fix:推荐接口

app/service/recommendation_system/milvus_client.py

@@ -203,39 +203,74 @@ def search_similar_vectors(
         query_vector: np.ndarray,
         category: str,
         topk: int = 500,
-        style: Optional[str] = None
+        style: Optional[str] = None,
+        style_boost_ratio: float = 0.2
 ) -> List[Dict]:
     """
     向量相似度检索
-    
+
     Args:
         query_vector: 查询向量（2048维）
         category: 类别过滤
         topk: 返回数量
-        style: 风格过滤（可选）
-    
+        style: 风格过滤（可选）- 当提供时，会给对应style的结果加分
+        style_boost_ratio: 风格加分比例（默认0.1，即10%）
+
     Returns:
         检索结果列表，每个元素包含 path, score, style, category 等字段
     """
     client = get_milvus_client()
 
     try:
-        # 构建过滤表达式
-        # 使用 filter 参数而不是 expr（根据 pymilvus MilvusClient API）
-        filter_expr = f"category == '{category}' && deprecated == 0"
-        if style:
-            filter_expr += f" && style == '{style}'"
+        # 如果没有指定style，使用原始逻辑
+        if not style:
+            filter_expr = f"category == '{category}' && deprecated == 0"
+            results = client.search(
+                collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
+                data=[query_vector.tolist()],
+                anns_field="feature_vector",
+                search_params={"metric_type": "IP", "params": {"nprobe": 10}},
+                limit=topk,
+                filter=filter_expr,
+                output_fields=["path", "style", "category", "sys_file_id"]
+            )
+        else:
+            # 有style参数时，使用两阶段搜索策略
 
-        # 搜索
-        results = client.search(
-            collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
-            data=[query_vector.tolist()],
-            anns_field="feature_vector",
-            search_params={"metric_type": "IP", "params": {"nprobe": 10}},
-            limit=topk,
-            filter=filter_expr,
-            output_fields=["path", "style", "category", "sys_file_id"]
-        )
+            # 第一阶段：搜索匹配style的向量，使用boosted query vector
+            filter_expr_style = f"category == '{category}' && deprecated == 0 && style == '{style}'"
+            boosted_query = query_vector * (1 + style_boost_ratio)
+            results_style = client.search(
+                collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
+                data=[boosted_query.tolist()],
+                anns_field="feature_vector",
+                search_params={"metric_type": "IP", "params": {"nprobe": 10}},
+                limit=topk,
+                filter=filter_expr_style,
+                output_fields=["path", "style", "category", "sys_file_id"]
+            )
+
+            # 第二阶段：搜索其他style的向量
+            filter_expr_others = f"category == '{category}' && deprecated == 0 && style != '{style}'"
+            results_others = client.search(
+                collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
+                data=[query_vector.tolist()],
+                anns_field="feature_vector",
+                search_params={"metric_type": "IP", "params": {"nprobe": 10}},
+                limit=topk,
+                filter=filter_expr_others,
+                output_fields=["path", "style", "category", "sys_file_id"]
+            )
+
+            # 合并结果
+            results = []
+            if results_style and len(results_style) > 0:
+                results.extend(results_style[0])
+            if results_others and len(results_others) > 0:
+                results.extend(results_others[0])
+
+            # 转换为单个结果列表格式
+            results = [results] if results else []
 
         # 格式化结果
         formatted_results = []
@@ -249,7 +284,10 @@ def search_similar_vectors(
                     "sys_file_id": hit.get("entity", {}).get("sys_file_id")
                 })
 
-        return formatted_results
+        # 按分数排序并返回topk
+        formatted_results.sort(key=lambda x: x["score"], reverse=True)
+        return formatted_results[:topk]
+
     except Exception as e:
         logger.error(f"向量检索失败: {e}", exc_info=True)
         return []
@@ -280,7 +318,7 @@ def query_random_candidates(category: str, style: Optional[str] = None, limit: i
             collection_name=MILVUS_COLLECTION_SKETCH_VECTORS,
             filter=filter_expr,
             output_fields=["path", "style", "category"],
-            limit=10000  # 先查询大量数据，然后随机选择
+            limit=10000
         )
 
         # 随机选择