feat: 表情包匹配从情绪匹配改成嵌入相似度匹配

src/plugins/chat/bot.py

@@ -174,7 +174,7 @@ class ChatBot:
             
             bot_response_time = tinking_time_point
             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/emoji_manager.py

@@ -15,11 +15,12 @@ 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 utils import get_embedding
+from ..chat.utils import get_embedding
 
 driver = get_driver()
 config = driver.config
@@ -39,7 +40,7 @@ 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)
         
     def _ensure_emoji_dir(self):
         """确保表情存储目录存在"""
@@ -99,44 +100,43 @@ class EmojiManager:
                 logger.error("无法获取文本的embedding")
                 return None
             
-            # 使用embedding进行相似度搜索，获取最相似的3个表情包
-            pipeline = [
-                {
-                    "$search": {
-                        "index": "default",
-                        "knnBeta": {
-                            "vector": text_embedding,
-                            "path": "embedding",
-                            "k": 3
-                        }
-                    }
-                }
-            ]
-            
             try:
-                # 获取搜索结果
+                # 获取所有表情包
-                results = list(self.db.db.emoji.aggregate(pipeline))
+                all_emojis = list(self.db.db.emoji.find({}, {'_id': 1, 'path': 1, 'embedding': 1, 'discription': 1}))
                 
-                if not results:
+                if not all_emojis:
-                    logger.warning("未找到匹配的表情包，尝试随机选择")
+                    logger.warning("数据库中没有任何表情包")
-                    # 如果没有匹配的表情，随机选择一个
+                    return None
-                    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:
-                        logger.error("数据库中没有任何表情")
-                        return None
                 
-                # 从最相似的3个表情包中随机选择一个
+                # 计算余弦相似度并排序
-                selected_emoji = random.choice(results)
+                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
@@ -144,7 +144,7 @@ class EmojiManager:
                         {'_id': selected_emoji['_id']},
                         {'$inc': {'usage_count': 1}}
                     )
-                    logger.success(f"找到匹配的表情包: {selected_emoji.get('discription', '无描述')}")
+                    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:
@@ -286,6 +286,14 @@ class EmojiManager:
             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:
+            print(f"\033[1;36m[表情包]\033[0m 开始扫描新表情包...")
+            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