feat(memory): 增强记忆构建系统并优化检索性能
- 添加记忆提取异常处理机制，提升系统稳定性 - 实现记忆内容格式化功能，增强可读性和结构化输出 - 优化LLM响应解析逻辑，避免系统标识误写入记忆 - 改进向量存储批量嵌入生成，提升处理效率 - 为记忆系统添加机器人身份上下文注入，避免自身信息记录 - 增强记忆检索接口，支持额外上下文参数传递 - 添加控制台记忆预览功能，便于人工检查 - 优化记忆融合算法，正确处理单记忆组情况 - 改进流循环管理器，支持未读消息积压强制分发机制
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Windpicker-owo
@@ -470,20 +470,114 @@ class DefaultReplyer:
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try:
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# 使用新的增强记忆系统
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from src.chat.memory_system.enhanced_memory_integration import recall_memories, remember_message
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stream = self.chat_stream
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user_info_obj = getattr(stream, "user_info", None)
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group_info_obj = getattr(stream, "group_info", None)
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memory_user_id = str(stream.stream_id)
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memory_user_display = None
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memory_aliases = []
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user_info_dict = {}
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if user_info_obj is not None:
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raw_user_id = getattr(user_info_obj, "user_id", None)
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if raw_user_id:
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memory_user_id = str(raw_user_id)
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if hasattr(user_info_obj, "to_dict"):
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try:
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user_info_dict = user_info_obj.to_dict() # type: ignore[attr-defined]
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except Exception:
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user_info_dict = {}
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candidate_keys = [
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"user_cardname",
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"user_nickname",
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"nickname",
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"remark",
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"display_name",
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"user_name",
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]
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for key in candidate_keys:
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value = user_info_dict.get(key)
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if isinstance(value, str) and value.strip():
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stripped = value.strip()
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if memory_user_display is None:
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memory_user_display = stripped
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elif stripped not in memory_aliases:
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memory_aliases.append(stripped)
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attr_keys = [
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"user_cardname",
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"user_nickname",
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"nickname",
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"remark",
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"display_name",
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"name",
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]
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for attr in attr_keys:
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value = getattr(user_info_obj, attr, None)
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if isinstance(value, str) and value.strip():
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stripped = value.strip()
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if memory_user_display is None:
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memory_user_display = stripped
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elif stripped not in memory_aliases:
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memory_aliases.append(stripped)
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alias_values = (
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user_info_dict.get("aliases")
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or user_info_dict.get("alias_names")
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or user_info_dict.get("alias")
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)
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if isinstance(alias_values, (list, tuple, set)):
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for alias in alias_values:
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if isinstance(alias, str) and alias.strip():
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stripped = alias.strip()
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if stripped not in memory_aliases and stripped != memory_user_display:
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memory_aliases.append(stripped)
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memory_context = {
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"user_id": memory_user_id,
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"user_display_name": memory_user_display or "",
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"user_name": memory_user_display or "",
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"nickname": memory_user_display or "",
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"sender_name": memory_user_display or "",
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"platform": getattr(stream, "platform", None),
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"chat_id": stream.stream_id,
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"stream_id": stream.stream_id,
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}
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if memory_aliases:
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memory_context["user_aliases"] = memory_aliases
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if group_info_obj is not None:
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group_name = getattr(group_info_obj, "group_name", None) or getattr(group_info_obj, "group_nickname", None)
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if group_name:
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memory_context["group_name"] = str(group_name)
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group_id = getattr(group_info_obj, "group_id", None)
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if group_id:
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memory_context["group_id"] = str(group_id)
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memory_context = {key: value for key, value in memory_context.items() if value}
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# 检索相关记忆
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enhanced_memories = await recall_memories(
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query=target,
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user_id=str(self.chat_stream.stream_id),
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chat_id=self.chat_stream.stream_id
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user_id=memory_user_id,
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chat_id=stream.stream_id,
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context=memory_context
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)
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# 异步存储聊天历史（非阻塞）
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asyncio.create_task(
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remember_message(
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message=chat_history,
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user_id=str(self.chat_stream.stream_id),
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chat_id=self.chat_stream.stream_id
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user_id=memory_user_id,
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chat_id=stream.stream_id,
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context=memory_context
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)
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)
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@@ -492,17 +586,20 @@ class DefaultReplyer:
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if enhanced_memories and enhanced_memories.get("has_memories"):
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for memory in enhanced_memories.get("memories", []):
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running_memories.append({
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'content': memory.get("content", ""),
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'score': memory.get("confidence", 0.0),
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'memory_type': memory.get("type", "unknown")
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"content": memory.get("content", ""),
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"memory_type": memory.get("type", "unknown"),
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"confidence": memory.get("confidence"),
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"importance": memory.get("importance"),
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"relevance": memory.get("relevance"),
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"source": memory.get("source"),
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"structure": memory.get("structure"),
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})
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# 构建瞬时记忆字符串
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if enhanced_memories and enhanced_memories.get("has_memories"):
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instant_memory = "\\n".join([
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f"{memory.get('content', '')} (相似度: {memory.get('confidence', 0.0):.2f})"
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for memory in enhanced_memories.get("memories", [])[:3] # 取前3条
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])
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top_memory = enhanced_memories.get("memories", [])[:1]
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if top_memory:
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instant_memory = top_memory[0].get("content", "")
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logger.info(f"增强记忆系统检索到 {len(running_memories)} 条记忆")
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@@ -511,6 +608,20 @@ class DefaultReplyer:
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running_memories = []
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instant_memory = ""
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def _format_confidence_label(value: Optional[float]) -> str:
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if value is None:
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return "未知"
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mapping = {4: "已验证", 3: "高", 2: "中等", 1: "较低"}
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rounded = int(value)
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return mapping.get(rounded, f"{value:.2f}")
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def _format_importance_label(value: Optional[float]) -> str:
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if value is None:
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return "未知"
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mapping = {4: "关键", 3: "高", 2: "一般", 1: "较低"}
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rounded = int(value)
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return mapping.get(rounded, f"{value:.2f}")
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# 构建记忆字符串，即使某种记忆为空也要继续
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memory_str = ""
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has_any_memory = False
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@@ -520,15 +631,26 @@ class DefaultReplyer:
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if not memory_str:
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memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
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for running_memory in running_memories:
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memory_str += f"- {running_memory['content']} (类型: {running_memory['memory_type']}, 相似度: {running_memory['score']:.2f})\n"
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details = []
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details.append(f"类型: {running_memory.get('memory_type', 'unknown')}")
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if running_memory.get("confidence") is not None:
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details.append(f"置信度: {_format_confidence_label(running_memory.get('confidence'))}")
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if running_memory.get("importance") is not None:
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details.append(f"重要性: {_format_importance_label(running_memory.get('importance'))}")
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if running_memory.get("relevance") is not None:
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details.append(f"相关度: {running_memory['relevance']:.2f}")
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detail_text = f" （{'，'.join(details)}）" if details else ""
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memory_str += f"- {running_memory['content']}{detail_text}\n"
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has_any_memory = True
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# 添加瞬时记忆
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if instant_memory:
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if not memory_str:
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memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
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memory_str += f"- {instant_memory}\n"
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has_any_memory = True
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if not any(rm["content"] == instant_memory for rm in running_memories):
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if not memory_str:
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memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
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memory_str += f"- 最相关记忆：{instant_memory}\n"
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has_any_memory = True
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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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