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 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

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(verbose=True)#model="gpt-4-1106-preview"

        #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()
        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
            ),
        ]


        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)

    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 = None
        try:
            result = self.agent.run(input_text.replace('执行任务-->', ''))
        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"))