feast-dev
diff --git a/‎sdk/python/feast/feature_store.py
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+143-2Lines changed: 143 additions & 2 deletions b/‎sdk/python/feast/feature_store.py
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+143-2Lines changed: 143 additions & 2 deletions
@@ -62,6 +62,7 @@
 from feast.feast_object import FeastObject
 from feast.feature_service import FeatureService
 from feast.feature_view import DUMMY_ENTITY, DUMMY_ENTITY_NAME, FeatureView
+from feast.field import Field
 from feast.inference import (
     update_data_sources_with_inferred_event_timestamp_col,
     update_feature_views_with_inferred_features_and_entities,
@@ -1833,7 +1834,6 @@ def retrieve_online_documents(
             top_k,
             distance_metric,
         )
-
         # TODO currently not return the vector value since it is same as feature value, if embedding is supported,
         # the feature value can be raw text before embedded
         entity_key_vals = [feature[1] for feature in document_features]
@@ -1861,6 +1861,66 @@ def retrieve_online_documents(
         )
         return OnlineResponse(online_features_response)
 
+    def retrieve_online_documents_v2(
+        self,
+        query: Union[str, List[float]],
+        top_k: int,
+        features: List[str],
+        distance_metric: Optional[str] = "L2",
+    ) -> OnlineResponse:
+        """
+        Retrieves the top k closest document features. Note, embeddings are a subset of features.
+
+        Args:
+            features: The list of features that should be retrieved from the online document store. These features can be
+                specified either as a list of string document feature references or as a feature service. String feature
+                references must have format "feature_view:feature", e.g, "document_fv:document_embeddings".
+            query: The query to retrieve the closest document features for.
+            top_k: The number of closest document features to retrieve.
+            distance_metric: The distance metric to use for retrieval.
+        """
+        if isinstance(query, str):
+            raise ValueError(
+                "Using embedding functionality is not supported for document retrieval. Please embed the query before calling retrieve_online_documents."
+            )
+
+        (
+            available_feature_views,
+            _,
+        ) = utils._get_feature_views_to_use(
+            registry=self._registry,
+            project=self.project,
+            features=features,
+            allow_cache=True,
+            hide_dummy_entity=False,
+        )
+        feature_view_set = set()
+        for feature in features:
+            feature_view_name = feature.split(":")[0]
+            feature_view = self.get_feature_view(feature_view_name)
+            feature_view_set.add(feature_view.name)
+        if len(feature_view_set) > 1:
+            raise ValueError("Document retrieval only supports a single feature view.")
+        requested_features = [
+            f.split(":")[1] for f in features if isinstance(f, str) and ":" in f
+        ]
+
+        requested_feature_view = available_feature_views[0]
+        if not requested_feature_view:
+            raise ValueError(
+                f"Feature view {requested_feature_view} not found in the registry."
+            )
+
+        provider = self._get_provider()
+        return self._retrieve_from_online_store_v2(
+            provider,
+            requested_feature_view,
+            requested_features,
+            query,
+            top_k,
+            distance_metric,
+        )
+
     def _retrieve_from_online_store(
         self,
         provider: Provider,
@@ -1878,6 +1938,10 @@ def _retrieve_from_online_store(
         """
         Search and return document features from the online document store.
         """
+        vector_field_metadata = _get_feature_view_vector_field_metadata(table)
+        if vector_field_metadata:
+            distance_metric = vector_field_metadata.vector_search_metric
+
         documents = provider.retrieve_online_documents(
             config=self.config,
             table=table,
@@ -1891,7 +1955,7 @@ def _retrieve_from_online_store(
         read_row_protos = []
         row_ts_proto = Timestamp()
 
-        for row_ts, entity_key, feature_val, vector_value, distance_val in documents:
+        for row_ts, entity_key, feature_val, vector_value, distance_val in documents:  # type: ignore[misc]
             # Reset timestamp to default or update if row_ts is not None
             if row_ts is not None:
                 row_ts_proto.FromDatetime(row_ts)
@@ -1916,6 +1980,70 @@ def _retrieve_from_online_store(
             )
         return read_row_protos
 
+    def _retrieve_from_online_store_v2(
+        self,
+        provider: Provider,
+        table: FeatureView,
+        requested_features: List[str],
+        query: List[float],
+        top_k: int,
+        distance_metric: Optional[str],
+    ) -> OnlineResponse:
+        """
+        Search and return document features from the online document store.
+        """
+        vector_field_metadata = _get_feature_view_vector_field_metadata(table)
+        if vector_field_metadata:
+            distance_metric = vector_field_metadata.vector_search_metric
+
+        documents = provider.retrieve_online_documents_v2(
+            config=self.config,
+            table=table,
+            requested_features=requested_features,
+            query=query,
+            top_k=top_k,
+            distance_metric=distance_metric,
+        )
+
+        entity_key_dict: Dict[str, List[ValueProto]] = {}
+        datevals, entityvals, list_of_feature_dicts = [], [], []
+        for row_ts, entity_key, feature_dict in documents:  # type: ignore[misc]
+            datevals.append(row_ts)
+            entityvals.append(entity_key)
+            list_of_feature_dicts.append(feature_dict)
+            if entity_key:
+                for key, value in zip(entity_key.join_keys, entity_key.entity_values):
+                    python_value = value
+                    if key not in entity_key_dict:
+                        entity_key_dict[key] = []
+                    entity_key_dict[key].append(python_value)
+
+        table_entity_values, idxs = utils._get_unique_entities_from_values(
+            entity_key_dict,
+        )
+
+        features_to_request: List[str] = []
+        if requested_features:
+            features_to_request = requested_features + ["distance"]
+        else:
+            features_to_request = ["distance"]
+        feature_data = utils._convert_rows_to_protobuf(
+            requested_features=features_to_request,
+            read_rows=list(zip(datevals, list_of_feature_dicts)),
+        )
+
+        online_features_response = GetOnlineFeaturesResponse(results=[])
+        utils._populate_response_from_feature_data(
+            feature_data=feature_data,
+            indexes=idxs,
+            online_features_response=online_features_response,
+            full_feature_names=False,
+            requested_features=features_to_request,
+            table=table,
+        )
+
+        return OnlineResponse(online_features_response)
+
     def serve(
         self,
         host: str,
@@ -2265,3 +2393,16 @@ def _validate_data_sources(data_sources: List[DataSource]):
             raise DataSourceRepeatNamesException(case_insensitive_ds_name)
         else:
             ds_names.add(case_insensitive_ds_name)
+
+
+def _get_feature_view_vector_field_metadata(
+    feature_view: FeatureView,
+) -> Optional[Field]:
+    vector_fields = [field for field in feature_view.schema if field.vector_index]
+    if len(vector_fields) > 1:
+        raise ValueError(
+            f"Feature view {feature_view.name} has multiple vector fields. Only one vector field per feature view is supported."
+        )
+    if not vector_fields:
+        return None
+    return vector_fields[0]