Datawhale 组队学习 camel-agents task1 notes: camel agent framework dem
datawhale 教程modelscope
poetry 换源
环境：
kubuntu
poetry
python3.10
camel
任务：
跑通 camel demo
demo code
from camel.agents import ChatAgent
from camel.models import ModelFactory
from camel.types import ModelPlatformType
from dotenv import load_dotenv
import os
load_dotenv()
api_key = os.getenv('QWEN_API_KEY')
model = ModelFactory.create(
model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
model_type="Qwen/Qwen2.5-72B-Instruct",
url='https://api-inference.modelscope.cn/v1/',
api_key=api_key
)
agent = ChatAgent(
model=model,
output_language='中文'
)
response = agent.step("你好，你是谁？")
print(response.msgs.content)from colorama import Fore
from camel.societies import RolePlaying
from camel.utils import print_text_animated
from camel.models import ModelFactory
from camel.types import ModelPlatformType
from dotenv import load_dotenv
import os
load_dotenv(dotenv_path='.env')
api_key = os.getenv('QWEN_API_KEY')
model = ModelFactory.create(
model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
model_type="Qwen/Qwen2.5-72B-Instruct",
url='https://api-inference.modelscope.cn/v1/',
api_key=api_key
)
def main(model=model, chat_turn_limit=50) -> None:
task_prompt = "为股票市场开发一个交易机器人"#设置任务目标
role_play_session = RolePlaying(
assistant_role_name="Python 程序员",#设置AI助手角色名
assistant_agent_kwargs=dict(model=model),
user_role_name="股票交易员",#设置用户角色名，在roleplay中，user用于指导AI助手完成任务
user_agent_kwargs=dict(model=model),
task_prompt=task_prompt,
with_task_specify=True,
task_specify_agent_kwargs=dict(model=model),
output_language='中文'#设置输出语言
)
print(
Fore.GREEN
+ f"AI 助手系统消息:\n{role_play_session.assistant_sys_msg}\n"
)
print(
Fore.BLUE + f"AI 用户系统消息:\n{role_play_session.user_sys_msg}\n"
)
print(Fore.YELLOW + f"原始任务提示:\n{task_prompt}\n")
print(
Fore.CYAN
+ "指定的任务提示:"
+ f"\n{role_play_session.specified_task_prompt}\n"
)
print(Fore.RED + f"最终任务提示:\n{role_play_session.task_prompt}\n")
n = 0
input_msg = role_play_session.init_chat()
while n < chat_turn_limit:
n += 1
assistant_response, user_response = role_play_session.step(input_msg)
if assistant_response.terminated:
print(
Fore.GREEN
+ (
"AI 助手已终止。原因: "
f"{assistant_response.info['termination_reasons']}."
)
)
break
if user_response.terminated:
print(
Fore.GREEN
+ (
"AI 用户已终止。"
f"原因: {user_response.info['termination_reasons']}."
)
)
break
print_text_animated(
Fore.BLUE + f"AI 用户:\n\n{user_response.msg.content}\n"
)
print_text_animated(
Fore.GREEN + "AI 助手:\n\n"
f"{assistant_response.msg.content}\n"
)
if "CAMEL_TASK_DONE" in user_response.msg.content:
break
input_msg = assistant_response.msg
if __name__ == "__main__":
main()experience
在服务器上跑了 task1
挺顺利的
camel repo 好像还有 gradio demo 等示例
学习内容：
poetry
camel-ai 基本利用
modelscope
ark
siliconflow
平台多得很，看你怎么用
further explore
camel/examples/
https://docs.camel-ai.org/
https://github.com/camel-ai/owl
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