a27ab9dfba
更换ReAct agent✓ 修复Thread.timer管理逻辑✓ 优化提示词减小返回格式出错概率（格式出错会导致重复执行）✓ 消息窗里加上执行任务标记✓ 更换gpt 3.5模型测试✓
144 lines
5.1 KiB
Python
144 lines
5.1 KiB
Python
from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.chat_models import ChatOpenAI
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from langchain.memory import VectorStoreRetrieverMemory
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import faiss
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from langchain.docstore import InMemoryDocstore
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from langchain.vectorstores import FAISS
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from langchain.agents import AgentExecutor, Tool, ZeroShotAgent, initialize_agent, agent_types
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from langchain.chains import LLMChain
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from agent.tools.MyTimer import MyTimer
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from agent.tools.QueryTime import QueryTime
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from agent.tools.Weather import Weather
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from agent.tools.Calculator import Calculator
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from agent.tools.CheckSensor import CheckSensor
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from agent.tools.Switch import Switch
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from agent.tools.Knowledge import Knowledge
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from agent.tools.Say import Say
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from agent.tools.QueryTimerDB import QueryTimerDB
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from agent.tools.DeleteTimer import DeleteTimer
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import utils.config_util as utils
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from core.content_db import Content_Db
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from core import wsa_server
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import os
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class FayAgentCore():
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def __init__(self):
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utils.load_config()
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os.environ['OPENAI_API_KEY'] = utils.key_gpt_api_key
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#使用open ai embedding
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embedding_size = 1536 # OpenAIEmbeddings 的维度
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index = faiss.IndexFlatL2(embedding_size)
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embedding_fn = OpenAIEmbeddings()
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#创建llm
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llm = ChatOpenAI(verbose=True)#model="gpt-4-1106-preview"
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#创建向量数据库
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vectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}), {})
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# 创建记忆
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retriever = vectorstore.as_retriever(search_kwargs=dict(k=2))
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memory = VectorStoreRetrieverMemory(memory_key="chat_history", retriever=retriever)
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# 保存基本信息到记忆
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utils.load_config()
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attr_info = ", ".join(f"{key}: {value}" for key, value in utils.config["attribute"].items())
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memory.save_context({"input": "我的基本信息是?"}, {"output": attr_info})
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#创建agent chain
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my_timer = MyTimer()
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query_time_tool = QueryTime()
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weather_tool = Weather()
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calculator_tool = Calculator()
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check_sensor_tool = CheckSensor()
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switch_tool = Switch()
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knowledge_tool = Knowledge()
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say_tool = Say()
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query_timer_db_tool = QueryTimerDB()
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delete_timer_tool = DeleteTimer()
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tools = [
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Tool(
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name=my_timer.name,
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func=my_timer.run,
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description=my_timer.description
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),
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Tool(
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name=query_time_tool.name,
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func=query_time_tool.run,
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description=query_time_tool.description
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),
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Tool(
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name=weather_tool.name,
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func=weather_tool.run,
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description=weather_tool.description
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),
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Tool(
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name=calculator_tool.name,
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func=calculator_tool.run,
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description=calculator_tool.description
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),
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Tool(
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name=check_sensor_tool.name,
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func=check_sensor_tool.run,
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description=check_sensor_tool.description
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),
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Tool(
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name=switch_tool.name,
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func=switch_tool.run,
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description=switch_tool.description
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),
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Tool(
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name=knowledge_tool.name,
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func=knowledge_tool.run,
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description=knowledge_tool.description
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),
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Tool(
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name=say_tool.name,
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func=say_tool.run,
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description=say_tool.description
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),
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Tool(
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name=query_timer_db_tool.name,
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func=query_timer_db_tool.run,
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description=query_timer_db_tool.description
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),
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Tool(
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name=delete_timer_tool.name,
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func=delete_timer_tool.run,
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description=delete_timer_tool.description
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),
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]
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self.agent = initialize_agent(agent_types=agent_types.AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
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tools=tools, llm=llm, verbose=True,
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max_history=5,
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memory=memory, handle_parsing_errors=True)
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def run(self, input_text):
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#消息保存
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contentdb = Content_Db()
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contentdb.add_content('member', 'agent', input_text.replace('主人语音说了：', '').replace('主人文字说了：', ''))
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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：', '')}})
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result = None
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try:
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result = self.agent.run(input_text.replace('执行任务-->', ''))
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except Exception as e:
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print(e)
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result = "执行完毕" if result is None or result == "N/A" else result
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#消息保存
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contentdb.add_content('fay','agent', result)
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wsa_server.get_web_instance().add_cmd({"panelReply": {"type":"fay","content":result}})
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return result
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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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