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

@@ -65,6 +65,43 @@ class CacheManager:
             """)
             conn.commit()
 
+    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:
@@ -102,7 +139,9 @@ class CacheManager:
         if semantic_query and self.embedding_model:
             embedding_result = await self.embedding_model.get_embedding(semantic_query)
             if embedding_result:
-                query_embedding = np.array([embedding_result], dtype='float32')
+                validated_embedding = self._validate_embedding(embedding_result)
+                if validated_embedding is not None:
+                    query_embedding = np.array([validated_embedding], dtype='float32')
 
         # 步骤 2a: L1 语义缓存 (FAISS)
         if query_embedding is not None and self.l1_vector_index.ntotal > 0:
@@ -153,10 +192,13 @@ class CacheManager:
                             # 回填 L1
                             self.l1_kv_cache[key] = {"data": data, "expires_at": expires_at}
                             if query_embedding is not None:
-                                new_id = self.l1_vector_index.ntotal
-                                faiss.normalize_L2(query_embedding)
-                                self.l1_vector_index.add(x=query_embedding)
-                                self.l1_vector_id_to_key[new_id] = key
+                                try:
+                                    new_id = self.l1_vector_index.ntotal
+                                    faiss.normalize_L2(query_embedding)
+                                    self.l1_vector_index.add(x=query_embedding)
+                                    self.l1_vector_id_to_key[new_id] = key
+                                except Exception as e:
+                                    logger.error(f"回填L1向量索引时发生错误: {e}")
                             return data
 
         logger.debug(f"缓存未命中: {key}")
@@ -186,14 +228,20 @@ class CacheManager:
         if semantic_query and self.embedding_model:
             embedding_result = await self.embedding_model.get_embedding(semantic_query)
             if embedding_result:
-                embedding = np.array([embedding_result], dtype='float32')
-                # 写入 L1 Vector
-                new_id = self.l1_vector_index.ntotal
-                faiss.normalize_L2(embedding)
-                self.l1_vector_index.add(x=embedding)
-                self.l1_vector_id_to_key[new_id] = key
-                # 写入 L2 Vector
-                self.chroma_collection.add(embeddings=embedding.tolist(), ids=[key])
+                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
+                        faiss.normalize_L2(embedding)
+                        self.l1_vector_index.add(x=embedding)
+                        self.l1_vector_id_to_key[new_id] = key
+                        # 写入 L2 Vector
+                        self.chroma_collection.add(embeddings=embedding.tolist(), ids=[key])
+                    except Exception as 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")