Merge branch 'debug' of https://github.com/SengokuCola/MaiMBot into refactor/unified_request

# Conflicts:
#	src/plugins/models/utils_model.py

src/plugins/chat/bot.py

@@ -171,7 +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()
 
@@ -96,6 +97,7 @@ 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
@@ -26,7 +29,7 @@ config = driver.config
 class EmojiManager:
     _instance = None
     EMOJI_DIR = "data/emoji"  # 表情包存储目录
-    
+
     EMOTION_KEYWORDS = {
         'happy': ['开心', '快乐', '高兴', '欢喜', '笑', '喜悦', '兴奋', '愉快', '乐', '好'],
         'angry': ['生气', '愤怒', '恼火', '不爽', '火大', '怒', '气愤', '恼怒', '发火', '不满'],
@@ -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)
             
@@ -89,79 +94,8 @@ 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
         Args:
@@ -171,54 +105,69 @@ class EmojiManager:
         """
         try:
             self._ensure_db()
-            # 获取情感标签
-            emotions = await self._get_emotion_from_text(text)
-            print("为  ‘"+ str(text) + "’  获取到的情感标签为：" + str(emotions))
-            if not emotions:
-                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}")
+            # 获取文本的embedding
+            text_for_search= await self._get_kimoji_for_text(text)
+            text_embedding = get_embedding(text_for_search)
+            if not text_embedding:
+                logger.error("无法获取文本的embedding")
+                return None
             
             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:
+                # 获取所有表情包
+                all_emojis = list(self.db.db.emoji.find({}, {'_id': 1, 'path': 1, 'embedding': 1, 'discription': 1}))
+                
+                if not all_emojis:
+                    logger.warning("数据库中没有任何表情包")
+                    return None
+                
+                # 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']},
                         {'$inc': {'usage_count': 1}}
                     )
-                    return emoji['path']
-            except StopIteration:
-                # 如果没有匹配的表情，从所有表情中随机选择一个
-                print(f"\033[1;33m[提示]\033[0m 未找到匹配的表情，随机选择一个")
-                try:
-                    emoji = self.db.db.emoji.aggregate([
-                        {'$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
+                    logger.success(f"找到匹配的表情包: {selected_emoji.get('discription', '无描述')} (相似度: {similarity:.4f})")
+                    return selected_emoji['path']
+                    
+            except Exception as search_error:
+                logger.error(f"搜索表情包失败: {str(search_error)}")
+                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:
@@ -237,11 +186,48 @@ 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
         Args:
@@ -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):
@@ -334,29 +320,39 @@ 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}")
-                    print(f"标签: {tag}")
+                    logger.success(f"注册新表情包: {filename}")
+                    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)}")
-            import traceback
-            print(traceback.format_exc())
-
+            logger.error(f"扫描表情包失败: {str(e)}")
+            logger.error(traceback.format_exc())
+    
     async def _periodic_scan(self, interval_MINS: int = 10):
         """定期扫描新表情包"""
         while True:
@@ -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")
-                print(f"\033[1;34m[统计]\033[0m 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)}")
+                logger.success(f"已清理 {removed_count} 个失效的表情包记录")
+                logger.info(f"清理前总数: {total_count} | 清理后总数: {remaining_count}")
             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)}")
-            import traceback
-            print(f"\033[1;31m[错误追踪]\033[0m\n{traceback.format_exc()}")
+            logger.error(f"检查表情包完整性失败: {str(e)}")
+            logger.error(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]