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from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Tuple, Union, Optional
from agentverse.agents.base import BaseAgent
from agentverse.utils import AGENT_TYPES
from agentverse.environments.tasksolving_env.rules.decision_maker import (
BaseDecisionMaker,
decision_maker_registry,
)
from agentverse.environments.tasksolving_env.rules.evaluator import (
BaseEvaluator,
evaluator_registry,
)
from agentverse.environments.tasksolving_env.rules.executor import (
BaseExecutor,
executor_registry,
)
from agentverse.environments.tasksolving_env.rules.role_assigner import (
BaseRoleAssigner,
role_assigner_registry,
)
from agentverse.environments import BaseRule
if TYPE_CHECKING:
from agentverse.message import SolverMessage, ExecutorMessage
class TasksolvingRule(BaseRule):
role_assigner: BaseRoleAssigner
decision_maker: BaseDecisionMaker
executor: BaseExecutor
evaluator: BaseEvaluator
role_assign_only_once: bool = False
add_execution_result_to_critic: bool = False
add_execution_result_to_solver: bool = False
def __init__(
self,
role_assigner_config,
decision_maker_config,
executor_config,
evaluator_config,
*args,
**kwargs,
):
def build_components(config: Dict, registry):
component_type = config.pop("type")
component = registry.build(component_type, **config)
return component
role_assigner = build_components(
role_assigner_config,
role_assigner_registry,
)
decision_maker = build_components(
decision_maker_config,
decision_maker_registry,
)
executor = build_components(executor_config, executor_registry)
evaluator = build_components(evaluator_config, evaluator_registry)
super().__init__(
role_assigner=role_assigner,
decision_maker=decision_maker,
executor=executor,
evaluator=evaluator,
*args,
**kwargs,
)
def role_assign(
self,
task_description: str,
agents: List[BaseAgent],
cnt_turn: int,
advice: str = "",
) -> List[BaseAgent]:
"""Assign roles to agents"""
if self.role_assign_only_once and cnt_turn > 0:
agents = [agents[AGENT_TYPES.SOLVER]] + agents[AGENT_TYPES.CRITIC]
else:
agents = self.role_assigner.step(
role_assigner=agents[AGENT_TYPES.ROLE_ASSIGNMENT],
group_members=[agents[AGENT_TYPES.SOLVER]] + agents[AGENT_TYPES.CRITIC],
advice=advice,
task_description=task_description,
)
if self.role_assign_only_once and cnt_turn == 0:
agents[AGENT_TYPES.SOLVER] = agents[0]
agents[AGENT_TYPES.CRITIC] = agents[1:]
return agents
async def decision_making(
self,
task_description: str,
agents: List[BaseAgent],
previous_plan: str,
advice: str = "No advice yet.",
) -> List[SolverMessage]:
# TODO: plan should be string or a special type of object?
# dynamic
if "dynamic" in self.decision_maker.name:
plan = await self.decision_maker.astep(
agents=[agents[AGENT_TYPES.SOLVER], *agents[AGENT_TYPES.CRITIC]],
manager=agents[AGENT_TYPES.MANAGER],
task_description=task_description,
previous_plan=previous_plan,
advice=advice,
)
else:
plan = await self.decision_maker.astep(
agents=[agents[AGENT_TYPES.SOLVER], *agents[AGENT_TYPES.CRITIC]],
task_description=task_description,
previous_plan=previous_plan,
advice=advice,
)
return plan
async def execute(
self,
task_description: str,
agents: List[BaseAgent],
final_solution: List[SolverMessage],
) -> Any:
"""execution stage.
Use the executor to finish the task.
"""
results = await self.executor.astep(
agents[AGENT_TYPES.EXECUTION], task_description, final_solution
)
if self.add_execution_result_to_critic:
for agent in agents[AGENT_TYPES.CRITIC]:
agent.add_message_to_memory(results)
if self.add_execution_result_to_solver:
agents[AGENT_TYPES.SOLVER].add_message_to_memory(results)
return results
def evaluate(
self,
task_description: str,
agents: List[BaseAgent],
solution: List[SolverMessage],
result: List[ExecutorMessage],
) -> Tuple[List[int], str]:
"""evaluation stage."""
# if self.human_eval:
# print("This round, LLM gave the following result:")
# print(result)
# comprehensiveness = input("Please evaluate the comprehensiveness>> ")
# detailedness = input("Please evaluate the detailedness>> ")
# feasibility = input("Please evaluate the feasibility>> ")
# novelty = input("Please evaluate the novelty>> ")
# advice = input("Please give some advice>>")
# try:
# comprehensiveness = int(comprehensiveness)
# detailedness = int(detailedness)
# feasibility = int(feasibility)
# novelty = int(novelty)
# except ValueError:
# logger.error("Bad response from human evaluator!")
# return ([comprehensiveness, detailedness, feasibility, novelty], advice)
# else:
evaluation = self.evaluator.step(
agent=agents[AGENT_TYPES.EVALUATION],
solution=solution,
result=result,
task_description=task_description,
all_role_description=[
agents[AGENT_TYPES.SOLVER].role_description,
*[agent.role_description for agent in agents[AGENT_TYPES.CRITIC]],
],
)
return evaluation.score, evaluation.advice
def reset(self) -> None:
self.role_assigner.reset()
self.decision_maker.reset()
self.executor.reset()
self.evaluator.reset()
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