diff --git a/src/memory_graph/core/node_merger.py b/src/memory_graph/core/node_merger.py index c63fbe800..8f91df149 100644 --- a/src/memory_graph/core/node_merger.py +++ b/src/memory_graph/core/node_merger.py @@ -4,8 +4,6 @@ from __future__ import annotations -from typing import Callable - from src.common.logger import get_logger from src.config.official_configs import MemoryConfig from src.memory_graph.models import MemoryNode, NodeType @@ -74,7 +72,7 @@ class NodeMerger: try: # 在向量存储中搜索相似节点 results = await self.vector_store.search_similar_nodes( - query_embedding=node.embedding, # type: ignore[arg-type] + query_embedding=node.embedding, limit=limit + 1, # +1 因为可能包含节点自己 node_types=[node.node_type], # 只搜索相同类型的节点 min_similarity=threshold, @@ -82,7 +80,7 @@ class NodeMerger: # 过滤掉节点自己，并构建结果 similar_nodes = [] - for node_id, similarity, _ in results: + for node_id, similarity, metadata in results: if node_id == node.id: continue # 跳过自己 @@ -97,7 +95,7 @@ class NodeMerger: logger.debug(f"找到 {len(similar_nodes)} 个相似节点 (阈值: {threshold})") return similar_nodes - except (RuntimeError, ValueError, KeyError, AttributeError) as e: + except Exception as e: logger.error(f"查找相似节点失败: {e}") return [] @@ -181,13 +179,13 @@ class NodeMerger: # 3. 检查邻居内容是否有重叠 source_neighbor_contents = set() - for neighbor_id, _ in source_neighbors: + for neighbor_id, edge_data in source_neighbors: neighbor_node = self._get_node_content(neighbor_id) if neighbor_node: source_neighbor_contents.add(neighbor_node.lower()) target_neighbor_contents = set() - for neighbor_id, _ in target_neighbors: + for neighbor_id, edge_data in target_neighbors: neighbor_node = self._get_node_content(neighbor_id) if neighbor_node: target_neighbor_contents.add(neighbor_node.lower()) @@ -245,7 +243,7 @@ class NodeMerger: logger.debug(f"节点合并成功: {source.id} → {target.id}") return True - except (RuntimeError, ValueError, KeyError, AttributeError) as e: + except Exception as e: logger.error(f"节点合并失败: {e}") return False @@ -278,7 +276,7 @@ class NodeMerger: async def batch_merge_similar_nodes( self, nodes: list[MemoryNode], - progress_callback: Callable[..., None] | None = None, + progress_callback: callable | None = None, ) -> dict: """ 批量处理节点合并 @@ -326,7 +324,7 @@ class NodeMerger: if progress_callback: progress_callback(i + 1, stats["total"], stats) - except (RuntimeError, ValueError, KeyError, AttributeError) as e: + except Exception as e: logger.error(f"处理节点 {node.id} 时失败: {e}") stats["skipped"] += 1 @@ -339,19 +337,19 @@ class NodeMerger: def get_merge_candidates( self, - _min_similarity: float = 0.85, - _limit: int = 100, + min_similarity: float = 0.85, + limit: int = 100, ) -> list[tuple[str, str, float]]: """ 获取待合并的候选节点对 Args: - _min_similarity: 最小相似度 - _limit: 最大返回数量 + min_similarity: 最小相似度 + limit: 最大返回数量 Returns: List of (node_id_1, node_id_2, similarity) """ - # 改进空间: 实现更智能的候选查找算法 + # TODO: 实现更智能的候选查找算法 # 目前返回空列表，后续可以基于向量存储进行批量查询 return [] diff --git a/src/memory_graph/long_term_manager.py b/src/memory_graph/long_term_manager.py index 5d5ccb497..8395de3b8 100644 --- a/src/memory_graph/long_term_manager.py +++ b/src/memory_graph/long_term_manager.py @@ -72,13 +72,13 @@ class LongTermMemoryManager: logger.debug("开始初始化长期记忆管理器...") # 确保底层 MemoryManager 已初始化 - if not self.memory_manager._initialized: # type: ignore[attr-defined,misc] + if not self.memory_manager._initialized: await self.memory_manager.initialize() self._initialized = True logger.debug("长期记忆管理器初始化完成") - except (OSError, RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"长期记忆管理器初始化失败: {e}") raise @@ -144,7 +144,7 @@ class LongTermMemoryManager: logger.debug(f"短期记忆转移完成: {result}") return result - except (OSError, RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"转移短期记忆失败: {e}") return {"error": str(e), "processed_count": 0} @@ -193,7 +193,7 @@ class LongTermMemoryManager: else: result["failed_count"] += 1 - except (KeyError, TypeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"处理短期记忆 {stm.id} 失败: {e}") result["failed_count"] += 1 @@ -211,12 +211,12 @@ class LongTermMemoryManager: from src.config.config import global_config # 检查是否启用了高级路径扩展算法 - use_path_expansion = getattr(global_config.memory, "enable_path_expansion", False) # type: ignore[union-attr] + use_path_expansion = getattr(global_config.memory, "enable_path_expansion", False) # 1. 检索记忆 # 如果启用了路径扩展，search_memories 内部会自动使用 PathScoreExpansion # 我们只需要传入合适的 expand_depth - expand_depth = getattr(global_config.memory, "path_expansion_max_hops", 2) if use_path_expansion else 0 # type: ignore[union-attr] + expand_depth = getattr(global_config.memory, "path_expansion_max_hops", 2) if use_path_expansion else 0 memories = await self.memory_manager.search_memories( query=stm.content, @@ -242,7 +242,7 @@ class LongTermMemoryManager: # 获取该记忆的直接关联记忆（1跳邻居） try: # 利用 MemoryManager 的底层图遍历能力 - related_ids = self.memory_manager._get_related_memories(mem.id, max_depth=1) # type: ignore[attr-defined,misc] + related_ids = self.memory_manager._get_related_memories(mem.id, max_depth=1) # 限制每个记忆扩展的邻居数量，避免上下文爆炸 max_neighbors = 2 @@ -259,7 +259,7 @@ class LongTermMemoryManager: if neighbor_count >= max_neighbors: break - except (KeyError, TypeError, ValueError, AttributeError) as e: + except Exception as e: logger.warning(f"获取关联记忆失败: {e}") # 总数限制 @@ -269,7 +269,7 @@ class LongTermMemoryManager: logger.debug(f"为短期记忆 {stm.id} 找到 {len(expanded_memories)} 个长期记忆 (含简单图扩展)") return expanded_memories - except (RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"检索相似长期记忆失败: {e}") return [] @@ -295,7 +295,7 @@ class LongTermMemoryManager: # 调用长期记忆构建模型 llm = LLMRequest( - model_set=model_config.model_task_config.memory_long_term_builder, # type: ignore[union-attr] + model_set=model_config.model_task_config.memory_long_term_builder, request_type="long_term_memory.graph_operations", ) @@ -311,7 +311,7 @@ class LongTermMemoryManager: logger.debug(f"LLM 生成 {len(operations)} 个图操作指令") return operations - except (RuntimeError, ValueError, KeyError) as e: + except Exception as e: logger.error(f"LLM 决策图操作失败: {e}") # 默认创建新记忆 return [ @@ -550,13 +550,13 @@ class LongTermMemoryManager: else: logger.warning(f"未实现的操作类型: {op.operation_type}") - except (KeyError, TypeError, ValueError, RuntimeError) as e: + except Exception as e: logger.error(f"执行图操作失败: {op}, 错误: {e}") logger.debug(f"执行了 {success_count}/{len(operations)} 个图操作") return success_count > 0 - except (KeyError, TypeError, ValueError, RuntimeError) as e: + except Exception as e: logger.error(f"执行图操作失败: {e}") return False @@ -715,7 +715,7 @@ class LongTermMemoryManager: logger.info(f"开始智能合并记忆: {memories_to_merge} -> {target_id}") # 1. 调用 GraphStore 的合并功能（转移节点和边） - merge_success = self.memory_manager.graph_store.merge_memories(target_id, memories_to_merge) # type: ignore[union-attr] + merge_success = self.memory_manager.graph_store.merge_memories(target_id, memories_to_merge) if merge_success: # 2. 更新目标记忆的元数据 @@ -731,7 +731,7 @@ class LongTermMemoryManager: ) # 3. 异步保存 - asyncio.create_task(self.memory_manager._async_save_graph_store("合并记忆")) # type: ignore[attr-defined,misc] + asyncio.create_task(self.memory_manager._async_save_graph_store("合并记忆")) logger.info(f"合并记忆完成: {source_ids} -> {target_id}") else: logger.error(f"合并记忆失败: {source_ids}") @@ -752,7 +752,7 @@ class LongTermMemoryManager: import uuid node_id = str(uuid.uuid4()) - success = self.memory_manager.graph_store.add_node( # type: ignore[union-attr] + success = self.memory_manager.graph_store.add_node( node_id=node_id, content=content, node_type=node_type, @@ -788,7 +788,7 @@ class LongTermMemoryManager: logger.warning("更新节点失败: 缺少 node_id") return - success = self.memory_manager.graph_store.update_node( # type: ignore[union-attr] + success = self.memory_manager.graph_store.update_node( node_id=node_id, content=updated_content ) @@ -815,11 +815,11 @@ class LongTermMemoryManager: # 更新目标节点内容 if merged_content: - self.memory_manager.graph_store.update_node(target_id, content=merged_content) # type: ignore[union-attr] + self.memory_manager.graph_store.update_node(target_id, content=merged_content) # 合并其他节点到目标节点 for source_id in sources: - self.memory_manager.graph_store.merge_nodes(source_id, target_id) # type: ignore[union-attr] + self.memory_manager.graph_store.merge_nodes(source_id, target_id) logger.info(f"合并节点: {sources} -> {target_id}") @@ -873,7 +873,7 @@ class LongTermMemoryManager: logger.warning("更新边失败: 缺少 edge_id") return - success = self.memory_manager.graph_store.update_edge( # type: ignore[union-attr] + success = self.memory_manager.graph_store.update_edge( edge_id=edge_id, relation=updated_relation, importance=updated_importance @@ -894,7 +894,7 @@ class LongTermMemoryManager: logger.warning("删除边失败: 缺少 edge_id") return - success = self.memory_manager.graph_store.remove_edge(edge_id) # type: ignore[union-attr] + success = self.memory_manager.graph_store.remove_edge(edge_id) if success: logger.info(f"删除边: {edge_id}") @@ -919,9 +919,9 @@ class LongTermMemoryManager: ) await self.memory_manager.vector_store.add_node(node) node.mark_vector_stored() - if self.memory_manager.graph_store.graph.has_node(node_id): # type: ignore[union-attr] - self.memory_manager.graph_store.graph.nodes[node_id]["has_vector"] = True # type: ignore[union-attr] - except (RuntimeError, ValueError, AttributeError) as e: + if self.memory_manager.graph_store.graph.has_node(node_id): + self.memory_manager.graph_store.graph.nodes[node_id]["has_vector"] = True + except Exception as e: logger.warning(f"生成节点 embedding 失败: {e}") async def apply_long_term_decay(self) -> dict[str, Any]: @@ -939,7 +939,7 @@ class LongTermMemoryManager: try: logger.info("开始应用长期记忆激活度衰减...") - all_memories = self.memory_manager.graph_store.get_all_memories() # type: ignore[union-attr] + all_memories = self.memory_manager.graph_store.get_all_memories() decayed_count = 0 for memory in all_memories: @@ -972,14 +972,14 @@ class LongTermMemoryManager: logger.warning(f"解析时间失败: {e}") # 保存更新 - await self.memory_manager.persistence.save_graph_store( # type: ignore[union-attr] - self.memory_manager.graph_store # type: ignore[arg-type] + await self.memory_manager.persistence.save_graph_store( + self.memory_manager.graph_store ) logger.info(f"长期记忆衰减完成: {decayed_count} 条记忆已更新") return {"decayed_count": decayed_count, "total_memories": len(all_memories)} - except (RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"应用长期记忆衰减失败: {e}") return {"error": str(e), "decayed_count": 0} @@ -1007,7 +1007,7 @@ class LongTermMemoryManager: self._initialized = False logger.info("长期记忆管理器已关闭") - except (RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"关闭长期记忆管理器失败: {e}") @@ -1015,9 +1015,9 @@ class LongTermMemoryManager: _long_term_manager_instance: LongTermMemoryManager | None = None -def get_long_term_manager() -> LongTermMemoryManager: # type: ignore +def get_long_term_manager() -> LongTermMemoryManager: """获取长期记忆管理器单例（需要先初始化记忆图系统）""" - global _long_term_manager_instance # type: ignore[name-defined,misc] + global _long_term_manager_instance if _long_term_manager_instance is None: from src.memory_graph.manager_singleton import get_memory_manager diff --git a/src/memory_graph/perceptual_manager.py b/src/memory_graph/perceptual_manager.py index e9dc7ac5b..76085d193 100644 --- a/src/memory_graph/perceptual_manager.py +++ b/src/memory_graph/perceptual_manager.py @@ -118,7 +118,7 @@ class PerceptualMemoryManager: f"(已加载 {len(self.perceptual_memory.blocks)} 个记忆块)" ) - except (OSError, RuntimeError, ValueError) as e: + except Exception as e: logger.error(f"感知记忆管理器初始化失败: {e}") raise @@ -146,24 +146,24 @@ class PerceptualMemoryManager: try: if not hasattr(self.perceptual_memory, "pending_messages"): - self.perceptual_memory.pending_messages = [] # type: ignore[union-attr] + self.perceptual_memory.pending_messages = [] self._cleanup_pending_messages() stream_id = message.get("stream_id", "unknown") self._normalize_message_timestamp(message) - self.perceptual_memory.pending_messages.append(message) # type: ignore[union-attr] + self.perceptual_memory.pending_messages.append(message) self._enforce_pending_limits(stream_id) logger.debug( f"消息已添加到待处理队列 (stream={stream_id[:8]}, " - f"总数={len(self.perceptual_memory.pending_messages)})" # type: ignore[union-attr] + f"总数={len(self.perceptual_memory.pending_messages)})" ) # 按 stream_id 检查是否达到创建块的条件 stream_messages = [ msg - for msg in self.perceptual_memory.pending_messages # type: ignore[union-attr] + for msg in self.perceptual_memory.pending_messages if msg.get("stream_id") == stream_id ] @@ -173,7 +173,7 @@ class PerceptualMemoryManager: return None - except (KeyError, TypeError, ValueError) as e: + except Exception as e: logger.error(f"添加消息失败: {e}") return None @@ -190,7 +190,7 @@ class PerceptualMemoryManager: try: self._cleanup_pending_messages() # 只取出指定 stream_id 的 block_size 条消息 - stream_messages = [msg for msg in self.perceptual_memory.pending_messages if msg.get("stream_id") == stream_id] # type: ignore[union-attr] + stream_messages = [msg for msg in self.perceptual_memory.pending_messages if msg.get("stream_id") == stream_id] if len(stream_messages) < self.block_size: logger.warning(f"stream {stream_id} 的消息不足 {self.block_size} 条，无法创建块") @@ -201,7 +201,7 @@ class PerceptualMemoryManager: # 从 pending_messages 中移除这些消息 for msg in messages: - self.perceptual_memory.pending_messages.remove(msg) # type: ignore[union-attr] + self.perceptual_memory.pending_messages.remove(msg) # 合并消息文本 combined_text = self._combine_messages(messages) @@ -219,21 +219,21 @@ class PerceptualMemoryManager: ) # 添加到记忆堆顶部 - self.perceptual_memory.blocks.insert(0, block) # type: ignore[union-attr] + self.perceptual_memory.blocks.insert(0, block) # 更新所有块的位置 - for i, b in enumerate(self.perceptual_memory.blocks): # type: ignore[union-attr] + for i, b in enumerate(self.perceptual_memory.blocks): b.position_in_stack = i # FIFO 淘汰：如果超过最大容量，移除最旧的块 - if len(self.perceptual_memory.blocks) > self.max_blocks: # type: ignore[union-attr] - removed_blocks = self.perceptual_memory.blocks[self.max_blocks :] # type: ignore[union-attr] - self.perceptual_memory.blocks = self.perceptual_memory.blocks[: self.max_blocks] # type: ignore[union-attr] + if len(self.perceptual_memory.blocks) > self.max_blocks: + removed_blocks = self.perceptual_memory.blocks[self.max_blocks :] + self.perceptual_memory.blocks = self.perceptual_memory.blocks[: self.max_blocks] logger.debug(f"记忆堆已满，移除 {len(removed_blocks)} 个旧块") logger.debug( f"✅ 创建新记忆块: {block.id} (stream={stream_id[:8]}, " - f"堆大小={len(self.perceptual_memory.blocks)}/{self.max_blocks})" # type: ignore[union-attr] + f"堆大小={len(self.perceptual_memory.blocks)}/{self.max_blocks})" ) # 异步保存 @@ -241,7 +241,7 @@ class PerceptualMemoryManager: return block - except (KeyError, TypeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"创建记忆块失败: {e}") return None @@ -249,7 +249,7 @@ class PerceptualMemoryManager: """确保消息包含 timestamp 字段并返回其值。""" raw_ts = message.get("timestamp", message.get("time")) try: - timestamp = float(raw_ts) if raw_ts is not None else time.time() + timestamp = float(raw_ts) except (TypeError, ValueError): timestamp = time.time() message["timestamp"] = timestamp @@ -270,7 +270,7 @@ class PerceptualMemoryManager: for msg in pending: ts = msg.get("timestamp") or msg.get("time") try: - ts_value = float(ts) if ts is not None else time.time() + ts_value = float(ts) except (TypeError, ValueError): ts_value = time.time() msg["timestamp"] = ts_value @@ -369,7 +369,7 @@ class PerceptualMemoryManager: embedding = await self.embedding_generator.generate(text) return embedding - except (RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"生成向量失败: {e}") return None @@ -391,7 +391,7 @@ class PerceptualMemoryManager: embeddings = await self.embedding_generator.generate_batch(texts) return embeddings - except (RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"批量生成向量失败: {e}") return [None] * len(texts) @@ -425,18 +425,18 @@ class PerceptualMemoryManager: logger.warning("查询向量生成失败，返回空列表") return [] - # 批量计算相似度（使用异步版本） + # 批量计算所有块的相似度（使用异步版本） blocks_with_embeddings = [ - block for block in self.perceptual_memory.blocks # type: ignore[union-attr] + block for block in self.perceptual_memory.blocks if block.embedding is not None ] if not blocks_with_embeddings: return [] - # 批量计算相似度，过滤掉 None 向量 + # 批量计算相似度 block_embeddings = [block.embedding for block in blocks_with_embeddings] - similarities = await batch_cosine_similarity_async(query_embedding, block_embeddings) # type: ignore[arg-type] + similarities = await batch_cosine_similarity_async(query_embedding, block_embeddings) # 过滤和排序 scored_blocks = [] @@ -489,7 +489,7 @@ class PerceptualMemoryManager: return recalled_blocks - except (KeyError, TypeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"召回记忆块失败: {e}") return [] @@ -503,20 +503,20 @@ class PerceptualMemoryManager: try: # 从原位置移除这些块 for block in blocks_to_promote: - if block in self.perceptual_memory.blocks: # type: ignore[union-attr] - self.perceptual_memory.blocks.remove(block) # type: ignore[union-attr] + if block in self.perceptual_memory.blocks: + self.perceptual_memory.blocks.remove(block) # 将它们插入到堆顶（保持原有的相对顺序） for block in reversed(blocks_to_promote): - self.perceptual_memory.blocks.insert(0, block) # type: ignore[union-attr] + self.perceptual_memory.blocks.insert(0, block) # 更新所有块的位置 - for i, block in enumerate(self.perceptual_memory.blocks): # type: ignore[union-attr] + for i, block in enumerate(self.perceptual_memory.blocks): block.position_in_stack = i logger.debug(f"提升 {len(blocks_to_promote)} 个块到堆顶") - except (ValueError, AttributeError) as e: + except Exception as e: logger.error(f"提升块失败: {e}") def get_activated_blocks(self) -> list[MemoryBlock]: @@ -531,7 +531,7 @@ class PerceptualMemoryManager: activated = [ block - for block in self.perceptual_memory.blocks # type: ignore[union-attr] + for block in self.perceptual_memory.blocks if block.recall_count >= self.activation_threshold ] @@ -552,12 +552,12 @@ class PerceptualMemoryManager: try: # 查找并移除块 - for i, block in enumerate(self.perceptual_memory.blocks): # type: ignore[union-attr] + for i, block in enumerate(self.perceptual_memory.blocks): if block.id == block_id: - self.perceptual_memory.blocks.pop(i) # type: ignore[union-attr] + self.perceptual_memory.blocks.pop(i) # 更新剩余块的位置 - for j, b in enumerate(self.perceptual_memory.blocks): # type: ignore[union-attr] + for j, b in enumerate(self.perceptual_memory.blocks): b.position_in_stack = j # 异步保存 @@ -568,7 +568,7 @@ class PerceptualMemoryManager: logger.warning(f"记忆块不存在: {block_id}") return False - except (ValueError, AttributeError) as e: + except Exception as e: logger.error(f"移除记忆块失败: {e}") return False @@ -607,22 +607,22 @@ class PerceptualMemoryManager: self._cleanup_pending_messages() # 保存到 JSON 文件 - import json + import orjson save_path = self.data_dir / "perceptual_memory.json" data = self.perceptual_memory.to_dict() - save_path.write_text(json.dumps(data, indent=2, ensure_ascii=False), encoding="utf-8") + save_path.write_bytes(orjson.dumps(data, option=orjson.OPT_INDENT_2)) logger.debug(f"感知记忆已保存到 {save_path}") - except (OSError, TypeError, ValueError) as e: + except Exception as e: logger.error(f"保存感知记忆失败: {e}") async def _load_from_disk(self) -> None: """从磁盘加载感知记忆""" try: - import json + import orjson load_path = self.data_dir / "perceptual_memory.json" @@ -630,13 +630,13 @@ class PerceptualMemoryManager: logger.debug("未找到感知记忆数据文件") return - data = json.loads(load_path.read_text(encoding="utf-8")) + data = orjson.loads(load_path.read_bytes()) self.perceptual_memory = PerceptualMemory.from_dict(data) # 重新加载向量数据 await self._reload_embeddings() - except (OSError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"加载感知记忆失败: {e}") async def _reload_embeddings(self) -> None: @@ -680,7 +680,7 @@ class PerceptualMemoryManager: self._initialized = False - except (OSError, RuntimeError, ValueError) as e: + except Exception as e: logger.error(f"关闭感知记忆管理器失败: {e}") @@ -690,7 +690,7 @@ _perceptual_manager_instance: PerceptualMemoryManager | None = None def get_perceptual_manager() -> PerceptualMemoryManager: """获取感知记忆管理器单例""" - global _perceptual_manager_instance # type: ignore + global _perceptual_manager_instance if _perceptual_manager_instance is None: _perceptual_manager_instance = PerceptualMemoryManager() return _perceptual_manager_instance diff --git a/src/memory_graph/short_term_manager.py b/src/memory_graph/short_term_manager.py index 5077f4ef9..2f94059ec 100644 --- a/src/memory_graph/short_term_manager.py +++ b/src/memory_graph/short_term_manager.py @@ -92,7 +92,7 @@ class ShortTermMemoryManager: self._initialized = True logger.debug(f"短期记忆管理器初始化完成 (已加载 {len(self.memories)} 条记忆)") - except (OSError, RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"短期记忆管理器初始化失败: {e}") raise @@ -144,7 +144,7 @@ class ShortTermMemoryManager: return result_memory - except (RuntimeError, ValueError, KeyError) as e: + except Exception as e: logger.error(f"添加短期记忆失败: {e}") return None @@ -198,7 +198,7 @@ class ShortTermMemoryManager: # 调用短期记忆构建模型 llm = LLMRequest( - model_set=model_config.model_task_config.memory_short_term_builder, # type: ignore[union-attr] + model_set=model_config.model_task_config.memory_short_term_builder, request_type="short_term_memory.extract", ) @@ -235,7 +235,7 @@ class ShortTermMemoryManager: logger.debug(f"提取结构化记忆: {memory.content[:50]}...") return memory - except (RuntimeError, ValueError, KeyError, AttributeError) as e: + except Exception as e: logger.error(f"提取结构化记忆失败: {e}") return None @@ -303,7 +303,7 @@ class ShortTermMemoryManager: # 调用短期记忆决策模型 llm = LLMRequest( - model_set=model_config.model_task_config.memory_short_term_decider, # type: ignore[union-attr] + model_set=model_config.model_task_config.memory_short_term_decider, request_type="short_term_memory.decide", ) @@ -340,7 +340,7 @@ class ShortTermMemoryManager: logger.debug(f"LLM 决策完成: {decision}") return decision - except (RuntimeError, ValueError, KeyError) as e: + except Exception as e: logger.error(f"LLM 决策失败: {e}") # 默认创建新记忆 return ShortTermDecision( @@ -431,7 +431,7 @@ class ShortTermMemoryManager: self.memories.append(new_memory) return new_memory - except (RuntimeError, ValueError, KeyError, AttributeError) as e: + except Exception as e: logger.error(f"执行决策失败: {e}") return None @@ -465,7 +465,7 @@ class ShortTermMemoryManager: return scored[:top_k] - except (RuntimeError, ValueError, KeyError, AttributeError) as e: + except Exception as e: logger.error(f"查找相似记忆失败: {e}") return [] @@ -490,7 +490,7 @@ class ShortTermMemoryManager: embedding = await self.embedding_generator.generate(text) return embedding - except (RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"生成向量失败: {e}") return None @@ -512,7 +512,7 @@ class ShortTermMemoryManager: embeddings = await self.embedding_generator.generate_batch(texts) return embeddings - except (RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"批量生成向量失败: {e}") return [None] * len(texts) @@ -532,11 +532,7 @@ class ShortTermMemoryManager: json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL) data = json_repair.loads(json_str) - if isinstance(data, dict): - return data - else: - logger.warning(f"JSON 解析返回非字典类型: {type(data)}") - return None + return data except json.JSONDecodeError as e: logger.warning(f"JSON 解析失败: {e}, 响应: {response[:200]}") @@ -586,7 +582,7 @@ class ShortTermMemoryManager: logger.debug(f"检索到 {len(results)} 条短期记忆") return results - except (RuntimeError, ValueError, KeyError, AttributeError) as e: + except Exception as e: logger.error(f"检索短期记忆失败: {e}") return [] @@ -646,7 +642,7 @@ class ShortTermMemoryManager: # 异步保存 asyncio.create_task(self._save_to_disk()) - except (RuntimeError, ValueError, KeyError, AttributeError) as e: + except Exception as e: logger.error(f"清除已转移记忆失败: {e}") def get_statistics(self) -> dict[str, Any]: @@ -670,6 +666,8 @@ class ShortTermMemoryManager: """保存短期记忆到磁盘""" async with self._save_lock: try: + import orjson + save_path = self.data_dir / "short_term_memory.json" data = { "memories": [mem.to_dict() for mem in self.memories], @@ -677,23 +675,25 @@ class ShortTermMemoryManager: "transfer_threshold": self.transfer_importance_threshold, } - save_path.write_text(json.dumps(data, indent=2, ensure_ascii=False), encoding="utf-8") + save_path.write_bytes(orjson.dumps(data, option=orjson.OPT_INDENT_2)) logger.debug(f"短期记忆已保存到 {save_path}") - except (OSError, TypeError, ValueError) as e: + except Exception as e: logger.error(f"保存短期记忆失败: {e}") async def _load_from_disk(self) -> None: """从磁盘加载短期记忆""" try: + import orjson + load_path = self.data_dir / "short_term_memory.json" if not load_path.exists(): - logger.debug("未找到短期记忆数据文件") + logger.info("未找到短期记忆数据文件") return - data = json.loads(load_path.read_text(encoding="utf-8")) + data = orjson.loads(load_path.read_bytes()) self.memories = [ShortTermMemory.from_dict(m) for m in data.get("memories", [])] # 重新生成向量 @@ -701,7 +701,7 @@ class ShortTermMemoryManager: logger.info(f"短期记忆已从 {load_path} 加载 ({len(self.memories)} 条)") - except (OSError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"加载短期记忆失败: {e}") async def _reload_embeddings(self) -> None: @@ -746,7 +746,7 @@ class ShortTermMemoryManager: self._initialized = False logger.info("短期记忆管理器已关闭") - except (RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"关闭短期记忆管理器失败: {e}") @@ -754,9 +754,9 @@ class ShortTermMemoryManager: _short_term_manager_instance: ShortTermMemoryManager | None = None -def get_short_term_manager() -> ShortTermMemoryManager: # type: ignore +def get_short_term_manager() -> ShortTermMemoryManager: """获取短期记忆管理器单例""" - global _short_term_manager_instance # type: ignore[name-defined,misc] + global _short_term_manager_instance if _short_term_manager_instance is None: _short_term_manager_instance = ShortTermMemoryManager() return _short_term_manager_instance diff --git a/src/memory_graph/storage/graph_store.py b/src/memory_graph/storage/graph_store.py index a54998247..1caedc345 100644 --- a/src/memory_graph/storage/graph_store.py +++ b/src/memory_graph/storage/graph_store.py @@ -9,7 +9,7 @@ from collections.abc import Iterable import networkx as nx from src.common.logger import get_logger -from src.memory_graph.models import EdgeType, Memory, MemoryEdge +from src.memory_graph.models import Memory, MemoryEdge logger = get_logger(__name__) @@ -204,7 +204,7 @@ class GraphStore: logger.debug(f"添加节点成功: {node_id} -> {memory_id}") return True - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"添加节点失败: {e}") return False @@ -253,7 +253,7 @@ class GraphStore: break return True - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"更新节点失败: {e}") return False @@ -288,7 +288,8 @@ class GraphStore: import uuid from datetime import datetime - # EdgeType 已在模块顶部导入 + from src.memory_graph.models import EdgeType, MemoryEdge + edge_id = str(uuid.uuid4()) created_at = datetime.now().isoformat() @@ -314,7 +315,7 @@ class GraphStore: # 尝试转换 edge_type try: - edge_type_enum: EdgeType = EdgeType(edge_type) + edge_type_enum = EdgeType(edge_type) except ValueError: edge_type_enum = EdgeType.RELATION @@ -337,7 +338,7 @@ class GraphStore: logger.debug(f"添加边成功: {source_id} -> {target_id} ({relation})") return edge_id - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"添加边失败: {e}") return None @@ -401,7 +402,7 @@ class GraphStore: break return True - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"更新边失败: {e}") return False @@ -451,7 +452,7 @@ class GraphStore: memory.edges = [e for e in memory.edges if e.id != edge_id] return True - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"删除边失败: {e}") return False @@ -507,7 +508,7 @@ class GraphStore: logger.info(f"成功合并记忆: {source_memory_ids} -> {target_memory_id}") return True - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"合并记忆失败: {e}") return False @@ -693,7 +694,7 @@ class GraphStore: except nx.NetworkXNoPath: return None - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"查找路径失败: {e}") return None @@ -745,8 +746,7 @@ class GraphStore: Returns: NetworkX 子图 """ - subgraph = self.graph.subgraph(node_ids) - return subgraph.copy() # type: ignore[return-value] + return self.graph.subgraph(node_ids).copy() def merge_nodes(self, source_id: str, target_id: str) -> None: """ @@ -870,7 +870,7 @@ class GraphStore: # 5. 同步图中的边到 Memory.edges（保证内存对象和图一致） try: store._sync_memory_edges_from_graph() - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError): + except Exception: logger.exception("同步图边到记忆.edges 失败") store._rebuild_node_edge_index() @@ -924,7 +924,7 @@ class GraphStore: try: # 使用 MemoryEdge.from_dict 构建对象 mem_edge = MemoryEdge.from_dict(edge_dict) - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError): + except Exception: # 兼容性：直接构造对象 mem_edge = MemoryEdge( id=edge_dict["id"] or "", @@ -978,7 +978,7 @@ class GraphStore: logger.debug(f"成功删除记忆: {memory_id}") return True - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"删除记忆失败 {memory_id}: {e}") return False diff --git a/src/memory_graph/storage/persistence.py b/src/memory_graph/storage/persistence.py index 434741967..12fe9f2a3 100644 --- a/src/memory_graph/storage/persistence.py +++ b/src/memory_graph/storage/persistence.py @@ -13,6 +13,7 @@ from datetime import datetime from pathlib import Path import aiofiles +import orjson from src.common.logger import get_logger from src.memory_graph.models import StagedMemory @@ -67,6 +68,7 @@ async def safe_atomic_write(temp_path: Path, target_path: Path, max_retries: int target_path.rename(old_file) except OSError: # 策略3: 使用时间戳后缀 + from datetime import datetime backup_file = target_path.with_suffix(f".bak_{datetime.now().strftime('%H%M%S')}") target_path.rename(backup_file) # 标记稍后清理 @@ -125,7 +127,7 @@ async def _cleanup_backup_files(directory: Path, file_stem: str, keep_recent: in except OSError as e: logger.debug(f"清理备份文件失败: {old_file.name}, {e}") - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.debug(f"清理备份文件任务失败: {e}") @@ -190,12 +192,11 @@ class PersistenceManager: "statistics": graph_store.get_statistics(), } - # 使用 json 序列化 - json_data = json.dumps( + # 使用 orjson 序列化（更快） + json_data = orjson.dumps( data, - indent=2, - ensure_ascii=False - ).encode("utf-8") + option=orjson.OPT_INDENT_2 | orjson.OPT_SERIALIZE_NUMPY, + ) # 原子写入（先写临时文件，再重命名） temp_file = self.graph_file.with_suffix(".tmp") @@ -207,7 +208,7 @@ class PersistenceManager: logger.debug(f"图数据已保存: {self.graph_file}, 大小: {len(json_data) / 1024:.2f} KB") - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"保存图数据失败: {e}") raise @@ -234,7 +235,7 @@ class PersistenceManager: try: async with aiofiles.open(self.graph_file, "rb") as f: json_data = await f.read() - data = json.loads(json_data.decode("utf-8")) + data = orjson.loads(json_data) break except OSError as e: if attempt == max_retries - 1: @@ -252,7 +253,7 @@ class PersistenceManager: logger.debug(f"图数据加载完成: {graph_store.get_statistics()}") return graph_store - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"加载图数据失败: {e}") # 尝试加载备份 return await self._load_from_backup() @@ -275,7 +276,7 @@ class PersistenceManager: "staged_memories": [sm.to_dict() for sm in staged_memories], } - json_data = json.dumps(data, indent=2, ensure_ascii=False).encode("utf-8") + json_data = orjson.dumps(data, option=orjson.OPT_INDENT_2 | orjson.OPT_SERIALIZE_NUMPY) temp_file = self.staged_file.with_suffix(".tmp") async with aiofiles.open(temp_file, "wb") as f: @@ -284,7 +285,7 @@ class PersistenceManager: # 使用安全的原子写入 await safe_atomic_write(temp_file, self.staged_file) - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"保存临时记忆失败: {e}") raise @@ -308,7 +309,7 @@ class PersistenceManager: try: async with aiofiles.open(self.staged_file, "rb") as f: json_data = await f.read() - data = json.loads(json_data.decode("utf-8")) + data = orjson.loads(json_data) break except OSError as e: if attempt == max_retries - 1: @@ -323,7 +324,7 @@ class PersistenceManager: staged_memories = [StagedMemory.from_dict(sm) for sm in data.get("staged_memories", [])] return staged_memories - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"加载临时记忆失败: {e}") return [] @@ -353,7 +354,7 @@ class PersistenceManager: return None - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"创建备份失败: {e}") return None @@ -377,7 +378,7 @@ class PersistenceManager: try: async with aiofiles.open(latest_backup, "rb") as f: json_data = await f.read() - data = json.loads(json_data.decode("utf-8")) + data = orjson.loads(json_data) break except OSError as e: if attempt == max_retries - 1: @@ -394,7 +395,7 @@ class PersistenceManager: return graph_store - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"从备份恢复失败: {e}") return None @@ -413,7 +414,7 @@ class PersistenceManager: backup_file.unlink() logger.debug(f"删除旧备份: {backup_file}") - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.warning(f"清理旧备份失败: {e}") async def start_auto_save( @@ -458,7 +459,7 @@ class PersistenceManager: if current_time.minute == 0: # 每个整点 await self.create_backup() - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"自动保存失败: {e}") self._auto_save_task = asyncio.create_task(auto_save_loop()) diff --git a/src/memory_graph/storage/vector_store.py b/src/memory_graph/storage/vector_store.py index a20460f55..ae79e9380 100644 --- a/src/memory_graph/storage/vector_store.py +++ b/src/memory_graph/storage/vector_store.py @@ -99,18 +99,16 @@ class VectorStore: # 处理额外的元数据，将 list 转换为 JSON 字符串 for key, value in node.metadata.items(): if isinstance(value, list | dict): - import json - metadata[key] = json.dumps(value, ensure_ascii=False) - elif isinstance(value, str): + import orjson + metadata[key] = orjson.dumps(value, option=orjson.OPT_NON_STR_KEYS).decode("utf-8") + elif isinstance(value, str | int | float | bool) or value is None: metadata[key] = value - elif isinstance(value, (int, float, bool)) or value is None: - metadata[key] = str(value) if value is not None else "" else: metadata[key] = str(value) self.collection.add( ids=[node.id], - embeddings=[node.embedding.tolist()] if node.embedding is not None else [[0.0]], # type: ignore[union-attr] + embeddings=[node.embedding.tolist()], metadatas=[metadata], documents=[node.content], # 文本内容用于检索 ) @@ -140,6 +138,7 @@ class VectorStore: try: # 准备元数据 + import orjson metadatas = [] for n in valid_nodes: metadata = { @@ -149,24 +148,21 @@ class VectorStore: } for key, value in n.metadata.items(): if isinstance(value, list | dict): - import json - metadata[key] = json.dumps(value, ensure_ascii=False) - elif isinstance(value, str): - metadata[key] = value - elif isinstance(value, (int, float, bool)) or value is None: - metadata[key] = str(value) if value is not None else "" + metadata[key] = orjson.dumps(value, option=orjson.OPT_NON_STR_KEYS).decode("utf-8") + elif isinstance(value, str | int | float | bool) or value is None: + metadata[key] = value # type: ignore else: metadata[key] = str(value) metadatas.append(metadata) self.collection.add( ids=[n.id for n in valid_nodes], - embeddings=[n.embedding.tolist() if n.embedding is not None else [0.0] for n in valid_nodes], # type: ignore[union-attr] + embeddings=[n.embedding.tolist() for n in valid_nodes], # type: ignore metadatas=metadatas, documents=[n.content for n in valid_nodes], ) - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"批量添加节点失败: {e}") raise @@ -206,6 +202,7 @@ class VectorStore: ) # 解析结果 + import orjson similar_nodes = [] # 修复：检查 ids 列表长度而不是直接判断真值（避免 numpy 数组歧义） ids = results.get("ids") @@ -226,9 +223,8 @@ class VectorStore: for key, value in list(metadata.items()): if isinstance(value, str) and (value.startswith("[") or value.startswith("{")): try: - import json - metadata[key] = json.loads(value) - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError): + metadata[key] = orjson.loads(value) + except Exception: pass # 保持原值 similar_nodes.append((node_id, similarity, metadata)) @@ -236,7 +232,7 @@ class VectorStore: logger.debug(f"相似节点搜索: 找到 {len(similar_nodes)} 个结果") return similar_nodes - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"相似节点搜索失败: {e}") raise @@ -288,7 +284,7 @@ class VectorStore: # 1. 对每个查询执行搜索 all_results: dict[str, dict[str, Any]] = {} # node_id -> {scores, metadata} - for _, (query_emb, weight) in enumerate(zip(query_embeddings, query_weights)): + for i, (query_emb, weight) in enumerate(zip(query_embeddings, query_weights)): # 搜索更多结果以提高融合质量 search_limit = limit * 3 results = await self.search_similar_nodes( @@ -381,7 +377,7 @@ class VectorStore: return None - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: # 节点不存在是正常情况，降级为 debug logger.debug(f"获取节点失败（节点可能不存在）: {e}") return None @@ -436,14 +432,13 @@ class VectorStore: try: # 删除并重新创建集合 - if self.client is not None: - self.client.delete_collection(self.collection_name) - self.collection = self.client.get_or_create_collection( - name=self.collection_name, - metadata={"description": "Memory graph node embeddings"}, - ) + self.client.delete_collection(self.collection_name) + self.collection = self.client.get_or_create_collection( + name=self.collection_name, + metadata={"description": "Memory graph node embeddings"}, + ) logger.warning(f"向量存储已清空: {self.collection_name}") - except (OSError, RuntimeError, ValueError, AttributeError, KeyError, TypeError) as e: + except Exception as e: logger.error(f"清空向量存储失败: {e}") raise diff --git a/src/memory_graph/unified_manager.py b/src/memory_graph/unified_manager.py index 385294819..784efec59 100644 --- a/src/memory_graph/unified_manager.py +++ b/src/memory_graph/unified_manager.py @@ -82,7 +82,7 @@ class UnifiedMemoryManager: self.long_term_manager: LongTermMemoryManager # 底层 MemoryManager（长期记忆） - self.memory_manager: MemoryManager = memory_manager # type: ignore[assignment] + self.memory_manager: MemoryManager = memory_manager # 配置参数存储（用于初始化） self._config = { @@ -163,7 +163,7 @@ class UnifiedMemoryManager: # 启动自动转移任务 self._start_auto_transfer_task() - except (OSError, RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"统一记忆管理器初始化失败: {e}") raise @@ -284,7 +284,7 @@ class UnifiedMemoryManager: return result - except (OSError, RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"智能检索失败: {e}") return { "perceptual_blocks": [], @@ -366,10 +366,10 @@ class UnifiedMemoryManager: 请输出JSON：""" # 调用记忆裁判模型 - if not model_config.model_task_config: # type: ignore[union-attr] + if not model_config.model_task_config: raise ValueError("模型任务配置未加载") llm = LLMRequest( - model_set=model_config.model_task_config.memory_judge, # type: ignore[union-attr] + model_set=model_config.model_task_config.memory_judge, request_type="unified_memory.judge", ) @@ -401,7 +401,7 @@ class UnifiedMemoryManager: return decision - except (RuntimeError, ValueError, KeyError) as e: + except Exception as e: logger.error(f"裁判模型评估失败: {e}") # 默认判定为不充足，需要检索长期记忆 return JudgeDecision( @@ -429,7 +429,7 @@ class UnifiedMemoryManager: done_task.result() except asyncio.CancelledError: logger.info(f"{task_name} 后台任务已取消") - except (RuntimeError, ValueError, KeyError) as exc: + except Exception as exc: logger.error(f"{task_name} 后台任务失败: {exc}") task.add_done_callback(_callback) @@ -472,7 +472,7 @@ class UnifiedMemoryManager: await self.perceptual_manager.remove_block(block.id) self._trigger_transfer_wakeup() logger.debug(f"✓ 记忆块 {block.id} 已被转移到短期记忆 {stm.id}") - except (RuntimeError, ValueError, KeyError, AttributeError) as exc: + except Exception as exc: logger.error(f"后台转移失败，记忆块 {block.id}: {exc}") def _build_manual_multi_queries(self, queries: list[str]) -> list[dict[str, float]]: @@ -523,6 +523,7 @@ class UnifiedMemoryManager: memories = await self.memory_manager.search_memories(**search_params) unique_memories = self._deduplicate_memories(memories) + len(manual_queries) if manual_queries else 1 return unique_memories def _deduplicate_memories(self, memories: list[Any]) -> list[Any]: @@ -632,7 +633,7 @@ class UnifiedMemoryManager: except asyncio.CancelledError: logger.debug("自动转移循环被取消") break - except (RuntimeError, ValueError, KeyError, AttributeError) as e: + except Exception as e: logger.error(f"自动转移循环异常: {e}") async def manual_transfer(self) -> dict[str, Any]: @@ -663,7 +664,7 @@ class UnifiedMemoryManager: logger.info(f"手动转移完成: {result}") return result - except (OSError, RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"手动转移失败: {e}") return {"error": str(e), "transferred_count": 0} @@ -715,5 +716,5 @@ class UnifiedMemoryManager: self._initialized = False logger.info("统一记忆管理器已关闭") - except (RuntimeError, ValueError, AttributeError) as e: + except Exception as e: logger.error(f"关闭统一记忆管理器失败: {e}")