mirror of
https://github.com/open-compass/opencompass.git
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b3f5d9e421
* support math agent * support gsm8k agent * support gsm8k agent * minor fix * minor fix * minor fix * Update configs/eval_codeagent.py
82 lines
3.5 KiB
Python
82 lines
3.5 KiB
Python
from typing import Union
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from lagent.actions import ActionExecutor
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from lagent.agents.base_agent import BaseAgent
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from lagent.agents.react import ReActProtocol
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from lagent.llms.base_api import BaseAPIModel
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from lagent.llms.base_llm import BaseModel
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from lagent.schema import ActionReturn, AgentReturn
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class ReAct(BaseAgent):
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"""An implementation of ReAct (https://arxiv.org/abs/2210.03629)
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Args:
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llm (BaseModel or BaseAPIModel): a LLM service which can chat
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and act as backend.
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action_executor (ActionExecutor): an action executor to manage
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all actions and their response.
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protocol (ReActProtocol): a wrapper to generate prompt and
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parse the response from LLM / actions.
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max_turn (int): the maximum number of trails for LLM to generate
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plans that can be successfully parsed by ReWOO protocol.
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"""
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def __init__(self,
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llm: Union[BaseModel, BaseAPIModel],
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action_executor: ActionExecutor,
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protocol: ReActProtocol = ReActProtocol(),
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max_turn: int = 2) -> None:
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self.max_turn = max_turn
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super().__init__(llm=llm,
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action_executor=action_executor,
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protocol=protocol)
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def opencompass_adapter(self, prompt):
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# adapter for prompt parsing
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from opencompass.utils.prompt import PromptList
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if isinstance(prompt, list):
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for p in prompt:
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if 'content' in p:
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p['prompt'] = p.pop('content')
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prompt = PromptList(prompt)
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return prompt
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def chat(self, message: str) -> AgentReturn:
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self._inner_history = []
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self._inner_history.append(dict(role='user', content=message))
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agent_return = AgentReturn()
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force_stop = False
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default_response = '对不起，我无法回答你的问题'
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for turn in range(self.max_turn):
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prompt = self._protocol.format(
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chat_history=self.session_history,
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inner_step=self._inner_history,
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action_executor=self._action_executor,
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force_stop=force_stop)
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prompt = self.opencompass_adapter(prompt)
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# allow single generation
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response = self._llm.generate_from_template([prompt], 512)[0]
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self._inner_history.append(dict(role='assistant',
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content=response))
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thought, action, action_input = self._protocol.parse(
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response, self._action_executor)
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action_return: ActionReturn = self._action_executor(
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action, action_input)
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action_return.thought = thought
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agent_return.actions.append(action_return)
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if action_return.type == self._action_executor.finish_action.name:
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agent_return.response = action_return.result['text']
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return agent_return
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self._inner_history.append(
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dict(role='system',
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content=self._protocol.format_response(action_return)))
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if turn == self.max_turn - 1:
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force_stop = True
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agent_return.response = default_response
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# only append the user and final response
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self._session_history.append(dict(role='user', content=message))
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self._session_history.append(
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dict(role='assistant', content=agent_return.response))
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return agent_return
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