TypeAdapter

Bases: Generic[T]

Usage Documentation

Type Adapter

Type adapters provide a flexible way to perform validation and serialization based on a Python type.

A TypeAdapter instance exposes some of the functionality from BaseModel instance methods for types that do not have such methods (such as dataclasses, primitive types, and more).

Note: TypeAdapter instances are not types, and cannot be used as type annotations for fields.

Attributes:

Name	Type	Description
core_schema	CoreSchema	The core schema for the type.
validator	SchemaValidator	The schema validator for the type.
serializer	SchemaSerializer	The schema serializer for the type.

Namespace management nuances and implementation details

Here, we collect some notes on namespace management, and subtle differences from BaseModel:

BaseModel uses its own __module__ to find out where it was defined and then looks for symbols to resolve forward references in those globals. On the other hand, TypeAdapter can be initialized with arbitrary objects, which may not be types and thus do not have a __module__ available. So instead we look at the globals in our parent stack frame.

It is expected that the ns_resolver passed to this function will have the correct namespace for the type we're adapting. See TypeAdapter.__init__ and TypeAdapter.rebuild for various ways to construct this namespace.

This works for the case where this function is called in a module that has the target of forward references in its scope, but does not always work for more complex cases.

For example, take the following:

a.py

from typing import Dict, List

IntList = List[int]
OuterDict = Dict[str, 'IntList']

b.py

from a import OuterDict

from pydantic import TypeAdapter

IntList = int  # replaces the symbol the forward reference is looking for
v = TypeAdapter(OuterDict)
v({'x': 1})  # should fail but doesn't

If OuterDict were a BaseModel, this would work because it would resolve the forward reference within the a.py namespace. But TypeAdapter(OuterDict) can't determine what module OuterDict came from.

In other words, the assumption that all forward references exist in the module we are being called from is not technically always true. Although most of the time it is and it works fine for recursive models and such, BaseModel's behavior isn't perfect either and can break in similar ways, so there is no right or wrong between the two.

But at the very least this behavior is subtly different from BaseModel's.

Parameters:

Name	Type	Description	Default
type	Any	The type associated with the TypeAdapter.	required
config	ConfigDict | None	Configuration for the TypeAdapter, should be a dictionary conforming to ConfigDict.	None
_parent_depth	int	Depth at which to search the for the parent namespace used for schema building. We also use this as a reference level to find the global namespace (_parent_depth - 1). Defaults to 2 because we expect to reference the frame that called the TypeAdapter constructor.	2
module	str | None	The module that passes to plugin if provided.	None

Note

You cannot use the config argument when instantiating a TypeAdapter if the type you're using has its own config that cannot be overridden (ex: BaseModel, TypedDict, and dataclass). A type-adapter-config-unused error will be raised in this case.

Note

The _parent_depth argument is named with an underscore to suggest its private nature and discourage use. It may be deprecated in a minor version, so we only recommend using it if you're comfortable with potential change in behavior / support.

Compatibility with mypy

Depending on the type used, mypy might raise an error when instantiating a TypeAdapter. As a workaround, you can explicitly annotate your variable:

from typing import Union

from pydantic import TypeAdapter

ta: TypeAdapter[Union[str, int]] = TypeAdapter(Union[str, int])  # type: ignore[arg-type]

Returns:

Type	Description
None	A type adapter configured for the specified type.

Source code in pydantic/type_adapter.py

def __init__(
    self,
    type: Any,
    *,
    config: ConfigDict | None = None,
    _parent_depth: int = 2,
    module: str | None = None,
) -> None:
    """Initializes the TypeAdapter object.

    Args:
        type: The type associated with the `TypeAdapter`.
        config: Configuration for the `TypeAdapter`, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
        _parent_depth: Depth at which to search the for the parent namespace used for schema building.
            We also use this as a reference level to find the global namespace (_parent_depth - 1).
            Defaults to 2 because we expect to reference the frame that called the `TypeAdapter` constructor.
        module: The module that passes to plugin if provided.

    !!! note
        You cannot use the `config` argument when instantiating a `TypeAdapter` if the type you're using has its own
        config that cannot be overridden (ex: `BaseModel`, `TypedDict`, and `dataclass`). A
        [`type-adapter-config-unused`](../errors/usage_errors.md#type-adapter-config-unused) error will be raised in this case.

    !!! note
        The `_parent_depth` argument is named with an underscore to suggest its private nature and discourage use.
        It may be deprecated in a minor version, so we only recommend using it if you're
        comfortable with potential change in behavior / support.

    ??? tip "Compatibility with `mypy`"
        Depending on the type used, `mypy` might raise an error when instantiating a `TypeAdapter`. As a workaround, you can explicitly
        annotate your variable:

        ```python
        from typing import Union

        from pydantic import TypeAdapter

        ta: TypeAdapter[Union[str, int]] = TypeAdapter(Union[str, int])  # type: ignore[arg-type]
        ```

    Returns:
        A type adapter configured for the specified `type`.
    """
    if _type_has_config(type) and config is not None:
        raise PydanticUserError(
            'Cannot use `config` when the type is a BaseModel, dataclass or TypedDict.'
            ' These types can have their own config and setting the config via the `config`'
            ' parameter to TypeAdapter will not override it, thus the `config` you passed to'
            ' TypeAdapter becomes meaningless, which is probably not what you want.',
            code='type-adapter-config-unused',
        )

    self._type = type
    self._config = config
    self._parent_depth = _parent_depth

    self.core_schema: CoreSchema
    self.validator: SchemaValidator | PluggableSchemaValidator
    self.serializer: SchemaSerializer
    self.pydantic_complete: bool = False

    localns = _typing_extra.parent_frame_namespace(parent_depth=self._parent_depth) or {}
    globalns = sys._getframe(max(self._parent_depth - 1, 1)).f_globals
    self._module_name = module or cast(str, globalns.get('__name__', ''))
    self._init_core_attrs(
        ns_resolver=_namespace_utils.NsResolver(
            namespaces_tuple=_namespace_utils.NamespacesTuple(locals=localns, globals=globalns),
            parent_namespace=localns,
        ),
        force=False,
    )

rebuild

rebuild(
    *,
    force: bool = False,
    raise_errors: bool = True,
    _parent_namespace_depth: int = 2,
    _types_namespace: MappingNamespace | None = None
) -> bool | None

Try to rebuild the pydantic-core schema for the adapter's type.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

Name	Type	Description	Default
force	bool	Whether to force the rebuilding of the type adapter's schema, defaults to False.	False
raise_errors	bool	Whether to raise errors, defaults to True.	True
_parent_namespace_depth	int	The depth level of the parent namespace, defaults to 2.	2
_types_namespace	MappingNamespace | None	The types namespace, defaults to None.	None

Returns:

Type	Description
bool | None	Returns None if the schema is already "complete" and rebuilding was not required.
bool | None	If rebuilding was required, returns True if rebuilding was successful, otherwise False.

Source code in pydantic/type_adapter.py

def rebuild(
    self,
    *,
    force: bool = False,
    raise_errors: bool = True,
    _parent_namespace_depth: int = 2,
    _types_namespace: _namespace_utils.MappingNamespace | None = None,
) -> bool | None:
    """Try to rebuild the pydantic-core schema for the adapter's type.

    This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
    the initial attempt to build the schema, and automatic rebuilding fails.

    Args:
        force: Whether to force the rebuilding of the type adapter's schema, defaults to `False`.
        raise_errors: Whether to raise errors, defaults to `True`.
        _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
        _types_namespace: The types namespace, defaults to `None`.

    Returns:
        Returns `None` if the schema is already "complete" and rebuilding was not required.
        If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
    """
    if not force and self.pydantic_complete:
        return None

    if _types_namespace is not None:
        rebuild_ns = _types_namespace
    elif _parent_namespace_depth > 0:
        rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
    else:
        rebuild_ns = {}

    # we have to manually fetch globals here because there's no type on the stack for the NsResolver
    # and so we skip the globalns = get_module_ns_of(typ) call that would normally happen
    globalns = sys._getframe(max(_parent_namespace_depth - 1, 1)).f_globals
    ns_resolver = _namespace_utils.NsResolver(
        namespaces_tuple=_namespace_utils.NamespacesTuple(locals=rebuild_ns, globals=globalns),
        parent_namespace=rebuild_ns,
    )
    return self._init_core_attrs(ns_resolver=ns_resolver, force=True, raise_errors=raise_errors)

validate_python

validate_python(
    object: Any,
    /,
    *,
    strict: bool | None = None,
    from_attributes: bool | None = None,
    context: dict[str, Any] | None = None,
    experimental_allow_partial: (
        bool | Literal["off", "on", "trailing-strings"]
    ) = False,
) -> T

Validate a Python object against the model.

Parameters:

Name	Type	Description	Default
object	Any	The Python object to validate against the model.	required
strict	bool | None	Whether to strictly check types.	None
from_attributes	bool | None	Whether to extract data from object attributes.	None
context	dict[str, Any] | None	Additional context to pass to the validator.	None
experimental_allow_partial	bool | Literal['off', 'on', 'trailing-strings']	Experimental whether to enable partial validation, e.g. to process streams. * False / 'off': Default behavior, no partial validation. * True / 'on': Enable partial validation. * 'trailing-strings': Enable partial validation and allow trailing strings in the input.	False

Note

When using TypeAdapter with a Pydantic dataclass, the use of the from_attributes argument is not supported.

Returns:

Type	Description
T	The validated object.

Source code in pydantic/type_adapter.py

def validate_python(
    self,
    object: Any,
    /,
    *,
    strict: bool | None = None,
    from_attributes: bool | None = None,
    context: dict[str, Any] | None = None,
    experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False,
) -> T:
    """Validate a Python object against the model.

    Args:
        object: The Python object to validate against the model.
        strict: Whether to strictly check types.
        from_attributes: Whether to extract data from object attributes.
        context: Additional context to pass to the validator.
        experimental_allow_partial: **Experimental** whether to enable
            [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.
            * False / 'off': Default behavior, no partial validation.
            * True / 'on': Enable partial validation.
            * 'trailing-strings': Enable partial validation and allow trailing strings in the input.

    !!! note
        When using `TypeAdapter` with a Pydantic `dataclass`, the use of the `from_attributes`
        argument is not supported.

    Returns:
        The validated object.
    """
    return self.validator.validate_python(
        object,
        strict=strict,
        from_attributes=from_attributes,
        context=context,
        allow_partial=experimental_allow_partial,
    )

validate_json

validate_json(
    data: str | bytes | bytearray,
    /,
    *,
    strict: bool | None = None,
    context: dict[str, Any] | None = None,
    experimental_allow_partial: (
        bool | Literal["off", "on", "trailing-strings"]
    ) = False,
) -> T

Usage Documentation

Json Parsing

Validate a JSON string or bytes against the model.

Parameters:

Name	Type	Description	Default
data	str | bytes | bytearray	The JSON data to validate against the model.	required
strict	bool | None	Whether to strictly check types.	None
context	dict[str, Any] | None	Additional context to use during validation.	None
experimental_allow_partial	bool | Literal['off', 'on', 'trailing-strings']	Experimental whether to enable partial validation, e.g. to process streams. * False / 'off': Default behavior, no partial validation. * True / 'on': Enable partial validation. * 'trailing-strings': Enable partial validation and allow trailing strings in the input.	False

Returns:

Type	Description
T	The validated object.

Source code in pydantic/type_adapter.py

def validate_json(
    self,
    data: str | bytes | bytearray,
    /,
    *,
    strict: bool | None = None,
    context: dict[str, Any] | None = None,
    experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False,
) -> T:
    """Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

    Validate a JSON string or bytes against the model.

    Args:
        data: The JSON data to validate against the model.
        strict: Whether to strictly check types.
        context: Additional context to use during validation.
        experimental_allow_partial: **Experimental** whether to enable
            [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.
            * False / 'off': Default behavior, no partial validation.
            * True / 'on': Enable partial validation.
            * 'trailing-strings': Enable partial validation and allow trailing strings in the input.

    Returns:
        The validated object.
    """
    return self.validator.validate_json(
        data, strict=strict, context=context, allow_partial=experimental_allow_partial
    )

validate_strings

validate_strings(
    obj: Any,
    /,
    *,
    strict: bool | None = None,
    context: dict[str, Any] | None = None,
    experimental_allow_partial: (
        bool | Literal["off", "on", "trailing-strings"]
    ) = False,
) -> T

Validate object contains string data against the model.

Parameters:

Name	Type	Description	Default
obj	Any	The object contains string data to validate.	required
strict	bool | None	Whether to strictly check types.	None
context	dict[str, Any] | None	Additional context to use during validation.	None
experimental_allow_partial	bool | Literal['off', 'on', 'trailing-strings']	Experimental whether to enable partial validation, e.g. to process streams. * False / 'off': Default behavior, no partial validation. * True / 'on': Enable partial validation. * 'trailing-strings': Enable partial validation and allow trailing strings in the input.	False

Returns:

Type	Description
T	The validated object.

Source code in pydantic/type_adapter.py

def validate_strings(
    self,
    obj: Any,
    /,
    *,
    strict: bool | None = None,
    context: dict[str, Any] | None = None,
    experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False,
) -> T:
    """Validate object contains string data against the model.

    Args:
        obj: The object contains string data to validate.
        strict: Whether to strictly check types.
        context: Additional context to use during validation.
        experimental_allow_partial: **Experimental** whether to enable
            [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams.
            * False / 'off': Default behavior, no partial validation.
            * True / 'on': Enable partial validation.
            * 'trailing-strings': Enable partial validation and allow trailing strings in the input.

    Returns:
        The validated object.
    """
    return self.validator.validate_strings(
        obj, strict=strict, context=context, allow_partial=experimental_allow_partial
    )

get_default_value

get_default_value(
    *,
    strict: bool | None = None,
    context: dict[str, Any] | None = None
) -> Some[T] | None

Get the default value for the wrapped type.

Parameters:

Name	Type	Description	Default
strict	bool | None	Whether to strictly check types.	None
context	dict[str, Any] | None	Additional context to pass to the validator.	None

Returns:

Type	Description
Some[T] | None	The default value wrapped in a Some if there is one or None if not.

Source code in pydantic/type_adapter.py

def get_default_value(self, *, strict: bool | None = None, context: dict[str, Any] | None = None) -> Some[T] | None:
    """Get the default value for the wrapped type.

    Args:
        strict: Whether to strictly check types.
        context: Additional context to pass to the validator.

    Returns:
        The default value wrapped in a `Some` if there is one or None if not.
    """
    return self.validator.get_default_value(strict=strict, context=context)

dump_python

dump_python(
    instance: T,
    /,
    *,
    mode: Literal["json", "python"] = "python",
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: (
        bool | Literal["none", "warn", "error"]
    ) = True,
    serialize_as_any: bool = False,
    context: dict[str, Any] | None = None,
) -> Any

Dump an instance of the adapted type to a Python object.

Parameters:

Name	Type	Description	Default
instance	T	The Python object to serialize.	required
mode	Literal['json', 'python']	The output format.	'python'
include	IncEx | None	Fields to include in the output.	None
exclude	IncEx | None	Fields to exclude from the output.	None
by_alias	bool	Whether to use alias names for field names.	False
exclude_unset	bool	Whether to exclude unset fields.	False
exclude_defaults	bool	Whether to exclude fields with default values.	False
exclude_none	bool	Whether to exclude fields with None values.	False
round_trip	bool	Whether to output the serialized data in a way that is compatible with deserialization.	False
warnings	bool | Literal['none', 'warn', 'error']	How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a PydanticSerializationError.	True
serialize_as_any	bool	Whether to serialize fields with duck-typing serialization behavior.	False
context	dict[str, Any] | None	Additional context to pass to the serializer.	None

Returns:

Type	Description
Any	The serialized object.

Source code in pydantic/type_adapter.py

def dump_python(
    self,
    instance: T,
    /,
    *,
    mode: Literal['json', 'python'] = 'python',
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    serialize_as_any: bool = False,
    context: dict[str, Any] | None = None,
) -> Any:
    """Dump an instance of the adapted type to a Python object.

    Args:
        instance: The Python object to serialize.
        mode: The output format.
        include: Fields to include in the output.
        exclude: Fields to exclude from the output.
        by_alias: Whether to use alias names for field names.
        exclude_unset: Whether to exclude unset fields.
        exclude_defaults: Whether to exclude fields with default values.
        exclude_none: Whether to exclude fields with None values.
        round_trip: Whether to output the serialized data in a way that is compatible with deserialization.
        warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
            "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
        serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
        context: Additional context to pass to the serializer.

    Returns:
        The serialized object.
    """
    return self.serializer.to_python(
        instance,
        mode=mode,
        by_alias=by_alias,
        include=include,
        exclude=exclude,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        round_trip=round_trip,
        warnings=warnings,
        serialize_as_any=serialize_as_any,
        context=context,
    )

dump_json

dump_json(
    instance: T,
    /,
    *,
    indent: int | None = None,
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: (
        bool | Literal["none", "warn", "error"]
    ) = True,
    serialize_as_any: bool = False,
    context: dict[str, Any] | None = None,
) -> bytes

Usage Documentation

JSON Serialization

Serialize an instance of the adapted type to JSON.

Parameters:

Name	Type	Description	Default
instance	T	The instance to be serialized.	required
indent	int | None	Number of spaces for JSON indentation.	None
include	IncEx | None	Fields to include.	None
exclude	IncEx | None	Fields to exclude.	None
by_alias	bool	Whether to use alias names for field names.	False
exclude_unset	bool	Whether to exclude unset fields.	False
exclude_defaults	bool	Whether to exclude fields with default values.	False
exclude_none	bool	Whether to exclude fields with a value of None.	False
round_trip	bool	Whether to serialize and deserialize the instance to ensure round-tripping.	False
warnings	bool | Literal['none', 'warn', 'error']	How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a PydanticSerializationError.	True
serialize_as_any	bool	Whether to serialize fields with duck-typing serialization behavior.	False
context	dict[str, Any] | None	Additional context to pass to the serializer.	None

Returns:

Type	Description
bytes	The JSON representation of the given instance as bytes.

Source code in pydantic/type_adapter.py

def dump_json(
    self,
    instance: T,
    /,
    *,
    indent: int | None = None,
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    serialize_as_any: bool = False,
    context: dict[str, Any] | None = None,
) -> bytes:
    """Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-serialization

    Serialize an instance of the adapted type to JSON.

    Args:
        instance: The instance to be serialized.
        indent: Number of spaces for JSON indentation.
        include: Fields to include.
        exclude: Fields to exclude.
        by_alias: Whether to use alias names for field names.
        exclude_unset: Whether to exclude unset fields.
        exclude_defaults: Whether to exclude fields with default values.
        exclude_none: Whether to exclude fields with a value of `None`.
        round_trip: Whether to serialize and deserialize the instance to ensure round-tripping.
        warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
            "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
        serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
        context: Additional context to pass to the serializer.

    Returns:
        The JSON representation of the given instance as bytes.
    """
    return self.serializer.to_json(
        instance,
        indent=indent,
        include=include,
        exclude=exclude,
        by_alias=by_alias,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        round_trip=round_trip,
        warnings=warnings,
        serialize_as_any=serialize_as_any,
        context=context,
    )

json_schema

json_schema(
    *,
    by_alias: bool = True,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[
        GenerateJsonSchema
    ] = GenerateJsonSchema,
    mode: JsonSchemaMode = "validation"
) -> dict[str, Any]

Generate a JSON schema for the adapted type.

Parameters:

Name	Type	Description	Default
by_alias	bool	Whether to use alias names for field names.	True
ref_template	str	The format string used for generating $ref strings.	DEFAULT_REF_TEMPLATE
schema_generator	type[GenerateJsonSchema]	The generator class used for creating the schema.	GenerateJsonSchema
mode	JsonSchemaMode	The mode to use for schema generation.	'validation'

Returns:

Type	Description
dict[str, Any]	The JSON schema for the model as a dictionary.

Source code in pydantic/type_adapter.py

def json_schema(
    self,
    *,
    by_alias: bool = True,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
    mode: JsonSchemaMode = 'validation',
) -> dict[str, Any]:
    """Generate a JSON schema for the adapted type.

    Args:
        by_alias: Whether to use alias names for field names.
        ref_template: The format string used for generating $ref strings.
        schema_generator: The generator class used for creating the schema.
        mode: The mode to use for schema generation.

    Returns:
        The JSON schema for the model as a dictionary.
    """
    schema_generator_instance = schema_generator(by_alias=by_alias, ref_template=ref_template)
    if isinstance(self.core_schema, _mock_val_ser.MockCoreSchema):
        self.core_schema.rebuild()
        assert not isinstance(self.core_schema, _mock_val_ser.MockCoreSchema), 'this is a bug! please report it'
    return schema_generator_instance.generate(self.core_schema, mode=mode)

json_schemas staticmethod

json_schemas(
    inputs: Iterable[
        tuple[
            JsonSchemaKeyT, JsonSchemaMode, TypeAdapter[Any]
        ]
    ],
    /,
    *,
    by_alias: bool = True,
    title: str | None = None,
    description: str | None = None,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[
        GenerateJsonSchema
    ] = GenerateJsonSchema,
) -> tuple[
    dict[
        tuple[JsonSchemaKeyT, JsonSchemaMode],
        JsonSchemaValue,
    ],
    JsonSchemaValue,
]

Generate a JSON schema including definitions from multiple type adapters.

Parameters:

Name	Type	Description	Default
inputs	Iterable[tuple[JsonSchemaKeyT, JsonSchemaMode, TypeAdapter[Any]]]	Inputs to schema generation. The first two items will form the keys of the (first) output mapping; the type adapters will provide the core schemas that get converted into definitions in the output JSON schema.	required
by_alias	bool	Whether to use alias names.	True
title	str | None	The title for the schema.	None
description	str | None	The description for the schema.	None
ref_template	str	The format string used for generating $ref strings.	DEFAULT_REF_TEMPLATE
schema_generator	type[GenerateJsonSchema]	The generator class used for creating the schema.	GenerateJsonSchema

Returns:

Type

Description

tuple[dict[tuple[JsonSchemaKeyT, JsonSchemaMode], JsonSchemaValue], JsonSchemaValue]

A tuple where: The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have JsonRef references to definitions that are defined in the second returned element.) The second element is a JSON schema containing all definitions referenced in the first returned element, along with the optional title and description keys.

Source code in pydantic/type_adapter.py

@staticmethod
def json_schemas(
    inputs: Iterable[tuple[JsonSchemaKeyT, JsonSchemaMode, TypeAdapter[Any]]],
    /,
    *,
    by_alias: bool = True,
    title: str | None = None,
    description: str | None = None,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
) -> tuple[dict[tuple[JsonSchemaKeyT, JsonSchemaMode], JsonSchemaValue], JsonSchemaValue]:
    """Generate a JSON schema including definitions from multiple type adapters.

    Args:
        inputs: Inputs to schema generation. The first two items will form the keys of the (first)
            output mapping; the type adapters will provide the core schemas that get converted into
            definitions in the output JSON schema.
        by_alias: Whether to use alias names.
        title: The title for the schema.
        description: The description for the schema.
        ref_template: The format string used for generating $ref strings.
        schema_generator: The generator class used for creating the schema.

    Returns:
        A tuple where:

            - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and
                whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have
                JsonRef references to definitions that are defined in the second returned element.)
            - The second element is a JSON schema containing all definitions referenced in the first returned
                element, along with the optional title and description keys.

    """
    schema_generator_instance = schema_generator(by_alias=by_alias, ref_template=ref_template)

    inputs_ = []
    for key, mode, adapter in inputs:
        # This is the same pattern we follow for model json schemas - we attempt a core schema rebuild if we detect a mock
        if isinstance(adapter.core_schema, _mock_val_ser.MockCoreSchema):
            adapter.core_schema.rebuild()
            assert not isinstance(
                adapter.core_schema, _mock_val_ser.MockCoreSchema
            ), 'this is a bug! please report it'
        inputs_.append((key, mode, adapter.core_schema))

    json_schemas_map, definitions = schema_generator_instance.generate_definitions(inputs_)

    json_schema: dict[str, Any] = {}
    if definitions:
        json_schema['$defs'] = definitions
    if title:
        json_schema['title'] = title
    if description:
        json_schema['description'] = description

    return json_schemas_map, json_schema