feat: Adding features field to retrieve_online_features to return mor… (

#4869)

sdk/python/feast/feature_store.py

@@ -1753,9 +1753,10 @@ async def get_online_features_async(
 
     def retrieve_online_documents(
         self,
-        feature: str,
+        feature: Optional[str],
         query: Union[str, List[float]],
         top_k: int,
+        features: Optional[List[str]] = None,
         distance_metric: Optional[str] = None,
     ) -> OnlineResponse:
         """
@@ -1765,6 +1766,7 @@ def retrieve_online_documents(
             feature: The list of document 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".
+            features: The list of features that should be retrieved from the online store.
             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.
@@ -1773,18 +1775,44 @@ def retrieve_online_documents(
             raise ValueError(
                 "Using embedding functionality is not supported for document retrieval. Please embed the query before calling retrieve_online_documents."
             )
+        feature_list: List[str] = (
+            features
+            if features is not None
+            else ([feature] if feature is not None else [])
+        )
+
         (
             available_feature_views,
             _,
         ) = utils._get_feature_views_to_use(
             registry=self._registry,
             project=self.project,
-            features=[feature],
+            features=feature_list,
             allow_cache=True,
             hide_dummy_entity=False,
         )
+        if features:
+            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_feature = None
+            requested_features = [
+                f.split(":")[1] for f in features if isinstance(f, str) and ":" in f
+            ]
+        else:
+            requested_feature = (
+                feature.split(":")[1] if isinstance(feature, str) else feature
+            )
+            requested_features = [requested_feature] if requested_feature else []
+
         requested_feature_view_name = (
-            feature.split(":")[0] if isinstance(feature, str) else feature
+            feature.split(":")[0] if feature else list(feature_view_set)[0]
         )
         for feature_view in available_feature_views:
             if feature_view.name == requested_feature_view_name:
@@ -1793,14 +1821,15 @@ def retrieve_online_documents(
             raise ValueError(
                 f"Feature view {requested_feature_view} not found in the registry."
             )
-        requested_feature = (
-            feature.split(":")[1] if isinstance(feature, str) else feature
-        )
+
+        requested_feature_view = available_feature_views[0]
+
         provider = self._get_provider()
         document_features = self._retrieve_from_online_store(
             provider,
             requested_feature_view,
             requested_feature,
+            requested_features,
             query,
             top_k,
             distance_metric,
@@ -1822,6 +1851,7 @@ def retrieve_online_documents(
         document_feature_vals = [feature[4] for feature in document_features]
         document_feature_distance_vals = [feature[5] for feature in document_features]
         online_features_response = GetOnlineFeaturesResponse(results=[])
+        requested_feature = requested_feature or requested_features[0]
         utils._populate_result_rows_from_columnar(
             online_features_response=online_features_response,
             data={
@@ -1836,7 +1866,8 @@ def _retrieve_from_online_store(
         self,
         provider: Provider,
         table: FeatureView,
-        requested_feature: str,
+        requested_feature: Optional[str],
+        requested_features: Optional[List[str]],
         query: List[float],
         top_k: int,
         distance_metric: Optional[str],
@@ -1852,6 +1883,7 @@ def _retrieve_from_online_store(
             config=self.config,
             table=table,
             requested_feature=requested_feature,
+            requested_features=requested_features,
             query=query,
             top_k=top_k,
             distance_metric=distance_metric,
@@ -1952,19 +1984,13 @@ def serve_ui(
         )
 
     def serve_registry(
-        self,
-        port: int,
-        tls_key_path: str = "",
-        tls_cert_path: str = "",
+        self, port: int, tls_key_path: str = "", tls_cert_path: str = ""
     ) -> None:
         """Start registry server locally on a given port."""
         from feast import registry_server
 
         registry_server.start_server(
-            self,
-            port=port,
-            tls_key_path=tls_key_path,
-            tls_cert_path=tls_cert_path,
+            self, port=port, tls_key_path=tls_key_path, tls_cert_path=tls_cert_path
         )
 
     def serve_offline(

sdk/python/feast/infra/key_encoding_utils.py

@@ -1,5 +1,7 @@
 import struct
-from typing import List, Tuple
+from typing import List, Tuple, Union
+
+from google.protobuf.internal.containers import RepeatedScalarFieldContainer
 
 from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto
 from feast.protos.feast.types.Value_pb2 import Value as ValueProto
@@ -163,3 +165,16 @@ def get_list_val_str(val):
         if val.HasField(accept_type):
             return str(getattr(val, accept_type).val)
     return None
+
+
+def serialize_f32(
+    vector: Union[RepeatedScalarFieldContainer[float], List[float]], vector_length: int
+) -> bytes:
+    """serializes a list of floats into a compact "raw bytes" format"""
+    return struct.pack(f"{vector_length}f", *vector)
+
+
+def deserialize_f32(byte_vector: bytes, vector_length: int) -> List[float]:
+    """deserializes a list of floats from a compact "raw bytes" format"""
+    num_floats = vector_length // 4  # 4 bytes per float
+    return list(struct.unpack(f"{num_floats}f", byte_vector))

sdk/python/feast/infra/online_stores/elasticsearch_online_store/elasticsearch.py

@@ -213,7 +213,8 @@ def retrieve_online_documents(
         self,
         config: RepoConfig,
         table: FeatureView,
-        requested_feature: str,
+        requested_feature: Optional[str],
+        requested_features: Optional[List[str]],
         embedding: List[float],
         top_k: int,
         *args,

sdk/python/feast/infra/online_stores/faiss_online_store.py

@@ -176,7 +176,8 @@ def retrieve_online_documents(
         self,
         config: RepoConfig,
         table: FeatureView,
-        requested_feature: str,
+        requested_feature: Optional[str],
+        requested_featres: Optional[List[str]],
         embedding: List[float],
         top_k: int,
         distance_metric: Optional[str] = None,

sdk/python/feast/infra/online_stores/online_store.py

@@ -390,7 +390,8 @@ def retrieve_online_documents(
         self,
         config: RepoConfig,
         table: FeatureView,
-        requested_feature: str,
+        requested_feature: Optional[str],
+        requested_features: Optional[List[str]],
         embedding: List[float],
         top_k: int,
         distance_metric: Optional[str] = None,
@@ -411,6 +412,7 @@ def retrieve_online_documents(
             config: The config for the current feature store.
             table: The feature view whose feature values should be read.
             requested_feature: The name of the feature whose embeddings should be used for retrieval.
+            requested_features: The list of features whose embeddings should be used for retrieval.
             embedding: The embeddings to use for retrieval.
             top_k: The number of documents to retrieve.
 
@@ -419,6 +421,10 @@ def retrieve_online_documents(
             where the first item is the event timestamp for the row, and the second item is a dict of feature
             name to embeddings.
         """
+        if not requested_feature and not requested_features:
+            raise ValueError(
+                "Either requested_feature or requested_features must be specified"
+            )
         raise NotImplementedError(
             f"Online store {self.__class__.__name__} does not support online retrieval"
         )

sdk/python/feast/infra/online_stores/postgres_online_store/postgres.py

@@ -347,7 +347,8 @@ def retrieve_online_documents(
         self,
         config: RepoConfig,
         table: FeatureView,
-        requested_feature: str,
+        requested_feature: Optional[str],
+        requested_features: Optional[List[str]],
         embedding: List[float],
         top_k: int,
         distance_metric: Optional[str] = "L2",
@@ -366,6 +367,7 @@ def retrieve_online_documents(
             config: Feast configuration object
             table: FeatureView object as the table to search
             requested_feature: The requested feature as the column to search
+            requested_features: The list of features whose embeddings should be used for retrieval.
             embedding: The query embedding to search for
             top_k: The number of items to return
             distance_metric: The distance metric to use for the search.G

sdk/python/feast/infra/online_stores/qdrant_online_store/qdrant.py

@@ -248,7 +248,8 @@ def retrieve_online_documents(
         self,
         config: RepoConfig,
         table: FeatureView,
-        requested_feature: str,
+        requested_feature: Optional[str],
+        requested_features: Optional[List[str]],
         embedding: List[float],
         top_k: int,
         distance_metric: Optional[str] = "cosine",

sdk/python/feast/infra/online_stores/sqlite.py

@@ -15,19 +15,20 @@
 import logging
 import os
 import sqlite3
-import struct
 import sys
 from datetime import date, datetime
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple
 
-from google.protobuf.internal.containers import RepeatedScalarFieldContainer
 from pydantic import StrictStr
 
 from feast import Entity
 from feast.feature_view import FeatureView
 from feast.infra.infra_object import SQLITE_INFRA_OBJECT_CLASS_TYPE, InfraObject
-from feast.infra.key_encoding_utils import serialize_entity_key
+from feast.infra.key_encoding_utils import (
+    serialize_entity_key,
+    serialize_f32,
+)
 from feast.infra.online_stores.online_store import OnlineStore
 from feast.infra.online_stores.vector_store import VectorStoreConfig
 from feast.protos.feast.core.InfraObject_pb2 import InfraObject as InfraObjectProto
@@ -330,7 +331,8 @@ def retrieve_online_documents(
         self,
         config: RepoConfig,
         table: FeatureView,
-        requested_feature: str,
+        requested_feature: Optional[str],
+        requested_featuers: Optional[List[str]],
         embedding: List[float],
         top_k: int,
         distance_metric: Optional[str] = None,
@@ -432,6 +434,7 @@ def retrieve_online_documents(
                 _build_retrieve_online_document_record(
                     entity_key,
                     string_value if string_value else b"",
+                    # This may be a bug
                     embedding,
                     distance,
                     event_ts,
@@ -459,19 +462,6 @@ def _table_id(project: str, table: FeatureView) -> str:
     return f"{project}_{table.name}"
 
 
-def serialize_f32(
-    vector: Union[RepeatedScalarFieldContainer[float], List[float]], vector_length: int
-) -> bytes:
-    """serializes a list of floats into a compact "raw bytes" format"""
-    return struct.pack(f"{vector_length}f", *vector)
-
-
-def deserialize_f32(byte_vector: bytes, vector_length: int) -> List[float]:
-    """deserializes a list of floats from a compact "raw bytes" format"""
-    num_floats = vector_length // 4  # 4 bytes per float
-    return list(struct.unpack(f"{num_floats}f", byte_vector))
-
-
 class SqliteTable(InfraObject):
     """
     A Sqlite table managed by Feast.

sdk/python/feast/infra/passthrough_provider.py

@@ -294,7 +294,8 @@ def retrieve_online_documents(
         self,
         config: RepoConfig,
         table: FeatureView,
-        requested_feature: str,
+        requested_feature: Optional[str],
+        requested_features: Optional[List[str]],
         query: List[float],
         top_k: int,
         distance_metric: Optional[str] = None,
@@ -305,6 +306,7 @@ def retrieve_online_documents(
                 config,
                 table,
                 requested_feature,
+                requested_features,
                 query,
                 top_k,
                 distance_metric,

sdk/python/feast/infra/provider.py

@@ -419,7 +419,8 @@ def retrieve_online_documents(
         self,
         config: RepoConfig,
         table: FeatureView,
-        requested_feature: str,
+        requested_feature: Optional[str],
+        requested_features: Optional[List[str]],
         query: List[float],
         top_k: int,
         distance_metric: Optional[str] = None,
@@ -440,6 +441,7 @@ def retrieve_online_documents(
             config: The config for the current feature store.
             table: The feature view whose embeddings should be searched.
             requested_feature: the requested document feature name.
+            requested_features: the requested document feature names.
             query: The query embedding to search for.
             top_k: The number of documents to return.
 

sdk/python/feast/utils.py

@@ -1192,6 +1192,10 @@ def _utc_now() -> datetime:
     return datetime.now(tz=timezone.utc)
 
 
+def _serialize_vector_to_float_list(vector: List[float]) -> ValueProto:
+    return ValueProto(float_list_val=FloatListProto(val=vector))
+
+
 def _build_retrieve_online_document_record(
     entity_key: Union[str, bytes],
     feature_value: Union[str, bytes],