franciscojavierarceo · Apr 29, 2025 · Apr 26, 2025 · Apr 27, 2025 · Apr 27, 2025 · Apr 27, 2025
@@ -1518,134 +1518,191 @@ def _offline_write():
 
             await run_in_threadpool(_offline_write)
 
-    def _get_feature_view_and_df_for_online_write(
+    def _validate_and_convert_input_data(
         self,
-        feature_view_name: str,
-        df: Optional[pd.DataFrame] = None,
-        inputs: Optional[Union[Dict[str, List[Any]], pd.DataFrame]] = None,
-        allow_registry_cache: bool = True,
-        transform_on_write: bool = True,
-    ):
-        feature_view_dict = {
-            fv_proto.name: fv_proto
-            for fv_proto in self.list_all_feature_views(allow_registry_cache)
-        }
-        try:
-            feature_view = feature_view_dict[feature_view_name]
-        except FeatureViewNotFoundException:
-            raise FeatureViewNotFoundException(feature_view_name, self.project)
+        df: Optional[pd.DataFrame],
+        inputs: Optional[Union[Dict[str, List[Any]], pd.DataFrame]],
+    ) -> Optional[pd.DataFrame]:
+        """
+        Validates input parameters and converts them to a pandas DataFrame.
+
+        Args:
+            df: Optional DataFrame input
+            inputs: Optional dictionary or DataFrame input
+
+        Returns:
+            Validated pandas DataFrame or None
+
+        Raises:
+            ValueError: If both df and inputs are provided
+            DataFrameSerializationError: If input data cannot be converted to DataFrame
+        """
         if df is not None and inputs is not None:
             raise ValueError("Both df and inputs cannot be provided at the same time.")
+
         if df is None and inputs is not None:
             if isinstance(inputs, dict) or isinstance(inputs, List):
                 try:
-                    df = pd.DataFrame(inputs)
+                    return pd.DataFrame(inputs)
                 except Exception as _:
                     raise DataFrameSerializationError(inputs)
             elif isinstance(inputs, pd.DataFrame):
-                pass
+                return inputs
             else:
                 raise ValueError("inputs must be a dictionary or a pandas DataFrame.")
+
         if df is not None and inputs is None:
             if isinstance(df, dict) or isinstance(df, List):
                 try:
-                    df = pd.DataFrame(df)
+                    return pd.DataFrame(df)
                 except Exception as _:
                     raise DataFrameSerializationError(df)
 
-        if feature_view.features[0].vector_index and df is not None:
+        return df
+
+    def _transform_on_demand_feature_view_df(
+        self, feature_view: OnDemandFeatureView, df: pd.DataFrame
+    ) -> pd.DataFrame:
+        """
+        Apply transformations for an OnDemandFeatureView to the input dataframe.
+
+        Args:
+            feature_view: The OnDemandFeatureView containing the transformation
+            df: The input dataframe to transform
+
+        Returns:
+            Transformed dataframe
+
+        Raises:
+            Exception: For unsupported OnDemandFeatureView modes
+        """
+        if feature_view.mode == "python" and isinstance(
+            feature_view.feature_transformation, PythonTransformation
+        ):
+            input_dict = (
+                df.to_dict(orient="records")[0]
+                if feature_view.singleton
+                else df.to_dict(orient="list")
+            )
+
+            if feature_view.singleton:
+                transformed_rows = []
+
+                for i, row in df.iterrows():
+                    output = feature_view.feature_transformation.udf(row.to_dict())
+                    if i == 0:
+                        transformed_rows = output
+                    else:
+                        for k in output:
+                            if isinstance(output[k], list):
+                                transformed_rows[k].extend(output[k])
+                            else:
+                                transformed_rows[k].append(output[k])
+
+                transformed_data = pd.DataFrame(transformed_rows)
+            else:
+                transformed_data = feature_view.feature_transformation.udf(input_dict)
+
+            if feature_view.write_to_online_store:
+                entities = [
+                    self.get_entity(entity) for entity in (feature_view.entities or [])
+                ]
+                join_keys = [entity.join_key for entity in entities if entity]
+                join_keys = [k for k in join_keys if k in input_dict.keys()]
+                transformed_df = (
+                    pd.DataFrame(transformed_data)
+                    if not isinstance(transformed_data, pd.DataFrame)
+                    else transformed_data
+                )
+                input_df = pd.DataFrame(
+                    [input_dict] if feature_view.singleton else input_dict
+                )
+                if input_df.shape[0] == transformed_df.shape[0]:
+                    for k in input_dict:
+                        if k not in transformed_data:
+                            transformed_data[k] = input_dict[k]
+                    transformed_df = pd.DataFrame(transformed_data)
+                else:
+                    transformed_df = pd.merge(
+                        transformed_df,
+                        input_df,
+                        how="left",
+                        on=join_keys,
+                    )
+            else:
+                # overwrite any transformed features and update the dictionary
+                for k in input_dict:
+                    if k not in transformed_data:
+                        transformed_data[k] = input_dict[k]
+
+            return pd.DataFrame(transformed_data)
+
+        elif feature_view.mode == "pandas" and isinstance(
+            feature_view.feature_transformation, PandasTransformation
+        ):
+            transformed_df = feature_view.feature_transformation.udf(df)
+            for col in df.columns:
+                transformed_df[col] = df[col]
+            return transformed_df
+        else:
+            raise Exception("Unsupported OnDemandFeatureView mode")
+
+    def _validate_vector_features(self, feature_view, df: pd.DataFrame) -> None:
+        """
+        Validates vector features in the DataFrame against the feature view specifications.
+
+        Args:
+            feature_view: The feature view containing vector feature specifications
+            df: The DataFrame to validate
+
+        Raises:
+            ValueError: If vector dimension constraints are violated
+        """
+        if feature_view.features and feature_view.features[0].vector_index:
             fv_vector_feature_name = feature_view.features[0].name
             df_vector_feature_index = df.columns.get_loc(fv_vector_feature_name)
+
             if feature_view.features[0].vector_length != 0:
                 if (
                     df.shape[df_vector_feature_index]
                     > feature_view.features[0].vector_length
                 ):
                     raise ValueError(
-                        f"The dataframe for {fv_vector_feature_name} column has {df.shape[1]} vectors which is greater than expected (i.e {feature_view.features[0].vector_length}) by feature view {feature_view.name}."
+                        f"The dataframe for {fv_vector_feature_name} column has {df.shape[1]} vectors "
+                        f"which is greater than expected (i.e {feature_view.features[0].vector_length}) "
+                        f"by feature view {feature_view.name}."
                     )
 
+    def _get_feature_view_and_df_for_online_write(
+        self,
+        feature_view_name: str,
+        df: Optional[pd.DataFrame] = None,
+        inputs: Optional[Union[Dict[str, List[Any]], pd.DataFrame]] = None,
+        allow_registry_cache: bool = True,
+        transform_on_write: bool = True,
+    ):
+        feature_view_dict = {
+            fv_proto.name: fv_proto
+            for fv_proto in self.list_all_feature_views(allow_registry_cache)
+        }
+        try:
+            feature_view = feature_view_dict[feature_view_name]
+        except FeatureViewNotFoundException:
+            raise FeatureViewNotFoundException(feature_view_name, self.project)
+
+        # Convert inputs/df to a consistent DataFrame format
+        df = self._validate_and_convert_input_data(df, inputs)
+
+        if df is not None:
+            self._validate_vector_features(feature_view, df)
+
         # # Apply transformations if this is an OnDemandFeatureView with write_to_online_store=True
         if (
             isinstance(feature_view, OnDemandFeatureView)
             and feature_view.write_to_online_store
             and transform_on_write
         ):
-            if (
-                feature_view.mode == "python"
-                and isinstance(
-                    feature_view.feature_transformation, PythonTransformation
-                )
-                and df is not None
-            ):
-                input_dict = (
-                    df.to_dict(orient="records")[0]
-                    if feature_view.singleton
-                    else df.to_dict(orient="list")
-                )
-                if feature_view.singleton:
-                    transformed_rows = []
-
-                    for i, row in df.iterrows():
-                        output = feature_view.feature_transformation.udf(row.to_dict())
-                        if i == 0:
-                            transformed_rows = output
-                        else:
-                            for k in output:
-                                if isinstance(output[k], list):
-                                    transformed_rows[k].extend(output[k])
-                                else:
-                                    transformed_rows[k].append(output[k])
-
-                    transformed_data = pd.DataFrame(transformed_rows)
-                else:
-                    transformed_data = feature_view.feature_transformation.udf(
-                        input_dict
-                    )
-                if feature_view.write_to_online_store:
-                    entities = [
-                        self.get_entity(entity)
-                        for entity in (feature_view.entities or [])
-                    ]
-                    join_keys = [entity.join_key for entity in entities if entity]
-                    join_keys = [k for k in join_keys if k in input_dict.keys()]
-                    transformed_df = (
-                        pd.DataFrame(transformed_data)
-                        if not isinstance(transformed_data, pd.DataFrame)
-                        else transformed_data
-                    )
-                    input_df = pd.DataFrame(
-                        [input_dict] if feature_view.singleton else input_dict
-                    )
-                    if input_df.shape[0] == transformed_df.shape[0]:
-                        for k in input_dict:
-                            if k not in transformed_data:
-                                transformed_data[k] = input_dict[k]
-                        transformed_df = pd.DataFrame(transformed_data)
-                    else:
-                        transformed_df = pd.merge(
-                            transformed_df,
-                            input_df,
-                            how="left",
-                            on=join_keys,
-                        )
-                else:
-                    # overwrite any transformed features and update the dictionary
-                    for k in input_dict:
-                        if k not in transformed_data:
-                            transformed_data[k] = input_dict[k]
-                df = pd.DataFrame(transformed_data)
-            elif feature_view.mode == "pandas" and isinstance(
-                feature_view.feature_transformation, PandasTransformation
-            ):
-                transformed_df = feature_view.feature_transformation.udf(df)
-                if df is not None:
-                    for col in df.columns:
-                        transformed_df[col] = df[col]
-                    df = transformed_df
-
-            else:
-                raise Exception("Unsupported OnDemandFeatureView mode")
+            df = self._transform_on_demand_feature_view_df(feature_view, df)
 
         return feature_view, df
 

diff --git a/ui/.npmrc.local b/ui/.npmrc.local
@@ -0,0 +1,3 @@
+# Dummy NPM token for local development
+# This file should not be committed to the repository
+//registry.npmjs.org/:_authToken=dummy-token-for-local-development
@@ -31,6 +31,7 @@ module.exports = {
     "^react-native$": "react-native-web",
     "^.+\\.module\\.(css|sass|scss)$": "identity-obj-proxy",
     "chroma-js": "<rootDir>/node_modules/chroma-js/dist/chroma.min.cjs",
+    "^reactflow/dist/style\\.css$": "identity-obj-proxy",
   },
   moduleFileExtensions: [
     "web.js",