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chatDev是什么？
ChatDev 是一个基于大型语言模型（如 GPT）的虚拟软件开发团队模拟器。它通过模拟软件开发团队中的不同角色（如程序员、测试员、设计师等），利用 AI 驱动的角色之间的对话和协作，来完成软件开发任务。ChatDev 的目标是探索如何通过 AI 模拟团队协作，自动生成代码、测试和文档等。
在本文中用到的代码地址是：下载地址。
- 下载源码
- 创建conda环境
conda create -n ChatDev_conda_env python=3.9 -y
conda activate ChatDev_conda_env
- 安装第三方库
pip3 install -r requirements.txt
代码修改
- 创建camel/model_config.py 大语言模型配置参数
主要了平台名称、网址、api类型、模型名称和token限制
格式如下：
MODELS = {"openai":{"base_url":"https://api.openai.com/v1","api_types":["openai"],"models":[{"name":"gpt-3.5-turbo","num_tokens":4096},{"name": "gpt-4","num_tokens": 8192},]},"deepseek":{"base_url":"https://api.deepseek.com","api_types":["openai"],"models":[{"name":"deepseek-chat","num_tokens":8192},{"name": "deepseek-reasoner","num_tokens": 8192},]},"qwen":{"base_url":"https://api.dashscope.aliyuncs.com/compatible-mode/v1","api_types":["openai", "self"],"models":[{"name":"qwen1.5-110-chat","num_tokens":8192},{"name": "qwen-plus","num_tokens": 8192},]}
}
- 创建.env 文件，配置OPENAI_API_KEY
在deepseel 官网下 https://platform.deepseek.com/usage进行充值，并生成APIKey 进行复制。
这里还有接口文档可供学习。
将复制的key粘贴到.env中
key一般以sk-开头 - run.py 导入OPENAI_API_KEY
from dotenv import load_dotenv
load_dotenv()
若环境中不存在dotenv ，进行pip安装
- run.py参数修改
1.1 增加api_type参数 ，表示api的类型，例如deepseek是支持openai类型的
1.2 修改model参数，默认为deepseek-chat
1.3 修改platform参数，默认改为deepseek平台
1.4 修改任务描述task，创建一个贪吃蛇游戏
1.5 修改任务名称
- run.py 初始化模型
可以删除不用的一部分代码，然后将modeltype 改成自己想要的类型
config_path, config_phase_path, config_role_path = get_config(args.config)
from camel.utils import get_model_info
base_url,num_tokens,api_type = get_model_info(args.platform,args.model,args.api_type)
model_type = ModelType(args.platform,name=args.model,base_url=base_url,num_tokens=num_tokens,api_type=api_type)
chat_chain = ChatChain(config_path=config_path,config_phase_path=config_phase_path,config_role_path=config_role_path,task_prompt=args.task,project_name=args.name,org_name=args.org,model_type=model_type,code_path=args.path)
- 创建ModelType类型（typing.py)
可以将原先类型，改成如下形式：
class ModelType(object):def __init__(self, platform,name,base_url,num_tokens,api_type):self.platform = platformself.name = nameself.base_url = base_urlself.num_tokens = num_tokensself.api_type = api_type
原先的：
- 在utils中创建get_model_info（）方法
def get_model_info(platform, model_name, api_type):if platform not in MODELS.keys():raise ValueError("platform 不存在")if api_type not in MODELS[platform]["api_types"]:raise ValueError("api_type 不存在")base_url = MODELS[platform]["base_url"]num_tokens = Nonefor model_info in MODELS[platform]["models"]:if model_info["name"] == model_name:num_tokens = model_info["num_tokens"]breakif not num_tokens:model_list = [model_info["name"] for model_info in MODELS[platform]["model"]]raise ValueError("num_tokens error shoule choice from {}".format(model_list))return base_url, num_tokens, api_type
- 因为ModelType类型被修改，所以run.py运行时所设计的文件中的ModelType修改被修改
例如：camel / agents / chat_agent.py 中
这里不在需要判断 改成：self.model: ModelType = model 即可
为了能够跑通代码，可以全局搜索一下ModelType的使用位置，进行修改
- 修改模型创建函数（model_backend.py）
将ModelFactory修改成如下形式
class ModelFactory:r"""Factory of backend models.Raises:ValueError: in case the provided model type is unknown."""@staticmethoddef create(model_type: ModelType, model_config_dict: Dict) -> ModelBackend:if model_type.api_type == "stub":return StubModel(model_type,model_config_dict)if model_type.api_type == "openai":return OpenAIModel(model_type,model_config_dict)if model_type.api_type == "qwen" and model_type.api_type == "self":return DataScopeAIModelelse:raise ValueError("model api backend create error, model_platform:{}, model_name:{}, ""base_url:{},api_type:{}".format(model_type.platform, model_type.name,model_type.base_url,model_type.api_type))# log_visualize("Model Type: {}".format(model_type))inst = model_class(model_type, model_config_dict)return inst将OpenAIModel修改成如下形式：
class OpenAIModel(ModelBackend):r"""OpenAI API in a unified ModelBackend interface."""def __init__(self, model_type: ModelType, model_config_dict: Dict) -> None:super().__init__()self.model_type = model_typeself.model_config_dict = model_config_dictdef run(self, *args, **kwargs):string = "\n".join([message["content"] for message in kwargs["messages"]])try: # 对于Token的计算encoding = tiktoken.encoding_for_model(self.model_type.name)num_prompt_tokens = len(encoding.encode(string))gap_between_send_receive = 15 * len(kwargs["messages"])num_prompt_tokens += gap_between_send_receiveexcept Exception as err:num_prompt_tokens = 0# Experimental, add base_urlclient = openai.OpenAI(api_key=OPENAI_API_KEY,base_url=self.model_type.base_url,)num_max_token = self.model_type.num_tokensnum_max_completion_tokens = num_max_token - num_prompt_tokensself.model_config_dict['max_tokens'] = num_max_completion_tokensprint("args:", args, "kwargs:", kwargs, "model:", self.model_type.name,"model_config_dict:", self.model_config_dict)response = client.chat.completions.create(*args, **kwargs, model=self.model_type.name,**self.model_config_dict)cost = prompt_cost(self.model_type.name,num_prompt_tokens=response.usage.prompt_tokens,num_completion_tokens=response.usage.completion_tokens)log_visualize("**[OpenAI_Usage_Info Receive]**\nprompt_tokens: {}\ncompletion_tokens: {}\ntotal_tokens: {}\ncost: ${:.6f}\n".format(response.usage.prompt_tokens, response.usage.completion_tokens,response.usage.total_tokens, cost))if not isinstance(response, ChatCompletion):raise RuntimeError("Unexpected return from OpenAI API")return response
创建DataScopeAIModel类
class DataScopeAIModel(ModelBackend):def __init__(self, model_type: ModelType, model_config_dict: Dict) -> None:super().__init__()self.model_type = model_typeself.model_config_dict = model_config_dicttry:import datascopeexcept Exception as err :raise ValueError("datascope 引入异常")self.client = datascope.Generation()def run(self, *args, **kwargs):string = "\n".join([message["content"] for message in kwargs["messages"]])try:encoding = tiktoken.encoding_for_model(self.model_type.name)num_prompt_tokens = len(encoding.encode(string))gap_between_send_receive = 15 * len(kwargs["messages"])num_prompt_tokens += gap_between_send_receiveexcept Exception as err:num_prompt_tokens = 0# Experimental, add base_urlnum_max_token = self.model_type.num_tokensnum_max_completion_tokens = num_max_token - num_prompt_tokensself.model_config_dict['max_tokens'] = num_max_completion_tokensprint("args:", args, "kwargs:", kwargs, "model:", self.model_type.name,"model_config_dict:", self.model_config_dict)api_key = os.environ.get("DASHSCOPE_API_KEY")if not api_key:api_key = OPENAI_API_KEYresponse = self.client.call(self.model_type.name,api_key = api_key,**kwargs)cost = prompt_cost(self.model_type.name,num_prompt_tokens=response.usage.prompt_tokens,num_completion_tokens=response.usage.completion_tokens)log_visualize("**[OpenAI_Usage_Info Receive]**\nprompt_tokens: {}\ncompletion_tokens: {}\ntotal_tokens: {}\ncost: ${:.6f}\n".format(response.usage.prompt_tokens, response.usage.completion_tokens,response.usage.total_tokens, cost))if not isinstance(response, ChatCompletion):raise RuntimeError("Unexpected return from OpenAI API")return response
下面这些无用的代码都可以删除
-
运行run.py文件
此时会爆出一些错误，根据错误提示修改，直到无误即可 -
运行成功后，会在WareHouse文件夹下生成对应文件
进入对应文件夹，运行main.py文件即可运行程序
