feat(memory): 更新记忆管理和配置，优化整合逻辑，添加语义相似度阈值

src/chat/message_receive/storage.py

@@ -182,12 +182,14 @@ class MessageStorageBatcher:
                 is_command = message.is_command or False
                 is_public_notice = message.is_public_notice or False
                 notice_type = message.notice_type
-                actions = message.actions
+                # 序列化actions列表为JSON字符串
+                actions = orjson.dumps(message.actions).decode("utf-8") if message.actions else None
                 should_reply = message.should_reply
                 should_act = message.should_act
                 additional_config = message.additional_config
-                key_words = ""
-                key_words_lite = ""
+                # 确保关键词字段是字符串格式（如果不是，则序列化）
+                key_words = MessageStorage._serialize_keywords(message.key_words)
+                key_words_lite = MessageStorage._serialize_keywords(message.key_words_lite)
                 memorized_times = 0
 
                 user_platform = message.user_info.platform if message.user_info else ""
@@ -254,7 +256,8 @@ class MessageStorageBatcher:
                     is_command = message.is_command
                     is_public_notice = getattr(message, "is_public_notice", False)
                     notice_type = getattr(message, "notice_type", None)
-                    actions = getattr(message, "actions", None)
+                    # 序列化actions列表为JSON字符串
+                    actions = orjson.dumps(getattr(message, "actions", None)).decode("utf-8") if getattr(message, "actions", None) else None
                     should_reply = getattr(message, "should_reply", None)
                     should_act = getattr(message, "should_act", None)
                     additional_config = getattr(message, "additional_config", None)
@@ -580,6 +583,11 @@ class MessageStorage:
                     is_picid = False
                     is_notify = False
                     is_command = False
+                    is_public_notice = False
+                    notice_type = None
+                    actions = None
+                    should_reply = False
+                    should_act = False
                     key_words = ""
                     key_words_lite = ""
                 else:
@@ -593,6 +601,12 @@ class MessageStorage:
                     is_picid = message.is_picid
                     is_notify = message.is_notify
                     is_command = message.is_command
+                    is_public_notice = getattr(message, "is_public_notice", False)
+                    notice_type = getattr(message, "notice_type", None)
+                    # 序列化actions列表为JSON字符串
+                    actions = orjson.dumps(getattr(message, "actions", None)).decode("utf-8") if getattr(message, "actions", None) else None
+                    should_reply = getattr(message, "should_reply", False)
+                    should_act = getattr(message, "should_act", False)
                     # 序列化关键词列表为JSON字符串
                     key_words = MessageStorage._serialize_keywords(message.key_words)
                     key_words_lite = MessageStorage._serialize_keywords(message.key_words_lite)
@@ -666,6 +680,11 @@ class MessageStorage:
                 is_picid=is_picid,
                 is_notify=is_notify,
                 is_command=is_command,
+                is_public_notice=is_public_notice,
+                notice_type=notice_type,
+                actions=actions,
+                should_reply=should_reply,
+                should_act=should_act,
                 key_words=key_words,
                 key_words_lite=key_words_lite,
             )

src/config/official_configs.py

@@ -416,6 +416,7 @@ class MemoryConfig(ValidatedConfigBase):
     search_min_importance: float = Field(default=0.3, description="最小重要性阈值")
     search_similarity_threshold: float = Field(default=0.5, description="向量相似度阈值")
     search_max_expand_depth: int = Field(default=2, description="检索时图扩展深度（0-3）")
+    search_expand_semantic_threshold: float = Field(default=0.3, description="图扩展时语义相似度阈值（建议0.3-0.5，过低可能引入无关记忆，过高无法扩展）")
     enable_query_optimization: bool = Field(default=True, description="启用查询优化")
     
     # 检索权重配置 (记忆图系统)
@@ -426,15 +427,21 @@ class MemoryConfig(ValidatedConfigBase):
     
     # 记忆整合配置
     consolidation_enabled: bool = Field(default=False, description="是否启用记忆整合")
-    consolidation_interval_hours: float = Field(default=6.0, description="整合任务执行间隔（小时）")
-    consolidation_similarity_threshold: float = Field(default=0.92, description="相似记忆去重阈值")
-    consolidation_time_window_hours: float = Field(default=6.0, description="整合时间窗口（小时）")
-    consolidation_max_batch_size: int = Field(default=50, description="单次最多处理的记忆数量")
+    consolidation_interval_hours: float = Field(default=2.0, description="整合任务执行间隔（小时）")
+    consolidation_deduplication_threshold: float = Field(default=0.93, description="相似记忆去重阈值")
+    consolidation_time_window_hours: float = Field(default=2.0, description="整合时间窗口（小时）- 统一用于去重和关联")
+    consolidation_max_batch_size: int = Field(default=30, description="单次最多处理的记忆数量")
 
-    # 自动关联配置
-    auto_link_enabled: bool = Field(default=True, description="是否启用自动关联")
-    auto_link_max_candidates: int = Field(default=5, description="每个记忆最多关联候选数")
-    auto_link_min_confidence: float = Field(default=0.7, description="最低置信度阈值")
+    # 记忆关联配置（整合功能的子模块）
+    consolidation_linking_enabled: bool = Field(default=True, description="是否启用记忆关联建立")
+    consolidation_linking_max_candidates: int = Field(default=10, description="每个记忆最多关联的候选数")
+    consolidation_linking_max_memories: int = Field(default=20, description="单次最多处理的记忆总数")
+    consolidation_linking_min_importance: float = Field(default=0.5, description="最低重要性阈值")
+    consolidation_linking_pre_filter_threshold: float = Field(default=0.7, description="向量相似度预筛选阈值")
+    consolidation_linking_max_pairs_for_llm: int = Field(default=5, description="最多发送给LLM分析的候选对数")
+    consolidation_linking_min_confidence: float = Field(default=0.7, description="LLM分析最低置信度阈值")
+    consolidation_linking_llm_temperature: float = Field(default=0.2, description="LLM分析温度参数")
+    consolidation_linking_llm_max_tokens: int = Field(default=1500, description="LLM分析最大输出长度")
     
     # 遗忘配置 (记忆图系统)
     forgetting_enabled: bool = Field(default=True, description="是否启用自动遗忘")

src/memory_graph/manager.py

@@ -25,6 +25,10 @@ from src.memory_graph.storage.vector_store import VectorStore
 from src.memory_graph.tools.memory_tools import MemoryTools
 from src.memory_graph.utils.embeddings import EmbeddingGenerator
 import uuid
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import numpy as np
 
 logger = logging.getLogger(__name__)
 
@@ -135,7 +139,8 @@ class MemoryManager:
             
             # 检查配置值
             expand_depth = self.config.search_max_expand_depth
-            logger.info(f"📊 配置检查: search_max_expand_depth={expand_depth}")
+            expand_semantic_threshold = self.config.search_expand_semantic_threshold
+            logger.info(f"📊 配置检查: search_max_expand_depth={expand_depth}, search_expand_semantic_threshold={expand_semantic_threshold}")
 
             self.tools = MemoryTools(
                 vector_store=self.vector_store,
@@ -143,6 +148,7 @@ class MemoryManager:
                 persistence_manager=self.persistence,
                 embedding_generator=self.embedding_generator,
                 max_expand_depth=expand_depth,  # 从配置读取图扩展深度
+                expand_semantic_threshold=expand_semantic_threshold,  # 从配置读取图扩展语义阈值
             )
             
             self._initialized = True
@@ -978,10 +984,11 @@ class MemoryManager:
         """
         整理记忆：直接合并去重相似记忆（不创建新边）
 
-        优化点：
-        1. 添加批量限制，避免长时间阻塞
-        2. 相似记忆直接覆盖合并，不创建关联边
-        3. 使用 asyncio.sleep 让出控制权，避免阻塞事件循环
+        性能优化版本：
+        1. 使用 asyncio.create_task 在后台执行，避免阻塞主流程
+        2. 向量计算批量处理，减少重复计算
+        3. 延迟保存，批量写入数据库
+        4. 更频繁的协作式多任务让出
 
         Args:
             similarity_threshold: 相似度阈值（默认0.85，建议提高到0.9减少误判）
@@ -989,14 +996,46 @@ class MemoryManager:
             max_batch_size: 单次最多处理的记忆数量
 
         Returns:
-            整理结果
+            整理结果（如果是异步执行，返回启动状态）
         """
         if not self._initialized:
             await self.initialize()
 
         try:
-            logger.info(f"开始记忆整理 (similarity_threshold={similarity_threshold}, time_window={time_window_hours}h, max_batch={max_batch_size})...")
+            logger.info(f"🚀 启动记忆整理任务 (similarity_threshold={similarity_threshold}, time_window={time_window_hours}h, max_batch={max_batch_size})...")
 
+            # 创建后台任务执行整理
+            task = asyncio.create_task(
+                self._consolidate_memories_background(
+                    similarity_threshold=similarity_threshold,
+                    time_window_hours=time_window_hours,
+                    max_batch_size=max_batch_size
+                )
+            )
+
+            # 返回任务启动状态，不等待完成
+            return {
+                "task_started": True,
+                "task_id": id(task),
+                "message": "记忆整理任务已在后台启动"
+            }
+
+        except Exception as e:
+            logger.error(f"启动记忆整理任务失败: {e}", exc_info=True)
+            return {"error": str(e), "task_started": False}
+
+    async def _consolidate_memories_background(
+        self,
+        similarity_threshold: float,
+        time_window_hours: float,
+        max_batch_size: int,
+    ) -> None:
+        """
+        后台执行记忆整理的具体实现
+
+        这个方法会在独立任务中运行，不阻塞主流程
+        """
+        try:
             result = {
                 "merged_count": 0,
                 "checked_count": 0,
@@ -1013,19 +1052,19 @@ class MemoryManager:
             ]
 
             if not recent_memories:
-                logger.info("没有需要整理的记忆")
-                return result
+                logger.info("✅ 记忆整理完成: 没有需要整理的记忆")
+                return
 
             # 限制批量处理数量
             if len(recent_memories) > max_batch_size:
-                logger.info(f"记忆数量 {len(recent_memories)} 超过批量限制 {max_batch_size}，仅处理最新的 {max_batch_size} 条")
+                logger.info(f"📊 记忆数量 {len(recent_memories)} 超过批量限制 {max_batch_size}，仅处理最新的 {max_batch_size} 条")
                 recent_memories = sorted(recent_memories, key=lambda m: m.created_at, reverse=True)[:max_batch_size]
                 result["skipped_count"] = len(all_memories) - max_batch_size
 
-            logger.info(f"找到 {len(recent_memories)} 条待整理记忆")
+            logger.info(f"📋 找到 {len(recent_memories)} 条待整理记忆")
             result["checked_count"] = len(recent_memories)
 
-            # 按记忆类型分组
+            # 按记忆类型分组，减少跨类型比较
             memories_by_type: Dict[str, List[Memory]] = {}
             for mem in recent_memories:
                 mem_type = mem.metadata.get("memory_type", "")
@@ -1033,7 +1072,8 @@ class MemoryManager:
                     memories_by_type[mem_type] = []
                 memories_by_type[mem_type].append(mem)
 
-            # 记录已删除的记忆ID，避免重复处理
+            # 记录需要删除的记忆，延迟批量删除
+            to_delete: List[Tuple[Memory, str]] = []  # (memory, reason)
             deleted_ids = set()
 
             # 对每个类型的记忆进行相似度检测
@@ -1041,71 +1081,129 @@ class MemoryManager:
                 if len(memories) < 2:
                     continue
 
-                logger.debug(f"检查类型 '{mem_type}' 的 {len(memories)} 条记忆")
+                logger.debug(f"🔍 检查类型 '{mem_type}' 的 {len(memories)} 条记忆")
 
-                # 使用向量相似度检测
-                for i in range(len(memories)):
-                    # 让出控制权，避免长时间阻塞
-                    if i % 10 == 0:
-                        await asyncio.sleep(0)
+                # 预提取所有主题节点的嵌入向量
+                embeddings_map: Dict[str, "np.ndarray"] = {}
+                valid_memories = []
 
-                    if memories[i].id in deleted_ids:
+                for mem in memories:
+                    topic_node = next((n for n in mem.nodes if n.node_type == NodeType.TOPIC), None)
+                    if topic_node and topic_node.embedding is not None:
+                        embeddings_map[mem.id] = topic_node.embedding
+                        valid_memories.append(mem)
+
+                # 批量计算相似度矩阵（比逐个计算更高效）
+                import numpy as np
+
+                for i in range(len(valid_memories)):
+                    # 更频繁的协作式多任务让出
+                    if i % 5 == 0:
+                        await asyncio.sleep(0.001)  # 1ms让出
+
+                    mem_i = valid_memories[i]
+                    if mem_i.id in deleted_ids:
                         continue
 
-                    for j in range(i + 1, len(memories)):
-                        if memories[j].id in deleted_ids:
+                    for j in range(i + 1, len(valid_memories)):
+                        if valid_memories[j].id in deleted_ids:
                             continue
 
-                        mem_i = memories[i]
-                        mem_j = memories[j]
+                        mem_j = valid_memories[j]
 
-                        # 获取主题节点的向量
-                        topic_i = next((n for n in mem_i.nodes if n.node_type == NodeType.TOPIC), None)
-                        topic_j = next((n for n in mem_j.nodes if n.node_type == NodeType.TOPIC), None)
+                        # 快速向量相似度计算
+                        embedding_i = embeddings_map[mem_i.id]
+                        embedding_j = embeddings_map[mem_j.id]
 
-                        if not topic_i or not topic_j:
-                            continue
-                        
-                        if topic_i.embedding is None or topic_j.embedding is None:
-                            continue
-                        
-                        # 计算余弦相似度
-                        import numpy as np
-                        similarity = np.dot(topic_i.embedding, topic_j.embedding) / (
-                            np.linalg.norm(topic_i.embedding) * np.linalg.norm(topic_j.embedding)
-                        )
+                        # 优化的余弦相似度计算
+                        similarity = self._fast_cosine_similarity(embedding_i, embedding_j)
 
                         if similarity >= similarity_threshold:
-                            # 直接去重：保留重要性高的，删除另一个（不创建关联边）
+                            # 决定保留哪个记忆
                             if mem_i.importance >= mem_j.importance:
                                 keep_mem, remove_mem = mem_i, mem_j
                             else:
                                 keep_mem, remove_mem = mem_j, mem_i
 
-                            logger.info(
-                                f"去重相似记忆 (similarity={similarity:.3f}): "
-                                f"保留 {keep_mem.id}, 删除 {remove_mem.id}"
+                            logger.debug(
+                                f"🔄 标记相似记忆 (similarity={similarity:.3f}): "
+                                f"保留 {keep_mem.id[:8]}, 删除 {remove_mem.id[:8]}"
                             )
 
-                            # 增强保留记忆的重要性（合并信息价值）
+                            # 增强保留记忆的重要性
                             keep_mem.importance = min(1.0, keep_mem.importance + 0.05)
-                            keep_mem.activation = min(1.0, keep_mem.activation + 0.05)
 
-                            # 将被删除记忆的访问次数累加到保留记忆
+                            # 累加访问次数
                             if hasattr(keep_mem, 'access_count') and hasattr(remove_mem, 'access_count'):
                                 keep_mem.access_count += remove_mem.access_count
 
-                            # 直接删除相似记忆（不创建边，简化图结构）
-                            await self.delete_memory(remove_mem.id)
+                            # 标记为待删除（不立即删除）
+                            to_delete.append((remove_mem, f"与记忆 {keep_mem.id[:8]} 相似度 {similarity:.3f}"))
                             deleted_ids.add(remove_mem.id)
                             result["merged_count"] += 1
 
-            logger.info(f"记忆整理完成: {result}")
-            return result
+                # 每处理完一个类型就让出控制权
+                await asyncio.sleep(0.005)  # 5ms让出
+
+            # 批量删除标记的记忆
+            if to_delete:
+                logger.info(f"🗑️ 开始批量删除 {len(to_delete)} 条相似记忆")
+
+                for memory, reason in to_delete:
+                    try:
+                        # 从向量存储删除节点
+                        for node in memory.nodes:
+                            if node.embedding is not None:
+                                await self.vector_store.delete_node(node.id)
+
+                        # 从图存储删除记忆
+                        self.graph_store.remove_memory(memory.id)
+
+                    except Exception as e:
+                        logger.warning(f"删除记忆 {memory.id[:8]} 失败: {e}")
+
+                # 批量保存（一次性写入，减少I/O）
+                await self.persistence.save_graph_store(self.graph_store)
+                logger.info(f"💾 批量保存完成")
+
+            logger.info(f"✅ 记忆整理完成: {result}")
 
         except Exception as e:
-            logger.error(f"记忆整理失败: {e}", exc_info=True)
-            return {"error": str(e), "merged_count": 0, "checked_count": 0}
+            logger.error(f"❌ 记忆整理失败: {e}", exc_info=True)
+
+    def _fast_cosine_similarity(self, vec1: "np.ndarray", vec2: "np.ndarray") -> float:
+        """
+        快速余弦相似度计算（优化版本）
+
+        Args:
+            vec1, vec2: 向量
+
+        Returns:
+            余弦相似度
+        """
+        try:
+            import numpy as np
+
+            # 避免重复的类型检查和转换
+            # 向量应该是numpy数组，如果不是，转换一次
+            if not isinstance(vec1, np.ndarray):
+                vec1 = np.asarray(vec1, dtype=np.float32)
+            if not isinstance(vec2, np.ndarray):
+                vec2 = np.asarray(vec2, dtype=np.float32)
+
+            # 使用更高效的范数计算
+            norm1 = np.linalg.norm(vec1)
+            norm2 = np.linalg.norm(vec2)
+
+            if norm1 == 0 or norm2 == 0:
+                return 0.0
+
+            # 直接计算点积和除法
+            return float(np.dot(vec1, vec2) / (norm1 * norm2))
+
+        except Exception as e:
+            logger.warning(f"计算余弦相似度失败: {e}")
+            return 0.0
 
     async def auto_link_memories(
         self,
@@ -1478,11 +1576,11 @@ class MemoryManager:
 
     async def maintenance(self) -> Dict[str, Any]:
         """
-        执行维护任务
+        执行维护任务（优化版本）
 
         包括：
-        - 记忆整理（合并相似记忆）
-        - 清理过期记忆
+        - 记忆整理（异步后台执行）
+        - 自动关联记忆（轻量级执行）
         - 自动遗忘低激活度记忆
         - 保存数据
 
@@ -1493,52 +1591,355 @@ class MemoryManager:
             await self.initialize()
 
         try:
-            logger.info("开始执行记忆系统维护...")
+            logger.info("🔧 开始执行记忆系统维护（优化版）...")
 
             result = {
-                "consolidated": 0,
+                "consolidation_task": "none",
+                "linked": 0,
                 "forgotten": 0,
-                "deleted": 0,
                 "saved": False,
+                "total_time": 0,
             }
 
-            # 1. 记忆整理（合并相似记忆）
-            # 默认禁用自动整理，因为可能阻塞主流程
-            # 建议：提高阈值到0.92以上，减少误判；限制批量大小避免阻塞
-            if getattr(self.config, 'consolidation_enabled', False):
-                consolidate_result = await self.consolidate_memories(
-                    similarity_threshold=getattr(self.config, 'consolidation_similarity_threshold', 0.92),
-                    time_window_hours=getattr(self.config, 'consolidation_time_window_hours', 24.0),
-                    max_batch_size=getattr(self.config, 'consolidation_max_batch_size', 50)
-                )
-                result["consolidated"] = consolidate_result.get("merged_count", 0)
+            start_time = datetime.now()
 
-            # 2. 自动关联记忆（发现和建立关系）
-            if getattr(self.config, 'auto_link_enabled', True):
-                link_result = await self.auto_link_memories()
+            # 1. 记忆整理（异步后台执行，不阻塞主流程）
+            if getattr(self.config, 'consolidation_enabled', False):
+                logger.info("🚀 启动异步记忆整理任务...")
+                consolidate_result = await self.consolidate_memories(
+                    similarity_threshold=getattr(self.config, 'consolidation_deduplication_threshold', 0.93),
+                    time_window_hours=getattr(self.config, 'consolidation_time_window_hours', 2.0),  # 统一时间窗口
+                    max_batch_size=getattr(self.config, 'consolidation_max_batch_size', 30)
+                )
+
+                if consolidate_result.get("task_started"):
+                    result["consolidation_task"] = f"background_task_{consolidate_result.get('task_id', 'unknown')}"
+                    logger.info("✅ 记忆整理任务已启动到后台执行")
+                else:
+                    result["consolidation_task"] = "failed"
+                    logger.warning("❌ 记忆整理任务启动失败")
+
+            # 2. 自动关联记忆（使用统一的时间窗口）
+            if getattr(self.config, 'consolidation_linking_enabled', True):
+                logger.info("🔗 执行轻量级自动关联...")
+                link_result = await self._lightweight_auto_link_memories()
                 result["linked"] = link_result.get("linked_count", 0)
 
-            # 3. 自动遗忘
+            # 3. 自动遗忘（快速执行）
             if getattr(self.config, 'forgetting_enabled', True):
+                logger.info("🗑️ 执行自动遗忘...")
                 forgotten_count = await self.auto_forget_memories(
                     threshold=getattr(self.config, 'forgetting_activation_threshold', 0.1)
                 )
                 result["forgotten"] = forgotten_count
 
-            # 4. 清理非常旧的已遗忘记忆（可选）
-            # TODO: 实现清理逻辑
-            
-            # 5. 保存数据
-            await self.persistence.save_graph_store(self.graph_store)
-            result["saved"] = True
+            # 4. 保存数据（如果记忆整理不在后台执行）
+            if result["consolidation_task"] == "none":
+                await self.persistence.save_graph_store(self.graph_store)
+                result["saved"] = True
+                logger.info("💾 数据保存完成")
 
             self._last_maintenance = datetime.now()
-            logger.info(f"维护完成: {result}")
+
+            # 计算维护耗时
+            total_time = (datetime.now() - start_time).total_seconds()
+            result["total_time"] = total_time
+
+            logger.info(f"✅ 维护完成 (耗时 {total_time:.2f}s): {result}")
             return result
 
         except Exception as e:
-            logger.error(f"维护失败: {e}", exc_info=True)
-            return {"error": str(e)}
+            logger.error(f"❌ 维护失败: {e}", exc_info=True)
+            return {"error": str(e), "total_time": 0}
+
+    async def _lightweight_auto_link_memories(
+        self,
+        time_window_hours: float = None,  # 从配置读取
+        max_candidates: int = None,  # 从配置读取
+        max_memories: int = None,  # 从配置读取
+    ) -> Dict[str, Any]:
+        """
+        智能轻量级自动关联记忆（保留LLM判断，优化性能）
+
+        优化策略：
+        1. 从配置读取处理参数，尊重用户设置
+        2. 使用向量相似度预筛选，仅对高相似度记忆调用LLM
+        3. 批量LLM调用，减少网络开销
+        4. 异步执行，避免阻塞
+        """
+        try:
+            result = {
+                "checked_count": 0,
+                "linked_count": 0,
+                "llm_calls": 0,
+            }
+
+            # 从配置读取参数，使用统一的时间窗口
+            if time_window_hours is None:
+                time_window_hours = getattr(self.config, 'consolidation_time_window_hours', 2.0)
+            if max_candidates is None:
+                max_candidates = getattr(self.config, 'consolidation_linking_max_candidates', 10)
+            if max_memories is None:
+                max_memories = getattr(self.config, 'consolidation_linking_max_memories', 20)
+
+            # 获取用户配置时间窗口内的记忆
+            time_threshold = datetime.now() - timedelta(hours=time_window_hours)
+            all_memories = self.graph_store.get_all_memories()
+
+            recent_memories = [
+                mem for mem in all_memories
+                if mem.created_at >= time_threshold
+                and not mem.metadata.get("forgotten", False)
+                and mem.importance >= getattr(self.config, 'consolidation_linking_min_importance', 0.5)  # 从配置读取重要性阈值
+            ]
+
+            if len(recent_memories) > max_memories:
+                recent_memories = sorted(recent_memories, key=lambda m: m.created_at, reverse=True)[:max_memories]
+
+            if len(recent_memories) < 2:
+                logger.debug("记忆数量不足，跳过智能关联")
+                return result
+
+            logger.debug(f"🧠 智能关联: 检查 {len(recent_memories)} 条重要记忆")
+
+            # 第一步：向量相似度预筛选，找到潜在关联对
+            candidate_pairs = []
+            import numpy as np
+
+            for i, memory in enumerate(recent_memories):
+                # 获取主题节点
+                topic_node = next(
+                    (n for n in memory.nodes if n.node_type == NodeType.TOPIC),
+                    None
+                )
+
+                if not topic_node or topic_node.embedding is None:
+                    continue
+
+                # 与其他记忆计算相似度
+                for j, other_memory in enumerate(recent_memories[i+1:], i+1):
+                    other_topic = next(
+                        (n for n in other_memory.nodes if n.node_type == NodeType.TOPIC),
+                        None
+                    )
+
+                    if not other_topic or other_topic.embedding is None:
+                        continue
+
+                    # 快速相似度计算
+                    similarity = self._fast_cosine_similarity(
+                        topic_node.embedding,
+                        other_topic.embedding
+                    )
+
+                    # 使用配置的预筛选阈值
+                    pre_filter_threshold = getattr(self.config, 'consolidation_linking_pre_filter_threshold', 0.7)
+                    if similarity >= pre_filter_threshold:
+                        candidate_pairs.append((memory, other_memory, similarity))
+
+                # 让出控制权
+                if i % 3 == 0:
+                    await asyncio.sleep(0.001)
+
+            logger.debug(f"🔍 预筛选找到 {len(candidate_pairs)} 个候选关联对")
+
+            if not candidate_pairs:
+                return result
+
+            # 第二步：批量LLM分析（使用配置的最大候选对数）
+            max_pairs_for_llm = getattr(self.config, 'consolidation_linking_max_pairs_for_llm', 5)
+            if len(candidate_pairs) <= max_pairs_for_llm:
+                link_relations = await self._batch_analyze_memory_relations(candidate_pairs)
+                result["llm_calls"] = 1
+
+                # 第三步：建立LLM确认的关联
+                for relation_info in link_relations:
+                    try:
+                        memory_a, memory_b = relation_info["memory_pair"]
+                        relation_type = relation_info["relation_type"]
+                        confidence = relation_info["confidence"]
+
+                        # 创建关联边
+                        edge = MemoryEdge(
+                            id=f"smart_edge_{uuid.uuid4().hex[:12]}",
+                            source_id=memory_a.subject_id,
+                            target_id=memory_b.subject_id,
+                            relation=relation_type,
+                            edge_type=EdgeType.RELATION,
+                            importance=confidence,
+                            metadata={
+                                "auto_linked": True,
+                                "method": "llm_analyzed",
+                                "vector_similarity": relation_info.get("vector_similarity", 0.0),
+                                "confidence": confidence,
+                                "reasoning": relation_info.get("reasoning", ""),
+                                "created_at": datetime.now().isoformat(),
+                            }
+                        )
+
+                        # 添加到图
+                        self.graph_store.graph.add_edge(
+                            edge.source_id,
+                            edge.target_id,
+                            edge_id=edge.id,
+                            relation=edge.relation,
+                            edge_type=edge.edge_type.value,
+                            importance=edge.importance,
+                            metadata=edge.metadata,
+                        )
+
+                        memory_a.edges.append(edge)
+                        result["linked_count"] += 1
+
+                        logger.debug(f"🧠 智能关联: {memory_a.id[:8]} --[{relation_type}]--> {memory_b.id[:8]} (置信度={confidence:.2f})")
+
+                    except Exception as e:
+                        logger.warning(f"建立智能关联失败: {e}")
+                        continue
+
+            # 保存关联结果
+            if result["linked_count"] > 0:
+                await self.persistence.save_graph_store(self.graph_store)
+
+            logger.debug(f"✅ 智能关联完成: 建立了 {result['linked_count']} 个关联，LLM调用 {result['llm_calls']} 次")
+            return result
+
+        except Exception as e:
+            logger.error(f"智能关联失败: {e}", exc_info=True)
+            return {"error": str(e), "checked_count": 0, "linked_count": 0}
+
+    async def _batch_analyze_memory_relations(
+        self,
+        candidate_pairs: List[Tuple[Memory, Memory, float]]
+    ) -> List[Dict[str, Any]]:
+        """
+        批量分析记忆关系（优化LLM调用）
+
+        Args:
+            candidate_pairs: 候选记忆对列表，每项包含 (memory_a, memory_b, vector_similarity)
+
+        Returns:
+            关系分析结果列表
+        """
+        try:
+            from src.llm_models.utils_model import LLMRequest
+            from src.config.config import model_config
+
+            llm = LLMRequest(
+                model_set=model_config.model_task_config.utils_small,
+                request_type="memory.batch_relation_analysis"
+            )
+
+            # 格式化所有候选记忆对
+            candidates_text = ""
+            for i, (mem_a, mem_b, similarity) in enumerate(candidate_pairs):
+                desc_a = self._format_memory_for_llm(mem_a)
+                desc_b = self._format_memory_for_llm(mem_b)
+                candidates_text += f"""
+候选对 {i+1}:
+记忆A: {desc_a}
+记忆B: {desc_b}
+向量相似度: {similarity:.3f}
+"""
+
+            # 构建批量分析提示词（使用配置的置信度阈值）
+            min_confidence = getattr(self.config, 'consolidation_linking_min_confidence', 0.7)
+
+            prompt = f"""你是记忆关系分析专家。请批量分析以下候选记忆对之间的关系。
+
+**关系类型说明：**
+- 导致: A的发生导致了B的发生（因果关系）
+- 引用: A提到或涉及B（引用关系）
+- 相似: A和B描述相似的内容（相似关系）
+- 相反: A和B表达相反的观点（对立关系）
+- 关联: A和B存在某种关联但不属于以上类型（一般关联）
+
+**候选记忆对：**
+{candidates_text}
+
+**任务要求：**
+1. 对每个候选对，判断是否存在有意义的关系
+2. 如果存在关系，指定关系类型和置信度(0.0-1.0)
+3. 简要说明判断理由
+4. 只返回置信度 >= {min_confidence} 的关系
+5. 优先考虑因果、引用等强关系，谨慎建立相似关系
+
+**输出格式（JSON）：**
+```json
+[
+  {{
+    "candidate_id": 1,
+    "has_relation": true,
+    "relation_type": "导致",
+    "confidence": 0.85,
+    "reasoning": "记忆A描述的原因导致记忆B的结果"
+  }},
+  {{
+    "candidate_id": 2,
+    "has_relation": false,
+    "reasoning": "两者无明显关联"
+  }}
+]
+```
+
+请分析并输出JSON结果："""
+
+            # 调用LLM（使用配置的参数）
+            llm_temperature = getattr(self.config, 'consolidation_linking_llm_temperature', 0.2)
+            llm_max_tokens = getattr(self.config, 'consolidation_linking_llm_max_tokens', 1500)
+
+            response, _ = await llm.generate_response_async(
+                prompt,
+                temperature=llm_temperature,
+                max_tokens=llm_max_tokens,
+            )
+
+            # 解析响应
+            import json
+            import re
+
+            # 提取JSON
+            json_match = re.search(r'```json\s*(.*?)\s*```', response, re.DOTALL)
+            if json_match:
+                json_str = json_match.group(1)
+            else:
+                json_str = response.strip()
+
+            try:
+                analysis_results = json.loads(json_str)
+            except json.JSONDecodeError:
+                logger.warning(f"LLM返回格式错误，尝试修复: {response[:200]}")
+                # 尝试简单修复
+                json_str = re.sub(r'[\r\n\t]', '', json_str)
+                analysis_results = json.loads(json_str)
+
+            # 转换为结果格式
+            relations = []
+            for result in analysis_results:
+                if not result.get("has_relation", False):
+                    continue
+
+                confidence = result.get("confidence", 0.0)
+                if confidence < min_confidence:  # 使用配置的置信度阈值
+                    continue
+
+                candidate_id = result.get("candidate_id", 0) - 1
+                if 0 <= candidate_id < len(candidate_pairs):
+                    mem_a, mem_b, vector_similarity = candidate_pairs[candidate_id]
+                    relations.append({
+                        "memory_pair": (mem_a, mem_b),
+                        "relation_type": result.get("relation_type", "关联"),
+                        "confidence": confidence,
+                        "reasoning": result.get("reasoning", ""),
+                        "vector_similarity": vector_similarity,
+                    })
+
+            logger.debug(f"🧠 LLM批量分析完成: 发现 {len(relations)} 个关系")
+            return relations
+
+        except Exception as e:
+            logger.error(f"LLM批量关系分析失败: {e}", exc_info=True)
+            return []
 
     async def start_maintenance_scheduler(self) -> None:
         """

src/memory_graph/tools/memory_tools.py

@@ -35,6 +35,7 @@ class MemoryTools:
         persistence_manager: PersistenceManager,
         embedding_generator: Optional[EmbeddingGenerator] = None,
         max_expand_depth: int = 1,
+        expand_semantic_threshold: float = 0.3,
     ):
         """
         初始化工具集
@@ -45,14 +46,16 @@ class MemoryTools:
             persistence_manager: 持久化管理器
             embedding_generator: 嵌入生成器（可选）
             max_expand_depth: 图扩展深度的默认值（从配置读取）
+            expand_semantic_threshold: 图扩展时语义相似度阈值（从配置读取）
         """
         self.vector_store = vector_store
         self.graph_store = graph_store
         self.persistence_manager = persistence_manager
         self._initialized = False
         self.max_expand_depth = max_expand_depth  # 保存配置的默认值
+        self.expand_semantic_threshold = expand_semantic_threshold  # 保存配置的语义阈值
 
-        logger.info(f"MemoryTools 初始化: max_expand_depth={max_expand_depth}")
+        logger.info(f"MemoryTools 初始化: max_expand_depth={max_expand_depth}, expand_semantic_threshold={expand_semantic_threshold}")
 
         # 初始化组件
         self.extractor = MemoryExtractor()
@@ -507,7 +510,7 @@ class MemoryTools:
                             initial_memory_ids=list(initial_memory_ids),
                             query_embedding=query_embedding,
                             max_depth=expand_depth,
-                            semantic_threshold=0.5,
+                            semantic_threshold=self.expand_semantic_threshold,  # 使用配置的阈值
                             max_expanded=top_k * 2
                         )
                         

template/bot_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "7.6.2"
+version = "7.6.4"
 
 #----以下是给开发人员阅读的，如果你只是部署了MoFox-Bot，不需要阅读----
 #如果你想要修改配置文件，请递增version的值
@@ -251,19 +251,32 @@ vector_db_path = "data/memory_graph/chroma_db" # 向量数据库路径 (使用
 # === 记忆检索配置 ===
 search_top_k = 10 # 默认检索返回数量
 search_min_importance = 0.3 # 最小重要性阈值 (0.0-1.0)
-search_similarity_threshold = 0.5 # 向量相似度阈值
+search_similarity_threshold = 0.6 # 向量相似度阈值
+search_expand_semantic_threshold = 0.3 # 图扩展时语义相似度阈值（建议0.3-0.5，过低可能引入无关记忆，过高无法扩展）
 
 # 智能查询优化
 enable_query_optimization = true # 启用查询优化（使用小模型分析对话历史，生成综合性搜索查询）
 
 # === 记忆整合配置 ===
+# 记忆整合包含两个功能：1)去重（合并相似记忆）2)关联（建立记忆关系）
 # 注意：整合任务会遍历所有记忆进行相似度计算，可能占用较多资源
 # 建议：1) 降低执行频率；2) 提高相似度阈值减少误判；3) 限制批量大小
 consolidation_enabled = true # 是否启用记忆整合
 consolidation_interval_hours = 1.0 # 整合任务执行间隔
-consolidation_similarity_threshold = 0.92 # 相似记忆去重阈值（建议>=0.92减少误判，0.85太低）
-consolidation_time_window_hours = 6.0 # 整合时间窗口（小时）
-consolidation_max_batch_size = 50 # 单次最多处理的记忆数量（限制批量避免阻塞）
+consolidation_deduplication_threshold = 0.93 # 相似记忆去重阈值
+consolidation_time_window_hours = 2.0 # 整合时间窗口（小时）- 统一用于去重和关联
+consolidation_max_batch_size = 100 # 单次最多处理的记忆数量
+
+# 记忆关联配置（整合功能的子模块）
+consolidation_linking_enabled = true # 是否启用记忆关联建立
+consolidation_linking_max_candidates = 10 # 每个记忆最多关联的候选数
+consolidation_linking_max_memories = 20 # 单次最多处理的记忆总数
+consolidation_linking_min_importance = 0.5 # 最低重要性阈值（低于此值的记忆不参与关联）
+consolidation_linking_pre_filter_threshold = 0.7 # 向量相似度预筛选阈值
+consolidation_linking_max_pairs_for_llm = 5 # 最多发送给LLM分析的候选对数
+consolidation_linking_min_confidence = 0.7 # LLM分析最低置信度阈值
+consolidation_linking_llm_temperature = 0.2 # LLM分析温度参数
+consolidation_linking_llm_max_tokens = 1500 # LLM分析最大输出长度
 
 # === 记忆遗忘配置 ===
 forgetting_enabled = true # 是否启用自动遗忘
@@ -273,7 +286,7 @@ forgetting_min_importance = 0.8 # 最小保护重要性（高于此值的记忆
 # === 记忆激活配置 ===
 activation_decay_rate = 0.9 # 激活度衰减率（每天衰减10%）
 activation_propagation_strength = 0.5 # 激活传播强度（传播到相关记忆的激活度比例）
-activation_propagation_depth = 2 # 激活传播深度（最多传播几层，建议1-2）
+activation_propagation_depth = 1 # 激活传播深度（最多传播几层，建议1-2）
 
 # === 记忆检索配置 ===
 search_max_expand_depth = 2 # 检索时图扩展深度（0=仅直接匹配，1=扩展1跳，2=扩展2跳，推荐1-2）