300 lines
12 KiB
Python
300 lines
12 KiB
Python
import re
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import time
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from random import random
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import json
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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, GroupInfo, Seg
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from src.think_flow_demo.heartflow import subheartflow_manager
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from src.think_flow_demo.outer_world import outer_world
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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 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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# message_json = json.loads(message_data)
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# 哦我嘞个json
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# 进入maimbot
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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, # 我嘞个gourp_info
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)
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message.update_chat_stream(chat)
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# 创建 心流 观察
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if global_config.enable_think_flow:
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await outer_world.check_and_add_new_observe()
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subheartflow_manager.create_subheartflow(chat.stream_id)
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await relationship_manager.update_relationship(
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chat_stream=chat,
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)
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await relationship_manager.update_relationship_value(chat_stream=chat, relationship_value=0)
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await message.process()
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# 过滤词
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for word in global_config.ban_words:
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if word in message.processed_plain_text:
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logger.info(
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f"[{chat.group_info.group_name if chat.group_info else '私聊'}]"
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f"{userinfo.user_nickname}:{message.processed_plain_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
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# 正则表达式过滤
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for pattern in global_config.ban_msgs_regex:
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if re.search(pattern, message.raw_message):
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logger.info(
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f"[{chat.group_info.group_name if chat.group_info else '私聊'}]"
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f"{userinfo.user_nickname}:{message.raw_message}"
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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
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current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(messageinfo.time))
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# 根据话题计算激活度
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topic = ""
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await self.storage.store_message(message, chat, topic[0] if topic else None)
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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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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 = (subheartflow_manager.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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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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logger.info(
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f"[{current_time}][{chat.group_info.group_name if chat.group_info else '私聊'}]"
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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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response = None
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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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# 开始组织语言
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if random() < reply_probability:
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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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# 开始思考的时间点
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thinking_time_point = round(time.time(), 2)
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# logger.debug(f"开始思考的时间点: {thinking_time_point}")
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think_id = "mt" + str(thinking_time_point)
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thinking_message = MessageThinking(
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message_id=think_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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response, raw_content = await self.gpt.generate_response(message)
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else:
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# 决定不回复时，也更新回复意愿
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willing_manager.change_reply_willing_not_sent(chat)
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# print(f"response: {response}")
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if response:
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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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if subheartflow_manager.get_subheartflow(stream_id):
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await subheartflow_manager.get_subheartflow(stream_id).do_after_reply(response, chat_talking_prompt)
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else:
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await subheartflow_manager.create_subheartflow(stream_id).do_after_reply(response, chat_talking_prompt)
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# print(f"有response: {response}")
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container = message_manager.get_container(chat.stream_id)
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thinking_message = None
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# 找到message,删除
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# print(f"开始找思考消息")
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for msg in container.messages:
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if isinstance(msg, MessageThinking) and msg.message_info.message_id == think_id:
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# print(f"找到思考消息: {msg}")
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thinking_message = msg
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container.messages.remove(msg)
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break
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# 如果找不到思考消息，直接返回
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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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# 记录开始思考的时间，避免从思考到回复的时间太久
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thinking_start_time = thinking_message.thinking_start_time
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message_set = MessageSet(chat, think_id)
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# 计算打字时间，1是为了模拟打字，2是避免多条回复乱序
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# accu_typing_time = 0
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mark_head = False
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for msg in response:
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# print(f"\033[1;32m[回复内容]\033[0m {msg}")
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# 通过时间改变时间戳
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# typing_time = calculate_typing_time(msg)
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# logger.debug(f"typing_time: {typing_time}")
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# accu_typing_time += typing_time
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# timepoint = thinking_time_point + accu_typing_time
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message_segment = Seg(type="text", data=msg)
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# logger.debug(f"message_segment: {message_segment}")
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bot_message = MessageSending(
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message_id=think_id,
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chat_stream=chat,
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bot_user_info=bot_user_info,
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sender_info=userinfo,
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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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if len(str(bot_message)) < 1000:
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logger.debug(f"bot_message: {bot_message}")
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logger.debug(f"添加消息到message_set: {bot_message}")
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else:
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logger.debug(f"bot_message: {str(bot_message)[:1000]}...{str(bot_message)[-10:]}")
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logger.debug(f"添加消息到message_set: {str(bot_message)[:1000]}...{str(bot_message)[-10:]}")
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# message_set 可以直接加入 message_manager
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# print(f"\033[1;32m[回复]\033[0m 将回复载入发送容器")
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logger.debug("添加message_set到message_manager")
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message_manager.add_message(message_set)
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bot_response_time = thinking_time_point
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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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# 检查是否 <没有找到> emoji
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if emoji_raw != None:
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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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if random() < 0.5:
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bot_response_time = thinking_time_point - 1
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else:
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bot_response_time = bot_response_time + 1
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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=think_id,
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chat_stream=chat,
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bot_user_info=bot_user_info,
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sender_info=userinfo,
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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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# 获取立场和情感标签，更新关系值
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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(
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chat_stream=chat, label=emotion, stance=stance
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)
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# 使用情绪管理器更新情绪
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self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
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# willing_manager.change_reply_willing_after_sent(
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# chat_stream=chat
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# )
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# 创建全局ChatBot实例
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chat_bot = ChatBot()
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