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from __future__ import annotations | |
import asyncio | |
from colorama import Fore | |
from typing import TYPE_CHECKING, List | |
from . import decision_maker_registry | |
from .base import BaseDecisionMaker | |
from agentverse.logging import logger | |
from agentverse.message import Message | |
if TYPE_CHECKING: | |
from agentverse.agents.base import BaseAgent | |
from agentverse.message import CriticMessage | |
class BrainstormingDecisionMaker(BaseDecisionMaker): | |
""" | |
Much like the horizontal decision maker, but with some twists: | |
(1) Solver acts as a summarizer, summarizing the discussion of this turn | |
(2) After summarizing, all the agents' memory are cleared, and replaced with | |
the summary (to avoid exceeding maximum context length of the model too fast) | |
""" | |
name: str = "brainstorming" | |
async def astep( | |
self, | |
agents: List[BaseAgent], | |
task_description: str, | |
previous_plan: str = "No solution yet.", | |
advice: str = "No advice yet.", | |
*args, | |
**kwargs, | |
) -> List[str]: | |
if advice != "No advice yet.": | |
self.broadcast_messages( | |
agents, [Message(content=advice, sender="Evaluator")] | |
) | |
for agent in agents[1:]: | |
review: CriticMessage = await agent.astep( | |
previous_plan, advice, task_description | |
) | |
if review.content != "": | |
self.broadcast_messages(agents, [review]) | |
logger.info("", "Reviews:", Fore.YELLOW) | |
logger.info( | |
"", | |
f"[{review.sender}]: {review.content}", | |
Fore.YELLOW, | |
) | |
result = agents[0].step(previous_plan, advice, task_description) | |
for agent in agents: | |
agent.memory.reset() | |
self.broadcast_messages( | |
agents, | |
[ | |
Message( | |
content=result.content, sender="Summary From Previous Discussion" | |
) | |
], | |
) | |
return [result] | |