Skip to content

pydantic_core.core_schema

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

WhenUsed module-attribute

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

Values have the following meanings:

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

CoreConfig

Bases: TypedDict

Base class for schema configuration options.

Attributes:

Name Type Description
title str

The name of the configuration.

strict bool

Whether the configuration should strictly adhere to specified rules.

extra_fields_behavior ExtraBehavior

The behavior for handling extra fields.

typed_dict_total bool

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

from_attributes bool

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

loc_by_alias bool

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

revalidate_instances Literal['always', 'never', 'subclass-instances']

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

validate_default bool

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

populate_by_name bool

Whether an aliased field may be populated by its name as given by the model attribute, as well as the alias. (Replaces 'allow_population_by_field_name' in Pydantic v1.) Default is False.

str_max_length int

The maximum length for string fields.

str_min_length int

The minimum length for string fields.

str_strip_whitespace bool

Whether to strip whitespace from string fields.

str_to_lower bool

Whether to convert string fields to lowercase.

str_to_upper bool

Whether to convert string fields to uppercase.

allow_inf_nan bool

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

ser_json_timedelta Literal['iso8601', 'float']

The serialization option for timedelta values. Default is 'iso8601'.

ser_json_bytes Literal['utf8', 'base64', 'hex']

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

ser_json_inf_nan Literal['null', 'constants']

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

hide_input_in_errors bool

Whether to hide input data from ValidationError representation.

validation_error_cause bool

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

coerce_numbers_to_str bool

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

regex_engine Literal['rust-regex', 'python-re']

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

ValidationInfo

Bases: Protocol

Argument passed to validation functions.

context property

context

Current validation context.

config property

config

The CoreConfig that applies to this validation.

mode property

mode

The type of input data we are currently validating

data property

data

The data being validated for this model.

field_name property

field_name

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

simple_ser_schema

simple_ser_schema(type)

Returns a schema for serialization with a custom type.

Parameters:

Name Type Description Default
type ExpectedSerializationTypes

The type to use for serialization

required
Source code in pydantic_core/core_schema.py
230
231
232
233
234
235
236
237
def simple_ser_schema(type: ExpectedSerializationTypes) -> SimpleSerSchema:
    """
    Returns a schema for serialization with a custom type.

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

plain_serializer_function_ser_schema

plain_serializer_function_ser_schema(
    function,
    *,
    is_field_serializer=None,
    info_arg=None,
    return_schema=None,
    when_used="always"
)

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

Parameters:

Name Type Description Default
function SerializerFunction

The function to use for serialization

required
is_field_serializer bool | None

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

None
info_arg bool | None

Whether the function takes an __info argument

None
return_schema CoreSchema | None

Schema to use for serializing return value

None
when_used WhenUsed

When the function should be called

'always'
Source code in pydantic_core/core_schema.py
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
def plain_serializer_function_ser_schema(
    function: SerializerFunction,
    *,
    is_field_serializer: bool | None = None,
    info_arg: bool | None = None,
    return_schema: CoreSchema | None = None,
    when_used: WhenUsed = 'always',
) -> PlainSerializerFunctionSerSchema:
    """
    Returns a schema for serialization with a function, can be either a "general" or "field" function.

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

wrap_serializer_function_ser_schema

wrap_serializer_function_ser_schema(
    function,
    *,
    is_field_serializer=None,
    info_arg=None,
    schema=None,
    return_schema=None,
    when_used="always"
)

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

Parameters:

Name Type Description Default
function WrapSerializerFunction

The function to use for serialization

required
is_field_serializer bool | None

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

None
info_arg bool | None

Whether the function takes an __info argument

None
schema CoreSchema | None

The schema to use for the inner serialization

None
return_schema CoreSchema | None

Schema to use for serializing return value

None
when_used WhenUsed

When the function should be called

'always'
Source code in pydantic_core/core_schema.py
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
def wrap_serializer_function_ser_schema(
    function: WrapSerializerFunction,
    *,
    is_field_serializer: bool | None = None,
    info_arg: bool | None = None,
    schema: CoreSchema | None = None,
    return_schema: CoreSchema | None = None,
    when_used: WhenUsed = 'always',
) -> WrapSerializerFunctionSerSchema:
    """
    Returns a schema for serialization with a wrap function, can be either a "general" or "field" function.

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

format_ser_schema

format_ser_schema(
    formatting_string, *, when_used="json-unless-none"
)

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

Parameters:

Name Type Description Default
formatting_string str

String defining the format to use

required
when_used WhenUsed

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

'json-unless-none'
Source code in pydantic_core/core_schema.py
379
380
381
382
383
384
385
386
387
388
389
390
def format_ser_schema(formatting_string: str, *, when_used: WhenUsed = 'json-unless-none') -> FormatSerSchema:
    """
    Returns a schema for serialization using python's `format` method.

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

to_string_ser_schema

to_string_ser_schema(*, when_used='json-unless-none')

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

Parameters:

Name Type Description Default
when_used WhenUsed

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

'json-unless-none'
Source code in pydantic_core/core_schema.py
398
399
400
401
402
403
404
405
406
407
408
409
def to_string_ser_schema(*, when_used: WhenUsed = 'json-unless-none') -> ToStringSerSchema:
    """
    Returns a schema for serialization using python's `str()` / `__str__` method.

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

model_ser_schema

model_ser_schema(cls, schema)

Returns a schema for serialization using a model.

Parameters:

Name Type Description Default
cls Type[Any]

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

required
schema CoreSchema

Internal schema to use to serialize the model dict

required
Source code in pydantic_core/core_schema.py
418
419
420
421
422
423
424
425
426
def model_ser_schema(cls: Type[Any], schema: CoreSchema) -> ModelSerSchema:
    """
    Returns a schema for serialization using a model.

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

computed_field

computed_field(
    property_name,
    return_schema,
    *,
    alias=None,
    metadata=None
)

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

Parameters:

Name Type Description Default
property_name str

The name of the property on the model or dataclass

required
return_schema CoreSchema

The schema used for the type returned by the computed field

required
alias str | None

The name to use in the serialized output

None
metadata Any

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

None
Source code in pydantic_core/core_schema.py
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
def computed_field(
    property_name: str, return_schema: CoreSchema, *, alias: str | None = None, metadata: Any = None
) -> ComputedField:
    """
    ComputedFields are properties of a model or dataclass that are included in serialization.

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

any_schema

any_schema(*, ref=None, metadata=None, serialization=None)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
def any_schema(*, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None) -> AnySchema:
    """
    Returns a schema that matches any value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

none_schema

none_schema(*, ref=None, metadata=None, serialization=None)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
def none_schema(*, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None) -> NoneSchema:
    """
    Returns a schema that matches a None value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

bool_schema

bool_schema(
    strict=None, ref=None, metadata=None, serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
strict bool | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
def bool_schema(
    strict: bool | None = None, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None
) -> BoolSchema:
    """
    Returns a schema that matches a bool value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

int_schema

int_schema(
    *,
    multiple_of=None,
    le=None,
    ge=None,
    lt=None,
    gt=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
multiple_of int | None

The value must be a multiple of this number

None
le int | None

The value must be less than or equal to this number

None
ge int | None

The value must be greater than or equal to this number

None
lt int | None

The value must be strictly less than this number

None
gt int | None

The value must be strictly greater than this number

None
strict bool | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
def int_schema(
    *,
    multiple_of: int | None = None,
    le: int | None = None,
    ge: int | None = None,
    lt: int | None = None,
    gt: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> IntSchema:
    """
    Returns a schema that matches a int value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

float_schema

float_schema(
    *,
    allow_inf_nan=None,
    multiple_of=None,
    le=None,
    ge=None,
    lt=None,
    gt=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
allow_inf_nan bool | None

Whether to allow inf and nan values

None
multiple_of float | None

The value must be a multiple of this number

None
le float | None

The value must be less than or equal to this number

None
ge float | None

The value must be greater than or equal to this number

None
lt float | None

The value must be strictly less than this number

None
gt float | None

The value must be strictly greater than this number

None
strict bool | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
def float_schema(
    *,
    allow_inf_nan: bool | None = None,
    multiple_of: float | None = None,
    le: float | None = None,
    ge: float | None = None,
    lt: float | None = None,
    gt: float | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> FloatSchema:
    """
    Returns a schema that matches a float value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

decimal_schema

decimal_schema(
    *,
    allow_inf_nan=None,
    multiple_of=None,
    le=None,
    ge=None,
    lt=None,
    gt=None,
    max_digits=None,
    decimal_places=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from decimal import Decimal
from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
allow_inf_nan bool

Whether to allow inf and nan values

None
multiple_of Decimal | None

The value must be a multiple of this number

None
le Decimal | None

The value must be less than or equal to this number

None
ge Decimal | None

The value must be greater than or equal to this number

None
lt Decimal | None

The value must be strictly less than this number

None
gt Decimal | None

The value must be strictly greater than this number

None
max_digits int | None

The maximum number of decimal digits allowed

None
decimal_places int | None

The maximum number of decimal places allowed

None
strict bool | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
def decimal_schema(
    *,
    allow_inf_nan: bool = None,
    multiple_of: Decimal | None = None,
    le: Decimal | None = None,
    ge: Decimal | None = None,
    lt: Decimal | None = None,
    gt: Decimal | None = None,
    max_digits: int | None = None,
    decimal_places: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> DecimalSchema:
    """
    Returns a schema that matches a decimal value, e.g.:

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

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

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

str_schema

str_schema(
    *,
    pattern=None,
    max_length=None,
    min_length=None,
    strip_whitespace=None,
    to_lower=None,
    to_upper=None,
    regex_engine=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
pattern str | None

A regex pattern that the value must match

None
max_length int | None

The value must be at most this length

None
min_length int | None

The value must be at least this length

None
strip_whitespace bool | None

Whether to strip whitespace from the value

None
to_lower bool | None

Whether to convert the value to lowercase

None
to_upper bool | None

Whether to convert the value to uppercase

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

The regex engine to use for pattern validation. Default is 'rust-regex'. - rust-regex uses the regex Rust crate, which is non-backtracking and therefore more DDoS resistant, but does not support all regex features. - python-re use the re module, which supports all regex features, but may be slower.

None
strict bool | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
def str_schema(
    *,
    pattern: str | None = None,
    max_length: int | None = None,
    min_length: int | None = None,
    strip_whitespace: bool | None = None,
    to_lower: bool | None = None,
    to_upper: bool | None = None,
    regex_engine: Literal['rust-regex', 'python-re'] | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> StringSchema:
    """
    Returns a schema that matches a string value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

bytes_schema

bytes_schema(
    *,
    max_length=None,
    min_length=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
max_length int | None

The value must be at most this length

None
min_length int | None

The value must be at least this length

None
strict bool | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
def bytes_schema(
    *,
    max_length: int | None = None,
    min_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> BytesSchema:
    """
    Returns a schema that matches a bytes value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

date_schema

date_schema(
    *,
    strict=None,
    le=None,
    ge=None,
    lt=None,
    gt=None,
    now_op=None,
    now_utc_offset=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from datetime import date
from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
strict bool | None

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

None
le date | None

The value must be less than or equal to this date

None
ge date | None

The value must be greater than or equal to this date

None
lt date | None

The value must be strictly less than this date

None
gt date | None

The value must be strictly greater than this date

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

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

None
now_utc_offset int | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
def date_schema(
    *,
    strict: bool | None = None,
    le: date | None = None,
    ge: date | None = None,
    lt: date | None = None,
    gt: date | None = None,
    now_op: Literal['past', 'future'] | None = None,
    now_utc_offset: int | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> DateSchema:
    """
    Returns a schema that matches a date value, e.g.:

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

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

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

time_schema

time_schema(
    *,
    strict=None,
    le=None,
    ge=None,
    lt=None,
    gt=None,
    tz_constraint=None,
    microseconds_precision="truncate",
    ref=None,
    metadata=None,
    serialization=None
)

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

from datetime import time
from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
strict bool | None

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

None
le time | None

The value must be less than or equal to this time

None
ge time | None

The value must be greater than or equal to this time

None
lt time | None

The value must be strictly less than this time

None
gt time | None

The value must be strictly greater than this time

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

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

None
microseconds_precision Literal['truncate', 'error']

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

'truncate'
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
def time_schema(
    *,
    strict: bool | None = None,
    le: time | None = None,
    ge: time | None = None,
    lt: time | None = None,
    gt: time | None = None,
    tz_constraint: Literal['aware', 'naive'] | int | None = None,
    microseconds_precision: Literal['truncate', 'error'] = 'truncate',
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> TimeSchema:
    """
    Returns a schema that matches a time value, e.g.:

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

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

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

datetime_schema

datetime_schema(
    *,
    strict=None,
    le=None,
    ge=None,
    lt=None,
    gt=None,
    now_op=None,
    tz_constraint=None,
    now_utc_offset=None,
    microseconds_precision="truncate",
    ref=None,
    metadata=None,
    serialization=None
)

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

from datetime import datetime
from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
strict bool | None

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

None
le datetime | None

The value must be less than or equal to this datetime

None
ge datetime | None

The value must be greater than or equal to this datetime

None
lt datetime | None

The value must be strictly less than this datetime

None
gt datetime | None

The value must be strictly greater than this datetime

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

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

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

The value must be timezone aware or naive, or an int to indicate required tz offset TODO: use of a tzinfo where offset changes based on the datetime is not yet supported

None
now_utc_offset int | None

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

None
microseconds_precision Literal['truncate', 'error']

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

'truncate'
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
def datetime_schema(
    *,
    strict: bool | None = None,
    le: datetime | None = None,
    ge: datetime | None = None,
    lt: datetime | None = None,
    gt: datetime | None = None,
    now_op: Literal['past', 'future'] | None = None,
    tz_constraint: Literal['aware', 'naive'] | int | None = None,
    now_utc_offset: int | None = None,
    microseconds_precision: Literal['truncate', 'error'] = 'truncate',
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> DatetimeSchema:
    """
    Returns a schema that matches a datetime value, e.g.:

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

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

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

timedelta_schema

timedelta_schema(
    *,
    strict=None,
    le=None,
    ge=None,
    lt=None,
    gt=None,
    microseconds_precision="truncate",
    ref=None,
    metadata=None,
    serialization=None
)

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

from datetime import timedelta
from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
strict bool | None

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

None
le timedelta | None

The value must be less than or equal to this timedelta

None
ge timedelta | None

The value must be greater than or equal to this timedelta

None
lt timedelta | None

The value must be strictly less than this timedelta

None
gt timedelta | None

The value must be strictly greater than this timedelta

None
microseconds_precision Literal['truncate', 'error']

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

'truncate'
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
def timedelta_schema(
    *,
    strict: bool | None = None,
    le: timedelta | None = None,
    ge: timedelta | None = None,
    lt: timedelta | None = None,
    gt: timedelta | None = None,
    microseconds_precision: Literal['truncate', 'error'] = 'truncate',
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> TimedeltaSchema:
    """
    Returns a schema that matches a timedelta value, e.g.:

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

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

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

literal_schema

literal_schema(
    expected, *, ref=None, metadata=None, serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
expected list[Any]

The value must be one of these values

required
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
def literal_schema(
    expected: list[Any], *, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None
) -> LiteralSchema:
    """
    Returns a schema that matches a literal value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

is_instance_schema

is_instance_schema(
    cls,
    *,
    cls_repr=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

class A:
    pass

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

Parameters:

Name Type Description Default
cls Any

The value must be an instance of this class

required
cls_repr str | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
def is_instance_schema(
    cls: Any,
    *,
    cls_repr: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> IsInstanceSchema:
    """
    Returns a schema that checks if a value is an instance of a class, equivalent to python's `isinstance` method, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    class A:
        pass

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

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

is_subclass_schema

is_subclass_schema(
    cls,
    *,
    cls_repr=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

class A:
    pass

class B(A):
    pass

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

Parameters:

Name Type Description Default
cls Type[Any]

The value must be a subclass of this class

required
cls_repr str | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
def is_subclass_schema(
    cls: Type[Any],
    *,
    cls_repr: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> IsInstanceSchema:
    """
    Returns a schema that checks if a value is a subtype of a class, equivalent to python's `issubclass` method, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

    class A:
        pass

    class B(A):
        pass

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

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

callable_schema

callable_schema(
    *, ref=None, metadata=None, serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
def callable_schema(
    *, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None
) -> CallableSchema:
    """
    Returns a schema that checks if a value is callable, equivalent to python's `callable` method, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

list_schema

list_schema(
    items_schema=None,
    *,
    min_length=None,
    max_length=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
items_schema CoreSchema | None

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

None
min_length int | None

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

None
max_length int | None

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

None
strict bool | None

The value must be a list with exactly this many items

None
ref str | None

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

None
metadata Any

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

None
serialization IncExSeqOrElseSerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
def list_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None,
) -> ListSchema:
    """
    Returns a schema that matches a list value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

tuple_positional_schema

tuple_positional_schema(
    items_schema,
    *,
    extras_schema=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
items_schema list[CoreSchema]

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

required
extras_schema CoreSchema | None

The value must be a tuple with items that match this schema This was inspired by JSON schema's prefixItems and items fields. In python's typing.Tuple, you can't specify a type for "extra" items -- they must all be the same type if the length is variable. So this field won't be set from a typing.Tuple annotation on a pydantic model.

None
strict bool | None

The value must be a tuple with exactly this many items

None
ref str | None

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

None
metadata Any

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

None
serialization IncExSeqOrElseSerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
def tuple_positional_schema(
    items_schema: list[CoreSchema],
    *,
    extras_schema: CoreSchema | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None,
) -> TupleSchema:
    """
    Returns a schema that matches a tuple of schemas, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

tuple_variable_schema

tuple_variable_schema(
    items_schema=None,
    *,
    min_length=None,
    max_length=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
items_schema CoreSchema | None

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

None
min_length int | None

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

None
max_length int | None

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

None
strict bool | None

The value must be a tuple with exactly this many items

None
ref str | None

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

None
metadata Any

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

None
serialization IncExSeqOrElseSerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
def tuple_variable_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None,
) -> TupleSchema:
    """
    Returns a schema that matches a tuple of a given schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

tuple_schema

tuple_schema(
    items_schema,
    *,
    variadic_item_index=None,
    min_length=None,
    max_length=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
items_schema list[CoreSchema]

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

required
variadic_item_index int | None

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

None
min_length int | None

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

None
max_length int | None

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

None
strict bool | None

The value must be a tuple with exactly this many items

None
ref str | None

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

None
metadata Any

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

None
serialization IncExSeqOrElseSerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
def tuple_schema(
    items_schema: list[CoreSchema],
    *,
    variadic_item_index: int | None = None,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None,
) -> TupleSchema:
    """
    Returns a schema that matches a tuple of schemas, with an optional variadic item, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

set_schema

set_schema(
    items_schema=None,
    *,
    min_length=None,
    max_length=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
items_schema CoreSchema | None

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

None
min_length int | None

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

None
max_length int | None

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

None
strict bool | None

The value must be a set with exactly this many items

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
def set_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> SetSchema:
    """
    Returns a schema that matches a set of a given schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

frozenset_schema

frozenset_schema(
    items_schema=None,
    *,
    min_length=None,
    max_length=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
items_schema CoreSchema | None

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

None
min_length int | None

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

None
max_length int | None

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

None
strict bool | None

The value must be a frozenset with exactly this many items

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
def frozenset_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> FrozenSetSchema:
    """
    Returns a schema that matches a frozenset of a given schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

generator_schema

generator_schema(
    items_schema=None,
    *,
    min_length=None,
    max_length=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from typing import Iterator
from pydantic_core import SchemaValidator, core_schema

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

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

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

Parameters:

Name Type Description Default
items_schema CoreSchema | None

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

None
min_length int | None

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

None
max_length int | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization IncExSeqOrElseSerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
def generator_schema(
    items_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: IncExSeqOrElseSerSchema | None = None,
) -> GeneratorSchema:
    """
    Returns a schema that matches a generator value, e.g.:

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

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

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

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

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

dict_schema

dict_schema(
    keys_schema=None,
    values_schema=None,
    *,
    min_length=None,
    max_length=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
keys_schema CoreSchema | None

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

None
values_schema CoreSchema | None

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

None
min_length int | None

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

None
max_length int | None

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

None
strict bool | None

Whether the keys and values should be validated with strict mode

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
def dict_schema(
    keys_schema: CoreSchema | None = None,
    values_schema: CoreSchema | None = None,
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> DictSchema:
    """
    Returns a schema that matches a dict value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

no_info_before_validator_function

no_info_before_validator_function(
    function,
    schema,
    *,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

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

Parameters:

Name Type Description Default
function NoInfoValidatorFunction

The validator function to call

required
schema CoreSchema

The schema to validate the output of the validator function

required
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
def no_info_before_validator_function(
    function: NoInfoValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> BeforeValidatorFunctionSchema:
    """
    Returns a schema that calls a validator function before validating, no `info` argument is provided, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

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

with_info_before_validator_function

with_info_before_validator_function(
    function,
    schema,
    *,
    field_name=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

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

Parameters:

Name Type Description Default
function WithInfoValidatorFunction

The validator function to call

required
field_name str | None

The name of the field

None
schema CoreSchema

The schema to validate the output of the validator function

required
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
def with_info_before_validator_function(
    function: WithInfoValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> BeforeValidatorFunctionSchema:
    """
    Returns a schema that calls a validator function before validation, the function is called with
    an `info` argument, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

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

no_info_after_validator_function

no_info_after_validator_function(
    function,
    schema,
    *,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

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

Parameters:

Name Type Description Default
function NoInfoValidatorFunction

The validator function to call after the schema is validated

required
schema CoreSchema

The schema to validate before the validator function

required
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
def no_info_after_validator_function(
    function: NoInfoValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> AfterValidatorFunctionSchema:
    """
    Returns a schema that calls a validator function after validating, no `info` argument is provided, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

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

with_info_after_validator_function

with_info_after_validator_function(
    function,
    schema,
    *,
    field_name=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

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

Parameters:

Name Type Description Default
function WithInfoValidatorFunction

The validator function to call after the schema is validated

required
schema CoreSchema

The schema to validate before the validator function

required
field_name str | None

The name of the field this validators is applied to, if any

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
def with_info_after_validator_function(
    function: WithInfoValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> AfterValidatorFunctionSchema:
    """
    Returns a schema that calls a validator function after validation, the function is called with
    an `info` argument, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

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

no_info_wrap_validator_function

no_info_wrap_validator_function(
    function,
    schema,
    *,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

Parameters:

Name Type Description Default
function NoInfoWrapValidatorFunction

The validator function to call

required
schema CoreSchema

The schema to validate the output of the validator function

required
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
def no_info_wrap_validator_function(
    function: NoInfoWrapValidatorFunction,
    schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> WrapValidatorFunctionSchema:
    """
    Returns a schema which calls a function with a `validator` callable argument which can
    optionally be used to call inner validation with the function logic, this is much like the
    "onion" implementation of middleware in many popular web frameworks, no `info` argument is passed, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

with_info_wrap_validator_function

with_info_wrap_validator_function(
    function,
    schema,
    *,
    field_name=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

Parameters:

Name Type Description Default
function WithInfoWrapValidatorFunction

The validator function to call

required
schema CoreSchema

The schema to validate the output of the validator function

required
field_name str | None

The name of the field this validators is applied to, if any

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
def with_info_wrap_validator_function(
    function: WithInfoWrapValidatorFunction,
    schema: CoreSchema,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> WrapValidatorFunctionSchema:
    """
    Returns a schema which calls a function with a `validator` callable argument which can
    optionally be used to call inner validation with the function logic, this is much like the
    "onion" implementation of middleware in many popular web frameworks, an `info` argument is also passed, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

no_info_plain_validator_function

no_info_plain_validator_function(
    function, *, ref=None, metadata=None, serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

Parameters:

Name Type Description Default
function NoInfoValidatorFunction

The validator function to call

required
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
def no_info_plain_validator_function(
    function: NoInfoValidatorFunction,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> PlainValidatorFunctionSchema:
    """
    Returns a schema that uses the provided function for validation, no `info` argument is passed, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

with_info_plain_validator_function

with_info_plain_validator_function(
    function,
    *,
    field_name=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

Parameters:

Name Type Description Default
function WithInfoValidatorFunction

The validator function to call

required
field_name str | None

The name of the field this validators is applied to, if any

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
def with_info_plain_validator_function(
    function: WithInfoValidatorFunction,
    *,
    field_name: str | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> PlainValidatorFunctionSchema:
    """
    Returns a schema that uses the provided function for validation, an `info` argument is passed, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

with_default_schema

with_default_schema(
    schema,
    *,
    default=PydanticUndefined,
    default_factory=None,
    on_error=None,
    validate_default=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
schema CoreSchema

The schema to add a default value to

required
default Any

The default value to use

PydanticUndefined
default_factory Callable[[], Any] | None

A function that returns the default value to use

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

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

None
validate_default bool | None

Whether the default value should be validated

None
strict bool | None

Whether the underlying schema should be validated with strict mode

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
def with_default_schema(
    schema: CoreSchema,
    *,
    default: Any = PydanticUndefined,
    default_factory: Callable[[], Any] | None = None,
    on_error: Literal['raise', 'omit', 'default'] | None = None,
    validate_default: bool | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> WithDefaultSchema:
    """
    Returns a schema that adds a default value to the given schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

nullable_schema

nullable_schema(
    schema,
    *,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
schema CoreSchema

The schema to wrap

required
strict bool | None

Whether the underlying schema should be validated with strict mode

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
def nullable_schema(
    schema: CoreSchema,
    *,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> NullableSchema:
    """
    Returns a schema that matches a nullable value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

union_schema

union_schema(
    choices,
    *,
    auto_collapse=None,
    custom_error_type=None,
    custom_error_message=None,
    custom_error_context=None,
    mode=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
choices list[CoreSchema | tuple[CoreSchema, str]]

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

required
auto_collapse bool | None

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

None
custom_error_type str | None

The custom error type to use if the validation fails

None
custom_error_message str | None

The custom error message to use if the validation fails

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

The custom error context to use if the validation fails

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

How to select which choice to return * smart (default) will try to return the choice which is the closest match to the input value * left_to_right will return the first choice in choices which succeeds validation

None
strict bool | None

Whether the underlying schemas should be validated with strict mode

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
def union_schema(
    choices: list[CoreSchema | tuple[CoreSchema, str]],
    *,
    auto_collapse: bool | None = None,
    custom_error_type: str | None = None,
    custom_error_message: str | None = None,
    custom_error_context: dict[str, str | int] | None = None,
    mode: Literal['smart', 'left_to_right'] | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> UnionSchema:
    """
    Returns a schema that matches a union value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

tagged_union_schema

tagged_union_schema(
    choices,
    discriminator,
    *,
    custom_error_type=None,
    custom_error_message=None,
    custom_error_context=None,
    strict=None,
    from_attributes=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
choices Dict[Hashable, CoreSchema]

The schemas to match When retrieving a schema from choices using the discriminator value, if the value is a str, it should be fed back into the choices map until a schema is obtained (This approach is to prevent multiple ownership of a single schema in Rust)

required
discriminator str | list[str | int] | list[list[str | int]] | Callable[[Any], Hashable]

The discriminator to use to determine the schema to use * If discriminator is a str, it is the name of the attribute to use as the discriminator * If discriminator is a list of int/str, it should be used as a "path" to access the discriminator * If discriminator is a list of lists, each inner list is a path, and the first path that exists is used * If discriminator is a callable, it should return the discriminator when called on the value to validate; the callable can return None to indicate that there is no matching discriminator present on the input

required
custom_error_type str | None

The custom error type to use if the validation fails

None
custom_error_message str | None

The custom error message to use if the validation fails

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

The custom error context to use if the validation fails

None
strict bool | None

Whether the underlying schemas should be validated with strict mode

None
from_attributes bool | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
def tagged_union_schema(
    choices: Dict[Hashable, CoreSchema],
    discriminator: str | list[str | int] | list[list[str | int]] | Callable[[Any], Hashable],
    *,
    custom_error_type: str | None = None,
    custom_error_message: str | None = None,
    custom_error_context: dict[str, int | str | float] | None = None,
    strict: bool | None = None,
    from_attributes: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> TaggedUnionSchema:
    """
    Returns a schema that matches a tagged union value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

chain_schema

chain_schema(
    steps, *, ref=None, metadata=None, serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

Parameters:

Name Type Description Default
steps list[CoreSchema]

The schemas to chain

required
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
def chain_schema(
    steps: list[CoreSchema], *, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None
) -> ChainSchema:
    """
    Returns a schema that chains the provided validation schemas, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

lax_or_strict_schema

lax_or_strict_schema(
    lax_schema,
    strict_schema,
    *,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

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

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

Parameters:

Name Type Description Default
lax_schema CoreSchema

The lax schema to use

required
strict_schema CoreSchema

The strict schema to use

required
strict bool | None

Whether the strict schema should be used

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
def lax_or_strict_schema(
    lax_schema: CoreSchema,
    strict_schema: CoreSchema,
    *,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> LaxOrStrictSchema:
    """
    Returns a schema that uses the lax or strict schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

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

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

json_or_python_schema

json_or_python_schema(
    json_schema,
    python_schema,
    *,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, ValidationError, core_schema

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

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

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

Parameters:

Name Type Description Default
json_schema CoreSchema

The schema to use for Json inputs

required
python_schema CoreSchema

The schema to use for Python inputs

required
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
def json_or_python_schema(
    json_schema: CoreSchema,
    python_schema: CoreSchema,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> JsonOrPythonSchema:
    """
    Returns a schema that uses the Json or Python schema depending on the input:

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

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

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

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

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

typed_dict_field

typed_dict_field(
    schema,
    *,
    required=None,
    validation_alias=None,
    serialization_alias=None,
    serialization_exclude=None,
    metadata=None
)

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

from pydantic_core import core_schema

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

Parameters:

Name Type Description Default
schema CoreSchema

The schema to use for the field

required
required bool | None

Whether the field is required

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

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

None
serialization_alias str | None

The alias to use as a key when serializing

None
serialization_exclude bool | None

Whether to exclude the field when serializing

None
metadata Any

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

None
Source code in pydantic_core/core_schema.py
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
def typed_dict_field(
    schema: CoreSchema,
    *,
    required: bool | None = None,
    validation_alias: str | list[str | int] | list[list[str | int]] | None = None,
    serialization_alias: str | None = None,
    serialization_exclude: bool | None = None,
    metadata: Any = None,
) -> TypedDictField:
    """
    Returns a schema that matches a typed dict field, e.g.:

    ```py
    from pydantic_core import core_schema

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

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

typed_dict_schema

typed_dict_schema(
    fields,
    *,
    computed_fields=None,
    strict=None,
    extras_schema=None,
    extra_behavior=None,
    total=None,
    populate_by_name=None,
    ref=None,
    metadata=None,
    serialization=None,
    config=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
fields Dict[str, TypedDictField]

The fields to use for the typed dict

required
computed_fields list[ComputedField] | None

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

None
strict bool | None

Whether the typed dict is strict

None
extras_schema CoreSchema | None

The extra validator to use for the typed dict

None
ref str | None

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

None
metadata Any

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

None
extra_behavior ExtraBehavior | None

The extra behavior to use for the typed dict

None
total bool | None

Whether the typed dict is total

None
populate_by_name bool | None

Whether the typed dict should populate by name

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
def typed_dict_schema(
    fields: Dict[str, TypedDictField],
    *,
    computed_fields: list[ComputedField] | None = None,
    strict: bool | None = None,
    extras_schema: CoreSchema | None = None,
    extra_behavior: ExtraBehavior | None = None,
    total: bool | None = None,
    populate_by_name: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
    config: CoreConfig | None = None,
) -> TypedDictSchema:
    """
    Returns a schema that matches a typed dict, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

model_field

model_field(
    schema,
    *,
    validation_alias=None,
    serialization_alias=None,
    serialization_exclude=None,
    frozen=None,
    metadata=None
)

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

from pydantic_core import core_schema

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

Parameters:

Name Type Description Default
schema CoreSchema

The schema to use for the field

required
validation_alias str | list[str | int] | list[list[str | int]] | None

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

None
serialization_alias str | None

The alias to use as a key when serializing

None
serialization_exclude bool | None

Whether to exclude the field when serializing

None
frozen bool | None

Whether the field is frozen

None
metadata Any

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

None
Source code in pydantic_core/core_schema.py
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
def model_field(
    schema: CoreSchema,
    *,
    validation_alias: str | list[str | int] | list[list[str | int]] | None = None,
    serialization_alias: str | None = None,
    serialization_exclude: bool | None = None,
    frozen: bool | None = None,
    metadata: Any = None,
) -> ModelField:
    """
    Returns a schema for a model field, e.g.:

    ```py
    from pydantic_core import core_schema

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

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

model_fields_schema

model_fields_schema(
    fields,
    *,
    model_name=None,
    computed_fields=None,
    strict=None,
    extras_schema=None,
    extra_behavior=None,
    populate_by_name=None,
    from_attributes=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
fields Dict[str, ModelField]

The fields to use for the typed dict

required
model_name str | None

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

None
computed_fields list[ComputedField] | None

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

None
strict bool | None

Whether the typed dict is strict

None
extras_schema CoreSchema | None

The extra validator to use for the typed dict

None
ref str | None

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

None
metadata Any

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

None
extra_behavior ExtraBehavior | None

The extra behavior to use for the typed dict

None
populate_by_name bool | None

Whether the typed dict should populate by name

None
from_attributes bool | None

Whether the typed dict should be populated from attributes

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
def model_fields_schema(
    fields: Dict[str, ModelField],
    *,
    model_name: str | None = None,
    computed_fields: list[ComputedField] | None = None,
    strict: bool | None = None,
    extras_schema: CoreSchema | None = None,
    extra_behavior: ExtraBehavior | None = None,
    populate_by_name: bool | None = None,
    from_attributes: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> ModelFieldsSchema:
    """
    Returns a schema that matches a typed dict, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

model_schema

model_schema(
    cls,
    schema,
    *,
    custom_init=None,
    root_model=None,
    post_init=None,
    revalidate_instances=None,
    strict=None,
    frozen=None,
    extra_behavior=None,
    config=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

Example:

from pydantic_core import CoreConfig, SchemaValidator, core_schema

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

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

Parameters:

Name Type Description Default
cls Type[Any]

The class to use for the model

required
schema CoreSchema

The schema to use for the model

required
custom_init bool | None

Whether the model has a custom init method

None
root_model bool | None

Whether the model is a RootModel

None
post_init str | None

The call after init to use for the model

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

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

None
strict bool | None

Whether the model is strict

None
frozen bool | None

Whether the model is frozen

None
extra_behavior ExtraBehavior | None

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

None
config CoreConfig | None

The config to use for the model

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
def model_schema(
    cls: Type[Any],
    schema: CoreSchema,
    *,
    custom_init: bool | None = None,
    root_model: bool | None = None,
    post_init: str | None = None,
    revalidate_instances: Literal['always', 'never', 'subclass-instances'] | None = None,
    strict: bool | None = None,
    frozen: bool | None = None,
    extra_behavior: ExtraBehavior | None = None,
    config: CoreConfig | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> ModelSchema:
    """
    A model schema generally contains a typed-dict schema.
    It will run the typed dict validator, then create a new class
    and set the dict and fields set returned from the typed dict validator
    to `__dict__` and `__pydantic_fields_set__` respectively.

    Example:

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

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

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

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

dataclass_field

dataclass_field(
    name,
    schema,
    *,
    kw_only=None,
    init=None,
    init_only=None,
    validation_alias=None,
    serialization_alias=None,
    serialization_exclude=None,
    metadata=None,
    frozen=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
name str

The name to use for the argument parameter

required
schema CoreSchema

The schema to use for the argument parameter

required
kw_only bool | None

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

None
init bool | None

Whether the field should be validated during initialization

None
init_only bool | None

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

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

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

None
serialization_alias str | None

The alias to use as a key when serializing

None
serialization_exclude bool | None

Whether to exclude the field when serializing

None
metadata Any

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

None
frozen bool | None

Whether the field is frozen

None
Source code in pydantic_core/core_schema.py
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
def dataclass_field(
    name: str,
    schema: CoreSchema,
    *,
    kw_only: bool | None = None,
    init: bool | None = None,
    init_only: bool | None = None,
    validation_alias: str | list[str | int] | list[list[str | int]] | None = None,
    serialization_alias: str | None = None,
    serialization_exclude: bool | None = None,
    metadata: Any = None,
    frozen: bool | None = None,
) -> DataclassField:
    """
    Returns a schema for a dataclass field, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

dataclass_args_schema

dataclass_args_schema(
    dataclass_name,
    fields,
    *,
    computed_fields=None,
    populate_by_name=None,
    collect_init_only=None,
    ref=None,
    metadata=None,
    serialization=None,
    extra_behavior=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
dataclass_name str

The name of the dataclass being validated

required
fields list[DataclassField]

The fields to use for the dataclass

required
computed_fields List[ComputedField] | None

Computed fields to use when serializing the dataclass

None
populate_by_name bool | None

Whether to populate by name

None
collect_init_only bool | None

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

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
extra_behavior ExtraBehavior | None

How to handle extra fields

None
Source code in pydantic_core/core_schema.py
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
def dataclass_args_schema(
    dataclass_name: str,
    fields: list[DataclassField],
    *,
    computed_fields: List[ComputedField] | None = None,
    populate_by_name: bool | None = None,
    collect_init_only: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
    extra_behavior: ExtraBehavior | None = None,
) -> DataclassArgsSchema:
    """
    Returns a schema for validating dataclass arguments, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

dataclass_schema

dataclass_schema(
    cls,
    schema,
    fields,
    *,
    cls_name=None,
    post_init=None,
    revalidate_instances=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None,
    frozen=None,
    slots=None,
    config=None
)

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

Parameters:

Name Type Description Default
cls Type[Any]

The dataclass type, used to perform subclass checks

required
schema CoreSchema

The schema to use for the dataclass fields

required
fields List[str]

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

required
cls_name str | None

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

None
post_init bool | None

Whether to call __post_init__ after validation

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

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

None
strict bool | None

Whether to require an exact instance of cls

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
frozen bool | None

Whether the dataclass is frozen

None
slots bool | None

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

None
Source code in pydantic_core/core_schema.py
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
def dataclass_schema(
    cls: Type[Any],
    schema: CoreSchema,
    fields: List[str],
    *,
    cls_name: str | None = None,
    post_init: bool | None = None,
    revalidate_instances: Literal['always', 'never', 'subclass-instances'] | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
    frozen: bool | None = None,
    slots: bool | None = None,
    config: CoreConfig | None = None,
) -> DataclassSchema:
    """
    Returns a schema for a dataclass. As with `ModelSchema`, this schema can only be used as a field within
    another schema, not as the root type.

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

arguments_parameter

arguments_parameter(name, schema, *, mode=None, alias=None)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
name str

The name to use for the argument parameter

required
schema CoreSchema

The schema to use for the argument parameter

required
mode Literal['positional_only', 'positional_or_keyword', 'keyword_only'] | None

The mode to use for the argument parameter

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

The alias to use for the argument parameter

None
Source code in pydantic_core/core_schema.py
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
def arguments_parameter(
    name: str,
    schema: CoreSchema,
    *,
    mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only'] | None = None,
    alias: str | list[str | int] | list[list[str | int]] | None = None,
) -> ArgumentsParameter:
    """
    Returns a schema that matches an argument parameter, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

arguments_schema

arguments_schema(
    arguments,
    *,
    populate_by_name=None,
    var_args_schema=None,
    var_kwargs_schema=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
arguments list[ArgumentsParameter]

The arguments to use for the arguments schema

required
populate_by_name bool | None

Whether to populate by name

None
var_args_schema CoreSchema | None

The variable args schema to use for the arguments schema

None
var_kwargs_schema CoreSchema | None

The variable kwargs schema to use for the arguments schema

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
def arguments_schema(
    arguments: list[ArgumentsParameter],
    *,
    populate_by_name: bool | None = None,
    var_args_schema: CoreSchema | None = None,
    var_kwargs_schema: CoreSchema | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> ArgumentsSchema:
    """
    Returns a schema that matches an arguments schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

call_schema

call_schema(
    arguments,
    function,
    *,
    function_name=None,
    return_schema=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

Parameters:

Name Type Description Default
arguments CoreSchema

The arguments to use for the arguments schema

required
function Callable[..., Any]

The function to use for the call schema

required
function_name str | None

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

None
return_schema CoreSchema | None

The return schema to use for the call schema

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
def call_schema(
    arguments: CoreSchema,
    function: Callable[..., Any],
    *,
    function_name: str | None = None,
    return_schema: CoreSchema | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> CallSchema:
    """
    Returns a schema that matches an arguments schema, then calls a function, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

custom_error_schema

custom_error_schema(
    schema,
    custom_error_type,
    *,
    custom_error_message=None,
    custom_error_context=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
schema CoreSchema

The schema to use for the custom error schema

required
custom_error_type str

The custom error type to use for the custom error schema

required
custom_error_message str | None

The custom error message to use for the custom error schema

None
custom_error_context dict[str, Any] | None

The custom error context to use for the custom error schema

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
def custom_error_schema(
    schema: CoreSchema,
    custom_error_type: str,
    *,
    custom_error_message: str | None = None,
    custom_error_context: dict[str, Any] | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> CustomErrorSchema:
    """
    Returns a schema that matches a custom error value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

json_schema

json_schema(
    schema=None,
    *,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

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

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

Parameters:

Name Type Description Default
schema CoreSchema | None

The schema to use for the JSON schema

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
def json_schema(
    schema: CoreSchema | None = None,
    *,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> JsonSchema:
    """
    Returns a schema that matches a JSON value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

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

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

url_schema

url_schema(
    *,
    max_length=None,
    allowed_schemes=None,
    host_required=None,
    default_host=None,
    default_port=None,
    default_path=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
max_length int | None

The maximum length of the URL

None
allowed_schemes list[str] | None

The allowed URL schemes

None
host_required bool | None

Whether the URL must have a host

None
default_host str | None

The default host to use if the URL does not have a host

None
default_port int | None

The default port to use if the URL does not have a port

None
default_path str | None

The default path to use if the URL does not have a path

None
strict bool | None

Whether to use strict URL parsing

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
def url_schema(
    *,
    max_length: int | None = None,
    allowed_schemes: list[str] | None = None,
    host_required: bool | None = None,
    default_host: str | None = None,
    default_port: int | None = None,
    default_path: str | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> UrlSchema:
    """
    Returns a schema that matches a URL value, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

multi_host_url_schema

multi_host_url_schema(
    *,
    max_length=None,
    allowed_schemes=None,
    host_required=None,
    default_host=None,
    default_port=None,
    default_path=None,
    strict=None,
    ref=None,
    metadata=None,
    serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
max_length int | None

The maximum length of the URL

None
allowed_schemes list[str] | None

The allowed URL schemes

None
host_required bool | None

Whether the URL must have a host

None
default_host str | None

The default host to use if the URL does not have a host

None
default_port int | None

The default port to use if the URL does not have a port

None
default_path str | None

The default path to use if the URL does not have a path

None
strict bool | None

Whether to use strict URL parsing

None
ref str | None

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

None
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580
3581
3582
3583
3584
3585
3586
3587
3588
3589
3590
3591
def multi_host_url_schema(
    *,
    max_length: int | None = None,
    allowed_schemes: list[str] | None = None,
    host_required: bool | None = None,
    default_host: str | None = None,
    default_port: int | None = None,
    default_path: str | None = None,
    strict: bool | None = None,
    ref: str | None = None,
    metadata: Any = None,
    serialization: SerSchema | None = None,
) -> MultiHostUrlSchema:
    """
    Returns a schema that matches a URL value with possibly multiple hosts, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

definitions_schema

definitions_schema(schema, definitions)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
schema CoreSchema

The inner schema

required
definitions list[CoreSchema]

List of definitions which can be referenced within inner schema

required
Source code in pydantic_core/core_schema.py
3602
3603
3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
3620
3621
3622
def definitions_schema(schema: CoreSchema, definitions: list[CoreSchema]) -> DefinitionsSchema:
    """
    Build a schema that contains both an inner schema and a list of definitions which can be used
    within the inner schema.

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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

definition_reference_schema

definition_reference_schema(
    schema_ref, ref=None, metadata=None, serialization=None
)

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

from pydantic_core import SchemaValidator, core_schema

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

Parameters:

Name Type Description Default
schema_ref str

The schema ref to use for the definition reference schema

required
metadata Any

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

None
serialization SerSchema | None

Custom serialization schema

None
Source code in pydantic_core/core_schema.py
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
def definition_reference_schema(
    schema_ref: str, ref: str | None = None, metadata: Any = None, serialization: SerSchema | None = None
) -> DefinitionReferenceSchema:
    """
    Returns a schema that points to a schema stored in "definitions", this is useful for nested recursive
    models and also when you want to define validators separately from the main schema, e.g.:

    ```py
    from pydantic_core import SchemaValidator, core_schema

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

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