Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

bot.py

@@ -7,12 +7,16 @@ from pathlib import Path
 import time
 import platform
 from dotenv import load_dotenv
-from src.common.logger import get_module_logger
+from src.common.logger import get_module_logger, LogConfig, CONFIRM_STYLE_CONFIG
 from src.common.crash_logger import install_crash_handler
 from src.main import MainSystem
 
 logger = get_module_logger("main_bot")
-
+confirm_logger_config = LogConfig(
+    console_format=CONFIRM_STYLE_CONFIG["console_format"],
+    file_format=CONFIRM_STYLE_CONFIG["file_format"],
+)
+confirm_logger = get_module_logger("main_bot", config=confirm_logger_config)
 # 获取没有加载env时的环境变量
 env_mask = {key: os.getenv(key) for key in os.environ}
 
@@ -166,8 +170,8 @@ def check_eula():
 
     # 如果EULA或隐私条款有更新，提示用户重新确认
     if eula_updated or privacy_updated:
-        print("EULA或隐私条款内容已更新，请在阅读后重新确认，继续运行视为同意更新后的以上两款协议")
-        print(
+        confirm_logger.critical("EULA或隐私条款内容已更新，请在阅读后重新确认，继续运行视为同意更新后的以上两款协议")
+        confirm_logger.critical(
             f'输入"同意"或"confirmed"或设置环境变量"EULA_AGREE={eula_new_hash}"和"PRIVACY_AGREE={privacy_new_hash}"继续运行'
         )
         while True:
@@ -176,14 +180,14 @@ def check_eula():
                 # print("确认成功，继续运行")
                 # print(f"确认成功，继续运行{eula_updated} {privacy_updated}")
                 if eula_updated:
-                    print(f"更新EULA确认文件{eula_new_hash}")
+                    logger.info(f"更新EULA确认文件{eula_new_hash}")
                     eula_confirm_file.write_text(eula_new_hash, encoding="utf-8")
                 if privacy_updated:
-                    print(f"更新隐私条款确认文件{privacy_new_hash}")
+                    logger.info(f"更新隐私条款确认文件{privacy_new_hash}")
                     privacy_confirm_file.write_text(privacy_new_hash, encoding="utf-8")
                 break
             else:
-                print('请输入"同意"或"confirmed"以继续运行')
+                confirm_logger.critical('请输入"同意"或"confirmed"以继续运行')
         return
     elif eula_confirmed and privacy_confirmed:
         return

src/common/logger.py

@@ -290,6 +290,12 @@ WILLING_STYLE_CONFIG = {
     },
 }
 
+CONFIRM_STYLE_CONFIG = {
+    "console_format": (
+        "<RED>{message}</RED>"
+    ),  # noqa: E501
+    "file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | EULA与PRIVACY确认 | {message}"),
+}
 
 # 根据SIMPLE_OUTPUT选择配置
 MEMORY_STYLE_CONFIG = MEMORY_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MEMORY_STYLE_CONFIG["advanced"]

src/heart_flow/heartflow.py

@@ -9,6 +9,7 @@ from src.common.logger import get_module_logger, LogConfig, HEARTFLOW_STYLE_CONF
 from src.individuality.individuality import Individuality
 import time
 import random
+from typing import Dict, Any
 
 heartflow_config = LogConfig(
     # 使用海马体专用样式
@@ -39,7 +40,7 @@ class Heartflow:
             model=global_config.llm_heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow"
         )
 
-        self._subheartflows = {}
+        self._subheartflows: Dict[Any, SubHeartflow] = {}
         self.active_subheartflows_nums = 0
 
     async def _cleanup_inactive_subheartflows(self):

src/plugins/chat/auto_speak.py

@@ -142,7 +142,11 @@ class AutoSpeakManager:
         message_manager.add_message(thinking_message)
 
         # 生成自主发言内容
+        try:
             response, raw_content = await self.gpt.generate_response(message)
+        except Exception as e:
+            logger.error(f"生成自主发言内容时发生错误: {e}")
+            return False
 
         if response:
             message_set = MessageSet(None, think_id)  # 不需要chat_stream

src/plugins/chat_module/reasoning_chat/reasoning_chat.py

@@ -59,11 +59,7 @@ class ReasoningChat:
 
         return thinking_id
 
-    async def _send_response_messages(self, 
-                                      message, 
-                                      chat, 
-                                      response_set:List[str], 
-                                      thinking_id) -> MessageSending:
+    async def _send_response_messages(self, message, chat, response_set: List[str], thinking_id) -> MessageSending:
         """发送回复消息"""
         container = message_manager.get_container(chat.stream_id)
         thinking_message = None
@@ -248,11 +244,15 @@ class ReasoningChat:
 
             # 生成回复
             timer1 = time.time()
+            try:
                 response_set = await self.gpt.generate_response(message, thinking_id)
                 timer2 = time.time()
                 timing_results["生成回复"] = timer2 - timer1
 
                 info_catcher.catch_after_generate_response(timing_results["生成回复"])
+            except Exception as e:
+                logger.error(f"回复生成出现错误：str{e}")
+                response_set = None
 
             if not response_set:
                 logger.info("为什么生成回复失败？")
@@ -266,7 +266,6 @@ class ReasoningChat:
 
             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

src/plugins/memory_system/Hippocampus.py

@@ -934,9 +934,437 @@ class EntorhinalCortex:
         logger.success(f"[数据库] 同步了 {len(memory_nodes)} 个节点和 {len(memory_edges)} 条边")
 
 
+# 海马体
+class Hippocampus:
+    def __init__(self):
+        self.memory_graph = Memory_graph()
+        self.llm_topic_judge = None
+        self.llm_summary_by_topic = None
+        self.entorhinal_cortex = None
+        self.parahippocampal_gyrus = None
+        self.config = None
+
+    def initialize(self, global_config):
+        self.config = MemoryConfig.from_global_config(global_config)
+        # 初始化子组件
+        self.entorhinal_cortex = EntorhinalCortex(self)
+        self.parahippocampal_gyrus = ParahippocampalGyrus(self)
+        # 从数据库加载记忆图
+        self.entorhinal_cortex.sync_memory_from_db()
+        self.llm_topic_judge = LLM_request(self.config.llm_topic_judge, request_type="memory")
+        self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic, request_type="memory")
+
+    def get_all_node_names(self) -> list:
+        """获取记忆图中所有节点的名字列表"""
+        return list(self.memory_graph.G.nodes())
+
+    def calculate_node_hash(self, concept, memory_items) -> int:
+        """计算节点的特征值"""
+        if not isinstance(memory_items, list):
+            memory_items = [memory_items] if memory_items else []
+        sorted_items = sorted(memory_items)
+        content = f"{concept}:{'|'.join(sorted_items)}"
+        return hash(content)
+
+    def calculate_edge_hash(self, source, target) -> int:
+        """计算边的特征值"""
+        nodes = sorted([source, target])
+        return hash(f"{nodes[0]}:{nodes[1]}")
+
+    def find_topic_llm(self, text, topic_num):
+        prompt = (
+            f"这是一段文字：{text}。请你从这段话中总结出最多{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，"
+            f"将主题用逗号隔开，并加上<>,例如<主题1>,<主题2>......尽可能精简。只需要列举最多{topic_num}个话题就好，不要有序号，不要告诉我其他内容。"
+            f"如果确定找不出主题或者没有明显主题，返回<none>。"
+        )
+        return prompt
+
+    def topic_what(self, text, topic, time_info):
+        prompt = (
+            f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，'
+            f"可以包含时间和人物，以及具体的观点。只输出这句话就好"
+        )
+        return prompt
+
+    def calculate_topic_num(self, text, compress_rate):
+        """计算文本的话题数量"""
+        information_content = calculate_information_content(text)
+        topic_by_length = text.count("\n") * compress_rate
+        topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
+        topic_num = int((topic_by_length + topic_by_information_content) / 2)
+        logger.debug(
+            f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
+            f"topic_num: {topic_num}"
+        )
+        return topic_num
+
+    def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
+        """从关键词获取相关记忆。
+
+        Args:
+            keyword (str): 关键词
+            max_depth (int, optional): 记忆检索深度，默认为2。1表示只获取直接相关的记忆，2表示获取间接相关的记忆。
+
+        Returns:
+            list: 记忆列表，每个元素是一个元组 (topic, memory_items, similarity)
+                - topic: str, 记忆主题
+                - memory_items: list, 该主题下的记忆项列表
+                - similarity: float, 与关键词的相似度
+        """
+        if not keyword:
+            return []
+
+        # 获取所有节点
+        all_nodes = list(self.memory_graph.G.nodes())
+        memories = []
+
+        # 计算关键词的词集合
+        keyword_words = set(jieba.cut(keyword))
+
+        # 遍历所有节点，计算相似度
+        for node in all_nodes:
+            node_words = set(jieba.cut(node))
+            all_words = keyword_words | node_words
+            v1 = [1 if word in keyword_words else 0 for word in all_words]
+            v2 = [1 if word in node_words else 0 for word in all_words]
+            similarity = cosine_similarity(v1, v2)
+
+            # 如果相似度超过阈值，获取该节点的记忆
+            if similarity >= 0.3:  # 可以调整这个阈值
+                node_data = self.memory_graph.G.nodes[node]
+                memory_items = node_data.get("memory_items", [])
+                if not isinstance(memory_items, list):
+                    memory_items = [memory_items] if memory_items else []
+
+                memories.append((node, memory_items, similarity))
+
+        # 按相似度降序排序
+        memories.sort(key=lambda x: x[2], reverse=True)
+        return memories
+
+    async def get_memory_from_text(
+        self,
+        text: str,
+        max_memory_num: int = 3,
+        max_memory_length: int = 2,
+        max_depth: int = 3,
+        fast_retrieval: bool = False,
+    ) -> list:
+        """从文本中提取关键词并获取相关记忆。
+
+        Args:
+            text (str): 输入文本
+            num (int, optional): 需要返回的记忆数量。默认为5。
+            max_depth (int, optional): 记忆检索深度。默认为2。
+            fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
+                如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
+                如果为False，使用LLM提取关键词，速度较慢但更准确。
+
+        Returns:
+            list: 记忆列表，每个元素是一个元组 (topic, memory_items, similarity)
+                - topic: str, 记忆主题
+                - memory_items: list, 该主题下的记忆项列表
+                - similarity: float, 与文本的相似度
+        """
+        if not text:
+            return []
+
+        if fast_retrieval:
+            # 使用jieba分词提取关键词
+            words = jieba.cut(text)
+            # 过滤掉停用词和单字词
+            keywords = [word for word in words if len(word) > 1]
+            # 去重
+            keywords = list(set(keywords))
+            # 限制关键词数量
+            keywords = keywords[:5]
+        else:
+            # 使用LLM提取关键词
+            topic_num = min(5, max(1, int(len(text) * 0.1)))  # 根据文本长度动态调整关键词数量
+            # logger.info(f"提取关键词数量: {topic_num}")
+            topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
+
+            # 提取关键词
+            keywords = re.findall(r"<([^>]+)>", topics_response[0])
+            if not keywords:
+                keywords = []
+            else:
+                keywords = [
+                    keyword.strip()
+                    for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
+                    if keyword.strip()
+                ]
+
+        # logger.info(f"提取的关键词: {', '.join(keywords)}")
+
+        # 过滤掉不存在于记忆图中的关键词
+        valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
+        if not valid_keywords:
+            logger.info("没有找到有效的关键词节点")
+            return []
+
+        logger.info(f"有效的关键词: {', '.join(valid_keywords)}")
+
+        # 从每个关键词获取记忆
+        all_memories = []
+        activate_map = {}  # 存储每个词的累计激活值
+
+        # 对每个关键词进行扩散式检索
+        for keyword in valid_keywords:
+            logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
+            # 初始化激活值
+            activation_values = {keyword: 1.0}
+            # 记录已访问的节点
+            visited_nodes = {keyword}
+            # 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
+            nodes_to_process = [(keyword, 1.0, 0)]
+
+            while nodes_to_process:
+                current_node, current_activation, current_depth = nodes_to_process.pop(0)
+
+                # 如果激活值小于0或超过最大深度，停止扩散
+                if current_activation <= 0 or current_depth >= max_depth:
+                    continue
+
+                # 获取当前节点的所有邻居
+                neighbors = list(self.memory_graph.G.neighbors(current_node))
+
+                for neighbor in neighbors:
+                    if neighbor in visited_nodes:
+                        continue
+
+                    # 获取连接强度
+                    edge_data = self.memory_graph.G[current_node][neighbor]
+                    strength = edge_data.get("strength", 1)
+
+                    # 计算新的激活值
+                    new_activation = current_activation - (1 / strength)
+
+                    if new_activation > 0:
+                        activation_values[neighbor] = new_activation
+                        visited_nodes.add(neighbor)
+                        nodes_to_process.append((neighbor, new_activation, current_depth + 1))
+                        logger.debug(
+                            f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})"
+                        )  # noqa: E501
+
+            # 更新激活映射
+            for node, activation_value in activation_values.items():
+                if activation_value > 0:
+                    if node in activate_map:
+                        activate_map[node] += activation_value
+                    else:
+                        activate_map[node] = activation_value
+
+        # 输出激活映射
+        # logger.info("激活映射统计:")
+        # for node, total_activation in sorted(activate_map.items(), key=lambda x: x[1], reverse=True):
+        #     logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}")
+
+        # 基于激活值平方的独立概率选择
+        remember_map = {}
+        # logger.info("基于激活值平方的归一化选择:")
+
+        # 计算所有激活值的平方和
+        total_squared_activation = sum(activation**2 for activation in activate_map.values())
+        if total_squared_activation > 0:
+            # 计算归一化的激活值
+            normalized_activations = {
+                node: (activation**2) / total_squared_activation for node, activation in activate_map.items()
+            }
+
+            # 按归一化激活值排序并选择前max_memory_num个
+            sorted_nodes = sorted(normalized_activations.items(), key=lambda x: x[1], reverse=True)[:max_memory_num]
+
+            # 将选中的节点添加到remember_map
+            for node, normalized_activation in sorted_nodes:
+                remember_map[node] = activate_map[node]  # 使用原始激活值
+                logger.debug(
+                    f"节点 '{node}' (归一化激活值: {normalized_activation:.2f}, 激活值: {activate_map[node]:.2f})"
+                )
+        else:
+            logger.info("没有有效的激活值")
+
+        # 从选中的节点中提取记忆
+        all_memories = []
+        # logger.info("开始从选中的节点中提取记忆:")
+        for node, activation in remember_map.items():
+            logger.debug(f"处理节点 '{node}' (激活值: {activation:.2f}):")
+            node_data = self.memory_graph.G.nodes[node]
+            memory_items = node_data.get("memory_items", [])
+            if not isinstance(memory_items, list):
+                memory_items = [memory_items] if memory_items else []
+
+            if memory_items:
+                logger.debug(f"节点包含 {len(memory_items)} 条记忆")
+                # 计算每条记忆与输入文本的相似度
+                memory_similarities = []
+                for memory in memory_items:
+                    # 计算与输入文本的相似度
+                    memory_words = set(jieba.cut(memory))
+                    text_words = set(jieba.cut(text))
+                    all_words = memory_words | text_words
+                    v1 = [1 if word in memory_words else 0 for word in all_words]
+                    v2 = [1 if word in text_words else 0 for word in all_words]
+                    similarity = cosine_similarity(v1, v2)
+                    memory_similarities.append((memory, similarity))
+
+                # 按相似度排序
+                memory_similarities.sort(key=lambda x: x[1], reverse=True)
+                # 获取最匹配的记忆
+                top_memories = memory_similarities[:max_memory_length]
+
+                # 添加到结果中
+                for memory, similarity in top_memories:
+                    all_memories.append((node, [memory], similarity))
+                    # logger.info(f"选中记忆: {memory} (相似度: {similarity:.2f})")
+            else:
+                logger.info("节点没有记忆")
+
+        # 去重（基于记忆内容）
+        logger.debug("开始记忆去重:")
+        seen_memories = set()
+        unique_memories = []
+        for topic, memory_items, activation_value in all_memories:
+            memory = memory_items[0]  # 因为每个topic只有一条记忆
+            if memory not in seen_memories:
+                seen_memories.add(memory)
+                unique_memories.append((topic, memory_items, activation_value))
+                logger.debug(f"保留记忆: {memory} (来自节点: {topic}, 激活值: {activation_value:.2f})")
+            else:
+                logger.debug(f"跳过重复记忆: {memory} (来自节点: {topic})")
+
+        # 转换为(关键词, 记忆)格式
+        result = []
+        for topic, memory_items, _ in unique_memories:
+            memory = memory_items[0]  # 因为每个topic只有一条记忆
+            result.append((topic, memory))
+            logger.info(f"选中记忆: {memory} (来自节点: {topic})")
+
+        return result
+
+    async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> float:
+        """从文本中提取关键词并获取相关记忆。
+
+        Args:
+            text (str): 输入文本
+            num (int, optional): 需要返回的记忆数量。默认为5。
+            max_depth (int, optional): 记忆检索深度。默认为2。
+            fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
+                如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
+                如果为False，使用LLM提取关键词，速度较慢但更准确。
+
+        Returns:
+            float: 激活节点数与总节点数的比值
+        """
+        if not text:
+            return 0
+
+        if fast_retrieval:
+            # 使用jieba分词提取关键词
+            words = jieba.cut(text)
+            # 过滤掉停用词和单字词
+            keywords = [word for word in words if len(word) > 1]
+            # 去重
+            keywords = list(set(keywords))
+            # 限制关键词数量
+            keywords = keywords[:5]
+        else:
+            # 使用LLM提取关键词
+            topic_num = min(5, max(1, int(len(text) * 0.1)))  # 根据文本长度动态调整关键词数量
+            # logger.info(f"提取关键词数量: {topic_num}")
+            topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
+
+            # 提取关键词
+            keywords = re.findall(r"<([^>]+)>", topics_response[0])
+            if not keywords:
+                keywords = []
+            else:
+                keywords = [
+                    keyword.strip()
+                    for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
+                    if keyword.strip()
+                ]
+
+        # logger.info(f"提取的关键词: {', '.join(keywords)}")
+
+        # 过滤掉不存在于记忆图中的关键词
+        valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
+        if not valid_keywords:
+            logger.info("没有找到有效的关键词节点")
+            return 0
+
+        logger.info(f"有效的关键词: {', '.join(valid_keywords)}")
+
+        # 从每个关键词获取记忆
+        activate_map = {}  # 存储每个词的累计激活值
+
+        # 对每个关键词进行扩散式检索
+        for keyword in valid_keywords:
+            logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
+            # 初始化激活值
+            activation_values = {keyword: 1.0}
+            # 记录已访问的节点
+            visited_nodes = {keyword}
+            # 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
+            nodes_to_process = [(keyword, 1.0, 0)]
+
+            while nodes_to_process:
+                current_node, current_activation, current_depth = nodes_to_process.pop(0)
+
+                # 如果激活值小于0或超过最大深度，停止扩散
+                if current_activation <= 0 or current_depth >= max_depth:
+                    continue
+
+                # 获取当前节点的所有邻居
+                neighbors = list(self.memory_graph.G.neighbors(current_node))
+
+                for neighbor in neighbors:
+                    if neighbor in visited_nodes:
+                        continue
+
+                    # 获取连接强度
+                    edge_data = self.memory_graph.G[current_node][neighbor]
+                    strength = edge_data.get("strength", 1)
+
+                    # 计算新的激活值
+                    new_activation = current_activation - (1 / strength)
+
+                    if new_activation > 0:
+                        activation_values[neighbor] = new_activation
+                        visited_nodes.add(neighbor)
+                        nodes_to_process.append((neighbor, new_activation, current_depth + 1))
+                        # logger.debug(
+                        # f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})")  # noqa: E501
+
+            # 更新激活映射
+            for node, activation_value in activation_values.items():
+                if activation_value > 0:
+                    if node in activate_map:
+                        activate_map[node] += activation_value
+                    else:
+                        activate_map[node] = activation_value
+
+        # 输出激活映射
+        # logger.info("激活映射统计:")
+        # for node, total_activation in sorted(activate_map.items(), key=lambda x: x[1], reverse=True):
+        #     logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}")
+
+        # 计算激活节点数与总节点数的比值
+        total_activation = sum(activate_map.values())
+        logger.info(f"总激活值: {total_activation:.2f}")
+        total_nodes = len(self.memory_graph.G.nodes())
+        # activated_nodes = len(activate_map)
+        activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0
+        activation_ratio = activation_ratio * 60
+        logger.info(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
+
+        return activation_ratio
+
+
 # 负责整合，遗忘，合并记忆
 class ParahippocampalGyrus:
-    def __init__(self, hippocampus):
+    def __init__(self, hippocampus: Hippocampus):
         self.hippocampus = hippocampus
         self.memory_graph = hippocampus.memory_graph
         self.config = hippocampus.config
@@ -1025,8 +1453,12 @@ class ParahippocampalGyrus:
         tasks = []
         for topic in filtered_topics:
             topic_what_prompt = self.hippocampus.topic_what(input_text, topic, time_info)
+            try:
                 task = self.hippocampus.llm_summary_by_topic.generate_response_async(topic_what_prompt)
                 tasks.append((topic.strip(), task))
+            except Exception as e:
+                logger.error(f"生成话题 '{topic}' 的摘要时发生错误: {e}")
+                continue
 
         # 等待所有任务完成
         compressed_memory = set()
@@ -1075,7 +1507,11 @@ class ParahippocampalGyrus:
             logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
 
             compress_rate = self.config.memory_compress_rate
+            try:
                 compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
+            except Exception as e:
+                logger.error(f"压缩记忆时发生错误: {e}")
+                continue
             logger.debug(f"压缩后记忆数量: {compressed_memory}，似曾相识的话题: {similar_topics_dict}")
 
             current_time = datetime.datetime.now().timestamp()
@@ -1247,7 +1683,6 @@ class ParahippocampalGyrus:
         logger.info(f"[遗忘] 总耗时: {end_time - start_time:.2f}秒")
 
 
-
 class HippocampusManager:
     _instance = None
     _hippocampus = None
@@ -1318,13 +1753,14 @@ class HippocampusManager:
         if not self._initialized:
             raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
         try:
-            response = await self._hippocampus.get_memory_from_text(text, max_memory_num, max_memory_length, max_depth, fast_retrieval)
+            response = await self._hippocampus.get_memory_from_text(
+                text, max_memory_num, max_memory_length, max_depth, fast_retrieval
+            )
         except Exception as e:
             logger.error(f"文本激活记忆失败: {e}")
             response = []
         return response
 
-
     async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> float:
         """从文本中获取激活值的公共接口"""
         if not self._initialized:

src/plugins/storage/storage.py

@@ -1,3 +1,4 @@
+import re
 from typing import Union
 
 from ...common.database import db
@@ -7,19 +8,34 @@ from src.common.logger import get_module_logger
 
 logger = get_module_logger("message_storage")
 
-
 class MessageStorage:
     async def store_message(self, message: Union[MessageSending, MessageRecv], chat_stream: ChatStream) -> None:
         """存储消息到数据库"""
         try:
+            # 莫越权 救世啊
+            pattern = r"<MainRule>.*?</MainRule>|<schedule>.*?</schedule>|<UserMessage>.*?</UserMessage>"
+
+            processed_plain_text = message.processed_plain_text
+            if processed_plain_text:
+                filtered_processed_plain_text = re.sub(pattern, "", processed_plain_text, flags=re.DOTALL)
+            else:
+                filtered_processed_plain_text = ""
+
+            detailed_plain_text = message.detailed_plain_text
+            if detailed_plain_text:
+                filtered_detailed_plain_text = re.sub(pattern, "", detailed_plain_text, flags=re.DOTALL)
+            else:
+                filtered_detailed_plain_text = ""
+
             message_data = {
                 "message_id": message.message_info.message_id,
                 "time": message.message_info.time,
                 "chat_id": chat_stream.stream_id,
                 "chat_info": chat_stream.to_dict(),
                 "user_info": message.message_info.user_info.to_dict(),
-                "processed_plain_text": message.processed_plain_text,
-                "detailed_plain_text": message.detailed_plain_text,
+                # 使用过滤后的文本
+                "processed_plain_text": filtered_processed_plain_text,
+                "detailed_plain_text": filtered_detailed_plain_text,
                 "memorized_times": message.memorized_times,
             }
             db.messages.insert_one(message_data)