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 import utils.config_util as utils from core.content_db import Content_Db from core import wsa_server import os 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 llm = ChatOpenAI(model="gpt-4-1106-preview", verbose=True) #创建向量数据库 vectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}), {}) # 创建记忆 retriever = vectorstore.as_retriever(search_kwargs=dict(k=2)) memory = VectorStoreRetrieverMemory(memory_key="chat_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()) memory.save_context({"input": "我的基本信息是?"}, {"output": attr_info}) #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() 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=tools, llm=llm, verbose=True, max_history=5, memory=memory, handle_parsing_errors=True) #创建llm chain用于聊天 tools_prompt = "[" tool_names = [tool.name for tool in tools if tool.name != set_chat_status_tool.name and tool.name != say_tool.name] tools_prompt += "、".join(tool_names) + "]" template = """你是一个智慧农业实验箱里的ai，你的责任是陪伴主人生活、工作，以及协助主人打理好农业种植箱里的农作物.现在主人正在和你聊天，若你在聊天过程中感觉到主人想使用以下工具，请按“agent:'主人刚刚的说话'”这样的格式回复，否则请直接回复我文字内容。工具如下： """ + tools_prompt +""" {chat_history} Human: {human_input} AI:""" prompt = PromptTemplate( input_variables=["chat_history", "human_input"], template=template ) self.llm_chain = LLMChain( llm=llm, prompt=prompt, verbose=True, memory=memory ) self.is_chat = False#聊天状态 def run(self, input_text): #消息保存 contentdb = Content_Db() contentdb.add_content('member', 'agent', input_text.replace('主人语音说了：', '').replace('主人文字说了：', '')) wsa_server.get_web_instance().add_cmd({"panelReply": {"type":"member","content":input_text.replace('主人语音说了：', '').replace('主人文字说了：', '')}}) result = "" 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: result = self.llm_chain.predict(human_input=input_text.replace('主人语音说了：', '').replace('主人文字说了：', '')) if "agent:" in result.lower() or not self.is_chat: print(result) print(self.is_chat) self.is_chat = False input_text = result if result.lower().replace("agent:", "") else input_text result = self.agent.run(input_text) except Exception as e: print(e) result = "执行完毕" if result is None or result == "N/A" else result #消息保存 contentdb.add_content('fay','agent', result) wsa_server.get_web_instance().add_cmd({"panelReply": {"type":"fay","content":result}}) return result if __name__ == "__main__": agent = FayAgentCore() print(agent.run("你好"))