feat(cache): 增强嵌入向量处理的健壮性和验证

- 新增 `_validate_embedding` 方法，用于在存入缓存前对嵌入向量进行严格的格式检查、维度验证和数值有效性校验。
- 在缓存查询 (`get`) 和写入 (`set`) 流程中，集成此验证逻辑，确保只有合规的向量才能被处理和存储。
- 增加了在L1和L2向量索引操作中的异常捕获，防止因向量处理失败导致缓存功能中断，提升了系统的整体稳定性。

src/common/cache_manager.py

@@ -90,6 +90,43 @@ class CacheManager:
             logger.error(f"验证嵌入向量时发生错误: {e}")
             return None
 
+    def _validate_embedding(self, embedding_result: Any) -> Optional[np.ndarray]:
+        """
+        验证和标准化嵌入向量格式
+        """
+        try:
+            if embedding_result is None:
+                return None
+                
+            # 确保embedding_result是一维数组或列表
+            if isinstance(embedding_result, (list, tuple, np.ndarray)):
+                # 转换为numpy数组进行处理
+                embedding_array = np.array(embedding_result)
+                
+                # 如果是多维数组，展平它
+                if embedding_array.ndim > 1:
+                    embedding_array = embedding_array.flatten()
+                
+                # 检查维度是否符合预期
+                expected_dim = global_config.lpmm_knowledge.embedding_dimension
+                if embedding_array.shape[0] != expected_dim:
+                    logger.warning(f"嵌入向量维度不匹配: 期望 {expected_dim}, 实际 {embedding_array.shape[0]}")
+                    return None
+                
+                # 检查是否包含有效的数值
+                if np.isnan(embedding_array).any() or np.isinf(embedding_array).any():
+                    logger.warning("嵌入向量包含无效的数值 (NaN 或 Inf)")
+                    return None
+                
+                return embedding_array.astype('float32')
+            else:
+                logger.warning(f"嵌入结果格式不支持: {type(embedding_result)}")
+                return None
+                
+        except Exception as e:
+            logger.error(f"验证嵌入向量时发生错误: {e}")
+            return None
+
     def _generate_key(self, tool_name: str, function_args: Dict[str, Any], tool_class: Any) -> str:
         """生成确定性的缓存键，包含代码哈希以实现自动失效。"""
         try:
@@ -179,30 +216,23 @@ class CacheManager:
                 )
 
         # 步骤 2c: L2 语义缓存 (ChromaDB)
-        if query_embedding is not None and self.chroma_collection:
+        if query_embedding is not None:
-            try:
             results = self.chroma_collection.query(query_embeddings=query_embedding.tolist(), n_results=1)
             if results and results['ids'] and results['ids'][0]:
                 distance = results['distances'][0][0] if results['distances'] and results['distances'][0] else 'N/A'
                 logger.debug(f"L2语义搜索找到最相似的结果: id={results['ids'][0]}, 距离={distance}")
                 if distance != 'N/A' and distance < 0.75:
-                        l2_hit_key = results['ids'][0][0] if isinstance(results['ids'][0], list) else results['ids'][0]
+                    l2_hit_key = results['ids'][0]
                     logger.info(f"命中L2语义缓存: key='{l2_hit_key}', 距离={distance:.4f}")
-                        
+                    with sqlite3.connect(self.db_path) as conn:
-                        # 从数据库获取缓存数据
+                        cursor = conn.cursor()
-                        semantic_cache_results = await db_query(
+                    cursor.execute("SELECT value, expires_at FROM cache WHERE key = ?", (l2_hit_key if isinstance(l2_hit_key, str) else l2_hit_key[0],))
-                            model_class=CacheEntries,
+                    row = cursor.fetchone()
-                            query_type="get",
+                    if row:
-                            filters={"cache_key": l2_hit_key},
+                        value, expires_at = row
-                            single_result=True
-                        )
-                        
-                        if semantic_cache_results:
-                            expires_at = semantic_cache_results["expires_at"]
                         if time.time() < expires_at:
-                                data = json.loads(semantic_cache_results["cache_value"])
+                            data = json.loads(value)
                             logger.debug(f"L2语义缓存返回的数据: {data}")
-                                
                             # 回填 L1
                             self.l1_kv_cache[key] = {"data": data, "expires_at": expires_at}
                             if query_embedding is not None:
@@ -214,8 +244,6 @@ class CacheManager:
                                 except Exception as e:
                                     logger.error(f"回填L1向量索引时发生错误: {e}")
                             return data
-            except Exception as e:
-                logger.warning(f"ChromaDB查询失败: {e}")
 
         logger.debug(f"缓存未命中: {key}")
         return None
@@ -252,12 +280,12 @@ class CacheManager:
         )
 
         # 写入语义缓存
-        if semantic_query and self.embedding_model and self.chroma_collection:
+        if semantic_query and self.embedding_model:
-            try:
             embedding_result = await self.embedding_model.get_embedding(semantic_query)
             if embedding_result:
                 validated_embedding = self._validate_embedding(embedding_result)
                 if validated_embedding is not None:
+                    try:
                         embedding = np.array([validated_embedding], dtype='float32')
                         # 写入 L1 Vector
                         new_id = self.l1_vector_index.ntotal
@@ -267,7 +295,8 @@ class CacheManager:
                         # 写入 L2 Vector
                         self.chroma_collection.add(embeddings=embedding.tolist(), ids=[key])
                     except Exception as e:
-                logger.warning(f"语义缓存写入失败: {e}")
+                        logger.error(f"写入语义缓存时发生错误: {e}")
+                        # 继续执行，不影响主要缓存功能
 
         logger.info(f"已缓存条目: {key}, TTL: {ttl}s")