🤖 自动格式化代码 [skip ci]
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@@ -1353,11 +1353,11 @@ class ParahippocampalGyrus:
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if not memory_items:
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try:
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self.memory_graph.G.remove_node(node)
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node_changes["removed"].append(f"{node}(空节点)") # 标记为空节点移除
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node_changes["removed"].append(f"{node}(空节点)") # 标记为空节点移除
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logger.debug(f"[遗忘] 移除了空的节点: {node}")
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except nx.NetworkXError as e:
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logger.warning(f"[遗忘] 移除空节点 {node} 时发生错误（可能已被移除）: {e}")
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continue # 处理下一个节点
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continue # 处理下一个节点
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# --- 如果节点不为空，则执行原来的不活跃检查和随机移除逻辑 ---
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last_modified = node_data.get("last_modified", current_time)
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@@ -1373,15 +1373,15 @@ class ParahippocampalGyrus:
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memory_items.remove(removed_item)
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# 条件3：检查移除后 memory_items 是否变空
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if memory_items: # 如果移除后列表不为空
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if memory_items: # 如果移除后列表不为空
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# self.memory_graph.G.nodes[node]["memory_items"] = memory_items # 直接修改列表即可
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self.memory_graph.G.nodes[node]["last_modified"] = current_time # 更新修改时间
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self.memory_graph.G.nodes[node]["last_modified"] = current_time # 更新修改时间
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node_changes["reduced"].append(f"{node} (数量: {current_count} -> {len(memory_items)})")
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else: # 如果移除后列表为空
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else: # 如果移除后列表为空
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# 尝试移除节点，处理可能的错误
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try:
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self.memory_graph.G.remove_node(node)
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node_changes["removed"].append(f"{node}(遗忘清空)") # 标记为遗忘清空
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node_changes["removed"].append(f"{node}(遗忘清空)") # 标记为遗忘清空
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logger.debug(f"[遗忘] 节点 {node} 因移除最后一项而被清空。")
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except nx.NetworkXError as e:
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logger.warning(f"[遗忘] 尝试移除节点 {node} 时发生错误（可能已被移除）：{e}")
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@@ -1464,9 +1464,9 @@ class ParahippocampalGyrus:
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node_data = self.memory_graph.G.nodes[node]
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memory_items = node_data.get("memory_items", [])
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if not isinstance(memory_items, list) or len(memory_items) < 2:
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continue # 双重检查，理论上不会进入
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continue # 双重检查，理论上不会进入
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items_copy = list(memory_items) # 创建副本以安全迭代和修改
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items_copy = list(memory_items) # 创建副本以安全迭代和修改
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# 遍历所有记忆项组合
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for item1, item2 in combinations(items_copy, 2):
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@@ -1495,21 +1495,24 @@ class ParahippocampalGyrus:
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# 从原始列表中移除信息量较低的项
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try:
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memory_items.remove(item_to_remove)
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logger.info(f"[整合] 已合并节点 '{node}' 中的记忆，保留: '{item_to_keep[:60]}...', 移除: '{item_to_remove[:60]}...'" )
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logger.info(
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f"[整合] 已合并节点 '{node}' 中的记忆，保留: '{item_to_keep[:60]}...', 移除: '{item_to_remove[:60]}...'"
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)
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merged_count += 1
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nodes_modified.add(node)
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node_data['last_modified'] = current_timestamp # 更新修改时间
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node_data["last_modified"] = current_timestamp # 更新修改时间
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_merged_in_this_node = True
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break # 每个节点每次检查只合并一对
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break # 每个节点每次检查只合并一对
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except ValueError:
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# 如果项已经被移除（例如，在之前的迭代中作为 item_to_keep），则跳过
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logger.warning(f"[整合] 尝试移除节点 '{node}' 中不存在的项 '{item_to_remove[:30]}...'，可能已被合并。")
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logger.warning(
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f"[整合] 尝试移除节点 '{node}' 中不存在的项 '{item_to_remove[:30]}...'，可能已被合并。"
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)
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continue
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# # 如果节点内发生了合并，更新节点数据 (这种方式不安全，会丢失其他属性)
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# if merged_in_this_node:
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# self.memory_graph.G.nodes[node]["memory_items"] = memory_items
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if merged_count > 0:
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logger.info(f"[整合] 共合并了 {merged_count} 对相似记忆项，分布在 {len(nodes_modified)} 个节点中。")
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sync_start = time.time()
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@@ -19,9 +19,9 @@ class MemoryConfig:
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memory_ban_words: List[str] # 记忆过滤词列表
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# 新增：记忆整合相关配置
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consolidation_similarity_threshold: float # 相似度阈值
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consolidate_memory_percentage: float # 检查节点比例
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consolidate_memory_interval: int # 记忆整合间隔
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consolidation_similarity_threshold: float # 相似度阈值
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consolidate_memory_percentage: float # 检查节点比例
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consolidate_memory_interval: int # 记忆整合间隔
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llm_topic_judge: str # 话题判断模型
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llm_summary_by_topic: str # 话题总结模型
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@@ -31,7 +31,9 @@ class MemoryConfig:
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"""从全局配置创建记忆系统配置"""
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# 使用 getattr 提供默认值，防止全局配置缺少这些项
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return cls(
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memory_build_distribution=getattr(global_config, "memory_build_distribution", (24, 12, 0.5, 168, 72, 0.5)), # 添加默认值
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memory_build_distribution=getattr(
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global_config, "memory_build_distribution", (24, 12, 0.5, 168, 72, 0.5)
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), # 添加默认值
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build_memory_sample_num=getattr(global_config, "build_memory_sample_num", 5),
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build_memory_sample_length=getattr(global_config, "build_memory_sample_length", 30),
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memory_compress_rate=getattr(global_config, "memory_compress_rate", 0.1),
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@@ -41,6 +43,8 @@ class MemoryConfig:
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consolidation_similarity_threshold=getattr(global_config, "consolidation_similarity_threshold", 0.7),
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consolidate_memory_percentage=getattr(global_config, "consolidate_memory_percentage", 0.01),
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consolidate_memory_interval=getattr(global_config, "consolidate_memory_interval", 1000),
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llm_topic_judge=getattr(global_config, "llm_topic_judge", "default_judge_model"), # 添加默认模型名
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llm_summary_by_topic=getattr(global_config, "llm_summary_by_topic", "default_summary_model"), # 添加默认模型名
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llm_topic_judge=getattr(global_config, "llm_topic_judge", "default_judge_model"), # 添加默认模型名
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llm_summary_by_topic=getattr(
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global_config, "llm_summary_by_topic", "default_summary_model"
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), # 添加默认模型名
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)
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