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♻️ refactor(core): 重构 AI 编排框架与记忆及 RAG 子系统 (#2149)
* ♻️ refactor(core): 重构 AI 编排框架与记忆及 RAG 子系统 - 【重构】重构 `BaseRunnable` 并引入统一的 `RunIntent` 意图载体，规范 Agent、Team 和 Workflow 的执行流 - 【解耦】将中期记忆槽和长期向量记忆从 `MemoryConfig` 中解耦，转为独立的能力组件与工具箱进行管理 - 【记忆】移除 `MemoryReader` 和 `MemoryWriter`，统一封装为 `SessionMemoryContext` 会话记忆门面 - 【RAG】重构检索器与存储后端接口，统一采用 `QueryRequest` 进行多维度联合检索，并引入 `InMemoryScorer` 提升打分性能 - 【事件】优化 `EventBus` 异步事件分发机制，引入队列机制确保事件按序处理，避免并发竞态问题 - 【依赖注入】移除 `memory` 注入项，优化 `DependencyInjector` 的签名解析缓存以提升性能 * 🚨 auto fix by pre-commit hooks --------- Co-authored-by: webjoin111 <455457521@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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webjoin111
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webjoin111
pre-commit-ci[bot]
parent
922d092650
commit
52f7dbdedf
@@ -6,7 +6,6 @@ from typing import Any, Literal
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from pydantic import Field, create_model
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from zhenxun.services.ai.context.memory.manager import memory_manager
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from zhenxun.services.ai.context.memory.models import MemoryConfig
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from zhenxun.services.ai.context.memory.types import (
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MemorySlot,
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SessionMetadata,
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@@ -16,6 +15,7 @@ from zhenxun.services.ai.tools.core.decorators import tool
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from zhenxun.services.ai.tools.core.tool import BaseTool
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from zhenxun.services.ai.tools.core.toolkit import BaseToolkit
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from zhenxun.services.ai.tools.models import ToolOptions, ToolResult
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from zhenxun.services.ai.utils.runtime import ContextUtils
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_SLOT_LOCKS: dict[str, asyncio.Lock] = {}
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_GLOBAL_LOCK = asyncio.Lock()
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@@ -45,119 +45,78 @@ class MemorySlotToolkit(BaseToolkit):
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shared_options = ToolOptions(silent=True)
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default_instructions = """\
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## 📋 状态与规则面板 (Memory Slots / 中期记忆)
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该系统是你的「桌面便利贴」或「共享黑板」，用于保存你当前需要随时查阅的核心状态与全局规范。
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_INTRO_TEXT = (
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"## 📋 状态与规则面板 (Memory Slots / 中期记忆)\n"
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"该系统是你的「桌面便利贴」或「共享黑板」，用于保存你当前需要随时查阅的核心状态与全局规范。\n\n"
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"### 💡 核心机制\n"
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"- 记忆槽内容会在每次对话时**直接注入提示词中**，你无需搜索即可看见。\n"
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"- 槽位容量极其有限，仅用于维持最新的运行状态。\n\n"
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"### 📝 职责说明\n"
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)
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_READ_GUIDE = (
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"- **探索可用面板**：接手新任务时，可使用 `list_slots` "
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"宏观查看当前存在哪些面板。\n"
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)
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_WRITE_GUIDE = (
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"- **维护规范与进度**：如设定'沟通口吻'等规范（`update_slot`），"
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"或记录'待办清单'（`append_slot`）。\n"
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"- **保持精简**：内容过长时，主动将其归档到长期记忆，"
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"再重新提炼或调用 `delete_slot` 删除。\n"
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)
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### 💡 核心机制
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- 被保存在记忆槽中的内容（如果已置顶），会在每次对话时**直接注入到你的上下文提示词中**，你无需任何搜索即可看见。
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- 槽位容量极其有限，仅用于维持当前最新的运行状态。
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default_instructions = _INTRO_TEXT + _READ_GUIDE + _WRITE_GUIDE
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### 📝 何时使用记忆槽？
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- **维护全局规则**：例如设定"用户整体偏好"、"沟通口吻"、"全局指导原则"等需要时刻遵守的规范（使用 `update_slot`）。
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- **追踪当前进度**：例如记录"待办事项清单"、"当前任务进度"、"上下文摘要"（使用 `append_slot` 列表或 `update_slot` 覆盖）。
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@classmethod
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def read_only(cls, **kwargs) -> "MemorySlotToolkit":
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"""[工厂方法] 创建一个只读模式的记忆槽工具箱。"""
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kwargs["include"] = ["list_slots", "read_slot"]
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kwargs.setdefault("instructions", cls._INTRO_TEXT + cls._READ_GUIDE)
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return cls(**kwargs)
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### ⚙️ 操作规范
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1. **探索可用面板**：接手新任务时，可使用 `list_slots` 宏观查看当前存在哪些状态面板。
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2. **保持精简**：槽位有严格的字符数限制。当内容过长时，请主动将其归档到长期记忆后，重新提炼并覆盖槽位，或直接调用 `delete_slot` 删除不再需要的槽位。\
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""" # noqa: E501
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@classmethod
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def write_only(cls, **kwargs) -> "MemorySlotToolkit":
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"""[工厂方法] 创建一个仅写入模式的记忆槽工具箱。"""
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kwargs["exclude"] = ["list_slots", "read_slot"]
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kwargs.setdefault("instructions", cls._INTRO_TEXT + cls._WRITE_GUIDE)
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return cls(**kwargs)
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def __init__(
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self,
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memory_config: MemoryConfig | None = None,
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scopes: dict[str, Any] | None = None,
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backend: Any = None,
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namespace: str | None = None,
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**kwargs: Any,
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):
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"""
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初始化中期记忆槽工具箱。
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参数：
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memory_config: 记忆系统的全局配置对象，为空则使用全局默认。
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namespace: 当前隔离环境的命名空间。
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kwargs: 其他透传给 BaseToolkit 的参数。
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"""
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super().__init__(**kwargs)
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self.memory_config = memory_config
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self.scopes = scopes or {}
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self.backend = backend
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self._namespace = namespace
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def _get_runtime_meta_and_ctx(
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self, context: RunContext, scope_name: str | None = None
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) -> tuple[Any, SessionMetadata]:
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ns = self._namespace or getattr(context.session, "namespace", "global")
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slot_ctx = memory_manager.get_slot_context(self.memory_config, namespace=ns)
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slot_ctx = self.backend or memory_manager.get_backend("slots", namespace=ns)
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scope_builder = None
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if (
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self.memory_config
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and self.memory_config.slots
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and self.memory_config.slots.scopes
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):
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scopes_dict = self.memory_config.slots.scopes
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if not scope_name:
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scope_builder = next(iter(scopes_dict.values()))
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else:
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scope_builder = scopes_dict.get(scope_name)
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if not scope_builder:
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scope_builder = getattr(self.memory_config, "base_isolation", None)
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if not scope_builder:
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from zhenxun.services.ai.context.memory.types import Isolation
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scope_builder = Isolation.AGENT_USER()
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selector = scope_builder.resolve(
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deps=context.deps,
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prefix="",
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default_namespace=ns,
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default_agent=context.run.agent_name,
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scope_builder = (
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self.scopes.get(scope_name)
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if scope_name
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else next(iter(self.scopes.values()), None)
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)
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parts = selector.get_scope_parts()
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all_scopes = {"/"}
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current_path = ""
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for part in parts:
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current_path += f"/{part}"
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all_scopes.add(current_path)
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accessible_scopes = list(all_scopes)
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accessible_scopes.sort(key=lambda x: len(x.split("/")))
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scope_name_mapping = {}
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if (
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self.memory_config
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and self.memory_config.slots
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and self.memory_config.slots.scopes
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):
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for name, builder in self.memory_config.slots.scopes.items():
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sel = builder.resolve(
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deps=context.deps,
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prefix="",
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default_namespace=ns,
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default_agent=context.run.agent_name,
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)
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scope_name_mapping[sel.scope_prefix] = name
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session_meta = SessionMetadata(
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session_id=context.session_id or "default_session",
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selector=selector,
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scope_prefix=selector.scope_prefix,
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accessible_scopes=accessible_scopes,
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scope_name_mapping=scope_name_mapping,
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session_meta = ContextUtils.build_session_meta(
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context=context,
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target_builder=scope_builder,
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extra_scopes=self.scopes,
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custom_namespace=self._namespace,
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)
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return slot_ctx, session_meta
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async def get_tools(self, context: RunContext | None = None) -> dict[str, BaseTool]:
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tools = await super().get_tools(context)
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if (
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not self.memory_config
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or not self.memory_config.slots
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or not self.memory_config.slots.enable
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):
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if not self.scopes:
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return tools
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scopes_dict = self.memory_config.slots.scopes
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if not scopes_dict:
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return tools
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scope_keys = tuple(scopes_dict.keys())
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scope_keys = tuple(self.scopes.keys())
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if len(scope_keys) > 1:
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ScopeType = Literal[scope_keys]
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@@ -237,12 +196,7 @@ class MemorySlotToolkit(BaseToolkit):
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res = ["已创建的记忆槽列表："]
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show_scope = False
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if (
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self.memory_config
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and self.memory_config.slots
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and self.memory_config.slots.scopes
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and len(self.memory_config.slots.scopes) > 1
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):
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if len(self.scopes) > 1:
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show_scope = True
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for s in slots:
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