@@ -192,8 +192,7 @@ def get_feature_vector(self, features: List[Union[str, Feature]],
192192
193193 return sql if return_sql else self .splice_ctx .df (sql ).toPandas ()
194194
195- def get_feature_vector_sql_from_training_view (self , training_view : str , features : List [Feature ],
196- include_insert : Optional [bool ] = True ) -> str :
195+ def get_feature_vector_sql_from_training_view (self , training_view : str , features : List [Feature ]) -> str :
197196 """
198197 Returns the parameterized feature retrieval SQL used for online model serving.
199198
@@ -206,25 +205,14 @@ def get_feature_vector_sql_from_training_view(self, training_view: str, features
206205 This function will error if the view SQL is missing a view key required to retrieve the\
207206 desired features
208207
209- :param include_insert: (Optional[bool]) determines whether insert into model table is included in the SQL statement
210208 :return: (str) the parameterized feature vector SQL
211209 """
212210
213211 # Get training view information (ctx primary key column(s), ctx primary key inference ts column, )
214212 vid = self .get_training_view_id (training_view )
215213 tctx = self .get_training_views (_filter = {'view_id' : vid })[0 ]
216214
217- # optional INSERT prefix
218- if (include_insert ):
219- sql = 'INSERT INTO {target_model_table} ('
220- for pkcol in tctx .pk_columns : # Select primary key column(s)
221- sql += f'{ pkcol } , '
222- for feature in features :
223- sql += f'{ feature .name } , ' # Collect all features over time
224- sql = sql .rstrip (', ' )
225- sql += ')\n SELECT '
226- else :
227- sql = 'SELECT '
215+ sql = 'SELECT '
228216
229217 # SELECT expressions
230218 for pkcol in tctx .pk_columns : # Select primary key column(s)
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