TypeAdapter
Bases: Generic[T]
Usage Documentation
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.
Note: By default, TypeAdapter
does not respect the
defer_build=True
setting in the
model_config
or in the TypeAdapter
constructor config
. You need to also
explicitly set experimental_defer_build_mode=('model', 'type_adapter')
of the
config to defer the model validator and serializer construction. Thus, this feature is opt-in to ensure backwards
compatibility.
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. |
Parameters:
Name | Type | Description | Default |
---|---|---|---|
type |
Any
|
The type associated with the |
required |
config |
ConfigDict | None
|
Configuration for the |
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 |
Source code in pydantic/type_adapter.py
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|
core_schema
cached
property
¶
core_schema: CoreSchema
The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
validator
cached
property
¶
validator: SchemaValidator | PluggableSchemaValidator
The pydantic-core SchemaValidator used to validate instances of the model.
serializer
cached
property
¶
serializer: SchemaSerializer
The pydantic-core SchemaSerializer used to dump instances of the model.
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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|
validate_json ¶
validate_json(
data: str | bytes,
/,
*,
strict: bool | None = None,
context: dict[str, Any] | None = None,
) -> T
Usage Documentation
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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|
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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|
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 |
Source code in pydantic/type_adapter.py
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|
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 |
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
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|
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
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 |
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 |
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
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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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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:
|
Source code in pydantic/type_adapter.py
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|