Extending the tests to validate more supported scorers for hybrid search. Adding experimental_method annotation to hybrid_search commands.

redis/commands/search/commands.py

@@ -11,7 +11,7 @@
     HybridQuery,
 )
 from redis.commands.search.hybrid_result import HybridCursorResult, HybridResult
-from redis.utils import deprecated_function
+from redis.utils import deprecated_function, experimental_method
 
 from ..helpers import get_protocol_version
 from ._util import to_string
@@ -560,6 +560,7 @@ def search(
             SEARCH_CMD, res, query=query, duration=(time.monotonic() - st) * 1000.0
         )
 
+    @experimental_method()
     def hybrid_search(
         self,
         query: HybridQuery,
@@ -1053,6 +1054,7 @@ async def search(
             SEARCH_CMD, res, query=query, duration=(time.monotonic() - st) * 1000.0
         )
 
+    @experimental_method()
     async def hybrid_search(
         self,
         query: HybridQuery,

redis/commands/search/hybrid_query.py

@@ -39,7 +39,8 @@ def query_string(self) -> str:
     def scorer(self, scorer: str) -> "HybridSearchQuery":
         """
         Scoring algorithm for text search query.
-        Allowed values are "TFIDF", "DISMAX", "DOCSCORE", "BM25", etc.
+        Allowed values are "TFIDF", "TFIDF.DOCNORM", "DISMAX", "DOCSCORE", "BM25",
+        "BM25STD", "BM25STD.TANH", "HAMMING", etc.
 
         For more information about supported scroring algorithms,
         see https://redis.io/docs/latest/develop/ai/search-and-query/advanced-concepts/scoring/

tests/test_search.py

@@ -4250,6 +4250,45 @@ def test_hybrid_search_query_with_scorer(self, client):
             )
             assert res["warnings"] == []
 
+    @pytest.mark.redismod
+    @skip_if_server_version_lt("8.3.224")
+    def test_hybrid_search_query_with_supported_scorer(self, client):
+        # Create index and add data
+        self._create_hybrid_search_index(client)
+        self._add_data_for_hybrid_search(client, items_sets=10)
+
+        # set search query
+        search_query = HybridSearchQuery("shoes")
+
+        vsim_query = HybridVsimQuery(
+            vector_field_name="@embedding",
+            vector_data="$vec",
+        )
+
+        hybrid_query = HybridQuery(search_query, vsim_query)
+
+        supported_scorers = [
+            "TFIDF",
+            "TFIDF.DOCNORM",
+            "BM25",
+            "BM25STD",
+            "BM25STD.TANH",
+            "DISMAX",
+            "DOCSCORE",
+            "HAMMING",
+        ]
+        for scorer in supported_scorers:
+            search_query.scorer(scorer)
+
+            res = client.ft().hybrid_search(
+                query=hybrid_query,
+                params_substitution={
+                    "vec": np.array([1, 2, 2, 3], dtype=np.float32).tobytes()
+                },
+                timeout=10,
+            )
+            assert res is not None
+
     @pytest.mark.redismod
     @skip_if_server_version_lt("8.3.224")
     def test_hybrid_search_query_with_vsim_method_defined_query_init(self, client):