[FSTORE-1202] Support similarity search in external fg (#1210)

* external fg api

* add find_neighbor

* add db client

* create fg with embedding

* set embedding object

* add read option

* fix style

* address comment

python/hsfs/feature_group.py

@@ -3277,6 +3277,7 @@ def __init__(
         notification_topic_name=None,
         spine=False,
         deprecated=False,
+        embedding_index=None,
         **kwargs,
     ):
         super().__init__(
@@ -3287,6 +3288,7 @@ def __init__(
             event_time=event_time,
             online_enabled=online_enabled,
             id=id,
+            embedding_index=embedding_index,
             expectation_suite=expectation_suite,
             online_topic_name=online_topic_name,
             topic_name=topic_name,
@@ -3347,7 +3349,7 @@ def __init__(
             )
         else:
             self._storage_connector = storage_connector
-
+        self._vector_db_client = None
         self._href = href
 
     def save(self):
@@ -3424,7 +3426,10 @@ def insert(
         )
 
     def read(
-        self, dataframe_type: Optional[str] = "default", online: Optional[bool] = False
+        self,
+        dataframe_type: Optional[str] = "default",
+        online: Optional[bool] = False,
+        read_options: Optional[dict] = None,
     ):
         """Get the feature group as a DataFrame.
 
@@ -3452,7 +3457,8 @@ def read(
                 `"pandas"`, `"numpy"` or `"python"`, defaults to `"default"`.
             online: bool, optional. If `True` read from online feature store, defaults
                 to `False`.
-
+            read_options: Additional options as key/value pairs to pass to the spark engine.
+                Defaults to `None`.
         # Returns
             `DataFrame`: The spark dataframe containing the feature data.
             `pyspark.DataFrame`. A Spark DataFrame.
@@ -3485,10 +3491,14 @@ def read(
                 self._name, self._feature_store_name
             ),
         )
-        return self.select_all().read(dataframe_type=dataframe_type, online=online)
+        return self.select_all().read(
+            dataframe_type=dataframe_type,
+            online=online,
+            read_options=read_options or {},
+        )
 
-    def show(self, n):
-        """Show the first n rows of the feature group.
+    def show(self, n: int, online: Optional[bool] = False):
+        """Show the first `n` rows of the feature group.
 
         !!! example
             ```python
@@ -3498,39 +3508,130 @@ def show(self, n):
             # get the Feature Group instance
             fg = fs.get_or_create_feature_group(...)
 
-            fg.show(5)
+            # make a query and show top 5 rows
+            fg.select(['date','weekly_sales','is_holiday']).show(5)
             ```
+
+        # Arguments
+            n: int. Number of rows to show.
+            online: bool, optional. If `True` read from online feature store, defaults
+                to `False`.
         """
         engine.get_instance().set_job_group(
             "Fetching Feature group",
             "Getting feature group: {} from the featurestore {}".format(
                 self._name, self._feature_store_name
             ),
         )
-        return self.select_all().show(n)
+        if online and self.embedding_index:
+            if self._vector_db_client is None:
+                self._vector_db_client = VectorDbClient(self.select_all())
+            results = self._vector_db_client.read(
+                self.id,
+                {},
+                pk=self.embedding_index.col_prefix + self.primary_key[0],
+                index_name=self.embedding_index.index_name,
+                n=n,
+            )
+            return [[result[f.name] for f in self.features] for result in results]
+        return self.select_all().show(n, online)
+
+    def find_neighbors(
+        self,
+        embedding: List[Union[int, float]],
+        col: Optional[str] = None,
+        k: Optional[int] = 10,
+        filter: Optional[Union[Filter, Logic]] = None,
+        min_score: Optional[float] = 0,
+    ) -> List[Tuple[float, List[Any]]]:
+        """
+        Finds the nearest neighbors for a given embedding in the vector database.
+
+        # Arguments
+            embedding: The target embedding for which neighbors are to be found.
+            col: The column name used to compute similarity score. Required only if there
+            are multiple embeddings (optional).
+            k: The number of nearest neighbors to retrieve (default is 10).
+            filter: A filter expression to restrict the search space (optional).
+            min_score: The minimum similarity score for neighbors to be considered (default is 0).
+
+        # Returns
+            A list of tuples representing the nearest neighbors.
+            Each tuple contains: `(The similarity score, A list of feature values)`
+
+        !!! Example
+            ```
+            embedding_index = EmbeddingIndex()
+            embedding_index.add_embedding(name="user_vector", dimension=3)
+            fg = fs.create_feature_group(
+                        name='air_quality',
+                        embedding_index = embedding_index,
+                        version=1,
+                        primary_key=['id1'],
+                        online_enabled=True,
+                    )
+            fg.insert(data)
+            fg.find_neighbors(
+                [0.1, 0.2, 0.3],
+                k=5,
+            )
+
+            # apply filter
+            fg.find_neighbors(
+                [0.1, 0.2, 0.3],
+                k=5,
+                filter=(fg.id1 > 10) & (fg.id1 < 30)
+            )
+            ```
+        """
+        if self._vector_db_client is None and self._embedding_index:
+            self._vector_db_client = VectorDbClient(self.select_all())
+        results = self._vector_db_client.find_neighbors(
+            embedding,
+            feature=(self.__getattr__(col) if col else None),
+            k=k,
+            filter=filter,
+            min_score=min_score,
+        )
+        return [
+            (result[0], [result[1][f.name] for f in self.features])
+            for result in results
+        ]
 
     @classmethod
     def from_response_json(cls, json_dict):
         json_decamelized = humps.decamelize(json_dict)
         if isinstance(json_decamelized, dict):
             _ = json_decamelized.pop("type", None)
+            if "embedding_index" in json_decamelized:
+                json_decamelized["embedding_index"] = EmbeddingIndex.from_json_response(
+                    json_decamelized["embedding_index"]
+                )
             return cls(**json_decamelized)
         for fg in json_decamelized:
             _ = fg.pop("type", None)
+            if "embedding_index" in fg:
+                fg["embedding_index"] = EmbeddingIndex.from_json_response(
+                    fg["embedding_index"]
+                )
         return [cls(**fg) for fg in json_decamelized]
 
     def update_from_response_json(self, json_dict):
         json_decamelized = humps.decamelize(json_dict)
         if "type" in json_decamelized:
             _ = json_decamelized.pop("type")
+        if "embedding_index" in json_decamelized:
+            json_decamelized["embedding_index"] = EmbeddingIndex.from_json_response(
+                json_decamelized["embedding_index"]
+            )
         self.__init__(**json_decamelized)
         return self
 
     def json(self):
         return json.dumps(self, cls=util.FeatureStoreEncoder)
 
     def to_dict(self):
-        return {
+        fg_meta_dict = {
             "id": self._id,
             "name": self._name,
             "description": self._description,
@@ -3554,6 +3655,9 @@ def to_dict(self):
             "notificationTopicName": self.notification_topic_name,
             "deprecated": self.deprecated,
         }
+        if self.embedding_index:
+            fg_meta_dict["embeddingIndex"] = self.embedding_index
+        return fg_meta_dict
 
     @property
     def id(self):

python/hsfs/feature_store.py

@@ -845,6 +845,7 @@ def create_external_feature_group(
         version: Optional[int] = None,
         description: Optional[str] = "",
         primary_key: Optional[List[str]] = [],
+        embedding_index: Optional[EmbeddingIndex] = None,
         features: Optional[List[feature.Feature]] = [],
         statistics_config: Optional[Union[StatisticsConfig, bool, dict]] = None,
         event_time: Optional[str] = None,
@@ -968,6 +969,7 @@ def create_external_feature_group(
             version=version,
             description=description,
             primary_key=primary_key,
+            embedding_index=embedding_index,
             featurestore_id=self._id,
             featurestore_name=self._name,
             features=features,