feat(embedding): 优化embedding缓存管理，避免无关标签的缓存影响内存使用

src/chat/interest_system/bot_interest_manager.py

@@ -331,7 +331,12 @@ class BotInterestManager:
         # 尝试从文件加载缓存
         file_cache = await self._load_embedding_cache_from_file(interests.personality_id)
         if file_cache:
-            self.embedding_cache.update(file_cache)
+            allowed_keys = {tag.tag_name for tag in interests.interest_tags}
+            filtered_cache = {key: value for key, value in file_cache.items() if key in allowed_keys}
+            dropped_cache = len(file_cache) - len(filtered_cache)
+            if dropped_cache > 0:
+                logger.debug(f"🧹 跳过 {dropped_cache} 个与当前兴趣标签无关的缓存embedding")
+            self.embedding_cache.update(filtered_cache)
 
         memory_cached_count = 0
         file_cached_count = 0
@@ -371,13 +376,16 @@ class BotInterestManager:
 
         interests.last_updated = datetime.now()
 
-    async def _get_embedding(self, text: str) -> list[float]:
+    async def _get_embedding(self, text: str, cache: bool = True) -> list[float]:
-        """获取文本的embedding向量"""
+        """获取文本的embedding向量
+
+        cache=False 用于消息内容，避免在 embedding_cache 中长期保留大文本导致内存膨胀。
+        """
         if not hasattr(self, "embedding_request"):
             raise RuntimeError("❌ Embedding请求客户端未初始化")
 
         # 检查缓存
-        if text in self.embedding_cache:
+        if cache and text in self.embedding_cache:
             return self.embedding_cache[text]
 
         # 使用LLMRequest获取embedding
@@ -389,10 +397,12 @@ class BotInterestManager:
             if isinstance(embedding[0], list):
                 # If it's a list of lists, take the first one (though get_embedding(str) should return list[float])
                 embedding = embedding[0]
-            
+
             # Now we can safely cast to list[float] as we've handled the nested list case
             embedding_float = cast(list[float], embedding)
-            self.embedding_cache[text] = embedding_float
+
+            if cache:
+                self.embedding_cache[text] = embedding_float
 
             current_dim = len(embedding_float)
             if self._detected_embedding_dimension is None:
@@ -424,7 +434,7 @@ class BotInterestManager:
             combined_text = message_text
 
         # 生成embedding
-        embedding = await self._get_embedding(combined_text)
+        embedding = await self._get_embedding(combined_text, cache=False)
         return embedding
 
     async def generate_embeddings_for_texts(
@@ -531,7 +541,8 @@ class BotInterestManager:
         # 生成消息的embedding
         logger.debug("正在生成消息 embedding...")
         if not message_embedding:
-            message_embedding = await self._get_embedding(message_text)
+            # 消息文本embedding不入全局缓存，避免缓存随着对话历史无限增长
+            message_embedding = await self._get_embedding(message_text, cache=False)
         logger.debug(f"消息 embedding 生成成功, 维度: {len(message_embedding)}")
 
         # 计算与每个兴趣标签的相似度（使用扩展标签）
@@ -1104,12 +1115,17 @@ class BotInterestManager:
             if self.embedding_config and hasattr(self.embedding_config, "model_list") and self.embedding_config.model_list:
                  current_embedding_model = self.embedding_config.model_list[0]
 
+            tag_embeddings = self.embedding_cache
+            if self.current_interests:
+                allowed_keys = {tag.tag_name for tag in self.current_interests.interest_tags}
+                tag_embeddings = {key: value for key, value in self.embedding_cache.items() if key in allowed_keys}
+
             cache_data = {
                 "version": 1,
                 "personality_id": personality_id,
                 "embedding_model": current_embedding_model,
                 "last_updated": datetime.now().isoformat(),
-                "embeddings": self.embedding_cache,
+                "embeddings": tag_embeddings,
                 "expanded_embeddings": self.expanded_embedding_cache,  # 同时保存扩展标签的embedding
             }