more plan operation, review update, add kaggle team

metagpt/actions/ml_da_action.py

@@ -0,0 +1,119 @@
+import json
+from typing import Dict, List, Union
+
+from metagpt.actions import Action
+from metagpt.schema import Message, Plan
+from metagpt.logs import logger
+
+
+def truncate(result: str, keep_len: int = 1000) -> str:
+    desc = "Truncated to show only the last 1000 characters\n"
+    if result.startswith(desc):
+        result = result[-len(desc) :]
+
+    if len(result) > keep_len:
+        result = result[-keep_len:]
+
+    if not result.startswith(desc):
+        return desc + result
+    return desc
+
+
+class ReviewConst:
+    TASK_REVIEW_TRIGGER = "task"
+    CODE_REVIEW_TRIGGER = "code"
+    CONTINUE_WORD = ["confirm", "continue", "c", "yes", "y"]
+    CHANGE_WORD = ["change"]
+    EXIT_WORD = ["exit"]
+    TASK_REVIEW_INSTRUCTION = (
+        f"If you want to change, add, delete a task or merge tasks in the plan, say '{CHANGE_WORD[0]} task task_id or current task, ... (things to change)' "
+        f"If you confirm the output from the current task and wish to continue, type: {CONTINUE_WORD[0]}"
+    )
+    CODE_REVIEW_INSTRUCTION = (
+        f"If you want the codes to be rewritten, say '{CHANGE_WORD[0]} ... (your change advice)' "
+        f"If you want to leave it as is, type: {CONTINUE_WORD[0]} or {CONTINUE_WORD[1]}"
+    )
+    EXIT_INSTRUCTION = f"If you want to terminate the process, type: {EXIT_WORD[0]}"
+
+
+class AskReview(Action):
+    async def run(
+        self, context: List[Message], plan: Plan = None, trigger: str = "task"
+    ):
+        logger.info("Current overall plan:")
+        logger.info(
+            "\n".join(
+                [
+                    f"{task.task_id}: {task.instruction}, is_finished: {task.is_finished}"
+                    for task in plan.tasks
+                ]
+            )
+        )
+
+        logger.info("most recent context:")
+        latest_action = context[-1].cause_by.__name__ if context[-1].cause_by else ""
+        review_instruction = (
+            ReviewConst.TASK_REVIEW_INSTRUCTION
+            if trigger == ReviewConst.TASK_REVIEW_TRIGGER
+            else ReviewConst.CODE_REVIEW_INSTRUCTION
+        )
+        prompt = (
+            f"This is a <{trigger}> review. Please review output from {latest_action}\n"
+            f"{review_instruction}\n"
+            f"{ReviewConst.EXIT_INSTRUCTION}\n"
+            "Please type your review below:\n"
+        )
+
+        rsp = input(prompt)
+
+        if rsp.lower() in ReviewConst.EXIT_WORD:
+            exit()
+
+        confirmed = rsp.lower() in ReviewConst.CONTINUE_WORD
+
+        return rsp, confirmed
+
+
+class SummarizeAnalysis(Action):
+    PROMPT_TEMPLATE = """
+    # Context
+    {context}
+    # Summary
+    Output a 30-word summary on analysis tool and modeling algorithms you have used, and the corresponding result. Make sure to announce the complete path to your test prediction file. Your summary:
+    """
+
+    def __init__(self, name: str = "", context=None, llm=None) -> str:
+        super().__init__(name, context, llm)
+
+    async def run(self, conmpleted_plan: Plan) -> str:
+        tasks = json.dumps(
+            [task.dict() for task in conmpleted_plan.tasks],
+            indent=4,
+            ensure_ascii=False,
+        )  # all tasks finished, return all task outputs
+        prompt = self.PROMPT_TEMPLATE.format(context=tasks)
+        summary = await self._aask(prompt)
+        return summary
+
+
+class Reflect(Action):
+    PROMPT_TEMPLATE = """
+    # User Requirement
+    {user_requirement}
+    # Context
+    {context}
+    # Summary
+    Above is all your attempts to tackle the user requirement. You plan, act, submit your output, and get the result and feedback.
+    First, summarize each of your previous trial in a triple of (your methods, the corresponding result, potential improvement), list them out.
+    # Takeaways
+    Second, carefully find key takeaways from your summarization in a step-by-step thinking process
+    # Guidance
+    Finally, make a concise one-sentence guidance for improving your future plan.
+    Your response:
+    """
+
+    async def run(self, context: str) -> str:
+        user_requirement = "Score as high as possible in a data modeling competition"
+        prompt = self.PROMPT_TEMPLATE.format(context=context, user_requirement=user_requirement)
+        rsp = await self._aask(prompt)
+        return rsp

metagpt/actions/write_plan.py

@@ -17,7 +17,7 @@ class WritePlan(Action):
     __context__
     # Task:
     Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to __max_tasks__ tasks.
-    If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes.
+    If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes. Give the whole plan unless instructed to modify only one task of the plan.
     Output a list of jsons following the format:
     ```json
     [