Merge branch 'SengokuCola:debug' into debug

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,38 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Describe the bug**
-A clear and concise description of what the bug is.
-
-**To Reproduce**
-Steps to reproduce the behavior:
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Desktop (please complete the following information):**
- - OS: [e.g. iOS]
- - Browser [e.g. chrome, safari]
- - Version [e.g. 22]
-
-**Smartphone (please complete the following information):**
- - Device: [e.g. iPhone6]
- - OS: [e.g. iOS8.1]
- - Browser [e.g. stock browser, safari]
- - Version [e.g. 22]
-
-**Additional context**
-Add any other context about the problem here.

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,47 @@
+name: Bug Report
+description: 提交 Bug
+labels: ["BUG"]
+body:
+- type: checkboxes
+  attributes:
+    label: "检查项"
+    description: "请检查下列项目，并勾选确认。"
+    options:
+    - label: "我确认此问题在所有分支的最新版本中依旧存在"
+      required: true
+    - label: "我确认在 Issues 列表中并无其他人已经提出过与此问题相同或相似的问题"
+      required: true
+    - label: "我使用了 Docker"
+- type: textarea
+  attributes:
+    label: 遇到的问题
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: 报错信息
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: 如何重现此问题？
+    placeholder: "若不知道请略过此问题"
+- type: textarea
+  attributes:
+    label: 可能造成问题的原因
+    placeholder: "若不知道请略过此问题"
+- type: textarea
+  attributes:
+    label: 系统环境 
+    placeholder: "例如：Windows 11 专业版 64位 24H2 / Debian Bookworm"
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Python 版本 
+    placeholder: "例如：Python 3.11"
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: 补充信息

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,20 +0,0 @@
----
-name: Feature request
-about: Suggest an idea for this project
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Is your feature request related to a problem? Please describe.**
-A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
-
-**Describe the solution you'd like**
-A clear and concise description of what you want to happen.
-
-**Describe alternatives you've considered**
-A clear and concise description of any alternative solutions or features you've considered.
-
-**Additional context**
-Add any other context or screenshots about the feature request here.

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,20 @@
+name: Feature Request
+description: 新功能请求
+labels: ["Feature"]
+body:
+- type: checkboxes
+  attributes:
+    label: "检查项"
+    description: "请检查下列项目，并勾选确认。"
+    options:
+    - label: "我确认在Issues列表中并无其他人已经建议过相似的功能"
+      required: true
+    - label: "这个新功能可以解决目前存在的某个问题或BUG"
+- type: textarea
+  attributes:
+    label: 期望的功能描述
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: 补充信息

README.md

@@ -18,6 +18,8 @@
 - 💾 MongoDB 提供数据持久化支持
 - 🐧 NapCat 作为QQ协议端支持
 
+**最新版本: v0.5.7**
+
 <div align="center">
 <a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
     <img src="docs/video.png" width="300" alt="麦麦演示视频">
@@ -31,6 +33,7 @@
 > - 文档未完善，有问题可以提交 Issue 或者 Discussion
 > - QQ机器人存在被限制风险，请自行了解，谨慎使用
 > - 由于持续迭代，可能存在一些已知或未知的bug
+> - 由于开发中，可能消耗较多token
 
 **交流群**: 766798517（仅用于开发和建议相关讨论）不建议在群内询问部署问题，我不一定有空回复，会优先写文档和代码
 

bot.py

@@ -8,7 +8,7 @@ from loguru import logger
 from colorama import init, Fore
 
 init()
-text = "多年以后，面对行刑队，张三将会回想起他2023年在会议上讨论人工智能的那个下午"
+text = "多年以后，面对AI行刑队，张三将会回想起他2023年在会议上讨论人工智能的那个下午"
 rainbow_colors = [Fore.RED, Fore.YELLOW, Fore.GREEN, Fore.CYAN, Fore.BLUE, Fore.MAGENTA]
 rainbow_text = ""
 for i, char in enumerate(text):

run_windows.bat

@@ -0,0 +1,67 @@
+@echo off
+setlocal enabledelayedexpansion
+chcp 65001
+
+REM 修正路径获取逻辑
+cd /d "%~dp0" || (
+    echo 错误：切换目录失败
+    exit /b 1
+)
+
+if not exist "venv\" (
+    echo 正在初始化虚拟环境...
+
+    where python >nul 2>&1
+    if %errorlevel% neq 0 (
+        echo 未找到Python解释器
+        exit /b 1
+    )
+
+    for /f "tokens=2" %%a in ('python --version 2^>^&1') do set version=%%a
+    for /f "tokens=1,2 delims=." %%b in ("!version!") do (
+        set major=%%b
+        set minor=%%c
+    )
+
+    if !major! lss 3 (
+        echo 需要Python大于等于3.0，当前版本 !version!
+        exit /b 1
+    )
+
+    if !major! equ 3 if !minor! lss 9 (
+        echo 需要Python大于等于3.9，当前版本 !version!
+        exit /b 1
+    )
+
+    echo 正在安装virtualenv...
+    python -m pip install virtualenv || (
+        echo virtualenv安装失败
+        exit /b 1
+    )
+
+    echo 正在创建虚拟环境...
+    python -m virtualenv venv || (
+        echo 虚拟环境创建失败
+        exit /b 1
+    )
+
+    call venv\Scripts\activate.bat
+
+    echo 正在安装依赖...
+    pip install -r requirements.txt
+) else (
+    call venv\Scripts\activate.bat
+)
+
+echo 当前代理设置：
+echo HTTP_PROXY=%HTTP_PROXY%
+echo HTTPS_PROXY=%HTTPS_PROXY%
+
+set HTTP_PROXY=
+set HTTPS_PROXY=
+echo 代理已取消。
+
+set no_proxy=0.0.0.0/32
+
+call nb run
+pause

src/plugins/chat/__init__.py

@@ -15,6 +15,8 @@ from .bot import chat_bot
 from .emoji_manager import emoji_manager
 import time
 
+# 添加标志变量
+_message_manager_started = False
 
 # 获取驱动器
 driver = get_driver()
@@ -70,18 +72,20 @@ async def init_relationships():
 @driver.on_bot_connect
 async def _(bot: Bot):
     """Bot连接成功时的处理"""
+    global _message_manager_started
     print(f"\033[1;38;5;208m-----------{global_config.BOT_NICKNAME}成功连接！-----------\033[0m")
     await willing_manager.ensure_started()
     
-    
     message_sender.set_bot(bot)
     print("\033[1;38;5;208m-----------消息发送器已启动！-----------\033[0m")
+    
+    if not _message_manager_started:
         asyncio.create_task(message_manager.start_processor())
+        _message_manager_started = True
         print("\033[1;38;5;208m-----------消息处理器已启动！-----------\033[0m")
     
     asyncio.create_task(emoji_manager._periodic_scan(interval_MINS=global_config.EMOJI_REGISTER_INTERVAL))
     print("\033[1;38;5;208m-----------开始偷表情包！-----------\033[0m")
-    # 启动消息发送控制任务
     
 @group_msg.handle()
 async def _(bot: Bot, event: GroupMessageEvent, state: T_State):
@@ -90,7 +94,7 @@ async def _(bot: Bot, event: GroupMessageEvent, state: T_State):
 # 添加build_memory定时任务
 @scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval, id="build_memory")
 async def build_memory_task():
-    """每30秒执行一次记忆构建"""
+    """每build_memory_interval秒执行一次记忆构建"""
     print("\033[1;32m[记忆构建]\033[0m -------------------------------------------开始构建记忆-------------------------------------------")
     start_time = time.time()
     await hippocampus.operation_build_memory(chat_size=20)

src/plugins/chat/bot.py

@@ -132,6 +132,7 @@ class ChatBot:
             accu_typing_time = 0
             
             # print(f"\033[1;32m[开始回复]\033[0m 开始将回复1载入发送容器")
+            mark_head = False
             for msg in response:
                 # print(f"\033[1;32m[回复内容]\033[0m {msg}")
                 #通过时间改变时间戳
@@ -152,6 +153,9 @@ class ChatBot:
                     thinking_start_time=thinking_start_time, #记录了思考开始的时间
                     reply_message_id=message.message_id
                 )
+                if not mark_head:
+                    bot_message.is_head = True
+                    mark_head = True
                 message_set.add_message(bot_message)
                 
             #message_set 可以直接加入 message_manager
@@ -167,7 +171,7 @@ class ChatBot:
             await relationship_manager.update_relationship_value(message.user_id, relationship_value=valuedict[emotion[0]])
 
             if random() < global_config.emoji_chance:
-                emoji_path = await emoji_manager.get_emoji_for_emotion(emotion)
+                emoji_path = await emoji_manager.get_emoji_for_text(response)
                 if emoji_path:
                     emoji_cq = CQCode.create_emoji_cq(emoji_path)
                     

src/plugins/chat/config.py

@@ -30,6 +30,7 @@ class BotConfig:
     forget_memory_interval: int = 300  # 记忆遗忘间隔（秒）
     EMOJI_CHECK_INTERVAL: int = 120  # 表情包检查间隔（分钟）
     EMOJI_REGISTER_INTERVAL: int = 10  # 表情包注册间隔（分钟）
+    EMOJI_CHECK_PROMPT: str = "不要包含违反公序良俗的内容" # 表情包过滤要求
 
     ban_words = set()
     
@@ -94,6 +95,7 @@ class BotConfig:
                 emoji_config = toml_dict["emoji"]
                 config.EMOJI_CHECK_INTERVAL = emoji_config.get("check_interval", config.EMOJI_CHECK_INTERVAL)
                 config.EMOJI_REGISTER_INTERVAL = emoji_config.get("register_interval", config.EMOJI_REGISTER_INTERVAL)
+                config.EMOJI_CHECK_PROMPT = emoji_config.get('check_prompt',config.EMOJI_CHECK_PROMPT)
             
             if "cq_code" in toml_dict:
                 cq_code_config = toml_dict["cq_code"]

src/plugins/chat/emoji_manager.py

@@ -14,10 +14,13 @@ import asyncio
 import time
 from PIL import Image
 import io
+from loguru import logger
+import traceback
 
 from nonebot import get_driver
 from ..chat.config import global_config
 from ..models.utils_model import LLM_request
+from ..chat.utils import get_embedding
 
 driver = get_driver()
 config = driver.config
@@ -47,7 +50,8 @@ class EmojiManager:
     def __init__(self):
         self.db = Database.get_instance()
         self._scan_task = None
-        self.llm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=50)
+        self.llm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000)
+        self.lm = LLM_request(model=global_config.llm_reasoning_minor, max_tokens=1000)
         
     def _ensure_emoji_dir(self):
         """确保表情存储目录存在"""
@@ -64,7 +68,7 @@ class EmojiManager:
                 # 启动时执行一次完整性检查
                 self.check_emoji_file_integrity()
             except Exception as e:
-                print(f"\033[1;31m[错误]\033[0m 初始化表情管理器失败: {str(e)}")
+                logger.error(f"初始化表情管理器失败: {str(e)}")
                 
     def _ensure_db(self):
         """确保数据库已初始化"""
@@ -77,6 +81,7 @@ class EmojiManager:
         """确保emoji集合存在并创建索引"""
         if 'emoji' not in self.db.db.list_collection_names():
             self.db.db.create_collection('emoji')
+            self.db.db.emoji.create_index([('embedding', '2dsphere')])
             self.db.db.emoji.create_index([('tags', 1)])
             self.db.db.emoji.create_index([('filename', 1)], unique=True)
             
@@ -89,78 +94,7 @@ class EmojiManager:
                 {'$inc': {'usage_count': 1}}
             )
         except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m 记录表情使用失败: {str(e)}")
+            logger.error(f"记录表情使用失败: {str(e)}")
-            
-    async def _get_emotion_from_text(self, text: str) -> List[str]:
-        """从文本中识别情感关键词
-        Args:
-            text: 输入文本
-        Returns:
-            List[str]: 匹配到的情感标签列表
-        """
-        try:
-            prompt = f'分析这段文本："{text}"，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签。只需要返回标签，不要输出其他任何内容。'
-            
-            content, _ = await self.llm.generate_response(prompt)
-            emotion = content.strip().lower()
-            
-            if emotion in self.EMOTION_KEYWORDS:
-                print(f"\033[1;32m[成功]\033[0m 识别到的情感: {emotion}")
-                return [emotion]
-            
-            return ['neutral']
-            
-        except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m 情感分析失败: {str(e)}")
-            return ['neutral']
-
-    async def get_emoji_for_emotion(self, emotion_tag: str) -> Optional[str]:
-        try:
-            self._ensure_db()
-                
-            # 构建查询条件：标签匹配任一情感
-            query = {'tags': {'$in': emotion_tag}}
-            
-            # print(f"\033[1;34m[调试]\033[0m 表情查询条件: {query}")
-            
-            try:
-                # 随机获取一个匹配的表情
-                emoji = self.db.db.emoji.aggregate([
-                    {'$match': query},
-                    {'$sample': {'size': 1}}
-                ]).next()
-                print(f"\033[1;32m[成功]\033[0m 找到匹配的表情")
-                if emoji and 'path' in emoji:
-                    # 更新使用次数
-                    self.db.db.emoji.update_one(
-                        {'_id': emoji['_id']},
-                        {'$inc': {'usage_count': 1}}
-                    )
-                    return emoji['path']
-            except StopIteration:
-                # 如果没有匹配的表情，从所有表情中随机选择一个
-                print(f"\033[1;33m[提示]\033[0m 未找到匹配的表情，随机选择一个")
-                try:
-                    emoji = self.db.db.emoji.aggregate([
-                        {'$sample': {'size': 1}}
-                    ]).next()
-                    if emoji and 'path' in emoji:
-                        # 更新使用次数
-                        self.db.db.emoji.update_one(
-                            {'_id': emoji['_id']},
-                            {'$inc': {'usage_count': 1}}
-                        )
-                        return emoji['path']
-                except StopIteration:
-                    print(f"\033[1;31m[错误]\033[0m 数据库中没有任何表情")
-                    return None
-            
-            return None
-            
-        except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m 获取表情包失败: {str(e)}")
-            return None
-
             
     async def get_emoji_for_text(self, text: str) -> Optional[str]:
         """根据文本内容获取相关表情包
@@ -171,54 +105,69 @@ class EmojiManager:
         """
         try:
             self._ensure_db()
-            # 获取情感标签
+            
-            emotions = await self._get_emotion_from_text(text)
+            # 获取文本的embedding
-            print("为  ‘"+ str(text) + "’  获取到的情感标签为：" + str(emotions))
+            text_for_search= await self._get_kimoji_for_text(text)
-            if not emotions:
+            text_embedding = get_embedding(text_for_search)
+            if not text_embedding:
+                logger.error("无法获取文本的embedding")
                 return None
             
-            # 构建查询条件：标签匹配任一情感
-            query = {'tags': {'$in': emotions}}
-            
-            print(f"\033[1;34m[调试]\033[0m 表情查询条件: {query}")
-            print(f"\033[1;34m[调试]\033[0m 匹配到的情感: {emotions}")
-            
             try:
-                # 随机获取一个匹配的表情
+                # 获取所有表情包
-                emoji = self.db.db.emoji.aggregate([
+                all_emojis = list(self.db.db.emoji.find({}, {'_id': 1, 'path': 1, 'embedding': 1, 'discription': 1}))
-                    {'$match': query},
+                
-                    {'$sample': {'size': 1}}
+                if not all_emojis:
-                ]).next()
+                    logger.warning("数据库中没有任何表情包")
-                print(f"\033[1;32m[成功]\033[0m 找到匹配的表情")
+                    return None
-                if emoji and 'path' in emoji:
+                
+                # 计算余弦相似度并排序
+                def cosine_similarity(v1, v2):
+                    if not v1 or not v2:
+                        return 0
+                    dot_product = sum(a * b for a, b in zip(v1, v2))
+                    norm_v1 = sum(a * a for a in v1) ** 0.5
+                    norm_v2 = sum(b * b for b in v2) ** 0.5
+                    if norm_v1 == 0 or norm_v2 == 0:
+                        return 0
+                    return dot_product / (norm_v1 * norm_v2)
+                
+                # 计算所有表情包与输入文本的相似度
+                emoji_similarities = [
+                    (emoji, cosine_similarity(text_embedding, emoji.get('embedding', [])))
+                    for emoji in all_emojis
+                ]
+                
+                # 按相似度降序排序
+                emoji_similarities.sort(key=lambda x: x[1], reverse=True)
+                
+                # 获取前3个最相似的表情包
+                top_3_emojis = emoji_similarities[:3]
+                
+                if not top_3_emojis:
+                    logger.warning("未找到匹配的表情包")
+                    return None
+                
+                # 从前3个中随机选择一个
+                selected_emoji, similarity = random.choice(top_3_emojis)
+                
+                if selected_emoji and 'path' in selected_emoji:
                     # 更新使用次数
                     self.db.db.emoji.update_one(
-                        {'_id': emoji['_id']},
+                        {'_id': selected_emoji['_id']},
                         {'$inc': {'usage_count': 1}}
                     )
-                    return emoji['path']
+                    logger.success(f"找到匹配的表情包: {selected_emoji.get('discription', '无描述')} (相似度: {similarity:.4f})")
-            except StopIteration:
+                    return selected_emoji['path']
-                # 如果没有匹配的表情，从所有表情中随机选择一个
+                    
-                print(f"\033[1;33m[提示]\033[0m 未找到匹配的表情，随机选择一个")
+            except Exception as search_error:
-                try:
+                logger.error(f"搜索表情包失败: {str(search_error)}")
-                    emoji = self.db.db.emoji.aggregate([
-                        {'$sample': {'size': 1}}
-                    ]).next()
-                    if emoji and 'path' in emoji:
-                        # 更新使用次数
-                        self.db.db.emoji.update_one(
-                            {'_id': emoji['_id']},
-                            {'$inc': {'usage_count': 1}}
-                        )
-                        return emoji['path']
-                except StopIteration:
-                    print(f"\033[1;31m[错误]\033[0m 数据库中没有任何表情")
                 return None
             
             return None
             
         except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m 获取表情包失败: {str(e)}")
+            logger.error(f"获取表情包失败: {str(e)}")
             return None
 
     async def _get_emoji_tag(self, image_base64: str) -> str:
@@ -237,10 +186,47 @@ class EmojiManager:
             
         except Exception as e:
             print(f"\033[1;31m[错误]\033[0m 获取标签失败: {str(e)}")
-            return "skip"
+            return "neutral"
         
         print(f"\033[1;32m[调试信息]\033[0m 使用默认标签: neutral")
-        return "skip"  # 默认标签
+        return "neutral"  # 默认标签
+
+    async def _get_emoji_discription(self, image_base64: str) -> str:
+        """获取表情包的标签"""
+        try:
+            prompt = '这是一个表情包，使用中文简洁的描述一下表情包的内容和表情包所表达的情感'
+            
+            content, _ = await self.llm.generate_response_for_image(prompt, image_base64)
+            logger.debug(f"输出描述: {content}")
+            return content
+            
+        except Exception as e:
+            logger.error(f"获取标签失败: {str(e)}")
+            return None
+    
+    async def _check_emoji(self, image_base64: str) -> str:
+        try:
+            prompt = f'这是一个表情包，请回答这个表情包是否满足\"{global_config.EMOJI_CHECK_PROMPT}\"的要求，是则回答是，否则回答否，不要出现任何其他内容'
+            
+            content, _ = await self.llm.generate_response_for_image(prompt, image_base64)
+            logger.debug(f"输出描述: {content}")
+            return content
+            
+        except Exception as e:
+            logger.error(f"获取标签失败: {str(e)}")
+            return None
+        
+    async def _get_kimoji_for_text(self, text:str):
+        try:
+            prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对内容的分析内容，只输出\"一种什么样的感觉\"中间的形容词部分。'
+            
+            content, _ = await self.lm.generate_response_async(prompt)
+            logger.info(f"输出描述: {content}")
+            return content
+            
+        except Exception as e:
+            logger.error(f"获取标签失败: {str(e)}")
+            return None
         
     async def _compress_image(self, image_path: str, target_size: int = 0.8 * 1024 * 1024) -> Optional[str]:
         """压缩图片并返回base64编码
@@ -303,12 +289,12 @@ class EmojiManager:
                 
                 # 获取压缩后的数据并转换为base64
                 compressed_data = output_buffer.getvalue()
-                print(f"\033[1;32m[成功]\033[0m 压缩图片: {os.path.basename(image_path)} ({original_width}x{original_height} -> {new_width}x{new_height})")
+                logger.success(f"压缩图片: {os.path.basename(image_path)} ({original_width}x{original_height} -> {new_width}x{new_height})")
                 
                 return base64.b64encode(compressed_data).decode('utf-8')
                 
         except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m 压缩图片失败: {os.path.basename(image_path)}, 错误: {str(e)}")
+            logger.error(f"压缩图片失败: {os.path.basename(image_path)}, 错误: {str(e)}")
             return None
             
     async def scan_new_emojis(self):
@@ -334,28 +320,38 @@ class EmojiManager:
                     os.remove(image_path)
                     continue
                 
-                # 获取表情包的情感标签
+                # 获取表情包的描述
+                discription = await self._get_emoji_discription(image_base64)
+                check = await self._check_emoji(image_base64)
+                if '是' not in check:
+                    os.remove(image_path)
+                    logger.info(f"描述: {discription}")
+                    logger.info(f"其不满足过滤规则，被剔除 {check}")
+                    continue
+                logger.info(f"check通过 {check}")
                 tag = await self._get_emoji_tag(image_base64)
-                if not tag == "skip":
+                embedding = get_embedding(discription)
+                if discription is not None:
                     # 准备数据库记录
                     emoji_record = {
                         'filename': filename,
                         'path': image_path,
-                        'tags': [tag],
+                        'embedding':embedding,
+                        'discription': discription,
+                        'tag':tag,
                         'timestamp': int(time.time())
                     }
                     
                     # 保存到数据库
                     self.db.db['emoji'].insert_one(emoji_record)
-                    print(f"\033[1;32m[成功]\033[0m 注册新表情包: {filename}")
+                    logger.success(f"注册新表情包: {filename}")
-                    print(f"标签: {tag}")
+                    logger.info(f"描述: {discription}")
                 else:
-                    print(f"\033[1;33m[警告]\033[0m 跳过表情包: {filename}")
+                    logger.warning(f"跳过表情包: {filename}")
                 
         except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m 扫描表情包失败: {str(e)}")
+            logger.error(f"扫描表情包失败: {str(e)}")
-            import traceback
+            logger.error(traceback.format_exc())
-            print(traceback.format_exc())
     
     async def _periodic_scan(self, interval_MINS: int = 10):
         """定期扫描新表情包"""
@@ -364,6 +360,7 @@ class EmojiManager:
             await self.scan_new_emojis()
             await asyncio.sleep(interval_MINS * 60)  # 每600秒扫描一次
 
+
     def check_emoji_file_integrity(self):
         """检查表情包文件完整性
         如果文件已被删除，则从数据库中移除对应记录
@@ -378,44 +375,42 @@ class EmojiManager:
             for emoji in all_emojis:
                 try:
                     if 'path' not in emoji:
-                        print(f"\033[1;33m[提示]\033[0m 发现无效记录（缺少path字段），ID: {emoji.get('_id', 'unknown')}")
+                        logger.warning(f"发现无效记录（缺少path字段），ID: {emoji.get('_id', 'unknown')}")
+                        self.db.db.emoji.delete_one({'_id': emoji['_id']})
+                        removed_count += 1
+                        continue
+                    
+                    if 'embedding' not in emoji:
+                        logger.warning(f"发现过时记录（缺少embedding字段），ID: {emoji.get('_id', 'unknown')}")
                         self.db.db.emoji.delete_one({'_id': emoji['_id']})
                         removed_count += 1
                         continue
                         
                     # 检查文件是否存在
                     if not os.path.exists(emoji['path']):
-                        print(f"\033[1;33m[提示]\033[0m 表情包文件已被删除: {emoji['path']}")
+                        logger.warning(f"表情包文件已被删除: {emoji['path']}")
                         # 从数据库中删除记录
                         result = self.db.db.emoji.delete_one({'_id': emoji['_id']})
                         if result.deleted_count > 0:
-                            print(f"\033[1;32m[成功]\033[0m 成功删除数据库记录: {emoji['_id']}")
+                            logger.success(f"成功删除数据库记录: {emoji['_id']}")
                             removed_count += 1
                         else:
-                            print(f"\033[1;31m[错误]\033[0m 删除数据库记录失败: {emoji['_id']}")
+                            logger.error(f"删除数据库记录失败: {emoji['_id']}")
                 except Exception as item_error:
-                    print(f"\033[1;31m[错误]\033[0m 处理表情包记录时出错: {str(item_error)}")
+                    logger.error(f"处理表情包记录时出错: {str(item_error)}")
                     continue
             
             # 验证清理结果
             remaining_count = self.db.db.emoji.count_documents({})
             if removed_count > 0:
-                print(f"\033[1;32m[成功]\033[0m 已清理 {removed_count} 个失效的表情包记录")
+                logger.success(f"已清理 {removed_count} 个失效的表情包记录")
-                print(f"\033[1;34m[统计]\033[0m 清理前总数: {total_count} | 清理后总数: {remaining_count}")
+                logger.info(f"清理前总数: {total_count} | 清理后总数: {remaining_count}")
-                # print(f"\033[1;34m[统计]\033[0m 应删除数量: {removed_count} | 实际删除数量: {total_count - remaining_count}")
-                # 执行数据库压缩
-                try:
-                    self.db.db.command({"compact": "emoji"})
-                    print(f"\033[1;32m[成功]\033[0m 数据库集合压缩完成")
-                except Exception as compact_error:
-                    print(f"\033[1;31m[错误]\033[0m 数据库压缩失败: {str(compact_error)}")
             else:
-                print(f"\033[1;36m[表情包]\033[0m 已检查 {total_count} 个表情包记录")
+                logger.info(f"已检查 {total_count} 个表情包记录")
                 
         except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m 检查表情包完整性失败: {str(e)}")
+            logger.error(f"检查表情包完整性失败: {str(e)}")
-            import traceback
+            logger.error(traceback.format_exc())
-            print(f"\033[1;31m[错误追踪]\033[0m\n{traceback.format_exc()}")
 
     async def start_periodic_check(self, interval_MINS: int = 120):
         while True:

src/plugins/chat/llm_generator.py

@@ -24,6 +24,7 @@ class ResponseGenerator:
         self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7,max_tokens=1000)
         self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7,max_tokens=1000)
         self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7,max_tokens=1000)
+        self.model_v25 = LLM_request(model=global_config.llm_normal_minor, temperature=0.7,max_tokens=1000)
         self.db = Database.get_instance()
         self.current_model_type = 'r1'  # 默认使用 R1
 
@@ -139,7 +140,7 @@ class ResponseGenerator:
             内容：{content}
             输出：
             '''
-            content, _ = await self.model_v3.generate_response(prompt)
+            content, _ = await self.model_v25.generate_response(prompt)
             content=content.strip()
             if content in ['happy','angry','sad','surprised','disgusted','fearful','neutral']:
                 return [content]

src/plugins/chat/message.py

@@ -169,6 +169,8 @@ class Message_Sending(Message):
     
     reply_message_id: int = None  # 存储 回复的 源消息ID
     
+    is_head: bool = False  # 是否是头部消息
+    
     def update_thinking_time(self):
         self.thinking_time = round(time.time(), 2) - self.thinking_start_time
         return self.thinking_time

src/plugins/chat/message_sender.py

@@ -166,11 +166,10 @@ class MessageManager:
             else:# 如果不是message_thinking就只能是message_sending    
                 print(f"\033[1;34m[调试]\033[0m 消息'{message_earliest.processed_plain_text}'正在发送中")
                 #直接发，等什么呢
-                if message_earliest.update_thinking_time() < 30:
+                if message_earliest.is_head and message_earliest.update_thinking_time() >30:
-                    await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, auto_escape=False)
-                else:
                     await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, auto_escape=False, reply_message_id=message_earliest.reply_message_id)
-                
+                else:
+                    await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, auto_escape=False)
         #移除消息
                 if message_earliest.is_emoji:
                     message_earliest.processed_plain_text = "[表情包]"
@@ -188,10 +187,11 @@ class MessageManager:
                         
                     try:
                         #发送
-                        if msg.update_thinking_time() < 30:
+                        if msg.is_head and msg.update_thinking_time() >30:
-                            await message_sender.send_group_message(group_id, msg.processed_plain_text, auto_escape=False)
-                        else:
                             await message_sender.send_group_message(group_id, msg.processed_plain_text, auto_escape=False, reply_message_id=msg.reply_message_id)
+                        else:
+                            await message_sender.send_group_message(group_id, msg.processed_plain_text, auto_escape=False)
+                            
                         
                         #如果是表情包，则替换为"[表情包]"
                         if msg.is_emoji:

src/plugins/chat/utils.py

@@ -395,13 +395,13 @@ def add_typos(text: str) -> str:
 
 def process_llm_response(text: str) -> List[str]:
     # processed_response = process_text_with_typos(content)
-    if len(text) > 200:
+    if len(text) > 300:
             print(f"回复过长 ({len(text)} 字符)，返回默认回复")
             return ['懒得说']
     # 处理长消息
     sentences = split_into_sentences_w_remove_punctuation(add_typos(text))
     # 检查分割后的消息数量是否过多（超过3条）
-    if len(sentences) > 3:
+    if len(sentences) > 4:
         print(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
         return [f'{global_config.BOT_NICKNAME}不知道哦']
     

src/plugins/models/utils_model.py

@@ -41,7 +41,7 @@ class LLM_request:
 
         # 发送请求到完整的chat/completions端点
         api_url = f"{self.base_url.rstrip('/')}/chat/completions"
-        logger.info(f"发送请求到URL: {api_url}{self.model_name}")  # 记录请求的URL
+        logger.info(f"发送请求到URL: {api_url}/{self.model_name}")  # 记录请求的URL
 
         max_retries = 3
         base_wait_time = 15
@@ -123,7 +123,7 @@ class LLM_request:
 
         # 发送请求到完整的chat/completions端点
         api_url = f"{self.base_url.rstrip('/')}/chat/completions"
-        logger.info(f"发送请求到URL: {api_url}{self.model_name}")  # 记录请求的URL
+        logger.info(f"发送请求到URL: {api_url}/{self.model_name}")  # 记录请求的URL
 
         max_retries = 3
         base_wait_time = 15
@@ -273,7 +273,7 @@ class LLM_request:
 
         # 发送请求到完整的chat/completions端点
         api_url = f"{self.base_url.rstrip('/')}/chat/completions"
-        logger.info(f"发送请求到URL: {api_url}{self.model_name}")  # 记录请求的URL
+        logger.info(f"发送请求到URL: {api_url}/{self.model_name}")  # 记录请求的URL
 
         max_retries = 2
         base_wait_time = 6
@@ -339,7 +339,7 @@ class LLM_request:
         }
 
         api_url = f"{self.base_url.rstrip('/')}/embeddings"
-        logger.info(f"发送请求到URL: {api_url}{self.model_name}")  # 记录请求的URL
+        logger.info(f"发送请求到URL: {api_url}/{self.model_name}")  # 记录请求的URL
 
         max_retries = 2
         base_wait_time = 6
@@ -396,7 +396,7 @@ class LLM_request:
         }
 
         api_url = f"{self.base_url.rstrip('/')}/embeddings"
-        logger.info(f"发送请求到URL: {api_url}{self.model_name}")  # 记录请求的URL
+        logger.info(f"发送请求到URL: {api_url}/{self.model_name}")  # 记录请求的URL
 
         max_retries = 3
         base_wait_time = 15