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

changelogs/changelog.md

@@ -2,13 +2,15 @@
 
 ## [0.9.1] - 2025-7-25
 
+- 修复reply导致的planner异常空跳
 - 修复表达方式迁移空目录问题
 - 修复reply_to空字段问题
 - 将metioned bot 和 at应用到focus prompt中
+- 更好的兴趣度计算
+- 修复部分模型由于enable_thinking导致的400问题
+- 优化关键词提取
 
-
-
-## [0.9.0] - 2025-7-25
+## [0.9.0] - 2025-7-24
 
 ### 摘要
 MaiBot 0.9.0 重磅升级！本版本带来两大核心突破：**全面重构的插件系统**提供更强大的扩展能力和管理功能；**normal和focus模式统一化处理**大幅简化架构并提升性能。同时新增s4u prompt模式优化、语音消息支持、全新情绪系统和mais4u直播互动功能，为MaiBot带来更自然、更智能的交互体验！

src/chat/chat_loop/heartFC_chat.py

@@ -330,13 +330,13 @@ class HeartFChatting:
 
             if self.loop_mode == ChatMode.NORMAL:
                 if action_type == "no_action":
-                    logger.info(f"[{self.log_prefix}] {global_config.bot.nickname} 决定进行回复")
+                    logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复")
                 elif is_parallel:
                     logger.info(
-                        f"[{self.log_prefix}] {global_config.bot.nickname} 决定进行回复, 同时执行{action_type}动作"
+                        f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复, 同时执行{action_type}动作"
                     )
                 else:
-                    logger.info(f"[{self.log_prefix}] {global_config.bot.nickname} 决定执行{action_type}动作")
+                    logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定执行{action_type}动作")
 
             if action_type == "no_action":
                 # 等待回复生成完毕
@@ -351,15 +351,15 @@ class HeartFChatting:
 
                 # 模型炸了，没有回复内容生成
                 if not response_set:
-                    logger.warning(f"[{self.log_prefix}] 模型未生成回复内容")
+                    logger.warning(f"{self.log_prefix}模型未生成回复内容")
                     return False
                 elif action_type not in ["no_action"] and not is_parallel:
                     logger.info(
-                        f"[{self.log_prefix}] {global_config.bot.nickname} 原本想要回复：{content}，但选择执行{action_type}，不发表回复"
+                        f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复：{content}，但选择执行{action_type}，不发表回复"
                     )
                     return False
 
-                logger.info(f"[{self.log_prefix}] {global_config.bot.nickname} 决定的回复内容: {content}")
+                logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定的回复内容: {content}")
 
                 # 发送回复 (不再需要传入 chat)
                 reply_text = await self._send_response(response_set, reply_to_str, loop_start_time,message_data)
@@ -563,7 +563,7 @@ class HeartFChatting:
             return reply_set
 
         except Exception as e:
-            logger.error(f"[{self.log_prefix}] 回复生成出现错误：{str(e)} {traceback.format_exc()}")
+            logger.error(f"{self.log_prefix}回复生成出现错误：{str(e)} {traceback.format_exc()}")
             return None
 
     async def _send_response(self, reply_set, reply_to, thinking_start_time, message_data):

src/chat/heart_flow/heartflow_message_processor.py

@@ -12,6 +12,7 @@ from src.chat.message_receive.storage import MessageStorage
 from src.chat.heart_flow.heartflow import heartflow
 from src.chat.utils.utils import is_mentioned_bot_in_message
 from src.chat.utils.timer_calculator import Timer
+from src.chat.utils.chat_message_builder import replace_user_references_in_content
 from src.common.logger import get_logger
 from src.person_info.relationship_manager import get_relationship_manager
 from src.mood.mood_manager import mood_manager
@@ -56,6 +57,7 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool]:
     with Timer("记忆激活"):
         interested_rate = await hippocampus_manager.get_activate_from_text(
             message.processed_plain_text,
+            max_depth= 5,
             fast_retrieval=False,
         )
         logger.debug(f"记忆激活率: {interested_rate:.2f}")
@@ -148,6 +150,14 @@ class HeartFCMessageReceiver:
             picid_pattern = r"\[picid:([^\]]+)\]"
             processed_plain_text = re.sub(picid_pattern, "[图片]", message.processed_plain_text)
             
+            # 应用用户引用格式替换，将回复<aaa:bbb>和@<aaa:bbb>格式转换为可读格式
+            processed_plain_text = replace_user_references_in_content(
+                processed_plain_text, 
+                message.message_info.platform, 
+                is_async=False, 
+                replace_bot_name=True
+            )
+
             logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[兴趣度：{interested_rate:.2f}]")  # type: ignore
 
             logger.debug(f"[{mes_name}][当前时段回复频率: {current_talk_frequency}]")

src/chat/memory_system/Hippocampus.py

@@ -224,10 +224,15 @@ class Hippocampus:
         return hash((source, target))
 
     @staticmethod
-    def find_topic_llm(text, topic_num):
-        # sourcery skip: inline-immediately-returned-variable
+    def find_topic_llm(text:str, topic_num:int|list[int]):
+        topic_num_str = ""
+        if isinstance(topic_num, list):
+            topic_num_str = f"{topic_num[0]}-{topic_num[1]}"
+        else:
+            topic_num_str = topic_num
+        
         prompt = (
-            f"这是一段文字：\n{text}\n\n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"这是一段文字：\n{text}\n\n请你从这段话中总结出最多{topic_num_str}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>。"
         )
@@ -300,7 +305,7 @@ class Hippocampus:
         memories.sort(key=lambda x: x[2], reverse=True)
         return memories
     
-    async def get_keywords_from_text(self, text: str, fast_retrieval: bool = False) -> list:
+    async def get_keywords_from_text(self, text: str) -> list:
         """从文本中提取关键词。
         
         Args:
@@ -312,33 +317,27 @@ class Hippocampus:
         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
-            logger.debug(f"提取关键词: {keywords}")
-
-        else:
         # 使用LLM提取关键词 - 根据详细文本长度分布优化topic_num计算
         text_length = len(text)
+        topic_num:str|list[int] = None
         if text_length <= 5:
-                topic_num = 1  # 1-5字符: 1个关键词 (26.57%的文本)
+            words = jieba.cut(text)
+            keywords = [word for word in words if len(word) > 1]
+            keywords = list(set(keywords))[:3]  # 限制最多3个关键词
+            logger.info(f"提取关键词: {keywords}")
+            return keywords
         elif text_length <= 10:
-                topic_num = 1  # 6-10字符: 1个关键词 (27.18%的文本)
+            topic_num = [1,3]  # 6-10字符: 1个关键词 (27.18%的文本)
         elif text_length <= 20:
-                topic_num = 2  # 11-20字符: 2个关键词 (22.76%的文本)
+            topic_num = [2,4]  # 11-20字符: 2个关键词 (22.76%的文本)
         elif text_length <= 30:
-                topic_num = 3  # 21-30字符: 3个关键词 (10.33%的文本)
+            topic_num = [3,5]  # 21-30字符: 3个关键词 (10.33%的文本)
         elif text_length <= 50:
-                topic_num = 4  # 31-50字符: 4个关键词 (9.79%的文本)
+            topic_num = [4,5]  # 31-50字符: 4个关键词 (9.79%的文本)
         else:
             topic_num = 5  # 51+字符: 5个关键词 (其余长文本)
         
-            # logger.info(f"提取关键词数量: {topic_num}")
+        
         topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(
             self.find_topic_llm(text, topic_num)
         )
@@ -354,6 +353,8 @@ class Hippocampus:
                 if keyword.strip()
             ]
         
+        logger.info(f"提取关键词: {keywords}")
+        
         return keywords 
         
 
@@ -382,7 +383,7 @@ class Hippocampus:
                 - memory_items: list, 该主题下的记忆项列表
                 - similarity: float, 与文本的相似度
         """
-        keywords = await self.get_keywords_from_text(text, fast_retrieval)
+        keywords = await self.get_keywords_from_text(text)
 
         # 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]
@@ -704,7 +705,7 @@ class Hippocampus:
         Returns:
             float: 激活节点数与总节点数的比值
         """
-        keywords = await self.get_keywords_from_text(text, fast_retrieval)
+        keywords = await self.get_keywords_from_text(text)
 
         # 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]
@@ -721,7 +722,7 @@ class Hippocampus:
         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}
+            activation_values = {keyword: 1.5}
             # 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)

src/chat/replyer/default_generator.py

@@ -17,7 +17,7 @@ from src.chat.message_receive.uni_message_sender import HeartFCSender
 from src.chat.utils.timer_calculator import Timer  # <--- Import Timer
 from src.chat.utils.utils import get_chat_type_and_target_info
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
-from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
+from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat, replace_user_references_in_content
 from src.chat.express.expression_selector import expression_selector
 from src.chat.knowledge.knowledge_lib import qa_manager
 from src.chat.memory_system.memory_activator import MemoryActivator
@@ -630,6 +630,14 @@ class DefaultReplyer:
 
         sender, target = self._parse_reply_target(reply_to)
         
+        target = replace_user_references_in_content(
+            target, 
+            chat_stream.platform, 
+            is_async=False, 
+            replace_bot_name=True
+        )
+        
+
         # 构建action描述 (如果启用planner)
         action_descriptions = ""
         if available_actions:

src/chat/utils/chat_message_builder.py

@@ -2,7 +2,7 @@ import time  # 导入 time 模块以获取当前时间
 import random
 import re
 
-from typing import List, Dict, Any, Tuple, Optional
+from typing import List, Dict, Any, Tuple, Optional, Union, Callable
 from rich.traceback import install
 
 from src.config.config import global_config
@@ -15,6 +15,155 @@ from src.chat.utils.utils import translate_timestamp_to_human_readable,assign_me
 install(extra_lines=3)
 
 
+def replace_user_references_in_content(
+    content: str,
+    platform: str,
+    name_resolver: Union[Callable[[str, str], str], Callable[[str, str], Any]] = None,
+    is_async: bool = False,
+    replace_bot_name: bool = True
+) -> Union[str, Any]:
+    """
+    替换内容中的用户引用格式，包括回复<aaa:bbb>和@<aaa:bbb>格式
+    
+    Args:
+        content: 要处理的内容字符串
+        platform: 平台标识
+        name_resolver: 名称解析函数，接收(platform, user_id)参数，返回用户名称
+                      如果为None，则使用默认的person_info_manager
+        is_async: 是否为异步模式
+        replace_bot_name: 是否将机器人的user_id替换为"机器人昵称(你)"
+    
+    Returns:
+        处理后的内容字符串（同步模式）或awaitable对象（异步模式）
+    """
+    if is_async:
+        return _replace_user_references_async(content, platform, name_resolver, replace_bot_name)
+    else:
+        return _replace_user_references_sync(content, platform, name_resolver, replace_bot_name)
+
+
+def _replace_user_references_sync(
+    content: str,
+    platform: str,
+    name_resolver: Optional[Callable[[str, str], str]] = None,
+    replace_bot_name: bool = True
+) -> str:
+    """同步版本的用户引用替换"""
+    if name_resolver is None:
+        person_info_manager = get_person_info_manager()
+        def default_resolver(platform: str, user_id: str) -> str:
+            # 检查是否是机器人自己
+            if replace_bot_name and user_id == global_config.bot.qq_account:
+                return f"{global_config.bot.nickname}(你)"
+            person_id = PersonInfoManager.get_person_id(platform, user_id)
+            return person_info_manager.get_value_sync(person_id, "person_name") or user_id
+        name_resolver = default_resolver
+    
+    # 处理回复<aaa:bbb>格式
+    reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
+    match = re.search(reply_pattern, content)
+    if match:
+        aaa = match.group(1)
+        bbb = match.group(2)
+        try:
+            # 检查是否是机器人自己
+            if replace_bot_name and bbb == global_config.bot.qq_account:
+                reply_person_name = f"{global_config.bot.nickname}(你)"
+            else:
+                reply_person_name = name_resolver(platform, bbb) or aaa
+            content = re.sub(reply_pattern, f"回复 {reply_person_name}", content, count=1)
+        except Exception:
+            # 如果解析失败，使用原始昵称
+            content = re.sub(reply_pattern, f"回复 {aaa}", content, count=1)
+    
+    # 处理@<aaa:bbb>格式
+    at_pattern = r"@<([^:<>]+):([^:<>]+)>"
+    at_matches = list(re.finditer(at_pattern, content))
+    if at_matches:
+        new_content = ""
+        last_end = 0
+        for m in at_matches:
+            new_content += content[last_end:m.start()]
+            aaa = m.group(1)
+            bbb = m.group(2)
+            try:
+                # 检查是否是机器人自己
+                if replace_bot_name and bbb == global_config.bot.qq_account:
+                    at_person_name = f"{global_config.bot.nickname}(你)"
+                else:
+                    at_person_name = name_resolver(platform, bbb) or aaa
+                new_content += f"@{at_person_name}"
+            except Exception:
+                # 如果解析失败，使用原始昵称
+                new_content += f"@{aaa}"
+            last_end = m.end()
+        new_content += content[last_end:]
+        content = new_content
+    
+    return content
+
+
+async def _replace_user_references_async(
+    content: str,
+    platform: str,
+    name_resolver: Optional[Callable[[str, str], Any]] = None,
+    replace_bot_name: bool = True
+) -> str:
+    """异步版本的用户引用替换"""
+    if name_resolver is None:
+        person_info_manager = get_person_info_manager()
+        async def default_resolver(platform: str, user_id: str) -> str:
+            # 检查是否是机器人自己
+            if replace_bot_name and user_id == global_config.bot.qq_account:
+                return f"{global_config.bot.nickname}(你)"
+            person_id = PersonInfoManager.get_person_id(platform, user_id)
+            return await person_info_manager.get_value(person_id, "person_name") or user_id
+        name_resolver = default_resolver
+    
+    # 处理回复<aaa:bbb>格式
+    reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
+    match = re.search(reply_pattern, content)
+    if match:
+        aaa = match.group(1)
+        bbb = match.group(2)
+        try:
+            # 检查是否是机器人自己
+            if replace_bot_name and bbb == global_config.bot.qq_account:
+                reply_person_name = f"{global_config.bot.nickname}(你)"
+            else:
+                reply_person_name = await name_resolver(platform, bbb) or aaa
+            content = re.sub(reply_pattern, f"回复 {reply_person_name}", content, count=1)
+        except Exception:
+            # 如果解析失败，使用原始昵称
+            content = re.sub(reply_pattern, f"回复 {aaa}", content, count=1)
+    
+    # 处理@<aaa:bbb>格式
+    at_pattern = r"@<([^:<>]+):([^:<>]+)>"
+    at_matches = list(re.finditer(at_pattern, content))
+    if at_matches:
+        new_content = ""
+        last_end = 0
+        for m in at_matches:
+            new_content += content[last_end:m.start()]
+            aaa = m.group(1)
+            bbb = m.group(2)
+            try:
+                # 检查是否是机器人自己
+                if replace_bot_name and bbb == global_config.bot.qq_account:
+                    at_person_name = f"{global_config.bot.nickname}(你)"
+                else:
+                    at_person_name = await name_resolver(platform, bbb) or aaa
+                new_content += f"@{at_person_name}"
+            except Exception:
+                # 如果解析失败，使用原始昵称
+                new_content += f"@{aaa}"
+            last_end = m.end()
+        new_content += content[last_end:]
+        content = new_content
+    
+    return content
+
+
 def get_raw_msg_by_timestamp(
     timestamp_start: float, timestamp_end: float, limit: int = 0, limit_mode: str = "latest"
 ) -> List[Dict[str, Any]]:
@@ -374,33 +523,8 @@ def _build_readable_messages_internal(
             else:
                 person_name = "某人"
 
-        # 检查是否有 回复<aaa:bbb> 字段
-        reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
-        match = re.search(reply_pattern, content)
-        if match:
-            aaa: str = match[1]
-            bbb: str = match[2]
-            reply_person_id = PersonInfoManager.get_person_id(platform, bbb)
-            reply_person_name = person_info_manager.get_value_sync(reply_person_id, "person_name") or aaa
-            # 在内容前加上回复信息
-            content = re.sub(reply_pattern, lambda m, name=reply_person_name: f"回复 {name}", content, count=1)
-
-        # 检查是否有 @<aaa:bbb> 字段 @<{member_info.get('nickname')}:{member_info.get('user_id')}>
-        at_pattern = r"@<([^:<>]+):([^:<>]+)>"
-        at_matches = list(re.finditer(at_pattern, content))
-        if at_matches:
-            new_content = ""
-            last_end = 0
-            for m in at_matches:
-                new_content += content[last_end : m.start()]
-                aaa = m.group(1)
-                bbb = m.group(2)
-                at_person_id = PersonInfoManager.get_person_id(platform, bbb)
-                at_person_name = person_info_manager.get_value_sync(at_person_id, "person_name") or aaa
-                new_content += f"@{at_person_name}"
-                last_end = m.end()
-            new_content += content[last_end:]
-            content = new_content
+        # 使用独立函数处理用户引用格式
+        content = replace_user_references_in_content(content, platform, is_async=False, replace_bot_name=replace_bot_name)
 
         target_str = "这是QQ的一个功能，用于提及某人，但没那么明显"
         if target_str in content and random.random() < 0.6:
@@ -916,38 +1040,14 @@ async def build_anonymous_messages(messages: List[Dict[str, Any]]) -> str:
             anon_name = get_anon_name(platform, user_id)
             # print(f"anon_name:{anon_name}")
 
-            # 处理 回复<aaa:bbb>
-            reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
-            match = re.search(reply_pattern, content)
-            if match:
-                # print(f"发现回复match:{match}")
-                bbb = match.group(2)
+            # 使用独立函数处理用户引用格式，传入自定义的匿名名称解析器
+            def anon_name_resolver(platform: str, user_id: str) -> str:
                 try:
-                    anon_reply = get_anon_name(platform, bbb)
-                    # print(f"anon_reply:{anon_reply}")
+                    return get_anon_name(platform, user_id)
                 except Exception:
-                    anon_reply = "?"
-                content = re.sub(reply_pattern, f"回复 {anon_reply}", content, count=1)
+                    return "?"
             
-            # 处理 @<aaa:bbb>，无嵌套def
-            at_pattern = r"@<([^:<>]+):([^:<>]+)>"
-            at_matches = list(re.finditer(at_pattern, content))
-            if at_matches:
-                # print(f"发现@match:{at_matches}")
-                new_content = ""
-                last_end = 0
-                for m in at_matches:
-                    new_content += content[last_end : m.start()]
-                    bbb = m.group(2)
-                    try:
-                        anon_at = get_anon_name(platform, bbb)
-                        # print(f"anon_at:{anon_at}")
-                    except Exception:
-                        anon_at = "?"
-                    new_content += f"@{anon_at}"
-                    last_end = m.end()
-                new_content += content[last_end:]
-                content = new_content
+            content = replace_user_references_in_content(content, platform, anon_name_resolver, is_async=False, replace_bot_name=False)
 
             header = f"{anon_name}说 "
             output_lines.append(header)

src/mood/mood_manager.py

@@ -78,7 +78,7 @@ class ChatMood:
         if interested_rate <= 0:
             interest_multiplier = 0
         else:
-            interest_multiplier = 3 * math.pow(interested_rate, 0.25)
+            interest_multiplier = 2 * math.pow(interested_rate, 0.25)
 
         logger.debug(
             f"base_probability: {base_probability}, time_multiplier: {time_multiplier}, interest_multiplier: {interest_multiplier}"

src/plugin_system/apis/send_api.py

@@ -19,6 +19,7 @@
     await send_api.custom_message("video", video_data, "123456", True)
 """
 
+import asyncio
 import traceback
 import time
 import difflib
@@ -30,7 +31,7 @@ from src.common.logger import get_logger
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.chat.message_receive.uni_message_sender import HeartFCSender
 from src.chat.message_receive.message import MessageSending, MessageRecv
-from src.chat.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat
+from src.chat.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat, replace_user_references_in_content
 from src.person_info.person_info import get_person_info_manager
 from maim_message import Seg, UserInfo
 from src.config.config import global_config
@@ -183,32 +184,8 @@ async def _find_reply_message(target_stream, reply_to: str) -> Optional[MessageR
             if person_name == sender:
                 translate_text = message["processed_plain_text"]
 
-                # 检查是否有 回复<aaa:bbb> 字段
-                reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
-                if match := re.search(reply_pattern, translate_text):
-                    aaa = match.group(1)
-                    bbb = match.group(2)
-                    reply_person_id = get_person_info_manager().get_person_id(platform, bbb)
-                    reply_person_name = await get_person_info_manager().get_value(reply_person_id, "person_name") or aaa
-                    # 在内容前加上回复信息
-                    translate_text = re.sub(reply_pattern, f"回复 {reply_person_name}", translate_text, count=1)
-
-                # 检查是否有 @<aaa:bbb> 字段
-                at_pattern = r"@<([^:<>]+):([^:<>]+)>"
-                at_matches = list(re.finditer(at_pattern, translate_text))
-                if at_matches:
-                    new_content = ""
-                    last_end = 0
-                    for m in at_matches:
-                        new_content += translate_text[last_end : m.start()]
-                        aaa = m.group(1)
-                        bbb = m.group(2)
-                        at_person_id = get_person_info_manager().get_person_id(platform, bbb)
-                        at_person_name = await get_person_info_manager().get_value(at_person_id, "person_name") or aaa
-                        new_content += f"@{at_person_name}"
-                        last_end = m.end()
-                    new_content += translate_text[last_end:]
-                    translate_text = new_content
+                # 使用独立函数处理用户引用格式
+                translate_text = await replace_user_references_in_content(translate_text, platform, is_async=True)
 
                 similarity = difflib.SequenceMatcher(None, text, translate_text).ratio()
                 if similarity >= 0.9: