TypeAdapter
Bases: Generic[T]
Usage docs: https://docs.pydantic.dev/2.6/concepts/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.
Default: core_schema
Default: validator
Default: serializer
def __init__(
type: type[T],
config: ConfigDict | None = ...,
_parent_depth: int = ...,
module: str | None = ...,
) -> None
def __init__(
type: T,
config: ConfigDict | None = ...,
_parent_depth: int = ...,
module: str | None = ...,
) -> None
Initializes the TypeAdapter object.
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]
None — A type adapter configured for the specified type.
The type associated with the TypeAdapter.
Configuration for the TypeAdapter, should be a dictionary conforming to ConfigDict.
depth at which to search the parent namespace to construct the local namespace.
The module that passes to plugin if provided.
def 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.
T — The validated object.
The Python object to validate against the model.
Whether to strictly check types.
Whether to extract data from object attributes.
Additional context to pass to the validator.
def validate_json(
__data: str | bytes,
strict: bool | None = None,
context: dict[str, Any] | None = None,
) -> T
Usage docs: https://docs.pydantic.dev/2.6/concepts/json/#json-parsing
Validate a JSON string or bytes against the model.
T — The validated object.
The JSON data to validate against the model.
Whether to strictly check types.
Additional context to use during validation.
def validate_strings(
__obj: Any,
strict: bool | None = None,
context: dict[str, Any] | None = None,
) -> T
Validate object contains string data against the model.
T — The validated object.
The object contains string data to validate.
Whether to strictly check types.
Additional context to use during validation.
def get_default_value(
strict: bool | None = None,
context: dict[str, Any] | None = None,
) -> Some[T] | None
Get the default value for the wrapped type.
Some[T] | None — The default value wrapped in a Some if there is one or None if not.
Whether to strictly check types.
Additional context to pass to the validator.
def 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.
Any — The serialized object.
The Python object to serialize.
The output format.
Fields to include in the output.
Fields to exclude from the output.
Whether to use alias names for field names.
Whether to exclude unset fields.
Whether to exclude fields with default values.
Whether to exclude fields with None values.
Whether to output the serialized data in a way that is compatible with deserialization.
Whether to display serialization warnings.
def 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 docs: https://docs.pydantic.dev/2.6/concepts/json/#json-serialization
Serialize an instance of the adapted type to JSON.
bytes — The JSON representation of the given instance as bytes.
The instance to be serialized.
Number of spaces for JSON indentation.
Fields to include.
Fields to exclude.
Whether to use alias names for field names.
Whether to exclude unset fields.
Whether to exclude fields with default values.
Whether to exclude fields with a value of None.
Whether to serialize and deserialize the instance to ensure round-tripping.
Whether to emit serialization warnings.
def 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.
dict[str, Any] — The JSON schema for the model as a dictionary.
Whether to use alias names for field names.
The format string used for generating $ref strings.
The generator class used for creating the schema.
The mode to use for schema generation.
@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.
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.
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.
Whether to use alias names.
The title for the schema.
The description for the schema.
The format string used for generating $ref strings.
The generator class used for creating the schema.