Spaces:
Runtime error
Runtime error
File size: 5,198 Bytes
9c48ae2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import re
import time
from autoagents.actions import Action, ActionOutput
from autoagents.roles import Role
from autoagents.system.logs import logger
from autoagents.system.schema import Message
from autoagents.actions import NextAction, CustomAction, Requirement
SLEEP_RATE = 30 # sleep between calls
CONTENT_TEMPLATE ="""
## Previous Steps and Responses
{previous}
## Current Step
{step}
"""
class Group(Role):
def __init__(self, roles, steps, watch_actions, name="Alex", profile="Group", goal="Effectively delivering information according to plan.", constraints="", **kwargs):
self.steps = steps
self.roles = roles
self.next_state = []
self._watch_action = watch_actions[-1]
super().__init__(name, profile, goal, constraints, **kwargs)
init_actions = []
for role in self.roles:
print('Add a new role:', role['name'])
class_name = role['name'].replace(' ', '_')+'_Action'
action_object = type(class_name, (CustomAction,), {"role_prompt":role['prompt'], "suggestions":role['suggestions'], "tool":role['tools']})
init_actions.append(action_object)
self._init_actions(init_actions)
self._watch(watch_actions)
self.next_action = NextAction()
self.necessary_information = ''
self.next_action.set_prefix(self._get_prefix(), self.profile, self._proxy, self._llm_api_key, self._serpapi_api_key)
async def _think(self) -> None:
if len(self.steps) > 1:
self.steps.pop(0)
states_prompt = ''
for i, step in enumerate(self.steps):
states_prompt += str(i+1) + ':' + step + '\n'
# logger.info(f"{self._setting}: ready to {self.next_action}")
# task = self._rc.important_memory[0]
# content = [task, str(self._rc.env.new_roles_args), str(self._rc.important_memory), states_prompt]
# rsp = await self.next_action.run(content)
self.next_step = self.steps[0]
next_state = 0
# self.necessary_information = rsp.instruct_content.NecessaryInformation
print('*******Next Steps********')
print(states_prompt)
print('************************')
self.next_state = []
for i, state in enumerate(self._actions):
name = str(state).replace('_Action', '').replace('_', ' ')
if name in self.next_step.split(':')[0]:
self.next_state.append(i)
else:
if len(self.steps) > 0:
self.steps.pop(0)
self.next_step = ''
self.next_role = ''
async def _act(self) -> Message:
if self.next_step == '':
return Message(content='', role='')
completed_steps, num_steps = '', 5
message = CONTENT_TEMPLATE.format(previous=str(self._rc.important_memory), step=self.next_step)
# context = str(self._rc.important_memory) + addition
steps, consensus = 0, [0 for i in self.next_state]
while len(self.next_state) > sum(consensus) and steps < num_steps:
if steps > num_steps - 2:
completed_steps += '\n You should synthesize the responses of previous steps and provide the final feedback.'
for i, state in enumerate(self.next_state):
self._set_state(state)
logger.info(f"{self._setting}: ready to {self._rc.todo}")
addition = f"\n### Completed Steps and Responses\n{completed_steps}\n###"
context = message + addition
response = await self._rc.todo.run(context)
if hasattr(response.instruct_content, 'Action'):
completed_steps += f'>{self._rc.todo} Substep:\n' + response.instruct_content.Action + '\n>Subresponse:\n' + response.instruct_content.Response + '\n'
else:
consensus[i] = 1
time.sleep(SLEEP_RATE)
steps += 1
# response.content = completed_steps
requirement_type = type('Requirement_Group', (Requirement,), {})
if isinstance(response, ActionOutput):
msg = Message(content=response.content, instruct_content=response.instruct_content, cause_by=self._watch_action)
else:
msg = Message(content=response, cause_by=self._watch_action)
# self._rc.memory.add(msg)
return msg
async def _observe(self) -> int:
"""从环境中观察，获得全部重要信息，并加入记忆"""
if not self._rc.env:
return 0
env_msgs = self._rc.env.memory.get()
observed = self._rc.env.memory.get_by_actions(self._rc.watch)
news = self._rc.memory.remember(observed) # remember recent exact or similar memories
for i in env_msgs:
self.recv(i)
news_text = [f"{i.role}: {i.content[:20]}..." for i in news]
if news_text:
logger.debug(f'{self._setting} observed: {news_text}')
return len(news) |