feat: 采用三层内存系统实现统一内存管理器
- 添加了UnifiedMemoryManager，以整合感知层、短期记忆层和长期记忆层。 - 实现了初始化、消息添加和内存搜索功能。 - 引入了记忆从短期存储到长期存储的自动转移机制。 - 开发了用于结构化内存表示的内存格式化工具。 - 增强日志记录功能，以便在内存操作过程中更好地进行追踪。
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Windpicker-owo
@@ -564,7 +564,7 @@ class DefaultReplyer:
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return f"{expression_habits_title}\n{expression_habits_block}"
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async def build_memory_block(self, chat_history: str, target: str) -> str:
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"""构建记忆块
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"""构建记忆块（使用三层记忆系统）
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Args:
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chat_history: 聊天历史记录
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@@ -573,149 +573,13 @@ class DefaultReplyer:
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Returns:
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str: 记忆信息字符串
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"""
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# 使用新的记忆图系统检索记忆（带智能查询优化）
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all_memories = []
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try:
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from src.memory_graph.manager_singleton import get_memory_manager, is_initialized
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if is_initialized():
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manager = get_memory_manager()
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if manager:
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# 构建查询上下文
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stream = self.chat_stream
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user_info_obj = getattr(stream, "user_info", None)
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sender_name = ""
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if user_info_obj:
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sender_name = getattr(user_info_obj, "user_nickname", "") or getattr(user_info_obj, "user_cardname", "")
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# 格式化聊天历史为更友好的格式
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formatted_history = ""
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if chat_history:
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# 移除过长的历史记录，只保留最近部分
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lines = chat_history.strip().split("\n")
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recent_lines = lines[-10:] if len(lines) > 10 else lines
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formatted_history = "\n".join(recent_lines)
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query_context = {
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"chat_history": formatted_history,
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"sender": sender_name,
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}
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# 使用记忆管理器的智能检索（多查询策略）
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memories = []
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if global_config.memory:
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memories = []
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if global_config.memory:
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top_k = global_config.memory.search_top_k
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min_importance = global_config.memory.search_min_importance
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memories = await manager.search_memories(
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query=target,
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top_k=top_k,
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min_importance=min_importance,
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include_forgotten=False,
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use_multi_query=True,
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context=query_context,
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)
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if memories:
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logger.info(f"[记忆图] 检索到 {len(memories)} 条相关记忆")
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# 使用新的格式化工具构建完整的记忆描述
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from src.memory_graph.utils.memory_formatter import (
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format_memory_for_prompt,
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get_memory_type_label,
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)
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for memory in memories:
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# 使用格式化工具生成完整的主谓宾描述
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content = format_memory_for_prompt(memory, include_metadata=False)
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# 获取记忆类型
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mem_type = memory.memory_type.value if memory.memory_type else "未知"
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if content:
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all_memories.append({
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"content": content,
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"memory_type": mem_type,
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"importance": memory.importance,
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"relevance": 0.7,
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"source": "memory_graph",
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})
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logger.debug(f"[记忆构建] 格式化记忆: [{mem_type}] {content[:50]}...")
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else:
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logger.debug("[记忆图] 未找到相关记忆")
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except Exception as e:
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logger.debug(f"[记忆图] 检索失败: {e}")
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all_memories = []
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# 构建记忆字符串，使用方括号格式
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memory_str = ""
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has_any_memory = False
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# 添加长期记忆（来自记忆图系统）
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if all_memories:
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# 使用方括号格式
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memory_parts = ["### 🧠 相关记忆 (Relevant Memories)", ""]
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# 按相关度排序，并记录相关度信息用于调试
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sorted_memories = sorted(all_memories, key=lambda x: x.get("relevance", 0.0), reverse=True)
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# 调试相关度信息
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relevance_info = [(m.get("memory_type", "unknown"), m.get("relevance", 0.0)) for m in sorted_memories]
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logger.debug(f"记忆相关度信息: {relevance_info}")
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logger.debug(f"[记忆构建] 准备将 {len(sorted_memories)} 条记忆添加到提示词")
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for idx, running_memory in enumerate(sorted_memories, 1):
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content = running_memory.get("content", "")
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memory_type = running_memory.get("memory_type", "unknown")
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# 跳过空内容
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if not content or not content.strip():
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logger.warning(f"[记忆构建] 跳过第 {idx} 条记忆：内容为空 (type={memory_type})")
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logger.debug(f"[记忆构建] 空记忆详情: {running_memory}")
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continue
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# 使用记忆图的类型映射（优先）或全局映射
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try:
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from src.memory_graph.utils.memory_formatter import get_memory_type_label
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chinese_type = get_memory_type_label(memory_type)
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except ImportError:
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# 回退到全局映射
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chinese_type = get_memory_type_chinese_label(memory_type)
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# 提取纯净内容（如果包含旧格式的元数据）
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clean_content = content
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if "（类型:" in content and "）" in content:
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clean_content = content.split("（类型:")[0].strip()
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logger.debug(f"[记忆构建] 添加第 {idx} 条记忆: [{chinese_type}] {clean_content[:50]}...")
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memory_parts.append(f"- **[{chinese_type}]** {clean_content}")
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memory_str = "\n".join(memory_parts) + "\n"
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has_any_memory = True
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logger.debug(f"[记忆构建] 成功构建记忆字符串，包含 {len(memory_parts) - 2} 条记忆")
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# 瞬时记忆由另一套系统处理，这里不再添加
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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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from src.memory_graph.manager_singleton import get_unified_memory_manager
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from src.memory_graph.utils.memory_formatter import format_memory_for_prompt
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unified_manager = get_unified_memory_manager()
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if not unified_manager:
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@@ -737,45 +601,45 @@ class DefaultReplyer:
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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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memory_parts = ["### 🧠 相关记忆 (Relevant Memories)", ""]
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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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memory_parts.append("#### 🌊 感知记忆")
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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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block_content = " → ".join([
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f"{msg.get('sender_name', msg.get('sender_id', ''))}: {msg.get('content', '')[:30]}"
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for msg in messages[:3]
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])
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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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memory_parts.append("#### 💭 短期记忆")
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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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content = format_memory_for_prompt(mem, include_metadata=False)
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if content:
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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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memory_parts.append("#### 🗄️ 长期记忆")
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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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content = format_memory_for_prompt(mem, include_metadata=False)
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if content:
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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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if total_count > 0:
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logger.info(
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f"[三层记忆] 检索到 {total_count} 条记忆 "
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f"(感知:{len(perceptual_blocks)}, 短期:{len(short_term_memories)}, 长期:{len(long_term_memories)})"
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)
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return "\n".join(memory_parts) if len(memory_parts) > 2 else ""
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@@ -783,6 +647,8 @@ class DefaultReplyer:
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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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@@ -1405,9 +1271,6 @@ 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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