# Programmable Friendly Conversationalist # Prefrontal cortex import datetime import asyncio from typing import List, Optional, Tuple, TYPE_CHECKING from src.common.logger import get_module_logger from ..chat.chat_stream import ChatStream from ..message.message_base import UserInfo, Seg from ..chat.message import Message from ..models.utils_model import LLM_request from ..config.config import global_config from src.plugins.chat.message import MessageSending from ..message.api import global_api from ..storage.storage import MessageStorage from .chat_observer import ChatObserver from .pfc_utils import get_items_from_json from src.individuality.individuality import Individuality from .conversation_info import ConversationInfo from .observation_info import ObservationInfo import time if TYPE_CHECKING: pass logger = get_module_logger("pfc") class GoalAnalyzer: """对话目标分析器""" def __init__(self, stream_id: str): self.llm = LLM_request( model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal" ) self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=2) self.name = global_config.BOT_NICKNAME self.nick_name = global_config.BOT_ALIAS_NAMES self.chat_observer = ChatObserver.get_instance(stream_id) # 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 goal_list = conversation_info.goal_list goal_text = "" for goal, reason in goal_list: goal_text += f"目标：{goal};" goal_text += f"原因：{reason}\n" # 获取聊天历史记录 chat_history_list = observation_info.chat_history chat_history_text = "" for msg in chat_history_list: chat_history_text += f"{msg}\n" if observation_info.new_messages_count > 0: new_messages_list = observation_info.unprocessed_messages chat_history_text += f"有{observation_info.new_messages_count}条新消息：\n" for msg in new_messages_list: chat_history_text += f"{msg}\n" observation_info.clear_unprocessed_messages() personality_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"""{personality_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： {goal_text} 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 请以JSON格式输出当前的所有对话目标，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"发送到LLM的提示词: {prompt}") content, _ = await self.llm.generate_response_async(prompt) logger.debug(f"LLM原始返回内容: {content}") # 使用简化函数提取JSON内容 success, result = get_items_from_json( content, "goal", "reasoning", required_types={"goal": str, "reasoning": str} ) #TODO conversation_info.goal_list.append(result) 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 self._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 def _calculate_similarity(self, 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 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_message_history() chat_history_text = "" for msg in messages: time_str = datetime.datetime.fromtimestamp(msg["time"]).strftime("%H:%M:%S") user_info = UserInfo.from_dict(msg.get("user_info", {})) sender = user_info.user_nickname or f"用户{user_info.user_id}" if sender == self.name: sender = "你说" chat_history_text += f"{time_str},{sender}:{msg.get('processed_plain_text', '')}\n" personality_text = f"你的名字是{self.name}，{self.personality_info}" prompt = f"""{personality_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"LLM原始返回内容: {content}") # 尝试解析JSON success, result = get_items_from_json( content, "goal_achieved", "stop_conversation", "reason", required_types={"goal_achieved": bool, "stop_conversation": bool, "reason": str} ) if not success: logger.error("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"分析对话状态时出错: {str(e)}") return False, False, f"分析出错: {str(e)}" class Waiter: """快 速 等 待""" def __init__(self, stream_id: str): self.chat_observer = ChatObserver.get_instance(stream_id) self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=2) self.name = global_config.BOT_NICKNAME async def wait(self) -> bool: """等待 Returns: bool: 是否超时（True表示超时） """ # 使用当前时间作为等待开始时间 wait_start_time = time.time() self.chat_observer.waiting_start_time = wait_start_time # 设置等待开始时间 while True: # 检查是否有新消息 if self.chat_observer.new_message_after(wait_start_time): logger.info("等待结束，收到新消息") return False # 检查是否超时 if time.time() - wait_start_time > 300: logger.info("等待超过300秒，结束对话") return True await asyncio.sleep(1) logger.info("等待中...") class DirectMessageSender: """直接发送消息到平台的发送器""" def __init__(self): self.logger = get_module_logger("direct_sender") self.storage = MessageStorage() 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() # 发送消息 try: message_json = message.to_dict() end_point = global_config.api_urls.get(chat_stream.platform, None) if not end_point: raise ValueError(f"未找到平台：{chat_stream.platform} 的url配置") await global_api.send_message_REST(end_point, message_json) # 存储消息 await self.storage.store_message(message, message.chat_stream) self.logger.info(f"直接发送消息成功: {content[:30]}...") except Exception as e: self.logger.error(f"直接发送消息失败: {str(e)}") raise