feat(Agent): format code style

format code style

dbgpt/agent/agents/agent.py

@@ -8,6 +8,7 @@
 from ..memory.gpts_memory import GptsMemory
 from dbgpt.core.interface.llm import ModelMetadata
 
+
 class Agent:
     """
     An interface for AI agent.
@@ -166,7 +167,7 @@ def to_dict(self) -> Dict[str, Any]:
 @dataclass
 class AgentContext:
     conv_id: str
-    llm_provider: Optional['LLMClient']
+    llm_provider: Optional["LLMClient"]
 
     gpts_name: Optional[str] = None
     resource_db: Optional[AgentResource] = None

dbgpt/agent/agents/base_agent.py

@@ -636,7 +636,6 @@ def _get_model_priority(self):
         else:
             return None
 
-
     def _filter_health_models(self, need_uses: Optional[list]):
         all_modes = self.agent_context.llm_models
         can_uses = []
@@ -645,7 +644,6 @@ def _filter_health_models(self, need_uses: Optional[list]):
                 can_uses.append(item)
         return can_uses
 
-
     def _select_llm_model(self, old_model: str = None):
         """
         LLM model selector, currently only supports manual selection, more strategies will be opened in the future
@@ -662,7 +660,7 @@ def _select_llm_model(self, old_model: str = None):
         if old_model:
             filtered_list = [item for item in all_modes if item.model != old_model]
             if filtered_list and len(filtered_list) >= 1:
-                now_model= filtered_list[0]
+                now_model = filtered_list[0]
         return now_model.model
 
     async def a_reasoning_reply(

dbgpt/agent/agents/expand/code_assistant_agent.py

@@ -171,7 +171,7 @@ async def a_verify(self, message: Optional[Dict]):
         check_reult, model = await self.a_reasoning_reply(
             [
                 {
-                    "role":  ModelMessageRoleType.HUMAN,
+                    "role": ModelMessageRoleType.HUMAN,
                     "content": f"""Please understand the following task objectives and results and give your judgment:
                         Task Gogal: {task_gogal}
                         Execution Result: {task_result}

dbgpt/agent/agents/expand/plugin_assistant_agent.py

@@ -113,9 +113,7 @@ async def tool_call(
             ### Answer failed, turn on automatic repair
             rensponse_succ = False
         else:
-
             try:
-
                 view = ""
             except Exception as e:
                 view = f"```vis-convert-error\n{content}\n```"

dbgpt/agent/agents/llm/llm_client.py

@@ -181,7 +181,9 @@ async def _completions_create(self, llm_model, params):
         try:
             model_request = _build_model_request(payload)
             model_output = await self._llm_client.generate(model_request)
-            parsed_output =  self._output_parser.parse_model_nostream_resp(model_output, "###")
+            parsed_output = self._output_parser.parse_model_nostream_resp(
+                model_output, "###"
+            )
             return parsed_output
         except Exception as e:
             logger.error(

dbgpt/agent/agents/plan_group_chat.py

@@ -226,7 +226,6 @@ def __init__(
         max_consecutive_auto_reply: Optional[int] = sys.maxsize,
         human_input_mode: Optional[str] = "NEVER",
         describe: Optional[str] = "Plan chat manager.",
-
         **kwargs,
     ):
         super().__init__(
@@ -431,7 +430,6 @@ async def a_run_chat(
                                 plan_result,
                             )
                     except Exception as e:
-
                         logger.exception(
                             f"An exception was encountered during the execution of the current plan step.{str(e)}"
                         )

dbgpt/agent/agents/planner_agent.py

@@ -86,7 +86,6 @@ def __init__(
         is_termination_msg: Optional[Callable[[Dict], bool]] = None,
         max_consecutive_auto_reply: Optional[int] = None,
         human_input_mode: Optional[str] = "NEVER",
-
         **kwargs,
     ):
         super().__init__(

dbgpt/agent/plugin/commands/built_in/disply_type/__init__.py

@@ -1 +1 @@
-from .show_chart_gen import static_message_img_path
+from .show_chart_gen import static_message_img_path

dbgpt/agent/plugin/plugin.py

@@ -4,10 +4,8 @@
 
 logger = logging.getLogger(__name__)
 
-class PluginLoader:
-
-
 
+class PluginLoader:
     def load_plugins(
         self, generator: PluginPromptGenerator, my_plugins: List[str]
     ) -> PluginPromptGenerator:
@@ -18,4 +16,4 @@ def load_plugins(
                 if not plugin.can_handle_post_prompt():
                     continue
                 generator = plugin.post_prompt(generator)
-        return generator
+        return generator

dbgpt/serve/agent/agents/controller.py

@@ -104,17 +104,15 @@ async def plan_chat(
         worker_manager = CFG.SYSTEM_APP.get_component(
             ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
         ).create()
-        llm_task =  DefaultLLMClient(worker_manager)
-        context: AgentContext = AgentContext(
-            conv_id=conv_id, llm_provider=llm_task
-        )
+        llm_task = DefaultLLMClient(worker_manager)
+        context: AgentContext = AgentContext(conv_id=conv_id, llm_provider=llm_task)
         context.gpts_name = gpts_instance.gpts_name
         context.resource_db = resource_db
         context.resource_internet = resource_internet
         context.resource_knowledge = resource_knowledge
         context.agents = agents_names
 
-        context.llm_models =  await llm_task.models()
+        context.llm_models = await llm_task.models()
         context.model_priority = llm_models_priority
 
         agent_map = defaultdict()
@@ -130,9 +128,6 @@ async def plan_chat(
             agents.append(agent)
             agent_map[name] = agent
 
-
-
-
         groupchat = PlanChat(agents=agents, messages=[], max_round=50)
         planner = PlannerAgent(
             agent_context=context,

dbgpt/util/code_utils.py

@@ -133,7 +133,6 @@ def extract_code(
 }
 
 
-
 def timeout_handler(signum, frame):
     raise TimeoutError("Timed out!")
 
@@ -202,14 +201,14 @@ def execute_code(
 
     try:
         import docker
+
         try:
             docker.version
         except AttributeError:
             docker = None
     except ImportError:
         docker = None
 
-
     if use_docker is None:
         if docker is None:
             use_docker = False
@@ -371,7 +370,6 @@ def execute_code(
 }
 
 
-
 def _remove_check(response):
     """Remove the check function from the response."""
     # find the position of the check function

examples/agents/auto_plan_agent_dialogue_example.py

@@ -35,7 +35,7 @@
 
     llm_client = OpenAILLMClient()
     context: AgentContext = AgentContext(conv_id="test456", llm_provider=llm_client)
-    context.llm_models =  [ModelMetadata(model="gpt-3.5-turbo")]
+    context.llm_models = [ModelMetadata(model="gpt-3.5-turbo")]
     context.gpts_name = "代码分析助手"
 
     default_memory = GptsMemory()
@@ -62,8 +62,8 @@
         user_proxy.a_initiate_chat(
             recipient=manager,
             reviewer=user_proxy,
-            message="download data from https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.",
-            # message="find papers on LLM applications from arxiv in the last week, create a markdown table of different domains.",
+            # message="load data from https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.",
+            message="find papers on LLM applications from arxiv in the last week, create a markdown table of different domains.",
         )
     )
 

examples/agents/single_agent_dialogue_example.py

@@ -24,7 +24,6 @@
 
 
 if __name__ == "__main__":
-
     from dbgpt.model import OpenAILLMClient
 
     llm_client = OpenAILLMClient()
@@ -40,7 +39,7 @@
         user_proxy.a_initiate_chat(
             recipient=coder,
             reviewer=user_proxy,
-            message="式计算下321 * 123等于多少", #用python代码的方式计算下321 * 123等于多少
+            message="式计算下321 * 123等于多少",  # 用python代码的方式计算下321 * 123等于多少
             # message="download data from https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.",
         )
     )