79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
import os
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from dotenv import load_dotenv
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load_dotenv()
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api_key = os.getenv("OPENAI_API_KEY")
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from langchain_openai import ChatOpenAI
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from langchain.agents import AgentExecutor, create_tool_calling_agent
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.tools import tool # 导入 @tool
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from langchain_community.chat_message_histories import ChatMessageHistory
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from langchain_core.runnables import RunnableWithMessageHistory
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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# 配置llm
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=api_key,
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base_url="https://api.deepseek.com",
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streaming=True,
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callbacks=[StreamingStdOutCallbackHandler()]
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)
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# 配置prompt(新增俩占位符 一个为对话历史记录，一个为agent的思考过程)
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prompt = ChatPromptTemplate.from_messages([
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('system','你是小智，一个帮助他人的智能助手。当你无法解答当前问题时，会调用工具来解决问题。'),
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MessagesPlaceholder(variable_name="history"),
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('human','{input}'),
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MessagesPlaceholder(variable_name="agent_scratchpad")
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])
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# 配置tool
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@tool
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def get_weather(location):
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"""模拟获得天气信息"""
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return f"{location}当前天气：23℃，晴，风力2级"
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tools = [get_weather]
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# 配置agent
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agent = create_tool_calling_agent(llm=llm,prompt=prompt,tools=tools)
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# 配置AgentExecutor
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agent_executor = AgentExecutor(agent=agent,tools=tools) # 这里没加verbose=True，想打印日志看思考链的可以自行打印
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# 记忆存储--包装agent_executor
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store = {}
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def get_session_history(session_id:str):
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if session_id not in store:
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store[session_id] = ChatMessageHistory()
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return store[session_id]
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agent_with_memory = RunnableWithMessageHistory(
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runnable=agent_executor,
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get_session_history=get_session_history,
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input_messages_key="input",
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history_messages_key="history"
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)
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# 打印测试
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session_id = 'user123'
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if __name__ == '__main__':
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while 1:
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user_input = input('\n你:')
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if user_input == 'quit':
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print('拜拜~')
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break
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# 用于标记"AI:"这个内容
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# flush=True保证"AI:"立即输出，而不是等缓存区存满再输出
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print("AI: ", end="", flush=True)
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response = agent_with_memory.invoke(
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{'input': user_input},
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config={'configurable': {'session_id': session_id}}
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)
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print()
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