feat:实现短期内存管理器和统一内存管理器
- 添加了ShortTermMemoryManager来管理短期记忆，包括提取、决策和记忆操作。 - 集成大型语言模型（LLM），用于结构化记忆提取和决策过程。 - 基于重要性阈值，实现了从短期到长期的内存转移逻辑。 - 创建了UnifiedMemoryManager，通过统一接口整合感知记忆、短期记忆和长期记忆的管理。 - 通过法官模型评估来增强记忆提取过程的充分性。 - 增加了自动和手动内存传输功能。 - 包含内存管理操作和决策的全面日志记录。
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Windpicker-owo
@@ -700,6 +700,89 @@ class DefaultReplyer:
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# 只有当完全没有任何记忆时才返回空字符串
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return memory_str if has_any_memory else ""
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async def build_three_tier_memory_block(self, chat_history: str, target: str) -> str:
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"""构建三层记忆块（感知记忆 + 短期记忆 + 长期记忆）
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Args:
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chat_history: 聊天历史记录
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target: 目标消息内容
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Returns:
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str: 三层记忆信息字符串
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"""
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# 检查是否启用三层记忆系统
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if not (global_config.three_tier_memory and global_config.three_tier_memory.enable):
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return ""
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try:
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from src.memory_graph.three_tier.manager_singleton import get_unified_memory_manager
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unified_manager = get_unified_memory_manager()
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if not unified_manager:
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logger.debug("[三层记忆] 管理器未初始化")
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return ""
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# 使用统一管理器的智能检索（Judge模型决策）
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search_result = await unified_manager.search_memories(
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query_text=target,
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use_judge=True,
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)
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if not search_result:
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logger.debug("[三层记忆] 未找到相关记忆")
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return ""
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# 分类记忆块
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perceptual_blocks = search_result.get("perceptual_blocks", [])
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short_term_memories = search_result.get("short_term_memories", [])
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long_term_memories = search_result.get("long_term_memories", [])
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memory_parts = ["### 🔮 三层记忆系统 (Three-Tier Memory)", ""]
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# 添加感知记忆（最近的消息块）
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if perceptual_blocks:
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memory_parts.append("#### 🌊 感知记忆 (Perceptual Memory)")
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for block in perceptual_blocks[:2]: # 最多显示2个块
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# MemoryBlock 对象有 messages 属性（列表）
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messages = block.messages if hasattr(block, 'messages') else []
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if messages:
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block_content = " → ".join([f"{msg.get('sender_name', msg.get('sender_id', ''))}: {msg.get('content', '')[:30]}" for msg in messages[:3]])
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memory_parts.append(f"- {block_content}")
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memory_parts.append("")
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# 添加短期记忆（结构化活跃记忆）
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if short_term_memories:
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memory_parts.append("#### 💭 短期记忆 (Short-Term Memory)")
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for mem in short_term_memories[:3]: # 最多显示3条
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# ShortTermMemory 对象有属性而非字典
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if hasattr(mem, 'subject') and hasattr(mem, 'topic') and hasattr(mem, 'object'):
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subject = mem.subject or ""
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topic = mem.topic or ""
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obj = mem.object or ""
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content = f"{subject} {topic} {obj}" if all([subject, topic, obj]) else (mem.content if hasattr(mem, 'content') else str(mem))
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else:
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content = mem.content if hasattr(mem, 'content') else str(mem)
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memory_parts.append(f"- {content}")
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memory_parts.append("")
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# 添加长期记忆（图谱记忆）
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if long_term_memories:
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memory_parts.append("#### 🧠 长期记忆 (Long-Term Memory)")
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for mem in long_term_memories[:3]: # 最多显示3条
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# Memory 对象有 content 属性
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content = mem.content if hasattr(mem, 'content') else str(mem)
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memory_parts.append(f"- {content}")
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memory_parts.append("")
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total_count = len(perceptual_blocks) + len(short_term_memories) + len(long_term_memories)
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logger.info(f"[三层记忆] 检索到 {total_count} 条记忆 (感知:{len(perceptual_blocks)}, 短期:{len(short_term_memories)}, 长期:{len(long_term_memories)})")
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return "\n".join(memory_parts) if len(memory_parts) > 2 else ""
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except Exception as e:
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logger.error(f"[三层记忆] 检索失败: {e}", exc_info=True)
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return ""
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async def build_tool_info(self, chat_history: str, sender: str, target: str, enable_tool: bool = True) -> str:
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"""构建工具信息块
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@@ -1322,6 +1405,9 @@ class DefaultReplyer:
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"memory_block": asyncio.create_task(
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self._time_and_run_task(self.build_memory_block(chat_talking_prompt_short, target), "memory_block")
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),
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"three_tier_memory": asyncio.create_task(
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self._time_and_run_task(self.build_three_tier_memory_block(chat_talking_prompt_short, target), "three_tier_memory")
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),
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"tool_info": asyncio.create_task(
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self._time_and_run_task(
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self.build_tool_info(chat_talking_prompt_short, sender, target, enable_tool=enable_tool),
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@@ -1355,6 +1441,7 @@ class DefaultReplyer:
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"expression_habits": "",
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"relation_info": "",
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"memory_block": "",
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"three_tier_memory": "",
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"tool_info": "",
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"prompt_info": "",
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"cross_context": "",
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@@ -1378,6 +1465,7 @@ class DefaultReplyer:
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"expression_habits": "选取表达方式",
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"relation_info": "感受关系",
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"memory_block": "回忆",
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"three_tier_memory": "三层记忆检索",
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"tool_info": "使用工具",
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"prompt_info": "获取知识",
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}
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@@ -1396,17 +1484,30 @@ class DefaultReplyer:
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expression_habits_block = results_dict["expression_habits"]
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relation_info = results_dict["relation_info"]
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memory_block = results_dict["memory_block"]
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three_tier_memory_block = results_dict["three_tier_memory"]
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tool_info = results_dict["tool_info"]
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prompt_info = results_dict["prompt_info"]
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cross_context_block = results_dict["cross_context"]
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notice_block = results_dict["notice_block"]
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# 合并三层记忆和原记忆图记忆
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# 如果三层记忆系统启用且有内容，优先使用三层记忆，否则使用原记忆图
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if three_tier_memory_block:
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# 三层记忆系统启用，使用新系统的结果
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combined_memory_block = three_tier_memory_block
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if memory_block:
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# 如果原记忆图也有内容，附加到后面
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combined_memory_block += "\n" + memory_block
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else:
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# 三层记忆系统未启用或无内容，使用原记忆图
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combined_memory_block = memory_block
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# 检查是否为视频分析结果，并注入引导语
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if target and ("[视频内容]" in target or "好的，我将根据您提供的" in target):
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video_prompt_injection = (
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"\n请注意，以上内容是你刚刚观看的视频，请以第一人称分享你的观后感，而不是在分析一份报告。"
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)
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memory_block += video_prompt_injection
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combined_memory_block += video_prompt_injection
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keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
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@@ -1537,7 +1638,7 @@ class DefaultReplyer:
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# 传递已构建的参数
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expression_habits_block=expression_habits_block,
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relation_info_block=relation_info,
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memory_block=memory_block,
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memory_block=combined_memory_block, # 使用合并后的记忆块
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tool_info_block=tool_info,
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knowledge_prompt=prompt_info,
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cross_context_block=cross_context_block,
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