fix black

Author: zhongyu09

openchatbi/code/docker_executor.py

@@ -36,9 +36,7 @@ def check_docker_status() -> tuple[bool, str]:
 
         # Check if Docker daemon is running
         # Fixed command and arguments; no user input reaches subprocess.
-        result = subprocess.run(  # nosec B603
-            [docker_cli_path, "info"], capture_output=True, text=True, timeout=10
-        )
+        result = subprocess.run([docker_cli_path, "info"], capture_output=True, text=True, timeout=10)  # nosec B603
 
         if result.returncode == 0:
             return True, "Docker is installed and running"
@@ -85,9 +83,7 @@ def _check_docker_availability() -> None:
         # Check if Docker daemon is running
         try:
             # Fixed command and arguments; no user input reaches subprocess.
-            result = subprocess.run(  # nosec B603
-                [docker_cli_path, "info"], capture_output=True, text=True, timeout=10
-            )
+            result = subprocess.run([docker_cli_path, "info"], capture_output=True, text=True, timeout=10)  # nosec B603
             if result.returncode != 0:
                 if "Cannot connect to the Docker daemon" in result.stderr:
                     raise RuntimeError(

openchatbi/context_manager.py

@@ -289,8 +289,10 @@ def _apply_conversation_summarization(self, messages: list[BaseMessage]) -> None
 
         if len(historical_messages) == 1:
             msg = historical_messages[0]
-            if isinstance(msg, AIMessage) and isinstance(msg.content, str) and msg.content.startswith(
-                "[Conversation Summary]"
+            if (
+                isinstance(msg, AIMessage)
+                and isinstance(msg.content, str)
+                and msg.content.startswith("[Conversation Summary]")
             ):
                 return
 

openchatbi/streaming.py

@@ -296,8 +296,9 @@ def _process_updates(self, depth: int, layer_label: str, event_value: Any) -> It
 
             elif node_name == "information_extraction":
                 message_obj = (_message_list(node_output.get("messages")) or [None])[0]
-                if message_obj and getattr(message_obj, "tool_calls", None):
-                    tool_name = getattr(message_obj, "tool_calls")[0]["name"]
+                tool_calls = getattr(message_obj, "tool_calls", None)
+                if message_obj and tool_calls:
+                    tool_name = tool_calls[0]["name"]
                     desc = f"🛠️ Using tool: {tool_name}"
                     kind = "tool"
                     data = {"tools": [tool_name]}

openchatbi/text2sql/data.py

@@ -21,7 +21,9 @@
     sql_example_retriever, sql_example_dicts, sql_example_vector_db = _init_sql_example_retriever(
         _catalog_store, config.get().vector_db_path
     )
-    learned_sql_store: LearnedSQLStore | None = LearnedSQLStore(sql_example_vector_db, sql_example_dicts, threading.Lock())
+    learned_sql_store: LearnedSQLStore | None = LearnedSQLStore(
+        sql_example_vector_db, sql_example_dicts, threading.Lock()
+    )
     table_selection_retriever, table_selection_example_dict = _init_table_selection_example_dict(
         _catalog_store, config.get().vector_db_path
     )

openchatbi/tool/memory.py

@@ -200,9 +200,7 @@ def tool_call_schema(self) -> Any:
         return tcs
 
 
-def get_memory_tools(
-    llm: BaseChatModel, sync_mode: bool = False, store: Any | None = None
-) -> list[BaseTool] | None:
+def get_memory_tools(llm: BaseChatModel, sync_mode: bool = False, store: Any | None = None) -> list[BaseTool] | None:
     # Get the appropriate store based on mode
     if not store:
         if sync_mode: