Update text2sql.py according to linter

llama-index-integrations/llms/llama-index-llms-ipex-llm/examples/text2sql.py

@@ -7,6 +7,7 @@
 from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, insert
 import argparse
 
+
 def create_database_schema():
     engine = create_engine("sqlite:///:memory:")
     metadata_obj = MetaData()
@@ -23,6 +24,7 @@ def create_database_schema():
     metadata_obj.create_all(engine)
     return engine, city_stats_table
 
+
 def define_sql_database(engine, city_stats_table):
     sql_database = SQLDatabase(engine, include_tables=["city_stats"])
 
@@ -43,21 +45,19 @@ def define_sql_database(engine, city_stats_table):
 
     return sql_database
 
+
 def main(args):
     engine, city_stats_table = create_database_schema()
 
     sql_database = define_sql_database(engine, city_stats_table)
 
-    model_id=args.embedding_model_path
+    model_id = args.embedding_model_path
     device_map = args.device
 
 
-    embed_model = IpexLLMEmbedding(
-            model_id,
-            device=device_map
-    )
+    embed_model = IpexLLMEmbedding(model_id, device=device_map)
 
-    llm =  IpexLLM.from_model_id(
+    llm = IpexLLM.from_model_id(
         model_name=args.model_path,
         tokenizer_name=args.model_path,
         context_window=512,
@@ -70,11 +70,11 @@ def main(args):
 
     # default retrieval (return_raw=True)
     nl_sql_retriever = NLSQLRetriever(
-                    sql_database,
-                    tables=["city_stats"],
-                    llm=llm,
-                    embed_model=embed_model,
-                    return_raw=True
+        sql_database,
+        tables=["city_stats"],
+        llm=llm,
+        embed_model=embed_model,
+        return_raw=True
     )
 
     query_engine = RetrieverQueryEngine.from_args(nl_sql_retriever, llm=llm)
@@ -84,13 +84,13 @@ def main(args):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='LlamaIndex IpexLLM Example')
+    parser = argparse.ArgumentParser(description="LlamaIndex IpexLLM Example")
     parser.add_argument(
-        '-m',
-        '--model-path',
+        "-m",
+        "--model-path",
         type=str,
         required=True,
-        help='the path to transformers model'
+        help="the path to transformers model"
     )
     parser.add_argument(
         "--device",
@@ -101,24 +101,24 @@ def main(args):
         help="The device (Intel CPU or Intel GPU) the LLM model runs on",
     )
     parser.add_argument(
-        '-q',
-        '--question',
+        "-q",
+        "--question",
         type=str,
-        default='Which city has the highest population?',
-        help='question you want to ask.'
+        default="Which city has the highest population?",
+        help="question you want to ask."
     )
     parser.add_argument(
-        '-e',
-        '--embedding-model-path',
+        "-e",
+        "--embedding-model-path",
         default="BAAI/bge-small-en",
         help="the path to embedding model path"
     )
     parser.add_argument(
-        '-n',
-        '--n-predict',
+        "-n",
+        "--n-predict",
         type=int,
         default=32,
-        help='max number of predict tokens'
+        help="max number of predict tokens"
     )
     args = parser.parse_args()