olivebot/agent/fay_agent.py

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"))