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

Bases: TypedDict

Base class for schema configuration options.

The name of the configuration.

Type: str

Whether the configuration should strictly adhere to specified rules.

Type: bool

The behavior for handling extra fields.

Type: ExtraBehavior

Whether the TypedDict should be considered total. Default is True.

Type: bool

Whether to use attributes for models, dataclasses, and tagged union keys.

Type: bool

Whether to use the used alias (or first alias for "field required" errors) instead of field_names to construct error locs. Default is True.

Type: bool

Whether instances of models and dataclasses should re-validate. Default is 'never'.

Type: Literal['always', 'never', 'subclass-instances']

Whether to validate default values during validation. Default is False.

Type: bool

The maximum length for string fields.

Type: int

The minimum length for string fields.

Type: int

Whether to strip whitespace from string fields.

Type: bool

Whether to convert string fields to lowercase.

Type: bool

Whether to convert string fields to uppercase.

Type: bool

Whether to allow infinity and NaN values for float fields. Default is True.

Type: bool

The serialization option for timedelta values. Default is 'iso8601'. Note that if ser_json_temporal is set, then this param will be ignored.

Type: Literal['iso8601', 'float']

The serialization option for datetime like values. Default is 'iso8601'. The types this covers are datetime, date, time and timedelta. If this is set, it will take precedence over ser_json_timedelta

Type: Literal['iso8601', 'seconds', 'milliseconds']

The serialization option for bytes values. Default is 'utf8'.

Type: Literal['utf8', 'base64', 'hex']

The serialization option for infinity and NaN values in float fields. Default is 'null'.

Type: Literal['null', 'constants', 'strings']

The validation option for bytes values, complementing ser_json_bytes. Default is 'utf8'.

Type: Literal['utf8', 'base64', 'hex']

Whether to hide input data from ValidationError representation.

Type: bool

Whether to add user-python excs to the cause of a ValidationError. Requires exceptiongroup backport pre Python 3.11.

Type: bool

Whether to enable coercion of any Number type to str (not applicable in strict mode).

Type: bool

The regex engine to use for regex pattern validation. Default is 'rust-regex'. See StringSchema.

Type: Literal['rust-regex', 'python-re']

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.

Type: Union[bool, Literal['all', 'keys', 'none']]

Whether to use the field's alias when validating against the provided input data. Default is True.

Type: bool

Whether to use the field's name when validating against the provided input data. Default is False. Replacement for populate_by_name.

Type: bool

Whether to serialize by alias. Default is False, expected to change to True in V3.

Type: bool

Whether to enable polymorphic serialization for models and dataclasses. Default is False.

Type: bool

Whether to preserve empty URL paths when validating values for a URL type. Defaults to False.

Type: bool

Bases: Protocol[ContextT]

Extra data used during serialization.

The include argument set during serialization.

Type: IncExCall

The exclude argument set during serialization.

Type: IncExCall

The current serialization context.

Type: ContextT

The serialization mode set during serialization.

Type: Literal['python', 'json'] | str

The by_alias argument set during serialization.

Type: bool

The exclude_unset argument set during serialization.

Type: bool

The exclude_defaults argument set during serialization.

Type: bool

The exclude_none argument set during serialization.

Type: bool

The exclude_computed_fields argument set during serialization.

Type: bool

The serialize_as_any argument set during serialization.

Type: bool

The polymorphic_serialization argument set during serialization, if any.

Type: bool | None

The round_trip argument set during serialization.

Type: bool

Bases: SerializationInfo[ContextT], Protocol

Extra data used during field serialization.

The name of the current field being serialized.

Type: str

Bases: Protocol[ContextT]

Extra data used during validation.

The current validation context.

Type: ContextT

The CoreConfig that applies to this validation.

Type: CoreConfig | None

The type of input data we are currently validating.

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

The data being validated for this model.

Type: dict[str, Any]

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

Type: str | None

def simple_ser_schema(type: ExpectedSerializationTypes) -> SimpleSerSchema

Returns a schema for serialization with a custom type.

SimpleSerSchema

type : ExpectedSerializationTypes

The type to use for serialization

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.

PlainSerializerFunctionSerSchema

function : SerializerFunction

The function to use for serialization

is_field_serializer : bool | None Default: None

Whether the serializer is for a field, e.g. takes model as the first argument, and info includes field_name

info_arg : bool | None Default: None

Whether the function takes an info argument

return_schema : CoreSchema | None Default: None

Schema to use for serializing return value

when_used : WhenUsed Default: 'always'

When the function should be called

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.

WrapSerializerFunctionSerSchema

function : WrapSerializerFunction

The function to use for serialization

is_field_serializer : bool | None Default: None

Whether the serializer is for a field, e.g. takes model as the first argument, and info includes field_name

info_arg : bool | None Default: None

Whether the function takes an info argument

schema : CoreSchema | None Default: None

The schema to use for the inner serialization

return_schema : CoreSchema | None Default: None

Schema to use for serializing return value

when_used : WhenUsed Default: 'always'

When the function should be called

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

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

FormatSerSchema

formatting_string : str

String defining the format to use

when_used : WhenUsed Default: 'json-unless-none'

Same meaning as for [general_function_plain_ser_schema], but with a different default

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

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

ToStringSerSchema

when_used : WhenUsed Default: 'json-unless-none'

Same meaning as for [general_function_plain_ser_schema], but with a different default

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

Returns a schema for serialization using a model.

ModelSerSchema

cls : type[Any]

The expected class type, used to generate warnings if the wrong type is passed

schema : CoreSchema

Internal schema to use to serialize the model dict

def invalid_schema(
    ref: str | None = None,
    metadata: dict[str, Any] | None = None,
) -> InvalidSchema

Returns an invalid schema, used to indicate that a schema is invalid.

InvalidSchema

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

def computed_field(
    property_name: str,
    return_schema: CoreSchema,
    *,
    alias: str | None = None,
    serialization_exclude_if: Callable[[Any], bool] | None = None,
    metadata: dict[str, Any] | None = None,
) -> ComputedField

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

ComputedField

property_name : str

The name of the property on the model or dataclass

return_schema : CoreSchema

The schema used for the type returned by the computed field

alias : str | None Default: None

The name to use in the serialized output

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

def any_schema(
    *,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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

AnySchema

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def none_schema(
    *,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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

NoneSchema

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def bool_schema(
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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

BoolSchema

strict : bool | None Default: None

Whether the value should be a bool or a value that can be converted to a bool

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = 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

IntSchema

multiple_of : int | None Default: None

The value must be a multiple of this number

le : int | None Default: None

The value must be less than or equal to this number

ge : int | None Default: None

The value must be greater than or equal to this number

lt : int | None Default: None

The value must be strictly less than this number

gt : int | None Default: None

The value must be strictly greater than this number

strict : bool | None Default: None

Whether the value should be a int or a value that can be converted to a int

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = 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

FloatSchema

allow_inf_nan : bool | None Default: None

Whether to allow inf and nan values

multiple_of : float | None Default: None

The value must be a multiple of this number

le : float | None Default: None

The value must be less than or equal to this number

ge : float | None Default: None

The value must be greater than or equal to this number

lt : float | None Default: None

The value must be strictly less than this number

gt : float | None Default: None

The value must be strictly greater than this number

strict : bool | None Default: None

Whether the value should be a float or a value that can be converted to a float

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def decimal_schema(
    *,
    allow_inf_nan: bool | None = 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: dict[str, Any] | None = 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')

DecimalSchema

allow_inf_nan : bool | None Default: None

Whether to allow inf and nan values

multiple_of : Decimal | None Default: None

The value must be a multiple of this number

le : Decimal | None Default: None

The value must be less than or equal to this number

ge : Decimal | None Default: None

The value must be greater than or equal to this number

lt : Decimal | None Default: None

The value must be strictly less than this number

gt : Decimal | None Default: None

The value must be strictly greater than this number

max_digits : int | None Default: None

The maximum number of decimal digits allowed

decimal_places : int | None Default: None

The maximum number of decimal places allowed

strict : bool | None Default: None

Whether the value should be a float or a value that can be converted to a float

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def complex_schema(
    *,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = None,
    serialization: SerSchema | None = None,
) -> ComplexSchema

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

from pydantic_core import SchemaValidator, core_schema

schema = core_schema.complex_schema()
v = SchemaValidator(schema)
assert v.validate_python('1+2j') == complex(1, 2)
assert v.validate_python(complex(1, 2)) == complex(1, 2)

ComplexSchema

strict : bool | None Default: None

Whether the value should be a complex object instance or a value that can be converted to a complex object

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def str_schema(
    *,
    pattern: str | 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: dict[str, Any] | None = 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'

StringSchema

pattern : str | Pattern[str] | None Default: None

A regex pattern that the value must match

max_length : int | None Default: None

The value must be at most this length

min_length : int | None Default: None

The value must be at least this length

strip_whitespace : bool | None Default: None

Whether to strip whitespace from the value

to_lower : bool | None Default: None

Whether to convert the value to lowercase

to_upper : bool | None Default: None

Whether to convert the value to uppercase

regex_engine : Literal['rust-regex', 'python-re'] | None Default: 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.

strict : bool | None Default: None

Whether the value should be a string or a value that can be converted to a string

coerce_numbers_to_str : bool | None Default: None

Whether to enable coercion of any Number type to str (not applicable in strict mode).

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def bytes_schema(
    *,
    max_length: int | None = None,
    min_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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'

BytesSchema

max_length : int | None Default: None

The value must be at most this length

min_length : int | None Default: None

The value must be at least this length

strict : bool | None Default: None

Whether the value should be a bytes or a value that can be converted to a bytes

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = 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)

DateSchema

strict : bool | None Default: None

Whether the value should be a date or a value that can be converted to a date

le : date | None Default: None

The value must be less than or equal to this date

ge : date | None Default: None

The value must be greater than or equal to this date

lt : date | None Default: None

The value must be strictly less than this date

gt : date | None Default: None

The value must be strictly greater than this date

now_op : Literal['past', 'future'] | None Default: None

The value must be in the past or future relative to the current date

now_utc_offset : int | None Default: None

The value must be in the past or future relative to the current date with this utc offset

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = 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)

TimeSchema

strict : bool | None Default: None

Whether the value should be a time or a value that can be converted to a time

le : time | None Default: None

The value must be less than or equal to this time

ge : time | None Default: None

The value must be greater than or equal to this time

lt : time | None Default: None

The value must be strictly less than this time

gt : time | None Default: None

The value must be strictly greater than this time

tz_constraint : Literal['aware', 'naive'] | int | None Default: None

The value must be timezone aware or naive, or an int to indicate required tz offset

microseconds_precision : Literal['truncate', 'error'] Default: 'truncate'

The behavior when seconds have more than 6 digits or microseconds is too large

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = 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

DatetimeSchema

strict : bool | None Default: None

Whether the value should be a datetime or a value that can be converted to a datetime

le : datetime | None Default: None

The value must be less than or equal to this datetime

ge : datetime | None Default: None

The value must be greater than or equal to this datetime

lt : datetime | None Default: None

The value must be strictly less than this datetime

gt : datetime | None Default: None

The value must be strictly greater than this datetime

now_op : Literal['past', 'future'] | None Default: None

The value must be in the past or future relative to the current datetime

tz_constraint : Literal['aware', 'naive'] | int | None Default: 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

now_utc_offset : int | None Default: None

The value must be in the past or future relative to the current datetime with this utc offset

microseconds_precision : Literal['truncate', 'error'] Default: 'truncate'

The behavior when seconds have more than 6 digits or microseconds is too large

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = 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)

TimedeltaSchema

strict : bool | None Default: None

Whether the value should be a timedelta or a value that can be converted to a timedelta

le : timedelta | None Default: None

The value must be less than or equal to this timedelta

ge : timedelta | None Default: None

The value must be greater than or equal to this timedelta

lt : timedelta | None Default: None

The value must be strictly less than this timedelta

gt : timedelta | None Default: None

The value must be strictly greater than this timedelta

microseconds_precision : Literal['truncate', 'error'] Default: 'truncate'

The behavior when seconds have more than 6 digits or microseconds is too large

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def literal_schema(
    expected: list[Any],
    *,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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'

LiteralSchema

expected : list[Any]

The value must be one of these values

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = 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

EnumSchema

cls : Any

The enum class

members : list[Any]

The members of the enum, generally list(MyEnum.__members__.values())

sub_type : Literal['str', 'int', 'float'] | None Default: None

The type of the enum, either 'str' or 'int' or None for plain enums

missing : Callable[[Any], Any] | None Default: None

A function to use when the value is not found in the enum, from _missing_

strict : bool | None Default: None

Whether to use strict mode, defaults to False

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def missing_sentinel_schema(
    metadata: dict[str, Any] | None = None,
    serialization: SerSchema | None = None,
) -> MissingSentinelSchema

Returns a schema for the MISSING sentinel.

MissingSentinelSchema

def is_instance_schema(
    cls: Any,
    *,
    cls_repr: str | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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())

IsInstanceSchema

cls : Any

The value must be an instance of this class

cls_repr : str | None Default: None

If provided this string is used in the validator name instead of repr(cls)

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def is_subclass_schema(
    cls: type[Any],
    *,
    cls_repr: str | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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)

IsInstanceSchema

cls : type[Any]

The value must be a subclass of this class

cls_repr : str | None Default: None

If provided this string is used in the validator name instead of repr(cls)

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def callable_schema(
    *,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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)

CallableSchema

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def list_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    fail_fast: bool | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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]

ListSchema

items_schema : CoreSchema | None Default: None

The value must be a list of items that match this schema

min_length : int | None Default: None

The value must be a list with at least this many items

max_length : int | None Default: None

The value must be a list with at most this many items

fail_fast : bool | None Default: None

Stop validation on the first error

strict : bool | None Default: None

The value must be a list with exactly this many items

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : IncExSeqOrElseSerSchema | None Default: None

Custom serialization schema

def tuple_positional_schema(
    items_schema: list[CoreSchema],
    *,
    extras_schema: CoreSchema | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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')

TupleSchema

items_schema : list[CoreSchema]

The value must be a tuple with items that match these schemas

extras_schema : CoreSchema | None Default: 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.

strict : bool | None Default: None

The value must be a tuple with exactly this many items

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : IncExSeqOrElseSerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = 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)

TupleSchema

items_schema : CoreSchema | None Default: None

The value must be a tuple with items that match this schema

min_length : int | None Default: None

The value must be a tuple with at least this many items

max_length : int | None Default: None

The value must be a tuple with at most this many items

strict : bool | None Default: None

The value must be a tuple with exactly this many items

ref : str | None Default: None

Optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : IncExSeqOrElseSerSchema | None Default: None

Custom serialization schema

def tuple_schema(
    items_schema: list[CoreSchema],
    *,
    variadic_item_index: int | None = None,
    min_length: int | None = None,
    max_length: int | None = None,
    fail_fast: bool | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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)

TupleSchema

items_schema : list[CoreSchema]

The value must be a tuple with items that match these schemas

variadic_item_index : int | None Default: None

The index of the schema in items_schema to be treated as variadic (following PEP 646)

min_length : int | None Default: None

The value must be a tuple with at least this many items

max_length : int | None Default: None

The value must be a tuple with at most this many items

fail_fast : bool | None Default: None

Stop validation on the first error

strict : bool | None Default: None

The value must be a tuple with exactly this many items

ref : str | None Default: None

Optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : IncExSeqOrElseSerSchema | None Default: None

Custom serialization schema

def set_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    fail_fast: bool | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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}

SetSchema

items_schema : CoreSchema | None Default: None

The value must be a set with items that match this schema

min_length : int | None Default: None

The value must be a set with at least this many items

max_length : int | None Default: None

The value must be a set with at most this many items

fail_fast : bool | None Default: None

Stop validation on the first error

strict : bool | None Default: None

The value must be a set with exactly this many items

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def frozenset_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    fail_fast: bool | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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})

FrozenSetSchema

items_schema : CoreSchema | None Default: None

The value must be a frozenset with items that match this schema

min_length : int | None Default: None

The value must be a frozenset with at least this many items

max_length : int | None Default: None

The value must be a frozenset with at most this many items

fail_fast : bool | None Default: None

Stop validation on the first error

strict : bool | None Default: None

The value must be a frozenset with exactly this many items

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def generator_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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.

GeneratorSchema

items_schema : CoreSchema | None Default: None

The value must be a generator with items that match this schema

min_length : int | None Default: None

The value must be a generator that yields at least this many items

max_length : int | None Default: None

The value must be a generator that yields at most this many items

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : IncExSeqOrElseSerSchema | None Default: None

Custom serialization schema

def dict_schema(
    keys_schema: CoreSchema | None = None,
    values_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    fail_fast: bool | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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}

DictSchema

keys_schema : CoreSchema | None Default: None

The value must be a dict with keys that match this schema

values_schema : CoreSchema | None Default: None

The value must be a dict with values that match this schema

min_length : int | None Default: None

The value must be a dict with at least this many items

max_length : int | None Default: None

The value must be a dict with at most this many items

fail_fast : bool | None Default: None

Stop validation on the first error

strict : bool | None Default: None

Whether the keys and values should be validated with strict mode

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def no_info_before_validator_function(
    function: NoInfoValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    json_schema_input_schema: CoreSchema | None = None,
    metadata: dict[str, Any] | None = 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'}

BeforeValidatorFunctionSchema

function : NoInfoValidatorFunction

The validator function to call

schema : CoreSchema

The schema to validate the output of the validator function

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

json_schema_input_schema : CoreSchema | None Default: None

The core schema to be used to generate the corresponding JSON Schema input type

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def with_info_before_validator_function(
    function: WithInfoValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    json_schema_input_schema: CoreSchema | None = None,
    metadata: dict[str, Any] | None = 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()
)
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'}

BeforeValidatorFunctionSchema

function : WithInfoValidatorFunction

The validator function to call

field_name : str | None Default: None

The name of the field this validator is applied to, if any (deprecated)

schema : CoreSchema

The schema to validate the output of the validator function

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

json_schema_input_schema : CoreSchema | None Default: None

The core schema to be used to generate the corresponding JSON Schema input type

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def no_info_after_validator_function(
    function: NoInfoValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    json_schema_input_schema: CoreSchema | None = None,
    metadata: dict[str, Any] | None = 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'}

AfterValidatorFunctionSchema

function : NoInfoValidatorFunction

The validator function to call after the schema is validated

schema : CoreSchema

The schema to validate before the validator function

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

json_schema_input_schema : CoreSchema | None Default: None

The core schema to be used to generate the corresponding JSON Schema input type

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def with_info_after_validator_function(
    function: WithInfoValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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()
)
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'}

AfterValidatorFunctionSchema

function : WithInfoValidatorFunction

The validator function to call after the schema is validated

schema : CoreSchema

The schema to validate before the validator function

field_name : str | None Default: None

The name of the field this validator is applied to, if any (deprecated)

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def no_info_wrap_validator_function(
    function: NoInfoWrapValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    json_schema_input_schema: CoreSchema | None = None,
    metadata: dict[str, Any] | None = 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'

WrapValidatorFunctionSchema

function : NoInfoWrapValidatorFunction

The validator function to call

schema : CoreSchema

The schema to validate the output of the validator function

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

json_schema_input_schema : CoreSchema | None Default: None

The core schema to be used to generate the corresponding JSON Schema input type

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def with_info_wrap_validator_function(
    function: WithInfoWrapValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    json_schema_input_schema: CoreSchema | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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'

WrapValidatorFunctionSchema

function : WithInfoWrapValidatorFunction

The validator function to call

schema : CoreSchema

The schema to validate the output of the validator function

field_name : str | None Default: None

The name of the field this validator is applied to, if any (deprecated)

json_schema_input_schema : CoreSchema | None Default: None

The core schema to be used to generate the corresponding JSON Schema input type

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def no_info_plain_validator_function(
    function: NoInfoValidatorFunction,
    *,
    ref: str | None = None,
    json_schema_input_schema: CoreSchema | None = None,
    metadata: dict[str, Any] | None = 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'

PlainValidatorFunctionSchema

function : NoInfoValidatorFunction

The validator function to call

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

json_schema_input_schema : CoreSchema | None Default: None

The core schema to be used to generate the corresponding JSON Schema input type

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def with_info_plain_validator_function(
    function: WithInfoValidatorFunction,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    json_schema_input_schema: CoreSchema | None = None,
    metadata: dict[str, Any] | None = 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'

PlainValidatorFunctionSchema

function : WithInfoValidatorFunction

The validator function to call

field_name : str | None Default: None

The name of the field this validator is applied to, if any (deprecated)

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

json_schema_input_schema : CoreSchema | None Default: None

The core schema to be used to generate the corresponding JSON Schema input type

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def with_default_schema(
    schema: CoreSchema,
    *,
    default: Any = PydanticUndefined,
    default_factory: Union[Callable[[], Any], Callable[[dict[str, Any]], Any], None] = None,
    default_factory_takes_data: bool | None = None,
    on_error: Literal['raise', 'omit', 'default'] | None = None,
    validate_default: bool | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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'})

WithDefaultSchema

schema : CoreSchema

The schema to add a default value to

default : Any Default: PydanticUndefined

The default value to use

default_factory : Union[Callable[[], Any], Callable[[dict[str, Any]], Any], None] Default: None

A callable that returns the default value to use

default_factory_takes_data : bool | None Default: None

Whether the default factory takes a validated data argument

on_error : Literal['raise', 'omit', 'default'] | None Default: None

What to do if the schema validation fails. One of 'raise', 'omit', 'default'

validate_default : bool | None Default: None

Whether the default value should be validated

strict : bool | None Default: None

Whether the underlying schema should be validated with strict mode

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def nullable_schema(
    schema: CoreSchema,
    *,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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

NullableSchema

schema : CoreSchema

The schema to wrap

strict : bool | None Default: None

Whether the underlying schema should be validated with strict mode

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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

UnionSchema

choices : list[CoreSchema | tuple[CoreSchema, str]]

The schemas to match. If a tuple, the second item is used as the label for the case.

auto_collapse : bool | None Default: None

whether to automatically collapse unions with one element to the inner validator, default true

custom_error_type : str | None Default: None

The custom error type to use if the validation fails

custom_error_message : str | None Default: None

The custom error message to use if the validation fails

custom_error_context : dict[str, str | int] | None Default: None

The custom error context to use if the validation fails

mode : Literal['smart', 'left_to_right'] | None Default: 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

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def tagged_union_schema(
    choices: dict[Any, CoreSchema],
    discriminator: str | list[str | int] | list[list[str | int]] | Callable[[Any], Any],
    *,
    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: dict[str, Any] | None = 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],
}

TaggedUnionSchema

choices : dict[Any, 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)

discriminator : str | list[str | int] | list[list[str | int]] | Callable[[Any], Any]

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 : str | None Default: None

The custom error type to use if the validation fails

custom_error_message : str | None Default: None

The custom error message to use if the validation fails

custom_error_context : dict[str, int | str | float] | None Default: None

The custom error context to use if the validation fails

strict : bool | None Default: None

Whether the underlying schemas should be validated with strict mode

from_attributes : bool | None Default: None

Whether to use the attributes of the object to retrieve the discriminator value

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def chain_schema(
    steps: list[CoreSchema],
    *,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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'

ChainSchema

steps : list[CoreSchema]

The schemas to chain

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def lax_or_strict_schema(
    lax_schema: CoreSchema,
    strict_schema: CoreSchema,
    *,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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

LaxOrStrictSchema

lax_schema : CoreSchema

The lax schema to use

strict_schema : CoreSchema

The strict schema to use

strict : bool | None Default: None

Whether the strict schema should be used

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def json_or_python_schema(
    json_schema: CoreSchema,
    python_schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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')

JsonOrPythonSchema

json_schema : CoreSchema

The schema to use for Json inputs

python_schema : CoreSchema

The schema to use for Python inputs

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = None,
    serialization_exclude_if: Callable[[Any], bool] | None = 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)

TypedDictField

schema : CoreSchema

The schema to use for the field

required : bool | None Default: None

Whether the field is required, otherwise uses the value from total on the typed dict

validation_alias : str | list[str | int] | list[list[str | int]] | None Default: None

The alias(es) to use to find the field in the validation data

serialization_alias : str | None Default: None

The alias to use as a key when serializing

serialization_exclude : bool | None Default: None

Whether to exclude the field when serializing

serialization_exclude_if : Callable[[Any], bool] | None Default: None

A callable that determines whether to exclude the field when serializing based on its value.

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

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

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

from typing_extensions import TypedDict

from pydantic_core import SchemaValidator, core_schema

class MyTypedDict(TypedDict):
    a: str

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

TypedDictSchema

fields : dict[str, TypedDictField]

The fields to use for the typed dict

cls : type[Any] | None Default: None

The class to use for the typed dict

cls_name : str | None Default: None

The name to use in error locations. Falls back to cls.__name__, or the validator name if no class is provided.

computed_fields : list[ComputedField] | None Default: None

Computed fields to use when serializing the model, only applies when directly inside a model

strict : bool | None Default: None

Whether the typed dict is strict

extras_schema : CoreSchema | None Default: None

The extra validator to use for the typed dict

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

extra_behavior : ExtraBehavior | None Default: None

The extra behavior to use for the typed dict

total : bool | None Default: None

Whether the typed dict is total, otherwise uses typed_dict_total from config

serialization : SerSchema | None Default: None

Custom serialization schema

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,
    serialization_exclude_if: Callable[[Any], bool] | None = None,
    frozen: bool | None = None,
    metadata: dict[str, Any] | None = 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())

ModelField

schema : CoreSchema

The schema to use for the field

validation_alias : str | list[str | int] | list[list[str | int]] | None Default: None

The alias(es) to use to find the field in the validation data

serialization_alias : str | None Default: None

The alias to use as a key when serializing

serialization_exclude : bool | None Default: None

Whether to exclude the field when serializing

serialization_exclude_if : Callable[[Any], bool] | None Default: None

A Callable that determines whether to exclude a field during serialization based on its value.

frozen : bool | None Default: None

Whether the field is frozen

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

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,
    extras_keys_schema: CoreSchema | None = None,
    extra_behavior: ExtraBehavior | None = None,
    from_attributes: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = None,
    serialization: SerSchema | None = None,
) -> ModelFieldsSchema

Returns a schema that matches the fields of a Pydantic model, 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'})

ModelFieldsSchema

fields : dict[str, ModelField]

The fields of the model

model_name : str | None Default: None

The name of the model, used for error messages, defaults to "Model"

computed_fields : list[ComputedField] | None Default: None

Computed fields to use when serializing the model, only applies when directly inside a model

strict : bool | None Default: None

Whether the model is strict

extras_schema : CoreSchema | None Default: None

The schema to use when validating extra input data

extras_keys_schema : CoreSchema | None Default: None

The schema to use when validating the keys of extra input data

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

extra_behavior : ExtraBehavior | None Default: None

The extra behavior to use for the model fields

from_attributes : bool | None Default: None

Whether the model fields should be populated from attributes

serialization : SerSchema | None Default: None

Custom serialization schema

def model_schema(
    cls: type[Any],
    schema: CoreSchema,
    *,
    generic_origin: type[Any] | None = None,
    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: dict[str, Any] | None = 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

ModelSchema

cls : type[Any]

The class to use for the model

schema : CoreSchema

The schema to use for the model

generic_origin : type[Any] | None Default: None

The origin type used for this model, if it's a parametrized generic. Ex, if this model schema represents SomeModel[int], generic_origin is SomeModel

custom_init : bool | None Default: None

Whether the model has a custom init method

root_model : bool | None Default: None

Whether the model is a RootModel

post_init : str | None Default: None

The call after init to use for the model

revalidate_instances : Literal['always', 'never', 'subclass-instances'] | None Default: None

whether instances of models and dataclasses (including subclass instances) should re-validate defaults to config.revalidate_instances, else 'never'

strict : bool | None Default: None

Whether the model is strict

frozen : bool | None Default: None

Whether the model is frozen

extra_behavior : ExtraBehavior | None Default: None

The extra behavior to use for the model, used in serialization

config : CoreConfig | None Default: None

The config to use for the model

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = None,
    serialization_exclude_if: Callable[[Any], bool] | None = 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)

DataclassField

name : str

The name to use for the argument parameter

schema : CoreSchema

The schema to use for the argument parameter

kw_only : bool | None Default: None

Whether the field can be set with a positional argument as well as a keyword argument

init : bool | None Default: None

Whether the field should be validated during initialization

init_only : bool | None Default: None

Whether the field should be omitted from __dict__ and passed to __post_init__

validation_alias : str | list[str | int] | list[list[str | int]] | None Default: None

The alias(es) to use to find the field in the validation data

serialization_alias : str | None Default: None

The alias to use as a key when serializing

serialization_exclude : bool | None Default: None

Whether to exclude the field when serializing

serialization_exclude_if : Callable[[Any], bool] | None Default: None

A callable that determines whether to exclude the field when serializing based on its value.

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

frozen : bool | None Default: None

Whether the field is frozen

def dataclass_args_schema(
    dataclass_name: str,
    fields: list[DataclassField],
    *,
    computed_fields: list[ComputedField] | None = None,
    collect_init_only: bool | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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)

DataclassArgsSchema

dataclass_name : str

The name of the dataclass being validated

fields : list[DataclassField]

The fields to use for the dataclass

computed_fields : list[ComputedField] | None Default: None

Computed fields to use when serializing the dataclass

collect_init_only : bool | None Default: None

Whether to collect init only fields into a dict to pass to __post_init__

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

extra_behavior : ExtraBehavior | None Default: None

How to handle extra fields

def dataclass_schema(
    cls: type[Any],
    schema: CoreSchema,
    fields: list[str],
    *,
    generic_origin: type[Any] | None = None,
    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: dict[str, Any] | None = 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.

DataclassSchema

cls : type[Any]

The dataclass type, used to perform subclass checks

schema : CoreSchema

The schema to use for the dataclass fields

fields : list[str]

Fields of the dataclass, this is used in serialization and in validation during re-validation and while validating assignment

generic_origin : type[Any] | None Default: None

The origin type used for this dataclass, if it's a parametrized generic. Ex, if this model schema represents SomeDataclass[int], generic_origin is SomeDataclass

cls_name : str | None Default: None

The name to use in error locs, etc; this is useful for generics (default: cls.__name__)

post_init : bool | None Default: None

Whether to call __post_init__ after validation

revalidate_instances : Literal['always', 'never', 'subclass-instances'] | None Default: None

whether instances of models and dataclasses (including subclass instances) should re-validate defaults to config.revalidate_instances, else 'never'

strict : bool | None Default: None

Whether to require an exact instance of cls

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

frozen : bool | None Default: None

Whether the dataclass is frozen

slots : bool | None Default: None

Whether slots=True on the dataclass, means each field is assigned independently, rather than simply setting __dict__, default false

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.:

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',), {})

ArgumentsParameter

name : str

The name to use for the argument parameter

schema : CoreSchema

The schema to use for the argument parameter

mode : Literal['positional_only', 'positional_or_keyword', 'keyword_only'] | None Default: None

The mode to use for the argument parameter

alias : str | list[str | int] | list[list[str | int]] | None Default: None

The alias to use for the argument parameter

def arguments_schema(
    arguments: list[ArgumentsParameter],
    *,
    validate_by_name: bool | None = None,
    validate_by_alias: bool | None = None,
    var_args_schema: CoreSchema | None = None,
    var_kwargs_mode: VarKwargsMode | None = None,
    var_kwargs_schema: CoreSchema | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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), {})

ArgumentsSchema

arguments : list[ArgumentsParameter]

The arguments to use for the arguments schema

validate_by_name : bool | None Default: None

Whether to populate by the parameter names, defaults to False.

validate_by_alias : bool | None Default: None

Whether to populate by the parameter aliases, defaults to True.

var_args_schema : CoreSchema | None Default: None

The variable args schema to use for the arguments schema

var_kwargs_mode : VarKwargsMode | None Default: None

The validation mode to use for variadic keyword arguments. If 'uniform', every value of the keyword arguments will be validated against the var_kwargs_schema schema. If 'unpacked-typed-dict', the var_kwargs_schema argument must be a typed_dict_schema

var_kwargs_schema : CoreSchema | None Default: None

The variable kwargs schema to use for the arguments schema

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def arguments_v3_parameter(
    name: str,
    schema: CoreSchema,
    *,
    mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only', 'var_args', 'var_kwargs_uniform', 'var_kwargs_unpacked_typed_dict'] | None = None,
    alias: str | list[str | int] | list[list[str | int]] | None = None,
) -> ArgumentsV3Parameter

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

from pydantic_core import SchemaValidator, core_schema

param = core_schema.arguments_v3_parameter(
    name='a', schema=core_schema.str_schema(), mode='positional_only'
)
schema = core_schema.arguments_v3_schema([param])
v = SchemaValidator(schema)
assert v.validate_python({'a': 'hello'}) == (('hello',), {})

ArgumentsV3Parameter

name : str

The name to use for the argument parameter

schema : CoreSchema

The schema to use for the argument parameter

mode : Literal['positional_only', 'positional_or_keyword', 'keyword_only', 'var_args', 'var_kwargs_uniform', 'var_kwargs_unpacked_typed_dict'] | None Default: None

The mode to use for the argument parameter

alias : str | list[str | int] | list[list[str | int]] | None Default: None

The alias to use for the argument parameter

def arguments_v3_schema(
    arguments: list[ArgumentsV3Parameter],
    *,
    validate_by_name: bool | None = None,
    validate_by_alias: bool | None = None,
    extra_behavior: Literal['forbid', 'ignore'] | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = None,
    serialization: SerSchema | None = None,
) -> ArgumentsV3Schema

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

from pydantic_core import SchemaValidator, core_schema

param_a = core_schema.arguments_v3_parameter(
    name='a', schema=core_schema.str_schema(), mode='positional_only'
)
param_b = core_schema.arguments_v3_parameter(
    name='kwargs', schema=core_schema.bool_schema(), mode='var_kwargs_uniform'
)
schema = core_schema.arguments_v3_schema([param_a, param_b])
v = SchemaValidator(schema)
assert v.validate_python({'a': 'hi', 'kwargs': {'b': True}}) == (('hi',), {'b': True})

This schema is currently not used by other Pydantic components. In V3, it will most likely become the default arguments schema for the 'call' schema.

ArgumentsV3Schema

arguments : list[ArgumentsV3Parameter]

The arguments to use for the arguments schema.

validate_by_name : bool | None Default: None

Whether to populate by the parameter names, defaults to False.

validate_by_alias : bool | None Default: None

Whether to populate by the parameter aliases, defaults to True.

extra_behavior : Literal['forbid', 'ignore'] | None Default: None

The extra behavior to use.

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places.

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core.

serialization : SerSchema | None Default: None

Custom serialization schema.

def call_schema(
    arguments: CoreSchema,
    function: Callable[..., Any],
    *,
    function_name: str | None = None,
    return_schema: CoreSchema | None = None,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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'

CallSchema

arguments : CoreSchema

The arguments to use for the arguments schema

function : Callable[..., Any]

The function to use for the call schema

function_name : str | None Default: None

The function name to use for the call schema, if not provided function.__name__ is used

return_schema : CoreSchema | None Default: None

The return schema to use for the call schema

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

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: dict[str, Any] | None = 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)

CustomErrorSchema

schema : CoreSchema

The schema to use for the custom error schema

custom_error_type : str

The custom error type to use for the custom error schema

custom_error_message : str | None Default: None

The custom error message to use for the custom error schema

custom_error_context : dict[str, Any] | None Default: None

The custom error context to use for the custom error schema

ref : str | None Default: None

optional unique identifier of the schema, used to reference the schema in other places

metadata : dict[str, Any] | None Default: None

Any other information you want to include with the schema, not used by pydantic-core

serialization : SerSchema | None Default: None

Custom serialization schema

def json_schema(
    schema: CoreSchema | None = None,
    *,
    ref: str | None = None,
    metadata: dict[str, Any] | None = 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)

JsonSchema

schema : CoreSchema | None Default: None