style: ruff format

Author: xerrors

Makefile

@@ -27,6 +27,7 @@ lint:
 format:
 	uv run ruff format .
 	uv run ruff check . --fix
+	uv run python -m ruff check --select I src --fix
 
 format_diff:
 	uv run ruff format --diff .

server/routers/chat_router.py

@@ -112,7 +112,8 @@ async def get_agent(current_user: User = Depends(get_required_user)):
             metadata = yaml.safe_load(f)
     return {"agents": agents, "metadata": metadata}
 
-#TODO:[未完成]这个thread_id在前端是直接生成的1234，最好传入thread_id时做校验只允许uuid4
+
+# TODO:[未完成]这个thread_id在前端是直接生成的1234，最好传入thread_id时做校验只允许uuid4
 @chat.post("/agent/{agent_id}")
 async def chat_agent(
     agent_id: str,
@@ -254,7 +255,8 @@ async def save_messages_from_langgraph_state(
             logger.error(f"Error saving messages from LangGraph state: {e}")
             logger.error(traceback.format_exc())
 
-    #TODO:[功能建议]针对需要人工审批后再执行的工具，可以使用langgraph的interrupt方法中断对话，等待用户输入后再使用command跳转回去
+    # TODO:[功能建议]针对需要人工审批后再执行的工具，
+    # 可以使用langgraph的interrupt方法中断对话，等待用户输入后再使用command跳转回去
     async def stream_messages():
         # 代表服务端已经收到了请求
         yield make_chunk(status="init", meta=meta, msg=HumanMessage(content=query).model_dump())

server/utils/lifespan.py

@@ -1,11 +1,11 @@
-import asyncio
 from contextlib import asynccontextmanager
 
 from fastapi import FastAPI
 
 from server.services import tasker
 
-#TODO:[已完成]使用lifespan进行统一生命周期管理
+# TODO:[已完成]使用lifespan进行统一生命周期管理
+
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):

server/utils/singleton.py

@@ -1,9 +1,11 @@
 from threading import Lock
 
+
 class SingletonMeta(type):
     """
     This is a thread-safe implementation of Singleton.
     """
+
     _instances = {}
     _lock: Lock = Lock()
 
@@ -12,4 +14,4 @@ def __call__(cls, *args, **kwargs):
             if cls not in cls._instances:
                 instance = super().__call__(*args, **kwargs)
                 cls._instances[cls] = instance
-        return cls._instances[cls]
+        return cls._instances[cls]

src/agents/chatbot/tools.py

@@ -9,8 +9,9 @@
 from src.storage.minio import upload_image_to_minio
 from src.utils import logger
 
-#TODO:[已完成]修改了tool定义的示例，使用更符合langgraph调用的方式
-@tool(name_or_callable="全能计算器",description="可以对给定的2个数字选择进行加减乘除四种计算")
+
+# TODO:[已完成]修改了tool定义的示例，使用更符合langgraph调用的方式
+@tool(name_or_callable="全能计算器", description="可以对给定的2个数字选择进行加减乘除四种计算")
 def calculator(a: float, b: float, operation: str) -> float:
     """
     可以对给定的2个数字选择进行加减乘除四种计算

src/agents/common/base.py

@@ -1,12 +1,12 @@
 from __future__ import annotations
 
 import os
-from pathlib import Path
 from abc import abstractmethod
+from pathlib import Path
 
-from langgraph.graph.state import CompiledStateGraph
 from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver, aiosqlite
+from langgraph.graph.state import CompiledStateGraph
 
 from src import config as sys_config
 from src.agents.common.context import BaseContext

src/agents/common/tools.py

@@ -11,7 +11,7 @@
 from src.utils import logger
 
 
-@tool
+@tool(name_or_callable="查询知识图谱", description="使用这个工具可以查询知识图谱中包含的三元组信息。")
 def query_knowledge_graph(query: Annotated[str, "The keyword to query knowledge graph."]) -> Any:
     """Use this to query knowledge graph, which include some food domain knowledge."""
     try:
@@ -35,7 +35,9 @@ def get_static_tools() -> list:
 
     # 检查是否启用网页搜索
     if config.enable_web_search:
-        static_tools.append(TavilySearch(max_results=10))
+        search = TavilySearch(max_results=10)
+        search.metadata = {"name": "Tavily 网页搜索"}
+        static_tools.append(search)
 
     return static_tools
 
@@ -76,9 +78,6 @@ async def async_retriever_wrapper(query_text: str) -> Any:
 
     for db_id, retrieve_info in retrievers.items():
         try:
-            # 使用改进的工具ID生成策略
-            tool_id = f"query_{db_id[:8]}"
-
             # 构建工具描述
             description = (
                 f"使用 {retrieve_info['name']} 知识库进行检索。\n"
@@ -88,10 +87,12 @@ async def async_retriever_wrapper(query_text: str) -> Any:
             # 使用工厂函数创建检索器包装函数，避免闭包问题
             retriever_wrapper = _create_retriever_wrapper(db_id, retrieve_info)
 
+            safename = retrieve_info["name"].replace(" ", "_")[:20]
+
             # 使用 StructuredTool.from_function 创建异步工具
             tool = StructuredTool.from_function(
                 coroutine=retriever_wrapper,
-                name=tool_id,
+                name=safename,
                 description=description,
                 args_schema=KnowledgeRetrieverModel,
                 metadata=retrieve_info["metadata"] | {"tag": ["knowledgebase"]},

src/config/app.py

@@ -13,7 +13,7 @@
 
 import tomli
 import tomli_w
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field
 
 from src.config.static.models import (
     DEFAULT_CHAT_MODEL_PROVIDERS,
@@ -172,13 +172,10 @@ def _handle_environment(self):
         self.model_dir = os.environ.get("MODEL_DIR", self.model_dir)
         if self.model_dir:
             if os.path.exists(self.model_dir):
-                logger.debug(
-                    f"Model directory ({self.model_dir}) contains: {os.listdir(self.model_dir)}"
-                )
+                logger.debug(f"Model directory ({self.model_dir}) contains: {os.listdir(self.model_dir)}")
             else:
                 logger.warning(
-                    f"Model directory ({self.model_dir}) does not exist. "
-                    "If not configured, please ignore it."
+                    f"Model directory ({self.model_dir}) does not exist. If not configured, please ignore it."
                 )
 
         # 检查模型提供商的环境变量
@@ -195,14 +192,10 @@ def _handle_environment(self):
             self.enable_web_search = True
 
         # 获取可用的模型提供商
-        self.valuable_model_provider = [
-            k for k, v in self.model_provider_status.items() if v
-        ]
+        self.valuable_model_provider = [k for k, v in self.model_provider_status.items() if v]
 
         if not self.valuable_model_provider:
-            raise ValueError(
-                "No model provider available, please check your `.env` file."
-            )
+            raise ValueError("No model provider available, please check your `.env` file.")
 
     def save(self):
         """保存配置到 TOML 文件（仅保存用户修改的字段）"""
@@ -251,15 +244,11 @@ def dump_config(self) -> dict[str, Any]:
         )
 
         # 添加模型信息（转换为字典格式供前端使用）
-        config_dict["model_names"] = {
-            provider: info.model_dump() for provider, info in self.model_names.items()
-        }
+        config_dict["model_names"] = {provider: info.model_dump() for provider, info in self.model_names.items()}
         config_dict["embed_model_names"] = {
             model_id: info.model_dump() for model_id, info in self.embed_model_names.items()
         }
-        config_dict["reranker_names"] = {
-            model_id: info.model_dump() for model_id, info in self.reranker_names.items()
-        }
+        config_dict["reranker_names"] = {model_id: info.model_dump() for model_id, info in self.reranker_names.items()}
 
         # 添加运行时状态信息
         config_dict["model_provider_status"] = self.model_provider_status
@@ -269,10 +258,12 @@ def dump_config(self) -> dict[str, Any]:
         for field_name, field_info in Config.model_fields.items():
             if not field_info.exclude:  # 排除内部字段
                 fields_info[field_name] = {
-                    'des': field_info.description,
-                    'default': field_info.default,
-                    'type': field_info.annotation.__name__ if hasattr(field_info.annotation, '__name__') else str(field_info.annotation),
-                    'exclude': field_info.exclude if hasattr(field_info, 'exclude') else False,
+                    "des": field_info.description,
+                    "default": field_info.default,
+                    "type": field_info.annotation.__name__
+                    if hasattr(field_info.annotation, "__name__")
+                    else str(field_info.annotation),
+                    "exclude": field_info.exclude if hasattr(field_info, "exclude") else False,
                 }
         config_dict["_config_items"] = fields_info
 
@@ -301,14 +292,12 @@ def get_reranker_choices(self) -> list[str]:
 
     def __getitem__(self, key: str) -> Any:
         """支持字典式访问 config[key]"""
-        logger.warning("Using deprecated dict-style access for Config. "
-                       "Please use attribute access instead.")
+        logger.warning("Using deprecated dict-style access for Config. Please use attribute access instead.")
         return getattr(self, key, None)
 
     def __setitem__(self, key: str, value: Any):
         """支持字典式赋值 config[key] = value"""
-        logger.warning("Using deprecated dict-style assignment for Config. "
-                       "Please use attribute access instead.")
+        logger.warning("Using deprecated dict-style assignment for Config. Please use attribute access instead.")
         setattr(self, key, value)
 
     def update(self, other: dict):
@@ -352,8 +341,7 @@ def _save_models_to_file(self, provider_name: str = None):
             else:
                 # 保存所有 model_names
                 user_config["model_names"] = {
-                    provider: info.model_dump()
-                    for provider, info in self.model_names.items()
+                    provider: info.model_dump() for provider, info in self.model_names.items()
                 }
                 # 记录整个 model_names 字段的修改
                 self._user_modified_fields.add("model_names")

src/knowledge/base.py

@@ -1,7 +1,7 @@
 import json
 import os
-import tempfile
 import shutil
+import tempfile
 from abc import ABC, abstractmethod
 from typing import Any
 
@@ -144,8 +144,8 @@ def create_database(
             "name": database_name,
             "description": description,
             "kb_type": self.kb_type,
-            "embed_info": embed_info.model_dump() if hasattr(embed_info, 'model_dump') else embed_info,
-            "llm_info": llm_info.model_dump() if hasattr(llm_info, 'model_dump') else llm_info,
+            "embed_info": embed_info.model_dump() if hasattr(embed_info, "model_dump") else embed_info,
+            "llm_info": llm_info.model_dump() if hasattr(llm_info, "model_dump") else llm_info,
             "metadata": kwargs,
             "created_at": utc_isoformat(),
         }
@@ -559,7 +559,7 @@ def _load_metadata(self):
 
     def _serialize_metadata(self, obj):
         """递归序列化元数据中的 Pydantic 模型"""
-        if hasattr(obj, 'dict'):
+        if hasattr(obj, "dict"):
             return obj.dict()
         elif isinstance(obj, dict):
             return {k: self._serialize_metadata(v) for k, v in obj.items()}
@@ -589,8 +589,7 @@ def _save_metadata(self):
 
             # 原子性写入（使用临时文件）
             with tempfile.NamedTemporaryFile(
-                mode='w', dir=os.path.dirname(meta_file),
-                prefix='.tmp_', suffix='.json', delete=False
+                mode="w", dir=os.path.dirname(meta_file), prefix=".tmp_", suffix=".json", delete=False
             ) as tmp_file:
                 json.dump(data, tmp_file, ensure_ascii=False, indent=2)
                 temp_path = tmp_file.name

src/knowledge/graph.py

@@ -299,7 +299,7 @@ def _batch_set_embeddings(tx, entity_embedding_pairs):
             logger.info(f"Adding entity to {kgdb_name}")
             session.execute_write(_create_graph, triples)
             logger.info(f"Creating vector index for {kgdb_name} with {config.embed_model}")
-            session.execute_write(_create_vector_index, getattr(cur_embed_info, 'dimension', 1024))
+            session.execute_write(_create_vector_index, getattr(cur_embed_info, "dimension", 1024))
 
             # 收集所有需要处理的实体名称，去重
             all_entities = []