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import copy
from typing import Dict, List
from lagent.actions import ActionExecutor
from lagent.agents.react import ReAct as _ReAct
from lagent.agents.react import ReActProtocol as _ReActProtocol
from lagent.schema import ActionReturn, ActionStatusCode, AgentReturn
class ReActProtocol(_ReActProtocol):
def __init__(self, **kwargs) -> None:
super().__init__(**kwargs)
# defaults to system
self.system_role = 'system'
self.first_system_role = 'system'
self.merge_adjacent_role = False
def format(self,
chat_history: List[Dict],
inner_step: List[Dict],
action_executor: ActionExecutor,
force_stop: bool = False) -> list:
"""Generate the ReAct format prompt.
Args:
chat_history (List[Dict]): The history log in previous runs.
inner_step (List[Dict]): The log in the current run.
action_executor (ActionExecutor): the action manager to
execute actions.
force_stop (boolean): whether force the agent to give responses
under pre-defined turns.
Returns:
List[Dict]: ReAct format prompt.
"""
call_protocol = self.call_protocol.format(
tool_description=action_executor.get_actions_info(),
action_names=action_executor.action_names(),
thought=self.thought['begin'],
action=self.action['begin'],
action_input=self.action_input['begin'],
response=self.response['begin'],
finish=self.finish['begin'],
)
formatted = []
formatted.append(
dict(role=self.first_system_role, content=call_protocol))
formatted += chat_history
formatted += inner_step
if force_stop:
formatted.append(
dict(role=self.system_role, content=self.force_stop))
if self.merge_adjacent_role and formatted:
merged = [formatted[0]] # Add the first dict
for d in formatted[1:]:
# If the 'role' of current dict matches with the 'role' of the
# last dict in merged list,
# append its 'content' to the 'content' of the last dict.
if d['role'] == merged[-1]['role']:
merged[-1]['content'] += d['content']
else:
# If 'role' does not match, add it as a new dict in the
# merged list
merged.append(d)
return merged
return formatted
class ReAct(_ReAct):
def __init__(self,
use_system_role: bool = True,
first_system_role: bool = True,
merge_adjacent_role: bool = False,
**kwargs) -> None:
super().__init__(**kwargs)
if use_system_role:
self.system_role = 'system'
else:
self.system_role = 'user'
if use_system_role or first_system_role:
first_system_role = 'system'
else:
first_system_role = 'user'
self._protocol.first_system_role = first_system_role
self._protocol.system_role = self.system_role
self._protocol.merge_adjacent_role = merge_adjacent_role
def chat(self, message: str) -> AgentReturn:
for hist in self._session_history:
if hist['role'] == 'system':
hist['role'] = self.system_role
self._inner_history = []
self._inner_history.append(dict(role='user', content=message))
agent_return = AgentReturn()
default_response = 'Sorry that I cannot answer your question.'
for turn in range(self.max_turn):
prompt = self._protocol.format(
chat_history=self.session_history,
inner_step=self._inner_history,
action_executor=self._action_executor,
force_stop=(turn == self.max_turn - 1))
response = self._llm.generate_from_template(prompt, 512)
self._inner_history.append(dict(role='assistant',
content=response))
thought, action, action_input = self._protocol.parse(
response, self._action_executor)
action_return: ActionReturn = self._action_executor(
action, action_input)
action_return.thought = thought
agent_return.actions.append(action_return)
if action_return.type == self._action_executor.finish_action.name:
agent_return.response = action_return.result['text']
break
self._inner_history.append(
dict(role=self.system_role,
content=self._protocol.format_response(action_return)))
else:
agent_return.response = default_response
agent_return.inner_steps = copy.deepcopy(self._inner_history)
# only append the user and final response
self._session_history.append(dict(role='user', content=message))
self._session_history.append(
dict(role='assistant', content=agent_return.response))
return agent_return
class CIReAct(ReAct):
"""Code Interpreter version of ReAct. The success state is different from
ReAct.
Args:
llm (BaseModel or BaseAPIModel): a LLM service which can chat
and act as backend.
action_executor (ActionExecutor): an action executor to manage
all actions and their response.
protocol (ReActProtocol): a wrapper to generate prompt and
parse the response from LLM / actions.
max_turn (int): the maximum number of trails for LLM to generate
plans that can be successfully parsed by ReWOO protocol.
"""
def reset(self):
"""Reset history and reset action if suit the case."""
self._session_history = []
# hard code here
from opencompass.lagent.actions.ipython_interpreter import \
IPythonInterpreter
b = IPythonInterpreter()
b.reset()
def chat(self, message: str) -> AgentReturn:
for hist in self._session_history:
if hist['role'] == 'system':
hist['role'] = self.system_role
self._inner_history = []
# append the user message for session history
self._session_history.append(dict(role='user', content=message))
agent_return = AgentReturn()
force_stop = False
default_response = '对不起，我无法回答你的问题'
for turn in range(self.max_turn):
prompt = self._protocol.format(
chat_history=self.session_history,
inner_step=self._inner_history,
action_executor=self._action_executor,
force_stop=force_stop)
response = self._llm.generate_from_template(prompt, 512)
self._inner_history.append(dict(role='assistant',
content=response))
thought, action, action_input = self._protocol.parse(
response, self._action_executor)
action_return: ActionReturn = self._action_executor(
action, action_input)
action_return.thought = thought
agent_return.actions.append(action_return)
if action_return.state == ActionStatusCode.SUCCESS:
# if success, stash model response and system response
self._session_history.append(
dict(role='assistant', content=response))
self._session_history.append(
dict(
role=self.system_role,
content=self._protocol.format_response(action_return)))
agent_return.response = action_return.result['text']
return agent_return
elif action_return.type == self._action_executor.invalid_action.name: # noqa
action_return.errmsg = 'The action is invalid, please check the action name.' # noqa
self._inner_history.append(
dict(role=self.system_role,
content=self._protocol.format_response(action_return)))
if turn == self.max_turn - 1:
force_stop = True
agent_return.response = default_response
self._session_history.append(
dict(role='assistant', content=agent_return.response))
return agent_return
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