app.py

from agent_build_sdk.builder import AgentBuilder
from agent_build_sdk.model.model import AgentResp, AgentReq, STATUS_DISTRIBUTION, STATUS_ROUND, STATUS_VOTE, \
    STATUS_START, STATUS_VOTE_RESULT, STATUS_RESULT
from agent_build_sdk.sdk.agent import BasicAgent
from agent_build_sdk.sdk.agent import format_prompt

from prompts import DESC_PROMPT, VOTE_PROMPT
from agent_build_sdk.utils.logger import logger

import openai
import os


class SpyAgent(BasicAgent):
    def __init__(self, api_key: str):
        super().__init__()
        self.api_key = api_key
        openai.api_key = self.api_key

    def generate_response(self, prompt: str) -> str:
        """
        Use GPT-3.5 to generate a response based on the provided prompt.
        """
        try:
            logger.info(f"Sending prompt to GPT-3.5: {prompt}")
            response = openai.ChatCompletion.create(
                model="gpt-3.5-turbo",
                messages=[
                    {"role": "system", "content": "You are a helpful AI assistant."},
                    {"role": "user", "content": prompt}
                ],
                max_tokens=1000,
                temperature=0.7
            )
            # Extract and return the content of the response
            return response['choices'][0]['message']['content'].strip()
        except Exception as e:
            logger.error(f"Error generating response: {e}")
            return "An error occurred while generating a response."

# Example usage
if __name__ == "__main__":
    # Assuming you have set your OpenAI API key in an environment variable
    api_key = os.getenv("OPENAI_API_KEY")
    if not api_key:
        raise ValueError("OPENAI_API_KEY environment variable not set.")
    
    agent = SpyAgent(api_key=api_key)
    prompt = "Describe the role of AI in modern society."
    response = agent.generate_response(prompt)
    print(f"GPT-3.5 Response: {response}")

    def perceive(self, req=AgentReq):
        logger.info("spy perceive: {}".format(req))
        if req.status == STATUS_START:  # 开始新的一局比赛
            self.memory.clear()
            self.memory.set_variable("name", req.message)
            self.memory.append_history(
                '主持人: 女士们先生们，欢迎来到《谁是卧底》游戏！我们有一个由6名玩家组成的小组，在其中有一名卧底。让我们开始吧！每个人都会收到一张纸。其中5人的纸上拥有相同的单词，而卧底则会收到含义上相似的单词。我们将大多数人拿到的单词称为"公共词"，将卧底拿到的单词称为"卧底词"。一旦你拿到了你的单词，首先需要根据其他人的发言判断自己是否拿到了卧底词。如果判断自己拿到了卧底词，请猜测公共词是什么，然后描述公共词来混淆视听，避免被投票淘汰。如果判断自己拿到了公共词，请思考如何巧妙地描述它而不泄露它，不能让卧底察觉，也要给同伴暗示。每人每轮用一句话描述自己拿到的词语，每个人的描述禁止重复，话中不能出现所持词语。每轮描述完毕，所有在场的人投票选出怀疑是卧底的那个人，得票数最多的人出局。卧底出局则游戏结束，若卧底未出局，游戏继续。现在游戏开始。')
        elif req.status == STATUS_DISTRIBUTION:  # 分配单词
            self.memory.set_variable("word", req.word)
            self.memory.append_history(
                '主持人: 你好，{}，你分配到的单词是:{}'.format(self.memory.load_variable("name"), req.word))
        elif req.status == STATUS_ROUND:  # 发言环节
            if req.name:
                # 其他玩家发言
                self.memory.append_history(req.name + ': ' + req.message)
            else:
                # 主持人发言
                self.memory.append_history('主持人: 现在进入第{}轮。'.format(str(req.round)))
                self.memory.append_history('主持人: 每个玩家描述自己分配到的单词。')
        elif req.status == STATUS_VOTE:  # 投票环节
            self.memory.append_history(req.name + ': ' + req.message)
        elif req.status == STATUS_VOTE_RESULT:  # 投票环节
            if req.name:
                self.memory.append_history('主持人: 投票结果是：{}。'.format(req.name))
            else:
                self.memory.append_history('主持人: 无人出局。')
        elif req.status == STATUS_RESULT:
            self.memory.append_history(req.message)
        else:
            raise NotImplementedError

    def interact(self, req=AgentReq) -> AgentResp:
        logger.info("spy interact: {}".format(req))
        if req.status == STATUS_ROUND:
            prompt = format_prompt(DESC_PROMPT,
                                   {"name": self.memory.load_variable("name"),
                                    "word": self.memory.load_variable("word"),
                                    "history": "\n".join(self.memory.load_history())
                                    })
            logger.info("prompt:" + prompt)
            result = self.llm_caller(prompt)
            logger.info("spy interact result: {}".format(result))
            return AgentResp(success=True, result=result, errMsg=None)

        elif req.status == STATUS_VOTE:
            self.memory.append_history('主持人: 到了投票的时候了。每个人，请指向你认为可能是卧底的人。')
            choices = [name for name in req.message.split(",") if name != self.memory.load_variable("name")]  # 排除自己
            self.memory.set_variable("choices", choices)
            prompt = format_prompt(VOTE_PROMPT, {"name": self.memory.load_variable("name"),
                                                 "choices": choices,
                                                 "history": "\n".join(self.memory.load_history())
                                                 })
            logger.info("prompt:" + prompt)
            result = self.llm_caller(prompt)
            logger.info("spy interact result: {}".format(result))
            return AgentResp(success=True, result=result, errMsg=None)
        else:
            raise NotImplementedError

    def llm_caller(self, prompt):
        client = OpenAI(
            api_key=os.getenv('API_KEY'),
            base_url=os.getenv('BASE_URL')
        )
        completion = client.chat.completions.create(
            model=self.model_name,
            messages=[
                {'role': 'system', 'content': 'You are a helpful assistant.'},
                {'role': 'user', 'content': prompt}
            ],
            temperature=0
        )
        try:
            return completion.choices[0].message.content
        except Exception as e:
            print(e)
            return None


if __name__ == '__main__':
    name = 'spy'
    agent_builder = AgentBuilder(name, agent=SpyAgent(name, model_name=os.getenv('MODEL_NAME')))
    agent_builder.start()