Python: Use storage_name for hybrid search fields and vector queries (#14684)

python/semantic_kernel/connectors/azure_ai_search.py

@@ -595,15 +595,23 @@ async def _inner_search(
                 if values is None:
                     raise VectorStoreOperationException("No vector and/or keywords provided for search.")
                 vector_field = self.definition.try_get_vector_field(options.vector_property_name)
-                search_args["search_fields"] = (
-                    [options.additional_property_name]
-                    if options.additional_property_name is not None
-                    else [
-                        field.name
+                if options.additional_property_name is not None:
+                    target_field = next(
+                        (field for field in self.definition.fields if field.name == options.additional_property_name),
+                        None,
+                    )
+                    search_field = (
+                        target_field.storage_name or target_field.name
+                        if target_field is not None
+                        else options.additional_property_name
+                    )
+                    search_args["search_fields"] = [search_field]
+                else:
+                    search_args["search_fields"] = [
+                        field.storage_name or field.name
                         for field in self.definition.fields
                         if field.field_type == FieldTypes.DATA and field.is_full_text_indexed
                     ]
-                )
                 if not search_args["search_fields"]:
                     raise VectorStoreOperationException("No searchable fields found for hybrid search.")
                 search_args["search_text"] = values
@@ -613,14 +621,14 @@ async def _inner_search(
                     search_args["vector_queries"] = [
                         VectorizedQuery(
                             vector=vector,  # type: ignore
-                            fields=vector_field.name if vector_field else None,
+                            fields=vector_field.storage_name or vector_field.name if vector_field else None,
                         )
                     ]
                 else:
                     search_args["vector_queries"] = [
                         VectorizableTextQuery(
                             text=values,
-                            fields=vector_field.name if vector_field else None,
+                            fields=vector_field.storage_name or vector_field.name if vector_field else None,
                         )
                     ]
         try:

python/tests/unit/connectors/memory/test_azure_ai_search.py

@@ -440,6 +440,54 @@ async def test_search_keyword_hybrid_search(collection, mock_search, include_vec
         assert call[1]["vector_queries"][0].fields == "vector"
 
 
+async def test_search_keyword_hybrid_search_with_storage_names(
+    azure_ai_search_unit_test_env, mock_search, dimensions, index_kind, distance_function, vector_property_type
+):
+    from semantic_kernel.data.vector import VectorStoreCollectionDefinition, VectorStoreField
+
+    storage_definition = VectorStoreCollectionDefinition(
+        fields=[
+            VectorStoreField("key", name="id", storage_name="doc_id", type="str"),
+            VectorStoreField("data", name="content", storage_name="body", type="str", is_full_text_indexed=True),
+            VectorStoreField("data", name="body", storage_name="summary", type="str", is_full_text_indexed=True),
+            VectorStoreField(
+                "vector",
+                name="vector",
+                storage_name="vec",
+                dimensions=dimensions,
+                index_kind=index_kind,
+                distance_function=distance_function,
+                type=vector_property_type,
+            ),
+        ]
+    )
+    col = AzureAISearchCollection(record_type=dict, definition=storage_definition)
+
+    # 1. Default search fields and vector fields use storage names
+    await col.hybrid_search(values="query", vector=[0.1, 0.2, 0.3])
+    call = mock_search.call_args_list[-1]
+    assert call[1]["search_fields"] == ["body", "summary"]
+    assert call[1]["vector_queries"][0].fields == "vec"
+
+    # 2. additional_property_name resolves declared property name to storage name
+    await col.hybrid_search(values="query", vector=[0.1, 0.2, 0.3], additional_property_name="content")
+    call = mock_search.call_args_list[-1]
+    assert call[1]["search_fields"] == ["body"]
+    assert call[1]["vector_queries"][0].fields == "vec"
+
+    # 3. Declared property name takes precedence over earlier storage alias
+    await col.hybrid_search(values="query", vector=[0.1, 0.2, 0.3], additional_property_name="body")
+    call = mock_search.call_args_list[-1]
+    assert call[1]["search_fields"] == ["summary"]
+    assert call[1]["vector_queries"][0].fields == "vec"
+
+    # 4. Fallback to direct storage name when no declared property matches
+    await col.hybrid_search(values="query", vector=[0.1, 0.2, 0.3], additional_property_name="summary")
+    call = mock_search.call_args_list[-1]
+    assert call[1]["search_fields"] == ["summary"]
+    assert call[1]["vector_queries"][0].fields == "vec"
+
+
 @mark.parametrize("filter, result", filter_lambda_list("ai_search"))
 def test_lambda_filter(collection, filter, result):
     if isinstance(result, type) and issubclass(result, Exception):