from typing import List, Optional from pydantic import BaseModel class Foo(BaseModel): count: int size: Optional[float] = None class Bar(BaseModel): apple: str = 'x' banana: str = 'y' class Spam(BaseModel): foo: Foo bars: List[Bar] m = Spam(foo={'count': 4}, bars=[{'apple...
#! -*-conding: UTF-8 -*-# @公众号: 海哥pythonfromenumimportEnumfromdatetimeimportdatetimefromtypingimportList,OptionalfrompydanticimportBaseModel,ValidationError,EmailStr# 导入pydantic对应的模型基类frompydanticimportconstr,conintclassGenderEnum(str,Enum):"""性别枚举"""male="男"female="女"classUser(B...
age=22, email="alice@example.com") data = User.model_dump(user) print(data) print(type(data...
class SimpleModelDumpable(BaseModel): password: SecretStr password_bytes: SecretBytes class Config: json_encoders = { SecretStr: lambda v: v.get_secret_value() if v else None, SecretBytes: lambda v: v.get_secret_value() if v else None, } sm2 = SimpleModelDumpable( password='IAmSensi...
@mathiasertl the combination of the two approaches would be great: Celery serializer should just .model_dump() all pydantic models and the wrapper should based on the typehint execute a model_validate for pydantic models. ManuelAngel99 commented on May 9, 2024 ManuelAngel99 on May 9, 2024 ...
Fix broken parametrized bases handling with GenericModels with complex sets of models, #5052 by @MarkusSintonen Invalidate mypy cache if plugin config changes, #5007 by @cdce8p Fix RecursionError when deep-copying dataclass types wrapped by pydantic, #4949 by @mbillingr Fix X | Y union synta...
You can also serialize Pydantic models as dictionaries and JSON: Python >>> new_employee.model_dump() { 'employee_id': UUID('d2e7b773-926b-49df-939a-5e98cbb9c9eb'), 'name': 'Eric Slogrenta', 'email': 'eslogrenta@example.com', 'date_of_birth': datetime.date(1990, 1, 2),...
success_status_code_by_response_model: Dict[Type[BaseModel], int] = None - If the return type of the method is a Union of multiple BaseModels, this dict can map those models to specific status codes merge_path_parameters: bool = False - See Path Parameter Folding request_fields_name: ...
使用[递归pydantic模型](# 3.1.2 递归模型)、typing 的标准类型 (如 List、Tuple 和 Dict 等) 和验证器,可以很清晰且容易地定义、验证和解析复杂数据模式。 可拓展 pydantic允许定义[自定义数据类型](# 3.2.7 自定义数据类型),或者您可以使用被 validator 装饰器装饰的模型上的方法来扩展验证。
执行查询,并将响应格式化为 JSON 字符串输出。model_dump_json(indent=2)方法将 Pydantic 模型转换为易于阅读的 JSON 格式。 输出将是结构化的,例如: {"response":"您可以通过以下方式追踪您的订单 #12345...","needs_escalation":false,"follow_up_required":true,"sentiment":"neutral"} ...