import os import time import math from langchain.embeddings.openai import OpenAIEmbeddings from langchain.chat_models import ChatOpenAI from langchain.memory import VectorStoreRetrieverMemory import faiss from langchain.docstore import InMemoryDocstore from langchain.vectorstores import FAISS from langchain.agents import AgentExecutor, Tool, ZeroShotAgent, initialize_agent, agent_types from langchain.chains import LLMChain from langchain.prompts import PromptTemplate from agent.tools.MyTimer import MyTimer from agent.tools.QueryTime import QueryTime from agent.tools.Weather import Weather from agent.tools.Calculator import Calculator from agent.tools.CheckSensor import CheckSensor from agent.tools.Switch import Switch from agent.tools.Knowledge import Knowledge from agent.tools.Say import Say from agent.tools.QueryTimerDB import QueryTimerDB from agent.tools.DeleteTimer import DeleteTimer from agent.tools.GetSwitchLog import GetSwitchLog from agent.tools.getOnRunLinkage import getOnRunLinkage from agent.tools.SetChatStatus import SetChatStatus from langchain.callbacks import get_openai_callback from langchain.retrievers import TimeWeightedVectorStoreRetriever from langchain.memory import ConversationBufferWindowMemory import utils.config_util as utils from core import wsa_server import fay_booter from utils import util class FayAgentCore(): def __init__(self): utils.load_config() os.environ['OPENAI_API_KEY'] = utils.key_gpt_api_key #使用open ai embedding embedding_size = 1536 # OpenAIEmbeddings 的维度 index = faiss.IndexFlatL2(embedding_size) embedding_fn = OpenAIEmbeddings() #创建llm self.llm = ChatOpenAI(model="gpt-4-1106-preview", verbose=True) #创建向量数据库 def relevance_score_fn(self, score: float) -> float: return 1.0 - score / math.sqrt(2) vectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}), {}, relevance_score_fn=relevance_score_fn) # 创建记忆(斯坦福小镇同款记忆检索机制:时间、相关性、重要性三个维度) retriever = TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, other_score_keys=["importance"], k=3) self.agent_memory = VectorStoreRetrieverMemory(memory_key="history", retriever=retriever) # 保存基本信息到记忆 utils.load_config() attr_info = ", ".join(f"{key}: {value}" for key, value in utils.config["attribute"].items()) self.agent_memory.save_context({"input": "我的基本信息是?"}, {"output": attr_info}) #内存保存聊天历史 self.chat_history = [] #创建agent chain my_timer = MyTimer() query_time_tool = QueryTime() weather_tool = Weather() calculator_tool = Calculator() check_sensor_tool = CheckSensor() switch_tool = Switch() knowledge_tool = Knowledge() say_tool = Say() query_timer_db_tool = QueryTimerDB() delete_timer_tool = DeleteTimer() get_switch_log = GetSwitchLog() get_on_run_linkage = getOnRunLinkage() set_chat_status_tool = SetChatStatus() self.tools = [ Tool( name=my_timer.name, func=my_timer.run, description=my_timer.description ), Tool( name=query_time_tool.name, func=query_time_tool.run, description=query_time_tool.description ), Tool( name=weather_tool.name, func=weather_tool.run, description=weather_tool.description ), Tool( name=calculator_tool.name, func=calculator_tool.run, description=calculator_tool.description ), Tool( name=check_sensor_tool.name, func=check_sensor_tool.run, description=check_sensor_tool.description ), Tool( name=switch_tool.name, func=switch_tool.run, description=switch_tool.description ), Tool( name=knowledge_tool.name, func=knowledge_tool.run, description=knowledge_tool.description ), Tool( name=say_tool.name, func=say_tool.run, description=say_tool.description ), Tool( name=query_timer_db_tool.name, func=query_timer_db_tool.run, description=query_timer_db_tool.description ), Tool( name=delete_timer_tool.name, func=delete_timer_tool.run, description=delete_timer_tool.description ), Tool( name=get_switch_log.name, func=get_switch_log.run, description=get_switch_log.description ), Tool( name=get_on_run_linkage.name, func=get_on_run_linkage.run, description=get_on_run_linkage.description ), Tool( name=set_chat_status_tool.name, func=set_chat_status_tool.run, description=set_chat_status_tool.description ), ] #agent用于执行任务 self.agent = initialize_agent(agent_types=agent_types.AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, tools=self.tools, llm=self.llm, verbose=True, max_history=5, handle_parsing_errors=True) #llm chain 用于聊天 self.is_chat = False#聊天状态 #记录一轮执行有无调用过say tool self.is_use_say_tool = False self.say_tool_text = "" self.total_tokens = 0 self.total_cost = 0 def format_history_str(self, str): result = "" history_string = str['history'] # Split the string into lines lines = history_string.split('input:') # Initialize an empty list to store the formatted history formatted_history = [] #处理记忆流格式 for line in lines: if "output" in line: input_line = line.split("output:")[0].strip() output_line = line.split("output:")[1].strip() formatted_history.append({"input": input_line, "output": output_line}) # 记忆流转换成字符串 result += "-以下是与用户说话关连度最高的记忆：\n" for i in range(len(formatted_history)): if i >= 3: break line = formatted_history[i] result += f"--input：{line['input']}\n--output：{line['output']}\n" if len(formatted_history) == 0: result += "--没有记录\n" #添加内存记忆 formatted_history = [] for line in self.chat_history: formatted_history.append({"input": line[0], "output": line[1]}) #格式化内存记忆字符串 result += "\n-以下刚刚的对话：\n" for i in range(len(formatted_history)): line = formatted_history[i] result += f"--input：{line['input']}\n--output：{line['output']}\n" if len(formatted_history) == 0: result += "--没有记录\n" return result def get_llm_chain(self, history): tools_prompt = "[" tool_names = [tool.name for tool in self.tools if tool.name != SetChatStatus().name and tool.name != Say().name] tools_prompt += "、".join(tool_names) + "]" template = """ 你是一个智能家居系统中的AI，负责协助主人处理日常事务和智能设备的操作。当主人提出要求时，如果需要使用特定的工具或执行特定的操作，请严格回复“agent: {human_input}”字符串。如果主人只是进行普通对话或询问信息，直接以文本内容回答即可。你可以使用的工具或执行的任务包括：。 """ + tools_prompt + "等。" +""" 现在时间是：now_time 请依据以下信息回复主人： chat_history input： {human_input} output：""".replace("chat_history", history).replace("now_time", QueryTime().run("")) prompt = PromptTemplate( input_variables=["human_input"], template=template ) llm_chain = LLMChain( llm=self.llm, prompt=prompt, verbose=True ) return llm_chain def run(self, input_text): self.is_use_say_tool = False self.say_tool_text = "" result = "" history = self.agent_memory.load_memory_variables({"input":input_text.replace('主人语音说了：', '').replace('主人文字说了：', '')}) history = self.format_history_str(history) try: #判断执行聊天模式还是agent模式，双模式在运行过程中会主动切换 if self.is_chat: llm_chain = self.get_llm_chain(history) with get_openai_callback() as cb: result = llm_chain.predict(human_input=input_text.replace('主人语音说了：', '').replace('主人文字说了：', '')) self.total_tokens = self.total_tokens + cb.total_tokens self.total_cost = self.total_cost + cb.total_cost util.log(1, "本次消耗token:{}， Cost (USD):{}，共消耗token:{}， Cost (USD):{}".format(cb.total_tokens, cb.total_cost, self.total_tokens, self.total_cost)) if "agent:" in result.lower() or not self.is_chat: self.is_chat = False input_text = result.lower().replace("agent:", "") if "agent:" in result.lower() else input_text.replace('主人语音说了：', '').replace('主人文字说了：', '') agent_prompt = """ 现在时间是：{now_time}。请依据以下信息为主人服务 ： {history} input：{input_text} output： """.format(history=history, input_text=input_text, now_time=QueryTime().run("")) print(agent_prompt) with get_openai_callback() as cb: result = self.agent.run(agent_prompt) self.total_tokens = self.total_tokens + cb.total_tokens self.total_cost = self.total_cost + cb.total_cost util.log(1, "本次消耗token:{}， Cost (USD):{}，共消耗token:{}， Cost (USD):{}".format(cb.total_tokens, cb.total_cost, self.total_tokens, self.total_cost)) except Exception as e: print(e) result = "执行完毕" if result is None or result == "N/A" else result chat_text = self.say_tool_text if self.is_use_say_tool else result #保存到记忆流和聊天对话 self.agent_memory.save_context({"input": input_text.replace('主人语音说了：', '').replace('主人文字说了：', '')},{"output": result}) self.chat_history.append((input_text.replace('主人语音说了：', '').replace('主人文字说了：', ''), chat_text)) if len(self.chat_history) > 5: self.chat_history.pop(0) return self.is_use_say_tool, chat_text if __name__ == "__main__": agent = FayAgentCore() print(agent.run("你好"))