新增RAG测试脚本

backend/services/crawler_service.py

@@ -142,9 +142,41 @@ class CrawlerService:
         
         return {"msg": "Batch processed", "count": processed}
 
-    def search(self, query: str, task_id, limit: int):
+    def search(self, query: str, task_id, return_num: int):
+        """
+        全链路搜索：向量生成 -> 混合检索(粗排) -> 重排序(精排)
+        """
+        # 1. 生成查询向量
         vector = llm_service.get_embedding(query)
         if not vector: return {"msg": "Embedding failed", "results": []}
-        return data_service.search(query_text=query, query_vector=vector, task_id=task_id, limit=limit)
 
+        # 2. 计算粗排召回数量
+        # 逻辑：至少召回 50 个，如果用户要很多，则召回 10 倍
+        coarse_limit = return_num * 10 if return_num * 10 > 50 else 50
+        
+        # 3. 执行混合检索 (粗排)
+        coarse_results = data_service.search(
+            query_text=query, 
+            query_vector=vector, 
+            task_id=task_id, 
+            candidates_num=coarse_limit # 使用计算出的粗排数量
+        )
+        
+        candidates = coarse_results.get('results', [])
+        
+        if not candidates:
+            return {"msg": "No documents found", "results": []}
+
+        # 4. 执行重排序 (精排)
+        final_results = llm_service.rerank(
+            query=query, 
+            documents=candidates, 
+            top_n=return_num # 最终返回用户需要的数量
+        )
+
+        return {
+            "results": final_results, 
+            "msg": f"Reranked {len(final_results)} from {len(candidates)} candidates"
+        }
+    
 crawler_service = CrawlerService()