python type_error ai_generated true

pydantic.errors.PydanticSchemaGenerationError: Unable to generate pydantic-core schema for <class 'decimal.Decimal'>. Set `arbitrary_types_allowed=True` in the model_config to ignore this error or implement `__get_pydantic_core_schema__` on your type to fully support it.

ID: python/pydantic-arbitrary-types-schema-generation

Also available as: JSON · Markdown · 中文
80%Fix Rate
89%Confidence
0Evidence
2024-02-08First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
2.x active — — —

Root Cause

The field type is a custom class (or a third-party type) that Pydantic v2 does not know how to validate, and no core schema has been registered.

generic

中文

字段类型是 Pydantic v2 无法识别的自定义类或第三方类型，且未注册对应的 core schema。

Workarounds

  1. 95% success 
    from pydantic import BaseModel, ConfigDict
class Foo(BaseModel):
    model_config = ConfigDict(arbitrary_types_allowed=True)
    value: MyCustomClass
  2. 90% success 
    from pydantic import GetCoreSchemaHandler
from pydantic_core import core_schema
class MyCustomClass:
    @classmethod
    def __get_pydantic_core_schema__(cls, source, handler):
        return core_schema.no_info_plain_validator_function(cls.validate)
  3. 85% success 
    from typing import Annotated
from pydantic import BeforeValidator
MyType = Annotated[decimal.Decimal, BeforeValidator(lambda v: decimal.Decimal(str(v)))]

Dead Ends

Common approaches that don't work:

  1. 55% fail

    Pydantic v2 uses model_config = ConfigDict(...); the inner Config class is deprecated and ignored in strict v2 builds.

  2. 70% fail

    Disables validation entirely, so invalid instances pass silently and errors surface later in business logic.

  3. 85% fail

    NewType is a static-typing alias; at runtime it is just the underlying type and does not register a schema.