From 0cb557695aeb50f03dcd511e7d119a71e5d839d6 Mon Sep 17 00:00:00 2001
From: Szymon Malewski <szymon.malewski@scylladb.com>
Date: Thu, 14 May 2026 11:28:38 +0200
Subject: [PATCH] test/vector_search: migrate wildcard select rescoring test to
 pytest

Migrate wildcard_select_is_correctly_rescored from rescoring_test.cc to
pytest. The test verifies that SELECT * with rescoring returns rows in
the correct similarity order with correct embedding values, covering a
slightly different processing path from the explicit-column SELECT test.
No semantic change.
---
 .../test_vector_search_rescoring_with_mock.py | 18 ++++++++++
 test/vector_search/rescoring_test.cc          | 36 -------------------
 2 files changed, 18 insertions(+), 36 deletions(-)

diff --git a/test/cqlpy/test_vector_search_rescoring_with_mock.py b/test/cqlpy/test_vector_search_rescoring_with_mock.py
index e41e0d993f..721d5ca36d 100644
--- a/test/cqlpy/test_vector_search_rescoring_with_mock.py
+++ b/test/cqlpy/test_vector_search_rescoring_with_mock.py
@@ -194,3 +194,21 @@ def test_similarity_function_returns_correctly_rescored_results(cql, test_keyspa
                 for row, d_row in zip(rows, expected):
                     assert row.similarity == pytest.approx(d_row.expected_similarity, abs=0.01)
                 assert len(rows[0]) == 2
+
+
+# Verifies that SELECT * with rescoring returns rows in the correct similarity
+# order with correct embedding values. Tests a slightly different processing
+# path compared to the explicit column SELECT in test_result_returned_by_vector_store_is_rescored.
+def test_wildcard_select_is_correctly_rescored(cql, test_keyspace, vector_store_mock, skip_without_tablets):
+    for func_name, data in TEST_DATA.items():
+        with rescoring_test_table(cql, test_keyspace, data,
+                extra_options={"similarity_function": func_name}) as table:
+            vector_store_mock.set_next_ann_response(200, reversed_ann_response(data))
+            rows = list(cql.execute(
+                f"SELECT * FROM {table} ORDER BY embedding ANN OF {ANN_QUERY_VECTOR_LITERAL} LIMIT 2"))
+
+            expected = data[:2]
+            assert [row.id for row in rows] == [d_row.id for d_row in expected]
+            for row, d_row in zip(rows, expected):
+                assert list(row.embedding) == pytest.approx(d_row.embedding)
+            assert len(rows[0]) == 2
diff --git a/test/vector_search/rescoring_test.cc b/test/vector_search/rescoring_test.cc
index adf1cc8b1a..bccc77e460 100644
--- a/test/vector_search/rescoring_test.cc
+++ b/test/vector_search/rescoring_test.cc
@@ -107,42 +107,6 @@ struct print_log_value<std::vector<float>> {
 };
 }
 
-SEASTAR_TEST_CASE(wildcard_select_is_correctly_rescored) {
-    // Another case with slightly different path of processing is "SELECT * ...".
-
-    for (const auto& params : test_data) {
-        auto server = co_await make_vs_mock_server();
-        co_await do_with_cql_env(
-                [&](cql_test_env& env) -> future<> {
-                    configure(env.local_qp().vector_store_client()).with_dns({{"server.node", std::vector<std::string>{server->host()}}});
-                    env.local_qp().vector_store_client().start_background_tasks();
-                    co_await create_index_and_insert_data(env, params);
-
-                    // Mock Response: Return all keys but in REVERSE similarity order.
-                    server->next_ann_response({http::reply::status_type::ok, R"({
-                        "primary_keys": { "id": [4, 3, 2, 1] },
-                        "similarity_scores": [0, 0, 0, 0]
-                    })"});
-                    auto msg = co_await env.execute_cql("SELECT * FROM ks.cf ORDER BY embedding ANN OF [0.1, 0.1] LIMIT 2;");
-
-                    auto rms = dynamic_pointer_cast<cql_transport::messages::result_message::rows>(msg);
-                    BOOST_REQUIRE(rms);
-                    const auto& rows = rms->rs().result_set().rows();
-                    BOOST_REQUIRE(rows.size() >= 2);
-                    BOOST_CHECK_EQUAL(rows.size(), 2);
-                    BOOST_CHECK_EQUAL(rms->rs().result_set().get_metadata().column_count(), 2);
-                    BOOST_CHECK_EQUAL(get_id_col_value(rows.at(0)), test_ids[0]);
-                    BOOST_CHECK_EQUAL(get_embedding_col_value(rows.at(0)), params.vectors[0]);
-                    BOOST_CHECK_EQUAL(get_id_col_value(rows.at(1)), 2);
-                    BOOST_CHECK_EQUAL(get_embedding_col_value(rows.at(1)), params.vectors[1]);
-                },
-                make_config(format("http://server.node:{}", server->port())))
-                .finally(seastar::coroutine::lambda([&] -> future<> {
-                    co_await server->stop();
-                }));
-    }
-}
-
 SEASTAR_TEST_CASE(select_similarity_function_other_than_ann_ordering) {
     // Another tricky case with similarity column with argument different from ANN ordering vector.
     // Especially if we use prepared statement and the difference is only seen at execution time.