feat：heartFC模式堂堂登场

src/plugins/chat_module/heartFC_chat/heartFC_chat.py

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+import time
+from random import random
+import traceback
+from typing import List
+from ...memory_system.Hippocampus import HippocampusManager
+from ...moods.moods import MoodManager
+from ....config.config import global_config
+from ...chat.emoji_manager import emoji_manager
+from .heartFC__generator import ResponseGenerator
+from ...chat.message import MessageSending, MessageRecv, MessageThinking, MessageSet
+from .messagesender import MessageManager
+from ...storage.storage import MessageStorage
+from ...chat.utils import is_mentioned_bot_in_message, get_recent_group_detailed_plain_text
+from ...chat.utils_image import image_path_to_base64
+from ...willing.willing_manager import willing_manager
+from ...message import UserInfo, Seg
+from src.heart_flow.heartflow import heartflow
+from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
+from ...chat.chat_stream import chat_manager
+from ...person_info.relationship_manager import relationship_manager
+from ...chat.message_buffer import message_buffer
+from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
+from ...utils.timer_calculater import Timer
+from src.do_tool.tool_use import ToolUser
+
+# 定义日志配置
+chat_config = LogConfig(
+    console_format=CHAT_STYLE_CONFIG["console_format"],
+    file_format=CHAT_STYLE_CONFIG["file_format"],
+)
+
+logger = get_module_logger("think_flow_chat", config=chat_config)
+
+
+class ThinkFlowChat:
+    def __init__(self):
+        self.storage = MessageStorage()
+        self.gpt = ResponseGenerator()
+        self.mood_manager = MoodManager.get_instance()
+        self.mood_manager.start_mood_update()
+        self.tool_user = ToolUser()
+
+    async def _create_thinking_message(self, message, chat, userinfo, messageinfo):
+        """创建思考消息"""
+        bot_user_info = UserInfo(
+            user_id=global_config.BOT_QQ,
+            user_nickname=global_config.BOT_NICKNAME,
+            platform=messageinfo.platform,
+        )
+
+        thinking_time_point = round(time.time(), 2)
+        thinking_id = "mt" + str(thinking_time_point)
+        thinking_message = MessageThinking(
+            message_id=thinking_id,
+            chat_stream=chat,
+            bot_user_info=bot_user_info,
+            reply=message,
+            thinking_start_time=thinking_time_point,
+        )
+
+        MessageManager().add_message(thinking_message)
+
+        return thinking_id
+
+    async def _send_response_messages(self, message, chat, response_set: List[str], thinking_id) -> MessageSending:
+        """发送回复消息"""
+        container = MessageManager().get_container(chat.stream_id)
+        thinking_message = None
+
+        for msg in container.messages:
+            if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id:
+                thinking_message = msg
+                container.messages.remove(msg)
+                break
+
+        if not thinking_message:
+            logger.warning("未找到对应的思考消息，可能已超时被移除")
+            return None
+
+        thinking_start_time = thinking_message.thinking_start_time
+        message_set = MessageSet(chat, thinking_id)
+
+        mark_head = False
+        first_bot_msg = None
+        for msg in response_set:
+            message_segment = Seg(type="text", data=msg)
+            bot_message = MessageSending(
+                message_id=thinking_id,
+                chat_stream=chat,
+                bot_user_info=UserInfo(
+                    user_id=global_config.BOT_QQ,
+                    user_nickname=global_config.BOT_NICKNAME,
+                    platform=message.message_info.platform,
+                ),
+                sender_info=message.message_info.user_info,
+                message_segment=message_segment,
+                reply=message,
+                is_head=not mark_head,
+                is_emoji=False,
+                thinking_start_time=thinking_start_time,
+            )
+            if not mark_head:
+                mark_head = True
+                first_bot_msg = bot_message
+
+            # print(f"thinking_start_time:{bot_message.thinking_start_time}")
+            message_set.add_message(bot_message)
+        MessageManager().add_message(message_set)
+        return first_bot_msg
+
+    async def _handle_emoji(self, message, chat, response, send_emoji=""):
+        """处理表情包"""
+        if send_emoji:
+            emoji_raw = await emoji_manager.get_emoji_for_text(send_emoji)
+        else:
+            emoji_raw = await emoji_manager.get_emoji_for_text(response)
+        if emoji_raw:
+            emoji_path, description = emoji_raw
+            emoji_cq = image_path_to_base64(emoji_path)
+
+            thinking_time_point = round(message.message_info.time, 2)
+
+            message_segment = Seg(type="emoji", data=emoji_cq)
+            bot_message = MessageSending(
+                message_id="mt" + str(thinking_time_point),
+                chat_stream=chat,
+                bot_user_info=UserInfo(
+                    user_id=global_config.BOT_QQ,
+                    user_nickname=global_config.BOT_NICKNAME,
+                    platform=message.message_info.platform,
+                ),
+                sender_info=message.message_info.user_info,
+                message_segment=message_segment,
+                reply=message,
+                is_head=False,
+                is_emoji=True,
+            )
+
+            MessageManager().add_message(bot_message)
+
+    async def _update_relationship(self, message: MessageRecv, response_set):
+        """更新关系情绪"""
+        ori_response = ",".join(response_set)
+        stance, emotion = await self.gpt._get_emotion_tags(ori_response, message.processed_plain_text)
+        await relationship_manager.calculate_update_relationship_value(
+            chat_stream=message.chat_stream, label=emotion, stance=stance
+        )
+        self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
+
+    async def process_message(self, message_data: str) -> None:
+        """处理消息并生成回复"""
+        timing_results = {}
+        response_set = None
+
+        message = MessageRecv(message_data)
+        groupinfo = message.message_info.group_info
+        userinfo = message.message_info.user_info
+        messageinfo = message.message_info
+
+        # 消息加入缓冲池
+        await message_buffer.start_caching_messages(message)
+
+        # 创建聊天流
+        chat = await chat_manager.get_or_create_stream(
+            platform=messageinfo.platform,
+            user_info=userinfo,
+            group_info=groupinfo,
+        )
+        message.update_chat_stream(chat)
+
+        # 创建心流与chat的观察
+        heartflow.create_subheartflow(chat.stream_id)
+
+        await message.process()
+        logger.trace(f"消息处理成功{message.processed_plain_text}")
+
+        # 过滤词/正则表达式过滤
+        if self._check_ban_words(message.processed_plain_text, chat, userinfo) or self._check_ban_regex(
+            message.raw_message, chat, userinfo
+        ):
+            return
+        logger.trace(f"过滤词/正则表达式过滤成功{message.processed_plain_text}")
+
+        await self.storage.store_message(message, chat)
+        logger.trace(f"存储成功{message.processed_plain_text}")
+
+        # 记忆激活
+        with Timer("记忆激活", timing_results):
+            interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
+                message.processed_plain_text, fast_retrieval=True
+            )
+        logger.trace(f"记忆激活: {interested_rate}")
+
+        # 查询缓冲器结果，会整合前面跳过的消息，改变processed_plain_text
+        buffer_result = await message_buffer.query_buffer_result(message)
+
+        # 处理提及
+        is_mentioned, reply_probability = is_mentioned_bot_in_message(message)
+
+        # 意愿管理器：设置当前message信息
+        willing_manager.setup(message, chat, is_mentioned, interested_rate)
+
+        # 处理缓冲器结果
+        if not buffer_result:
+            await willing_manager.bombing_buffer_message_handle(message.message_info.message_id)
+            willing_manager.delete(message.message_info.message_id)
+            F_type = "seglist"
+            if message.message_segment.type != "seglist":
+                F_type =message.message_segment.type
+            else:
+                if (isinstance(message.message_segment.data, list) 
+                and all(isinstance(x, Seg) for x in message.message_segment.data)
+                and len(message.message_segment.data) == 1):
+                    F_type = message.message_segment.data[0].type
+            if F_type == "text":
+                logger.info(f"触发缓冲，已炸飞消息：{message.processed_plain_text}")
+            elif F_type == "image":
+                logger.info("触发缓冲，已炸飞表情包/图片")
+            elif F_type == "seglist":
+                logger.info("触发缓冲，已炸飞消息列")
+            return
+
+        # 获取回复概率
+        is_willing = False
+        if reply_probability != 1:
+            is_willing = True
+            reply_probability = await willing_manager.get_reply_probability(message.message_info.message_id)
+
+            if message.message_info.additional_config:
+                if "maimcore_reply_probability_gain" in message.message_info.additional_config.keys():
+                    reply_probability += message.message_info.additional_config["maimcore_reply_probability_gain"]
+
+        # 打印消息信息
+        mes_name = chat.group_info.group_name if chat.group_info else "私聊"
+        current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time))
+        willing_log = f"[回复意愿:{await willing_manager.get_willing(chat.stream_id):.2f}]" if is_willing else ""
+        logger.info(
+            f"[{current_time}][{mes_name}]"
+            f"{chat.user_info.user_nickname}:"
+            f"{message.processed_plain_text}{willing_log}[概率:{reply_probability * 100:.1f}%]"
+        )
+
+        do_reply = False
+        if random() < reply_probability:
+            try:
+                do_reply = True
+
+                # 回复前处理
+                await willing_manager.before_generate_reply_handle(message.message_info.message_id)
+
+                # 创建思考消息
+                try:
+                    with Timer("创建思考消息", timing_results):
+                        thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
+                except Exception as e:
+                    logger.error(f"心流创建思考消息失败: {e}")
+
+                logger.trace(f"创建捕捉器，thinking_id:{thinking_id}")
+
+                info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
+                info_catcher.catch_decide_to_response(message)
+
+                # 观察
+                try:
+                    with Timer("观察", timing_results):
+                        await heartflow.get_subheartflow(chat.stream_id).do_observe()
+                except Exception as e:
+                    logger.error(f"心流观察失败: {e}")
+                    logger.error(traceback.format_exc())
+
+                info_catcher.catch_after_observe(timing_results["观察"])
+
+                # 思考前使用工具
+                update_relationship = ""
+                get_mid_memory_id = []
+                tool_result_info = {}
+                send_emoji = ""
+                try:
+                    with Timer("思考前使用工具", timing_results):
+                        tool_result = await self.tool_user.use_tool(
+                            message.processed_plain_text,
+                            message.message_info.user_info.user_nickname,
+                            chat,
+                            heartflow.get_subheartflow(chat.stream_id),
+                        )
+                        # 如果工具被使用且获得了结果，将收集到的信息合并到思考中
+                        # collected_info = ""
+                        if tool_result.get("used_tools", False):
+                            if "structured_info" in tool_result:
+                                tool_result_info = tool_result["structured_info"]
+                                # collected_info = ""
+                                get_mid_memory_id = []
+                                update_relationship = ""
+
+                                # 动态解析工具结果
+                                for tool_name, tool_data in tool_result_info.items():
+                                    # tool_result_info += f"\n{tool_name} 相关信息:\n"
+                                    # for item in tool_data:
+                                    # tool_result_info += f"- {item['name']}: {item['content']}\n"
+
+                                    # 特殊判定：mid_chat_mem
+                                    if tool_name == "mid_chat_mem":
+                                        for mid_memory in tool_data:
+                                            get_mid_memory_id.append(mid_memory["content"])
+
+                                    # 特殊判定：change_mood
+                                    if tool_name == "change_mood":
+                                        for mood in tool_data:
+                                            self.mood_manager.update_mood_from_emotion(
+                                                mood["content"], global_config.mood_intensity_factor
+                                            )
+
+                                    # 特殊判定：change_relationship
+                                    if tool_name == "change_relationship":
+                                        update_relationship = tool_data[0]["content"]
+
+                                    if tool_name == "send_emoji":
+                                        send_emoji = tool_data[0]["content"]
+
+                except Exception as e:
+                    logger.error(f"思考前工具调用失败: {e}")
+                    logger.error(traceback.format_exc())
+
+                # 处理关系更新
+                if update_relationship:
+                    stance, emotion = await self.gpt._get_emotion_tags_with_reason(
+                        "你还没有回复", message.processed_plain_text, update_relationship
+                    )
+                    await relationship_manager.calculate_update_relationship_value(
+                        chat_stream=message.chat_stream, label=emotion, stance=stance
+                    )
+
+                # 思考前脑内状态
+                try:
+                    with Timer("思考前脑内状态", timing_results):
+                        current_mind, past_mind = await heartflow.get_subheartflow(
+                            chat.stream_id
+                        ).do_thinking_before_reply(
+                            message_txt=message.processed_plain_text,
+                            sender_info=message.message_info.user_info,
+                            chat_stream=chat,
+                            obs_id=get_mid_memory_id,
+                            extra_info=tool_result_info,
+                        )
+                except Exception as e:
+                    logger.error(f"心流思考前脑内状态失败: {e}")
+                    logger.error(traceback.format_exc())
+                    # 确保变量被定义，即使在错误情况下
+                    current_mind = ""
+                    past_mind = ""
+
+                info_catcher.catch_afer_shf_step(timing_results["思考前脑内状态"], past_mind, current_mind)
+
+                # 生成回复
+                with Timer("生成回复", timing_results):
+                    response_set = await self.gpt.generate_response(message, thinking_id)
+
+                info_catcher.catch_after_generate_response(timing_results["生成回复"])
+
+                if not response_set:
+                    logger.info("回复生成失败，返回为空")
+                    return
+
+                # 发送消息
+                try:
+                    with Timer("发送消息", timing_results):
+                        first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id)
+                except Exception as e:
+                    logger.error(f"心流发送消息失败: {e}")
+
+                info_catcher.catch_after_response(timing_results["发送消息"], response_set, first_bot_msg)
+
+                info_catcher.done_catch()
+
+                # 处理表情包
+                try:
+                    with Timer("处理表情包", timing_results):
+                        if send_emoji:
+                            logger.info(f"麦麦决定发送表情包{send_emoji}")
+                            await self._handle_emoji(message, chat, response_set, send_emoji)
+
+                except Exception as e:
+                    logger.error(f"心流处理表情包失败: {e}")
+
+
+                # 回复后处理
+                await willing_manager.after_generate_reply_handle(message.message_info.message_id)
+
+
+            except Exception as e:
+                logger.error(f"心流处理消息失败: {e}")
+                logger.error(traceback.format_exc())
+
+        # 输出性能计时结果
+        if do_reply:
+            timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()])
+            trigger_msg = message.processed_plain_text
+            response_msg = " ".join(response_set) if response_set else "无回复"
+            logger.info(f"触发消息: {trigger_msg[:20]}... | 思维消息: {response_msg[:20]}... | 性能计时: {timing_str}")
+        else:
+            # 不回复处理
+            await willing_manager.not_reply_handle(message.message_info.message_id)
+
+        # 意愿管理器：注销当前message信息
+        willing_manager.delete(message.message_info.message_id)
+
+    def _check_ban_words(self, text: str, chat, userinfo) -> bool:
+        """检查消息中是否包含过滤词"""
+        for word in global_config.ban_words:
+            if word in text:
+                logger.info(
+                    f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
+                )
+                logger.info(f"[过滤词识别]消息中含有{word}，filtered")
+                return True
+        return False
+
+    def _check_ban_regex(self, text: str, chat, userinfo) -> bool:
+        """检查消息是否匹配过滤正则表达式"""
+        for pattern in global_config.ban_msgs_regex:
+            if pattern.search(text):
+                logger.info(
+                    f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
+                )
+                logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
+                return True
+        return False