diff --git a/src/chat/replyer/default_generator.py b/src/chat/replyer/default_generator.py index c03115c74..90d85853b 100644 --- a/src/chat/replyer/default_generator.py +++ b/src/chat/replyer/default_generator.py @@ -555,164 +555,8 @@ class DefaultReplyer: instant_memory = None - # 使用新的增强记忆系统检索记忆 - running_memories = [] - instant_memory = None - - if global_config.memory.enable_memory: - try: - # 使用新的统一记忆系统 - from src.chat.memory_system import get_memory_system - - stream = self.chat_stream - user_info_obj = getattr(stream, "user_info", None) - group_info_obj = getattr(stream, "group_info", None) - - memory_user_id = str(stream.stream_id) - memory_user_display = None - memory_aliases = [] - user_info_dict = {} - - if user_info_obj is not None: - raw_user_id = getattr(user_info_obj, "user_id", None) - if raw_user_id: - memory_user_id = str(raw_user_id) - - if hasattr(user_info_obj, "to_dict"): - try: - user_info_dict = user_info_obj.to_dict() # type: ignore[attr-defined] - except Exception: - user_info_dict = {} - - candidate_keys = [ - "user_cardname", - "user_nickname", - "nickname", - "remark", - "display_name", - "user_name", - ] - - for key in candidate_keys: - value = user_info_dict.get(key) - if isinstance(value, str) and value.strip(): - stripped = value.strip() - if memory_user_display is None: - memory_user_display = stripped - elif stripped not in memory_aliases: - memory_aliases.append(stripped) - - attr_keys = [ - "user_cardname", - "user_nickname", - "nickname", - "remark", - "display_name", - "name", - ] - - for attr in attr_keys: - value = getattr(user_info_obj, attr, None) - if isinstance(value, str) and value.strip(): - stripped = value.strip() - if memory_user_display is None: - memory_user_display = stripped - elif stripped not in memory_aliases: - memory_aliases.append(stripped) - - alias_values = ( - user_info_dict.get("aliases") - or user_info_dict.get("alias_names") - or user_info_dict.get("alias") - ) - if isinstance(alias_values, list | tuple | set): - for alias in alias_values: - if isinstance(alias, str) and alias.strip(): - stripped = alias.strip() - if stripped not in memory_aliases and stripped != memory_user_display: - memory_aliases.append(stripped) - - memory_context = { - "user_id": memory_user_id, - "user_display_name": memory_user_display or "", - "user_name": memory_user_display or "", - "nickname": memory_user_display or "", - "sender_name": memory_user_display or "", - "platform": getattr(stream, "platform", None), - "chat_id": stream.stream_id, - "stream_id": stream.stream_id, - } - - if memory_aliases: - memory_context["user_aliases"] = memory_aliases - - if group_info_obj is not None: - group_name = getattr(group_info_obj, "group_name", None) or getattr( - group_info_obj, "group_nickname", None - ) - if group_name: - memory_context["group_name"] = str(group_name) - group_id = getattr(group_info_obj, "group_id", None) - if group_id: - memory_context["group_id"] = str(group_id) - - memory_context = {key: value for key, value in memory_context.items() if value} - - # 获取记忆系统实例 - memory_system = get_memory_system() - - # 使用统一记忆系统检索相关记忆 - enhanced_memories = await memory_system.retrieve_relevant_memories( - query=target, user_id=memory_user_id, scope_id=stream.stream_id, context=memory_context, limit=10 - ) - - # 注意：记忆存储已迁移到回复生成完成后进行，不在查询阶段执行 - - # 转换格式以兼容现有代码 - running_memories = [] - if enhanced_memories: - logger.debug(f"[记忆转换] 收到 {len(enhanced_memories)} 条原始记忆") - for idx, memory_chunk in enumerate(enhanced_memories, 1): - # 获取结构化内容的字符串表示 - structure_display = str(memory_chunk.content) if hasattr(memory_chunk, "content") else "unknown" - - # 获取记忆内容，优先使用display - content = memory_chunk.display or memory_chunk.text_content or "" - - # 调试：记录每条记忆的内容获取情况 - logger.debug( - f"[记忆转换] 第{idx}条: display={repr(memory_chunk.display)[:80]}, text_content={repr(memory_chunk.text_content)[:80]}, final_content={repr(content)[:80]}" - ) - - running_memories.append( - { - "content": content, - "memory_type": memory_chunk.memory_type.value, - "confidence": memory_chunk.metadata.confidence.value, - "importance": memory_chunk.metadata.importance.value, - "relevance": getattr(memory_chunk.metadata, "relevance_score", 0.5), - "source": memory_chunk.metadata.source, - "structure": structure_display, - } - ) - - # 构建瞬时记忆字符串 - if running_memories: - top_memory = running_memories[:1] - if top_memory: - instant_memory = top_memory[0].get("content", "") - - logger.info( - f"增强记忆系统检索到 {len(enhanced_memories)} 条原始记忆，转换为 {len(running_memories)} 条可用记忆" - ) - - except Exception as e: - logger.warning(f"增强记忆系统检索失败: {e}") - running_memories = [] - instant_memory = "" - - # 尝试从记忆图系统检索记忆 - graph_memories = [] + # 使用新的记忆图系统检索记忆 + all_memories = [] try: from src.memory_graph.manager_singleton import get_memory_manager, is_initialized @@ -722,8 +566,8 @@ class DefaultReplyer: # 搜索相关记忆 memories = await manager.search_memories( query=target, - top_k=5, - min_importance=0.5, + top_k=10, # 增加检索数量 + min_importance=0.3, # 降低最低重要性阈值，获取更多记忆 include_forgotten=False ) @@ -733,27 +577,29 @@ class DefaultReplyer: topic = memory.metadata.get("topic", "") mem_type = memory.metadata.get("memory_type", "未知") if topic: - graph_memories.append({ + all_memories.append({ "content": topic, "memory_type": mem_type, "importance": memory.importance, "relevance": 0.7, # 默认相关度 "source": "memory_graph", }) + + # 提取最重要的记忆作为瞬时记忆 + if all_memories: + top_memory = max(all_memories, key=lambda m: m.get("importance", 0)) + instant_memory = top_memory.get("content", "") else: logger.debug("[记忆图] 未找到相关记忆") except Exception as e: logger.debug(f"[记忆图] 检索失败: {e}") - graph_memories = [] - - # 合并记忆（增强记忆系统 + 记忆图系统） - all_memories = running_memories + graph_memories + all_memories = [] # 构建记忆字符串，使用方括号格式 memory_str = "" has_any_memory = False - # 添加长期记忆（来自增强记忆系统 + 记忆图系统） + # 添加长期记忆（来自记忆图系统） if all_memories: # 使用方括号格式 memory_parts = ["### 🧠 相关记忆 (Relevant Memories)", ""] @@ -2159,23 +2005,9 @@ class DefaultReplyer: show_actions=True, ) - # 异步存储聊天历史（完全非阻塞） - memory_system = get_memory_system() - task = asyncio.create_task( - memory_system.process_conversation_memory( - context={ - "conversation_text": chat_history, - "user_id": memory_user_id, - "scope_id": stream.stream_id, - **memory_context, - } - ) - ) - # 将任务添加到全局集合以防止被垃圾回收 - _background_tasks.add(task) - task.add_done_callback(_background_tasks.discard) - - logger.debug(f"已启动记忆存储任务，用户: {memory_user_display or memory_user_id}") + # 旧记忆系统的自动存储已禁用 + # 新记忆系统通过 LLM 工具调用（create_memory）来创建记忆 + logger.debug(f"记忆创建通过 LLM 工具调用进行，用户: {memory_user_display or memory_user_id}") except asyncio.CancelledError: logger.debug("记忆存储任务被取消") diff --git a/src/chat/utils/prompt.py b/src/chat/utils/prompt.py index b08887e9e..6782fecad 100644 --- a/src/chat/utils/prompt.py +++ b/src/chat/utils/prompt.py @@ -398,6 +398,9 @@ class Prompt: """ start_time = time.time() + # 初始化预构建参数字典 + pre_built_params = {} + try: # --- 步骤 1: 准备构建任务 --- tasks = [] @@ -406,7 +409,6 @@ class Prompt: # --- 步骤 1.1: 优先使用预构建的参数 --- # 如果参数对象中已经包含了某些block，说明它们是外部预构建的， # 我们将它们存起来，并跳过对应的实时构建任务。 - pre_built_params = {} if self.parameters.expression_habits_block: pre_built_params["expression_habits_block"] = self.parameters.expression_habits_block if self.parameters.relation_info_block: @@ -428,11 +430,9 @@ class Prompt: tasks.append(self._build_expression_habits()) task_names.append("expression_habits") - # 记忆块构建非常耗时，强烈建议预构建。如果没有预构建，这里会运行一个快速的后备版本。 - if self.parameters.enable_memory and not pre_built_params.get("memory_block"): - logger.debug("memory_block未预构建，执行快速构建作为后备方案") - tasks.append(self._build_memory_block_fast()) - task_names.append("memory_block") + # 记忆块构建已移至 default_generator.py 的 build_memory_block 方法 + # 使用新的记忆图系统，不再在 prompt.py 中构建记忆 + # 如果需要记忆，必须通过 pre_built_params 传入 if self.parameters.enable_relation and not pre_built_params.get("relation_info_block"): tasks.append(self._build_relation_info()) @@ -637,8 +637,12 @@ class Prompt: logger.error(f"构建表达习惯失败: {e}") return {"expression_habits_block": ""} - async def _build_memory_block(self) -> dict[str, Any]: - """构建与当前对话相关的记忆上下文块（完整版）.""" + # _build_memory_block 和 _build_memory_block_fast 已移除 + # 记忆构建现在完全由 default_generator.py 的 build_memory_block 方法处理 + # 使用新的记忆图系统，通过 pre_built_params 传入 + + async def _REMOVED_build_memory_block(self) -> dict[str, Any]: + """已废弃：构建与当前对话相关的记忆上下文块（完整版）.""" if not global_config.memory.enable_memory: return {"memory_block": ""} @@ -753,8 +757,8 @@ class Prompt: logger.error(f"构建记忆块失败: {e}") return {"memory_block": ""} - async def _build_memory_block_fast(self) -> dict[str, Any]: - """快速构建记忆块（简化版），作为未预构建时的后备方案.""" + async def _REMOVED_build_memory_block_fast(self) -> dict[str, Any]: + """已废弃：快速构建记忆块（简化版），作为未预构建时的后备方案.""" if not global_config.memory.enable_memory: return {"memory_block": ""} diff --git a/src/memory_graph/plugin_tools/memory_plugin_tools.py b/src/memory_graph/plugin_tools/memory_plugin_tools.py index 38eb4f79a..4771507b6 100644 --- a/src/memory_graph/plugin_tools/memory_plugin_tools.py +++ b/src/memory_graph/plugin_tools/memory_plugin_tools.py @@ -107,7 +107,7 @@ class LinkMemoriesTool(BaseTool): ("strength", ToolParamType.FLOAT, "关系强度（0.0-1.0），默认0.7", False, None), ] - available_for_llm = True + available_for_llm = False # 暂不对 LLM 开放 async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]: """执行关联记忆""" @@ -166,7 +166,7 @@ class SearchMemoriesTool(BaseTool): ("min_importance", ToolParamType.FLOAT, "最低重要性阈值（0.0-1.0），只返回重要性不低于此值的记忆", False, None), ] - available_for_llm = True + available_for_llm = False # 暂不对 LLM 开放，记忆检索在提示词构建时自动执行 async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]: """执行搜索记忆""" @@ -183,7 +183,7 @@ class SearchMemoriesTool(BaseTool): query = function_args.get("query", "") top_k = function_args.get("top_k", 5) min_importance_raw = function_args.get("min_importance") - min_importance = float(min_importance_raw) if min_importance_raw is not None else None + min_importance = float(min_importance_raw) if min_importance_raw is not None else 0.0 # 搜索记忆 memories = await manager.search_memories(