zhaoxi
d30c7e37a6
##前端美化和bug修复 #### 💄 美化 - **前端美化**：对于整个前端效果进行了重新设计，现在的前端看起来会更立体。 #### 🐛 修复 - **前端演示**：修复了前端展示workflow列表的bug，但是workflow的具体条目显示由于序列化导致仍然有问题。 - **密钥修复**：对于secret_key现在在使用默认情况时，会强制生成一个安全的密钥。
41 lines
1.7 KiB
Python
41 lines
1.7 KiB
Python
# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Type, TypeVar
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from pydantic import BaseModel
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T = TypeVar("T", bound=Type[BaseModel])
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def pickle(cls: T) -> T:
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"""
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类装饰器pickle
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通过装饰继承了BaseModel的类，用pydantic的高效序列化替代python原生__reduce__魔术方法,实现ray在通讯时的高效序列化
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Args:
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cls: 继承了BaseModel类的类，需要被装饰的对象
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Returns:
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返回被重写了__reduce__魔术方法的cls类
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"""
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def __reduce__(self):
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# 1. 序列化：触发 Pydantic-core (Rust) 的极速序列化
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"""执行与 reduce 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
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Returns: : 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。 """
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data = self.model_dump_json()
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# 2. 反序列化：告诉 Pickle 重建时调用 cls.model_validate_json
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return cls.model_validate_json, (data,)
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cls.__reduce__ = __reduce__
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return cls
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