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Showing content from https://docs.pydantic.dev/latest/api/type_adapter/ below:

TypeAdapter - Pydantic

TypeAdapter

Bases: Generic[T]

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.

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.

Note

You cannot provide a configuration 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.

None _parent_depth int

Depth at which to search for the parent frame. This frame is used when resolving forward annotations during schema building, by looking for the globals and locals of this frame. Defaults to 2, which will result in the frame where the TypeAdapter was instantiated.

Note

This parameter 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. It's default value is 2 because internally, the TypeAdapter class makes another call to fetch the frame.

2 module str | None

The module that passes to plugin if provided.

None

Attributes:

Name Type Description core_schema CoreSchema

The core schema for the type.

validator SchemaValidator | PluggableSchemaValidator

The schema validator for the type.

serializer SchemaSerializer

The schema serializer for the type.

pydantic_complete bool

Whether the core schema for the type is successfully built.

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]

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 the source code for 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

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.

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:
    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.pydantic_complete = False

    parent_frame = self._fetch_parent_frame()
    if parent_frame is not None:
        globalns = parent_frame.f_globals
        # Do not provide a local ns if the type adapter happens to be instantiated at the module level:
        localns = parent_frame.f_locals if parent_frame.f_locals is not globalns else {}
    else:
        globalns = {}
        localns = {}

    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

Depth at which to search for the parent frame. This frame is used when resolving forward annotations during schema rebuilding, by looking for the locals of this frame. Defaults to 2, which will result in the frame where the method was called.

2 _types_namespace MappingNamespace | None

An explicit types namespace to use, instead of using the local namespace from the parent frame. 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: Depth at which to search for the [parent frame][frame-objects]. This
            frame is used when resolving forward annotations during schema rebuilding, by looking for
            the locals of this frame. Defaults to 2, which will result in the frame where the method
            was called.
        _types_namespace: An explicit types namespace to use, instead of using the local namespace
            from the parent frame. 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 of 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,
    by_alias: bool | None = None,
    by_name: bool | None = None,
) -> 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 by_alias bool | None

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

None by_name bool | None

Whether to use the field's name when validating against the provided input data.

None

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,
    by_alias: bool | None = None,
    by_name: bool | None = None,
) -> 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.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

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

    Returns:
        The validated object.
    """
    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return self.validator.validate_python(
        object,
        strict=strict,
        from_attributes=from_attributes,
        context=context,
        allow_partial=experimental_allow_partial,
        by_alias=by_alias,
        by_name=by_name,
    )

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,
    by_alias: bool | None = None,
    by_name: bool | None = None,
) -> T

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']

False by_alias bool | None

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

None by_name bool | None

Whether to use the field's name when validating against the provided input data.

None

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,
    by_alias: bool | None = None,
    by_name: bool | None = None,
) -> T:
    """!!! abstract "Usage Documentation"
        [JSON Parsing](../concepts/json.md#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.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Returns:
        The validated object.
    """
    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return self.validator.validate_json(
        data,
        strict=strict,
        context=context,
        allow_partial=experimental_allow_partial,
        by_alias=by_alias,
        by_name=by_name,
    )

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,
    by_alias: bool | None = None,
    by_name: bool | None = None,
) -> 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']

False by_alias bool | None

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

None by_name bool | None

Whether to use the field's name when validating against the provided input data.

None

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,
    by_alias: bool | None = None,
    by_name: bool | None = None,
) -> 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.
        by_alias: Whether to use the field's alias when validating against the provided input data.
        by_name: Whether to use the field's name when validating against the provided input data.

    Returns:
        The validated object.
    """
    if by_alias is False and by_name is not True:
        raise PydanticUserError(
            'At least one of `by_alias` or `by_name` must be set to True.',
            code='validate-by-alias-and-name-false',
        )

    return self.validator.validate_strings(
        obj,
        strict=strict,
        context=context,
        allow_partial=experimental_allow_partial,
        by_alias=by_alias,
        by_name=by_name,
    )

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 | None = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: (
        bool | Literal["none", "warn", "error"]
    ) = True,
    fallback: Callable[[Any], Any] | None = None,
    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 | None

Whether to use alias names for field names.

None 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 fallback Callable[[Any], Any] | None

A function to call when an unknown value is encountered. If not provided, a PydanticSerializationError error is raised.

None 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 | None = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    fallback: Callable[[Any], Any] | None = None,
    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].
        fallback: A function to call when an unknown value is encountered. If not provided,
            a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
        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,
        fallback=fallback,
        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 | None = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: (
        bool | Literal["none", "warn", "error"]
    ) = True,
    fallback: Callable[[Any], Any] | None = None,
    serialize_as_any: bool = False,
    context: dict[str, Any] | None = None,
) -> bytes

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 | None

Whether to use alias names for field names.

None 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 fallback Callable[[Any], Any] | None

A function to call when an unknown value is encountered. If not provided, a PydanticSerializationError error is raised.

None 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 | None = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: bool | Literal['none', 'warn', 'error'] = True,
    fallback: Callable[[Any], Any] | None = None,
    serialize_as_any: bool = False,
    context: dict[str, Any] | None = None,
) -> bytes:
    """!!! abstract "Usage Documentation"
        [JSON Serialization](../concepts/json.md#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].
        fallback: A function to call when an unknown value is encountered. If not provided,
            a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
        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,
        fallback=fallback,
        serialize_as_any=serialize_as_any,
        context=context,
    )

json_schema ¶

Generate a JSON schema for the adapted type.

Parameters:

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

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