我们来尝试用微软的Autogen创建自己的AI小团队来实现完成一些简单的任务

pip install pyautogen

autogen
│
├── groupchat
│   └── cars.csv
└── analyze_cars.py

import autogen

# 这里添加我们使用的大模型，如果你使用chatgpt-4,可以改成下面的方式
# config_list = [
#     {
#         "model": "gpt-4",
#         "api_key": "<your OpenAI API key here>"
#     },
# ]
config_list = [
        {
            "model": "chatglm2-6b",
            "api_base": "http://localhost:1234/v1", #这里添加我们开启的本地API服务地址
            "api_type": "open_ai",
            "api_key": "NULL", # 这里仅仅是一个占位，因为我们本地的服务没有设置认证
        }
]

llm_config = {
    "request_timeout": 600,
    "seed": 42,
    "config_list": config_list,
    "temperature":0
}

# 创建一个用户代理，我们通过这个代理来作为输入我们需求的入口
user_proxy = autogen.UserProxyAgent(
   name="User_proxy",
   system_message="A human admin.",
   code_execution_config={"last_n_messages": 3, "work_dir": "groupchat","use_docker":False},
   human_input_mode="NEVER",
)

# 创建一个开发的助理，AutoGen通过这个AI助理来根据评判助理的建议来写代码
coder = autogen.AssistantAgent(
    name="Coder",  
    llm_config=llm_config,
)

# 创建一个评判助理，能够分析用户代理提出的需求，并对开发助理写的代码进行评估并提出意见
critic = autogen.AssistantAgent(
    name="Critic",
    system_message="""Critic. You are a helpful assistant highly skilled in evaluating the quality of a given visualization code by providing a score from 1 (bad) - 10 (good) while providing clear rationale. YOU MUST CONSIDER VISUALIZATION BEST PRACTICES for each evaluation. Specifically, you can carefully evaluate the code across the following dimensions
- bugs (bugs):  are there bugs, logic errors, syntax error or typos? Are there any reasons why the code may fail to compile? How should it be fixed? If ANY bug exists, the bug score MUST be less than 5.
- Data transformation (transformation): Is the data transformed appropriately for the visualization type? E.g., is the dataset appropriated filtered, aggregated, or grouped  if needed? If a date field is used, is the date field first converted to a date object etc?
- Goal compliance (compliance): how well the code meets the specified visualization goals?
- Visualization type (type): CONSIDERING BEST PRACTICES, is the visualization type appropriate for the data and intent? Is there a visualization type that would be more effective in conveying insights? If a different visualization type is more appropriate, the score MUST BE LESS THAN 5.
- Data encoding (encoding): Is the data encoded appropriately for the visualization type?
- aesthetics (aesthetics): Are the aesthetics of the visualization appropriate for the visualization type and the data?

YOU MUST PROVIDE A SCORE for each of the above dimensions.
{bugs: 0, transformation: 0, compliance: 0, type: 0, encoding: 0, aesthetics: 0}
Do not suggest code. 
Finally, based on the critique above, suggest a concrete list of actions that the coder should take to improve the code.
""",
    llm_config=llm_config,
)

# 讲用户代理，开发助理以及评判助理组成一个团队并讲需求输入让开发处理和评判助理共同解决用户提出的问题
groupchat = autogen.GroupChat(agents=[user_proxy, coder, critic], messages=[], max_round=20)
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)

user_proxy.initiate_chat(manager, message="use data from cars.csv in work dir and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.")
# type exit to terminate the chat

python analyze_cars.py