移除mai4u：s4u_watching_manager.py, screen_manager.py, super_chat_manager.py, yes_or_no.py, openai_client.py, and s4u_config.py. These changes streamline the codebase by eliminating unused components and improving maintainability.

src/chat/message_receive/bot.py

@@ -9,13 +9,12 @@ from maim_message import UserInfo
 from src.chat.antipromptinjector import initialize_anti_injector
 from src.chat.message_manager import message_manager
 from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
-from src.chat.message_receive.message import MessageRecv, MessageRecvS4U
+from src.chat.message_receive.message import MessageRecv
 from src.chat.message_receive.storage import MessageStorage
 from src.chat.utils.prompt import create_prompt_async, global_prompt_manager
 from src.chat.utils.utils import is_mentioned_bot_in_message
 from src.common.logger import get_logger
 from src.config.config import global_config
-from src.mais4u.mais4u_chat.s4u_msg_processor import S4UMessageProcessor
 from src.mood.mood_manager import mood_manager  # 导入情绪管理器
 from src.plugin_system.base import BaseCommand, EventType
 from src.plugin_system.core import component_registry, event_manager, global_announcement_manager
@@ -73,9 +72,6 @@ class ChatBot:
         self.bot = None  # bot 实例引用
         self._started = False
         self.mood_manager = mood_manager  # 获取情绪管理器单例
-        # 亲和力流消息处理器 - 直接使用全局afc_manager
-
-        self.s4u_message_processor = S4UMessageProcessor()
 
         # 初始化反注入系统
         self._initialize_anti_injector()
@@ -364,27 +360,6 @@ class ChatBot:
         except Exception as e:
             logger.error(f"处理适配器响应时出错: {e}")
 
-    async def do_s4u(self, message_data: dict[str, Any]):
-        message = MessageRecvS4U(message_data)
-        group_info = message.message_info.group_info
-        user_info = message.message_info.user_info
-
-        get_chat_manager().register_message(message)
-        chat = await get_chat_manager().get_or_create_stream(
-            platform=message.message_info.platform,  # type: ignore
-            user_info=user_info,  # type: ignore
-            group_info=group_info,
-        )
-
-        message.update_chat_stream(chat)
-
-        # 处理消息内容
-        await message.process()
-
-        await self.s4u_message_processor.process_message(message)
-
-        return
-
     async def message_process(self, message_data: dict[str, Any]) -> None:
         """处理转化后的统一格式消息"""
         try:
@@ -419,10 +394,6 @@ class ChatBot:
 
             platform = message_info.get("platform")
 
-            if platform == "amaidesu_default":
-                await self.do_s4u(message_data)
-                return
-
             if message_info.get("group_info") is not None:
                 message_info["group_info"]["group_id"] = str(
                     message_info["group_info"]["group_id"]

src/chat/message_receive/message.py

@@ -309,206 +309,6 @@ class MessageRecv(Message):
             return f"[处理失败的{segment.type}消息]"
 
 
-@dataclass
-class MessageRecvS4U(MessageRecv):
-    def __init__(self, message_dict: dict[str, Any]):
-        super().__init__(message_dict)
-        self.is_gift = False
-        self.is_fake_gift = False
-        self.is_superchat = False
-        self.gift_info = None
-        self.gift_name = None
-        self.gift_count: int | None = None
-        self.superchat_info = None
-        self.superchat_price = None
-        self.superchat_message_text = None
-        self.is_screen = False
-        self.is_internal = False
-        self.voice_done = None
-
-        self.chat_info = None
-
-    async def process(self) -> None:
-        self.processed_plain_text = await self._process_message_segments(self.message_segment)
-
-    async def _process_single_segment(self, segment: Seg) -> str:
-        """处理单个消息段
-
-        Args:
-            segment: 消息段
-
-        Returns:
-            str: 处理后的文本
-        """
-        try:
-            if segment.type == "text":
-                self.is_voice = False
-                self.is_picid = False
-                self.is_emoji = False
-                return segment.data  # type: ignore
-            elif segment.type == "image":
-                self.is_voice = False
-                # 如果是base64图片数据
-                if isinstance(segment.data, str):
-                    self.has_picid = True
-                    self.is_picid = True
-                    self.is_emoji = False
-                    image_manager = get_image_manager()
-                    # print(f"segment.data: {segment.data}")
-                    _, processed_text = await image_manager.process_image(segment.data)
-                    return processed_text
-                return "[发了一张图片，网卡了加载不出来]"
-            elif segment.type == "emoji":
-                self.has_emoji = True
-                self.is_emoji = True
-                self.is_picid = False
-                if isinstance(segment.data, str):
-                    return await get_image_manager().get_emoji_description(segment.data)
-                return "[发了一个表情包，网卡了加载不出来]"
-            elif segment.type == "voice":
-                self.has_picid = False
-                self.is_picid = False
-                self.is_emoji = False
-                self.is_voice = True
-
-                # 检查消息是否由机器人自己发送
-                # 检查消息是否由机器人自己发送
-                if self.message_info and self.message_info.user_info and str(self.message_info.user_info.user_id) == str(global_config.bot.qq_account):
-                    logger.info(f"检测到机器人自身发送的语音消息 (User ID: {self.message_info.user_info.user_id})，尝试从缓存获取文本。")
-                    if isinstance(segment.data, str):
-                        cached_text = consume_self_voice_text(segment.data)
-                        if cached_text:
-                            logger.info(f"成功从缓存中获取语音文本: '{cached_text[:70]}...'")
-                            return f"[语音：{cached_text}]"
-                        else:
-                            logger.warning("机器人自身语音消息缓存未命中，将回退到标准语音识别。")
-
-                # 标准语音识别流程 (也作为缓存未命中的后备方案)
-                if isinstance(segment.data, str):
-                    return await get_voice_text(segment.data)
-                return "[发了一段语音，网卡了加载不出来]"
-            elif segment.type == "mention_bot":
-                self.is_voice = False
-                self.is_picid = False
-                self.is_emoji = False
-                self.is_mentioned = float(segment.data)  # type: ignore
-                return ""
-            elif segment.type == "priority_info":
-                self.is_voice = False
-                self.is_picid = False
-                self.is_emoji = False
-                if isinstance(segment.data, dict):
-                    # 处理优先级信息
-                    self.priority_mode = "priority"
-                    self.priority_info = segment.data
-                    """
-                    {
-                        'message_type': 'vip', # vip or normal
-                        'message_priority': 1.0, # 优先级，大为优先，float
-                    }
-                    """
-                return ""
-            elif segment.type == "gift":
-                self.is_voice = False
-                self.is_gift = True
-                # 解析gift_info，格式为"名称:数量"
-                name, count = segment.data.split(":", 1)  # type: ignore
-                self.gift_info = segment.data
-                self.gift_name = name.strip()
-                self.gift_count = int(count.strip())
-                return ""
-            elif segment.type == "voice_done":
-                msg_id = segment.data
-                logger.info(f"voice_done: {msg_id}")
-                self.voice_done = msg_id
-                return ""
-            elif segment.type == "superchat":
-                self.is_superchat = True
-                self.superchat_info = segment.data
-                price, message_text = segment.data.split(":", 1)  # type: ignore
-                self.superchat_price = price.strip()
-                self.superchat_message_text = message_text.strip()
-
-                self.processed_plain_text = str(self.superchat_message_text)
-                self.processed_plain_text += (
-                    f"（注意：这是一条超级弹幕信息，价值{self.superchat_price}元，请你认真回复）"
-                )
-
-                return self.processed_plain_text
-            elif segment.type == "screen":
-                self.is_screen = True
-                self.screen_info = segment.data
-                return "屏幕信息"
-            elif segment.type == "file":
-                if isinstance(segment.data, dict):
-                    file_name = segment.data.get('name', '未知文件')
-                    file_size = segment.data.get('size', '未知大小')
-                    return f"[文件：{file_name} ({file_size}字节)]"
-                return "[收到一个文件]"
-            elif segment.type == "video":
-                self.is_voice = False
-                self.is_picid = False
-                self.is_emoji = False
-
-                logger.info(f"接收到视频消息，数据类型: {type(segment.data)}")
-
-                # 检查视频分析功能是否可用
-                if not is_video_analysis_available():
-                    logger.warning("⚠️ Rust视频处理模块不可用，跳过视频分析")
-                    return "[视频]"
-
-                if global_config.video_analysis.enable:
-                    logger.info("已启用视频识别,开始识别")
-                    if isinstance(segment.data, dict):
-                        try:
-                            # 从Adapter接收的视频数据
-                            video_base64 = segment.data.get("base64")
-                            filename = segment.data.get("filename", "video.mp4")
-
-                            logger.info(f"视频文件名: {filename}")
-                            logger.info(f"Base64数据长度: {len(video_base64) if video_base64 else 0}")
-
-                            if video_base64:
-                                # 解码base64视频数据
-                                video_bytes = base64.b64decode(video_base64)
-                                logger.info(f"解码后视频大小: {len(video_bytes)} 字节")
-
-                                # 使用video analyzer分析视频
-                                video_analyzer = get_video_analyzer()
-                                result = await video_analyzer.analyze_video_from_bytes(
-                                    video_bytes, filename
-                                )
-
-                                logger.info(f"视频分析结果: {result}")
-
-                                # 返回视频分析结果
-                                summary = result.get("summary", "")
-                                if summary:
-                                    return f"[视频内容] {summary}"
-                                else:
-                                    return "[已收到视频，但分析失败]"
-                            else:
-                                logger.warning("视频消息中没有base64数据")
-                                return "[收到视频消息，但数据异常]"
-                        except Exception as e:
-                            logger.error(f"视频处理失败: {e!s}")
-                            import traceback
-
-                            logger.error(f"错误详情: {traceback.format_exc()}")
-                            return "[收到视频，但处理时出现错误]"
-                    else:
-                        logger.warning(f"视频消息数据不是字典格式: {type(segment.data)}")
-                    return "[发了一个视频，但格式不支持]"
-                else:
-                    return ""
-            else:
-                logger.warning(f"未知的消息段类型: {segment.type}")
-                return f"[{segment.type} 消息]"
-        except Exception as e:
-            logger.error(f"处理消息段失败: {e!s}, 类型: {segment.type}, 数据: {segment.data}")
-            return f"[处理失败的{segment.type}消息]"
-
-
 @dataclass
 class MessageProcessBase(Message):
     """消息处理基类，用于处理中和发送中的消息"""

src/mais4u/config/old/s4u_config_20250715_141713.toml

@@ -1,36 +0,0 @@
-[inner]
-version = "1.0.0"
-
-#----以下是S4U聊天系统配置文件----
-# S4U (Smart 4 U) 聊天系统是MaiBot的核心对话模块
-# 支持优先级队列、消息中断、VIP用户等高级功能
-#
-# 如果你想要修改配置文件，请在修改后将version的值进行变更
-# 如果新增项目，请参考src/mais4u/s4u_config.py中的S4UConfig类
-#
-# 版本格式：主版本号.次版本号.修订号
-#----S4U配置说明结束----
-
-[s4u]
-# 消息管理配置
-message_timeout_seconds = 120  # 普通消息存活时间（秒），超过此时间的消息将被丢弃
-recent_message_keep_count = 6  # 保留最近N条消息，超出范围的普通消息将被移除
-
-# 优先级系统配置
-at_bot_priority_bonus = 100.0  # @机器人时的优先级加成分数
-vip_queue_priority = true  # 是否启用VIP队列优先级系统
-enable_message_interruption = true  # 是否允许高优先级消息中断当前回复
-
-# 打字效果配置
-typing_delay = 0.1  # 打字延迟时间（秒），模拟真实打字速度
-enable_dynamic_typing_delay = false  # 是否启用基于文本长度的动态打字延迟
-
-# 动态打字延迟参数（仅在enable_dynamic_typing_delay=true时生效）
-chars_per_second = 15.0  # 每秒字符数，用于计算动态打字延迟
-min_typing_delay = 0.2  # 最小打字延迟（秒）
-max_typing_delay = 2.0  # 最大打字延迟（秒）
-
-# 系统功能开关
-enable_old_message_cleanup = true  # 是否自动清理过旧的普通消息
-enable_loading_indicator = true  # 是否显示加载提示 
-

src/mais4u/config/s4u_config.toml

@@ -1,132 +0,0 @@
-[inner]
-version = "1.1.0"
-
-#----以下是S4U聊天系统配置文件----
-# S4U (Smart 4 U) 聊天系统是MaiBot的核心对话模块
-# 支持优先级队列、消息中断、VIP用户等高级功能
-#
-# 如果你想要修改配置文件，请在修改后将version的值进行变更
-# 如果新增项目，请参考src/mais4u/s4u_config.py中的S4UConfig类
-#
-# 版本格式：主版本号.次版本号.修订号
-#----S4U配置说明结束----
-
-[s4u]
-# 消息管理配置
-message_timeout_seconds = 80  # 普通消息存活时间（秒），超过此时间的消息将被丢弃
-recent_message_keep_count = 8 # 保留最近N条消息，超出范围的普通消息将被移除
-
-# 优先级系统配置
-at_bot_priority_bonus = 100.0  # @机器人时的优先级加成分数
-vip_queue_priority = true  # 是否启用VIP队列优先级系统
-enable_message_interruption = true  # 是否允许高优先级消息中断当前回复
-
-# 打字效果配置
-typing_delay = 0.1  # 打字延迟时间（秒），模拟真实打字速度
-enable_dynamic_typing_delay = false  # 是否启用基于文本长度的动态打字延迟
-
-# 动态打字延迟参数（仅在enable_dynamic_typing_delay=true时生效）
-chars_per_second = 15.0  # 每秒字符数，用于计算动态打字延迟
-min_typing_delay = 0.2  # 最小打字延迟（秒）
-max_typing_delay = 2.0  # 最大打字延迟（秒）
-
-# 系统功能开关
-enable_old_message_cleanup = true  # 是否自动清理过旧的普通消息
-enable_loading_indicator = true  # 是否显示加载提示 
-
-enable_streaming_output = false  # 是否启用流式输出，false时全部生成后一次性发送
-
-max_context_message_length = 30
-max_core_message_length = 20
-
-# 模型配置
-[models]
-# 主要对话模型配置
-[models.chat]
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-enable_thinking = false
-
-# 规划模型配置
-[models.motion]
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-enable_thinking = false
-
-# 情感分析模型配置
-[models.emotion]
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-
-# 记忆模型配置
-[models.memory]
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-
-# 工具使用模型配置
-[models.tool_use]
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-
-# 嵌入模型配置
-[models.embedding]
-name = "text-embedding-v1"
-provider = "OPENAI"
-dimension = 1024
-
-# 视觉语言模型配置
-[models.vlm]
-name = "qwen-vl-plus"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-
-# 知识库模型配置
-[models.knowledge]
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-
-# 实体提取模型配置
-[models.entity_extract]
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-
-# 问答模型配置
-[models.qa]
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-
-# 兼容性配置（已废弃，请使用models.motion）
-[model_motion] # 在麦麦的一些组件中使用的小模型，消耗量较大，建议使用速度较快的小模型
-# 强烈建议使用免费的小模型
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-enable_thinking = false # 是否启用思考

src/mais4u/config/s4u_config_template.toml

@@ -1,67 +0,0 @@
-[inner]
-version = "1.1.0"
-
-#----以下是S4U聊天系统配置文件----
-# S4U (Smart 4 U) 聊天系统是MaiBot的核心对话模块
-# 支持优先级队列、消息中断、VIP用户等高级功能
-#
-# 如果你想要修改配置文件，请在修改后将version的值进行变更
-# 如果新增项目，请参考src/mais4u/s4u_config.py中的S4UConfig类
-#
-# 版本格式：主版本号.次版本号.修订号
-#----S4U配置说明结束----
-
-[s4u]
-# 消息管理配置
-message_timeout_seconds = 120  # 普通消息存活时间（秒），超过此时间的消息将被丢弃
-recent_message_keep_count = 6  # 保留最近N条消息，超出范围的普通消息将被移除
-
-# 优先级系统配置
-at_bot_priority_bonus = 100.0  # @机器人时的优先级加成分数
-vip_queue_priority = true  # 是否启用VIP队列优先级系统
-enable_message_interruption = true  # 是否允许高优先级消息中断当前回复
-
-# 打字效果配置
-typing_delay = 0.1  # 打字延迟时间（秒），模拟真实打字速度
-enable_dynamic_typing_delay = false  # 是否启用基于文本长度的动态打字延迟
-
-# 动态打字延迟参数（仅在enable_dynamic_typing_delay=true时生效）
-chars_per_second = 15.0  # 每秒字符数，用于计算动态打字延迟
-min_typing_delay = 0.2  # 最小打字延迟（秒）
-max_typing_delay = 2.0  # 最大打字延迟（秒）
-
-# 系统功能开关
-enable_old_message_cleanup = true  # 是否自动清理过旧的普通消息
-
-enable_streaming_output = true  # 是否启用流式输出，false时全部生成后一次性发送
-
-max_context_message_length = 20
-max_core_message_length = 30
-
-# 模型配置
-[models]
-# 主要对话模型配置
-[models.chat]
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-enable_thinking = false
-
-# 规划模型配置
-[models.motion]
-name = "qwen3-32b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7
-enable_thinking = false
-
-# 情感分析模型配置
-[models.emotion]
-name = "qwen3-8b"
-provider = "BAILIAN"
-pri_in = 0.5
-pri_out = 2
-temp = 0.7

src/mais4u/constant_s4u.py

@@ -1 +0,0 @@
-ENABLE_S4U = False

src/mais4u/mai_think.py

@@ -1,178 +0,0 @@
-import time
-
-from src.chat.message_receive.chat_stream import get_chat_manager
-from src.chat.message_receive.message import MessageRecvS4U
-from src.chat.utils.prompt import Prompt, global_prompt_manager
-from src.common.logger import get_logger
-from src.config.config import model_config
-from src.llm_models.utils_model import LLMRequest
-from src.mais4u.mais4u_chat.internal_manager import internal_manager
-from src.mais4u.mais4u_chat.s4u_msg_processor import S4UMessageProcessor
-
-logger = get_logger(__name__)
-
-
-def init_prompt():
-    Prompt(
-        """
-你之前的内心想法是：{mind}
-
-{memory_block}
-{relation_info_block}
-
-{chat_target}
-{time_block}
-{chat_info}
-{identity}
-
-你刚刚在{chat_target_2},你你刚刚的心情是：{mood_state}
----------------------
-在这样的情况下，你对上面的内容，你对 {sender} 发送的 消息 “{target}” 进行了回复
-你刚刚选择回复的内容是：{reponse}
-现在，根据你之前的想法和回复的内容，推测你现在的想法，思考你现在的想法是什么，为什么做出上面的回复内容
-请不要浮夸和夸张修辞，不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出想法：""",
-        "after_response_think_prompt",
-    )
-
-
-class MaiThinking:
-    def __init__(self, chat_id):
-        self.chat_id = chat_id
-        # 这些将在异步初始化中设置
-        self.chat_stream = None  # type: ignore
-        self.platform = None
-        self.is_group = False
-        self._initialized = False
-
-        self.s4u_message_processor = S4UMessageProcessor()
-
-        self.mind = ""
-
-        self.memory_block = ""
-        self.relation_info_block = ""
-        self.time_block = ""
-        self.chat_target = ""
-        self.chat_target_2 = ""
-        self.chat_info = ""
-        self.mood_state = ""
-        self.identity = ""
-        self.sender = ""
-        self.target = ""
-
-        self.thinking_model = LLMRequest(model_set=model_config.model_task_config.replyer, request_type="thinking")
-
-    async def _initialize(self):
-        """异步初始化方法"""
-        if not self._initialized:
-            self.chat_stream = await get_chat_manager().get_stream(self.chat_id)
-            if self.chat_stream:
-                self.platform = self.chat_stream.platform
-                self.is_group = bool(self.chat_stream.group_info)
-            self._initialized = True
-
-    async def do_think_before_response(self):
-        pass
-
-    async def do_think_after_response(self, reponse: str):
-        prompt = await global_prompt_manager.format_prompt(
-            "after_response_think_prompt",
-            mind=self.mind,
-            reponse=reponse,
-            memory_block=self.memory_block,
-            relation_info_block=self.relation_info_block,
-            time_block=self.time_block,
-            chat_target=self.chat_target,
-            chat_target_2=self.chat_target_2,
-            chat_info=self.chat_info,
-            mood_state=self.mood_state,
-            identity=self.identity,
-            sender=self.sender,
-            target=self.target,
-        )
-
-        result, _ = await self.thinking_model.generate_response_async(prompt)
-        self.mind = result
-
-        logger.info(f"[{self.chat_id}] 思考前想法：{self.mind}")
-        # logger.info(f"[{self.chat_id}] 思考前prompt：{prompt}")
-        logger.info(f"[{self.chat_id}] 思考后想法：{self.mind}")
-
-        msg_recv = await self.build_internal_message_recv(self.mind)
-        await self.s4u_message_processor.process_message(msg_recv)
-        internal_manager.set_internal_state(self.mind)
-
-    async def do_think_when_receive_message(self):
-        pass
-
-    async def build_internal_message_recv(self, message_text: str):
-        # 初始化
-        await self._initialize()
-
-        msg_id = f"internal_{time.time()}"
-
-        message_dict = {
-            "message_info": {
-                "message_id": msg_id,
-                "time": time.time(),
-                "user_info": {
-                    "user_id": "internal",  # 内部用户ID
-                    "user_nickname": "内心",  # 内部昵称
-                    "platform": self.platform,  # 平台标记为 internal
-                    # 其他 user_info 字段按需补充
-                },
-                "platform": self.platform,  # 平台
-                # 其他 message_info 字段按需补充
-            },
-            "message_segment": {
-                "type": "text",  # 消息类型
-                "data": message_text,  # 消息内容
-                # 其他 segment 字段按需补充
-            },
-            "raw_message": message_text,  # 原始消息内容
-            "processed_plain_text": message_text,  # 处理后的纯文本
-            # 下面这些字段可选，根据 MessageRecv 需要
-            "is_emoji": False,
-            "has_emoji": False,
-            "is_picid": False,
-            "has_picid": False,
-            "is_voice": False,
-            "is_mentioned": False,
-            "is_command": False,
-            "is_internal": True,
-            "priority_mode": "interest",
-            "priority_info": {"message_priority": 10.0},  # 内部消息可设高优先级
-            "interest_value": 1.0,
-        }
-
-        if self.is_group:
-            message_dict["message_info"]["group_info"] = {
-                "platform": self.platform,
-                "group_id": self.chat_stream.group_info.group_id,
-                "group_name": self.chat_stream.group_info.group_name,
-            }
-
-        msg_recv = MessageRecvS4U(message_dict)
-        msg_recv.chat_info = self.chat_info
-        msg_recv.chat_stream = self.chat_stream
-        msg_recv.is_internal = True
-
-        return msg_recv
-
-
-class MaiThinkingManager:
-    def __init__(self):
-        self.mai_think_list = []
-
-    def get_mai_think(self, chat_id):
-        for mai_think in self.mai_think_list:
-            if mai_think.chat_id == chat_id:
-                return mai_think
-        mai_think = MaiThinking(chat_id)
-        self.mai_think_list.append(mai_think)
-        return mai_think
-
-
-mai_thinking_manager = MaiThinkingManager()
-
-
-init_prompt()

src/mais4u/mais4u_chat/body_emotion_action_manager.py

@@ -1,306 +0,0 @@
-import time
-
-import orjson
-from json_repair import repair_json
-
-from src.chat.message_receive.message import MessageRecv
-from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_by_timestamp_with_chat_inclusive
-from src.chat.utils.prompt import Prompt, global_prompt_manager
-from src.common.logger import get_logger
-from src.config.config import global_config, model_config
-from src.llm_models.utils_model import LLMRequest
-from src.mais4u.s4u_config import s4u_config
-from src.manager.async_task_manager import AsyncTask, async_task_manager
-from src.plugin_system.apis import send_api
-
-logger = get_logger("action")
-
-HEAD_CODE = {
-    "看向上方": "(0,0.5,0)",
-    "看向下方": "(0,-0.5,0)",
-    "看向左边": "(-1,0,0)",
-    "看向右边": "(1,0,0)",
-    "随意朝向": "random",
-    "看向摄像机": "camera",
-    "注视对方": "(0,0,0)",
-    "看向正前方": "(0,0,0)",
-}
-
-BODY_CODE = {
-    "双手背后向前弯腰": "010_0070",
-    "歪头双手合十": "010_0100",
-    "标准文静站立": "010_0101",
-    "双手交叠腹部站立": "010_0150",
-    "帅气的姿势": "010_0190",
-    "另一个帅气的姿势": "010_0191",
-    "手掌朝前可爱": "010_0210",
-    "平静，双手后放": "平静，双手后放",
-    "思考": "思考",
-    "优雅，左手放在腰上": "优雅，左手放在腰上",
-    "一般": "一般",
-    "可爱，双手前放": "可爱，双手前放",
-}
-
-
-def init_prompt():
-    Prompt(
-        """
-{chat_talking_prompt}
-以上是群里正在进行的聊天记录
-
-{indentify_block}
-你现在的动作状态是：
-- 身体动作：{body_action}
-
-现在，因为你发送了消息，或者群里其他人发送了消息，引起了你的注意，你对其进行了阅读和思考，请你更新你的动作状态。
-身体动作可选：
-{all_actions}
-
-请只按照以下json格式输出，描述你新的动作状态，确保每个字段都存在：
-{{
-  "body_action": "..."
-}}
-""",
-        "change_action_prompt",
-    )
-    Prompt(
-        """
-{chat_talking_prompt}
-以上是群里最近的聊天记录
-
-{indentify_block}
-你之前的动作状态是
-- 身体动作：{body_action}
-
-身体动作可选：
-{all_actions}
-
-距离你上次关注群里消息已经过去了一段时间，你冷静了下来，你的动作会趋于平缓或静止，请你输出你现在新的动作状态，用中文。
-请只按照以下json格式输出，描述你新的动作状态，确保每个字段都存在：
-{{
-  "body_action": "..."
-}}
-""",
-        "regress_action_prompt",
-    )
-
-
-class ChatAction:
-    def __init__(self, chat_id: str):
-        self.chat_id: str = chat_id
-        self.body_action: str = "一般"
-        self.head_action: str = "注视摄像机"
-
-        self.regression_count: int = 0
-        # 新增：body_action冷却池，key为动作名，value为剩余冷却次数
-        self.body_action_cooldown: dict[str, int] = {}
-
-        print(s4u_config.models.motion)
-        print(model_config.model_task_config.emotion)
-
-        self.action_model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="motion")
-
-        self.last_change_time: float = 0
-
-    async def send_action_update(self):
-        """发送动作更新到前端"""
-
-        body_code = BODY_CODE.get(self.body_action, "")
-        await send_api.custom_to_stream(
-            message_type="body_action",
-            content=body_code,
-            stream_id=self.chat_id,
-            storage_message=False,
-            show_log=True,
-        )
-
-    async def update_action_by_message(self, message: MessageRecv):
-        self.regression_count = 0
-
-        message_time: float = message.message_info.time  # type: ignore
-        message_list_before_now = await get_raw_msg_by_timestamp_with_chat_inclusive(
-            chat_id=self.chat_id,
-            timestamp_start=self.last_change_time,
-            timestamp_end=message_time,
-            limit=15,
-            limit_mode="last",
-        )
-        chat_talking_prompt = await build_readable_messages(
-            message_list_before_now,
-            replace_bot_name=True,
-            merge_messages=False,
-            timestamp_mode="normal_no_YMD",
-            read_mark=0.0,
-            truncate=True,
-            show_actions=True,
-        )
-
-        bot_name = global_config.bot.nickname
-        if global_config.bot.alias_names:
-            bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
-        else:
-            bot_nickname = ""
-
-        prompt_personality = global_config.personality.personality_core
-        indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
-
-        try:
-            # 冷却池处理：过滤掉冷却中的动作
-            self._update_body_action_cooldown()
-            available_actions = [k for k in BODY_CODE.keys() if k not in self.body_action_cooldown]
-            all_actions = "\n".join(available_actions)
-
-            prompt = await global_prompt_manager.format_prompt(
-                "change_action_prompt",
-                chat_talking_prompt=chat_talking_prompt,
-                indentify_block=indentify_block,
-                body_action=self.body_action,
-                all_actions=all_actions,
-            )
-
-            logger.info(f"prompt: {prompt}")
-            response, (reasoning_content, _, _) = await self.action_model.generate_response_async(
-                prompt=prompt, temperature=0.7
-            )
-            logger.info(f"response: {response}")
-            logger.info(f"reasoning_content: {reasoning_content}")
-
-            if action_data := orjson.loads(repair_json(response)):
-                # 记录原动作，切换后进入冷却
-                prev_body_action = self.body_action
-                new_body_action = action_data.get("body_action", self.body_action)
-                if new_body_action != prev_body_action and prev_body_action:
-                    self.body_action_cooldown[prev_body_action] = 3
-                self.body_action = new_body_action
-                self.head_action = action_data.get("head_action", self.head_action)
-                # 发送动作更新
-                await self.send_action_update()
-
-            self.last_change_time = message_time
-        except Exception as e:
-            logger.error(f"update_action_by_message error: {e}")
-
-    async def regress_action(self):
-        message_time = time.time()
-        message_list_before_now = await get_raw_msg_by_timestamp_with_chat_inclusive(
-            chat_id=self.chat_id,
-            timestamp_start=self.last_change_time,
-            timestamp_end=message_time,
-            limit=10,
-            limit_mode="last",
-        )
-        chat_talking_prompt = await build_readable_messages(
-            message_list_before_now,
-            replace_bot_name=True,
-            merge_messages=False,
-            timestamp_mode="normal_no_YMD",
-            read_mark=0.0,
-            truncate=True,
-            show_actions=True,
-        )
-
-        bot_name = global_config.bot.nickname
-        if global_config.bot.alias_names:
-            bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
-        else:
-            bot_nickname = ""
-
-        prompt_personality = global_config.personality.personality_core
-        indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
-        try:
-            # 冷却池处理：过滤掉冷却中的动作
-            self._update_body_action_cooldown()
-            available_actions = [k for k in BODY_CODE.keys() if k not in self.body_action_cooldown]
-            all_actions = "\n".join(available_actions)
-
-            prompt = await global_prompt_manager.format_prompt(
-                "regress_action_prompt",
-                chat_talking_prompt=chat_talking_prompt,
-                indentify_block=indentify_block,
-                body_action=self.body_action,
-                all_actions=all_actions,
-            )
-
-            logger.info(f"prompt: {prompt}")
-            response, (reasoning_content, _, _) = await self.action_model.generate_response_async(
-                prompt=prompt, temperature=0.7
-            )
-            logger.info(f"response: {response}")
-            logger.info(f"reasoning_content: {reasoning_content}")
-
-            if action_data := orjson.loads(repair_json(response)):
-                prev_body_action = self.body_action
-                new_body_action = action_data.get("body_action", self.body_action)
-                if new_body_action != prev_body_action and prev_body_action:
-                    self.body_action_cooldown[prev_body_action] = 6
-                self.body_action = new_body_action
-                # 发送动作更新
-                await self.send_action_update()
-
-            self.regression_count += 1
-            self.last_change_time = message_time
-        except Exception as e:
-            logger.error(f"regress_action error: {e}")
-
-    # 新增：冷却池维护方法
-    def _update_body_action_cooldown(self):
-        remove_keys = []
-        for k in self.body_action_cooldown:
-            self.body_action_cooldown[k] -= 1
-            if self.body_action_cooldown[k] <= 0:
-                remove_keys.append(k)
-        for k in remove_keys:
-            del self.body_action_cooldown[k]
-
-
-class ActionRegressionTask(AsyncTask):
-    def __init__(self, action_manager: "ActionManager"):
-        super().__init__(task_name="ActionRegressionTask", run_interval=3)
-        self.action_manager = action_manager
-
-    async def run(self):
-        logger.debug("Running action regression task...")
-        now = time.time()
-        for action_state in self.action_manager.action_state_list:
-            if action_state.last_change_time == 0:
-                continue
-
-            if now - action_state.last_change_time > 10:
-                if action_state.regression_count >= 3:
-                    continue
-
-                logger.info(f"chat {action_state.chat_id} 开始动作回归, 这是第 {action_state.regression_count + 1} 次")
-                await action_state.regress_action()
-
-
-class ActionManager:
-    def __init__(self):
-        self.action_state_list: list[ChatAction] = []
-        """当前动作状态"""
-        self.task_started: bool = False
-
-    async def start(self):
-        """启动动作回归后台任务"""
-        if self.task_started:
-            return
-
-        logger.info("启动动作回归任务...")
-        task = ActionRegressionTask(self)
-        await async_task_manager.add_task(task)
-        self.task_started = True
-        logger.info("动作回归任务已启动")
-
-    def get_action_state_by_chat_id(self, chat_id: str) -> ChatAction:
-        for action_state in self.action_state_list:
-            if action_state.chat_id == chat_id:
-                return action_state
-
-        new_action_state = ChatAction(chat_id)
-        self.action_state_list.append(new_action_state)
-        return new_action_state
-
-
-init_prompt()
-
-action_manager = ActionManager()
-"""全局动作管理器"""

src/mais4u/mais4u_chat/context_web_manager.py

@@ -1,692 +0,0 @@
-import asyncio
-from collections import deque
-from datetime import datetime
-
-import aiohttp_cors
-import orjson
-from aiohttp import WSMsgType, web
-
-from src.chat.message_receive.message import MessageRecv
-from src.common.logger import get_logger
-
-logger = get_logger("context_web")
-
-
-class ContextMessage:
-    """上下文消息类"""
-
-    def __init__(self, message: MessageRecv):
-        self.user_name = message.message_info.user_info.user_nickname
-        self.user_id = message.message_info.user_info.user_id
-        self.content = message.processed_plain_text
-        self.timestamp = datetime.now()
-        self.group_name = message.message_info.group_info.group_name if message.message_info.group_info else "私聊"
-
-        # 识别消息类型
-        self.is_gift = getattr(message, "is_gift", False)
-        self.is_superchat = getattr(message, "is_superchat", False)
-
-        # 添加礼物和SC相关信息
-        if self.is_gift:
-            self.gift_name = getattr(message, "gift_name", "")
-            self.gift_count = getattr(message, "gift_count", "1")
-            self.content = f"送出了 {self.gift_name} x{self.gift_count}"
-        elif self.is_superchat:
-            self.superchat_price = getattr(message, "superchat_price", "0")
-            self.superchat_message = getattr(message, "superchat_message_text", "")
-            if self.superchat_message:
-                self.content = f"[¥{self.superchat_price}] {self.superchat_message}"
-            else:
-                self.content = f"[¥{self.superchat_price}] {self.content}"
-
-    def to_dict(self):
-        return {
-            "user_name": self.user_name,
-            "user_id": self.user_id,
-            "content": self.content,
-            "timestamp": self.timestamp.strftime("%m-%d %H:%M:%S"),
-            "group_name": self.group_name,
-            "is_gift": self.is_gift,
-            "is_superchat": self.is_superchat,
-        }
-
-
-class ContextWebManager:
-    """上下文网页管理器"""
-
-    def __init__(self, max_messages: int = 10, port: int = 8765):
-        self.max_messages = max_messages
-        self.port = port
-        self.contexts: dict[str, deque] = {}  # chat_id -> deque of ContextMessage
-        self.websockets: list[web.WebSocketResponse] = []
-        self.app = None
-        self.runner = None
-        self.site = None
-        self._server_starting = False  # 添加启动标志防止并发
-
-    async def start_server(self):
-        """启动web服务器"""
-        if self.site is not None:
-            logger.debug("Web服务器已经启动，跳过重复启动")
-            return
-
-        if self._server_starting:
-            logger.debug("Web服务器正在启动中，等待启动完成...")
-            # 等待启动完成
-            while self._server_starting and self.site is None:
-                await asyncio.sleep(0.1)
-            return
-
-        self._server_starting = True
-
-        try:
-            self.app = web.Application()
-
-            # 设置CORS
-            cors = aiohttp_cors.setup(
-                self.app,
-                defaults={
-                    "*": aiohttp_cors.ResourceOptions(
-                        allow_credentials=True, expose_headers="*", allow_headers="*", allow_methods="*"
-                    )
-                },
-            )
-
-            # 添加路由
-            self.app.router.add_get("/", self.index_handler)
-            self.app.router.add_get("/ws", self.websocket_handler)
-            self.app.router.add_get("/api/contexts", self.get_contexts_handler)
-            self.app.router.add_get("/debug", self.debug_handler)
-
-            # 为所有路由添加CORS
-            for route in list(self.app.router.routes()):
-                cors.add(route)
-
-            self.runner = web.AppRunner(self.app)
-            await self.runner.setup()
-
-            self.site = web.TCPSite(self.runner, "localhost", self.port)
-            await self.site.start()
-
-            logger.info(f"🌐 上下文网页服务器启动成功在 http://localhost:{self.port}")
-
-        except Exception as e:
-            logger.error(f"❌ 启动Web服务器失败: {e}")
-            # 清理部分启动的资源
-            if self.runner:
-                await self.runner.cleanup()
-            self.app = None
-            self.runner = None
-            self.site = None
-            raise
-        finally:
-            self._server_starting = False
-
-    async def stop_server(self):
-        """停止web服务器"""
-        if self.site:
-            await self.site.stop()
-        if self.runner:
-            await self.runner.cleanup()
-        self.app = None
-        self.runner = None
-        self.site = None
-        self._server_starting = False
-
-    async def index_handler(self, request):
-        """主页处理器"""
-        html_content = (
-            """
-<!DOCTYPE html>
-<html>
-<head>
-    <meta charset="UTF-8">
-    <title>聊天上下文</title>
-    <style>
-        html, body {
-            background: transparent !important;
-            background-color: transparent !important;
-            margin: 0;
-            padding: 20px;
-            font-family: 'Microsoft YaHei', Arial, sans-serif;
-            color: #ffffff;
-            text-shadow: 2px 2px 4px rgba(0,0,0,0.8);
-        }
-        .container {
-            max-width: 800px;
-            margin: 0 auto;
-            background: transparent !important;
-        }
-        .message {
-            background: rgba(0, 0, 0, 0.3);
-            margin: 10px 0;
-            padding: 15px;
-            border-radius: 10px;
-            border-left: 4px solid #00ff88;
-            backdrop-filter: blur(5px);
-            animation: slideIn 0.3s ease-out;
-            transform: translateY(0);
-            transition: transform 0.5s ease, opacity 0.5s ease;
-        }
-        .message:hover {
-            background: rgba(0, 0, 0, 0.5);
-            transform: translateX(5px);
-            transition: all 0.3s ease;
-        }
-        .message.gift {
-            border-left: 4px solid #ff8800;
-            background: rgba(255, 136, 0, 0.2);
-        }
-        .message.gift:hover {
-            background: rgba(255, 136, 0, 0.3);
-        }
-        .message.gift .username {
-            color: #ff8800;
-        }
-        .message.superchat {
-            border-left: 4px solid #ff6b6b;
-            background: linear-gradient(135deg, rgba(255, 107, 107, 0.2), rgba(107, 255, 107, 0.2), rgba(107, 107, 255, 0.2));
-            background-size: 200% 200%;
-            animation: rainbow 3s ease infinite;
-        }
-        .message.superchat:hover {
-            background: linear-gradient(135deg, rgba(255, 107, 107, 0.4), rgba(107, 255, 107, 0.4), rgba(107, 107, 255, 0.4));
-            background-size: 200% 200%;
-        }
-        .message.superchat .username {
-            background: linear-gradient(45deg, #ff6b6b, #4ecdc4, #45b7d1, #96ceb4, #feca57);
-            background-size: 300% 300%;
-            animation: rainbow-text 2s ease infinite;
-            -webkit-background-clip: text;
-            -webkit-text-fill-color: transparent;
-            background-clip: text;
-        }
-        @keyframes rainbow {
-            0% { background-position: 0% 50%; }
-            50% { background-position: 100% 50%; }
-            100% { background-position: 0% 50%; }
-        }
-        @keyframes rainbow-text {
-            0% { background-position: 0% 50%; }
-            50% { background-position: 100% 50%; }
-            100% { background-position: 0% 50%; }
-        }
-        .message-line {
-            line-height: 1.4;
-            word-wrap: break-word;
-            font-size: 24px;
-        }
-        .username {
-            color: #00ff88;
-        }
-        .content {
-            color: #ffffff;
-        }
-
-        .new-message {
-            animation: slideInNew 0.6s ease-out;
-        }
-
-        .debug-btn {
-            position: fixed;
-            bottom: 20px;
-            right: 20px;
-            background: rgba(0, 0, 0, 0.7);
-            color: #00ff88;
-            font-size: 12px;
-            padding: 8px 12px;
-            border-radius: 20px;
-            backdrop-filter: blur(10px);
-            z-index: 1000;
-            text-decoration: none;
-            border: 1px solid #00ff88;
-        }
-        .debug-btn:hover {
-            background: rgba(0, 255, 136, 0.2);
-        }
-        @keyframes slideIn {
-            from {
-                opacity: 0;
-                transform: translateY(-20px);
-            }
-            to {
-                opacity: 1;
-                transform: translateY(0);
-            }
-        }
-        @keyframes slideInNew {
-            from {
-                opacity: 0;
-                transform: translateY(50px) scale(0.95);
-            }
-            to {
-                opacity: 1;
-                transform: translateY(0) scale(1);
-            }
-        }
-        .no-messages {
-            text-align: center;
-            color: #666;
-            font-style: italic;
-            margin-top: 50px;
-        }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <a href="/debug" class="debug-btn">🔧 调试</a>
-        <div id="messages">
-            <div class="no-messages">暂无消息</div>
-        </div>
-    </div>
-
-    <script>
-        let ws;
-        let reconnectInterval;
-        let currentMessages = []; // 存储当前显示的消息
-
-                 function connectWebSocket() {
-             console.log('正在连接WebSocket...');
-             ws = new WebSocket('ws://localhost:"""
-            + str(self.port)
-            + """/ws');
-
-             ws.onopen = function() {
-                 console.log('WebSocket连接已建立');
-                 if (reconnectInterval) {
-                     clearInterval(reconnectInterval);
-                     reconnectInterval = null;
-                 }
-             };
-
-             ws.onmessage = function(event) {
-                 console.log('收到WebSocket消息:', event.data);
-                 try {
-                     const data = orjson.parse(event.data);
-                     updateMessages(data.contexts);
-                 } catch (e) {
-                     console.error('解析消息失败:', e, event.data);
-                 }
-             };
-
-             ws.onclose = function(event) {
-                 console.log('WebSocket连接关闭:', event.code, event.reason);
-
-                 if (!reconnectInterval) {
-                     reconnectInterval = setInterval(connectWebSocket, 3000);
-                 }
-             };
-
-             ws.onerror = function(error) {
-                 console.error('WebSocket错误:', error);
-             };
-         }
-
-                 function updateMessages(contexts) {
-             const messagesDiv = document.getElementById('messages');
-
-             if (!contexts || contexts.length === 0) {
-                 messagesDiv.innerHTML = '<div class="no-messages">暂无消息</div>';
-                 currentMessages = [];
-                 return;
-             }
-
-             // 如果是第一次加载或者消息完全不同，进行完全重新渲染
-             if (currentMessages.length === 0) {
-                 console.log('首次加载消息，数量:', contexts.length);
-                 messagesDiv.innerHTML = '';
-
-                 contexts.forEach(function(msg) {
-                     const messageDiv = createMessageElement(msg);
-                     messagesDiv.appendChild(messageDiv);
-                 });
-
-                 currentMessages = [...contexts];
-                 window.scrollTo(0, document.body.scrollHeight);
-                 return;
-             }
-
-             // 检测新消息 - 使用更可靠的方法
-             const newMessages = findNewMessages(contexts, currentMessages);
-
-             if (newMessages.length > 0) {
-                 console.log('添加新消息，数量:', newMessages.length);
-
-                 // 先检查是否需要移除老消息（保持DOM清洁）
-                 const maxDisplayMessages = 15; // 比服务器端稍多一些，确保流畅性
-                 const currentMessageElements = messagesDiv.querySelectorAll('.message');
-                 const willExceedLimit = currentMessageElements.length + newMessages.length > maxDisplayMessages;
-
-                 if (willExceedLimit) {
-                     const removeCount = (currentMessageElements.length + newMessages.length) - maxDisplayMessages;
-                     console.log('需要移除老消息数量:', removeCount);
-
-                     for (let i = 0; i < removeCount && i < currentMessageElements.length; i++) {
-                         const oldMessage = currentMessageElements[i];
-                         oldMessage.style.transition = 'opacity 0.3s ease, transform 0.3s ease';
-                         oldMessage.style.opacity = '0';
-                         oldMessage.style.transform = 'translateY(-20px)';
-
-                         setTimeout(() => {
-                             if (oldMessage.parentNode) {
-                                 oldMessage.parentNode.removeChild(oldMessage);
-                             }
-                         }, 300);
-                     }
-                 }
-
-                 // 添加新消息
-                 newMessages.forEach(function(msg) {
-                     const messageDiv = createMessageElement(msg, true); // true表示是新消息
-                     messagesDiv.appendChild(messageDiv);
-
-                     // 移除动画类，避免重复动画
-                     setTimeout(() => {
-                         messageDiv.classList.remove('new-message');
-                     }, 600);
-                 });
-
-                 // 更新当前消息列表
-                 currentMessages = [...contexts];
-
-                 // 平滑滚动到底部
-                 setTimeout(() => {
-                     window.scrollTo({
-                         top: document.body.scrollHeight,
-                         behavior: 'smooth'
-                     });
-                 }, 100);
-             }
-         }
-
-         function findNewMessages(contexts, currentMessages) {
-             // 如果当前消息为空，所有消息都是新的
-             if (currentMessages.length === 0) {
-                 return contexts;
-             }
-
-             // 找到最后一条当前消息在新消息列表中的位置
-             const lastCurrentMsg = currentMessages[currentMessages.length - 1];
-             let lastIndex = -1;
-
-             // 从后往前找，因为新消息通常在末尾
-             for (let i = contexts.length - 1; i >= 0; i--) {
-                 const msg = contexts[i];
-                 if (msg.user_id === lastCurrentMsg.user_id &&
-                     msg.content === lastCurrentMsg.content &&
-                     msg.timestamp === lastCurrentMsg.timestamp) {
-                     lastIndex = i;
-                     break;
-                 }
-             }
-
-             // 如果找到了，返回之后的消息；否则返回所有消息（可能是完全刷新）
-             if (lastIndex >= 0) {
-                 return contexts.slice(lastIndex + 1);
-             } else {
-                 console.log('未找到匹配的最后消息，可能需要完全刷新');
-                 return contexts.slice(Math.max(0, contexts.length - (currentMessages.length + 1)));
-             }
-         }
-
-         function createMessageElement(msg, isNew = false) {
-             const messageDiv = document.createElement('div');
-             let className = 'message';
-
-             // 根据消息类型添加对应的CSS类
-             if (msg.is_gift) {
-                 className += ' gift';
-             } else if (msg.is_superchat) {
-                 className += ' superchat';
-             }
-
-             if (isNew) {
-                 className += ' new-message';
-             }
-
-             messageDiv.className = className;
-             messageDiv.innerHTML = `
-                 <div class="message-line">
-                     <span class="username">${escapeHtml(msg.user_name)}：</span><span class="content">${escapeHtml(msg.content)}</span>
-                 </div>
-             `;
-             return messageDiv;
-         }
-
-         function escapeHtml(text) {
-             const div = document.createElement('div');
-             div.textContent = text;
-             return div.innerHTML;
-         }
-
-        // 初始加载数据
-        fetch('/api/contexts')
-            .then(response => response.json())
-            .then(data => {
-                console.log('初始数据加载成功:', data);
-                updateMessages(data.contexts);
-            })
-            .catch(err => console.error('加载初始数据失败:', err));
-
-        // 连接WebSocket
-        connectWebSocket();
-    </script>
-</body>
-</html>
-        """
-        )
-        return web.Response(text=html_content, content_type="text/html")
-
-    async def websocket_handler(self, request):
-        """WebSocket处理器"""
-        ws = web.WebSocketResponse()
-        await ws.prepare(request)
-
-        self.websockets.append(ws)
-        logger.debug(f"WebSocket连接建立，当前连接数: {len(self.websockets)}")
-
-        # 发送初始数据
-        await self.send_contexts_to_websocket(ws)
-
-        async for msg in ws:
-            if msg.type == WSMsgType.ERROR:
-                logger.error(f"WebSocket错误: {ws.exception()}")
-                break
-
-        # 清理断开的连接
-        if ws in self.websockets:
-            self.websockets.remove(ws)
-        logger.debug(f"WebSocket连接断开，当前连接数: {len(self.websockets)}")
-
-        return ws
-
-    async def get_contexts_handler(self, request):
-        """获取上下文API"""
-        all_context_msgs = []
-        for contexts in self.contexts.values():
-            all_context_msgs.extend(list(contexts))
-
-        # 按时间排序，最新的在最后
-        all_context_msgs.sort(key=lambda x: x.timestamp)
-
-        # 转换为字典格式
-        contexts_data = [msg.to_dict() for msg in all_context_msgs[-self.max_messages :]]
-
-        logger.debug(f"返回上下文数据，共 {len(contexts_data)} 条消息")
-        return web.json_response({"contexts": contexts_data})
-
-    async def debug_handler(self, request):
-        """调试信息处理器"""
-        debug_info = {
-            "server_status": "running",
-            "websocket_connections": len(self.websockets),
-            "total_chats": len(self.contexts),
-            "total_messages": sum(len(contexts) for contexts in self.contexts.values()),
-        }
-
-        # 构建聊天详情HTML
-        chats_html = ""
-        for chat_id, contexts in self.contexts.items():
-            messages_html = ""
-            for msg in contexts:
-                timestamp = msg.timestamp.strftime("%H:%M:%S")
-                content = msg.content[:50] + "..." if len(msg.content) > 50 else msg.content
-                messages_html += f'<div class="message">[{timestamp}] {msg.user_name}: {content}</div>'
-
-            chats_html += f"""
-            <div class="chat">
-                <h3>聊天 {chat_id} ({len(contexts)} 条消息)</h3>
-                {messages_html}
-            </div>
-            """
-
-        html_content = f"""
-<!DOCTYPE html>
-<html>
-<head>
-    <meta charset="UTF-8">
-    <title>调试信息</title>
-    <style>
-        body {{ font-family: monospace; margin: 20px; }}
-        .section {{ margin: 20px 0; padding: 10px; border: 1px solid #ccc; }}
-        .chat {{ margin: 10px 0; padding: 10px; background: #f5f5f5; }}
-        .message {{ margin: 5px 0; padding: 5px; background: white; }}
-    </style>
-</head>
-<body>
-    <h1>上下文网页管理器调试信息</h1>
-
-    <div class="section">
-        <h2>服务器状态</h2>
-        <p>状态: {debug_info["server_status"]}</p>
-        <p>WebSocket连接数: {debug_info["websocket_connections"]}</p>
-        <p>聊天总数: {debug_info["total_chats"]}</p>
-        <p>消息总数: {debug_info["total_messages"]}</p>
-    </div>
-
-    <div class="section">
-        <h2>聊天详情</h2>
-        {chats_html}
-    </div>
-
-    <div class="section">
-        <h2>操作</h2>
-        <button onclick="location.reload()">刷新页面</button>
-        <button onclick="window.location.href='/'">返回主页</button>
-        <button onclick="window.location.href='/api/contexts'">查看API数据</button>
-    </div>
-
-    <script>
-        console.log('调试信息:', {orjson.dumps(debug_info, option=orjson.OPT_INDENT_2).decode("utf-8")});
-        setTimeout(() => location.reload(), 5000); // 5秒自动刷新
-    </script>
-</body>
-</html>
-        """
-
-        return web.Response(text=html_content, content_type="text/html")
-
-    async def add_message(self, chat_id: str, message: MessageRecv):
-        """添加新消息到上下文"""
-        if chat_id not in self.contexts:
-            self.contexts[chat_id] = deque(maxlen=self.max_messages)
-            logger.debug(f"为聊天 {chat_id} 创建新的上下文队列")
-
-        context_msg = ContextMessage(message)
-        self.contexts[chat_id].append(context_msg)
-
-        # 统计当前总消息数
-        total_messages = sum(len(contexts) for contexts in self.contexts.values())
-
-        logger.info(
-            f"✅ 添加消息到上下文 [总数: {total_messages}]: [{context_msg.group_name}] {context_msg.user_name}: {context_msg.content}"
-        )
-
-        # 调试：打印当前所有消息
-        logger.info("📝 当前上下文中的所有消息：")
-        for cid, contexts in self.contexts.items():
-            logger.info(f"  聊天 {cid}: {len(contexts)} 条消息")
-            for i, msg in enumerate(contexts):
-                logger.info(
-                    f"    {i + 1}. [{msg.timestamp.strftime('%H:%M:%S')}] {msg.user_name}: {msg.content[:30]}..."
-                )
-
-        # 广播更新给所有WebSocket连接
-        await self.broadcast_contexts()
-
-    async def send_contexts_to_websocket(self, ws: web.WebSocketResponse):
-        """向单个WebSocket发送上下文数据"""
-        all_context_msgs = []
-        for contexts in self.contexts.values():
-            all_context_msgs.extend(list(contexts))
-
-        # 按时间排序，最新的在最后
-        all_context_msgs.sort(key=lambda x: x.timestamp)
-
-        # 转换为字典格式
-        contexts_data = [msg.to_dict() for msg in all_context_msgs[-self.max_messages :]]
-
-        data = {"contexts": contexts_data}
-        await ws.send_str(orjson.dumps(data).decode("utf-8"))
-
-    async def broadcast_contexts(self):
-        """向所有WebSocket连接广播上下文更新"""
-        if not self.websockets:
-            logger.debug("没有WebSocket连接，跳过广播")
-            return
-
-        all_context_msgs = []
-        for contexts in self.contexts.values():
-            all_context_msgs.extend(list(contexts))
-
-        # 按时间排序，最新的在最后
-        all_context_msgs.sort(key=lambda x: x.timestamp)
-
-        # 转换为字典格式
-        contexts_data = [msg.to_dict() for msg in all_context_msgs[-self.max_messages :]]
-
-        data = {"contexts": contexts_data}
-        message = orjson.dumps(data).decode("utf-8")
-
-        logger.info(f"广播 {len(contexts_data)} 条消息到 {len(self.websockets)} 个WebSocket连接")
-
-        # 创建WebSocket列表的副本，避免在遍历时修改
-        websockets_copy = self.websockets.copy()
-        removed_count = 0
-
-        for ws in websockets_copy:
-            if ws.closed:
-                if ws in self.websockets:
-                    self.websockets.remove(ws)
-                    removed_count += 1
-            else:
-                try:
-                    await ws.send_str(message)
-                    logger.debug("消息发送成功")
-                except Exception as e:
-                    logger.error(f"发送WebSocket消息失败: {e}")
-                    if ws in self.websockets:
-                        self.websockets.remove(ws)
-                        removed_count += 1
-
-        if removed_count > 0:
-            logger.debug(f"清理了 {removed_count} 个断开的WebSocket连接")
-
-
-# 全局实例
-_context_web_manager: ContextWebManager | None = None
-
-
-def get_context_web_manager() -> ContextWebManager:
-    """获取上下文网页管理器实例"""
-    global _context_web_manager
-    if _context_web_manager is None:
-        _context_web_manager = ContextWebManager()
-    return _context_web_manager
-
-
-async def init_context_web_manager():
-    """初始化上下文网页管理器"""
-    manager = get_context_web_manager()
-    await manager.start_server()
-    return manager

src/mais4u/mais4u_chat/gift_manager.py

@@ -1,147 +0,0 @@
-import asyncio
-from collections.abc import Callable
-from dataclasses import dataclass
-
-from src.chat.message_receive.message import MessageRecvS4U
-from src.common.logger import get_logger
-
-logger = get_logger("gift_manager")
-
-
-@dataclass
-class PendingGift:
-    """等待中的礼物消息"""
-
-    message: MessageRecvS4U
-    total_count: int
-    timer_task: asyncio.Task
-    callback: Callable[[MessageRecvS4U], None]
-
-
-class GiftManager:
-    """礼物管理器，提供防抖功能"""
-
-    def __init__(self):
-        """初始化礼物管理器"""
-        self.pending_gifts: dict[tuple[str, str], PendingGift] = {}
-        self.debounce_timeout = 5.0  # 3秒防抖时间
-
-    async def handle_gift(
-        self, message: MessageRecvS4U, callback: Callable[[MessageRecvS4U], None] | None = None
-    ) -> bool:
-        """处理礼物消息，返回是否应该立即处理
-
-        Args:
-            message: 礼物消息
-            callback: 防抖完成后的回调函数
-
-        Returns:
-            bool: False表示消息被暂存等待防抖，True表示应该立即处理
-        """
-        if not message.is_gift:
-            return True
-
-        # 构建礼物的唯一键：(发送人ID, 礼物名称)
-        gift_key = (message.message_info.user_info.user_id, message.gift_name)
-
-        # 如果已经有相同的礼物在等待中，则合并
-        if gift_key in self.pending_gifts:
-            await self._merge_gift(gift_key, message)
-            return False
-
-        # 创建新的等待礼物
-        await self._create_pending_gift(gift_key, message, callback)
-        return False
-
-    async def _merge_gift(self, gift_key: tuple[str, str], new_message: MessageRecvS4U) -> None:
-        """合并礼物消息"""
-        pending_gift = self.pending_gifts[gift_key]
-
-        # 取消之前的定时器
-        if not pending_gift.timer_task.cancelled():
-            pending_gift.timer_task.cancel()
-
-        # 累加礼物数量
-        try:
-            new_count = int(new_message.gift_count)
-            pending_gift.total_count += new_count
-
-            # 更新消息为最新的（保留最新的消息，但累加数量）
-            pending_gift.message = new_message
-            pending_gift.message.gift_count = str(pending_gift.total_count)
-            pending_gift.message.gift_info = f"{pending_gift.message.gift_name}:{pending_gift.total_count}"
-
-        except ValueError:
-            logger.warning(f"无法解析礼物数量: {new_message.gift_count}")
-            # 如果无法解析数量，保持原有数量不变
-
-        # 重新创建定时器
-        pending_gift.timer_task = asyncio.create_task(self._gift_timeout(gift_key))
-
-        logger.debug(f"合并礼物: {gift_key}, 总数量: {pending_gift.total_count}")
-
-    async def _create_pending_gift(
-        self, gift_key: tuple[str, str], message: MessageRecvS4U, callback: Callable[[MessageRecvS4U], None] | None
-    ) -> None:
-        """创建新的等待礼物"""
-        try:
-            initial_count = int(message.gift_count)
-        except ValueError:
-            initial_count = 1
-            logger.warning(f"无法解析礼物数量: {message.gift_count}，默认设为1")
-
-        # 创建定时器任务
-        timer_task = asyncio.create_task(self._gift_timeout(gift_key))
-
-        # 创建等待礼物对象
-        pending_gift = PendingGift(message=message, total_count=initial_count, timer_task=timer_task, callback=callback)
-
-        self.pending_gifts[gift_key] = pending_gift
-
-        logger.debug(f"创建等待礼物: {gift_key}, 初始数量: {initial_count}")
-
-    async def _gift_timeout(self, gift_key: tuple[str, str]) -> None:
-        """礼物防抖超时处理"""
-        try:
-            # 等待防抖时间
-            await asyncio.sleep(self.debounce_timeout)
-
-            # 获取等待中的礼物
-            if gift_key not in self.pending_gifts:
-                return
-
-            pending_gift = self.pending_gifts.pop(gift_key)
-
-            logger.info(f"礼物防抖完成: {gift_key}, 最终数量: {pending_gift.total_count}")
-
-            message = pending_gift.message
-            message.processed_plain_text = f"用户{message.message_info.user_info.user_nickname}送出了礼物{message.gift_name} x{pending_gift.total_count}"
-
-            # 执行回调
-            if pending_gift.callback:
-                try:
-                    pending_gift.callback(message)
-                except Exception as e:
-                    logger.error(f"礼物回调执行失败: {e}", exc_info=True)
-
-        except asyncio.CancelledError:
-            # 定时器被取消，不需要处理
-            pass
-        except Exception as e:
-            logger.error(f"礼物防抖处理异常: {e}", exc_info=True)
-
-    def get_pending_count(self) -> int:
-        """获取当前等待中的礼物数量"""
-        return len(self.pending_gifts)
-
-    async def flush_all(self) -> None:
-        """立即处理所有等待中的礼物"""
-        for gift_key in list(self.pending_gifts.keys()):
-            pending_gift = self.pending_gifts.get(gift_key)
-            if pending_gift and not pending_gift.timer_task.cancelled():
-                pending_gift.timer_task.cancel()
-                await self._gift_timeout(gift_key)
-
-
-# 创建全局礼物管理器实例
-gift_manager = GiftManager()

src/mais4u/mais4u_chat/internal_manager.py

@@ -1,15 +0,0 @@
-class InternalManager:
-    def __init__(self):
-        self.now_internal_state = ""
-
-    def set_internal_state(self, internal_state: str):
-        self.now_internal_state = internal_state
-
-    def get_internal_state(self):
-        return self.now_internal_state
-
-    def get_internal_state_str(self):
-        return f"你今天的直播内容是直播QQ水群，你正在一边回复弹幕，一边在QQ群聊天，你在QQ群聊天中产生的想法是：{self.now_internal_state}"
-
-
-internal_manager = InternalManager()

src/mais4u/mais4u_chat/s4u_chat.py

@@ -1,611 +0,0 @@
-import asyncio
-import random
-import time
-import traceback
-
-import orjson
-from maim_message import Seg, UserInfo
-
-from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
-from src.chat.message_receive.message import MessageRecv, MessageRecvS4U, MessageSending
-from src.chat.message_receive.storage import MessageStorage
-from src.common.logger import get_logger
-from src.common.message.api import get_global_api
-from src.config.config import global_config
-from src.mais4u.constant_s4u import ENABLE_S4U
-from src.mais4u.s4u_config import s4u_config
-from src.person_info.person_info import PersonInfoManager
-from src.person_info.relationship_builder_manager import relationship_builder_manager
-
-from .s4u_mood_manager import mood_manager
-from .s4u_stream_generator import S4UStreamGenerator
-from .s4u_watching_manager import watching_manager
-from .super_chat_manager import get_super_chat_manager
-from .yes_or_no import yes_or_no_head
-
-logger = get_logger("S4U_chat")
-
-
-class MessageSenderContainer:
-    """一个简单的容器，用于按顺序发送消息并模拟打字效果。"""
-
-    def __init__(self, chat_stream: ChatStream, original_message: MessageRecv):
-        self.chat_stream = chat_stream
-        self.original_message = original_message
-        self.queue = asyncio.Queue()
-        self.storage = MessageStorage()
-        self._task: asyncio.Task | None = None
-        self._paused_event = asyncio.Event()
-        self._paused_event.set()  # 默认设置为非暂停状态
-
-        self.msg_id = ""
-
-        self.last_msg_id = ""
-
-        self.voice_done = ""
-
-    async def add_message(self, chunk: str):
-        """向队列中添加一个消息块。"""
-        await self.queue.put(chunk)
-
-    async def close(self):
-        """表示没有更多消息了，关闭队列。"""
-        await self.queue.put(None)  # Sentinel
-
-    def pause(self):
-        """暂停发送。"""
-        self._paused_event.clear()
-
-    def resume(self):
-        """恢复发送。"""
-        self._paused_event.set()
-
-    @staticmethod
-    def _calculate_typing_delay(text: str) -> float:
-        """根据文本长度计算模拟打字延迟。"""
-        chars_per_second = s4u_config.chars_per_second
-        min_delay = s4u_config.min_typing_delay
-        max_delay = s4u_config.max_typing_delay
-
-        delay = len(text) / chars_per_second
-        return max(min_delay, min(delay, max_delay))
-
-    async def _send_worker(self):
-        """从队列中取出消息并发送。"""
-        while True:
-            try:
-                # This structure ensures that task_done() is called for every item retrieved,
-                # even if the worker is cancelled while processing the item.
-                chunk = await self.queue.get()
-            except asyncio.CancelledError:
-                break
-
-            try:
-                if chunk is None:
-                    break
-
-                # Check for pause signal *after* getting an item.
-                await self._paused_event.wait()
-
-                # 根据配置选择延迟模式
-                if s4u_config.enable_dynamic_typing_delay:
-                    delay = self._calculate_typing_delay(chunk)
-                else:
-                    delay = s4u_config.typing_delay
-                await asyncio.sleep(delay)
-
-                message_segment = Seg(type="tts_text", data=f"{self.msg_id}:{chunk}")
-                bot_message = MessageSending(
-                    message_id=self.msg_id,
-                    chat_stream=self.chat_stream,
-                    bot_user_info=UserInfo(
-                        user_id=global_config.bot.qq_account,
-                        user_nickname=global_config.bot.nickname,
-                        platform=self.original_message.message_info.platform,
-                    ),
-                    sender_info=self.original_message.message_info.user_info,
-                    message_segment=message_segment,
-                    reply=self.original_message,
-                    is_emoji=False,
-                    apply_set_reply_logic=True,
-                    reply_to=f"{self.original_message.message_info.user_info.platform}:{self.original_message.message_info.user_info.user_id}",
-                )
-
-                await bot_message.process()
-
-                await get_global_api().send_message(bot_message)
-                logger.info(f"已将消息 '{self.msg_id}:{chunk}' 发往平台 '{bot_message.message_info.platform}'")
-
-                message_segment = Seg(type="text", data=chunk)
-                bot_message = MessageSending(
-                    message_id=self.msg_id,
-                    chat_stream=self.chat_stream,
-                    bot_user_info=UserInfo(
-                        user_id=global_config.bot.qq_account,
-                        user_nickname=global_config.bot.nickname,
-                        platform=self.original_message.message_info.platform,
-                    ),
-                    sender_info=self.original_message.message_info.user_info,
-                    message_segment=message_segment,
-                    reply=self.original_message,
-                    is_emoji=False,
-                    apply_set_reply_logic=True,
-                    reply_to=f"{self.original_message.message_info.user_info.platform}:{self.original_message.message_info.user_info.user_id}",
-                )
-                await bot_message.process()
-
-                await self.storage.store_message(bot_message, self.chat_stream)
-
-            except Exception as e:
-                logger.error(f"[消息流: {self.chat_stream.stream_id}] 消息发送或存储时出现错误: {e}", exc_info=True)
-
-            finally:
-                # CRUCIAL: Always call task_done() for any item that was successfully retrieved.
-                self.queue.task_done()
-
-    def start(self):
-        """启动发送任务。"""
-        if self._task is None:
-            self._task = asyncio.create_task(self._send_worker())
-
-    async def join(self):
-        """等待所有消息发送完毕。"""
-        if self._task:
-            await self._task
-
-    @property
-    def task(self):
-        return self._task
-
-
-class S4UChatManager:
-    def __init__(self):
-        self.s4u_chats: dict[str, "S4UChat"] = {}
-
-    async def get_or_create_chat(self, chat_stream: ChatStream) -> "S4UChat":
-        if chat_stream.stream_id not in self.s4u_chats:
-            stream_name = await get_chat_manager().get_stream_name(chat_stream.stream_id) or chat_stream.stream_id
-            logger.info(f"Creating new S4UChat for stream: {stream_name}")
-            self.s4u_chats[chat_stream.stream_id] = S4UChat(chat_stream)
-        return self.s4u_chats[chat_stream.stream_id]
-
-
-if not ENABLE_S4U:
-    s4u_chat_manager = None
-else:
-    s4u_chat_manager = S4UChatManager()
-
-
-def get_s4u_chat_manager() -> S4UChatManager:
-    return s4u_chat_manager
-
-
-class S4UChat:
-    def __init__(self, chat_stream: ChatStream):
-        """初始化 S4UChat 实例。"""
-
-        self.last_msg_id = self.msg_id
-        self.chat_stream = chat_stream
-        self.stream_id = chat_stream.stream_id
-        self.stream_name = self.stream_id  # 初始化时使用stream_id，稍后异步更新
-        self.relationship_builder = relationship_builder_manager.get_or_create_builder(self.stream_id)
-
-        # 两个消息队列
-        self._vip_queue = asyncio.PriorityQueue()
-        self._normal_queue = asyncio.PriorityQueue()
-
-        self._entry_counter = 0  # 保证FIFO的全局计数器
-        self._new_message_event = asyncio.Event()  # 用于唤醒处理器
-
-        self._processing_task = asyncio.create_task(self._message_processor())
-        self._current_generation_task: asyncio.Task | None = None
-        # 当前消息的元数据：(队列类型, 优先级分数, 计数器, 消息对象)
-        self._current_message_being_replied: tuple[str, float, int, MessageRecv] | None = None
-
-        self._is_replying = False
-        self.gpt = S4UStreamGenerator()
-        self.gpt.chat_stream = self.chat_stream
-        self.interest_dict: dict[str, float] = {}  # 用户兴趣分
-
-        self.internal_message: list[MessageRecvS4U] = []
-
-        self.msg_id = ""
-        self.voice_done = ""
-
-        logger.info(f"[{self.stream_name}] S4UChat with two-queue system initialized.")
-        self._stream_name_initialized = False
-
-    async def _initialize_stream_name(self):
-        """异步初始化stream_name"""
-        if not self._stream_name_initialized:
-            self.stream_name = await get_chat_manager().get_stream_name(self.stream_id) or self.stream_id
-            self._stream_name_initialized = True
-
-    @staticmethod
-    def _get_priority_info(message: MessageRecv) -> dict:
-        """安全地从消息中提取和解析 priority_info"""
-        priority_info_raw = message.priority_info
-        priority_info = {}
-        if isinstance(priority_info_raw, str):
-            try:
-                priority_info = orjson.loads(priority_info_raw)
-            except orjson.JSONDecodeError:
-                logger.warning(f"Failed to parse priority_info JSON: {priority_info_raw}")
-        elif isinstance(priority_info_raw, dict):
-            priority_info = priority_info_raw
-        return priority_info
-
-    @staticmethod
-    def _is_vip(priority_info: dict) -> bool:
-        """检查消息是否来自VIP用户。"""
-        return priority_info.get("message_type") == "vip"
-
-    def _get_interest_score(self, user_id: str) -> float:
-        """获取用户的兴趣分，默认为1.0"""
-        return self.interest_dict.get(user_id, 1.0)
-
-    def go_processing(self):
-        if self.voice_done == self.last_msg_id:
-            return True
-        return False
-
-    def _calculate_base_priority_score(self, message: MessageRecv, priority_info: dict) -> float:
-        """
-        为消息计算基础优先级分数。分数越高，优先级越高。
-        """
-        score = 0.0
-
-        # 加上消息自带的优先级
-        score += priority_info.get("message_priority", 0.0)
-
-        # 加上用户的固有兴趣分
-        score += self._get_interest_score(message.message_info.user_info.user_id)
-        return score
-
-    def decay_interest_score(self):
-        for person_id, score in self.interest_dict.items():
-            if score > 0:
-                self.interest_dict[person_id] = score * 0.95
-            else:
-                self.interest_dict[person_id] = 0
-
-    async def add_message(self, message: MessageRecvS4U | MessageRecv) -> None:
-        # 初始化stream_name
-        await self._initialize_stream_name()
-
-        self.decay_interest_score()
-
-        """根据VIP状态和中断逻辑将消息放入相应队列。"""
-        user_id = message.message_info.user_info.user_id
-        platform = message.message_info.platform
-        person_id = PersonInfoManager.get_person_id(platform, user_id)
-
-        try:
-            is_gift = message.is_gift
-            is_superchat = message.is_superchat
-            # print(is_gift)
-            # print(is_superchat)
-            if is_gift:
-                await self.relationship_builder.build_relation(immediate_build=person_id)
-                # 安全地增加兴趣分，如果person_id不存在则先初始化为1.0
-                current_score = self.interest_dict.get(person_id, 1.0)
-                self.interest_dict[person_id] = current_score + 0.1 * message.gift_count
-            elif is_superchat:
-                await self.relationship_builder.build_relation(immediate_build=person_id)
-                # 安全地增加兴趣分，如果person_id不存在则先初始化为1.0
-                current_score = self.interest_dict.get(person_id, 1.0)
-                self.interest_dict[person_id] = current_score + 0.1 * float(message.superchat_price)
-
-                # 添加SuperChat到管理器
-                super_chat_manager = get_super_chat_manager()
-                await super_chat_manager.add_superchat(message)
-            else:
-                await self.relationship_builder.build_relation(20)
-        except Exception:
-            traceback.print_exc()
-
-        logger.info(f"[{self.stream_name}] 消息处理完毕，消息内容：{message.processed_plain_text}")
-
-        priority_info = self._get_priority_info(message)
-        is_vip = self._is_vip(priority_info)
-        new_priority_score = self._calculate_base_priority_score(message, priority_info)
-
-        should_interrupt = False
-        if (
-            s4u_config.enable_message_interruption
-            and self._current_generation_task
-            and not self._current_generation_task.done()
-        ):
-            if self._current_message_being_replied:
-                current_queue, current_priority, _, current_msg = self._current_message_being_replied
-
-                # 规则：VIP从不被打断
-                if current_queue == "vip":
-                    pass  # Do nothing
-
-                # 规则：普通消息可以被打断
-                elif current_queue == "normal":
-                    # VIP消息可以打断普通消息
-                    if is_vip:
-                        should_interrupt = True
-                        logger.info(f"[{self.stream_name}] VIP message received, interrupting current normal task.")
-                    # 普通消息的内部打断逻辑
-                    else:
-                        new_sender_id = message.message_info.user_info.user_id
-                        current_sender_id = current_msg.message_info.user_info.user_id
-                        # 新消息优先级更高
-                        if new_priority_score > current_priority:
-                            should_interrupt = True
-                            logger.info(f"[{self.stream_name}] New normal message has higher priority, interrupting.")
-                        # 同用户，新消息的优先级不能更低
-                        elif new_sender_id == current_sender_id and new_priority_score >= current_priority:
-                            should_interrupt = True
-                            logger.info(f"[{self.stream_name}] Same user sent new message, interrupting.")
-
-        if should_interrupt:
-            if self.gpt.partial_response:
-                logger.warning(
-                    f"[{self.stream_name}] Interrupting reply. Already generated: '{self.gpt.partial_response}'"
-                )
-            self._current_generation_task.cancel()
-
-        # asyncio.PriorityQueue 是最小堆，所以我们存入分数的相反数
-        # 这样，原始分数越高的消息，在队列中的优先级数字越小，越靠前
-        item = (-new_priority_score, self._entry_counter, time.time(), message)
-
-        if is_vip and s4u_config.vip_queue_priority:
-            await self._vip_queue.put(item)
-            logger.info(f"[{self.stream_name}] VIP message added to queue.")
-        else:
-            await self._normal_queue.put(item)
-
-        self._entry_counter += 1
-        self._new_message_event.set()  # 唤醒处理器
-
-    def _cleanup_old_normal_messages(self):
-        """清理普通队列中不在最近N条消息范围内的消息"""
-        if not s4u_config.enable_old_message_cleanup or self._normal_queue.empty():
-            return
-
-        # 计算阈值：保留最近 recent_message_keep_count 条消息
-        cutoff_counter = max(0, self._entry_counter - s4u_config.recent_message_keep_count)
-
-        # 临时存储需要保留的消息
-        temp_messages = []
-        removed_count = 0
-
-        # 取出所有普通队列中的消息
-        while not self._normal_queue.empty():
-            try:
-                item = self._normal_queue.get_nowait()
-                neg_priority, entry_count, timestamp, message = item
-
-                # 如果消息在最近N条消息范围内，保留它
-                logger.info(
-                    f"检查消息:{message.processed_plain_text},entry_count:{entry_count} cutoff_counter:{cutoff_counter}"
-                )
-
-                if entry_count >= cutoff_counter:
-                    temp_messages.append(item)
-                else:
-                    removed_count += 1
-                    self._normal_queue.task_done()  # 标记被移除的任务为完成
-
-            except asyncio.QueueEmpty:
-                break
-
-        # 将保留的消息重新放入队列
-        for item in temp_messages:
-            self._normal_queue.put_nowait(item)
-
-        if removed_count > 0:
-            logger.info(
-                f"消息{message.processed_plain_text}超过{s4u_config.recent_message_keep_count}条，现在counter:{self._entry_counter}被移除"
-            )
-            logger.info(
-                f"[{self.stream_name}] Cleaned up {removed_count} old normal messages outside recent {s4u_config.recent_message_keep_count} range."
-            )
-
-    async def _message_processor(self):
-        """调度器：优先处理VIP队列，然后处理普通队列。"""
-        while True:
-            try:
-                # 等待有新消息的信号，避免空转
-                await self._new_message_event.wait()
-                self._new_message_event.clear()
-
-                # 清理普通队列中的过旧消息
-                self._cleanup_old_normal_messages()
-
-                # 优先处理VIP队列
-                if not self._vip_queue.empty():
-                    neg_priority, entry_count, _, message = self._vip_queue.get_nowait()
-                    priority = -neg_priority
-                    queue_name = "vip"
-                # 其次处理普通队列
-                elif not self._normal_queue.empty():
-                    neg_priority, entry_count, timestamp, message = self._normal_queue.get_nowait()
-                    priority = -neg_priority
-                    # 检查普通消息是否超时
-                    if time.time() - timestamp > s4u_config.message_timeout_seconds:
-                        logger.info(
-                            f"[{self.stream_name}] Discarding stale normal message: {message.processed_plain_text[:20]}..."
-                        )
-                        self._normal_queue.task_done()
-                        continue  # 处理下一条
-                    queue_name = "normal"
-                else:
-                    if self.internal_message:
-                        message = self.internal_message[-1]
-                        self.internal_message = []
-
-                        priority = 0
-                        neg_priority = 0
-                        entry_count = 0
-                        queue_name = "internal"
-
-                        logger.info(
-                            f"[{self.stream_name}] normal/vip 队列都空，触发 internal_message 回复: {getattr(message, 'processed_plain_text', str(message))[:20]}..."
-                        )
-                    else:
-                        continue  # 没有消息了，回去等事件
-
-                self._current_message_being_replied = (queue_name, priority, entry_count, message)
-                self._current_generation_task = asyncio.create_task(self._generate_and_send(message))
-
-                try:
-                    await self._current_generation_task
-                except asyncio.CancelledError:
-                    logger.info(
-                        f"[{self.stream_name}] Reply generation was interrupted externally for {queue_name} message. The message will be discarded."
-                    )
-                    # 被中断的消息应该被丢弃，而不是重新排队，以响应最新的用户输入。
-                    # 旧的重新入队逻辑会导致所有中断的消息最终都被回复。
-
-                except Exception as e:
-                    logger.error(f"[{self.stream_name}] _generate_and_send task error: {e}", exc_info=True)
-                finally:
-                    self._current_generation_task = None
-                    self._current_message_being_replied = None
-                    # 标记任务完成
-                    if queue_name == "vip":
-                        self._vip_queue.task_done()
-                    elif queue_name == "internal":
-                        # 如果使用 internal_message 生成回复，则不从 normal 队列中移除
-                        pass
-                    else:
-                        self._normal_queue.task_done()
-
-                    # 检查是否还有任务，有则立即再次触发事件
-                    if not self._vip_queue.empty() or not self._normal_queue.empty():
-                        self._new_message_event.set()
-
-            except asyncio.CancelledError:
-                logger.info(f"[{self.stream_name}] Message processor is shutting down.")
-                break
-            except Exception as e:
-                logger.error(f"[{self.stream_name}] Message processor main loop error: {e}", exc_info=True)
-                await asyncio.sleep(1)
-
-    def get_processing_message_id(self):
-        self.msg_id = f"{time.time()}_{random.randint(1000, 9999)}"
-
-    async def _generate_and_send(self, message: MessageRecv):
-        """为单个消息生成文本回复。整个过程可以被中断。"""
-        self._is_replying = True
-        total_chars_sent = 0  # 跟踪发送的总字符数
-
-        self.get_processing_message_id()
-
-        # 视线管理：开始生成回复时切换视线状态
-        chat_watching = watching_manager.get_watching_by_chat_id(self.stream_id)
-
-        if message.is_internal:
-            await chat_watching.on_internal_message_start()
-        else:
-            await chat_watching.on_reply_start()
-
-        sender_container = MessageSenderContainer(self.chat_stream, message)
-        sender_container.start()
-
-        async def generate_and_send_inner():
-            nonlocal total_chars_sent
-            logger.info(f"[S4U] 开始为消息生成文本和音频流: '{message.processed_plain_text[:30]}...'")
-
-            if s4u_config.enable_streaming_output:
-                logger.info("[S4U] 开始流式输出")
-                # 流式输出，边生成边发送
-                gen = self.gpt.generate_response(message, "")
-                async for chunk in gen:
-                    sender_container.msg_id = self.msg_id
-                    await sender_container.add_message(chunk)
-                    total_chars_sent += len(chunk)
-            else:
-                logger.info("[S4U] 开始一次性输出")
-                # 一次性输出，先收集所有chunk
-                all_chunks = []
-                gen = self.gpt.generate_response(message, "")
-                async for chunk in gen:
-                    all_chunks.append(chunk)
-                    total_chars_sent += len(chunk)
-                # 一次性发送
-                sender_container.msg_id = self.msg_id
-                await sender_container.add_message("".join(all_chunks))
-
-        try:
-            try:
-                await asyncio.wait_for(generate_and_send_inner(), timeout=10)
-            except asyncio.TimeoutError:
-                logger.warning(f"[{self.stream_name}] 回复生成超时，发送默认回复。")
-                sender_container.msg_id = self.msg_id
-                await sender_container.add_message("麦麦不知道哦")
-                total_chars_sent = len("麦麦不知道哦")
-
-            mood = mood_manager.get_mood_by_chat_id(self.stream_id)
-            await yes_or_no_head(
-                text=total_chars_sent,
-                emotion=mood.mood_state,
-                chat_history=message.processed_plain_text,
-                chat_id=self.stream_id,
-            )
-
-            # 等待所有文本消息发送完成
-            await sender_container.close()
-            await sender_container.join()
-
-            await chat_watching.on_thinking_finished()
-
-            start_time = time.time()
-            logged = False
-            while not self.go_processing():
-                if time.time() - start_time > 60:
-                    logger.warning(f"[{self.stream_name}] 等待消息发送超时（60秒），强制跳出循环。")
-                    break
-                if not logged:
-                    logger.info(f"[{self.stream_name}] 等待消息发送完成...")
-                    logged = True
-                await asyncio.sleep(0.2)
-
-            logger.info(f"[{self.stream_name}] 所有文本块处理完毕。")
-
-        except asyncio.CancelledError:
-            logger.info(f"[{self.stream_name}] 回复流程（文本）被中断。")
-            raise  # 将取消异常向上传播
-        except Exception as e:
-            traceback.print_exc()
-            logger.error(f"[{self.stream_name}] 回复生成过程中出现错误: {e}", exc_info=True)
-            # 回复生成实时展示：清空内容（出错时）
-        finally:
-            self._is_replying = False
-
-            # 视线管理：回复结束时切换视线状态
-            chat_watching = watching_manager.get_watching_by_chat_id(self.stream_id)
-            await chat_watching.on_reply_finished()
-
-            # 确保发送器被妥善关闭（即使已关闭，再次调用也是安全的）
-            sender_container.resume()
-            if not sender_container.task.done():
-                await sender_container.close()
-                await sender_container.join()
-            logger.info(f"[{self.stream_name}] _generate_and_send 任务结束，资源已清理。")
-
-    async def shutdown(self):
-        """平滑关闭处理任务。"""
-        logger.info(f"正在关闭 S4UChat: {self.stream_name}")
-
-        # 取消正在运行的任务
-        if self._current_generation_task and not self._current_generation_task.done():
-            self._current_generation_task.cancel()
-
-        if self._processing_task and not self._processing_task.done():
-            self._processing_task.cancel()
-
-        # 等待任务响应取消
-        try:
-            await self._processing_task
-        except asyncio.CancelledError:
-            logger.info(f"处理任务已成功取消: {self.stream_name}")
-
-    @property
-    def new_message_event(self):
-        return self._new_message_event

src/mais4u/mais4u_chat/s4u_mood_manager.py

@@ -1,458 +0,0 @@
-import asyncio
-import time
-
-import orjson
-
-from src.chat.message_receive.message import MessageRecv
-from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_by_timestamp_with_chat_inclusive
-from src.chat.utils.prompt import Prompt, global_prompt_manager
-from src.common.logger import get_logger
-from src.config.config import global_config, model_config
-from src.llm_models.utils_model import LLMRequest
-from src.mais4u.constant_s4u import ENABLE_S4U
-from src.manager.async_task_manager import AsyncTask, async_task_manager
-from src.plugin_system.apis import send_api
-
-"""
-情绪管理系统使用说明：
-
-1. 情绪数值系统：
-   - 情绪包含四个维度：joy(喜), anger(怒), sorrow(哀), fear(惧)
-   - 每个维度的取值范围为1-10
-   - 当情绪发生变化时，会自动发送到ws端处理
-
-2. 情绪更新机制：
-   - 接收到新消息时会更新情绪状态
-   - 定期进行情绪回归（冷静下来）
-   - 每次情绪变化都会发送到ws端，格式为：
-     type: "emotion"
-     data: {"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}
-
-3. ws端处理：
-   - 本地只负责情绪计算和发送情绪数值
-   - 表情渲染和动作由ws端根据情绪数值处理
-"""
-
-logger = get_logger("mood")
-
-
-def init_prompt():
-    Prompt(
-        """
-{chat_talking_prompt}
-以上是直播间里正在进行的对话
-
-{indentify_block}
-你刚刚的情绪状态是：{mood_state}
-
-现在，发送了消息，引起了你的注意，你对其进行了阅读和思考，请你输出一句话描述你新的情绪状态，不要输出任何其他内容
-请只输出情绪状态，不要输出其他内容：
-""",
-        "change_mood_prompt_vtb",
-    )
-    Prompt(
-        """
-{chat_talking_prompt}
-以上是直播间里最近的对话
-
-{indentify_block}
-你之前的情绪状态是：{mood_state}
-
-距离你上次关注直播间消息已经过去了一段时间，你冷静了下来，请你输出一句话描述你现在的情绪状态
-请只输出情绪状态，不要输出其他内容：
-""",
-        "regress_mood_prompt_vtb",
-    )
-    Prompt(
-        """
-{chat_talking_prompt}
-以上是直播间里正在进行的对话
-
-{indentify_block}
-你刚刚的情绪状态是：{mood_state}
-具体来说，从1-10分，你的情绪状态是：
-喜(Joy): {joy}
-怒(Anger): {anger}
-哀(Sorrow): {sorrow}
-惧(Fear): {fear}
-
-现在，发送了消息，引起了你的注意，你对其进行了阅读和思考。请基于对话内容，评估你新的情绪状态。
-请以JSON格式输出你新的情绪状态，包含"喜怒哀惧"四个维度，每个维度的取值范围为1-10。
-键值请使用英文: "joy", "anger", "sorrow", "fear".
-例如: {{"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}}
-不要输出任何其他内容，只输出JSON。
-""",
-        "change_mood_numerical_prompt",
-    )
-    Prompt(
-        """
-{chat_talking_prompt}
-以上是直播间里最近的对话
-
-{indentify_block}
-你之前的情绪状态是：{mood_state}
-具体来说，从1-10分，你的情绪状态是：
-喜(Joy): {joy}
-怒(Anger): {anger}
-哀(Sorrow): {sorrow}
-惧(Fear): {fear}
-
-距离你上次关注直播间消息已经过去了一段时间，你冷静了下来。请基于此，评估你现在的情绪状态。
-请以JSON格式输出你新的情绪状态，包含"喜怒哀惧"四个维度，每个维度的取值范围为1-10。
-键值请使用英文: "joy", "anger", "sorrow", "fear".
-例如: {{"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}}
-不要输出任何其他内容，只输出JSON。
-""",
-        "regress_mood_numerical_prompt",
-    )
-
-
-class ChatMood:
-    def __init__(self, chat_id: str):
-        self.chat_id: str = chat_id
-        self.mood_state: str = "感觉很平静"
-        self.mood_values: dict[str, int] = {"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}
-
-        self.regression_count: int = 0
-
-        self.mood_model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="mood_text")
-        self.mood_model_numerical = LLMRequest(
-            model_set=model_config.model_task_config.emotion, request_type="mood_numerical"
-        )
-
-        self.last_change_time: float = 0
-
-        # 发送初始情绪状态到ws端
-        asyncio.create_task(self.send_emotion_update(self.mood_values))
-
-    @staticmethod
-    def _parse_numerical_mood(response: str) -> dict[str, int] | None:
-        try:
-            # The LLM might output markdown with json inside
-            if "```json" in response:
-                response = response.split("```json")[1].split("```")[0]
-            elif "```" in response:
-                response = response.split("```")[1].split("```")[0]
-
-            data = orjson.loads(response)
-
-            # Validate
-            required_keys = {"joy", "anger", "sorrow", "fear"}
-            if not required_keys.issubset(data.keys()):
-                logger.warning(f"Numerical mood response missing keys: {response}")
-                return None
-
-            for key in required_keys:
-                value = data[key]
-                if not isinstance(value, int) or not (1 <= value <= 10):
-                    logger.warning(f"Numerical mood response invalid value for {key}: {value} in {response}")
-                    return None
-
-            return {key: data[key] for key in required_keys}
-
-        except orjson.JSONDecodeError:
-            logger.warning(f"Failed to parse numerical mood JSON: {response}")
-            return None
-        except Exception as e:
-            logger.error(f"Error parsing numerical mood: {e}, response: {response}")
-            return None
-
-    async def update_mood_by_message(self, message: MessageRecv):
-        self.regression_count = 0
-
-        message_time: float = message.message_info.time  # type: ignore
-        message_list_before_now = await get_raw_msg_by_timestamp_with_chat_inclusive(
-            chat_id=self.chat_id,
-            timestamp_start=self.last_change_time,
-            timestamp_end=message_time,
-            limit=10,
-            limit_mode="last",
-        )
-        chat_talking_prompt = await build_readable_messages(
-            message_list_before_now,
-            replace_bot_name=True,
-            merge_messages=False,
-            timestamp_mode="normal_no_YMD",
-            read_mark=0.0,
-            truncate=True,
-            show_actions=True,
-        )
-
-        bot_name = global_config.bot.nickname
-        if global_config.bot.alias_names:
-            bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
-        else:
-            bot_nickname = ""
-
-        prompt_personality = global_config.personality.personality_core
-        indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
-
-        async def _update_text_mood():
-            prompt = await global_prompt_manager.format_prompt(
-                "change_mood_prompt_vtb",
-                chat_talking_prompt=chat_talking_prompt,
-                indentify_block=indentify_block,
-                mood_state=self.mood_state,
-            )
-            logger.debug(f"text mood prompt: {prompt}")
-            response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
-                prompt=prompt, temperature=0.7
-            )
-            logger.info(f"text mood response: {response}")
-            logger.debug(f"text mood reasoning_content: {reasoning_content}")
-            return response
-
-        async def _update_numerical_mood():
-            prompt = await global_prompt_manager.format_prompt(
-                "change_mood_numerical_prompt",
-                chat_talking_prompt=chat_talking_prompt,
-                indentify_block=indentify_block,
-                mood_state=self.mood_state,
-                joy=self.mood_values["joy"],
-                anger=self.mood_values["anger"],
-                sorrow=self.mood_values["sorrow"],
-                fear=self.mood_values["fear"],
-            )
-            logger.debug(f"numerical mood prompt: {prompt}")
-            response, (reasoning_content, _, _) = await self.mood_model_numerical.generate_response_async(
-                prompt=prompt, temperature=0.4
-            )
-            logger.info(f"numerical mood response: {response}")
-            logger.debug(f"numerical mood reasoning_content: {reasoning_content}")
-            return self._parse_numerical_mood(response)
-
-        results = await asyncio.gather(_update_text_mood(), _update_numerical_mood())
-        text_mood_response, numerical_mood_response = results
-
-        if text_mood_response:
-            self.mood_state = text_mood_response
-
-        if numerical_mood_response:
-            _old_mood_values = self.mood_values.copy()
-            self.mood_values = numerical_mood_response
-
-            # 发送情绪更新到ws端
-            await self.send_emotion_update(self.mood_values)
-
-            logger.info(f"[{self.chat_id}] 情绪变化: {_old_mood_values} -> {self.mood_values}")
-
-        self.last_change_time = message_time
-
-    async def regress_mood(self):
-        message_time = time.time()
-        message_list_before_now = await get_raw_msg_by_timestamp_with_chat_inclusive(
-            chat_id=self.chat_id,
-            timestamp_start=self.last_change_time,
-            timestamp_end=message_time,
-            limit=5,
-            limit_mode="last",
-        )
-        chat_talking_prompt = await build_readable_messages(
-            message_list_before_now,
-            replace_bot_name=True,
-            merge_messages=False,
-            timestamp_mode="normal_no_YMD",
-            read_mark=0.0,
-            truncate=True,
-            show_actions=True,
-        )
-
-        bot_name = global_config.bot.nickname
-        if global_config.bot.alias_names:
-            bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
-        else:
-            bot_nickname = ""
-
-        prompt_personality = global_config.personality.personality_core
-        indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
-
-        async def _regress_text_mood():
-            prompt = await global_prompt_manager.format_prompt(
-                "regress_mood_prompt_vtb",
-                chat_talking_prompt=chat_talking_prompt,
-                indentify_block=indentify_block,
-                mood_state=self.mood_state,
-            )
-            logger.debug(f"text regress prompt: {prompt}")
-            response, (reasoning_content, _, _) = await self.mood_model.generate_response_async(
-                prompt=prompt, temperature=0.7
-            )
-            logger.info(f"text regress response: {response}")
-            logger.debug(f"text regress reasoning_content: {reasoning_content}")
-            return response
-
-        async def _regress_numerical_mood():
-            prompt = await global_prompt_manager.format_prompt(
-                "regress_mood_numerical_prompt",
-                chat_talking_prompt=chat_talking_prompt,
-                indentify_block=indentify_block,
-                mood_state=self.mood_state,
-                joy=self.mood_values["joy"],
-                anger=self.mood_values["anger"],
-                sorrow=self.mood_values["sorrow"],
-                fear=self.mood_values["fear"],
-            )
-            logger.debug(f"numerical regress prompt: {prompt}")
-            response, (reasoning_content, _, _) = await self.mood_model_numerical.generate_response_async(
-                prompt=prompt,
-                temperature=0.4,
-            )
-            logger.info(f"numerical regress response: {response}")
-            logger.debug(f"numerical regress reasoning_content: {reasoning_content}")
-            return self._parse_numerical_mood(response)
-
-        results = await asyncio.gather(_regress_text_mood(), _regress_numerical_mood())
-        text_mood_response, numerical_mood_response = results
-
-        if text_mood_response:
-            self.mood_state = text_mood_response
-
-        if numerical_mood_response:
-            _old_mood_values = self.mood_values.copy()
-            self.mood_values = numerical_mood_response
-
-            # 发送情绪更新到ws端
-            await self.send_emotion_update(self.mood_values)
-
-            logger.info(f"[{self.chat_id}] 情绪回归: {_old_mood_values} -> {self.mood_values}")
-
-        self.regression_count += 1
-
-    async def send_emotion_update(self, mood_values: dict[str, int]):
-        """发送情绪更新到ws端"""
-        emotion_data = {
-            "joy": mood_values.get("joy", 5),
-            "anger": mood_values.get("anger", 1),
-            "sorrow": mood_values.get("sorrow", 1),
-            "fear": mood_values.get("fear", 1),
-        }
-
-        await send_api.custom_to_stream(
-            message_type="emotion",
-            content=emotion_data,
-            stream_id=self.chat_id,
-            storage_message=False,
-            show_log=True,
-        )
-
-        logger.info(f"[{self.chat_id}] 发送情绪更新: {emotion_data}")
-
-
-class MoodRegressionTask(AsyncTask):
-    def __init__(self, mood_manager: "MoodManager"):
-        super().__init__(task_name="MoodRegressionTask", run_interval=30)
-        self.mood_manager = mood_manager
-        self.run_count = 0
-
-    async def run(self):
-        self.run_count += 1
-        logger.info(f"[回归任务] 第{self.run_count}次检查，当前管理{len(self.mood_manager.mood_list)}个聊天的情绪状态")
-
-        now = time.time()
-        regression_executed = 0
-
-        for mood in self.mood_manager.mood_list:
-            chat_info = f"chat {mood.chat_id}"
-
-            if mood.last_change_time == 0:
-                logger.debug(f"[回归任务] {chat_info} 尚未有情绪变化，跳过回归")
-                continue
-
-            time_since_last_change = now - mood.last_change_time
-
-            # 检查是否有极端情绪需要快速回归
-            high_emotions = {k: v for k, v in mood.mood_values.items() if v >= 8}
-            has_extreme_emotion = len(high_emotions) > 0
-
-            # 回归条件：1. 正常时间间隔(120s) 或 2. 有极端情绪且距上次变化>=30s
-            should_regress = False
-            regress_reason = ""
-
-            if time_since_last_change > 120:
-                should_regress = True
-                regress_reason = f"常规回归(距上次变化{int(time_since_last_change)}秒)"
-            elif has_extreme_emotion and time_since_last_change > 30:
-                should_regress = True
-                high_emotion_str = ", ".join([f"{k}={v}" for k, v in high_emotions.items()])
-                regress_reason = f"极端情绪快速回归({high_emotion_str}, 距上次变化{int(time_since_last_change)}秒)"
-
-            if should_regress:
-                if mood.regression_count >= 3:
-                    logger.debug(f"[回归任务] {chat_info} 已达到最大回归次数(3次)，停止回归")
-                    continue
-
-                logger.info(
-                    f"[回归任务] {chat_info} 开始情绪回归 ({regress_reason}，第{mood.regression_count + 1}次回归)"
-                )
-                await mood.regress_mood()
-                regression_executed += 1
-            else:
-                if has_extreme_emotion:
-                    remaining_time = 5 - time_since_last_change
-                    high_emotion_str = ", ".join([f"{k}={v}" for k, v in high_emotions.items()])
-                    logger.debug(
-                        f"[回归任务] {chat_info} 存在极端情绪({high_emotion_str})，距离快速回归还需等待{int(remaining_time)}秒"
-                    )
-                else:
-                    remaining_time = 120 - time_since_last_change
-                    logger.debug(f"[回归任务] {chat_info} 距离回归还需等待{int(remaining_time)}秒")
-
-        if regression_executed > 0:
-            logger.info(f"[回归任务] 本次执行了{regression_executed}个聊天的情绪回归")
-        else:
-            logger.debug("[回归任务] 本次没有符合回归条件的聊天")
-
-
-class MoodManager:
-    def __init__(self):
-        self.mood_list: list[ChatMood] = []
-        """当前情绪状态"""
-        self.task_started: bool = False
-
-    async def start(self):
-        """启动情绪回归后台任务"""
-        if self.task_started:
-            return
-
-        logger.info("启动情绪管理任务...")
-
-        # 启动情绪回归任务
-        regression_task = MoodRegressionTask(self)
-        await async_task_manager.add_task(regression_task)
-
-        self.task_started = True
-        logger.info("情绪管理任务已启动（情绪回归）")
-
-    def get_mood_by_chat_id(self, chat_id: str) -> ChatMood:
-        for mood in self.mood_list:
-            if mood.chat_id == chat_id:
-                return mood
-
-        new_mood = ChatMood(chat_id)
-        self.mood_list.append(new_mood)
-        return new_mood
-
-    def reset_mood_by_chat_id(self, chat_id: str):
-        for mood in self.mood_list:
-            if mood.chat_id == chat_id:
-                mood.mood_state = "感觉很平静"
-                mood.mood_values = {"joy": 5, "anger": 1, "sorrow": 1, "fear": 1}
-                mood.regression_count = 0
-                # 发送重置后的情绪状态到ws端
-                asyncio.create_task(mood.send_emotion_update(mood.mood_values))
-                return
-
-        # 如果没有找到现有的mood，创建新的
-        new_mood = ChatMood(chat_id)
-        self.mood_list.append(new_mood)
-        # 发送初始情绪状态到ws端
-        asyncio.create_task(new_mood.send_emotion_update(new_mood.mood_values))
-
-
-if ENABLE_S4U:
-    init_prompt()
-    mood_manager = MoodManager()
-else:
-    mood_manager = None
-
-"""全局情绪管理器"""

src/mais4u/mais4u_chat/s4u_msg_processor.py

@@ -1,282 +0,0 @@
-import asyncio
-import math
-
-from maim_message.message_base import GroupInfo
-
-from src.chat.message_receive.chat_stream import get_chat_manager
-
-# 旧的Hippocampus系统已被移除，现在使用增强记忆系统
-# from src.chat.memory_system.enhanced_memory_manager import enhanced_memory_manager
-from src.chat.message_receive.message import MessageRecv, MessageRecvS4U
-from src.chat.message_receive.storage import MessageStorage
-from src.chat.utils.timer_calculator import Timer
-from src.chat.utils.utils import is_mentioned_bot_in_message
-from src.common.logger import get_logger
-from src.config.config import global_config
-from src.mais4u.mais4u_chat.body_emotion_action_manager import action_manager
-from src.mais4u.mais4u_chat.context_web_manager import get_context_web_manager
-from src.mais4u.mais4u_chat.gift_manager import gift_manager
-from src.mais4u.mais4u_chat.s4u_mood_manager import mood_manager
-from src.mais4u.mais4u_chat.s4u_watching_manager import watching_manager
-from src.mais4u.mais4u_chat.screen_manager import screen_manager
-
-from .s4u_chat import get_s4u_chat_manager
-
-# from ..message_receive.message_buffer import message_buffer
-
-logger = get_logger("chat")
-
-
-async def _calculate_interest(message: MessageRecv) -> tuple[float, bool]:
-    """计算消息的兴趣度
-
-    Args:
-        message: 待处理的消息对象
-
-    Returns:
-        Tuple[float, bool]: (兴趣度, 是否被提及)
-    """
-    is_mentioned, _ = is_mentioned_bot_in_message(message)
-    interested_rate = 0.0
-
-    if global_config.memory.enable_memory:
-        with Timer("记忆激活"):
-            # 使用新的统一记忆系统计算兴趣度
-            try:
-                from src.chat.memory_system import get_memory_system
-
-                memory_system = get_memory_system()
-                enhanced_memories = await memory_system.retrieve_relevant_memories(
-                    query_text=message.processed_plain_text,
-                    user_id=str(message.user_info.user_id),
-                    scope_id=message.chat_id,
-                    limit=5,
-                )
-
-                # 基于检索结果计算兴趣度
-                if enhanced_memories:
-                    # 有相关记忆，兴趣度基于相似度计算
-                    max_score = max(getattr(memory, "relevance_score", 0.5) for memory in enhanced_memories)
-                    interested_rate = min(max_score, 1.0)  # 限制在0-1之间
-                else:
-                    # 没有相关记忆，给予基础兴趣度
-                    interested_rate = 0.1
-
-                logger.debug(f"增强记忆系统兴趣度: {interested_rate:.2f}")
-
-            except Exception as e:
-                logger.warning(f"增强记忆系统兴趣度计算失败: {e}")
-                interested_rate = 0.1  # 默认基础兴趣度
-
-    text_len = len(message.processed_plain_text)
-    # 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算
-    # 基于实际分布：0-5字符(26.57%), 6-10字符(27.18%), 11-20字符(22.76%), 21-30字符(10.33%), 31+字符(13.86%)
-
-    if text_len == 0:
-        base_interest = 0.01  # 空消息最低兴趣度
-    elif text_len <= 5:
-        # 1-5字符：线性增长 0.01 -> 0.03
-        base_interest = 0.01 + (text_len - 1) * (0.03 - 0.01) / 4
-    elif text_len <= 10:
-        # 6-10字符：线性增长 0.03 -> 0.06
-        base_interest = 0.03 + (text_len - 5) * (0.06 - 0.03) / 5
-    elif text_len <= 20:
-        # 11-20字符：线性增长 0.06 -> 0.12
-        base_interest = 0.06 + (text_len - 10) * (0.12 - 0.06) / 10
-    elif text_len <= 30:
-        # 21-30字符：线性增长 0.12 -> 0.18
-        base_interest = 0.12 + (text_len - 20) * (0.18 - 0.12) / 10
-    elif text_len <= 50:
-        # 31-50字符：线性增长 0.18 -> 0.22
-        base_interest = 0.18 + (text_len - 30) * (0.22 - 0.18) / 20
-    elif text_len <= 100:
-        # 51-100字符：线性增长 0.22 -> 0.26
-        base_interest = 0.22 + (text_len - 50) * (0.26 - 0.22) / 50
-    else:
-        # 100+字符：对数增长 0.26 -> 0.3，增长率递减
-        base_interest = 0.26 + (0.3 - 0.26) * (math.log10(text_len - 99) / math.log10(901))  # 1000-99=901
-
-    # 确保在范围内
-    base_interest = min(max(base_interest, 0.01), 0.3)
-
-    interested_rate += base_interest
-
-    if is_mentioned:
-        interest_increase_on_mention = 1
-        interested_rate += interest_increase_on_mention
-
-    return interested_rate, is_mentioned
-
-
-class S4UMessageProcessor:
-    """心流处理器，负责处理接收到的消息并计算兴趣度"""
-
-    def __init__(self):
-        """初始化心流处理器，创建消息存储实例"""
-        self.storage = MessageStorage()
-
-    async def process_message(self, message: MessageRecvS4U, skip_gift_debounce: bool = False) -> None:
-        """处理接收到的原始消息数据
-
-        主要流程:
-        1. 消息解析与初始化
-        2. 消息缓冲处理
-        3. 过滤检查
-        4. 兴趣度计算
-        5. 关系处理
-
-        Args:
-            message_data: 原始消息字符串
-        """
-
-        # 1. 消息解析与初始化
-        groupinfo = message.message_info.group_info
-        userinfo = message.message_info.user_info
-        message_info = message.message_info
-
-        chat = await get_chat_manager().get_or_create_stream(
-            platform=message_info.platform,
-            user_info=userinfo,
-            group_info=groupinfo,
-        )
-
-        if await self.handle_internal_message(message):
-            return
-
-        if await self.hadle_if_voice_done(message):
-            return
-
-        # 处理礼物消息，如果消息被暂存则停止当前处理流程
-        if not skip_gift_debounce and not await self.handle_if_gift(message):
-            return
-        await self.check_if_fake_gift(message)
-
-        # 处理屏幕消息
-        if await self.handle_screen_message(message):
-            return
-
-        await self.storage.store_message(message, chat)
-
-        s4u_chat = await get_s4u_chat_manager().get_or_create_chat(chat)
-
-        await s4u_chat.add_message(message)
-
-        _interested_rate, _ = await _calculate_interest(message)
-
-        await mood_manager.start()
-
-        # 一系列llm驱动的前处理
-        chat_mood = mood_manager.get_mood_by_chat_id(chat.stream_id)
-        asyncio.create_task(chat_mood.update_mood_by_message(message))
-        chat_action = action_manager.get_action_state_by_chat_id(chat.stream_id)
-        asyncio.create_task(chat_action.update_action_by_message(message))
-        # 视线管理：收到消息时切换视线状态
-        chat_watching = watching_manager.get_watching_by_chat_id(chat.stream_id)
-        await chat_watching.on_message_received()
-
-        # 上下文网页管理：启动独立task处理消息上下文
-        asyncio.create_task(self._handle_context_web_update(chat.stream_id, message))
-
-        # 日志记录
-        if message.is_gift:
-            logger.info(f"[S4U-礼物] {userinfo.user_nickname} 送出了 {message.gift_name} x{message.gift_count}")
-        else:
-            logger.info(f"[S4U]{userinfo.user_nickname}:{message.processed_plain_text}")
-
-    @staticmethod
-    async def handle_internal_message(message: MessageRecvS4U):
-        if message.is_internal:
-            group_info = GroupInfo(platform="amaidesu_default", group_id=660154, group_name="内心")
-
-            chat = await get_chat_manager().get_or_create_stream(
-                platform="amaidesu_default", user_info=message.message_info.user_info, group_info=group_info
-            )
-            s4u_chat = await get_s4u_chat_manager().get_or_create_chat(chat)
-            message.message_info.group_info = s4u_chat.chat_stream.group_info
-            message.message_info.platform = s4u_chat.chat_stream.platform
-
-            s4u_chat.internal_message.append(message)
-            s4u_chat.new_message_event.set()
-
-            logger.info(
-                f"[{s4u_chat.stream_name}] 添加内部消息-------------------------------------------------------: {message.processed_plain_text}"
-            )
-
-            return True
-        return False
-
-    @staticmethod
-    async def handle_screen_message(message: MessageRecvS4U):
-        if message.is_screen:
-            screen_manager.set_screen(message.screen_info)
-            return True
-        return False
-
-    @staticmethod
-    async def hadle_if_voice_done(message: MessageRecvS4U):
-        if message.voice_done:
-            s4u_chat = await get_s4u_chat_manager().get_or_create_chat(message.chat_stream)
-            s4u_chat.voice_done = message.voice_done
-            return True
-        return False
-
-    @staticmethod
-    async def check_if_fake_gift(message: MessageRecvS4U) -> bool:
-        """检查消息是否为假礼物"""
-        if message.is_gift:
-            return False
-
-        gift_keywords = ["送出了礼物", "礼物", "送出了", "投喂"]
-        if any(keyword in message.processed_plain_text for keyword in gift_keywords):
-            message.is_fake_gift = True
-            return True
-
-        return False
-
-    async def handle_if_gift(self, message: MessageRecvS4U) -> bool:
-        """处理礼物消息
-
-        Returns:
-            bool: True表示应该继续处理消息，False表示消息已被暂存不需要继续处理
-        """
-        if message.is_gift:
-            # 定义防抖完成后的回调函数
-            def gift_callback(merged_message: MessageRecvS4U):
-                """礼物防抖完成后的回调"""
-                # 创建异步任务来处理合并后的礼物消息，跳过防抖处理
-                asyncio.create_task(self.process_message(merged_message, skip_gift_debounce=True))
-
-            # 交给礼物管理器处理，并传入回调函数
-            # 对于礼物消息，handle_gift 总是返回 False（消息被暂存）
-            await gift_manager.handle_gift(message, gift_callback)
-            return False  # 消息被暂存，不继续处理
-
-        return True  # 非礼物消息，继续正常处理
-
-    @staticmethod
-    async def _handle_context_web_update(chat_id: str, message: MessageRecv):
-        """处理上下文网页更新的独立task
-
-        Args:
-            chat_id: 聊天ID
-            message: 消息对象
-        """
-        try:
-            logger.debug(f"🔄 开始处理上下文网页更新: {message.message_info.user_info.user_nickname}")
-
-            context_manager = get_context_web_manager()
-
-            # 只在服务器未启动时启动（避免重复启动）
-            if context_manager.site is None:
-                logger.info("🚀 首次启动上下文网页服务器...")
-                await context_manager.start_server()
-
-            # 添加消息到上下文并更新网页
-            await asyncio.sleep(1.5)
-
-            await context_manager.add_message(chat_id, message)
-
-            logger.debug(f"✅ 上下文网页更新完成: {message.message_info.user_info.user_nickname}")
-
-        except Exception as e:
-            logger.error(f"❌ 处理上下文网页更新失败: {e}", exc_info=True)

src/mais4u/mais4u_chat/s4u_prompt.py

@@ -1,443 +0,0 @@
-import asyncio
-
-# 旧的Hippocampus系统已被移除，现在使用增强记忆系统
-# from src.chat.memory_system.enhanced_memory_manager import enhanced_memory_manager
-import random
-import time
-from datetime import datetime
-
-from src.chat.express.expression_selector import expression_selector
-from src.chat.message_receive.chat_stream import ChatStream
-from src.chat.message_receive.message import MessageRecvS4U
-from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
-from src.chat.utils.prompt import Prompt, global_prompt_manager
-from src.chat.utils.utils import get_recent_group_speaker
-from src.common.logger import get_logger
-from src.config.config import global_config
-from src.mais4u.mais4u_chat.internal_manager import internal_manager
-from src.mais4u.mais4u_chat.screen_manager import screen_manager
-from src.mais4u.mais4u_chat.super_chat_manager import get_super_chat_manager
-from src.mais4u.s4u_config import s4u_config
-from src.person_info.person_info import PersonInfoManager, get_person_info_manager
-from src.person_info.relationship_fetcher import relationship_fetcher_manager
-
-from .s4u_mood_manager import mood_manager
-
-logger = get_logger("prompt")
-
-
-def init_prompt():
-    Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt")
-    Prompt("\n关于你们的关系，你需要知道：\n{relation_info}\n", "relation_prompt")
-    Prompt("你回想起了一些事情：\n{memory_info}\n", "memory_prompt")
-
-    Prompt(
-        """
-你的名字是麦麦, 是千石可乐开发的程序，可以在QQ，微信等平台发言，你现在正在哔哩哔哩作为虚拟主播进行直播
-虽然是个AI，但你在努力扮演真人, 人设的配色是橙色和紫色和绿色
-你有一头法式橙色卷发，你很可爱，穿戴英式侦探风格学院风裙子和帽子，你正在一个教室场景里进行虚拟线上直播。不过不要刻意提及这些内容，自然回复
-你可以看见用户发送的弹幕，礼物和superchat
-{screen_info}
-{internal_state}
-
-{relation_info_block}
-{memory_block}
-{expression_habits_block}
-
-你现在的主要任务是和 {sender_name} 发送的弹幕聊天。同时，也有其他用户会参与你们的聊天，你可以参考他们的回复内容，但是你主要还是关注你和{sender_name}的聊天内容。
-
-{sc_info}
-
-{background_dialogue_prompt}
---------------------------------
-{time_block}
-这是你和{sender_name}的对话，你们正在交流中：
-{core_dialogue_prompt}
-
-对方最新发送的内容：{message_txt}
-{gift_info}
-回复简短一些，平淡一些，可以参考贴吧，知乎和微博的回复风格，回复不要浮夸，不要用夸张修辞。
-表现的有个性，不要随意服从他人要求，积极互动。你现在的心情是：{mood_state}
-不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容，现在{sender_name}正在等待你的回复。
-你的回复风格不要浮夸，有逻辑和条理，请你继续回复{sender_name}。
-你的发言：
-""",
-        "s4u_prompt",  # New template for private CHAT chat
-    )
-
-    Prompt(
-        """
-你的名字是麦麦, 是千石可乐开发的程序，可以在QQ，微信等平台发言，你现在正在哔哩哔哩作为虚拟主播进行直播
-虽然是个AI，但你在努力扮演真人, 人设的配色是橙色和紫色和绿色
-你有一头法式橙色卷发，你很可爱，穿戴英式侦探风格学院风裙子和帽子，你正在一个教室场景里进行虚拟线上直播。不过不要刻意提及这些内容，自然回复
-你可以看见用户发送的弹幕，礼物和superchat
-你可以看见面前的屏幕，目前屏幕的内容是:
-{screen_info}
-
-{memory_block}
-{expression_habits_block}
-
-{sc_info}
-
-{time_block}
-{chat_info_danmu}
---------------------------------
-以上是你和弹幕的对话，与此同时，你在与QQ群友聊天，聊天记录如下：
-{chat_info_qq}
---------------------------------
-你刚刚回复了QQ群，你内心的想法是：{mind}
-请根据你内心的想法，组织一条回复，在直播间进行发言，可以点名吐槽对象，让观众知道你在说谁
-{gift_info}
-回复简短一些，平淡一些，可以参考贴吧，知乎和微博的回复风格。不要浮夸，有逻辑和条理。
-表现的有个性，不要随意服从他人要求，积极互动。你现在的心情是：{mood_state}
-不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。
-你的发言：
-""",
-        "s4u_prompt_internal",  # New template for private CHAT chat
-    )
-
-
-class PromptBuilder:
-    def __init__(self):
-        self.prompt_built = ""
-        self.activate_messages = ""
-
-    @staticmethod
-    async def build_expression_habits(chat_stream: ChatStream, chat_history, target):
-        style_habits = []
-        grammar_habits = []
-
-        # 使用统一的表达方式选择入口（支持classic和exp_model模式）
-        selected_expressions = await expression_selector.select_suitable_expressions(
-            chat_id=chat_stream.stream_id,
-            chat_history=chat_history,
-            target_message=target,
-            max_num=12,
-            min_num=5
-        )
-
-        if selected_expressions:
-            logger.debug(f" 使用处理器选中的{len(selected_expressions)}个表达方式")
-            for expr in selected_expressions:
-                if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                    expr_type = expr.get("type", "style")
-                    if expr_type == "grammar":
-                        grammar_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
-                    else:
-                        style_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
-        else:
-            logger.debug("没有从处理器获得表达方式，将使用空的表达方式")
-            # 不再在replyer中进行随机选择，全部交给处理器处理
-
-        style_habits_str = "\n".join(style_habits)
-        grammar_habits_str = "\n".join(grammar_habits)
-
-        # 动态构建expression habits块
-        expression_habits_block = ""
-        if style_habits_str.strip():
-            expression_habits_block += f"你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中：\n{style_habits_str}\n\n"
-        if grammar_habits_str.strip():
-            expression_habits_block += f"请你根据情景使用以下句法：\n{grammar_habits_str}\n"
-
-        return expression_habits_block
-
-    @staticmethod
-    async def build_relation_info(chat_stream) -> str:
-        is_group_chat = bool(chat_stream.group_info)
-        who_chat_in_group = []
-        if is_group_chat:
-            who_chat_in_group = get_recent_group_speaker(
-                chat_stream.stream_id,
-                (chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None,
-                limit=global_config.chat.max_context_size,
-            )
-        elif chat_stream.user_info:
-            who_chat_in_group.append(
-                (chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
-            )
-
-        relation_prompt = ""
-        if global_config.affinity_flow.enable_relationship_tracking and who_chat_in_group:
-            relationship_fetcher = relationship_fetcher_manager.get_fetcher(chat_stream.stream_id)
-
-            # 将 (platform, user_id, nickname) 转换为 person_id
-            person_ids = []
-            for person in who_chat_in_group:
-                person_id = PersonInfoManager.get_person_id(person[0], person[1])
-                person_ids.append(person_id)
-
-            # 构建用户关系信息和聊天流印象信息
-            user_relation_tasks = [relationship_fetcher.build_relation_info(person_id, points_num=3) for person_id in person_ids]
-            stream_impression_task = relationship_fetcher.build_chat_stream_impression(chat_stream.stream_id)
-            
-            # 并行获取所有信息
-            results = await asyncio.gather(*user_relation_tasks, stream_impression_task)
-            relation_info_list = results[:-1]  # 用户关系信息
-            stream_impression = results[-1]     # 聊天流印象
-            
-            # 组合用户关系信息和聊天流印象
-            combined_info_parts = []
-            if user_relation_info := "".join(relation_info_list):
-                combined_info_parts.append(user_relation_info)
-            if stream_impression:
-                combined_info_parts.append(stream_impression)
-            
-            if combined_info := "\n\n".join(combined_info_parts):
-                relation_prompt = await global_prompt_manager.format_prompt(
-                    "relation_prompt", relation_info=combined_info
-                )
-        return relation_prompt
-
-    @staticmethod
-    async def build_memory_block(text: str) -> str:
-        # 使用新的统一记忆系统检索记忆
-        try:
-            from src.chat.memory_system import get_memory_system
-
-            memory_system = get_memory_system()
-            enhanced_memories = await memory_system.retrieve_relevant_memories(
-                query_text=text,
-                user_id="system",  # 系统查询
-                scope_id="system",
-                limit=5,
-            )
-
-            related_memory_info = ""
-            if enhanced_memories:
-                for memory_chunk in enhanced_memories:
-                    related_memory_info += memory_chunk.display or memory_chunk.text_content or ""
-                return await global_prompt_manager.format_prompt(
-                    "memory_prompt", memory_info=related_memory_info.strip()
-                )
-            return ""
-
-        except Exception as e:
-            logger.warning(f"增强记忆系统检索失败: {e}")
-            return ""
-
-    @staticmethod
-    async def build_chat_history_prompts(chat_stream: ChatStream, message: MessageRecvS4U):
-        message_list_before_now = await get_raw_msg_before_timestamp_with_chat(
-            chat_id=chat_stream.stream_id,
-            timestamp=time.time(),
-            limit=300,
-        )
-
-        talk_type = f"{message.message_info.platform}:{message.chat_stream.user_info.user_id!s}"
-
-        core_dialogue_list = []
-        background_dialogue_list = []
-        bot_id = str(global_config.bot.qq_account)
-        target_user_id = str(message.chat_stream.user_info.user_id)
-
-        for msg_dict in message_list_before_now:
-            try:
-                msg_user_id = str(msg_dict.get("user_id"))
-                if msg_user_id == bot_id:
-                    if msg_dict.get("reply_to") and talk_type == msg_dict.get("reply_to"):
-                        core_dialogue_list.append(msg_dict)
-                    elif msg_dict.get("reply_to") and talk_type != msg_dict.get("reply_to"):
-                        background_dialogue_list.append(msg_dict)
-                    # else:
-                    # background_dialogue_list.append(msg_dict)
-                elif msg_user_id == target_user_id:
-                    core_dialogue_list.append(msg_dict)
-                else:
-                    background_dialogue_list.append(msg_dict)
-            except Exception as e:
-                logger.error(f"无法处理历史消息记录: {msg_dict}, 错误: {e}")
-
-        background_dialogue_prompt = ""
-        if background_dialogue_list:
-            context_msgs = background_dialogue_list[-s4u_config.max_context_message_length :]
-            background_dialogue_prompt_str = await build_readable_messages(
-                context_msgs,
-                timestamp_mode="normal_no_YMD",
-                show_pic=False,
-            )
-            background_dialogue_prompt = f"这是其他用户的发言：\n{background_dialogue_prompt_str}"
-
-        core_msg_str = ""
-        if core_dialogue_list:
-            core_dialogue_list = core_dialogue_list[-s4u_config.max_core_message_length :]
-
-            first_msg = core_dialogue_list[0]
-            start_speaking_user_id = first_msg.get("user_id")
-            if start_speaking_user_id == bot_id:
-                last_speaking_user_id = bot_id
-                msg_seg_str = "你的发言：\n"
-            else:
-                start_speaking_user_id = target_user_id
-                last_speaking_user_id = start_speaking_user_id
-                msg_seg_str = "对方的发言：\n"
-
-            msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(first_msg.get('time')))}: {first_msg.get('processed_plain_text')}\n"
-
-            all_msg_seg_list = []
-            for msg in core_dialogue_list[1:]:
-                speaker = msg.get("user_id")
-                if speaker == last_speaking_user_id:
-                    msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(msg.get('time')))}: {msg.get('processed_plain_text')}\n"
-                else:
-                    msg_seg_str = f"{msg_seg_str}\n"
-                    all_msg_seg_list.append(msg_seg_str)
-
-                    if speaker == bot_id:
-                        msg_seg_str = "你的发言：\n"
-                    else:
-                        msg_seg_str = "对方的发言：\n"
-
-                    msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(msg.get('time')))}: {msg.get('processed_plain_text')}\n"
-                    last_speaking_user_id = speaker
-
-            all_msg_seg_list.append(msg_seg_str)
-            for msg in all_msg_seg_list:
-                core_msg_str += msg
-
-        all_dialogue_prompt = await get_raw_msg_before_timestamp_with_chat(
-            chat_id=chat_stream.stream_id,
-            timestamp=time.time(),
-            limit=20,
-        )
-        all_dialogue_prompt_str = await build_readable_messages(
-            all_dialogue_prompt,
-            timestamp_mode="normal_no_YMD",
-            show_pic=False,
-        )
-
-        return core_msg_str, background_dialogue_prompt, all_dialogue_prompt_str
-
-    @staticmethod
-    def build_gift_info(message: MessageRecvS4U):
-        if message.is_gift:
-            return f"这是一条礼物信息，{message.gift_name} x{message.gift_count}，请注意这位用户"
-        else:
-            if message.is_fake_gift:
-                return f"{message.processed_plain_text}（注意：这是一条普通弹幕信息，对方没有真的发送礼物，不是礼物信息，注意区分，如果对方在发假的礼物骗你，请反击）"
-
-        return ""
-
-    @staticmethod
-    def build_sc_info(message: MessageRecvS4U):
-        super_chat_manager = get_super_chat_manager()
-        return super_chat_manager.build_superchat_summary_string(message.chat_stream.stream_id)
-
-    async def build_prompt_normal(
-        self,
-        message: MessageRecvS4U,
-        message_txt: str,
-    ) -> str:
-        chat_stream = message.chat_stream
-
-        person_id = PersonInfoManager.get_person_id(
-            message.chat_stream.user_info.platform, message.chat_stream.user_info.user_id
-        )
-        person_info_manager = get_person_info_manager()
-        person_name = await person_info_manager.get_value(person_id, "person_name")
-
-        if message.chat_stream.user_info.user_nickname:
-            if person_name:
-                sender_name = f"[{message.chat_stream.user_info.user_nickname}]（你叫ta{person_name}）"
-            else:
-                sender_name = f"[{message.chat_stream.user_info.user_nickname}]"
-        else:
-            sender_name = f"用户({message.chat_stream.user_info.user_id})"
-
-        relation_info_block, memory_block, expression_habits_block = await asyncio.gather(
-            self.build_relation_info(chat_stream),
-            self.build_memory_block(message_txt),
-            self.build_expression_habits(chat_stream, message_txt, sender_name),
-        )
-
-        core_dialogue_prompt, background_dialogue_prompt, all_dialogue_prompt = await self.build_chat_history_prompts(
-            chat_stream, message
-        )
-
-        gift_info = self.build_gift_info(message)
-
-        sc_info = self.build_sc_info(message)
-
-        screen_info = screen_manager.get_screen_str()
-
-        internal_state = internal_manager.get_internal_state_str()
-
-        time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
-
-        mood = mood_manager.get_mood_by_chat_id(chat_stream.stream_id)
-
-        template_name = "s4u_prompt"
-
-        if not message.is_internal:
-            prompt = await global_prompt_manager.format_prompt(
-                template_name,
-                time_block=time_block,
-                expression_habits_block=expression_habits_block,
-                relation_info_block=relation_info_block,
-                memory_block=memory_block,
-                screen_info=screen_info,
-                internal_state=internal_state,
-                gift_info=gift_info,
-                sc_info=sc_info,
-                sender_name=sender_name,
-                core_dialogue_prompt=core_dialogue_prompt,
-                background_dialogue_prompt=background_dialogue_prompt,
-                message_txt=message_txt,
-                mood_state=mood.mood_state,
-            )
-        else:
-            prompt = await global_prompt_manager.format_prompt(
-                "s4u_prompt_internal",
-                time_block=time_block,
-                expression_habits_block=expression_habits_block,
-                relation_info_block=relation_info_block,
-                memory_block=memory_block,
-                screen_info=screen_info,
-                gift_info=gift_info,
-                sc_info=sc_info,
-                chat_info_danmu=all_dialogue_prompt,
-                chat_info_qq=message.chat_info,
-                mind=message.processed_plain_text,
-                mood_state=mood.mood_state,
-            )
-
-        # print(prompt)
-
-        return prompt
-
-
-def weighted_sample_no_replacement(items, weights, k) -> list:
-    """
-    加权且不放回地随机抽取k个元素。
-
-    参数：
-        items: 待抽取的元素列表
-        weights: 每个元素对应的权重（与items等长，且为正数）
-        k: 需要抽取的元素个数
-    返回：
-        selected: 按权重加权且不重复抽取的k个元素组成的列表
-
-        如果 items 中的元素不足 k 个，就只会返回所有可用的元素
-
-    实现思路：
-        每次从当前池中按权重加权随机选出一个元素，选中后将其从池中移除，重复k次。
-        这样保证了：
-        1. count越大被选中概率越高
-        2. 不会重复选中同一个元素
-    """
-    selected = []
-    pool = list(zip(items, weights, strict=False))
-    for _ in range(min(k, len(pool))):
-        total = sum(w for _, w in pool)
-        r = random.uniform(0, total)
-        upto = 0
-        for idx, (item, weight) in enumerate(pool):
-            upto += weight
-            if upto >= r:
-                selected.append(item)
-                pool.pop(idx)
-                break
-    return selected
-
-
-init_prompt()
-prompt_builder = PromptBuilder()

src/mais4u/mais4u_chat/s4u_stream_generator.py

@@ -1,168 +0,0 @@
-import asyncio
-import re
-from collections.abc import AsyncGenerator
-
-from src.chat.message_receive.message import MessageRecvS4U
-from src.common.logger import get_logger
-from src.config.config import model_config
-from src.mais4u.mais4u_chat.s4u_prompt import prompt_builder
-from src.mais4u.openai_client import AsyncOpenAIClient
-
-logger = get_logger("s4u_stream_generator")
-
-
-class S4UStreamGenerator:
-    def __init__(self):
-        replyer_config = model_config.model_task_config.replyer
-        model_to_use = replyer_config.model_list[0]
-        model_info = model_config.get_model_info(model_to_use)
-        if not model_info:
-            logger.error(f"模型 {model_to_use} 在配置中未找到")
-            raise ValueError(f"模型 {model_to_use} 在配置中未找到")
-        provider_name = model_info.api_provider
-        provider_info = model_config.get_provider(provider_name)
-        if not provider_info:
-            logger.error("`replyer` 找不到对应的Provider")
-            raise ValueError("`replyer` 找不到对应的Provider")
-
-        api_key = provider_info.api_key
-        base_url = provider_info.base_url
-
-        if not api_key:
-            logger.error(f"{provider_name}没有配置API KEY")
-            raise ValueError(f"{provider_name}没有配置API KEY")
-
-        self.client_1 = AsyncOpenAIClient(api_key=api_key, base_url=base_url)
-        self.model_1_name = model_to_use
-        self.replyer_config = replyer_config
-
-        self.current_model_name = "unknown model"
-        self.partial_response = ""
-
-        # 正则表达式用于按句子切分，同时处理各种标点和边缘情况
-        # 匹配常见的句子结束符，但会忽略引号内和数字中的标点
-        self.sentence_split_pattern = re.compile(
-            r'([^\s\w"\'([{]*["\'([{].*?["\'}\])][^\s\w"\'([{]*|'  # 匹配被引号/括号包裹的内容
-            r'[^.。!?？！\n\r]+(?:[.。!?？！\n\r](?![\'"])|$))',  # 匹配直到句子结束符
-            re.UNICODE | re.DOTALL,
-        )
-
-        self.chat_stream = None
-
-    @staticmethod
-    async def build_last_internal_message(message: MessageRecvS4U, previous_reply_context: str = ""):
-        # person_id = PersonInfoManager.get_person_id(
-        #     message.chat_stream.user_info.platform, message.chat_stream.user_info.user_id
-        # )
-        # person_info_manager = get_person_info_manager()
-        # person_name = await person_info_manager.get_value(person_id, "person_name")
-
-        # if message.chat_stream.user_info.user_nickname:
-        #     if person_name:
-        #         sender_name = f"[{message.chat_stream.user_info.user_nickname}]（你叫ta{person_name}）"
-        #     else:
-        #         sender_name = f"[{message.chat_stream.user_info.user_nickname}]"
-        # else:
-        #     sender_name = f"用户({message.chat_stream.user_info.user_id})"
-
-        # 构建prompt
-        if previous_reply_context:
-            message_txt = f"""
-            你正在回复用户的消息，但中途被打断了。这是已有的对话上下文:
-            [你已经对上一条消息说的话]: {previous_reply_context}
-            ---
-            [这是用户发来的新消息, 你需要结合上下文，对此进行回复]:
-            {message.processed_plain_text}
-            """
-            return True, message_txt
-        else:
-            message_txt = message.processed_plain_text
-            return False, message_txt
-
-    async def generate_response(
-        self, message: MessageRecvS4U, previous_reply_context: str = ""
-    ) -> AsyncGenerator[str, None]:
-        """根据当前模型类型选择对应的生成函数"""
-        # 从global_config中获取模型概率值并选择模型
-        self.partial_response = ""
-        message_txt = message.processed_plain_text
-        if not message.is_internal:
-            interupted, message_txt_added = await self.build_last_internal_message(message, previous_reply_context)
-            if interupted:
-                message_txt = message_txt_added
-
-        message.chat_stream = self.chat_stream
-        prompt = await prompt_builder.build_prompt_normal(
-            message=message,
-            message_txt=message_txt,
-        )
-
-        logger.info(
-            f"{self.current_model_name}思考:{message_txt[:30] + '...' if len(message_txt) > 30 else message_txt}"
-        )
-
-        current_client = self.client_1
-        self.current_model_name = self.model_1_name
-
-        extra_kwargs = {}
-        if self.replyer_config.get("enable_thinking") is not None:
-            extra_kwargs["enable_thinking"] = self.replyer_config.get("enable_thinking")
-        if self.replyer_config.get("thinking_budget") is not None:
-            extra_kwargs["thinking_budget"] = self.replyer_config.get("thinking_budget")
-
-        async for chunk in self._generate_response_with_model(
-            prompt, current_client, self.current_model_name, **extra_kwargs
-        ):
-            yield chunk
-
-    async def _generate_response_with_model(
-        self,
-        prompt: str,
-        client: AsyncOpenAIClient,
-        model_name: str,
-        **kwargs,
-    ) -> AsyncGenerator[str, None]:
-        buffer = ""
-        delimiters = "，。！？,.!?\n\r"  # For final trimming
-        punctuation_buffer = ""
-
-        async for content in client.get_stream_content(
-            messages=[{"role": "user", "content": prompt}], model=model_name, **kwargs
-        ):
-            buffer += content
-
-            # 使用正则表达式匹配句子
-            last_match_end = 0
-            for match in self.sentence_split_pattern.finditer(buffer):
-                sentence = match.group(0).strip()
-                if sentence:
-                    # 如果句子看起来完整（即不只是等待更多内容），则发送
-                    if match.end(0) < len(buffer) or sentence.endswith(tuple(delimiters)):
-                        # 检查是否只是一个标点符号
-                        if sentence in [",", "，", ".", "。", "!", "！", "?", "？"]:
-                            punctuation_buffer += sentence
-                        else:
-                            # 发送之前累积的标点和当前句子
-                            to_yield = punctuation_buffer + sentence
-                            if to_yield.endswith((",", "，")):
-                                to_yield = to_yield.rstrip(",，")
-
-                            self.partial_response += to_yield
-                            yield to_yield
-                            punctuation_buffer = ""  # 清空标点符号缓冲区
-                            await asyncio.sleep(0)  # 允许其他任务运行
-
-                        last_match_end = match.end(0)
-
-            # 从缓冲区移除已发送的部分
-            if last_match_end > 0:
-                buffer = buffer[last_match_end:]
-
-        # 发送缓冲区中剩余的任何内容
-        to_yield = (punctuation_buffer + buffer).strip()
-        if to_yield:
-            if to_yield.endswith(("，", ",")):
-                to_yield = to_yield.rstrip("，,")
-            if to_yield:
-                self.partial_response += to_yield
-                yield to_yield

src/mais4u/mais4u_chat/s4u_watching_manager.py

@@ -1,106 +0,0 @@
-from src.common.logger import get_logger
-from src.plugin_system.apis import send_api
-
-"""
-视线管理系统使用说明：
-
-1. 视线状态：
-   - wandering: 随意看
-   - danmu: 看弹幕
-   - lens: 看镜头
-
-2. 状态切换逻辑：
-   - 收到消息时 → 切换为看弹幕，立即发送更新
-   - 开始生成回复时 → 切换为看镜头或随意，立即发送更新
-   - 生成完毕后 → 看弹幕1秒，然后回到看镜头直到有新消息，状态变化时立即发送更新
-
-3. 使用方法：
-   # 获取视线管理器
-   watching = watching_manager.get_watching_by_chat_id(chat_id)
-
-   # 收到消息时调用
-   await watching.on_message_received()
-
-   # 开始生成回复时调用
-   await watching.on_reply_start()
-
-   # 生成回复完毕时调用
-   await watching.on_reply_finished()
-
-4. 自动更新系统：
-   - 状态变化时立即发送type为"watching"，data为状态值的websocket消息
-   - 使用定时器自动处理状态转换（如看弹幕时间结束后自动切换到看镜头）
-   - 无需定期检查，所有状态变化都是事件驱动的
-"""
-
-logger = get_logger("watching")
-
-HEAD_CODE = {
-    "看向上方": "(0,0.5,0)",
-    "看向下方": "(0,-0.5,0)",
-    "看向左边": "(-1,0,0)",
-    "看向右边": "(1,0,0)",
-    "随意朝向": "random",
-    "看向摄像机": "camera",
-    "注视对方": "(0,0,0)",
-    "看向正前方": "(0,0,0)",
-}
-
-
-class ChatWatching:
-    def __init__(self, chat_id: str):
-        self.chat_id: str = chat_id
-
-    async def on_reply_start(self):
-        """开始生成回复时调用"""
-        await send_api.custom_to_stream(
-            message_type="state", content="start_thinking", stream_id=self.chat_id, storage_message=False
-        )
-
-    async def on_reply_finished(self):
-        """生成回复完毕时调用"""
-        await send_api.custom_to_stream(
-            message_type="state", content="finish_reply", stream_id=self.chat_id, storage_message=False
-        )
-
-    async def on_thinking_finished(self):
-        """思考完毕时调用"""
-        await send_api.custom_to_stream(
-            message_type="state", content="finish_thinking", stream_id=self.chat_id, storage_message=False
-        )
-
-    async def on_message_received(self):
-        """收到消息时调用"""
-        await send_api.custom_to_stream(
-            message_type="state", content="start_viewing", stream_id=self.chat_id, storage_message=False
-        )
-
-    async def on_internal_message_start(self):
-        """收到消息时调用"""
-        await send_api.custom_to_stream(
-            message_type="state", content="start_internal_thinking", stream_id=self.chat_id, storage_message=False
-        )
-
-
-class WatchingManager:
-    def __init__(self):
-        self.watching_list: list[ChatWatching] = []
-        """当前视线状态列表"""
-        self.task_started: bool = False
-
-    def get_watching_by_chat_id(self, chat_id: str) -> ChatWatching:
-        """获取或创建聊天对应的视线管理器"""
-        for watching in self.watching_list:
-            if watching.chat_id == chat_id:
-                return watching
-
-        new_watching = ChatWatching(chat_id)
-        self.watching_list.append(new_watching)
-        logger.info(f"为chat {chat_id}创建新的视线管理器")
-
-        return new_watching
-
-
-# 全局视线管理器实例
-watching_manager = WatchingManager()
-"""全局视线管理器"""

src/mais4u/mais4u_chat/screen_manager.py

@@ -1,15 +0,0 @@
-class ScreenManager:
-    def __init__(self):
-        self.now_screen = ""
-
-    def set_screen(self, screen_str: str):
-        self.now_screen = screen_str
-
-    def get_screen(self):
-        return self.now_screen
-
-    def get_screen_str(self):
-        return f"你可以看见面前的屏幕，目前屏幕的内容是:现在千石可乐在和你一起直播，这是他正在操作的屏幕内容：{self.now_screen}"
-
-
-screen_manager = ScreenManager()

src/mais4u/mais4u_chat/super_chat_manager.py

@@ -1,304 +0,0 @@
-import asyncio
-import time
-from dataclasses import dataclass
-
-from src.chat.message_receive.message import MessageRecvS4U
-from src.common.logger import get_logger
-
-# 全局SuperChat管理器实例
-from src.mais4u.constant_s4u import ENABLE_S4U
-
-logger = get_logger("super_chat_manager")
-
-
-@dataclass
-class SuperChatRecord:
-    """SuperChat记录数据类"""
-
-    user_id: str
-    user_nickname: str
-    platform: str
-    chat_id: str
-    price: float
-    message_text: str
-    timestamp: float
-    expire_time: float
-    group_name: str | None = None
-
-    def is_expired(self) -> bool:
-        """检查SuperChat是否已过期"""
-        return time.time() > self.expire_time
-
-    def remaining_time(self) -> float:
-        """获取剩余时间（秒）"""
-        return max(0, self.expire_time - time.time())
-
-    def to_dict(self) -> dict:
-        """转换为字典格式"""
-        return {
-            "user_id": self.user_id,
-            "user_nickname": self.user_nickname,
-            "platform": self.platform,
-            "chat_id": self.chat_id,
-            "price": self.price,
-            "message_text": self.message_text,
-            "timestamp": self.timestamp,
-            "expire_time": self.expire_time,
-            "group_name": self.group_name,
-            "remaining_time": self.remaining_time(),
-        }
-
-
-class SuperChatManager:
-    """SuperChat管理器，负责管理和跟踪SuperChat消息"""
-
-    def __init__(self):
-        self.super_chats: dict[str, list[SuperChatRecord]] = {}  # chat_id -> SuperChat列表
-        self._cleanup_task: asyncio.Task | None = None
-        self._is_initialized = False
-        logger.info("SuperChat管理器已初始化")
-
-    def _ensure_cleanup_task_started(self):
-        """确保清理任务已启动（延迟启动）"""
-        if self._cleanup_task is None or self._cleanup_task.done():
-            try:
-                loop = asyncio.get_running_loop()
-                self._cleanup_task = loop.create_task(self._cleanup_expired_superchats())
-                self._is_initialized = True
-                logger.info("SuperChat清理任务已启动")
-            except RuntimeError:
-                # 没有运行的事件循环，稍后再启动
-                logger.debug("当前没有运行的事件循环，将在需要时启动清理任务")
-
-    def _start_cleanup_task(self):
-        """启动清理任务（已弃用，保留向后兼容）"""
-        self._ensure_cleanup_task_started()
-
-    async def _cleanup_expired_superchats(self):
-        """定期清理过期的SuperChat"""
-        while True:
-            try:
-                total_removed = 0
-
-                for chat_id in list(self.super_chats.keys()):
-                    original_count = len(self.super_chats[chat_id])
-                    # 移除过期的SuperChat
-                    self.super_chats[chat_id] = [sc for sc in self.super_chats[chat_id] if not sc.is_expired()]
-
-                    removed_count = original_count - len(self.super_chats[chat_id])
-                    total_removed += removed_count
-
-                    if removed_count > 0:
-                        logger.info(f"从聊天 {chat_id} 中清理了 {removed_count} 个过期的SuperChat")
-
-                    # 如果列表为空，删除该聊天的记录
-                    if not self.super_chats[chat_id]:
-                        del self.super_chats[chat_id]
-
-                if total_removed > 0:
-                    logger.info(f"总共清理了 {total_removed} 个过期的SuperChat")
-
-                # 每30秒检查一次
-                await asyncio.sleep(30)
-
-            except Exception as e:
-                logger.error(f"清理过期SuperChat时出错: {e}", exc_info=True)
-                await asyncio.sleep(60)  # 出错时等待更长时间
-
-    @staticmethod
-    def _calculate_expire_time(price: float) -> float:
-        """根据SuperChat金额计算过期时间"""
-        current_time = time.time()
-
-        # 根据金额阶梯设置不同的存活时间
-        if price >= 500:
-            # 500元以上：保持4小时
-            duration = 4 * 3600
-        elif price >= 200:
-            # 200-499元：保持2小时
-            duration = 2 * 3600
-        elif price >= 100:
-            # 100-199元：保持1小时
-            duration = 1 * 3600
-        elif price >= 50:
-            # 50-99元：保持30分钟
-            duration = 30 * 60
-        elif price >= 20:
-            # 20-49元：保持15分钟
-            duration = 15 * 60
-        elif price >= 10:
-            # 10-19元：保持10分钟
-            duration = 10 * 60
-        else:
-            # 10元以下：保持5分钟
-            duration = 5 * 60
-
-        return current_time + duration
-
-    async def add_superchat(self, message: MessageRecvS4U) -> None:
-        """添加新的SuperChat记录"""
-        # 确保清理任务已启动
-        self._ensure_cleanup_task_started()
-
-        if not message.is_superchat or not message.superchat_price:
-            logger.warning("尝试添加非SuperChat消息到SuperChat管理器")
-            return
-
-        try:
-            price = float(message.superchat_price)
-        except (ValueError, TypeError):
-            logger.error(f"无效的SuperChat价格: {message.superchat_price}")
-            return
-
-        user_info = message.message_info.user_info
-        group_info = message.message_info.group_info
-        chat_id = getattr(message, "chat_stream", None)
-        if chat_id:
-            chat_id = chat_id.stream_id
-        else:
-            # 生成chat_id的备用方法
-            chat_id = f"{message.message_info.platform}_{user_info.user_id}"
-            if group_info:
-                chat_id = f"{message.message_info.platform}_{group_info.group_id}"
-
-        expire_time = self._calculate_expire_time(price)
-
-        record = SuperChatRecord(
-            user_id=user_info.user_id,
-            user_nickname=user_info.user_nickname,
-            platform=message.message_info.platform,
-            chat_id=chat_id,
-            price=price,
-            message_text=message.superchat_message_text or "",
-            timestamp=message.message_info.time,
-            expire_time=expire_time,
-            group_name=group_info.group_name if group_info else None,
-        )
-
-        # 添加到对应聊天的SuperChat列表
-        if chat_id not in self.super_chats:
-            self.super_chats[chat_id] = []
-
-        self.super_chats[chat_id].append(record)
-
-        # 按价格降序排序（价格高的在前）
-        self.super_chats[chat_id].sort(key=lambda x: x.price, reverse=True)
-
-        logger.info(f"添加SuperChat记录: {user_info.user_nickname} - {price}元 - {message.superchat_message_text}")
-
-    def get_superchats_by_chat(self, chat_id: str) -> list[SuperChatRecord]:
-        """获取指定聊天的所有有效SuperChat"""
-        # 确保清理任务已启动
-        self._ensure_cleanup_task_started()
-
-        if chat_id not in self.super_chats:
-            return []
-
-        # 过滤掉过期的SuperChat
-        valid_superchats = [sc for sc in self.super_chats[chat_id] if not sc.is_expired()]
-        return valid_superchats
-
-    def get_all_valid_superchats(self) -> dict[str, list[SuperChatRecord]]:
-        """获取所有有效的SuperChat"""
-        # 确保清理任务已启动
-        self._ensure_cleanup_task_started()
-
-        result = {}
-        for chat_id, superchats in self.super_chats.items():
-            valid_superchats = [sc for sc in superchats if not sc.is_expired()]
-            if valid_superchats:
-                result[chat_id] = valid_superchats
-        return result
-
-    def build_superchat_display_string(self, chat_id: str, max_count: int = 10) -> str:
-        """构建SuperChat显示字符串"""
-        superchats = self.get_superchats_by_chat(chat_id)
-
-        if not superchats:
-            return ""
-
-        # 限制显示数量
-        display_superchats = superchats[:max_count]
-
-        lines = ["📢 当前有效超级弹幕："]
-        for i, sc in enumerate(display_superchats, 1):
-            remaining_minutes = int(sc.remaining_time() / 60)
-            remaining_seconds = int(sc.remaining_time() % 60)
-
-            time_display = (
-                f"{remaining_minutes}分{remaining_seconds}秒" if remaining_minutes > 0 else f"{remaining_seconds}秒"
-            )
-
-            line = f"{i}. 【{sc.price}元】{sc.user_nickname}: {sc.message_text}"
-            if len(line) > 100:  # 限制单行长度
-                line = f"{line[:97]}..."
-            line += f" (剩余{time_display})"
-            lines.append(line)
-
-        if len(superchats) > max_count:
-            lines.append(f"... 还有{len(superchats) - max_count}条SuperChat")
-
-        return "\n".join(lines)
-
-    def build_superchat_summary_string(self, chat_id: str) -> str:
-        """构建SuperChat摘要字符串"""
-        superchats = self.get_superchats_by_chat(chat_id)
-
-        if not superchats:
-            return "当前没有有效的超级弹幕"
-        lines = []
-        for sc in superchats:
-            single_sc_str = f"{sc.user_nickname} - {sc.price}元 - {sc.message_text}"
-            if len(single_sc_str) > 100:
-                single_sc_str = f"{single_sc_str[:97]}..."
-            single_sc_str += f" (剩余{int(sc.remaining_time())}秒)"
-            lines.append(single_sc_str)
-
-        total_amount = sum(sc.price for sc in superchats)
-        count = len(superchats)
-        highest_amount = max(sc.price for sc in superchats)
-
-        final_str = f"当前有{count}条超级弹幕，总金额{total_amount}元，最高单笔{highest_amount}元"
-        if lines:
-            final_str += "\n" + "\n".join(lines)
-        return final_str
-
-    def get_superchat_statistics(self, chat_id: str) -> dict:
-        """获取SuperChat统计信息"""
-        superchats = self.get_superchats_by_chat(chat_id)
-
-        if not superchats:
-            return {"count": 0, "total_amount": 0, "average_amount": 0, "highest_amount": 0, "lowest_amount": 0}
-
-        amounts = [sc.price for sc in superchats]
-
-        return {
-            "count": len(superchats),
-            "total_amount": sum(amounts),
-            "average_amount": sum(amounts) / len(amounts),
-            "highest_amount": max(amounts),
-            "lowest_amount": min(amounts),
-        }
-
-    async def shutdown(self):  # sourcery skip: use-contextlib-suppress
-        """关闭管理器，清理资源"""
-        if self._cleanup_task and not self._cleanup_task.done():
-            self._cleanup_task.cancel()
-            try:
-                await self._cleanup_task
-            except asyncio.CancelledError:
-                pass
-        logger.info("SuperChat管理器已关闭")
-
-
-# sourcery skip: assign-if-exp
-if ENABLE_S4U:
-    super_chat_manager = SuperChatManager()
-else:
-    super_chat_manager = None
-
-
-def get_super_chat_manager() -> SuperChatManager:
-    """获取全局SuperChat管理器实例"""
-
-    return super_chat_manager

src/mais4u/mais4u_chat/yes_or_no.py

@@ -1,46 +0,0 @@
-from src.common.logger import get_logger
-from src.config.config import model_config
-from src.llm_models.utils_model import LLMRequest
-from src.plugin_system.apis import send_api
-
-logger = get_logger(__name__)
-
-head_actions_list = ["不做额外动作", "点头一次", "点头两次", "摇头", "歪脑袋", "低头望向一边"]
-
-
-async def yes_or_no_head(text: str, emotion: str = "", chat_history: str = "", chat_id: str = ""):
-    prompt = f"""
-{chat_history}
-以上是对方的发言：
-
-对这个发言，你的心情是：{emotion}
-对上面的发言，你的回复是：{text}
-请判断时是否要伴随回复做头部动作，你可以选择：
-
-不做额外动作
-点头一次
-点头两次
-摇头
-歪脑袋
-低头望向一边
-
-请从上面的动作中选择一个，并输出，请只输出你选择的动作就好，不要输出其他内容。"""
-    model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="motion")
-
-    try:
-        # logger.info(f"prompt: {prompt}")
-        response, _ = await model.generate_response_async(prompt=prompt, temperature=0.7)
-        logger.info(f"response: {response}")
-
-        head_action = response if response in head_actions_list else "不做额外动作"
-        await send_api.custom_to_stream(
-            message_type="head_action",
-            content=head_action,
-            stream_id=chat_id,
-            storage_message=False,
-            show_log=True,
-        )
-
-    except Exception as e:
-        logger.error(f"yes_or_no_head error: {e}")
-        return "不做额外动作"

src/mais4u/openai_client.py

@@ -1,287 +0,0 @@
-from collections.abc import AsyncGenerator
-from dataclasses import dataclass
-
-from openai import AsyncOpenAI
-from openai.types.chat import ChatCompletion, ChatCompletionChunk
-
-
-@dataclass
-class ChatMessage:
-    """聊天消息数据类"""
-
-    role: str
-    content: str
-
-    def to_dict(self) -> dict[str, str]:
-        return {"role": self.role, "content": self.content}
-
-
-class AsyncOpenAIClient:
-    """异步OpenAI客户端，支持流式传输"""
-
-    def __init__(self, api_key: str, base_url: str | None = None):
-        """
-        初始化客户端
-
-        Args:
-            api_key: OpenAI API密钥
-            base_url: 可选的API基础URL，用于自定义端点
-        """
-        self.client = AsyncOpenAI(
-            api_key=api_key,
-            base_url=base_url,
-            timeout=10.0,  # 设置60秒的全局超时
-        )
-
-    async def chat_completion(
-        self,
-        messages: list[ChatMessage | dict[str, str]],
-        model: str = "gpt-3.5-turbo",
-        temperature: float = 0.7,
-        max_tokens: int | None = None,
-        **kwargs,
-    ) -> ChatCompletion:
-        """
-        非流式聊天完成
-
-        Args:
-            messages: 消息列表
-            model: 模型名称
-            temperature: 温度参数
-            max_tokens: 最大token数
-            **kwargs: 其他参数
-
-        Returns:
-            完整的聊天回复
-        """
-        # 转换消息格式
-        formatted_messages = []
-        for msg in messages:
-            if isinstance(msg, ChatMessage):
-                formatted_messages.append(msg.to_dict())
-            else:
-                formatted_messages.append(msg)
-
-        extra_body = {}
-        if kwargs.get("enable_thinking") is not None:
-            extra_body["enable_thinking"] = kwargs.pop("enable_thinking")
-        if kwargs.get("thinking_budget") is not None:
-            extra_body["thinking_budget"] = kwargs.pop("thinking_budget")
-
-        response = await self.client.chat.completions.create(
-            model=model,
-            messages=formatted_messages,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            stream=False,
-            extra_body=extra_body if extra_body else None,
-            **kwargs,
-        )
-
-        return response
-
-    async def chat_completion_stream(
-        self,
-        messages: list[ChatMessage | dict[str, str]],
-        model: str = "gpt-3.5-turbo",
-        temperature: float = 0.7,
-        max_tokens: int | None = None,
-        **kwargs,
-    ) -> AsyncGenerator[ChatCompletionChunk, None]:
-        """
-        流式聊天完成
-
-        Args:
-            messages: 消息列表
-            model: 模型名称
-            temperature: 温度参数
-            max_tokens: 最大token数
-            **kwargs: 其他参数
-
-        Yields:
-            ChatCompletionChunk: 流式响应块
-        """
-        # 转换消息格式
-        formatted_messages = []
-        for msg in messages:
-            if isinstance(msg, ChatMessage):
-                formatted_messages.append(msg.to_dict())
-            else:
-                formatted_messages.append(msg)
-
-        extra_body = {}
-        if kwargs.get("enable_thinking") is not None:
-            extra_body["enable_thinking"] = kwargs.pop("enable_thinking")
-        if kwargs.get("thinking_budget") is not None:
-            extra_body["thinking_budget"] = kwargs.pop("thinking_budget")
-
-        stream = await self.client.chat.completions.create(
-            model=model,
-            messages=formatted_messages,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            stream=True,
-            extra_body=extra_body if extra_body else None,
-            **kwargs,
-        )
-
-        async for chunk in stream:
-            yield chunk
-
-    async def get_stream_content(
-        self,
-        messages: list[ChatMessage | dict[str, str]],
-        model: str = "gpt-3.5-turbo",
-        temperature: float = 0.7,
-        max_tokens: int | None = None,
-        **kwargs,
-    ) -> AsyncGenerator[str, None]:
-        """
-        获取流式内容（只返回文本内容）
-
-        Args:
-            messages: 消息列表
-            model: 模型名称
-            temperature: 温度参数
-            max_tokens: 最大token数
-            **kwargs: 其他参数
-
-        Yields:
-            str: 文本内容片段
-        """
-        async for chunk in self.chat_completion_stream(
-            messages=messages, model=model, temperature=temperature, max_tokens=max_tokens, **kwargs
-        ):
-            if chunk.choices and chunk.choices[0].delta.content:
-                yield chunk.choices[0].delta.content
-
-    async def collect_stream_response(
-        self,
-        messages: list[ChatMessage | dict[str, str]],
-        model: str = "gpt-3.5-turbo",
-        temperature: float = 0.7,
-        max_tokens: int | None = None,
-        **kwargs,
-    ) -> str:
-        """
-        收集完整的流式响应
-
-        Args:
-            messages: 消息列表
-            model: 模型名称
-            temperature: 温度参数
-            max_tokens: 最大token数
-            **kwargs: 其他参数
-
-        Returns:
-            str: 完整的响应文本
-        """
-        full_response = ""
-        async for content in self.get_stream_content(
-            messages=messages, model=model, temperature=temperature, max_tokens=max_tokens, **kwargs
-        ):
-            full_response += content
-
-        return full_response
-
-    async def close(self):
-        """关闭客户端"""
-        await self.client.close()
-
-    async def __aenter__(self):
-        """异步上下文管理器入口"""
-        return self
-
-    async def __aexit__(self, exc_type, exc_val, exc_tb):
-        """异步上下文管理器退出"""
-        await self.close()
-
-
-class ConversationManager:
-    """对话管理器，用于管理对话历史"""
-
-    def __init__(self, client: AsyncOpenAIClient, system_prompt: str | None = None):
-        """
-        初始化对话管理器
-
-        Args:
-            client: OpenAI客户端实例
-            system_prompt: 系统提示词
-        """
-        self.client = client
-        self.messages: list[ChatMessage] = []
-
-        if system_prompt:
-            self.messages.append(ChatMessage(role="system", content=system_prompt))
-
-    def add_user_message(self, content: str):
-        """添加用户消息"""
-        self.messages.append(ChatMessage(role="user", content=content))
-
-    def add_assistant_message(self, content: str):
-        """添加助手消息"""
-        self.messages.append(ChatMessage(role="assistant", content=content))
-
-    async def send_message_stream(
-        self, content: str, model: str = "gpt-3.5-turbo", **kwargs
-    ) -> AsyncGenerator[str, None]:
-        """
-        发送消息并获取流式响应
-
-        Args:
-            content: 用户消息内容
-            model: 模型名称
-            **kwargs: 其他参数
-
-        Yields:
-            str: 响应内容片段
-        """
-        self.add_user_message(content)
-
-        response_content = ""
-        async for chunk in self.client.get_stream_content(messages=self.messages, model=model, **kwargs):
-            response_content += chunk
-            yield chunk
-
-        self.add_assistant_message(response_content)
-
-    async def send_message(self, content: str, model: str = "gpt-3.5-turbo", **kwargs) -> str:
-        """
-        发送消息并获取完整响应
-
-        Args:
-            content: 用户消息内容
-            model: 模型名称
-            **kwargs: 其他参数
-
-        Returns:
-            str: 完整响应
-        """
-        self.add_user_message(content)
-
-        response = await self.client.chat_completion(messages=self.messages, model=model, **kwargs)
-
-        response_content = response.choices[0].message.content
-        self.add_assistant_message(response_content)
-
-        return response_content
-
-    def clear_history(self, keep_system: bool = True):
-        """
-        清除对话历史
-
-        Args:
-            keep_system: 是否保留系统消息
-        """
-        if keep_system and self.messages and self.messages[0].role == "system":
-            self.messages = [self.messages[0]]
-        else:
-            self.messages = []
-
-    def get_message_count(self) -> int:
-        """获取消息数量"""
-        return len(self.messages)
-
-    def get_conversation_history(self) -> list[dict[str, str]]:
-        """获取对话历史"""
-        return [msg.to_dict() for msg in self.messages]

src/mais4u/s4u_config.py

@@ -1,373 +0,0 @@
-import os
-import shutil
-from dataclasses import MISSING, dataclass, field, fields
-from datetime import datetime
-from typing import Any, Literal, TypeVar, get_args, get_origin
-
-import tomlkit
-from tomlkit import TOMLDocument
-from tomlkit.items import Table
-from typing_extensions import Self
-
-from src.common.logger import get_logger
-from src.mais4u.constant_s4u import ENABLE_S4U
-
-logger = get_logger("s4u_config")
-
-
-# 新增：兼容dict和tomlkit Table
-def is_dict_like(obj):
-    return isinstance(obj, dict | Table)
-
-
-# 新增：递归将Table转为dict
-def table_to_dict(obj):
-    if isinstance(obj, Table):
-        return {k: table_to_dict(v) for k, v in obj.items()}
-    elif isinstance(obj, dict):
-        return {k: table_to_dict(v) for k, v in obj.items()}
-    elif isinstance(obj, list):
-        return [table_to_dict(i) for i in obj]
-    else:
-        return obj
-
-
-# 获取mais4u模块目录
-MAIS4U_ROOT = os.path.dirname(__file__)
-CONFIG_DIR = os.path.join(MAIS4U_ROOT, "config")
-TEMPLATE_PATH = os.path.join(CONFIG_DIR, "s4u_config_template.toml")
-CONFIG_PATH = os.path.join(CONFIG_DIR, "s4u_config.toml")
-
-# S4U配置版本
-S4U_VERSION = "1.1.0"
-
-T = TypeVar("T", bound="S4UConfigBase")
-
-
-@dataclass
-class S4UConfigBase:
-    """S4U配置类的基类"""
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> Self:
-        """从字典加载配置字段"""
-        data = table_to_dict(data)  # 递归转dict，兼容tomlkit Table
-        if not is_dict_like(data):
-            raise TypeError(f"Expected a dictionary, got {type(data).__name__}")
-
-        init_args: dict[str, Any] = {}
-
-        for f in fields(cls):
-            field_name = f.name
-
-            if field_name.startswith("_"):
-                # 跳过以 _ 开头的字段
-                continue
-
-            if field_name not in data:
-                if f.default is not MISSING or f.default_factory is not MISSING:
-                    # 跳过未提供且有默认值/默认构造方法的字段
-                    continue
-                else:
-                    raise ValueError(f"Missing required field: '{field_name}'")
-
-            value = data[field_name]
-            field_type = f.type
-
-            try:
-                init_args[field_name] = cls._convert_field(value, field_type)  # type: ignore
-            except TypeError as e:
-                raise TypeError(f"Field '{field_name}' has a type error: {e}") from e
-            except Exception as e:
-                raise RuntimeError(f"Failed to convert field '{field_name}' to target type: {e}") from e
-
-        return cls()
-
-    @classmethod
-    def _convert_field(cls, value: Any, field_type: type[Any]) -> Any:
-        """转换字段值为指定类型"""
-        # 如果是嵌套的 dataclass，递归调用 from_dict 方法
-        if isinstance(field_type, type) and issubclass(field_type, S4UConfigBase):
-            if not is_dict_like(value):
-                raise TypeError(f"Expected a dictionary for {field_type.__name__}, got {type(value).__name__}")
-            return field_type.from_dict(value)
-
-        # 处理泛型集合类型（list, set, tuple）
-        field_origin_type = get_origin(field_type)
-        field_type_args = get_args(field_type)
-
-        if field_origin_type in {list, set, tuple}:
-            if not isinstance(value, list):
-                raise TypeError(f"Expected an list for {field_type.__name__}, got {type(value).__name__}")
-
-            if field_origin_type is list:
-                if (
-                    field_type_args
-                    and isinstance(field_type_args[0], type)
-                    and issubclass(field_type_args[0], S4UConfigBase)
-                ):
-                    return [field_type_args[0].from_dict(item) for item in value]
-                return [cls._convert_field(item, field_type_args[0]) for item in value]
-            elif field_origin_type is set:
-                return {cls._convert_field(item, field_type_args[0]) for item in value}
-            elif field_origin_type is tuple:
-                if len(value) != len(field_type_args):
-                    raise TypeError(
-                        f"Expected {len(field_type_args)} items for {field_type.__name__}, got {len(value)}"
-                    )
-                return tuple(cls._convert_field(item, arg) for item, arg in zip(value, field_type_args, strict=False))
-
-        if field_origin_type is dict:
-            if not is_dict_like(value):
-                raise TypeError(f"Expected a dictionary for {field_type.__name__}, got {type(value).__name__}")
-
-            if len(field_type_args) != 2:
-                raise TypeError(f"Expected a dictionary with two type arguments for {field_type.__name__}")
-            key_type, value_type = field_type_args
-
-            return {cls._convert_field(k, key_type): cls._convert_field(v, value_type) for k, v in value.items()}
-
-        # 处理基础类型，例如 int, str 等
-        if field_origin_type is type(None) and value is None:  # 处理Optional类型
-            return None
-
-        # 处理Literal类型
-        if field_origin_type is Literal or get_origin(field_type) is Literal:
-            allowed_values = get_args(field_type)
-            if value in allowed_values:
-                return value
-            else:
-                raise TypeError(f"Value '{value}' is not in allowed values {allowed_values} for Literal type")
-
-        if field_type is Any or isinstance(value, field_type):
-            return value
-
-        # 其他类型，尝试直接转换
-        try:
-            return field_type(value)
-        except (ValueError, TypeError) as e:
-            raise TypeError(f"Cannot convert {type(value).__name__} to {field_type.__name__}") from e
-
-
-@dataclass
-class S4UModelConfig(S4UConfigBase):
-    """S4U模型配置类"""
-
-    # 主要对话模型配置
-    chat: dict[str, Any] = field(default_factory=lambda: {})
-    """主要对话模型配置"""
-
-    # 规划模型配置（原model_motion）
-    motion: dict[str, Any] = field(default_factory=lambda: {})
-    """规划模型配置"""
-
-    # 情感分析模型配置
-    emotion: dict[str, Any] = field(default_factory=lambda: {})
-    """情感分析模型配置"""
-
-    # 记忆模型配置
-    memory: dict[str, Any] = field(default_factory=lambda: {})
-    """记忆模型配置"""
-
-    # 工具使用模型配置
-    tool_use: dict[str, Any] = field(default_factory=lambda: {})
-    """工具使用模型配置"""
-
-    # 嵌入模型配置
-    embedding: dict[str, Any] = field(default_factory=lambda: {})
-    """嵌入模型配置"""
-
-    # 视觉语言模型配置
-    vlm: dict[str, Any] = field(default_factory=lambda: {})
-    """视觉语言模型配置"""
-
-    # 知识库模型配置
-    knowledge: dict[str, Any] = field(default_factory=lambda: {})
-    """知识库模型配置"""
-
-    # 实体提取模型配置
-    entity_extract: dict[str, Any] = field(default_factory=lambda: {})
-    """实体提取模型配置"""
-
-    # 问答模型配置
-    qa: dict[str, Any] = field(default_factory=lambda: {})
-    """问答模型配置"""
-
-
-@dataclass
-class S4UConfig(S4UConfigBase):
-    """S4U聊天系统配置类"""
-
-    message_timeout_seconds: int = 120
-    """普通消息存活时间（秒），超过此时间的消息将被丢弃"""
-
-    at_bot_priority_bonus: float = 100.0
-    """@机器人时的优先级加成分数"""
-
-    recent_message_keep_count: int = 6
-    """保留最近N条消息，超出范围的普通消息将被移除"""
-
-    typing_delay: float = 0.1
-    """打字延迟时间（秒），模拟真实打字速度"""
-
-    chars_per_second: float = 15.0
-    """每秒字符数，用于计算动态打字延迟"""
-
-    min_typing_delay: float = 0.2
-    """最小打字延迟（秒）"""
-
-    max_typing_delay: float = 2.0
-    """最大打字延迟（秒）"""
-
-    enable_dynamic_typing_delay: bool = False
-    """是否启用基于文本长度的动态打字延迟"""
-
-    vip_queue_priority: bool = True
-    """是否启用VIP队列优先级系统"""
-
-    enable_message_interruption: bool = True
-    """是否允许高优先级消息中断当前回复"""
-
-    enable_old_message_cleanup: bool = True
-    """是否自动清理过旧的普通消息"""
-
-    enable_streaming_output: bool = True
-    """是否启用流式输出，false时全部生成后一次性发送"""
-
-    max_context_message_length: int = 20
-    """上下文消息最大长度"""
-
-    max_core_message_length: int = 30
-    """核心消息最大长度"""
-
-    # 模型配置
-    models: S4UModelConfig = field(default_factory=S4UModelConfig)
-    """S4U模型配置"""
-
-    # 兼容性字段，保持向后兼容
-
-
-@dataclass
-class S4UGlobalConfig(S4UConfigBase):
-    """S4U总配置类"""
-
-    s4u: S4UConfig
-    S4U_VERSION: str = S4U_VERSION
-
-
-def update_s4u_config():
-    """更新S4U配置文件"""
-    # 创建配置目录（如果不存在）
-    os.makedirs(CONFIG_DIR, exist_ok=True)
-
-    # 检查模板文件是否存在
-    if not os.path.exists(TEMPLATE_PATH):
-        logger.error(f"S4U配置模板文件不存在: {TEMPLATE_PATH}")
-        logger.error("请确保模板文件存在后重新运行")
-        raise FileNotFoundError(f"S4U配置模板文件不存在: {TEMPLATE_PATH}")
-
-    # 检查配置文件是否存在
-    if not os.path.exists(CONFIG_PATH):
-        logger.info("S4U配置文件不存在，从模板创建新配置")
-        shutil.copy2(TEMPLATE_PATH, CONFIG_PATH)
-        logger.info(f"已创建S4U配置文件: {CONFIG_PATH}")
-        return
-
-    # 读取旧配置文件和模板文件
-    with open(CONFIG_PATH, encoding="utf-8") as f:
-        old_config = tomlkit.load(f)
-    with open(TEMPLATE_PATH, encoding="utf-8") as f:
-        new_config = tomlkit.load(f)
-
-    # 检查version是否相同
-    if old_config and "inner" in old_config and "inner" in new_config:
-        old_version = old_config["inner"].get("version")  # type: ignore
-        new_version = new_config["inner"].get("version")  # type: ignore
-        if old_version and new_version and old_version == new_version:
-            logger.info(f"检测到S4U配置文件版本号相同 (v{old_version})，跳过更新")
-            return
-        else:
-            logger.info(f"检测到S4U配置版本号不同: 旧版本 v{old_version} -> 新版本 v{new_version}")
-    else:
-        logger.info("S4U配置文件未检测到版本号，可能是旧版本。将进行更新")
-
-    # 创建备份目录
-    old_config_dir = os.path.join(CONFIG_DIR, "old")
-    os.makedirs(old_config_dir, exist_ok=True)
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    old_backup_path = os.path.join(old_config_dir, f"s4u_config_{timestamp}.toml")
-
-    # 移动旧配置文件到old目录
-    shutil.move(CONFIG_PATH, old_backup_path)
-    logger.info(f"已备份旧S4U配置文件到: {old_backup_path}")
-
-    # 复制模板文件到配置目录
-    shutil.copy2(TEMPLATE_PATH, CONFIG_PATH)
-    logger.info(f"已创建新S4U配置文件: {CONFIG_PATH}")
-
-    def update_dict(target: TOMLDocument | dict | Table, source: TOMLDocument | dict):
-        """
-        将source字典的值更新到target字典中（如果target中存在相同的键）
-        """
-        for key, value in source.items():
-            # 跳过version字段的更新
-            if key == "version":
-                continue
-            if key in target:
-                target_value = target[key]
-                if isinstance(value, dict) and isinstance(target_value, dict | Table):
-                    update_dict(target_value, value)
-                else:
-                    try:
-                        # 对数组类型进行特殊处理
-                        if isinstance(value, list):
-                            target[key] = tomlkit.array(str(value)) if value else tomlkit.array()
-                        else:
-                            # 其他类型使用item方法创建新值
-                            target[key] = tomlkit.item(value)
-                    except (TypeError, ValueError):
-                        # 如果转换失败，直接赋值
-                        target[key] = value
-
-    # 将旧配置的值更新到新配置中
-    logger.info("开始合并S4U新旧配置...")
-    update_dict(new_config, old_config)
-
-    # 保存更新后的配置（保留注释和格式）
-    with open(CONFIG_PATH, "w", encoding="utf-8") as f:
-        f.write(tomlkit.dumps(new_config))
-
-    logger.info("S4U配置文件更新完成")
-
-
-def load_s4u_config(config_path: str) -> S4UGlobalConfig:
-    """
-    加载S4U配置文件
-    :param config_path: 配置文件路径
-    :return: S4UGlobalConfig对象
-    """
-    # 读取配置文件
-    with open(config_path, encoding="utf-8") as f:
-        config_data = tomlkit.load(f)
-
-    # 创建S4UGlobalConfig对象
-    try:
-        return S4UGlobalConfig.from_dict(config_data)
-    except Exception as e:
-        logger.critical("S4U配置文件解析失败")
-        raise e
-
-
-if not ENABLE_S4U:
-    s4u_config = None
-    s4u_config_main = None
-else:
-    # 初始化S4U配置
-    logger.info(f"S4U当前版本: {S4U_VERSION}")
-    update_s4u_config()
-
-    logger.info("正在加载S4U配置文件...")
-    s4u_config_main = load_s4u_config(config_path=CONFIG_PATH)
-    logger.info("S4U配置文件加载完成！")
-
-    s4u_config: S4UConfig = s4u_config_main.s4u