236 lines
8.0 KiB
Python
236 lines
8.0 KiB
Python
import sys
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import os
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current_directory = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(current_directory)
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import agentscope
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from agentscope.message import Msg
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import os
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import asyncio
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import threading
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import json
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from agent.flex_service_toolkit import *
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from AlgoriAgent.projects.algoriAgent.agent.algori_agent import *
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from agentscope.service import (
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ServiceToolkit,
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ServiceResponse,
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ServiceExecStatus,
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)
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import configparser
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config = configparser.ConfigParser()
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config.read('config.ini')
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openai_api_key = config['Global']['api_key']
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OPENAI_CFG_DICT = {
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"config_name": "openai_cfg", # 此配置的名称，必须保证唯一
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"model_type": "openai_chat", # 模型类型
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"model_name": "gpt-4o-mini", # 模型名称
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#"model_name": "gpt-4", # 模型名称
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#"model_name": "llama3",
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"api_key": openai_api_key, # OpenAI API key. 如果没有设置，将使用环境变量中的 OPENAI_API_KEY
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"client_args": {
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"base_url": config['Global']['base_chat_url']
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},
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}
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import uuid
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from AlgoriAgent.projects.algoriAgent.tools.judge_tools import judge
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EMPTY_CHAPTER_CHAIN = [ Chapter(1, CHAPTER_FOCUS, "本章是未打开某个具体章节时的默认章节。", "处于本章节时不会有任何章节跳转。请作为一名经验丰富的算法教师，回答用户的问题。") ]
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def tool_name_to_tool(tool_name_list):
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tools = []
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for tool_name in tool_name_list:
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if tool_name == "":
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continue
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if tool_name == "judge":
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tools.append(judge)
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return tools
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class AgentManager:
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def __init__(self):
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agentscope.init(model_configs=[OPENAI_CFG_DICT])#, studio_url="http://0.0.0.0:5000")
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self.agents = {}
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def new_agent(self, markdown:str, markdown_prompt:str, score_prompt:str, id = None):
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'''
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markdown: 教案的markdown文件内容
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markdown_prompt: 教案的prompt的markdown文件内容
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score_prompt: 评分的prompt的markdown文件内容
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根据3个教案的prompt，生成一个agent，返回agent的id与agent
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'''
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markdown_list = markdown.split("\n")
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markdown_prompt_list = markdown_prompt.split("\n")
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score_prompt_list = score_prompt.split("\n")
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# 获取 H1 标题
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title = ""
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for line in markdown_list:
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if line.startswith("# "):
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title = line[2:]
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break
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# 对于 H3 标题 构造每一个 Chapter
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# 首先找到所有的 H3 标题
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chapter_dict = {}
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chapter_sequence = []
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for line in markdown_list:
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if line.startswith("### "):
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chapter_name = line[4:]
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chapter_dict[chapter_name] = {}
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chapter_sequence.append(chapter_name)
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# 将 H3 标题 和 其对应的内容 构造成一个 Chapter
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h3_name = ""
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content = ""
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for i in range(len(markdown_list)):
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if markdown_list[i].startswith("### "):
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if(h3_name != ""):
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chapter_dict[h3_name]["markdown"] = content
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h3_name = markdown_list[i][4:]
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content = ""
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continue
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if h3_name != "":
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content += markdown_list[i]+"\n"
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if(h3_name != ""):
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chapter_dict[h3_name]["markdown"] = content
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h3_name = ""
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content = ""
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require_tools = []
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for i in range(len(markdown_prompt_list)):
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if markdown_prompt_list[i].startswith("### "):
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if(h3_name != ""):
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chapter_dict[h3_name]["markdown_prompt"] = content
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chapter_dict[h3_name]["require_tools"] = tool_name_to_tool(require_tools)
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h3_name = markdown_prompt_list[i][4:]
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content = ""
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continue
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if h3_name != "":
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if markdown_prompt_list[i].startswith("_require_tools"):
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require_tools.append(markdown_prompt_list[i].split("=")[1].strip())
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continue
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content += markdown_prompt_list[i]+"\n"
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if(h3_name != ""):
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chapter_dict[h3_name]["markdown_prompt"] = content
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chapter_dict[h3_name]["require_tools"] = tool_name_to_tool(require_tools)
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h3_name = ""
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content = ""
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for i in range(len(score_prompt_list)):
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if score_prompt_list[i].startswith("### "):
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if(h3_name != ""):
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chapter_dict[h3_name]["score_prompt"] = content
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h3_name = score_prompt_list[i][4:]
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content = ""
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continue
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if h3_name != "":
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content += score_prompt_list[i]+"\n"
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if(h3_name != ""):
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chapter_dict[h3_name]["score_prompt"] = content
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chapter_chain = []
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No = 1
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print (chapter_dict)
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for chapter_name in chapter_sequence:
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chapter_chain.append(Chapter(No, CHAPTER_LATTER, title, chapter_dict[chapter_name]["markdown"], chapter_dict[chapter_name]["markdown_prompt"], chapter_dict[chapter_name]["score_prompt"],chapter_dict[chapter_name]["require_tools"]))
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No+=1
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chapter_chain[0].Focus()
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# Prepare the tools for the agent
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service_toolkit = FlexServiceToolkit()
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# for tool_function in unity_function_list:
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# service_toolkit.add(tool_function)
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agent = ChapterChainAgent(
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name="assistant",
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model_config_name="openai_cfg",
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verbose=True,
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service_toolkit=service_toolkit,
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max_iters=5,
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chapter_chain=chapter_chain
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)
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self.agents[id] = agent
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return id, agent
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def new_agent_with_chain(self, chapter_chain = None, id = None):
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'''
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'''
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if chapter_chain is None:
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chapter_chain = EMPTY_CHAPTER_CHAIN
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# Prepare the tools for the agent
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service_toolkit = FlexServiceToolkit()
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# for tool_function in unity_function_list:
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# service_toolkit.add(tool_function)
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agent = ChapterChainAgent(
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name="assistant",
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model_config_name="openai_cfg",
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verbose=True,
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service_toolkit=service_toolkit,
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max_iters=5,
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chapter_chain=EMPTY_CHAPTER_CHAIN
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)
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if id is None: id = uuid.uuid4()
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self.agents[id] = agent
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return id, agent
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def get_agent(self, id=None):
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'''
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如果在get agent之前没有new agent，并用id进行访问，就返回一个章节链为空的agent
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'''
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if id:
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if id in self.agents:
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return self.agents[id]
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self.agents[id] = self.new_agent(id)[1]
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else:
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id = str(uuid.uuid4())
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self.agents[id] = self.new_agent()[1]
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return id, self.agents[id]
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def invoke(self,id, query, user_backboard):
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msg = Msg("user", query, role="user")
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return self.agents[id](msg, user_backboard = user_backboard)
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import time
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if __name__ == '__main__':
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# tool_demo = ToolDemo("Lava")
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# response = tool_demo.invoke("I want such a Gurouce, when use it, it will be thrown and fly towards the mouse position for a short time, and stop. And will damage Enemy who step on it. I call it Lava Gurouce.")
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# print(response)
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# Start the WebSocket server in a separate thread
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manager = AgentManager()
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id, agent = manager.new_agent()
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# Main thread logic
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try:
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# Your code that might be interrupted
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while True:
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input_data = input("用户：")
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print(manager.invoke(id, input_data))
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except KeyboardInterrupt:
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print("Process interrupted by user.")
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# You can add any cleanup code here if needed
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finally:
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print("Exiting program.")
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# Code to run before the program exits
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