'commit'

dsSchoolBuddy/ElasticSearch/T7_XiangLiangQuery.py

@@ -1,6 +1,4 @@
 import logging
-import warnings
-from typing import List, Tuple, Dict
 
 from Config.Config import ES_CONFIG
 from ElasticSearch.Utils.EsSearchUtil import EsSearchUtil
@@ -10,29 +8,6 @@ logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
 
-def merge_results(keyword_results: List[Tuple[Dict, float]], vector_results: List[Tuple[Dict, float]]) -> List[Tuple[Dict, float, str]]:
-    """
-    合并关键字搜索和向量搜索结果
-    """
-    # 标记结果来源并合并
-    all_results = []
-    for doc, score in keyword_results:
-        all_results.append((doc, score, "关键字搜索"))
-    for doc, score in vector_results:
-        all_results.append((doc, score, "向量搜索"))
-
-    # 去重并按分数排序
-    unique_results = {}
-    for doc, score, source in all_results:
-        doc_id = doc['_id']
-        if doc_id not in unique_results or score > unique_results[doc_id][1]:
-            unique_results[doc_id] = (doc, score, source)
-
-    # 按分数降序排序
-    sorted_results = sorted(unique_results.values(), key=lambda x: x[1], reverse=True)
-    return sorted_results
-
-
 if __name__ == "__main__":
     # 初始化EsSearchUtil
     search_util = EsSearchUtil(ES_CONFIG)
@@ -77,7 +52,7 @@ if __name__ == "__main__":
         print("\n=== 合并搜索结果 ===")
         # 为关键字结果添加分数
         keyword_results_with_scores = [(doc, doc['_score']) for doc in keyword_hits]
-        merged_results = merge_results(keyword_results_with_scores, reranked_vector_results)
+        merged_results = search_util.merge_results(keyword_results_with_scores, reranked_vector_results)
         print(f"合并后唯一结果数量: {len(merged_results)}")
 
         # 4. 打印最终结果

dsSchoolBuddy/ElasticSearch/Utils/EsSearchUtil.py

@@ -411,3 +411,32 @@ class EsSearchUtil:
                 print(f"分数: {score:.4f}")
             print("---")
 
+    def merge_results(self, keyword_results: List[Tuple[Dict, float]], vector_results: List[Tuple[Dict, float]]) -> List[Tuple[Dict, float, str]]:
+        """
+        合并关键字搜索和向量搜索结果
+
+        参数:
+            keyword_results: 关键字搜索结果列表，每个元素是(文档, 分数)元组
+            vector_results: 向量搜索结果列表，每个元素是(文档, 分数)元组
+
+        返回:
+            list: 合并后的结果列表，每个元素是(文档, 分数, 来源)元组
+        """
+        # 标记结果来源并合并
+        all_results = []
+        for doc, score in keyword_results:
+            all_results.append((doc, score, "关键字搜索"))
+        for doc, score in vector_results:
+            all_results.append((doc, score, "向量搜索"))
+
+        # 去重并按分数排序
+        unique_results = {}
+        for doc, score, source in all_results:
+            doc_id = doc['_id']
+            if doc_id not in unique_results or score > unique_results[doc_id][1]:
+                unique_results[doc_id] = (doc, score, source)
+
+        # 按分数降序排序
+        sorted_results = sorted(unique_results.values(), key=lambda x: x[1], reverse=True)
+        return sorted_results
+