Multi-Agent实践第7期：智能体的高效协作

git clone https://github.com/modelscope/agentscope
cd agentscope
# On windows
pip install -e .[distribute]
# On mac or linux
pip install -e .\[distribute\]

// model_configs.json
[
  {
    "model_type": "dashscope_chat",
    "config_name": "tongyi_qwen_config",
    "model_name": "qwen-max",
    "api_key": "************"  // 在这里填写你DashScope中的API-KEY
  }
]

dialog_agent = DialogAgent(
        name="Assistant",
        sys_prompt="You're a helpful assistant.",
        model_config_name="tongyi_qwen_config",
    )
    # Add to_dist()
    dialog_agent = dialog_agent.to_dist()
    user_agent = UserAgent()

# -*- coding: utf-8 -*-
"""An example use multiple agents to search the Internet for answers"""
import time
import argparse
from loguru import logger
from searcher_agent import SearcherAgent
from answerer_agent import AnswererAgent
import agentscope
from agentscope.agents.user_agent import UserAgent
from agentscope.message import Msg
def parse_args() -> argparse.Namespace:
    # 处理上文提到的输入参数...
    # 在此省略一些代码，完整版代码见GitHub仓库
if __name__ == "__main__":
    args = parse_args()
    agentscope.init(
        model_configs="configs/model_configs.json",
    )
    WORKER_NUM = 3
    searcher = SearcherAgent(
        name="Searcher",
        model_config_name="my_model",
        result_num=args.num_workers,
        search_engine_type=args.search_engine,
        api_key=args.api_key,
        cse_id=args.cse_id,
    )
    answerers = []
    for i in range(args.num_workers):
        answerer = AnswererAgent(
            name=f"Answerer-{i}",
            model_config_name="my_model",
        )
        if args.use_dist:
            answerer = answerer.to_dist(lazy_launch=False)
        answerers.append(answerer)
    user_agent = UserAgent()
    msg = user_agent()
    while not msg.content == "exit":
        start_time = time.time()
        msg = searcher(msg)
        results = []
        for page, worker in zip(msg.content, answerers):
            results.append(worker(Msg(**page)))
        for result in results:
            logger.chat(result)
        end_time = time.time()
        logger.chat(
            Msg(
                name="system",
                role="system",
                content=f"Finish in [{end_time - start_time}]s",
            ),
        )
        msg = user_agent()

python main.py --num-workers 10 --search-engine bing --api-key xxxxx --use-dist

User input: Sora 是什么时候发布的
...
Answerer-2: Sora是在2月16日凌晨由美国人工智能公司OpenAI发布的。
Answerer-2: https://www.stcn.com/article/detail/1121797.html
Answerer-1: OpenAI Sora是在2024年2月15日正式发布的。
Answerer-1: https://shengpu.cc/sora-release-timeline/
...
system: Completed in [13.2] s

User input: Sora 是什么时候发布的
...
Answerer-1: OpenAI Sora是在2024年2月15日正式发布的。
Answerer-1: https://shengpu.cc/sora-release-timeline/
Answerer-2: Sora是在2月16日凌晨由美国人工智能公司OpenAI发布的。
Answerer-2: https://www.stcn.com/article/detail/1121797.html
...
system: Completed in [51.3] s

def setup_server(parsed_args: argparse.Namespace) -> None:
    """Setup rpc server for participant agent"""
    # ...
    # 在此省略一些代码，完整版代码见GitHub仓库
    server_launcher = RpcAgentServerLauncher(
        agent_class=agent_class,
        agent_kwargs=config,
        host=host,
        port=port,
    )
    server_launcher.launch(in_subprocess=False)
    server_launcher.wait_until_terminate()

python distributed_debate.py --role pro --pro-host localhost --pro-port 12011 --is-human

python distributed_debate.py --role con --con-host localhost --con-port 12012 --is-human

def run_main_process(parsed_args: argparse.Namespace) -> None:
    """Setup the main debate competition process"""
    pro_agent, con_agent, judge_agent = agentscope.init(
        model_configs="configs/model_configs.json",
        agent_configs="configs/debate_agent_configs.json",
    )
    pro_agent = pro_agent.to_dist(
        host=parsed_args.pro_host,
        port=parsed_args.pro_port,
        launch_server=False,
    )
    con_agent = con_agent.to_dist(
        host=parsed_args.con_host,
        port=parsed_args.con_port,
        launch_server=False,
    )
    # ...
    # 在此省略一些代码，完整版代码见GitHub仓库

python distributed_debate.py --role main \
    --pro-host localhost --pro-port 12011 \
    --con-host localhost --con-port 12012

System: Welcome to the debate on whether Artificial General Intelligence (AGI) can be achieved
...
User input: Some of Pro arguments

System: Welcome to the debate on whether Artificial General Intelligence (AGI) can be achieved
...
Pro: Some of Pro arguments
User input: Some of Con arguments

System: Welcome to the debate on whether Artificial General Intelligence (AGI) can be achieved
...
User input: Some of Pro arguments
Con: Some of Con arguments
Judge: Some reviews