370 lines
14 KiB
Python
370 lines
14 KiB
Python
import re
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import time
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from random import random
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from ..memory_system.Hippocampus import HippocampusManager
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from ..moods.moods import MoodManager # 导入情绪管理器
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from ..config.config import global_config
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from .emoji_manager import emoji_manager # 导入表情包管理器
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from .llm_generator import ResponseGenerator
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from .message import MessageSending, MessageRecv, MessageThinking, MessageSet
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from .chat_stream import chat_manager
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from .message_sender import message_manager # 导入新的消息管理器
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from .relationship_manager import relationship_manager
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from .storage import MessageStorage
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from .utils import is_mentioned_bot_in_message, get_recent_group_detailed_plain_text
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from .utils_image import image_path_to_base64
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from ..willing.willing_manager import willing_manager # 导入意愿管理器
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from ..message import UserInfo, Seg
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from src.heart_flow.heartflow import heartflow
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from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
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# 定义日志配置
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chat_config = LogConfig(
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# 使用消息发送专用样式
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console_format=CHAT_STYLE_CONFIG["console_format"],
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file_format=CHAT_STYLE_CONFIG["file_format"],
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)
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# 配置主程序日志格式
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logger = get_module_logger("chat_bot", config=chat_config)
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class ChatBot:
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def __init__(self):
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self.storage = MessageStorage()
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self.gpt = ResponseGenerator()
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self.bot = None # bot 实例引用
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self._started = False
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self.mood_manager = MoodManager.get_instance() # 获取情绪管理器单例
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self.mood_manager.start_mood_update() # 启动情绪更新
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async def _ensure_started(self):
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"""确保所有任务已启动"""
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if not self._started:
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self._started = True
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async def _create_thinking_message(self, message, chat, userinfo, messageinfo):
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"""创建思考消息
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Args:
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message: 接收到的消息
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chat: 聊天流对象
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userinfo: 用户信息对象
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messageinfo: 消息信息对象
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Returns:
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str: thinking_id
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"""
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=messageinfo.platform,
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)
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thinking_time_point = round(time.time(), 2)
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thinking_id = "mt" + str(thinking_time_point)
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thinking_message = MessageThinking(
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message_id=thinking_id,
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chat_stream=chat,
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bot_user_info=bot_user_info,
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reply=message,
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thinking_start_time=thinking_time_point,
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)
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message_manager.add_message(thinking_message)
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willing_manager.change_reply_willing_sent(chat)
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return thinking_id
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async def message_process(self, message_data: str) -> None:
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"""处理转化后的统一格式消息
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1. 过滤消息
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2. 记忆激活
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3. 意愿激活
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4. 生成回复并发送
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5. 更新关系
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6. 更新情绪
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"""
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timing_results = {} # 用于收集所有计时结果
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response_set = None # 初始化response_set变量
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message = MessageRecv(message_data)
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groupinfo = message.message_info.group_info
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userinfo = message.message_info.user_info
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messageinfo = message.message_info
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# 消息过滤，涉及到config有待更新
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# 创建聊天流
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chat = await chat_manager.get_or_create_stream(
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platform=messageinfo.platform,
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user_info=userinfo,
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group_info=groupinfo,
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)
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message.update_chat_stream(chat)
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# 创建 心流与chat的观察
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heartflow.create_subheartflow(chat.stream_id)
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await message.process()
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# 过滤词/正则表达式过滤
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if self._check_ban_words(message.processed_plain_text, chat, userinfo) or self._check_ban_regex(
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message.raw_message, chat, userinfo
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):
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return
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await self.storage.store_message(message, chat)
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timer1 = time.time()
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interested_rate = 0
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interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
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message.processed_plain_text, fast_retrieval=True
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)
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timer2 = time.time()
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timing_results["记忆激活"] = timer2 - timer1
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is_mentioned = is_mentioned_bot_in_message(message)
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if global_config.enable_think_flow:
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current_willing_old = willing_manager.get_willing(chat_stream=chat)
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current_willing_new = (heartflow.get_subheartflow(chat.stream_id).current_state.willing - 5) / 4
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print(f"旧回复意愿：{current_willing_old}，新回复意愿：{current_willing_new}")
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current_willing = (current_willing_old + current_willing_new) / 2
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else:
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current_willing = willing_manager.get_willing(chat_stream=chat)
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willing_manager.set_willing(chat.stream_id, current_willing)
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timer1 = time.time()
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reply_probability = await willing_manager.change_reply_willing_received(
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chat_stream=chat,
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is_mentioned_bot=is_mentioned,
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config=global_config,
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is_emoji=message.is_emoji,
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interested_rate=interested_rate,
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sender_id=str(message.message_info.user_info.user_id),
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)
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timer2 = time.time()
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timing_results["意愿激活"] = timer2 - timer1
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# 神秘的消息流数据结构处理
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if chat.group_info:
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mes_name = chat.group_info.group_name
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else:
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mes_name = "私聊"
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# 打印收到的信息的信息
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current_time = time.strftime("%H:%M:%S", time.localtime(messageinfo.time))
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logger.info(
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f"[{current_time}][{mes_name}]"
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f"{chat.user_info.user_nickname}:"
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f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]"
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)
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if message.message_info.additional_config:
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if "maimcore_reply_probability_gain" in message.message_info.additional_config.keys():
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reply_probability += message.message_info.additional_config["maimcore_reply_probability_gain"]
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do_reply = False
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# 开始组织语言
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if random() < reply_probability:
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do_reply = True
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timer1 = time.time()
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thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
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timer2 = time.time()
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timing_results["创建思考消息"] = timer2 - timer1
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timer1 = time.time()
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await heartflow.get_subheartflow(chat.stream_id).do_observe()
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timer2 = time.time()
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timing_results["观察"] = timer2 - timer1
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timer1 = time.time()
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await heartflow.get_subheartflow(chat.stream_id).do_thinking_before_reply(message.processed_plain_text)
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timer2 = time.time()
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timing_results["思考前脑内状态"] = timer2 - timer1
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timer1 = time.time()
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response_set = await self.gpt.generate_response(message)
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timer2 = time.time()
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timing_results["生成回复"] = timer2 - timer1
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if not response_set:
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logger.info("为什么生成回复失败？")
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return
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# 发送消息
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timer1 = time.time()
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await self._send_response_messages(message, chat, response_set, thinking_id)
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timer2 = time.time()
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timing_results["发送消息"] = timer2 - timer1
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# 处理表情包
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timer1 = time.time()
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await self._handle_emoji(message, chat, response_set)
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timer2 = time.time()
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timing_results["处理表情包"] = timer2 - timer1
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timer1 = time.time()
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await self._update_using_response(message, response_set)
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timer2 = time.time()
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timing_results["更新心流"] = timer2 - timer1
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# 在最后统一输出所有计时结果
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if do_reply:
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timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()])
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trigger_msg = message.processed_plain_text
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response_msg = " ".join(response_set) if response_set else "无回复"
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logger.info(f"触发消息: {trigger_msg[:20]}... | 生成消息: {response_msg[:20]}... | 性能计时: {timing_str}")
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async def _update_using_response(self, message, response_set):
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# 更新心流状态
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stream_id = message.chat_stream.stream_id
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chat_talking_prompt = ""
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if stream_id:
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chat_talking_prompt = get_recent_group_detailed_plain_text(
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stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
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)
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await heartflow.get_subheartflow(stream_id).do_thinking_after_reply(response_set, chat_talking_prompt)
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async def _send_response_messages(self, message, chat, response_set, thinking_id):
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container = message_manager.get_container(chat.stream_id)
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thinking_message = None
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# logger.info(f"开始发送消息准备")
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for msg in container.messages:
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if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id:
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thinking_message = msg
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container.messages.remove(msg)
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break
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if not thinking_message:
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logger.warning("未找到对应的思考消息，可能已超时被移除")
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return
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# logger.info(f"开始发送消息")
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thinking_start_time = thinking_message.thinking_start_time
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message_set = MessageSet(chat, thinking_id)
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mark_head = False
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for msg in response_set:
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message_segment = Seg(type="text", data=msg)
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bot_message = MessageSending(
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message_id=thinking_id,
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chat_stream=chat,
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bot_user_info=UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=message.message_info.platform,
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),
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sender_info=message.message_info.user_info,
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message_segment=message_segment,
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reply=message,
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is_head=not mark_head,
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is_emoji=False,
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thinking_start_time=thinking_start_time,
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)
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if not mark_head:
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mark_head = True
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message_set.add_message(bot_message)
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# logger.info(f"开始添加发送消息")
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message_manager.add_message(message_set)
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async def _handle_emoji(self, message, chat, response):
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"""处理表情包
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Args:
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message: 接收到的消息
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chat: 聊天流对象
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response: 生成的回复
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"""
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if random() < global_config.emoji_chance:
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emoji_raw = await emoji_manager.get_emoji_for_text(response)
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if emoji_raw:
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emoji_path, description = emoji_raw
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emoji_cq = image_path_to_base64(emoji_path)
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thinking_time_point = round(message.message_info.time, 2)
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message_segment = Seg(type="emoji", data=emoji_cq)
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bot_message = MessageSending(
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message_id="mt" + str(thinking_time_point),
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chat_stream=chat,
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bot_user_info=UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=message.message_info.platform,
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),
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sender_info=message.message_info.user_info,
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message_segment=message_segment,
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reply=message,
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is_head=False,
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is_emoji=True,
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)
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message_manager.add_message(bot_message)
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async def _update_emotion_and_relationship(self, message, chat, response, raw_content):
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"""更新情绪和关系
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Args:
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message: 接收到的消息
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chat: 聊天流对象
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response: 生成的回复
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raw_content: 原始内容
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"""
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stance, emotion = await self.gpt._get_emotion_tags(raw_content, message.processed_plain_text)
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logger.debug(f"为 '{response}' 立场为：{stance} 获取到的情感标签为：{emotion}")
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await relationship_manager.calculate_update_relationship_value(chat_stream=chat, label=emotion, stance=stance)
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self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
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def _check_ban_words(self, text: str, chat, userinfo) -> bool:
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"""检查消息中是否包含过滤词
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Args:
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text: 要检查的文本
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chat: 聊天流对象
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userinfo: 用户信息对象
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Returns:
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bool: 如果包含过滤词返回True，否则返回False
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"""
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for word in global_config.ban_words:
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if word in text:
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logger.info(
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f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
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)
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logger.info(f"[过滤词识别]消息中含有{word}，filtered")
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return True
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return False
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def _check_ban_regex(self, text: str, chat, userinfo) -> bool:
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"""检查消息是否匹配过滤正则表达式
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Args:
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text: 要检查的文本
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chat: 聊天流对象
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userinfo: 用户信息对象
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Returns:
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bool: 如果匹配过滤正则返回True，否则返回False
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"""
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for pattern in global_config.ban_msgs_regex:
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if re.search(pattern, text):
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logger.info(
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f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
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)
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logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
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return True
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return False
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# 创建全局ChatBot实例
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chat_bot = ChatBot()
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