feat: Add hybrid query example to vector search sample.

PiperOrigin-RevId: 643779254

samples/model-builder/test_constants.py

@@ -350,6 +350,26 @@
 VECTOR_SEARCH_INDEX_ENDPOINT = "456"
 VECTOR_SEARCH_DEPLOYED_INDEX_ID = "789"
 VECTOR_SERACH_INDEX_QUERIES = [[0.1]]
+VECTOR_SERACH_INDEX_HYBRID_QUERIES = [
+    aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
+        dense_embedding=[1, 2, 3],
+        sparse_embedding_dimensions=[10, 20, 30],
+        sparse_embedding_values=[1.0, 1.0, 1.0],
+        rrf_ranking_alpha=0.5,
+    ),
+    aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
+        dense_embedding=[1, 2, 3],
+        sparse_embedding_dimensions=[10, 20, 30],
+        sparse_embedding_values=[0.1, 0.2, 0.3],
+    ),
+    aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
+        sparse_embedding_dimensions=[10, 20, 30],
+        sparse_embedding_values=[0.1, 0.2, 0.3],
+    ),
+    aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
+        dense_embedding=[1, 2, 3]
+    ),
+]
 VECTOR_SEARCH_INDEX_DISPLAY_NAME = "my-vector-search-index"
 VECTOR_SEARCH_GCS_URI = "gs://fake-dir"
 VECTOR_SEARCH_INDEX_ENDPOINT_DISPLAY_NAME = "my-vector-search-index-endpoint"

samples/model-builder/vector_search/vector_search_find_neighbors_sample.py

@@ -55,5 +55,32 @@ def vector_search_find_neighbors(
     )
     print(resp)
 
+    # Query hybrid datapoints, sparse-only datapoints, and dense-only datapoints.
+    hybrid_queries = [
+        aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
+            dense_embedding=[1, 2, 3],
+            sparse_embedding_dimensions=[10, 20, 30],
+            sparse_embedding_values=[1.0, 1.0, 1.0],
+            rrf_ranking_alpha=0.5,
+        ),
+        aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
+            dense_embedding=[1, 2, 3],
+            sparse_embedding_dimensions=[10, 20, 30],
+            sparse_embedding_values=[0.1, 0.2, 0.3],
+        ),
+        aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
+            sparse_embedding_dimensions=[10, 20, 30],
+            sparse_embedding_values=[0.1, 0.2, 0.3],
+        ),
+        aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
+            dense_embedding=[1, 2, 3]
+        ),
+    ]
+
+    hybrid_resp = my_index_endpoint.find_neighbors(
+            deployed_index_id=deployed_index_id,
+            queries=hybrid_queries,
+            num_neighbors=num_neighbors,)
+    print(hybrid_resp)
 
 #  [END aiplatform_sdk_vector_search_find_neighbors_sample]

samples/model-builder/vector_search/vector_search_find_neighbors_sample_test.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from unittest.mock import call
+
 import test_constants as constants
 from vector_search import vector_search_find_neighbors_sample
 
@@ -38,8 +40,18 @@ def test_vector_search_find_neighbors_sample(
         index_endpoint_name=constants.VECTOR_SEARCH_INDEX_ENDPOINT)
 
     # Check index_endpoint.find_neighbors is called with right params.
-    mock_index_endpoint_find_neighbors.assert_called_with(
-      deployed_index_id=constants.VECTOR_SEARCH_DEPLOYED_INDEX_ID,
-      queries=constants.VECTOR_SERACH_INDEX_QUERIES,
-      num_neighbors=10
+    mock_index_endpoint_find_neighbors.assert_has_calls(
+        [
+            call(
+                deployed_index_id=constants.VECTOR_SEARCH_DEPLOYED_INDEX_ID,
+                queries=constants.VECTOR_SERACH_INDEX_QUERIES,
+                num_neighbors=10,
+            ),
+            call(
+                deployed_index_id=constants.VECTOR_SEARCH_DEPLOYED_INDEX_ID,
+                queries=constants.VECTOR_SERACH_INDEX_HYBRID_QUERIES,
+                num_neighbors=10,
+            ),
+        ],
+        any_order=False,
     )