feat: support read_index_datapoints for private network.

PiperOrigin-RevId: 589281257

google/cloud/aiplatform/matching_engine/matching_engine_index_endpoint.py

@@ -216,6 +216,7 @@ def __init__(
         )
         self._gca_resource = self._get_gca_resource(resource_name=index_endpoint_name)
 
+        self._public_match_client = None
         if self.public_endpoint_domain_name:
             self._public_match_client = self._instantiate_public_match_client()
 
@@ -518,6 +519,36 @@ def _instantiate_public_match_client(
             api_path_override=self.public_endpoint_domain_name,
         )
 
+    def _instantiate_private_match_service_stub(
+        self,
+        deployed_index_id: str,
+    ) -> match_service_pb2_grpc.MatchServiceStub:
+        """Helper method to instantiate private match service stub.
+        Args:
+            deployed_index_id (str):
+                Required. The user specified ID of the
+                DeployedIndex.
+        Returns:
+            stub (match_service_pb2_grpc.MatchServiceStub):
+                Initialized match service stub.
+        """
+        # Find the deployed index by id
+        deployed_indexes = [
+            deployed_index
+            for deployed_index in self.deployed_indexes
+            if deployed_index.id == deployed_index_id
+        ]
+
+        if not deployed_indexes:
+            raise RuntimeError(f"No deployed index with id '{deployed_index_id}' found")
+
+        # Retrieve server ip from deployed index
+        server_ip = deployed_indexes[0].private_endpoints.match_grpc_address
+
+        # Set up channel and stub
+        channel = grpc.insecure_channel("{}:10000".format(server_ip))
+        return match_service_pb2_grpc.MatchServiceStub(channel)
+
     @property
     def public_endpoint_domain_name(self) -> Optional[str]:
         """Public endpoint DNS name."""
@@ -1233,7 +1264,8 @@ def read_index_datapoints(
         deployed_index_id: str,
         ids: List[str] = [],
     ) -> List[gca_index_v1beta1.IndexDatapoint]:
-        """Reads the datapoints/vectors of the given IDs on the specified deployed index which is deployed to public endpoint.
+        """Reads the datapoints/vectors of the given IDs on the specified
+        deployed index which is deployed to public or private endpoint.
 
         ```
         Example Usage:
@@ -1252,9 +1284,25 @@ def read_index_datapoints(
             List[gca_index_v1beta1.IndexDatapoint] - A list of datapoints/vectors of the given IDs.
         """
         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."
+            # Call private match service stub with BatchGetEmbeddings request
+            response = self._batch_get_embeddings(
+                deployed_index_id=deployed_index_id, ids=ids
             )
+            return [
+                gca_index_v1beta1.IndexDatapoint(
+                    datapoint_id=embedding.id,
+                    feature_vector=embedding.float_val,
+                    restricts=gca_index_v1beta1.IndexDatapoint.Restriction(
+                        namespace=embedding.restricts.name,
+                        allow_list=embedding.restricts.allow_tokens,
+                    ),
+                    deny_list=embedding.restricts.deny_tokens,
+                    crowding_attributes=gca_index_v1beta1.CrowdingEmbedding(
+                        str(embedding.crowding_tag)
+                    ),
+                )
+                for embedding in response.embeddings
+            ]
 
         # Create the ReadIndexDatapoints request
         read_index_datapoints_request = (
@@ -1273,6 +1321,38 @@ def read_index_datapoints(
         # Wrap the results and return
         return response.datapoints
 
+    def _batch_get_embeddings(
+        self,
+        *,
+        deployed_index_id: str,
+        ids: List[str] = [],
+    ) -> List[List[match_service_pb2.Embedding]]:
+        """
+        Reads the datapoints/vectors of the given IDs on the specified index
+        which is deployed to private endpoint.
+
+        Args:
+            deployed_index_id (str):
+                Required. The ID of the DeployedIndex to match the queries against.
+            ids (List[str]):
+                Required. IDs of the datapoints to be searched for.
+        Returns:
+            List[match_service_pb2.Embedding] - A list of datapoints/vectors of the given IDs.
+        """
+        stub = self._instantiate_private_match_service_stub(
+            deployed_index_id=deployed_index_id
+        )
+
+        # Create the batch get embeddings request
+        batch_request = match_service_pb2.BatchGetEmbeddingsRequest()
+        batch_request.deployed_index_id = deployed_index_id
+
+        for id in ids:
+            batch_request.id.append(id)
+        response = stub.BatchGetEmbeddings(batch_request)
+
+        return response.embeddings
+
     def match(
         self,
         deployed_index_id: str,
@@ -1310,23 +1390,9 @@ def match(
         Returns:
             List[List[MatchNeighbor]] - A list of nearest neighbors for each query.
         """
-
-        # Find the deployed index by id
-        deployed_indexes = [
-            deployed_index
-            for deployed_index in self.deployed_indexes
-            if deployed_index.id == deployed_index_id
-        ]
-
-        if not deployed_indexes:
-            raise RuntimeError(f"No deployed index with id '{deployed_index_id}' found")
-
-        # Retrieve server ip from deployed index
-        server_ip = deployed_indexes[0].private_endpoints.match_grpc_address
-
-        # Set up channel and stub
-        channel = grpc.insecure_channel("{}:10000".format(server_ip))
-        stub = match_service_pb2_grpc.MatchServiceStub(channel)
+        stub = self._instantiate_private_match_service_stub(
+            deployed_index_id=deployed_index_id
+        )
 
         # Create the batch match request
         batch_request = match_service_pb2.BatchMatchRequest()

tests/unit/aiplatform/test_matching_engine_index_endpoint.py

@@ -493,6 +493,31 @@ def index_endpoint_match_queries_mock():
         yield index_endpoint_match_queries_mock
 
 
+@pytest.fixture
+def index_endpoint_batch_get_embeddings_mock():
+    with patch.object(
+        grpc._channel._UnaryUnaryMultiCallable,
+        "__call__",
+    ) as index_endpoint_batch_get_embeddings_mock:
+        index_endpoint_batch_get_embeddings_mock.return_value = (
+            match_service_pb2.BatchGetEmbeddingsResponse(
+                embeddings=[
+                    match_service_pb2.Embedding(
+                        id="1",
+                        float_val=[0.1, 0.2, 0.3],
+                        crowding_attribute=1,
+                    ),
+                    match_service_pb2.Embedding(
+                        id="2",
+                        float_val=[0.5, 0.2, 0.3],
+                        crowding_attribute=1,
+                    ),
+                ]
+            )
+        )
+        yield index_endpoint_batch_get_embeddings_mock
+
+
 @pytest.fixture
 def index_public_endpoint_match_queries_mock():
     with patch.object(
@@ -1204,3 +1229,23 @@ def test_index_public_endpoint_read_index_datapoints(
         index_public_endpoint_read_index_datapoints_mock.assert_called_with(
             read_index_datapoints_request
         )
+
+    @pytest.mark.usefixtures("get_index_endpoint_mock")
+    def test_index_endpoint_batch_get_embeddings(
+        self, index_endpoint_batch_get_embeddings_mock
+    ):
+        aiplatform.init(project=_TEST_PROJECT)
+
+        my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(
+            index_endpoint_name=_TEST_INDEX_ENDPOINT_ID
+        )
+
+        my_index_endpoint._batch_get_embeddings(
+            deployed_index_id=_TEST_DEPLOYED_INDEX_ID, ids=["1", "2"]
+        )
+
+        batch_request = match_service_pb2.BatchGetEmbeddingsRequest(
+            deployed_index_id=_TEST_DEPLOYED_INDEX_ID, id=["1", "2"]
+        )
+
+        index_endpoint_batch_get_embeddings_mock.assert_called_with(batch_request)