add fix issue plan, modify Editor

metagpt/roles/di/data_analyst.py

@@ -25,6 +25,7 @@ from metagpt.utils.common import CodeParser
 class DataAnalyst(DataInterpreter):
     name: str = "David"
     profile: str = "DataAnalyst"
+    goal: str = "Take on any data-related tasks, such as data analysis, machine learning, deep learning, web browsing, web scraping, web searching, web deployment, terminal operation, git and github operation, etc."
     react_mode: Literal["react"] = "react"
     max_react_loop: int = 20  # used for react mode
     task_result: TaskResult = None
@@ -33,6 +34,7 @@ class DataAnalyst(DataInterpreter):
         Command.RESET_TASK,
         Command.REPLACE_TASK,
         Command.FINISH_CURRENT_TASK,
+        Command.CONTINUE_WITH_CURRENT_TASK,
         # Command.PUBLISH_MESSAGE,
         Command.ASK_HUMAN,
         Command.REPLY_TO_HUMAN,
@@ -55,7 +57,7 @@ class DataAnalyst(DataInterpreter):
         self._set_state(0)
 
         # HACK: Init Planner, control it through dynamic thinking; Consider formalizing as a react mode
-        self.planner = Planner(goal=self.goal, working_memory=self.rc.working_memory, auto_run=True)
+        self.planner = Planner(goal="", working_memory=self.rc.working_memory, auto_run=True)
 
         return self
 
@@ -69,11 +71,14 @@ class DataAnalyst(DataInterpreter):
             example = KeywordExpRetriever().retrieve(self.user_requirement)
         else:
             self.working_memory.add_batch(self.rc.news)
+            context = "\n\n".join([str(mem) for mem in self.working_memory.get()])
+            example = KeywordExpRetriever().retrieve(context)
 
         plan_status = self.planner.plan.model_dump(include=["goal", "tasks"])
         for task in plan_status["tasks"]:
             task.pop("code")
             task.pop("result")
+            task.pop("is_success")
         prompt = CMD_PROMPT.format(
             plan_status=plan_status,
             example=example,
@@ -96,6 +101,8 @@ class DataAnalyst(DataInterpreter):
         self.planner.plan.current_task.is_success = (
             is_success  # mark is_success, determine is_finished later in thinking
         )
+
+        # FIXME: task result is always overwritten by the last act, whereas it can be made of of multiple acts
         self.task_result = TaskResult(code=code, result=result, is_success=is_success)
         return Message(content="Task completed", role="assistant", sent_from=self._setting, cause_by=WriteAnalysisCode)