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Merge pull request #628 from iorisa/fixbug/role/assistant
fixbug: 修复通用智能体role及其相关的TalkAction和SkillAction
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geekan
GitHub
commit
59586f30d6
12 changed files with 541 additions and 512 deletions
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@ -14,36 +14,45 @@ import traceback
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from copy import deepcopy
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from typing import Dict, Optional
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from metagpt.actions import Action, ActionOutput
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from metagpt.actions import Action
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from metagpt.learn.skill_loader import Skill
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from metagpt.logs import logger
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from metagpt.schema import Message
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# TOTEST
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class ArgumentsParingAction(Action):
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skill: Skill
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ask: str
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rsp: Optional[ActionOutput]
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args: Optional[Dict]
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rsp: Optional[Message] = None
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args: Optional[Dict] = None
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@property
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def prompt(self):
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prompt = f"{self.skill.name} function parameters description:\n"
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prompt = "You are a function parser. You can convert spoken words into function parameters.\n"
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prompt += "\n---\n"
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prompt += f"{self.skill.name} function parameters description:\n"
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for k, v in self.skill.arguments.items():
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prompt += f"parameter `{k}`: {v}\n"
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prompt += "\n"
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prompt += "\n---\n"
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prompt += "Examples:\n"
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for e in self.skill.examples:
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prompt += f"If want you to do `{e.ask}`, return `{e.answer}` brief and clear.\n"
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prompt += f"\nNow I want you to do `{self.ask}`, return in examples format above, brief and clear."
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prompt += "\n---\n"
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prompt += (
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f"\nRefer to the `{self.skill.name}` function description, and fill in the function parameters according "
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'to the example "I want you to do xx" in the Examples section.'
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f"\nNow I want you to do `{self.ask}`, return function parameters in Examples format above, brief and "
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"clear."
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)
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return prompt
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async def run(self, *args, **kwargs) -> ActionOutput:
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async def run(self, with_message=None, **kwargs) -> Message:
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prompt = self.prompt
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rsp = await self.llm.aask(msg=prompt, system_msgs=[])
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logger.debug(f"SKILL:{prompt}\n, RESULT:{rsp}")
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self.args = ArgumentsParingAction.parse_arguments(skill_name=self.skill.name, txt=rsp)
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self.rsp = ActionOutput(content=rsp)
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self.rsp = Message(content=rsp, role="assistant", instruct_content=self.args, cause_by=self)
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return self.rsp
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@staticmethod
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@ -72,9 +81,9 @@ class ArgumentsParingAction(Action):
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class SkillAction(Action):
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skill: Skill
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args: Dict
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rsp: str = ""
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rsp: Optional[Message] = None
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async def run(self, *args, **kwargs) -> str | ActionOutput | None:
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async def run(self, with_message=None, **kwargs) -> Message:
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"""Run action"""
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options = deepcopy(kwargs)
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if self.args:
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@ -82,26 +91,21 @@ class SkillAction(Action):
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if k in options:
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options.pop(k)
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try:
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self.rsp = await self.find_and_call_function(self.skill.name, args=self.args, **options)
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rsp = await self.find_and_call_function(self.skill.name, args=self.args, **options)
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self.rsp = Message(content=rsp, role="assistant", cause_by=self)
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except Exception as e:
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logger.exception(f"{e}, traceback:{traceback.format_exc()}")
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self.rsp = f"Error: {e}"
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return ActionOutput(content=self.rsp, instruct_content=self.skill.json())
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self.rsp = Message(content=f"Error: {e}", role="assistant", cause_by=self)
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return self.rsp
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@staticmethod
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async def find_and_call_function(function_name, args, **kwargs):
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async def find_and_call_function(function_name, args, **kwargs) -> str:
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try:
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module = importlib.import_module("metagpt.learn")
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function = getattr(module, function_name)
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# 调用函数并返回结果
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# Invoke function and return result
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result = await function(**args, **kwargs)
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return result
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except (ModuleNotFoundError, AttributeError):
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logger.error(f"{function_name} not found")
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return None
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if __name__ == "__main__":
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ArgumentsParingAction.parse_arguments(
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skill_name="text_to_image", txt='`text_to_image(text="Draw an apple", size_type="512x512")`'
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)
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raise ValueError(f"{function_name} not found")
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@ -6,27 +6,21 @@
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@File : talk_action.py
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@Desc : Act as it’s a talk
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"""
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import json
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from typing import Optional
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from metagpt.actions import Action, ActionOutput
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from metagpt.actions import Action
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from metagpt.config import CONFIG
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from metagpt.const import DEFAULT_LANGUAGE
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from metagpt.llm import LLMType
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from metagpt.logs import logger
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from metagpt.schema import Message
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# TOTEST
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class TalkAction(Action):
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def __init__(self, name: str = "", talk="", history_summary="", knowledge="", context=None, llm=None, **kwargs):
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context = context or {}
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context["talk"] = talk
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context["history_summery"] = history_summary
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context["knowledge"] = knowledge
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super(TalkAction, self).__init__(name=name, context=context, llm=llm)
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self._talk = talk
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self._history_summary = history_summary
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self._knowledge = knowledge
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self._rsp = None
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context: str
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history_summary: str = ""
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knowledge: str = ""
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rsp: Optional[Message] = None
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@property
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def prompt(self):
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@ -37,15 +31,15 @@ class TalkAction(Action):
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f"maintaining the character's persona and habits. When faced with unrelated questions, playfully "
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f"decline to answer without revealing your AI nature to preserve the character's image.\n\n"
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)
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prompt += f"Knowledge:\n{self._knowledge}\n\n" if self._knowledge else ""
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prompt += f"{self._history_summary}\n\n"
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prompt += f"Knowledge:\n{self.knowledge}\n\n" if self.knowledge else ""
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prompt += f"{self.history_summary}\n\n"
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prompt += (
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"If the information is insufficient, you can search in the historical conversation or knowledge above.\n"
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)
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language = CONFIG.language or DEFAULT_LANGUAGE
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prompt += (
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f"Answer the following questions strictly in {language}, and the answers must follow the Markdown format.\n "
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f"{self._talk}"
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f"{self.context}"
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)
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logger.debug(f"PROMPT: {prompt}")
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return prompt
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@ -54,23 +48,23 @@ class TalkAction(Action):
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def prompt_gpt4(self):
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kvs = {
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"{role}": CONFIG.agent_description or "",
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"{history}": self._history_summary or "",
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"{knowledge}": self._knowledge or "",
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"{history}": self.history_summary or "",
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"{knowledge}": self.knowledge or "",
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"{language}": CONFIG.language or DEFAULT_LANGUAGE,
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"{ask}": self._talk,
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"{ask}": self.context,
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}
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prompt = TalkAction.__FORMATION_LOOSE__
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prompt = TalkActionPrompt.FORMATION_LOOSE
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for k, v in kvs.items():
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prompt = prompt.replace(k, v)
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logger.info(f"PROMPT: {prompt}")
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return prompt
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async def run_old(self, *args, **kwargs) -> ActionOutput:
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prompt = self.prompt
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rsp = await self.llm.aask(msg=prompt, system_msgs=[])
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logger.debug(f"PROMPT:{prompt}\nRESULT:{rsp}\n")
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self._rsp = ActionOutput(content=rsp)
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return self._rsp
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# async def run_old(self, *args, **kwargs) -> ActionOutput:
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# prompt = self.prompt
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# rsp = await self.llm.aask(msg=prompt, system_msgs=[])
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# logger.debug(f"PROMPT:{prompt}\nRESULT:{rsp}\n")
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# self._rsp = ActionOutput(content=rsp)
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# return self._rsp
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@property
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def aask_args(self):
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@ -84,22 +78,21 @@ class TalkAction(Action):
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f"Answer the following questions strictly in {language}, and the answers must follow the Markdown format.",
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]
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format_msgs = []
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if self._knowledge:
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format_msgs.append({"role": "assistant", "content": self._knowledge})
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if self._history_summary:
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if CONFIG.LLM_TYPE == LLMType.METAGPT.value:
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format_msgs.extend(json.loads(self._history_summary))
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else:
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format_msgs.append({"role": "assistant", "content": self._history_summary})
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return self._talk, format_msgs, system_msgs
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if self.knowledge:
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format_msgs.append({"role": "assistant", "content": self.knowledge})
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if self.history_summary:
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format_msgs.append({"role": "assistant", "content": self.history_summary})
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return self.context, format_msgs, system_msgs
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async def run(self, *args, **kwargs) -> ActionOutput:
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async def run(self, with_message=None, **kwargs) -> Message:
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msg, format_msgs, system_msgs = self.aask_args
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rsp = await self.llm.aask(msg=msg, format_msgs=format_msgs, system_msgs=system_msgs)
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self._rsp = ActionOutput(content=rsp)
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return self._rsp
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self.rsp = Message(content=rsp, role="assistant", cause_by=self)
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return self.rsp
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__FORMATION__ = """Formation: "Capacity and role" defines the role you are currently playing;
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class TalkActionPrompt:
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FORMATION = """Formation: "Capacity and role" defines the role you are currently playing;
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"[HISTORY_BEGIN]" and "[HISTORY_END]" tags enclose the historical conversation;
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"[KNOWLEDGE_BEGIN]" and "[KNOWLEDGE_END]" tags enclose the knowledge may help for your responses;
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"Statement" defines the work detail you need to complete at this stage;
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@ -135,7 +128,7 @@ Statement: Unless you are a language professional, answer the following question
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{ask}
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"""
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__FORMATION_LOOSE__ = """Formation: "Capacity and role" defines the role you are currently playing;
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FORMATION_LOOSE = """Formation: "Capacity and role" defines the role you are currently playing;
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"[HISTORY_BEGIN]" and "[HISTORY_END]" tags enclose the historical conversation;
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"[KNOWLEDGE_BEGIN]" and "[KNOWLEDGE_END]" tags enclose the knowledge may help for your responses;
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"Statement" defines the work detail you need to complete at this stage;
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