fix: Add hybridsearch result cases (#30834)

#30694

Signed-off-by: luzhang <luzhang@zilliz.com>
Co-authored-by: luzhang <luzhang@zilliz.com>

tests/python_client/testcases/test_search.py

@@ -11709,3 +11709,72 @@ class TestCollectionHybridSearchValid(TestcaseBase):
         for i in range(len(score_answer[:limit])):
             assert score_answer[i] - hybrid_res[0].distances[i] < hybrid_search_epsilon
 
+    @pytest.mark.tags(CaseLabel.L1)
+    def test_hybrid_search_result_L2_order(self):
+        """
+        target: test hybrid search result having correct order for L2 distance
+        method: create connection, collection, insert and search
+        expected: hybrid search successfully and result order is correct
+        """
+        # 1. initialize collection with data
+        collection_w, _, _, insert_ids, time_stamp = \
+            self.init_collection_general(prefix, True, is_index=False, multiple_dim_array=[default_dim, default_dim])[0:5]
+
+        # 2. create index
+        vector_name_list = cf.extract_vector_field_name_list(collection_w)
+        vector_name_list.append(ct.default_float_vec_field_name)
+        for i  in range(len(vector_name_list)) :
+            default_index = { "index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 128},}
+            collection_w.create_index(vector_name_list[i], default_index)
+        collection_w.load()
+
+        # 3. prepare search params
+        req_list = []
+        weights = [0.2, 0.3, 0.5]
+        for i in range(len(vector_name_list)):
+            vectors = [[random.random() for _ in range(default_dim)] for _ in range(1)]
+            search_param = {
+                "data": vectors,
+                "anns_field": vector_name_list[i],
+                "param": {"metric_type": "L2", "offset": 0},
+                "limit": default_limit,
+                "expr": "int64 > 0"}
+            req = AnnSearchRequest(**search_param)
+            req_list.append(req)
+        # 4. hybrid search 
+        res = collection_w.hybrid_search(req_list, WeightedRanker(*weights), 10)
+        is_sorted_decrease = lambda lst: all(lst[i]['distance'] >= lst[i+1]['distance'] for i in range(len(lst)-1))
+        assert is_sorted_decrease(res[0])
+        print(res)
+
+    @pytest.mark.tags(CaseLabel.L1)
+    def test_hybrid_search_result_order(self):
+        """
+        target: test hybrid search result having correct order for cosine distance
+        method: create connection, collection, insert and search
+        expected: hybrid search successfully and result order is correct
+        """
+        # 1. initialize collection with data
+        collection_w, _, _, insert_ids, time_stamp = \
+            self.init_collection_general(prefix, True, multiple_dim_array=[default_dim, default_dim])[0:5]
+        # 2. extract vector field name
+        vector_name_list = cf.extract_vector_field_name_list(collection_w)
+        vector_name_list.append(ct.default_float_vec_field_name)
+        # 3. prepare search params
+        req_list = []
+        weights = [0.2, 0.3, 0.5]
+        for i in range(len(vector_name_list)):
+            vectors = [[random.random() for _ in range(default_dim)] for _ in range(1)]
+            search_param = {
+                "data": vectors,
+                "anns_field": vector_name_list[i],
+                "param": {"metric_type": "COSINE", "offset": 0},
+                "limit": default_limit,
+                "expr": "int64 > 0"}
+            req = AnnSearchRequest(**search_param)
+            req_list.append(req)
+        # 4. hybrid search 
+        res = collection_w.hybrid_search(req_list, WeightedRanker(*weights), 10)
+        is_sorted_ascend = lambda lst: all(lst[i]['distance'] <= lst[i+1]['distance'] for i in range(len(lst)-1))
+        assert is_sorted_ascend(res[0])
+        print(res)