style: apply pre-commit fixes (black, isort)

Author: github-actions[bot]

cli/__init__.py

@@ -1,5 +1,7 @@
-import click
 import subprocess
+
+import click
+
 from llm_utils.tools import set_gms_server
 
 

data_utils/datahub_source.py

@@ -1,13 +1,18 @@
-from datahub.metadata.schema_classes import DatasetPropertiesClass, SchemaMetadataClass
-from datahub.emitter.rest_emitter import DatahubRestEmitter
-from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
-from datahub.metadata.schema_classes import UpstreamLineageClass
 from collections import defaultdict
+
 import requests
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
+from datahub.metadata.schema_classes import (
+    DatasetPropertiesClass,
+    SchemaMetadataClass,
+    UpstreamLineageClass,
+)
+
 from data_utils.queries import (
-    ROOT_GLOSSARY_NODES_QUERY,
     GLOSSARY_NODE_QUERY,
     LIST_QUERIES_QUERY,
+    ROOT_GLOSSARY_NODES_QUERY,
 )
 
 

evaluation/gen_answer.py

@@ -1,10 +1,9 @@
+import uuid
 from argparse import ArgumentParser
-from langchain_core.messages import HumanMessage
-
-from utils import load_question_json, save_answer_json
 
+from langchain_core.messages import HumanMessage
 from tqdm import tqdm
-import uuid
+from utils import load_question_json, save_answer_json
 
 from llm_utils.graph import builder
 

evaluation/gen_persona.py

@@ -1,12 +1,12 @@
 import os
+from argparse import ArgumentParser
 
-from utils import save_persona_json, pretty_print_persona
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai.chat_models import ChatOpenAI
 from persona_class import PersonaList
+from utils import pretty_print_persona, save_persona_json
 
 from llm_utils.tools import _get_table_info
-from langchain_openai.chat_models import ChatOpenAI
-from langchain_core.prompts import ChatPromptTemplate
-from argparse import ArgumentParser
 
 
 def get_table_des_string(tables_desc):

evaluation/gen_question.py

@@ -1,10 +1,10 @@
-from utils import load_persona_json, save_question_json
+import os
+from argparse import ArgumentParser
+
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai.chat_models import ChatOpenAI
 from tqdm import tqdm
-
-from argparse import ArgumentParser
-import os
+from utils import load_persona_json, save_question_json
 
 
 def get_persona_prompt(persona):

evaluation/persona_class.py

@@ -1,6 +1,7 @@
-from pydantic import BaseModel
 from typing import List
 
+from pydantic import BaseModel
+
 
 class Persona(BaseModel):
     name: str

evaluation/utils.py

@@ -1,7 +1,8 @@
 import json
-from persona_class import PersonaList
-from glob import glob
 import os
+from glob import glob
+
+from persona_class import PersonaList
 
 
 def save_persona_json(data, filepath):

interface/lang2sql.py

@@ -1,15 +1,15 @@
-import streamlit as st
-from langchain_core.messages import HumanMessage
-from llm_utils.graph import builder
-from langchain.chains.sql_database.prompt import SQL_PROMPTS
 import os
 from typing import Union
-import pandas as pd
 
+import pandas as pd
+import streamlit as st
 from clickhouse_driver import Client
-from llm_utils.connect_db import ConnectDB
 from dotenv import load_dotenv
+from langchain.chains.sql_database.prompt import SQL_PROMPTS
+from langchain_core.messages import HumanMessage
 
+from llm_utils.connect_db import ConnectDB
+from llm_utils.graph import builder
 
 # Clickhouse 연결
 db = ConnectDB()

interface/viz_eval.py

@@ -1,9 +1,9 @@
-import streamlit as st
-import json
 import glob
-import pandas as pd
+import json
 import os
 
+import pandas as pd
+import streamlit as st
 
 st.set_page_config(layout="wide", page_title="Lang2SQL 평가 시각화")
 

llm_utils/chains.py

@@ -1,15 +1,16 @@
 import os
+
+from dotenv import load_dotenv
 from langchain_core.prompts import (
     ChatPromptTemplate,
     MessagesPlaceholder,
     SystemMessagePromptTemplate,
 )
 
-from .llm_factory import get_llm
-
-from dotenv import load_dotenv
 from prompt.template_loader import get_prompt_template
 
+from .llm_factory import get_llm
+
 env_path = os.path.join(os.getcwd(), ".env")
 
 if os.path.exists(env_path):