Type Adapter

You may have types that are not BaseModels that you want to validate data against. Or you may want to validate a List[SomeModel], or dump it to JSON.

API Documentation

pydantic.type_adapter.TypeAdapter

For use cases like this, Pydantic provides TypeAdapter, which can be used for type validation, serialization, and JSON schema generation without needing to create a BaseModel.

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):

Python 3.8 and abovePython 3.9 and above

from typing import List

from typing_extensions import TypedDict

from pydantic import TypeAdapter, ValidationError


class User(TypedDict):
    name: str
    id: int


user_list_adapter = TypeAdapter(List[User])
user_list = user_list_adapter.validate_python([{'name': 'Fred', 'id': '3'}])
print(repr(user_list))
#> [{'name': 'Fred', 'id': 3}]

try:
    user_list_adapter.validate_python(
        [{'name': 'Fred', 'id': 'wrong', 'other': 'no'}]
    )
except ValidationError as e:
    print(e)
    """
    1 validation error for list[typed-dict]
    0.id
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='wrong', input_type=str]
    """

print(repr(user_list_adapter.dump_json(user_list)))
#> b'[{"name":"Fred","id":3}]'

from typing_extensions import TypedDict

from pydantic import TypeAdapter, ValidationError


class User(TypedDict):
    name: str
    id: int


user_list_adapter = TypeAdapter(list[User])
user_list = user_list_adapter.validate_python([{'name': 'Fred', 'id': '3'}])
print(repr(user_list))
#> [{'name': 'Fred', 'id': 3}]

try:
    user_list_adapter.validate_python(
        [{'name': 'Fred', 'id': 'wrong', 'other': 'no'}]
    )
except ValidationError as e:
    print(e)
    """
    1 validation error for list[typed-dict]
    0.id
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='wrong', input_type=str]
    """

print(repr(user_list_adapter.dump_json(user_list)))
#> b'[{"name":"Fred","id":3}]'

dump_json returns bytes

TypeAdapter's dump_json methods returns a bytes object, unlike the corresponding method for BaseModel, model_dump_json, which returns a str. The reason for this discrepancy is that in V1, model dumping returned a str type, so this behavior is retained in V2 for backwards compatibility. For the BaseModel case, bytes are coerced to str types, but bytes are often the desired end type. Hence, for the new TypeAdapter class in V2, the return type is simply bytes, which can easily be coerced to a str type if desired.

Note

Despite some overlap in use cases with RootModel, TypeAdapter should not be used as a type annotation for specifying fields of a BaseModel, etc.

Parsing data into a specified type¶

TypeAdapter can be used to apply the parsing logic to populate Pydantic models in a more ad-hoc way. This function behaves similarly to BaseModel.model_validate, but works with arbitrary Pydantic-compatible types.

This is especially useful when you want to parse results into a type that is not a direct subclass of BaseModel. For example:

Python 3.8 and abovePython 3.9 and above

from typing import List

from pydantic import BaseModel, TypeAdapter


class Item(BaseModel):
    id: int
    name: str


# `item_data` could come from an API call, eg., via something like:
# item_data = requests.get('https://my-api.com/items').json()
item_data = [{'id': 1, 'name': 'My Item'}]

items = TypeAdapter(List[Item]).validate_python(item_data)
print(items)
#> [Item(id=1, name='My Item')]

from pydantic import BaseModel, TypeAdapter


class Item(BaseModel):
    id: int
    name: str


# `item_data` could come from an API call, eg., via something like:
# item_data = requests.get('https://my-api.com/items').json()
item_data = [{'id': 1, 'name': 'My Item'}]

items = TypeAdapter(list[Item]).validate_python(item_data)
print(items)
#> [Item(id=1, name='My Item')]

TypeAdapter is capable of parsing data into any of the types Pydantic can handle as fields of a BaseModel.

Performance considerations

When creating an instance of TypeAdapter, the provided type must be analyzed and converted into a pydantic-core schema. This comes with some non-trivial overhead, so it is recommended to create a TypeAdapter for a given type just once and reuse it in loops or other performance-critical code.

Rebuilding a `TypeAdapter`'s schema¶

In v2.10+, TypeAdapter's support deferred schema building and manual rebuilds. This is helpful for the case of:

Types with forward references
Types for which core schema builds are expensive

When you initialize a TypeAdapter with a type, Pydantic analyzes the type and creates a core schema for it. This core schema contains the information needed to validate and serialize data for that type. See the architecture documentation for more information on core schemas.

If you set defer_build to True when initializing a TypeAdapter, Pydantic will defer building the core schema until the first time it is needed (for validation or serialization).

In order to manually trigger the building of the core schema, you can call the rebuild method on the TypeAdapter instance:

from pydantic import ConfigDict, TypeAdapter

ta = TypeAdapter('MyInt', config=ConfigDict(defer_build=True))

# some time later, the forward reference is defined
MyInt = int

ta.rebuild()
assert ta.validate_python(1) == 1

Type Adapter

Parsing data into a specified type¶

Rebuilding a TypeAdapter's schema¶

Rebuilding a `TypeAdapter`'s schema¶