diff --git a/src/chat/chat_loop/proactive/event_scheduler.py b/src/chat/chat_loop/proactive/event_scheduler.py index 767360dd8..5e6ca0c51 100644 --- a/src/chat/chat_loop/proactive/event_scheduler.py +++ b/src/chat/chat_loop/proactive/event_scheduler.py @@ -205,7 +205,7 @@ async def schedule_reminder( ) -async def _execute_reminder_callback(subheartflow_id: str, reminder_text: str): +async def _execute_reminder_callback(subheartflow_id: str, reminder_text: str, original_message: str = None): """执行提醒回调函数""" try: # 获取对应的subheartflow实例 @@ -219,11 +219,15 @@ async def _execute_reminder_callback(subheartflow_id: str, reminder_text: str): # 创建主动思考事件，触发完整的思考流程 from src.chat.chat_loop.proactive.events import ProactiveTriggerEvent + # 使用原始消息来构造reason，如果没有原始消息则使用处理后的内容 + reason_content = original_message if original_message else reminder_text + event = ProactiveTriggerEvent( source="reminder_system", - reason=f"定时提醒：{reminder_text}", + reason=f"定时提醒：{reason_content}", # 这里传递完整的原始消息 metadata={ "reminder_text": reminder_text, + "original_message": original_message, "trigger_time": datetime.now().isoformat() } ) @@ -231,7 +235,7 @@ async def _execute_reminder_callback(subheartflow_id: str, reminder_text: str): # 通过subflow的HeartFChatting实例触发主动思考 await subflow.heart_fc_instance.proactive_thinker.think(event) - logger.info(f"已触发提醒的主动思考，内容: {reminder_text}") + logger.info(f"已触发提醒的主动思考，内容: {reminder_text},没有传递那条消息吗？{original_message}") except Exception as e: logger.error(f"执行提醒回调时发生错误: {e}") diff --git a/src/chat/chat_loop/proactive/proactive_thinker.py b/src/chat/chat_loop/proactive/proactive_thinker.py index e4086aec1..0a28c6e98 100644 --- a/src/chat/chat_loop/proactive/proactive_thinker.py +++ b/src/chat/chat_loop/proactive/proactive_thinker.py @@ -120,75 +120,60 @@ class ProactiveThinker: trigger_event (ProactiveTriggerEvent): 触发事件。 """ try: - # 如果是提醒事件，跳过规划器，直接构建默认动作 + # 如果是提醒事件，直接使用当前上下文执行at_user动作 if trigger_event.source == "reminder_system": # 1. 获取上下文信息 metadata = trigger_event.metadata or {} - action_message = metadata reminder_content = trigger_event.reason.replace("定时提醒：", "").strip() - # 2. 确定目标用户名 + # 2. 使用LLM智能解析目标用户名 target_user_name = None - match = re.search(r"艾特一下([^,，\s]+)", reminder_content) - if match: - target_user_name = match.group(1) - else: - from src.person_info.person_info import get_person_info_manager - user_id = metadata.get("user_id") - platform = metadata.get("platform") - if user_id and platform: - person_id = get_person_info_manager().get_person_id(platform, user_id) - target_user_name = await get_person_info_manager().get_value(person_id, "person_name") + + # 首先尝试从完整的原始信息中解析（如果有的话） + full_content = trigger_event.reason + logger.info(f"{self.context.log_prefix} 解析提醒内容: '{full_content}'") + + target_user_name = await self._extract_target_user_with_llm(full_content) if not target_user_name: logger.warning(f"无法从提醒 '{reminder_content}' 中确定目标用户，回退") - raise Exception("无法确定目标用户") - - # 3. 构建动作 - action_result = { - "action_type": "at_user", - "reasoning": "执行定时提醒", - "action_data": { - "user_name": target_user_name, - "at_message": reminder_content - }, - "action_message": action_message - } - - # 4. 执行或回退 - try: - original_chat_id = metadata.get("chat_id") - if not original_chat_id: - if trigger_event.related_message_id: - db_message = await db_get(Messages, {"message_id": trigger_event.related_message_id}, single_result=True) or {} - original_chat_id = db_message.get("chat_id") - - if not original_chat_id: - raise Exception("提醒事件中缺少chat_id") - - from src.chat.heart_flow.heartflow import heartflow - subflow = await heartflow.get_or_create_subheartflow(original_chat_id) - if not subflow: - raise Exception(f"无法为chat_id {original_chat_id} 获取subflow") - - success, _, _ = await subflow.heart_fc_instance.cycle_processor._handle_action( - action=action_result["action_type"], - reasoning=action_result["reasoning"], - action_data=action_result["action_data"], - cycle_timers={}, - thinking_id="", - action_message=action_result["action_message"], - ) - if not success: - raise Exception("at_user action failed") - except Exception as e: - logger.warning(f"{self.context.log_prefix} at_user动作执行失败: {e}，回退到proactive_reply") + # 回退到生成普通提醒消息 fallback_action = { "action_type": "proactive_reply", - "action_data": {"topic": trigger_event.reason}, - "action_message": action_message + "action_data": {"topic": f"定时提醒：{reminder_content}"}, + "action_message": metadata } - await self._generate_proactive_content_and_send(fallback_action, trigger_event) + await self._generate_reminder_proactive_reply(fallback_action, trigger_event, reminder_content) + return + + # 3. 直接使用当前上下文的cycle_processor执行at_user动作 + try: + success, _, _ = await self.cycle_processor._handle_action( + action="at_user", + reasoning="执行定时提醒", + action_data={ + "user_name": target_user_name, + "at_message": reminder_content + }, + cycle_timers={}, + thinking_id="", + action_message=metadata, + ) + if success: + logger.info(f"{self.context.log_prefix} 成功执行定时提醒艾特用户 {target_user_name}") + return + else: + raise Exception("at_user action failed") + except Exception as e: + logger.warning(f"{self.context.log_prefix} at_user动作执行失败: {e}，回退到专用提醒回复") + # 回退到专用的定时提醒回复 + fallback_action = { + "action_type": "proactive_reply", + "action_data": {"topic": f"定时提醒：{reminder_content}"}, + "action_message": metadata + } + await self._generate_reminder_proactive_reply(fallback_action, trigger_event, reminder_content) + return else: # 对于其他来源的主动思考，正常调用规划器 @@ -213,6 +198,145 @@ class ProactiveThinker: except Exception as e: logger.error(f"{self.context.log_prefix} 主动思考执行异常: {e}") logger.error(traceback.format_exc()) + async def _extract_target_user_with_llm(self, reminder_content: str) -> str: + """ + 使用LLM从提醒内容中提取目标用户名 + + Args: + reminder_content: 完整的提醒内容 + + Returns: + 提取出的用户名，如果找不到则返回None + """ + try: + from src.llm_models.utils_model import LLMRequest + from src.config.config import model_config + + bot_name = global_config.bot.nickname + user_extraction_prompt = f''' +从以下提醒消息中提取需要被提醒的目标用户名。 + +**重要认知**：你的名字是"{bot_name}"。当消息中提到"{bot_name}"时，通常是在称呼你，而不是要提醒的目标。你需要找出除了你自己之外的那个目标用户。 + +提醒消息: "{reminder_content}" + +规则: +1. 用户名通常在"提醒"、"艾特"、"叫"等动词后面。 +2. **绝对不能**提取你自己的名字("{bot_name}")作为目标。 +3. 只提取最关键的人名，不要包含多余的词语（比如时间、动作）。 +4. 如果消息中除了你自己的名字外，没有明确提到其他目标用户名，请回答"无"。 + +示例: +- 消息: "定时提醒：{bot_name}，提醒阿范一分钟后去写模组" -> "阿范" +- 消息: "定时提醒：一分钟后提醒一闪喝水" -> "一闪" +- 消息: "定时提醒：艾特绿皮" -> "绿皮" +- 消息: "定时提醒：喝水" -> "无" +- 消息: "定时提醒：{bot_name}，记得休息" -> "无" + +请直接输出提取到的用户名，如果不存在则输出"无"。 +''' + + llm_request = LLMRequest( + model_set=model_config.model_task_config.utils_small, + request_type="reminder_user_extraction" + ) + + response, _ = await llm_request.generate_response_async(prompt=user_extraction_prompt) + + if response and response.strip() != "无": + logger.info(f"LLM成功提取目标用户: '{response.strip()}'") + return response.strip() + else: + logger.warning(f"LLM未能从 '{reminder_content}' 中提取目标用户") + return None + + except Exception as e: + logger.error(f"使用LLM提取用户名时出错: {e}") + return None + + async def _generate_reminder_proactive_reply(self, action_result: Dict[str, Any], trigger_event: ProactiveTriggerEvent, reminder_content: str): + """ + 为定时提醒事件生成专用的主动回复 + + Args: + action_result: 动作结果 + trigger_event: 触发事件 + reminder_content: 提醒内容 + """ + try: + logger.info(f"{self.context.log_prefix} 生成定时提醒专用回复: '{reminder_content}'") + + # 获取基本信息 + bot_name = global_config.bot.nickname + personality = global_config.personality + identity_block = ( + f"你的名字是{bot_name}。\n" + f"关于你：{personality.personality_core}，并且{personality.personality_side}。\n" + f"你的身份是{personality.identity}，平时说话风格是{personality.reply_style}。" + ) + mood_block = f"你现在的心情是：{mood_manager.get_mood_by_chat_id(self.context.stream_id).mood_state}" + + # 获取日程信息 + schedule_block = "你今天没有日程安排。" + if global_config.planning_system.schedule_enable: + if current_activity := schedule_manager.get_current_activity(): + schedule_block = f"你当前正在：{current_activity}。" + + # 为定时提醒定制的专用提示词 + reminder_prompt = f""" +## 你的角色 +{identity_block} + +## 你的心情 +{mood_block} + +## 你今天的日程安排 +{schedule_block} + +## 定时提醒任务 +你收到了一个定时提醒："{reminder_content}" +这是一个自动触发的提醒事件，你需要根据提醒内容发送一条友好的提醒消息。 + +## 任务要求 +- 这是一个定时提醒，要体现出你的贴心和关怀 +- 根据提醒内容的具体情况（如"喝水"、"休息"等）给出相应的提醒 +- 保持你一贯的温暖、俏皮风格 +- 可以加上一些鼓励或关心的话语 +- 直接输出提醒消息，不要解释为什么要提醒 + +请生成一条温暖贴心的提醒消息。 +""" + + response_text = await generator_api.generate_response_custom( + chat_stream=self.context.chat_stream, + prompt=reminder_prompt, + request_type="chat.replyer.reminder", + ) + + if response_text: + response_set = process_human_text( + content=response_text, + enable_splitter=global_config.response_splitter.enable, + enable_chinese_typo=global_config.chinese_typo.enable, + ) + await self.cycle_processor.response_handler.send_response( + response_set, time.time(), action_result.get("action_message") + ) + await store_action_info( + chat_stream=self.context.chat_stream, + action_name="reminder_reply", + action_data={"reminder_content": reminder_content, "response": response_text}, + action_prompt_display=f"定时提醒回复: {reminder_content}", + action_done=True, + ) + logger.info(f"{self.context.log_prefix} 成功发送定时提醒回复: {response_text}") + else: + logger.error(f"{self.context.log_prefix} 定时提醒回复生成失败。") + + except Exception as e: + logger.error(f"{self.context.log_prefix} 生成定时提醒回复时异常: {e}") + logger.error(traceback.format_exc()) + async def _get_reminder_context(self, message_id: str) -> str: """获取提醒消息的上下文""" diff --git a/src/chat/heart_flow/heartflow_message_processor.py b/src/chat/heart_flow/heartflow_message_processor.py index 37db99947..3bf083e33 100644 --- a/src/chat/heart_flow/heartflow_message_processor.py +++ b/src/chat/heart_flow/heartflow_message_processor.py @@ -139,9 +139,13 @@ class HeartFCMessageReceiver: from src.chat.chat_loop.proactive.events import ProactiveTriggerEvent reminder_content = metadata.get('content', '提醒时间到了') + # 使用原始消息内容作为reason，如果没有则使用处理后的内容 + original_message = metadata.get('original_message', '') + reason_content = original_message if original_message else reminder_content + event = ProactiveTriggerEvent( source="reminder_system", - reason=f"定时提醒：{reminder_content}", + reason=f"定时提醒：{reason_content}", metadata=metadata, related_message_id=metadata.get("original_message_id") ) @@ -179,7 +183,8 @@ class HeartFCMessageReceiver: "content": reminder_event.content, "confidence": reminder_event.confidence, "created_at": datetime.now().isoformat(), - "original_message_id": message.message_info.message_id + "original_message_id": message.message_info.message_id, + "original_message": message.processed_plain_text # 保存完整的原始消息 } success = await event_scheduler.schedule_event( diff --git a/src/plugins/built_in/at_user_plugin/plugin.py b/src/plugins/built_in/at_user_plugin/plugin.py index e01f9bf9c..f05de7672 100644 --- a/src/plugins/built_in/at_user_plugin/plugin.py +++ b/src/plugins/built_in/at_user_plugin/plugin.py @@ -70,15 +70,49 @@ class AtAction(BaseAction): if not member_list: return False, "群成员列表为空" - # 使用模糊匹配找到最接近的用户名 - choices = {member["card"] or member["nickname"]: member["user_id"] for member in member_list} - best_match, score = process.extractOne(user_name, choices.keys()) + # 优化用户匹配逻辑 + best_match = None + user_id = None + + # 1. 完全精确匹配 + for member in member_list: + card = member.get("card", "") + nickname = member.get("nickname", "") + if user_name == card or user_name == nickname: + best_match = card if user_name == card else nickname + user_id = member["user_id"] + logger.info(f"找到完全精确匹配: '{user_name}' -> '{best_match}' (ID: {user_id})") + break - if score < 30: # 设置一个匹配度阈值 - logger.info(f"找不到与 '{user_name}' 高度匹配的用户 (最佳匹配: {best_match}, 分数: {score})") - return False, "用户不存在" + # 2. 包含关系匹配 + if not best_match: + containing_matches = [] + for member in member_list: + card = member.get("card", "") + nickname = member.get("nickname", "") + if user_name in card: + containing_matches.append((card, member["user_id"])) + elif user_name in nickname: + containing_matches.append((nickname, member["user_id"])) - user_id = choices[best_match] + if containing_matches: + # 选择最短的匹配项，因为通常更精确 + best_match, user_id = min(containing_matches, key=lambda x: len(x[0])) + logger.info(f"找到包含关系匹配: '{user_name}' -> '{best_match}' (ID: {user_id})") + + # 3. 模糊匹配作为兜底 + if not best_match: + choices = {member["card"] or member["nickname"]: member["user_id"] for member in member_list} + fuzzy_match, score = process.extractOne(user_name, choices.keys()) + if score >= 60: # 维持较高的阈值 + best_match = fuzzy_match + user_id = choices[best_match] + logger.info(f"找到模糊匹配: '{user_name}' -> '{best_match}' (ID: {user_id}, Score: {score})") + + if not best_match: + logger.warning(f"所有匹配策略都未能找到用户: '{user_name}'") + return False, "用户不存在" + user_info = {"user_id": user_id, "user_nickname": best_match} try: @@ -93,13 +127,17 @@ class AtAction(BaseAction): return False, "聊天流不存在" replyer = DefaultReplyer(chat_stream) - extra_info = f"你需要艾特用户 {user_name} 并回复他们说: {at_message}" - + # 优化提示词，消除记忆割裂感 + reminder_task = at_message.replace("定时提醒：", "").strip() + extra_info = f"""你之前记下了一个提醒任务：'{reminder_task}' +现在时间到了，你需要去提醒用户 '{user_name}'。 +请像一个朋友一样，自然地完成这个提醒，而不是生硬地复述任务。""" + success, llm_response, _ = await replyer.generate_reply_with_context( - reply_to=f"{user_name}:{at_message}", + reply_to=f"是时候提醒'{user_name}'了", # 内部上下文，更符合执行任务的语境 extra_info=extra_info, enable_tool=False, - from_plugin=False + from_plugin=True # 标记为插件调用，以便LLM更好地理解上下文 ) if not success or not llm_response: