feat: add per_crowding_attribute_neighbor_count, `approx_num_neighb…

…ors`, `fraction_leaf_nodes_to_search_override`, and `return_full_datapoint` to MatchingEngineIndexEndpoint `find_neighbors`

PiperOrigin-RevId: 579967420

google/cloud/aiplatform/matching_engine/matching_engine_index_endpoint.py

@@ -956,6 +956,10 @@ def find_neighbors(
         queries: List[List[float]],
         num_neighbors: int = 10,
         filter: Optional[List[Namespace]] = [],
+        per_crowding_attribute_neighbor_count: Optional[int] = None,
+        approx_num_neighbors: Optional[int] = None,
+        fraction_leaf_nodes_to_search_override: Optional[float] = None,
+        return_full_datapoint: bool = False,
     ) -> List[List[MatchNeighbor]]:
         """Retrieves nearest neighbors for the given embedding queries on the specified deployed index which is deployed to public endpoint.
 
@@ -979,25 +983,58 @@ def find_neighbors(
                 For example, [Namespace("color", ["red"], []), Namespace("shape", [], ["squared"])] will match datapoints
                 that satisfy "red color" but not include datapoints with "squared shape".
                 Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.
+
+            per_crowding_attribute_neighbor_count (int):
+                Optional. Crowding is a constraint on a neighbor list produced
+                by nearest neighbor search requiring that no more than some
+                value k' of the k neighbors returned have the same value of
+                crowding_attribute. It's used for improving result diversity.
+                This field is the maximum number of matches with the same crowding tag.
+
+            approx_num_neighbors (int):
+                Optional. The number of neighbors to find via approximate search
+                before exact reordering is performed. If not set, the default
+                value from scam config is used; if set, this value must be > 0.
+
+            fraction_leaf_nodes_to_search_override (float):
+                Optional. The fraction of the number of leaves to search, set at
+                query time allows user to tune search performance. This value
+                increase result in both search accuracy and latency increase.
+                The value should be between 0.0 and 1.0.
+
+            return_full_datapoint (bool):
+                Optional. If set to true, the full datapoints (including all
+                vector values and of the nearest neighbors are returned.
+                Note that returning full datapoint will significantly increase the
+                latency and cost of the query.
+
         Returns:
             List[List[MatchNeighbor]] - A list of nearest neighbors for each query.
         """
 
         if not self._public_match_client:
             raise ValueError(
-                "Please make sure index has been deployed to public endpoint, and follow the example usage to call this method."
+                "Please make sure index has been deployed to public endpoint,and follow the example usage to call this method."
             )
 
         # Create the FindNeighbors request
         find_neighbors_request = gca_match_service_v1beta1.FindNeighborsRequest()
         find_neighbors_request.index_endpoint = self.resource_name
         find_neighbors_request.deployed_index_id = deployed_index_id
+        find_neighbors_request.return_full_datapoint = return_full_datapoint
 
         for query in queries:
             find_neighbors_query = (
                 gca_match_service_v1beta1.FindNeighborsRequest.Query()
             )
             find_neighbors_query.neighbor_count = num_neighbors
+            find_neighbors_query.per_crowding_attribute_neighbor_count = (
+                per_crowding_attribute_neighbor_count
+            )
+            find_neighbors_query.approximate_neighbor_count = approx_num_neighbors
+            find_neighbors_query.fraction_leaf_nodes_to_search_override = (
+                fraction_leaf_nodes_to_search_override
+            )
             datapoint = gca_index_v1beta1.IndexDatapoint(feature_vector=query)
             for namespace in filter:
                 restrict = gca_index_v1beta1.IndexDatapoint.Restriction()

tests/unit/aiplatform/test_matching_engine_index_endpoint.py

@@ -234,6 +234,8 @@
 _TEST_IDS = ["123", "456", "789"]
 _TEST_PER_CROWDING_ATTRIBUTE_NUM_NEIGHBOURS = 3
 _TEST_APPROX_NUM_NEIGHBORS = 2
+_TEST_FRACTION_LEAF_NODES_TO_SEARCH_OVERRIDE = 0.8
+_TEST_RETURN_FULL_DATAPOINT = True
 
 
 def uuid_mock():
@@ -954,6 +956,10 @@ def test_index_public_endpoint_match_queries(
             queries=_TEST_QUERIES,
             num_neighbors=_TEST_NUM_NEIGHBOURS,
             filter=_TEST_FILTER,
+            per_crowding_attribute_neighbor_count=_TEST_PER_CROWDING_ATTRIBUTE_NUM_NEIGHBOURS,
+            approx_num_neighbors=_TEST_APPROX_NUM_NEIGHBORS,
+            fraction_leaf_nodes_to_search_override=_TEST_FRACTION_LEAF_NODES_TO_SEARCH_OVERRIDE,
+            return_full_datapoint=_TEST_RETURN_FULL_DATAPOINT,
         )
 
         find_neighbors_request = gca_match_service_v1beta1.FindNeighborsRequest(
@@ -972,8 +978,12 @@ def test_index_public_endpoint_match_queries(
                             )
                         ],
                     ),
+                    per_crowding_attribute_neighbor_count=_TEST_PER_CROWDING_ATTRIBUTE_NUM_NEIGHBOURS,
+                    approximate_neighbor_count=_TEST_APPROX_NUM_NEIGHBORS,
+                    fraction_leaf_nodes_to_search_override=_TEST_FRACTION_LEAF_NODES_TO_SEARCH_OVERRIDE,
                 )
             ],
+            return_full_datapoint=_TEST_RETURN_FULL_DATAPOINT,
         )
 
         index_public_endpoint_match_queries_mock.assert_called_with(