Gpts app v0.4 (#1169)

dbgpt/serve/agent/team/layout/agent_operator.py

@@ -135,22 +135,30 @@ class AwelAgentOperator(
         self,
         input_value: AgentGenerateContext,
     ) -> AgentGenerateContext:
-        now_rely_messages: List[Dict] = []
-
+        now_message = input_value.message
         agent = await self.get_agent(input_value)
+        if agent.fixed_subgoal and len(agent.fixed_subgoal) > 0:
+            # Isolate the message delivery mechanism and pass it to the operator
+            input_value.message["current_gogal"] = (
+                f"[{agent.name if agent.name else agent.profile}]:"
+                + agent.fixed_subgoal
+            )
+            now_message["content"] = agent.fixed_subgoal
+        else:
+            # Isolate the message delivery mechanism and pass it to the operator
+            input_value.message["current_gogal"] = (
+                f"[{agent.name if agent.name else agent.profile}]:"
+                + input_value.message["content"]
+            )
 
-        # Isolate the message delivery mechanism and pass it to the operator
-        input_value.message["current_gogal"] = (
-            f"[{agent.name if agent.name else agent.profile}]:"
-            + input_value.message["content"]
-        )
+        now_rely_messages: List[Dict] = []
         ###What was received was the User message
         human_message = input_value.message.copy()
         human_message["role"] = ModelMessageRoleType.HUMAN
         now_rely_messages.append(human_message)
 
         ###Send a message (no reply required) and pass the message content
-        now_message = input_value.message
+
         if input_value.rely_messages and len(input_value.rely_messages) > 0:
             now_message = input_value.rely_messages[-1]
         await input_value.sender.a_send(now_message, agent, input_value.reviewer, False)
@@ -200,9 +208,13 @@ class AwelAgentOperator(
                 llm_config = LLMConfig(llm_client=input_value.llm_client)
             else:
                 llm_config = LLMConfig(llm_client=self.llm_client)
-
+        kwargs = {}
+        if self.awel_agent.role_name:
+            kwargs["name"] = self.awel_agent.role_name
+        if self.awel_agent.fixed_subgoal:
+            kwargs["fixed_subgoal"] = self.awel_agent.fixed_subgoal
         agent = (
-            await agent_cls(name=self.awel_agent.role_name)
+            await agent_cls(**kwargs)
             .bind(input_value.memory)
             .bind(llm_config)
             .bind(input_value.agent_context)

dbgpt/serve/agent/team/layout/agent_operator_resource.py

@@ -139,6 +139,14 @@ class AwelAgentConfig(LLMConfig):
             default=None,
             description="The agent role name.",
         ),
+        Parameter.build_from(
+            label="Fixed Gogal",
+            name="fixed_subgoal",
+            type=str,
+            optional=True,
+            default=None,
+            description="The agent fixed gogal.",
+        ),
         Parameter.build_from(
             label="Agent Resource",
             name="agent_resource",
@@ -162,6 +170,7 @@ class AwelAgent(BaseModel):
     role_name: Optional[str] = None
     llm_config: Optional[LLMConfig] = None
     resources: List[AgentResource] = Field(default_factory=list)
+    fixed_subgoal: Optional[str] = None
 
     class Config:
         arbitrary_types_allowed = True

dbgpt/serve/agent/team/plan/team_auto_plan.py

@@ -117,6 +117,7 @@ class AutoPlanChatManager(ManagerAgent):
         reviewer: Optional[ConversableAgent] = None,
     ) -> Optional[ActionOutput]:
         speaker = sender
+        final_message = message
         for i in range(self.max_round):
             plans = self.memory.plans_memory.get_by_conv_id(self.agent_context.conv_id)
 
@@ -153,7 +154,7 @@ class AutoPlanChatManager(ManagerAgent):
                     # complete
                     return ActionOutput(
                         is_exe_success=True,
-                        content=f"{plans[-1].result}",  # work results message
+                        content=final_message,  # work results message
                     )
                 else:
                     try:
@@ -201,11 +202,14 @@ class AutoPlanChatManager(ManagerAgent):
                         )
 
                         plan_result = ""
+                        final_message = reply_message["content"]
                         if is_success:
                             if reply_message:
                                 action_report = reply_message.get("action_report", None)
                                 if action_report:
                                     plan_result = action_report.get("content", "")
+                                    final_message = action_report["view"]
+
                             ### The current planned Agent generation verification is successful
                             ##Plan executed successfully
                             self.memory.plans_memory.complete_task(
@@ -213,7 +217,6 @@ class AutoPlanChatManager(ManagerAgent):
                                 now_plan.sub_task_num,
                                 plan_result,
                             )
-
                         else:
                             plan_result = reply_message["content"]
                             self.memory.plans_memory.update_task(
@@ -228,6 +231,7 @@ class AutoPlanChatManager(ManagerAgent):
                             return ActionOutput(
                                 is_exe_success=False, content=plan_result
                             )
+
                     except Exception as e:
                         logger.exception(
                             f"An exception was encountered during the execution of the current plan step.{str(e)}"