fix: Fix Feast UI failure with new way of specifying entities

ui/feature_repo/features.py

@@ -1,18 +1,11 @@
 from datetime import timedelta
 
-from feast import (
-    Entity,
-    FeatureService,
-    FeatureView,
-    Field,
-    FileSource,
-    ValueType,
-)
+import pandas as pd
+
+from feast import Entity, FeatureService, FeatureView, Field, FileSource, ValueType
 from feast.data_source import RequestSource
-from feast.request_feature_view import RequestFeatureView
 from feast.on_demand_feature_view import on_demand_feature_view
 from feast.types import Bool, Int64, String
-import pandas as pd
 
 zipcode = Entity(
     name="zipcode",
@@ -128,19 +121,14 @@
 # Define a request data source which encodes features / information only
 # available at request time (e.g. part of the user initiated HTTP request)
 input_request = RequestSource(
-    name="transaction",
-    schema=[
-        Field(name="transaction_amt", dtype=Int64),
-    ],
+    name="transaction", schema=[Field(name="transaction_amt", dtype=Int64),],
 )
 
 # Define an on demand feature view which can generate new features based on
 # existing feature views and RequestSource features
 @on_demand_feature_view(
     sources=[credit_history, input_request],
-    schema=[
-        Field(name="transaction_gt_last_credit_card_due", dtype=Bool),
-    ],
+    schema=[Field(name="transaction_gt_last_credit_card_due", dtype=Bool),],
 )
 def transaction_gt_last_credit_card_due(inputs: pd.DataFrame) -> pd.DataFrame:
     df = pd.DataFrame()
@@ -149,6 +137,7 @@ def transaction_gt_last_credit_card_due(inputs: pd.DataFrame) -> pd.DataFrame:
     )
     return df
 
+
 model_v1 = FeatureService(
     name="credit_score_v1",
     features=[

ui/feature_repo/test_get_features.py

@@ -1,12 +1,11 @@
 import pandas as pd
-from feast import FeatureStore
-from feast.infra.offline_stores.file import SavedDatasetFileStorage
-
-from feast.dqm.profilers.ge_profiler import ge_profiler
-
 from great_expectations.core.expectation_suite import ExpectationSuite
 from great_expectations.dataset import PandasDataset
 
+from feast import FeatureStore
+from feast.dqm.profilers.ge_profiler import ge_profiler
+from feast.infra.offline_stores.file import SavedDatasetFileStorage
+
 DELTA = 0.1  # controlling allowed window in fraction of the value on scale [0, 1]
 # Note: the GE integration allows asserting differences between datasets. The "ds" below is the reference dataset to check and this generates the expectation suite which can be used against future datasets.
 # It's used via ge.validate(new_dataset, ExpectationSuite)

ui/src/parsers/feastEntities.ts

@@ -4,7 +4,7 @@ import { FEAST_FEATURE_VALUE_TYPES } from "./types";
 const FeastEntitySchema = z.object({
   spec: z.object({
     name: z.string(),
-    valueType: z.nativeEnum(FEAST_FEATURE_VALUE_TYPES),
+    valueType: z.nativeEnum(FEAST_FEATURE_VALUE_TYPES).optional(),
     joinKey: z.string(),
     description: z.string().optional(),
     labels: z.record(z.string()).optional(),