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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

The core schema for the type.

validator SchemaValidator

The schema validator for the type.

serializer

The schema serializer for the type.

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 parent namespace to construct the local namespace.

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
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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 parent namespace to construct the local namespace.
        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:

        ```py
        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`.
    """
    type_is_annotated: bool = _typing_extra.is_annotated(type)
    annotated_type: Any = get_args(type)[0] if type_is_annotated else None
    type_has_config: bool = _type_has_config(annotated_type if type_is_annotated else type)

    if type_has_config 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',
        )

    config_wrapper = _config.ConfigWrapper(config)

    core_schema: CoreSchema
    try:
        core_schema = _getattr_no_parents(type, '__pydantic_core_schema__')
    except AttributeError:
        core_schema = _get_schema(type, config_wrapper, parent_depth=_parent_depth + 1)

    core_config = config_wrapper.core_config(None)
    validator: SchemaValidator
    try:
        validator = _getattr_no_parents(type, '__pydantic_validator__')
    except AttributeError:
        if module is None:
            f = sys._getframe(1)
            module = cast(str, f.f_globals.get('__name__', ''))
        validator = create_schema_validator(
            core_schema, type, module, str(type), 'TypeAdapter', core_config, config_wrapper.plugin_settings
        )  # type: ignore

    serializer: SchemaSerializer
    try:
        serializer = _getattr_no_parents(type, '__pydantic_serializer__')
    except AttributeError:
        serializer = SchemaSerializer(core_schema, core_config)

    self.core_schema = core_schema
    self.validator = validator
    self.serializer = serializer

validate_python

validate_python(
    __object: Any,
    *,
    strict: bool | None = None,
    from_attributes: bool | None = None,
    context: dict[str, Any] | 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

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
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def validate_python(
    self,
    __object: Any,
    *,
    strict: bool | None = None,
    from_attributes: bool | None = None,
    context: dict[str, Any] | 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.

    !!! 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)

validate_json

validate_json(
    __data: str | bytes,
    *,
    strict: bool | None = None,
    context: dict[str, Any] | None = None
) -> T

Usage Documentation

Json Parsing

Validate a JSON string or bytes against the model.

Parameters:

Name Type Description Default
__data str | bytes

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

Returns:

Type Description
T

The validated object.

Source code in pydantic/type_adapter.py
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def validate_json(
    self, __data: str | bytes, *, strict: bool | None = None, context: dict[str, Any] | None = None
) -> T:
    """Usage docs: https://docs.pydantic.dev/2.7/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.

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

validate_strings

validate_strings(
    __obj: Any,
    *,
    strict: bool | None = None,
    context: dict[str, Any] | 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

Returns:

Type Description
T

The validated object.

Source code in pydantic/type_adapter.py
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def validate_strings(self, __obj: Any, *, strict: bool | None = None, context: dict[str, Any] | 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.

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

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
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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 = True
) -> 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

Whether to display serialization warnings.

True

Returns:

Type Description
Any

The serialized object.

Source code in pydantic/type_adapter.py
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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 = True,
) -> 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: Whether to display serialization warnings.

    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,
    )

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 = True
) -> 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

Whether to emit serialization warnings.

True

Returns:

Type Description
bytes

The JSON representation of the given instance as bytes.

Source code in pydantic/type_adapter.py
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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 = True,
) -> bytes:
    """Usage docs: https://docs.pydantic.dev/2.7/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: Whether to emit serialization warnings.

    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,
    )

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
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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)
    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
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@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 = [(key, mode, adapter.core_schema) for key, mode, adapter in __inputs]

    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