from typing import List, Tuple, TYPE_CHECKING from src.common.logger import get_module_logger from src.chat.models.utils_model import LLMRequest from src.config.config import global_config from src.experimental.PFC.chat_observer import ChatObserver from src.experimental.PFC.pfc_utils import get_items_from_json from src.individuality.individuality import Individuality from src.experimental.PFC.conversation_info import ConversationInfo from src.experimental.PFC.observation_info import ObservationInfo from src.chat.utils.chat_message_builder import build_readable_messages from rich.traceback import install install(extra_lines=3) if TYPE_CHECKING: pass logger = get_module_logger("pfc") def _calculate_similarity(goal1: str, goal2: str) -> float: """简单计算两个目标之间的相似度 这里使用一个简单的实现，实际可以使用更复杂的文本相似度算法 Args: goal1: 第一个目标 goal2: 第二个目标 Returns: float: 相似度得分 (0-1) """ # 简单实现：检查重叠字数比例 words1 = set(goal1) words2 = set(goal2) overlap = len(words1.intersection(words2)) total = len(words1.union(words2)) return overlap / total if total > 0 else 0 class GoalAnalyzer: """对话目标分析器""" def __init__(self, stream_id: str, private_name: str): # TODO: API-Adapter修改标记 self.llm = LLMRequest( model=global_config.model.normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal" ) self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3) self.name = global_config.bot.nickname self.nick_name = global_config.bot.alias_names self.private_name = private_name self.chat_observer = ChatObserver.get_instance(stream_id, private_name) # 多目标存储结构 self.goals = [] # 存储多个目标 self.max_goals = 3 # 同时保持的最大目标数量 self.current_goal_and_reason = None async def analyze_goal(self, conversation_info: ConversationInfo, observation_info: ObservationInfo): """分析对话历史并设定目标 Args: conversation_info: 对话信息 observation_info: 观察信息 Returns: Tuple[str, str, str]: (目标, 方法, 原因) """ # 构建对话目标 goals_str = "" if conversation_info.goal_list: for goal_reason in conversation_info.goal_list: if isinstance(goal_reason, dict): goal = goal_reason.get("goal", "目标内容缺失") reasoning = goal_reason.get("reasoning", "没有明确原因") else: goal = str(goal_reason) reasoning = "没有明确原因" goal_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n" goals_str += goal_str else: goal = "目前没有明确对话目标" reasoning = "目前没有明确对话目标，最好思考一个对话目标" goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n" # 获取聊天历史记录 chat_history_text = observation_info.chat_history_str if observation_info.new_messages_count > 0: new_messages_list = observation_info.unprocessed_messages new_messages_str = await build_readable_messages( new_messages_list, replace_bot_name=True, merge_messages=False, timestamp_mode="relative", read_mark=0.0, ) chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}" # await observation_info.clear_unprocessed_messages() persona_text = f"你的名字是{self.name}，{self.personality_info}。" # 构建action历史文本 action_history_list = conversation_info.done_action action_history_text = "你之前做的事情是：" for action in action_history_list: action_history_text += f"{action}\n" prompt = f"""{persona_text}。现在你在参与一场QQ聊天，请分析以下聊天记录，并根据你的性格特征确定多个明确的对话目标。 这些目标应该反映出对话的不同方面和意图。 {action_history_text} 当前对话目标： {goals_str} 聊天记录： {chat_history_text} 请分析当前对话并确定最适合的对话目标。你可以： 1. 保持现有目标不变 2. 修改现有目标 3. 添加新目标 4. 删除不再相关的目标 5. 如果你想结束对话，请设置一个目标，目标goal为"结束对话"，原因reasoning为你希望结束对话 请以JSON数组格式输出当前的所有对话目标，每个目标包含以下字段： 1. goal: 对话目标（简短的一句话） 2. reasoning: 对话原因，为什么设定这个目标（简要解释） 输出格式示例： [ {{ "goal": "回答用户关于Python编程的具体问题", "reasoning": "用户提出了关于Python的技术问题，需要专业且准确的解答" }}, {{ "goal": "回答用户关于python安装的具体问题", "reasoning": "用户提出了关于Python的技术问题，需要专业且准确的解答" }} ]""" logger.debug(f"[私聊][{self.private_name}]发送到LLM的提示词: {prompt}") try: content, _ = await self.llm.generate_response_async(prompt) logger.debug(f"[私聊][{self.private_name}]LLM原始返回内容: {content}") except Exception as e: logger.error(f"[私聊][{self.private_name}]分析对话目标时出错: {str(e)}") content = "" # 使用改进后的get_items_from_json函数处理JSON数组 success, result = get_items_from_json( content, self.private_name, "goal", "reasoning", required_types={"goal": str, "reasoning": str}, allow_array=True, ) if success: # 判断结果是单个字典还是字典列表 if isinstance(result, list): # 清空现有目标列表并添加新目标 conversation_info.goal_list = [] for item in result: conversation_info.goal_list.append(item) # 返回第一个目标作为当前主要目标（如果有） if result: first_goal = result[0] return first_goal.get("goal", ""), "", first_goal.get("reasoning", "") else: # 单个目标的情况 conversation_info.goal_list.append(result) return goal, "", reasoning # 如果解析失败，返回默认值 return "", "", "" async def _update_goals(self, new_goal: str, method: str, reasoning: str): """更新目标列表 Args: new_goal: 新的目标 method: 实现目标的方法 reasoning: 目标的原因 """ # 检查新目标是否与现有目标相似 for i, (existing_goal, _, _) in enumerate(self.goals): if _calculate_similarity(new_goal, existing_goal) > 0.7: # 相似度阈值 # 更新现有目标 self.goals[i] = (new_goal, method, reasoning) # 将此目标移到列表前面（最主要的位置） self.goals.insert(0, self.goals.pop(i)) return # 添加新目标到列表前面 self.goals.insert(0, (new_goal, method, reasoning)) # 限制目标数量 if len(self.goals) > self.max_goals: self.goals.pop() # 移除最老的目标 async def get_all_goals(self) -> List[Tuple[str, str, str]]: """获取所有当前目标 Returns: List[Tuple[str, str, str]]: 目标列表，每项为(目标, 方法, 原因) """ return self.goals.copy() async def get_alternative_goals(self) -> List[Tuple[str, str, str]]: """获取除了当前主要目标外的其他备选目标 Returns: List[Tuple[str, str, str]]: 备选目标列表 """ if len(self.goals) <= 1: return [] return self.goals[1:].copy() async def analyze_conversation(self, goal, reasoning): messages = self.chat_observer.get_cached_messages() chat_history_text = await build_readable_messages( messages, replace_bot_name=True, merge_messages=False, timestamp_mode="relative", read_mark=0.0, ) persona_text = f"你的名字是{self.name}，{self.personality_info}。" # ===> Persona 文本构建结束 <=== # --- 修改 Prompt 字符串，使用 persona_text --- prompt = f"""{persona_text}。现在你在参与一场QQ聊天， 当前对话目标：{goal} 产生该对话目标的原因：{reasoning} 请分析以下聊天记录，并根据你的性格特征评估该目标是否已经达到，或者你是否希望停止该次对话。 聊天记录： {chat_history_text} 请以JSON格式输出，包含以下字段： 1. goal_achieved: 对话目标是否已经达到（true/false） 2. stop_conversation: 是否希望停止该次对话（true/false） 3. reason: 为什么希望停止该次对话（简要解释） 输出格式示例： {{ "goal_achieved": true, "stop_conversation": false, "reason": "虽然目标已达成，但对话仍然有继续的价值" }}""" try: content, _ = await self.llm.generate_response_async(prompt) logger.debug(f"[私聊][{self.private_name}]LLM原始返回内容: {content}") # 尝试解析JSON success, result = get_items_from_json( content, self.private_name, "goal_achieved", "stop_conversation", "reason", required_types={"goal_achieved": bool, "stop_conversation": bool, "reason": str}, ) if not success: logger.error(f"[私聊][{self.private_name}]无法解析对话分析结果JSON") return False, False, "解析结果失败" goal_achieved = result["goal_achieved"] stop_conversation = result["stop_conversation"] reason = result["reason"] return goal_achieved, stop_conversation, reason except Exception as e: logger.error(f"[私聊][{self.private_name}]分析对话状态时出错: {str(e)}") return False, False, f"分析出错: {str(e)}" # 先注释掉，万一以后出问题了还能开回来（（（ # class DirectMessageSender: # """直接发送消息到平台的发送器""" # def __init__(self, private_name: str): # self.logger = get_module_logger("direct_sender") # self.storage = MessageStorage() # self.private_name = private_name # async def send_via_ws(self, message: MessageSending) -> None: # try: # await global_api.send_message(message) # except Exception as e: # raise ValueError(f"未找到平台：{message.message_info.platform} 的url配置，请检查配置文件") from e # async def send_message( # self, # chat_stream: ChatStream, # content: str, # reply_to_message: Optional[Message] = None, # ) -> None: # """直接发送消息到平台 # Args: # chat_stream: 聊天流 # content: 消息内容 # reply_to_message: 要回复的消息 # """ # # 构建消息对象 # message_segment = Seg(type="text", data=content) # bot_user_info = UserInfo( # user_id=global_config.BOT_QQ, # user_nickname=global_config.BOT_NICKNAME, # platform=chat_stream.platform, # ) # message = MessageSending( # message_id=f"dm{round(time.time(), 2)}", # chat_stream=chat_stream, # bot_user_info=bot_user_info, # sender_info=reply_to_message.message_info.user_info if reply_to_message else None, # message_segment=message_segment, # reply=reply_to_message, # is_head=True, # is_emoji=False, # thinking_start_time=time.time(), # ) # # 处理消息 # await message.process() # _message_json = message.to_dict() # # 发送消息 # try: # await self.send_via_ws(message) # await self.storage.store_message(message, chat_stream) # logger.success(f"[私聊][{self.private_name}]PFC消息已发送: {content}") # except Exception as e: # logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {str(e)}")