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diff --git a/.venv/lib/python3.12/site-packages/pydantic_core/core_schema.py b/.venv/lib/python3.12/site-packages/pydantic_core/core_schema.py
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+"""
+This module contains definitions to build schemas which `pydantic_core` can
+validate and serialize.
+"""
+
+from __future__ import annotations as _annotations
+
+import sys
+import warnings
+from collections.abc import Mapping
+from datetime import date, datetime, time, timedelta
+from decimal import Decimal
+from typing import TYPE_CHECKING, Any, Callable, Dict, Hashable, List, Pattern, Set, Tuple, Type, Union
+
+from typing_extensions import deprecated
+
+if sys.version_info < (3, 12):
+    from typing_extensions import TypedDict
+else:
+    from typing import TypedDict
+
+if sys.version_info < (3, 11):
+    from typing_extensions import Protocol, Required, TypeAlias
+else:
+    from typing import Protocol, Required, TypeAlias
+
+if sys.version_info < (3, 9):
+    from typing_extensions import Literal
+else:
+    from typing import Literal
+
+if TYPE_CHECKING:
+    from pydantic_core import PydanticUndefined
+else:
+    # The initial build of pydantic_core requires PydanticUndefined to generate
+    # the core schema; so we need to conditionally skip it. mypy doesn't like
+    # this at all, hence the TYPE_CHECKING branch above.
+    try:
+        from pydantic_core import PydanticUndefined
+    except ImportError:
+        PydanticUndefined = object()
+
+
+ExtraBehavior = Literal['allow', 'forbid', 'ignore']
+
+
+class CoreConfig(TypedDict, total=False):
+    """
+    Base class for schema configuration options.
+
+    Attributes:
+        title: The name of the configuration.
+        strict: Whether the configuration should strictly adhere to specified rules.
+        extra_fields_behavior: The behavior for handling extra fields.
+        typed_dict_total: Whether the TypedDict should be considered total. Default is `True`.
+        from_attributes: Whether to use attributes for models, dataclasses, and tagged union keys.
+        loc_by_alias: Whether to use the used alias (or first alias for "field required" errors) instead of
+            `field_names` to construct error `loc`s. Default is `True`.
+        revalidate_instances: Whether instances of models and dataclasses should re-validate. Default is 'never'.
+        validate_default: Whether to validate default values during validation. Default is `False`.
+        populate_by_name: 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: The maximum length for string fields.
+        str_min_length: The minimum length for string fields.
+        str_strip_whitespace: Whether to strip whitespace from string fields.
+        str_to_lower: Whether to convert string fields to lowercase.
+        str_to_upper: Whether to convert string fields to uppercase.
+        allow_inf_nan: Whether to allow infinity and NaN values for float fields. Default is `True`.
+        ser_json_timedelta: The serialization option for `timedelta` values. Default is 'iso8601'.
+        ser_json_bytes: The serialization option for `bytes` values. Default is 'utf8'.
+        ser_json_inf_nan: The serialization option for infinity and NaN values
+            in float fields. Default is 'null'.
+        val_json_bytes: The validation option for `bytes` values, complementing ser_json_bytes. Default is 'utf8'.
+        hide_input_in_errors: Whether to hide input data from `ValidationError` representation.
+        validation_error_cause: Whether to add user-python excs to the __cause__ of a ValidationError.
+            Requires exceptiongroup backport pre Python 3.11.
+        coerce_numbers_to_str: Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode).
+        regex_engine: The regex engine to use for regex pattern validation. Default is 'rust-regex'. See `StringSchema`.
+        cache_strings: Whether to cache strings. Default is `True`, `True` or `'all'` is required to cache strings
+            during general validation since validators don't know if they're in a key or a value.
+    """
+
+    title: str
+    strict: bool
+    # settings related to typed dicts, model fields, dataclass fields
+    extra_fields_behavior: ExtraBehavior
+    typed_dict_total: bool  # default: True
+    # used for models, dataclasses, and tagged union keys
+    from_attributes: bool
+    # whether to use the used alias (or first alias for "field required" errors) instead of field_names
+    # to construct error `loc`s, default True
+    loc_by_alias: bool
+    # whether instances of models and dataclasses (including subclass instances) should re-validate, default 'never'
+    revalidate_instances: Literal['always', 'never', 'subclass-instances']
+    # whether to validate default values during validation, default False
+    validate_default: bool
+    # used on typed-dicts and arguments
+    populate_by_name: bool  # replaces `allow_population_by_field_name` in pydantic v1
+    # fields related to string fields only
+    str_max_length: int
+    str_min_length: int
+    str_strip_whitespace: bool
+    str_to_lower: bool
+    str_to_upper: bool
+    # fields related to float fields only
+    allow_inf_nan: bool  # default: True
+    # the config options are used to customise serialization to JSON
+    ser_json_timedelta: Literal['iso8601', 'float']  # default: 'iso8601'
+    ser_json_bytes: Literal['utf8', 'base64', 'hex']  # default: 'utf8'
+    ser_json_inf_nan: Literal['null', 'constants', 'strings']  # default: 'null'
+    val_json_bytes: Literal['utf8', 'base64', 'hex']  # default: 'utf8'
+    # used to hide input data from ValidationError repr
+    hide_input_in_errors: bool
+    validation_error_cause: bool  # default: False
+    coerce_numbers_to_str: bool  # default: False
+    regex_engine: Literal['rust-regex', 'python-re']  # default: 'rust-regex'
+    cache_strings: Union[bool, Literal['all', 'keys', 'none']]  # default: 'True'
+
+
+IncExCall: TypeAlias = 'set[int | str] | dict[int | str, IncExCall] | None'
+
+
+class SerializationInfo(Protocol):
+    @property
+    def include(self) -> IncExCall: ...
+
+    @property
+    def exclude(self) -> IncExCall: ...
+
+    @property
+    def context(self) -> Any | None:
+        """Current serialization context."""
+
+    @property
+    def mode(self) -> str: ...
+
+    @property
+    def by_alias(self) -> bool: ...
+
+    @property
+    def exclude_unset(self) -> bool: ...
+
+    @property
+    def exclude_defaults(self) -> bool: ...
+
+    @property
+    def exclude_none(self) -> bool: ...
+
+    @property
+    def serialize_as_any(self) -> bool: ...
+
+    def round_trip(self) -> bool: ...
+
+    def mode_is_json(self) -> bool: ...
+
+    def __str__(self) -> str: ...
+
+    def __repr__(self) -> str: ...
+
+
+class FieldSerializationInfo(SerializationInfo, Protocol):
+    @property
+    def field_name(self) -> str: ...
+
+
+class ValidationInfo(Protocol):
+    """
+    Argument passed to validation functions.
+    """
+
+    @property
+    def context(self) -> Any | None:
+        """Current validation context."""
+        ...
+
+    @property
+    def config(self) -> CoreConfig | None:
+        """The CoreConfig that applies to this validation."""
+        ...
+
+    @property
+    def mode(self) -> Literal['python', 'json']:
+        """The type of input data we are currently validating"""
+        ...
+
+    @property
+    def data(self) -> Dict[str, Any]:
+        """The data being validated for this model."""
+        ...
+
+    @property
+    def field_name(self) -> str | None:
+        """
+        The name of the current field being validated if this validator is
+        attached to a model field.
+        """
+        ...
+
+
+ExpectedSerializationTypes = Literal[
+    'none',
+    'int',
+    'bool',
+    'float',
+    'str',
+    'bytes',
+    'bytearray',
+    'list',
+    'tuple',
+    'set',
+    'frozenset',
+    'generator',
+    'dict',
+    'datetime',
+    'date',
+    'time',
+    'timedelta',
+    'url',
+    'multi-host-url',
+    'json',
+    'uuid',
+    'any',
+]
+
+
+class SimpleSerSchema(TypedDict, total=False):
+    type: Required[ExpectedSerializationTypes]
+
+
+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)
+
+
+# (input_value: Any, /) -> Any
+GeneralPlainNoInfoSerializerFunction = Callable[[Any], Any]
+# (input_value: Any, info: FieldSerializationInfo, /) -> Any
+GeneralPlainInfoSerializerFunction = Callable[[Any, SerializationInfo], Any]
+# (model: Any, input_value: Any, /) -> Any
+FieldPlainNoInfoSerializerFunction = Callable[[Any, Any], Any]
+# (model: Any, input_value: Any, info: FieldSerializationInfo, /) -> Any
+FieldPlainInfoSerializerFunction = Callable[[Any, Any, FieldSerializationInfo], Any]
+SerializerFunction = Union[
+    GeneralPlainNoInfoSerializerFunction,
+    GeneralPlainInfoSerializerFunction,
+    FieldPlainNoInfoSerializerFunction,
+    FieldPlainInfoSerializerFunction,
+]
+
+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`
+"""
+
+
+class PlainSerializerFunctionSerSchema(TypedDict, total=False):
+    type: Required[Literal['function-plain']]
+    function: Required[SerializerFunction]
+    is_field_serializer: bool  # default False
+    info_arg: bool  # default False
+    return_schema: CoreSchema  # if omitted, AnySchema is used
+    when_used: WhenUsed  # default: 'always'
+
+
+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,
+    )
+
+
+class SerializerFunctionWrapHandler(Protocol):  # pragma: no cover
+    def __call__(self, input_value: Any, index_key: int | str | None = None, /) -> Any: ...
+
+
+# (input_value: Any, serializer: SerializerFunctionWrapHandler, /) -> Any
+GeneralWrapNoInfoSerializerFunction = Callable[[Any, SerializerFunctionWrapHandler], Any]
+# (input_value: Any, serializer: SerializerFunctionWrapHandler, info: SerializationInfo, /) -> Any
+GeneralWrapInfoSerializerFunction = Callable[[Any, SerializerFunctionWrapHandler, SerializationInfo], Any]
+# (model: Any, input_value: Any, serializer: SerializerFunctionWrapHandler, /) -> Any
+FieldWrapNoInfoSerializerFunction = Callable[[Any, Any, SerializerFunctionWrapHandler], Any]
+# (model: Any, input_value: Any, serializer: SerializerFunctionWrapHandler, info: FieldSerializationInfo, /) -> Any
+FieldWrapInfoSerializerFunction = Callable[[Any, Any, SerializerFunctionWrapHandler, FieldSerializationInfo], Any]
+WrapSerializerFunction = Union[
+    GeneralWrapNoInfoSerializerFunction,
+    GeneralWrapInfoSerializerFunction,
+    FieldWrapNoInfoSerializerFunction,
+    FieldWrapInfoSerializerFunction,
+]
+
+
+class WrapSerializerFunctionSerSchema(TypedDict, total=False):
+    type: Required[Literal['function-wrap']]
+    function: Required[WrapSerializerFunction]
+    is_field_serializer: bool  # default False
+    info_arg: bool  # default False
+    schema: CoreSchema  # if omitted, the schema on which this serializer is defined is used
+    return_schema: CoreSchema  # if omitted, AnySchema is used
+    when_used: WhenUsed  # default: 'always'
+
+
+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,
+    )
+
+
+class FormatSerSchema(TypedDict, total=False):
+    type: Required[Literal['format']]
+    formatting_string: Required[str]
+    when_used: WhenUsed  # default: 'json-unless-none'
+
+
+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)
+
+
+class ToStringSerSchema(TypedDict, total=False):
+    type: Required[Literal['to-string']]
+    when_used: WhenUsed  # default: 'json-unless-none'
+
+
+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
+
+
+class ModelSerSchema(TypedDict, total=False):
+    type: Required[Literal['model']]
+    cls: Required[Type[Any]]
+    schema: Required[CoreSchema]
+
+
+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)
+
+
+SerSchema = Union[
+    SimpleSerSchema,
+    PlainSerializerFunctionSerSchema,
+    WrapSerializerFunctionSerSchema,
+    FormatSerSchema,
+    ToStringSerSchema,
+    ModelSerSchema,
+]
+
+
+class InvalidSchema(TypedDict, total=False):
+    type: Required[Literal['invalid']]
+    ref: str
+    metadata: Dict[str, Any]
+    # note, we never plan to use this, but include it for type checking purposes to match
+    # all other CoreSchema union members
+    serialization: SerSchema
+
+
+def invalid_schema(ref: str | None = None, metadata: Dict[str, Any] | None = None) -> InvalidSchema:
+    """
+    Returns an invalid schema, used to indicate that a schema is invalid.
+
+        Returns a schema that matches any value, e.g.:
+
+    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
+    """
+
+    return _dict_not_none(type='invalid', ref=ref, metadata=metadata)
+
+
+class ComputedField(TypedDict, total=False):
+    type: Required[Literal['computed-field']]
+    property_name: Required[str]
+    return_schema: Required[CoreSchema]
+    alias: str
+    metadata: Dict[str, Any]
+
+
+def computed_field(
+    property_name: str, return_schema: CoreSchema, *, alias: str | None = None, metadata: Dict[str, Any] | None = 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
+    )
+
+
+class AnySchema(TypedDict, total=False):
+    type: Required[Literal['any']]
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def any_schema(
+    *, ref: str | None = None, metadata: Dict[str, Any] | None = None, serialization: SerSchema | None = None
+) -> AnySchema:
+    """
+    Returns a schema that matches any value, e.g.:
+
+    ```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)
+
+
+class NoneSchema(TypedDict, total=False):
+    type: Required[Literal['none']]
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def none_schema(
+    *, ref: str | None = None, metadata: Dict[str, Any] | None = None, serialization: SerSchema | None = None
+) -> NoneSchema:
+    """
+    Returns a schema that matches a None value, e.g.:
+
+    ```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)
+
+
+class BoolSchema(TypedDict, total=False):
+    type: Required[Literal['bool']]
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def bool_schema(
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> BoolSchema:
+    """
+    Returns a schema that matches a bool value, e.g.:
+
+    ```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)
+
+
+class IntSchema(TypedDict, total=False):
+    type: Required[Literal['int']]
+    multiple_of: int
+    le: int
+    ge: int
+    lt: int
+    gt: int
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def int_schema(
+    *,
+    multiple_of: int | None = None,
+    le: int | None = None,
+    ge: int | None = None,
+    lt: int | None = None,
+    gt: int | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> IntSchema:
+    """
+    Returns a schema that matches a int value, e.g.:
+
+    ```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,
+    )
+
+
+class FloatSchema(TypedDict, total=False):
+    type: Required[Literal['float']]
+    allow_inf_nan: bool  # whether 'NaN', '+inf', '-inf' should be forbidden. default: True
+    multiple_of: float
+    le: float
+    ge: float
+    lt: float
+    gt: float
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def float_schema(
+    *,
+    allow_inf_nan: bool | None = None,
+    multiple_of: float | None = None,
+    le: float | None = None,
+    ge: float | None = None,
+    lt: float | None = None,
+    gt: float | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> FloatSchema:
+    """
+    Returns a schema that matches a float value, e.g.:
+
+    ```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,
+    )
+
+
+class DecimalSchema(TypedDict, total=False):
+    type: Required[Literal['decimal']]
+    allow_inf_nan: bool  # whether 'NaN', '+inf', '-inf' should be forbidden. default: False
+    multiple_of: Decimal
+    le: Decimal
+    ge: Decimal
+    lt: Decimal
+    gt: Decimal
+    max_digits: int
+    decimal_places: int
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def decimal_schema(
+    *,
+    allow_inf_nan: bool | None = None,
+    multiple_of: Decimal | None = None,
+    le: Decimal | None = None,
+    ge: Decimal | None = None,
+    lt: Decimal | None = None,
+    gt: Decimal | None = None,
+    max_digits: int | None = None,
+    decimal_places: int | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> DecimalSchema:
+    """
+    Returns a schema that matches a decimal value, e.g.:
+
+    ```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,
+    )
+
+
+class ComplexSchema(TypedDict, total=False):
+    type: Required[Literal['complex']]
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def complex_schema(
+    *,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> ComplexSchema:
+    """
+    Returns a schema that matches a complex value, e.g.:
+
+    ```py
+    from pydantic_core import SchemaValidator, core_schema
+
+    schema = core_schema.complex_schema()
+    v = SchemaValidator(schema)
+    assert v.validate_python('1+2j') == complex(1, 2)
+    assert v.validate_python(complex(1, 2)) == complex(1, 2)
+    ```
+
+    Args:
+        strict: Whether the value should be a complex object instance or a value that can be converted to a complex object
+        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='complex',
+        strict=strict,
+        ref=ref,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+class StringSchema(TypedDict, total=False):
+    type: Required[Literal['str']]
+    pattern: Union[str, Pattern[str]]
+    max_length: int
+    min_length: int
+    strip_whitespace: bool
+    to_lower: bool
+    to_upper: bool
+    regex_engine: Literal['rust-regex', 'python-re']  # default: 'rust-regex'
+    strict: bool
+    coerce_numbers_to_str: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def str_schema(
+    *,
+    pattern: str | Pattern[str] | None = None,
+    max_length: int | None = None,
+    min_length: int | None = None,
+    strip_whitespace: bool | None = None,
+    to_lower: bool | None = None,
+    to_upper: bool | None = None,
+    regex_engine: Literal['rust-regex', 'python-re'] | None = None,
+    strict: bool | None = None,
+    coerce_numbers_to_str: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> StringSchema:
+    """
+    Returns a schema that matches a string value, e.g.:
+
+    ```py
+    from pydantic_core import SchemaValidator, core_schema
+
+    schema = core_schema.str_schema(max_length=10, min_length=2)
+    v = SchemaValidator(schema)
+    assert v.validate_python('hello') == 'hello'
+    ```
+
+    Args:
+        pattern: A regex pattern that the value must match
+        max_length: The value must be at most this length
+        min_length: The value must be at least this length
+        strip_whitespace: Whether to strip whitespace from the value
+        to_lower: Whether to convert the value to lowercase
+        to_upper: Whether to convert the value to uppercase
+        regex_engine: The regex engine to use for pattern validation. Default is 'rust-regex'.
+            - `rust-regex` uses the [`regex`](https://docs.rs/regex) Rust
+              crate, which is non-backtracking and therefore more DDoS
+              resistant, but does not support all regex features.
+            - `python-re` use the [`re`](https://docs.python.org/3/library/re.html) module,
+              which supports all regex features, but may be slower.
+        strict: Whether the value should be a string or a value that can be converted to a string
+        coerce_numbers_to_str: Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode).
+        ref: optional unique identifier of the schema, used to reference the schema in other places
+        metadata: Any other information you want to include with the schema, not used by pydantic-core
+        serialization: Custom serialization schema
+    """
+    return _dict_not_none(
+        type='str',
+        pattern=pattern,
+        max_length=max_length,
+        min_length=min_length,
+        strip_whitespace=strip_whitespace,
+        to_lower=to_lower,
+        to_upper=to_upper,
+        regex_engine=regex_engine,
+        strict=strict,
+        coerce_numbers_to_str=coerce_numbers_to_str,
+        ref=ref,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+class BytesSchema(TypedDict, total=False):
+    type: Required[Literal['bytes']]
+    max_length: int
+    min_length: int
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def bytes_schema(
+    *,
+    max_length: int | None = None,
+    min_length: int | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> BytesSchema:
+    """
+    Returns a schema that matches a bytes value, e.g.:
+
+    ```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,
+    )
+
+
+class DateSchema(TypedDict, total=False):
+    type: Required[Literal['date']]
+    strict: bool
+    le: date
+    ge: date
+    lt: date
+    gt: date
+    now_op: Literal['past', 'future']
+    # defaults to current local utc offset from `time.localtime().tm_gmtoff`
+    # value is restricted to -86_400 < offset < 86_400 by bounds in generate_self_schema.py
+    now_utc_offset: int
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def date_schema(
+    *,
+    strict: bool | None = None,
+    le: date | None = None,
+    ge: date | None = None,
+    lt: date | None = None,
+    gt: date | None = None,
+    now_op: Literal['past', 'future'] | None = None,
+    now_utc_offset: int | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> DateSchema:
+    """
+    Returns a schema that matches a date value, e.g.:
+
+    ```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,
+    )
+
+
+class TimeSchema(TypedDict, total=False):
+    type: Required[Literal['time']]
+    strict: bool
+    le: time
+    ge: time
+    lt: time
+    gt: time
+    tz_constraint: Union[Literal['aware', 'naive'], int]
+    microseconds_precision: Literal['truncate', 'error']
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def time_schema(
+    *,
+    strict: bool | None = None,
+    le: time | None = None,
+    ge: time | None = None,
+    lt: time | None = None,
+    gt: time | None = None,
+    tz_constraint: Literal['aware', 'naive'] | int | None = None,
+    microseconds_precision: Literal['truncate', 'error'] = 'truncate',
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> TimeSchema:
+    """
+    Returns a schema that matches a time value, e.g.:
+
+    ```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,
+    )
+
+
+class DatetimeSchema(TypedDict, total=False):
+    type: Required[Literal['datetime']]
+    strict: bool
+    le: datetime
+    ge: datetime
+    lt: datetime
+    gt: datetime
+    now_op: Literal['past', 'future']
+    tz_constraint: Union[Literal['aware', 'naive'], int]
+    # defaults to current local utc offset from `time.localtime().tm_gmtoff`
+    # value is restricted to -86_400 < offset < 86_400 by bounds in generate_self_schema.py
+    now_utc_offset: int
+    microseconds_precision: Literal['truncate', 'error']  # default: 'truncate'
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def datetime_schema(
+    *,
+    strict: bool | None = None,
+    le: datetime | None = None,
+    ge: datetime | None = None,
+    lt: datetime | None = None,
+    gt: datetime | None = None,
+    now_op: Literal['past', 'future'] | None = None,
+    tz_constraint: Literal['aware', 'naive'] | int | None = None,
+    now_utc_offset: int | None = None,
+    microseconds_precision: Literal['truncate', 'error'] = 'truncate',
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> DatetimeSchema:
+    """
+    Returns a schema that matches a datetime value, e.g.:
+
+    ```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,
+    )
+
+
+class TimedeltaSchema(TypedDict, total=False):
+    type: Required[Literal['timedelta']]
+    strict: bool
+    le: timedelta
+    ge: timedelta
+    lt: timedelta
+    gt: timedelta
+    microseconds_precision: Literal['truncate', 'error']
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def timedelta_schema(
+    *,
+    strict: bool | None = None,
+    le: timedelta | None = None,
+    ge: timedelta | None = None,
+    lt: timedelta | None = None,
+    gt: timedelta | None = None,
+    microseconds_precision: Literal['truncate', 'error'] = 'truncate',
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> TimedeltaSchema:
+    """
+    Returns a schema that matches a timedelta value, e.g.:
+
+    ```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,
+    )
+
+
+class LiteralSchema(TypedDict, total=False):
+    type: Required[Literal['literal']]
+    expected: Required[List[Any]]
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def literal_schema(
+    expected: list[Any],
+    *,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> LiteralSchema:
+    """
+    Returns a schema that matches a literal value, e.g.:
+
+    ```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)
+
+
+class EnumSchema(TypedDict, total=False):
+    type: Required[Literal['enum']]
+    cls: Required[Any]
+    members: Required[List[Any]]
+    sub_type: Literal['str', 'int', 'float']
+    missing: Callable[[Any], Any]
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def enum_schema(
+    cls: Any,
+    members: list[Any],
+    *,
+    sub_type: Literal['str', 'int', 'float'] | None = None,
+    missing: Callable[[Any], Any] | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> EnumSchema:
+    """
+    Returns a schema that matches an enum value, e.g.:
+
+    ```py
+    from enum import Enum
+    from pydantic_core import SchemaValidator, core_schema
+
+    class Color(Enum):
+        RED = 1
+        GREEN = 2
+        BLUE = 3
+
+    schema = core_schema.enum_schema(Color, list(Color.__members__.values()))
+    v = SchemaValidator(schema)
+    assert v.validate_python(2) is Color.GREEN
+    ```
+
+    Args:
+        cls: The enum class
+        members: The members of the enum, generally `list(MyEnum.__members__.values())`
+        sub_type: The type of the enum, either 'str' or 'int' or None for plain enums
+        missing: A function to use when the value is not found in the enum, from `_missing_`
+        strict: Whether to use strict mode, defaults to False
+        ref: optional unique identifier of the schema, used to reference the schema in other places
+        metadata: Any other information you want to include with the schema, not used by pydantic-core
+        serialization: Custom serialization schema
+    """
+    return _dict_not_none(
+        type='enum',
+        cls=cls,
+        members=members,
+        sub_type=sub_type,
+        missing=missing,
+        strict=strict,
+        ref=ref,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+# must match input/parse_json.rs::JsonType::try_from
+JsonType = Literal['null', 'bool', 'int', 'float', 'str', 'list', 'dict']
+
+
+class IsInstanceSchema(TypedDict, total=False):
+    type: Required[Literal['is-instance']]
+    cls: Required[Any]
+    cls_repr: str
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def is_instance_schema(
+    cls: Any,
+    *,
+    cls_repr: str | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> IsInstanceSchema:
+    """
+    Returns a schema that checks if a value is an instance of a class, equivalent to python's `isinstance` method, e.g.:
+
+    ```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
+    )
+
+
+class IsSubclassSchema(TypedDict, total=False):
+    type: Required[Literal['is-subclass']]
+    cls: Required[Type[Any]]
+    cls_repr: str
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def is_subclass_schema(
+    cls: Type[Any],
+    *,
+    cls_repr: str | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> IsInstanceSchema:
+    """
+    Returns a schema that checks if a value is a subtype of a class, equivalent to python's `issubclass` method, e.g.:
+
+    ```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
+    )
+
+
+class CallableSchema(TypedDict, total=False):
+    type: Required[Literal['callable']]
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def callable_schema(
+    *, ref: str | None = None, metadata: Dict[str, Any] | None = None, serialization: SerSchema | None = None
+) -> CallableSchema:
+    """
+    Returns a schema that checks if a value is callable, equivalent to python's `callable` method, e.g.:
+
+    ```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)
+
+
+class UuidSchema(TypedDict, total=False):
+    type: Required[Literal['uuid']]
+    version: Literal[1, 3, 4, 5]
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def uuid_schema(
+    *,
+    version: Literal[1, 3, 4, 5] | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> UuidSchema:
+    return _dict_not_none(
+        type='uuid', version=version, strict=strict, ref=ref, metadata=metadata, serialization=serialization
+    )
+
+
+class IncExSeqSerSchema(TypedDict, total=False):
+    type: Required[Literal['include-exclude-sequence']]
+    include: Set[int]
+    exclude: Set[int]
+
+
+def filter_seq_schema(*, include: Set[int] | None = None, exclude: Set[int] | None = None) -> IncExSeqSerSchema:
+    return _dict_not_none(type='include-exclude-sequence', include=include, exclude=exclude)
+
+
+IncExSeqOrElseSerSchema = Union[IncExSeqSerSchema, SerSchema]
+
+
+class ListSchema(TypedDict, total=False):
+    type: Required[Literal['list']]
+    items_schema: CoreSchema
+    min_length: int
+    max_length: int
+    fail_fast: bool
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: IncExSeqOrElseSerSchema
+
+
+def list_schema(
+    items_schema: CoreSchema | None = None,
+    *,
+    min_length: int | None = None,
+    max_length: int | None = None,
+    fail_fast: bool | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: IncExSeqOrElseSerSchema | None = None,
+) -> ListSchema:
+    """
+    Returns a schema that matches a list value, e.g.:
+
+    ```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
+        fail_fast: Stop validation on the first error
+        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,
+        fail_fast=fail_fast,
+        strict=strict,
+        ref=ref,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+# @deprecated('tuple_positional_schema is deprecated. Use pydantic_core.core_schema.tuple_schema instead.')
+def tuple_positional_schema(
+    items_schema: list[CoreSchema],
+    *,
+    extras_schema: CoreSchema | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: IncExSeqOrElseSerSchema | None = None,
+) -> TupleSchema:
+    """
+    Returns a schema that matches a tuple of schemas, e.g.:
+
+    ```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,
+    )
+
+
+# @deprecated('tuple_variable_schema is deprecated. Use pydantic_core.core_schema.tuple_schema instead.')
+def tuple_variable_schema(
+    items_schema: CoreSchema | None = None,
+    *,
+    min_length: int | None = None,
+    max_length: int | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: IncExSeqOrElseSerSchema | None = None,
+) -> TupleSchema:
+    """
+    Returns a schema that matches a tuple of a given schema, e.g.:
+
+    ```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,
+    )
+
+
+class TupleSchema(TypedDict, total=False):
+    type: Required[Literal['tuple']]
+    items_schema: Required[List[CoreSchema]]
+    variadic_item_index: int
+    min_length: int
+    max_length: int
+    fail_fast: bool
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: IncExSeqOrElseSerSchema
+
+
+def tuple_schema(
+    items_schema: list[CoreSchema],
+    *,
+    variadic_item_index: int | None = None,
+    min_length: int | None = None,
+    max_length: int | None = None,
+    fail_fast: bool | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: IncExSeqOrElseSerSchema | None = None,
+) -> TupleSchema:
+    """
+    Returns a schema that matches a tuple of schemas, with an optional variadic item, e.g.:
+
+    ```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
+        fail_fast: Stop validation on the first error
+        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,
+        fail_fast=fail_fast,
+        strict=strict,
+        ref=ref,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+class SetSchema(TypedDict, total=False):
+    type: Required[Literal['set']]
+    items_schema: CoreSchema
+    min_length: int
+    max_length: int
+    fail_fast: bool
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def set_schema(
+    items_schema: CoreSchema | None = None,
+    *,
+    min_length: int | None = None,
+    max_length: int | None = None,
+    fail_fast: bool | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> SetSchema:
+    """
+    Returns a schema that matches a set of a given schema, e.g.:
+
+    ```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
+        fail_fast: Stop validation on the first error
+        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,
+        fail_fast=fail_fast,
+        strict=strict,
+        ref=ref,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+class FrozenSetSchema(TypedDict, total=False):
+    type: Required[Literal['frozenset']]
+    items_schema: CoreSchema
+    min_length: int
+    max_length: int
+    fail_fast: bool
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def frozenset_schema(
+    items_schema: CoreSchema | None = None,
+    *,
+    min_length: int | None = None,
+    max_length: int | None = None,
+    fail_fast: bool | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> FrozenSetSchema:
+    """
+    Returns a schema that matches a frozenset of a given schema, e.g.:
+
+    ```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
+        fail_fast: Stop validation on the first error
+        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,
+        fail_fast=fail_fast,
+        strict=strict,
+        ref=ref,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+class GeneratorSchema(TypedDict, total=False):
+    type: Required[Literal['generator']]
+    items_schema: CoreSchema
+    min_length: int
+    max_length: int
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: IncExSeqOrElseSerSchema
+
+
+def generator_schema(
+    items_schema: CoreSchema | None = None,
+    *,
+    min_length: int | None = None,
+    max_length: int | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: IncExSeqOrElseSerSchema | None = None,
+) -> GeneratorSchema:
+    """
+    Returns a schema that matches a generator value, e.g.:
+
+    ```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,
+    )
+
+
+IncExDict = Set[Union[int, str]]
+
+
+class IncExDictSerSchema(TypedDict, total=False):
+    type: Required[Literal['include-exclude-dict']]
+    include: IncExDict
+    exclude: IncExDict
+
+
+def filter_dict_schema(*, include: IncExDict | None = None, exclude: IncExDict | None = None) -> IncExDictSerSchema:
+    return _dict_not_none(type='include-exclude-dict', include=include, exclude=exclude)
+
+
+IncExDictOrElseSerSchema = Union[IncExDictSerSchema, SerSchema]
+
+
+class DictSchema(TypedDict, total=False):
+    type: Required[Literal['dict']]
+    keys_schema: CoreSchema  # default: AnySchema
+    values_schema: CoreSchema  # default: AnySchema
+    min_length: int
+    max_length: int
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: IncExDictOrElseSerSchema
+
+
+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: Dict[str, Any] | None = 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,
+    )
+
+
+# (input_value: Any, /) -> Any
+NoInfoValidatorFunction = Callable[[Any], Any]
+
+
+class NoInfoValidatorFunctionSchema(TypedDict):
+    type: Literal['no-info']
+    function: NoInfoValidatorFunction
+
+
+# (input_value: Any, info: ValidationInfo, /) -> Any
+WithInfoValidatorFunction = Callable[[Any, ValidationInfo], Any]
+
+
+class WithInfoValidatorFunctionSchema(TypedDict, total=False):
+    type: Required[Literal['with-info']]
+    function: Required[WithInfoValidatorFunction]
+    field_name: str
+
+
+ValidationFunction = Union[NoInfoValidatorFunctionSchema, WithInfoValidatorFunctionSchema]
+
+
+class _ValidatorFunctionSchema(TypedDict, total=False):
+    function: Required[ValidationFunction]
+    schema: Required[CoreSchema]
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+class BeforeValidatorFunctionSchema(_ValidatorFunctionSchema, total=False):
+    type: Required[Literal['function-before']]
+    json_schema_input_schema: CoreSchema
+
+
+def no_info_before_validator_function(
+    function: NoInfoValidatorFunction,
+    schema: CoreSchema,
+    *,
+    ref: str | None = None,
+    json_schema_input_schema: CoreSchema | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> BeforeValidatorFunctionSchema:
+    """
+    Returns a schema that calls a validator function before validating, no `info` argument is provided, e.g.:
+
+    ```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
+        json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type
+        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,
+        json_schema_input_schema=json_schema_input_schema,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+def with_info_before_validator_function(
+    function: WithInfoValidatorFunction,
+    schema: CoreSchema,
+    *,
+    field_name: str | None = None,
+    ref: str | None = None,
+    json_schema_input_schema: CoreSchema | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> BeforeValidatorFunctionSchema:
+    """
+    Returns a schema that calls a validator function before validation, the function is called with
+    an `info` argument, e.g.:
+
+    ```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
+        json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type
+        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,
+        json_schema_input_schema=json_schema_input_schema,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+class AfterValidatorFunctionSchema(_ValidatorFunctionSchema, total=False):
+    type: Required[Literal['function-after']]
+
+
+def no_info_after_validator_function(
+    function: NoInfoValidatorFunction,
+    schema: CoreSchema,
+    *,
+    ref: str | None = None,
+    json_schema_input_schema: CoreSchema | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> AfterValidatorFunctionSchema:
+    """
+    Returns a schema that calls a validator function after validating, no `info` argument is provided, e.g.:
+
+    ```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
+        json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type
+        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,
+        json_schema_input_schema=json_schema_input_schema,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+def with_info_after_validator_function(
+    function: WithInfoValidatorFunction,
+    schema: CoreSchema,
+    *,
+    field_name: str | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> AfterValidatorFunctionSchema:
+    """
+    Returns a schema that calls a validator function after validation, the function is called with
+    an `info` argument, e.g.:
+
+    ```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,
+    )
+
+
+class ValidatorFunctionWrapHandler(Protocol):
+    def __call__(self, input_value: Any, outer_location: str | int | None = None, /) -> Any:  # pragma: no cover
+        ...
+
+
+# (input_value: Any, validator: ValidatorFunctionWrapHandler, /) -> Any
+NoInfoWrapValidatorFunction = Callable[[Any, ValidatorFunctionWrapHandler], Any]
+
+
+class NoInfoWrapValidatorFunctionSchema(TypedDict):
+    type: Literal['no-info']
+    function: NoInfoWrapValidatorFunction
+
+
+# (input_value: Any, validator: ValidatorFunctionWrapHandler, info: ValidationInfo, /) -> Any
+WithInfoWrapValidatorFunction = Callable[[Any, ValidatorFunctionWrapHandler, ValidationInfo], Any]
+
+
+class WithInfoWrapValidatorFunctionSchema(TypedDict, total=False):
+    type: Required[Literal['with-info']]
+    function: Required[WithInfoWrapValidatorFunction]
+    field_name: str
+
+
+WrapValidatorFunction = Union[NoInfoWrapValidatorFunctionSchema, WithInfoWrapValidatorFunctionSchema]
+
+
+class WrapValidatorFunctionSchema(TypedDict, total=False):
+    type: Required[Literal['function-wrap']]
+    function: Required[WrapValidatorFunction]
+    schema: Required[CoreSchema]
+    ref: str
+    json_schema_input_schema: CoreSchema
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def no_info_wrap_validator_function(
+    function: NoInfoWrapValidatorFunction,
+    schema: CoreSchema,
+    *,
+    ref: str | None = None,
+    json_schema_input_schema: CoreSchema | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> WrapValidatorFunctionSchema:
+    """
+    Returns a schema which calls a function with a `validator` callable argument which can
+    optionally be used to call inner validation with the function logic, this is much like the
+    "onion" implementation of middleware in many popular web frameworks, no `info` argument is passed, e.g.:
+
+    ```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
+        json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type
+        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,
+        json_schema_input_schema=json_schema_input_schema,
+        ref=ref,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+def with_info_wrap_validator_function(
+    function: WithInfoWrapValidatorFunction,
+    schema: CoreSchema,
+    *,
+    field_name: str | None = None,
+    json_schema_input_schema: CoreSchema | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> WrapValidatorFunctionSchema:
+    """
+    Returns a schema which calls a function with a `validator` callable argument which can
+    optionally be used to call inner validation with the function logic, this is much like the
+    "onion" implementation of middleware in many popular web frameworks, an `info` argument is also passed, e.g.:
+
+    ```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
+        json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type
+        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,
+        json_schema_input_schema=json_schema_input_schema,
+        ref=ref,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+class PlainValidatorFunctionSchema(TypedDict, total=False):
+    type: Required[Literal['function-plain']]
+    function: Required[ValidationFunction]
+    ref: str
+    json_schema_input_schema: CoreSchema
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def no_info_plain_validator_function(
+    function: NoInfoValidatorFunction,
+    *,
+    ref: str | None = None,
+    json_schema_input_schema: CoreSchema | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> PlainValidatorFunctionSchema:
+    """
+    Returns a schema that uses the provided function for validation, no `info` argument is passed, e.g.:
+
+    ```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
+        json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type
+        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,
+        json_schema_input_schema=json_schema_input_schema,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+def with_info_plain_validator_function(
+    function: WithInfoValidatorFunction,
+    *,
+    field_name: str | None = None,
+    ref: str | None = None,
+    json_schema_input_schema: CoreSchema | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> PlainValidatorFunctionSchema:
+    """
+    Returns a schema that uses the provided function for validation, an `info` argument is passed, e.g.:
+
+    ```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
+        json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type
+        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,
+        json_schema_input_schema=json_schema_input_schema,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+class WithDefaultSchema(TypedDict, total=False):
+    type: Required[Literal['default']]
+    schema: Required[CoreSchema]
+    default: Any
+    default_factory: Union[Callable[[], Any], Callable[[Dict[str, Any]], Any]]
+    default_factory_takes_data: bool
+    on_error: Literal['raise', 'omit', 'default']  # default: 'raise'
+    validate_default: bool  # default: False
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def with_default_schema(
+    schema: CoreSchema,
+    *,
+    default: Any = PydanticUndefined,
+    default_factory: Union[Callable[[], Any], Callable[[Dict[str, Any]], Any], None] = None,
+    default_factory_takes_data: bool | None = None,
+    on_error: Literal['raise', 'omit', 'default'] | None = None,
+    validate_default: bool | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> WithDefaultSchema:
+    """
+    Returns a schema that adds a default value to the given schema, e.g.:
+
+    ```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 callable that returns the default value to use
+        default_factory_takes_data: Whether the default factory takes a validated data argument
+        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,
+        default_factory_takes_data=default_factory_takes_data,
+        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
+
+
+class NullableSchema(TypedDict, total=False):
+    type: Required[Literal['nullable']]
+    schema: Required[CoreSchema]
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def nullable_schema(
+    schema: CoreSchema,
+    *,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> NullableSchema:
+    """
+    Returns a schema that matches a nullable value, e.g.:
+
+    ```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
+    )
+
+
+class UnionSchema(TypedDict, total=False):
+    type: Required[Literal['union']]
+    choices: Required[List[Union[CoreSchema, Tuple[CoreSchema, str]]]]
+    # default true, whether to automatically collapse unions with one element to the inner validator
+    auto_collapse: bool
+    custom_error_type: str
+    custom_error_message: str
+    custom_error_context: Dict[str, Union[str, int, float]]
+    mode: Literal['smart', 'left_to_right']  # default: 'smart'
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+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: Dict[str, Any] | None = 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,
+    )
+
+
+class TaggedUnionSchema(TypedDict, total=False):
+    type: Required[Literal['tagged-union']]
+    choices: Required[Dict[Hashable, CoreSchema]]
+    discriminator: Required[Union[str, List[Union[str, int]], List[List[Union[str, int]]], Callable[[Any], Hashable]]]
+    custom_error_type: str
+    custom_error_message: str
+    custom_error_context: Dict[str, Union[str, int, float]]
+    strict: bool
+    from_attributes: bool  # default: True
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def tagged_union_schema(
+    choices: Dict[Any, CoreSchema],
+    discriminator: str | list[str | int] | list[list[str | int]] | Callable[[Any], Any],
+    *,
+    custom_error_type: str | None = None,
+    custom_error_message: str | None = None,
+    custom_error_context: dict[str, int | str | float] | None = None,
+    strict: bool | None = None,
+    from_attributes: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> TaggedUnionSchema:
+    """
+    Returns a schema that matches a tagged union value, e.g.:
+
+    ```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,
+    )
+
+
+class ChainSchema(TypedDict, total=False):
+    type: Required[Literal['chain']]
+    steps: Required[List[CoreSchema]]
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def chain_schema(
+    steps: list[CoreSchema],
+    *,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> ChainSchema:
+    """
+    Returns a schema that chains the provided validation schemas, e.g.:
+
+    ```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)
+
+
+class LaxOrStrictSchema(TypedDict, total=False):
+    type: Required[Literal['lax-or-strict']]
+    lax_schema: Required[CoreSchema]
+    strict_schema: Required[CoreSchema]
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def lax_or_strict_schema(
+    lax_schema: CoreSchema,
+    strict_schema: CoreSchema,
+    *,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> LaxOrStrictSchema:
+    """
+    Returns a schema that uses the lax or strict schema, e.g.:
+
+    ```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,
+    )
+
+
+class JsonOrPythonSchema(TypedDict, total=False):
+    type: Required[Literal['json-or-python']]
+    json_schema: Required[CoreSchema]
+    python_schema: Required[CoreSchema]
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def json_or_python_schema(
+    json_schema: CoreSchema,
+    python_schema: CoreSchema,
+    *,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> JsonOrPythonSchema:
+    """
+    Returns a schema that uses the Json or Python schema depending on the input:
+
+    ```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,
+    )
+
+
+class TypedDictField(TypedDict, total=False):
+    type: Required[Literal['typed-dict-field']]
+    schema: Required[CoreSchema]
+    required: bool
+    validation_alias: Union[str, List[Union[str, int]], List[List[Union[str, int]]]]
+    serialization_alias: str
+    serialization_exclude: bool  # default: False
+    metadata: Dict[str, Any]
+
+
+def typed_dict_field(
+    schema: CoreSchema,
+    *,
+    required: bool | None = None,
+    validation_alias: str | list[str | int] | list[list[str | int]] | None = None,
+    serialization_alias: str | None = None,
+    serialization_exclude: bool | None = None,
+    metadata: Dict[str, Any] | None = None,
+) -> 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, otherwise uses the value from `total` on the typed dict
+        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,
+    )
+
+
+class TypedDictSchema(TypedDict, total=False):
+    type: Required[Literal['typed-dict']]
+    fields: Required[Dict[str, TypedDictField]]
+    cls: Type[Any]
+    computed_fields: List[ComputedField]
+    strict: bool
+    extras_schema: CoreSchema
+    # all these values can be set via config, equivalent fields have `typed_dict_` prefix
+    extra_behavior: ExtraBehavior
+    total: bool  # default: True
+    populate_by_name: bool  # replaces `allow_population_by_field_name` in pydantic v1
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+    config: CoreConfig
+
+
+def typed_dict_schema(
+    fields: Dict[str, TypedDictField],
+    *,
+    cls: Type[Any] | None = None,
+    computed_fields: list[ComputedField] | None = None,
+    strict: bool | None = None,
+    extras_schema: CoreSchema | None = None,
+    extra_behavior: ExtraBehavior | None = None,
+    total: bool | None = None,
+    populate_by_name: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+    config: CoreConfig | None = None,
+) -> TypedDictSchema:
+    """
+    Returns a schema that matches a typed dict, e.g.:
+
+    ```py
+    from typing_extensions import TypedDict
+
+    from pydantic_core import SchemaValidator, core_schema
+
+    class MyTypedDict(TypedDict):
+        a: str
+
+    wrapper_schema = core_schema.typed_dict_schema(
+        {'a': core_schema.typed_dict_field(core_schema.str_schema())}, cls=MyTypedDict
+    )
+    v = SchemaValidator(wrapper_schema)
+    assert v.validate_python({'a': 'hello'}) == {'a': 'hello'}
+    ```
+
+    Args:
+        fields: The fields to use for the typed dict
+        cls: The class 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, otherwise uses `typed_dict_total` from config
+        populate_by_name: Whether the typed dict should populate by name
+        serialization: Custom serialization schema
+    """
+    return _dict_not_none(
+        type='typed-dict',
+        fields=fields,
+        cls=cls,
+        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,
+    )
+
+
+class ModelField(TypedDict, total=False):
+    type: Required[Literal['model-field']]
+    schema: Required[CoreSchema]
+    validation_alias: Union[str, List[Union[str, int]], List[List[Union[str, int]]]]
+    serialization_alias: str
+    serialization_exclude: bool  # default: False
+    frozen: bool
+    metadata: Dict[str, Any]
+
+
+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: Dict[str, Any] | None = 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,
+    )
+
+
+class ModelFieldsSchema(TypedDict, total=False):
+    type: Required[Literal['model-fields']]
+    fields: Required[Dict[str, ModelField]]
+    model_name: str
+    computed_fields: List[ComputedField]
+    strict: bool
+    extras_schema: CoreSchema
+    # all these values can be set via config, equivalent fields have `typed_dict_` prefix
+    extra_behavior: ExtraBehavior
+    populate_by_name: bool  # replaces `allow_population_by_field_name` in pydantic v1
+    from_attributes: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+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: Dict[str, Any] | None = 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,
+    )
+
+
+class ModelSchema(TypedDict, total=False):
+    type: Required[Literal['model']]
+    cls: Required[Type[Any]]
+    generic_origin: Type[Any]
+    schema: Required[CoreSchema]
+    custom_init: bool
+    root_model: bool
+    post_init: str
+    revalidate_instances: Literal['always', 'never', 'subclass-instances']  # default: 'never'
+    strict: bool
+    frozen: bool
+    extra_behavior: ExtraBehavior
+    config: CoreConfig
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def model_schema(
+    cls: Type[Any],
+    schema: CoreSchema,
+    *,
+    generic_origin: Type[Any] | None = None,
+    custom_init: bool | None = None,
+    root_model: bool | None = None,
+    post_init: str | None = None,
+    revalidate_instances: Literal['always', 'never', 'subclass-instances'] | None = None,
+    strict: bool | None = None,
+    frozen: bool | None = None,
+    extra_behavior: ExtraBehavior | None = None,
+    config: CoreConfig | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> ModelSchema:
+    """
+    A model schema generally contains a typed-dict schema.
+    It will run the typed dict validator, then create a new class
+    and set the dict and fields set returned from the typed dict validator
+    to `__dict__` and `__pydantic_fields_set__` respectively.
+
+    Example:
+
+    ```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
+        generic_origin: The origin type used for this model, if it's a parametrized generic. Ex,
+            if this model schema represents `SomeModel[int]`, generic_origin is `SomeModel`
+        custom_init: 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,
+        generic_origin=generic_origin,
+        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,
+    )
+
+
+class DataclassField(TypedDict, total=False):
+    type: Required[Literal['dataclass-field']]
+    name: Required[str]
+    schema: Required[CoreSchema]
+    kw_only: bool  # default: True
+    init: bool  # default: True
+    init_only: bool  # default: False
+    frozen: bool  # default: False
+    validation_alias: Union[str, List[Union[str, int]], List[List[Union[str, int]]]]
+    serialization_alias: str
+    serialization_exclude: bool  # default: False
+    metadata: Dict[str, Any]
+
+
+def dataclass_field(
+    name: str,
+    schema: CoreSchema,
+    *,
+    kw_only: bool | None = None,
+    init: bool | None = None,
+    init_only: bool | None = None,
+    validation_alias: str | list[str | int] | list[list[str | int]] | None = None,
+    serialization_alias: str | None = None,
+    serialization_exclude: bool | None = None,
+    metadata: Dict[str, Any] | None = None,
+    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,
+    )
+
+
+class DataclassArgsSchema(TypedDict, total=False):
+    type: Required[Literal['dataclass-args']]
+    dataclass_name: Required[str]
+    fields: Required[List[DataclassField]]
+    computed_fields: List[ComputedField]
+    populate_by_name: bool  # default: False
+    collect_init_only: bool  # default: False
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+    extra_behavior: ExtraBehavior
+
+
+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: Dict[str, Any] | None = 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,
+    )
+
+
+class DataclassSchema(TypedDict, total=False):
+    type: Required[Literal['dataclass']]
+    cls: Required[Type[Any]]
+    generic_origin: Type[Any]
+    schema: Required[CoreSchema]
+    fields: Required[List[str]]
+    cls_name: str
+    post_init: bool  # default: False
+    revalidate_instances: Literal['always', 'never', 'subclass-instances']  # default: 'never'
+    strict: bool  # default: False
+    frozen: bool  # default False
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+    slots: bool
+    config: CoreConfig
+
+
+def dataclass_schema(
+    cls: Type[Any],
+    schema: CoreSchema,
+    fields: List[str],
+    *,
+    generic_origin: Type[Any] | None = None,
+    cls_name: str | None = None,
+    post_init: bool | None = None,
+    revalidate_instances: Literal['always', 'never', 'subclass-instances'] | None = None,
+    strict: bool | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+    frozen: bool | None = None,
+    slots: bool | None = None,
+    config: CoreConfig | None = None,
+) -> DataclassSchema:
+    """
+    Returns a schema for a dataclass. As with `ModelSchema`, this schema can only be used as a field within
+    another schema, not as the root type.
+
+    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
+        generic_origin: The origin type used for this dataclass, if it's a parametrized generic. Ex,
+            if this model schema represents `SomeDataclass[int]`, generic_origin is `SomeDataclass`
+        cls_name: 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,
+        generic_origin=generic_origin,
+        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,
+    )
+
+
+class ArgumentsParameter(TypedDict, total=False):
+    name: Required[str]
+    schema: Required[CoreSchema]
+    mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only']  # default positional_or_keyword
+    alias: Union[str, List[Union[str, int]], List[List[Union[str, int]]]]
+
+
+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)
+
+
+VarKwargsMode: TypeAlias = Literal['uniform', 'unpacked-typed-dict']
+
+
+class ArgumentsSchema(TypedDict, total=False):
+    type: Required[Literal['arguments']]
+    arguments_schema: Required[List[ArgumentsParameter]]
+    populate_by_name: bool
+    var_args_schema: CoreSchema
+    var_kwargs_mode: VarKwargsMode
+    var_kwargs_schema: CoreSchema
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def arguments_schema(
+    arguments: list[ArgumentsParameter],
+    *,
+    populate_by_name: bool | None = None,
+    var_args_schema: CoreSchema | None = None,
+    var_kwargs_mode: VarKwargsMode | None = None,
+    var_kwargs_schema: CoreSchema | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> ArgumentsSchema:
+    """
+    Returns a schema that matches an arguments schema, e.g.:
+
+    ```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_mode: The validation mode to use for variadic keyword arguments. If `'uniform'`, every value of the
+            keyword arguments will be validated against the `var_kwargs_schema` schema. If `'unpacked-typed-dict'`,
+            the `var_kwargs_schema` argument must be a [`typed_dict_schema`][pydantic_core.core_schema.typed_dict_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_mode=var_kwargs_mode,
+        var_kwargs_schema=var_kwargs_schema,
+        ref=ref,
+        metadata=metadata,
+        serialization=serialization,
+    )
+
+
+class CallSchema(TypedDict, total=False):
+    type: Required[Literal['call']]
+    arguments_schema: Required[CoreSchema]
+    function: Required[Callable[..., Any]]
+    function_name: str  # default function.__name__
+    return_schema: CoreSchema
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def call_schema(
+    arguments: CoreSchema,
+    function: Callable[..., Any],
+    *,
+    function_name: str | None = None,
+    return_schema: CoreSchema | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> CallSchema:
+    """
+    Returns a schema that matches an arguments schema, then calls a function, e.g.:
+
+    ```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,
+    )
+
+
+class CustomErrorSchema(TypedDict, total=False):
+    type: Required[Literal['custom-error']]
+    schema: Required[CoreSchema]
+    custom_error_type: Required[str]
+    custom_error_message: str
+    custom_error_context: Dict[str, Union[str, int, float]]
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def custom_error_schema(
+    schema: CoreSchema,
+    custom_error_type: str,
+    *,
+    custom_error_message: str | None = None,
+    custom_error_context: dict[str, Any] | None = None,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> CustomErrorSchema:
+    """
+    Returns a schema that matches a custom error value, e.g.:
+
+    ```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,
+    )
+
+
+class JsonSchema(TypedDict, total=False):
+    type: Required[Literal['json']]
+    schema: CoreSchema
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def json_schema(
+    schema: CoreSchema | None = None,
+    *,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = None,
+    serialization: SerSchema | None = None,
+) -> JsonSchema:
+    """
+    Returns a schema that matches a JSON value, e.g.:
+
+    ```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)
+
+
+class UrlSchema(TypedDict, total=False):
+    type: Required[Literal['url']]
+    max_length: int
+    allowed_schemes: List[str]
+    host_required: bool  # default False
+    default_host: str
+    default_port: int
+    default_path: str
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+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: Dict[str, Any] | None = 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,
+    )
+
+
+class MultiHostUrlSchema(TypedDict, total=False):
+    type: Required[Literal['multi-host-url']]
+    max_length: int
+    allowed_schemes: List[str]
+    host_required: bool  # default False
+    default_host: str
+    default_port: int
+    default_path: str
+    strict: bool
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+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: Dict[str, Any] | None = 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,
+    )
+
+
+class DefinitionsSchema(TypedDict, total=False):
+    type: Required[Literal['definitions']]
+    schema: Required[CoreSchema]
+    definitions: Required[List[CoreSchema]]
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+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)
+
+
+class DefinitionReferenceSchema(TypedDict, total=False):
+    type: Required[Literal['definition-ref']]
+    schema_ref: Required[str]
+    ref: str
+    metadata: Dict[str, Any]
+    serialization: SerSchema
+
+
+def definition_reference_schema(
+    schema_ref: str,
+    ref: str | None = None,
+    metadata: Dict[str, Any] | None = 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
+    )
+
+
+MYPY = False
+# See https://github.com/python/mypy/issues/14034 for details, in summary mypy is extremely slow to process this
+# union which kills performance not just for pydantic, but even for code using pydantic
+if not MYPY:
+    CoreSchema = Union[
+        InvalidSchema,
+        AnySchema,
+        NoneSchema,
+        BoolSchema,
+        IntSchema,
+        FloatSchema,
+        DecimalSchema,
+        StringSchema,
+        BytesSchema,
+        DateSchema,
+        TimeSchema,
+        DatetimeSchema,
+        TimedeltaSchema,
+        LiteralSchema,
+        EnumSchema,
+        IsInstanceSchema,
+        IsSubclassSchema,
+        CallableSchema,
+        ListSchema,
+        TupleSchema,
+        SetSchema,
+        FrozenSetSchema,
+        GeneratorSchema,
+        DictSchema,
+        AfterValidatorFunctionSchema,
+        BeforeValidatorFunctionSchema,
+        WrapValidatorFunctionSchema,
+        PlainValidatorFunctionSchema,
+        WithDefaultSchema,
+        NullableSchema,
+        UnionSchema,
+        TaggedUnionSchema,
+        ChainSchema,
+        LaxOrStrictSchema,
+        JsonOrPythonSchema,
+        TypedDictSchema,
+        ModelFieldsSchema,
+        ModelSchema,
+        DataclassArgsSchema,
+        DataclassSchema,
+        ArgumentsSchema,
+        CallSchema,
+        CustomErrorSchema,
+        JsonSchema,
+        UrlSchema,
+        MultiHostUrlSchema,
+        DefinitionsSchema,
+        DefinitionReferenceSchema,
+        UuidSchema,
+        ComplexSchema,
+    ]
+elif False:
+    CoreSchema: TypeAlias = Mapping[str, Any]
+
+
+# to update this, call `pytest -k test_core_schema_type_literal` and copy the output
+CoreSchemaType = Literal[
+    'invalid',
+    'any',
+    'none',
+    'bool',
+    'int',
+    'float',
+    'decimal',
+    'str',
+    'bytes',
+    'date',
+    'time',
+    'datetime',
+    'timedelta',
+    'literal',
+    'enum',
+    'is-instance',
+    'is-subclass',
+    'callable',
+    'list',
+    'tuple',
+    'set',
+    'frozenset',
+    'generator',
+    'dict',
+    'function-after',
+    'function-before',
+    'function-wrap',
+    'function-plain',
+    'default',
+    'nullable',
+    'union',
+    'tagged-union',
+    'chain',
+    'lax-or-strict',
+    'json-or-python',
+    'typed-dict',
+    'model-fields',
+    'model',
+    'dataclass-args',
+    'dataclass',
+    'arguments',
+    'call',
+    'custom-error',
+    'json',
+    'url',
+    'multi-host-url',
+    'definitions',
+    'definition-ref',
+    'uuid',
+    'complex',
+]
+
+CoreSchemaFieldType = Literal['model-field', 'dataclass-field', 'typed-dict-field', 'computed-field']
+
+
+# used in _pydantic_core.pyi::PydanticKnownError
+# to update this, call `pytest -k test_all_errors` and copy the output
+ErrorType = Literal[
+    'no_such_attribute',
+    'json_invalid',
+    'json_type',
+    'needs_python_object',
+    'recursion_loop',
+    'missing',
+    'frozen_field',
+    'frozen_instance',
+    'extra_forbidden',
+    'invalid_key',
+    'get_attribute_error',
+    'model_type',
+    'model_attributes_type',
+    'dataclass_type',
+    'dataclass_exact_type',
+    'none_required',
+    'greater_than',
+    'greater_than_equal',
+    'less_than',
+    'less_than_equal',
+    'multiple_of',
+    'finite_number',
+    'too_short',
+    'too_long',
+    'iterable_type',
+    'iteration_error',
+    'string_type',
+    'string_sub_type',
+    'string_unicode',
+    'string_too_short',
+    'string_too_long',
+    'string_pattern_mismatch',
+    'enum',
+    'dict_type',
+    'mapping_type',
+    'list_type',
+    'tuple_type',
+    'set_type',
+    'bool_type',
+    'bool_parsing',
+    'int_type',
+    'int_parsing',
+    'int_parsing_size',
+    'int_from_float',
+    'float_type',
+    'float_parsing',
+    'bytes_type',
+    'bytes_too_short',
+    'bytes_too_long',
+    'bytes_invalid_encoding',
+    'value_error',
+    'assertion_error',
+    'literal_error',
+    'date_type',
+    'date_parsing',
+    'date_from_datetime_parsing',
+    'date_from_datetime_inexact',
+    'date_past',
+    'date_future',
+    'time_type',
+    'time_parsing',
+    'datetime_type',
+    'datetime_parsing',
+    'datetime_object_invalid',
+    'datetime_from_date_parsing',
+    'datetime_past',
+    'datetime_future',
+    'timezone_naive',
+    'timezone_aware',
+    'timezone_offset',
+    'time_delta_type',
+    'time_delta_parsing',
+    'frozen_set_type',
+    'is_instance_of',
+    'is_subclass_of',
+    'callable_type',
+    'union_tag_invalid',
+    'union_tag_not_found',
+    'arguments_type',
+    'missing_argument',
+    'unexpected_keyword_argument',
+    'missing_keyword_only_argument',
+    'unexpected_positional_argument',
+    'missing_positional_only_argument',
+    'multiple_argument_values',
+    'url_type',
+    'url_parsing',
+    'url_syntax_violation',
+    'url_too_long',
+    'url_scheme',
+    'uuid_type',
+    'uuid_parsing',
+    'uuid_version',
+    'decimal_type',
+    'decimal_parsing',
+    'decimal_max_digits',
+    'decimal_max_places',
+    'decimal_whole_digits',
+    'complex_type',
+    'complex_str_parsing',
+]
+
+
+def _dict_not_none(**kwargs: Any) -> Any:
+    return {k: v for k, v in kwargs.items() if v is not None}
+
+
+###############################################################################
+# All this stuff is deprecated by #980 and will be removed eventually
+# They're kept because some code external code will be using them
+
+
+@deprecated('`field_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.')
+def field_before_validator_function(function: WithInfoValidatorFunction, field_name: str, schema: CoreSchema, **kwargs):
+    warnings.warn(
+        '`field_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.',
+        DeprecationWarning,
+    )
+    return with_info_before_validator_function(function, schema, field_name=field_name, **kwargs)
+
+
+@deprecated('`general_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.')
+def general_before_validator_function(*args, **kwargs):
+    warnings.warn(
+        '`general_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.',
+        DeprecationWarning,
+    )
+    return with_info_before_validator_function(*args, **kwargs)
+
+
+@deprecated('`field_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.')
+def field_after_validator_function(function: WithInfoValidatorFunction, field_name: str, schema: CoreSchema, **kwargs):
+    warnings.warn(
+        '`field_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.',
+        DeprecationWarning,
+    )
+    return with_info_after_validator_function(function, schema, field_name=field_name, **kwargs)
+
+
+@deprecated('`general_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.')
+def general_after_validator_function(*args, **kwargs):
+    warnings.warn(
+        '`general_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.',
+        DeprecationWarning,
+    )
+    return with_info_after_validator_function(*args, **kwargs)
+
+
+@deprecated('`field_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.')
+def field_wrap_validator_function(
+    function: WithInfoWrapValidatorFunction, field_name: str, schema: CoreSchema, **kwargs
+):
+    warnings.warn(
+        '`field_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.',
+        DeprecationWarning,
+    )
+    return with_info_wrap_validator_function(function, schema, field_name=field_name, **kwargs)
+
+
+@deprecated('`general_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.')
+def general_wrap_validator_function(*args, **kwargs):
+    warnings.warn(
+        '`general_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.',
+        DeprecationWarning,
+    )
+    return with_info_wrap_validator_function(*args, **kwargs)
+
+
+@deprecated('`field_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.')
+def field_plain_validator_function(function: WithInfoValidatorFunction, field_name: str, **kwargs):
+    warnings.warn(
+        '`field_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.',
+        DeprecationWarning,
+    )
+    return with_info_plain_validator_function(function, field_name=field_name, **kwargs)
+
+
+@deprecated('`general_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.')
+def general_plain_validator_function(*args, **kwargs):
+    warnings.warn(
+        '`general_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.',
+        DeprecationWarning,
+    )
+    return with_info_plain_validator_function(*args, **kwargs)
+
+
+_deprecated_import_lookup = {
+    'FieldValidationInfo': ValidationInfo,
+    'FieldValidatorFunction': WithInfoValidatorFunction,
+    'GeneralValidatorFunction': WithInfoValidatorFunction,
+    'FieldWrapValidatorFunction': WithInfoWrapValidatorFunction,
+}
+
+if TYPE_CHECKING:
+    FieldValidationInfo = ValidationInfo
+
+
+def __getattr__(attr_name: str) -> object:
+    new_attr = _deprecated_import_lookup.get(attr_name)
+    if new_attr is None:
+        raise AttributeError(f"module 'pydantic_core' has no attribute '{attr_name}'")
+    else:
+        import warnings
+
+        msg = f'`{attr_name}` is deprecated, use `{new_attr.__name__}` instead.'
+        warnings.warn(msg, DeprecationWarning, stacklevel=1)
+        return new_attr