style: auto-format with ruff [skip ci]

server/routers/chat_router.py

@@ -658,11 +658,11 @@ async def update_thread(
     )
 
 
-
 # ================================
 # > === 附件管理分组 ===
 # ================================
 
+
 @chat.post("/thread/{thread_id}/attachments", response_model=AttachmentResponse)
 async def upload_thread_attachment(
     thread_id: str,

src/agents/deep_agent/graph.py

@@ -13,10 +13,9 @@ from src.agents.common import BaseAgent, load_chat_model
 from src.agents.common.middlewares import RuntimeConfigMiddleware, SummaryOffloadMiddleware, save_attachments_to_fs
 from src.agents.common.tools import get_tavily_search
 from src.services.mcp_service import get_tools_from_all_servers
+from src.utils import logger
 
 from .context import DeepContext
-from .prompts import DEEP_PROMPT
-from src.utils import logger
 
 
 def _create_fs_backend(rt):

src/models/embed.py

@@ -74,8 +74,8 @@ class BaseEmbeddingModel(ABC):
             task_id = hashstr(messages)
             self.embed_state[task_id] = {"status": "in-progress", "total": len(messages), "progress": 0}
 
-        #保留原有逻辑：
-        #使用 asyncio.gather 并发执行所有 embedding 批次请求：
+        # 保留原有逻辑：
+        # 使用 asyncio.gather 并发执行所有 embedding 批次请求：
         # tasks = []
         # for i in range(0, len(messages), batch_size):
         #     group_msg = messages[i : i + batch_size]
@@ -90,7 +90,7 @@ class BaseEmbeddingModel(ABC):
         #     self.embed_state[task_id]["status"] = "completed"
 
         # return data
-        
+
         for i in range(0, len(messages), batch_size):
             group_msg = messages[i : i + batch_size]
             logger.info(f"Async encoding [{i}/{len(messages)}] messages (bsz={batch_size})")