100 lines
4.4 KiB
Python
100 lines
4.4 KiB
Python
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import os
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import time
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from llm.agent.tools.MyTimer import MyTimer
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from llm.agent.tools.Weather import Weather
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from llm.agent.tools.QueryTimerDB import QueryTimerDB
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from llm.agent.tools.DeleteTimer import DeleteTimer
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from llm.agent.tools.QueryTime import QueryTime
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from llm.agent.tools.PythonExecutor import PythonExecutor
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from llm.agent.tools.WebPageRetriever import WebPageRetriever
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from llm.agent.tools.WebPageScraper import WebPageScraper
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from llm.agent.tools.ToRemind import ToRemind
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from langgraph.prebuilt import create_react_agent
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from langchain_openai import ChatOpenAI
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from langgraph.checkpoint.memory import MemorySaver
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import utils.config_util as cfg
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from utils import util
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from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
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from core import content_db
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from core import member_db
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class FayAgentCore():
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def __init__(self, uid=0, observation=""):
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self.observation=observation
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cfg.load_config()
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os.environ["OPENAI_API_KEY"] = cfg.key_gpt_api_key
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os.environ["OPENAI_API_BASE"] = cfg.gpt_base_url
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os.environ["LANGCHAIN_TRACING_V2"] = "true"
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os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
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os.environ["LANGCHAIN_API_KEY"] = "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"
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os.environ["LANGCHAIN_PROJECT"] = "pr-best-artist-21"
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#创建llm
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self.llm = ChatOpenAI(model=cfg.gpt_model_engine)
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#创建agent graph
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my_timer = MyTimer(uid=uid)#传入uid
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weather_tool = Weather()
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query_timer_db_tool = QueryTimerDB()
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delete_timer_tool = DeleteTimer()
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python_executor = PythonExecutor()
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web_page_retriever = WebPageRetriever()
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web_page_scraper = WebPageScraper()
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to_remind = ToRemind()
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self.tools = [my_timer, weather_tool, query_timer_db_tool, delete_timer_tool, python_executor, web_page_retriever, web_page_scraper, to_remind]
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self.attr_info = ", ".join(f"{key}: {value}" for key, value in cfg.config["attribute"].items())
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self.prompt_template = """
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现在时间是：{now_time}。你是一个数字人，负责协助主人处理问题和陪伴主人生活、工作。你的个人资料是：{attr_info}。通过外部设备观测到：{observation}。\n请依据以信息为主人服务。
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""".format(now_time=QueryTime().run(""), attr_info=self.attr_info, observation=self.observation)
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self.memory = MemorySaver()
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self.agent = create_react_agent(self.llm, self.tools, checkpointer=self.memory)
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self.total_tokens = 0
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self.total_cost = 0
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#载入记忆
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def get_history_messages(self, uid):
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chat_history = []
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history = content_db.new_instance().get_list('all','desc', 100, uid)
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if history and len(history) > 0:
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i = 0
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while i < len(history):
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if history[i][0] == "member":
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chat_history.append(HumanMessage(content=history[i][2], user=member_db.new_instance().find_username_by_uid(uid=uid)))
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else:
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chat_history.append(AIMessage(content=history[i][2]))
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i += 1
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return chat_history
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def run(self, input_text, uid=0):
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result = ""
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messages = self.get_history_messages(uid)
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messages.insert(0, SystemMessage(self.prompt_template))
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messages.append(HumanMessage(content=input_text))
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try:
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for chunk in self.agent.stream(
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{"messages": messages}, {"configurable": {"thread_id": "tid{}".format(uid)}}
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):
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if chunk.get("agent"):
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if chunk['agent']['messages'][0].content:
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result = chunk['agent']['messages'][0].content
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cb = chunk['agent']['messages'][0].response_metadata['token_usage']['total_tokens']
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self.total_tokens = self.total_tokens + cb
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util.log(1, "本次消耗token:{}，共消耗token:{}".format(cb, self.total_tokens))
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except Exception as e:
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print(e)
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return result
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def question(cont, uid=0, observation=""):
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starttime = time.time()
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agent = FayAgentCore(uid=uid, observation=observation)
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response_text = agent.run(cont, uid)
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util.log(1, "接口调用耗时 :" + str(time.time() - starttime))
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return response_text
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if __name__ == "__main__":
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agent = FayAgentCore()
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print(agent.run("你好"))
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