♻️ 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>

zhenxun/services/ai/context/memory/__init__.py

@@ -1,9 +1,7 @@
 from .builder import MemoryBuilder
 from .compression import MemoryPolicy
-from .facades import AgentSessionFacade
 from .manager import memory_manager
 from .models import (
-    BaseMemoryIngestionMiddleware,
     MemoryConfig,
 )
 from .types import (
@@ -12,8 +10,6 @@ from .types import (
 )
 
 __all__ = [
-    "AgentSessionFacade",
-    "BaseMemoryIngestionMiddleware",
     "Isolation",
     "MemoryBuilder",
     "MemoryConfig",

zhenxun/services/ai/context/memory/builder.py

@@ -6,27 +6,19 @@ from typing_extensions import Self
 
 from pydantic import BaseModel
 
-from zhenxun.services.ai.context.memory.compression import MemoryPolicy
-from zhenxun.services.ai.context.memory.models import (
+from zhenxun.services.ai.utils.scope import ScopeBuilder
+
+from .compression import MemoryPolicy
+from .models import (
     ContextCompressionConfig,
     IngestionConfig,
-    LongTermConfig,
     MemoryConfig,
-    MemorySlot,
     ShortTermConfig,
-    SlotMemoryConfig,
 )
-from zhenxun.services.ai.context.memory.storage.interfaces import (
+from .storage.interfaces import (
     BaseChatContext,
     BaseMemoryIngestionMiddleware,
-    BaseSlotContext,
 )
-from zhenxun.services.ai.context.memory.types import (
-    AutoRecallPolicy,
-)
-from zhenxun.services.ai.context.rag.backends import Embedder, StorageBackend
-from zhenxun.services.ai.context.rag.engine import ScopedRAGClient
-from zhenxun.services.ai.utils.scope import ScopeBuilder
 
 
 class MemoryBuilder:
@@ -42,8 +34,6 @@ class MemoryBuilder:
         """
         self._config = MemoryConfig(
             short_term=ShortTermConfig(enable=False),
-            slots=SlotMemoryConfig(enable=False),
-            long_term=LongTermConfig(enable=False),
             compression=ContextCompressionConfig(),
             ingestion=IngestionConfig(),
         )
@@ -68,17 +58,10 @@ class MemoryBuilder:
         if isinstance(memory, bool):
             return MemoryConfig(
                 short_term=ShortTermConfig(enable=memory),
-                long_term=LongTermConfig(enable=memory),
             )
 
         return MemoryConfig(short_term=ShortTermConfig(enable=False))
 
-    def with_base_isolation(self, isolation: ScopeBuilder) -> Self:
-        """设置顶层基准隔离级别，短期/中期/长期记忆将默认继承此级别"""
-        self._config.base_isolation = isolation
-        self._config.short_term.isolation = isolation
-        return self
-
     def with_short_term(
         self,
         enable: bool = True,
@@ -95,77 +78,11 @@ class MemoryBuilder:
         """
         self._config.short_term.enable = enable
         if isolation is not None:
-            self._config.base_isolation = isolation
             self._config.short_term.isolation = isolation
         if backend is not None:
             self._config.short_term.backend = backend
         return self
 
-    def with_slots(
-        self,
-        enable: bool = True,
-        scopes: dict[str, ScopeBuilder] | None = None,
-        default_slots: list[MemorySlot] | None = None,
-        backend: str | BaseSlotContext | None = None,
-        instructions: str | None = None,
-    ) -> Self:
-        """
-        配置核心槽位记忆 (Memory Slots)。
-
-        参数:
-            enable: 是否启用槽位记忆。
-            scopes: 语义化作用域映射字典。如果只有一个键值对，则大模型不可见该参数。
-            default_slots: 首次初始化时自动写入的默认槽位列表。
-            backend: 槽位记忆存储后端，如果为 None 则使用全局默认后端。
-            instructions: 覆写内置槽位管理工具箱的默认系统提示词规则。
-        """
-        self._config.slots.enable = enable
-        if scopes is not None:
-            self._config.slots.scopes = scopes
-        if default_slots is not None:
-            self._config.slots.default_slots = default_slots
-        if backend is not None:
-            self._config.slots.backend = backend
-        if instructions is not None:
-            self._config.slots.instructions = instructions
-        return self
-
-    def with_long_term(
-        self,
-        enable: bool = True,
-        scopes: dict[str, ScopeBuilder] | None = None,
-        engine: ScopedRAGClient | None = None,
-        backend: str | StorageBackend | None = None,
-        embedder: Embedder | str | None = None,
-        agentic: bool = True,
-        auto_recall: AutoRecallPolicy = False,
-        instructions: str | None = None,
-    ) -> Self:
-        """
-        配置长期向量记忆与 RAG 设定。
-
-        参数:
-            enable: 是否启用长期记忆。
-            scopes: 语义化作用域映射字典。如果只有一个键值对，则大模型不可见该参数。
-            engine: 高级 RAG 检索引擎实例 (推荐)。若提供，将接管记忆的底层检索、混合与重排。
-            backend: 长期记忆存储后端。
-            embedder: 用于向量化的文本嵌入模型实例。
-            agentic: 是否开启主动智能体记忆管理 (增删改查工具自动注入)。
-            auto_recall: 长期记忆的自动召回策略，支持 bool 或 Callable 函数。
-            instructions: 覆写内置长期记忆工具箱的默认系统提示词规则。
-        """  # noqa: E501
-        self._config.long_term.enable = enable
-        self._config.long_term.engine = engine
-        if scopes is not None:
-            self._config.long_term.scopes = scopes
-        self._config.long_term.backend = backend
-        self._config.long_term.embedder = embedder
-        self._config.long_term.agentic = agentic
-        self._config.long_term.auto_recall = auto_recall
-        if instructions is not None:
-            self._config.long_term.instructions = instructions
-        return self
-
     def with_multimodal_window(self, window_size: int = 5) -> Self:
         """
         配置多模态历史视窗大小。
@@ -261,8 +178,4 @@ class MemoryBuilder:
         """
         生成最终构建好的 MemoryConfig 配置对象。
         """
-        if not self._config.slots.scopes:
-            self._config.slots.scopes = {"私有": self._config.base_isolation}
-        if not self._config.long_term.scopes:
-            self._config.long_term.scopes = {"私有": self._config.base_isolation}
         return self._config

zhenxun/services/ai/context/memory/capabilities.py

@@ -1,52 +1,270 @@
+import inspect
 from typing import Any
 
 from zhenxun.services.ai.capabilities.base import AbstractCapability
-from zhenxun.services.ai.context.memory.models import MemoryConfig
+from zhenxun.services.ai.context.rag.backends import Embedder, StorageBackend
+from zhenxun.services.ai.context.rag.engine import ScopedRAGClient
 from zhenxun.services.ai.run.context import RunContext
+from zhenxun.services.ai.tools.core.toolkit import BaseToolkit
 from zhenxun.services.ai.tools.providers.builtin.memory import MemoryManagementToolkit
+from zhenxun.services.ai.utils.logger import log_memory as logger
+from zhenxun.services.ai.utils.runtime import ContextUtils
+from zhenxun.services.ai.utils.scope import ScopeBuilder
+
+from .manager import memory_manager
+from .models import MemoryScoringConfig
+from .storage.backends import MemoryScope
+from .storage.interfaces import BaseSlotContext
+from .types import (
+    AutoRecallPolicy,
+    Isolation,
+    MemorySlot,
+    SessionMetadata,
+)
 
 
-class AgenticMemoryCapability(AbstractCapability):
+class LongTermMemoryCapability(AbstractCapability):
     """
-    智能体主动记忆管理能力 (Agentic Memory Management)。
-    当 `MemoryConfig.long_term.enable == True` 且 `agentic == True` 时隐式挂载，
-    在运行时动态组装并向大模型提供 `MemoryManagementToolkit` 工具箱。
+    长期向量记忆 (RAG) 核心能力组件。
+    负责静默执行自动召回 (Auto Recall)，并在必要时提供读写 RAG 数据库的工具链。
     """
 
-    def __init__(self, memory_config: MemoryConfig, namespace: str):
-        self.memory_config = memory_config
+    def __init__(
+        self,
+        engine: ScopedRAGClient | None = None,
+        storage_backend: StorageBackend | None = None,
+        embedder: Embedder | str | None = None,
+        scopes: dict[str, ScopeBuilder] | ScopeBuilder | None = None,
+        toolkit: bool | BaseToolkit = True,
+        scoring_config: MemoryScoringConfig | None = None,
+        auto_recall: AutoRecallPolicy = False,
+        recall_limit: int = 5,
+        recall_threshold: float = 0.5,
+        namespace: str | None = None,
+    ):
+        """
+        初始化长期记忆能力组件。
+
+        参数：
+            engine: RAG 客户端引擎实例。若为 None 则在运行时按需构建。
+            storage_backend: RAG 向量存储后端。若为 None 则从管理器按命名空间提取。
+            embedder: 嵌入模型实例或模型名称。
+            scopes: 控制长期记忆的隔离级别。支持单 ScopeBuilder 或 映射字典。
+            toolkit: 是否启用记忆管理工具箱，或传入自定义的工具箱实例。
+            scoring_config: 记忆检索打分配置（包含时间衰减等参数）。
+            auto_recall: 自动召回策略，可以是布尔值或自定义的回调函数。
+            recall_limit: 自动召回的记忆条数限制。
+            recall_threshold: 自动召回的相似度分数阈值。
+            namespace: 指定的命名空间，用于自动路由存储后端及构建器。
+        """
+        self.engine = engine
+        self.storage_backend = storage_backend
+        self.embedder = embedder
+        if isinstance(scopes, ScopeBuilder):
+            self.scopes = {"默认": scopes}
+        else:
+            self.scopes = scopes or {"私有": Isolation.AGENT_USER()}
+        self.toolkit = toolkit
+        self.scoring_config = scoring_config or MemoryScoringConfig()
+        self.auto_recall = auto_recall
+        self.recall_limit = recall_limit
+        self.recall_threshold = recall_threshold
         self.namespace = namespace
 
-    async def get_tools(self, context: RunContext) -> list[Any]:
-        kwargs = self.memory_config.long_term.toolkit_kwargs.copy()
-        kwargs["memory_config"] = self.memory_config
-        kwargs["namespace"] = self.namespace
-        if self.memory_config.long_term.instructions is not None:
-            kwargs["instructions"] = self.memory_config.long_term.instructions
+    def _build_session_meta(self, context: RunContext) -> SessionMetadata:
+        scope_builder = next(iter(self.scopes.values())) if self.scopes else None
+        return ContextUtils.build_session_meta(
+            context=context,
+            target_builder=scope_builder,
+            extra_scopes=self.scopes,
+            custom_namespace=self.namespace,
+        )
 
-        toolkit = MemoryManagementToolkit(**kwargs)
-        return [toolkit]
+    def _ensure_engine(self, context: RunContext) -> ScopedRAGClient | None:
+        if self.engine is not None:
+            return self.engine
+
+        ns = self.namespace or getattr(context.session, "namespace", "global")
+
+        storage_instance = self.storage_backend
+        if not storage_instance:
+            factory = memory_manager._storage_factories.get(
+                ns
+            ) or memory_manager._storage_factories.get("global")
+            if factory:
+                storage_instance = factory()
+
+        if not storage_instance:
+            return None
+
+        embedder_instance = self.embedder
+        if isinstance(embedder_instance, str):
+            from zhenxun.services.ai.context.rag.backends.embedders import (
+                DefaultEmbedder,
+            )
+
+            embedder_instance = DefaultEmbedder(model_name=embedder_instance)
+
+        from zhenxun.services.ai.context.rag.builder import RAGBuilder
+
+        builder = RAGBuilder(storage_instance).with_scope("/")
+        if embedder_instance:
+            builder.with_embedder(embedder_instance)
+
+        builder.enable_lifecycle_scoring(
+            half_life_days=self.scoring_config.recency_half_life_days,
+            decay_weight=self.scoring_config.recency_weight,
+            semantic_weight=self.scoring_config.semantic_weight,
+            importance_weight=self.scoring_config.importance_weight,
+            reinforcement_weight=self.scoring_config.reinforcement_weight,
+        )
+
+        self.engine = builder.build()
+        return self.engine
+
+    async def get_system_prompts(self, context: RunContext) -> list[str]:
+        engine = self._ensure_engine(context)
+        user_input = context.run.user_input
+        if not user_input or not engine:
+            return []
+
+        should_recall = False
+        session_meta = self._build_session_meta(context)
+
+        if isinstance(self.auto_recall, bool):
+            should_recall = self.auto_recall
+        elif callable(self.auto_recall):
+            try:
+                res = self.auto_recall(user_input, session_meta)
+                if inspect.isawaitable(res):
+                    should_recall = await res
+                else:
+                    should_recall = bool(res)
+            except Exception as e:
+                logger.error(
+                    f"[LongTermMemoryCapability] auto_recall 函数执行失败: {e}"
+                )
+                should_recall = False
+
+        if not should_recall:
+            return []
+
+        scope = MemoryScope(rag_client=engine)
+        matches = await scope.recall(
+            session=session_meta,
+            query=user_input,
+            limit=self.recall_limit,
+        )
+        if matches:
+            valid_matches = [m for m in matches if m.score >= self.recall_threshold]
+            if valid_matches:
+                fact_str = "\n".join(f"- {m.record.content}" for m in valid_matches)
+                return [f"[系统补充：有关用户的长期记忆设定]\n{fact_str}"]
+        return []
+
+    async def get_tools(self, context: RunContext) -> list[Any]:
+        if self.toolkit is False:
+            return []
+
+        engine = self._ensure_engine(context)
+        if not engine:
+            return []
+
+        if isinstance(self.toolkit, BaseToolkit):
+            tk = self.toolkit.clone_with(
+                rag_client=engine, scopes=self.scopes, _namespace=self.namespace
+            )
+            return [tk]
+
+        return [
+            MemoryManagementToolkit(
+                rag_client=engine, scopes=self.scopes, namespace=self.namespace
+            )
+        ]
 
 
 class SlotMemoryCapability(AbstractCapability):
     """
-    槽位记忆能力组件。
-    当 `MemoryConfig.slots.enable == True` 时隐式挂载，
-    在运行时动态组装并向大模型提供 `MemorySlotToolkit` 工具箱。
+    独立的槽位记忆 (Memory Slots) 能力组件。
+    直接作为插件挂载至 Agent 的 capabilities 列表中。
     """
 
-    def __init__(self, memory_config: MemoryConfig, namespace: str):
-        self.memory_config = memory_config
+    def __init__(
+        self,
+        scopes: dict[str, ScopeBuilder] | ScopeBuilder | None = None,
+        default_slots: list[MemorySlot] | None = None,
+        backend: BaseSlotContext | None = None,
+        toolkit: bool | BaseToolkit = True,
+        namespace: str | None = None,
+    ):
+        """
+        初始化槽位记忆能力组件。
+
+        参数：
+            scopes: 控制槽位记忆的隔离级别。支持单 ScopeBuilder 或 映射字典。
+            default_slots: 默认记忆槽列表，初始时自动创建未存在的槽位。
+            backend: 中期记忆槽持久化后端。若为 None 则从管理器按命名空间提取。
+            toolkit: 是否启用记忆槽管理工具箱，或传入自定义的工具箱实例。
+            namespace: 指定的命名空间，用于自动路由后端及工具箱。
+        """
+        if isinstance(scopes, ScopeBuilder):
+            self.scopes = {"默认": scopes}
+        else:
+            self.scopes = scopes or {"私有": Isolation.AGENT_USER()}
+        self.default_slots = default_slots or []
+        self.backend = backend
+        self.toolkit = toolkit
         self.namespace = namespace
 
+    async def _get_slot_ctx_and_meta(self, context: RunContext):
+        ns = self.namespace or getattr(context.session, "namespace", "global")
+        slot_ctx = self.backend or memory_manager.get_backend("slots", namespace=ns)
+
+        target_builder = next(iter(self.scopes.values())) if self.scopes else None
+        session_meta = ContextUtils.build_session_meta(
+            context=context,
+            target_builder=target_builder,
+            extra_scopes=self.scopes,
+            custom_namespace=self.namespace,
+        )
+        return slot_ctx, session_meta
+
+    async def get_system_prompts(self, context: RunContext) -> list[str]:
+        slot_ctx, session_meta = await self._get_slot_ctx_and_meta(context)
+        if not slot_ctx:
+            return []
+
+        for default_slot in self.default_slots:
+            if not await slot_ctx.get_slot(session_meta, default_slot.label):
+                await slot_ctx.set_slot(session_meta, default_slot)
+
+        slots = await slot_ctx.list_pinned_slots(session_meta)
+        if not slots:
+            return []
+
+        xml_parts = ["<memory_slots>"]
+        for slot in slots:
+            semantic_name = session_meta.scope_name_mapping.get(slot.scope, "未知")
+            xml_parts.append(
+                f'  <slot name="{slot.label}" scope="{semantic_name}">\n'
+                f"    {slot.content}\n"
+                "  </slot>"
+            )
+        xml_parts.append("</memory_slots>")
+        return ["\n".join(xml_parts)]
+
     async def get_tools(self, context: RunContext) -> list[Any]:
         from zhenxun.services.ai.tools.providers.builtin.slots import MemorySlotToolkit
 
-        kwargs = self.memory_config.slots.toolkit_kwargs.copy()
-        kwargs["memory_config"] = self.memory_config
-        kwargs["namespace"] = self.namespace
-        if self.memory_config.slots.instructions is not None:
-            kwargs["instructions"] = self.memory_config.slots.instructions
+        if self.toolkit is False:
+            return []
 
-        toolkit = MemorySlotToolkit(**kwargs)
+        if isinstance(self.toolkit, BaseToolkit):
+            toolkit = self.toolkit.clone_with(
+                scopes=self.scopes, backend=self.backend, _namespace=self.namespace
+            )
+        else:
+            toolkit = MemorySlotToolkit(
+                scopes=self.scopes, backend=self.backend, namespace=self.namespace
+            )
         return [toolkit]

zhenxun/services/ai/context/memory/compression.py

@@ -4,6 +4,7 @@ from typing import Any, Generic, TypeVar
 
 from pydantic import BaseModel, Field
 
+from zhenxun.services.ai.context.memory.models import MemoryConfig
 from zhenxun.services.ai.core.engine.token_counter import token_counter
 from zhenxun.services.ai.core.messages import (
     AudioPart,
@@ -386,7 +387,7 @@ class LLMSummarizerReducer(AbstractSummarizerReducer):
     ) -> LLMMessage | None:
         prompt_text = f"### 📋 [对话摘要任务]\n{self.summarization_prompt}\n\n"
         if prev_summary:
-            prompt_text += "####  önceki_summary (参考先前的快照):\n"
+            prompt_text += "####  prev_summary (参考先前的快照):\n"
             prompt_text += f"> {prev_summary}\n\n"
         prompt_text += "#### 待处理的历史消息流：\n"
         for m in to_summarize:
@@ -512,7 +513,7 @@ class CondenserPipeline:
 
     @classmethod
     def create_from_configs(
-        cls, memory_config: Any, model_name: str
+        cls, memory_config: MemoryConfig | None, model_name: str
     ) -> "CondenserPipeline":
         """基于全局和局部配置组装压缩管线工厂方法"""
         from zhenxun.services.ai.config import get_llm_config
@@ -602,7 +603,7 @@ class CondenserPipeline:
 
 class MemoryPolicy:
     """
-    记忆策略工厂 (Strategy Factory Facade)。
+    记忆策略工厂。
     为开发者提供开箱即用的上下文压缩管线组装方案。
     """
 

zhenxun/services/ai/context/memory/engine.py

@@ -1,8 +1,9 @@
 from collections.abc import Sequence
-from typing import Any, cast
+from typing import cast
 
 from zhenxun.services.ai.core.engine.context_renderer import ContextConverter
 from zhenxun.services.ai.core.messages import AgentMessage
+from zhenxun.services.ai.run.context import RunContext
 from zhenxun.services.ai.utils.logger import log_memory as logger
 from zhenxun.utils.pydantic_compat import model_copy
 
@@ -14,144 +15,37 @@ from .models import MemoryConfig
 from .types import SessionMetadata
 
 
-class MemoryReader:
+class SessionMemoryContext:
     """
-    记忆读取器 (Memory Reader)。
-    负责从数据库中提取短期上下文历史，召回长期的背景知识，并执行自动压缩。
+    会话记忆门面。
+    封装了当前会话记忆的读写操作、上下文压缩管线以及入库清洗中间件。
     """
 
     def __init__(
-        self, session_meta: SessionMetadata, memory_config: MemoryConfig | None
+        self,
+        session_meta: SessionMetadata,
+        memory_config: MemoryConfig | None,
+        context: RunContext,
     ):
         """
-        初始化记忆读取器。
+        初始化会话记忆门面。
 
         参数:
             session_meta: 会话元数据，包含 Namespace 与作用域映射等上下文信息。
-            memory_config: 记忆系统的配置对象，控制长期、短期及槽位记忆的启用与逻辑。
+            memory_config: 记忆系统的配置对象，控制长期、短期记忆的启用与逻辑。
+            context: 必填的运行时上下文环境 (RunContext)，供中间件进行依赖注入。
         """
         self.session_meta = session_meta
         self.memory_config = memory_config
+        self.context = context
 
-    async def get_long_term_context(self, user_input: str) -> str:
-        """
-        基于用户输入召回长期记忆（RAG），返回格式化后的背景提示词。
-        """
-        if (
-            not self.memory_config
-            or not self.memory_config.long_term.enable
-            or not user_input
-        ):
-            return ""
-
-        policy = self.memory_config.long_term.auto_recall
-        should_recall = False
-
-        if isinstance(policy, bool):
-            should_recall = policy
-        elif callable(policy):
-            import inspect
-
-            try:
-                res = policy(user_input, self.session_meta)
-                if inspect.isawaitable(res):
-                    should_recall = await res
-                else:
-                    should_recall = bool(res)
-            except Exception as e:
-                logger.error(f"[MemoryReader] 自定义 auto_recall 函数执行失败: {e}")
-                should_recall = False
-
-        if not should_recall:
-            return ""
-
-        ltm_scope = memory_manager.get_long_term_memory(
-            self.memory_config,
-            namespace=self.session_meta.selector.namespace or "global",
-        )
-        if not ltm_scope:
-            return ""
-
-        matches = await ltm_scope.recall(session=self.session_meta, query=user_input)
-        if matches:
-            logger.debug(f"🧠 [MemoryReader] 长期记忆召回详情 (Query: '{user_input}'):")
-            for i, m in enumerate(matches):
-                logger.debug(
-                    f"  [{i + 1}] 得分: {m.score:.4f} | 内容: {m.record.content}"
-                )
-
-            threshold = self.memory_config.long_term.recall_threshold
-            valid_matches = [m for m in matches if m.score >= threshold]
-            if not valid_matches:
-                logger.debug("🧠 [MemoryReader] 召回的记忆均未达到相关性阈值，已丢弃。")
-                return ""
-
-            fact_str = "\n".join(f"- {m.record.content}" for m in valid_matches)
-            logger.debug(
-                f"🧠 [MemoryReader]"
-                f"成功截取并注入 {len(valid_matches)} 条高价值长期记忆。"
-            )
-            return f"[系统补充：有关用户的长期记忆设定]\n{fact_str}"
-        return ""
-
-    async def get_slots_context(self) -> str:
-        """
-        读取并组装核心槽位记忆 (Memory Slots)，返回 XML 格式字符串供大模型使用。
-        """
-        if not self.memory_config or not self.memory_config.slots.enable:
-            return ""
-        slot_ctx = memory_manager.get_slot_context(
-            self.memory_config,
-            namespace=self.session_meta.selector.namespace or "global",
-        )
-        if not slot_ctx:
-            return ""
-
-        if self.memory_config.slots.default_slots:
-            for default_slot in self.memory_config.slots.default_slots:
-                existing = await slot_ctx.get_slot(
-                    self.session_meta, default_slot.label
-                )
-                if not existing:
-                    await slot_ctx.set_slot(self.session_meta, default_slot)
-
-        slots = await slot_ctx.list_pinned_slots(self.session_meta)
-        if not slots:
-            return ""
-
-        show_scope = False
-        if (
-            self.memory_config
-            and self.memory_config.slots.scopes
-            and len(self.memory_config.slots.scopes) > 1
-        ):
-            show_scope = True
-
-        xml_parts = ["<memory_slots>"]
-        for slot in slots:
-            if show_scope:
-                semantic_name = self.session_meta.scope_name_mapping.get(
-                    slot.scope, "未知"
-                )
-                xml_parts.append(
-                    f'  <slot name="{slot.label}" scope="{semantic_name}">\n'
-                    f"    {slot.content}\n"
-                    "  </slot>"
-                )
-            else:
-                xml_parts.append(
-                    f'  <slot name="{slot.label}">\n    {slot.content}\n  </slot>'
-                )
-        xml_parts.append("</memory_slots>")
-        return "\n".join(xml_parts)
-
-    async def get_short_term_context(
+    async def read(
         self,
         model_name: str,
         override_history: Sequence[AgentMessage] | None = None,
     ) -> list[AgentMessage]:
         """
-        拉取短期对话历史，并执行 Token 压缩。
+        拉取短期对话历史，并执行 Token 压缩与管线修剪。
         """
         current_history: list[AgentMessage] = []
         if override_history is not None:
@@ -187,43 +81,18 @@ class MemoryReader:
                 if changed:
                     await chat_context.set_messages(self.session_meta, new_history)
                     logger.info(
-                        "💾 [MemoryReader] 压缩截断完毕，已同步覆写数据库。"
+                        "💾 [SessionMemory] 压缩截断完毕，已同步覆写数据库。"
                         f"压缩后条数: {len(new_history)}"
                     )
                 current_history = cast(list[AgentMessage], new_history)
 
         return current_history
 
-
-class MemoryWriter:
-    """
-    记忆写入器 (Memory Writer)。
-    负责将对话增量安全地写入数据库。
-    """
-
-    def __init__(
-        self,
-        session_meta: SessionMetadata,
-        memory_config: MemoryConfig | None,
-        context: Any = None,
-    ):
-        """
-        初始化记忆写入器。
-
-        参数:
-            session_meta: 会话元数据，包含 Namespace 与作用域映射等上下文信息。
-            memory_config: 记忆系统的配置对象，控制记忆存入的逻辑。
-            context: 运行时上下文环境，作为可选参数传入，供中间件使用，默认 None。
-        """
-        self.session_meta = session_meta
-        self.memory_config = memory_config
-        self.context = context
-
-    async def save_new_messages(
+    async def write(
         self,
         new_messages: Sequence[AgentMessage],
-    ):
-        """将新产生的对话增量保存到数据库"""
+    ) -> None:
+        """将新产生的对话增量，经过入库中间件清洗后保存到数据库"""
         if not new_messages:
             return
 

zhenxun/services/ai/context/memory/facades.py

@@ -1,129 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Sequence
-from typing import Literal
-
-from zhenxun.services.ai.core.messages import AgentMessage, LLMMessage
-
-from .manager import GlobalMemoryManager
-from .storage.interfaces import (
-    BaseChatContext,
-    BaseSlotContext,
-)
-from .types import (
-    MemorySlot,
-    SessionMetadata,
-)
-
-
-class ChatHistoryFacade:
-    """短期对话历史门面"""
-
-    def __init__(self, manager: GlobalMemoryManager, session_meta: SessionMetadata):
-        self.manager = manager
-        self.session_meta = session_meta
-
-    @property
-    def _backend(self) -> BaseChatContext | None:
-        return self.manager.get_chat_context(
-            None, self.session_meta.namespace or "global"
-        )
-
-    async def get(self, limit: int | None = None) -> list[LLMMessage]:
-        """获取当前会话的历史消息"""
-        if not self._backend:
-            return []
-        msgs = await self._backend.get_messages(self.session_meta)
-        return msgs[-limit:] if limit else msgs
-
-    async def add(self, messages: Sequence[AgentMessage] | AgentMessage) -> None:
-        """向当前会话追加一条或多条历史消息"""
-        if not self._backend:
-            return
-        from zhenxun.services.ai.core.engine.context_renderer import ContextConverter
-
-        msgs = messages if isinstance(messages, Sequence) else [messages]
-        flattened = ContextConverter.flatten_to_llm_messages(msgs)
-        if flattened:
-            await self._backend.add_messages(self.session_meta, flattened)
-
-    async def clear(self) -> None:
-        """清空当前会话的短期对话历史"""
-        if not self._backend:
-            return
-        await self._backend.clear(self.session_meta)
-
-
-class SlotFacade:
-    """中期记忆槽门面"""
-
-    def __init__(self, manager: GlobalMemoryManager, session_meta: SessionMetadata):
-        self.manager = manager
-        self.session_meta = session_meta
-
-    @property
-    def _backend(self) -> BaseSlotContext | None:
-        """获取底层槽位存储后端"""
-        return self.manager.get_slot_context(
-            None, self.session_meta.namespace or "global"
-        )
-
-    async def get(self, label: str) -> str | None:
-        """获取指定标识的槽位记忆内容"""
-        if not self._backend:
-            return None
-        slot = await self._backend.get_slot(self.session_meta, label)
-        return slot.content if slot else None
-
-    async def set(
-        self,
-        label: str,
-        content: str,
-        scope: Literal["session", "global"] = "session",
-        size_limit: int = 2000,
-        pinned: bool = True,
-    ) -> None:
-        """设置或更新指定的槽位记忆"""
-        if not self._backend:
-            return
-        slot = MemorySlot(
-            label=label,
-            content=content,
-            scope=scope,
-            size_limit=size_limit,
-            pinned=pinned,
-        )
-        await self._backend.set_slot(self.session_meta, slot)
-
-    async def delete(
-        self, label: str, scope: Literal["session", "global"] = "session"
-    ) -> None:
-        """删除指定的槽位记忆"""
-        if not self._backend:
-            return
-        await self._backend.delete_slot(self.session_meta, label, scope)
-
-    async def list_all(self) -> dict[str, str]:
-        """获取当前会话所有被置顶的槽位记忆"""
-        if not self._backend:
-            return {}
-        slots = await self._backend.list_pinned_slots(self.session_meta)
-        return {s.label: s.content for s in slots}
-
-
-class AgentSessionFacade:
-    """
-    提供给第三方开发者的会话记忆访问聚合门面 (Facade)。
-    """
-
-    def __init__(self, manager: GlobalMemoryManager, session_meta: SessionMetadata):
-        self.manager = manager
-        self.session_meta = session_meta
-        self.history = ChatHistoryFacade(manager, session_meta)
-        self.slots = SlotFacade(manager, session_meta)
-
-    async def clear_all(self) -> None:
-        """一键清空当前会话下的短期对话历史与记忆槽"""
-        cleaner = self.manager.cleaner().session(self.session_meta.session_id)
-        await cleaner.clear_short_term()
-        await cleaner.clear_slots()

zhenxun/services/ai/context/memory/manager.py

@@ -1,7 +1,8 @@
+from collections import defaultdict
 from collections.abc import Callable
 from typing import Any, cast
 
-from zhenxun.services.ai.context.rag.backends import Embedder, StorageBackend
+from zhenxun.services.ai.context.rag.backends import StorageBackend
 from zhenxun.services.ai.utils.logger import log_memory as logger
 from zhenxun.services.ai.utils.scope import BaseScopeBuilder
 from zhenxun.utils.utils import infer_plugin_namespace
@@ -9,11 +10,10 @@ from zhenxun.utils.utils import infer_plugin_namespace
 from .models import MemoryConfig
 from .storage.backends import (
     InMemoryChatContext,
-    MemoryScope,
 )
 from .storage.interfaces import (
     BaseChatContext,
-    BaseSlotContext,
+    IClearableBackend,
 )
 
 
@@ -33,41 +33,46 @@ class MemoryCleaner(BaseScopeBuilder["MemoryCleaner"]):
         self._config = cfg.build() if hasattr(cfg, "build") else cfg
         return self
 
+    async def clear_target(self, target_name: str):
+        """底层派发器：定向清理指定注册名称的泛型扩展后端数据"""
+        ns_dict = self.manager._backends.get(target_name, {})
+        for backend in ns_dict.values():
+            if isinstance(backend, IClearableBackend):
+                await backend.clear_by_query(self._selector)
+            else:
+                logger.warning(
+                    f"后端 {backend.__class__.__name__}"
+                    "未实现 IClearableBackend 协议，已跳过清理。"
+                )
+
     async def clear_short_term(self):
         """一键清理目标范围下的短期对话历史记忆"""
-        if self._config and self._config.short_term.backend:
+        if isinstance(self._config, MemoryConfig) and isinstance(
+            self._config.short_term.backend, IClearableBackend
+        ):
             await self._config.short_term.backend.clear_by_query(self._selector)
         else:
-            for backend in self.manager._chat_backends.values():
-                await backend.clear_by_query(self._selector)
+            await self.clear_target("chat")
 
     async def clear_slots(self):
-        """一键清理目标范围下的中期记忆槽 (Memory Slots)"""
-        if self._config and self._config.slots.backend:
-            await self._config.slots.backend.clear_by_query(self._selector)
-        else:
-            for backend in self.manager._slot_backends.values():
-                await backend.clear_by_query(self._selector)
+        """一键清理目标范围下的记忆槽数据"""
+        await self.clear_target("slots")
 
     async def clear_long_term(self):
         """一键清理目标范围下的长期向量记忆 (RAG Vector Database)"""
-        if self._config and self._config.long_term.backend:
-            from zhenxun.services.ai.context.rag.backends import StorageBackend
-
-            storage = cast(StorageBackend, self._config.long_term.backend)
-            await storage.clear_by_query(self._selector)
-        else:
-            for factory in self.manager._storage_factories.values():
-                storage = factory()
-                if hasattr(storage, "clear_by_query"):
-                    await storage.clear_by_query(self._selector)
-                else:
-                    await storage.delete(scope_prefix=self._selector.scope_prefix)
+        for factory in self.manager._storage_factories.values():
+            storage = factory()
+            if isinstance(storage, IClearableBackend):
+                await storage.clear_by_query(self._selector)
 
     async def clear_all(self):
-        """一键清理指定范围下的所有生命周期记忆（对话、槽位、RAG）"""
-        await self.clear_short_term()
-        await self.clear_slots()
+        """一键清理指定范围下的所有生命周期记忆（对话、记忆槽、RAG、及其他泛型扩展后端）"""
+        if isinstance(self._config, MemoryConfig) and isinstance(
+            self._config.short_term.backend, IClearableBackend
+        ):
+            await self._config.short_term.backend.clear_by_query(self._selector)
+        for target_name in self.manager._backends:
+            await self.clear_target(target_name)
         await self.clear_long_term()
         logger.info(
             f"🧹 成功清理作用域 '{self._selector.scope_prefix}'下的所有记忆痕迹！"
@@ -81,10 +86,8 @@ class GlobalMemoryManager:
     """
 
     def __init__(self):
-        self._chat_backends: dict[str, BaseChatContext] = {
-            "global": InMemoryChatContext()
-        }
-        self._slot_backends: dict[str, BaseSlotContext] = {}
+        self._backends: dict[str, dict[str, Any]] = defaultdict(dict)
+        self.register_backend("chat", InMemoryChatContext(), "global")
 
         from zhenxun.services.ai.context.rag.backends import DictStorageBackend
 
@@ -92,19 +95,23 @@ class GlobalMemoryManager:
             "global": lambda: DictStorageBackend()
         }
 
+    def register_backend(
+        self, backend_type: str, backend: Any, scope: str | None = None
+    ) -> None:
+        """泛型注册：注册任意类型的存储后端"""
+        ns = scope if scope is not None else infer_plugin_namespace()
+        self._backends[backend_type][ns] = backend
+
+    def get_backend(self, backend_type: str, namespace: str = "global") -> Any | None:
+        """泛型获取：获取任意类型的存储后端"""
+        backends = self._backends.get(backend_type, {})
+        return backends.get(namespace) or backends.get("global")
+
     def register_chat_backend(
         self, backend: BaseChatContext, scope: str | None = None
     ) -> None:
         """注册特定命名空间的短期记忆存储后端。"""
-        ns = scope if scope is not None else infer_plugin_namespace()
-        self._chat_backends[ns] = backend
-
-    def register_slot_backend(
-        self, backend: BaseSlotContext, scope: str | None = None
-    ) -> None:
-        """注册特定命名空间的中期记忆槽存储后端。"""
-        ns = scope if scope is not None else infer_plugin_namespace()
-        self._slot_backends[ns] = backend
+        self.register_backend("chat", backend, scope)
 
     def register_storage_factory(
         self, factory: Callable[[], StorageBackend], scope: str | None = None
@@ -117,20 +124,6 @@ class GlobalMemoryManager:
         """获取声明式记忆清理构建器，供第三方开发者极速清理指定记忆"""
         return MemoryCleaner(self)
 
-    def get_embedder(self, embedder_val: "Embedder | str | None") -> Embedder | None:
-        """获取向量化引擎实例。如果传入的是字符串，则视为 API 模型名称。"""
-        if not embedder_val:
-            return None
-
-        if isinstance(embedder_val, str):
-            from zhenxun.services.ai.context.rag.backends.embedders import (
-                DefaultEmbedder,
-            )
-
-            return DefaultEmbedder(model_name=embedder_val)
-
-        return embedder_val
-
     def get_chat_context(
         self, config: MemoryConfig | None, namespace: str = "global"
     ) -> BaseChatContext | None:
@@ -142,69 +135,7 @@ class GlobalMemoryManager:
         if backend_cfg is not None:
             return cast(BaseChatContext, backend_cfg)
 
-        return self._chat_backends.get(namespace) or self._chat_backends["global"]
-
-    def get_slot_context(
-        self, config: MemoryConfig | None, namespace: str = "global"
-    ) -> BaseSlotContext | None:
-        """根据配置分配对应的槽位记忆实例"""
-        if not config or not config.slots.enable:
-            return None
-
-        backend_cfg = config.slots.backend
-        if backend_cfg is not None:
-            return cast(BaseSlotContext, backend_cfg)
-
-        return self._slot_backends.get(namespace) or self._slot_backends["global"]
-
-    def get_long_term_memory(
-        self, config: MemoryConfig | None, namespace: str = "global"
-    ) -> MemoryScope | None:
-        """根据声明式配置动态组装长期向量记忆实例"""
-        if not config or not config.long_term.enable:
-            return None
-
-        if config.long_term.engine is not None:
-            return MemoryScope(
-                rag_client=config.long_term.engine,
-            )
-
-        storage_instance = None
-        backend_cfg = config.long_term.backend
-        if backend_cfg is not None:
-            storage_instance = cast(StorageBackend, backend_cfg)
-        else:
-            factory = (
-                self._storage_factories.get(namespace)
-                or self._storage_factories["global"]
-            )
-            storage_instance = factory()
-
-        embedder = self.get_embedder(config.long_term.embedder)
-
-        from zhenxun.services.ai.context.rag.builder import RAGBuilder
-
-        builder = RAGBuilder(storage_instance).with_scope("/")
-        if embedder:
-            builder.with_embedder(embedder)
-
-        from .models import MemoryScoringConfig
-
-        scoring_cfg = MemoryScoringConfig()
-
-        builder.enable_lifecycle_scoring(
-            half_life_days=scoring_cfg.recency_half_life_days,
-            decay_weight=scoring_cfg.recency_weight,
-            semantic_weight=scoring_cfg.semantic_weight,
-            importance_weight=scoring_cfg.importance_weight,
-            reinforcement_weight=scoring_cfg.reinforcement_weight,
-        )
-
-        client = builder.build()
-
-        return MemoryScope(
-            rag_client=client,
-        )
+        return self.get_backend("chat", namespace)
 
 
 memory_manager = GlobalMemoryManager()

zhenxun/services/ai/context/memory/models.py

@@ -2,50 +2,21 @@
 记忆域类型定义
 """
 
-from typing import Any
-
 from pydantic import BaseModel, ConfigDict, Field
 
-from zhenxun.services.ai.context.rag.backends import Embedder, StorageBackend
-from zhenxun.services.ai.context.rag.engine import ScopedRAGClient
 from zhenxun.services.ai.utils.scope import ScopeBuilder
 
 from .storage.interfaces import (
     BaseChatContext,
     BaseMemoryIngestionMiddleware,
     BaseMemoryReducer,
-    BaseSlotContext,
 )
 from .types import (
-    AutoRecallPolicy,
     Isolation,
-    MemorySlot,
     SessionMetadata,
 )
 
 
-class SlotMemoryConfig(BaseModel):
-    """槽位记忆 (Memory Slots) 配置"""
-
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
-    enable: bool = Field(default=False)
-    """是否启用中期记忆槽"""
-    scopes: dict[str, ScopeBuilder] | None = Field(default=None)
-    """语义化作用域映射字典，供大模型作为 Literal 选择。如果只有一个，则自动隐藏参数"""
-    default_slots: list[MemorySlot] = Field(default_factory=list)
-    """首次初始化时自动写入的默认槽位列表"""
-    backend: str | BaseSlotContext | None = Field(default=None)
-    """
-    指定底层槽位记忆数据库注册名称，或直接传入 BaseSlotContext 实例。
-    为空则使用全局默认
-    """
-    instructions: str | None = Field(default=None)
-    """覆写内置槽位管理工具箱的系统提示词"""
-    toolkit_kwargs: dict[str, Any] = Field(default_factory=dict)
-    """透传给底层 MemorySlotToolkit 的高级参数 (如 prefix, exclude, shared_options)"""
-
-
 class MemoryScoringConfig(BaseModel):
     """长期记忆的复合打分与检索配置"""
 
@@ -57,7 +28,6 @@ class MemoryScoringConfig(BaseModel):
     """重要性权重"""
     recency_half_life_days: int = Field(default=30)
     """时间衰减的半衰期(天)"""
-
     reinforcement_weight: float = Field(default=0.2)
     """访问强化的加权权重 (被检索越多得分越高)"""
 
@@ -78,44 +48,6 @@ class ShortTermConfig(BaseModel):
     """单一的记忆隔离级别 (ScopeBuilder)，决定短期记忆存储边界"""
 
 
-class LongTermConfig(BaseModel):
-    """长期向量记忆配置"""
-
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
-    enable: bool = Field(default=False)
-    """是否启用长期记忆（开启后自动赋予 Agent 存取记忆的工具，并附加 RAG 召回能力）"""
-    engine: ScopedRAGClient | None = Field(default=None)
-    """
-    [推荐] 指定底层的高级 RAG 检索引擎实例。若传入此项，将覆盖默认的 backend
-    和 embedder 配置。
-    """
-    backend: str | StorageBackend | None = Field(default=None)
-    """
-    指定底层长期向量数据库 (Storage) 注册名称，或直接传入 StorageBackend 实例。
-    为空则使用全局默认
-    """
-    scopes: dict[str, ScopeBuilder] | None = Field(default=None)
-    """语义化作用域映射字典，决定长期记忆存储边界。如果只有一个，则自动隐藏参数"""
-    embedder: str | Embedder | None = Field(default=None)
-    """
-    指定底层向量化引擎 (Embedder) 实例，若为字符串则视为 API 模型名称。
-    为空则使用全局默认
-    """
-
-    agentic: bool = Field(default=True)
-    """是否赋予大模型主动管理记忆的能力 (Agentic Memory)"""
-    auto_recall: AutoRecallPolicy = Field(default=False)
-    """长期记忆的自动召回策略，默认 False (从不自动召回)，由大模型自主
-    决定调用搜索工具"""
-    recall_threshold: float = Field(default=0.5)
-    """长期记忆召回的最低余弦相似度要求"""
-    instructions: str | None = Field(default=None)
-    """覆写内置长期记忆管理工具箱的系统提示词"""
-    toolkit_kwargs: dict[str, Any] = Field(default_factory=dict)
-    """透传给底层 MemoryManagementToolkit 的高级参数"""
-
-
 class ContextCompressionConfig(BaseModel):
     """上下文压缩与管理配置"""
 
@@ -144,14 +76,8 @@ class MemoryConfig(BaseModel):
     """统一的记忆配置项声明"""
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    base_isolation: ScopeBuilder = Field(default_factory=Isolation.AGENT_USER)
-    """顶层基准隔离级别，短期/中期/长期记忆将默认继承此级别"""
     short_term: ShortTermConfig = Field(default_factory=ShortTermConfig)
     """短期对话记忆配置"""
-    slots: SlotMemoryConfig = Field(default_factory=SlotMemoryConfig)
-    """槽位记忆配置"""
-    long_term: LongTermConfig = Field(default_factory=LongTermConfig)
-    """长期向量记忆配置"""
     compression: ContextCompressionConfig = Field(
         default_factory=ContextCompressionConfig
     )
@@ -161,12 +87,10 @@ class MemoryConfig(BaseModel):
 
 
 __all__ = [
-    "AutoRecallPolicy",
     "BaseMemoryIngestionMiddleware",
     "ContextCompressionConfig",
     "IngestionConfig",
     "Isolation",
-    "LongTermConfig",
     "MemoryConfig",
     "MemoryScoringConfig",
     "SessionMetadata",

zhenxun/services/ai/context/memory/storage/backends.py

@@ -95,7 +95,9 @@ class DBMessageSerializer:
         return content_parts
 
     @staticmethod
-    def serialize_content(content_payload: Any) -> list[dict[str, Any]]:
+    def serialize_content(
+        content_payload: list[LLMContentPart] | str,
+    ) -> list[dict[str, Any]]:
         """将 LLMMessage 消息内容序列化为可存储于数据库的 JSON 格式"""
         if isinstance(content_payload, str):
             return [{"type": "text", "text": content_payload}]
@@ -134,7 +136,7 @@ class MemoryScope:
     ):
         """初始化长期记忆作用域与 RAG 客户端"""
         self.rag_client = rag_client
-        self._background_tasks: set[Any] = set()
+        self._background_tasks: set[asyncio.Task[Any]] = set()
 
     async def remember(
         self,
@@ -492,3 +494,13 @@ def get_orm_slot_context(model_class: type[AbstractSlotRecord]) -> TortoiseSlotC
     [工厂方法] 供第三方开发者调用，将 Tortoise ORM 表直接包装为记忆槽存储系统。
     """
     return TortoiseSlotContext(model_class=model_class)
+
+
+__all__ = [
+    "AbstractMemoryRecord",
+    "AbstractSlotRecord",
+    "InMemoryChatContext",
+    "MemoryScope",
+    "TortoiseChatContext",
+    "TortoiseSlotContext",
+]

zhenxun/services/ai/context/memory/storage/interfaces.py

@@ -1,5 +1,6 @@
 from abc import ABC, abstractmethod
 from collections.abc import Sequence
+from typing import Protocol, runtime_checkable
 
 from zhenxun.services.ai.context.memory.types import (
     MemorySlot,
@@ -10,7 +11,14 @@ from zhenxun.services.ai.run.context import RunContext
 from zhenxun.services.ai.utils.scope import ScopeSelector
 
 
-class BaseChatContext(ABC):
+@runtime_checkable
+class IClearableBackend(Protocol):
+    """支持声明式清理的作用域后端协议"""
+
+    async def clear_by_query(self, query: ScopeSelector) -> int | None: ...
+
+
+class BaseChatContext(IClearableBackend, ABC):
     """短期对话历史记忆接口"""
 
     @abstractmethod
@@ -44,13 +52,8 @@ class BaseChatContext(ABC):
         """清空当前会话的历史消息。"""
         ...
 
-    @abstractmethod
-    async def clear_by_query(self, query: ScopeSelector) -> None:
-        """根据条件领域查询对象清理对话历史。"""
-        ...
 
-
-class BaseSlotContext(ABC):
+class BaseSlotContext(IClearableBackend, ABC):
     """中期记忆槽持久化接口"""
 
     @abstractmethod
@@ -80,11 +83,6 @@ class BaseSlotContext(ABC):
         """列出当前会话的所有记忆槽（包括未置顶的）。"""
         ...
 
-    @abstractmethod
-    async def clear_by_query(self, query: ScopeSelector) -> None:
-        """根据条件领域查询对象清理记忆槽。"""
-        ...
-
 
 class BaseMemoryReducer(ABC):
     """记忆压缩器基类"""
@@ -97,7 +95,19 @@ class BaseMemoryReducer(ABC):
         model_name: str,
         base_overhead: int = 0,
     ) -> tuple[list[LLMMessage], bool, int]:
-        """对消息列表进行压缩处理。"""
+        """
+        执行记忆压缩处理，精简或提炼对话上下文以降低 Token 消耗。
+
+        参数:
+            messages: 需要进行压缩的原始 LLM 消息历史列表。
+            current_tokens: 压缩前消息列表的当前 Token 总数。
+            model_name: 用于判定压缩阈值或计算 Token 的底层大模型名称。
+            base_overhead: 基础系统提示词等静态开销的 Token 计数。
+
+        返回:
+            tuple[list[LLMMessage], bool, int]: 包含压缩后的新消息历史列表、
+                本次是否实际触发了压缩的布尔标记、以及压缩后的新 Token 总数。
+        """
         ...