanticUndefined,
    default_factory: Callable[[], Any] | None = None,
    on_error: Literal['raise', 'omit', 'default'] | None = None,
    validate_default: bool | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> WithDefaultSchema:
    """
    Returns a schema that adds a default value to the given schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.with_default_schema(core_schema.str_schema(), default='hello')
    wrapper_schema = core_schema.typed_dict_schema(
        {'a': core_schema.typed_dict_field(schema)}
    )
    v = SchemaValidator(wrapper_schema)
    assert v.validate_python({}) == v.validate_python({'a': 'hello'})
    ```

    Args:
        schema: The schema to add a default value to
        default: The default value to use
        default_factory: A function that returns the default value to use
        on_error: What to do if the schema validation fails. One of 'raise', 'omit', 'default'
        validate_default: Whether the default value should be validated
        strict: Whether the underlying schema should be validated with strict mode
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    s = _dict_not_none(
        type='default',
        schema=schema,
        default_factory=default_factory,
        on_error=on_error,
        validate_default=validate_default,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )
    if default is not PydanticUndefined:
        s['default'] = default
    return s

nullable_schema ¶

nullable_schema(
    schema: CoreSchema,
    *,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> NullableSchema

Returns a schema that matches a nullable value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.nullable_schema(core_schema.str_schema())
v = SchemaValidator(schema)
assert v.validate_python(None) is None

Parameters:

Name	Type	Description	Default
`schema`	`CoreSchema`	The schema to wrap	required
`strict`	`bool \| None`	Whether the underlying schema should be validated with strict mode	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def nullable_schema(
    schema: CoreSchema,
    *,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> NullableSchema:
    """
    Returns a schema that matches a nullable value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.nullable_schema(core_schema.str_schema())
    v = SchemaValidator(schema)
    assert v.validate_python(None) is None
    ```

    Args:
        schema: The schema to wrap
        strict: Whether the underlying schema should be validated with strict mode
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='nullable', schema=schema, strict=strict, ref=ref, metadata=metadata, serialization=serialization
    )

union_schema ¶

union_schema(
    choices: list[CoreSchema | tuple[CoreSchema, str]],
    *,
    auto_collapse: bool | None = None,
    custom_error_type: str | None = None,
    custom_error_message: str | None = None,
    custom_error_context: dict[str, str | int]
    | None = None,
    mode: Literal["smart", "left_to_right"] | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> UnionSchema

Returns a schema that matches a union value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.union_schema([core_schema.str_schema(), core_schema.int_schema()])
v = SchemaValidator(schema)
assert v.validate_python('hello') == 'hello'
assert v.validate_python(1) == 1

Parameters:

Name	Type	Description	Default
`choices`	`list[CoreSchema \| tuple[CoreSchema, str]]`	The schemas to match. If a tuple, the second item is used as the label for the case.	required
`auto_collapse`	`bool \| None`	whether to automatically collapse unions with one element to the inner validator, default true	`None`
`custom_error_type`	`str \| None`	The custom error type to use if the validation fails	`None`
`custom_error_message`	`str \| None`	The custom error message to use if the validation fails	`None`
`custom_error_context`	`dict[str, str \| int] \| None`	The custom error context to use if the validation fails	`None`
`mode`	`Literal['smart', 'left_to_right'] \| None`	How to select which choice to return * `smart` (default) will try to return the choice which is the closest match to the input value * `left_to_right` will return the first choice in `choices` which succeeds validation	`None`
`strict`	`bool \| None`	Whether the underlying schemas should be validated with strict mode	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def union_schema(
    choices: list[CoreSchema | tuple[CoreSchema, str]],
    *,
    auto_collapse: bool | None = None,
    custom_error_type: str | None = None,
    custom_error_message: str | None = None,
    custom_error_context: dict[str, str | int] | None = None,
    mode: Literal['smart', 'left_to_right'] | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> UnionSchema:
    """
    Returns a schema that matches a union value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.union_schema([core_schema.str_schema(), core_schema.int_schema()])
    v = SchemaValidator(schema)
    assert v.validate_python('hello') == 'hello'
    assert v.validate_python(1) == 1
    ```

    Args:
        choices: The schemas to match. If a tuple, the second item is used as the label for the case.
        auto_collapse: whether to automatically collapse unions with one element to the inner validator, default true
        custom_error_type: The custom error type to use if the validation fails
        custom_error_message: The custom error message to use if the validation fails
        custom_error_context: The custom error context to use if the validation fails
        mode: How to select which choice to return
            * `smart` (default) will try to return the choice which is the closest match to the input value
            * `left_to_right` will return the first choice in `choices` which succeeds validation
        strict: Whether the underlying schemas should be validated with strict mode
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='union',
        choices=choices,
        auto_collapse=auto_collapse,
        custom_error_type=custom_error_type,
        custom_error_message=custom_error_message,
        custom_error_context=custom_error_context,
        mode=mode,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

tagged_union_schema ¶

tagged_union_schema(
    choices: Dict[Hashable, CoreSchema],
    discriminator: str
    | list[str | int]
    | list[list[str | int]]
    | Callable[[Any], Hashable],
    *,
    custom_error_type: str | None = None,
    custom_error_message: str | None = None,
    custom_error_context: dict[str, int | str | float]
    | None = None,
    strict: bool | None = None,
    from_attributes: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> TaggedUnionSchema

Returns a schema that matches a tagged union value, e.g.:

from pydantic_core import SchemaValidator, core_schema

apple_schema = core_schema.typed_dict_schema(
    {
        'foo': core_schema.typed_dict_field(core_schema.str_schema()),
        'bar': core_schema.typed_dict_field(core_schema.int_schema()),
    }
)
banana_schema = core_schema.typed_dict_schema(
    {
        'foo': core_schema.typed_dict_field(core_schema.str_schema()),
        'spam': core_schema.typed_dict_field(
            core_schema.list_schema(items_schema=core_schema.int_schema())
        ),
    }
)
schema = core_schema.tagged_union_schema(
    choices={
        'apple': apple_schema,
        'banana': banana_schema,
    },
    discriminator='foo',
)
v = SchemaValidator(schema)
assert v.validate_python({'foo': 'apple', 'bar': '123'}) == {'foo': 'apple', 'bar': 123}
assert v.validate_python({'foo': 'banana', 'spam': [1, 2, 3]}) == {
    'foo': 'banana',
    'spam': [1, 2, 3],
}

Parameters:

Name	Type	Description	Default
`choices`	`Dict[Hashable, CoreSchema]`	The schemas to match When retrieving a schema from `choices` using the discriminator value, if the value is a str, it should be fed back into the `choices` map until a schema is obtained (This approach is to prevent multiple ownership of a single schema in Rust)	required
`discriminator`	`str \| list[str \| int] \| list[list[str \| int]] \| Callable[[Any], Hashable]`	The discriminator to use to determine the schema to use * If `discriminator` is a str, it is the name of the attribute to use as the discriminator * If `discriminator` is a list of int/str, it should be used as a "path" to access the discriminator * If `discriminator` is a list of lists, each inner list is a path, and the first path that exists is used * If `discriminator` is a callable, it should return the discriminator when called on the value to validate; the callable can return `None` to indicate that there is no matching discriminator present on the input	required
`custom_error_type`	`str \| None`	The custom error type to use if the validation fails	`None`
`custom_error_message`	`str \| None`	The custom error message to use if the validation fails	`None`
`custom_error_context`	`dict[str, int \| str \| float] \| None`	The custom error context to use if the validation fails	`None`
`strict`	`bool \| None`	Whether the underlying schemas should be validated with strict mode	`None`
`from_attributes`	`bool \| None`	Whether to use the attributes of the object to retrieve the discriminator value	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def tagged_union_schema(
    choices: Dict[Hashable, CoreSchema],
    discriminator: str | list[str | int] | list[list[str | int]] | Callable[[Any], Hashable],
    *,
    custom_error_type: str | None = None,
    custom_error_message: str | None = None,
    custom_error_context: dict[str, int | str | float] | None = None,
    strict: bool | None = None,
    from_attributes: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> TaggedUnionSchema:
    """
    Returns a schema that matches a tagged union value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    apple_schema = core_schema.typed_dict_schema(
        {
            'foo': core_schema.typed_dict_field(core_schema.str_schema()),
            'bar': core_schema.typed_dict_field(core_schema.int_schema()),
        }
    )
    banana_schema = core_schema.typed_dict_schema(
        {
            'foo': core_schema.typed_dict_field(core_schema.str_schema()),
            'spam': core_schema.typed_dict_field(
                core_schema.list_schema(items_schema=core_schema.int_schema())
            ),
        }
    )
    schema = core_schema.tagged_union_schema(
        choices={
            'apple': apple_schema,
            'banana': banana_schema,
        },
        discriminator='foo',
    )
    v = SchemaValidator(schema)
    assert v.validate_python({'foo': 'apple', 'bar': '123'}) == {'foo': 'apple', 'bar': 123}
    assert v.validate_python({'foo': 'banana', 'spam': [1, 2, 3]}) == {
        'foo': 'banana',
        'spam': [1, 2, 3],
    }
    ```

    Args:
        choices: The schemas to match
            When retrieving a schema from `choices` using the discriminator value, if the value is a str,
            it should be fed back into the `choices` map until a schema is obtained
            (This approach is to prevent multiple ownership of a single schema in Rust)
        discriminator: The discriminator to use to determine the schema to use
            * If `discriminator` is a str, it is the name of the attribute to use as the discriminator
            * If `discriminator` is a list of int/str, it should be used as a "path" to access the discriminator
            * If `discriminator` is a list of lists, each inner list is a path, and the first path that exists is used
            * If `discriminator` is a callable, it should return the discriminator when called on the value to validate;
              the callable can return `None` to indicate that there is no matching discriminator present on the input
        custom_error_type: The custom error type to use if the validation fails
        custom_error_message: The custom error message to use if the validation fails
        custom_error_context: The custom error context to use if the validation fails
        strict: Whether the underlying schemas should be validated with strict mode
        from_attributes: Whether to use the attributes of the object to retrieve the discriminator value
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='tagged-union',
        choices=choices,
        discriminator=discriminator,
        custom_error_type=custom_error_type,
        custom_error_message=custom_error_message,
        custom_error_context=custom_error_context,
        strict=strict,
        from_attributes=from_attributes,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

chain_schema ¶

chain_schema(
    steps: list[CoreSchema],
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> ChainSchema

Returns a schema that chains the provided validation schemas, e.g.:

from pydantic_core import SchemaValidator, core_schema

def fn(v: str, info: core_schema.ValidationInfo) -> str:
    assert 'hello' in v
    return v + ' world'

fn_schema = core_schema.with_info_plain_validator_function(function=fn)
schema = core_schema.chain_schema(
    [fn_schema, fn_schema, fn_schema, core_schema.str_schema()]
)
v = SchemaValidator(schema)
assert v.validate_python('hello') == 'hello world world world'

Parameters:

Name	Type	Description	Default
`steps`	`list[CoreSchema]`	The schemas to chain	required
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def chain_schema(
    steps: list[CoreSchema], *, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None
) -> ChainSchema:
    """
    Returns a schema that chains the provided validation schemas, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    def fn(v: str, info: core_schema.ValidationInfo) -> str:
        assert 'hello' in v
        return v + ' world'

    fn_schema = core_schema.with_info_plain_validator_function(function=fn)
    schema = core_schema.chain_schema(
        [fn_schema, fn_schema, fn_schema, core_schema.str_schema()]
    )
    v = SchemaValidator(schema)
    assert v.validate_python('hello') == 'hello world world world'
    ```

    Args:
        steps: The schemas to chain
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(type='chain', steps=steps, ref=ref, metadata=metadata, serialization=serialization)

lax_or_strict_schema ¶

lax_or_strict_schema(
    lax_schema: CoreSchema,
    strict_schema: CoreSchema,
    *,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> LaxOrStrictSchema

Returns a schema that uses the lax or strict schema, e.g.:

from pydantic_core import SchemaValidator, core_schema

def fn(v: str, info: core_schema.ValidationInfo) -> str:
    assert 'hello' in v
    return v + ' world'

lax_schema = core_schema.int_schema(strict=False)
strict_schema = core_schema.int_schema(strict=True)

schema = core_schema.lax_or_strict_schema(
    lax_schema=lax_schema, strict_schema=strict_schema, strict=True
)
v = SchemaValidator(schema)
assert v.validate_python(123) == 123

schema = core_schema.lax_or_strict_schema(
    lax_schema=lax_schema, strict_schema=strict_schema, strict=False
)
v = SchemaValidator(schema)
assert v.validate_python('123') == 123

Parameters:

Name	Type	Description	Default
`lax_schema`	`CoreSchema`	The lax schema to use	required
`strict_schema`	`CoreSchema`	The strict schema to use	required
`strict`	`bool \| None`	Whether the strict schema should be used	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def lax_or_strict_schema(
    lax_schema: CoreSchema,
    strict_schema: CoreSchema,
    *,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> LaxOrStrictSchema:
    """
    Returns a schema that uses the lax or strict schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    def fn(v: str, info: core_schema.ValidationInfo) -> str:
        assert 'hello' in v
        return v + ' world'

    lax_schema = core_schema.int_schema(strict=False)
    strict_schema = core_schema.int_schema(strict=True)

    schema = core_schema.lax_or_strict_schema(
        lax_schema=lax_schema, strict_schema=strict_schema, strict=True
    )
    v = SchemaValidator(schema)
    assert v.validate_python(123) == 123

    schema = core_schema.lax_or_strict_schema(
        lax_schema=lax_schema, strict_schema=strict_schema, strict=False
    )
    v = SchemaValidator(schema)
    assert v.validate_python('123') == 123
    ```

    Args:
        lax_schema: The lax schema to use
        strict_schema: The strict schema to use
        strict: Whether the strict schema should be used
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='lax-or-strict',
        lax_schema=lax_schema,
        strict_schema=strict_schema,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

json_or_python_schema ¶

json_or_python_schema(
    json_schema: CoreSchema,
    python_schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> JsonOrPythonSchema

Returns a schema that uses the Json or Python schema depending on the input:

from pydantic_core import SchemaValidator, ValidationError, core_schema

v = SchemaValidator(
    core_schema.json_or_python_schema(
        json_schema=core_schema.int_schema(),
        python_schema=core_schema.int_schema(strict=True),
    )
)

assert v.validate_json('"123"') == 123

try:
    v.validate_python('123')
except ValidationError:
    pass
else:
    raise AssertionError('Validation should have failed')

Parameters:

Name	Type	Description	Default
`json_schema`	`CoreSchema`	The schema to use for Json inputs	required
`python_schema`	`CoreSchema`	The schema to use for Python inputs	required
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def json_or_python_schema(
    json_schema: CoreSchema,
    python_schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> JsonOrPythonSchema:
    """
    Returns a schema that uses the Json or Python schema depending on the input:

    ```py
    from pydantic_core import SchemaValidator, ValidationError, core_schema

    v = SchemaValidator(
        core_schema.json_or_python_schema(
            json_schema=core_schema.int_schema(),
            python_schema=core_schema.int_schema(strict=True),
        )
    )

    assert v.validate_json('"123"') == 123

    try:
        v.validate_python('123')
    except ValidationError:
        pass
    else:
        raise AssertionError('Validation should have failed')
    ```

    Args:
        json_schema: The schema to use for Json inputs
        python_schema: The schema to use for Python inputs
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='json-or-python',
        json_schema=json_schema,
        python_schema=python_schema,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

typed_dict_field ¶

typed_dict_field(
    schema: CoreSchema,
    *,
    required: bool | None = None,
    validation_alias: str
    | list[str | int]
    | list[list[str | int]]
    | None = None,
    serialization_alias: str | None = None,
    serialization_exclude: bool | None = None,
    metadata: Any = None
) -> TypedDictField

Returns a schema that matches a typed dict field, e.g.:

from pydantic_core import core_schema

field = core_schema.typed_dict_field(schema=core_schema.int_schema(), required=True)

Parameters:

Name	Type	Description	Default
`schema`	`CoreSchema`	The schema to use for the field	required
`required`	`bool \| None`	Whether the field is required	`None`
`validation_alias`	`str \| list[str \| int] \| list[list[str \| int]] \| None`	The alias(es) to use to find the field in the validation data	`None`
`serialization_alias`	`str \| None`	The alias to use as a key when serializing	`None`
`serialization_exclude`	`bool \| None`	Whether to exclude the field when serializing	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`

Source code in pydantic_core/core_schema.py

def typed_dict_field(
    schema: CoreSchema,
    *,
    required: bool | None = None,
    validation_alias: str | list[str | int] | list[list[str | int]] | None = None,
    serialization_alias: str | None = None,
    serialization_exclude: bool | None = None,
    metadata: Any = None,
) -> TypedDictField:
    """
    Returns a schema that matches a typed dict field, e.g.:

    ```py
    from pydantic_core import core_schema

    field = core_schema.typed_dict_field(schema=core_schema.int_schema(), required=True)
    ```

    Args:
        schema: The schema to use for the field
        required: Whether the field is required
        validation_alias: The alias(es) to use to find the field in the validation data
        serialization_alias: The alias to use as a key when serializing
        serialization_exclude: Whether to exclude the field when serializing
        metadata: Any other information you want to include with the schema, not used by pydantic-core
    """
    return _dict_not_none(
        type='typed-dict-field',
        schema=schema,
        required=required,
        validation_alias=validation_alias,
        serialization_alias=serialization_alias,
        serialization_exclude=serialization_exclude,
        metadata=metadata,
    )

typed_dict_schema ¶

typed_dict_schema(
    fields: Dict[str, TypedDictField],
    *,
    computed_fields: list[ComputedField] | None = None,
    strict: bool | None = None,
    extras_schema: CoreSchema | None = None,
    extra_behavior: ExtraBehavior | None = None,
    total: bool | None = None,
    populate_by_name: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
    config: CoreConfig | None = None
) -> TypedDictSchema

Returns a schema that matches a typed dict, e.g.:

from pydantic_core import SchemaValidator, core_schema

wrapper_schema = core_schema.typed_dict_schema(
    {'a': core_schema.typed_dict_field(core_schema.str_schema())}
)
v = SchemaValidator(wrapper_schema)
assert v.validate_python({'a': 'hello'}) == {'a': 'hello'}

Parameters:

Name	Type	Description	Default
`fields`	`Dict[str, TypedDictField]`	The fields to use for the typed dict	required
`computed_fields`	`list[ComputedField] \| None`	Computed fields to use when serializing the model, only applies when directly inside a model	`None`
`strict`	`bool \| None`	Whether the typed dict is strict	`None`
`extras_schema`	`CoreSchema \| None`	The extra validator to use for the typed dict	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`extra_behavior`	`ExtraBehavior \| None`	The extra behavior to use for the typed dict	`None`
`total`	`bool \| None`	Whether the typed dict is total	`None`
`populate_by_name`	`bool \| None`	Whether the typed dict should populate by name	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def typed_dict_schema(
    fields: Dict[str, TypedDictField],
    *,
    computed_fields: list[ComputedField] | None = None,
    strict: bool | None = None,
    extras_schema: CoreSchema | None = None,
    extra_behavior: ExtraBehavior | None = None,
    total: bool | None = None,
    populate_by_name: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
    config: CoreConfig | None = None,
) -> TypedDictSchema:
    """
    Returns a schema that matches a typed dict, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    wrapper_schema = core_schema.typed_dict_schema(
        {'a': core_schema.typed_dict_field(core_schema.str_schema())}
    )
    v = SchemaValidator(wrapper_schema)
    assert v.validate_python({'a': 'hello'}) == {'a': 'hello'}
    ```

    Args:
        fields: The fields to use for the typed dict
        computed_fields: Computed fields to use when serializing the model, only applies when directly inside a model
        strict: Whether the typed dict is strict
        extras_schema: The extra validator to use for the typed dict
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        extra_behavior: The extra behavior to use for the typed dict
        total: Whether the typed dict is total
        populate_by_name: Whether the typed dict should populate by name
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='typed-dict',
        fields=fields,
        computed_fields=computed_fields,
        strict=strict,
        extras_schema=extras_schema,
        extra_behavior=extra_behavior,
        total=total,
        populate_by_name=populate_by_name,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
        config=config,
    )

model_field ¶

model_field(
    schema: CoreSchema,
    *,
    validation_alias: str
    | list[str | int]
    | list[list[str | int]]
    | None = None,
    serialization_alias: str | None = None,
    serialization_exclude: bool | None = None,
    frozen: bool | None = None,
    metadata: Any = None
) -> ModelField

Returns a schema for a model field, e.g.:

from pydantic_core import core_schema

field = core_schema.model_field(schema=core_schema.int_schema())

Parameters:

Name	Type	Description	Default
`schema`	`CoreSchema`	The schema to use for the field	required
`validation_alias`	`str \| list[str \| int] \| list[list[str \| int]] \| None`	The alias(es) to use to find the field in the validation data	`None`
`serialization_alias`	`str \| None`	The alias to use as a key when serializing	`None`
`serialization_exclude`	`bool \| None`	Whether to exclude the field when serializing	`None`
`frozen`	`bool \| None`	Whether the field is frozen	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`

Source code in pydantic_core/core_schema.py

def model_field(
    schema: CoreSchema,
    *,
    validation_alias: str | list[str | int] | list[list[str | int]] | None = None,
    serialization_alias: str | None = None,
    serialization_exclude: bool | None = None,
    frozen: bool | None = None,
    metadata: Any = None,
) -> ModelField:
    """
    Returns a schema for a model field, e.g.:

    ```py
    from pydantic_core import core_schema

    field = core_schema.model_field(schema=core_schema.int_schema())
    ```

    Args:
        schema: The schema to use for the field
        validation_alias: The alias(es) to use to find the field in the validation data
        serialization_alias: The alias to use as a key when serializing
        serialization_exclude: Whether to exclude the field when serializing
        frozen: Whether the field is frozen
        metadata: Any other information you want to include with the schema, not used by pydantic-core
    """
    return _dict_not_none(
        type='model-field',
        schema=schema,
        validation_alias=validation_alias,
        serialization_alias=serialization_alias,
        serialization_exclude=serialization_exclude,
        frozen=frozen,
        metadata=metadata,
    )

model_fields_schema ¶

model_fields_schema(
    fields: Dict[str, ModelField],
    *,
    model_name: str | None = None,
    computed_fields: list[ComputedField] | None = None,
    strict: bool | None = None,
    extras_schema: CoreSchema | None = None,
    extra_behavior: ExtraBehavior | None = None,
    populate_by_name: bool | None = None,
    from_attributes: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> ModelFieldsSchema

Returns a schema that matches a typed dict, e.g.:

from pydantic_core import SchemaValidator, core_schema

wrapper_schema = core_schema.model_fields_schema(
    {'a': core_schema.model_field(core_schema.str_schema())}
)
v = SchemaValidator(wrapper_schema)
print(v.validate_python({'a': 'hello'}))
#> ({'a': 'hello'}, None, {'a'})

Parameters:

Name	Type	Description	Default
`fields`	`Dict[str, ModelField]`	The fields to use for the typed dict	required
`model_name`	`str \| None`	The name of the model, used for error messages, defaults to "Model"	`None`
`computed_fields`	`list[ComputedField] \| None`	Computed fields to use when serializing the model, only applies when directly inside a model	`None`
`strict`	`bool \| None`	Whether the typed dict is strict	`None`
`extras_schema`	`CoreSchema \| None`	The extra validator to use for the typed dict	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`extra_behavior`	`ExtraBehavior \| None`	The extra behavior to use for the typed dict	`None`
`populate_by_name`	`bool \| None`	Whether the typed dict should populate by name	`None`
`from_attributes`	`bool \| None`	Whether the typed dict should be populated from attributes	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def model_fields_schema(
    fields: Dict[str, ModelField],
    *,
    model_name: str | None = None,
    computed_fields: list[ComputedField] | None = None,
    strict: bool | None = None,
    extras_schema: CoreSchema | None = None,
    extra_behavior: ExtraBehavior | None = None,
    populate_by_name: bool | None = None,
    from_attributes: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> ModelFieldsSchema:
    """
    Returns a schema that matches a typed dict, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    wrapper_schema = core_schema.model_fields_schema(
        {'a': core_schema.model_field(core_schema.str_schema())}
    )
    v = SchemaValidator(wrapper_schema)
    print(v.validate_python({'a': 'hello'}))
    #> ({'a': 'hello'}, None, {'a'})
    ```

    Args:
        fields: The fields to use for the typed dict
        model_name: The name of the model, used for error messages, defaults to "Model"
        computed_fields: Computed fields to use when serializing the model, only applies when directly inside a model
        strict: Whether the typed dict is strict
        extras_schema: The extra validator to use for the typed dict
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        extra_behavior: The extra behavior to use for the typed dict
        populate_by_name: Whether the typed dict should populate by name
        from_attributes: Whether the typed dict should be populated from attributes
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='model-fields',
        fields=fields,
        model_name=model_name,
        computed_fields=computed_fields,
        strict=strict,
        extras_schema=extras_schema,
        extra_behavior=extra_behavior,
        populate_by_name=populate_by_name,
        from_attributes=from_attributes,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

model_schema ¶

model_schema(
    cls: Type[Any],
    schema: CoreSchema,
    *,
    custom_init: bool | None = None,
    root_model: bool | None = None,
    post_init: str | None = None,
    revalidate_instances: Literal[
        "always", "never", "subclass-instances"
    ]
    | None = None,
    strict: bool | None = None,
    frozen: bool | None = None,
    extra_behavior: ExtraBehavior | None = None,
    config: CoreConfig | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> ModelSchema

A model schema generally contains a typed-dict schema. It will run the typed dict validator, then create a new class and set the dict and fields set returned from the typed dict validator to __dict__ and __pydantic_fields_set__ respectively.

Example:

from pydantic_core import CoreConfig, SchemaValidator, core_schema

class MyModel:
    __slots__ = (
        '__dict__',
        '__pydantic_fields_set__',
        '__pydantic_extra__',
        '__pydantic_private__',
    )

schema = core_schema.model_schema(
    cls=MyModel,
    config=CoreConfig(str_max_length=5),
    schema=core_schema.model_fields_schema(
        fields={'a': core_schema.model_field(core_schema.str_schema())},
    ),
)
v = SchemaValidator(schema)
assert v.isinstance_python({'a': 'hello'}) is True
assert v.isinstance_python({'a': 'too long'}) is False

Parameters:

Name	Type	Description	Default
`cls`	`Type[Any]`	The class to use for the model	required
`schema`	`CoreSchema`	The schema to use for the model	required
`custom_init`	`bool \| None`	Whether the model has a custom init method	`None`
`root_model`	`bool \| None`	Whether the model is a `RootModel`	`None`
`post_init`	`str \| None`	The call after init to use for the model	`None`
`revalidate_instances`	`Literal['always', 'never', 'subclass-instances'] \| None`	whether instances of models and dataclasses (including subclass instances) should re-validate defaults to config.revalidate_instances, else 'never'	`None`
`strict`	`bool \| None`	Whether the model is strict	`None`
`frozen`	`bool \| None`	Whether the model is frozen	`None`
`extra_behavior`	`ExtraBehavior \| None`	The extra behavior to use for the model, used in serialization	`None`
`config`	`CoreConfig \| None`	The config to use for the model	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def model_schema(
    cls: Type[Any],
    schema: CoreSchema,
    *,
    custom_init: bool | None = None,
    root_model: bool | None = None,
    post_init: str | None = None,
    revalidate_instances: Literal['always', 'never', 'subclass-instances'] | None = None,
    strict: bool | None = None,
    frozen: bool | None = None,
    extra_behavior: ExtraBehavior | None = None,
    config: CoreConfig | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> ModelSchema:
    """
    A model schema generally contains a typed-dict schema.
    It will run the typed dict validator, then create a new class
    and set the dict and fields set returned from the typed dict validator
    to `__dict__` and `__pydantic_fields_set__` respectively.

    Example:

    ```py
    from pydantic_core import CoreConfig, SchemaValidator, core_schema

    class MyModel:
        __slots__ = (
            '__dict__',
            '__pydantic_fields_set__',
            '__pydantic_extra__',
            '__pydantic_private__',
        )

    schema = core_schema.model_schema(
        cls=MyModel,
        config=CoreConfig(str_max_length=5),
        schema=core_schema.model_fields_schema(
            fields={'a': core_schema.model_field(core_schema.str_schema())},
        ),
    )
    v = SchemaValidator(schema)
    assert v.isinstance_python({'a': 'hello'}) is True
    assert v.isinstance_python({'a': 'too long'}) is False
    ```

    Args:
        cls: The class to use for the model
        schema: The schema to use for the model
        custom_init: Whether the model has a custom init method
        root_model: Whether the model is a `RootModel`
        post_init: The call after init to use for the model
        revalidate_instances: whether instances of models and dataclasses (including subclass instances)
            should re-validate defaults to config.revalidate_instances, else 'never'
        strict: Whether the model is strict
        frozen: Whether the model is frozen
        extra_behavior: The extra behavior to use for the model, used in serialization
        config: The config to use for the model
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='model',
        cls=cls,
        schema=schema,
        custom_init=custom_init,
        root_model=root_model,
        post_init=post_init,
        revalidate_instances=revalidate_instances,
        strict=strict,
        frozen=frozen,
        extra_behavior=extra_behavior,
        config=config,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

dataclass_field ¶

dataclass_field(
    name: str,
    schema: CoreSchema,
    *,
    kw_only: bool | None = None,
    init: bool | None = None,
    init_only: bool | None = None,
    validation_alias: str
    | list[str | int]
    | list[list[str | int]]
    | None = None,
    serialization_alias: str | None = None,
    serialization_exclude: bool | None = None,
    metadata: Any = None,
    frozen: bool | None = None
) -> DataclassField

Returns a schema for a dataclass field, e.g.:

from pydantic_core import SchemaValidator, core_schema

field = core_schema.dataclass_field(
    name='a', schema=core_schema.str_schema(), kw_only=False
)
schema = core_schema.dataclass_args_schema('Foobar', [field])
v = SchemaValidator(schema)
assert v.validate_python({'a': 'hello'}) == ({'a': 'hello'}, None)

Parameters:

Name	Type	Description	Default
`name`	`str`	The name to use for the argument parameter	required
`schema`	`CoreSchema`	The schema to use for the argument parameter	required
`kw_only`	`bool \| None`	Whether the field can be set with a positional argument as well as a keyword argument	`None`
`init`	`bool \| None`	Whether the field should be validated during initialization	`None`
`init_only`	`bool \| None`	Whether the field should be omitted from `__dict__` and passed to `__post_init__`	`None`
`validation_alias`	`str \| list[str \| int] \| list[list[str \| int]] \| None`	The alias(es) to use to find the field in the validation data	`None`
`serialization_alias`	`str \| None`	The alias to use as a key when serializing	`None`
`serialization_exclude`	`bool \| None`	Whether to exclude the field when serializing	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`frozen`	`bool \| None`	Whether the field is frozen	`None`

Source code in pydantic_core/core_schema.py

def dataclass_field(
    name: str,
    schema: CoreSchema,
    *,
    kw_only: bool | None = None,
    init: bool | None = None,
    init_only: bool | None = None,
    validation_alias: str | list[str | int] | list[list[str | int]] | None = None,
    serialization_alias: str | None = None,
    serialization_exclude: bool | None = None,
    metadata: Any = None,
    frozen: bool | None = None,
) -> DataclassField:
    """
    Returns a schema for a dataclass field, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    field = core_schema.dataclass_field(
        name='a', schema=core_schema.str_schema(), kw_only=False
    )
    schema = core_schema.dataclass_args_schema('Foobar', [field])
    v = SchemaValidator(schema)
    assert v.validate_python({'a': 'hello'}) == ({'a': 'hello'}, None)
    ```

    Args:
        name: The name to use for the argument parameter
        schema: The schema to use for the argument parameter
        kw_only: Whether the field can be set with a positional argument as well as a keyword argument
        init: Whether the field should be validated during initialization
        init_only: Whether the field should be omitted  from `__dict__` and passed to `__post_init__`
        validation_alias: The alias(es) to use to find the field in the validation data
        serialization_alias: The alias to use as a key when serializing
        serialization_exclude: Whether to exclude the field when serializing
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        frozen: Whether the field is frozen
    """
    return _dict_not_none(
        type='dataclass-field',
        name=name,
        schema=schema,
        kw_only=kw_only,
        init=init,
        init_only=init_only,
        validation_alias=validation_alias,
        serialization_alias=serialization_alias,
        serialization_exclude=serialization_exclude,
        metadata=metadata,
        frozen=frozen,
    )

dataclass_args_schema ¶

dataclass_args_schema(
    dataclass_name: str,
    fields: list[DataclassField],
    *,
    computed_fields: List[ComputedField] | None = None,
    populate_by_name: bool | None = None,
    collect_init_only: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
    extra_behavior: ExtraBehavior | None = None
) -> DataclassArgsSchema

Returns a schema for validating dataclass arguments, e.g.:

from pydantic_core import SchemaValidator, core_schema

field_a = core_schema.dataclass_field(
    name='a', schema=core_schema.str_schema(), kw_only=False
)
field_b = core_schema.dataclass_field(
    name='b', schema=core_schema.bool_schema(), kw_only=False
)
schema = core_schema.dataclass_args_schema('Foobar', [field_a, field_b])
v = SchemaValidator(schema)
assert v.validate_python({'a': 'hello', 'b': True}) == ({'a': 'hello', 'b': True}, None)

Parameters:

Name	Type	Description	Default
`dataclass_name`	`str`	The name of the dataclass being validated	required
`fields`	`list[DataclassField]`	The fields to use for the dataclass	required
`computed_fields`	`List[ComputedField] \| None`	Computed fields to use when serializing the dataclass	`None`
`populate_by_name`	`bool \| None`	Whether to populate by name	`None`
`collect_init_only`	`bool \| None`	Whether to collect init only fields into a dict to pass to `__post_init__`	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`
`extra_behavior`	`ExtraBehavior \| None`	How to handle extra fields	`None`

Source code in pydantic_core/core_schema.py

def dataclass_args_schema(
    dataclass_name: str,
    fields: list[DataclassField],
    *,
    computed_fields: List[ComputedField] | None = None,
    populate_by_name: bool | None = None,
    collect_init_only: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
    extra_behavior: ExtraBehavior | None = None,
) -> DataclassArgsSchema:
    """
    Returns a schema for validating dataclass arguments, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    field_a = core_schema.dataclass_field(
        name='a', schema=core_schema.str_schema(), kw_only=False
    )
    field_b = core_schema.dataclass_field(
        name='b', schema=core_schema.bool_schema(), kw_only=False
    )
    schema = core_schema.dataclass_args_schema('Foobar', [field_a, field_b])
    v = SchemaValidator(schema)
    assert v.validate_python({'a': 'hello', 'b': True}) == ({'a': 'hello', 'b': True}, None)
    ```

    Args:
        dataclass_name: The name of the dataclass being validated
        fields: The fields to use for the dataclass
        computed_fields: Computed fields to use when serializing the dataclass
        populate_by_name: Whether to populate by name
        collect_init_only: Whether to collect init only fields into a dict to pass to `__post_init__`
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
        extra_behavior: How to handle extra fields
    """
    return _dict_not_none(
        type='dataclass-args',
        dataclass_name=dataclass_name,
        fields=fields,
        computed_fields=computed_fields,
        populate_by_name=populate_by_name,
        collect_init_only=collect_init_only,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
        extra_behavior=extra_behavior,
    )

dataclass_schema ¶

dataclass_schema(
    cls: Type[Any],
    schema: CoreSchema,
    fields: List[str],
    *,
    cls_name: str | None = None,
    post_init: bool | None = None,
    revalidate_instances: Literal[
        "always", "never", "subclass-instances"
    ]
    | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
    frozen: bool | None = None,
    slots: bool | None = None,
    config: CoreConfig | None = None
) -> DataclassSchema

Returns a schema for a dataclass. As with ModelSchema, this schema can only be used as a field within another schema, not as the root type.

Parameters:

Name	Type	Description	Default
`cls`	`Type[Any]`	The dataclass type, used to perform subclass checks	required
`schema`	`CoreSchema`	The schema to use for the dataclass fields	required
`fields`	`List[str]`	Fields of the dataclass, this is used in serialization and in validation during re-validation and while validating assignment	required
`cls_name`	`str \| None`	The name to use in error locs, etc; this is useful for generics (default: `cls.__name__`)	`None`
`post_init`	`bool \| None`	Whether to call `__post_init__` after validation	`None`
`revalidate_instances`	`Literal['always', 'never', 'subclass-instances'] \| None`	whether instances of models and dataclasses (including subclass instances) should re-validate defaults to config.revalidate_instances, else 'never'	`None`
`strict`	`bool \| None`	Whether to require an exact instance of `cls`	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`
`frozen`	`bool \| None`	Whether the dataclass is frozen	`None`
`slots`	`bool \| None`	Whether `slots=True` on the dataclass, means each field is assigned independently, rather than simply setting `__dict__`, default false	`None`

Source code in pydantic_core/core_schema.py

def dataclass_schema(
    cls: Type[Any],
    schema: CoreSchema,
    fields: List[str],
    *,
    cls_name: str | None = None,
    post_init: bool | None = None,
    revalidate_instances: Literal['always', 'never', 'subclass-instances'] | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
    frozen: bool | None = None,
    slots: bool | None = None,
    config: CoreConfig | None = None,
) -> DataclassSchema:
    """
    Returns a schema for a dataclass. As with `ModelSchema`, this schema can only be used as a field within
    another schema, not as the root type.

    Args:
        cls: The dataclass type, used to perform subclass checks
        schema: The schema to use for the dataclass fields
        fields: Fields of the dataclass, this is used in serialization and in validation during re-validation
            and while validating assignment
        cls_name: The name to use in error locs, etc; this is useful for generics (default: `cls.__name__`)
        post_init: Whether to call `__post_init__` after validation
        revalidate_instances: whether instances of models and dataclasses (including subclass instances)
            should re-validate defaults to config.revalidate_instances, else 'never'
        strict: Whether to require an exact instance of `cls`
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
        frozen: Whether the dataclass is frozen
        slots: Whether `slots=True` on the dataclass, means each field is assigned independently, rather than
            simply setting `__dict__`, default false
    """
    return _dict_not_none(
        type='dataclass',
        cls=cls,
        fields=fields,
        cls_name=cls_name,
        schema=schema,
        post_init=post_init,
        revalidate_instances=revalidate_instances,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
        frozen=frozen,
        slots=slots,
        config=config,
    )

arguments_parameter ¶

arguments_parameter(
    name: str,
    schema: CoreSchema,
    *,
    mode: Literal[
        "positional_only",
        "positional_or_keyword",
        "keyword_only",
    ]
    | None = None,
    alias: str
    | list[str | int]
    | list[list[str | int]]
    | None = None
) -> ArgumentsParameter

Returns a schema that matches an argument parameter, e.g.:

from pydantic_core import SchemaValidator, core_schema

param = core_schema.arguments_parameter(
    name='a', schema=core_schema.str_schema(), mode='positional_only'
)
schema = core_schema.arguments_schema([param])
v = SchemaValidator(schema)
assert v.validate_python(('hello',)) == (('hello',), {})

Parameters:

Name	Type	Description	Default
`name`	`str`	The name to use for the argument parameter	required
`schema`	`CoreSchema`	The schema to use for the argument parameter	required
`mode`	`Literal['positional_only', 'positional_or_keyword', 'keyword_only'] \| None`	The mode to use for the argument parameter	`None`
`alias`	`str \| list[str \| int] \| list[list[str \| int]] \| None`	The alias to use for the argument parameter	`None`

Source code in pydantic_core/core_schema.py

def arguments_parameter(
    name: str,
    schema: CoreSchema,
    *,
    mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only'] | None = None,
    alias: str | list[str | int] | list[list[str | int]] | None = None,
) -> ArgumentsParameter:
    """
    Returns a schema that matches an argument parameter, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    param = core_schema.arguments_parameter(
        name='a', schema=core_schema.str_schema(), mode='positional_only'
    )
    schema = core_schema.arguments_schema([param])
    v = SchemaValidator(schema)
    assert v.validate_python(('hello',)) == (('hello',), {})
    ```

    Args:
        name: The name to use for the argument parameter
        schema: The schema to use for the argument parameter
        mode: The mode to use for the argument parameter
        alias: The alias to use for the argument parameter
    """
    return _dict_not_none(name=name, schema=schema, mode=mode, alias=alias)

arguments_schema ¶

arguments_schema(
    arguments: list[ArgumentsParameter],
    *,
    populate_by_name: bool | None = None,
    var_args_schema: CoreSchema | None = None,
    var_kwargs_schema: CoreSchema | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> ArgumentsSchema

Returns a schema that matches an arguments schema, e.g.:

from pydantic_core import SchemaValidator, core_schema

param_a = core_schema.arguments_parameter(
    name='a', schema=core_schema.str_schema(), mode='positional_only'
)
param_b = core_schema.arguments_parameter(
    name='b', schema=core_schema.bool_schema(), mode='positional_only'
)
schema = core_schema.arguments_schema([param_a, param_b])
v = SchemaValidator(schema)
assert v.validate_python(('hello', True)) == (('hello', True), {})

Parameters:

Name	Type	Description	Default
`arguments`	`list[ArgumentsParameter]`	The arguments to use for the arguments schema	required
`populate_by_name`	`bool \| None`	Whether to populate by name	`None`
`var_args_schema`	`CoreSchema \| None`	The variable args schema to use for the arguments schema	`None`
`var_kwargs_schema`	`CoreSchema \| None`	The variable kwargs schema to use for the arguments schema	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def arguments_schema(
    arguments: list[ArgumentsParameter],
    *,
    populate_by_name: bool | None = None,
    var_args_schema: CoreSchema | None = None,
    var_kwargs_schema: CoreSchema | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> ArgumentsSchema:
    """
    Returns a schema that matches an arguments schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    param_a = core_schema.arguments_parameter(
        name='a', schema=core_schema.str_schema(), mode='positional_only'
    )
    param_b = core_schema.arguments_parameter(
        name='b', schema=core_schema.bool_schema(), mode='positional_only'
    )
    schema = core_schema.arguments_schema([param_a, param_b])
    v = SchemaValidator(schema)
    assert v.validate_python(('hello', True)) == (('hello', True), {})
    ```

    Args:
        arguments: The arguments to use for the arguments schema
        populate_by_name: Whether to populate by name
        var_args_schema: The variable args schema to use for the arguments schema
        var_kwargs_schema: The variable kwargs schema to use for the arguments schema
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='arguments',
        arguments_schema=arguments,
        populate_by_name=populate_by_name,
        var_args_schema=var_args_schema,
        var_kwargs_schema=var_kwargs_schema,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

call_schema ¶

call_schema(
    arguments: CoreSchema,
    function: Callable[..., Any],
    *,
    function_name: str | None = None,
    return_schema: CoreSchema | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> CallSchema

Returns a schema that matches an arguments schema, then calls a function, e.g.:

from pydantic_core import SchemaValidator, core_schema

param_a = core_schema.arguments_parameter(
    name='a', schema=core_schema.str_schema(), mode='positional_only'
)
param_b = core_schema.arguments_parameter(
    name='b', schema=core_schema.bool_schema(), mode='positional_only'
)
args_schema = core_schema.arguments_schema([param_a, param_b])

schema = core_schema.call_schema(
    arguments=args_schema,
    function=lambda a, b: a + str(not b),
    return_schema=core_schema.str_schema(),
)
v = SchemaValidator(schema)
assert v.validate_python((('hello', True))) == 'helloFalse'

Parameters:

Name	Type	Description	Default
`arguments`	`CoreSchema`	The arguments to use for the arguments schema	required
`function`	`Callable[..., Any]`	The function to use for the call schema	required
`function_name`	`str \| None`	The function name to use for the call schema, if not provided `function.__name__` is used	`None`
`return_schema`	`CoreSchema \| None`	The return schema to use for the call schema	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def call_schema(
    arguments: CoreSchema,
    function: Callable[..., Any],
    *,
    function_name: str | None = None,
    return_schema: CoreSchema | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> CallSchema:
    """
    Returns a schema that matches an arguments schema, then calls a function, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    param_a = core_schema.arguments_parameter(
        name='a', schema=core_schema.str_schema(), mode='positional_only'
    )
    param_b = core_schema.arguments_parameter(
        name='b', schema=core_schema.bool_schema(), mode='positional_only'
    )
    args_schema = core_schema.arguments_schema([param_a, param_b])

    schema = core_schema.call_schema(
        arguments=args_schema,
        function=lambda a, b: a + str(not b),
        return_schema=core_schema.str_schema(),
    )
    v = SchemaValidator(schema)
    assert v.validate_python((('hello', True))) == 'helloFalse'
    ```

    Args:
        arguments: The arguments to use for the arguments schema
        function: The function to use for the call schema
        function_name: The function name to use for the call schema, if not provided `function.__name__` is used
        return_schema: The return schema to use for the call schema
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='call',
        arguments_schema=arguments,
        function=function,
        function_name=function_name,
        return_schema=return_schema,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

custom_error_schema ¶

custom_error_schema(
    schema: CoreSchema,
    custom_error_type: str,
    *,
    custom_error_message: str | None = None,
    custom_error_context: dict[str, Any] | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> CustomErrorSchema

Returns a schema that matches a custom error value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.custom_error_schema(
    schema=core_schema.int_schema(),
    custom_error_type='MyError',
    custom_error_message='Error msg',
)
v = SchemaValidator(schema)
v.validate_python(1)

Parameters:

Name	Type	Description	Default
`schema`	`CoreSchema`	The schema to use for the custom error schema	required
`custom_error_type`	`str`	The custom error type to use for the custom error schema	required
`custom_error_message`	`str \| None`	The custom error message to use for the custom error schema	`None`
`custom_error_context`	`dict[str, Any] \| None`	The custom error context to use for the custom error schema	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def custom_error_schema(
    schema: CoreSchema,
    custom_error_type: str,
    *,
    custom_error_message: str | None = None,
    custom_error_context: dict[str, Any] | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> CustomErrorSchema:
    """
    Returns a schema that matches a custom error value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.custom_error_schema(
        schema=core_schema.int_schema(),
        custom_error_type='MyError',
        custom_error_message='Error msg',
    )
    v = SchemaValidator(schema)
    v.validate_python(1)
    ```

    Args:
        schema: The schema to use for the custom error schema
        custom_error_type: The custom error type to use for the custom error schema
        custom_error_message: The custom error message to use for the custom error schema
        custom_error_context: The custom error context to use for the custom error schema
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='custom-error',
        schema=schema,
        custom_error_type=custom_error_type,
        custom_error_message=custom_error_message,
        custom_error_context=custom_error_context,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

json_schema ¶

json_schema(
    schema: CoreSchema | None = None,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> JsonSchema

Returns a schema that matches a JSON value, e.g.:

from pydantic_core import SchemaValidator, core_schema

dict_schema = core_schema.model_fields_schema(
    {
        'field_a': core_schema.model_field(core_schema.str_schema()),
        'field_b': core_schema.model_field(core_schema.bool_schema()),
    },
)

class MyModel:
    __slots__ = (
        '__dict__',
        '__pydantic_fields_set__',
        '__pydantic_extra__',
        '__pydantic_private__',
    )
    field_a: str
    field_b: bool

json_schema = core_schema.json_schema(schema=dict_schema)
schema = core_schema.model_schema(cls=MyModel, schema=json_schema)
v = SchemaValidator(schema)
m = v.validate_python('{"field_a": "hello", "field_b": true}')
assert isinstance(m, MyModel)

Parameters:

Name	Type	Description	Default
`schema`	`CoreSchema \| None`	The schema to use for the JSON schema	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def json_schema(
    schema: CoreSchema | None = None,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> JsonSchema:
    """
    Returns a schema that matches a JSON value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    dict_schema = core_schema.model_fields_schema(
        {
            'field_a': core_schema.model_field(core_schema.str_schema()),
            'field_b': core_schema.model_field(core_schema.bool_schema()),
        },
    )

    class MyModel:
        __slots__ = (
            '__dict__',
            '__pydantic_fields_set__',
            '__pydantic_extra__',
            '__pydantic_private__',
        )
        field_a: str
        field_b: bool

    json_schema = core_schema.json_schema(schema=dict_schema)
    schema = core_schema.model_schema(cls=MyModel, schema=json_schema)
    v = SchemaValidator(schema)
    m = v.validate_python('{"field_a": "hello", "field_b": true}')
    assert isinstance(m, MyModel)
    ```

    Args:
        schema: The schema to use for the JSON schema
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(type='json', schema=schema, ref=ref, metadata=metadata, serialization=serialization)

url_schema ¶

url_schema(
    *,
    max_length: int | None = None,
    allowed_schemes: list[str] | None = None,
    host_required: bool | None = None,
    default_host: str | None = None,
    default_port: int | None = None,
    default_path: str | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> UrlSchema

Returns a schema that matches a URL value, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.url_schema()
v = SchemaValidator(schema)
print(v.validate_python('https://example.com'))
#> https://example.com/

Parameters:

Name	Type	Description	Default
`max_length`	`int \| None`	The maximum length of the URL	`None`
`allowed_schemes`	`list[str] \| None`	The allowed URL schemes	`None`
`host_required`	`bool \| None`	Whether the URL must have a host	`None`
`default_host`	`str \| None`	The default host to use if the URL does not have a host	`None`
`default_port`	`int \| None`	The default port to use if the URL does not have a port	`None`
`default_path`	`str \| None`	The default path to use if the URL does not have a path	`None`
`strict`	`bool \| None`	Whether to use strict URL parsing	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def url_schema(
    *,
    max_length: int | None = None,
    allowed_schemes: list[str] | None = None,
    host_required: bool | None = None,
    default_host: str | None = None,
    default_port: int | None = None,
    default_path: str | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> UrlSchema:
    """
    Returns a schema that matches a URL value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.url_schema()
    v = SchemaValidator(schema)
    print(v.validate_python('https://example.com'))
    #> https://example.com/
    ```

    Args:
        max_length: The maximum length of the URL
        allowed_schemes: The allowed URL schemes
        host_required: Whether the URL must have a host
        default_host: The default host to use if the URL does not have a host
        default_port: The default port to use if the URL does not have a port
        default_path: The default path to use if the URL does not have a path
        strict: Whether to use strict URL parsing
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='url',
        max_length=max_length,
        allowed_schemes=allowed_schemes,
        host_required=host_required,
        default_host=default_host,
        default_port=default_port,
        default_path=default_path,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

multi_host_url_schema ¶

multi_host_url_schema(
    *,
    max_length: int | None = None,
    allowed_schemes: list[str] | None = None,
    host_required: bool | None = None,
    default_host: str | None = None,
    default_port: int | None = None,
    default_path: str | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None
) -> MultiHostUrlSchema

Returns a schema that matches a URL value with possibly multiple hosts, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.multi_host_url_schema()
v = SchemaValidator(schema)
print(v.validate_python('redis://localhost,0.0.0.0,127.0.0.1'))
#> redis://localhost,0.0.0.0,127.0.0.1

Parameters:

Name	Type	Description	Default
`max_length`	`int \| None`	The maximum length of the URL	`None`
`allowed_schemes`	`list[str] \| None`	The allowed URL schemes	`None`
`host_required`	`bool \| None`	Whether the URL must have a host	`None`
`default_host`	`str \| None`	The default host to use if the URL does not have a host	`None`
`default_port`	`int \| None`	The default port to use if the URL does not have a port	`None`
`default_path`	`str \| None`	The default path to use if the URL does not have a path	`None`
`strict`	`bool \| None`	Whether to use strict URL parsing	`None`
`ref`	`str \| None`	optional unique identifier of the schema, used to reference the schema in other places	`None`
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def multi_host_url_schema(
    *,
    max_length: int | None = None,
    allowed_schemes: list[str] | None = None,
    host_required: bool | None = None,
    default_host: str | None = None,
    default_port: int | None = None,
    default_path: str | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> MultiHostUrlSchema:
    """
    Returns a schema that matches a URL value with possibly multiple hosts, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.multi_host_url_schema()
    v = SchemaValidator(schema)
    print(v.validate_python('redis://localhost,0.0.0.0,127.0.0.1'))
    #> redis://localhost,0.0.0.0,127.0.0.1
    ```

    Args:
        max_length: The maximum length of the URL
        allowed_schemes: The allowed URL schemes
        host_required: Whether the URL must have a host
        default_host: The default host to use if the URL does not have a host
        default_port: The default port to use if the URL does not have a port
        default_path: The default path to use if the URL does not have a path
        strict: Whether to use strict URL parsing
        ref: optional unique identifier of the schema, used to reference the schema in other places
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='multi-host-url',
        max_length=max_length,
        allowed_schemes=allowed_schemes,
        host_required=host_required,
        default_host=default_host,
        default_port=default_port,
        default_path=default_path,
        strict=strict,
        ref=ref,
        metadata=metadata,
        serialization=serialization,
    )

definitions_schema ¶

definitions_schema(
    schema: CoreSchema, definitions: list[CoreSchema]
) -> DefinitionsSchema

Build a schema that contains both an inner schema and a list of definitions which can be used within the inner schema.

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.definitions_schema(
    core_schema.list_schema(core_schema.definition_reference_schema('foobar')),
    [core_schema.int_schema(ref='foobar')],
)
v = SchemaValidator(schema)
assert v.validate_python([1, 2, '3']) == [1, 2, 3]

Parameters:

Name	Type	Description	Default
`schema`	`CoreSchema`	The inner schema	required
`definitions`	`list[CoreSchema]`	List of definitions which can be referenced within inner schema	required

Source code in pydantic_core/core_schema.py

def definitions_schema(schema: CoreSchema, definitions: list[CoreSchema]) -> DefinitionsSchema:
    """
    Build a schema that contains both an inner schema and a list of definitions which can be used
    within the inner schema.

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema = core_schema.definitions_schema(
        core_schema.list_schema(core_schema.definition_reference_schema('foobar')),
        [core_schema.int_schema(ref='foobar')],
    )
    v = SchemaValidator(schema)
    assert v.validate_python([1, 2, '3']) == [1, 2, 3]
    ```

    Args:
        schema: The inner schema
        definitions: List of definitions which can be referenced within inner schema
    """
    return DefinitionsSchema(type='definitions', schema=schema, definitions=definitions)

definition_reference_schema ¶

definition_reference_schema(
    schema_ref: str,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> DefinitionReferenceSchema

Returns a schema that points to a schema stored in "definitions", this is useful for nested recursive models and also when you want to define validators separately from the main schema, e.g.:

from pydantic_core import SchemaValidator, core_schema

schema_definition = core_schema.definition_reference_schema('list-schema')
schema = core_schema.definitions_schema(
    schema=schema_definition,
    definitions=[
        core_schema.list_schema(items_schema=schema_definition, ref='list-schema'),
    ],
)
v = SchemaValidator(schema)
assert v.validate_python([()]) == [[]]

Parameters:

Name	Type	Description	Default
`schema_ref`	`str`	The schema ref to use for the definition reference schema	required
`metadata`	`Any`	Any other information you want to include with the schema, not used by pydantic-core	`None`
`serialization`	`SerSchema \| None`	Custom serialization schema	`None`

Source code in pydantic_core/core_schema.py

def definition_reference_schema(
    schema_ref: str, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None
) -> DefinitionReferenceSchema:
    """
    Returns a schema that points to a schema stored in "definitions", this is useful for nested recursive
    models and also when you want to define validators separately from the main schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    schema_definition = core_schema.definition_reference_schema('list-schema')
    schema = core_schema.definitions_schema(
        schema=schema_definition,
        definitions=[
            core_schema.list_schema(items_schema=schema_definition, ref='list-schema'),
        ],
    )
    v = SchemaValidator(schema)
    assert v.validate_python([()]) == [[]]
    ```

    Args:
        schema_ref: The schema ref to use for the definition reference schema
        metadata: Any other information you want to include with the schema, not used by pydantic-core
        serialization: Custom serialization schema
    """
    return _dict_not_none(
        type='definition-ref', schema_ref=schema_ref, ref=ref, metadata=metadata, serialization=serialization
    )

pydantic_core.core_schema

WhenUsed module-attribute ¶

CoreConfig ¶

SerializationInfo ¶

context property ¶

ValidationInfo ¶

context property ¶

config property ¶

mode property ¶

data property ¶

field_name property ¶

simple_ser_schema ¶

plain_serializer_function_ser_schema ¶

wrap_serializer_function_ser_schema ¶

format_ser_schema ¶

to_string_ser_schema ¶

model_ser_schema ¶

computed_field ¶

any_schema ¶

none_schema ¶

bool_schema ¶

int_schema ¶

float_schema ¶

decimal_schema ¶

str_schema ¶

bytes_schema ¶

date_schema ¶

time_schema ¶

datetime_schema ¶

timedelta_schema ¶

literal_schema ¶

enum_schema ¶

is_instance_schema ¶

is_subclass_schema ¶

callable_schema ¶

list_schema ¶

tuple_positional_schema ¶

tuple_variable_schema ¶

tuple_schema ¶

set_schema ¶

frozenset_schema ¶

generator_schema ¶

dict_schema ¶

no_info_before_validator_function ¶

with_info_before_validator_function ¶

no_info_after_validator_function ¶

with_info_after_validator_function ¶

no_info_wrap_validator_function ¶

with_info_wrap_validator_function ¶

no_info_plain_validator_function ¶

with_info_plain_validator_function ¶

with_default_schema ¶

nullable_schema ¶

union_schema ¶

tagged_union_schema ¶

chain_schema ¶

lax_or_strict_schema ¶

json_or_python_schema ¶

typed_dict_field ¶

typed_dict_schema ¶

model_field ¶

model_fields_schema ¶

model_schema ¶

dataclass_field ¶

dataclass_args_schema ¶

dataclass_schema ¶

arguments_parameter ¶

arguments_schema ¶

call_schema ¶

custom_error_schema ¶

json_schema ¶

url_schema ¶

multi_host_url_schema ¶

definitions_schema ¶

definition_reference_schema ¶

WhenUsed `module-attribute` ¶

context `property` ¶

context `property` ¶

config `property` ¶

mode `property` ¶

data `property` ¶

field_name `property` ¶