From d5ee00d40407f4c5fdb7edae83379859db429e87 Mon Sep 17 00:00:00 2001
From: QingGang <qinggang.deng@dxc.com>
Date: Tue, 13 Jan 2026 02:23:27 +0800
Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90=E6=B7=B7=E5=90=88=E6=A3=80?=
 =?UTF-8?q?=E7=B4=A2?=
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---
 backend/services/crawler_service.py |  4 +-
 backend/services/data_service.py    | 68 +++++++++++++++++++++++++----
 scripts/test_apis.py                |  2 +-
 3 files changed, 63 insertions(+), 11 deletions(-)

diff --git a/backend/services/crawler_service.py b/backend/services/crawler_service.py
index 0293c22..3eed083 100644
--- a/backend/services/crawler_service.py
+++ b/backend/services/crawler_service.py
@@ -142,9 +142,9 @@ class CrawlerService:
         
         return {"msg": "Batch processed", "count": processed}
 
-    def search(self, query: str, task_id: int, limit: int):
+    def search(self, query: str, task_id, limit: int):
         vector = llm_service.get_embedding(query)
         if not vector: return {"msg": "Embedding failed", "results": []}
-        return data_service.search(vector, task_id, limit)
+        return data_service.search(query_text=query, query_vector=vector, task_id=task_id, limit=limit)
 
 crawler_service = CrawlerService()
\ No newline at end of file
diff --git a/backend/services/data_service.py b/backend/services/data_service.py
index a52d5a5..1e75700 100644
--- a/backend/services/data_service.py
+++ b/backend/services/data_service.py
@@ -1,4 +1,4 @@
-from sqlalchemy import select, insert, update, and_
+from sqlalchemy import select, insert, update, and_, text, func, desc
 from backend.core.database import db
 from backend.utils.common import normalize_url
 
@@ -91,7 +91,63 @@ class DataService:
 
         return {"msg": f"Saved {count} chunks", "count": count}
 
-    def search(self, vector: list, task_id: int = None, limit: int = 5):
+    def search(self, query_text: str, query_vector: list, task_id = None, limit: int = 5):
+        """
+        Phase 2: 混合检索 (Hybrid Search)
+        综合 向量相似度 (Semantic) 和 关键词匹配度 (Keyword)
+        """
+
+        results = []
+        with self.db.engine.connect() as conn:
+            # 定义混合检索的 SQL 逻辑
+            
+            # 使用 websearch_to_tsquery 处理用户输入 (支持 "firecrawl or dify" 这种语法)
+            keyword_query = func.websearch_to_tsquery('english', query_text)
+            
+            vector_score = (1 - self.db.chunks.c.embedding.cosine_distance(query_vector))
+            keyword_score = func.ts_rank(self.db.chunks.c.content_tsvector, keyword_query)
+            
+            # 综合打分列: 0.7 * Vector + 0.3 * Keyword
+            # coalesce 确保如果关键词得分为 NULL (无匹配)，则视为 0
+            final_score = (vector_score * 0.7 + func.coalesce(keyword_score, 0) * 0.3).label("score")
+
+            stmt = select(
+                self.db.chunks.c.task_id,
+                self.db.chunks.c.source_url,
+                self.db.chunks.c.title,
+                self.db.chunks.c.content,
+                self.db.chunks.c.meta_info,
+                final_score
+            )
+
+            if task_id:
+                stmt = stmt.where(self.db.chunks.c.task_id == task_id)
+            
+            # 按综合分数倒序
+            stmt = stmt.order_by(desc("score")).limit(limit)
+            
+            try:
+                rows = conn.execute(stmt).fetchall()
+                results = [
+                    {
+                        "task_id": r[0], 
+                        "source_url": r[1], 
+                        "title": r[2], 
+                        "content": r[3], 
+                        "meta_info": r[4],
+                        "score": float(r[5])
+                    } 
+                    for r in rows
+                ]
+            except Exception as e:
+                print(f"[ERROR] Hybrid search failed: {e}")
+                return self._fallback_vector_search(query_vector, task_id, limit)
+
+        return {"results": results, "msg": f"Hybrid found {len(results)}"}
+
+    def _fallback_vector_search(self, vector, task_id, limit):
+        """降级兜底：纯向量搜索"""
+        print("[WARN] Fallback to pure vector search")
         with self.db.engine.connect() as conn:
             stmt = select(
                 self.db.chunks.c.task_id, self.db.chunks.c.source_url, self.db.chunks.c.title,
@@ -100,12 +156,8 @@ class DataService:
             
             if task_id:
                 stmt = stmt.where(self.db.chunks.c.task_id == task_id)
-            
+                
             rows = conn.execute(stmt).fetchall()
-            results = [
-                {"task_id": r[0], "source_url": r[1], "title": r[2], "content": r[3], "meta_info": r[4]} 
-                for r in rows
-            ]
-            return {"results": results, "msg": f"Found {len(results)}"}
+            return {"results": [{"content": r[3], "meta_info": r[4]} for r in rows], "msg": "Fallback found"}
 
 data_service = DataService()
\ No newline at end of file
diff --git a/scripts/test_apis.py b/scripts/test_apis.py
index 7503fda..4a1a132 100644
--- a/scripts/test_apis.py
+++ b/scripts/test_apis.py
@@ -86,7 +86,7 @@ def run_e2e_test():
     # Step 3: 轮询搜索结果 (Polling)
     # ---------------------------------------------------------
     log("STEP 3", "轮询搜索接口，等待数据入库...")
-    
+    task_id = 6
     max_retries = 12
     found_data = False
     search_results = []